The goal of this study was to elaborate the differences between COVID-19 induced and a potentially restriction free consumer behavior in 2024 regarding a sustained change of online shopping and the usage of digital services. Two different scenarios were developed (I) asking participants about their behavior during the COVID-19 pandemic and (II) asking participants about their behavior in a pandemic and restriction free environment in 2024. In order to elaborate potential effects, a research model was derived based on a conceptual framework investigating the long-term adherence of behavioral changes combined with item relationships regarding the sustained usage of services. The data was derived through online questionnaires. Afterwards, the conducted data was operationalized and elaborated through multiple (M)ANOVA. The key findings were that the satisfaction levels for both online shopping as well as for the digital services significantly decreased in scenario II. According to the research model this allows the conclusion that a sustained usage is negatively affected. Therefore, the observed acceleration of online shopping could potentially slow down and return to average growth levels. The reduction of satisfaction levels was supported by a correlation reduction of the frequency of use and money spent items which confirmed the reduction of online shopping as well as the usage of digital services. For the retail and e-commerce industry the findings allow strategic implications to prepare for the post pandemic consumer behavior. Consumers will potentially carry their positive experiences with them and translate those into future expectations towards retail and e-commerce. To mention are that due to the decreased satisfaction levels the traditional stationary retail store remains relevant and therefore should be strategically emphasized within retailer’s channel strategy.
Table of Contents
List of abbreviations
List of tables
List of figures
1 Introduction
1.1 Initial situation
1.2 Research question
1.3 Thesis structure
2 Determinates of purchasing patterns
2.1 Influences of consumer decisions
2.1.1 Activation
2.1.2 Motivation
2.1.3 Emotion
2.1.4 Attitude
2.1.5 Influence of environment and surrounding
2.2 Buying behavior for e-commerce
2.3 Consumer behavior models
3 Impact of the Corona Crisis
3.1 Economic impact
3.1.1 Impact on the German economy
3.1.2 Projected economic development of Germany
3.2 Economic performance of the retail and e-commerce market
3.3 Crisis management
3.4 Impact on consumer behavior
3.4.1 Overview of changes
3.4.2 Impact for retail and e-commerce
4 Methodology and research design
4.1 Research design
4.1.1 State of research
4.1.2 Deviation of research model
4.1.3 Research questions and hypothesis
4.2 Methodology
4.2.1 Elaboration design
4.2.2 Operationalizing and scenario design
4.2.3 Relationships between samples
4.2.4 Data collection
4.2.5 Data processing
5 Elaboration and analysis
5.1 Demographic profile of the respondents
5.2 Data analysis
5.2.1 Preparation of data and elaboration of premises
5.2.2 Analysis of variance (M)ANOVA
5.3 Additional elaborations
5.4 Elaboration of hypothesis
5.5 Answering of research question
6 Discussion
6.1 Critical Discussion
6.2 Limitation and further research
7 Conclusion
Bibliography
List of tables
Table 1- Overview of hypothesis
Table 2 - Sources of participants
Table 3 - Quality criterion (Backhaus 2018, S. 379, Weiber 2014, 128ff.)
Table 4 - Overview of factor and reliability analysis
Table 5 - Premises ANOVA and MANOVA (Huber 2014, S, 63; Eschweiler 2007, 546f.)
Table 6 - Summary of premises ANOVA and MANOVA
Table 7 – Summary results MANOVA 1 and
Table 8 - Summary ANOVA results
Table 9 - Summary results Welch-test
Table 10 - Descriptive statistics
Table 11 - Overview age groups for sustained usage items
Table 12 - Overview urbanity groups for sustained usage items
Table 13 - Overview gender for sustained usage items
List of figures
Figure 1 - Thesis design
Figure 2 - Shell model of consumer behavior
Figure 3 - Maslow’s Need hierarchy
Figure 4 - SOR-Model
Figure 5 - Development of German GDP
Figure 6 - German business climate
Figure 7 - Development of German retail
Figure 8 - Development of retail sales
Figure 9 - Chronic of COVID-19 infection and federal measures
Figure 10 - Daily corona infections in Germany
Figure 11 - Changed consumer behavior (McKinsey 2021, 2020)
Figure 12 - Evaluation of COVID-19 pandemic (Appinio 2021)
Figure 13 - Research model
Figure 14 - Difference product categories between groups
Abstract
The goal of this study was to elaborate the differences between COVID-19 induced and a potentially restriction free consumer behavior in 2024 regarding a sustained change of online shopping and the usage of digital services. Two different scenarios were developed (I) asking participants about their behavior during the COVID-19 pandemic and (II) asking participants about their behavior in a pandemic and restriction free environment in 2024. In order to elaborate potential effects, a research model was derived based on a conceptual framework investigating the long-term adherence of behavioral changes combined with item relationships regarding the sustained usage of services. The data was derived through online questionnaires. Afterwards, the conducted data was operationalized and elaborated through multiple (M)ANOVA. The key findings were that the satisfaction levels for both online shopping as well as for the digital services significantly decreased in scenario II. According to the research model this allows the conclusion that a sustained usage is negatively affected. Therefore, the observed acceleration of online shopping could potentially slow down and return to average growth levels. The reduction of satisfaction levels was supported by a correlation reduction of the frequency of use and money spent items which confirmed the reduction of online shopping as well as the usage of digital services. For the retail and e-commerce industry the findings allow strategic implications to prepare for the post pandemic consumer behavior. Consumers will potentially carry their positive experiences with them and translate those into future expectations towards retail and e-commerce. To mention are that due to the decreased satisfaction levels the traditional stationary retail store remains relevant and therefore should be strategically emphasized within retailer’s channel strategy.
List of abbreviations
Abbildung in dieser Leseprobe nicht enthalten
1 Introduction
1.1 Initial situation
The appearance of COVID-19 and its spread over the globe has changed the lives of millions of people. Chancellor Angela Merkel called the crisis the biggest challenge since World War II, which emphasizes the significance of COVID-19(BReg 2020). During the pandemic, Germany was exposed to the imposition of travel restrictions, lockdowns and the closure of shops and service points. Consequently, COVID-19 changed routines and habits of consumers as well as shopping channel preferences(Deloitte 2021a). Germany`s retail environment was the subject to change long before the pandemic. The growth of online shopping reshapes the German retail market and causes companies to adapt to changed market requirements(Jahn 2017, 25).
In reaction to COVID-19 online channels gained even more importance for consumers(McKinsey 2021; W&V 2020). Companies were trying to counteract the pandemic situation with new sales and service solutions to secure a minimum level of business. Some have adapted to the current situation by changing their business models(Deloitte 2020).
It was observed that consumers changed their behavior with the appearance of COVID-19 and adjusted it over the course of the crisis(Deloitte 2021a; Sheth 2020). The future of shopping is still in flux and even so if the newly adopted behavior will be part of the post pandemic behavior(Remes 2021). Previous studies have investigated the adoption of online shopping. However, there is a lack of published research examining the sustained use and especially the effects of COIVD-19. Research carried out on the sustained usage of online shopping identified the following variables to be influential: relative advantage, enjoyment, and risk. It was shown that relative advantage and enjoyment have a positive correlation for the sustained usage of online purchasing as well as cross-channel usage. Whereas, risk influences the sustained usage negatively(Liu 2011). Recently carried out investigations identified that the stickiness of the behavioral change is the product of forced behavior change and the satisfaction with the new behavior. The definition of stickiness was adopted by previous research as behaviors that are likely to persist(Remes 2021).
1.2 Research question
Firstly, it is the aim of this study to contribute towards the academical refinement process of the COVID-19 crisis. The study conducted through online questionnaires examined whether the COVID-19 induced consumer behavior differs from a potential pandemic and restriction free consumer behavior.
Based on the underlying literature research, it became apparent that COVID-19 and the effects on sustained usage of online shopping have not yet been subject to research. This paper aims to combine both COVID-19 and the effects on sustained usage of online shopping and digital services. Thus, the following research question was developed: Which differences can be identified between COVID-19 induced and potentially restriction free consumer behavior in 2024 regarding a sustained change of online shopping and the usage of digital services? A quantitative analysis was performed. The methods applied were (M)ANOVA (Multivariate Analysis of Variance) of the dependent variables. The (M)ANOVA allows to analyze group differences synchronously across the dependent variables(Backhaus 2018, 165). Based on the findings, further recommendations were derived for retailers.
1.3 Thesis structure
The structure of this thesis orientates on the empirical research process according to Döring preceded by an extensive literature research and followed by a critical discussion and conclusion. The thesis deviates occasionally from the empirical research structure in order to allow more reassuring reading.
The following figure illustrates steps and the attached chapters of the thesis:
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Figure1- Thesis design (in emphasis on Döring 2016, S. 24f.)
Generally, the thesis is structured in four major blocks. Firstly, findings of the literature review were brought together within the chapters two and three. Chapter four provides the methodology as well as the research design containing the most of the empirical research steps. In the fifth chapter, the data analysis provides the basis for evaluating the hypotheses and answering the research question. The sixth chapter presents the findings for discussion. A summary of the collected findings is presented. In addition, the chapter deals with the application of the findings and possible limitations. The last chapter seven contains the conclusion which summarizes the key findings and implications presented in the preceding discussion.
2 Determinates of purchasing patterns
This chapter aims to provide a condensed foundation for the consumer behavior related research performed within this study.
2.1 Influences of consumer decisions
Consumer behavior is influenced by psychological and individual factors. Activation, emotion, involvement and attitude are conceived as psychological factors. In addition, influences like environment, culture and society are also influencing the consumer behavior(Trommsdorff 2004, 31). The influences are either affecting from the outside or are evolving within the individual itself. The relative relevance of the concentric layers illustrated in figure 2 for the consumer behavior are decreasing from the inside to the outside. Further explanations variables are shown in figure 2.
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Figure2- Shell model of consumer behavior (in emphasis on Foscht 2017, S. 33)
2.1.1 Activation
Activation is a human state of arousal that causes customers to behave in a certain way. Activation is considered to be the basis of human commitment and enables other layers, such as motivation, emotion, involvement and attitudes. Activation can be triggered by internal and external stimuli. Cognitive processes or the human metabolism are examples for internal stimuli. The environment or surroundings determine the external stimuli, which are divided into emotional, cognitive, and physical stimuli. Emotional stimuli generate activation through arousal states that are evoked inside of the human and often cause naturally programmed reactions to these stimuli(Pepels 2013, 48f.; Homburg 2009, 16).
Activation impacts consumer's purchase decisions and information processing. Higher levels of activation increase the likelihood that the information from advertisements or other stimuli like environmental changes will be processed and thus impact purchase decisions(Foscht 2017, 38; Homburg 2009, 17).
2.1.2 Motivation
Motivation determines why people engage in a certain behavior and consists of several motives, which are influenced by various human needs. Motivation serves to the satisfy human needs. The Maslow's pyramid of needs illustrated in figure 3 shows which needs drive people. Maslow assumes that the needs of the lowest level must be satisfied first to achieve needs of the upper levels. Therefore, physiological needs must be satisfied first to meet safety needs, and so on.(Hawkins 2019, 367; Homburg 2009, 17).
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Figure3- Maslow’s Need hierarchy (in emphasis on Hawkins 2020, S. 367)
2.1.3 Emotion
Emotions also determine human behavior and influence their actions. Emotions are not goal-oriented, but rather a short- to long-term state of mind. Examples for human emotions can be anger, joy, or fear. Emotions are usually connected to state of arousal(Homburg 2009, 18). Emotions are often automatic reactions which humans have little control of. Nevertheless, emotions are an important component of reasoning. Without emotions it would not be possible to come to rational decisions(Munzinger 2012, 107). Consumer behavior is highly influenced by emotions, as these play an important role within the processing of information. They are impactful for evaluation of information, and thus ultimately on the purchase decision(Homburg 2009, 18f.). In addition, emotions often lead to quick and easy purchase decisions(Munzinger 2012, 106).
2.1.4 Attitude.
The attitude of consumers has a significant influence on its purchasing behavior(Pepels 2013, 58). Attitude is associated with the expectations and inner mindset of a person regarding a product, service, or other things. It is perceived that attitude is learned and is strongly anchored within the consumer(Homburg 2009, 20). Attitudes can be differentiated based by various characteristics. Cognitive attitudes are based on information about objects, while emotional attitudes are based on feelings. Attitudes that are formed by personal experience are called experience-based attitudes. The opposite are adopted attitudes formed from external information, such as expert opinions. Consumer attitudes can be influenced and directed by marketing measures, such as the design of the brand image. The attitudes that consumers have toward a brand or service are key determinants of the purchase decision(Homburg 2009, 21).
2.1.5 Influence of environment and surrounding
Human behavior is not only determined by internal, but also by external influences, which determine psychological processes. Environmental factors are especially the physical and psychological surrounding(Foscht 2017, 33f.). Whereby the physical environment is formed by nature and climate, or by infrastructure and objects created by man. The psychological environment, on the other hand, is formed by personal contacts, such as family, friends, or acquaintances. However, religion, social class or culture are also part of the social environment. The social environment can have a direct influence on the purchase decision. The cultural environment includes habits, customs, attitudes or values that are shared by several people(Homburg 2009, 25).
2.2 Buying behavior for e-commerce
Today, online shopping is experiencing an explosive growth and with it changing consumer behavior(Xiaozhou 2019). Generally, consumer behavior is impacted fundamentally by the usage of online shopping. Multiple possibilities are available to prepare shopping experiences. Online shopping provides many benefits compared to stationary shopping. Of which are to mention, multimedia support of product information, 24-hours availability of online stores, or accessibility of information at any time(Michelis 2014, 30; Kuß 2007, 165). Consumers are aware of the respective advantages and disadvantages of different channels and combine channel benefits. Because of multiple shopping channels, customers can easily switch from one channel to another one to create more value for them(Pourabedin 2016). At the same time, gained experiences in one channel influence the behavior and expectations of customers in another(Hölter 2020, 6). The utilitarian value or respectively the relative advantage is highly relevant for consumers to consider different channels(Pourabedin 2016).
When comparing online shopping to traditional shopping, studies were able to identify similarities as well as differences(Constantinides 2004, 121). External as well as personal influences, which impact consumer behavior, are very similar for online and stationary shopping. However, regarding marketing measures differences can be identified. Three major differences can be distinguished: (1) usability and interaction with user, (2) psychological elements which help to build and generate trust, (3) content and aesthetics(Ozok 2010; Constantinides 2004, 121).
Sociological, situational, and psychological influences are among the most impactful for online shopping behavior of consumers. Sociological influences are for example gender, age, culture, or lifestyle. According to recent research the differences between men and women are dissolving slowly. Therefore, men and women are almost head-to-head regarding online purchasing frequency(de la Motte 2019). However, gender preferences regarding product categories can be identified(McMahan 2009). A more impactful influence for online shopping behavior than gender is age. There exists a considerable difference between age groups. It can be observed that people in the middle age group particularly (25 - 44 years and 45 - 64 years) show a higher frequency to use the internet for shopping compared to other age groups(de la Motte 2019).
Situational influences, such as pressure, urgency of demand, availability, ease of use or user experience of a webstore are influential for online shopping. If consumers are short of time, they tend to buy online. The availability of products in stores has a considerably influence for online shopping as well. If products are not available in stores consumers switch channels and use online shops to buy the desired product(Fritz 2004, 125f.).
Psychological influences are considered to play a key role regarding online shopping. Positive experiences help to reduce fears that consumers relate towards online shopping(Fritz 2004, 125f.). Research regarding sustained usage of online shopping points out, that enjoyment and risk are crucial for consumer to continue using online channels either for shopping or information search(Liu 2011). Other influence factors that can have a positive influence for online engagement are enjoyment and perceived relative advantage(Liu 2011).
Summarizing, consumers develop a stronger desire for flexible solutions with a high degree of self-determination(Hölter 2020, 7).
2.3 Consumer behavior models
Numerous models are used to explain consumer behavior. In the context of this study neo-behaviorist models were put into further consideration.
Neo-behaviorism is an approach to psychology influenced by logical positivism that emphasized the development of comprehensive theories and frameworks of behavior through empirical observation and the use of consciousness and mental events as explanatory devices(APA 2021).
These approaches aim to explore the so-called “black box” of humans. The SR model is extended by the inner behavior, which is represented by the organism (O)(Foscht 2017, 23f.). In the SOR model, the focus lies on the mental processes in the organism. The observable variables represent the stimuli (S), which affect an organism, as well as the response (R). Through the interceding variables the “black-box” can be described(Ş. Sabah 2017). In order to explain processes within the organism theoretical frameworks need to be applied. The interceding variables can be distinguished between (1) activation processes, e.g., emotions or motivation, (2) cognitive processes, e.g., perception or learning. Observable and interceding variables relate to each other as illustrated within figure 4. In order to measure the interceding variables they need to be connected to observable stimuli and reactions(Foscht 2017, 29f.).
According to current research, interceding variables are the foundation for explaining consumer behavior. Ideally, every consumer behavior can be related to activating and cognitive processes as a foundation for explanation. It should be noted that activating processes do not have to precede cognitive processes, but there can be inverse or mediating and moderating relationships between the constructs as well. Furthermore, due to the complexity of consumer behavior, partial analyses dominate, which also take into account further psychological, personal, social and cultural determinants(Foscht 2017, 30). In the context of this study it is important to point out that external stimuli are not limited to marketing stimuli, but also include environmental stimuli which include e.g. political, economic or social stimuli(Michelis 2014, 53).
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Figure4- SOR-Model (in emphasis on Foscht 2017, S. 30)
3 Impact of the Corona Crisis
COVID-19 and its spread across Germany and the rest of the world has changed the lives of millions of people since its initial appearance. During this crisis, the world faced the imposition of international travel bans, lockdowns and the closure of shops and communities(Deloitte 2020). The following chapter aims to provide a found understanding about the impact of the COVID-19 for the German economy, business industries and customers. Since the appearance of corona is a very recent topic multiple sources from leading consulting companies, research institutes and journals are brought together to provide the most recent information.
3.1 Economic impact
The following chapter provides an introduction regarding the impact of the corona-pandemic on the German economy.
3.1.1 Impact on the German economy
In reaction, the GDP in Germany decreased by about 5.0% in 2020 compared to the previous year.(DESTATIS 2021b). Almost every industry was significantly affected in 2020(Feld 2020). One major influence was, that production sites scaled down or had to shut down completely. The impact was especially noticeable for the service sector(Stephany 2020). Examples for service industries that were affected very hard are retail, transportation, hospitality, food and leisure. The productivity of these industries decreased by 6.3% compared to the previous year. However, opposite developments were noticed as well. For example e-commerce increased significantly and was able to profit from the accelerating digitization (DESTATIS 2021a; Charm 2020).
The pandemic has also left its mark on the German labor market. In average about 2.7 million people were unemployed in 2020. This summed up to about 429,000 less people employed than in the previous year. The unemployment rate increased to 5.9%, which was 0.9% higher than the previous year(BA 2021). The German government intends to stabilize the economy and safeguard jobs with extensive support measures. However, it is projected that because of the corona pandemic almost 800 tsd. jobs could be eliminated(Ragnitz 2020).
In general, the economy was influenced strongly by the degree of the infections over the course of the pandemic. In reaction to the occurrence of infections and the connected governmental regulations the economy was impacted as well. After the virus was controlled in spring of 2020 the economy experienced a noticeable recovery for the subsequent quarters(DESTATIS 2021a; Feld 2020). Figure 5 illustrates the economic development over the course of the pandemic. The average decrease of the German GDP for the year 2020 was lower compared to the average GDP growth of about 1.7% for the years 2014-2019. As figure 5 also illustrates the degree of GDP reduction differentiates between the quarters of 2020 and 2021. As stated above the economy was influenced significantly by the occurrence of infections and the in reaction to that discharged governmental implications. The strong collapse in the second quarter of 2020 was also caused by the initial shock the pandemic evoked among the population as well as companies(Michelsen 2020).
Another important variable that needs to be put into consideration is the global scale of the pandemic. The corona virus led to global economic reactions that also impacted the German economy. Since, the German economy is highly demanded on its trade routes and trade balance(Feld 2020).
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Figure5- Development of German GDP (in emphasis on DESTATIS 2021)
3.1.2 Projected economic development of Germany
Assessments of the current business situation have improved since the beginning of 2021(Becker 2021). Among different sectors of the economy the assessment is very different, mainly according to how severely they continue to be affected by restrictions on business activities. Figure 6 illustrates the German business climate index for different industries. It can be perceived that the overall climate improved. However, industry differences need to be distinguished(Deloitte 2021b). For example, the production industry is experiencing a strong recovery. On the other hand, retail along with other industries remain on a slow recovery. By far the least optimistic industry is the hospitality and accommodation sector. Focusing on retail the business climate index is still far away from its average of the last years.
The optimism of business leaders is supported by projections regarding potential growth of the German economy. For 2021 it is estimated that the German economy could grow up to 3.7%. The growth could also continue in 2022 for which a GDP growth of 3.2% is projected(IFO 2021). Overall, the post-corona development of the German economy is perceived positively. However, among the most feared risks is the uncertainty of the pandemic development(DIHK 2021).
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Figure6- German business climate (in emphasis on DIHK 2021)
3.2 Economic performance of the retail and e-commerce market
Even before the COVID-19 crisis the retail and e-commerce market in Germany was subject to major changes(Jahn 2017, 25). In reaction to the corona-pandemic the market experienced further changes and reallocations(McKinsey 2021). This chapter aims to provide an overview of the current German retail and e-commerce market.
Stationary retail and e-commerce took two different routes over the course of the pandemic. In March 2020, the federal states agreed to close most of the stores to slow down the spread of the virus for the first time. This led to very opposite developments for stationary retail and e-commerce. Whilst many stationary retail concepts had to readjust and enter into crisis management, on the other hand e-commerce businesses faced an unexperienced increase of consumer demand(Adhi 2020). Figure 7 illustrates the development of the German retail market and the uneven distribution over the course of the pandemic. The index showed that since overcoming the initial shock the e-commerce index underwent a strong development to a new all-time high well above its average growth rates. Conversely, the stationary retail and other retail sectors had to recover from a significant decline.
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Figure7- Development of German retail (in emphasis on Destatis 2021)
Figure 8 illustrates the development of retail and e-commerce sales in Germany from 2014-2020. It can be derived that retail sales underwent a steady development from approx. €458 Bill. in 2014 to approx.- €552 Bill. in 2020. However, the development of e-commerce sales showed a more dynamic development from approx. €36 Bill. in 2014 to approx. €73 Bill. in 2020(Statista 2021a). The average CAGR of retail sales accumulated to 2.9% and the average e-commerce sales to 12.5%. However, the growth of e-commerce for 2020 accumulated to almost 23.0% which represents the exceptional growth that could already be seen in figure 7.
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Figure8- Development of retail sales (in emphasis on Statista 2021, 2020)
3.3 Crisis management
This chapter aims to provide a brief overview of the federal and to some extent federal state COVID-19 crisis management.
Recent studies identified governmental crisis management influential for consumer behavior(Keane 2021). Since, the crisis management went through many iterations a timeline of the crisis development and its major management milestones is presented in the following figure.
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Figure9- Chronic of COVID-19 infection and federal measures (in emphasis on BMG 2021)
[...]
- Arbeit zitieren
- Michel Brandes (Autor:in), 2021, Impact of Covid-19 on German Consumer Behavior. Elaboration Regarding a Sustained Change of Online Shopping, München, GRIN Verlag, https://www.grin.com/document/1138163
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