The Intention of Consumers
to Engage in Digital Food
Sharing Platforms
MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 30 Credits
PROGRAMME OF STUDY: Global Management AUTHOR: Fatlum Sadrijaj & Tim Rösing
JÖNKÖPING May 2021
An Analysis and Investigation of the Behavioural Intention from a
Consumer Perspective by Extending the Theory of Planned
Master Thesis in Global Management
Title: The Intention of Consumers to Engage in Digital Food Sharing Platforms Authors: Fatlum Sadrijaj & Tim Rösing
Tutor: Tommaso Minola, PhD Date: 2021-05-23
Key terms: Food Sharing, Digital Food Sharing, Food Waste, Sharing Economy, Behavioural Intention, Theory of Planned Behaviour
Abstract
The sharing economy, which has been receiving significant attention from research due to its unprecedented growth in the recent past, is being seen as a potential driving force to transform and rethink society’s unsustainable approach to consumption. Especially, the concept of food sharing as part of the sharing economy is being considered as essential for a more sustainable world and thus aims at counteracting the unsustainable consumption behaviour of individuals. Even though the importance of food sharing concepts for society is undisputed, academia lags extensive research of this domain from a consumer perspective.
The aim of this study is to investigate the behavioural intention of consumers to engage and use digital, for profit food sharing platforms in a business to consumer setting to obtain an in-depth understanding of the key determinants by extending the Theory of Planned Behaviour. Additionally, a cross-cultural comparison has been undertaken to acknowledge the international importance of this field.
For the purpose of data collection, an online survey has been conducted. This yielded 4353 responses of which 2995 have been taken into account for the data analysis procedures in SPSS and SmartPLS. The software SmartPLS has been utilized to perform a partial least square structural equation modelling (PLS-SEM) assessing the measurement as well as structural model including the testing of the proposed hypotheses. Additionally, a multigroup analysis has been performed to investigate behavioural differences between cultures.
The empirical findings show that perceived usefulness and attitude are the strongest predictors of the behavioural intention followed by perceived behavioural control, economic benefit as well as subjective norm. Moreover, the attitude of consumers is strongly driven by sustainable considerations and the perceived trust of consumers towards digital food sharing platforms. Lastly, no statistically significant moderating effect could be identified with regards to culture.
Acknowledgement
We want to express our appreciation towards everyone, who has supported us in the procedure of writing this master thesis. To begin with, we want to thank our assigned supervisor Tommaso Minola for guiding us through the overall research process and additionally sharing valuable insights as well as information with us. Further, we express special appreciation for our seminar group, which provided us with fruitful and constructive feedback. Additionally, we are highly grateful for every individual, who participated in our survey and thus helped us to fulfil our research purpose. Lastly, we thank our families and friends for the outstanding support throughout the entire process.
Jönköping International Business School, Jönköping University
May 2021
Table of Contents
1 Introduction ... 1
1.1 Background ... 1
1.2 Problem Statement ... 2
1.3 Research Purpose and Questions ... 3
1.4 Structure of the Thesis ... 4
2 Literature Review ... 5
2.1 Sharing Economy ... 5
2.2 Food Sharing ... 7
2.3 Theory of Planned Behaviour ... 10
2.3.1 Attitude (A) ... 11
2.3.2 Subjective Norm (SN) ... 13
2.3.3 Perceived Behavioural Control (PBC) ... 14
2.3.4 Behavioural Intention (BI) ... 15
2.4 Possible Antecedents ... 16
2.4.1 Perceived Usefulness & Perceived Ease of Use (PU & PEOU) ... 16
2.4.2 Ecological Sustainability (SUS) ... 17
2.4.3 Economic Benefit (EB) ... 18
2.4.4 Trust (T) ... 18
2.4.5 Product Scarcity (PS) ... 19
3
Methodology and Method ... 22
3.1 Research Philosophy ... 22 3.2 Research Approach ... 23 3.3 Research Design ... 24 3.4 Research Strategy ... 25 3.5 Data Collection ... 26 3.5.1 Primary Data ... 27 3.5.2 Secondary Data ... 28 3.6 Questionnaire Design ... 29 3.6.1 Constructs ... 30 3.6.2 Pilot test ... 32 3.7 Sampling ... 32 3.7.1 Population ... 33 3.8 Time Horizon ... 33
3.9 Data Analysis Procedure and Techniques ... 34
3.10 Research Quality ... 37
3.11 Research Ethics ... 38
4 Data Analysis and Findings ... 40
4.1 Description of Empirical Data ... 40
4.2 Partial Least Square Structural Equation Modelling Analysis (PLS-SEM) ... 46
4.2.1 Model Fit ... 46
4.2.2 Measurement Model... 46
4.2.3 Structural Model... 50
4.2.4 Testing of Hypothesized Relations ... 52
4.3 Multigroup Analysis (MGA) ... 55
4.3.2 Multigroup Analysis Results ... 58
4.4 Summary and Interpretation of Empirical Findings ... 60
5 Conclusion ... 64
6 Discussion ... 66
6.1 Discussion of Findings ... 66
6.2 Practical and Theoretical Implications ... 67
6.3 Limitations ... 70
6.3.1 Theoretical Background and Self-Reporting of Data... 70
6.3.2 Limitations of Data Collection Process and Data Set ... 71
6.3.3 Data Analysis Procedure ... 72
6.4 Future Research ... 72
6.4.1 Research on Cultural Differences and Demographics ... 72
6.4.2 Further Investigation of Trust ... 73
6.4.3 Adaption of Research Design ... 73
6.4.4 Investigation of Intention-Behaviour Relationship ... 74
Reference List ... 75
Figures
Figure 1: Theory of Reasoned Action & Planned Behaviour ... 11
Figure 2: Conceptual Model ... 20
Figure 3: Tree Metaphor ... 22
Figure 4: Structural & Measurement Model ... 36
Figure 5: Gender ... 41
Figure 6: Age ... 41
Figure 7: Country of Residence ... 42
Figure 8: Occupation ... 43
Figure 9: Have you downloaded the "Too Good To Go" application? ... 44
Figure 10: Have you heard about the digital food sharing platform "Too Good To Go"?44 Figure 12: Have you used the "Too Good To Go" application before? ... 44
Figure 11: Have you used any other food sharing services before? ... 44
Figure 13: How often do you use the "Too Good To Go" application? ... 44
Figure 14: Extended TPB Model and Summary of Empirical Findings ... 60
Tables
Table 1: List of Hypotheses ... 21Table 2: Constructs ... 31
Table 3: Demographic Profile of Respondents ... 45
Table 4: Assessment of the Measurement Model... 49
Table 5: HTMT Assessment ... 50
Table 6: Assessment of the Structural Model ... 51
Table 7: Hypothesis Testing ... 54
Table 8: Formed Groups for MGA ... 56
Table 9: MICOM Permutation Results... 58
Table 10: MGA Results ... 59
Appendix
Appendix 1: English and German Questionnaire.………...…..84Appendix 2: SmartPLS Model.………...92
List of Abbreviations
A – Attitude
AVE – Average Variance Extracted BI – Behavioural Intention B2C – Business-to-Consumer CR – Composite Reliability C2C – Consumer-to-Consumer EB – Economic Benefit F2 – Effect Size HTMT – Heterotrait-Monotrait Ratio MGA – Multigroup Analysis
MICOM – Measurement Invariance Assessment PBC – Perceived Behavioural Control
PEOU – Perceived Ease of Use PLS – Partial Least Square PS – Product Scarcity PU – Perceived Usefulness
Q2 – Cross-Validated Redundancy
R2 – Coefficient of Determination
SEM – Structural Equation Modelling SN – Subjective Norm
SRMR – Standardized Root Mean Square Residual SUS – Ecological Sustainability
T – Trust
TAM – Technology Acceptance Model TPB – Theory of Planned Behaviour TRA – Theory of Reasoned Action VIF – Variance Inflation Facto
1 Introduction
_____________________________________________________________________________________ The purpose of the introduction is to provide background information regarding the sharing economy as well as food sharing domain. Further, it is being pointed out why the topic of this research deserves to be studied and what the purpose of this thesis is including the formulation of the research questions. The chapter closes by providing an overview of the thesis structure.
______________________________________________________________________
1.1 Background
The phenomenon of the sharing economy industry has been facing increased popularity in recent years. PwC (2015) is expecting the industry to reach a valuation of 335 billion USD by 2025, which represents a 22-fold growth in the period between 2014 and 2025. Simultaneous to the recent growth of the industry, researchers have also been showing increased interest in the sharing economy domain. This has led to the publication of a magnitude of research, definitions as well as synonyms (Hossain, 2020; Vallas & Schor, 2020). While there is no universally valid definition (Cheng, 2016; Pargman et al., 2016), academia generally agrees that the sharing economy incorporates aspects of sharing and collaboration with regards to the consumption and usage of resources into business models, which additionally leads to higher resource efficiency (Hossain, 2020; Schlagwein et al., 2020).
Even though the sharing economy has been enjoying increased popularity in recent years, it can be said that the concept of sharing per se is certainly not novel and has always been a vital part of human life and interaction (Belk, 2014; Hossain, 2020). Nonetheless, business models in the sharing economy are being seen as potential force to transform society and its concept of consumption, which is frequently being categorized as unsustainable (Cheng, 2016; Michelini et al., 2018).
This aspect can be observed in the context of food waste: one third of all food being produced is being thrown away, which amounts to a total of 1.3 billion tonnes of food
waste annually worldwide. This points towards a paradox the world is facing: while there are 1.3 billion tonnes of wasted food annually, people in the economically weakest countries are starving to death. The ramifications of this unsustainable consumption behaviour are manifold and harming individuals in need as well as earth’s ecosystem by significantly contributing to the production of greenhouse gases (UN-Environment, 2021; Vittuari et al., 2016). Thus, the concept of food sharing as part of the sharing economy is being categorized as essential for a more sustainable world (Davies, Donald, et al., 2017; Falcone & Imbert, 2017). This has led to the formation of several food sharing initiatives in the recent past ranging from non-profit to for-profit (Michelini et al., 2018). Additionally, digital and technological advancements such as smartphones have generated new business opportunities and models, which further led to the introduction of disruptive innovations within the food sharing domain. Digital innovations have enabled firms as well as individuals to connect and share food in novel and fast ways (Davies, Donald, et al., 2017; Mazzucchelli et al., 2021; Michelini et al., 2018).
1.2 Problem Statement
While the sharing economy has received significant attention from scholars, the food sharing domain lacks substantial research (Mazzucchelli et al., 2021; Schanes & Stagl, 2019). Although food sharing can be seen as a part of the sharing economy, it would be deceptive to apply general assumptions and findings since differences between these domains appear to be present. Further, the motivation of consumers to engage in sharing economy services has been researched extensively (Ek Styvén & Mariani, 2020; French et al., 2017; Möhlmann, 2015; Oliveira et al., 2021; Tussyadiah, 2016; Wang et al., 2020). However, only a few studies have been investigating it in the food sharing context (Schanes & Stagl, 2019). These studies were also limited to a consumer-to-consumer (C2C) and non-profit context (Harvey et al., 2020). In recent years new food sharing business models arose through the utilization of advanced technologies (Mazzucchelli et al., 2021). Examples are food sharing smartphone applications, which operate in a for-profit context. So far, there has not been any research investigating the intention of consumers to engage in the services of digital, for-profit food sharing platforms in a business-to-consumer (B2C) context. It is important to understand consumers’ intention to engage in these services for a multitude of reasons. Firstly, this field deserves to be investigated in detail because of the huge potential of food sharing services to resolve
sustainability and food waste issues (Michelini et al., 2018; Falcone & Imbert, 2017). It has never been more important to engage in sustainable actions and movements since resource scarcity and environmental issues seem to be omnipresent in today’s society. Subsequently, this thesis is contributing to enhance the efficiency of these movements.
Secondly, differences between the sharing economy and the food sharing domain seem to exist. Therefore, applying general assumptions about the sharing economy to the food sharing context would be insufficient to capture the uniqueness of this field. Since it remains predominantly unclear what mainly drives consumers’ intentions within the food sharing domain, findings of this thesis will contribute to an overall understanding of these business models from a consumer perspective. The market insights derived from this study are highly useful for digital food sharing platform providers because an in-depth understanding of consumers’ intentions could potentially be utilized to enhance the overall market appearance and effectivity of these services. Digital food sharing companies can use these insights to further educate consumers and create awareness for the societal problem of food waste, which seems to result from unsuitable habits and behaviours of end consumers. The contribution or novelty in our research lays in the fact that we combined the theory of planned behaviour (TPB) as well as the technology acceptance model (TAM) and additionally extended the combined models by further antecedents, which have been incorporated in existing literature before, and applied our conceptual framework to a new context.
1.3 Research Purpose and Questions
The purpose of this thesis is to investigate the intention of consumers to engage and use digital, for profit food sharing platforms in a B2C context and additionally undertake a cross-cultural comparison. Further, the factors influencing the intention under investigation will be analysed as well. Due to the fact that digital food sharing platforms operate internationally to tackle the global food waste problem, a comparison in a cross-cultural context will be drawn to investigate variances with regards to the behavioural intention. To fulfil the purpose of this thesis, a model extending the theory of planned behaviour is being developed and geared towards the usage intention of digital food sharing platforms.
Hence following research questions have been derived and will be answered:
RQ1: “What are the key determinants influencing the intention of consumers to engage
in digital food sharing platforms?”
RQ2: “What are the main differences with regards to the intention of consumers to
engage in digital food sharing platforms in a cross-cultural context?”
1.4 Structure of the Thesis
The thesis starts by analysing and investigating the relevant literature regarding the sharing economy as well as food sharing domain. Further, the TPB is introduced and discussed and has been used as the foundation for our proposed conceptual model. The literature review is being followed by the methods and methodologies of this study, which took a closer look on the philosophical positions adopted as well as the methodological decisions taken. Additionally, this section outlines the questionnaire design, the data collection and analysis procedure and contains remarks regarding the research quality and ethical considerations. Prior to the conclusion of the study, the data analysis and empirical findings are being presented including descriptive statistics, partial least square structural equation modelling as well as a multigroup analysis. The chapter concludes with a summary and interpretation of the empirical data. The study ends with a discussion about the empirical results including the limitations, implications for theory and practice and additionally avenues for future research are being outlined.
2 Literature Review
_____________________________________________________________________________________ The purpose of this chapter is to provide the theoretical background to the topic. It starts by looking at the literature in the context of the sharing economy followed by an in-depth investigation of the food sharing domain. Further, the theoretical frame is being introduced, which refers to the Theory of Planned Behaviour. The chapter closes with the conceptual model as well as the hypothesized relations.
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2.1 Sharing Economy
The sharing economy has been progressing rapidly in the recent past of the 21st century with prominent examples such as Uber and Airbnb. The phenomenon has been gaining popularity from multiple directions including researchers, regulators and individuals. With regards to research, it can be stated that scholars have been starting to pay significant attention to this field of research since 2012. Furthermore, the global industry of the sharing economy itself has experienced significant financial growth as well: in the period between 2014 to 2025, the industry is being expected to grow 22-fold reaching an overall evaluation of 335 billion USD (Hossain, 2020; Vallas & Schor, 2020). PwC (2015) identifies four main drivers of this rapid growth: digital platforms and technological devices, change in consumer needs and expectations, increased efficiency in resource usage as well as changes resulting from globalization and urbanization forces.
The main idea of the sharing economy is certainly not novel and can be traced back to ancient times since sharing has always been part of human interaction (Belk, 2014; Hossain, 2020). In response to an increasing scarcity of critical resources, the emergence of the internet around the new millennia has been utilized as an opportunity to increase the efficiency concerning resource usage. The sharing economy can be seen as one initiative to tackle the problem of resource inefficiency. The original concept of the sharing economy was rather classified as non-profit initiative but steadily grew into the large industry it is today (Cheng, 2016).
Scholars have been attempting to define the sharing economy resulting in a plurality of definitions. Similarly, a multitude of synonyms surfaced including collaborative
consumption (Hamari et al., 2016) and community/platform-based economy (Hossain, 2020). Generally, the phenomenon includes business models, which incorporate the aspects of sharing and collaborating with regards to the use and consumption of resources and tries to bring the core players including platform and service providers as well as consumers together frequently enabled through technology (Hossain, 2020; Schlagwein et al., 2020). Schlagwein et al. (2020, p. 818) define the sharing economy as: “[…] an IT-facilitated peer-to-peer model for commercial or noncommercial sharing of underutilized goods and service capacity through an intermediary […]”. Muñoz and Cohen (2017, p. 21) on the other hand describe sharing economy as “a socio-economic system enabling an intermediated set of exchanges of goods and services between individuals and organizations which aim to increase efficiency and optimization of sub-utilized resources in society.”. Frenken et al. (2015) further define the sharing economy as “consumers granting each other temporary access to underutilized physical assets […], possibly for money”. Deriving from these definitions it can be observed that there is a disparity in describing the key elements of the sharing economy. However, most scholars emphasize the increased usage of underutilized goods through the aspect of sharing. Concluding, it has to be stressed that there is no universally valid definition of the sharing economy concept and additionally existing definitions often appear to be contradicting (Cheng, 2016; Pargman et al., 2016). This is due to the fact that a variety of existing businesses and companies ranging from ride sharing to hospitality operate in this fast-evolving field (Hossain, 2020). Furthermore, according to Belk (2014) the word sharing is generally too broad and can take many forms, which further contributes to the dilemma of defining the notion of the sharing economy.
The sharing economy concept has been employed by existing incumbent organisations as well as start-ups and is acting as a transformative force regarding social, environmental and economic perceptions. According to Cheng (2016, p. 61) the sharing economy is in a position to “transform many aspects of our current social economic system by allowing individuals, communities, organizations and policy makers to re-think the way we live, grow, connect and sustain”. Essentially, attempts have been made to substitute and rethink the current concept and approach to consumption, which is frequently termed hyper-consumption. Further, it appears that individuals engage in unsustainable practices
and behaviours, which appears to be evident by taking a closer look on the issue of food waste (Hossain, 2020; Michelini et al., 2018).
2.2 Food Sharing
The world is facing a paradox: while it is being estimated that one third of all the food that is produced for consumption purposes is being thrown away, poverty is still an omnipresent issue (Michelini et al., 2018). This amounts to 88 million tonnes of food waste in Europe alone, while the global food waste totals approximately 1.3 billion tonnes annually. The ramifications of this unsustainable habit are manifold going beyond ethical and economic considerations. On one side, wasted food contributes significantly to the production of greenhouse gases (approximately 8%) causing harm to earth’s ecosystem. On the other side, a more conscious approach to food waste could benefit individuals in need (UN-Environment, 2021; Vittuari et al., 2016). The concept of food sharing as part of the sharing economy is trying to tackle this issue and is being seen as fundamental for a more sustainable world (Davies, Donald, et al., 2017; Falcone & Imbert, 2017).
The concept of recovered and redistributed food waste has been receiving increased popularity in recent years due to its important role in reducing and preventing food waste (Falcone & Imbert, 2017). The matter of food sharing has also been examined from an anthropological perspective noting that the sharing of food has always been a constant part of human life to secure livelihood as well as strengthen communal relations (Kaplan et al., 2005). Families or other small communities provide the initial context for humans to get familiar with the concept of food sharing (Falcone & Imbert, 2017; Harvey et al., 2020). As a matter of fact, food sharing in intra and inter family contexts enabled and gave rise to division of labour leading to specialisation, which shaped relationships and connections in communities. Hence, it appears to be evident that sharing has been a strong force in influencing human history (Davies, Donald, et al., 2017). In recent years this has enabled a plurality of food sharing concepts to shape going beyond the family context and taking various forms in different contexts. Furthermore, recent food sharing concepts include different forms ranging from digital food sharing networks to restaurants and private initiatives. Additionally, food sharing concepts can either incorporate the aspects of donating or swapping as well as for-profit selling. Michelini et al. (2018, pp. 210-211) identify a threefold categorization of food sharing models: (1) “sharing for money”,
which takes place in a B2C, for profit context (2) “sharing for charity”, which is mainly about collecting surplus food and donating them to non-profit charities and (3) “sharing for community” relating to sharing activities in a C2C setting. Main distinctions can be observed by taking a closer look on whether the food sharing services are performed for profit, which role the consumer is going to take in the process and what kind of food is shared. Further the organisational form of the food sharing service itself determines the socio-economic impact of the enterprise (Davies, Donald, et al., 2017; Falcone & Imbert, 2017; Harvey et al., 2020).
Technological and digital advancements in the recent past have generated new business opportunities changing the value creation of businesses and giving rise to disruptive innovations. The incorporation of digital technology has also been observed in the field of food sharing. According to Mazzucchelli et al. (2021, p. 47) industries have been fundamentally changed by “introducing new business models that exploit the new digital technologies and embed environmental, social and economic issues”. Digital platforms as well as smartphone applications, which prevent food from being wasted, can be seen as most disruptive business models in the field of food sharing since enables consumers to connect and share food in novel ways (Mazzucchelli et al., 2021). The rise in the popularity of food sharing services can be seen as a response to an increase in digitalization with transformative potential (Davies, Donald, et al., 2017; Michelini et al., 2018). A digital platform organisation providing food sharing services takes advantage of the ease of connecting users offering food, which is likely to become waste, with consumers willing to consume it and thereby filling “circularity holes” within the supply chain (Ciulli et al., 2020). These food sharing organisations further play a key role in educating the consumer with regards to food waste, protecting the consumers from potential risks arising from using the service, and mobilizing other key players needed to fill the gaps within the supply chain. Further, integrating new technological tools and other processes into the supply chain enables the food sharing organisation to guarantee constant circularity of the food sharing service. (Ciulli et al., 2020).
The concept of food sharing can be considered a central connection point of multiple sustainability issues ranging from the reduction of food waste to social inclusion and the securing of food supplies for individuals in need. Even though the interest of academia
with regards to food sharing has been growing in recent years, knowledge on this topic appears to be scarce (Schanes & Stagl, 2019).
While research appears to be plentiful in the context of the sharing economy in general (Hossain, 2020), literature and detailed understanding in the context of food sharing tends to be rather limited (Mazzucchelli et al., 2021; Schanes & Stagl, 2019). Current research publications on food sharing initiatives predominantly focus on the classification, characterisation as well as nomenclature regarding business models (Corbo & Fraticelli, 2015; Davies, Edwards, et al., 2017; Michelini et al., 2018). Additionally, the potentials of food sharing initiatives and its characteristics have been pointed out by scholars (Davies, Edwards, et al., 2017). Falcone & Imbert (2017) & Michelini et al. (2018) analysed the potential of food sharing in connection with sustainability and the reduction of food waste. Furthermore, scholars have also been examining the barriers for participating in food sharing initiatives, which for instance can be categorized into supply and demand side barriers (Barnes & Mattsson, 2016; Bielefeldt et al., 2016; Ciulli et al., 2020). Recently, scholars have drawn a connection between the implementation of digital technologies into the food sharing domain including the success factors and potentials of these platforms (Ciulli et al., 2020; Davies, Donald, et al., 2017; Davies, Edwards, et al., 2017; Harvey et al., 2020; Mazzucchelli et al., 2021).
Moreover, the intention of consumers to engage in sharing economy activities has received significant attention from scholars resulting in a multitude of research (Ek Styvén & Mariani, 2020; French et al., 2017; Möhlmann, 2015; Oliveira et al., 2021; Tussyadiah, 2016; Wang et al., 2020). Additionally, these studies covered a multitude of industries and contexts e.g. car sharing (Bielefeldt et al., 2016; Möhlmann, 2015), ride sharing (Wang et al., 2020), tourism (French et al., 2017; Tussyadiah, 2016) as well as second-hand clothing (Ek Styvén & Mariani, 2020). However, as the overall literature about food sharing seems to be scarce also a lack of analysing the intention of consumers to use food sharing services appears to be present. Since food sharing can be seen as a distinct domain of the sharing economy and because there are different incentives to engage in food sharing, it remains unclear what mainly drives consumers’ intention. Only a fraction of research has focused on the motivation of people to engage in food sharing activities. Schanes & Stangl (2019) have investigated the motivation of individuals to
participate in sharing and rescuing of food from a social identity perspective. Further, Ganglbauer et al. (2014) have investigated the motivation of individuals with reference to social media through thematical analysis of postings. While scholars have analysed the impact of digital technologies on food sharing, this has been primarily done in a peer-to-peer or C2C, non-profit context (Harvey et al., 2020). Yet, research lacks extensive analysis of the intention of individuals to engage in food sharing through digital platforms as well as smartphone applications in a B2C, for profit context, which would correspond to Michelini et al. (2018) categorisation of “sharing for money”.
2.3 Theory of Planned Behaviour
In order to analyse the main drivers with regards to consumers’ intention to engage in digital food sharing, a proper theoretical model is needed. For this purpose, the theory of planned behaviour (TPB) seems to be appropriate. The theory has been chosen as an adequate basis for this research due to the fact that it is comprehensive to allow for in-depth examination of individuals’ intention deriving from attitudes, subjective norms and perceived behavioural control. An informed examination of multiple sources of literature enables us to reach a comprehensive understanding of the intention and its antecedents. In turn, this makes it possible to incorporate further already existing and/or new constructs. Hence, a model to understand the intention of consumers to engage in digital food sharing platforms in a B2C context is being developed.
The TPB has been introduced in 1991 by Ajzen as an extension to the theory of reasoned action (TRA). The TRA was believed to have several limitations and by adding the Perceived Behavioural Control determinant into the model, a more solid understanding regarding the connections between attitude, intentions as well as behaviours has been reached (Ajzen, 1991; Montaño & Kasprzyk, 2015). Ajzen (1991, p. 181) claims that TPB was “designed to predict and explain human behaviour in specific contexts”. Since the publication of the TPB model it has been used in various contexts and empirically tested numerous times over making it one of the most popular models for analysing, understanding and predicting individual human behaviour (Ajzen, 2011b). In total, the theory has been cited over 85.000 times, which is further evidence for its popularity and quality (Google-Scholar, 2021).
Ajzen (2005) claims that in the TPB the main determinant of an action or behaviour is the intention of an individual to execute or not execute the behaviour in question. He further argues that the behavioural intention of an individual is influenced by three determinants. These include the “attitude towards a behaviour”, which can be classified as personal determinant, the “subjective norm”, which mirrors the social influence of an individual, and lastly the “perceived behavioural control” referring to control issues encountered by individuals or the ability of individuals to undertake a behaviour in question (Ajzen, 2005). The TRA has been extended by adding the construct of perceived behavioural control, which considers that behaviours are not necessarily fully controllable by individuals. The result of adding the additional construct led to the publication of the TPB (Manstead, 1996), which is being illustrated in Figure 1 below and shall be discussed in more detail in the subsequent paragraphs. Overall Ajzen (2005, p. 118) concludes: “[…] people intend to perform a behaviour when they evaluate it positively, when they experience social pressure to perform it, and when they believe that they have the means and opportunities to do so”.
Figure 1: Theory of Reasoned Action & Planned Behaviour
Source: adapted from Ajzen (2005)
2.3.1 Attitude (A)
One of the determinants influencing the intention within the TPB model is attitude. The determinant can be ascribed as “the degree to which a person has a favourable or unfavourable evaluation or appraisal of the behaviour in question” (Ajzen, 1991, p. 188). The intention of performing a certain behaviour is generally assumed to be stronger if an
individual’s attitude towards the behaviour in question is favourable and subsequently weaker if the attitude towards the behaviour in question is unfavourable (Ajzen, 1991). According to Fishbein and Ajzen’s (1975) expectancy-value model, attitude is further determined by people’s belief, where each belief is directly linked to the end result of the behaviour performed. If the respondent links the behaviour in question to a positive outcome, these believes strengthen the attitude towards performing the behaviour. When the behaviour is believed to lead to negative outcomes on the other side, the attitude towards performing the behaviour is weakened. The beliefs and attributes, which come along with the evaluation of the outcome are consolidated beforehand. The attitude is built up simultaneously and therefore is acquired before actually performing the behaviour in question as well.
Direct observation of an individual’s attitude is impossible. Therefore, it has to be interfered from measurable responses when analysed. Further, attitude can be categorized into three kind of responses: affect, cognition and conation. Someone’s attitude can be interfered by analysing the evaluation of the respondent with regards to feelings towards the object under consideration, which refers to affective responses. Cognitive responses, on the other hand, refer to the respondent’s thoughts about and perception of the analysed object. Lastly, conative responses reflect the behavioural intention of a respondent through expression (Ajzen, 2005).
In the context of the sharing economy literature many studies pointed out that consumers’ intention to engage in theses service is mainly influenced by their attitude towards this behaviour. Examples of these studies are research regarding the buying intention of second hand clothing on a sharing economy platform (Ek Styvén & Mariani, 2020), the continued intention to engage in social networking tourism (French et al., 2017) and a study of the antecedents of behavioural intention and preferences in an online peer-to-peer resource sharing setting (Laurenti & Acuña, 2020).
In the context of this thesis the TPB would suggest that a positive attitude towards a digital food sharing platform leads to a positive intention to use its services. Thus, we hypothesise:
Hypothesis 1: A positive attitude towards the services of a digital food sharing platform
positively influences the intention to use these services.
2.3.2 Subjective Norm (SN)
The behavioural intention incorporated in the TPB is also influenced by subjective norm (SN). SN “refers to the perceived social pressure to perform or not to perform the behaviour” (Ajzen, 1991, p. 188). Similar to the attitude component, the more positive the SN towards a certain behaviour is perceived the stronger the behavioural intention tends to be and vice versa (Ajzen, 1991). An individual’s social pressure is directly influenced by people, which are important to that individual (e.g. family, close friends). The SN is generated out of normative beliefs. Normative beliefs refer to the probability of approval or disapproval of the behaviour by significant others while SN is describing the overall resulting social pressure (Armitage & Conner, 2001).
When applying the TPB model in the sharing economy context, it was shown that the SN determinant has significant impact on the consumers intention. Although its influence on the behavioural intention was found to be weaker compared to the attitude determinant, the impact of SN is non-neglectable (Kim et al., 2018; Laurenti & Acuña, 2020). Further, Sheeran et al. (1999) found that when an individual is feeling generally pressured to perform a behaviour, the motivational impact on intention is lower compared to motivation resulting from the individual’s own beliefs. The weakness of the predictive power of SN within the TPB model is sometimes also related to a weakness in measurement. However, analysing SN by using multiple measures might enhance its predictive power towards behavioural intention (Armitage & Conner, 2001).
The TPB would suggest that a positive SN of a digital food sharing platform perceived by the consumer leads to a positive intention to use its services. Thus, we hypothesise:
Hypothesis 2: A positive subjective norm towards the service of a digital food sharing
2.3.3 Perceived Behavioural Control (PBC)
The predictive power of the TRA is limited to easy to perform and voluntary behaviours, where individuals perceive to have high level of control and in situation with little constraints to an action. This means that behaviours with lower levels of perceived control could not sufficiently be captured and explained by the model. Therefore, the explanatory capacity of the TRA had to be widened leading the addition of the determinant of perceived behavioural control (PBC) and the postulation of the TPB (Ajzen, 1991, 2011b; Madden et al., 1992). Generally speaking, a person’s perception of how difficult or easy it is to execute a behaviour is being reflected in the PBC. This further means that high PBC is related to a perception that a behaviour is rather easy to execute and vice versa (Manstead, 1996).
The introduction of the construct of PBC has been partly derived from the notion that behaviours are not solely dependent on intentions but additionally ability, which refers to the level of control regarding a behaviour, appears to play a role as well. Ajzen (2005, p. 110) states that “personal deficiencies and external obstacles can interfere with the performance of any behaviour […] these factors represent people’s actual control or lack of control over the behaviour”. However, the actual control of an individual cannot be fully measured. This is due to the fact that the collection of information regarding actual facilitating as well as preventing factors of a behaviour in question is lacking sufficiency. Thus, the perception of individuals regarding control can be measured instead, which mirrors the actual control of an individual making PBC a solid proximation (Ajzen, 2005). What is more, two aspects have an impact on PBC: on one hand control beliefs and on the other hand perceived power. The former refers to the exitance or non-exitance of resources and opportunities as facilitating and impeding circumstances concerning a behaviour. The latter reflects a way to weight the potential impact of these circumstances in the context of a behaviour to perform (Ajzen, 1991; Montaño & Kasprzyk, 2015). Ajzen (1991) states a high number of perceived resources and opportunities in combination with low expected barriers will lead to a perception of high behavioural control of an individual.
A person who perceives the engagement in digital food sharing platforms as easy to perform is according to the TPB likely to have positive intention to engage in these services. Accordingly, we hypothesise:
Hypothesis 3: A high perceived behavioural control towards the services of a digital
food sharing platform positively influences the intention to use these services.
2.3.4 Behavioural Intention (BI)
As already mentioned, in the TPB it is being assumed that the main determinate of a behaviour is the intention of an individual regarding the behaviour in question (Ajzen, 1991). Additionally, BI is a function of beliefs concerning the chances that particular results will be obtained by undertaking a behaviour (Fishbein & Ajzen, 1975; Madden et al., 1992). According to Ajzen (1991, p. 181) behavioural “intentions are assumed to capture the motivational factors that influence a behaviour; they are indications of how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behaviour.” The author further adds that the likelihood of performing a behaviour will increase the greater the BI of an individual is.
It appears that scholars generally support the idea of intention as main predictor of behaviour (Ajzen, 2005; Fishbein & Ajzen, 1975; Gollwitzer, 1993), which led to the publication of a plurality of studies showing a significant correlation between intention and behaviour (Armitage & Conner, 2001; Notani, 1998; Randall & Wolff, 1994; Sheppard et al., 1988). Additionally, studies in this regard have been carried out in various contexts such as the health domain (Godin & Kok, 1996) including smoking (Godin et al., 1992), birth control (Ajzen & Fishbein, 1980), drug use (Orbell et al., 2001) and exercise behaviour (Hausenblas et al., 1997) as well as other domains such as voting behaviour (Ajzen & Fishbein, 1980) and investment decisions (East, 1993).
As it is evident from the results of these various studies, academia has accumulated solid evidence for the predictive power of intention with reference to behaviour. Nevertheless, studies in this context have also led to inconsistency and variability in terms of correlation between intention and behaviour including low correlations. According to Ajzen (2005)
several factors could explain these inconsistencies including the incompatibility of behaviour and intention, stability of intentions as well as literal inconstancy.
It has to be mentioned that generally, when applying the TPB, the intention in question might impact the relative importance of the three antecedents, attitude, SN and PBC. Depending on the intention that is being investigated, attitude might play a more noteworthy role than SN and PBC. In other cases, SN or PBC might be of higher importance than attitude. Simultaneously this implies that for explaining a given intention it might be sufficient to only investigate attitude and SN. For another given intention it might be necessary to take all three antecedents into consideration and investigation. Lastly, different populations of respondents might further impact the antecedents’ relative weighting (Ajzen, 2005).
2.4 Possible Antecedents
2.4.1 Perceived Usefulness & Perceived Ease of Use (PU & PEOU)
Due to the fact that this thesis examines digital food sharing platforms, it is necessary to incorporate the aspect of information technology. Davis (1989) investigated what factors drive individuals to adopt or abandon information technology, which led to the postulation of the Technology Acceptance Model (TAM). In this model, which is grounded in the TRA as well as TPB, the author suggests two constructs, namely perceived usefulness (PU) and perceived ease of use (PEOU), which influence individuals’ attitude towards a technology and attitude in turn influences the BI. PU refers to the perception of an individual concerning the ability of an information technology to enhance their job performance while PEOU refers to how easy or difficult an individual perceives the usage of a technology to be (Chen et al., 2007; Davis, 1989). Since users of digital food sharing platforms interact with smartphone applications, we shall incorporate and integrate the two constructs PU as well as PEOU of using these applications into our proposed framework. As mentioned, Davis (1989) relates PU to the job performance of individuals. However, it could be possible to link PU to the general performance in life. This would mean that digital food sharing platforms could boost individuals’ performance in terms of practicality and availability of food options. Hence, we hypothesise following relationships, which additionally are in line with Davis (1989) proposed relationships between PU, PEOU, attitude as well as BI:
Hypothesis 4: Perceived usefulness of digital food sharing platforms positively
influences the attitude towards these platforms.
Hypothesis 5: Perceived ease of use of digital food sharing platforms positively
influences the attitude towards these platforms.
Hypothesis 6: Perceived usefulness of digital food sharing platforms positively
influences the intention to use these platforms.
Hypothesis 7: Perceived ease of use of digital food sharing platforms positively
increases the perceived usefulness of these platforms.
2.4.2 Ecological Sustainability (SUS)
The aspect of ecological sustainability (SUS) of sharing economy companies is one of the main factors considered to influence the consumer’s intention to engage in these services (Hamari et al., 2016). Overall, an engagement in sharing economy services is perceived to be ecological sustainable by the consumers (Prothero et al., 2011). Especially the food sharing domain is seen as a solution for ecological problems (Davies, Donald, et al., 2017; Falcone & Imbert, 2017). However, the ecological impact of companies varies and therefore, it is important to assess the perceived ecological impact in the context of this thesis.
Previous studies in the sharing economy field support the use of a company’s SUS perceived by the consumer as an antecedent of attitude within an TPB analysis (Hamari et al., 2016; Hawlitschek et al., 2018; Laurenti & Acuña, 2020; Paul et al., 2016). In all of these studies SUS was found to influence the consumer’s attitude significantly. Since digital food sharing platforms are addressing the problem of food waste, which subsequently is connected to an ecological sustainable behaviour, we also assume that the attitude of the consumer is influenced by the perceived SUS positively. Hence, we incorporate SUS as an antecedent of attitude and hypothesise:
Hypothesis 8: Ecological sustainability influences the attitude towards digital food
2.4.3 Economic Benefit (EB)
The results of a report published by PwC (2015) indicates that the majority of adults perceive sharing goods and services in the sharing economy as cheaper compared to conventional consumption. This further implies that the potential saving of financial resources might have an impact on the participation of consumers in the sharing economy and further with regards to food sharing as well. In a qualitative study Hellwig and colleagues (2015) found out that saving of financial resources can be seen as a motivational factor for sharing economy participation as stated by the respondents. Further studies in connection with EB in the context of sharing economy showed the impact of these benefits on the consumer’s attitude within a TPB analysis (Akande et al., 2020; Hamari et al., 2016; Hawlitschek et al., 2018). It was found that overall EB is a proper determinant of attitude within the sharing economy (Akande et al., 2020).
With regards to digital food sharing platforms it is common that the food, which otherwise would be thrown away, is being offered for a lower price and hence the EB is evident. Therefore, we incorporate EB as an antecedent of attitude and hypothesise:
Hypothesis 9: Economic Benefit influences the attitude towards digital food sharing
platforms positively.
2.4.4 Trust (T)
As mentioned earlier, sharing has always been an important and vital part of human life. Even though this aspect is true, sharing usually takes place in familiar contexts with e.g. family and friends. However, on sharing economy platforms the aspect of sharing happens predominantly with strangers implying some extent of risk encountered by users of these platforms (Frenken & Schor, 2019; Räisänen et al., 2020). Thus, it appears that trust plays a central role in the context of sharing economy (Räisänen et al., 2020).
Moreover, the concept of trust has been examined with regards to consumer intention in the context of online peer-to-peer resource sharing (Laurenti & Acuña, 2020), Airbnb (Liang et al., 2018) and car-sharing (Barnes & Mattsson, 2017) showing its relevance for a TPB analysis of the sharing economy. In the digital food sharing domain, the consumer cannot influence which choice of food and quality is going to be supplied and therefore
has to trust the supplier. This might result in a bond of trust between consumer and supplier of the leftover food. Additionally, Akande et al. (2020) found that the trust on attitude relationship is one of the strongest in the context of the sharing economy. Because of the proven predictive power of trust on attitude and the context of this thesis, where trust is a prerequisite of an engagement in digital food sharing, we incorporate trust as antecedent of attitude and hypothesise:
Hypothesis 10: Trust influences the attitude towards digital food sharing platforms
positively.
2.4.5 Product Scarcity (PS)
Since the services of digital food sharing platforms depend on collaborating food providers, it cannot be ensured by the company where and how much food will be left over. This factor might influence the consumer’s attitude to engage in these services. Studies have already examined this factor within the sharing economy. Lamberton and Rose (2012) for example displayed that the perceived risk of PS influences likelihood to engage in sharing services negatively. Concluding, research generally has examined the relationship between attitude and PS within a TPB analysis and hence the factor of PS is incorporated into our proposed framework as antecedent of attitude (Laurenti & Acuña, 2020). Thus, we hypothesise the following:
Hypothesis 11: Product scarcity influences the attitude towards digital food sharing
Figure 2: Conceptual Model
Source: Own Figure
As illustrated all antecedents of the new developed model, which have been derived from similar TPB models used to explain similar intention patterns within the sharing economy domain, are hypothesised to mainly influence attitude. This is due to the fact that all these studies pointed out that these antecedents are key predictors of attitude in their research and an influence on SN or PBC of these factors was either non-existing or non-significant. Because the examined studies used for the development of the model have been applied in a similar context, the connection between the different elements of the model will be applied analogously. Although this new developed model will be applied to a new context, the similarities to the contexts are evident and hence, these connections will be taken over. Additionally, it was shown that, depending on the context, that attitude is the main and strongest predictor of intention (Ajzen, 2005) and therefore it is a fundamental aspect to analyse within our study due to the fact that we research a rather new domain and hence the natural first step would be to investigate the most crucial aspects to build a solid foundation for future research. This decision has also been influenced by the fact that the time frame of our study is rather short limiting the resources we have to investigate this topic. The original TPB model extended by the TAM has been incorporated in our conceptual framework without any adjustments since these models have been successfully applied and empirically tested multiple times over. Table 1 provides an overview of all hypotheses, which emerged out of the literature review. Since
we are investigating a new context, the data analysis will be further utilised to investigate whether there are any additional relationships between constructs, which have not been captured by the current conceptual model. If new connections appear to influence the BI of people to engage in digital food sharing platforms, these connections will be considered, and new hypotheses will be added. Thus, there is the possibility that additional hypotheses will emerge throughout the process of the data analysis.
Table 1: List of Hypotheses
(+) H1 A positive attitude towards the services of a digital
food sharing platform positively influences the intention to use these services.
(+) H2 A positive subjective norm towards the service of
a digital food sharing platform positively influences the intention to use these services.
(+) H3 A high perceived behavioural control towards the
service of a digital food sharing platform positively influences the intention to use these services.
(+) H4
Perceived usefulness of digital food sharing platforms positively influences the attitude towards these platforms.
(+) H5 Perceived ease of use of digital food sharing
platforms positively influences the attitude towards these platforms.
(+) H6 Perceived usefulness of digital food sharing
platforms positively influences the intention to use these platforms.
(+) H7 Perceived ease of use of digital food sharing
platforms positively increases the perceived usefulness of these platforms.
(+) H8 Ecological sustainability influences the attitude
towards digital food sharing platforms positively.
(+) H9 Economic Benefit influences the attitude towards
digital food sharing platforms positively.
(+) H10 Trust influences the attitude towards digital food
sharing platforms positively.
(-) H11 Product scarcity influences the attitude towards
3
Methodology and Method
_____________________________________________________________________________________ This section of the thesis looks at the philosophical positions adopted within this study, the methodological decisions taken, the questionnaire design, the data collection procedures as well as the data analysis procedures and techniques. This section ends by making remarks concerning the research quality of this thesis and ethical considerations. ____________________________________________________________________
Figure 3: Tree Metaphor
Source: Adapted from Easterby-Smith et al. (2018)
3.1 Research Philosophy
Easterby-Smith et al. (2018) make use of a tree as a metaphor to refer to the research philosophy and process in general. As it is evident in Figure 3, the inner most ring refers to the research ontology. According to the authors (p.109), ontology refers to “[…] the basic assumptions that the researcher makes about the nature of reality”. As ontology relates to the way researchers view the world, it can be situated within a continuum, which ranges from realism to nominalism. The former makes the assumption that the observers’ or researchers’ perception and the physical as well as social world are independent from each other while the latter makes the opposite argument claiming that there is no independence between the observer and the physical and social world as reality is formed by our perceptions, language and labels we put on phenomena (Easterby-Smith et al., 2018).
Following the logic of the tree metaphor, the next ring refers to the research epistemology. Epistemology has the nature of knowledge at its core and deals with the different ways of inquiring the social as well as physical world. Similar to the ontology, observers can take different epistemological standpoints. On one hand there is positivism, which argues that the world is rather external in nature and can therefore be measured objectively. Social constructionism on the other hand claims that reality is not external or objective in nature but rather determined by the observer or research and the way one makes sense of it (Easterby-Smith et al., 2018).
As described in the chapter before (Chapter 2: Literature Review), our research is based on existing literature in the context of the sharing economy as well as the TPB. The theory has been used as a base and has been extended by several antecedents including ones referring to technology acceptance in order to investigate the intention of individuals to use digital, for profit food sharing platforms in a B2C context. It appears that our approach rather resembles a realistic as well as positivistic point of view. This is due to the fact that our frame of reference is aiming at describing the physical and social world from an external standpoint. The aim is to extract general conclusions from the collected and subsequently analysed data through hypothesis testing and apply them to a larger population, which also points towards to the necessity to incorporate a more objective way of research. Otherwise it would be rather challenging to draw general conclusions from the sample. Due to this fact the other philosophical positions, nominalism and social constructionism, have been excluded as possible approaches to our research.
3.2 Research Approach
Based on the chosen research philosophy, the next step of the research process is according to Easterby-Smith et al. (2018) and Saunders et al. (2012) defining the research approach. The definition of the research approach includes outlining whether authors are using a deductive, inductive or abductive approach. Deduction generally refers to a research process, which is testing an already established theory. Hypotheses about the theory are developed and tested using statistical and logical methods. After this process the hypotheses are either rejected or confirmed. Induction on the other hand refers according to Saunders et al. (2012, p. 109) to “a research approach which involves the development of theory as a result of analysing data already collected”. Lastly, an
abductive research approach is combining the two previous mentioned research approaches and aims at the completion of observations (Easterby-Smith et al., 2018; Saunders et al., 2012).
We began the thesis by defining all relevant denominations based on reviewing the existing literature, which has received credibility within the research field. First, the sharing economy is defined followed by a description of food sharing and its different forms leading to the lack of research in understanding the intention of users of digital food sharing platforms and subsequently to the TPB model. The TPB is an already established theory and has been tested empirically multiple times in studies regarding motivational intention of consumers. We extended the theory by antecedents used in a variety of studies and applied it to a new context. Out of the literature review eleven hypotheses were formulated, but no empirical data was assessed beforehand. This process can be ascribed as deductive approach. Further it matches with the realistic and positivistic research philosophy chosen in this thesis. The chosen research approach further leads to a quantitative approach, which will be discussed in the next chapter (Easterby-Smith et al., 2018).
3.3 Research Design
In more general terms, when conducting research two main designs can be distinguished. On one hand a qualitative and on the other hand a quantitative research design. The former, which most commonly relates to a more inductive approach, focuses on words and perceptions rather than numerical circumstances and frequently starts with a broad research questions with the aim of investigating social phenomena in more detail. (Bell et al., 2018; Saunders et al., 2012). A quantitative research design focuses, in contrast to the qualitative approach, on numerical circumstances rather than words and perceptions of individuals and additionally follows a more linear path while taking a rather positivistic position to obtain more objective results and describe causal relations. Further, the possibility of generalisation and replicability of the data should be given in the context of quantitative research (Bell et al., 2018; Easterby-Smith et al., 2018; Saunders et al., 2012). The latter design has been chosen for our research for two main reasons. First, the theory used to develop the frame of reference, the TPB, has been widely used and applied to various contexts while incorporating a quantitative research design. The theory has been
quantitively tested multiple times showing the reliability of the proposed constructs (Ajzen, 2011a). Second, this approach allows us to draw more precise, objective and generalisable results with regards to the intention of consumers to use digital food sharing platforms. These objective and generalised results can further be used to project the intention of a representative sample to a larger population. Due to these circumstances the possibility of conducting qualitative research has been ruled out.
3.4 Research Strategy
The next step in the research process is to choose an appropriate research strategy. Generally, it is divided between the following: Experiment, Survey, Case Study, Action Research, Grounded Theory, Ethnography, and Archival Research. All of these strategies can be used for explanatory, exploratory and descriptive research. Further, all of the research strategies are equivalent in importance meaning that there is no research strategy superior to any other and it is possible to combine them, if necessary. The research strategy is being chosen upon several factors. It has to answer the research questions and meet the research objectives. Further criteria for evaluating possible research strategies are the existing knowledge, time constraints and other resources (e.g. money and work force) (Saunders et al., 2012).
The several factors incorporated in our developed TPB model conclusively influence the intention of consumers to engage in digital food sharing. It has been argued for each assumed effect of the different factors towards each other and hypotheses have been developed in the research process. This relationship between factors can be best explained from a detached observational position taken from a survey obtained from an appropriate sample size (Easterby-Smith et al., 2018). Choosing a survey as a research strategy is in line with a realistic and positivistic position, which is chosen for the context if this thesis (Saunders et al., 2012).
Developing and distributing a survey to answer our research questions comes along with several advantages. Conducting a survey to generate data from a large number of people is cost-effective and being in control of the process is another advantage. Other strategies, which tend to be more qualitative, would make us more dependent on others. Further, the survey allows us to analyse the data with statistical tools and methods straightforward
(Saunders et al., 2012). Because of these advantages and literature arguing for the use of a survey in our given context, we chose a survey as our research strategy.
We conducted our survey by making use of Qualtrics. It is known to be reliable, easy to use and further it is freely accessible. Moreover, it is easily accessible on mobile and desktop devices, which makes it comfortable for the respondents. Additionally, using Qualtrics enabled us to transfer our obtained data easily into statistical programs such as SPSS for the purposes of data analysis. Before asking the respondents questions regarding our research in specific, our topic was introduced, anonymity was guaranteed and demographic questions relevant for our studies were incorporated in our survey. In addition, it was assessed whether the respondents used digital food sharing platforms before and if this question was affirmed, it was asked about the respondents’ frequency of using these services. For each developed hypothesis survey questions have been framed in order to test them. Since all of the factors have been used and tested sufficiently in other studies, we used previous research as a basis for our own survey (see Table 2). The respondents were asked to assess to what degree they agree or disagree with a given statement. A likert scale was used for this purpose. The respondents could choose between seven possible answers on the scale indicating their thoughts ranging from “strongly agree” to “strongly disagree”. A seven-point likert scale allows to assess the respondents’ tendency since there are three possible answers indicating an agreement and also three possible answers indicating a disagreement. It further gives the respondent the opportunity to take a neutral position (Malhotra, 2012). Other questions regarding demographics have been mainly raised in the questionnaire by using a nominal scale (see Appendix 1 for the whole questionnaire). After the survey was developed, we performed a pilot test with 22 people to make sure the survey is reliable before being published. When changes have been suggested by the respondents in the pilot test, they have been examined and applied if seen as valid.
3.5 Data Collection
The data incorporated into this research can be classified into primary as well as secondary data. In the subsequent paragraphs this classification will be discussed in more detail.
3.5.1 Primary Data
According to Saunders et al. (2012) primary data refers to data, which are being obtained to exclusively fulfil the objectives of a research undertaken. The primary data necessary to fulfil our research objective, which is the investigation of the intention of consumers to use digital food sharing platforms, have been obtained through the distribution of an online survey using Qualtrics. The advantages of this approach have already been discussed above (see 3.3. Research Strategy). To obtain the data, a multichannel approach has been chosen. First, the survey has been shared by the company “Too Good To Go”, which is a digital food sharing platform. In this context, it is necessary to specify that the Austrian as well as Swedish office of Too Good To Go shared the survey on Instagram, a social media platform, and within its e-mail newsletter enabling us to reach approximately 121.000 potential respondents, which are relevant to our study. For a detailed explanation of the sampling strategy see point 3.7. Sampling. Too Good To Go, a company which was founded 2015 in Copenhagen, Denmark, is trying to fight food waste by offering digital food sharing services to its customers. In order to fulfil its mission “to inspire and empower everyone to take action against food waste” (TooGoodToGo, 2021) the company employs technology-based solutions in form of a mobile application. The application has been used by more than 31 million people in nine countries in total so far (TooGoodToGo, 2021). Through the application, customers are able to identify stores and restaurants, which collaborate with Too Good To Go, and offer to sell surplus food, which would be thrown away otherwise, for a cheaper price. Customers are able to place an order via the application, digitally pay in advance and have to pick up the food at the location of the collaborating store (Google-Play, 2021). Further, customers do not know in specific what kind of food will be sold as a surplus. Therefore, Too Good To Go is marketing the surplus food as “surprise bags”. Moreover, more than 59 million meals have been saved since 2016 by using the Too Good To Go application (TooGoodToGo, 2021). Apart from reducing food waste in general, the customer profits from a lower price paid for the surplus meals. Further, it has to be mentioned that the company connects businesses and consumers with each other through the use of its mobile application and hence can be categorized as “sharing for money” according to Michelini’s et al. (2018) classification of food sharing activities.
