Each day business leaders and administrators are leading their entities as different processes and sub-processes are being performed. Processes take place within every domain of entities, such as accounting, advertising, human resources, information technology, management, and production. These entities rely on the performance of these processes to determine their success or failure. Due to this, it is vital that the business leaders implement strategies to effectively manage the growth and maturation of the entity’s processes. Previous literature includes examining such process models as the Defining Advertising Goals for Measured Advertising model, the Foote, Cone, & Belding strategy matrix, benchmarking, Total Quality Management, Six-Sigma, business process improvement, business process management, business process reengineering, standards, policies, and legislation. However, these previous models do not approach business process management through using a model that progressively matures the processes. The Capability Maturity Model is a model that approaches business process management through a five-step model that seeks progressive improvement and maturation of processes. This model has been previously studied in areas such as project management, logistics, information technology, finance, software, industrial management, and criminal justice. None of the previous literature has studied adapting the Capability Maturity Model within the context of advertising processes. Therefore, this study proposes a derivative of the model for advertising processes. The model, crafted as the Advertising Maturity Model, will serve to approach advertising process improvement through progressive maturation. The conclusions and recommendations from this research stem from the results of a questionnaire distributed to employees who currently or previously have worked in business settings that administer advertising processes. Overall, the conclusions show that this research failed to exhibit that all five levels of the Capability Maturity Model’s framework are perceived with the respondents. Due to changes in the management of processes amongst organizations, it was recommended that future studies further examine the use of the Capability Maturity Model with additional approaches. This research provides future researchers with a reference from which multiple further studies may be conducted pertaining to the Capability Maturity Model’s use.
TABLE OF CONTENTS
RESEARCHER QUALIFICATIONS
EXECUTIVE SUMMARY
ACKNOWLEDGEMENTS
TABLE OF CONTENTS
CHAPTER ONE
1.1 Introduction to the Study
1.2 Review of Previous Paradigms
1.3 Rationale and Motivation of Research Study
1.3.1 Advertising Processes
1.3.2 The Capability Maturity Model Integrated
1.3.3 Derivatives of the CMMi
1.4 Research Question and Hypothesis
1.5 Overview of Research Goals and Expectations
1.5.1 Research Goals
1.5.2 Research Expectations
1.6 Research Value
1.7 Overview of Research Design and Methodology
1.7.1 Research Design
1.7.2 Data Collection
1.7.3 Target Population and Sample Size
1.7.4 Methods for Analyzing Data
1.8 Overview of Scope, Limitations, and Bias
1.9 Research Ethics
CHAPTER TWO
2.1 Introduction to the Literature Findings
2.2 Understanding Processes
2.3 Processes in the Advertising Context
2.3.1 Examining Advertising Processes
2.4 Defining Advertising Goals for Measured Advertising Results Model
2.4.1 Description of the DAGMAR Model
2.4.2 Purpose of the DAGMAR Model
2.4.3 Uses of the DAGMAR Model
2.5 Foote, Cone, and Belding Matrix
2.5.1 Description of the Foote, Cone, and Belding Matrix
2.5.2 Purpose of the Foote, Cone, and Belding Matrix
2.5.3 Uses of the Foote, Cone, and Belding Matrix
2.6 Benchmarking
2.6.1 Description of Benchmarking
2.6.2 Purpose of Benchmarking in Advertising
2.6.3 Uses of Benchmarking
2.7 Total Quality Management
2.7.1 Description of Total Quality Management
2.7.2 Purpose of Total Quality Management
2.7.3 Uses of Total Quality Management
2.8 Business Process Reengineering
2.8.1 Description of Business Process Reengineering
2.8.2 Purpose of Business Process Reengineering
2.8.3 Uses of Business Process Reengineering
2.9 Business Process Improvement
2.9.1 Description of Business Process Improvement
2.9.2 Purpose of Business Process Improvement
2.9.3 Uses of Business Process Improvement
2.10 Business Process Management
2.10.1 Description of Business Process Management
2.10.2 Purpose of Business Process Management
2.10.3 Uses of Business Process Management
2.11 Six-Sigma
2.11.1 Description of Six-Sigma
2.11.2 Purpose of Six-Sigma
2.11.3 Uses of Six-Sigma
2.12 Standards
2.12.1 Description of Standards
2.12.2 Purpose of Standards
2.12.3 Uses of Standards
2.13 Legislation
2.13.1 Description of Legislation
2.13.2 Purpose of Legislation
2.13.3 Uses of Legislation
2.14 Policies
2.14.1 Description of Policies
2.14.2 Purpose of Policies
2.14.3 Use of Policies
2.15 Capability Maturity Model Integrated
2.15.1 Description of the Capability Maturity Model Integrated
2.15.2 Purpose of the Capability Maturity Model Integrated
2.15.3 Uses of the Capability Maturity Model Integrated
2.16 Literature Analysis to Propose an Advertising Maturity Model
2.17 Motivation for the Study
CHAPTER THREE
3.1 Introduction to the Methodology
3.2 Variable Motivation
3.3 Overview of Scope, Limitations, and Bias
3.3.1 Scope
3.3.2 Limitations and Bias
3.4 Survey Design Overview
3.4.1 Design
3.4.2 Data Collection
3.4.3 Target Population and Sample Size
3.5 Methods of Analyzing the Data
3.6 Ethics
CHAPTER FOUR
4.1 Introduction to the Findings
4.2 Demographic Overview of the Respondents
4.3 Bias
4.4 Reliability Findings
4.5 Response Rate Findings
4.6 Summary of the Survey Findings
4.6.1 Urban versus Rural
4.6.2 Previous Initiative versus No Previous Initiative
4.6.3 Current Initiative versus No Current Initiative
4.7 Summary of the Survey Responses
4.7.1 Summary of Responses - Overall Framework Questions
4.7.2 Summary of Responses - Level One of the CMMi
4.7.3 Summary of Responses - Level Two of the CMMi
4.7.4 Summary of the Responses - Level Three of the CMMi
4.7.5 Summary of the Responses - Level Four of the CMMi
4.7.6 Summary of the Responses - Level Five of the CMMi
4.7.7 Summary of the Responses - Ancillary Questions
4.8 Highlights of the Findings
CHAPTER FIVE
5.1 Introduction to the Conclusions and Recommendations
5.2 Summary of the Study
5.3 Summary of the Literature Review
5.4 Summary of the Research
5.4.1 Summary of the Survey
5.4.2 Summary of the Methodology
5.5 Conclusions of the Hypotheses
5.5.1 Urban versus Rural
5.5.2 Previous Initiatives versus No Previous Initiatives
5.5.3 Current Initiatives versus No Current Initiatives
5.6 Conclusions of the Survey Responses
5.6.1 Overall Framework Questions
5.6.2 Level One Questions
5.6.3 Level Two Questions
5.6.4 Level Three Questions
5.6.5 Level Four Questions
5.6.6 Level Five Questions
5.6.7 Ancillary Questions
5.7 Conclusions: Goals, Objectives, and the Research Question
5.7.1 Research Goals
5.7.2 Research Expectations
5.7.3 Overall Research Question
5.8 Recommendations and Future Research
APPENDIX A - CONSENT FORM
APPENDIX B - SURVEY INSTRUMENT
REFERENCES
RESEARCHER QUALIFICATIONS
Mr. Charles Pickett III is currently pursuing a Master of Business Administration degree with an emphasis in General Business. Mr. Pickett previously earned his Bachelor of Business Administration in Accounting degree. Both degrees were awarded to Mr. Pickett by the University of West Alabama. While attending the University of West Alabama, Mr. Pickett achieved membership into collegiate honor societies such as Blue Key Honor Society, Delta Mu Delta International Business Honor Society, Omicron Delta Kappa National Leadership Honor Society, Order of Omega Honor Society, and Who’s Who among American Colleges and Universities. As an undergraduate, Mr. Pickett was also awarded a Trustee Award Scholarship, an India L. Shields Memorial Scholarship, and an Alabama Society of Certified Public Accountants Scholarship.
Mr. Pickett is a native of Demopolis, Alabama and he currently is a resident of Tuscaloosa, Alabama. The researcher has prior work experience as a Staff Accountant and then an Accounts Payable Manager at Progressive Pipeline in Meridian, Mississippi. Currently, Mr. Pickett works as a Cost Accountant at McAbee Construction in Tuscaloosa, Alabama. This researcher’s work experience has given him ample experience with basic and complex accounting functions in business settings. Also, Mr. Pickett’s work experiences have exposed him to different processes in multiple business settings.
FILING ABSTRACT AND SUMMARY
illustration not visible in this excerpt
EXECUTIVE SUMMARY
Background:
Each day business leaders and administrators are leading their entities as different processes and sub-processes are being performed. Processes take place within every domain of entities, such as accounting, advertising, human resources, information technology, management, and production. These entities rely on the performance of these processes to determine their success or failure. Due to this, it is vital that the business leaders implement strategies to effectively manage the growth and maturation of the entity’s processes. Previous literature includes examining such process models as the Defining Advertising Goals for Measured Advertising model, the Foote, Cone, & Belding strategy matrix, benchmarking, Total Quality Management, Six-Sigma, business process improvement, business process management, business process reengineering, standards, policies, and legislation. However, these previous models do not approach business process management through using a model that progressively matures the processes. The Capability Maturity Model is a model that approaches business process management through a five-step model that seeks progressive improvement and maturation of processes. This model has been previously studied in areas such as project management, logistics, information technology, finance, software, industrial management, and criminal justice. None of the previous literature has studied adapting the Capability Maturity Model within the context of advertising processes. Therefore, this study proposes a derivative of the model for advertising processes. The model, crafted as the Advertising Maturity Model, will serve to approach advertising process improvement through progressive maturation.
Conclusions and Recommendations:
The conclusions and recommendations from this research stem from the results of a questionnaire distributed to employees who currently or previously have worked in business settings that administer advertising processes. Overall, the conclusions show that this research failed to exhibit that all five levels of the Capability Maturity Model’s framework are perceived with the respondents. Thus, each level of the framework of the model did not exist amongst business organization in this research that perform advertising processes. Due to changes in the management of processes amongst organizations, it was recommended that future studies further examine the use of the Capability Maturity Model with additional approaches. This research provides future researchers with a reference from which multiple further studies may be conducted pertaining to the Capability Maturity Model’s use.
ACKNOWLEDGEMENTS
There are many people who I owe a great amount of thanks to for the completion of this work. First and foremost, I must thank God for making this research endeavor possible. My research committee of Dr. Adrian Doss, Dr. Brie Winkles, and Dr. Russ Henley spent countless hours devoting their time to guiding me through this study. Without their supervision, guidance, dedication, encouragement, and feedback, I would not have succeeded in this study. Each of my committee members exhibited their knowledge and experience, both of which I will forever be grateful for.
To my family who were always there for support, I cannot express my appreciation enough for always believing in me. To my parents, Angela Cormier and Charles Pickett Jr., and my stepfather, Mark Cormier, I would have not made it this far without the encouragement you each have given me. To my sister, Chelsea Pickett, thank you for providing me with your support and encouragement also. To my friends, I also show great appreciation for believing in me and showing your support. I have been blessed beyond measure with incredible friends and family who support me in achieving my goals.
I dedicate this work to my late grandfather, Charles Pickett Sr. He taught me numerous lessons in life. Thank you for showing me how to remain dedicated while working hard in life to continue to achieve any goals I set.
CHAPTER ONE INTRODUCTION
1.1 Introduction to the Study
Modern media presents American society with any number of messages, ranging from periodic updates of overseas warfare in the Middle East and overviews of ongoing domestic emergencies to the details of local criminality and politics (McElreath, 2013; McElreath, 2014a; McElreath, 2014b). Reaching any audience, whether international, national, regional, or local, necessitates some consideration of marketing processes wherein various procedures are followed sequentially. Although a process exists whereby marketing and advertising activities occur, no guarantees exist that the implemented processes are optimized or efficient. Even organizations themselves are subject to cyclical process that progress through phases of growth, maturity, and decline (He, 2016).
Daily, leaders of businesses, executives, business decision-makers, and so forth study methods to improve their current processes in all facets of their respective organizations. This notion applies to all possible levels of any business, from the top level to the bottom level decision makers. By studying methods to improve the current business processes of a business, the executives and business leaders can advance their processes. They also use intelligence processes to gather information for supporting decisions (Doss, et al., 2016). Improving the processes of a business allows for a company to place itself in better position to improve all around, which includes all areas of a business. Process improvement is a crucial function for management to consider for a company to improve its processes. When looking at processes, the improvement of those processes can be applied to many different areas of the company, including information technology, operations management, logistics, security, industrial production, and personnel, just to name a few. Without process improvement, businesses will continue to execute the same processes without knowing how well they are performing or how they could improve their processes.
To implement effective processes within a business, the administrators and leaders of businesses will need to examine a plethora of models and controls to manage, compare, examine, improve, and regulate the performance of their company’s individual operations. Without this performance evaluation, a company’s progression moving forward will be highly unlikely. In the advertising field, this idea of performance evaluation and process is key as well to achieve the goal of advertising: maximizing awareness of the information that is advertised to a group of individuals (Lavidge & Steiner, 2000). If advertising agencies are not performing well, their clients will not have success in producing higher revenues and improving their company’s image. Performance is why the evaluation, control, review, and improvement aspects of processes are vital to success in advertising agencies.
Business organizations must implement effective management models and processes to successfully provide the best opportunity for success from their employees, who in return will effectively contribute to meeting the external expectations. Management within these organizations must consider a wide variety of measurement instruments and processes to evaluate their current processes and determine which decisions to make to better serve themselves in future endeavors. When examining different strategies to enforce to achieve future successes with advertising processes, managers consider the Defining Advertising Goals for Measured Advertising Results model (Colley, 1984), the Foote, Cone, & Belding strategy matrix (Vaughn, 1980) , the evaluation methods of benchmarking for process improvement in relation to goals and standards, (Broderick, Garry, & Beasley, 2010), an adaptive Total Quality Management model for facilitation of services (Ghosh & Ling, 1994), the Six-sigma method of function improvement (Drake, Sutterfield, & Ngassam, 2008), and restructuring of advertising processes through business process reengineering (Teng, Grover, & Fiedler, 1994).
Using these process controls, business leaders are given the opportunity to improve their day-to-day functionalities. Each of the process evaluation methods provide ample opportunities for advertising agencies to examine their current strategies and process, but implementation of the Capability Maturity Model integrated (CMMi) has not previously been pursued by any personnel performing advertising functions. Marketing processes often have cyclical natures and exhibit economic attributes with respect servicing the needs and wants of humans (Doss, Glover, Goza, & Wigginton, 2015). Thus, the marketing process begins with society (Doss, Guo, & Lee, 2011). Given these notions, such reasoning allows for probable cause for research to be conducted to determine if the CMMi can be adapted for processes within advertising firms.
1.2 Review of Previous Paradigms
Tim Glowa (2002) previously conducted a study to review models that advertisers used to facilitate their processes. One of the earliest established prominent advertising process models was the Defining Advertising Goals for Measured Advertising Results (DAGMAR) model, which was a book written by Russell H. Colley (Glowa, 2002). This model was derived based on four different types of communication responses to products. These four areas included awareness, comprehension, conviction, and action (Colley, 1984). In his book, Colley stated that it was appropriate to choose one of the four areas of the communication with consumers and in return apply the chosen area to the creation of advertising goals.
Another model that was studied by Glowa was the Foote, Cone, & Belding (FCB) strategic matrix. This model recommended that advertising functions based on the perception of the commodity being sold (Glowa, 2002). The FCB matrix was derived to investigate the functionality of advertising in relation to the products. This model, comprised by Richard Vaughn, provided advertisers with the opportunity to choose the approach in which they deliver their advertising pitch to those interested in buying their product and choose the demeanor that consumers will exhibit towards their product (Vaughn, 1986).
Benchmarking refers to the actions taken to analyze, interpret, and acclimate current practices currently in place in a business or practices from an outside business to better business processes and performance (Vermeulen, 2003). This can include comparisons between current business operations and historical operations, and comparisons between what a company is doing in relation to what the competition is doing. Information from benchmarking can be used to provide operational advantages to a company.
Total Quality Management (TQM) is a method used by management to govern the functions of all departments, resources, processes, and operations of a company (Ghosh & Ling, 1994). TQM mostly is concerned with feedback form customers that can be used to better processes that a company conducts to operate. TQM can be implemented to better serve customers of a company.
The paradigm of Six-Sigma is a model that is a well-organized process that strives to analyze quantitative data of a business from which information will be derived to influence business operations and lower the faults that the business was previously making (Drake, Sutterfield, & Ngassam, 2008). More specifically, Six-Sigma is concerned with analyzing statistical data to monitor the changeability of business processes involved with the statistical information.
The act of business process reengineering (BPR) refers to the strategic analysis of a business’s current operations with the goal of achieving optimal output (Teng, Grover, & Fiedler, 1994). This model of process evaluation also calls for redesigning of the current business processes of a firm to expedite the improvement, efficiency, and effectiveness of current business processes (Mohapatara, 2013).
The act of business process improvement (BPI) involves scanning the external and internal environment to decide exactly where an organization can improve the current processes to remain competitive with the changing environment (Zellner, 2011). Firms can use business process improvement to better their processes to provide themselves with the best chance of maintaining efficiency and effectiveness over time (Vergidis, Tiwari, & Majeed, 2006). Business process management (BPM) is a process paradigm that seeks to model business processes, simplify business processes, analyze business process performance, and improve business processes (Margherita, 2014). Organizations using this model of business process improvement want to properly govern their processes so that the processes are performing optimally (Zairi, 1997).
Standardization is a method of setting minimal requirements that an organization must meet to perform and operate (Laroche, Kirpalani, Pons, & Zhou, 2001). In advertising, standardization must occur for a firm to function and conduct its advertising processes in an adequate manner.
Governing bodies at each level of civilization develop different types of legislative actions to govern companies and organizations in their everyday functions and activities (Furlong, 1994). Advertising functions within an organization should adhere to the governing bodies or they will face potential fines, penalties, and other punishment. Policies exist in most any organization for providing guidelines for business activities in the context of what is considered ethical and unethical when conducting business (Swami & Tirupati, 2012). With advertising functions in a business, organizations must follow policies that outline what is deemed acceptable for the business to do.
1.3 Rationale and Motivation of Research Study
The rationale and motivation causing this research study stem from the literary works in advertising. The preexisting literature lacks a model that investigates process improvement within the advertising field and seeks to progress and mature the current and future processes in advertising. There is a plethora of existing process maturity models that contribute a wide variety of methods of implementing process enhancement within organizations, but none of the preexisting methods attacks this through process maturity.
Because of a lack of previous research relating to the development of a process maturity framework within the context of advertising, there currently is a demand for the creation of an advertising process enhancement paradigm. With this being noted, the motivation surrounding the purpose of this study relates to an absence of any previous study directed towards a process enhancement model paradigm in the field of advertising. With the lack of research of a progressive process maturation model in the advertising domain, a look into modeling a process maturity framework for the purpose of advertising processes could provide management within these businesses with a new means of process improvement that would contribute to more efficient and effective processes moving forward.
Even without the literary presence of process maturity framework in the field of advertising, the literary works using this model in other areas provide a plethora of studies that indicate that the process maturity paradigm would prove successful within their respective areas of work. For example, implementing a process maturity framework within the logistics domain has provided logistics processing with an applicable framework for process enhancement over time (Benmoussa, Abdelkabir, Abd, & Hassou, 2015). A model of such stature provided a new process evaluation method that is both valid and available for use again. Another example of successful implementation of a process maturity paradigm is within the software industry. The use of a process maturity framework in the software firms has provided the results of increased process potential that concludes with a higher customer satisfaction (Padma, Ganesh, & Rajendran, 2008). When examining shareholder feedback, adapting a process maturity model framework has provided shareholders with a higher share price per stock from successful implantation of the process enhancement paradigm. Also, firms that invest in a process maturity framework will provide higher returns for the near future and for the long-term (Filbeck, Swinarski, & Zhao, 2013).
With the availability of prior process improvement modeling studies in areas not related to advertising agencies, the purpose of this research study will be to achieve an added process maturity model literature. For this study, the research will add an advertising view with the existing studies done with process maturity frameworks. Another motivational purpose of this research will be to further the previous studies completed in maturity frameworks by constructing another perspective relating to advertising processes. A further source of motivation for this study relates to the continuous need for business process that will provide for more efficient and effective functionalities. More importantly, the implication of more efficient process will allow for business administrators and leaders to observe where costs can be lowered which will allow for more efficient operations with lowered operational costs (Burian & Maffei, 2013). Business organizations are constantly searching for methods of producing more output at lower costs, which will provide higher returns for the business. Per the studies of Silk and Berndt (1993), advertising agencies are concerned with aligning minimal costs with larger outputs. This goal can be achieved through effective evaluation and maturity of advertising processes.
Using the CMMi for process maturity, businesses leaders can best apply the operations of their organization in the most efficient way which will reduce operational costs (Ahern, Clouse, & Turner, 2004). To better serve the customers of advertising agencies, the administrators of such agencies must constantly investigate better means of improving their operations (Calantone & Drury, 1979). Due to this, process enhancement with the use of progressive maturity levels can allow for the needed enhancements in efficiency that will provide lower costs for the company. By examining the molding of a CMMi for advertising processes, a new evolutionary process maturity paradigm may possibly be derived to provide these agencies with lower costs and optimal output from the operations that are conducted.
A rational purpose for conducting this study can be considered through analytical and realistic instances. From the analytical perspective, there remains a lack of preexisting literature for the CMMi within advertising agencies, which will allow for an authentic approach for such model. For realistic instances, introducing an adapted CMMi framework for advertising can allow for operational efficiency that may lower financial costs of performing previous processes.
1.3.1 Advertising Processes
Advertising agencies are managed organizations that require the use of a plethora of business operations. These operations may relate specifically to the advertising agency itself or they may be specific functions that any business performs. For example, advertising agencies require the functions of management, management information systems, accounting and finance, and human resources. These basic business operations relate to all businesses. A brief listing of business processes that take place in an advertising agency are as follows:
- Advise on which advertising strategies for clients to use (Na, Marshall, & Woodside, 2009)
- Demonstrate how the agency plans to implement its skills for acting on advertising schemes (Na, Marshall, & Woodside, 2009)
- Regulated organizational functions (Na, Marshall, & Woodside, 2009)
- Conducting marketing research activities (Na, Marshall, & Woodside, 2009) x Internal organizational scanning to improve productivity (Vyas & Manwani, 2012)
- External environment scanning to improve productivity (Vyas & Manwani, 2012)
- Processes to improve organizational performance (Vyas & Manwani, 2012) x Measures to assess staff performance (Lace, 1998)
- Measures to train and reward staff (Lace, 1998)
The above listing of business processes is just a sampling of the processes that occur in any advertising agencies across the different functional departments. There are many other business processes and operations that take place in advertising agencies. However, the listing shows that business processes take place across different functions of advertising agencies.
1.3.2 The Capability Maturity Model Integrated
During the 1980s, the Software Engineering Institute (SEI) was established at Carnegie Mellon University to help further research and understand how administrative processes within businesses are matured and improved over time (Carcary, 2013). Through research, the SEI studied a management perspective of how companies analyze their respective administrative process to determine the progress of them in the present time and in future times. The Capability Maturity Model integrated (CMMi) was established from this research with emphasis on five levels of process maturity. From the first (initial) level of the process maturity paradigm to the final level (optimizing), business processes go through different levels to improve and mature effectively. The order of the five levels of the process maturity paradigm is: initial, repeatable, defined, managed, and optimized. Processes start as immature and end as mature using the CMMi’s framework. A brief description of each of the five levels of the primary maturity model framework are listed below (Doss, 2014; Doss, 2004):
Level One: processes are erratic and unplanned; processes are not well defined; processes are responsive; processes are potentially sporadic and ad hoc
Level Two: processes are reoccurring and reactive
Level Three: processes are characterized and expressed
Level Four: processes are measured, contained, and investigated
Level Five: processes are now optimized
Overall, the levels of the CMMi provide business administrators and leaders of any business organization with a process maturity framework that will allow for the maturity and improvement of business processes. Each level provides a unique approach in the maturity of business processes. This process improvement paradigm offers a method of improving administrative processes from the original erratic and unpredictable state to finishing in the optimized state. The basic architecture and framework of the maturity model concept have shown the potential of adaptability across multiple domains (Doss & Kamery, 2006).
1.3.3 Derivatives of the CMMi
Since its establishment, maturity modeling has been used in a wide variety of different business areas to help organizations better evaluate, control, and improve their business processes. Examples of derivative forms of this model include project management (Doss & Kamery, 2006), logistics (Benmoussa, Abdelkabir, Abd, & Hassou, 2015), information technology (Carcary, 2013), finance (Doss, Chen, & Holland, 2008), software (Doss & Kamery, 2006), people (Wademan, Spuches, & Doughty, 2007), industrial management (Doss, 2004), environmental management (Doss, et al., n.d.; Doss & Kamery, 2005), industrial management (Doss, et al., n.d.; Doss & Kamery, 2006), and criminal justice (Doss, et al. 2015; Doss, 2014). Despite the existence of derivative maturity models, the existing advertising agency research lacks the presence of any process enhancement paradigm that seeks to establish a process maturity framework. The prior models show a wide variety of distinctive views to evaluate, control, and improve advertising processes, but none of these paradigms establish a process maturity framework to be used for the advertising processes. Due to this shortcoming, the purpose of this study is to examine how business conducting advertising functions view their administrative processes, improvement of the processes, and maturity of the processes with respect to the potential of CMMi portability.
1.4 Research Question and Hypothesis
The research surrounding this study seeks to answer the research question that has been constructed for this study. Answering research questions seek to serve as the purpose for conducting extensive research (Allen, 2006). For this study, the question surrounding the research in this study is stated below:
Can the maturity framework of the Capability Maturity Model integrated be adapted to create a process improvement maturity model for advertising processes?
Once a research question is developed, consideration for a research hypothesis or research hypotheses should be examined. A research hypothesis statement seeks to develop an expected outcome from the research question for the research study (van Wesel, Boeije, & Hoijtink, 2013). For this study, three hypotheses were developed that are applicable to all levels of the CMMi. This study seeks to look at the perspectives of process maturity modeling with those employed in urban and rural locations, those with and without previous process improvement initiatives, and those with and without current process improvement initiatives. The hypothesis statements for this study that is derived from the research question is listed in the table below.
Table 1.1 - Hypothesis Statements
illustration not visible in this excerpt
The survey questions used for this study can be broken down into questions that are specific to each level of the CMMi. The levels of the CMMi have been listed in Table 1.2.
Table 1.2 - Capability Maturity Model Levels
illustration not visible in this excerpt
The formation of the previously stated hypothesis also occurred after a review of the levels respective to the survey questions. The survey questions are listed in Table 1.3. Both tables are listed below.
Table 1.3 - Survey Questions
illustration not visible in this excerpt
1.5 Overview of Research Goals and Expectations
From the development of the hypotheses statements, research expectations and goals must be developed. When conducting research, it is very important to align the expectations and goals of the study with the hypothesis that is based on the research question (Allen, 2006). The expectations and goals serve as important means of direction and purpose when conducting intensive research and disseminating surveys (van Wesel, Boeije, & Hoijtink, 2013). These expectations and goals of the research take the hypothesis further and determine what exactly the study should result with.
1.5.1 Research Goals
For this study, the research goals are as follows:
1. This study is predicted to demonstrate how the process maturity structure of the Capability Maturity Model integrated can be implemented with advertising processes.
2. This study is predicted to demonstrate that prior process and procedure enhancement models have not acknowledged the problems of process improvement in advertisement using a process maturity foundation.
3. This study is predicted to demonstrate that prior advertising processes management procedures do not align with the principles of the Capability Maturity Model integrated.
1.5.2 Research Expectations
The expected outcomes of this study are as follows:
1. Evaluate the viewpoint of rural against urban staffs relating to the process maturity model foundation.
2. Evaluate the process maturity model foundation viewpoint of employees with prior process improvement initiatives against employees without prior process improvement initiatives.
3. Evaluate the process maturity model foundation viewpoint of employees with current process improvement initiatives against employees without current process improvement initiatives.
1.6 Research Value
Prior to the dissemination of the survey included with this research, this researcher examined employees’ views of different business process in a multitude of different areas of business to determine how processes in each area of business have been matured efficiently and effectively. More specifically, the use of the CMMi across different areas of business was reviewed to assess the success that the process maturity paradigm has had in those areas. For this research, advertising processes and how they are progressed over time was examined with prior research. The preliminary readings of literature showed that no previous display of publications cover how business persons address the maturation of advertising processes within their own organization, especially when comparing between rural and urban settings. Thus, this study will allow for the future research into process maturity paradigms in other areas of business. This study may provide beneficial results to current business organizations that use advertising processes on a normal basis. The results may allow for these organizat ions to better how they perform advertising functions so that in the future they perform these advertising functions in a more efficient and effective manner.
1.7 Overview of Research Design and Methodology
1.7.1 Research Design
For this research, a Likert-scale survey was developed for conducting a cross-sectional study. This type of study design was chosen because cross-sectional studies allow for the analysis of similar information gathered from any size of a selected population regardless of the period (Doss, 2014). Cross-sectional studies provide the best opportunity for researchers to analyze data with consideration to the different responses that occur from the dissemination of a survey to the respondents of the survey (Allen & Seaman, 2007).
The survey used during this research was crafted with a Likert-scale for understanding the perspectives of personnel in business organizations that use advertising processes. Likertscales provide researchers with an opportunity to examine cross-sectional data using a survey (Alexandrov, 2010). This form of surveying allows respondents to answer questions with the emphasis on scaling their view or views on the questions that were asked (Allen & Seaman, 2007). The choice of using the Likert-scale formatted survey provided the best opportunity to view statistical information across a wide variety of responses.
The Likert-scale survey used for this research provided five different answering options that allowed for respondents to scale their answers based on their personal viewpoint towards the questions in the survey. The five possible answer choices were scaled on a five-point scaling system. Respondents’ answers ranged from the first answer choice of “strongly disagree” to the fifth answer choice of “strongly agree.” The answer choices for the survey are listed as follows:
1. Strongly disagree
2. Disagree
3. Indifferent
4. Agree
5. Strongly agree
Questions that were included in this survey sought to gain more knowledge on the thoughts towards process improvement that any employee in a business organization may have. To understand these employee perceptions, there were questions pertaining to process characteristics, success and convenience of processes, and questions associated with each level of the CMMi. Furthermore, the survey questioned information about the demographics, location, and other basic characteristics of the employee’s business organization that was being surveyed.
1.7.2 Data Collection
The propagation of this survey occurred using the online surveying tool of SurveyMonkey. This website provides multiple surveying features to its users and allows the users of the website to create and design a plethora of different types of survey to be sent out for data collection. One useful function of SurveyMonkey is the option to allow the SurveyMonkey staff to disseminate a survey based on the target population and sample size of the survey. This allows for both a timely and effective distribution of the survey so that the appropriate amount of responses can be achieved. Also, the use of this surveying service allowed for respondents to complete the survey at any time during any day of the week.
As previously mentioned, the survey included a multitude of questions regarding the viewpoints of current and prior process improvement paradigms to better understand the employee perceptions of these models. There were also questions included in the survey that sought to understand the demographic and geographic features of each business organization being referred to in the responses to the surveys.
Included with the questions in the survey were statements before the survey began regarding the specifics of the survey. This included information on the CMMi and the different levels of the process improvement paradigm. There was also a statement included in the beginning sections informing participants of their voluntary participation in the survey.
1.7.3 Target Population and Sample Size
This research, with the use of the cross-sectional designed Likert-scale, sought to question individuals working in business settings that perform advertising processes and functions over time. It is acceptable to believe that most businesses perform some shape or form 33 of advertising over the business’s lifetime. For this study, the viewpoints of those working in said business settings were to be analyzed from the perspective of the geographic regions of rural businesses to urban businesses.
This survey sought to question those individuals in business settings to understand their knowledge of process improvement in their workplace. Questions were included in the survey that would allow for understanding of just how well current business personnel understand process improvement. Also, questions related to each level of the CMMi were asked so that perceptions of how employees felt and understood process improvement at the different levels could be understood.
For this research study, it was decided that 115 individuals should be questioned to retrieve answers based on the survey. The 115 respondents were required to answer questions regarding process maturity and questions relating to the CMMi at each level. On top of that, respondents were asked to identify their location within the United States which will better help understand the locations of the different respondents.
1.7.4 Methods for Analyzing Data
This research considered a variety of quantitative analysis approaches to further examine the data once it was collected. The analytical methods used for data analysis in this research included the analysis of variance (ANOVA) method, the Cronbach method, and descriptive statistics.
The Analysis of Variance (ANOVA) method of analyzing data gives the opportunity to examine the collected data for meaningful differences among the means of at least two groups of data from the study that are not related (Mendes & Yigit, 2013). Using this method of data analysis gives a useful tool for analyzing groups of collected data within a study.
The Cronbach method of analyzing data is a way of examining the reliability of a group data based on how closely related the data in that group are (Sijtsma, 2009). For the purposes of a study involving a survey, this method of data analysis will allow for any outliers to be examined and potentially omitted from the research.
The Chi-Ssquared method is used for comparing data that has been collected with an expected outcome of data based on a hypothesis that has been determined prior to the collection of the data (Lobato & Velasco, 2004). This method of data analysis allows for ample opportunity to analyze data when conducting a research that involves the use of a survey.
The Omega squared method of testing for effectiveness size is helpful for understanding differences when conducting ANOVA tests. Omega squared shows the estimated amount of variance distribution by the dependent variable that is shown by the independent variable (Doss, 2014). This method provides a beneficial opportunity of analyzing data when a survey is used.
Descriptive statistics are important to use when analyzing data because they provide some general feedback based on the data. These statistics provide researchers with such information as the mean, median, mode, standard deviation, and sample variance within the set of data.
1.8 Overview of Scope, Limitations, and Bias
This study was conducted for understanding advertising processes that take place across multiple business domains. No matter what the area of work may be, each organization performs some type of advertising that requires a process to complete the advertisement for the entity (Malmelin, 2010). Service organizations, for example, must advertise their services to attract customers. Another example of this is with sales companies. These types of businesses heavily rely on their advertisements to attract customers. When natural disasters strike an area, it is important for the media to properly advertise this so that the residents in the area will be informed. These instances require a process to take place to advertise the appropriate message to the audience.
The predetermined understanding in this study was that some fashion of advertising takes place in business environments, regardless of the industry or field of work. This study sought to research individuals that have been employed in organizations currently or recently. The questions involved in the survey were tailored towards gaining responses that will help understand how employees in a business setting view the processes that take place for an advertisement within their organization to occur.
Once the survey was constructed and ready for dissemination, it was determined that there would be a goal of 115 responses from those taking the survey. The respondents involved in this research were those that reside in the United States with no specific region or area targeted. Given this limitation on the size of respondents, some bias may have resulted from a smaller sample size. However, this research allows for further research to be conducted on this topic in the future.
1.9 Research Ethics
The research conducted for this study followed the ethical protocols and standards previously set forth by the University of West Alabama. This study also followed the ethical policies and guidelines established with SurveyMonkey.
Information regarding the importance of ethics was tracked after the completion of this study. Images of the information regarding confidentiality and ethics included in a section prior to taking the survey can be found in the appendix. This information was available to any respondent upon request. Also, the questions that made up the survey itself can be found in the appendix.
CHAPTER TWO LITERATURE REVIEW
2.1 Introduction to the Literature Findings
The research includes literature findings that view business process enhancement and improvement with different approaches. These literature findings covered a variety of different studies that reflect on quality control as it relates to businesses processes that are conducted. These studies included quality management models that have been used in the fields of advertising, healthcare, banking, manufacturing, and business settings in general.
This chapter defines processes on a generic level first, followed by a discussion of processes that are categorized as advertising processes. This chapter will also discuss, in sections, the process improvement models that were discovered through the research involved in this study. The models researched that entail business process enhancement include the Defining Advertising Goals for Measured Advertising Results model, the FCB Matrix, benchmarking, Total Quality Management, Business Process Reengineering, Business Process Improvement, Business Process Management, Six-Sigma, standardization, governmental legislation, and policies. Generally, such quality paradigms do not approach process improvement from the perspective of process maturity (Doss & Kamery, 2006).
These models will first be defined to understand what the models entail and what they mean. Next, the models will be described to garner a better understanding of how different process improvement paradigms have been used previously for process improvement over time in business organizations. Furthermore, the models will also be described in relation to the advertising field. Thorough description and understanding of the application of these models will help to explain the nature and purpose of the existing process enhancement models.
The models researched and compiled through the literature findings cover the concept of process improvement. However, none of the previous models for process enhancement includes the aspect of progressively maturing processes through an established framework for process improvement. Due to this, an absence of a progressive advertising process maturity model was exposed from the collection of literature found throughout the research process. Furthermore, this discovery lead to the interest for crafting a progressive process maturity model for advertising processes within business organizations.
Even though the literature findings did not show a model for progressive process improvement within advertising processes, a model was discovered that enhances processes over any period. This process improvement paradigm allows for businesses to look at the processes used in different areas and determine just how to approach improving these processes. The model discovered, the Capability Maturity Model integrated (CMMi), approaches process improvement with the purpose of enhancing business process progressively.
The CMMi allows for the opportunity for the model to be adapted for advertising processes. Literature findings of the CMMi’s adaptive uses in a variety of fields are discussed within a derivative context termed the Advertising Maturity Model (AMM). This model allows for a new perspective of approaching process improvement within the field of advertising and for companies that use advertising processes.
2.2 Understanding Processes
The term “process” can have a wide variety of definitions that relate to how the term is used. This is partly because the term is available for use in many different purposes. One method of defining processes is that the term stands for any grouping or set of activities that are completed for the sole purpose of achieving a common goal (Kock & McQueen, 1996). For example, how an individual prepares a meal involves a set of actions that make up the process of preparing and eventually completing the meal. In other cases, processes may be intangible, such as the psychological processes involved with human memory (Sumrall, Sumrall, & Doss, 2016).
Another definition for processes refers to processes in a somewhat similar manner to the previously described meaning of the term. This secondary definition of a process can best be explained as an action or actions that may arise as common or uncommon in manner and are continuous over short or long periods of time (Goldenberg, 2006). An example of this meaning for a process is when someone breathes because this action is repetitive over time. Although the result of this process is to gain oxygen, this process continuously occurs over time.
With these descriptions of processes in mind, it is understandable that processes are used across a wide variety of domains in business. In this context, business processes can best be explained as an individual task or a group of tasks that allow for the overall goals of the business to be met within a specific period (Gorbach, 2002). Business processes also include actions that will allow for the customers to receive any product or service that they have requested (Goldenberg, 2006). All business processes play a vital role in assuring that the day-to-day operations of the business are performed smoothly so that the company can continue to function.
The described meanings of the term processes and its business derivative, business processes, allow for one to gather conclusions on the purpose of processes. All processes in these instances possess similar characteristics, regardless of how one may view a process (Kock & McQueen, 1996). An individual action or a group of actions occurs during the action of performing a process. In some instances, processes are common and reoccurring, but in others some processes occur randomly on a case-to-case basis. Whatever the reason may be, processes are performed with the purpose of achieving a result or goal.
2.3 Processes in the Advertising Context
Conducting advertising processes in any business organization requires a plethora of methods to properly manage and execute these processes. Specifically, for advertising agencies, the proper management of processes that take place within the firm is vital.
The Defining Advertising Goals for Measured Advertising Results (DAGMAR) model, for example, was established to locate which step in the communication process that the advertisement is and understand how to determine the appropriate goals and results for it (Glowa, 2002). This model itself exhibits a method for reviewing and improving advertising processes to understand where the advertisement stands in its lifespan.
There are a multitude of other methods available for advertising agencies to administer to provide the opportunity for process improvement. For example, the Foote, Cone, & Belding (FCB) matrix analyzes how advertising processes should be executed in relation to the promotion of the product or service (Atkinson, 2003). Other models used for execution of advertising processes include benchmarking, Total Quality Management (TQM), Six-Sigma, Business Process Reengineering (BPR), Business Process Improvement (BPI), Business Process Management (BPM), standardization, legislation, and policies. When organizations administer these processes, they allow for processes execution that will be for the betterment of their entity. Numerical analysis of productivity also takes place for these firms to perform at a higher level presently and in the future.
Although these models exist for process management in advertising, none of the paradigms approach process enhancement from the viewpoint of progressively maturing processes. Due to that, this section will thoroughly examine advertising processes and the need for such a model in the administering of advertising processes.
2.3.1 Examining Advertising Processes
Advertising is a vital part of success for any business organization that is in any field of work. The activity of advertising is understood as any process or processes involved in the promotion of products or services that may be offered by a business organization (Vaughn, 1986). Advertising takes place across every type of organization, and it also occurs amongst individuals.
The processes involved in advertising vary based on the nature of the product, service, event, or item of interest that is advertised. Regardless, advertisement in any form seeks to promote the point of the advertisement and reach as many potential customers or consumers as possible (Grunert, 1996). This fact is important in understanding why it is important to see to it that advertising processes are optimized.
Due to the large volume of situations that require the use of effective advertising, it becomes very important for those involved with the advertising process to see to it that the message being delivered is executed appropriately (Na, Marshall, & Woodside, 2009). The advertisement of important information in pressing situations can vary based on how populated an area is. If an unusual event occurs in an urban area a larger audience will be impacted, but if this event occurs in a rural area then a smaller audience will be involved in receiving the message (Fang, Luo, & Keith, 2015). Events such as the ones previously mentioned tend to occur at random; therefore, the processes involved in advertisement of these instances can leave room for error. For example, during a natural disaster it is important for information regarding the phenomenon to be transmitted to the effected audience efficiently. Another example of an important instance where advertisement must be handled efficiently would be during a terrorist attack or shooting. Effective advertising is vital in any area regardless of the location and timing of the event.
When products or services are advertised, this same concept of efficient advertising applies. These promotional processes will usually occur in a more defined manner. During the holiday season, for example, processes exist that involve the appropriate communication of information regarding the sale of specific products or services. If products and services are not promoted to the potentially interested parties appropriately, the goal of maximizing the audience sized may see a negative effect (Fang, Luo, & Keith, 2015). This concept can be applied to those individuals that live in urban areas that are highly populated and to those that reside in urban areas that may involve lower populations of residents.
To assure that these advertising processes are optimized and executed appropriately, the CMMi can be used to observe the current processes to understand their current state. The CMMi paradigm examines processes from the viewpoint of a five-step progressive process maturation model (Doss, 2014). As in the scenarios mentioned previously, it is important that the transmittance of information be executed in an efficient manner. Through this CMMi model, the processes used in advertising can be examined and further improved to assure that the operations that take place in the promotion of events, products, or services are optimized.
2.4 Defining Advertising Goals for Measured Advertising Results Model
2.4.1 Description of the DAGMAR Model
Over 50 years ago, it was understood by those in advertising that there was a need for a model in that field that would demonstrate how the process of advertising functions. The Defining Advertising Goals for Measured Advertising Results (DAGMAR) model was developed in 1961 to help meet this preexisting need in the field of advertising (Glowa, 2002).
This model that was developed helped advertisers and business administrators involved with advertising understand the purpose and function of the steps taken when advertising occurs. Furthermore, there was a minimal amount of studies conducted to develop a model that would describe the advertising processes that take place (Calantone & Drury, 1979). The DAGMAR model allowed advertisers to grasp knowledge of how their advertising functions were performing.
2.4.2 Purpose of the DAGMAR Model
The DAGMAR model was established with four principles relating to the exchange of information in mind. The four foundations surrounding this advertising model include attention, understand, belief, and reaction (Colley, 1984). These foundations were based upon how a customer or person viewing an advertisement interpreted the message being delivered.
From this model, it is believed that the viewers of an advertisement first notice the advertisement (attention) and eventually perceive (understand) what they are seeing (Glowa, 2002). After those two steps occur, the viewer will then develop a perception of the advertisement (belief) and act on their perception (reaction) of the message they have processed (Colley, 1984). A modified derivative of this model is shown below:
Figure 2.1 - DAGMAR Model
illustration not visible in this excerpt
Due to the establishment of the DAGMAR model, advertisers gained the ability to understand the performance of their advertisements. The model allowed for users of advertising to analyze which parts of the advertising process were performing efficiently and which areas of the process need improvement (Glowa, 2002). This newly found knowledge provided a useful tool for advertisers.
2.4.3 Uses of the DAGMAR Model
In any business environment where advertising takes place, the DAGMAR model is used by purpose or by coincidence. When advertisers decide to promote their product, service, or item of interest they will have some type of interaction with this model due to how communication occurs in the perceptions of advertisements (Preston, 1982). The use of the DAGMAR model occurs because advertisers will analyze, review, and sometimes even reconstruct their advertisements based on how the potential audience reacts to the message.
For example, when a company selling products promotes its products they will first need to analyze how their advertisement is perceived by the viewers. This idea of a viewer’s perception of the advertisement is related to the four steps that take place in the DAGMAR model (Colley, 1984). When the advertisement of the product is publicized, the audience will first become aware of the advertisement. Next, an audience will understand the message in the advertisement and develop their own opinion of the message (Preston, 1982). Finally, the potential customers will make their decision on how they feel about the product and decide which action they should take in the purchasing process. Because of this four-step process, users of advertising will encounter the DAGMAR model because how their audience reacts to their advertisement will determine on which changes, if any, should be made to the message that is sent to the audience (Glowa, 2002).
2.5 Foote, Cone, and Belding Matrix
2.5.1 Description of the Foote, Cone, and Belding Matrix
With the development of the DAGMAR model, advertisers and business administrators began to take different approaches to further understand their customers’ perceptions of advertisements. After extensive research and study into advertising and the viewers of advertisements, there was an understanding that the perceptions of advertisements differed based on the products and services that were advertised at any given time (Vaughn, 1980). This newfound knowledge led to the establishment of the Foote, Cone, and Belding (FCB) matrix).
The FCB model was developed based on how an advertisement was viewed by the audience in relation to the different products or services that were promoted. This model is highly directed by how the product or service stands within the thinking of the potential customers (Marshall, 2006). In a later research conducted by Vaughn to follow up with the FCB matrix, he stated that:
“Not all advertising works in the same way. Sometimes communication of key information and salient emotion will be needed to get a sale; at other times, consumers will need one, but not both; and often, [a purchase] may occur with little or no information and emotion. The purpose of strategy planning is to identify the information, emotion or action leverage for a particular product, build the appropriate advertising model and then execute it” (Glowa, 2002).
The FCB matrix allows advertisers to gain knowledge of the thoughts, feelings, and levels of involvement that take place when customers make decisions on the advertised products or services. This model provided advertisers with a new method of understanding just how well their advertisements are being executed and where advantages or disadvantages of the message may occur.
2.5.2 Purpose of the Foote, Cone, and Belding Matrix
The establishment of the FCB matrix provided many purposes and functions for advertisers to analyze. Prior to the development of this model, there was a lack of a model that sought to understand the thought processes and ideas that are involved in the process of making a purchase of an item or use of services (Vaughn, 1980). During the purchasing process, potential customers make decisions based on prior experiences or knowledge gained on the product or service that is being sold (Vaughn, 1986). The perceptions built by those interested in buying can be highly impacted based on how well the promotion of the product is executed. For the best opportunity of successful execution of promotion of the product or service, advertisers analyze purchasing patterns of customers that relate to the FCB matrix. This model provided a four- section grid where the purchasing patterns of the different advertised material would fall under, which is shown in the diagram below:
Figure 2.2 - FCB Matrix (Glowa, 2002)
illustration not visible in this excerpt
The four sections of the FCB matrix were based on how much of the decision to purchase involved different amounts of thinking or feeling towards the product or service (Marshall, 2006). Per the model, decision-making on purchases can range from extensive thought processes to decisions that involve very little input from predetermined feelings and thoughts (Vaughn, 1986). Due to this, this model’s purpose allowed the opportunity to gain knowledge of how much attention should be dedicated to different advertisement audiences so that the amount of purchases resulting from advertisements may be optimized.
2.5.3 Uses of the Foote, Cone, and Belding Matrix
In advertising, the FCB matrix plays a vital role in its use. This model allows users of advertisements to understand the thought processes that take place for a purchase to be made (Glowa, 2002). When products are advertised by companies the business administrators involved in the advertising process must take into consideration the types of customers that purchase the products. With the use of the FCB matrix, advertisers can determine how customers think or feel when making purchases which can help them better plan their advertising tactics (Rossiter, 1982). This provides an advantage to those involved in the process of advertising so that they may better understand their audience.
The FCB matrix’s four different quadrants further provide usefulness for promotion of products and services. These four sections of the FCB matrix provide the opportunity for the development of advertisements to occur at a quicker and simplified pace (Vaughn, 1986). Different products and services will fall under the four different sections based on the thought processes or feelings that occur when a potential buyer is deciding on purchasing. Products and services that fall on the left side, Quadrant 1 and Quadrant 3, of the model require different levels of thinking depending on the type of product or service that is sold (Rossiter, 1982). A higher level of thinking would occur for an item such as a house or vehicle compared to a lower level of thinking for items like paper towels or napkins. Any products and services falling on the right side of the matrix, Quadrant 2 and Quadrant 4, will require high or low amounts of feeling towards them and little thinking (Glowa, 2002). More emotion or feeling towards a product is required when purchasing higher quality items, such as jewelry or expensive perfume. Less emotion or feeling is involved with items such as alcohol or snack drinks that a customer consumes regularly.
Due to the differentiated types of products or services that can fall under the different sections of the matrix, this matrix allows advertisers to use the model to determine how they should attack their advertisements (Rossiter, 1982). When the planning process begins for advertisements, those involved in the process will look to understand what types of interactions occur from the customers in the purchasing process. By doing so, advertisers can better target their customers by understanding the emotion or thinking that will take place from a potential buyer before a purchase is made.
2.6 Benchmarking
2.6.1 Description of Benchmarking
Benchmarking has been around for quite some time. It occurs over a wide variety of fields of business and may be used in each department or functioning division of a company. This action of benchmarking can best be described as any type of assessment or evaluation method that compares something to a predetermined standard (Hong, Hong, James, & Park, 2012). Benchmarking may be useful in many ways for performance evaluation.
In business, benchmarking occurs in some shape or form. Benchmarking for business will usually occur internally or externally, and sometimes it occurs using both methods depending on the situation. Internal benchmarking for businesses will occur using preexisting data (Broderick, Garry, & Beasley, 2010). This form of evaluation is helpful for comparing performance over different periods of time within one organization. External benchmarking for an entity takes place by scanning the exterior environment to compare data over time (Vermeulen, 2003). This type of benchmarking provides an effective means of comparing statistics of an organization to ones in its competition. Essentially, benchmarking is a resource whereby organizations may judge their performances internally or externally (Doss, et al., 2016).
Both methods of benchmarking provide an effective means of understanding how well a company’s operations and functions are performing (Doss, 2013; 2012). Benchmarking allows management and business administrators of an entity to examine their current processes and operations to understand where the entity is efficiently operating and where enhancements are needed (Broderick, Garry, & Beasley, 2010). The uses of benchmarking also are pertinent for advertising in understanding the success of advertising functions in an organization.
2.6.2 Purpose of Benchmarking in Advertising
Benchmarking plays an important role in any organization that performs any type of advertising functions. This evaluation tool allows business administrators to analyze the success of their advertising processes to understand their strengths and weaknesses (Hong, Hong, James, & Park, 2012). Furthermore, benchmarking in advertising can provide organizations with the opportunity to review, analyze, and potentially reconstruct advertisements so that the promotion of their products and services allow for the optimal amounts of customers to be reached.
When internal benchmarking takes place, this action provides the business leaders and users of advertising within an organization the purpose of comparing their own advertisement and promotional efforts (Broderick, Garry, & Beasley, 2010). By using this form of benchmarking, the success of sales based on advertisements used in prior time periods may be compared to current ones to understand how well the current promotions are performing. This type of performance evaluation will allow businesses to adapt their advertising efforts based on changes over time.
The use of external benchmarking can allow companies to gain a competitive advantage by analyzing their advertisements with those of the competition to see where improvements should take place to make the company’s current advertisements more successful (Vermeulen, 2003). This form of benchmarking makes it where companies can adjust and change their advertisements in comparison to the competition’s advertising. This may render the first company’s advertising more appealing.
2.6.3 Uses of Benchmarking
In the domain of advertising, benchmarking can be used to effectively manage and control just how good advertisements are performing. When setting benchmarks for sales, business administrators can understand the importance of advertising functions and determine the success or failure based on the benchmarks set (Luo, 2000). This method of evaluation allows an organization use benchmarking to enhance their current advertising processes.
During the use of an internal advertising benchmark, a company will decide to compare current advertising statistics and data to similar information gathered from the past (Hong, Hong, James, & Park, 2012). For example, during a crisis or random chaotic event that would need to be advertised efficiently, it would be important that a company or organization refer to how the process was handled in the past. In the case of such event, the importance of bettering the message to the audience would be a main goal to keep in mind. Referencing past experiences with these similar events could provide the entity with chance to exploit weaknesses and advertise their message more effectively than before (Broderick, Garry, & Beasley, 2010).
When an external advertising benchmark is used, an organization will examine its current advertisement data with those of similar entities within the industry (Hong, Hong, James, & Park, 2012). During times of high product sales, this method of advertising could allow a company to understand where their advertisements could improve to attract more customers. If a competitor seems to be attracting more customers, the company could use their competition’s sales information and advertising data as a benchmark to improve their current advertising processes (Vermeulen, 2003). This form of benchmarking can give a company an advantage over their competition and allow for sales volumes to increase.
Regardless of the situation or use, benchmarking in advertising can allow business administrators and leaders to gain more knowledge of their current processes so that improvement can be made. The uses of internal and external benchmarking are contributing factors.
2.7 Total Quality Management
2.7.1 Description of Total Quality Management
Total Quality Management (TQM) is believed to have been introduced in the United States during the early 1900s. The idea surrounding this business process enhancement model is that TQM may be used in business environments to effectively manage and govern the different activities and processes that take place (Weckenmann, Akkasoglu, & Werner, 2015). This business process evaluation model is based on how an organization may improve its current operations to achieve an optimized level of customer satisfaction The TQM model includes many foundations that attribute to providing the best chance at success from the use of TQM. This paradigm models around serving the customer as best as possible while involving all employees of the organization so that ultimately the highest rate of customer satisfaction may be achieved (Askey & Malcolm, 1997). Engaging all employees during the application of TQM practices allows for the whole business may understand how quality is improved. TQM also includes assessing the processes strategically that take place within an entity so that any enhancements to the processes can be made (Weckenmann, Akkasoglu, & Werner, 2015). Analyzing the current processes within the organization will allow for improvements to be made to the processes also. However, using TQM as a model for improving operating efficiency requires that the business leaders effectively communicate implemented plans so that all responsible parties will be informed correctly (Ghosh & Ling, 1994).
Overall, the TQM model provides another form of analyzing an organization’s current processes to achieve optimal efficiency. This model of business management evaluation may be used across a wide variety of businesses and organizations, which makes this model appealing for entities to use. Using TQM, organizations may experience various benefits, including (Doss & Kamery, 2006):
Customer emphasis - total customer satisfaction
Process - reductions of process variations and continuous improvement
Cultural - pervasive quality throughout the organizational enterprise
Analytical - Uses of various measurement systems to enhance quality and facilitate continuous improvement
2.7.2 Purpose of Total Quality Management
Across any domain in which it is used, Total Quality Management may be implemented by the business administrators of an entity to improve their functionality. One of the main purposes of TQM includes improving the overall quality of the operations that take place within an entity with the result of enhancing customer satisfaction (Ghosh & Ling, 1994). This model allows a company to perform better than the competition while seeing an improvement in the overall image of the company.
The use of TQM in any business or departments of a business allows for improvement in multiple facets of the organization. Implementing the use of a TQM requires some thought and goal-setting that will provide the best opportunity for an entity to outlast its competition (Askey & Malcolm, 1997). This is because TQM requires high emphasis on the customer. When a customer’s perception of an entity is put first, this causes the business leaders within the organization to reevaluate how their current operations can better serve the customer (Ghosh & Ling, 1994). To achieve higher customer satisfaction, many different areas relative to the organization must be addressed. In relation to the TQM model in Figure 2.3 below, customer satisfaction can be achieved through attention to some of the key principles involved in the TQM model.
Figure 2.3 - TQM Principles
illustration not visible in this excerpt
Through the effective implementation of a TQM model within an organization, the overall quality of the entity may be improved (Weckenmann, Akkasoglu, & Werner, 2015). Furthermore, the flexibility of using the TQM allows for its use across a wide variety of organizations. This can allow for goal-setting, process improvement, and other quality management practices to take place differently within each organization based on what actions will serve them best.
2.7.3 Uses of Total Quality Management
Due to its potential use in multiple areas, TQM opens the possibility to be used within many different organizations. For this study, studies relating to TQM’s use in the field of advertising will be analyzed using a study by Ghosh and Ling (1994). Also, implementing a study by Askey and Malcolm (1997) will show another effective use of TQM in advertising.
The study conducted by Ghosh and Ling examined the use of quality management services, specifically TQM, with advertising functions. This research conducted by the two analyzed the difficult threats that are faced by advertising agencies on a day-to-day basis.
Advertising agencies face the struggle of implementing advertising strategies that will reach the maximum audience while appealing to those viewers so that sales may be maximized (Ghosh & Ling, 1994). This same concept can be applied to any organization that uses advertising because advertising seeks to reach those same goals mentioned in the study. Ghosh and Ling concluded that TQM should be used in advertising because the model “focuses clearly on the needs of the audience, manages the processes involved in advertising, and enhances communication among all employees” (Ghosh & Ling, 1994).
Askey and Malcolm conducted a separate study to further analyze more uses of TQM in the advertising industry. This study was conducted using a questionnaire to understand the types of quality management systems, if any, are being used in the advertising industry. The research endeavor also sought to understand the results from the use of a TQM model in advertising. Due to the importance of advertising within companies, it is of high importance for entities to analyze their advertising functions to understand where improvements are needed (Askey & Malcolm, 1997). This relates directly to the TQM and how it could be used to provide the best quality of advertising services. Askey and Malcolm’s study ended by stating that “most agencies implement Total Quality Management processes with the purpose of improving the quality of services provided to customers” (Askey & Malcolm, 1997). This study’s results and conclusions were very supportive in the use of a TQM model for advertising functions.
Whether it is in advertising agencies or examining an organization’s advertising functions, the implementation of a TQM model has provided successful opportunities for the entities to improve. This model allows organizations to approach organizational improvement through customer satisfaction.
2.8 Business Process Reengineering
2.8.1 Description of Business Process Reengineering
Some shape or form of Business process reengineering (BPR) occurs in almost all organizations. BPR is described as an evaluation tool in business settings that allow for the evaluation and reconstruction of processes within the organization (Manganelli & Klein, 1994). This analytic tool allows for advancement of the operations within any given entity.
BPR provides organizations with the use of a functional tool to analyze the current business processes that take place on a day-to-day basis. Teng, Grover, and Fiedler (1994) define BPR as in-depth assessment of an organization that involves redesigning the structure and functions that take place within an entity. BPR allows for management within an organization to redesign their current processes to provide a better opportunity for efficiency.
When BPR takes place within an organization, the entity can examine areas that may need improvement. BPR, if used optimally, can allow an organization to thoroughly use information technology resources to analyze how the business processes that take place can be improved to provide benefits such as lowering cost and increasing output (Polpinij, Ghose, & Dam, 2015). This method of business management can be a useful planning tool for entities when addressing deficiencies within different operations that take place.
2.8.2 Purpose of Business Process Reengineering
The use of Business process reengineering can provide multiple purposes and positive outcomes from the successful use of it. The restructuring tool of BPR allows for the management within an organization to evaluate the performance over a certain period to understand what changes need to be made with the current processes that take place (Teng, Grover, & Fiedler, 1994). BPR provides organizations a means of ensuring that processes conducted are performed as efficiently and effectively as possible at any given time.
Beyond just the purpose of improving business processes and overall efficiency, implementing BPR allows companies to explore methods of improving the different functions and departments that work together (Manganelli & Klein, 1994). Performing such actions as this allows for the layout of the organization to be restructured for the organization to perform smoother. Also, analyzing ways of improving the different departments allows for responsibility relating to certain processes to be assigned to the appropriate parties so that these groups may become more adept at carrying out the processes (Polpinij, Ghose, & Dam, 2015). Effective use of BPR for these purposes will improve the organization’s functionality.
Outside of looking at processes relating to specific departments and teams, administering BPR also includes analyzing common issues that reoccur so that these can be addressed individually (Teng, Grover, & Fiedler, 1994). For example, if a process takes place that consistently seems to lack optimal output then the management will need to address this by reconstructing the process. Analyzing and addressing the current common issues that result from processes also allows for an organization to improve its problem-solving abilities (Manganelli & Klein, 1994). This will improve the company’s ability to react to other issues in the future also.
2.8.3 Uses of Business Process Reengineering
There has been an abundance of research conducted to understand the structure of Business Process Reengineering and to understand its use. This model of organizational redesign has provided successful outcomes for businesses across a wide variety of business functions, including healthcare, accounting, information technology, and many other fields of business. There have been instances of success and instances of failure when using this model.
When referencing healthcare and its information systems, Patwardhan and Patwardhan (2008) conducted an in-depth study on the potential use of BPR in healthcare processes. This study analyzed a sample of some of the different processes that take place in healthcare facilities, such as hospitals. The two researchers in this study noted that in healthcare settings BPR could be used to accomplish higher levels of efficiency and effectiveness resulting from performing services on patients (Patwardhan and Patwardhan, 2008). This could be due to using BPR to reconstruct certain processes that take place when dealing with the patients in healthcare facilities. The study on healthcare also concluded that “BPR can be used as a tool for improving some sub-processes or sub-unit activity” (Patwardhan and Patwardhan, 2008). This idea comes from the notion that many different smaller processes take place in healthcare settings to make up one complete process.
In advertising, BPR can be used to allow for advertisers to reevaluate the process or processes involved in establishing an advertisement and then effectively communicating the information to the audience. With the successful use of BPR, management within an organization can construct a plan for redesigning how current processes transpire (Teng, Grover, & Fiedler, 1994). In advertising, this idea can be applied to communicating information to an audience at any given time. When a natural disaster occurs or catastrophic event takes place, BPR could be used to understand the deficiencies in carrying out those duties. A study conducted to analyze BPR’s flexibility and use across multiple domains concluded that BPR activities should be used to continue to sustain success and stay competitive over time (Polpinij, Ghose, & Dam, 2015). This is due to constant change that takes place over time. BPR in this case would assist companies in transforming their current processes to respond to the constant changes that take place.
2.9 Business Process Improvement
2.9.1 Description of Business Process Improvement
The Business process improvement (BPI) paradigm is a method of examining current processes and current sub-processes to attain operating results that are more desirable (Doss & Kamery, 2006). BPI may also be described as an in-depth approach to determining which processes, steps involved in processes, or skills involved in processes should be enhanced so that the entity may perform more efficiently and develop its processes (Vergidis, Tiwari, & Majeed, 2006).
Developing a strategic model for BPI in organizations did not occur until the early 1990s. James Harrington is recorded as the first to develop the concept of BPI in his book Business Process Improvement: The Breakthrough Strategy for Total Quality, Productivity, and Competitiveness (Klefsjo, 1998). Since its establishment, BPI has been used by many different organizations to improve their current processes.
Regardless of what definition of the model is used, BPI provides companies with a business model that will analyze how the company is currently performing. This paradigm will also allow the entity to make the appropriate improvements to the processes that take place so that better results will occur.
2.9.2 Purpose of Business Process Improvement
The main purpose of using Business process improvement is to allow organizations to modify the current processes that they perform to function more efficiently and meet the expectations of customers as best as possible (Vergidis, Tiwari, & Mareed, 2006). Making changes to the current processes that exist within a company may range from small changes to drastic changes. The severity of the changes will be determined based on a performance evaluation of the organization.
Another purpose that BPI provides to its users is that the paradigm allows entities to analyze the customers they serve. This is because the company will see operating results based on the processes that take place. Due to this, BPI can require business leaders to revisit goals that have been set. If an organization’s current processes are causing the entity to stray away from meeting the goals and outcomes set, then BPI will enhance the current processes so that the goals may be achieved (Klefsjo, 1998). This provides a method of aligning the current business processes with the goals and expected outcomes that were previously set.
2.9.3 Uses of Business Process Improvement
Organizations utilize BPI as a method of adjusting their current business processes to remain competitive with the constant developments and innovation that takes place in business settings (Zellner, 2011). As mentioned previously, BPI can be used to align the current processes with the goals of the organization. This notion also relates to the idea that BPI can be used to allow an entity to conform to the business world that is constantly changing.
A study conducted by Islam and Daud Ahmed (2012) explained how BPI could be used to analyze the current business processes within a credit card department of a multinational bank. The research conducted by the two involved examining real business processes that take place within an actual company. After ample research, the study concluded that “the proposed business process reduces the cycle time effectively and uses the organizational resources efficiently to achieve better customer satisfaction” (Islam & Daud Ahmed, 2012). This use of BPI allowed two researchers to interpret current processes, identify the issues, and make improvements to the processes so that customers will be more pleased.
Regardless of the situation, BPI can be used by business administrators to enhance an organization’s processes. This model also allows an entity to do the following: examine processes, define processes, implanting methods of automating processes, provide feedback, and improve continuously (Doss & Kamery, 2006). Through effective use of the model and actions such as those, an entity will provide itself with the best opportunity at sustaining success over periods of time.
2.10 Business Process Management
2.10.1 Description of Business Process Management
Business process management (BPM) can be defined as a method of assessing any business processes that allow an organization to meet previously established goals (Margherita, 2014). BPM may also be described as actions taken by management within an entity to govern the activities that take place with the purpose of achieving adequate and successful performance (Gulledge & Sommer, 2002).
The administrative paradigm of BPM is widely understood as one of the most commonly used methods of process management (Zairi, 1997). This is logically understood because businesses involve management and executives that analyze their current performance based on the processes they conduct. If the processes are managed and assessed correctly, an organization will provide itself with an opportunity for sustained success. BPM may also be described in a plethora of different ways due to its wide variety of uses (Vivas, Sobreiro, & Claudino, 2014).
From the definitions and descriptions of BPM mentioned above, it can be rationalized that BPM involves some type of regulation of the operations that take place within businesses and other entities. No matter the specific definition used, BPM can be instilled by executives within organizations to grasp a better understanding on the current processes that take place.
2.10.2 Purpose of Business Process Management
When BPM is implemented in business settings, the purpose of this model includes multiple beneficial outcomes for the entity. Through his research, Doss stated that “BPM transcends both management functions and organizational designs to facilitate process improvement” (Doss, 2014). From this notion, it is understood that BPM provides the purpose of improving processes within an organization in an easier manner.
Going a little further, the BPM model calls for more than just simply managing the processes that take place within an organization. BPM requires that executives of an entity analyze the individual sub processes and components of processes to manage the business activities (Zairi, 1997). When examining the current processes to properly manage them, the business leaders look at components such as who is involved, determine which individuals are responsible for certain actions in the process, and determine how the performance of the process is judged (Vivas, Sobreiro, & Claudino, 2014). Through these actions, BPM provides the purpose of gaining more knowledge and understanding of an organizations current business activities so that they may be properly governed.
Through the actions taken to properly use the BPM paradigm, business administrators also gain beneficial results in other areas. When organizations thoroughly analyze the current processes that take place within the entity, effective communication must take place amongst the responsible parties that are involved with the process (Vivas, Sobreiro, & Claudino, 2014). Due to this, BPM allows for ideas and concepts involved with the process to be shared. This provides the opportunity for employees within the company to build their teamwork skills and communication skills while gaining more knowledge on the day-today business activities.
2.10.3 Uses of Business Process Management
Many different studies have been instituted to understand how BPM has been used in different business settings. Gulledge and Sommer (2002) investigated how business process management could be implemented in the public sector and the benefits that could result from its use. The research conducted by the two shows that implementing effectively BPM includes the following:
- Record each process to garner more knowledge on the activities that take place throughout the process (Gulledge & Summer, 2002)
- Designate process responsibility so the responsible parties may be held accountable (Gulledge & Summer, 2002)
- Oversee the whole process and its components to enhance the measures of process performance (Gulledge & Summer, 2002)
- Enhance the process components to improve the eventual quality and performance of the process (Gulledge & Summer, 2002)
These noted characteristics from this research align with BPM’s meaning and purpose. Through effective use of the business process paradigm, a result of smoother processes that are properly managed may result.
Margherita (2014) conducted a study to understand the concept of BPM in real world settings. Within that study, the researcher discussed the different advantages and positive outcomes from using a BPM approach to process improvement. Margherita stated that BPM was 65 important because it was concerned with “bringing employees to develop a broader organizational view rather than focusing on the goals of a single functional area” (Margherita, 2014). This use of BPM allowed the individuals within organizations to gain more knowledge of their entity rather than the individual departments that made up the company.
Regardless of how it may be used, BPM provides organizations with a chance at assessing the current activities that take place. From these assessments, executives may use other business process initiatives and paradigms to improve the current operations and functions that occur within the organization.
2.11 Six-Sigma
2.11.1 Description of Six-Sigma
Six-Sigma is a method of analyzing an organization’s current processes that may be used across a wide variety of domains within an entity. The discipline was first used for assessing production and manufacturing processes that take place for a company (Maddox, 2004). From there, Six-sigma was molded to be used with other functions that occur within an organization.
Per Drake, Sutterfield, and Ngassam (2008), Six-Sigma is a paradigm that involves the assessment of statistical data from an entity to understand where defective processes are taking place. Six-Sigma may also be defined as a quality management evaluation tool that allows for improvement of processes, enhancement of services and products, and overall better experiences for the customers (Quelch & Harris, 2005). The diagram in Figure 2.4 below best shows a simplified flow of the activities that take place when Six-Sigma is used.
Figure 2.4 (From Six-Sigma Daily)
illustration not visible in this excerpt
Six-Sigma, regardless of the specific description that is referred to, involves reviewing statistical data resulting from the current processes that take place (Sony & Naik, 2012). From doing this, improvements may be made to improve products and services offered by an organization. However, no description exists for Six-Sigma that approaches process improvement progressive maturation of the processes.
2.11.2 Purpose of Six-Sigma
The implementation of the Six-Sigma quality management paradigm provides organizations with multiple different purposes of its effective use. The main purposes of Sixsigma are listed below:
- Organizational assessment takes place (Maddox, 2004).
- Analysis of statistical data occurs (Drake, Sutterfield, & Ngassam, 2008).
- Realization of defective processes occurs (Drake, Sutterfield, & Ngassam, 2008).
- Process improvement occurs (Quelch & Harris, 2005).
- Improvement of products and services results (Quelch & Harris, 2005).
- Customers’ experiences improve (Quelch & Harris, 2005).
The first action taken when implementing the Six-Sigma paradigm would be analyzing the organization. When analyzing the company this will help business leaders and executives grasp an idea of the performance of the company (Maddox, 2004). From analyzing the company, a step further must be taken to better understand the evaluation of the entity. This next step involves investigating statistical data from reports to determine the results that have occurred from the organization’s operations (Drake, Sutterfield, & Ngassam, 2008). Thus, from analyzing statistics, business administrators will understand the areas that are not performing to their best.
Once certain departments or areas within an entity are discovered, further steps must be taken. Processes must be analyzed within the underperforming departments to understand which processes are currently deficient (Drake, Sutterfield, & Ngassam, 2008). However, once the deficient process occurs then the improvement of those processes may begin. Realizing the defective processes involved in the underperforming areas of an organization allow for the process or processes to be improved by the responsible parties relating to them (Quelch & Harris, 2005). These actions will allow for improved efficiency in the future. Also, because of process improvement, the products and services relating to these processes will improve which will also improve the experiences of the customers (Quelch & Harris, 2005). Overall, Six-Sigma provides the purpose of improving processes within an organization.
2.11.3 Uses of Six-Sigma
Six-sigma, due to its wide variety of uses and opportunities, can be used across a plethora of functions, departments, and different industries. Many researchers have conducted studies on Six-Sigma and its uses. For example, Sony and Naik (2012) engineered a study on Six-Sigma from multiple perspectives. The studies of the two combined previous literature pertaining to the matter along with some research of their own. The two concluded that “Six-Sigma promotes organizational learning and innovativeness because organizational learning and innovativeness jointly promote organizational entrepreneurship and increase competitive advantages” (Sony & Naik, 2012).
Within the domain of marketing, Maddox (2004) conducted research by herself to determine the implications of using Six-sigma. This researcher analyzed certain companies that have used the Six-Sigma discipline within their marketing departments to improve the processes. For example, her research was interested in the types of product development and promotion that takes place within those entities and how those functions can be improved. From the study, Maddox stated that one of the companies she researched achieved “a 104% increase in repeat visitors and reduced development costs by 84%” (Maddox, 2004). As a result from the use of Six-sigma in this instance, customer satisfaction increased as well as lowering costs.
2.12 Standards
2.12.1 Description of Standards
Standards exist within all facets of any organization, regardless of the type of business or how the business may be operated. Per Samiee, Jeong, Pae, and Tai (2003), a standard may be defined as a predetermined regulation as to which business operations must conform to. These types of standards may be developed by the different governmental organizations that exist or by another form of organization that publicly publishes the standard and it becomes an ethical reference and generally acceptable for use (Samiee, Jeong, Pae, & Tai, 2003). Once a standard is established and becomes widely accepted, changing the standard may become tough.
Laroche, Kirpalani, Pons, and Zhou (2001) determined that standards may best be understood as processes that have previously been established with the purpose of making business activities more uniform. This description of standards refers to simplifying how various processes are conducted within an organization so that the processes may become simpler and quicker to perform (Laroche, Kirpalani, Pons, & Zhou, 2001). Standards within this sense are also previously established and accepted amongst wide groups of individuals.
Regardless of the description of standards used, the term itself calls for simplification of processes that take place. The use of standards in an organization may also be referred to as a form of external benchmarking due to the use of predetermined regulations (Samiee, Jeong, Pae, & Tai, 2003). The processes within an entity may be streamlined based on standards that have been established before by the government or other organizations with the idea of widespread use of the standards in mind.
2.12.2 Purpose of Standards
The purpose of using standards depends on how an organization determines that the entity will use the standards. One purpose of standards is to provide companies with a point of reference to determine if the processes they perform are ethical and conform to regulations (Krugman & Ferrell, 1981). This means that standards in advertising provide organization with an opportunity to view which types of advertisements are considered ethical or unethical according to society.
Another important purpose of standards involves providing another point of reference for those in an organization. In this instance, standards may be used as a method of referring to prior instances where a process took place and then using that outcome as a potential goal or benchmark for current processes (Boddewyn, 2007). For example, referring to how other 70 organizations have performed from their advertising efforts would allow another entity to set goals based on those results.
Continuing with the purpose of standards is the idea that using standards provides a simplified method of performing processes within an organization (Laroche, Kirpalani, Pons, & Zhou, 2001). When determining how to carry out business processes and actions within an entity, some business administrators will choose to follow the actions of their peers initially. Some organizations will use standards to create uniform means of performing their business process that take place to allow for a repetitive process that may be easier to perform over a period (Krugman & Ferrell, 1981). Regardless of how standards are used in these situations, they allow for providing easier means of conducting business within entities.
2.12.3 Uses of Standards
There have been many studies conducted on standards and how they have played roles in different industries. For advertising, Laroche, Kirpalani, Pons, and Zhou (2001) conducted a study to analyze how advertising strategies have been standardized amongst different entities. The researchers in this study were concerned with developing as model of standardization for advertisements that could be reused at any given time by entities to simplify their advertising processes. The study, about multinational corporations (MNCs), concluded that “understanding the similarities in market position, getting familiar with foreign contexts, and developing shared values and beliefs” all were important to standardizing advertising processes within organizations (Laroche, Kirpalani, Ponz, & Zhou, 2001).
Another study discovered through this research related to advertising and the importance of standards with advertising. Boddewyn (2007) conducted research to educate others on the importance of controlling and regulating advertising. During this study, lengthy discussions of activities that must take place to control advertising took place. From the research, the list below states activities that must take place to govern advertising:
- Establish standards (Boddewyn, 2007).
- Making the standards distinguished and generally agreed upon by other professionals (Boddewyn, 2007).
- Offer recommendations to others using advertising of ambiguous advertising situations (Boddewyn, 2007).
- Govern conformity to the standards (Boddewyn, 2007).
From the points in the study, it can be understood that standards provide opportunities for advertisers to use them as means of regulating their advertisements. Developing standards in this instance also allows advertisers to assist others in potentially unethical or confusing situations (Boddewyn, 2007). Overall, standards can be used in advertising in different ways for controlling and governing their advertisements.
2.13 Legislation
2.13.1 Description of Legislation
Legislation exists across all domains of business and across other areas involved in today’s society. Doss (2014) explains legislation as the construction of a rule or system of rules by an authoritative party that involve some legally binding consequence if broken. This description of legislation refers to the creation of a law or a group of laws by the relevant government entity which requires some form of legally binding occurrence (Doss, 2014).
The Legal Information Institute at Cornell Law School referred to legislation as the enactment of ideas and beliefs that have been molded into laws for citizens to abide by. This explanation of legislation falls in line with the description because it involves the creation of a law by governmental entity. Legislation may involve different beliefs and ideas at different levels of government based on their location (Benady, 2000). Culture and ethics impact legislation in these instances. Legislation also may involve statutes, decrees, and other regulations that are passed by government that pertain to any form of an entity that conducts business (Furlong, 1994). In this instance, legislation that is enacted by government for businesses must be followed.
Given these descriptions of legislation, it can be understood that legislation involves some form of creating laws that are passed by any governmental organization at any level. These laws must be followed by individuals and entities.
2.13.2 Purpose of Legislation
Given the previously discussed descriptions that exist for legislation, determining the purpose of the term becomes an easier task. Legislation was developed to establish a means of governing individuals and groups of individuals, such as entities, companies, associations, cities, counties, states, and other groupings of citizens (Benady, 2000). Legislation is needed to establish a means of sanity and control over individuals of any location.
Per the Legal Information Institute at Cornell Law School, legislation serves the purpose of providing a framework of rules or laws that should be followed by the appropriate persons or society. Citizens within the U.S., for example, can vote on which laws and other forms of legislation are enacted (Cornell Law School). Due to this, legislation also provides the purpose of giving citizens an opportunity to have some type of input on which legislation will be passed to govern the country.
In relation to the business world, legislation serves the purpose of governing corporations, companies, partnerships, and any other form of business. When organizations perform their business activities, they must conform with the appropriate governing body (Benady, 2000). There are different types of actions entities may take that are considered legal and illegal, but these entities must follow legislation or potentially face fines.
2.13.3 Uses of Legislation
Many different instances, situations, and scenarios exist in which legislation has been used amongst businesses. When referencing advertising, legislation has been crafted to make sure that advertisements are truthful and following the law. Mills (1994) wrote about how the use of radio advertisement for vehicle sales and the associated leases has been impacted by the enactment of legislation. Mills also discussed similar advertisements involving leases that are in print, stating that “newspaper and magazine advertisements are also required to disclose leasing provisions” (Mills, 1994).
Advertisement of tobacco products is also another area that requires specific legislation to govern certain circumstances. Furlong (1994) further researched advertisements in the tobacco industry to determine what types of legislation impact this industry. This research included examining the specific advertisement laws related to tobacco in Australia. During the early 1990s, the advertisement of tobacco within the country was shut down due to the harmful effects of using tobacco products like cigarettes and smokeless tobacco (Furlong, 1994). However, Australia is not the only country that has monitored the advertisement of tobacco products closely. Keller (2007) conducted research on Congress’s impact on advertising in the tobacco industry within the United States in recent years. Congress eventually passed legislation requiring the U.S. Food and Drug Administration (FDA) to regulate all advertisement of tobacco products (Keller, 2007). Due to this legislation, organizations that are involved with those products must make sure that the advertising processes that take place adhere to guidelines established by the FDA. Thus, advertising must be done legally (Glover & Doss, n.d.).
Other instances where legislation has impacted the advertisement of goods and services exist also. Benady (2000) published research on legislation involved with advertisements by companies across many different industries. Different countries and groups of countries across the world view advertising legislation differently. In places, such as Belgium, Austria, and Italy, limitations on advertisements to children exist due to the legislation passed by the governing bodies of each country (Benady, 2000). These restrictions are for protecting children from what they may see at such a young age.
Regardless of the specific use, legislation exists in many different countries that pertain to restrictions for advertisements. These different types of legislations impact how business administrators must structure the processes that exist in relation to performing advertising functions
2.14 Policies
2.14.1 Description of Policies
Based on research involved with this study, policy may be defined in two manners that are both similar with their descriptions. Dubé, Hitsch, and Manchanda (2005) define a policy as a rule, foundation, or principle that is established by a governing body, an entity conducting business, or an individual. In this instance, a policy is understood as a regulation proposed to be 75 used as a guide for companies, employees within companies, or for certain individuals (Dubé, Hitsch, and Manchanda, 2005). How the policy is created depends on the specific party it applies to.
Another beneficial description of policies states that policies best symbolize the ethical beliefs and principles of those in leadership and executive level positions involved in managing different organizations (Doss, 2014). This description of the term best relates to the business use of policies. In this instance, policies can be created by businesses to be used for governing their employees based on the entity’s ethical and cultural beliefs (Poundstone, 2015).
Regardless of the specific terminology or description used, one can logically understand that policies are proposed by governing bodies or individuals. These policies are like legislation and standards, but also have differing characteristics that set them apart from the two.
2.14.2 Purpose of Policies
Given the description of policies, it is important to also understand the purpose of the term. Policies, in one meaning, are established to provide the purpose of established guidelines that govern organizations on which actions are appropriate behavior (Doss, 2014). Executives that direct and manage these organizations will establish policies in these organizations based on what their beliefs are. Policies also provide the purpose of establishing specific procedures that must be followed within organizations (Doss, 2014). Processes and other business activities that take place must adhere to the organization’s policies.
Per Poundstone (2016), policies exist to provide employees working for organizations with guidelines that exemplify the cultural beliefs of the top executives involved in the entity. These individuals in these management roles have played some role in the crafting of the policies, which explains why their cultural beliefs are shown through the policies. Policies within organizations also exist for providing employees with an understanding of the conduct that should take place within the entity (Poundstone, 2015). Furthermore, policies may also allow employees to understand the vision of the organization so that they will understand how to perform the business activities that take place (Swami & Tirupati, 2012).
Basically, policies show organizations and employees of organizations a set of guidelines to follow that exhibit some fashion of ethics. The specific policies within different entities will differ in some way due to individuals having different beliefs on certain ethical situations.
2.14.3 Use of Policies
Policies are used by the government, by organizations, by groups of individuals, and by individuals themselves. During the research for this study, published literature by Krishnan and Jain (2006) was reviewed pertaining to how advertising policies may be established for new products. The two researchers in the study analyzed the importance of developing advertisement strategies for new products, and they also stressed the importance of how difficult it can be to develop advertising policies for these new products. The study conducted by the two concluded that “it is the interplay between the advertising effectiveness and the prevailing advertising-sales ratio that decides whether advertising should be increased or decreased” (Krishnan & Jain, 2006). In other words, establishing an advertising policy for new products is dependent on the sales performance of the product.
Caraher, Landon, and Dalmeny (2006) conducted in-depth research on the uses of policies within advertising. This study specifically analyzed developing policies for advertisements used on television that seen by children. Within this context, television advertising that may be viewed by children must consider the well-being, health, and demeanor 77 of adolescents (Caraher, Landon, & Dalmeny, 2006). These considerations can be understood due to the willingness of children to react to what they may view on their televisions. The study concluded by stating that “the monitoring and impact of advertising inputs are required from the perspective of the impact on public health” (Caraher, Landon, & Dalmeny, 2006). Due to this, administrators must carefully develop policies for advertisements that consider the impact that the advertisements may have on children.
Many other instances exist where policies have been used in within the context of advertising. Policies exist to provide organizations with a set of guidelines that adverting processes may conform to. However, the use of policies does not include improving the advertising processes within an entity.
2.15 Capability Maturity Model Integrated
2.15.1 Description of the Capability Maturity Model Integrated
Through a vast array of different business functions, an existing progressive process improvement framework exists in the integrated Capability Maturity Model (CMMi). The establishment of the CMMi can be traced back to the late 1980s when the Software Engineering Institute conducted a study to begin the creation of a process maturity model to assist in enhancing processes used in software functionalities (McCollum, 2004). After a couple years of experimenting with this software process maturity framework, the Software Engineering Institute fully created the Capability Maturity Model for Software (Carcary, 2013). With its creation, the CMMi opened the door for use across multiple business functions.
The CMMi itself is made up of five different progressive maturity levels that business processes may be defined as at any given time. Level one of the CMMi, the initial level, is where processes lack organization or any repeatable characteristics (Benmoussa, Abdelkabir, Abd, & Hassou, 2015). Processes may then fall under the second level, the repeatable level, where processes begin to reoccur and may be managed at any given time (Doss & Kamery, 2006). The third level of the CMMi, the defined level, is where processes begin to be defined and proactive (Filbeck, Swinarski, & Zhao, 2013). Finally, the last two levels within the CMMi are the quantitatively managed level and the optimized level. During these two levels, processes are quantitatively managed based on their performance and improvements are made so that the processes which eventually reach optimal performance (Padma, Ganesh, & Rajendran, 2008). A further description of the CMMi and its maturity levels may be found in the table below.
Table 2.1 - Capability Maturity Model Levels
illustration not visible in this excerpt
2.15.2 Purpose of the Capability Maturity Model Integrated
Based off the understanding of the CMMi’s description, the model itself serves a multitude of purposes for organizations who decide to use it. This process maturity paradigm seeks to evaluate processes within a firm, from all facets of the organization (accounting, marketing, advertising, human resources, etc.) and see how the current operations can be enhanced and positively altered to achieve ultimate production output of these current operations (Doss, 2014). The purpose of the CMMi initially is to look at existing processes within an organization and determine whether they are chaotic, managed, defined, quantitatively managed, and optimized (Filbeck, Swinarski, & Zhao, 2013). Understanding which maturity level the processes are in will help to understand what improvements need to be made.
Level One of the CMMi, the initial level, is where processes that need the most improvement may be found. This level of the process maturity model serves the purpose of understanding which processes occur at random, which processes are amiss, and how reactive processes are (McCollum, 2004). If processes are to be determined to be in Level Two, the repeatable level, these processes have occurred previously. This level of the CMMi allows an understanding of which processes are repeatable and what results occur from the repetitiveness of the processes (Carcary, 2013). Level Three of the CMMi, the defined level, includes defined process that occur regularly and have proactive characteristics. During this level of the model, descriptions of the processes may be established and expressed as well (Benmoussa, Abdelkabir, Abd, & Hassou, 2015). The penultimate level of the CMMi, Level Four, involves processes that are measured, controlled, and analyzed. This level, the quantitatively managed level, involves examining reports and outputs that occur from business processes (McCollum, 2004). Level Five of the CMMi, the optimized level, provides the purpose of determining whether processes are optimized or not. During this level of the model, business administrators must use performance results to determine whether improved processes exist (Padma, Ganesh, & Rajendran, 2008).
When using the CMMi, business processes will be categorized in one of the levels of the model. Regardless of which level it is, actions must be taken by business leaders to progressively improve the processes. If done correctly, processes will result in an optimized state.
2.15.3 Uses of the Capability Maturity Model Integrated
Since its establishment, the CMMi has been derived from its original form to be used across several other business domains. Some business domains that have been used for researching the CMMi’s capabilities include project management (Doss & Kamery, 2006), logistics (Benmoussa, Abdelkabir, Abd, & Hassou, 2015), information technology (Carcary, 2013), finance (Doss, Chen, & Holland, 2008), software (Doss & Kamery, 2006), people (Wademan, Spuches, & Doughty, 2007), industrial management (Doss, 2004; Doss, et. al., n.d.), environmental management (Doss & Kamery, 2005; Doss, et al., n.d.), and criminal justice (Doss, 2014). These examples are all instances where the CMMi has been derived into process improvement paradigms for different business functions. Only a sampling of the uses of these models will be discussed in this research.
Doss and Kamery (2006) examined the Project Management Maturity Model (PMMM) from the CMMi. The PMMM includes each of the five levels derived from the CMMi, but its levels are slightly different. The five levels of the PMMM include ad hoc, abbreviated, organized, managed, and adaptive (Doss & Kamery, 2006). Benmoussa, Abdelkabir, Abd, and Hassou (2015) researched the use of CMMi within the domain of logistics. The researchers then derived their own rendering of the CMMi for logistics, which included five different levels that involved specific steps, goals, and indications of achievement within the level (Benmoussa, Abdelkabir, Abd, & Hassou, 2015). Carcary (2013) researched how information technology processes could benefit from the use of the CMMi. The research concluded by deriving an Information Technology Capability Maturity Model (ITCMM). Carcary modeled this new process improvement model from the CMMi to have maturity levels of initial, basic, intermediate, advanced, and optimized (Carcary, 2013).
Wademan, Puches, and Doughty (2007) researched the uses of previously crafted CMMi for people and how it could be used within a workplace. The study discussed the five maturity levels involved in this derivative of the CMMi. Cultural characteristics and descriptions of each level of the model exemplify that the CMMi’s use with people is very beneficial (Wademan, Spuches, & Doughty, 2007). Doss (2014) researched prior process improvement methods, along with prior derivatives of the CMMi, to develop a maturity model for the criminal justice domain. From the study, the Criminal Justice Maturity Model was derived (Doss, 2014). Within this model, there were five different maturity levels established like the CMMi maturity levels. Each level of the CJMM includes multiple requirements that allow for the processes to be classified within each level (Doss, 2014).
2.16 Literature Analysis to Propose an Advertising Maturity Model
Many quality management models within this study approach business process improvement. Such models include the DAGMAR Model, the FCB Matrix, benchmarking, TQM, BPR, BPI, BPM, Six-Sigma, standards, legislation, and the use of policies. Given the preexisting studies surrounding quality management and process controls in advertising, there remains the lack of a model that exhibits process maturity with a progressive foundation.
A previously crafted business process paradigm, the CMMi, exists that matures processes over time with a progressive foundation. This model has not yet been derived for advertising. However, the CMMI exists within other areas of expertise, such as project management, logistics, information technology, finance, software, people, industrial management, environmental management, and criminal justice.
Given its derivative forms amongst other domains, the CMMi may be adapted for the use within advertising processes. The five maturity levels that exist within the CMMi may be modified to fit an advertising maturity model (AMM), but they will also remain similar with the original maturity levels within the CMMi. The proposed derivative of the CMMi, the AMM, and its maturity levels are listed in the table below.
Table 2.2 - AMM Maturity Levels
illustration not visible in this excerpt
Given the levels listed in the proposed AMM above, processes may begin as chaotic or random and eventually reach an optimized level. This is due to the progressive maturation that occurs with the CMM. With each level having certain characteristics, it is also vital to understand the requirements that encompass each level within the AMM. These requirements will be used to control the progressive maturation of processes. The requirements for each level of the AMM are described in the table below.
Table 2.3 - Requirements for Each AMM Level
illustration not visible in this excerpt
The first level of the AMM, the initial level, requires that advertising processes be analyzed, their occurrence determined, and the reasoning of the processes be understood. This allows for processes to be classified in the second, or repeatable level, of the AMM. These processes must be understood for reoccurrence, planned, and tracked as they occur. Processes then will be defined, or in the third maturity level. Processes at this level must be documented, their weaknesses must be acknowledged, and the processes must be managed. Once third level requirements are met, the processes must then be analyzed to understand performance and then improved based on their performance results. This will classify processes as in the fourth, or quantitatively managed, level. Processes will finally reach the optimal level when they are continuously monitored, modified over time, and any improvements are documented for future reference.
2.17 Motivation for the Study
This research has examined a plethora of quality control models that are used for monitoring business processes within organizations. These models have been used within the advertising context as well as with other unrelated contexts. However, these models have not approached process management through a maturity model that exhibits progressive improvement over time. Such a model, the CMMi, does exist that has been used in other unconnected domains. Given these facts, initial motivation exists for a study to understand whether the CMMi could be derived within the field of advertising. Further motivation exists through studies that relate to advertising and to studies that examine the same perspectives, urban versus rural, as this study.
When determining motivation beyond the lack of literature surrounding a CMMi derivative in advertising, it is understandable to examine quality control research that has been completed within the field of advertising. By doing so, an understanding of the success or failures of such studies can be grasped to determine whether further studies should be conducted. Ghosh and Ling (1994) research the use of a TQM model within the advertising industry in Singapore. The two researchers issued a survey to understand how advertising clients perceive certain aspects of advertisements. Of the most importance to customers was quickness of service, knowledge of the business, attracting sales from advertisements, and the product or service’s reliability (Ghosh & Ling, 1994). These aspects of advertising can be improved with the use of TQM. This study also exemplified a plan and implementation of the plan that would involve using TQM with advertising functions. The successful use of a quality control model within the context of advertising provides further motivation for this study.
With the motivation from the successful use of prior quality control models within advertising, further motivation may be established through examining studies that have used similar aspects. One hypothesis surrounding this study involves examining the aspects of individuals in urban and rural settings in relation to quality control. Amponsah (2010) researched different project management methods within Ghanaian agriculture, banking, and construction sectors. In this study, the researcher exhibits comparisons between urban and rural perspectives of different projects that have had success from the use of quality control measures (Amponsah, 2010). Prior studies exhibiting comparing urban versus rural perspectives with the use of a quality control model further motivate this study.
As previously concluded, motivation from this study already exists through examining studies that have used quality control models and similar perspectives that this study will use. A further motivational purpose for this study involves analyzing any research involving the CMMi and the perspectives of urban and rural individuals in relation to the model. Doss (2014) researched the CMMi and the potential for adaptive purposes within the context of criminal justice. This research included a conclusion stating that “no statistically significant difference exists between the perceptions of urban versus rural personnel regarding the notion that ‘organizational evidence of the process maturity model framework exists’” (Doss, 2014). Due to these findings, motivation exists to further understand urban versus rural perspectives of the CMMi.
Given the prior literature findings, multiple motivational purposes for this study exist. Prior studies exist that exhibit the use of quality control models in advertising, that analyze the perspectives of urban versus rural individuals when using quality control models, and that view how urban and rural individuals perceive the use of the CMMi in business settings. These findings provide motivation and purpose towards understanding deriving the CMMi for advertising and understanding how individuals in urban and will perceive such actions.
CHAPTER THREE METHODOLOGY
3.1 Introduction to the Methodology
This chapter involves describing different characteristics involved with the methodology in this study. This third chapter will begin by describing the different variable motivations and then discuss the scope, limitations, and bias. Following that, a description of the design of the survey within this research will take place, which includes the survey design, data collection, target population, and sample size. The methods that were used to analyze the statistics gathered from the research will next be discussed. Finally, the consideration of ethics with this study will be discussed to understand the importance of confidentiality.
3.2 Variable Motivation
When conducting extensive research, it is important to develop different variables that are dependent upon the research being conducted. These variables are used to determine the appropriate hypothesis or hypotheses. The first variable surrounding this study includes determining the geographic classification of the respondents. Individuals who participated in the survey were asked to classify themselves as either “urban” or “rural” based on their past or current employer’s location. According to the 2010 Census conducted by the United States Census Bureau, there are two classifications to describe an area as urban and anything smaller is a rural area. The two classifications of urban areas include “urbanized areas” that are made up of 50,000 people or more and “urban clusters” that are made up of 2,500 to 49,999 people (U.S. Census Bureau, 2015). A rural area can best be defined as a city or town that occupies less than 2,499 individuals within the city limits (U.S. Census Bureau, 2015). Differentiating between the two classifications was very important to understanding the two variables. One question pertaining to geographic size was included within the survey, which included the differing characteristics of urban and rural areas.
Two other variables motivated this study. The two variables used in determining the hypotheses pertained to how responses to the survey were recorded. Question 28 for this survey was stated as “Our previous process improvement initiative was:” followed by the different answer options. Question 29 for this survey was stated as “Our current process improvement initiative is:” followed by the answer options. These answer choices for both questions included no previous initiative, TQM, BPR, BPI, BPM, benchmarking, Six-Sigma, regulation, ISO standards, process maturity modeling, and proprietary initiatives. These two questions were used to decide on two more variables for this research. One variable was the respondents that had previous process improvement initiatives versus the respondents that did not have previous process improvement initiatives. The other variable included a similar variable: comparing those respondents with current process improvement initiatives versus those that do not have current process improvement initiatives. These two variables provide fascinating aspects for analyzing the results in this study.
3.3 Overview of Scope, Limitations, and Bias
3.3.1 Scope
When conducting an in-depth study, it is important to understand the scope of the study before research is conducted. The scope of a study will help to understand the different aspects that should be considered when determining the motivational purposes of conducting the research. A scope of study includes the different prior ideologies and methods that relate to the research itself (Søgaard, Lindholt, & Gyrd-Hansen, 2012).
The demographic scope of this study included questioning individuals across the United States on their perceptions of the Capability Maturity Model (CMM). This study sought to gain more knowledge of these perceptions from those across all regions of the country. Based on the survey results, respondents from all regions of the country were questioned. However, some states did not have participants in this survey.
For this research, many different aspects were considered when determining the motivation behind this study. Research for this study was conducted to understand how employees view advertising processes within their organizations. Malmelin (2010) outlined characteristics that may be exhibited by advertising agencies and other agencies conducting advertising functions. One understanding of this research was that most businesses exhibit some form of advertising activities at any given time. From this understanding, individuals within business settings were questioned on their perceptions of the advertising processes. The questions involved in the survey sought to understand how employees viewed advertising processes in relation to process maturity model.
The CMMi and a selection of its prior applications were also considered as a part of the scope of this study. Doss and Kamery (2006) researched different derivate uses of the CMMi across numerous domains that are separate from one another. The CMMi’s derivative forms with people, project management, and industrial processes were all analyzed to provide more insight on the purpose and direction of this study. These prior models contributed to the overall scope of this research.
Prior research involving different motivational perspectives were also considered when determining the scope of this study. It was determined that analyzing the perspectives of employees in urban and rural regions would be used with the responses to the survey. Doss (2014) and Amponsah (2010) successfully conducted separate studies that used urban versus rural perspectives, which help support the reasoning behind using this perspective within the scope of this study.
3.3.2 Limitations and Bias
Within any research, some form of limitations will exist due to a plethora of different potential reasons. For example, some limitations may exist because of time or financial constraints. Determining limitations of a research are important because these constraints allow for an opportunity to think critically about the results and understand what could have limited the research (Price & Murnan, 2004). Limitations on a research endeavor also open the opportunity for further research.
This study involved research examining previous methods of business process controls and improvements. A survey was disseminated to question employees of organizations to understand those individuals’ perceptions of process improvement within their current or former organization. This research yielded 115 total responses, of which 103 of the respondents completed the survey in full. The sample size of 115 responses came from individuals working in business environments that exhibit some form of advertising. Given these circumstances, the following limitations have been yielded:
- Small sample size of 115
- Respondents of any business domain were sampled x Small budget for research
- Respondents were not queried to understand if they specifically perform advertising functions or not
Due to these limitations, bias could exist towards the sample size that was surveyed during this research. Bias could exist due to individual responses coming from those working in general business environments instead of strictly advertisers. Also, the incomplete responses resulting from the survey respondents not finishing the survey exist. The respondents that did not complete the survey were removed before any statistical analysis occurred.
3.4 Survey Design Overview
3.4.1 Design
As a part of this quantitative research, a survey was disseminated to obtain responses to questions related to the study. For the design of the responses, a Likert-scale was used to give survey respondents the opportunity to scale their answers. A Likert-scale allows for the analysis of data across multiple sections of a survey (Alexandrov, 2010). This design method was also chosen since it gives respondents the opportunity to rank their perception of how they feel about the question asked or statement made (Allen & Seaman, 2007). In this survey, the questions and statements were given five potential responses. These responses included “Strongly Disagree”, “Disagree”, “Indifferent”, “Agree”, and “Strongly Agree”. In that same order, the responses were ranked from 1 to 5. “Strongly Disagree” was ranked at the low end with 1, and “Strongly Agree” was ranked at the high end with 5.
With this survey being disseminated through SurveyMonkey, the Likert-scale format was set up for each question through the survey. Prior to beginning the survey, a description of the background and purpose of the study was given to help respondents understand the research purposes better. Respondents were also given a description of the process improvement model and the levels within the model to help them understand the CMMi. After the first dissemination of the survey, issues existed with incomplete surveys. Because of abandonment, the survey was redesigned to include a progress bar and questions were grouped on pages together to display a smoother layout of the questions.
The survey included 23 questions in total that questioned respondents’ perceptions of advertising processes within their current or former place of employment. The questions involved with the survey were based on the Capability Maturity Model Integrated (CMMi) progressive maturity framework. Different sets of questions were about different level of the CMMi. Questions 1-5 were general questions that exhibited characteristics of each level of the CMMi framework. Questions 6-19 were in relation to each level of the CMMi. Questions 1-19 and their relation to the CMMi levels (if applicable) are listed in the table below.
Table 3.1 - CMMi Questions
illustration not visible in this excerpt
The next grouping of questions, which were questions 20-23, sought to understand the process management procedures that the respondent’s current or former employer used. After those, questions 24-27 were demographic and geographic questions to understand more specifics on the respondents. Questions 28 and 29 were used to understand current and previous business process improvement initiatives used. Finally, the remaining three questions asked respondents about their organization’s size, type of advertising, and what type of customers are served by them. The specific questions are listed in the tables below.
Table 3.2 - Questions 20 through 23
illustration not visible in this excerpt
Table 3.3 - Questions 24 through 32
illustration not visible in this excerpt
3.4.2 Data Collection
The survey involved with this quantitative research was disseminated using through the online website SurveyMonkey. This website provides its users with the function of creating surveys for audiences to take. There are multiple different types of features and design options to suit customers based on their specific needs. SurveyMonkey allows for users to create surveys through their website that provide a link for audiences to go to for completing the survey.
The website also offers a function, called Audience, where users can pick their audience based on geographic, demographic, and other characteristics that will help them to reach their target audience. SurveyMonkey will then assess fee for the survey and collection of the responses based on the target audience. This function was used for collecting survey responses in relation to this study. Collecting survey responses through SurveyMonkey’s Audience function allowed for quick and collection of data for this study.
3.4.3 Target Population and Sample Size
Before the dissemination of the survey, a target population and sample size were established to help meet the purpose of this study. The Audience function of Survey Monkey gives users different options that would narrow down the requirements for the respondent to meet to participate in the survey. The target population of this research involved 115 individuals who currently worked in business settings, full-time or part-time. Respondents to the survey were either in management or non-management positions within their respective organizations. The businesses referenced by respondents involved a variety of different areas of work. These respondents also were at least 18 years of age. The survey included a description of the project prior to beginning so that participants could understand the perspective of this research.
From the established target population, the sample size was then decided. The use of SurveyMonkey’s Audience feature required paying service fees, which factored into the sample size established for this study. Based on the budget set for this study, a target population of 100 responses was initially set. When the survey was first disseminated, issues existed with respondents abandoning the survey before completion. Only 35% of surveys were returned complete after the survey’s first disbursement. The survey was then redesigned and dispersed again. This second effort saw a higher completion rate of 64%, but design characteristics were adjusted once again before sending the survey out for a third time. After the third effort of disseminating the survey, the completion rate climbed a little higher to 66%. A total of 115 responses had been recorded through all three attempts. This set the total target population to 115. Of those 115, there were 103 completed surveys. The 12 that lacked completion were removed before any statistical analysis was performed, giving this study a total sample size of 103 respondents.
3.5 Methods of Analyzing the Data
This research involved analyzing statistical results from survey responses that were recorded through SurveyMonkey. Once the data was compiled and the sample size was complete, analytical methods of reviewing the data were used for this study. The analysis of variance (ANOVA) method of testing for data was the first statistical analysis method decided to be used with the data. Other methods of analyzing the data in this study included the Cronbach method, the Chi-Squared method, the Omega squared method, and the use of descriptive statistics.
An ANOVA test is a useful method for testing data compiled from a research that involves the use of a survey. ANOVA testing is important because it shows the variances amongst different groups of means within a study. Results from conducting an ANOVA test include information that will show if sets of data are significantly different and will also show information to help determine whether the hypothesis should be accepted or rejected (Mendes & Yigit, 2013). One- way ANOVA testing was performed using the responses from the basic framework questions (questions 1 through 5) for analyzing results pertaining to those in urban versus rural areas, those with and without previous process improvement initiatives, and those with and without current process improvement initiatives. One of the key results from ANOVA testing is the p-value. The p-value helps researchers to understand how whether to reject the hypotheses within their research (Doss, 2014). The results from the ANOVA tests ran for the hypotheses and research questions involved in this study will be discussed in further detail in the following chapter.
The Cronbach method is a helpful test based on compiled data within a research. This statistical analysis method provides researchers with Cronbach’s alpha, which a measure that is used to determine the reliability of a group of data (Sijtsma, 2009). Since this study involved a survey, it was determined that computing Cronbach’s alpha would be an effective way to determine the reliability value. This method of reliability results in a number between 0 and 1 that is scaled based on different reliability standards (Koning & Franses, 2003). For any grouping of data, Cronbach’s alpha should exceed 0.7 for the data’s reliability to be deemed sufficient and the alpha should exceed 0.8 for the data’s reliability to be understood as good (Doss, 2014).
The Chi-Squared method of analyzing data is useful for understanding whether bias exists within sets of data in a research study. When analyzing data from a research, the Chi- Squared method will investigate expected outcomes compared to the actual outcomes to understand whether bias exists (Lind, Marchal, & Wathen, 2008). The use of this method of analyzing data will test the results of this research to understand if any bias has occurred amongst the respondents. Chi-Squared testing will also be used in this research examine the expected versus observed responses from the survey’s distribution amongst the geographical regions.
Testing for effectiveness is a method of examining data to garner information on how large a noticed effect may be no matter what size the sample may be (Doss, 2014). There are multiple different methods of calculating effectiveness, but the Omega squared method will be used for this research. The size of the effect is scaled based on the following:
- Effect size less than 0.2 is deemed “small” (Priviteria, 2013)
- Effect size between 0.2 and 0.8 is deemed “medium” (Priviteria, 2013)
- Effect size greater than 0.8 is deemed as “large” (Priviteria, 2013)
The effect size calculation for this research will be used with the ANOVA testing results to determine how significant of a difference exists between groupings of data. This information will be important to use in understanding how respondents have answered the questions involved with the survey.
Descriptive statistics are another method of analyzing the data. Descriptive statistics used in this research present information such as the mean, median, mode, standard deviation, and variance. Each of these statistics provide important feedback to better understanding the data. The mean is useful for understanding the average response for the individual questions or for a set of questions. This number will be used to analyze the overall view of each question, which is shown as follows:
- A mean less than 2.5 yields an overall view of “disagree” (Doss, 2014)
- A mean between 2.5 and 3.5 yields an overall view of “indifferent” (Doss, 2014)
- A mean larger than 3.5 yields an overall view of “agree” (Doss, 20014)
The mode will be used to understand which responses appeared the most. The median shows which response sits in the middle of the data. Standard deviation will be displayed to show the amount of variance from the mean.
3.6 Ethics
Appropriate steps were taken prior to and during this research to meet the ethical standards and guidelines set forth by the University of West Alabama. Prior to conducting quantitative research, approval to move forward with the research was granted from the Internal Review Board with the University of West Alabama’s Office of Sponsored Programs and Research. This process involved a research proposal that included information on the research itself, confidentiality, and other ethical procedures taken to assure that responses are kept confidential.
Before beginning the questionnaire involved with this study, respondents were informed that this study was in conjunction with a graduate study taking place, the participation in this survey was completely voluntary and anonymous, and that all data gathered from the survey would be kept classified and used solely for this research. Those involved in completing the survey were notified that their completion of the questionnaire signified their agreement to take part in this study. A consent form was included with the survey prior to respondents beginning the survey. A copy of the consent form can be found in the Appendix. Also, participants were made aware of the background information surrounding the research, the purpose of the study, and the potential gain as an outcome from this research. Below is an excerpt from the introduction to the survey, which outlined some of the ethical procedures previously mentioned:
“This survey is a part of a study being conducted by a graduate student. Your participation is based on your own will to contribute. Your participation is your implied consent to participate in this survey. Your answers will be kept completely confidential and anonymous.”
Once the survey responses were received, the answers and any other data related to the survey were kept confidential through SurveyMonkey’s website. In future uses of this study, including presentations or publications, or any references to this study will involve complete confidentiality of individual responses related to this survey.
CHAPTER FOUR FINDINGS
4.1 Introduction to the Findings
This chapter involves discussions of the analyzed data that has been compiled for this research. This fourth chapter will first involve showing the demographics of the respondents, the discussion of the findings from the test for bias, and explaining the results for the test for reliability. The findings from the response rate will also be discussed. Displaying and then discussing the analysis of variance (ANOVA) tests that were completed for each hypothesis will follow the findings. The discussion of each ANOVA test will include statistical results from the data that will help to accept or reject the hypotheses. Some descriptive statistics will be used for this as well. Following the completion of those analyses, graphs from questions 1 through 2 will be inserted into this chapter to further discuss the responses received to each question. These methods of analyzing and discussing the results will provide for an ample understanding of this survey’s results.
The first five questions involved with the survey disseminated in this research involve questions related to each level of the Capability Maturity Model Integrated (CMMi) itself. These questions are what make up the overall foundation of the CMMi. This grouping of questions is what was used to conduct ANOVA tests to determine if there were or were not significant differences amongst different groupings of respondents. According to Mendes and Yigit (2013), any p-value that is less than 0.05 will show a statistical difference amongst the groupings of examined data. This information will help to decide on the hypotheses. Within these same grouping of questions from the survey, the results from mean analysis testing will be included to garner information on the overall question of this research.
At the end of this chapter, a summary of the findings will be displayed in tables to show the highlights of the findings. A table displaying the responses for each question will be shown at the end of this chapter. The descriptive statistics for each question will also be included in a table.
4.2 Demographic Overview of the Respondents
Respondents in the survey were questioned on which region of the United States that they live in. This question is a supplemental feature provided by SurveyMonkey to help understand where the respondents to surveys they disseminate are located. These results provide useful information for the purposes of this research. The question gave the respondents the option to classify themselves by which regions they lived in. These regions were predetermined by SurveyMonkey. The possible choices were: New England, Middle Atlantic, East North Central, West North Central, South Atlantic, East South Central, West South Central, Mountain, and Pacific. The states within each region are shown in the table below.
Table 4.1 - Regional Characteristics
illustration not visible in this excerpt
Based on the information above, the 103 respondents classified themselves based on the region they reside. The results are displayed in the graph below.
Graph 4.1 - Respondents by U.S. Region
illustration not visible in this excerpt
Within the different regions, it is also helpful to understand how respondents perceived process improvement initiatives within their organization. Respondents were asked to state their organization’s current and previous process improvement initiatives, if their entity used process improvement initiatives. These respondents were sorted based on if they have current process improvement initiatives or if they had previous process initiatives. These respondents were grouped together based on those demographics. Graph 4.2 below displays the number of respondents with current or previous process improvement initiative.
Graph 4.2 - Demographics of Respondents with Process Improvement Initiatives
illustration not visible in this excerpt
Graph 4.2 shows that there were respondents surveyed from each of the regions listed for this question. A numerical breakdown of the respondents with current or previous process improvement initiative is as follows:
- 8 (6.5%) classified themselves in the New England region
- 4 (3.3%) classified themselves in the Middle Atlantic region
- 26 (21.1%) classified themselves in the East North Central
- 13 (10.6%) classified themselves in the West North Central region
- 23 (18.7%) classified themselves in the South Atlantic region
- 8 (6.5%) classified themselves in the East South Central region
- 5 (4.1%) classified themselves in the West South Central region
- 15 (12.2%) classified themselves in the Mountain region
- 21 (17.1%) classified themselves in the Pacific region
Respondents were also sorted based on if they have no current process improvement initiative or had no previous process. Graph 4.3 displays the number of respondents without current or previous process improvement initiatives.
Graph 4.3 - Demographics of Respondents without Process Improvement Initiatives
illustration not visible in this excerpt
Graph 4.3 displays that there were respondents involved in this survey from each region of the United States A numerical breakdown of respondents without current or previous process improvement initiatives is as follows:
- 2 (2.4%) classified themselves in the New England region
- 12 (14.5%) classified themselves in the Middle Atlantic region
- 22 (26.5%) classified themselves in the East North Central region
- 5 (6.0%) classified themselves in the West North Central region
- 9 (10.8%) classified themselves in the South Atlantic region
- 4 (4.8%) classified themselves in the East South Central region
- 3 (3.6%) classified themselves in the West South Central region
- 9 (10.8%) classified themselves in the Mountain region
- 17 (20.5%) classified themselves in the Pacific region
4.3 Bias
To determine if bias amongst the geographical regions existed for this study, testing using the Chi-Squared method was performed. Lind, Marchal, and Wathen (2008) state that this form of statistical testing examines predicted outcomes in comparison to actual data gathered. This study’s expected and actual outcomes are shown in Table 4.2 below. The total of 103 responses was averaged across the nine total geographic regions to provide an expected 11 responses per region.
Table 4.2 - Expected and Actual Outcomes by Region
illustration not visible in this excerpt
The null and alternative hypotheses crafted for examining the possibility of bias amongst the regions are shown below:
H0: No relationship exists between actual and expected outcomes by region
Ha: A relationship exists between actual and expected outcomes by region
The total number of completed surveys resulted as 103 out of the total 115 disseminated surveys. The New England region accounted for 5 responses, the Middle Atlantic region accounted for 8 responses, the East North Central region accounted for 24 responses, the West North Central region accounted for 9 responses, the South Atlantic region accounted for 16 responses, the East South Central region accounted for 6 responses, the West South Central region accounted for 4 responses, the Mountain region accounted for 12 responses, and the Pacific region accounted for 19 responses. Based on these actual outcomes for each region, the Chi-Squared statistic resulted as 0.000056136. This statistic shows that some bias could exist due to the larger amount of responses from some of the regions.
4.4 Reliability Findings
Testing for the reliability was conducted on the overall sample size of this study, which originally involved the total 115 returned surveys. The decision to use the whole sample rather than section it off based on the hypotheses and CMMi levels was because the sample size was smaller. The initial calculation of Cronbach’s alpha found that the value was between 0.6 and 0.7, which classifies the data’s reliability and closeness as questionable (Koning & Franses, 2003). This initial calculation caused for concern and led to further investigation into why the alpha was not stronger than the first calculation.
Lowering Cronbach’s alpha involved the decision to remove the incomplete surveys to narrow the data used for Cronbach’s alpha down to the sample size of 103. The data was removed out of the set and the calculation for Cronbach’s alpha was computed again. Given the completed data within this research, the Cronbach’s alpha for this study resulted as 0.851. When Cronbach’s alpha is larger than 0.8, the data within the set is considered acceptable (Doss, 2014). Due to this, the data compiled for this research is deemed as reliable and closely related.
4.5 Response Rate Findings
The target population for this research included Based on the target population of 115 individuals that meet the criteria of currently working full-time or part-time. These individuals were also over the age of 18. Of the 115 surveys disseminated to the respondents, 103 were returned as completed. The sample size chosen from this target population then included the 103 respondents who completed each question of the survey. According to Lewis and Slack (2003), a typical research with a response rate in the range of 40% to 60% shows a favorable survey to be used for analysis. The response rate for this research was calculated as 89.6%, which shows a favorable response rate. Favorable response rates also show a strong indicator that the sample size is reflective of the population (Lewis & Slack, 2003). Due to this, the sample size for this study is reflective of the population.
4.6 Summary of the Survey Findings
4.6.1 Urban versus Rural
The ANOVA test ran was for the comparison of the urban and rural respondents based on the results provided. This was based on the first of the three hypotheses involved with this study. The hypothesis statement for these two groupings of respondents was as follows: “no difference exists between the perceptions of rural versus urban personnel in the perspective that evidence of the maturity model framework exists.” Of the 103 total respondents used for statistical analysis, 81 classified themselves as working in an urban area. The remaining 22 respondents classified themselves as working in a rural area.
Once the ANOVA test was completed, the p-value was used to determine if there was any significant difference amongst the respondents of the two groups. The mean analysis testing was also completed for these survey responses. Summary data from the two statistical analyses methods can be found in the table below.
Table 4.2 - Urban vs. Rural Findings for the CMMi Basic Framework
illustration not visible in this excerpt
Given that the p-value for this grouping of respondents resulted as 0.76, there is no statistically significant difference between urban and rural personnel when referring to the framework of the CMMi. The mean for the urban respondents is 3.18, while the rural respondents provided a mean of 3.21 for all responses to questions 1 through 5. Due to this, both groups of respondents generally felt neutral to all levels of the CMMi. The median for both groups resulted as 3, meaning that the middle response given was “indifferent.” Urban respondents had a mode of 3, while rural respondents had a mode of 4. More urban respondents answered with “indifferent”, and more rural respondents answered with “agree.” The standard deviation for the urban respondents is 1.04, while the standard deviation for the rural respondents resulted as 0.99. Sample variance for the urban respondents is 1.09, and the sample variance for rural respondents totaled 0.99. The variance and deviation show that the answers are not widely spread out.
4.6.2 Previous Initiative versus No Previous Initiative
The next grouping of respondents from the survey to undergo the ANOVA testing as well as mean analysis testing involved those respondents who worked in organizations with previous process improvement initiatives versus those that did not have previous process improvement initiative. This grouping was based on the second hypothesis for this study. The second hypothesis is stated as: “No difference exists between the perceptions of employees with previous process improvement initiatives and employees without previous process improvement initiatives from the perspective that evidence of the maturity model framework exists.” Based on the responses given, 42 answered with the no previous initiative option. The remaining 61 respondents answered with the previous initiative that their organization had used. Once the data was sorted to show the respondents with previous initiative versus the respondents without previous initiatives, the ANOVA test was conducted. This test provided the p-value needed to provide feedback on the two groups examined by the ANOVA test. The mean analysis testing was also conducted to gain information on how the respondents answered the CMMi basic framework questions. The data used from the two tests are in the table below.
Table 4.3 - Previous Initiative versus No Previous Initiative
illustration not visible in this excerpt
The p-value resulting from the ANOVA testing is 0.052, which is slightly higher than 0.05. This means that there is no statistically significant difference between the personnel with prior process initiatives and the personnel without prior process initiatives when referring to the framework of the CMMi. The mean of the respondents with previous process improvement initiatives resulted as 3.26, while the mean of the respondents without previous process improvement initiatives provided a result of 3.08. Given these two means, the average respondent for these questions is best described as “indifferent” to the questions. The median for each grouping of response is 3, meaning “indifferent” is the middle answer of all responses. The mode for those with previous process improvement initiatives is 4, while the respondents without previous process improvement initiatives have a mode of 3. This shows that those with previous initiatives more commonly answered the framework questions with agree. The respondents without previous process improvement initiatives answered the framework questions more commonly as “indifferent.” The standard deviation for the respondents with previous process improvement initiatives resulted as 1.11, while the respondents without prior process improvement initiatives yielded a standard deviation of 0.89. This information shows the amount of variation from the mean. The sample variance for the previous initiative respondents is 1.24, while the sample variance for the respondents with no previous initiative is 0.80. The deviation and variance show that responses for those with previous initiatives were slightly more spread out than those without previous initiatives.
4.6.3 Current Initiative versus No Current Initiative
The third grouping of questions involved sorting the data based on those respondents whose organizations had current process improvement initiatives compared to those who did not have current process improvement initiatives. This grouping of respondents was based on the third hypothesis that states “No difference exists between the perceptions of employees with current process improvement initiatives and employees without current process improvement initiatives from the perspective that evidence of the maturity model framework exists.” Of the 103 total respondents, 41 responded by stating that their organization does not currently have process improvement initiatives. The remaining 62 respondents stated that they did currently have process improvement initiatives within their organization.
The data was then sorted to show the results of those with current process improvement initiatives compared to those without current process improvement initiatives. The ANOVA test was then conducted on the sets of data so that the p-value could be used to determine if there were or were not significant differences amongst the data groupings. The mean analysis test followed the ANOVA to understand how respondents viewed the basic CMMi framework questions. The mean and standard deviation for all five questions were calculated to provide the appropriate statistics for analysis of the questions. The data used for this research from the two tests is listed in the table below.
Table 4.4 - Current Initiative versus No Current Initiative
illustration not visible in this excerpt
Based on the results of the ANOVA testing, the p-value for these two groups of respondents results as much less than 0.05. This means that there is a statistical difference between those with current process improvement initiatives and those without current process improvement initiatives. However, the effect size is 0.027. This shows that the statistical difference is small. The mean of the respondents with current process improvement initiatives resulted as 3.33, while the mean of those without current process improvement initiatives calculated as 2.97. These two groupings overall averaged the “indifferent” response. The median for those with current process improvement initiatives is 4, which shows that the middle answer for this group is agree. The median for those without current process improvement is 3, which shows that the middle response for this group is “indifferent.” Those with current process improvement initiatives had mode of 4, while those without current process improvement initiatives had a mode of 3. The standard deviation for those with current process improvement initiatives resulted as 1.12, while those without current process improvement initiatives yielded a standard deviation of 0.83. The sample variance for those with current process improvement initiatives resulted in 1.26. Those without current process improvement initiatives resulted with a sample variance of 0.69. The variances for those with current process improvement initiatives is more widely spread out compared to those without current process improvement initiatives.
4.7 Summary of the Survey Responses
The questions involved with the survey that was disseminated for this research involved questions that pertained to the CMMi at its different levels. All the questions at each level and the ancillary questions will be displayed and summarized to better understand this research’s findings. The responses for each question will be displayed through a graph to help better represent which answers received which percentages of the total responses.
4.7.1 Summary of Responses - Overall Framework Questions
Questions 1 through 5 of the survey related to the overall framework of the CMMi. The overall framework of this model involved a summary question relating to each level of the CMMi. These questions help to understand if any evidence of the CMMi currently exists when using advertising processes.
Question 1 Synopsis
The first question of the survey stated “Agency processes may be defined as ad hoc, chaotic, or random.” This question presented a statement related to the overall state of processes within the first level of the CMMi. The responses for this question are listed in the graph below.
Graph 4.3 - Question 1 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “indifferent” answer choice. The responses to the statement are as follows:
- 11 (10.7%) responses were “strongly disagree”
- 26 (25.2%) responses were “disagree”
- 35 (34.0%) responses were “indifferent”
- 25 (24.3%) responses were the “agree”
- 6 (5.8%) answered with the “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.5 - Question 1 Descriptive Statistics
illustration not visible in this excerpt
Based on the findings in Table 4.5, the first question yielded a mean of 2.89. This shows that respondents average an “indifferent” answer. The median and mode were both 3, which means that the middle answer and most common answer was “indifferent.” The standard deviation resulted as 1.07 and the sample variance resulted as 1.16, which shows that the answers in this data set were not widely scattered from the mean.
Question 2 Synopsis
The second question of the survey stated “Advertising processes within your workplace are managed.” This question displayed a statement related to the overall state of the second level of the CMMi. The responses for this question are listed in the graph below.
Graph 4.4 - Question 2 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “agree” answer choice. The responses to the statement are as follows:
- 4 (3.9%) responses were “strongly disagree”
- 10 (9.7%) responses were “disagree”
- 37 (35.9%) responses were “indifferent”
- 40 (38.8%) responses were “agree”
- 12 (11.7%) responses were “strongly agree”
Further statistics used to analyze the responses from this question are presented within the following table.
Table 4.6 - Question 2 Descriptive Statistics
illustration not visible in this excerpt
Given the statistics shown in table 4.6, this question resulted with a calculated mean of 3.45. This shows that the average response was “indifferent.” The median and the mode for this question resulted as 4, which shows that the middle answer of all responses was “agree” and the most common answer was also “agree.” The standard deviation was 0.96 and sample variance resulted as 0.92, which shows that the answers in the data set were not spread out far from the mean.
Question 3 Synopsis
The third question of the survey stated “Advertising processes in your workplace are defined/specific.” This question provided a statement that is a basic framework that relates to the overall state of the third level of the CMMi. The responses for this question are listed in the graph below.
Graph 4.5 - Question 3 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “agree” answer choice. The responses to the statement are as follows:
- 5 (4.9%) responses were “strongly disagree”
- 16 (15.5%) responses were “disagree”
- 32 (31.1%) responses were “indifferent”
- 38 (36.9%) responses were “agree”
- 12 (11.7%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.7 - Question 3 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistical results provided in Table 4.7, the mean for this question was calculated as 3.35. This shows that the average response was “indifferent.” The median for this question resulted as 3, which means that the middle answer of all the responses was
“indifferent.” The most common answer for this question was “agree.” The standard deviation was 1.04 and sample variance was 1.07, which shows that that the distribution of answers in this data set was not very scattered out.
Question 4 Synopsis
The fourth question of the survey stated “Advertising processes in your workplace are quantitatively managed.” This question is a basic framework question that relates to the overall state of the fourth level of the CMMi. The responses for this question are listed in the graph shown below.
Graph 4.6 - Question 4 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “agree” answer choice. The responses to the statement are as follows:
- 5 (4.9%) responses were “strongly disagree”
- 18 (17.5%) responses were “disagree”
- 36 (35.0%) responses were “indifferent”
- 37 (35.9%) responses were “agree”
- 7 (6.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.8 - Question 4 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.8, the mean for this question was calculated as 3.22. This shows that the average response was “indifferent.” The median for this question resulted as 3, which means that the middle answer of all responses was “indifferent.” The most common answer for this question was “agree.” The standard deviation was calculated as 0.98 and the sample variance was calculated as 0.96. These two statistics show that the answers within this data set were not widely scattered from the mean.
Question 5 Synopsis
The fifth question of this survey stated “Advertising processes in your workplace are optimized.” This is a basic framework question that relates to the over standing of the fifth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.7 - Question 5 Responses
illustration not visible in this excerpt
The results from the fifth question show that the highest percentage of responses was the “indifferent” answer choice. The responses to the statement are as follows:
- 9 (8.7%) responses were “strongly disagree”
- 21 (20.4%) responses were “disagree”
- 39 (37.9%) responses were “indifferent”
- 29 (28.2%) responses were “agree”
- 5 (4.9%) responses were “strongly agree”
Further statistics to describe the total responses given to this question are presented within the following table.
Table 4.9 - Question 5 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.9 above, the mean for this question was calculated as 3.00. This shows that the average response was “indifferent.” The median and mode both resulted as 3, which show that the middle answer of all responses was “indifferent” and that the most common answer was also “indifferent.” The standard deviation resulted as 1.02 and the sample variance resulted as 1.04. Those two statistics show that the responses were not widely scattered from the mean.
4.7.2 Summary of Responses - Level One of the CMMi
The first level of the CMMi involves questions 6 through 8 in the survey. These questions provide the opportunity to see if the respondents’ perceptions of the questions show any evidence of the CMMi’s first level, or initial level, within their organization.
Question 6 Synopsis
The sixth question of this survey stated “Advertising processes in your workplace are unpredictable.” This question presents a statement showing a characteristic of the first level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.8 - Question 6 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “agree” answer choice. The responses to the statement are as follows: x 11 (10.7%) responses were “strongly disagree”
- 29 (28.2%) responses were “disagree”
- 29 (28.2%) responses were “indifferent”
- 31 (30.1%) responses were “agree”
- 3 (2.9%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.10 - Question 6 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.10, the mean for this question was calculated as 2.86. This shows that the average response was closer to “indifferent.” The median for this question resulted as 3, which means that the middle response of all the responses was “indifferent.” The most common answer, or the mode, resulted as 4. This shows that the most common answer was “agree.” The standard deviation for this question resulted as 1.06, and the sample variance resulted as 1.12. These statistics show that the answers were not widely scattered from the mean.
Question 7 Synopsis
The seventh question of this survey stated “Advertising processes in your workplace are reactive.” This question presents a statement that relates to a characteristic of processes that exist within the first level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.9 - Question 7 Responses
illustration not visible in this excerpt
The results from this question show that that the highest percentage of responses was the “indifferent” answer choice. The responses to the statement are as follows:
- 2 (1.9%) responses were “strongly disagree”
- 16 (15.5%) responses were “disagree”
- 42 (40.8%) responses were “indifferent”
- 36 (35.0%) responses were “agree”
- 7 (6.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.11 - Question 7 Descriptive Statistics
illustration not visible in this excerpt
Based on the results provided in Table 4.11, the calculated mean for this question resulted as 3.29. This shows that the average response was closest to “indifferent.” The median and mode both resulted as 3, which shows that the most common answer and the answer in the middle of all the responses resulted as “indifferent.” The standard deviation was calculated as 0.88, and the sample variance resulted as 0.78. These two statistics show that the answers were not widely scattered from the mean.
Question 8 Synopsis
The eight question for this survey stated “Advertising processes within your workplace are uncoordinated.” This question presents a statement that is reflective of advertising processes classified in the first level of the CMMi. The responses given to this question are shown in the graph below.
Graph 4.10 - Question 8 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “disagree” answer choice. The responses to the statement are as follows:
- 14 (13.6%) responses were “strongly disagree”
- 36 (35.0%) responses were “disagree”
- 34 (33.0%) responses were “indifferent”
- 17 (16.5%) responses were “agree”
- 2 (1.9%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.12 - Question 8 Descriptive Statistics
illustration not visible in this excerpt
Based on the results provided in Table 4.12, the mean for this question was calculated as 2.58. This shows that the average response for this question was “indifferent.” The median resulted as 3, which means the middle answer of all responses was “indifferent.” The mode for this question resulted as 2, which shows that the most common answer of all responses resulted as “disagree.” This also shows that the most common answer choice for this question was “indifferent.” The standard deviation resulted as 0.99, while the sample variance resulted as 0.97. These two statistics show that the answers were not widely scattered from the mean.
4.7.3 Summary of Responses - Level Two of the CMMi
For the second level of the CMMi, Questions 9 and 10 were used to question respondents on their perceptions of the CMMi. These questions provide the chance to understand if the respondents’ perceptions of the questions show any evidence of the CMMi’s second level, or repeatable level, within their organization.
Question 9 Synopsis
The ninth question of this survey stated “Advertising processes in your workplace are planned.” This question presents a statement that shows a characteristic of the second level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.11 - Question 9 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “agree” answer choice. The responses to this statement are as follows:
- 2 (1.9%) responses were “strongly disagree”
- 10 (9.7%) responses were “disagree”
- 33 (32.0%) responses were “indifferent”
- 43 (41.7%) responses were “agree”
- 15 (14.6%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.13 - Question 9 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.13, the mean for this group of data resulted as 3.57. This shows that the average response to the statement was “agree.” The median and mode both resulted as 4. This means that the middle response of all the responses was “agree.” The most common answer choice resulted as “agree” also. The standard deviation resulted as 0.92, and the sample variance resulted as 0.85. These statistics show that the data as a whole was not widely scattered from the mean.
Question 10 Synopsis
The tenth question for this survey stated “Advertising processes in your workplace are controlled.” This question provides a statement that is also a characteristic of the second level of the CMMi. The responses for the question are in the graph below.
Graph 4.12 - Question 10 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “agree” answer choice. The responses to this statement are as follows:
- 2 (1.9%) responses were “strongly disagree”
- 10 (9.7%) responses were “disagree”
- 32 (31.1%) responses were “indifferent”
- 44 (42.7%) responses were “agree”
- 15 (14.6%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.14 - Question 10 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.14, the mean from this grouping of data resulted as 3.58. This shows that the average response to this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the middle answer of all responses was “agree” and the most common answer choice was also “agree.” The standard deviation resulted as 0.92, and the sample variance resulted as 0.85. These statistics show that the data is not widely scattered from the mean.
4.7.4 Summary of the Responses - Level Three of the CMMi
The third level of the CMMi involved questions 11 through 13 to survey the respondents on their perceptions of the CMMi. These questions each provided the opportunity to understand if respondents’ perceptions of the questions show any evidence of the CMMi’s third level, or defined level, within their organization.
Question 11 Synopsis
The eleventh question of this survey stated “Advertising processes in your workplace are well-defined.” This question displays a statement that is a characteristic of the third level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.13 - Question 11 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “indifferent” response. The responses to the statement are as follows:
- 4 (3.9%) responses were “strongly disagree”
- 14 (13.6%) responses were “disagree”
- 46 (44.7%) responses were “indifferent”
- 31 (30.1%) responses were “agree”
- 8 (7.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.15 - Question 11 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.15, the mean for this question was calculated as 3.25. This shows that the average response was “indifferent.” The median and mode were both 3, which means that the middle answer of all responses and the most common answer were both “indifferent.” The standard deviation resulted as 0.90, and the sample variance resulted as 0.82. These statistics show that the responses as a whole were not widely scattered from the mean.
Question 12 Synopsis
The twelfth question of this survey stated “Advertising processes in your workplace are consistent.” This question presents a statement that reflects another characteristic of the third level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.14 - Question 12 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “indifferent” response. The responses to the statement are as follows:
- 4 (3.9%) responses were “strongly disagree”
- 29 (28.2%) responses were “disagree”
- 33 (32.0%) responses were “indifferent”
- 29 (28.2%) responses were “agree”
- 8 (7.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.16 - Question 12 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.16, the mean for this question was calculated as 3.08. This shows that the average response for each question was “indifferent.” The median and mode both resulted as 3. This means that the “indifferent” answer choice was the most commonly chosen response and also the middle answer of all responses. The standard deviation was 1.02, and the sample variance was 1.03. This shows that the data as a whole is not widely scattered from the mean.
Question 13 Synopsis
The thirteenth question of this survey stated “Advertising processes in your workplace are followed.” This question provided a statement that showed a characteristic that relates to the third level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.15 - Question 13 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “indifferent” answer choice. The responses to the statement are as follows:
- 2 (1.9%) responses were “strongly disagree”
- 16 (15.5%) responses were “disagree”
- 45 (43.7%) responses were “indifferent”
- 31 (30.1%) responses were “agree”
- 9 (8.7%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
illustration not visible in this excerpt
Based on the statistics shown in Table 4.17, the mean for this grouping of data was calculated as 3.28. This shows that the average response for this question was “indifferent.” The median and mode for this question resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 0.90, and the sample variance was calculated as 0.91. These statistics show that the data as a whole was not widely scattered from the mean.
4.7.5 Summary of the Responses - Level Four of the CMMi
The fourth level of the CMMi involved questions 14 through 16 to survey the respondents on their perceptions of the CMMi. These questions individually provided the opportunity to understand if respondents’ perceptions of the questions show any evidence of the CMMi’s fourth level, or quantitatively managed level, within their organization.
Question 14 Synopsis
The fourteenth question of this survey stated “Advertising processes in your workplace involve quantitative objectives.” This question showed a statement that represented a characteristic relevant to the overall state of the fourth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.16 - Question 14 Responses
illustration not visible in this excerpt
The results from this question show that the highest percentage of responses was the “agree” answer choice. The responses to the statement are as follows:
- 4 (3.9%) responses were “strongly disagree”
- 14 (13.6%) responses were “disagree”
- 36 (35.0%) responses were “indifferent”
- 38 (36.9%) responses were “agree”
- 11 (10.7%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.18 - Question 14 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics shown in Table 4.18, the mean for this question was calculated as 3.36. This shows that the average response for this question was “indifferent.” The median for this question resulted as 3, which means that the middle answer of all responses was “indifferent.” The mode for this question resulted as 4, which means that the most common answer choice was “agree.” The standard deviation was calculated as 0.98, and the sample variance was calculated as 0.96. These statistics show that the data as a whole was not widely scattered from the mean.
Question 15 Synopsis
The fifteenth question of this survey stated “Advertising processes in your workplace are analyzed numerically.” This question displayed a statement that represented a characteristic relevant to the overall state of the fourth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.17 - Question 15 Responses
illustration not visible in this excerpt
The responses to this question show that the highest number of responses was the “indifferent” answer choice. The answers to this question are as follows:
- 8 (7.8%) responses were “strongly disagree”
- 20 (19.4%) responses were “disagree”
- 42 (40.8%) responses were “indifferent”
- 25 (24.3%) responses were “agree”
- 8 (7.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.19 - Question 15 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.19, the mean for this grouping of data was calculated as 3.05. This shows that the average response for this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 1.03, and the sample variance was calculated as 1.07. These statistics show that the data as a whole was not widely scattered from the mean.
Question 16 Synopsis
The sixteenth question of this survey stated “Advertising processes in your workplace involve statistical analysis.” This question provided a statement that is relevant to the overall state of the fourth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.18 - Question 16 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “agree” answer choice. The answers given to this question are as follows:
- 6 (5.8%) responses were “strongly disagree”
- 16 (15.5%) responses were “disagree”
- 36 (35.0%) responses were “indifferent”
- 37 (35.9%) responses were “agree”
- 8 (7.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.20 - Question 16 Responses
illustration not visible in this excerpt
Based on the statistics shown in Table 4.20, the mean for this grouping of data was calculated as 3.24. This shows that the average response for this question was “indifferent.” The median for this question resulted as 3, which means that the middle answer of all responses was “indifferent.” The mode for this question resulted as 4, which means that the most common answer choice was “agree.” The standard deviation was calculated as 1.00, and the sample variance was calculated as 1.01. These statistics show that the data as a whole was not widely scattered from the mean.
4.7.6 Summary of the Responses - Level Five of the CMMi
The fifth level of the CMMi involved questions 17 through 19 to survey the respondents on their perceptions of the CMMi. These questions each provided the opportunity to understand if respondents’ perceptions of the questions show any evidence of the CMMi’s fifth level, or optimized level, within their organization.
Question 17 Synopsis
The seventeenth question of this survey stated “Advertising processes in your workplace are improved incrementally.” This questioned presented a statement that is a characteristic of the fifth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.19 - Question 17 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “indifferent” answer choice. The answers given to this question are as follows:
- 3 (2.9%) responses were “strongly disagree”
- 14 (13.6%) responses were “disagree”
- 53 (51.5%) responses were “indifferent”
- 28 (27.2%) responses were “agree”
- 5 (4.9%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.21 - Question 17 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.21, the mean for this grouping of data was calculated as 3.17. This shows that the average response for this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 0.83, and the sample variance was calculated as 0.69. These statistics show that the data as a whole was not widely scattered from the mean.
Question 18 Synopsis
The eighteenth question of this survey stated “Advertising processes in your workplace are efficient.” This questioned displayed a statement that is relevant to the fifth level of the overall state of the CMMi. The responses for this question are shown in the graph below.
Graph 4.20 - Question 18 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “indifferent” answer choice. The answers given to this question are as follows:
- 6 (5.8%) responses were “strongly disagree”
- 18 (17.5%) responses were “disagree”
- 50 (48.5%) responses were “indifferent”
- 26 (25.2%) responses were “agree”
- 3 (2.9%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.22 - Question 18 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.22, the mean for this grouping of data was calculated as 3.02. This shows that the average response for this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 0.88, and the sample variance was calculated as 0.78. These statistics show that the data as a whole was not widely scattered from the mean.
Question 19 Synopsis
The nineteenth question involved with this survey stated “Advertising processes in your workplace are effective.” This provided a statement that is a characteristic of the fifth level of the CMMi. The responses for this question are shown in the graph below.
Graph 4.21 - Question 19 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “indifferent” answer choice. The answers given to this question are as follows:
- 2 (1.9%) responses were “strongly disagree”
- 13 (12.6%) responses were “disagree”
- 51 (49.5%) responses were “indifferent”
- 33 (32.0%) responses were “agree”
- 4 (3.9%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.23 - Question 19 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.23, the mean for this question was calculated as 3.23. This shows that the average response for this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 0.79, and the sample variance was calculated as 0.63. These statistics show that the data as a whole was not widely scattered from the mean.
4.7.7 Summary of the Responses - Ancillary Questions
The next grouping of questions involved questions 20 through 23. These questions did not relate to any particular level of the CMMi. Instead of relating to a specific CMMi level, these questions provided opportunities to understand the respondents’ perceptions of management within their organization.
Question 20 Synopsis
The twentieth question of this survey stated “Process initiatives are tracked to examine process performance.” This ancillary question presented a statement related to understanding process management initiatives within these organizations. The results for this question are shown in the graph below.
Graph 4.22 - Question 20 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “agree” answer choice. The answers given to this question are as follows:
- 3 (2.9%) responses were “strongly disagree”
- 20 (19.4%) responses were “disagree”
- 35 (34.0%) responses were “indifferent”
- 39 (37.9%) responses were “agree”
- 6 (5.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.24 - Question 20 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.24, the mean for this group of data was calculated as 3.24. This shows that the average response for this question was “indifferent.” The median for this question resulted as 3, which means that the middle answer of all responses was “indifferent.” The mode for this question resulted as 4, which means that the most common answer choice was “agree.” The standard deviation was calculated as 0.93, and the sample variance was calculated as 0.87. These statistics show that the data as a whole was not widely scattered from the mean.
Question 21 Synopsis
The twenty-first question of this survey stated “Policies in your workplace influence processes.” This ancillary question presented a statement related to understanding how policies are used or are not used in an organization to govern processes. The results for this question are shown in the graph below.
Graph 4.23 - Question 21 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “agree” answer choice. The answers given to this question are as follows:
- 3 (2.9%) responses were “strongly disagree”
- 3 (2.9%) responses were “disagree”
- 36 (35.0%) responses were “indifferent”
- 48 (46.6%) responses were “agree”
- 13 (12.6%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.25 - Question 21 Responses
illustration not visible in this excerpt
Based on the statistics presented in Table 4.25, the mean for this grouping of data was calculated as 3.63. This shows that the average response for this question was “agree.” The median and mode for this question both resulted as 4. This means that the most common answer choice and the middle answer of all responses both were “agree.” The standard deviation was calculated as 0.85, and the sample variance was calculated as 0.73. These statistics show that the data as a whole was not widely scattered from the mean.
Question 22 Synopsis
The twenty-second question of this survey stated “My business advocates process training.” This ancillary question displayed a statement that would be helpful for gaining an understanding on if organizations do or do not conduct process training. The responses for this question are shown in the graph below.
Graph 4.24 - Question 22 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “indifferent” answer choice. The answers given to this question are as follows:
- 5 (4.9%) responses were “strongly disagree”
- 17 (16.5%) responses were “disagree”
- 40 (38.8%) responses were “indifferent”
- 33 (32.0%) responses were “agree”
- 8 (7.8%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.26 - Question 22 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.26, the mean for this question was calculated as 3.21. This shows that the average response for this question was “indifferent.” The median and mode for this question both resulted as 3. This means that the most common answer choice and the middle answer of all responses both were “indifferent.” The standard deviation was calculated as 0.98, and the sample variance was calculated as 0.95. These statistics show that the data as a whole was not widely scattered from the mean.
Question 23 Synopsis
The twenty-third question of this survey stated “Processes exist that govern decisions within my company.” This ancillary question presented a statement that provided the opportunity to understand respondents’ perception of how organizations are governed. The responses for this question are shown in the graph below.
Graph 4.25 - Question 23 Responses
illustration not visible in this excerpt
The results to this question show that the highest percentage of responses was the “agree” answer choice. The answers given to this question are as follows:
- 4 (3.9%) responses were “strongly disagree”
- 11 (10.7%) responses were “disagree”
- 35 (34.0%) responses were “indifferent”
- 41 (39.8%) responses were “agree”
- 12 (11.7%) responses were “strongly agree”
Further statistics used to analyze the results from this question are presented within the following table.
Table 4.27 - Question 23 Descriptive Statistics
illustration not visible in this excerpt
Based on the statistics presented in Table 4.26, the mean for this grouping of data was calculated as 3.45. This shows that the average response for this question was “agree.” The median and mode for this question both resulted as 4. This means that the most common answer choice and the middle answer of all responses both were “agree.” The standard deviation was calculated as 0.97, and the sample variance was calculated as 0.94. These statistics show that the data as a whole was not widely scattered from the mean.
4.8 Highlights of the Findings
Based on the results of the survey for this study, the findings are shown in the given tables below. Table 4.28 displays the findings of the hypotheses tests.
Table 4.28 - Summarized Hypotheses Findings
illustration not visible in this excerpt
. The findings of the urban versus rural stratification show that no statistically significant difference exists between the two groupings of data. The findings of the respondents with previous process improvement initiatives versus respondents with no previous process improvement initiatives stratification shows that no statistically significant difference exists between the two groupings of data. The findings of the respondents with current process improvement initiatives versus respondents with no current process improvement initiatives stratification shows that a statistically significant difference does exits, but it is a small significant difference.
The descriptive statistics to support the data used for the hypotheses tests are shown below in Table 4.29.
Table 4.29 - Descriptive Statistics for Hypotheses
illustration not visible in this excerpt
Urban respondents had a calculated mean of 3.18, while the median and mode both were 3. The standard deviation resulted as 1.04 and the sample variance resulted as 1.09. Rural respondents had a calculated mean of 3.21. This grouping of respondents also had a median of 3 and a mode of 4. The standard deviation resulted as 0.97 and the sample variance resulted as 0.99. Respondents with previous process improvement initiatives had a calculated mean of 3.26. This grouping of respondents had a median of 3 and a mode of 4. The standard deviation resulted as 1.04 and the sample variance resulted a 1.21. Respondents without previous process improvement initiatives had a calculated mean of 3.08. This grouping of respondents had median and mode both resulting as 3. The standard deviation resulted as 0.89 and the sample variance resulted as 0.80. Respondents with current process improvement initiatives had a calculated mean of 3.33. This grouping of respondents had a median and mode both resulting as 4. The standard deviation resulted as 1.12 and the sample variance resulted as 1.23.
Respondents without current process improvement initiatives had a calculated mean of 2.97. This grouping of respondents had a median and mode both resulting as 3. The standard deviation resulted as 0.83 and the sample variance resulted as 0.69.
A summarized listing of the responses to each individual question can be found in Table 4.30 below. This information shows the response percentages for each of the questions that related to the CMMi and organizational processes.
Table 4.30 - Summarized Responses by Percentages
illustration not visible in this excerpt
A summarized listing of the descriptive statistics can be found in Table 4.31 below. This table provides statistical feedback for how the respondents answered each individual question that related to the CMMi and organizational processes.
Table 4.31 - Summarized Descriptive Statistics for Survey Responses
illustration not visible in this excerpt
CHAPTER FIVE CONCLUSIONS AND RECOMMENDATIONS
5.1 Introduction to the Conclusions and Recommendations
This chapter will summarize the study, the literature review, and the methodology used to appropriately analyze the data and hypotheses made for this study. This chapter primarily encompasses discussions of the conclusions and recommendations from the statistical analysis portion of this study. Based on the findings from the statistical analysis, conclusions will be drawn to further understand the supporting data for this study. The research goals, expectations, and overall research question will be revisited for conclusions on those. Finally, recommendations will be provided for future research.
5.2 Summary of the Study
This study sought to further express the importance of business process management across any domain within the field of business. These domains include those such as accounting, advertising, human resources, information technology, and management. The emphasis of business process management and progressively improving these processes was discussed to garner understanding of these two principles. The fields of advertising, healthcare, banking, manufacturing, and business settings in general were examined for their use of different process management models amongst their organizations. Within the context of advertising processes, no prior business process models existed that provided the opportunity for continuous process improvement. These factors played a vital role in providing purpose for this study.
5.3 Summary of the Literature Review
The literature review surrounding this study involved discussions of business processes in general, processes specific to advertising, previously used business process management models, an introduction of the Advertising Process Maturity model, and the motivation for the study. The business models explained include the Defining Advertising Goals for Measured Advertising Results (DAGMAR) Model, the Foote, Cone, and Belding (FCB) Matrix, benchmarking, Total Quality Management (TQM), business process reengineering (BPR), business process improvement (BPI), business process management (BPM), Six-Sigma, standards, legislation, and policies.
For the previously used business process management models, this study covered a discussion of each model. The models pertaining to this study were described to gain a better understanding of each paradigm. Each section pertaining to the models in this research also provided the purpose of the business process models and their uses. The uses of these models pertained to many different business domains. Each previously used model was discussed for their use in advertising, and for their use within unrelated fields of business.
The literature review also introduced and discussed the Capability Maturity Model integrated (CMMi) as a part of this study. The CMMi approaches business process management through a progressive maturation foundation, which is something that is nonexistent in the advertising field. The CMMi also has shown its multi-purpose use over a variety of business domains and settings. Due to the lack of a business process management model in advertising that seeks to progressively mature processes over time, motivation to introduce and propose such a model existed. The proposed derivative form of the CMMi, the Advertising Maturity Model (AMM) is shown in the table below.
Table 5.1 - The Advertising Maturity Model
illustration not visible in this excerpt
The AMM was constructed with the approach of a maturity model that requires progressive maturation of the business processes. To achieve process improvement and maturation to the next stage of the model, processes must show the characteristics to classify itself at a certain level of the AMM. With growth and process improvement following the model, these processes will mature over time and achieve the optimal state.
Due to the introduction of the AMM, a derivative of the CMMi, it is concluded that this model provides an opportunity for business organizations to use a new business process management model with characteristics unmatched by previous methods. The CMMi’s flexible framework allows for business process management across multiple business domains. This model provides business users with a method of managing their business processes based on the principle of progressive improvement of the processes. In advertising, this type of model would provide vital use in many different circumstances. For example, optimized processes are crucial when performing advertising functions in the event of a natural disaster. The use of the CMMi for managing advertising processes would provide efficient and effective processes given these circumstances.
The use of the CMMi in advertising would allow for advertisers to begin to define and track advertising processes and functions that repeatedly occur. With the model’s progressive framework, these processes could then be tracked quantitatively for progress. Any needed adjustments, upgrades, or other improvements could then be made to ensure that optimized processes exist. Based on this framework, business users can ensure that their advertising processes are performing optimally. The progressive maturation of the CMMi provides unique characteristics to business process management that will provide business organizations with a new method of ensuring that their business processes are performing at optimal levels.
5.4 Summary of the Research
5.4.1 Summary of the Survey
The overall research question surrounding this study is stated as: “Can the maturity framework of the Capability Maturity Model integrated be adapted to create a process improvement maturity model for advertising processes?” To gather the data necessary for this research, research methods were implemented. The methodology surrounding this study involved using a Likert-scale survey through SurveyMonkey’s Audience tool. The survey was structured with 23 questions pertaining to the CMMi and its different levels and 9 demographic- based questions to gain a further understanding of the respondents. Five different response choices were given, which included “strongly disagree”, “disagree”, “indifferent”, “agree”, and “strongly agree.”
For the dissemination of this survey, 115 total respondents were targeted to provide survey responses. Once the surveys were completed, it was determined that a total of 103 were fully complete and would be used for further statistical analysis to support this study. These individuals included those that previously or currently worked for business organizations that perform or have performed advertising functions.
5.4.2 Summary of the Methodology
After the completion of the data collection, this research involved analyzing the gathered statistical information to better understand the audience. Cronbach’s alpha was implemented to determine the reliability of the data. This research yielded a Cronbach’s alpha of 0.851, which classifies the data as reliable. The Chi-Squared method was also used for determining if any bias existed amongst the data set. Performing the Chi-Squared test returned a Chi-Squared statistic of 0.000056136. This statistic showed that some bias exists, which could be due to larger numbers of responses coming from certain regions.
For hypotheses testing purposes, the three hypotheses were tested by grouping the responses by variable to see how each variable responded to the CMMi basic framework questions. The three hypotheses for this research are shown in the table below.
Table 5.2 - Research Hypotheses
illustration not visible in this excerpt
The one-way analysis of variance (ANOVA) method was used for providing statistical information on the data. This ANOVA test was used for analyzing the groups of data pertaining to the hypotheses developed for this study. Those hypotheses are displayed within the table below.
The Omega squared method was used for determining effectiveness when statistically significant differences exist. This information helped to determine any differences in the groupings of data used for the hypotheses. Descriptive statistics were also used as another method of analyzing the data. The mean, median, mode, standard deviation, and sample variance were calculated for each grouping of hypotheses data as well as each individual question.
5.5 Conclusions of the Hypotheses
5.5.1 Urban versus Rural
The first hypotheses sought to understand how urban and rural employees perceived the overall basic framework of the CMMi. Given means of 3.18 and 3.21, it is concluded that both urban and rural respondents felt a sense of indifference towards the CMMi framework. For both urban and rural respondents, the median resulted as 3. This concludes that the middle-most response showed neutrality. The most commonly chosen answer choice for the urban respondents showed neutrality, while the rural respondents’ most common answer choice showed agreement with the CMMi framework. The standard deviation and sample variance for each of the variables concluded that the data set was not very widely scattered. The p-value resulting from the ANOVA testing resulted as 0.761. This statistic concluded that no statistically significant difference exists between urban and rural respondents. Due to this, it is concluded that the hypothesis stating “No difference is shown between the perceptions of rural versus urban employees when examining evidence of the CMMi framework” is accepted based on the statistical information gathered from this research.
Given the findings and their associated conclusions, it is understood that the findings of this study support the notion that employees working in urban and rural settings do not have any differences in their perceptions of the CMMi’s framework. This is important when factoring workplace location (urban or rural) when determining whether to use the CMMi as a process improvement initiative. Given the mean analysis, these two groupings of data show a neutral viewpoint of the CMMi. The literature review showed that no previous derivative of the CMMi exists to acknowledge progressive maturation of process in advertising. Thus, it is concluded that very few (if any) characteristics of a business process model that addresses progressive maturation are recognized amongst those working in urban and rural settings. This conclusion is anticipated due to the findings showing that a very small amount (roughly 2.4%) of all respondents previously or currently used process maturity modeling.
5.5.2 Previous Initiatives versus No Previous Initiatives
The second hypothesis sought to understand how employees with and without previous process improvement initiatives perceived the overall basic framework of the CMMi. Given a mean of 3.26 for those with previous process improvement initiatives and a mean of 3.08 for those without previous process improvement initiatives, it is concluded that both groupings of respondents felt indifferent towards the overall basic framework of the CMMi. The median for those with previous process improvement initiatives and those without previous process improvement initiatives resulted as 3. This statistic allows for the conclusion that the middle- most answer choice shows neutrality. The mode for respondents with previous process improvement initiatives was 4, which concludes that these respondents felt in agreement with the CMMi framework more than any other answer. The mode for respondents without previous process improvement initiatives resulted as 3, which concludes that these respondents felt neutral towards the CMMi framework more than any other answer. The standard deviation and sample variance statistics both allow for the conclusion that the data set was not widely scattered. The p-value resulting from the ANOVA test was calculated as 0.052, which shows no statistically significant difference between the two groupings of data. Due to the p-value resulting as 0.052, it is concluded that the hypothesis stating “No difference is shown between the perceptions of employees with previous process improvement initiatives and employees without previous process improvement when examining evidence of the CMM framework” may be accepted based on the supporting data from this study.
Based on the findings and conclusions drawn, it is understood that this study does support the notion that employees with or without previous process improvement initiatives do not have differing perceptions of the basic framework of the CMMi. This is important when considering the use of a CMMi derivative for process maturity in organizations that previously have or have not used process improvement initiatives. Given the mean analysis, these two groupings of data show a feeling of neutrality towards the overall basic framework of the CMMi. However, it is important to note that respondents with previous process improvement initiatives more commonly agreed with the framework of the CMMi. The literature review showed that previously no derivative form of the CMMi existed to acknowledge the progressive maturation of advertising processes. Thus, it is concluded that it is more common for those with previous process improvement initiatives to recognize the characteristics of a business process model that addresses progressive maturation. This conclusion does not come as a surprise when understanding that those that have been previously exposed to some form of process improvement are more likely to recognize similar characteristics of another model. Even though a small portion of the respondents (roughly 2.4%) have used a progressive maturation business process model, those with previous exposure to some form of process improvement more commonly still recognized a model with progressive maturation characteristics.
5.5.3 Current Initiatives versus No Current Initiatives
The third hypothesis sought to analyze how those with and without current process improvement initiatives perceived the overall basic framework of the CMMi. With means of 3.33 for respondents with current process improvement initiatives and 2.97 for respondents without current process improvement, it is concluded that both groupings of data generally feel indifferent towards the overall basic framework of the CMMi. The median response for those with current process improvement initiatives resulted as 4, and the median for those without current process improvement initiatives resulted as 3. This statistic allows for the conclusion that the middle-most response for those with current process improvement initiatives showed agreement with the CMMi framework, and the middle-most response for those without current process improvement initiative showed neutrality. The mode for those with current process improvement initiatives resulted as 4, which concludes that the most common answer choice for those with current process improvements shows agreement. The mode for those without any current process improvement initiative resulted as 3, which shows that the most common answer choice for those without current process improvement initiatives shows neutrality. The standard deviation and sample variance allow for the conclusion that both groupings of data are not widely scattered.
The p-value resulting from the ANOVA test resulted as 0.0001, which shows that a statistically significant difference exists between the groupings of data. Due to the p-value, the hypothesis stating “No difference exists between the perceptions of employees with current process improvement initiatives and employees without current process improvement initiatives when examining evidence of the CMM framework” may be rejected. The existence of a statistically significant difference led to the Omega squared test, which resulted with an effect size of 0.027. This effect size concludes that the statistically significant difference amongst the two groupings of data is small.
Based on the findings and the conclusions drawn, it is understood that the results of this study do not support the notion that employees with or without current process improvement initiatives have differing perceptions of the basic framework of the CMMi. This is significant when considering using a derivative of the CMMi for process maturity in organizations that do or do not currently have process improvement initiatives. Given the mean analysis, these two groupings of data show a feeling of neutrality towards the overall basic framework of the CMMi. However, it is important to note that those with current process improvement initiatives more commonly agreed with the CMMi’s framework. The literature review showed that a previous derivative form of the CMMi did not exist to address progressive maturation of processes in advertising. Thus, it is concluded that those with current process improvement initiatives are more likely to recognize the characteristics of a business process model that addresses progressive maturation. This is not surprising given that those with a current process improvement initiative are more likely to recognize process improvement characteristics in another model. Even though a small portion of the survey participants (roughly 2.4%) have used a progressive maturation business process model, those with currents experience with some form of process improvement more frequently acknowledged a model with progressive maturation characteristics.
5.6 Conclusions of the Survey Responses
5.6.1 Overall Framework Questions
The overall framework questions involved in this study related to the first five statements of the questionnaire. Each question number corresponded to characteristics of each level of the CMMi. Question 1 was related to CMMi Level 1, question 2 was related to CMMi level 2, and so on. The mean for this grouping of questions were all found to be below 3.5, which concludes that the respondents generally felt neutral towards the basic framework of the CMMi. All but one of five basic framework questions yielded a median of 3, which concludes that the middle- most answer choice showed neutrality. The second question had a median of 4, which concludes that the middle-most answer choice showed agreement. Question 1 and question 5 of the survey had calculated modes of 3, which concludes the respondents most commonly felt neutral towards these questions. Questions 2 through 4 had calculated modes of 4, which concludes that respondents more commonly felt in agreement with these questions.
Given the findings and conclusions drawn from the overall framework questions of the CMMi, it is important to acknowledge a few points from the responses to these questions. The mean analysis conducted for each of the questions revealed that all basic framework questions show a feeling of indifference from the respondents. The findings of these questions are important when addressing the research question, which sought to understand if the framework of the CMMi is adaptable with advertising processes. The resulting means are not surprising given that only 2.4% of respondents currently or previously used a business process model that addressed progressive maturation of processes. However, it is worth noting that respondents more commonly agreed with the characteristics of Level 2, Level 3, and Level 4 of the CMMi. This shows that these respondents did more commonly agree with the CMMi’s characteristics at those levels. Some agreement with the models characteristics shows that there is evidence of the CMMi’s characteristics in organizations.
5.6.2 Level One Questions
The statements relating to Level 1 of the CMMi were located within questions 6 through 8 of the questionnaire. Question 6 stated “Advertising processes in your workplace are unpredictable.” Question 7 stated “Advertising processes in your workplace are reactive.”
Question 8 question stated “Advertising processes within your workplace are uncoordinated.” Each mean calculated for these questions resulted below 3.5, which concludes that the respondents generally had a feeling of neutrality towards Level 1 of the CMMi. Each of the three questions in this group had a median of 3, which concludes that the middle-most answer choice amongst the respondents showed neutrality. Question 6 had a mode of 4, which concludes that more respondents agreed with that statement. Question 7 had a mode of 3, which concludes that more respondents felt neutral towards that statement. Question 8 had a mode of 2, which concludes that respondents more commonly disagreed with the statement. Based on the findings and conclusions drawn from the Level 1 questions, a few points of interest must be acknowledged. The mean analysis conducted for each of the questions showed that the respondents felt neutral towards each statement in this grouping of questions. These findings are important for addressing if the framework of the CMMi may be used with advertising processes. The findings of the mean analysis are not surprising given that a low number of respondents (2.4%) had previously or currently implemented a business process model to manage the progressive maturation of processes. However, it is important to acknowledge that respondents more commonly agreed with Question 6 and more commonly disagreed with Question 8. This shows that the respondents of this survey more commonly agreed with these statements, which shows some evidence of Level 1 of the CMMi in business organizations.
5.6.3 Level Two Questions
The statements relating to Level 2 of the CMMi were found in questions 9 and 10 of the questionnaire. Question 9 stated “Advertising processes in your workplace are planned.” Question 10 stated “Advertising processes in your workplace are controlled.” Both means calculated for these questions resulted above 3.5, which concludes that the respondents generally felt in agreement with the statements for Level 2 of the CMMi. Each of these two questions had a median of 4, which concludes that the middle-most answer choice for these questions showed agreement with Level 2 of the CMMi. The mode of both questions was calculated as 4, which concludes that the most common answer choice showed agreement with the statements of Level 2 of the CMMi.
Given the findings and conclusions drawn from the Level 2 questions, it is important to point out some key details for these questions. The mean analysis conducted for each question showed that the respondents generally agreed with both statements in this grouping of questions. These findings are essential for analyzing whether the CMMi’s framework may be used with advertising processes. The mean analysis findings are somewhat surprising given that the low number of respondents (2.4%) with previous or current business process management models that addressed the progressive maturation of processes. This shows that most respondents more commonly showed some characteristics of Level 2 of the CMMi within their organization’s business processes.
5.6.4 Level Three Questions
The statements relating to Level 3 of the CMMi were found within questions 11 through 13 of the questionnaire. Question 11 stated “Advertising processes in your workplace are well- defined.” Question 12 stated “Advertising processes in your workplace are consistent.” Question 13 stated “Advertising processes in your workplace are followed.” The means calculated for these questions all were calculated below 3.5, which concludes that the respondents generally felt a sense of neutrality towards these statements relating to Level 3 of the CMMi. The median for each of these questions resulted as 3, which concludes that the middle- most response showed neutrality towards Level 3 of the CMMi. The mode for each of these questions also resulted 3, which concludes that the most common answer choice showed neutrality towards this level of the CMMi.
Given the findings and conclusions drawn from the Level 3 questions, it is important to note a few points of interest. The mean analysis performed for each of these questions showed that the respondents felt neutral towards each statement relating to Level 3 of the CMMi. This information is vital when determining whether the CMMi’s basic framework may be adapted for advertising processes. These findings are not shocking with it being understood that a small portion of the total respondents (2.9%) previously or currently use a business process model that addresses progressive maturation of the business processes. This shows that the respondents do not exhibit any evidence of Level 3 of the CMMi.
5.6.5 Level Four Questions
The statements that support level 4 of the CMMi can be found within questions 14 through 16 of the questionnaire. Question 14 stated “Advertising processes in your workplace involve quantitative objectives.” Question 15 stated “Advertising processes in your workplace are analyzed numerically.” Question 16 stated “Advertising processes in your workplace involve statistical analysis.” Each mean calculated for these questions resulted below 3.5, which concludes that the respondents generally felt neutral towards questions relating to Level 4 of the CMMi. Each median for these questions resulted as 3, which concludes that the middle-most answer choice showed neutrality. The mode for two of the questions resulted as 4, which concludes that respondents more commonly felt in agreement with these two questions. One of the questions in this grouping resulted with a mode of 3, which concludes that respondents more commonly felt neutral towards one of the three statements relating to Level 4 of the CMMi.
Given the findings and conclusions drawn from the Level 4 questions, a few points of interest must be noted. The mean analysis conducted for each of the questions showed that the respondents felt neutral towards each statement in this grouping of questions. This is important for addressing whether framework of the CMMi can be derived for a business process model that acknowledges the progressive maturation of advertising processes. The mean analysis findings do not come as a surprise given that such a small amount (2.4%) of the respondents previously or currently use a business process management model that acknowledges the progressive maturation of processes. However, it is important to note that two of the three questions had responses that more commonly agreed with the statements relating to Level 3 of the CMMi. This shows that most of the respondents do show some characteristics of Level 3 of the CMMi within the business processes performed by their organization.
5.6.6 Level Five Questions
The statements that relate to Level 5 of the CMMi can be found within questions 17 through 19 of the questionnaire. Question 17 stated “Advertising processes in your workplace are improved incrementally.” Question 18 stated “Advertising processes in your workplace are efficient.” Question 19 stated “Advertising processes in your workplace are effective.” The calculated mean for each of these questions resulted below 3.5, which concludes that the respondents generally felt a sense of neutrality towards Level 4 of the CMMi. The median for each of these questions resulted as 3, which concludes that the middle-most answer choice showed neutrality for each statement relating to Level 5 of the CMMi. The mode of each of these questions resulted as 3, which concludes that the most common answer choice amongst these respondents showed a feeling of neutrality towards Level 5 of the CMMi. Based on the findings and conclusions drawn from the Level 5 questions, a few key points must be addressed. The mean analysis performed for each question showed that the respondents generally felt neutral towards Level 5 of the CMMi. The means found are important for understanding if the CMMi’s framework can be used with advertising processes. The findings of the mean analysis do not come as a surprise given that a low amount (2.4%) of the respondents with previous or current business process management models that seek progressive maturation of the processes. These conclusions show that respondents do not see evidence of Level 5 of the CMMi within their organization’s business processes.
5.6.7 Ancillary Questions
The statements involved in questions 20 through 23 of the questionnaire related to ancillary questions to assess the management of processes amongst organizations. Question 20 stated “Process initiatives are tracked to examine process performance” Question 21 stated “Policies in your workplace influence processes.” Question 22 stated “My business advocates process training.” Question 23 stated “Processes exist that govern decisions within my company.” The means calculated for questions 20, 22, and 23 all resulted below 3.5, which concludes that respondents generally felt neutral towards these questions. Question 21 had a calculated mean resulting as 3.63, which concludes that the respondents generally agreed with the statement for this question. Questions 20 and 22 had medians that resulted as 3, which concludes that the middle-most answer choice for these questions shows a sense of neutrality. Questions 21 and 23 had medians that resulted as 4, which concludes that the middle-most answer choice showed agreement with these statements. Questions 20, 21, and 23 had modes of 4, which concludes that respondents generally agreed with these statements. Question 22 had a mode of 3, which concludes that respondents generally felt neutral towards this statement. Given the findings and conclusions drawn from these questions, it is important to note a few points of interest. The mean analysis performed for questions 20, 22, and 23 all show that respondents felt neutral towards these questions. Question 21’s mean analysis revealed that respondents agreed with the statements on process management. These means are vital in understanding how organizations manage their processes. The respondents more commonly answered that their organizations tracked process initiatives to understand process performance, process training takes place in their organization, and that processes are in place to govern their organization’s decisions. Thus, there are business process management characteristics in place in these organizations even though the average response may not show agreement with these statements.
5.7 Conclusions: Goals, Objectives, and the Research Question
5.7.1 Research Goals
Prior to the start of this research, goals were established to provide benchmarks to assess the research’s progress. The goals for this research were set to investigate using a derivative form of the CMMi within the context of advertising processes. The goals for this research were as follows:
1. This study is predicted to demonstrate how the process maturity structure of the Capability Maturity Model integrated can be implemented with advertising processes.
2. This study is predicted to demonstrate that prior process and procedure enhancement models have not acknowledged the problems of progressively maturing advertising processes.
3. This study is predicted to demonstrate that prior advertising procedures do not align with the principles of the Capability Maturity Model integrated.
The first goal set for this research involved understanding how the progressive process maturation framework of the CMMi could be implemented within advertising processes. This research explained advertising processes discussed in the studies conducted by Na, Marshall, and Woodside (2009), Vyas and Manwani (2012), and Lace (1998). Examples of where progressive maturation of advertising processes could be implemented, such as during natural disasters or during holiday advertisements, were also discussed. The studies of Filbeck, Swinarski, and Zhao (2013) were also discussed for how processes within an organization are perceived in relation to the CMMi and levels of progressive maturity. Given these discussions, it is concluded that the use of the CMMi’s progressive maturation structure and how it could be implemented in advertising was demonstrated. Therefore, the first goal of this research was achieved. The second goal of this research included understanding how previous process enhancement models do not acknowledge the progressive maturation of the advertising processes. This research used multiple studies that explained models such as the DAGMAR Model, the FCB Matrix, benchmarking, TQM, BPR, BPI, BPM, Six-Sigma, standards, legislation, and policies. Within the discussions of each model, it was noted that each model had its own characteristics in improvement of processes, but none of the prior models approached process improvement with a progressive maturation foundation. Based on the discussions of previous business process models and their characteristics, it is concluded that no prior business process management paradigm addresses the problem of progressively maturing business processes. Thus, the second goal of this research was achieved.
The third and final goal of this research sought to understand how prior business process management models do not conform with the principles of the CMMi’s progressive maturation framework. The CMMi’s basic framework of progressively maturing processes was discussed through different studies). McCollum (2004) explained the CMMi as a unique business process model crafted to address the progressive maturation of business process, which is something that previous models lacked. To complement the work of Benmoussa, Adbdelkabir, and Hassou (2015), discussions of the CMMi’s five unique levels of progressive process maturation also took place. It was also determined that none of the prior business process management models in advertising approach business process improvement like the CMMi. Due to this, it is concluded that this research found that no previous business process management model aligned with the characteristics of the CMMi’s framework. Therefore, the final goal of this research was also achieved.
5.7.2 Research Expectations
Before the research for this study began, expectations were set to assess the potential outcomes. The expectations for this study were set to further examine how the progressive maturation framework of the CMMi could be used with advertising processes. The expectations for this research were as follows:
1. Evaluate the viewpoint of rural against urban staffs relating to the process maturity model foundation.
2. Evaluate the process maturity model foundation viewpoint of employees with prior process improvement initiatives against employees without prior process improvement initiatives.
3. Evaluate the process maturity model foundation viewpoint of employees with current process improvement initiatives against employees without current process improvement initiatives.
The first expectation for this study included analyzing how employees in urban and rural settings view the process maturity framework of the CMMi. The characteristics set forth by the U.S. Census Bureau (2015) were used to identify those in urban and rural settings. An urban area was classified as an area with 2,500 residents or more, and a rural area was classified as an area with less than 2,500 residents (U.S. Census Bureau, 2015). With respect to the CMMi and its framework, respondents in both settings were questioned to understand how they perceive the CMMi’s framework. One hypothesis tested for this research stated “No difference is shown between the perceptions of rural versus urban employees when examining evidence of the CMM framework.” This hypothesis required that this study analyze these two variables. Based on the findings, it was concluded that this hypothesis was accepted. Therefore, it is concluded that this expectation for this research was met.
The next expectation involved examining how employees with previous process improvement initiatives and employees without previous process improvement initiatives perceive the CMMi’s framework. In the literature review, research conducted by Ghosh and Ling (1994) was used to provide motivation for analyzing these two variables in this study.
Their research involved understanding how TQM has previously been used in advertising processes. The research conducted by the two researchers found that the TQM model could further improve certain aspects of advertising processes (Ghosh & Ling, 1994). This research crafted a hypothesis stating “No difference is shown between the perceptions of employees with previous process improvement initiatives and employees without previous process improvement when examining evidence of the CMM framework.” The findings from this study concluded that this hypothesis was supported by this research. Thus, the second expectation of this research was achieved.
Lastly, the final expectation set included understanding how employees with current process improvement initiatives and employees without current process improvement initiatives view the CMMi’s framework. Patwardham and Patwardham (2008) studied business organizations that currently use the BPR model for business process improvement. This study concluded that BPR can be currently bused to improve business processes (Patwardham & Patwardham, 2008). This study served as a motivation for the final hypothesis that stated “No difference exists between the perceptions of employees with current process improvement initiatives and employees without current process improvement initiatives when examining evidence of the CMM framework.” The findings within this research concluded that this hypothesis was not supported. Even though the hypothesis was not supported, this variable still managed to analyze the two variables. Therefore, the final expectation for this research was met.
5.7.3 Overall Research Question
Prior to the research portion of this study, the overall research question was stated as:
“Can the maturity framework of the Capability Maturity Model integrated be adapted to create a process improvement maturity model for advertising processes?” The domain studied for this research involved a grouping of 103 respondents in the United States that currently or previously worked in settings that performed advertising functions. Due to this, the conclusions of this research should not be derived nationwide amongst those that currently or previously work in business settings that perform advertising functions.
Each hypothesis involved its own variables requiring that the data be grouped to analyze the hypotheses. The respondents in urban and rural settings were compared to understand if any significant difference existed between how the two groups viewed the CMMi’s overall basic framework, and the findings showed that no significant difference exists between the two groupings of data. Examining the mean for this grouping of data displayed indifference towards the overall framework of the CMMi. Perceptions of the respondents with previous process improvement initiatives were compared to those without previous process improvement initiatives to see if any significant difference existed between the two groupings. The findings for these two variables showed that no statistically significant difference existed. Analyzing the mean exhibited that these respondents felt indifferent towards the CMMi and its overall framework. The perceptions of respondents with current process improvement initiatives were compared with those without current process improvement to understand if any significant difference existed among the groupings of data. The findings showed that a small statistically significant difference existed between the variables. Analyzing the means of these two groupings showed that these respondents generally felt indifferent towards the CMMi’s basic framework. Due to a lack of consistency between the respondents, there is not enough evidence to support the idea that the CMMi’s basic framework is adaptable for advertising processes. From the findings of this research, the overall research question had a calculated mean that resulted as 3.22. From this statistic, it can be concluded that the respondents showed neutrality towards the overall framework of the CMMi. This does not come as a surprise given that a small portion (roughly 2.4%) of the respondents had prior or current experience with a business process management model that exhibited progressive maturation of the business processes. Due to the lack of experience with such a model like the CMMi, the respondents in this research may not have recognized the characteristics of the CMMi within their business organizations. However, management within businesses may choose to change their process management models to a maturity model framework such as the CMMi. This allows for the opportunity for future maturity modeling supports the idea that business organization would be exposed to maturity modeling characteristics. Unique with its progressive maturation characteristics, the wide use of a model like the CMMi does not exist currently. With more frequent use of a model like the CMMi, there will be a higher chance of recognizing the CMMi’s traits and progressions through the model’s framework. Thus, these beliefs may result in varied findings and conclusions with future research relating to this study’s overall research question.
5.8 Recommendations and Future Research
Based on the findings from the literature, a lack of previous research exists that discusses the use of a progressive maturation of advertising processes. Due to this, it is recommended that further studies be performed beyond the context of this research. These studies may research the use of a process maturity models in other advertising domains, such as strictly within advertising firms. A study such as this could help provide further understanding of the impact of using a model to progressively mature advertising processes.
The respondents in this research involved a small grouping of respondents across the United States that currently work or have previously worked in business environments that perform advertising functions. Thus, the results and conclusions from this research should not be applied to the general population of the United States. Future studies on this topic may seek to understand the use of a progressive maturation business process model within smaller constraints and different variables.
The total sample size from this study resulted as 103 total respondents who currently or previously work in business settings that perform advertising functions. This sample size from the research provided a look at a smaller sample of the United States population that worked in such settings. Further studies including larger sample sizes could be conducted to analyze specific geographic regions of the country. Therefore, it is recommended that future studies analyze larger sample sizes in specific geographic regions, such as the Northeast, Southeast, Midwest, etc.
This study analyzed the perceptions of a progressive process maturity model within urban and rural settings. Future research on this topic may seek to understand perceptions of those within different constraints, such as personnel in management versus non-management positions, personnel in for-profit organizations versus personnel in nonprofit organizations, and personnel in service advertising versus personnel in product advertising. Further studies using such constraints would provide a new understanding of how a maturity modeling framework is perceived in advertising.
The adaptability of the CMMi’s progressive maturation framework allows for the model’s potential use across different business domains. Previous literature shows that the CMMi’s use has been studied within domains such as project management (Doss & Kamery, 2006), logistics (Benmoussa, Abdelkabir, Abd, & Hassou, 2015), information technology (Carcary, 2013), finance (Doss, Chen, & Holland, 2008), software (Doss & Kamery, 2006), people (Wademan, Spuches, & Doughty, 2007), industrial management (Doss, 2004), environmental management (Doss & Kamery, 2005), and criminal justice (Doss, 2014). Thus, it is recommended that future studies also research the use of the CMMi’s framework within a new domain that has not previously been researched.
This study included a variable to analyze the perceptions of personnel with previous process improvement initiatives versus those without previous process improvement initiatives and a variable to analyze the perceptions of personnel with current process improvement initiatives versus personnel without current process improvement initiatives. All businesses can experience change in management procedures over the course of time. For this study, roughly 2.4% of all respondents identified their current or previous employers as having experience with using a maturity modeling framework for the management of business processes. This percentage may change over time due to organizations deciding to adapt their process management procedures to such a model. Due to this, it is recommended that this study be repeated.
APPENDIX A - CONSENT FORM
PRINCIPAL INVESTIGATOR:
WHO: Charles R. Pickett III
WHERE: University of West Alabama
DESCRIPTION:
Participants in this survey are asked to complete this survey with accuracy and full disclosure. Those that participate will be surveyed based on processes within their agency, information about their agency, and their demographics.
CONFIDENTIALITY:
Your name will not be attached to any responses you give during the session. Any identifying information you give will be secured so that it will only be accessible to the Principal Investigator and will be destroyed upon completion of the study. Any information given in this survey will be kept confidential with the sole purpose of completing this study.
BENEFITS:
Participants will receive the benefit of providing the Principal Investigator with necessary information to complete the study.
RISKS:
There are no risks from participating in this study.
CONTACT PEOPLE:
If you have any questions about this research project, please contact the Principal Investigator. If you have any questions regarding your rights as a research participant, please contact the Principal Investigator at x or y.
VOLUNTARY NATURE OF PARTICIPATION:
Your participation in this study is entirely voluntary. If you do not wish to participate, or wish to end your participation in this study, there will be no penalty to you and no loss of benefit. If you prefer not to see or give any information you may end this session immediately, or if you feel uneasy either session you may end the session at any time.
SIGNATURE:
Your signature on this consent form indicates you fully understand the nature of this study, what is being asked of you, and that you are signing this voluntarily. If you have any questions, please feel free to ask them now or at any time during the study.
illustration not visible in this excerpt
A copy of this consent form can be made available for you to keep.
APPENDIX B - SURVEY INSTRUMENT
Figure B.1 - Survey Introduction
illustration not visible in this excerpt
Figure B.2 - Questions 1 and 2
illustration not visible in this excerpt
Figure B.3 - Questions 3 through 5
illustration not visible in this excerpt
Figure B.4 - Questions 6 through 8
illustration not visible in this excerpt
Figure B.5 - Questions 9 and 10
illustration not visible in this excerpt
Figure B.6 - Questions 11 through 13
illustration not visible in this excerpt
Figure B.7 - Questions 14 and 15
illustration not visible in this excerpt
Figure B.8 - Questions 16 through 18
illustration not visible in this excerpt
Figure B.9 - Questions 19 and 20
illustration not visible in this excerpt
Figure B.10 - Questions 21 through 23
illustration not visible in this excerpt
Figure B.11 - Question 24
illustration not visible in this excerpt
Figure B.12 - Question 24 (continued)
illustration not visible in this excerpt
Figure B.13 - Question 24 continued
illustration not visible in this excerpt
Figure B.14 - Question 25
illustration not visible in this excerpt
Figure B.15 - Questions 26 through 28
illustration not visible in this excerpt
Figure B.16 - Question 29
illustration not visible in this excerpt
Figure B.17 - Question 30
illustration not visible in this excerpt
Figure B.18 - Questions 31
illustration not visible in this excerpt
REFERENCES
Ahern, D.M., Clouse, A., & Turner, R. 2004. CMMi Distilled: A Practical Introduction to Integrated Process Improvement. Boston, MA: Pearson Education Publishing.
Alexandrov, A. (2010). Characteristics of single-item measures in likert scale format. Electronic Journal of Business Research Methods, 8(1), 1-12. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/856419472?a ccountid=14786
Allen, C. A. (2006). Survey Research: Question Wording and Design. Arden: Jordan Whitney Enterprises, Inc. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/216277107?a ccountid=14786.
Allen, I. E., & Seaman, C. A. (2007). Likert scales and data analyses. Quality Progress, 40(7), 64-65. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/214764202?a ccountid=14786.
Amponsah, R. (2010). Improving project management practice in Ghana with focus on agriculture, banking, and construction sectors of the Ghanaian economy. From: researchbank.rmit.edu.au/eserv/rmit:10389/Amponsah.pdf.
Askey, J. M., & Malcolm, A. (1997). Quality management in the UK advertising industry. The International Journal of Quality & Reliability Management, 14(2), 186-196. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/197612716?a ccountid=14786.
Atkinson, C. (2003). Samsung hands FCB more business. Advertising Age, 74(28), 3-3,26. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/208348763?a ccountid=14786.
Benady, A. (2000, Oct 12). ISBA and ad freedom. Marketing, 16-19. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/214987033?a ccountid=14786.
Benmoussa, R., Abdelkabir, C., Abd, A., & Hassou, M. (2015). Capability/Maturity Based Model for Logistics Processes Assessment. International Journal of Productivity and Performance Management, 64(1), 28-51.
Boddewyn, J. J. (2007). ADVERTISING CONTROL WITH EMPHASIS ON SELF- REGULATION AND INTERNET ADVERTISING. Paper presented at the 171-183.
Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/192404972?a ccountid=14786.
Broderick, A., Garry, T., & Beasley, M. (2010). The need for adaptive processes of benchmarking in small business-to-business services. The Journal of Business & Industrial Marketing, 25(5), 324-337.
Burian, P. E., & "Skip" Maffei, F. R. (2013). Operations Strategy: A Broader View of Threading the Vision to the Customer. The International Business & Economics Research Journal (Online), 12(12), 1513-n/a. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1468618537? accountid=14786.
Calantone, R. J., & Drury, D. H. (1979). Advertising Agency Compensation: A Model for
Incentive and Control. Management Science (Pre-1986), 25(7), 632. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/205850778?a ccountid=14786.
Caraher, M., Landon, J., & Dalmeny, K. (2006). Television advertising and children: Lessons from policy development. Public Health Nutrition, 9(5), 596-605. doi:http://dx.doi.org/10.1079/PHN2005879.
Carcary, M. (2013). IT Risk Management: A Capability Maturity Model Perspective. Electronic Journal of Information Systems Evaluation, 16(1), 3-13. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1520563034? accountid=14786.
Colley, R. H. (1984). Defining advertising goals for measured results. Association of National Advertisers, New York, NY.
Cornell Law School. (n.d.). Legislation. Retrieved August 02, 2016, from https://www.law.cornell.edu/wex/legislation.
Doss, D. (2004). An investigation of adapting the software capability maturity model architecture and framework within traditional industrial environments. Prescott, AZ: Northcentral University.
Doss, D. (2014). The capability maturity model as a criminal justice process improvement paradigm. Pretoria, South Africa: University of South Africa.
Doss, D., Chen, I., & Holland, L. (2008). A proposed variation of the capability maturity model framework among financial management settings. Proceedings of the Academy of Accounting and Financial Studies, 13 (1), 15-18.
Doss, D., Glover, W., Goza, R., & Wigginton, M. (2015). The foundations of communication in criminal justice systems. Boca Raton, FL: CRC Press.
Doss, D., Goza, R., Tesiero, R., Gokaraju, B., & McElreath, D. (IN PRESS). The Capability Maturity Model as an industrial process improvement model. Manufacturing Science and Technology, 4 (2), IN PRESS.
Doss, D., Henley, R., Gokaraju, B., McElreath, D., & He, F. (2016). Integrating the GOMS model and the intelligence cycle. Journal of Business and Economics, 7 (8), 1260-1268.
Doss, D., Henley, R., McElreath, D., Gokaraju, B., Goza, R., & Lusk, G. (IN PRESS). Process improvement: Urban vs. rural personnel perspectives of a derivative CMMi process maturity framework. Southwest Academy of Management.
Doss, D., Henley, R., McElreath, D., Gokaraju, B., Lin, S., Lusk, G., & Goza, R. (IN PRESS). Managerial perspectives of the integrated capability maturity model among law enforcement personnel. Southwest Academy of Management.
Doss, D. & Kamery, R. (2006a). A review of existing capability maturity model (CMM) derivative frameworks. Proceedings of the Academy of Educational Leadership, 11 (1), 125-130.
Doss, D. & Kamery, R. (2006b). Derivation of the CMM to support the generation of a baseline IPMM. Proceedings of the Academy of Educational Leadership, 11 (1), 185-190.
Doss, D. & Kamery, R. (2006c). The software capability maturity model architecture and potential applications to process improvements: Preliminary study. Proceedings of the Academy of Educational Leadership, 11 (1), 137-142.
Doss, D. & Kamery, R. (2006d). A study of the benefits of adapting software process improvement frameworks and architectures in traditional settings. Proceedings of the Academy of Educational Leadership, 11 (1), 107-112.
Doss, D., & Kamery, R. (2006e). A review of two improvement initiatives: Business process improvement (BPI) and business process reengineering (BPR). Proceedings of the Academy of Educational Leadership, 11 (1), 95-100.
Doss, D. & Kamery, R. (2006f). Adapting the capability maturity model (CMM) to unrelated industries as a process maturity framework. Proceedings of the Academy of Educational Leadership, 11 (1), 155-160.
Doss, D. & Kamery, R. (2006g). Derivation of the CMM to support the generation of a baseline IPMM. Proceedings of the Academy of Educational Leadership, 11 (1), 185-190.
Doss, D. & Kamery, R. (2006h). Exploring total quality management (TQM) and derivative frameworks of the capability maturity model (CMM). Proceedings of the Academy of Educational Leadership, 11 (1), 131-135.
Doss, D. & Kamery, R. (2006i). The software capability maturity model architecture and potential applications to process improvements: Preliminary study. Proceedings of the Academy of Educational Leadership, 11 (1), 137-142.
Doss, D., & Kamery, R. (2006j). The capability maturity model (CMM) architecture and framework within traditional industrial environments: An overview. Proceedings of the Academy of Educational Leadership, 11 (1), 101-106.
Doss, D. & Kamery, R. (2005a). Implementation of the capability maturity model to support continuous process improvement tenets of the TQM philosophy. Proceedings of the Academy of Information and Management Sciences, 9 (2), 7-12.
Doss, D. & Kamery, R. (2005b). The capability maturity model: A valid architecture to support a baseline environmental maturity model. Proceedings of the Academy of Information Management Sciences, 9 (2), 1-5.
Doss, D., McElreath, D., Henley, R., Goza, R., Lusk, G., & Gokaraju, B. (IN PRESS). Process improvement and Total Quality Management for justice system organizations. Southwest Academy of Management.
Doss, D., McElreath, D., Jensen, C., Wigginton, M., Goza, R., Becker, U., & Roberts, R. (2016). Management and administration for criminal justice organizations. Dubuque, IA: Kendall-Hunt.
Doss, D., McElreath, S., Mallory, S., Wigginton, M., Jensen, C., & Lande, U. (2015). Mississippi vs. Alabama: Examining the management vs. non-management personnel perceptions of the Capability Maturity Model among justice system organizations. Proceedings of the Mustang International Academic Conference, 7 (1), 27.
Doss, D., Sumrall, W., McElreath, D., & Jones. D. (2013). Economic and financial analysis for criminal justice organizations. Boca Raton, FL: CRC Press.
Doss, D., Sumrall, W., & Jones, D. (2012). Strategic finance for criminal justice organizations. Boca Raton, FL: CRC Press.
Doss, D., Tesiero, R., Gokaraju, B., McElreath, D., & Goza, R. (IN PRESS). Proposed derivation of the integrated Capability Maturity Model as an environmental management maturity model. Energy and Environmental Engineering, 5 (3), IN PRESS.
Drake, D., Sutterfield, J. S., & Ngassam, C. (2008). The revolution of six-sigma: An analysis of its theory and application. Academy of Information and Management Sciences Journal, 11(1), 29-44. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/214629398?a ccountid=14786.
Dubé, J., Hitsch, G., & Manchanda, P. (2005). An empirical model of advertising dynamics. Quantitative Marketing and Economics, 3(2), 107-144. doi:http://dx.doi.org/10.1007/s11129-005-0334-2.
Fang, Z., Luo, X., & Keith, M. E. (2015). How effective is location-targeted mobile advertising? MIT Sloan Management Review, 56(2), 14-15. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1650910764? accountid=14786.
Filbeck, G., Swinarski, M., & Zhao, X. (2013). Shareholder reaction to firm investments in the capability maturity model: An event study. European Journal of Information Systems, 22(2), 170-190. doi:http://dx.doi.org/10.1057/ejis.2012.54.
Furlong, R. (1994). Tobacco Advertising Legislation and the Sponsorship of Sport. Australian Business Law Review, 22 (3), 159. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/223517099?a ccountid=14786.
Ghosh, B. C., & Ling, M. T. (1994). Total quality management in services: The case of Singapore's advertising industry. The TQM Magazine, 6(4), 34. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/227615194?a ccountid=14786.
Glover, W. & Doss, D. (IN PRESS). Business law for people in business. Austin, TX: Sentia Publishing.
Glowa, T. (2002, June 24). White Paper: Advertising Process Models. Retrieved February 22, 2016, from http://www.academia.edu/5302471/Advertising_Process_Models_White_Paper_White_P aper_Advertising.
Goldenberg, B. (2006, 10). Business Processes Must Precede Technology. Customer Relationship Management, 10, 16. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/222784094?a ccountid=14786.
Grunert, K. G. (1996). Automatic and strategic processes in advertising effects. Journal of Marketing, 60(4), 88-101.
Gulledge, Thomas R., Jr, & Sommer, R. A. (2002). Business process management: Public sector implications. Business Process Management Journal, 8(4), 364. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/220305589?a ccountid=14786.
He, F., Liu, M., Yang, D., Li., M., & Doss, D. (2016). Cognitive models: Piaget, McCarthy, and organizational management. Proceedings of the Academy of Organizational Culture, Communications, and Conflict, 21 (1), 17-22.
Hong, P., Hong, S. W., James, J. R., & Park, K. (2012). Evolving benchmarking practices: A review for research perspectives. Benchmarking, 19(4), 444-462. doi:http://dx.doi.org/10.1108/14635771211257945.
Islam, S., & Daud Ahmed, M. (2012). Business process improvement of credit card department: Case study of a multinational bank. Business Process Management Journal, 18(2), 284- 303. doi:http://dx.doi.org/10.1108/14637151211225207.
Keller, M. (2007). FUTURE GOVERNING OF THE TOBACCO INDUSTRY. NPN, National Petroleum News, 99(7), 28-31. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/223288150?a ccountid=14786.
Klefsjo, B. (1998). Business process improvement workbook: Documentation, analysis, design, and management of business process improvement. Quality Progress, 31(6), 110. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/214771762?a ccountid=14786.
Kock Jr, N. F., & McQueen, R. J. (1996). Product Flow, Breadth and Complexity of Business Processes: An Empirical Study of 15 Business processes in Three Organizations. Business Process Re-Engineering & Management Journal, 2(2), 8. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/220292849?a ccountid=14786.
Koning, A. J., & Franses, P. B. F. (2003). Confidence intervals for cronbach's coefficient alpha values. St. Louis: Federal Reserve Bank of St Louis. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1698319484? accountid=14786.
Krishnan, T. V., & Jain, D. C. (2006) Optimal Dynamic Advertising Policy for New Products. Management Science52(12):1957-1969. Retrieved from http://dx.doi.org/10.1287/mnsc.1060.0585.
Krugman, D. M., & Ferrell, O. C. (1981). The organizational ethics of advertising: Corporate and agency views. Journal of Advertising, 10(1), 21. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/236592752?a ccountid=14786.
Lace, J. M. (1998). Evaluating Advertising Agency Performance: Actions to Enhance the Client/Agency Relationship. Management Research News, 21(7), 47-59. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/223540878?a ccountid=14786.
Laroche, M., Kirpalani, V. H., Pons, F., & Zhou, L. (2001). A model of advertising standardization in multinational corporations. Journal of International Business Studies, 32(2), 249-266. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/197168131?a ccountid=14786.
Lavidge, Robert J. and Gary A. Steiner. 1961. A model for predictive measurements of advertising effectiveness. Journal of Marketing. 25(October), 59-62.
Lewis, M. & Slack, N. 2003. Operations Management: Critical Perspectives on Business and Management. New York, NY: Routledge Publishing.
Lind, D., Marchal, W., & Wathen, S. 2008. Statistical Techniques in Business and Economics. 13th edition. Boston, MA: McGraw-Hill Publishing.
Lobato, I. N., & Velasco, C. (2004). A Simple Test of Normality for Time Series. Econometric Theory, 20(4), 671. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/212391282?a ccountid=14786.
Luo, X. (2000). Benchmarking and improving advertising efficiency of the top 100 advertisers: New applications of data envelopment analysis. American Marketing Association.Conference Proceedings, 11, 318. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/199502235?a ccountid=14786.
Maddox, K. (2004, November). Six Sigma Helps Marketing Improve Design, Save Money. Retrieved May 24, 2016, from http://adage.com/article/btob/sigma-helps-marketing- improve-design-save-money/258785/.
Malmelin, N. (2010). Diverging responsibilities: Reflections on emerging issues of responsibility in the advertising business. Business Strategy Series, 11(1), 43-53. doi:http://dx.doi.org/10.1108/17515631011013104.
Manganelli, R. L., & Klein, M. M. (1994). A framework for reengineering (part I). Management Review, 83(6), 10. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/206706530?a ccountid=14786
Margherita, A. (2014). Business process management system and activities. Business Process Management Journal, 20(5), 642. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1658148144? accountid=14786.
Marshall, S. W. (2006). Advertising message strategies and executional devices in television commercials from award -winning “effective” campaigns from 1999 to 2004 (Order No. 3228782). Available from ABI/INFORM Collection. (305329043). Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/305329043?a ccountid=14786.
McCollum, W. R. (2004). Process improvement in the quality management system: An analysis of Carnegie Mellon's capability maturity model (Order No. 3118900). Available from ABI/INFORM Complete. (305082771). Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/305082771?a ccountid=14786.
McElreath, D., Doss, D., Jensen, C., Wigginton, M., Nations, R., Van Slyke, J., & Nations, J. (2014a). Foundations of emergency management. Dubuque, IA: Kendall-Hunt.
McElreath, D., Jensen, C., Wigginton, M., Doss, D., Nations, R., & Van Slyke, J. (2014b). Introduction to homeland security. (2nd ed.). Boca Raton, FL: CRC Press.
McElreath, D., Doss, D., Jensen, C., Wigginton, M., Kennedy, R., Winter, K., Mongue, R., Bounds, J., & Estis-Sumerel, J. (2013). Introduction to law enforcement. Boca Raton, FL: CRC Press.
Mendes, M., & Yigit, S. (2013). Comparison of ANOVA-F and ANOM tests with regard to type I error rate and test power. Journal of Statistical Computation and Simulation, 83(11), 2093. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1442996640? accountid=14786.
Mills, J. (1994, Aug 08). THE MEDIA BUSINESS; legislation may calm frantic radio ads for car leases. New York Times Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/429855880?a ccountid=14786.
Mohapatra, S. (2013). Business Process Reengineering: Automation Decision Points in Process Reengineering. New York, NY: Springer Publishing.
Na, W., Marshall, R., & Woodside, A. G. (2009). Decision System Analysis of Advertising Agency Decisions. Qualitative Market Research, 12(2), 153-170. Retrieved from http://dx.doi.org/10.1108/1352275091094876.
Padma, P., Ganesh, L. S., & Rajendran, C. (2008). An exploratory study of the impact of the capability maturity model on the organizational performance of Indian software firms. The Quality Management Journal, 15(2), 20-34. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/213623722?a ccountid=14786.
Patwardhan, A., & Patwardhan, D. (2008). Business process re-engineering - saviour or just another fad? International Journal of Health Care Quality Assurance, 21(3), 289-96. doi:http://dx.doi.org/10.1108/09526860810868229.
Polpinij, J., Ghose, A., & Dam, H. K. (2015). Mining business rules from business process model repositories. Business Process Management Journal, 21(4), 820-836. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1692261894 ?accountid=14786.
Poundstone, J. (2015, June 16). Ten Things Every Employee Should Understand About the Purpose of Workplace Policies. Retrieved from http://www.navexglobal.com/blog/ten- things-every-employee-should-understand-about-purpose-workplace-policies.
Preston, I. L. (1982). The association model of the advertising communication process. Journal of Advertising (Pre-1986), 11(000002), 3. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/208477647? accountid=14786.
Price, J. H. & Murnan, J (2004). Research Limitations and the Necessity of Reporting Them. American Journal of Health Education, 66-67. Retrieved from: http://www.tandfonline.com/doi/abs/10.1080/19325037.2004.10603611.
Priviteria, G.J. 2013. Student Study Guide With IBM SPSS Workbook for Research Methods for the Behavioral Sciences. Thousand Oaks, CA: Sage Publishing.
Quelch, J., & Harris, B. (2005, Autumn). SIX SIGMA COMES TO MARKETING. European Business Forum, 32-35. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/224663358? accountid=14786.
Rossiter, J. R., Percy, L., & Donovan, R. J. (1991, October/November). A Better Advertising Planning Grid. Journal of Advertising Research, 11-21. Retrieved from https://www.researchgate.net/publication/232573942_A_better_advertising_planning_gr id.
Samiee, S., Jeong, I., Pae, J. H., & Tai, S. (2003). Advertising standardization in multinational corporations: The subsidiary perspective. Journal of Business Research, 56(8), 613-626. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/196323278? accountid=14786.
Sijtsma, K. (2009). On the use, the misuse, and the very limited usefulness of cronbach's alpha. Psychometrika, 74(1), 107-120. doi:http://dx.doi.org/10.1007/s11336-008-9101-0.
Silk, A. J., & Berndt, E. R. (1993). SCALE AND SCOPE EFFECTS ON ADVERTISING AGENCY COSTS. Marketing Science (1986-1998), 12(1), 53. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/207346792? accountid=14786.
Six-Sigma Daily. Retrieved May 25, 2016, from http://www.sixsigmadaily.com/.
Søgaard, R., Lindholt, J., & Gyrd-Hansen, D. (2012). Insensitivity to scope in contingent valuation studies. Applied Health Economics and Health Policy, 10(6), 397-405. doi:http://dx.doi.org/10.2165/11633800-000000000-00000.
Sony, M., & Naik, S. (2012). Six sigma, organizational learning and innovation. The International Journal of Quality & Reliability Management, 29(8), 915-936. doi:http://dx.doi.org/10.1108/02656711211258535.
Sumrall, W., Sumrall, R., & Doss, D. (2016). A review of memory theory. International Journal of Humanities and Social Sciences, 6 (5), 23-30.
Swami, S., & Tirupati, D. (2012). Optimal Pricing and Advertising Policies for Bottom of Pyramid Markets: An Analytical Approach. Technology Operation Management, 3(1-2), 32-49. doi:http://dx.doi.org/10.1007/s13727-013-0014-5.
Teng, J. T. C., Grover, V., & Fiedler, K. D. (1994). Business process reengineering: Charting a strategic path for the information age. California Management Review, 36(3), 9. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/216128261?a ccountid=14786.
U.S. Census Bureau. (2015.). 2010 Census Urban and Rural Classification and Urban Area Criteria. Retrieved September 25, 2016, from https://www.census.gov/geo/reference/ua/urban-rural-2010.html
van Wesel, F., Boeije, H., & Hoijtink, H. (2013). Use of hypotheses for analysis of variance models: Challenging the current practice. Quality and Quantity, 47(1), 137-150. doi:http://dx.doi.org/10.1007/s11135-011-9508-z.
Vaughn, R.(1980). How Advertising Works: A Planning Model. Advertising & Society Review 1(1), Advertising Educational Foundation. Retrieved March 2, 2016, from Project MUSE database.
Vaughn, R. (1986), “How Advertising Works: A Planning Model Revisited”, Journal of Advertising Research, Vol. 26, February / March.
Vergidis, K., Tiwari, A., & Majeed, B. (2006). Business process improvement using multi- objective optimisation. BT Technology Journal, 24(2), 229. Retrieved from doi:http://dx.doi.org/10.1007/s10550-006-0065-2.
Vermeulen, W. (2003). Benchmarking as an enabler of business excellence in the south african financial sector. Benchmarking, 10(1), 65-72. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/217366133?a ccountid=14786.
Vivas, C., Sobreiro, P., & Claudino, R. (2014). Integrating knowledge management in a business strategy process operationalized using process management approach. Paper presented at the European Conference on Knowledge Management. 3, 1045-1054. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1673123860? accountid=14786.
Vyas, S., & Manwani, D. T. (2012). Organizational Internal Environment Scanning- A Productivity Enrichment Perspective. International Journal of Organizational Behaviour & Management Perspectives, 1(1), 5-9. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1648114429? accountid=14786.
Wademan, M. R., Spuches, C. M., & Doughty, P. L. (2007). The people capability maturity model: Its approach and potential to improve workforce performance. Performance Improvement Quarterly, 20(1), 97-123.
Weckenmann, A., Akkasoglu, G., & Werner, T. (2015). Quality management - history and trends. TQM Journal, 27(3), 281. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/1672078844? accountid=14786.
Zairi, M. (1997). Business process management: A boundaryless approach to modern competitiveness. Business Process Management Journal, 3(1), 64. Retrieved from http://ezproxy.uwa.edu:2048/login?url=http://search.proquest.com/docview/220292989?a ccountid=14786.
Zellner, G. (2011). A structured evaluation of business process improvement approaches. Business Process Management Journal, 17(2), 203-237. doi:http://dx.doi.org/10.1108/14637151111122329.
- Citar trabajo
- Charles R. Pickett (Autor), 2016, The Capability Maturity Model as an Advertising Process Maturity Paradigm, Múnich, GRIN Verlag, https://www.grin.com/document/421250
-
-
-
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X. -
-
¡Carge sus propios textos! Gane dinero y un iPhone X.