An Extension of the Technology Acceptance Model: Understanding Farmers’ Behavioral Intention towards Using Agricultural E-commerce
محورهای موضوعی : Education and training
رها زارعی
1
(Ph.D., Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran)
غلامحسین زمانی
2
(Professor, Department of Agricultural Extension and Education, School of Agriculture, Shiraz University, Shiraz, Iran)
حمید کریمی
3
(استادیار گروه ترویج و آموزش کشاورزی، دانشکده کشاورزی دانشگاه زابل)
اریک مایکلز
4
(Associate Professor Department of Agricultural and Resource Economics, University of Saskatchewan, 51 Campus Drive, Saskatoon, SK S7N 5A8, Canada)
کلید واژه: Facilitating Conditions, Technology acceptance model, Citrus growers, social influence, Agricultural e-commerce,
چکیده مقاله :
The emergence of agricultural e‐commerce can solve the challenges of agricultural marketing especially in developing countries such as Iran. The aim of this study was to extend the Technology Acceptance Model (TAM) through adding the variables social influence and facilitating conditions to understand farmers’ behavioral intention towards using agricultural e-commerce. This descriptive-correlational research was conducted through a cross-sectional survey. The statistical population of the study was Jahrom citrus growers, Iran, (N=3566). The stratified random sampling method was used and 360 respondents selected in the survey. The research instrument was a structured questionnaire, who’s the research instrument was a structured questionnaire, whose face, convergent, and discriminant validity of the questionnaire were confirmed. Cronbach's alpha and composite reliability were obtained using SPSS24 and AMOS24 software, respectively to examine the reliability of research tool (0.75< α< 0.85). The results showed that the extended form of TAM constructs was significant in explaining farmers’ behavioral intention. This means that attitude, perceived usefulness, facilitating conditions, social influence and perceived ease of use explained 50.3% of the variance in behavioral intention. The hypotheses of the extended TAM constructs showed that social influence and facilitating conditions have a positive and significant effect on intention towards using agricultural e-commerce. In this context, the government and agricultural e-commerce planners need to raise the awareness of all social influence groups about the benefits of using agricultural e-commerce through all media. In addition, providing technical and educational e-commerce facilities for farmers is recommended.
پیدایش تجارت الکترونیک کشاورزی میتواند چالشهای بازاریابی کشاورزی به ویژه در کشورهای در حال توسعه مانند ایران را حل نماید. هدف پژوهش حاضر، توسعه الگوی پذیرش فناوری (TAM) از طریق افزودن متغیرهای تأثیر اجتماعی و شرایط تسهیل شده برای درک نیت رفتاری کشاورزان نسبت به استفاده از تجارت الکترونیک کشاورزی بود. این مطالعه توصیفی- همبستگی با استفاده از یک پیمایش مقطعی انجام شد. جامعه آماری مطالعه، مرکباتکاران شهرستان جهرم در ایران بودند (3566=N). از روش نمونهگیری تصادفی طبقهبندی شده استفاده گردید و 360 پاسخ دهنده برای پیمایش انتخاب شدند. ابزار پژوهش، پرسشنامهای ساختارمند بود که روایی ظاهری، همگرا و تشخیصی آن مورد تایید قرار گرفت. ضرایب آلفای کرونباخ و روایی ترکیبی برای بررسی پایایی ابزار پژوهش مورد استفاده قرار گرفت ( 85/0< α< 75/0). نتایج نشان داد که متغیرهای نگرش، سودمندی ادرک شده، تأثیر اجتماعی و شرایط تسهیل شده اثرات مستقیم، مثبت و معناداری بر نیت رفتاری نسبت به استفاده از تجارت الکترونیکی کشاورزی داشتند. سهولت استفاده ادراک شده نیز تأثیر غیر مستقیمی بر نیت رفتاری داشت. به طور کلی، نتایج نشان داد که سازههای الگوی توسعه یافته پذیرش فناوری برای تبیین نیت رفتاری کشاورزان معنادار میباشند. بدین معنی که متغیرهای نگرش، سودمندی ادراک شده، شرایط تسهیل شده، تأثیر اجتماعی و سهولت استفاده ادراک شده 3/50 درصد از واریانس نیت رفتاری را توضیح میدهند. در نهایت نیز کاربردهای این مطالعه مورد بحث قرار گرفت.
Agarwal, R., & Karahanna, E. (2000). Time flies when you’re having fun: Cognitive absorption and beliefs about information technology usage. MIS Quarterly, 24(4), 665-694.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211.
Alavion, S. J., Allahyari, M. S., Al-Rimawi, A. S., & Surujlal, J. (2017). Adoption of agricultural E-marketing: application of the theory of planned behavior. Journal of international food & agribusiness marketing, 29(1), 1-15.
Al-Busaidi, Z., Kotagama, H., Boughanmi, H., Dharmapala, S., & Waelti, J. (2009). Adoption of e-Commerce in the agricultural and fisheries business sector in Oman. Journal of Agricultural and Marine Sciences, 14, 41-47.
Alipour, H., Hoseinbeyki, A., Johed, M., Rahnama, H., & Sharifnia, M. (2013). A review on citrus production and export marketing strategies in Mazandaran province, Iran. Middle-East of Scientific Research, 14(10), 1375-1380.
Alraja, M. N. (2016). The effect of social influence and facilitating conditions on e-government acceptance from the individual employees’ perspective. Polish Journal of Management Studies, 14, 18-27.
ALraja, M. N., & Aref, M. (2015). Customer acceptance of e-commerce: Integrating perceived risk with TAM. International Journal of Applied Business and Economic Research, 13(2), 913-921.
Alryalat, M. A. A. (2017). Measuring citizens' adoption of electronic complaint service (ECS) in Jordan: Validation of the extended technology acceptance model (TAM). International Journal of Electronic Government Research, 13(2), 47-65.
Al-Sharafi, M. A., Arshah, R. A., Abo-Shanab, E. A., & Elayah, N. (2016). The effect of security and privacy perceptions on customers’ trust to accept internet banking services: An extension of TAM. Journal of Engineering and Applied Sciences, 11(3), 545-552.
Alshurideh, M., Al-Kurdi, B., & Salloum, S. A. (2019). Examining the main mobile learning system drivers’ effects: A mix empirical examination of both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In International Conference on Advanced Intelligent Systems and Informatics (pp. 406-417). Springer, Cham.
Arromdee, J., & Suntrayuth, S. (2020). E-commerce adoption for rice selling in Thailand: an empirical study. Thailand and The World Economy, 38(2), 88-118.
Beldad, A. D., & Hegner, S. M. (2018). Expanding the technology acceptance model with the inclusion of trust, social influence, and health valuation to determine the predictors of German users’ willingness to continue using a fitness app: A structural equation modeling approach. International Journal of Human–Computer Interaction, 34(9), 882-893.
Bodini, A., & Zanoli, R. (2011). Competitive factors of the agro-food e-commerce. Journal of Food Products Marketing., 17(3), 241-260.
Browen, M., & Cudeck, R. (1993). Alternative ways of assessing model fit in testing structural equation model. Mark Science, 18(2), 31-43.
Cai, Y., Lang, Y., Zheng, S., & Zhang, Y. (2015). Research on the influence of e-commerce platform to agricultural logistics: an empirical analysis based on agricultural product marketing. International Journal of Security and Its Applications, 9(10), 287-296.
Chau, P. Y. (2001). Influence of computer attitude and self-efficacy on IT usage behavior. Journal of Organizational and End User Computing, 13(1), 26-33.
Cheung, W., Chang, M. K., & Lai, V. S. (2000). Prediction of internet and world wide web usage at work: a test of an extended triandis model. Decision Support Systems, 30(1), 83-100.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
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.
Doan, T. (2020). Factors affecting online purchase intention: A study of Vietnam online customers. Management Science Letters, 10(10), 2337-2342.
Dsouza, D. J., & Joshi, H. (2014). Development of agricultural e-commerce framework for India, a strategic approach. International Journal of Engineering Research and Applications, 4(11), 1-4.
Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: A conceptual framework. Procedia economics Economics and financeFinance, 26, 1000-1006.
Fedorko, I., Bacik, R., & Gavurova, B. (2018). Technology acceptance model in e-commerce segment. Management & Marketing. Challenges for the Knowledge Society, 13(4), 1242-1256.
Fishbein, M., & Ajzen, I. (1975). Intention and Behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
Guzzo, T., Ferri, F., & Grifoni, P. (2016). A model of e-commerce adoption (MOCA): Consumer's perceptions and behaviours. Behaviour and Information Technology, 35(3), 196-209.
Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis (7th ed.). Upper Saddle River, NJ: Prentice-Hall, Inc.
Hajli, M. (2012). An integrated model for e-commerce adoption at the customer level with the impact of social commerce. International Journal of Information Science and Management, 10, 77-97.
Henderson, J. R., Akridge, J. T., & Dooley, F. J. (2006). Internet and e-commerce use by agribusiness firms: 2004. Journal of Agribusiness, 24, 17-39.
Hsu, C. L., & Lu, H. P. (2004). Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 41(7), 853-868.
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.
Imam, G. (2005). Model persamaan struktural: Konsep dan aplikasi dengan program AMOS Ver. 5.0. Semarang: Badan Penerbit Universitas Diponegoro.
Jones, E., Sundaram, S., & Chin, W. (2002). Factors leading to sales force automation use: A longitudinal analysis. Journal of Personal Selling and Sales Management, 22(3), 145-156.
Kabugumila, M. S., Lushakuzi, S., & Mtui, J. E. (2016). E-commerce: An overview of adoption and its effective implementation. International Journal of Business and Social Science, 7(4), 243.
Kamal, S. A., Shafiq, M., & Kakria, P. (2020). Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technology in Society, 60, 101-212.
Kock, N. (2010). E-Collaboration Technologies and organizational performance: Current and future trends: Current and future trends. Texas A&M International University: IGI Global.
Koufaris, M. (2002). Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research, 13(2), 205-223.
Lane, P. P. M., Wafa, S. A., Hassan, R. A., & Belkhamza, Z. (2014). Perceived usefulness and perceived ease of use of e-commerce adoption among entrepreneurs in Sabah. Kuwait Chapter of Arabian Journal of Business and Management Review, 33, 1-10.
Lavidas, K., Achriani, A., Athanassopoulos, S., Messinis, I., & Kotsiantis, S. (2019). University students’ intention to use search engines for research purposes: A structural equation modeling approach. Education and Information Technologies, 25, 1-17.
Lee, L., & Mun, Y. Y. (2011). Group-level effects of facilitating conditions on individual acceptance of information systems. Information technology and management, 12(4), 315-334.
Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: Past, present, and future. Communications of the Association for information systems, 12(1), 751-781.
Li, X., Guo, H., Jin, S., Ma, W., & Zeng, Y. (2021). Do farmers gain internet dividends from E-commerce adoption? Evidence from China. Food Policy, 101, 102024.
Lin, Y. (2018). E-urbanism: E-commerce, migration, and the transformation of Taobao villages in urban China. Cities, 91, 202-212.
Lv, D., & Zhou, Q. (2014). Development model of agricultural E-commerce in the context of social commerce. Journal of Chemical and Pharmaceutical Research, 6(7), 1341-1345.
Ma, W., Zhou, X., & Liu, M. (2020). What drives farmers’ willingness to adopt e‐commerce in rural China? The role of internet use. Agribusiness, 36(1), 159-163.
Malik, A., Suresh, S., & Sharma, S. (2017). Factors influencing consumers’ attitude towards adoption and continuous use of mobile applications: a conceptual model. Procedia computer science, 122, 106-113.
McFarlane, D., Chembezi, D., & Befecadu, J. (2003). Internet adoption and use of e-commerce strategies by agribusiness firms in Alabama. Southern Agricultural Economics Association, Annual Meeting, Mobile, February 1-5, Alabama.
McLean, G., Osei-Frimpong, K., Al-Nabhani, K., & Marriott, H. (2020). Examining consumer attitudes towards retailers' m-commerce mobile applications–An initial adoption vs. continuous use perspective. Journal of Business Research, 106, 139-157.
Mohd, F., Ahmad, F., Samsudin, N., & Sudin, S. (2011). Extending the Technology Acceptance Model to account for social influence, trust and integration for pervasive computing environment: A case study in university industry. American Journal of Economics and Business Administration, 3(3), 552-559.
Montealegre, F., Thompson, S. R., & Eales, J. S. (2007). An empirical analysis of the determinants of success of food and agribusiness e-commerce firms. International Food and Agribusiness Management Review, 10, 1-21.
Mugo, D. G., Njagi, K., Chemwei, B., & Motanya, J. O. (2017). The technology acceptance model (TAM) and its application to the utilization of mobile learning technologies. Journal of Advances in Mathematics and Computer Science, 20(4), 1-8.
Mutahar, A. M., Daud, N. M., Thurasamy, R., Isaac, O., & Abdulsalam, R. (2018). The mediating of perceived usefulness and perceived ease of use: The case of mobile banking in Yemen. International Journal of Technology Diffusion, 9(2), 21-40.
Nguyen, T. T. H., Nguyen, N., Nguyen, T. B. L., Phan, T. T. H., Bui, L. P., & Moon, H. C. (2019). Investigating consumer attitude and intention towards online food purchasing in an emerging economy: An extended TAM approach. Foods, 8(11), 576.
Roca, J. C., Chiu, C.-M., & Martínez, F. J. (2006). Understanding e-learning continuance intention: An extension of the technology acceptance model. International Journal of Human-computer Studies, 64(8), 683-696.
Sadi, A. H. M. S., & Noordin, M. F. (2011). Factors influencing the adoption of M-commerce: An exploratory analysis. In International Conference on Industrial Engineering and Operations Management (pp. 492-498).
Seyal, A. H., & Rahman, M. N. A. (2009). An extension of the technology acceptance model: The case of Bruneian managers. Asian Journal of Business and Accounting, 2, 25-45.
Sombultawee, K. (2017). Mobile commerce switching intentions in Thai consumers. Mediterranean Journal of Social Sciences, 8(6), 123-134.
Stage, F. K., Carter, H. C., & Nora, A. (2004). Path analysis: An introduction and analysis of a decade of research. The Journal of Educational Research, 98(1), 5-13.
Taherdoost, H. (2018). A review of technology acceptance and adoption models and theories. Procedia Manufacturing, 22, 960-967.
Tam, C., Santos, D., & Oliveira, T. (2020). Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model. Information Systems Frontiers, 22(1), 243-257.
Teo, T. (2009). The impact of subjective norm and facilitating conditions on pre-service teachers' attitude toward computer use: A structural equation modeling of an extended technology acceptance model. Journal of Educational Computing Research, 40(1), 89-109.
Teo, T. (2010). Examining the influence of subjective norm and facilitating conditions on the intention to use technology among pre-service teachers: a structural equation modeling of an extended technology acceptance model. Asia Pacific Education Review, 11(2), 253-262.
Thongsri, N., Shen, L., & Bao, Y. (2019). Investigating academic major differences in perception of computer self-efficacy and intention toward e-learning adoption in China. Innovations in Education and Teaching International, 57(5), 577-589.
Tucker, L. R., & Lewis, C. (1973). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1-10.
Uzoka, F. M. E., Shemi, A. P., & Seleka, G. G. (2007). Behavioral influences on e-Commerce adoption in a developing country context. The Electronic Journal of Information Systems in Developing Countries, 31(1), 1-15.
Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Waheed, M., & Jam, F. A. (2010). Teacher's intention to accept online education: extended TAM model. Interdisciplinary Journal of Contemporary Research in Business, 2(5), 330-344.
Wheaton, B. (1987). Assessment of fit in over identified models with latent variables. Sociological Methods and Research, 16(1), 118-154.
Yeop, M. A., Yaakob, M. F. M., Wong, K. T., Don, Y., & Zain, F. M. (2019). Implementation of ICT policy (blended learning approach): Investigating factors of behavioral intention and use behavior. International Journal of Instruction, 12(1), 767-782.
Yu, C. S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104-121.
Zhang, T., Tao, D., Qu, X., Zhang, X., Zeng, J., Zhu, H., & Zhu, H. (2020). Automated vehicle acceptance in China: Social influence and initial trust are key determinants. Transportation Research Part C: Emerging Technologies, 112, 220-233.
Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational Technology Research and Development, 86, 1485-1519.
