References

• Abbasi, M., & El Hanandeh, A. (2016). Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Management, 56, 13–22. https://doi.org/10.1016/j.wasman.2016.05.018
   PubMed Web of Science ®Google Scholar
• AbuShanab, E., & Pearson, J. M. (2007). Internet banking in Jordan: The unified theory of acceptance and use of technology (UTAUT) perspective. Journal of Systems and Information Technology, 9(1), 78–97. https://doi.org/10.1108/13287260710817700
   Google Scholar
• Abu-Taieh, E. M., AlHadid, I., Abu-Tayeh, S., Masa’deh, R. e., Alkhawaldeh, R. S., Khwaldeh, S., & Alrowwad, A. A. (2022). Continued intention to Use of M-banking in Jordan by integrating UTAUT, TPB, TAM and service quality with ML. Journal of Open Innovation: Technology, Market, and Complexity, 8(3), 120. https://doi.org/10.3390/joitmc8030120
   Google Scholar
• Abu Zayyad, H. M., Obeidat, Z. M., Alshurideh, M. T., Abuhashesh, M., Maqableh, M., & Masa’deh, R. e. (2021). Corporate social responsibility and patronage intentions: The mediating effect of brand credibility. Journal of Marketing Communications, 27(5), 510–533. https://doi.org/10.1080/13527266.2020.1728565
   Google Scholar
• Ahmad, W., & Zhang, Q. (2020). Green purchase intention: Effects of electronic service quality and customer green psychology. Journal of Cleaner Production, 267, 122053. https://doi.org/10.1016/j.jclepro.2020.122053
   Web of Science ®Google Scholar
• Aksah, H., Ani, A. I. C., Johar, S., & Husain, S. H. (2023). Evaluating the content validity: Development of An instrument for measuring functional building performance. International Journal of Global Optimization and Its Application, 2(1), 12–19. https://doi.org/10.56225/ijgoia.v2i1.161
   Google Scholar
• Alamanda, D. T., Wibowo, L. A., Munawar, S., & Nisa, A. K. (2021). The interest of technology adoption in e-commerce mobile apps using modified unified theory of acceptance and use of technology 2 in Indonesia. International Journal of Applied Business and International Management (IJABIM), 6, 35–45.
   Google Scholar
• Aleedy, M., Shaiba, H., & Bezbradica, M. (2019). Generating and analyzing chatbot responses using natural language processing. International Journal of Advanced Computer Science and Applications, 10(9), https://doi.org/10.14569/IJACSA.2019.0100910
   Web of Science ®Google Scholar
• Ameen, N., Viglia, G., & Altinay, L. (2023). Revolutionizing services with cutting-edge technologies post major exogenous shocks. The Service Industries Journal, 43(3-4), 125–133. https://doi.org/10.1080/02642069.2023.2185934
   Web of Science ®Google Scholar
• Anabel, T. W. V., & Simanjuntak, D. C. (2022). Obtaining preferences from a hybrid learning system to promote English-speaking ability through focus group discussion. Journal of Languages and Language Teaching, 10(2), 118–133. https://doi.org/10.33394/jollt.v10i2.4994
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Appolloni, A., Jabbour, C. J. C., D'Adamo, I., Gastaldi, M., & Settembre-Blundo, D. (2022). Green recovery in the mature manufacturing industry: The role of the green-circular premium and sustainability certification in innovative efforts. Ecological Economics, 193, 107311. https://doi.org/10.1016/j.ecolecon.2021.107311
   Web of Science ®Google Scholar
• Armitage, C. J., & Conner, M. (2000). Attitudinal ambivalence: A test of three key hypotheses. Personality and Social Psychology Bulletin, 26(11), 1421–1432. https://doi.org/10.1177/0146167200263009
   Web of Science ®Google Scholar
• Avató, J. L., & Mannheim, V. (2022). Life cycle assessment model of a catering product: Comparing environmental impacts for different end-of-life scenarios. Energies, 15(15), 5423. https://doi.org/10.3390/en15155423
   Web of Science ®Google Scholar
• Bae, J. H., Rishi, M., & Li, D. (2021). Consumer preferences for a green certificate program in South Korea. Energy, 230, 120726. https://doi.org/10.1016/j.energy.2021.120726
   Web of Science ®Google Scholar
• Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484.
   Google Scholar
• Bangsa, A. B., & Schlegelmilch, B. B. (2020). Linking sustainable product attributes and consumer decision- making: Insights from a systematic review. Journal of Cleaner Production, 245, 118902. https://doi.org/10.1016/j.jclepro.2019.118902
   Web of Science ®Google Scholar
• Beerbaum Dr., D. O. (2023). <2023 - Generative Artificial Intelligence (GAI) with ChatGPT.pdf>.
   Google Scholar
• Bendary, N., & Al-Sahouly, I. (2018). Exploring the extension of unified theory of acceptance and use of technology, UTAUT2, factors effect on perceived usefulness and ease of use on mobile commerce in Egypt.
   Google Scholar
• Berger, J. (2019). Signaling can increase consumers’ willingness to pay for green products. Theoretical model and experimental evidence. Journal of Consumer Behaviour, 18(3), 233–246. https://doi.org/10.1002/cb.1760
   Web of Science ®Google Scholar
• Bertoldi, P. (2022). Policies for energy conservation and sufficiency: Review of existing policies and recommendations for new and effective policies in OECD countries. Energy and Buildings, 264, 112075. https://doi.org/10.1016/j.enbuild.2022.112075
   Web of Science ®Google Scholar
• Biswas, A., & Roy, M. (2016). A study of consumers’ willingness to pay for green products. Journal of Advanced Management Science, 4, 211–215.
   Google Scholar
• Biswas, S. (2023a). The Function of chat GPT in social media: According to chat GPT. Available at SSRN 4405389.
   Google Scholar
• Biswas, S. (2023b). Importance of chat GPT in Agriculture: According to chat GPT. Available at SSRN 4405391.
   Google Scholar
• Biswas, S. S. (2023). Potential use of chat gpt in global warming. Annals of Biomedical Engineering, 51(6), 1126–1127. https://doi.org/10.1007/s10439-023-03171-8
   PubMed Web of Science ®Google Scholar
• Burnett, J. (2007). City buildings—Eco-labels and shades of green!. Landscape and Urban Planning, 83(1), 29–38. https://doi.org/10.1016/j.landurbplan.2007.09.003
   Web of Science ®Google Scholar
• Campbell, J., DiPietro, R. B., & Remar, D. (2014). Local foods in a university setting: Price consciousness, product involvement, price/quality inference and consumer's willingness- to-pay. International Journal of Hospitality Management, 42, 39–49. https://doi.org/10.1016/j.ijhm.2014.05.014
   Web of Science ®Google Scholar
• Cardoso, A. G. (2023). Do we need a Chat-GPT-Gov? The importance of technology for effective access to public information. The importance of technology for effective access to public information. (January 7, 2023).
   Google Scholar
• Chen, C. C., Chen, C. W., & Tung, Y. C. (2018). Exploring the consumer behavior of intention to purchase green products in belt and road countries: An empirical analysis. Sustainability, 10(3), 854.
   Web of Science ®Google Scholar
• Chen, H. (2017). The effect of life cost to consumer expenditure behavior. International Management Review, 13(1), 85–91.
   Google Scholar
• Chen, H., Wan, K., Zhang, Y., & Wang, Y. (2021). Waste to wealth: Chemical recycling and chemical upcycling of waste plastics for a great future. ChemSusChem, 14(19), 4123–4136. https://doi.org/10.1002/cssc.202100652
   PubMed Web of Science ®Google Scholar
• Chen, Y. S., & Chang, C. H. (2012). Enhance green purchase intentions: The roles of green perceived value, green perceived risk, and green trust. Management Decision, 50(3), 502–520. https://doi.org/10.1108/00251741211216250
   Web of Science ®Google Scholar
• Cheng, Y., & Jiang, H. (2022). Customer–brand relationship in the era of artificial intelligence: Understanding the role of chatbot marketing efforts. Journal of Product & Brand Management, 31(2), 252–264. https://doi.org/10.1108/JPBM-05-2020-2907
   Web of Science ®Google Scholar
• Cheung, G. W., Cooper-Thomas, H. D., Lau, R. S., & Wang, L. C. (2023). Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pacific Journal of Management, 1–39.
   Web of Science ®Google Scholar
• Chiemeke, S., & Evwiekpaefe, A. (2011). A conceptual framework of a modified unified theory of acceptance and use of technology (UTAUT) model with Nigerian factors in E-commerce adoption. Educational Research, 2, 1719–1726.
   Google Scholar
• Chin, P. N., Isa, S. M., & Alodin, Y. (2020). The impact of endorser and brand credibility on consumers’ purchase intention: The mediating effect of attitude towards brand and brand credibility. Journal of Marketing Communications, 26(8), 896–912. https://doi.org/10.1080/13527266.2019.1604561
   Google Scholar
• Chopdar, P. K., Korfiatis, N., Sivakumar, V., & Lytras, M. D. (2018). Mobile shopping apps adoption and perceived risks: A cross-country perspective utilizing the unified theory of acceptance and Use of technology. Computers in Human Behavior, 86, 109–128. https://doi.org/10.1016/j.chb.2018.04.017
   Web of Science ®Google Scholar
• Connell, J., Carlton, J., Grundy, A., Taylor Buck, E., Keetharuth, A. D., Ricketts, T., Barkham, M., Robotham, D., Rose, D., & Brazier, J. (2018). The importance of content and face validity in instrument development: Lessons learnt from service users when developing the recovering quality of life measure (ReQoL). Quality of Life Research, 27, 1893–1902. https://doi.org/10.1007/s11136-018-1847-y
   PubMed Web of Science ®Google Scholar
• Dajani, D. (2016). Using the unified theory of acceptance and use of technology to explain e-commerce acceptance by Jordanian travel agencies. Journal of Comparative International Management, 19, 99–118.
   Google Scholar
• Das, K. P., & Chandra, J. (2023). A survey on artificial intelligence for reducing the climate footprint in healthcare. Energy Nexus, 9, 100167. https://doi.org/10.1016/j.nexus.2022.100167
   Google Scholar
• Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7
   Web of Science ®Google Scholar
• Denton, F. T., & Mountain, D. C. (2011). Taxing a commodity with and without revenue neutrality: A calibrated theoretical consumer equilibrium model. Atlantic Economic Journal, 39, 261–271. https://doi.org/10.1007/s11293-011-9276-0
   Google Scholar
• Diao, J., Hu, Y., Tian, Y., Carr, R., & Moon, T. S. (2023). Upcycling of poly (ethylene terephthalate) to produce high-value bio-products. Cell Reports, 42(1), 1–16.
   Web of Science ®Google Scholar
• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., & Ahuja, M. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
   Web of Science ®Google Scholar
• Elahi, K., & Reardon, J. (2022). Consumer Equilibrium Conditions in the Cardinal Versus Ordinal Approaches: Any Difference? X Euro-Asian Symposium on Economic Theory” Viability of Economic Theories: through Order and Chaos”.
   Google Scholar
• Erdem, T., & Swait, J. (2004). Brand credibility, brand consideration, and choice. Journal of Consumer Research, 31(1), 191–198. https://doi.org/10.1086/383434
   Web of Science ®Google Scholar
• Escursell, S., Llorach-Massana, P., & Roncero, M. B. (2021). Sustainability in e-commerce packaging: A review. Journal of Cleaner Production, 280, 124314. https://doi.org/10.1016/j.jclepro.2020.124314
   PubMed Web of Science ®Google Scholar
• Falcão, D., & Roseira, C. (2022). Mapping the socially responsible consumption gap research: Review and future research agenda. International Journal of Consumer Studies, 46(5), 1718–1760. https://doi.org/10.1111/ijcs.12803
   Web of Science ®Google Scholar
• Flavián, C., & Casaló, L. V. (2021). Artificial intelligence in services: Current trends, benefits and challenges. The Service Industries Journal, 41(13–14), 853–859. https://doi.org/10.1080/02642069.2021.1989177
   Web of Science ®Google Scholar
• Forgas-Coll, S., Huertas-Garcia, R., Andriella, A., & Alenyà, G. (2023). Social robot-delivered customer-facing services: An assessment of the experience. The Service Industries Journal, 43(3-4), 154–184. https://doi.org/10.1080/02642069.2022.2163995
   Web of Science ®Google Scholar
• George, A. S., George, A. H., & Martin, A. G. (2023). The environmental impact of AI: A case study of water consumption by ChatGPT. Partners Universal International Innovation Journal, 1, 97–104.
   Google Scholar
• Ghosal, I., Prasad, B., & Gupta, B. (2022). Restructuring the green consumerism through e-commerce portals: A behavioural congruence during post-covid-19. In R. N. Subudhi, S. Mishra, & A. S. D. Khezrimotlagh (Eds.), Future of work and Business in Covid-19 Era: Proceedings of IMC-2021 (pp. 89–99). Springer.
   Google Scholar
• Gómez-Llanos, E., Durán-Barroso, P., & Robina-Ramírez, R. (2020). Analysis of consumer awareness of sustainable water consumption by the water footprint concept. Science of the Total Environment, 721, 137743. https://doi.org/10.1016/j.scitotenv.2020.137743
   PubMed Web of Science ®Google Scholar
• Gray-Hawkins, M., & Lăzăroiu, G. (2020). Industrial artificial intelligence, sustainable product lifecycle management, and internet of things sensing networks in cyber-physical smart manufacturing systems. Journal of Self-Governance and Management Economics, 8, 19–28.
   Google Scholar
• Guo, W., & Luo, Q. (2023). Investigating the impact of intelligent personal assistants on the purchase intentions of generation Z consumers: The moderating role of brand credibility. Journal of Retailing and Consumer Services, 73, 103353. https://doi.org/10.1016/j.jretconser.2023.103353
   Web of Science ®Google Scholar
• Gürlek, M., & Koseoglu, M. A. (2021). Green innovation research in the field of hospitality and tourism: The construct, antecedents, consequences, and future outlook. The Service Industries Journal, 41(11–12), 734–766. https://doi.org/10.1080/02642069.2021.1929930
   Web of Science ®Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: A comparative evaluation of composite-based structural equation modeling methods. Journal of the Academy of Marketing Science, 45, 616–632. https://doi.org/10.1007/s11747-017-0517-x
   Web of Science ®Google Scholar
• Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
   Web of Science ®Google Scholar
• Hao, S., & Huang, L. (2023). How the time-scarcity feature of live-streaming e-commerce affects impulsive buying 直播电商的时间性稀缺特征如何影响冲动购买. The Service Industries Journal, 1–21.
   Web of Science ®Google Scholar
• Henseler, J. (2017). Partial least squares path modeling. Advanced Methods for Modeling Markets, 361–381.
   Google Scholar
• Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: Updated guidelines. Industrial Management & Data Systems, 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382
   Web of Science ®Google Scholar
• Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115–135. https://doi.org/10.1007/s11747-014-0403-8
   Web of Science ®Google Scholar
• Howitt, P., & Özak, Ö. (2014). Adaptive consumption behavior. Journal of Economic Dynamics and Control, 39, 37–61. https://doi.org/10.1016/j.jedc.2013.11.003
   Web of Science ®Google Scholar
• Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
   Web of Science ®Google Scholar
• Huang, Y.-C., Yang, M., & Wang, Y.-C. (2014). Effects of green brand on green purchase intention. Marketing Intelligence & Planning, 32(3), 250–268. https://doi.org/10.1108/MIP-10-2012-0105
   Google Scholar
• Huang, Y.-S., & Kao, W.-K. (2021). Chatbot service usage during a pandemic: Fear and social distancing. The Service Industries Journal, 41(13–14), 964–984.
   Web of Science ®Google Scholar
• Ihemezie, E. J., Ukwuaba, I. C., & Nnaji, A. P. (2018). Impact of ‘Green’product label standards on consumer behaviour: A systematic review analysis. International Journal of Academic Research in Business and Social Sciences, 8(9), 666–684. https://doi.org/10.6007/IJARBSS/v8-i9/4647
   Google Scholar
• Ikumoro, A. O., & Jawad, M. S. (2019). Intention to use intelligent conversational agents in e-commerce among Malaysian SMEs: An integrated conceptual framework based on tri-theories including unified theory of acceptance, use of technology (UTAUT), and TOE. International Journal of Academic Research in Business and Social Sciences, 9(11), 205–235. https://doi.org/10.6007/IJARBSS/v9-i11/6544
   Google Scholar
• Indriani, I. A. D., Rahayu, M., & Hadiwidjojo, D. (2019). The influence of environmental knowledge on green purchase intention the role of attitude as mediating variable. International Journal of Multicultural and Multireligious Understanding, 6(2), 627–635. https://doi.org/10.18415/ijmmu.v6i2.706
   Google Scholar
• Jeng, S.-P. (2016). The influences of airline brand credibility on consumer purchase intentions. Journal of Air Transport Management, 55, 1–8. https://doi.org/10.1016/j.jairtraman.2016.04.005
   Web of Science ®Google Scholar
• Jiang, H., & Chen, L. (2023). Analysis of consumer recommendation behavior and market equilibrium in E-commerce from the perspective of social media. Advances in Multimedia, 2023.
   Web of Science ®Google Scholar
• Joshi, Y., & Rahman, Z. (2017). Investigating the determinants of consumers’ sustainable purchase behaviour. Sustainable Production and Consumption, 10, 110–120. https://doi.org/10.1016/j.spc.2017.02.002
   Web of Science ®Google Scholar
• Joshi, Y., & Rahman, Z. (2019). Consumers’ sustainable purchase behaviour: Modeling the impact of psychological factors. Ecological Economics, 159, 235–243. https://doi.org/10.1016/j.ecolecon.2019.01.025
   Web of Science ®Google Scholar
• Kachamas, P., Akkaradamrongrat, S., Sinthupinyo, S., & Chandrachai, A. (2019). Application of artificial intelligent in the prediction of consumer behavior from Facebook posts analysis. International Journal of Machine Learning and Computing, 9(1), 91–97. https://doi.org/10.18178/ijmlc.2019.9.1.770
   Google Scholar
• Kalla, D., & Smith, N. (2023). Study and analysis of ChatGPT and its impact on different fields of study. International Journal of Innovative Science and Research Technology, 8, 827–833.
   Google Scholar
• Kan, M., & Miller, S. A. (2022). Environmental impacts of plastic packaging of food products. Resources, Conservation and Recycling, 180, 106156. https://doi.org/10.1016/j.resconrec.2022.106156
   Web of Science ®Google Scholar
• Khan, S., & Rabbani, M. R. (2020). Chatbot as islamic finance expert (caife) when finance meets artificial intelligence. Proceedings of the 2020 4th international symposium on computer science and intelligent control.
   Google Scholar
• Khatimah, H., & Halim, F. (2014). Consumers’ intention to use e-money in Indonesia based on Unified Theory of Acceptance and Use of Technology (UTAUT). American-Eurasian Journal of Sustainable Agriculture, 8, 34–40.
   Google Scholar
• Khrais, L. T. (2020). Role of artificial intelligence in shaping consumer demand in E-commerce. Future Internet, 12(12), 1–14. https://doi.org/10.3390/fi12120226.
   Web of Science ®Google Scholar
• Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (Ijec), 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
   Web of Science ®Google Scholar
• Kumar, P., Polonsky, M., Dwivedi, Y. K., & Kar, A. (2021). Green information quality and green brand evaluation: The moderating effects of eco-label credibility and consumer knowledge. European Journal of Marketing, 55(7), 2037–2071. https://doi.org/10.1108/EJM-10-2019-0808
   Web of Science ®Google Scholar
• Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general. Psychology, 9(08), 2207. https://doi.org/10.4236/psych.2018.98126
   Google Scholar
• Lasuin, C. A., & Ng, Y. C. (2014). Factors influencing green purchase intention among university students. Malaysian Journal of Business and Economics (MJBE), 1(2), 1–14.
   Google Scholar
• Lee, C. T., Pan, L.-Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: A two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329–1356. https://doi.org/10.1108/INTR-01-2021-0030
   Web of Science ®Google Scholar
• Lee, S., & Kim, E. (2020). Influencer marketing on Instagram: How sponsorship disclosure, influencer credibility, and brand credibility impact the effectiveness of Instagram promotional post. Journal of Global Fashion Marketing, 11(3), 232–249.
   Web of Science ®Google Scholar
• Liao, M., Lan, K., & Yao, Y. (2022). Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework. Journal of Industrial Ecology, 26(1), 164–182. https://doi.org/10.1111/jiec.13214
   Web of Science ®Google Scholar
• Lim, P. K., Koay, K. Y., & Chong, W. Y. (2021). The effects of abusive supervision, emotional exhaustion and organizational commitment on cyberloafing: A moderated-mediation examination. Internet Research, 31(2), 497–518. https://doi.org/10.1108/INTR-03-2020-0165
   Web of Science ®Google Scholar
• Lin, C.-J., & Chen, H.-Y. (2016). User expectancies for green products: A case study on the internal customers of a social enterprise. Social Enterprise Journal, 12(3), 281–301. https://doi.org/10.1108/SEJ-02-2016-0004
   Web of Science ®Google Scholar
• Lin, J., Guo, J., Turel, O., & Liu, S. (2020). Purchasing organic food with social commerce: An integrated food-technology consumption values perspective. International Journal of Information Management, 51, 102033. https://doi.org/10.1016/j.ijinfomgt.2019.11.001
   Web of Science ®Google Scholar
• Lubis, A. R., Majid, M. S. A., & Bachri, N. (2016). Credibility and consumer behavior of Islamic Bank in Indonesia: A literature review. Expert Journal of Marketing, 4, 20–23.
   Google Scholar
• Mariani, M. M., Hashemi, N., & Wirtz, J. (2023). Artificial intelligence empowered conversational agents: A systematic literature review and research agenda. Journal of Business Research, 161, 113838. https://doi.org/10.1016/j.jbusres.2023.113838
   Web of Science ®Google Scholar
• Mashaabi, M., Alotaibi, A., Qudaih, H., Alnashwan, R., & Al-Khalifa, H. (2022). Natural Language Processing in Customer Service: A Systematic Review. arXiv preprint arXiv:2212.09523.
   Google Scholar
• McGee, R. W. (2023). <2023 - Is ChatGPT Biased against Conservatives An Empirical Study.pdf>.
   Google Scholar
• Migliore, M. (2019). Circular economy and upcycling of waste and pre-consumer scraps in construction sector. The role of information to facilitate the exchange of resources through a virtual marketplace. Environmental Engineering and Management Journal, 18, 2297–2303.
   Web of Science ®Google Scholar
• Ming, L., & Tunca, T. I. (2022). Consumer equilibrium, demand effects, and efficiency in group buying. Manufacturing & Service Operations Management, 24(3), 1437–1456. https://doi.org/10.1287/msom.2022.1083
   Google Scholar
• Mogyoros, A. (2023). Improving eco-labels: Are green certification marks up to the task? Journal of Intellectual Property Law and Practice, 18(5), 367–373. https://doi.org/10.1093/jiplp/jpad029
   Web of Science ®Google Scholar
• Mook, A., Overdevest, C., & Lusk, J. (2023). World society and the convergence of consumer values: Buying patterns of eco-certification in the UAE. Business Strategy & Development.
   Google Scholar
• Moran, D., Wood, R., Hertwich, E., Mattson, K., Rodriguez, J. F., Schanes, K., & Barrett, J. (2020). Quantifying the potential for consumer-oriented policy to reduce European and foreign carbon emissions. Climate Policy, 20(sup1), S28–S38. https://doi.org/10.1080/14693062.2018.1551186
   Web of Science ®Google Scholar
• Musa, H. M., Hayes, C., Bradley, M. J., Clayson, A., & Gillibrand, G. (2013). Measures aimed at reducing plastic carrier bag use: A consumer behaviour focused study. Natural Environment, 1(1), 17–23. https://doi.org/10.12966/ne.06.02.2013
   Google Scholar
• Ngai, E. W., Lee, M. C., Luo, M., Chan, P. S., & Liang, T. (2021). An intelligent knowledge-based chatbot for customer service. Electronic Commerce Research and Applications, 50, 101098. https://doi.org/10.1016/j.elerap.2021.101098
   Web of Science ®Google Scholar
• Northey, G., Hunter, V., Mulcahy, R., Choong, K., & Mehmet, M. (2022). Man vs machine: How artificial intelligence in banking influences consumer belief in financial advice. International Journal of Bank Marketing, 40(6), 1182–1199. https://doi.org/10.1108/IJBM-09-2021-0439
   Web of Science ®Google Scholar
• Olan, F., Suklan, J., Arakpogun, E. O., & Robson, A. (2021). Advancing consumer behavior: The role of artificial intelligence technologies and knowledge sharing. IEEE Transactions on Engineering Management, 1–39.
   Web of Science ®Google Scholar
• O. Nyumba, T., Wilson, K., Derrick, C. J., & Mukherjee, N. (2018). The use of focus group discussion methodology: Insights from two decades of application in conservation. Methods in Ecology and Evolution, 9(1), 20–32. https://doi.org/10.1111/2041-210X.12860
   Web of Science ®Google Scholar
• Park, H. J., & Lin, L. M. (2020). Exploring attitude–behavior gap in sustainable consumption: Comparison of recycled and upcycled fashion products. Journal of Business Research, 117, 623–628. https://doi.org/10.1016/j.jbusres.2018.08.025
   Web of Science ®Google Scholar
• Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, https://doi.org/10.1016/j.ijresmar.2023.03.001
   Web of Science ®Google Scholar
• Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
   PubMed Web of Science ®Google Scholar
• Popescu, G. H. (2015). E-commerce effects on social sustainability. Economics, Management, and Financial Markets, 10, 80–85.
   Google Scholar
• Porumb, V. A., Maier, G., & Anghel, I. (2020). The impact of building location on green certification price premiums: Evidence from three European countries. Journal of Cleaner Production, 272, 122080.
   Web of Science ®Google Scholar
• Prakash, A. V., Joshi, A., Nim, S., & Das, S. (2023). Determinants and consequences of trust in AI-based customer service chatbots: 基于人工智能的客户服务聊天机器人信任的决定因素和后果. The Service Industries Journal, 43(9–10), 642–675. https://doi.org/10.1080/02642069.2023.2166493
   Web of Science ®Google Scholar
• Praneetvatakul, S., Vijitsrikamol, K., & Schreinemachers, P. (2022). Ecolabeling to improve product quality and reduce environmental impact: A choice experiment with vegetable farmers in Thailand. Frontiers in Sustainable Food Systems, 5, 704233. https://doi.org/10.3389/fsufs.2021.704233
   Google Scholar
• Puntoni, S., Reczek, R. W., Giesler, M., & Botti, S. (2021). Consumers and artificial intelligence: An experiential perspective. Journal of Marketing, 85(1), 131–151. https://doi.org/10.1177/0022242920953847
   Web of Science ®Google Scholar
• Rabby, F., Chimhundu, R., & Hassan, R. (2021). Artificial intelligence in digital marketing influences consumer behaviour: A review and theoretical foundation for future research. Academy of Marketing Studies Journal, 25, 1–7.
   Google Scholar
• Rahi, S., Ghani, M., Alnaser, F., & Ngah, A. (2018). Investigating the role of unified theory of acceptance and use of technology (UTAUT) in internet banking adoption context. Management Science Letters, 8(3), 173–186. https://doi.org/10.5267/j.msl.2018.1.001
   Google Scholar
• Ramaswamy, S., & DeClerck, N. (2018). Customer perception analysis using deep learning and NLP. Procedia Computer Science, 140, 170–178. https://doi.org/10.1016/j.procs.2018.10.326
   Google Scholar
• Raubenheimer, J. (2004). An item selection procedure to maximize scale reliability and validity. SA Journal of Industrial Psychology, 30(4), 59–64. https://doi.org/10.4102/sajip.v30i4.168
   Google Scholar
• Razmus, W., Grabner-Kräuter, S., Kostyra, M., & Zawadzka, A. M. (2022). Buying happiness: How brand engagement in self-concept affects purchase happiness. Psychology & Marketing, 39(11), 2096–2109. https://doi.org/10.1002/mar.21714
   Web of Science ®Google Scholar
• Richter, N. F., Cepeda-Carrión, G., Roldán Salgueiro, J. L., & Ringle, C. M. (2016). European management research using partial least squares structural equation modeling (PLS-SEM). European Management Journal, 34(6), 589–597. https://doi.org/10.1016/j.emj.2016.08.001
   Web of Science ®Google Scholar
• Sadiq, M. W., Hameed, J., Huo, C., & Abdullah, M. I. (2022). Service innovation in small neighborhood family firms: An advanced approach to enhance employee's performance through social and psychological rewards. Frontiers in Public Health, 10, 984848. https://doi.org/10.3389/fpubh.2022.984848
   PubMed Web of Science ®Google Scholar
• Sadiq, W., Abdullah, I., Aslam, K., & Zulfiqar, S. (2020). Engagement marketing: the innovative perspective to enhance the viewer’s loyalty in social media and blogging e-commerce websites.
   Google Scholar
• Shafeeg, A., Shazhaev, I., Mihaylov, D., Tularov, A., & Shazhaev, I. (2023). Voice assistant integrated with chat gpt. Indonesian Journal of Computer Science, 12(1), https://doi.org/10.33022/ijcs.v12i1.3146
   Google Scholar
• Sheeraz, M., Iqbal, N., & Ahmed, N. (2012). Impact of brand credibility and consumer values on consumer purchase intentions in Pakistan. International Journal of Academic Research in Business and Social Sciences, 2, 1.
   Google Scholar
• Singh, L., Ahmed Pihlgren, S., Holmes, E. A., & Moulds, M. L. (2023). Using a daily diary for monitoring intrusive memories of trauma: A translational data synthesis study exploring convergent validity. International Journal of Methods in Psychiatric Research, 32(1), e1936. https://doi.org/10.1002/mpr.1936
   PubMed Web of Science ®Google Scholar
• Sreen, N., Purbey, S., & Sadarangani, P. (2018). Impact of culture, behavior and gender on green purchase intention. Journal of Retailing and Consumer Services, 41, 177–189. https://doi.org/10.1016/j.jretconser.2017.12.002
   Web of Science ®Google Scholar
• Stichnothe, H. (2022). Life cycle assessment of peat for growing media and evaluation of the suitability of using the product environmental footprint methodology for peat. The International Journal of Life Cycle Assessment, 27(12), 1270–1282. https://doi.org/10.1007/s11367-022-02106-0
   Web of Science ®Google Scholar
• Stiglitz, J. E. (1979). Equilibrium in product markets with imperfect information. The American Economic Review, 69, 339–345.
   Web of Science ®Google Scholar
• Suki, N. M., Suki, N. M., & Azman, N. S. (2016). Impacts of corporate social responsibility on the links between green marketing awareness and consumer purchase intentions. Procedia Economics and Finance, 37, 262–268. https://doi.org/10.1016/S2212-5671(16)30123-X
   Google Scholar
• Sun, H., Zafar, M. Z., & Hasan, N. (2022). Employing natural language processing as artificial intelligence for analyzing consumer opinion toward advertisement. Frontiers in Psychology, 13, 856663. https://doi.org/10.3389/fpsyg.2022.856663
   PubMed Web of Science ®Google Scholar
• Surameery, N. M. S., & Shakor, M. Y. (2023). Use ChatGPT to solve programming bugs. International Journal of Information Technology and Computer Engineering, 31, 17–22. https://doi.org/10.55529/ijitc.31.17.22
   Google Scholar
• Teo, C. B.-C. (2016). Recycling behaviour of Malaysian urban households and upcycling prospects. Journal of International Business, Economics and Entrepreneurship, 1, 915.
   Google Scholar
• Testa, F., Iraldo, F., Vaccari, A., & Ferrari, E. (2015). Why eco-labels can be effective marketing tools: Evidence from a study on Italian consumers. Business Strategy and the Environment, 24(4), 252–265. https://doi.org/10.1002/bse.1821
   Web of Science ®Google Scholar
• Thowfeek, M. H., Samsudeen, S., & Sanjeetha, M. B. F. (2020). Drivers of artificial intelligence in banking service sectors. Solid State Technology, 63, 6400–6411.
   Google Scholar
• Trujillo-Ortiz, A., Hernandez-Walls, R., Castro-Perez, A., Barba-Rojo, K., & Otero-Limon, A. (2006). kmo: Kaiser–Meyer-Olkin Measure of Sampling Adequacy. A MATLAB file.[WWW document] URL http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do
   Google Scholar
• Tukker, A., & Jansen, B. (2006). Environmental impacts of products: A detailed review of studies. Journal of Industrial Ecology, 10(3), 159–182. https://doi.org/10.1162/jiec.2006.10.3.159
   Web of Science ®Google Scholar
• Vashisht, V., & Dharia, P. (2020). Integrating chatbot application with qlik sense business intelligence (BI) tool using natural language processing (NLP). In Micro-Electronics and Telecommunication Engineering: Proceedings of 3rd ICMETE 2019 (pp. 683-692). Springer.
   Google Scholar
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478. https://doi.org/10.2307/30036540
   Web of Science ®Google Scholar
• Vitezić, V., & Perić, M. (2021). Artificial intelligence acceptance in services: Connecting with generation Z. The Service Industries Journal, 41(13–14), 926–946. https://doi.org/10.1080/02642069.2021.1974406
   Web of Science ®Google Scholar
• Wang, H., & Wang, L. (2022). Product line strategy and environmental impact oriented to carbon tax constraints. Sustainable Production and Consumption, 32, 198–213. https://doi.org/10.1016/j.spc.2022.04.015
   Web of Science ®Google Scholar
• Wang, S., Liao, Y. K., Wu, W. Y., & Le, K. B. H. (2021). The role of corporate social responsibility perceptions in brand equity, brand credibility, brand reputation, and purchase intentions. Sustainability, 13(21), 11975.
   Web of Science ®Google Scholar
• Wang, S.-M., Huang, Y.-K., Shih, C.-W., & Li, P.-C. (2023). Evaluation of service quality on natural language processing service: A case on train station AI service. Review of Integrative Business and Economics Research, 12, 71–87.
   Google Scholar
• Wang, Y., Fan, R., Shen, L., & Jin, M. (2020). Decisions and coordination of green e-commerce supply chain considering green manufacturer's fairness concerns. International Journal of Production Research, 58(24), 7471–7489. https://doi.org/10.1080/00207543.2020.1765040
   Web of Science ®Google Scholar
• West, A., Clifford, J., & Atkinson, D. (2018). “Alexa, build me a brand” An investigation into the impact of artificial intelligence on branding. The Business & Management Review, 9, 321–330.
   Google Scholar
• Wilson, M. (2016). When creative consumers go green: Understanding consumer upcycling. Journal of Product & Brand Management, 25(4), 394–399. https://doi.org/10.1108/JPBM-09-2015-0972
   Web of Science ®Google Scholar
• Worthington, R. L., & Whittaker, T. A. (2006). Scale development research: A content analysis and recommendations for best practices. The Counseling Psychologist, 34(6), 806–838. https://doi.org/10.1177/0011000006288127
   Web of Science ®Google Scholar
• Xie, H., Chang, S., Wang, Y., & Afzal, A. (2023). The impact of e-commerce on environmental sustainability targets in selected European countries. Economic Research-Ekonomska Istraživanja, 36(1), 230–242. https://doi.org/10.1080/1331677X.2022.2117718
   Web of Science ®Google Scholar
• Xiong, Y. (2022). The impact of artificial intelligence and digital economy consumer online shopping behavior on market changes. Discrete Dynamics in Nature and Society, 1–12.
   Web of Science ®Google Scholar
• Yabe, M., Hayashi, T., & Nishimura, B. (2013). Economic analysis of consumer behaviour and agricultural products based on biodiversity conservation value. In J. R. Pillarisetti, R. Lawrey, & A. Ahmad (Eds.), Multifunctional agriculture, ecology and food security: International perspectives (pp. 21–37). Nova Science Publishers, Inc.
   Google Scholar
• Yaqub, R., Ahmad, S., Ahmad, A., & Amin, M. (2016). Smart energy-consumption management system considering consumers’ spending goals (SEMS-CCSG). International Transactions on Electrical Energy Systems, 26(7), 1570–1584. https://doi.org/10.1002/etep.2167
   Web of Science ®Google Scholar
• Zhang, L., & Li, R. (2022). Impacts of green certification programs on energy consumption and GHG emissions in buildings: A spatial regression approach. Energy and Buildings, 256, 111677.
   Web of Science ®Google Scholar
• Zhang, X., Shao, X., Jeong, E., & Olson, E. (2021). I am worth more than you think I am: Investigating the effects of upcycling on event attendees’ recycling intention. International Journal of Hospitality Management, 94, 102888. https://doi.org/10.1016/j.ijhm.2021.102888
   Web of Science ®Google Scholar
• Zhang, Z., Guo, C., & Goes, P. (2013). Product comparison networks for competitive analysis of online word-of-mouth. ACM Transactions on Management Information Systems (TMIS), 3, 1–22.
   Google Scholar
• Zhou, S., Silvasstar, J., Clark, C., Salyers, A. J., Chavez, C., & Bull, S. S. (2023). An artificially intelligent, natural language processing chatbot designed to promote COVID-19 vaccination: A proof-of-concept pilot study. Digital Health, 9, 20552076231155679.
   Web of Science ®Google Scholar
• Zhuang, W., Luo, X., & Riaz, M. U. (2021). On the factors influencing green purchase intention: A meta-analysis approach. Frontiers in Psychology, 12, 644020. https://doi.org/10.3389/fpsyg.2021.644020
   PubMed Web of Science ®Google Scholar