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Kaizenshogun77
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Kaizenshogun77 commits to:
Write at least 1000 words per week for 10 weeks in order to finish my thesis.
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Kaizenshogun77
Kaizenshogun77
May 26, 2021, 4:15 PM
Methodology
Study design
To study the demographic, psychological, and behavioural characteristics of consumers interested in purchasing sustainable foods an online survey was developed. The survey takes on average 18 minutes to complete. Questions from the survey were crafted based on two model of behavior change frameworks. First, the Theory of Planned Behavior (TPB; Ajzen 1985) will be used to understand the target users’ motivations, challenges, and goals regarding the purchase of different types of sustainable food (e.g. local foods, organic foods, plant-based proteins) since findings have shown the importance of taking product category differences into account in studying consumer food motivations and intentions (Verain et al., 2017).

The TPB has been extensively and effectively employed as the predominant model for the understanding, prediction and change of numerous human behaviors over the past decades (Sniehotta et al. 2014; Steinmetz et al., 2016). The theory of planned behavior is established as the most commonly used theory in behavior change interventions of sustainable behaviors (Klaniecki et al., 2018) as well as being one of the chief theory of behavior change that can reliably measure consumer intentions and behaviors in numerous contexts surrounding food choice (Nardi et al., 2019). For example, several adaptations of this model have been tailored to study healthy eating (Conner et al., 2002), environmental behaviors (Gkargkavouzi et al., 2019), organic food consomption (Donahue, 2017; Bagher et al., 2018) and buying environmentally sustainable products (Kumar, 2012). 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 (Ajzen, 2020). 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). In the survey, 55 Likert type items (i.e. 1= Strongly Disagree, 5= Strongly Agree; Likert 1= Never, and 5 =Always) measure the intention to purchase sustainable foods, as well as its motivations and barriers. Those questions were based on questionnaires from multiple studies using adapted versions of the TPB to measure the personal determinants of organic food consumption (Aertsens et al., 2009; Wang et al., 2019; Al-Swidi et al., 2014). The participant’s understanding of which type of aliment constitutes the most sustainable food (e.g. local foods, organic foods, plant-based proteins) was measured using 3 items that were ranked from 1 to 3 by the participant. Since the TPB has been successfully applied to studies investigating dietary and sustainable food consumption in the past, it is deemed a suitable model for the study of consumer purchase intentions towards sustainable foods.

Secondly, to inform how those characteristics could be used to inform the design of targeted gamified interventions that would promote, facilitate, and maintain sustainable food purchasing, the TPB will be used in conjunction with Marczewski’s (2015) Gamification User Types Hexad Scale (GUTHS). The Hexad scale is a gamification framework that analyzes the target users’ player type and was empirically validated (Tondello et al. 2016; Akgün and Topal, 2018). It was designed to match each user’s personality to specific game elements, for the purpose of tailoring personalized behavior change application (Mora et al. 2017); 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 (TSD) (Deci and Ryan, 2012). The SDT addresses both intrinsic and extrinsic motives for action (Berger & Schrader, 2016). According to Ryan & Deci (2000), intrinsic motivation, refers to doing something because it is inherently interesting or enjoyable, and extrinsic motivation, refers to doing something because it leads to a separable outcome. The SDT theory is based on three psychological needs: autonomy, competence and relatedness, which can be addressed by gamified interventions contributing to enjoyment, regardless of the specific content, complexity, or genre of games (Przybylski et al., 2010). The resulting ‘Hexad player type scale’(HPTS) 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). The HPTS is measured in this study using 18 Likert type items ( i.e. 1= Strongly Disagree, and 5= Strongly agree). This information will inform practitioners and academics on how to implement game elements based on the characteristics and preferences of the target users.

Finally, 12 ranked items ( i.e. ranked from 1 to 12) will established each participant’s preferred medium in this survey, and 5 items (i.e. Likert 1= Never, and 5 =Always) measure previous behavior towards different types of games. Attitude towards gamified systems have to be considered when investigating the effect of gamification on behavior change (Berger…). It was demonstrated that different user characteristics determine the attitude they each have towards gamification, thus explaining why in certain environment or only with certain users, gamification has significant effects (Hamari et al., 2014). For example, it was found that using gamification with a smart phone, (i.e. fantasy and challenge), was effective to improve customer retailing experience (Poncin et al. 2017). Moreover, people who have a passion for gaming or individuals who grew up with the internet, the use of smartphones and social media (i.e., digital natives) will probably respond differently to gamified interventions using different behavior change tools and mediums than digital immigrants who were not born learning the digital language of computers, video games and the Internet (Prenzy, 2001).
Consequently, to investigate the effectiveness of gamification in a context as realistic as possible, we have to figure out the best medium or tool (e.g. website, application for smartphone) for implementing gamification based on the context and type of behavior targeted (Klaniecki et al., 2018). Because gamification studies should “ focus on the relationships between game dynamics, gamification contexts, gaming personalities or preferences, dynamic gaming engagement styles etc” (Tu et al., 2015), the online survey will measure the demographic information of the participants as well as their motivations, challenges, and goals regarding the purchase of different types of sustainable food (e.g. local foods, organic foods, plant-based proteins), their player types, and their past gaming behavior based on game genre as well as their preferred smartphone application type that can be used as a medium for gamified interventions.

Sampling and data collection
Several sample size calculators suggested between 383 and 400 participants when provided with a margin of error at a confidence level of 95% for a population of 100,000 people. I chose this level of confidence because it is the most widely used in research (Finch and Cumming, 2009). I selected a population size of 100,000 people because the sample size does not change much once it becomes larger than 50,000 people. The mathematics of probability shows that the size of the population is irrelevant, unless the size of the sample exceeds a few percent of the total population examined. This means that a sample of 400 people is equally useful in examining the opinions of a province of 15,000,000 as it would a city of 100,000. For instance, Qualtrics sample calculator suggests 383 participants. Creative Research System calculator as well as Checkmarket calculator also suggest 383 participants. SurveyMonkey calculator suggests that 400 participants should be used when provided with the same variables (i.e. a margin of error at a confidence level of 95% and a population of 100,000 people). Thus, I expect 400 participants to take part in this study.


The survey was designed and administered online through the Qualtrics survey software in both French and English and was then distributed through a panel participants recruitement company named ‘Quest mindshare’. The data was collected in the month of March 2020.
The student investigator paid Quest Mindshare for their service of providing adequate participants to the study based on quotas regarding Gender, Income, and age. Those quotas were implemented within the data collection process to ensure that the participants were representative of Ontario population distribution based on 2019 census of Canada. The participation in the study was voluntary, anonymous, and rewarded with a financial incentive provided by Quest MindShare to the respondents. The study received ethics approval from the University of Waterloo, Ontario.

Hypotheses and objectives

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.


Statistical Tool

(…Will be completed next time)

Statistical Analysis

Marco77
Marco77
May 21, 2021, 10:34 AM
Almost over ! Stay Focus do not scatter !
Kaizenshogun77
Kaizenshogun77
May 21, 2021, 12:46 AM
Methodology


P. the more theories used the better.
Ex.
Exp
L: 3 models


This research draws on three 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.

+ Stages of change theory !


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).

(SDT+ TPB+ Stages of Change theory)


Hypotheses and ovjectives

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.
1. Methods
1.1. Study design
To examine the factors affecting eating behaviours among young adults in Canada an online questionnaire was developed and administered. The questionnaire included items from the U.S. National Cancer Institute’s (NCI) Food Attitudes and Behaviors (FAB) Survey (National Cancer Institute (NCI), 2020), and other similar studies investigating eating behaviours (Booth et al., 2001; Deliens, Deforche, De Bourdeaudhuij, & Clarys, 2014; Erinosho, Moser, Oh, Nebeling, & Yaroch, 2012; Glanz et al., 2005; Markovina et al., 2015). The constructs were based on Social Cognitive Theory, and were related to personal, environmental and behavioural factors affecting eating behaviours. More specifically, 52 items were included in the questionnaire which addressed personal preferences, health and wellbeing, convenience and familiarity, environmental impact considerations, weight control and body image, food neophobia, food involvement, price, food culture, food choice influencers and sociability. For each question the respondents were asked to rate the item based how important it was for choosing food. Items were scored on a 7-point Likert scale (1 = not important at all and 7 = very important).
Respondents were also asked to answer questions related to their socio-demographic status including gender, highest level of education (no certificate, secondary school diploma, apprenticeship or trades certificate or diploma, college or university certificate or diploma below or equal to bachelor level, University certificate or diploma above bachelor level), type of community (large urban center, small urban center and rural), immigration status (Canadian citizen, permanent resident), and province (excluding territories). Income was also included in the questions; however, since many of the respondents declined to answer the question, it was eliminated from the analysis.
1.2. Sampling and data collection
The questionnaire was administered online using Qualtrics Survey Software, in both French (for province of Quebec residents) and English (for the rest of Canada). It was distributed through Quest Mindshare and social media. Data collection took place during the month of November 2020. The sampling procedure ensured that the respondents were representative of population distribution based on provinces/territories and gender according to Statistics Canada (Statistics Canada, 2020b, 2020a). Since the focus of this study was on the young adult population in Canada, only the responses from the age group of 18-24 was used for the current study.
The data was gathered during the COVID-19 pandemic. However, respondents were asked to choose their responses based on their opinions and actions prior to the pandemic. Participation in the study was voluntary and anonymous. The study received ethics approval from the University of Waterloo.
1.3. Statistical analysis
As the first step, the analysis included methods to examine frequencies and distributions and to detect possible errors or missing data. To assess the reliability of the survey Cronbach’s alpha was used to ensure internal consistency.
In order to reduce the number of factors assessed in the questionnaire, an Exploratory Factor Analysis (EFA) using a Varimax rotation was performed. To ensure data is suitable for factor analysis, Kaiser-Meyer-Olkin (KMO) and Bartlett’s test of sphericity were used (Hair, Black, Tatham, & Anderson, 2010). The number of factors were determined based on the Scree plot and eigen values using the web-based parallel analysis engine by Patil et al. (Patil, Singh, Mishra, & Donavan, 2017). Correlation between socio-demographic data was assessed using chi-square, and a non-parametric correlation test (Spearman rank correlation) was used to assess the correlation between the extracted factors and socio-demographic data.
The extracted factors were standardized and used as weights for a cluster analysis in order to segment respondents. Cluster analysis is a commonly used method for segmentation of consumers (Brečić et al., 2017; Espinoza-Ortega, Martínez-García, Thomé-Ortiz, & Vizcarra-Bordi, 2016). The clusters were determined using the K-mean method which is suitable for clustering cases with similar characteristics and a predetermined number of clusters (Hair et al., 2010). All statistical analyses were performed using IBM SPSS 27 (SPSS Inc., Chicago, IL).
Marco77
Marco77
May 16, 2021, 3:39 PM
nice work ! not finish yet ! So finish the job ! almost done
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