Consumer Behavior on Online Grocery Shopping Adoption. A Quantitative Analysis in the Context of the COVID-19 Pandemic, Contrasting the Markets of Peru and Germany

Master's Thesis , 2022 , 137 Pages , Grade: 1,3

Business economics - Market research

The COVID-19 pandemic raised the demand for online food shopping in traditional Peru and Germany. This urged an in-depth examination since consumer attitudes in specific cultural contexts had received little attention. Thus, this research aims to ascertain the factors that significantly influence Germans' and Peruvians' intention to use online grocery shopping, as well as to identify significant differences between the two countries.

Two particular research topics serve as the framework for this study, which are as follows: Are there significant differences in the usage of online grocery shopping between Germans and Peruvians during the COVID-19 pandemic? What factors significantly influence Germans and Peruvians' online grocery shopping usage during the COVID-19 pandemic? As indicated by both research questions, this study's primary emphasis is on online grocery shopping behavior during the COVID-19 pandemic that peaked one year ago, in the year 2020, yet has persisted worldwide. Therefore, this study focuses only on empirical data from the COVID-19 outbreak since online grocery shopping in both nations was not a significant event before the pandemic.

Furthermore, there was no previous scientific research on online grocery shopping’s behavior in Peruvian literature, preventing comparison between the two countries. Given that the pandemic's breakout aided in the partial adoption of online grocery shopping in both nations, the researcher judged it appropriate to frame both research questions exclusively in terms of the COVID-19 pandemic to capture better the influence of the digital transition that was experienced in 2020.

Table of Contents

List of Figures

List of Tables

List of Formulae

List of Abbreviations

1 Introduction.
1.1 Context and Problem Definition.
1.2 Research Aim and Objectives.
1.3 Research Questions and Study Relevance.
1.4 Research Design
1.5 Research Structure

2 Literature Review
2.1 Background of e-commerce in Germany.
2.1.1 E-commerce context before the COVID-19 pandemic in Germany.
2.1.2 E-commerce context during the COVID-19 pandemic in Germany.
2.1.3 Germany's e-commerce adoption compared to other European countries.
2.1.4 Motivating and demotivating factors: Online shopping in Germany.
2.2 Background of e-commerce in Peru.
2.2.1 E-commerce context before the COVID-19 pandemic in Peru.
2.2.2 E-commerce context during the COVID-19 pandemic in Peru.
2.2.3 Peru's e-commerce adoption compared to other Latin American countries.
2.2.4 Motivating and demotivating factors: Online shopping in Peru.
2.3 Introduction to the Theoretical Frameworks.
2.3.1 The Technology Acceptance Model
2.3.2 Limitations of the Technology Acceptance Model
2.3.3 Hypotheses based on the Technology Acceptance Model
2.3.4 The Theory of Planned Behavior
2.3.5 Limitations of the Theory of Planned Behavior
2.3.6 Hypotheses based on the Theory of Planned Behavior
2.4 Introduction to the intrinsic and extrinsic motivation factors.
2.4.1 Perceived External Pressure (Pressure)
2.4.2 Perceived Lack of Alternatives (Alt)
2.4.3 Risk-taking Propensity (RTP)
2.4.4 Perceived Punishable Infractions (PPI)
2.4.5 Perceived Risk (Risk)
2.4.6 Government Support (Gov)
2.5 Conceptual Framework.
2.6 Chapter Conclusion.

3 Research Methodology.
3.1 Research Design: Research Philosophy and Research Logic
3.2 Research Strategy and Data Collection Instrument
3.2.1 The Survey Method
3.2.2 Structured Online Questionnaire
3.3 Target Population Definition and Sampling Technique
3.4 Research Ethics
3.5 Pilot Studies
3.6 Methodology for Data Analysis
3.6.1 First Assessment: The Partial Least Squares-Structural Equation Model
3.6.2 PLS-SEM: Introduction to Validity Assessment in the Measurement Models
3.6.3 PLS-SEM: Convergent Validity in the Measurement Models
3.6.4 PLS-SEM: Discriminant Validity in the Measurement Models
3.6.5 PLS-SEM: Assessment in the Structural Model
3.6.6 Second Assessment: The one-way ANOVA
3.6.7 Second Assessment: Hypothesis testing using the one-way ANOVA

4 Data Analysis
4.1 Consumer Socio-demographic Findings
4.2 One-way ANOVA Findings
4.3 PLS-SEM Model: Measurement Models Findings
4.3.1 Convergent Validity Findings
4.3.2 Discriminant Validity Findings
4.4 PLS-SEM Model: Structural Model Findings
4.4.1 Collinearity Findings
4.4.2 Hypothesis Testing Findings
4.4.3 Coefficient of Determination (R2) Findings
4.4.4 Structural Model’s Effect Size (F2) Findings
4.4.5 Predictive Relevance (Q2) Findings
4.4.6 Summary of Structural Model’s Findings

5 Conclusion
5.1 Introduction: Implication & Interpretation of Findings.
5.1.1 Interpretation of Significant Relationships for Peru.
5.1.2 Interpretation of Non-Significant Relationships for Peru.
5.1.3 Interpretation of Significant Relationships for Germany.
5.1.4 Interpretation of Non-Significant Relationships for Germany.
5.1.5 Interpretation of one-way ANOVA Findings for Peru and Germany.
5.2 Practical Recommendations for Governments and Food Retailers.
5.2.1 Recommendations for the Peruvian Government and Food Retailers.
5.2.2 Recommendations for the German Government and Food Retailers.
5.3 Limitations and Delimitations.
5.3.1 Limitations.
5.3.2 Delimitations.
5.4 Recommendations for Further Research

Appendix
Appendix 1: Sample Online Questionnaire in German with responses in %
Appendix 2: Sample Online Questionnaire in Spanish with responses in %
Appendix 3: Sample Online Questionnaire in English
Appendix 4: ANOVA reports – Non-significant differences Peru & Germany
Appendix 5: Average Variance Extracted Bar Chart for Germany
Appendix 6: Average Variance Extracted Bar Chart for Peru
Appendix 7: Composite Reliability Bar Chart for Germany
Appendix 8: Composite Reliability Bar Chart for Peru
Appendix 9: Outer Weights / Loadings in SmartPLS - Peru
Appendix 10: Outer Weights / Loadings in SmartPLS - Germany

References.

Abstract

Purpose: The COVID-19 pandemic raised the demand for online food shopping in traditional Peru and Germany. This urged an in-depth examination since consumer attitudes in specific cultural contexts had received little attention. Thus, this research aims to ascertain the factors that significantly influence Germans' and Peruvians' intention to use online grocery shopping, as well as to identify significant differences between the two countries.

Design: The Technology Acceptance Model, Theory of Planned Behavior, and six situational factors were proposed as a conceptual framework. The Partial Least Square-Structural Equation Modeling (PLS-SEM) approach assessed the reliability and validity of the model. A semi-structured online questionnaire was used to test the literature review findings.

Findings: PLS-SEM showed that significant predictors for adoption in Germany were: Perceived Ease of Use (PEOU), Risk-taking Propensity, Perceived Punishable Infractions (PPI), and Government Support. For Peru: Perceived Behavioral Control, Subjective Norms, Behavioral Intention to Use, and Perceived Risks. The one-way ANOVA test indicated significant differences in 4 out of the 31 questionnaire items: PU_2, PU_3, PEOU_2, and PPI_2.

Research limitations and implications: This research focused on two countries, a specified age range, and individuals with previous experience shopping online. Further, the two samples were limited to 100 participants each. Thus, it is advised that future research use a bigger sample size, a broader age range, and those with no experience with online shopping.

Theoretical implications: This study contributes to the literature on online grocery shopping acceptance in Peru and Germany by highlighting the importance of well-known technology acceptance models such as the TAM and TPB while incorporating some frequently overlooked situational factors representing the COVID-19 pandemic context.

Practical implications: Grocery retailers must show why online grocery shopping is inherently better than other choices. Further, retailers should address the key hurdles to online food shopping, e.g., data privacy and payment security, as well as consider customers' physiological preferences. On the other hand, governments must raise consumer awareness about online platforms and spend more on online grocery shopping policies.

Keywords: Online Grocery Shopping, Consumer Behaviour, Technology Acceptance Model

Originality and Value: This study identifies significant predictors of the intention to adopt online food shopping in Peru and Germany. Thus, online retailers and governments might use the findings of this research to focus their efforts better and shape the spread of e-commerce.

List of Figures

Figure 1: Germany's top online shopping sales in 2019

Figure 2: Germany’s most popular consumer goods online in the third quarter of 2020

Figure 3: Transition from on-site to digital grocery shopping in Germany amid the pandemic.

Figure 4: Consumers who are likely to buy food online in the next 12 months (in %)

Figure 5: Satisfaction with shopping channel (respondents in %)

Figure 6: Payment method survey in Germany during the pandemic

Figure 7: Peruvian e-commerce market size’s development from 2015-2020..

Figure 8: Growth of diverse industries during the pandemic in Peru (per trimester).

Figure 9: E-commerce’s sales growth in Peru from 2005-2020 (in million $)

Figure 10: Summary of e-commerce‘s critical figures throughout 2020 for Peru.

Figure 11: Growth of online shopping demand in provinces in 2020

Figure 12: Growth of e-commerce in Peru, Chile, Colombia & Argentina

Figure 13: Forecast of Latin America's online retail market growth (sales in billion $)

Figure 14: Growth in claims linked to online transactions between 2016 and 2020

Figure 15: Top 5 most reported claims between 2020 and 2021

Figure 16: Claims per month between March 2020 and February 2021

Figure 17: Graphical representation of the Technology Acceptance Model

Figure 18: Graphical representation of the Theory of Planned Behavior

Figure 19: Theoretical model for online grocery shopping’s adoption

Figure 20: Final PLS-SEM Model in Microsoft Office Word

Figure 21: Final PLS-SEM Model in SmartPLS statistical software

Figure 22: Example of 5 Measurement Models within the proposed PLS-SEM Model

Figure 23: Example of the Structural Model within the PLS-SEM Model

Figure 24: Structural Model’s path coefficients and R2[] value for Peru

Figure 25: Structural Model’s path coefficients and R2[] value for Germany

List of Tables

Table 1: Constructs, sources, and number of items in the online questionnaire

Table 2: Socio-demographic results from the Peruvian online questionnaire

Table 3: Socio-demographic results from the German online questionnaire

Table 4: ANOVA results summarized

Table 5: One-way ANOVA findings - Perceived Usefulness item 2 (PU_2)

Table 6: One-way ANOVA findings - Perceived Usefulness item 3 (PU_3)

Table 7: One-way ANOVA findings - Perceived Ease of Use item 2 (PEOU_2)

Table 8: One-way ANOVA findings - Perceived Punishable Infractions item 2 (PPI_2)

Table 9: Convergent validity findings for Peru

Table 10: Convergent validity findings for Germany

Table 11: Fornell-Larcker Scale findings for Peru

Table 12: Fornell-Larcker Scale findings for Germany

Table 13: HTMT criterion findings for Peru

Table 14: HTMT criterion findings for Germany

Table 15: Cross Loading criterion findings for Peru

Table 16: Cross Loading Criterion findings for Germany

Table 17: Collinearity findings for Peru & Germany

Table 18: Direct relationships‘ results for hypothesis testing for Peru

Table 19: Direct relationships‘ results for hypothesis testing for Germany

Table 20: Effect Size (F2[]) findings for Peru

Table 21: Effect Size (F2[]) findings for Germany

Table 22: Predictive Relevance (Q2[]) findings for Peru

Table 23: Predictive Relevance (Q2[]) findings for Germany

Table 24: Structural Model's summary findings for Peru

Table 25: Structural Model's summary findings for Germany

Table 26: Summary of significant factors for Peru

Table 27: Summary of non-significant factors for Peru

Table 28: Summary of significant factors for Germany

Table 29: Summary of non-significant factors for Germany

Table 30: Statistically significant ANOVA findings summarized

List of Formulae

Formula 1: Average Variance Extracted (AVE)

Formula 2: Composite Reliability (CR)

Formula 3: Structural Model's Effect Size (F2[])

Formula 4: Structural Model's Predictive Relevance (Q2[])

List of Abbreviations

ALT Perceived Lack of Alternatives

ANOVA Analysis of Variance

AU Actual Usage

AVE Average Variance Extracted

CAPECE Peruvian Chamber of Online Commerce

COVID-19 Coronavirus Disease of 2019

CR Composite Reliability

E-Commerce Electronic Commerce

ECM Expectaction-Confirmation Model

EHI EuroHandelsinstitut

EUR Euro

F2[] Effect Size

GDP Gross Domestic Product

GfK Growth of Knowledge

Gov Government Support

IAB Interactive Advertising Bureau

IT Information Technology

IU Behavioral Intention to Use

LCC Lima Chamber of Commerce

No. Number

PBC Perceived Behavior Control

PCs Personal computers

PEOU Perception of Ease of Use

PLS Partial Least Squares

PLS-SEM Partial Least Squares-Structural Equation Modeling

PNP Peruvian National Police

POD Pay on Delivery

PPI Perceived Punishable Infractions

Pressure Perceived External Pressure

PU Perceived Usefulness

PwC PricewaterhouseCoopers

Q2[] Predictive Relevance

Risk Perceived Risk

RTP Risk-taking Propensity

SEM Structural Equation Modeling

SN Subjective Norms

SUNAT Superintendency of Tax Administration

TAM Technology Acceptance Model

TPB Theory of Planned Behavior

TR Technology Readiness Model

TRA Theory of Reasoned Action

TRAM Technology Readiness and Acceptance Model

UK United Kingdom

USD United States Dollar

VIF Variance inflation factor

1 Introduction

1.1 Context and Problem Definition

Despite being a relatively new phenomenon and therefore modest compared to different product categories, online grocery shopping has seen continued growth and considerable popularity in recent years, thus capturing the great attention of consumers and retailers worldwide. Online grocery shopping is defined as the way to purchase food and other household supplies through a web-based shopping service (Driediger & Bhatiasevi, 2019). Mckinsey's (2019) research has designated online food shopping as one of the fastest-growing markets of the future. Its annual growth between 2014 and 2019 was estimated at 21% worldwide (EuroMonitor International, 2020). However, although online grocery shopping has spread rapidly, the field of consumer attitudes has not received sufficient attention (ibid.).

Despite the rapid development of online grocery shopping, current literature shows that consumer adoption of online grocery retailing has been heterogeneous and thus differs widely from country to country (Anesbury et al., 2016). This can be attributed to different consumer habits (Sreeram et al., 2017) and the convenience of online shopping (Huang & Oppewal, 2006). According to Lee et al. (2017), developed economies such as China, the United States, and the United Kingdom have been quick to support the introduction of online food shopping. On the other hand, some studies have also highlighted, for instance, that customers in some Western European countries such as Germany tend to remain loyal to brick-and-mortar retail, which contributes to slower adoption of online food retail (Internetworld, 2019; Schulz et al. 2013).

Several studies have attempted to explain this behavior by presenting theoretical explanations of the apparent rejection of the online grocery retail trade in the Western European territory (Yeo et al., 2017); yet, little empirical research has been conducted to investigate the state of consumer behavior in distinct national and cultural situations. The existing literature that is based on specific national contexts is primarily directed at developed countries with already large and well-established online retail markets and thus high consumer acceptance of online food purchases, such as the United States (Hansen, 2005; Hansen 2008) and China (Shang & Wu, 2017). Meanwhile, little is known about the acceptance of online food practices and consumer behavior in developed and developing countries with emerging online food retail markets such as Germany and Peru. Both Germany and Peru have always been characterized by their slow acceptance of online food purchases (Ogonowski, 2019; European Investment Bank, 2019).

According to FreshPlaza (2020), online food sales in the German market accounted for less than 1% of total German food sales in 2019. Based on current literature, it could be argued that German reluctance to accept new technologies can come from cultural patterns. However, this argument is not entirely accurate given the Germans' widespread use of new technologies in other fields (Wunderlich et al., 2019). Therefore, the barriers to technology acceptance in online food shopping in Germany cannot be easily anticipated. The particular case of Germany raises an important question. The country enjoys state-of-the-art technological development, a well-planned infrastructure, sufficient availability, and a large community of Internet-savvy consumers. These factors should work as a trigger to rapidly adopt online food shopping, but not necessarily in Germany's case (Mintel, 2019).

As for Peru, the country's e-commerce sales had grown from $276 million in 2009 to $4 billion in 2019, representing a total increase of 31% (Cáceda, 2020 as cited in Peru Legal Team, 2020). Peru is part of the emerging digital market in Latin America, where around 24% of the population acquires their purchases of goods and services virtually (PagoEfectivo, 2020). However, buying groceries online was not always a significant activity in Peru. As indicated by the reports of PagoEfectivo (2020), although Peru counts with sufficient digital infrastructure to promote online purchases, both distrust in the form of payment and the level of education of customers about the use of online channels prevail as the main factors hampering the growth of electronic commerce in the country. Arellano (2020, as cited PagoEfectivo 2020) highlights that although Peru is one of the South American countries with the highest Internet penetration rates (70%), this figure was not reflected in sales – at least until the pandemic took place.

The pandemic outbreak of March 2020 triggered a global emergency that has been considered the most severe health emergency in history and has immediately forced many customers to adopt digital channels to obey social distancing rules, as highlighted by recent research on consumer behavior (Sheth, 2020). The pandemic crisis led to an unexpectedly sharp rise in online food services worldwide, mainly triggered by the rigid curfew rules preventing people from going to physical stores. Peru and Germany, both of which had low levels of online grocery shopping before the pandemic, saw significant growth during the pandemic since specific segments (bakeries, small neighborhood stores, and wholesalers) of the online food retail industry began to digitalize their services for the first time, resulting in a surge in demand.

Nonetheless, in comparison to other nations such as the United Kingdom, France, Brazil, or Argentina; and to different online categories, the sales volume produced by online grocery shopping in Peru and Germany seems to be modest and encountering still some resistance (Statista, 2021; Ecommerce News, 2021). Thus, the acceptance rates of e-commerce in Peru and Germany demanded a more in-depth assessment of the factors that influence consumer decisions to accept online grocery purchases during a pandemic context.

1.2 Research Aim and Objectives

Although various scientific publications on consumer behavior and online food shopping, most have approached the subject differently. Some existing literature has focused instead on how factors such as product or seller characteristics influence the acceptance of buying food online (Chu et al., 2010; Sheehan et al., 2019), or how product information can lead to a higher frequency of online purchases (Benn et al., 2015). Other researchers explored the issue by comparing online food shopping and offline shopping (Anesbury et al., 2016; Van Droogenbroeck & Van Hove, 2020; Rogus et al., 2020). Last but not least, the impact of contextual variables on online grocery purchasing has been examined (Hand et al., 2009; Muhammad et al., 2016, Salem & Nor, 2020). Yet, what almost all of these studies have in common is that the research and the respective conclusions were based on everyday situations (before the COVID-19 pandemic).

Rogus et al. (2020) explained that situational factors, such as a change in the environment, can significantly affect buying food online. The COVID-19 outbreak represents an unusual event and, therefore, an excellent example of a situational factor, which has made online food shopping a necessity for a large part of the community. Thus, a more in-depth look at this phenomenon would contribute to obtaining enriching information on consumer behavior in country-specific contexts and providing elements of knowledge to the retail industry, governments, and researchers.

This research identifies a research gap in the academic literature and attempts to address it with four primary objectives that are assessed using a quantitative analysis:

1.Conduct quantitative comparative analysis of online grocery shopping behavior in Peru and Germany by incorporating various theoretical variables from the literature into a model. In particular, it is intended to demonstrate the relevance of situational variables in determining technology adoption, which is often overlooked in theoretical models, yet was included in this model. The intrinsic and extrinsic motivation factors were chosen to represent situational variables in this research.
2.Compare the findings with similar studies. The inclusion of situational factors allows for direct comparison to previous studies such as Salem & Nor (2020), which investigated online grocery shopping behavior in Saudi Arabia during the COVID-19 pandemic using the same model and urged for more country-specific research to compare results in different cultural environments.
3.Determine the reasons that significantly motivate or dissuade Germans and Peruvians from abstaining from/utilizing online grocery shopping during the COVID-19 pandemic. The findings would aid in scientifically explaining which factors substantially impact each country's demand growth and which do not. Identifying these factors, particularly those that are not statistically significant, helps provide recommendations to the Peruvian and German governments and food retailers on improving citizens‘ involvement in online practices such as online grocery shopping.
4.Identify significant differences between empirical data from Peruvian and German samples. This enables a more nuanced appreciation of cultural and social distinctions.

1.3 Research Questions and Study Relevance

Two particular research topics serve as the framework for this study, which are as follows:

-Are there significant differences in the usage of online grocery shopping between Germans and Peruvians during the COVID-19 pandemic?
-What factors significantly influence Germans and Peruvians' online grocery shopping usage during the COVID-19 pandemic?

As indicated by both research questions, this study's primary emphasis is on online grocery shopping behavior during the COVID-19 pandemic that peaked one year ago, in the year 2020, yet has persisted worldwide. Therefore, this study focuses only on empirical data from the COVID-19 outbreak since online grocery shopping in both nations was not a significant event before the pandemic.

Furthermore, there was no previous scientific research on online grocery shopping’s behavior in Peruvian literature, preventing comparison between the two countries. Given that the pandemic's breakout aided in the partial adoption of online grocery shopping in both nations, the researcher judged it appropriate to frame both research questions exclusively in terms of the COVID-19 pandemic to capture better the influence of the digital transition that was experienced in 2020.

Moving on to the academic contribution of this research, this study is relevant since a) there is little research on online grocery buying behavior in conservative countries with emerging online food markets, such as Germany and Peru; b) there is currently no study assessing online grocery shopping acceptability in Germany and Peru during the COVID-19 pandemic utilizing the PLS-SEM modelling technique in conjunction with the Technology Acceptance Model (TAM), the Theory of Planned Behavior (TPB), and individual situational variables, c) there is no previous empirical comparison between Germany and Peru to explore online grocery shopping behavior, d) there is a little investigation on the effect of the COVID-19 pandemic on online grocery shopping behavior in specific national and cultural contexts, in particular considering the effect of situational factors, and e) the outcomes of this study may give guidance to both governments and online grocery retailers on how to increase the spread of online grocery shopping.

1.4 Research Design

To fill the research gap in this study, it was determined that a quantitative strategy focusing primarily on the COVID-19 pandemic and the Peruvian and German populations would be the most effective approach. A cross-sectional and semi-structured online questionnaire was employed for this aim, with the primary variables and question items retrieved from theoretical models in the literature.

The variables that are assessed within the scope of this research are derived from the following two models on technology acceptance and consumer behavior:

1.Technology Acceptance Model (TAM)
2.Theory of Planned Behavior (TPB)

Furthermore, the literature review suggests a practical gap in understanding the motivations for adopting a particular technology, which broader external factors may explain. As a result, variables from the literature that are not explicitly included in any model but are still considered in certain studies were retrieved for this study to describe the pandemic context best, the so-called situational factors. These were divided into the two following categories:

1.Intrinsic motivation factors
2.Extrinsic motivation factors

In total, 100 Peruvians and 100 Germans aged 18-55+ years old participated in this study regarding online grocery shopping behavior, allowing for the examination of differences between the population samples and the identification of significant predictive variables associated with its adoption and use. The sample size for each population group had to be limited to 100 individuals, since the free version of the statistical tool SmartPLS did not allow for the assessment of a higher sample size.

This section is expanded in Chapters 2 and 3, which explain the literature review and research methodology in depth.

1.5 Research Structure

This chapter’s purpose is designed to enlighten the reader on the structure of this research project, which is divided into five chapters and maintains the following format:

Chapter 1 provides an overview of the study's introduction, including a short discussion of the topic’s background and issue, the research goals, objectives, research questions, the research design and rationale, and finally, the study’s structure.

Chapter 2 discusses the theoretical underpinnings of consumer perceptions of online grocery shopping in Peru and Germany and explains the two theoretical frameworks and associated variables used in this study to explain consumer behavior related to online grocery shopping usage. Additionally, this part includes a critical assessment of prior empirical results in the literature about the variables that promote and inhibit online grocery shopping adoption.

Chapter 3 outlines and justifies the methodology of the research, including the research philosophy and logic, the research strategy and data collection instrument, the target population definition and sampling technique, research ethics, pilot studies, and the methodology for data analysis which details the procedure used to analyze the empirical data.

Chapter 4 addresses the most noteworthy findings of the model's validity, reliability, and hypothesis testing for significant correlations and differences between Peru and Germany.

Chapter 5 concludes the research by summarizing the preceding chapter's results and interpreting them. Furthermore, some practical recommendations for online grocery shopping are given based on the results. Finally, the research limitations and delimitations are discussed, along with suggestions for further research.

2 Literature Review

2.1 Background of e-commerce in Germany

2.1.1 E-commerce context before the COVID-19 pandemic in Germany

According to both Statista's Digital Market Outlook (2020, as cited in EcommerceDB, 2021) and Dannenberg et al. (2020), online grocery shopping in Germany began as an experiment in the 1990s, gradually grew in the 2000s, and peaked in the late 2010s. Since then, online grocery shopping has grown at a higher rate. Yet, despite the growth of online food shopping in Germany, revenues are still significantly lower than in other online retail segments and countries (Rabe, 2021; EHI Handelsdaten, 2018).

As Bitkom Research (2019) shows, purchasing groceries online has always been a niche sector and was not a common habit among Germans before the pandemic outbreak in early 2020. This assertion is backed up by Statista's 2020 Digital Market Outlook research, which states that the online food category has historically been the lowest in the German online market.

As seen in Figure 1, before the pandemic, online purchases in Germany were focused on other categories, including clothing (93%), electric home appliances (81%), books (79%), and cosmetics (72%), rather than online grocery shopping. According to FreshPlaza (2020), online food sales in the German market accounted for less than 1% of total German food sales in 2019.

Figure 1: Germany's top online shopping sales in 2019

Abbildung in dieser Leseprobe nicht enthalten

Source: Bitkom Research, 2019

2.1.2 E-commerce context during the COVID-19 pandemic in Germany

The COVID-19 pandemic and its preventive measures, such as face-to-face contact limitations or stay-at-home protocols adopted in March 2020 in Germany, imposed social and economic limits and accelerated the development of digitalization (World Economic Forum, 2020).

In Germany, violators of curfew laws, quarantine rules (14-day isolation period), or other hygienic procedures faced harsh penalties from authorities. As in section 75 (1) of the Infection Protection Law, violations of quarantine restrictions may result in fines of up to EUR 25,000 or potentially five years in jail. Under German law, violating quarantine may be seen as a physical injury or attempted bodily damage, explaining its stringent regulations. As of October 2020, about EUR 1,000,000 in penalties were imposed in Hamburg alone (Busse, 2020).

Throughout 2020, the COVID-19 pandemic significantly altered consumer behavior in Germany's online retail market, resulting in emerging consumer trends and unprecedented rapid spikes in demand for various online services, including sports courses, educational training, video streaming, and even online grocery shopping. German consumers were pushed to make substantial lifestyle changes quickly due to nationwide restrictions and newly defined norms of conduct.

Numerous Germans began using the services mentioned above for the first time at a period when analog solutions were severely hampered by mobility restrictions and developing concerns about exposure to other individuals in indoor public spaces (Institut für Verbraucherpolitik, 2020). As a result, according to the German Federal Association of E-commerce (2021, as cited in Fischer, 2021), e-commerce grew dramatically by +14.6% to reach $83.3 billion (EUR 73,5 billion) in sales by 2020.

The German Federal Association of E-commerce (2021, as cited in Fischer, 2021) highlighted that the online grocery sector registered the highest growth among all online segments in the third quarter of 2020. The online grocery market grew by 52.9% (year-on-year) to EUR 633 million in sales by 2020.

Overall, as shown in Figure 2, purchasing behavior towards consumer goods changed drastically during the pandemic, with the most significant finding being the unprecedented surge in online food shopping (52.9 %) that did not show up in 2019.

Figure 2: Germany’s most popular consumer goods online in the third quarter of 2020

Abbildung in dieser Leseprobe nicht enthalten

Source: Bundesverband E-Commerce und Versandhandel, 2021

Numerous research has been undertaken in Germany since the pandemic's emergence to ascertain the shift of online consumer behavior. Some studies, in particular, have evaluated the change in consumer behavior toward online grocery shopping.

One study that examined online food purchasing behavior in the European region during the coronavirus pandemic was undertaken by Bitkom Research, in which 1,003 Germans over the age of 16 (843 of whom were Internet users) were questioned about their online grocery shopping habits. According to the report (as cited in Institut für Verbraucherpolitik, 2020), 30% of all respondents purchased groceries online frequently during the pandemic, compared to 16% before the pandemic.

Further, the same 2020 Bitkom research indicated that online grocery consumption climbed from 7% to 19% in online supermarkets such as rewe.de, bringmeister.de, and Amazon Fresh at the pandemic beginning, as shown in Figure 3. Meanwhile, physical establishments' consumption dropped from 75% to 65%, respectively. The German grocery market sector is estimated to be over EUR 250 billion (Fischer, 2021), making it an extremely lucrative market for food merchants. As a result, several of them, including Rewe, EDEKA, and Dr. Oetker, launched their delivery services some years ago to meet the expanding demands of online shoppers.

Figure 3: Transition from on-site to digital grocery shopping in Germany amid the pandemic

Abbildung in dieser Leseprobe nicht enthalten

Source: Bitkom Research, 2020

While adoption in Germany was relatively high in contrast to prior years, other nations in Europe have consistently had a higher rate of adoption; and their users showed more satisfaction with their current usage, suggesting a more significant possibility of using online grocery shopping services in the future. The following section addresses this disparity in further depth.

2.1.3 Germany's e-commerce adoption compared to other European countries

Although demand for online grocery shopping has increased significantly throughout Europe, the magnitude of the rise and adoption varies by nation, with some enjoying a substantial increase (the United Kingdom and Italy) and others having a more modest increase (Germany) when compared in proportion. With a population of 82 million, Germany is one of Europe's major retail food marketplaces, yet is regarded to have a modest level of digitization compared to other European nations (European Investment Bank, 2019, p.52).

As Wulff and Rumpff (2018, p.8) demonstrate in a 2018 PwC investigation of online purchasing behavior among 9,700 European participants, the German online food segment has a low market share (1.2 %) in comparison to the United Kingdom's market share of 7%. The same study found that when participants were questioned about their likelihood of purchasing groceries online in the next 12 months, just 32% of Germans responded highly likely or likely.

Meanwhile, consumers from other countries showed a greater propensity to purchase online in the future, as shown in Figure 4.

Figure 4: Consumers who are likely to buy food online in the next 12 months (in %)

Abbildung in dieser Leseprobe nicht enthalten

Source: Wulff and Rumpff, 2018

Furthermore, the study indicated that although many participants expressed satisfaction with online food shopping, some did not intend to continue using this practice after the pandemic. As the survey notes, consumers in the United Kingdom are projected to grow their online grocery consumption (+5%), while customers in France (-1%), Germany (-10%), Spain (-12%), and Italy (-14%) are likely to decrease their online grocery usage after the pandemic.

Customer satisfaction with the online grocery buying experience is also a key differentiator across European nations. McKinsey's research (Günday et al., 2020) indicates that only 16% of 1,000 respondents in Germany were satisfied with the online ordering and home delivery service during the pandemic – compared to higher satisfaction levels in Spain (29%) and the United Kingdom (33%) (Figure 5).

Yet, as the survey highlights, the comparatively low level of satisfaction in Germany is tied to the capacity of grocery shops to keep up with demand, namely having the necessary infrastructure to serve online clients. As of 2020, the United Kingdom has the most extensive online grocery market penetration in Europe at 6.9%, followed by France (5%), Spain (1.7%), Germany (1.5%), and Italy (0.7 %).

Figure 5: Satisfaction with shopping channel, respondents in %

Abbildung in dieser Leseprobe nicht enthalten

Source: Günday et al., 2020, McKinsey & Company

2.1.4 Motivating and demotivating factors: Online shopping in Germany

According to the literature (Dannenberg & Dederichs, 2019; Zook, 2002), Germany's most significant barriers to online food shopping adoption are worries about data and payment security, the difficulties associated with online payment methods, and the inability to see and feel the product in advance. In addition, Fuchs (2019) states that the slow adoption might be partly due to worries about digital spying and monopolization. As indicated before, another source of concern for Germans is the ability of food stores to meet demand. Local supermarkets' inability to provide the essential infrastructure to support online customers, such as the inability to complete deliveries on schedule or a shortage of stock, is a barrier to online grocery shopping adoption in Germany (Günday et al., 2020). Finally, as Schulz et al. (2013) indicate, German buyers also have a strong affinity for neighborhood retailers and are particularly price-aware due to the success of discounters such as Aldi and Lidl. According to Schulz et al.‘s (2013) research findings, Germans are very price-conscious when it comes to food shopping and are unwilling to pay more for services such as delivery; thus, food retailers must persuade consumers of the service's added value while maintaining a high level of trust and customer loyalty.

On the other hand, the Bitkom Research (2020) survey revealed that 74% of respondents criticized local grocery shops for failing to adhere to social distance and hygiene regulations, compelling them to use internet services. Overall, there was considerable worry about infection risk. Respondents rated internet grocery shopping as not only safer but also more convenient. As a result, it's unsurprising that many persons who recognized the benefits of online food shopping during the pandemic have become used to them and continued to utilize them.

Bitkom's July 2020 research (Figure 6) also revealed a significant trend toward Germans wanting to avoid paying in cash and wishing for more cashless means to complete purchases in stores, which may have aided in the expansion of online food shopping in Germany.

Figure 6: Payment method survey during the pandemic in Germany

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Source: Bitkom Research, 2020

Overall, the coronavirus pandemic has played a significant role in Germany's quick adoption of online food shopping, creating new opportunities for Germans and companies. According to Fischer (2021), there are many factors relating to the nation and the features of the German people that have aided in the rapid adoption of online grocery shopping. For instance, as the author notes, Germany is a highly developed nation with a high rate of mobile phone adoption, rapid Gross Domestic Product (GDP) development, and a highly educated population.

The next part discusses the evolution of online shopping in Peru, where online grocery purchasing was essential in the country's quick adoption of online purchasing habits.

2.2 Background of e-commerce in Peru

2.2.1 E-commerce context before the COVID-19 pandemic in Peru

Peru is South America's fourth most populated country with 32,6 million inhabitants. As of 2021, 76.2% of households have an Internet connection, and 36.1% are active online shoppers, using smartphones (78%), PCs (36.9%), laptops (23.4%), or tablets (11%), as stated by the Peruvian Chamber of Online Commerce’s “CAPECE” official annual report (2021, p. 17).

The usage of e-commerce in Peru has drastically evolved. Nine years ago, in 2012, most Peruvian internet users purchased mostly flight tickets on online platforms. 60% of internet transactions were thus devoted exclusively to tourism (Rojas, 2019). Nonetheless, the proportion of internet users was fewer than 5%, with the majority residing in Lima's capital.

Later, between 2015 and 2019, consumer demand started to diversify. In 2015, the increased accessibility of products in other categories and the maturing of the market resulted in increased purchase frequency. As a result, card payments for online purchases increased by 33%. Tourism (13.43 %) remained a popular category, yet now sharing the top spot with domestic appliances (13.43 %), and was closely followed by home accessories (11.22 %) and fashion (10.48 %). In 2015, total online sales were around S/. 4 billion ($982 million) (ibid.).

According to the Lima Chamber of Commerce “LCC” study (2019, as cited in Gestión 2019), 9 million Peruvians (20%) made purchases online in 2019. In that year, online transactions totaled around S/. 11,4 billion ($ 2,8 billion) (Rojas, 2019). Compared to 2018, in 2019, the number of online customers climbed by 20%, while transaction volume increased by 10% (from S/. 10,4 billion to S/. 11,4 billion). As Rosales (2019) noted, as of December 2019, seven out of every hundred retail purchases (7.7%) in the nation were made online; by the end of the first semester of 2020, this proportion had jumped to twelve out of every hundred (12 %).

According to research conducted by LCC (2019, as cited in Rojas, 2019), Millennials aged 25-34 years old made the most online purchases in 2019. Furthermore, the same poll showed that 50% of internet users resided in the capital, while the remaining 50% resided in other regions. This fact, in turn, shows a trend with high internet penetration across the nation, not only in the capital.

In 2020, due to the COVID-19 outbreak, overall internet sales increased significantly (Figure 7). According to the Peruvian Chamber of Online Commerce (2021), these transactions totaled around S/. 21,6 billion ($5,3 billion).

Figure 7: Peruvian e-commerce market size’s development from 2015-2020 (in millions S/.)

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 32

2.2.2 E-commerce context during the COVID-19 pandemic in Peru

COVID-19 impacted negatively on several economies, most notably in Latin America. Peru was the second nation to have the worst economic impact from this health disaster in the region. According to CAPECE (2021), GDP contracted by -13.9 % in 2021. Yet, online sales were one of the segments favored during the pandemic period (Figure 8).

Figure 8: Growth of diverse industries during the pandemic in Peru (per trimester)

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 21

The Peruvian government implemented a mandatory and targeted quarantine during 2020 and 2021, which included strict curfews and restrictions on freedom of movement to contain the spread of the virus. As a result, first-need items were urged to be purchased online to reduce the risk of viral infection.

Penalties for violating the requirements of the Government's laws governing the state of emergency were less stringent than those in Germany, yet they still led to a reduction in the propensity to violate the regulations. According to Legislative Decree No. 1458 (Gob.pe, 2021), the economic punishment imposed by a member of the Peruvian National Police (PNP) varied between S/. 88 ($21) and S/. 440 ($108). If the fine were not paid within five business days, the person would not qualify to receive benefits from any state's economic, food, or health assistance programs. Among the most severe penalties were walking on public roads without wearing the mandatory mask (S/. 352, $86), failing to adhere to the specified distance meter in supermarkets (S/. 88, $21), being on public roadways on Sundays while subject to mandatory social immobility (S/. 396, $ 97), and failing to bring any identification documents when on the street during the mandatory curfew (S/. 440, $108) (ibid.). Thus, digital transactions became one of Peruvians' preferred methods of purchasing goods and services securely, without leaving their homes, while adhering to quarantines and avoiding crowds.

According to El Economista (2020), online purchases of first-need products such as groceries, hygiene, and pharmaceutical supplies produced more than S/. 10 million ($2,4 million) in sales only during the first week of the quarantine as of early 2020, contributing to the country's online sales boom. Online transactions soared by 120 % only in the first half of 2020 (Laurante, 2020). According to Americas Market Intelligence's research (2021), the gastronomic sector (supermarkets, bakeries, restaurants) experienced the most remarkable growth across industries, presenting a significant change in consumer behavior. Moreover, the study showed that online fast food consumption climbed by 24 %, while online restaurant food consumption increased by 10.19 % (ibid). Furthermore, CAPECE (2021, p. 24) highlighted an unprecedented development in several gastronomic segments that had previously relied exclusively on cash transactions. The 2020 research revealed that online consumption increased by 2,683 % in bakeries, 97 % in small neighborhood grocery stores, 414 % in supermarkets, and 2,171 % in wholesalers. As the Americas Market Intelligence's report (2021) underlined, there was no e-commerce for bakers, small local grocers, and wholesalers before the pandemic. These findings are more surprising compared to 2017, when online grocery sales in Latin America amounted to less than 2% of total sales (Euromonitor, 2019, p. 40). In contrast, prominent e-commerce players of 2019, such as the tourism industry, experienced a -75% contraction (Peruvian Chamber of Online Commerce, 2021, p. 15).

By early 2020 (before COVID-19), online commerce was predicted to rise by 30%. However, due to the pandemic, e-commerce was officially reported with a growth of +50% in 2020 (Figure 9), driven mainly by the retail e-commerce sector, such as the online grocery shopping segment. Only retail e-commerce grew by 250% in 2020 (La Republica, 2021). The Superintendency of Tax Administration (SUNAT) (2021, as cited in La Republica, 2021) yet highlighted that around 60% of online vendors operate avoiding taxes, implying that the rise of e-commerce sales might have been far more than the official figure reported in 2020 (+50%). Indeed, as noted by Americas Market Intelligence’s report (2021), in 2020, the Peruvian e-commerce industry might have grown far more than 50 %.

Figure 9: E-commerce’s sales growth in Peru from 2005-2020 (in million $)

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 15

La Republica (2021), based on data provided by the Lima Chamber of Commerce (LCC), predicted a 25% increase of internet consumers in Peru from 9 million in 2020 to 11 million 250 by the end of 2021. However, this projection contradicts official statistics from the Peruvian Chamber of Online Commerce (2021, p. 16), which indicates that by the end of 2020, Peru had closed already with 11,8 million online shoppers, which constitutes 36% of the Peruvian population (Figure 10). The discrepancies between the two reported figures indicate room for improvement in collecting consumer data related to online consumption in Peru.

Figure 10: Summary of e-commerce‘s critical figures throughout 2020 for Peru

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 16

Undoubtedly, COVID-19 represents a before and after in the country's digital economy. The pandemic aided in the acquisition of 30% of new online buyers. 44% of Peruvian customers made their first internet purchase. 64% of these reported having an excellent or decent experience with their first online purchase (Think with Google, 2020). A shift in the penetration of online purchasing among regions was also perceived. Between June-December 2020, there was a considerable increase in online buying from 3% to 10% in provinces (Figure 11). According to El Economista (2020), this trend is highly likely to continue.

Figure 11: Growth of online shopping demand in provinces in 2020

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 53

2.2.3 Peru's e-commerce adoption compared to other Latin American countries

As previous research from the data analytics company Growth of Knowledge (GfK) has shown (2019, as cited in Rosales, 2019), online sales billing in Peru increased 44.2 % in the first half of 2019, outpacing Argentina (43 %), Chile (25.3 %), and Brazil (7 %), which have been in the e-commerce industry for a longer length of time. As Euromonitor International (2019) notes, the reason for this might be that Peruvians use the Internet more than five times each week, making them more sensitive to online advertising than consumers in other Latin American countries, resulting in increased online buying.

Like other nations, the Peruvian e-commerce industry increased dramatically between 2020 and 2021, despite the recession induced by the COVID-19 pandemic and the limitations on e-commerce operations concerning delivery services and logistics imposed by the Peruvian government. Because online commerce operations were limited to basic needs, the increase of 58% experienced in the week preceding social isolation (March 16, 2020) could not be maintained in the following months (Figure 12).

Figure 12: Growth of e-commerce in Peru, Chile, Colombia & Argentina (January-July 2020

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p.12

As seen in the graphic above, e-commerce in Peru increased by just 25% in March and plummeted by -11% in April. Except for Peru, no nation in Latin America drastically restricted e-commerce activities such as delivery services between provinces during the early stages of the pandemic. After two months of restrictions, the government, pressured by the Peruvian Chamber of Online Commerce, ultimately allowed the reactivation of e-commerce for all categories in mid-May 2020 to serve as a catalyst for economic recovery and help contain the spread of the virus. As a result, e-commerce rebounded and increased 56 % in May 2020. Later in June, it increased by 86%, and in July, it hit a record high of 160 %. Overall, there was a 40% rise in the first trimester and a 60% increase in the second trimester, demonstrating that the absence of barriers to e-commerce aided the adoption of online shopping in Peru (Peruvian Chamber of Online Commerce, 2021).

As shown in Figure 13, as of 2021, Peru has one of the fastest growth rates in Latin America's e-commerce sector, although it lags behind other countries such as Brazil, Colombia, Mexico, Argentina, and Chile in terms of sales volume (Pasquali, 2021). According to Rosales (2019), Peru has a high ceiling for online sales development, which explains why its growth rate is highest in Latin America. However, Rosales (2019) adds that at the regional level in Latin America, 20% of retail transactions are conducted online, compared to just 12% nationwide in Peru, indicating a gap to narrow. According to the Statista Digital Industry Outlook’s forecast (2021, as cited in Pasquali, 2021), the Latin American online retail market - a segment of the whole e-commerce market - exceeded $80.5 billion in retail sales in 2021. This value is, in turn, expected to hit $105.5 billion by 2025.

Figure 13: Forecast of Latin America's online retail market growth (sales in billion $)

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Source: Adapted from Pasquali, 2021, Statista

According to Americas Market Intelligence's report (2021), Peru's whole e-commerce market is expected to grow by 42% between 2020 and 2024. Similarly, according to the chart above, the online retail sector will expand by 29% between 2020 and 2025.

2.2.4 Motivating and demotivating factors: Online shopping in Peru

According to the Lima Chamber of Commerce (2020, as cited in El Economista 2020), the most important factors motivating Peruvians to make an online purchase are low prices (73.70 %), a positive shopping experience (71.10 %), the diversity of the offer (48.70 %), and convenience and time savings (40.80 %). Rojas (2019) supports LCC’s research in that the price is critical for the Peruvian customer and adds that 79.8 % of Peruvians value the special discounts offered by online retailers above physical stores, which usually offer higher prices. Rosales (2019) concurs with both Rojas (2019) and LCC (2020) and shows that, in addition to low prices, other motivating elements include the ability to examine the goods before purchasing them, the ability to purchase more items, and the speed of the transaction.

Rojas (2019) highlights that Peruvians still place a premium on physically inspecting and handling the food before purchasing. 65.8 % of respondents stated that they avoid online purchases because of concern that the goods would not be as described in the photograph. Moreover, 63.9 % feel safer when they visit a real shop for fear of being scammed, making the money transaction, and never receiving the merchandise. The theft of bank card information (38.4 %) is the third reason why Peruvians continue to avoid online shopping.

As Rojas (2019) highlights, the country's bankization rate does not reach 30%. Less than 30% of the Peruvian population has a bank card. In Peru, a bank card is typically needed to make a purchase online; however, as CAPECE (2021) indicated, several Peruvian customers prefer to pay in cash out of concern that their actual income can be traced. However, as Rojas’s study shows, this fact has not prevented the Peruvian market from seeing rapid development in online commerce. However, it is critical to note that Peru's low bankization rate may be a barrier to the broader adoption of online food shopping.

Moreover, as noted in CAPECE's report (2021, p. 85), some Peruvians think that some online grocery stores may not be legitimate since there is no list of trusted retailers available on the Internet.

Furthermore, concerning e-commerce in general, Peruvians identified additional factors that demotivated them to make online purchases and thus require improvement. According to PagoEfectivo's (2020) reports, while Peru has an adequate digital infrastructure to facilitate online purchases, payment distrust and a lack of customer education regarding online channels are the primary factors impeding the country's expansion of electronic trade. Likewise, security (23%), data privacy (70%), and negative prior experiences (16%) are cited as significant barriers in the Interactive Advertising Bureau (IAB) report (2017).

Further, as indicated by Rojas (2019), the exchange/return process/policies (54.60 %), the speed with which complaints are addressed (47.30 %), the clarity of product information (45.80 %), the security of the purchasing process (40.10 %), and compliance with specified delivery dates (35.80 %) are aspects that should also be improved. Indeed, according to CAPECE (2021, p.13), the historic number of 60,000 claims linked to online transactions were handled in just ten months, from March 2020 to December 2020, increasing online distrust (Figure 14).

Figure 14: Growth in claims linked to online transactions between 2016 and 2020

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Source: Peruvian Chamber of Online Commerce, 2021, p. 90

According to the same source, 75% of online businesses in Peru are informal to a certain extent. Some firms lack detailed and precise terms and conditions on their websites and a complaint book, and delivery and transaction terms are not always granted. Figure 15 summarizes the top 5 reported claims regarding online shopping in Peru; meanwhile, Figure 16 depicts the number of claims per month.

Figure 15: Top 5 most reported claims between 2020 and 2021

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 90

Figure 16: Claims per month between March 2020 and February 2021

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Source: Adapted from Peruvian Chamber of Online Commerce, 2021, p. 90

2020 was a year filled with several lessons for everyone. The graph above shows that claims declined between July 2020 and February 2021. This demonstrates that the logistics ecosystem became more resilient as the months passed, increasing operating efficiency in Peru.

The following section outlines the theoretical frameworks utilized to evaluate the coronavirus pandemic's online grocery shopping uptake in Peru and Germany.

2.3 Introduction to the Theoretical Frameworks

Few studies have been conducted in the literature on online grocery shopping acceptance that combines many theoretical perspectives. This study offers a model that integrates two theoretical frameworks and independent components. The two theoretical theories, notably the Technological Adoption Model (TAM) and the Theory of Planned Behavior (TPB), build exclusively on technology acceptance and consumer behavior.

Meanwhile, in terms of independent variables (not belonging to any model), the proposed model includes intrinsic and extrinsic motivation factors to account for the influence of the COVID-19 pandemic on technology adoption. The TAM and TPB models were combined because they have been extensively utilized in the literature to describe consumer behavior and technological adoption. Meanwhile, the selected intrinsic and extrinsic motivation components were included in this study based on the research of Salem & Nor (2020), who acquired them from the work of other authors, including Kurnia et al. (2015), Hansen et al. (2018), Liao (2009), and Looi (2005).

Both the TAM (Davis et al., 1989) and TPB (Ajzen, 1991) have been widely recognized by researchers as the leading theoretical approaches that best explain and predict human behavior; that is, the relationships between individuals' beliefs, attitudes, and intentions; and, as such, are frequently combined with increasing the predictive power of new technology acceptance in the field of information systems research (Yu et al., 2018; Venkatesh et al., 2003).

2.3.1 The Technology Acceptance Model

Fred Davis created the Technology Acceptance Model (TAM) in 1980 to explain computer adoption (Dimitrova & Chen, 2016). As Kamble et al. (2019) note, one of the advantages of employing the TAM model is that it helps identify and close the gap between what customers say they would do and what they actually do.

As Davis et al. (1989) describe, the Technology Acceptance Model (TAM) is based on two psychological constructs that play a crucial role in technology adoption: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). These both are a function of Behavioral Intention to Use (IU) and Actual System Use (AU). According to Figure 17, the higher the Perceived Ease of Use, the greater the Perceived Usefulness, and hence the greater the likelihood that an individual would embrace and use new technology.

Figure 17: Graphical representation of the Technology Acceptance Model

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Source: Davis et al., 1989

Perceived Usefulness (PU) is defined as the user's perception that new technology helps in work performance improvement. Whereas, Perceived Ease of Use (PEOU) denotes the perception of the amount of effort required by the user to adopt the new technology (Venkatesh, 1999; Davis et al., 1989). Davis et al. (1989) assert that PU and PEOU directly affect the Behavioral Intention to Use (IU), which, in turn, influences the adoption and usage of new technology. The Behavioral Intention to Use (IU) was defined as the extent to which an individual has made intentional decisions to engage in or abstain from certain future behaviors (Davis et al. 1989).

As previously stated, the TAM Model has been widely used and substantially supported in the Information Systems and Business & Management community to explain information systems adoption. However, the TAM Model has sometimes been modified or extended by researchers to incorporate certain individual-specific features in what is known as the TRAM Model; a merger of the TAM Model and the Technology Readiness Model (TR) to increase the model's explanatory potential (Wu & Lederer, 2009, Lin et al., 2007). This study uses only the original TAM model developed by Davis et al. (1989) to compare its results to previous studies that have employed only the TAM Model.

2.3.2 Limitations of the Technology Acceptance Model

While the Technology Acceptance Model has been extensively utilized and endorsed in the scientific community, it has also attracted significant criticism. The model has been criticized for its simplicity (Ajibade, 2018). The model assumes that technology adoption is mainly determined by two explanatory variables: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Malatji et al. (2020) claim that this assumption precludes the model from detecting other factors that may influence an individuals' propensity to embrace a particular technology, such as costs and structural imperatives. Another frequent criticism, as Malatji et al. (2020) note, is that the TAM model is not very accurate in quantifying observed behavior.

Ajibade (2018) concurs with Malatji (2020) and contends that the TAM model is insufficiently robust to adequately forecast an individual user's attitude toward new technology uptake. Last but not least, Hsu and Lu (2004, as cited by Ajibade, 2018) argued that the concept of Perceived Usefulness is not always appropriate for predicting technological adoption. Notably, in online gaming, when technology is utilized for recreational reasons, Perceived Usefulness is irrelevant since the technology is not used to solve problems but rather to relax.

2.3.3 Hypotheses based on the Technology Acceptance Model

After having identified and explained the variables of the Technology Acceptance Model, the following three hypotheses are formulated:

H1: Perceived Usefulness positively affects online grocery shopping ’ s use during the COVID-19 pandemic.
H2: Perceived Ease of Use positively affects online grocery shopping ’ s use during the COVID-19 pandemic.
H3: Behavioral Intention to Use positively affects online grocery shopping ’ s use during the COVID-19 pandemic.

2.3.4 The Theory of Planned Behavior

Icek Ajzen established the Theory of Planned Behavior (TPB) in 1985 to extend and improve the predictive power of the then-popular Theory of Reasoned Action (TRA) of 1975, which, principally, omitted the construct Perceived Behavioral Control (PBC) for anticipating volitional human intents and acts (Ajzen, 1991; Sniehotta et al., 2014). Since then, academics have used this theory widely and efficiently in correlational studies to explain and anticipate consumer behavior in various diverse disciplines of study, including health-service behavior and technology adoption behavior, among others (Sniehotta et al., 2014). As LaMorte (2019) mentions, the TPB model connects beliefs and behavior to explain and anticipate all behavioral circumstances in which a person has self-control. The fact that this theory covers behavioral actions in which people exhibit self-control is important because it solves one of the shortcomings of its predecessor, the TRA model, which dealt with circumstances in which individuals lacked total volitional control (Ajzen, 1991).

According to Figure 18, the construct Behavior is a function of the Intention to conduct the underlying behavior and the Perceived Behavioral Control. As Sniehotta et al. (2014) explain, the extent to which Perceived Behavioral Control (PBC) directly impacts Behavior (as opposed to indirectly through Intention) depends, according to the theory, on the degree of self-control over the behavior. Likewise, it is hypothesized that Intention can be used directly to Behavior, which is dependent on an individual's evaluation of the behavior of interest (Attitude), the belief that engaging in that behavior earns them approval or disapproval from society (S ubjective Norms), and the individual's perception of the ease or difficulty of performing that behavior (Perceived Behavioral Control).

As Ajzen (1991) explains, if two individuals have equal intentions to perform a particular activity, the one who succeeds is more likely to be the one who is confident in his ability to act. In other words, the one who perceives the activity as easier to accomplish is more likely to succeed. Thus, a critical component of this model is Perceived Behavioral Control (PBC).

Figure 18: Graphical representation of the Theory of Planned Behavior (TPB)

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Source: Ajzen, 1991

As previously noted, Perceived Behavioral Control (PBC) is defined as the ability to modify one's motivation in response to the perceived difficulty or ease of a task. Thus, if individuals view something as difficult to do, their intention to carry out the desired activity decreases. Continuing with the model's other constructs, Subjective Norms (SN) relate to an individual's opinion of whether society would accept or disapprove of the activity they are engaging in. The word "society" refers to close relatives or friends who may significantly influence an individual's choice to undertake an activity. If the individual's most intimate circle disapproves of the notion, they are less likely to embrace a new habit. Finally, the construct Attitude might be defined as the subjective value attached to behavior modification (Ajzen, 1991). Attitude is not addressed in this study since it was not included in Salem & Nor‘s (2020) research, to which this study is compared.

2.3.5 Limitations of the Theory of Planned Behavior

As with any other model, the Theory of Planned Behavior has some limitations that researchers have criticized, and hence it is worth noting them. One of the shortcomings of this theory is that it does not take environmental or economic considerations into account when predicting intentions to adopt a new habit. The approach is only based on four explanatory concepts, and thus an issue regarding the validity of the model is claimed (Sniehotta et al., 2014). For instance, it evaluates the influence of a subset of society only through the construct of S ubjective Norms, which is manifestly insufficient to cover external factors' effect on behavior.

Additionally, as LaMorte (2019) notes, the model implies that eventual behavior adoption occurs by a linear decision-making process, which is not accurate in reality since it is known that it might vary over time. Furthermore, there is no clear explanation for what occurs between the construct "intention to adopt" (intention) and the construct "actual adoption" (behavior) (ibid.). Finally, Sheeran et al. (2013) criticize the model for emphasizing only logical thinking. The approach excludes non-rational elements that may impact the desire to adopt a behavior, such as emotions or previous experiences.

2.3.6 Hypotheses based on the Theory of Planned Behavior

After having identified and explained the variables in the Theory of Planned Behavior, the following two hypotheses were formulated in the context of the COVID-19 pandemic:

H4: Perceived Behavioral Control positively affects online grocery shopping ’ s use during the COVID-19 pandemic.
H5: Subjective Norms positively affect online grocery shopping ’ s use during the COVID-19 pandemic.

2.4 Introduction to the intrinsic and extrinsic motivation factors

Additionally, the proposed model for this research adds the following six (6) situational factors to more accurately portray the COVID-19 pandemic's impact: 1) Risk-taking Propensity, 2) Perceived External Pressure, 3) Perceived Lack of Alternatives, 4) Perceived Risk, 5) Perceived Punishable Infractions, and 6) Government Support. These additional components were included to account for situational factors, sometimes overlooked yet affecting consumer attitudes (Hand et al., 2009), especially in a pandemic setting. It is important to emphasize that these variables do not belong to any theoretical model, yet there is a vast precedent for the Technology Acceptance Model to include intrinsic and extrinsic driving aspects (Dimitrova & Chen, 2016; Salem & Nor, 2020; Lee et al., 2005; Venkatesh, 1999).

Due to the coronavirus pandemic, customers were compelled to suspend interactions in order to prevent the transmission of the infectious illness. This extraordinary transition may ultimately result in a considerable change in customers' thinking, leading to the adoption of certain new behaviors. The factors that motivate someone to participate in specific behaviors may be intrinsic or extrinsic. Intrinsic motivation refers to engaging in an activity just for the sake of enjoyment or intrigue, with no expectation of external profit. On the other side, extrinsic motivation is driven by external rewards. In this vein, 1) Risk-taking Propensity constitutes an intrinsic motivation construct; meanwhile, 2) Perceived External Pressure, 3) Perceived Lack of Alternatives, 4) Perceived Risk, and 5) Perceived Punishable Infractions are extrinsic motivation constructs in this research. Government Support does not fit under the category of motivation variables but was included in this research to highlight the impact of government on technology adoption. The literature also supports the inclusion of government support to the TAM Model (Ilin et al., 2017; Dimitrova & Chen, 2016; Salem & Nor, 2020). It is relevant to note that these six situational factors were selected specifically because they were included in Salem's & Nor‘s (2020) study, which is compared directly to the findings of this research.

Based on this knowledge, it is critical to remember that the ultimate assessment of a customer's willingness to adopt and use new technologies, in this case, purchasing food online, must be made not only based on perceived benefits or risks but also based on specific life events that trigger particular customer behaviors.

The following subsections provide further details on each intrinsic and extrinsic motivation variable and their associated hypotheses.

2.4.1 Perceived External Pressure (Pressure)

As Kurnia et al. (2015) and Muhammad et al. (2018) note, environmental variables such as Perceived External Pressure are among the most relevant since they often impact people's adoption of a particular technology. Thus, this variable has been extensively used in the literature as a predictor of technology adoption (Kurnia et al., 2015; Muhammad et al., 2018; Salem & Nor, 2020; Gabryelczyk, 2018; Matta et al., 2011).

[...]

• College: University of applied sciences, Munich
• Grade: 1,3
• Author: José Alonso Pisfil Manchego (Author)
• Publication Year: 2022
• Pages: 137
• Catalog Number: V1330976
• ISBN (PDF): 9783346831095
• ISBN (Book): 9783346831101
• Language: English
• Tags: Consumer Behavior, Online Grocery Shopping, COVID-19 pandemic, Peru, Germany, Quantitative analysis