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Kaizenshogun77
Kaizenshogun77
January 23, 2021, 4:18 PM
Food systems have a devastating impact on the environment. They are contributing to numerous environmental issues, such as global warming, species extinction, as well as land and water degradation (Behrens et al., 2017; Heller et al., 2018). Therefore, food systems in their current form threaten human survival on Earth (Chao and Feng, 2018; O’Neill et al., 2017). For instance, food systems have been estimated to produce around 29% of all greenhouse gas emissions produced by human activities (Vermeulen et al., 2012). Not only do food systems pollute the earth, they can also create persistent and harmful social effects on humanity, potentially damaging the wellbeing of labor force and customers on a local and provincial level (Buttel, 2003). Consumers have the power to minimize the negative effects of existing food systems by purchasing lower impact foods (Joshi and Rahman, 2015; Redman and Redman, 2014). Because of the damaging consequences of food systems operations, changing people’s current eating habits to adopt diets with low impact in order to build sustainable food systems has become more crucial than ever (Heller et al., 2018; Hartmann and Siegrist., 2017; Magrini et al., 2018; Hedin et al., 2019). Unfortunately, ingrained food habits are difficult to change because they are the result of many complex factors interacting with each other (Aranceta et al., 2003). Therefore, there is a need to understand how to design interventions that can efficiently promote, facilitate and maintain sustainable food choices.



Food behaviors are influenced by a broad variety of factors including social, economic, psychological, and environmental factors (Bublitz et al., 2010; Pollard et al., 2002; Renner et al., 2012), but they are mainly predicted by habits (i.e. frequently repeated behaviors) (Cornelis et al., 2017; Conner et al., 2002). For example, Mäkiniemi and Vainio (2014) discovered that although people perceived ‘price’ to be the main barrier preventing lower impact food choices, the two most important barriers actually preventing low impact food purchase (i.e. foods considered more environmentally friendly than conventional ones) were: habits (i.e. wanting to eat the same way as before), and the belief that food choices have little to no effects on climate change. There is indeed a growing amount of evidence that habits are a major predictor for: eating behaviors (Riet et al., 2011; Flaherty et al., 2018; Cornelis et al., 2017; Conner et al., 2002), food purchasing behaviors (Machín et al., 2020), and even environmental behaviors (Gkargkavouzi et al., 2019). Therefore, there is a need to understand how to alter habitual behaviors in order to design interventions that can efficiently promote, facilitate and maintain sustainable food purchases.



Habitual behaviors (i.e. behaviors frequently repeated) are harder to change than non-habitual behaviors. Interventions focused on providing educational information in order to change habitual behaviors are not considered effective (Aarts et al., 1997; Rothman et al., 2009). For example, a meta-analysis measuring the effect of information-based interventions on health behaviors found that there was less success when the targeted behavior was habitual rather than new (Snyder et al., 2004). This discrepancy is due to the fact that when an individual frequently engages in a behavior that has consistently rewarding consequences, he learns to recognize the situational cues linked to the rewarding behavior, then the reactions to those cues become less conscious and more automatic each time he acts the behavior (Riet et al., 2011; Lally and Gardner, 2013). The individual thus becomes less attentive and receptive to new information and new ways of doing the same action (Verplanken and Aarts, 2011; Kurz et al, 2015). Habitual behaviors have proven difficult to change even when contradicted by the intention to modify the habitual behavior (Lally and Gardner, 2013). This phenomenon partly explains why studies on lower impact purchasing report an inconsistency between what consumers think and what they actually do (Joshi and Rahman, 2015; Tanner and Kast, 2003). The formation and maintenance of habits primarily depends on the situational cues found within the environment where the behavior occurs (Riet et al., 2011). In his book: “Designing for behavior change” Wendel, (2013) states that in order to change a habit, one must interrupt its automatic routine by delivering immediate feedback to the individual based on the specific behavior, thus disrupting the usual pattern of action with a new situational cue. Therefore, there is a need to understand what specific situational cues could efficiently disrupt food choice habits in order to design interventions that can promote, facilitate and maintain sustainable food purchases.



The situational cues that influence food behaviors are found within social environments. Experiments showed that both adults and children are more likely to eat a larger amount of food when eating with someone who eats giant portions and eat less when eating with someone who eats little portions (Herman, 2015; Robinson et al., 2013). In a field experiment, it was demonstrated that participants were more likely to select healthy foods after they were exposed to information indicating that most people chose the healthy foods (Mollen et al., 2013). It has also been observed that sustainable food behaviors were harder to change in the long term than sustainable waste behaviors, because social norms around existing food behaviors are more deeply rooted than those related to waste behaviors (Redman., 2013). Those findings demonstrate that our perception of social norms concerning what and how much others eat, has a strong effect on our personal food behaviors (Higgs and Thomas, 2016), even though we may not be consciously aware of this external influence (Robinson and Field, 2015). Our perception of social norms related to food affect our behavior by informing us of what is the most appropriate behavior to adopt in a given situation (Herman et al., 2003), and we tend to feel emotionally rewarded when adopting these norms (Higgs, 2015). The use of social norms messaging as an immediate feedback to change food behaviors was shown in multiple studies to be efficient (Robinson et al., 2013; Higgs, 2015; Berger, 2019; Higgs and Ruddock, 2020). Therefore, there is a need to understand how to use social norm feedbacks about food behaviors in order to design interventions that can efficiently promote and facilitate sustainable food purchases.



Effective sustainable food consumption interventions should provide social norm messages digitally (i.e. through websites, mobile phones applications, wearable devices). In a recent systematic review of digital interventions promoting sustainable food consumption Hedin et al. (2019) found that the most common strategy employed was: “feedback and monitoring”. They also discovered that many of these interventions used behavior change techniques based on social theories (e.g. social support and disapproval, social comparison, social incentives, restructuring the social environment). Because of the rise of digital technology, the use of digital interventions aimed at changing and maintaining behavior change has become increasingly widespread (Michie et al., 2017). With the rise of digital application, a growing number of people are trying mobile applications that use social and game mechanics to help people regularly exercise, consume sustainably, and eat healthy foods (Hamari and Koivisto, 2015). Systematic reviews demonstrated that dietary mobile applications are effective tools to improve nutritional behaviors (Paramastri et al., 2020; Fakih El Khoury et al., 2019). Digital technologies have become the customary medium for behavior change interventions because they provide a secure way to reach more people at a low cost (Murray et al., 2016). However, a common challenge restricting the effectiveness of digital behavior change interventions was poor user engagement (i.e. a large percentage of users stop using the intervention) (Sucala et al., 2019; Kelders et al., 2012; Eysenbach, 2005). This might be explained by the fact that repeating a new behavior enough times to turn it into a habit is proven more likely to fail when the new behavior is not rewarding enough, at least in the early stages of adoption (Verplanken and Aarts, 2011). Previous findings showed that when a behavior is particularly rewarding, it is more likely to be repeated (Lally and Gardner, 2013; Skinner, 1938; Thorndike, 1911) and therefore more likely to be maintained over time and become a habit. For example, people were found more likely to keep low impact behaviors, such as buying environmentally friendly washing products, when the new behavior provides rewarding experiences (Kurz et al., 2015). In other words, when the benefits or rewards of the newly implemented behavior are not immediate but instead emerge later in time (e.g. such as changing your diet to achieve a certain weight goal), or the presence of reward is simply nonexistent, it becomes more likely that the individual stops trying to change and decides to return to the previous habit (Verplanken and Aarts, 2011). Rewarding a new behavior in order to turn it into a habit can have the opposite effect for several reasons. It was proven that people with different personality types tend to favour different types of rewards (Nienaber et al., 2011). There is also a distinction between extrinsic rewards (i.e. tangible, external rewards such as financial incentives) and intrinsic rewards (i.e. intangible, innately pleasurable or enjoyable feelings created from performing an interesting activity) (Ryan and Deci, 2000; Deci and Ryan, 2012). Moreover, it was found that giving external rewards (e.g. money) to someone for completing a given task could actually decrease their intrinsic motivation to engage with the task (Deci, 1971). Therefore, more research is needed to understand how to reward users adequately to keep them engaged with a dietary mobile application in order to promote and facilitate sustainable food purchase.



An effective digital intervention promoting sustainable food purchase should combine social norm feedback with gamification in order to keep the users engaged long enough to produce the desired habitual behavior. Gamification was born in the digital media industry (Berger and Schrader, 2016) and became a worldwide trend around 2010 (Dicheva et al, 2015). Gamification focuses on understanding the ability of games to keep someone’s attention and maintain their engagement level with the goal of applying this knowledge to non-game activities (Rutledge et al., 2018; Deterding et al., 2011). Gamification is most commonly defined as “the use of game design elements in a non-game context” (Deterding et al, 2011), and has been established as an effective instrument for engagement and behavior change (Johnson et al., 2016). Similar concepts have been referred to as: ‘Pervasive games’, ‘Serious-games’ and ‘Game-based technologies’ (Deterding et al, 2011). A more descriptive and recent definition of gamification is “using game-design elements in any non-game system context to increase users’ intrinsic and extrinsic motivation, help them to process information, help them to better achieve goals, and/or help them to change their behavior”(Treiblmaier et al., 2018). For example, gamification has been widely used in numerous areas (Sardi et al., 2017), such as education (e.g. to foster the engagement of students) (Dreimane, 2019; Barata et al., 2013), business (e.g. to engage employees) (Aziz et al., 2017), health (Guarneri and Andreoni, 2014; Hagberg et al., 2009; Lindberg et al., 2016), and environmental sustainability ( e.g. energy and water conservation) (Albertarelli et al., 2018; Ro et al., 2017). More specifically, gamification can be successfully used to change nutritional behaviors. For instance it has been used to decrease the intake of unhealthy foods (Majumdar et al., 2015; Majumdar et al., 2013), teaching food literacy while shopping (Bomfim and Wallace, 2018; Bomfim et al., 2020), promoting fruits and vegetables intake in adolescents (Yoshida et al., 2020), and promoting healthy breakfasts intake (Luhanga et al., 2016). For example, academics at Cornell University researched and developed the use of mobile games that motivate adolescents to eat more healthy breakfasts by providing them with instant feedback through virtual pets (Byrne et al. 2012; Pollak et al. 2010). Gamified interventions also had success in increasing the time participants were engaged in interventions designed to promote sustainable behaviors, like reducing energy use (Oppong-Tawiah et al., 2020) and increasing sustainable travel (Wells et al., 2014).



The potential of gamification has been identified for the promotion of sustainable nutrition behaviors (Berger et al., 2014; Berger and Schrader, 2016; Kronisch, 2019; Vermeir et al., 2020) and even more specifically in promoting eco-friendly shopping (Lounis et al., 2013). For example, Berger (2019) conducted an experiment in which an online shop was designed to resemble a real shopping environment. Online shoppers were given different types of immediate social norm-based feedback adapted to their purchase. She found that using social norms feedback with gamification elements was effective at increasing the amount of eco-friendly foods the participants would place in their online baskets. Berger, (2019) accomplished this by providing a green meter that indicated the level of eco-friendliness of products accompanied by social norm information as soon as a participant placed a product in their online basket. Digital gamification has the potential to increase low impact food choice because it can use the power of social interactions to promote behavior change (Hamari and Koivisto, 2013; (Koivisto and Hamari 2019; Hamari and Koivisto, 2015) through game design elements (e.g. points, badges, leaderboards, narratives, customization tools, levels, notifications, progress bars, and time constraints) that can be communicated instantaneously, therefore providing immediate feedback based on certain activities (McGonigal, 2011). With the rise of digital applications, a growing number of people are using mobile applications using social and game mechanics to help them regularly exercise, consume sustainably, and eat healthy foods (Hamari and Koivisto, 2015). Unfortunately, even though gamification has positive effects such as increasing user engagement and motivation (Dicheva et al., 2015), the results about its effectiveness can be quite inconsistent (Seaborn and Fels, 2015; Koivisto and Hamari, 2019) because the results of gamified interventions are extremely dependent on the context being gamified and on the user (Hamari et al., 2014). Therefore, there is a need to understand how to design interventions that can efficiently promote and facilitate sustainable food purchases according to the context and user.



There are agreements amongst gamification experts and existing studies on what an effective gamification framework must concentrate on. There is an accord amongst gamification experts that gamification design frameworks must not only analyze the context being gamified, but must also properly analyze what characterizes the user within the context being gamified (Morschheuser et al., 2017; Knutas et al., 2019; Dale, 2014) and that such analysis needs to be founded on theories of motivation proven effective within the psychology field (DeSmet et al., 2014; Seaborn and Fels, 2015). For example, a meta-analysis review of 54 games for health concluded that personalization should be used according to the behavior change needs and demographics of the users (DeSmet et al., 2014). Multiple gamification researchers also suggested that in order to reach a maximum level of efficiency, gamified systems should be personalized based on who the user is (Hakulinen et al., 2015; Carreño, 2018; Mora et al., 2018; Monterrat et al., 2015). For instance, a systematic review on gamification design frameworks used in higher education proved that despite existing differences amongst frameworks, there is a growing consensus on three guidelines: 1) the targeted behaviour (e.g. increased purchases of sustainable foods) must be clearly defined, 2) it is important to analyze the target users and identify player types ( e.g. socialisers, killers, achiever, explorers) (Bartle, 1996), and 3) it must employ the appropriate game design principles based on the player types of the users (Mora et al., 2017). For example, a framework for guiding gamification design, called “activity-challenge-motivation triplets” (Deterding, 2015) has been extensively cited in numerous studies investigating how to effectively and systematically apply gamification for behavior change (Hansen, 2017; Knutas et al., 2019; Morschheuser et al., 2017; Li, 2017). Deterding’s personalized gamification model focuses on combining the examination of both the user and the context being gamified together (Deterding, 2015). The user analysis commonly involves defining the target users by examining relevant information such as socio-demographics information, as well as the needs, motivations and hurdles the users have within the specific context (Deterding, 2015; Morschheuser et al., 2017). Different frameworks such as Personas, or Player types are then typically used to categorize the target users into different groups, segments or clusters in order to inform the design of the personalized gamification (Morschheuser et al., 2017; Knutas et al., 2019; Monterrat et al., 2015; Orji et al., 2018). There is a need to segment and personalize the gamification design based on the users within a certain context because it was demonstrated that different users understand and react differently to the same game element (i.e. badges) and that the context in which the badge is presented influences how user interpret the meaning of the badge (Antin and Churchill., 2011). Essentially, gamification researchers have learnt that a game that motivates a given individual can discourage another one (Dale, 2014; Monterrat et al., 2014; Orji et al., 2013). Therefore, the design of a gamified intervention promoting and facilitating sustainable food procurement should be based on a segmentation and in-depth analysis of its potential users within the context of sustainable food purchase.



Previous studies researching how to understand and change sustainable food choice state that future researchers should segment the consumers. For example, based on the findings of their survey investigating the influences of environmentally friendly product choice, Dahlstrand and Biel (1997) suggested a behavioral change model that would segment users by the strength of their behavioral habit. Next, after reviewing 53 empirical articles that studied what influences consumers purchasing environmentally friendly products, Joshi & Rahman (2015) ask future researchers to consider consumer segmentation. Moreover, after studying what determines organic food consumption, (Aertsens et al., 2009) state that future research should analyze the motivations and barriers of different user segments based on their organic food behaviors (e.g. non-, light-, medium- and heavy organic food users). This is because an intervention application that can adapt to the user specific situation (e.g. social and environmental context) can improve the effectiveness of the digital intervention (Sucala et al., 2019). Professor B.J. Fogg at the University of Stanford explains in his book ‘Persuasive technology’ (2003) how new technology offers behavior change proponents the advantage of tailor content towards individual needs, interests, personality characteristics, and contexts. Therefore, the design of a gamified intervention promoting and facilitating sustainable food procurement should be based on a segmentation of its potential users and adapted to the context of sustainable food purchase.



Previous studies researching how to understand and change food behaviors using gamification techniques state that future research should segment the users as well. A systematic review of gamification interventions promoting fruits and vegetables intake (Yoshida et al., 2020) asks future researchers to adapt gameful design elements according to population subgroups (i.e. segments). More recently, after experimenting with social norm-based feedback in a gamified online shopping environment, Berger (2019) stated that future studies should consider “target group specific gamification interventions”. A user segmentation is relevant in the context of sustainable food purchasing since interviews with participants demonstrated that people react very differently to the same aspects of gamification within the same gamified shopping context aimed at promoting eco-friendly purchases (Lounis et al., 2013). Consequently, the purpose of this study is to conduct a consumer segmentation study aimed at informing the design of an effective digital intervention promoting and facilitating sustainable food behaviors. To the best of my knowledge, no previous research has ever segmented a population in order to inform the design of gamified interventions aimed at promoting sustainable food purchasing.



Framework



This research draws on two frameworks. First, the Theory of Planned Behavior (TPB; Ajzen 1985) will be used to understand the target users’ motives, challenges, and goals regarding the purchase of different types of sustainable food (e.g. local foods, organic foods, plant-based proteins). The TPB has been extensively and effectively employed as the predominant model for the understanding, prediction and change of human behavior over the past three decades (Sniehotta et al. 2014; Steinmetz et al. 2016). The TPB states that the main predictor of behavior is the intention to execute the behavior (Ajzen 1985, 1991, 2012). The TPB also suggests that the stronger the intention to perform a behavior, the more likely the behavior will actually occur. The TPB also clarifies that the strength of the intention depends on three variables: attitude towards the behavior, subjective norms, and perceived behavioral control (Ajzen 1991, 2012).The TPB has been successfully applied toawide variety of behavioral domains, such as recycling, eating, drug use, and technology adoption, helping to explain and predict patterns in these behaviours(Steinmetz et al. 2016; Ajzen 2020).



The use of the TPB is appropriate for the study of consumer purchase intentions towards sustainable foods. The TPB has previously been adapted to investigate the determinants of organic food purchase (Aertsens et al., 2009, Bagher et al., 2018, Wang et al., 2019), resulting in the addition of validated predictors (e.g. environmental and health concerns) to the original theory. Original and adapted versions of TPB have also been used in previous research, which investigates the various ways of understanding sustainable product consumption (Kumar 2012; Paul et al. 2016; Yang et al. 2018; Zhang et al. 2019). Therefore, because the TPB has been successfully applied to studies investigating dietary and sustainable food consumption, it is suitable for the study of consumer purchase intentions towards sustainable foods.





The TPB will be used in conjunction with Marczewski’s (2015) Gamification User Types Hexad Scale (GUTHS), a gamification framework that analyzes the target users’ player type. The Hexad model was empirically validated by Tondello et al. in 2016 and Akgün and Topal in 2018. It was created to match the user’s personality to specific game elements, for the purpose of tailoring personalized behavior change application (Zhao et al., 2020). The Hexad scale is grounded in a combination of Bartle’s player type framework (Bartle, 1996) and the Theory of Self-Determination (Deci and Ryan 2012). The resulting ‘Hexad player type scale’ was found to be correlated to the Big 5 personality traits (Tondello et al. 2016), indicating the user’s preferences towards different game design elements guidelines (Orji et al. 2013).
Kaizenshogun77
Kaizenshogun77
January 23, 2021, 4:18 PM
Gamification “the use of game design elements in non-game context” (Deterding et al, 2011) is a behavior change concept that has been established as a promising tool to promote change in human nutritional behaviors (Chow et al., 2020; Ezezika et al., 2018; Yoshida et al., 2020; Jones et al., 2014). Gamification has also shown great potential in promoting health behaviors (King et al., 2013; Edwards et al., 2018), and various environmentally friendly behaviors (Ro et al., 2017; Kronisch, 2019), including sustainable food purchasing (Lounis et al., 2013; Berger, 2019).

The purpose of this study is to investigate the characteristics of consumers who are interested in purchasing sustainable foods, in order to inform the design of efficient behavior change interventions that promote and facilitate sustainable food purchasing.

The first aim of this study is to segment consumers based on their level of intention to buy sustainable foods (e.g. non-, low-, medium- and high- intent to buy sustainable foods). The second aim is to analyze the characteristics of the target consumer in order to adapt the gamification design of interventions that would effectively promote, facilitate, and maintain sustainable food purchasing.

Research question(s):

What are the demographic and behavioural characteristics of consumers interested in purchasing sustainable foods and how can they be used to inform the design of targeted gamified interventions that promote and facilitate sustainable food purchasing?


Specific objectives are to:

A. Identify how the target market defines a sustainable diet.

B. Identify the demographic characteristics of the target market.


C. Determine which personal variables (e.g. attitudes, behavioral control, social norm, personal moral norm, emotions) are associated with the intent to purchase sustainable food.

D. Identify the barriers and drivers associated with those who have a high level of intent to purchase sustainable food.

E. Identify the factors (i.e. player types, game playing habits, preferred mobile application types) required for the design of a gamified intervention which would aim to promote, facilitate and maintain the sustainable food purchase of users who have a high level of intent to purchase sustainable food.

Hypothesis 1: Gender, education and income are related to the intent to purchase sustainable food.

Hypothesis 2: There is a relationship between the personal variables (i.e. attitudes, behavioral control, social norm, personal moral norm, emotions) and the intent to purchase sustainable food.
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