Determinants of Green Smartphone Application Adoption for Sustainable Food Consumption Among University Students

Arian Matin, Tornike Khoshtaria, Nugzar Todua, Ola Bareja-Wawryszuk, Tomasz Pajewski, Nia Todua


This study aims to investigate the determinants of green smartphone application adoption among users. The study employs content richness model and modified Unified Theory of Acceptance and Use of Technology (UTAUT) as well as extrinsic constructs such as customisation and environmental concerns. A quantitative approach using a survey is utilised by collecting 700 responses. The data is analysed using Structural Equation Modelling (SEM) and three machine learning techniques including Artificial Neural Networks (ANN), Classification Regression Tree (CRT) and Chi-Squared Automatic Interaction Detection (CHAID). The results indicate that UTAUT, customisation and environmental concerns positively impact the adoption of green applications. Further analysis revealed fitness of analytical methods and the importance of variables for the overall sample and the subsamples derived. The study provides theoretical and practical contributions to academics, marketers and software developers in understanding consumer behaviour in the field. The result assist developers and marketers to decipher consumer behaviour towards green applications for sustainable consumption. The research contributes to theory and practice by employing an integrative model to investigate the role of technology in sustainable consumption. Moreover, the findings revealed the fitness of three machine learning methods to analyse the data collected for green consumption and the importance of variables in the model. The data is collected by employing convenience sampling. Hence, the results cannot be generalised accurately. Furthermore, data collection is conducted using a cross-sectional approach. Future researchers can add to the findings using a probability sampling and/or longitudinal data collection to generalise the results and reveal the changes in consumer behaviour. 




Technology adoption; Green smartphone applications; Sustainability; Food consumption behaviour; UTAUT; SEM; Machine Learning

Full Text:



Abbad, M. M. (2021). Using the UTAUT model to understand students’ usage of e-learning systems in developing countries. Education and Information Technologies, 26, 7205–7224.

Alantari, H. J., Currim, I. S., Deng, Y., & Singh, S. (2022). An empirical comparison of machine learning methods for text-based sentiment analysis of online consumer reviews. International Journal of Research in Marketing, 39(1), 1-19.

Aldrich, G., Grimsrud, K., Thacher, J., & Kotchen, M. (2007). Relating environmental attitudes and contingent values: how robust are methods for identifying preference heterogeneity? Environmental and Resource Economics volume, 37, 757–775. DOI:

Almousa, M.; Alsaikhan, A. & Aloud. A. (2020). The Influence of Social Media on Nutritional Behavior and Purchase Intention Among Millennials. International Journal of Marketing, Communication and New Media, Special Issue 8 – Social Media Marketing, 78-9

Alnawas, I., & Aburub, F. (2016). The effect of benefits generated from interacting with branded mobile apps on consumer satisfaction and purchase intentions. Journal of Retailing and Consumer Services, 31, 313–322. DOI:

Almarzouqi, A., Aburayya, A., & Salloum, S. A. (2022). Determinants of intention to use medical smartwatch-based dual-stage SEM-ANN analysis. Informatics in Medicine Unlocked, 28. DOI:

Aryadoust, V & Baghaei, P (2016) Does EFL Readers' Lexical and Grammatical Knowledge Predict Their Reading Ability? Insights From a Perceptron Artificial Neural Network Study, Educational Assessment, 21(2), 135-156, DOI: 10.1080/10627197.2016.1166343

Bamberg, S. (2003). How does environmental concern influence specific environmentally related behaviors? A new answer to an old question. Journal of Environmental Psychology, 23 (1), 21–32. DOI:

Bekaroo, G., Sungkur, R., Ramsamy, P., Okolo, A., & Moedeen, W. (2018). Enhancing awareness on green consumption of electronic devices: The application of augmented reality. Sustainable Energy Technologies and Assessments, 30, 27-291. DOI:

Bengtsson, F., & Ågerfalk, P. J. (2011). Information technology as a change actant in sustainability innovation: Insights from Uppsala. The Journal of Strategic Information Systems, 20 (1), 96–112. DOI:

Bilgihan, A., Kandampully, J., & Zhang, T. (2015). Towards a unified customer experience in online shopping environments- Antecedents and outcomes. International Journal of Quality and Service Science, 8 (1), 102–119. DOI:

Borges, A. P.; Vieira, E.; Rodrigues, P. & Tavares, V. (2021). Brand knowledge and Satisfaction Explained by the Attributes of a Regional Food Product, International Journal of Marketing, Communication and New Media. Vol. 9, Nº 16, 25-50.

Brauer, B., Ebermann, C., Hildebrandt, B., Remané, G., & Kolbe, L. M. (2016). Grenn by app: The contribution of mobile applications to environmental sustainability. 20th Pacific Asia Conference on Information Systems (PACIS 2016), (p. Chiayi.

Breiman, L. (1984). Classification and Regression Trees. New York: Routledge. DOI:

Brüggemann, P., Pauwels, K. (2022). Consumers’ Attitudes and Purchases in Online Versus Offline Grocery Shopping. In: Martínez-López, F.J., Gázquez-Abad, J.C., Ieva, M. (eds) Advances in National Brand and Private Label Marketing. Springer Proceedings in Business and Economics. Springer, Cham.

Bryne, B. M. (2013). Structural equation modelling with AMOS, basic concepts, applications and programming. New York: Taylor and Francis Group LLP. DOI:

Cabero, J., Barroso, J., & Llorente, M. (2016). Technology acceptance model & augmented reality: study in progress. Revista Lasallista de Investigación, 13(2), 18–26.

Chang, S., & Tung, F. (2008). An empirical investigation of students’ behavioural intentions to use the online learning course websites. British Journal of Educational Technologies, 399 (1), 71-83. DOI:

Chen, J. L. (2011). The effects of education compatibility and technological expectancy on e-learning acceptance. Computers & Education, 57, 1501-1511

Chen, Y., & Lan, Y. (2014). An empirical study of the factors affecting mobile shopping in Taiwan. International Journal of Technology and Human, 10 (1), 19-30. DOI: 10.4018/ijthi.2014010102

Chin, W. W., & Todd, P. A. (1995). On the use, usefulness, and ease of use of structural equation modelling in MIS research: A note of caution. MIS Quarterly, 237-246. DOI:

Chiu, C.-Y.; Chen, C.-L.; Chen, S. (2022) Broadband Mobile Applications’ Adoption by SMEs in Taiwan—A Multi-Perspective Study of Determinants. Appl. Sci., 12, 7002.

Choi, A., & Fielding, K. (2013). Environmental attitudes as WTP predictors: a case study involving endangered species. Ecological Economics, 89, 24–32. DOI:

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13 (3), 319–340. DOI: 10.2307/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35 (8), 982–1003. DOI:

Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behaviour and Information Technology, 37 (2), 145-158. DOI:

De Wulf, K., Schillewaert, N., Muylle, S., & Rangarajan, D. (2006). The role of pleasure in web site success. Information And Management, 43 (4), 434-446. DOI:

Doligalski, T., Goliński, M., & Kozłowski, K. (Eds.). (2021). Disruptive Platforms: Markets, Ecosystems, and Monopolists (1st ed.). Routledge.

Doll, W., & Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly, 12, 259-272. DOI:

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734.

Ebrahimi, P., Basirat, M., Yousefi, A., Nekmahmud, M., Gholampour, A., & Fekete-Farkas, M. (2022). Social networks marketing and consumer purchase behavior: the combination of SEM and unsupervised machine learning approaches. Big Data and Cognitive Computing, 6(2), 35. DOI:

Eiriksdottir, E., & Catrambone, R. (2011). Procedural Instructions, Principles, and Examples How to Structure Instructions for Procedural Tasks to Enhance Performance, Learning, and Transfer. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53 (6), 749-770. DOI: 10.1177/0018720811419154

Enginkaya Erkent, E., Köse, Ş. G., & Çizer, E. O. (2021). Will you carry that watch? investigating factors affecting continuance intention of Smartwatches. International Journal of Contemporary Economics and Administrative Sciences, 11 (2), 354-373. DOI: 10.5281/zenodo.5831643

Farjam, M., Nikolaychuk, O., & Bravo, G. (2019). Experimental evidence of an environmental attitude-behavior gap in high-cost situations. Ecological Economics, 166, 106434. DOI:

Fraj-Andres, E., & Martínez-Salinas, E. (2007). Impact of environmental knowledge on ecological consumer behaviour: an empirical analysis. International Journal of Consumer Marketing, 19 (3), 73–102. DOI:

Franzen, A., & Meyer, R. (2010). Environmental attitudes in cross-national perspective: a multilevel analysis of the ISSP 1993 and 2000. analy Sociological Review, 26 (2), 219–234. DOI:

Ghose, A., Ipeirotis, P. G., & Li, B. (2019). Modeling consumer footprints on search engines: An interplay with social media. Management Science, 65(3), 1363–1385.

Givens, J. J. (2013). Individual environmental concern in the world polity: a multilevel analysis. Social Science Research, 42 (2), 418–431. DOI:

Greene, G., & Weller, K. (2012, July 1). Exploring Demographic and Behavioral Variables Associated with Motivational Readiness to Adopt Green Eating Behaviors. Journal of Nutrition Education and Behavior, 44 (4) , S19 DOI:

Grunert, K. G., Hieke, S., & Wills, J. (2014). Sustainability labels on food products: Consumer motivation, understanding and use. Food Policy, 44, 177–189. DOI:

Gupta, A., Dhiman, N., Yousaf, A., & Arora, N. (2020). Social comparison and continuance intention of smart fitness wearables: an extended expectation confirmation theory perspective. Behaviour & Information Technology, 40 (13), 1341-1354. DOI:

Hadler, M., & Haller, M. (2013). A shift from public to private environmental behavior: findings from Hadler and Haller (2011) revisited and extended. International Sociology, 28 (4), 484–489. DOI:10.1177/0268580913494661

Haraty, R. A., & Bitar, G. (2019). Associating learning technology to sustain the environment through green mobile applications. Heliyon , 5 (1), 01141. DOI:

Hilpert, H., Kranz, J., & Schumann, M. (2014). An Information System Design Theory for Green Information Systems for Sustainability Reporting - Integrating Theory with Evidence from Multiple Case Studies. European Conference on Information Systems (ECIS). Tel-Aviv: ECIS 2014 Proceedings.

Hsiao, C.-H., Chang, J.-J., & Tang, K.-Y. (2016). Exploring the influential factors in continuance usage of mobile social apps: Satisfaction, habit, and customer value perspectives. Telematics and Informatics, 33, 342–355. DOI:

Huang, W., Hood, D., & Yoo, S. (2013). Gender divide and acceptance of collaborative Web 2.0 applications for learning in higher education. Internet and Higher Education, 16, 57-65. DOI:

Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing, 37, 646-664. DOI:

Jain, K., Sharma, I., & Singh, G. (2019). An empirical study of factors determining wearable fitness tracker continuance among actual users. International Journal of Technology Marketing, 13 (1), 83–109. DOI: 10.1504/IJTMKT.2018.10020929

Kannan, P. K., & Li, H. A. (2017). Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing, 34 (1), 22–45. DOI:

Kemp, A., Palmer, E., & Strelan, P. (2019). A taxonomy of factors affecting attitudes towards educational technologies for use with technology acceptance models. British Journal of Educational Technology, 50(5), 2394–2413.

Kim, H. Y., Lee, J. Y., Mun, J. M., & Johnson, K. K. (2017). Consumer adoption of smart in-store technology: assessing the predictive value of attitude versus beliefs in the technology acceptance model. International Journal of Fashion Design, Technology and Education, 10(1), 26-36. DOI:

Kim, J., & Lee, K. S. (2022). Conceptual model to predict Filipino teachers’ adoption of ICT-based instruction in class: using the UTAUT model. Asia Pacific Journal of Education, 42(4), 699-713 .

Kim, J. S. (2016). An extended technology acceptance model in behavioral intention toward hotel tablet apps with moderating effects of gender and age. International Journal of Contemporary Hospitality Management, 28(8), 1535–1553. DOI:

Kim, S., & Baek, T. (2018). Examining the antecedents and consequences of mobile app engagement. Telematics and Informatics, 35 (1), 148–158. DOI:

Kim, Y., & Choi, S. M. (2005). Antecedents of green purchase behavior: An examinationof collectivism, environmental concern, and PCE. Advances in Consumer Research, 32 (1), 592–599. DOI:

Khoshtaria, T., Matin, A., Mercan, M., & Datuashvili, D. (2021). The impact of customers’ purchasing patterns on their showrooming and webrooming behaviour: an empirical evidence from the Georgian retail sector. International Journal of Electronic Marketing and Retailing, 12 (4), 394-413. DOI: 10.1504/IJEMR.2021.10040527

Lazzarini, G. A., Visschers, V. H., & Siegrist, M. (2018). How to improve consumers’ environmental sustainability judgements of foods. Journal of Cleaner Production, 198, 564–574. DOI:

Lea, E., & Worsley, T. (2005). Australians’ organic food beliefs, demographics and values. British Food Journal, 107 (11), 855 - 869. DOI:

Lee, C. H., & Crange, D. A. (2011). Personalisation - privacy paradox: The effects of personalisation and privacy assurance on customer responses to travel web sites. Tourism Management, 32, 987–994. DOI:

Lee, M. C., & Tsai, T. R. (2010). What drives people to continue to play online games? An extension of technology model and theory of planned behavior. International Journal of Human-Computer Interaction, 26 (6), 601–620.


Lee, Y.-C. (2006). An empirical investigation into factors influencing the adoption of an e-Learning system. Online Information Review, 30, 517-541. DOI:

Lee, Y. H., Hsieh, Y. C., & Hsu, C. N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124-137. DOI:

Lemos Barboza, M. N., & Arruda Filho, E. J. (2019). Green Consumption Values in Mobile Apps. Journal of International Consumer Marketing, 31 (1), 66-83. DOI:

Leong L-Y., Hew T-S., Ooi K-B., Lee V-H., Hew J-J., (2019) A hybrid SEM-neural network analysis of social media addiction. Expert Systems with Applications,133, 296–316. DOI:

Liebana-Cabanillas, F., Marinkovic, V., de Luna, I. R., Kalinic, Z., (2018) Predicting the determinants of mobile payment acceptance: a hybrid SEM-neural network approach. Technological Forecasting and Social Change,129, 117–30. DOI:

Lin, C. A., & Kim, T. (2016). Predicting user response to sponsored advertising on social media via the technology acceptance model. Computers in human behavior, 64, 710-718. DOI:

Lubowiecki-Vikuk, A., Dąbrowska, A., & Machnik, A. (2021). Responsibleconsumer and lifestyle: Sustainability insights. Sustainable Production and Consumption, 25, 91–101.

Magrath, V., & McCormick, H. (2013). Marketing design elements of mobile fashion retail apps. Journal of Fashion Marketing and Management, 17 (1), 115–134. DOI:

Matin, A., Khoshtaria, T., Marcan, M., & Datuashvili, D. (2021). The roles of hedonistic, utilitarian incentives and government policies affecting customer attitudes and purchase intention towards green products. International Review on Public and Nonprofit Marketing ( DOI:

Matin, A., Khoshtaria, T., Mercan, M., & Botsvadze, I. (2023). Digital consumer-based branding among football clubs: determinants of brand loyalty and purchase intention towards green brand extensions offered through digital platforms. International Journal of Technology Marketing, 17(4), 378-408. doi:10.1504/IJTMKT.2023.133971

Matthes, J., & Wonneberger, A. (2014). The skeptical green consumer revisited: testing the relationship between green consumerism and skepticism toward advertising. Journal of Advertising, 43 (2), 115–127. DOI: 10.1080/00913367.2013.834804

Mclean, G., & Wilson, A. (2016). Evolving the online customer experience … is there a role for online customer support? Computers in Human Behavior, 60, 602–610. DOI: 10.1016/j.chb.2016.02.084

Meyer, R., & Liebe, U. (2010). Are the affluent prepared to pay for the planet? Explaining willingness to pay for public and quasi-private environmental goods in Switzerland. Population and Environment, 32, 42–65. DOI: 10.1007/s11111-010-0116-y

Morales-Solana, D., Esteban-Millat, I. & Alegret Cotas, A. (2022) Experiences in consumer flow in online supermarkets. Electronic Commerce Research, 22, 1195–1226.

Mortenson, M. J., & Vidgen, R. (2016). A computational literature review of the technology acceptance model. International Journal of Information Management, 36(6), 1248-1259. DOI:

Mróz, B. (2021). Consumer Shopping Behaviours on Social Media Platforms:Trends, Challenges, Business Opportunities (pp. 113–129). Doligalski, T.,Goliński, M., & Kozłowski, K. (Eds.), Disruptive Platforms: Markets,Ecosystems, Monopolists. Routledge.

Mousa, A. H., Mousa, S. H., Mousa, S. H., & Obaid, H. A. (2020). Advance acceptance status model for E-learning based on university academics and students. In IOP Conference Series: Materials Science and Engineering. 671(1), p. 012031). IOP Publishing. DOI: 10.1088/1757-899X/671/1/012031

Nasidi, Q. Y.; Hassan, I.; Ahmad, M. F.; Garba, M.; & Gamji, M. B. (2022). Effects of Advertising, Online Risk, Perceived Usefulness, and Reliability on Online Shopping Behavior. International Journal of Marketing, Communication and New Media, Vol. 10, Nº 18, 206-228

O’Rourke, D., & Ringer, A. (2016). The impact of sustainability information on consumer decision making. Journal of Industrial Ecology, 20 (4), 882–892. DOI: 10.1111/jiec.12310

Osman, M., & Thornton, K. (2019). Traffic light labelling of meals to promote sustainable consumption and healthy eating. Appetite, 138, 60–71. DOI:

Osman, M., Schwartz, P., & Wodak, S. (2021). Sustainable consumption: What works best, carbon taxes, subsidies and/or nudges? Basic and Applied Social Psychology, 43 (3), 169–194. DOI:

Park, N., Roman, R., Lee, S., & Chung, J. E. (2009). User acceptance of a digital library system in developing countries: an application of the Technology Acceptance Model. International Journal of Information Management, 29 (3), 196-209. DOI:

Park, Y., Son, H., & Kim, C. (2012). Investigating the determinants of construction professionals’ acceptance of web-based training: an extension of the technology acceptance model. Automation in Construction, 22, 377-386. DOI:

Parasuraman, A., & Colby, C.L. (2015). An Updated and Streamlined Technology Readiness Index: TRI 2.0. Journal of Service Research, 18(1), 59–74.

Perera, C., Auger, P., & Klein, J. (2018). Green Consumption Practices Among Young Environmentalists: A Practice Theory Perspective. Journal of Business Ethics, 152, 843–864. DOI: 10.1007/s10551-016-3376-3

Pindeh, N., Mohd Suki, N., & Mohd Suki, N. (2016). User acceptance on mobile apps as an effective medium to learn Kadazandusun language. Fifth International Conference On Marketing And Retailing (pp. 372 – 378). Penang: Procedia Economics and Finance. DOI:

Podsakoff, P. M., MacKenzie, S., Lee, J. Y., & Podsakoff, N. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88 (5), 879-903. DOI: 10.1037/0021-9010.88.5.879

Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. DOI:

Rafique, H., Anwer, F., Shamim, A., Minaei-Bidgoli, B., Qureshi, M. A., & Shamshirband, S. (2018). Factors affecting acceptance of mobile library applications: Structural equation model. Libri, 68(2), 99-112.

Remondes, J. (2021). The growing publication of scientific articles on marketing and digital communication, International Journal of Marketing, Communication and New Media, Vol. 9, Nº 16, 1-3.

Romano, R., Davino, C., & Næs, T. (2014). Classification trees in consumer studies for combining both product attributes and consumer preferences with additional consumer characteristics. Food Quality and Preference, 33, 27-36.

Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88, 308–322. DOI:

Saaria, U. A., Damberg, S., Frombling, L., & Ringle, C. M. (2021). Sustainable consumption behavior of Europeans: The influence of environmental knowledge and risk perception on environmental concern and behavioral intention. Ecological Economics, 189, 107155. DOI:

Sapci, O., & Considine, T. (2014). The link between environmental attitudes and energy consumption behavior. Journal of Behavioral and Experimental Economics, 52, 29–34. DOI:

Shaikh, A. A., Lakshmi, K. S., Tongkachok, K., Alanya-Beltran, J., Ramirez-Asis, E., & Perez-Falcon, J. (2022). Empirical analysis in analysing the major factors of machine learning in enhancing the e-business through structural equation modelling (SEM) approach. 13, 681–689. DOI:

Sharma, S. K., Sharma, M., (2019) Examining the role of trust and quality dimensions in the actual usage of mobile banking services: an empirical investigation. International Journal of Information Management, 44, 65–75. DOI:

Shukla, A., & Sharma, S. K. (2018). Evaluating Consumers’ Adoption of Mobile Technology for Grocery Shopping: An Application of Technology Acceptance Model. Vision-The Journal of Business Perspective, 22 (2), 185-198. DOI: 10.1177/0972262918766136

Singh, L. (2021). Relationship Between Green Marketing Mix and Consumer Behavior: A study of hospitably firms in North India, International Journal of Marketing, Communication and New Media. Special Issue on Sustainable Marketing, June 2021, 82-103.

Song, L., Lim, Y., Chang, P., Guo, Y., Zhang, M., Wang, X., et al. (2019). Ecolabel’s role in informing sustainable consumption: A naturalistic decision making study using eye tracking glasses. Journal of Cleaner Production, 218, 685–695. DOI:

Song, S. Y., & Kim, Y. K. (2018). A human-centered approach to green apparel advertising: decision tree predictive modeling of consumer choice. Sustainability, 10(10),

Sousa, B.; Lubowiecki-Vikuk, A.; Rodrigues, M. A. & Remondes, J. (2021). Challenges for Marketing Research in the Concept of Sustainable Development, International Journal of Marketing, Communication and New Media. Special Issue on Sustainable Marketing, June 2021, 1-5

Surendran, P. (2012). Technology acceptance model: A survey of literature. International Journal of Business and Social Research, 2(4), 175-178. DOI:

Tam, K. Y., & Ho, S. Y. (2005). Web personalization as a persuasion strategy: An elaboration likelihood model perspective. Information Systems Research, 16 (3), 271–291. DOI: 10.1287/isre.1050.0058

Tan, B., Lau, T., Sarwar, A., & Khan, N. (2022). The effects of consumer consciousness, food safety concern and healthy lifestyle on attitudes toward eating “green”. British Food Journal, 124 (4), 1187-1203. DOI:

Tarka, P. (2017). The comparison of estimation methods on the parameterestimates and fit indices in SEM model under a 7-point Likert scale. Archives of Data Science. Series A, 2(1).

Temple, N. J. (2020). Front-of-package food labels: A narrative review. Appetite, 144, 104485. DOI: 10.1016/j.appet.2019.104485

Torlak, N.G., Demir, A. and Budur, T. (2019), “Impact of operations management strategies on customer satisfaction and behavioral intentions at cafe-restaurants”, International Journal of Productivity and Performance Management, Vol. 69 No. 9, pp. 1903-1924. DOI:

Vainio, A., & Paloniemi, R. .. (2014). The complex role of attitudes toward science in pro- environmental consumption in the Nordic countries. Ecological Economics, 108, 18–27. DOI:

Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204. DOI: 10.1287/mnsc.

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27 (3), 425–78. DOI:

Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157-178 .

vom Brocke, J., Watson, R. T., Dwyer, C., & Melville, N. (2013). Green information systems: directives for the IS discipline. Communications of the Association for Information Systems, 33 (1), DOI:

Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information & Management, 51(2), 249-259. DOI:

Wang, R. J., Malthouse, E. C., & Krishnamurthi, L. (2015). On the go: How mobile shopping affects customer purchase behavior. Journal of Retailing, 91, 217–234. DOI:

Wang, Y. (2017). Promoting sustainable consumption behaviors: the impacts of environmental attitudes and governance in a cross-national context. Environment and Behavior, 49 (10), 1128–1155. DOI:

Wang, Y. S., Wu, S. C., Lin, H. H., Wang, Y. M., & He, T. R. (2012). Determinants of user adoption of web ''Automatic Teller Machines': an integrated model of Transaction Cost Theory and Innovation Diffusion Theory. The Service Industries Journal, 32(9), 1505-1525. DOI:

Wang, Y., Wang, S., Wang, J., Wei, J., & Wang, C. (2020). An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model. Transportation, 47(1), 397-415. DOI:

Weber, A. (2021). Mobile apps as a sustainable shopping guide: The effect of eco-score rankings on sustainable food choice. Appetite, 167, 105616. DOI:

Weiss, M., Staake, T., Mattern, F., & Fleisch, E. (2012). PowerPedia: changing energy usage with the help of a community-based smartphone application. Personal and Ubiquitous Computing, 16, 655–664. DOI:

Weller, K. E., G. W., Redding, C. A., Paiva, A. L., Lofgren, I., Nash, J. T., & Kobayashi, H. (2014, September 1). Development and Validation of Green Eating Behaviors, Stage of Change, Decisional Balance, and Self-Efficacy Scales in College Students. Journal of Nutrition Education and Behavior, 46(5), 324-333. DOI:

Wen, D., Zhang, X., & Lei, J. (2017). Consumers’ perceived attitudes to wearable devices in health monitoring in China: a survey study. Computer Methods and Programs in Biomedicine, 140, 131–137. DOI:

Wen, L. Y., & Li, S. H. (2013). A study on the relationship amidst health conscisousness, ecological effect, and purchase intention of green products. International Journal of Organizational Innovation, 5 (4), 124-137.

Yau, H. K., Tang, H. Y. H. (2018) Analyzing customer satisfaction in self-service technology adopted in airports. Journal of Marketing. 6, 6–18. DOI:

Yoon, H. Y. (2016). User acceptance of mobile library applications in academic libraries: an application of the technology acceptance model. The Journal of Academic Librarianship, 42(6), 687-693. DOI:

Young, D., & Lehto, M. (2013). User acceptance of youtube for procedural learning: An extension of the technology acceptance model. Computers and Education, 61, 193-208. DOI:

Zhang, Y., & Godes, D. (2018). Learning from online social ties. Marketing Science, 37(3), 425–444.


  • There are currently no refbacks.

Copyright (c) 2024 Arian Matin, Tornike Khoshtaria, Nugzar Todua, Ola Bareja-Wawryszuk, Tomasz Pajewski, Nia Todua

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

International Journal of Marketing, Communication and New Media

ISSN: 2182-9306

DOI: 10.54663/2182-9306

Qualis Periódicos - CAPES: B2



Web of Science - Emerging Sources Citation Index - Clarivate Analytics

Journal Citation Reports (JCR) 2021