Green Consumer Profiling and Online Shopping of Imperfect Foods: Extending UTAUT with Web-Based Label Quality for Misshapen Organic Produce
Abstract
:1. Introduction
2. Literature Review and Related Works
2.1. Ugly Vegetables e-Commerce Site
2.2. Green Consumers and Segmentation
2.3. Online Green Purchase Intention (OGPI)
3. Theoretical Framework and Hypothesis Development
3.1. Unified Theory of Acceptance and Use of Technology (UTAUT)
3.1.1. Performance Expectancy (PE)
3.1.2. Effort Expectancy (EE)
3.1.3. Facilitating Condition (FC)
3.1.4. Social Influence (SI)
3.2. Extended UTAUT Theory
Web-Based Label Quality Perception (WLQ)
3.3. Moderating Roles of Green Consumers
3.3.1. Dark-Green Consumer
3.3.2. Semi/Light-Green Consumer
3.3.3. Non-Green Consumer
4. Research Methodology
4.1. Research Model
4.2. Data Collection
4.3. Measurement Items
4.4. Data Analysis
5. Result
5.1. Cluster Analysis
5.2. Measurement Model
5.3. Structural Equation Model
5.4. Multigroup Moderation Analysis
5.4.1. Measurement Invariance
5.4.2. Z-Test for Loading Differences
6. Discussion
General Behaviors of Dark, Semi/Light, and Non-Greens and Their Online Green Purchase Intention (OGPI)
7. Research Implication
7.1. Theoretical Contribution
7.2. Practical Implication
8. Limitations and Future Research
9. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Questionnaire
Appendix B. Other Results
Fit Index | Value | Threshold | Assessment |
---|---|---|---|
p-value | 0.000 | Acceptable | |
CMIN/df | 1.922 | <3.00 | Passed |
TLI | 0.967 | >0.90 | Passed |
CFI | 0.971 | >0.90 | Passed |
IFI | 0.971 | >0.90 | Passed |
RMSEA | 0.037 | <0.10 | Passed |
Constructs | PE | EE | FE | SI | PI | WLQ |
---|---|---|---|---|---|---|
PE | 0.60 | |||||
EE | 0.79 | 0.66 | ||||
FC | 0.76 | 0.81 | 0.56 | |||
SI | 0.59 | 0.60 | 0.71 | 0.59 | ||
PI | 0.65 | 0.64 | 0.69 | 0.59 | 0.69 | |
WLQ | 0.61 | 0.69 | 0.68 | 0.55 | 0.58 | 0.61 |
Fit Index | Value | Threshold | Assessment |
---|---|---|---|
p-value | 0.000 | Acceptable | |
CMIN/df | 1.922 | <3.00 | Passed |
TLI | 0.967 | >0.90 | Passed |
CFI | 0.971 | >0.90 | Passed |
IFI | 0.971 | >0.90 | Passed |
RMSEA | 0.037 | <0.10 | Passed |
Fit Index | Value | Threshold | Assessment |
---|---|---|---|
p-value | 0.000 | Acceptable | |
CMIN/df | 1.588 | <3.00 | Passed |
TLI | 0.935 | >0.90 | Passed |
CFI | 0.944 | >0.90 | Passed |
IFI | 0.944 | >0.90 | Passed |
RMSEA | 0.030 | <0.10 | Passed |
References
- Chen, L.; Rashidin, M.S.; Song, F.; Wang, Y.; Javed, S.; Wang, J. Determinants of Consumer’s Purchase Intention on Fresh E-Commerce Platform: Perspective of UTAUT Model. Sage Open 2021, 11, 21582440211027875. [Google Scholar] [CrossRef]
- Tedesco DE, A.; Scarioni, S.; Tava, A.; Panseri, S.; Zuorro, A. Fruit and vegetable wholesale market waste: Safety and nutritional characterisation for their potential re-use in livestock nutrition. Sustainability 2021, 13, 9478. [Google Scholar] [CrossRef]
- Naruetharadhol, P.; Wongsaichia, S.; Pienwisetkaew, T.; Schrank, J.; Chaiwongjarat, K.; Thippawong, P.; Khotsombat, T.; Ketkaew, C. Consumer Intention to Utilize an E-Commerce Platform for Imperfect Vegetables Based on Health-Consciousness. Foods 2023, 12, 1166. [Google Scholar] [CrossRef] [PubMed]
- Grewal, L.; Hmurovic, J.; Lamberton, C.; Reczek, R.W. Why Consumers Devalue Unattractive Produce. J. Mark. 2019, 83, 89–107. [Google Scholar] [CrossRef]
- Mookerjee, S.; Cornil, Y.; Hoegg, J.A. From Waste to Taste: How “Ugly” Labels Can Increase Purchase of Unattractive Produce. J. Mark. 2021, 85, 62–77. [Google Scholar] [CrossRef]
- Hingston, S.T.; Noseworthy, T.J. On the epidemic of food waste: Idealized prototypes and the aversion to misshapen fruits and vegetables. Food Qual. Prefer. 2020, 86, 103999. [Google Scholar] [CrossRef]
- Tiwari, U.; Jerome, L.; Jahnke, B. Take it or leave it? Investigating the ambivalence and willingness to pay for suboptimal fruits and vegetables among organic consumers in Germany. Front. Sustain. Food Syst. 2022, 6, 934954. [Google Scholar] [CrossRef]
- Xu, Y.; Jeong, E.; Jang, S.; Shao, X. Would you bring home ugly produce? Motivators and demotivators for ugly food consumption. J. Retail. Consum. Serv. 2021, 59, 102376. [Google Scholar] [CrossRef]
- Hussain, S.; Huang, J. The impact of cultural values on green purchase intentions through ecological awareness and perceived consumer effectiveness: An empirical investigation. Front. Environ. Sci. 2022, 10, 985200. [Google Scholar] [CrossRef]
- Whitmarsh, L.; O’Neill, S. Green identity, green living? The role of pro-environmental self-identity in determining consistency across diverse pro-environmental behaviours. J. Environ. Psychol. 2010, 30, 305–314. [Google Scholar] [CrossRef]
- Zhang, X.; Dong, F. Why do consumers make green purchase decisions? Insights from a systematic review. Int. J. Environ. Res. Public Health 2020, 17, 6607. [Google Scholar] [CrossRef] [PubMed]
- Roman, T.; Bostan, I.; Manolică, A.; Mitrica, I. Profile of green consumers in Romania in light of sustainability challenges and opportunities. Sustainability 2015, 7, 6394–6411. [Google Scholar] [CrossRef]
- Susanty, A.; Akshinta, P.Y.; Ulkhaq, M.M.; Puspitasari, N.B. Analysis of the tendency of transition between segments of green consumer behavior with a Markov chain approach. J. Model. Manag. 2022, 17, 1177–1212. [Google Scholar] [CrossRef]
- Gupta, K.P.; Manrai, R.; Goel, U. Factors influencing adoption of payments banks by Indian customers: Extending UTAUT with perceived credibility. J. Asia Bus. Stud. 2019, 13, 173–195. [Google Scholar] [CrossRef]
- Lin, J.; Li, T.; Guo, J. Factors influencing consumers’ continuous purchase intention on fresh food e-commerce platforms: An organic foods-centric empirical investigation. Electron. Commer. Res. Appl. 2021, 50, 101103. [Google Scholar] [CrossRef]
- Ramezani Nia, M.; Shokouhyar, S. Analyzing the effects of visual aesthetic of Web pages on users’ responses in online retailing using the VisAWI method. J. Res. Interact. Mark. 2020, 14, 357–389. [Google Scholar] [CrossRef]
- Shin, S.; Lee, W.J. Factors affecting user acceptance for NFC mobile wallets in the U.S. and Korea. Innov. Manag. Rev. 2021, 18, 417–433. [Google Scholar] [CrossRef]
- Armutcu, B.; Ramadani, V.; Zeqiri, J.; Dana, L.-P. The Role of Social Media in Consumers’ Intentions to Buy Green Food: Evidence from Türkiye. BFJ 2024, 126, 1923–1940. [Google Scholar] [CrossRef]
- Palomino Rivera, H.J.; Barcellos-Paula, L. Personal Variables in Attitude toward Green Purchase Intention of Organic Products. Foods 2024, 13, 213. [Google Scholar] [CrossRef]
- Polonsky, M.J. A stakeholder theory approach to designing environmental marketing strategy. J. Bus. Ind. Mark. 1995, 10, 29–46. [Google Scholar] [CrossRef]
- Pienwisetkaew, T.; Wongsaichia, S.; Pinyosap, B.; Prasertsil, S.; Poonsakpaisarn, K.; Ketkaew, C. The Behavioral Intention to Adopt Circular Economy-Based Digital Technology for Agricultural Waste Valorization. Foods 2023, 12, 2341. [Google Scholar] [CrossRef] [PubMed]
- Dong, X.; Zhao, H.; Li, T. The Role of Live-Streaming E-Commerce on Consumers’ Purchasing Intention regarding Green Agricultural Products. Sustainability 2022, 14, 4374. [Google Scholar] [CrossRef]
- Hong, C.; Choi, E.K.; Joung, H.W. Determinants of customer purchase intention toward online food delivery services: The moderating role of usage frequency. J. Hosp. Tour. Manag. 2023, 54, 76–87. [Google Scholar] [CrossRef]
- Martínez-López, F.J.; Gázquez-Abad, J.C.; Sousa, C.M.P. Structural equation modelling in marketing and business research: Critical issues and practical recommendations. Eur. J. Mark. 2013, 47, 115–152. [Google Scholar] [CrossRef]
- Heidenstrøm, N.; Hebrok, M. Towards realizing the sustainability potential within digital food provisioning platforms: The case of meal box schemes and online grocery shopping in Norway. Sustain. Prod. Consum. 2022, 29, 831–850. [Google Scholar] [CrossRef]
- Khan SA, R.; Piprani, A.Z.; Yu, Z. Digital technology and circular economy practices: Future of supply chains. Oper. Manag. Res. 2022, 15, 676–688. [Google Scholar] [CrossRef]
- Qalati, S.A.; Vela, E.G.; Li, W.; Dakhan, S.A.; Hong Thuy, T.T.; Merani, S.H. Effects of perceived service quality, website quality, and reputation on purchase intention: The mediating and moderating roles of trust and perceived risk in online shopping. Cogent Bus. Manag. 2021, 8, 1869363. [Google Scholar] [CrossRef]
- Aydinli, A.; Lu, F.C.; Baskin, E.; Sinha, J.; Jain, S.P. Preference for imperfect produce: The influence of political ideology and openness to experience. Appetite 2023, 191, 107068. [Google Scholar] [CrossRef] [PubMed]
- Hueppe, R.; Zander, K. Perfect apples or sustainable production?—Consumer perspectives from Germany. J. Consum. Behav. 2024, 23, 698–710. [Google Scholar] [CrossRef]
- Roy, M. Green Consumerism: An A-to-Z Guide; Mansvelt, J., Robbins, P., Eds.; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2011; ISBN 9781452266190. [Google Scholar]
- Shen, M.; Wang, J. The Impact of Pro-environmental Awareness Components on Green Consumption Behavior: The Moderation Effect of Consumer Perceived Cost, Policy Incentives, and Face Culture. Front. Psychol. 2022, 13, 580823. [Google Scholar] [CrossRef]
- Wax, J. The Environment: Public Attitudes and Individual Behavior; Roper Organization, Cornell University: New York, NY, USA, 1990. [Google Scholar]
- Ottman, J. A Smart Way to Segment Green Consumers. 2010. Available online: https://hbr.org/2010/02/a-Smart-Way-to-Segment-Green-c (accessed on 3 February 2023).
- Akkucuk, U. (Ed.) The Circular Economy and Its Implications on Sustainability and the Green Supply Chain; IGI Global: Hershey, PA, USA, 2019. [Google Scholar] [CrossRef]
- Roberts, S. ‘Light Green’ Consumers Differ from ‘Dark Green’ Consumers. 2009. Available online: https://www.environmentalleader.com/2009/10/Light-Green-Consumers-Differ-from-Dark-Green-Consumers/ (accessed on 16 September 2023).
- Chen, T.; Chai, L.T. Attitude towards the Environment and Green Products: Consumers’ Perspective. Manag. Sci. Eng. 2010, 4, 27–39. [Google Scholar]
- Buldeo Rai, H. The net environmental impact of online shopping, beyond the substitution bias. J. Transp. Geogr. 2021, 93, 103058. [Google Scholar] [CrossRef]
- Alcock, I. Measuring Commitment to Environmental Sustainability: The Development of a Valid and Reliable Measure. Methodol. Innov. Online 2012, 7, 13–26. [Google Scholar] [CrossRef]
- Štofejová, L.; Kráľ, Š.; Fedorko, R.; Bačík, R.; Tomášová, M. Sustainability and Consumer Behavior in Electronic Commerce. Sustainability 2023, 15, 15902. [Google Scholar] [CrossRef]
- Teresa Foti, V.; Ingrassia, M.; Bellia, C.; Zia, A.; Alzahrani, M.; Alomari, A.; AlGhamdi, F. Investigating the Drivers of Sustainable Consumption and Their Impact on Online Purchase Intentions for Agricultural Products. Sustainability 2022, 14, 6563. [Google Scholar] [CrossRef]
- Srisathan, W.A.; Wongsaichia, S.; Gebsombut, N.; Naruetharadhol, P.; Ketkaew, C. The Green-Awakening Customer Attitudes towards Buying Green Products on an Online Platform in Thailand: The Multigroup Moderation Effects of Age, Gender, and Income. Sustainability 2023, 15, 2497. [Google Scholar] [CrossRef]
- Venkatesh, V.; Morris, M.G.; Davis, G.B.; Davis, F.D. User Acceptance of Information Technology: Toward a Unified View. MIS Q. 2003, 27, 425–478. [Google Scholar] [CrossRef]
- Erjavec, J.; Manfreda, A. Online shopping adoption during COVID-19 and social isolation: Extending the UTAUT model with herd behavior. J. Retail. Consum. Serv. 2022, 65, 102867. [Google Scholar] [CrossRef]
- Lee, U.K.; Kim, H. UTAUT in Metaverse: An “Ifland” Case. J. Theor. Appl. Electron. Commer. Res. 2022, 17, 613–635. [Google Scholar] [CrossRef]
- Muangmee, C.; Kot, S.; Meekaewkunchorn, N.; Kassakorn, N.; Khalid, B. Factors Determining the Behavioral Intention of Using Food Delivery Apps during COVID-19 Pandemics. J. Theor. Appl. Electron. Commer. Res. 2021, 16, 1297–1310. [Google Scholar] [CrossRef]
- Salem, M.A.; Elshaer, I.A. Educators’ Utilizing One-Stop Mobile Learning Approach amid Global Health Emergencies: Do Technology Acceptance Determinants Matter? Electronics 2023, 12, 441. [Google Scholar] [CrossRef]
- Wang, C.; Liu, T.; Zhu, Y.; Wang, H.; Wang, X.; Zhao, S. The influence of consumer perception on purchase intention: Evidence from cross-border E-commerce platforms. Heliyon 2023, 9, e21617. [Google Scholar] [CrossRef]
- Chauhan, H.; Pandey, A.; Mishra, S.; Rai, S.K. Modeling the predictors of consumers’ online purchase intention of green products: The role of personal innovativeness and environmental drive. Environ. Dev. Sustain. 2021, 23, 16769–16785. [Google Scholar] [CrossRef]
- Doan, T. Factors affecting online purchase intention: A study of Vietnam online customers. Manag. Sci. Lett. 2020, 10, 2337–2342. [Google Scholar] [CrossRef]
- Dirgantara, R.R.; Hartono, A. View of What drives consumers to purchase green innovation product? Empirical evidence from Indonesian consumers. Int. J. Res. Bus. Soc. Sci. 2022, 11, 376–386. [Google Scholar] [CrossRef]
- Zhang, M.; Hassan, H.; Migin, M.W. Exploring the Consumers’ Purchase Intention on Online Community Group Buying Platform during Pandemic. Sustainability 2023, 15, 2433. [Google Scholar] [CrossRef]
- Wijekoon, R.; Sabri, M.F. Determinants that influence green product purchase intention and behavior: A literature review and guiding framework. Sustainability 2021, 13, 6219. [Google Scholar] [CrossRef]
- Ahmad, W.; Zhang, Q. Green purchase intention: Effects of electronic service quality and customer green psychology. J. Clean. Prod. 2020, 267, 122053. [Google Scholar] [CrossRef]
- Widyanto, H.A.; Kusumawardani, K.A.; Yohanes, H. Safety first: Extending UTAUT to better predict mobile payment adoption by incorporating perceived security, perceived risk and trust. J. Sci. Technol. Policy Manag. 2021, 13, 952–973. [Google Scholar] [CrossRef]
- Enriquez, J.P.; Archila-Godinez, J.C. Social and cultural influences on food choices: A review. Crit. Rev. Food Sci. Nutr. 2022, 62, 3698–3704. [Google Scholar] [CrossRef]
- Santaliestra-Pasías, A.M.; Felez, A.P.; Huybrechts, I.; Censi, L.; González-Gross, M.; Forsner, M.; Sjöström, M.; Lambrinou, C.P.; Amaro, F.; Kersting, M.; et al. Social Environment and Food and Beverage Intake in European Adolescents: The Helena Study. J. Am. Nutr. Assoc. 2022, 41, 468–480. [Google Scholar] [CrossRef] [PubMed]
- Sokolova, K.; Kefi, H. Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J. Retail. Consum. Serv. 2020, 53, 101742. [Google Scholar] [CrossRef]
- Attar, R.W.; Shanmugam, M.; Hajli, N. Investigating the antecedents of e-commerce satisfaction in social commerce context. Br. Food J. 2021, 123, 849–868. [Google Scholar] [CrossRef]
- Manrai, R.; Goel, U.; Yadav, P.D. Factors affecting adoption of digital payments by semi-rural Indian women: Extension of UTAUT-2 with self-determination theory and perceived credibility. Aslib J. Inf. Manag. 2021, 73, 814–838. [Google Scholar] [CrossRef]
- Solvalier, I. Green Marketing Strategies-Case Study about ICA Group AB; Karlstads Universitet: Karlstad, Sweden, 2010. [Google Scholar]
- Olson, E.L. It’s Not Easy Being Green: The Effects of Attribute Tradeoffs on Green Product Preference and Choice. J. Acad. Mark. Sci. 2013, 41, 171–184. [Google Scholar] [CrossRef]
- Roberts, J.A.; Bacon, D.R. Exploring the Subtle Relationships between Environmental Concern and Ecologically Conscious Consumer Behavior. J. Bus. Res. 1997, 40, 79–89. [Google Scholar] [CrossRef]
- Zeynalova, Z.; Namazova, N. Revealing Consumer Behavior toward Green Consumption. Sustainability 2022, 14, 5806. [Google Scholar] [CrossRef]
- Diamantopoulos, A.; Schlegelmilch, B.B.; Sinkovics, R.R.; Bohlen, G.M. Can Socio-Demographics Still Play a Role in Profiling Green Consumers? A Review of the Evidence and an Empirical Investigation. J. Bus. Res. 2003, 56, 465–480. [Google Scholar] [CrossRef]
- Kollmuss, A.; Agyeman, J. Mind the Gap: Why Do People Act Environmentally and What Are the Barriers to pro-Environmental Behavior? Environ. Educ. Res. 2002, 8, 239–260. [Google Scholar] [CrossRef]
- Urbański, M.; Ul Haque, A. Are You Environmentally Conscious Enough to Differentiate between Greenwashed and Sustainable Items? A Global Consumers Perspective. Sustainability 2020, 12, 1786. [Google Scholar] [CrossRef]
- Afridi, S.A.; Khan, W.; Haider, M.; Shahjehan, A.; Afsar, B. Generativity and Green Purchasing Behavior: Moderating Role of Man-Nature Orientation and Perceived Behavioral Control. Sage Open 2021, 11, 21582440211054480. [Google Scholar] [CrossRef]
- Borau, S.; Elgaaied-Gambier, L.; Barbarossa, C. The Green Mate Appeal: Men’s Pro-environmental Consumption Is an Honest Signal of Commitment to Their Partner. Psychol. Mark. 2021, 38, 266–285. [Google Scholar] [CrossRef]
- Le, X.C. What triggers mobile application-based purchase behavior during COVID-19 pandemic: Evidence from Vietnam. Int. J. Emerg. Mark. 2021, 18, 4108–4129. [Google Scholar] [CrossRef]
- Saprikis, V.; Avlogiaris, G.; Katarachia, A. A Comparative Study of Users versus Non-Users’ Behavioral Intention towards M-Banking Apps’ Adoption. Information 2022, 13, 30. [Google Scholar] [CrossRef]
- Gupta, V.; Duggal, S. How the consumer’s attitude and behavioural intentions are influenced: A case of online food delivery applications in India. Int. J. Cult. Tour. Hosp. Res. 2020, 15, 77–93. [Google Scholar] [CrossRef]
- Jayanti, R.; Hafidzi, A.; Izzuddin, A. The Influence of Information Quality, Online Customer Reviews and Postage Subsidy Promos on Purchasing Decisions on E-Commerce Shopee. In Proceedings of the 3rd International Conference of Business, Accounting, and Economics, ICBAE 2022, Purwokerto, Central Java, Indonesia, 10–11 August 2022. [Google Scholar] [CrossRef]
- Calculator.net, Sample Size Calculator. Available online: https://www.calculator.net/sample-size-calculator.html (accessed on 7 January 2023).
- Kline, R.B. Principles and Practice of Structural Equation Modeling, 4th ed.; Guilford Press: New York, NY, USA, 2016. [Google Scholar]
- Malhotra, N.K.; Birks, D.F. Marketing Research: An Applied Approach; Financial Times/Prentice Hall: Harlow, UK, 2006; p. 753. ISBN 0-273-69530-4. [Google Scholar]
- Lee, Y.H.; Hsiao, C.; Weng, J.; Chen, Y.H. The impacts of relational capital on self-disclosure in virtual communities: A cross-level analysis of key moderators. Inf. Technol. People 2021, 34, 228–249. [Google Scholar] [CrossRef]
- Lefever, S.; Dal, M.; Matthíasdóttir, Á. Online data collection in academic research: Advantages and limitations. Br. J. Educ. Technol. 2007, 38, 574–582. [Google Scholar] [CrossRef]
- Van Selm, M.; Jankowski, N.W. Conducting online surveys. Qual. Quant. 2006, 40, 435–456. [Google Scholar] [CrossRef]
- Upadhyay, N.; Upadhyay, S.; Abed, S.S.; Dwivedi, Y.K. Consumer Adoption of Mobile Payment Services during COVID-19: Extending Meta-UTAUT with Perceived Severity and Self-Efficacy. Int. J. Bank Mark. 2022, 40, 960–991. [Google Scholar] [CrossRef]
- Alshurideh, M.; Gasaymeh, A.; Ahmed, G.; Alzoubi, H.; Kurd, B.A. Loyalty program effectiveness: Theoretical reviews and practical proofs. Uncertain Supply Chain Manag. 2020, 8, 599–612. [Google Scholar] [CrossRef]
- Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common Method Biases in Behavioral Research: A Critical Review of the Literature and Recommended Remedies. J. Appl. Psychol. 2003, 88, 879–903. [Google Scholar] [CrossRef]
- Ahmed, W. Understanding self-directed learning behavior towards digital competence among business research students: SEM-neural analysis. Educ. Inf. Technol. 2023, 28, 4173–4202. [Google Scholar] [CrossRef]
- Pillai, S.G.; Kim, W.G.; Haldorai, K.; Kim, H.S. Online food delivery services and consumers’ purchase intention: Integration of theory of planned behavior, theory of perceived risk, and the elaboration likelihood model. Int. J. Hosp. Manag. 2022, 105, 103275. [Google Scholar] [CrossRef]
- Chen, C.L. Conceptualising Customer Relationship Management and Its Impact on Customer Lifetime Value in the Taiwanese Banking Sector; De Montfort University: Leicester, UK, 2012; Available online: https://dora.dmu.ac.uk/server/api/core/bitstreams/06f2d22f-66bd-4d30-a91b-09a9be25395b/content (accessed on 7 January 2023).
- Dandis, A.O.; Jarrad, A.A.; Joudeh JM, M.; Mukattash, I.L.; Hassouneh, A.G. The effect of multidimensional service quality on word of mouth in university on-campus healthcare centers. TQM J. 2022, 34, 701–727. [Google Scholar] [CrossRef]
- Rodu, B.; Plurphanswat, N. Cross-sectional e-cigarette studies are unreliable without timing of exposure and disease diagnosis. Intern. Emerg. Med. 2023, 18, 319–323. [Google Scholar] [CrossRef]
- Bindah, E. Practical Assessment of Good Practices in Structural Equation Modeling (SEM) Analysis: A Study on Consumer Socialization. Acad. Mark. Stud. J. 2023, 27, 1–16. [Google Scholar]
- Field, A.P. Discovering Statistics Using SPSS: (And Sex, Drugs and Rock “n” Roll), 4th ed.; Sage Publications: Los Angeles, CA, USA, 2013; p. 779. ISBN 0-7619-4451-6. [Google Scholar]
- Kim, H.-Y. Statistical notes for clinical researchers: Assessing normal distribution (2) using skewness and kurtosis. Restor. Dent. Endod. 2013, 38, 52–54. [Google Scholar] [CrossRef] [PubMed]
- David Garson, G. Testing Statistical Assumptions; Statistical Associates Publishing: Asheboro, NC, USA, 2012; Available online: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=sAfLteIAAAAJ&citation_for_view=sAfLteIAAAAJ:PoWvk5oyLR8C (accessed on 17 January 2023).
- Benitez, J.; Henseler, J.; Castillo, A.; Schuberth, F. How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. Inf. Manag. 2020, 57, 103168. [Google Scholar] [CrossRef]
- Hair, J.F.; Howard, M.C.; Nitzl, C. Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. J. Bus. Res. 2020, 109, 101–110. [Google Scholar] [CrossRef]
- Afthanorhan, A.; Ghazali, P.L.; Rashid, N. Discriminant Validity: A Comparison of CBSEM and Consistent PLS using Fornell & Larcker and HTMT Approaches. J. Phys. Conf. Ser. 2021, 1874, 012085. [Google Scholar] [CrossRef]
- Amora, J.T. Convergent validity assessment in PLS-SEM: A loadings-driven approach. Data Anal. Perspect. J. 2021, 2, 1–6. [Google Scholar]
- Baumgartner, H.; Homburg, C. Applications of structural equation modeling in marketing and consumer research: A review. Int. J. Res. Mark. 1996, 13, 139–161. [Google Scholar] [CrossRef]
- Byrne, B.M. Testing for Multigroup Invariance Using AMOS Graphics: A Road Less Traveled. Struct. Equ. Model. Multidiscip. J. 2004, 11, 272–300. [Google Scholar] [CrossRef]
- Pienwisetkaew, T.; Wongthahan, P.; Naruetharadhol, P.; Wongsaichia, S.; Vonganunsuntree, C.; Padthar, S.; Nee, S.; He, P.; Ketkaew, C. Consumers’ Intention to Purchase Functional Non-Dairy Milk and Gender-Based Market Segmentation. Sustainability 2022, 14, 11957. [Google Scholar] [CrossRef]
- Putnick, D.L.; Bornstein, M.H. Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Dev. Rev. 2016, 41, 71–90. [Google Scholar] [CrossRef] [PubMed]
- Pandis, N. Comparison of 2 means (independent z test or independent t test). Am. J. Orthod. Dentofac. Orthop. 2015, 148, 350–351. [Google Scholar] [CrossRef] [PubMed]
- Milovanov, O. Marketing and Sustainability: Identifying the Profile of Green Consumers. 2016. Available online: https://www.researchgate.net/publication/289345272 (accessed on 3 February 2023).
- Wang, L.; Wong, P.P.; Narayanan, E.A. The demographic impact of consumer green purchase intention toward Green Hotel Selection in China. Tour. Hosp. Res. 2020, 20, 210–222. [Google Scholar] [CrossRef]
- Scherman, A.; Valenzuela, S.; Rivera, S. Youth environmental activism in the age of social media: The case of Chile (2009–2019). J. Youth Stud. 2022, 25, 751–770. [Google Scholar] [CrossRef]
- Al-Swidi, A.; Saleh, R.M. How green our future would be? An investigation of the determinants of green purchasing behavior of young citizens in a developing Country. Environ. Dev. Sustain. 2021, 23, 13436–13468. [Google Scholar] [CrossRef]
- Chauke, O.F.; Tlapana, T.; Hawkins-Mofokeng, R. Adoption and consumption patterns of green products: An exploratory study amongst green consumers in Durban. Int. J. Res. Bus. Soc. Sci. 2021, 10, 78–86. [Google Scholar] [CrossRef]
- Castellini, G.; Savarese, M.; Graffigna, G. Online fake news about food: Self-evaluation, social influence and the stages of change moderation. Int. J. Environ. Res. Public Health 2021, 18, 2934. [Google Scholar] [CrossRef] [PubMed]
- Sylvia, M.L.; Terhaar, M.F. Clinical Analytics and Data Management for the DNP, 3rd ed.; Springer Publishing: New York, NY, USA, 2024. [Google Scholar]
- Atinafu, B. Higher education students’ social media literacy in Ethiopia: A case of Bahir Dar University. J. Media Lit. Educ. 2021, 13, 86–96. [Google Scholar] [CrossRef]
- Singh, S.; Singh, U.S. A Study Assessing the Brand Loyalty Creation by Promotion Mix for KOTON Brand. Cross Curr. Int. J. Econ. Manag. Media Stud. 2021, 3, 19–28. [Google Scholar] [CrossRef]
- Berkman, E.T.; Reise, S.P. A Conceptual Guide to Statistics Using SPSS; Sage: Thousand Oaks, CA, USA, 2012. [Google Scholar]
- Wongsaichia, S.; Naruetharadhol, P.; Schrank, J.; Phoomsom, P.; Sirisoonthonkul, K.; Paiyasen, V.; Srichaingwang, S.; Ketkaew, C. Influences of Green Eating Behaviors Underlying the Extended Theory of Planned Behavior: A Study of Market Segmentation and Purchase Intention. Sustainability 2022, 14, 8050. [Google Scholar] [CrossRef]
- Hossan, D.; Aktar, A.; Zhang, Q.; Malaysia, P. Partial Least Squares Structural Equation Modeling (PLS-SEM) as Emerging Tool in Action Research. LC Int. J. STEM 2020, 1. [Google Scholar]
- Henseler, J.; Ringle, C.M.; Sarstedt, M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J. Acad. Mark. Sci. 2015, 43, 115–135. [Google Scholar] [CrossRef]
- Kim, N.; Lee, K. Environmental Consciousness, Purchase Intention, and Actual Purchase Behavior of Eco-Friendly Products: The Moderating Impact of Situational Context. Int. J. Environ. Res. Public Health 2023, 20, 5312. [Google Scholar] [CrossRef] [PubMed]
- Bae, Y.; Choi, J.; Gantumur, M.; Kim, N. Technology-Based Strategies for Online Secondhand Platforms Promoting Sustainable Retailing. Sustainability 2022, 14, 3259. [Google Scholar] [CrossRef]
- Chen, S.; Qiu, H.; Xiao, H.; He, W.; Mou, J.; Siponen, M. Consumption behavior of eco-friendly products and applications of ICT innovation. J. Clean. Prod. 2021, 287, 125436. [Google Scholar] [CrossRef]
- Shihong, L. Study on the Method for Evaluation on Rural Informatisation in China. N. Z. J. Agric. Res. 2007, 50, 749–755. [Google Scholar] [CrossRef]
- Gil-Gomez, H.; Guerola-Navarro, V.; Oltra-Badenes, R.; Lozano-Quilis, J.A. Customer Relationship Management: Digital Transformation and Sustainable Business Model Innovation. Econ. Res. -Ekon. Istraživanja 2020, 33, 2733–2750. [Google Scholar] [CrossRef]
- Shi, Y.; Siddik, A.B.; Masukujjaman, M.; Zheng, G.; Hamayun, M.; Ibrahim, A.M. The Antecedents of Willingness to Adopt and Pay for the IoT in the Agricultural Industry: An Application of the UTAUT 2 Theory. Sustainability 2022, 14, 6640. [Google Scholar] [CrossRef]
- Steenkamp JB, E.M.; Baumgartner, H. Assessing measurement invariance in cross-national consumer research. J. Consum. Res. 1998, 25, 78–90. [Google Scholar] [CrossRef]
- Luong, R.; Flake, J.K. Measurement invariance testing using confirmatory factor analysis and alignment optimization: A tutorial for transparent analysis planning and reporting. Psychol. Methods 2022, 28, 905–924. [Google Scholar] [CrossRef]
- Aji, H.M.; Berakon, I.; Md Husin, M. COVID-19 and e-wallet usage intention: A multigroup analysis between Indonesia and Malaysia. Cogent Bus. Manag. 2020, 7, 1804181. [Google Scholar] [CrossRef]
- Morris, S.B.; Lobsenz, R.E. Significance Tests and Confidence Intervals for the Adverse Impact Ratio. Pers. Psychol. 2000, 53, 89–111. [Google Scholar] [CrossRef]
- Brough, A.R.; Wilkie JE, B.; Ma, J.; Isaac, M.S.; Gal, D. Is Eco-Friendly Unmanly? The Green-Feminine Stereotype and Its Effect on Sustainable Consumption. J. Consum. Res. 2016, 43, 567–582. [Google Scholar] [CrossRef]
- Lim MS, C.; Molenaar, A.; Brennan, L.; Reid, M.; McCaffrey, T. Young Adults’ Use of Different Social Media Platforms for Health Information: Insights From Web-Based Conversations. J. Med. Internet Res. 2022, 24, e23656. [Google Scholar] [CrossRef] [PubMed]
- Mehta, P.; Chahal, H.S. Consumer attitude towards green products: Revisiting the profile of green consumers using segmentation approach. Manag. Environ. Qual. Int. J. 2021, 32, 902–928. [Google Scholar] [CrossRef]
- Dewi, C.K.; Mohaidin, Z.; Murshid, M.A. Determinants of online purchase intention: A PLS-SEM approach: Evidence from Indonesia. J. Asia Bus. Stud. 2020, 14, 281–306. [Google Scholar] [CrossRef]
- Haryanti, T.; Subriadi, A. E-commerce acceptance in the dimension of sustainability. J. Model. Manag. 2021. ahead-of-print. [Google Scholar] [CrossRef]
- Ekren, B.Y.; Mangla, S.K.; Turhanlar, E.E.; Kazancoglu, Y.; Li, G. Lateral inventory share-based models for IoT-enabled E-commerce sustainable food supply networks. Comput. Oper. Res. 2021, 130, 105237. [Google Scholar] [CrossRef]
- Vahdat, A.; Alizadeh, A.; Quach, S.; Hamelin, N. Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australas. Mark. J. 2021, 29, 187–197. [Google Scholar] [CrossRef]
- White, K.; Habib, R.; Hardisty, D.J. How to SHIFT consumer behaviors to be more sustainable: A literature review and guiding framework. J. Mark. 2019, 83, 22–49. [Google Scholar] [CrossRef]
- Chang, Y.W.; Chen, J. What Motivates Customers to Shop in Smart Shops? The Impacts of Smart Technology and Technology Readiness. J. Retail. Consum. Serv. 2021, 58, 102325. [Google Scholar] [CrossRef]
- Venkatesh, V.; Walton, S.M.; Thong JY, L. Quarterly Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Q. 2012, 36, 157–178. [Google Scholar] [CrossRef]
- Lombart, C.; Millan, E.; Normand, J.M.; Verhulst, A.; Labbé-Pinlon, B.; Moreau, G. Consumer perceptions and purchase behavior toward imperfect fruits and vegetables in an immersive virtual reality grocery store. J. Retail. Consum. Serv. 2019, 48, 28–40. [Google Scholar] [CrossRef]
- Srisuwannaket, T.; Liumpetch, C. Tackling Thailand’s Food-Waste Crisis. Available online: https://tdri.or.th/en/2019/10/tackling-thailands-food-waste-crisis/#:~:text=Thailand%20urgently%20needs%20a%20food,which%20policy%20can%20be%20formulated (accessed on 25 February 2023).
- Xiao, L.; Guo, F.; Yu, F.; Liu, S. The Effects of Online Shopping Context Cues on Consumers’ Purchase Intention for Cross-Border E-Commerce Sustainability. Sustainability 2019, 11, 2777. [Google Scholar] [CrossRef]
Segmented by | Characteristics |
---|---|
Polonsky (1995) [20] | Dark-green: Their motivation to actively seek information about green products, services, and purchases is derived from their inner intentions. Semi/Light-green: The intention to seek information on green products and services is lower than that of dark-green consumers. They decide to purchase green products sometimes but not all the time. Non-green: Rarely buy and consume green products or services. If they purchase such green products, it unintentionally happens. |
Ottman (2010) [33] | Resource conservers (hate waste): They prioritize economic value, long-lasting, and reusability advantages of products. In addition, the products that enable them to recycle, compost, and save energy. Health fanatics: They are concerned about excessive sun exposure, fear of chemicals used in products, and fear of contaminants in children’s toys. They consider organic elements, health benefits, trust, and natural ingredients. Additionally, they encourage cross-promotion with organic food parties, sponsorships, or promotions in natural life magazines. Animal Lovers: People prefer vegetarianism, consider PETA (People for the Ethical Treatment of Animals), and boycott animal exploitation. Also, they seek products that are “cruelty-free.” Outdoor Enthusiasts: People entertain themselves by doing nature tourism, such as camping, mountain climbing, skiing, hiking, and visiting national parks. They are eager to cut the environmental impact of recreational activities. They are additionally concerned with labeled items and recyclable materials. |
Organization and Wax (1990) [32] | True Blue Greens (9%): Using strong environmental values to affect change positively, they repeatedly avoid products manufactured by an environmentally unconcerned corporation. Greenbacks Green (6%): Greenbacks differ from True Blues in many ways, including their environmental beliefs. They are, nonetheless, more prone to buy green items than the average green customer. Sprouts (31%): Sprouts believed more in theory than practice. They seldom order a green product if it is more costly. In fact, they can afford eco-friendly products and are willing to use them when people can persuade them the right way. Grousers (19%): Grousers tend to neglect the environment and are cynical about their ability to contribute to it. They think environmentally friendly products are expensive and do not greatly impact product competition. Basic Browns (33%): They are solely concerned with their daily lives and are disinterested in environmental and social issues. |
Constructs | Item | Observed Variables | Source |
---|---|---|---|
Green Consumer | GC1 | I tend to buy degradable products that easily blend in with the environment. | [63,67] |
GC2 | I prefer to purchase a similar item in a larger package. | ||
GC3 | I procure used things to cut down on unneeded consumption. | ||
GC4 | The food that has not been completely consumed is stored, processed, or given to others. | ||
GC5 | I try not to waste anything in my home. | ||
GC6 | If I am aware of the possible environmental harm that any products may cause, I will not purchase them. | ||
GC7 | I usually try to choose items that are environmentally friendly and contain less pollution when shopping. | ||
GC8 | When offered the option of two similar products, I definitely choose the one that poses the least risk to other people and the environment. |
Constructs | Item | Observed Variables | Source |
---|---|---|---|
Performance Expectancy (PE) | PE1 | E-commerce selling imperfect organic fruits and vegetables has the potential to boost my daily performance. | [42,43,49,69] |
PE2 | Buying fruit and vegetables on e-commerce can save me some time. | ||
PE3 | Purchasing through e-commerce can be accessed from anywhere. | ||
PE4 | The e-commerce selling imperfect organic fruits and vegetables helps me make purchases more efficiently. | ||
Effort Expectancy (EE) | EE1 | It is simple to figure out how to operate an e-commerce site selling imperfect organic fruits and vegetables. | [42,43,49,69,79] |
EE2 | The e-commerce selling imperfect organic fruits and vegetables provides a user-friendly interface. | ||
EE3 | The e-commerce selling imperfect organic fruits and vegetables is less confusing to adopt | ||
EE4 | It doesn’t take long to become an expert user who understands e-commerce selling imperfect organic fruits and vegetables. | ||
Facilitating Condition (FC) | FC1 | I own the required resources to use e-commerce selling imperfect organic fruits and vegetables. | [42,43,49,69] |
FC2 | I am knowledgeable enough to use e-commerce. | ||
FC3 | I assume that assistance from the company is available if I have trouble with the platform. | ||
FC4 | The e-commerce selling imperfect organic fruits and vegetables will function similarly to other e-commerce systems. | ||
Social Influence (SI) | SI1 | I feel that the people around me will recommend using e-commerce to purchase imperfect organic fruits and vegetables online. | [42,43,48,49,69] |
SI2 | I believe that those influencing my consumption behavior will advise me to adopt e-commerce selling imperfect organic fruits and vegetables. | ||
SI3 | People adopting e-commerce selling imperfect organic fruits and vegetables will look more prestigious. | ||
SI4 | I have feelings that the people closest to me will recommend that I purchase imperfect organic fruits and vegetables from e-commerce. | ||
Web-Based Label Quality Perception (WLQ) | WLQ1 | I have confidence that an e-commerce platform offering information on imperfect organic fruits and vegetables provides trustworthy label quality for making a purchase decision. | [14,15,16,17] |
WLQ2 | I believe that stakeholders involved in the online sale of imperfect organic fruits and vegetables provide trustworthy label quality for making a purchase decision. | ||
WLQ3 | The quality and accuracy of label information on the e-commerce website for imperfect organic fruits and vegetables are sufficient to proceed confidently with a purchase transaction. | ||
WLQ4 | The e-commerce platform selling imperfect organic fruits and vegetables will continually update and improve label information to ensure the security and trustworthiness of a transaction. | ||
Online Green Purchase Intention (OGPI) | OGPI1 | I plan to use e-commerce to purchase imperfect organic fruits and vegetables in the near future. | [15,43,48,49,53] |
OGPI2 | I perceive that I will purchase imperfect organic fruits and vegetables through e-commerce in daily life. | ||
OGPI3 | I will find myself frequently ordering imperfect organic fruits and vegetables through e-commerce. |
Demographic Variable | Categories | Segment 1 (Dark-Green) | Segment 2 (Semi/Light-Green) | Segment 3 (Non-Green) | Total | Significance Chi-Square Test | ||||
---|---|---|---|---|---|---|---|---|---|---|
n | % | n | % | n | % | n | % | |||
Segment size | 225 | 34.14 | 241 | 36.57 | 202 | 30.65 | 668 | 100 | ||
Gender | Male | 65 | 9.7 | 83 | 12.4 | 105 | 15.7 | 253 | 37.9 | <0.001 * |
Female | 160 | 24 | 158 | 23.7 | 97 | 14.5 | 415 | 62.1 | ||
Status | Single | 163 | 24.4 | 191 | 28.6 | 147 | 22 | 501 | 75 | 0.232 |
Married | 56 | 8.4 | 45 | 6.7 | 53 | 7.9 | 154 | 23.1 | ||
Divorced | 6 | 0.9 | 5 | 0.7 | 2 | 0.3 | 13 | 1.9 | ||
Family | 1 | 15 | 2.3 | 5 | 0.8 | 9 | 1.4 | 29 | 4.4 | <0.001 * |
2 | 22 | 3.3 | 10 | 1.5 | 21 | 3.2 | 53 | 8.1 | ||
3 | 34 | 5.1 | 74 | 11.1 | 44 | 6.6 | 152 | 22.8 | ||
4 | 83 | 12.4 | 83 | 12.4 | 83 | 12.4 | 249 | 37.3 | ||
>4 | 71 | 10.6 | 69 | 10.3 | 45 | 6.7 | 185 | 27.7 | ||
Age | 18–24 | 85 | 12.7 | 109 | 16.3 | 34 | 5.1 | 228 | 34.1 | <0.001 * |
25–34 | 60 | 9 | 78 | 11.7 | 72 | 10.8 | 210 | 31.4 | ||
35–44 | 49 | 7.3 | 32 | 4.8 | 43 | 6.4 | 124 | 18.6 | ||
>44 | 31 | 4.6 | 22 | 3.3 | 53 | 7.9 | 106 | 15.9 | ||
Income | <10,000 | 62 | 9.3 | 36 | 5.4 | 13 | 1.9 | 111 | 16.6 | <0.001 * |
10,001–20,000 | 75 | 11.2 | 134 | 20.1 | 64 | 9.7 | 274 | 41 | ||
20,001–30,000 | 33 | 4.9 | 33 | 4.9 | 37 | 5.5 | 103 | 15.4 | ||
30,001–40,000 | 23 | 3.4 | 18 | 2.7 | 48 | 7.2 | 89 | 13.3 | ||
>40,001 | 32 | 4.8 | 20 | 3 | 39 | 5.8 | 91 | 13.6 | ||
Education | Diploma | 35 | 5.2 | 15 | 2.2 | 23 | 3.4 | 73 | 10.9 | <0.001 * |
Bachelor | 152 | 22.8 | 207 | 31 | 169 | 25.3 | 528 | 79 | ||
Graduate | 38 | 5.7 | 19 | 2.8 | 10 | 1.5 | 67 | 10 | ||
Occupation | Student | 86 | 12.9 | 112 | 16.8 | 30 | 4.5 | 228 | 34.1 | <0.001 * |
Government | 39 | 5.8 | 53 | 7.9 | 49 | 7.3 | 141 | 21.1 | ||
State Enterprise | 11 | 1.6 | 16 | 2.4 | 13 | 1.9 | 40 | 6 | ||
Employee | 43 | 6.4 | 42 | 6.3 | 77 | 11.5 | 162 | 24.3 | ||
Business Owner | 46 | 6.9 | 18 | 2.7 | 33 | 4.9 | 97 | 14.5 | ||
Regular buyer of green food | Yes | 215 | 32.2 | 231 | 34.6 | 79 | 11.8 | 525 | 78.6 | <0.001 * |
No | 10 | 1.5 | 10 | 1.5 | 123 | 18.4 | 143 | 21.4 | ||
An active online user | Yes | 222 | 33.2 | 236 | 35.3 | 168 | 25.1 | 626 | 93.7 | <0.001 * |
No | 3 | 0.4 | 5 | 0.7 | 34 | 5.1 | 42 | 6.3 |
Measure | Segment 1 (Dark-Green) | Segment 2 (Semi/Light-Green) | Segment 3 (Non-Green) | Welch’s Statistic | p-Value | |||
---|---|---|---|---|---|---|---|---|
Mean | SD | Mean | SD | Mean | SD | |||
GC1 | 8.13 | 1.176 | 5.87 | 1.118 | 4.25 | 0.973 | 694.23 | <0.001 * |
GC2 | 7.67 | 1.570 | 5.71 | 0.920 | 3.81 | 0.868 | 574.508 | <0.001 * |
GC3 | 7.45 | 1.734 | 5.49 | 1.159 | 3.89 | 1.069 | 347.280 | <0.001 * |
GC4 | 7.73 | 1.509 | 5.45 | 1.264 | 3.83 | 0.995 | 513.505 | <0.001 * |
GC5 | 8.18 | 1.058 | 5.74 | 1.115 | 3.77 | 0.940 | 1036.880 | <0.001 * |
GC6 | 8.24 | 0.853 | 5.80 | 0.924 | 3.91 | 0.950 | 1253.824 | <0.001 * |
GC7 | 8.19 | 0.997 | 5.85 | 0.999 | 3.88 | 1.017 | 983.377 | <0.001 * |
GC8 | 8.42 | 0.746 | 6.06 | 1.047 | 3.68 | 1.003 | 1554.542 | <0.001 * |
Measure | Segment 1 (Dark-Green) | Segment 2 (Semi/Light-Green) | Segment 3 (Non-Green) | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | t | One-Sided p | Mean | t | One-Sided p | Mean | t | One-Sided p | |
OGPI1 | 4.24 | 4.985 * | <0.001 * | 4.04 | 0.988 | 0.162 | 3.92 | −1.456 | 0.147 |
OGPI2 | 4.17 | 3.463 * | <0.001 * | 3.98 | −0.507 | 0.306 | 3.91 | −2.989 | 0.003 |
OGPI3 | 4.16 | 3.310 * | <0.001 * | 4.03 | 0.784 | 0.217 | 3.76 | −4.115 | <0.001 |
Constructs | Indicator | Loading | p-Value | Cronbach α | AVE | CR |
---|---|---|---|---|---|---|
Performance Expectancy | PE1 | 0.77 | *** | 0.855 | 0.596 | 0.855 |
PE2 | 0.763 | *** | ||||
PE3 | 0.749 | *** | ||||
PE4 | 0.806 | *** | ||||
Effort Expectancy | EE1 | 0.832 | *** | 0.884 | 0.659 | 0.885 |
EE2 | 0.773 | *** | ||||
EE3 | 0.812 | *** | ||||
EE4 | 0.828 | *** | ||||
Facilitating Condition | FC1 | 0.81 | *** | 0.831 | 0.559 | 0.835 |
FC2 | 0.699 | *** | ||||
FC3 | 0.738 | *** | ||||
FC4 | 0.739 | *** | ||||
Social Influence | SI1 | 0.791 | *** | 0.849 | 0.595 | 0.854 |
SI2 | 0.785 | *** | ||||
SI3 | 0.691 | *** | ||||
SI4 | 0.814 | *** | ||||
Web-Based Label Quality | WLQ1 | 0.799 | *** | 0.862 | 0.6159 | 0.865 |
WLQ2 | 0.725 | *** | ||||
WLQ3 | 0.79 | *** | ||||
WLQ4 | 0.822 | *** | ||||
Online Green Purchase Intention | OGPI1 | 0.838 | *** | 0.869 | 0.689 | 0.870 |
OGPI2 | 0.854 | *** | ||||
OGPI3 | 0.799 | *** |
Path | Relationship | Standardized Estimate | p-Value | Result |
---|---|---|---|---|
H1 | Performance Expectancy (PE) Online Green Purchase Intention (OGPI) | 0.237 | *** | Supported |
H2 | Effort Expectancy (EE) Online Green Purchase Intention (OGPI) | 0.021 | 0.811 | Rejected |
H3 | Facilitating Condition (FC) Online Green Purchase Intention (OGPI) | 0.265 | 0.003 ** | Supported |
H4 | Social Influence (SI) Online Green Purchase Intention (PI) | 0.153 | 0.009 ** | Supported |
H5 | Web-Based Label Quality (WLQ) Online Green Purchase Intention (OGPI) | 0.128 | 0.025 * | Supported |
Fit Index | Configural Invariance (Unconstrained) | Metric Invariance (Measurement Weight) | Scalar Invariance (Measurement Intercepts) | Threshold |
---|---|---|---|---|
p-value | 0.000 | 0.000 | 0.000 | |
CMIN/df | 1.597 | 1.586 | 1.637 | <3.00 |
TLI | 0.934 | 0.935 | 0.930 | >0.90 |
CFI | 0.943 | 0.942 | 0.933 | >0.90 |
IFI | 0.943 | 0.942 | 0.934 | >0.90 |
RMSEA | 0.030 | 0.030 | 0.031 | <0.10 |
Assessment | Passed | Passed | Passed |
Hypothesis | Causal Relationship | Dark-Green | Semi/Light Green | Non-Green | Critical Ratio Differences | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Std. Est. | p-Value | Std. Est | p-Value | Std. Est | p-Value | Dark vs. Semi | Dark vs. Non | Semi vs. Non | Threshold | ||
H1 | PE | 0.206 | 0.041 * | 0.385 | 0.007 ** | 0.105 | 0.450 | 1.146 | −1.548 | −1.651 | |1.96| |
H2 | EE | 0.093 | 0.389 | −0.193 | 0.512 | −0.094 | 0.605 | −0.926 | −0.920 | 0.319 | |1.96| |
H3 | FC | 0.116 | 0.317 | 0.173 | 0.627 | 0.577 | *** | 0.241 | 2.373 * | 0.604 | |1.96| |
H4 | SI | 0.209 | 0.038 * | 0.101 | 0.157 | 0.051 | 0.579 | −0.475 | −1.131 | −0.263 | |1.96| |
H5 | 0.170 | 0.043 * | 0.128 | 0.421 | −0.005 | 0.960 | −0.358 | −1.443 | −0.707 | |1.96| |
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Oktaviani, R.D.; Naruetharadhol, P.; Padthar, S.; Ketkaew, C. Green Consumer Profiling and Online Shopping of Imperfect Foods: Extending UTAUT with Web-Based Label Quality for Misshapen Organic Produce. Foods 2024, 13, 1401. https://doi.org/10.3390/foods13091401
Oktaviani RD, Naruetharadhol P, Padthar S, Ketkaew C. Green Consumer Profiling and Online Shopping of Imperfect Foods: Extending UTAUT with Web-Based Label Quality for Misshapen Organic Produce. Foods. 2024; 13(9):1401. https://doi.org/10.3390/foods13091401
Chicago/Turabian StyleOktaviani, Rara Dwi, Phaninee Naruetharadhol, Siraphat Padthar, and Chavis Ketkaew. 2024. "Green Consumer Profiling and Online Shopping of Imperfect Foods: Extending UTAUT with Web-Based Label Quality for Misshapen Organic Produce" Foods 13, no. 9: 1401. https://doi.org/10.3390/foods13091401