Facilitating the trade of raw foods on vietnamese e commerce platforms through online product introduction videos

VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN THI HAI YEN FACILITATING THE TRADE OF RAW FOODS ON VIETNAMESE E-COMMERCE PLATFORMS THROUGH ONLINE PRODUCT INTRODUCTION VIDEOS: THE IMPACT ON PURCHASE INTENTION AND PERCEPTION OF TASTE MASTER'S THESIS BUSINESS ADMINISTRATION Hanoi, 2018 VIETNAM NATIONAL UNIVERSITY, HANOI VIETNAM JAPAN UNIVERSITY TRAN THI HAI YEN FACILITATING THE TRADE OF RAW FOODS ON VIETNAMESE E-COMMERCE PLATFORMS THROUGH ONLINE PRODUCT INTRODUCTION VIDEOS: THE IMPACT ON PURCHASE INTENTION AND PERCEPTION OF TASTE MAJOR: BUSINESS ADMINISTRATION CODE: 60340102 RESEARCH SUPERVISORS: DR TRAN THI BICH HANH PROF DR MOTONARI TANABU Hanoi, 2018 TABLE OF CONTENTS LIST OF TABLE LIST OF FIGURE LIST OF ABBREVIATION CHAPTER 1: INTRODUCTION 1.1 Research background 1.2 Research objectives 1.3 Scope of research .7 1.4 Research structure CHAPTER 2: LITERATURE REVIEW 2.1 Theory of reasoned actions (TRA) 2.2 Theory of planned behaviours (TPB) 11 2.3 Benefit-risk analysis (BRA) 15 2.4 Perceived risk 17 2.5 Perceived benefit 19 2.5.1 Definition of perceived benefit and dual aspect of perceived benefit 19 2.5.2 Sources of perception of food benefit 20 2.6 Benefit-risk analysis (BRA) in an E-commerce context 21 2.6.1 Product types 21 2.6.2 Online communication practices .24 CHAPTER 3: HYPOTHESES 28 3.1 Online Product introduction video (OPIV) affects customer perception 28 3.1.1 OPIV and perception of healthiness 28 3.1.2 OPIV and perception of food performance 30 3.1.3 OPIV and perception of risk 32 3.2 The relationship between perceived risk and perceived benefit 33 3.3 The moderating effect of product type on the relationship between product perceptions and attitude 34 3.4 Attitude toward product and purchase intention 35 3.5 Research model 36 CHAPTER 4: METHODOLOGY 37 4.1 Experimental design 37 4.2 Choice of product and the format of product introduction 38 4.3 Dependent variables 41 4.4 Participants 43 4.5 Experimental procedures 44 CHAPTER 5: RESULTS AND FINDINGS 46 5.1 Test on the demographic difference 46 5.3 Manipulation check on product type 47 5.4 Reliability, validity, and model fit testing 47 5.5 Hypotheses testing 50 CHAPTER 6: DISCUSSION, IMPLICATION, AND LIMITATIONS 59 6.1 Discussion 59 6.2 Contribution to theory and practice 62 6.3 Limitations 63 CONCLUSION 65 REFERENCE APPENDIX LIST OF TABLE Page Table 2.1 Overview of EU activities in the benefit-risk assessment of food and nutrition 16 Table 3.1 Experiment result of Maga (Maga, 1974) 31 Table 5.1 Number of participants in each treatment 45 Table 5.2 Manipulation check on product type 46 Table 5.3 Descriptive statistics, correlation coefficients, reliability, validity 47 Table 5.4 Factor Loadings 48 Table 5.5 Summary of model fit indices of CFA 49 Table 5.6 Results of Pillai's multivariate tests of significance 50 Table 5.7 Result of univariate ANOVAs 51 Table 5.8 Result of Pearson correlation test 54 Table 5.9 Comparing model fit indices of the original and revised model 55 Table 5.10 Summary of findings 56 LIST OF FIGURE Page Figure 2.1 The Theory of Reasoned 10 Figure 2.2 The Theory of Planned Behaviour (TPB) 11 Figure 2.3 The theoretical framework of benefit-risk analysis 17 Figure 3.1 Research model 35 Figure 4.1 Counterbalancing product type and format of online product introduction 37 Figure 4.2 Experiment procedures 44 Figure 5.1 Interaction effect of product type and format of product introduction 53 Figure 5.2 Standardized regression coefficients of multi-group SEM 56 LIST OF ABBREVIATION BRA: Benefit-risk analysis CFI: Comparative fit index Df: Degree of freedom GFI: Goodness of fix index GM: Genetically modified IFI: Incremental fit index OPPVs: Online product presentation videos RMSEA: Root mean square errors of approximation SEC: The framework of Search, Experience, and Credence product SRMR: Standardized root mean square residual TLI: Tucker-Lewis index TPB: Theory of planned behavior CHAPTER 1: INTRODUCTION 1.1 Research background Vietnam enjoys numerous favorable natural conditions to develop diversified and rich agriculture Theoretically, Vietnam agriculture should highly develop, farmers‟ income should be compatible with other sectors Also, farmers should be able to produce agricultural products high value in large-scale efficiently and effectively However, the reality of Vietnamese agriculture is entirely the opposite In fact, the production force is majorly small household or agricultural cooperative, who produce at a small scale with low productivity Therefore, in comparison with other developed countries, Vietnamese agriculture faces chronic problems associating with low economic value, delicate supply chain, unstainable growth, fluctuating market price More severely, due to the large proportion of smallholders in production, the ability to capture market demand is limited Consequently, overproduction happens frequently In order to solve the problem as well as improve competitiveness or agriculture, it was widely suggested that cooperation between farmers and agricultural enterprises should be strengthened However, the number of enterprises operating in agriculture only accounts for about 1% of all enterprises in the country Annually, the proportion of agricultural enterprises filing for bankruptcy keeps increasing compared to newly register ones (Dương, 2017) In the recent efforts of Government to create more incentives for investors to invest in agriculture, and facilitate the development of agricultural enterprises, the trade of agricultural product on electronic marketplaces was started to be focused This study was inspired by those obstacles to the development of Vietnamese agriculture With interest in boosting the development of agriculture, improving the income of farmers, an approach to the stimulation of raw food consumption sold in virtual groceries was made E-commerce in Vietnam has been rapidly increasing in recent years and become a highly lucrative market In 2015, E-commerce expanded 37%, and the e-commerce market value potentially reaches $10 billion (Nikkei, 2016) Likewise, the online retailing is booming and recorded approximately double growth rate in compared with the entire e-commerce industry Therefore, in Vietnam, there is a fierce competition of giant retailer chain like AEON, Lotte Mart, Vinmart in occupying larger market share for online groceries (Anh, 2018) However, raw food still takes the back seat in that trend Although large supermarket chain intensively invests in virtual groceries to expand their sales of fast-moving consumer goods, yet raw foods are hardly available The reasons are related to not only raw food‟s characteristics, which are perishable, low monetary value compared with cue volume, seasonal (Strzębicki, 2011) but also the constant fear of unsafety food, which makes consumers prefer shop food physical stores Food safety is currently is a highly controversial topic of the whole society, especially in Vietnam Food supply chain is widely questioned about its integrity and its ability to provide riskfree food to Vietnamese It is frequently reported that farmers often overuse chemicals, antibiotics, and growth promoters to get higher yields and desired product characteristics Moreover, food processing system in Vietnam also majorly consists of micro and small producers who cannot afford to establish and maintain a certified food processing system (Worldbank, 2017) Evidence about serious food safety problem in Vietnam is found in thousands of scientific researchers Notably, according to works of the International Livestock Research Institutes, food in Vietnam tend to be contaminated by Salmonella, Escherichia coli Furthermore, among ten thousands of food sample, Grace et al., (2012) realized that approximately 10 – 40% of food is contaminated with microbes or parasites, which likely lead to foodborne illness (Grace, D et al., 2012) This study also pointed out that food is severely positive for a high proportion of pesticides, heavy metals and antibiotic residues (Grace, D et al., 2012) Recently, the Pasteur Institute of Hochiminh city found out that among 150 samples of raw meat and 147 sample of raw fishes, which were collected in five different provinces, 100% of meat sample and 64% of the fish sample are positive for Escherichia coli at a high level (L.TH.H., 2017) There are various sources of risk related food like microbiological, chemical and technological threats Regarding microbiological threats, they have the main cause bacteria, which harm human health directly During 2017, in Vietnam, there are 139 cases of serious food poisoning, which involved 3869 people and among that, 24 people died (H.V., 2018) The common bacteria in food are Salmonella, Campylobacter coli, Listeria monocytogenes and Escherichia coli (Yeung et al 2012) Regarding chemical hazards, it is related to the excessive use of agricultural chemicals, growth control hormone, antibiotic treatment, etc., in agriculture and food industries Chemicals intentionally or by accidents can contaminate food because of lax control of government over supply chain of chemicals or of input materials in agriculture Concerning technological threats, they are unwanted results caused by the application of advanced technology in food production, namely food irradiation and genetically modified (GM) food In addition, owing to the development of social media and social networks, information about many food safety scandals are speeded widely and constantly As a result, Vietnamese consumers gradually lose trust in product introduction provided by sellers and have a trend to shop in regular groceries, where they can judge the quality of food by themselves Electronic commerce (E-commerce) is the virtualization of all business activities, transactions of commodities, information, and relationships between e-commerce participants (Fruhling & Digman, 2002) The virtualization of the market is believed to superior to the traditional one since it is more transparent, competitive and geographically limitless (Batte & Ernst, 2007; Montealegre, Thompson, & Eales, 2007) Likewise, E-commerce is suggested to sharply boost the development in agricultural markets regarding increasing sales, expanding accessibility and cutting various transaction costs (Elia, Lefebvre, & Lefebvre, 2007) However, relative short history of E-commerce‟s application in agricultural business, there is a shortage of the scientific papers about its impact on the marketing of agri-products Strzębicki, D (2011) Selling of agricultural products via the Internet Polityki Europejskie, Finanse i Marketing, 5(54), 176–196 Suh, K.-S., & Lee, Y E (2005) the Effects of Virtual Reality on Consumer Learning: an Empirical Investigation Virtual Reality on Consumer Learning MIS Quarterly, 29(4), 673–697 https://doi.org/25148705 Sütterlin, B., & Siegrist, M (2015) Simply adding the word “fruit” makes sugar healthier: The misleading effect of symbolic information on the perceived healthiness of food Appetite, 95, 252–261 https://doi.org/10.1016/j.appet.2015.07.011 Taylor, S., & Todd, P (1995) Assessing IT Usage: The Role of Prior Experience MIS Quarterly, 19(4), 561–570 Retrieved from http://www.jstor.org/stable/249633 Tepper, B J., & Trail, A C (1998) Taste or health: A study on consumer acceptance of corn chips Food Quality and Preference, 9(4), 267–272 https://doi.org/https://doi.org/10.1016/S0950-3293(98)00006-8 Tijhuis, M J., de Jong, N., Pohjola, M V., Gunnlaugsdóttir, H., Hendriksen, M., Hoekstra, J., … Verhagen, H (2012) State of the art in benefit-risk analysis: Food and nutrition Food and Chemical Toxicology, 50(1), 5–25 https://doi.org/10.1016/j.fct.2011.06.010 Tonsor, G T., Schroeder, T C., & Pennings, J M E (2009) Factors Impacting Food Safety Risk Perceptions Journal of Agricultural Economics, 60(3), 625–644 https://doi.org/10.1111/j.1477-9552.2009.00209.x Towler, G., & Shepherd, R (1992) Application of Fishbein and Ajzen‟s expectancyvalue model to understanding fat intake Appetite, 18(1), 15–27 https://doi.org/10.1016/0195-6663(92)90207-M Truong, Y (2009) An Evaluation of the Theory of Planned Behaviour in Consumer Acceptance of Online Video and Television Services The Electronic Journal Information System Evaluation, 12(2), 177–186 Ueland, Gunnlaugsdottir, H., Holm, F., Kalogeras, N., Leino, O., Luteijn, J M., … Verhagen, H (2012) State of the art in benefit-risk analysis: Consumer perception Food and Chemical Toxicology, 50(1), 67–76 https://doi.org/10.1016/j.fct.2011.06.006 Urala, N., & Lähteenmäki, L (2006) Hedonic ratings and perceived healthiness in experimental functional food choices Appetite, 47(3), 302–314 https://doi.org/10.1016/j.appet.2006.04.007 van Kleef, E., van Trijp, H C M., & Luning, P (2005) Consumer research in the early stages of new product development: a critical review of methods and techniques Food Quality and Preference, 16(3), 181–201 https://doi.org/10.1016/j.foodqual.2004.05.012 Venkatesh, V., Davis, F D., & College, S M W (2000) Theoretical Acceptance Extension Model : Field Four Studies of the Technology Longitudinal Management Science, 46(2), 186–204 https://doi.org/10.1287/mnsc.46.2.186.11926 Venkatraman, M P (1989) Involvement and risk Psychology & Marketing, 6(3), 229–247 https://doi.org/https://doi.org/10.1002/mar.4220060306 Verbeke, W., Frewer, L J., Scholderer, J., & De Brabander, H F (2007) Why consumers behave as they with respect to food safety and risk information Analytica Chimica Acta, 586(1–2 SPEC ISS.), 2–7 https://doi.org/10.1016/j.aca.2006.07.065 Verbeke, W., Sioen, I., Brunsø, K., Henauw, S., & Camp, J (2007) Consumer perception versus scientific evidence of farmed and wild fish: Exploratory insights from Belgium Aquaculture International, 15(2), 121–136 https://doi.org/10.1007/s10499-007-9072-7 Weathers, D., Sharma, S., & Wood, S L (2007) Effects of online communication practices on consumer perceptions of performance uncertainty for search and experience goods Journal of Retailing, 83(4), 393–401 https://doi.org/10.1016/j.jretai.2007.03.009 Wheeler, E (1938) Tested sentences that sell New York: Prentice & Co Hall World Bank (2016) Vietnam Food Safety Risks Management: Challenges and Opportunities Retrieved from http://documents.worldbank.org/curated/en/157501490724011125/Vietnam-foodsafety-risks-management-challenges-and-opportunities-policy-note Worsfold, D., & Griffith, C (1995) A generic model for evaluating consumer food safety behaviour Food Control, 6(6), 357–363 https://doi.org/10.1016/0956- 7135(95)00048-8 Xiao, Z., Zhang, J., Li, D., & Chen, C (2015) Trust in Online Food Purchase Behavior: An Exploration in Food Safety Problem for Produce E-retailers Advance Journal of Food Science and Technology, 8, 751–757 https://doi.org/10.19026/ajfst.8.1602 Xu, P., Chen, L., & Santhanam, R (2015) Will video be the next generation of ecommerce product reviews? Presentation format and the role of product type Decision Support Systems, 73(Supplement C), 85–96 https://doi.org/https://doi.org/10.1016/j.dss.2015.03.001 Yeung, R M W., & Morris, J (2001) Food safety risk British Food Journal, 103(3), 170–187 https://doi.org/10.1108/00070700110386728 Zampini, M., & Spence, C (2004) The role of auditory cues in modulating the perceived crispness and staleness of potato chips Journal of Sensory Studies, 19(5), 347–363 https://doi.org/10.1111/j.1745-459x.2004.080403.x Zandstra, E H., De Graaf, C., & Van Staveren, W A (2001) Influence of health and taste attitudes on consumption of low- and high-fat foods Food Quality and Preference, 12(1), 75–82 https://doi.org/10.1016/S0950-3293(00)00032-X Zanna, M P., & John Karl Rempel (1998) Attitudes: A new look at an old concept In The social psychology of knowledge (pp 315–333) Zhang, Y., Jing, L., Bai, Q., Shao, W., Feng, Y., Yin, S., & Zhang, M (2017) Application of an integrated framework to examine Chinese consumers‟ purchase intention toward genetically modified food Food Quality and Preference, 65, 118–128 https://doi.org/10.1016/j.foodqual.2017.11.001 APPENDIX APPENDIX 1: KMO and Bartlett's Test Kaiser-Meyer-Olkin Measure of Sampling Adequacy Bartlett's Test of Sphericity 760 Approx Chi-Square 2261.367 df 120 Sig 0.000 APPENDIX 2: Box's Test of Equality of Covariance Matrices Box's M 337.223 F 2.934 df1 110 df2 289421.603 Sig .000 Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups a Design: Intercept + Format + product + Format * product APPENDIX 3: Levene's Test of Equality of Error Variances F df1 df2 Sig Healthy3 4.433 327 013 Healthy4 1.750 327 175 Healthy5 662 327 516 Taste1 2.908 327 056 Taste3 4.528 327 011 Taste4 2.567 327 078 risk1 2.668 327 071 risk5 247 327 781 risk6 150 327 861 risk7 2.493 327 084 Tests the null hypothesis that the error variance of the dependent variable is equal across groups a Design: Intercept + Format + product + Format * product APPENDIX 4: MULTIVARIATE TESTS FOR MANCOVA Multivariate Testsa Hypothesi Value F s df Effect Intercept Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root Format Partial Eta Squared 810 456.203b 3.000 322.000 000 810 b 3.000 322.000 000 810 b 3.000 322.000 000 810 b 3.000 322.000 000 810 190 456.203 4.250 456.203 4.250 456.203 110 6.296 6.000 646.000 000 055 Wilks' Lambda 891 b 644.000 000 056 Hotelling's Trace 122 6.403 6.510 6.000 6.000 642.000 000 057 c 111 11.945 232 32.379b 3.000 323.000 000 100 3.000 322.000 000 232 b 768 32.379 302 32.379b 3.000 322.000 000 232 3.000 322.000 000 232 b 302 32.379 219 13.250 3.000 322.000 000 232 6.000 646.000 000 110 b 784 13.880 271 14.510 6.000 644.000 000 115 6.000 642.000 000 119 c 3.000 323.000 000 203 Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root Format * product Sig Pillai's Trace Roy's Largest Root product Error df Pillai's Trace Wilks' Lambda Hotelling's Trace Roy's Largest Root 255 27.451 a Design: Intercept + Format + product + Format * product b Exact statistic c The statistic is an upper bound on F that yields a lower bound on the significance level APPENDIX 5: SERIES OF UNIVARIATE ANOVAS Tests of Between-Subjects Effects Dependent Variable: Risk Source Type III Sum of Squares Mean Square df F Sig 81.204a 1149.376 16.241 9.178 000 1149.376 649.510 000 Format 21.828 10.914 6.167 002 product 6.703 6.703 3.788 052 47.497 23.748 13.420 000 Error 573.352 324 1.770 Total 7614.375 330 654.556 a R Squared = 124 (Adjusted R Squared = 111) 329 Corrected Model Intercept Format * product Corrected Total Tests of Be Tests of Between-Subjects Effects Dependent Variable: Healthiness Dependent Variable: Taste Source Type III Sum of Squares Corrected Model Mean Square df a F Sig 61.522 854.231 12.304 6.033 854.231 418.845 Format 12.618 6.309 3.093 047 product 1.769 1.769 867 32.946 16.473 8.077 Error 660.795 324 2.039 Total 6198.778 330 722.317 a R Squared = 085 (Adjusted R Squared = 071) 329 Intercept Format * product Corrected Total b Computed using alpha = 05 APPENDIX 6: STRUCTURAL EQUATION MODELING 000 Noncent Paramet Observed er Powerb Source Type III Sum of Squares Corrected Model 30.587a Intercept 845.401 30.165 995 000 418.845 1.000 Format 6.187 594 product 352 867 153 Format * product 000 16.154 957 Error 584.661 Total 6543.333 Corrected Total 4.915 001 13.201 615.247 a R Squared = 050 (Adjusted R Squared = 035) b Computed using alpha = 05 APPENDIX 7: MODEL FIT OF MULTI-GROUP STRUCTURAL EQUATION MODELING CMIN Model Default model Saturated model Independence model NPAR 120 408 48 CMIN 405.447 000 2649.449 DF 288 360 P 000 CMIN/DF 1.408 000 7.360 RMR, GFI Model Default model Saturated model Independence model RMR 232 000 700 GFI 874 1.000 443 AGFI 822 PGFI 617 369 391 NFI Delta1 847 1.000 000 RFI rho1 809 IFI Delta2 950 1.000 000 TLI rho2 936 Baseline Comparisons Model Default model Saturated model Independence model 000 000 CFI 949 1.000 000 RMSEA Model Default model Independence model RMSEA 035 139 LO 90 027 135 HI 90 043 144 PCLOSE 999 000 APPENDIX 8: RESULTS OF SEM Standardized Regression Weights: (search - Default model) Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude Estimate 0.624 -0.138 0.039 0.419 0.571 0.826 0.815 0.664 0.958 0.912 0.577 0.779 0.958 0.681 0.866 0.721 1.013 0.347 0.501 0.876 0.589 Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 Regression Weights: (search - Default model) < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude Estimate 0.311 -0.049 0.014 0.343 0.306 0.918 0.621 0.997 0.528 1.156 0.784 0.894 1.05 0.436 2.218 1.22 S.E 0.086 0.034 0.041 0.07 0.314 C.R P -3.61 *** -1.44 0.15 0.338 0.74 4.878 *** -4.129 *** Label par_12 par_16 par_18 par_17 par_19 0.111 0.091 8.274 6.852 *** *** par_1 par_2 0.071 0.078 14.04 6.813 *** *** par_3 par_4 0.113 0.105 10.271 7.485 *** *** par_5 par_6 0.095 0.074 0.114 9.429 14.222 3.828 *** *** *** par_7 par_8 par_9 0.457 0.285 4.854 4.279 *** *** par_10 par_11 Standardized Regression Weights: (experience - Default model) Regression Weights: (experience - Default model) Estimate Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude 0.594 -0.303 0.17 0.486 0.264 0.96 0.858 0.683 0.975 0.824 0.603 0.65 0.887 0.671 0.772 0.616 0.177 0.378 0.491 0.869 0.378 Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude Estimate S.E 0.194 -0.109 0.053 0.299 0.522 0.922 0.658 0.863 0.575 1.4 0.863 0.804 0.309 0.444 2.111 1.015 0.058 0.054 0.034 0.073 0.236 C.R P -3.354 *** 2.015 0.04 1.547 0.12 4.12 *** -2.21 0.03 Label par_31 par_35 par_37 par_36 par_38 0.077 0.078 11.96 8.438 *** *** par_20 par_21 0.085 0.084 10.153 6.851 *** *** par_22 par_23 0.227 0.149 6.177 5.807 *** *** par_24 par_25 0.228 0.205 0.154 3.526 *** 1.507 0.13 2.884 par_26 par_27 par_28 0.555 0.332 3.804 3.057 par_29 par_30 *** Standardized Regression Weights: (credence - Default model) Regression Weights: (credence - Default model) Estimate Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude 0.436 -0.157 0.088 0.429 0.253 0.871 0.887 0.657 0.912 0.878 0.788 0.851 0.714 0.688 0.863 0.801 0.6 0.578 0.767 0.909 0.455 Attitude Attitude Attitude Purchase Purchase Healthy5 Healthy4 Healthy3 Taste3 Taste4 Taste1 Purchase2 Purchase3 Puchase1 risk6 risk7 risk5 risk1 Attitude4 Attitude2 Attitude1 < < < < < < < < < < < < < < < < < < < < < - Healthiness Risk Taste Taste Attitude Healthiness Healthiness Healthiness Taste Taste Taste Purchase Purchase Purchase Risk Risk Risk Risk Attitude Attitude Attitude Estimate S.E C.R P Label 0.471 -0.139 0.075 0.367 0.255 0.97 0.697 1.025 0.81 0.923 0.857 0.902 0.9 0.621 1.024 0.541 0.134 0.09 0.094 0.097 0.115 -3.509 *** 1.545 0.12 -0.798 0.43 3.793 *** 2.214 0.03 par_50 par_54 par_56 par_55 par_57 0.097 0.095 10.028 7.38 *** *** par_39 par_40 0.085 0.078 12.094 10.326 *** *** par_41 par_42 0.141 0.134 6.558 6.405 *** *** par_43 par_44 0.113 0.147 0.105 7.972 6.137 5.892 *** *** *** par_45 par_46 par_47 0.149 0.12 6.86 4.513 *** *** par_48 par_49 APPENDIX 9: MEASUREMENT ITEMS Perceived healthiness Perceived taste Fee from poisonous chemicals Nutritious Good for physical condition 7-point Likert scale 7-point Likert scale 7-point Likert scale I recalled a similar food/ dish I have consumed While going through online product introduction I had a good appetite While going through online product introduction, I was salivating I choose food suppliers and food brands carefully to buy safety food? (Ergönül, 2013) I have suffered from a food-born originated diseases (diarrhea, vomit, etc.) (Ergönül, 2013) I am likely to be attracted by food scandals and TV programs about food like "Say no to contaminated food" 7-point Likert scale 7-point Likert scale I likely share information about food scandals with my friends I likely recall the news about food scandals when buying food of the same category I will purchase this product later when I need I likely purchase this product more frequently 7-point Likert scale 7-point Likert scale I want to try this product 7-point Likert scale This product is inconvenient This product is unsafe This product is undesirable 7-point Likert scale 7-point Likert scale 7-point Likert scale perceived risk Purchase intention (Choe, Park, Chung, & Moon, 2009; (Xu, Chen, & Santhanam, 2015) Attitude toward product (Orú C et al 2017) 7-point Likert scale 7-point Likert scale Yes/ No 7-point Likert scale 7-point Likert scale 7-point Likert scale Ability to assess taste? Product type* (Xu, Ability to assess healthiness of product Chen, & Santhanam, Ability to assess price 2015) Ability to assess brand *This scale is not a measurement for statistic analysis but for the manipulation of product type 7-point Likert scale 7-point Likert scale 7-point Likert scale 7-point Likert scale ... introduction of the purpose of the online experiment and the instructions on how to navigate through eight pages of the website Regarding the main content of the experiment, there are four sections... instruction In the video, one way to use the food is introduced The content of the video consists of only the essence of essential steps so that the length of the video is under three minutes There is... 19 eliminated from the menu (Ueland et al., 2012) The fact that the benefit of foods is personalized to the preference of single consumer creates differences or even contradictions in the benefit