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Information Systems Frontiers

, Volume 21, Issue 3, pp 565–580 | Cite as

Farmer Cooperatives’ Intention to Adopt Agricultural Information Technology—Mediating Effects of Attitude

  • Ya-na WangEmail author
  • Lifu Jin
  • Hanping Mao
Article
First Online:

Abstract

Based on the technology acceptance model and technology–organization–environment framework, this study has developed a new model to explain the adoption of agricultural information technology (AIT) by farmer cooperatives from the combination perspective of individual and organization using four factors: cooperatives’ factors, environmental factors, technological factors, and leaders’ attitude towards AIT. Data was collected from a survey of farmer cooperatives and was analyzed using structural equation modelling. The model showed a good fit with findings indicating that the cooperatives’ and environmental factors positively influence the intentions of farmer cooperatives to adopt AIT. Again, the results depict that the technological factors have no significant effect. Leaders’ attitude towards AIT has a mediating effect on the relationships between the environmental, the cooperatives’, as well as the technological factors and the intentions to adopt AIT. The results will guide promotional strategies by technology providers and help government agencies better optimize policies to promote widespread use of AIT.

Keywords

Farmer cooperatives Intention to adopt Agricultural information technology Structural equation modelling 
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Notes

Acknowledgements

This work was financially supported by the Key projects of the National Social Science Fund of China (No. 16AJL006); Jiangsu Provincial Education Department Project(No. 2017SJB1064); a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (No.PAPD-2014-37).

References

  1. Aboelmaged, M., & Hashem, G. (2018). RFID application in patient and medical asset operations management: A technology, organizational and environmental (TOE) perspective into key enablers and impediments. International Journal of Medical Informatics, 10(118), 58–64.CrossRefGoogle Scholar
  2. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.CrossRefGoogle Scholar
  3. Ali, A., Rahut, D. B., & Behera, B. (2016). Factors influencing farmers’ adoption of energy-based water pumps and impacts on crop productivity and household income in Pakistan. Renewable & Sustainable Energy Reviews, 2(54), 48–57.CrossRefGoogle Scholar
  4. Baidu-Forson, J. (1999). Factors influencing adoption of land-enhancing Technology in the Sahel: Lessons from a case study in Niger[J]. Agricultural Economics, 20, 231–239.CrossRefGoogle Scholar
  5. Banerjee, P. K., & Ma, L. C. (2012). Routinisation of B2B E-commerce by small firms: A process perspective. Information Systems Frontiers, 14, 1033–1046.CrossRefGoogle Scholar
  6. Batz, F. J., Peters, K. J., & Janssen, W. (1999). The influence of technology characteristics on the rate and speed of adoption. Agricultural Economics, 21, 121–130.CrossRefGoogle Scholar
  7. Bhattacherjee, A., & Sanford, C. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS Quarterly, 30(4), 805–825.CrossRefGoogle Scholar
  8. Chong, A., & Chan, F. (2012). Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry.Expert Systems with Applications 39, 8645–8654.Google Scholar
  9. Chong, S., & Pervan, G. (2007). Factors influencing the extent of deployment of electronic commerce for small-and medium sized enterprises. Journal of Electronic Commerce in Organizations, 5(1), 1–29.CrossRefGoogle Scholar
  10. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319–340.CrossRefGoogle Scholar
  11. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies, 45(1), 19–45.CrossRefGoogle Scholar
  12. Deshpande, R., Grinstein, A., & Ofek, E. (2012). Strategic orientations in a competitive context: The role of strategic orientation differentiation. Marketing Letters, 23(3), 629–643.CrossRefGoogle Scholar
  13. Dey, B., Newman, D., & Prendergast, R. (2011). Analysing appropriation and usability in social and occupational lives-an investigation of Bangladeshi farmers’ use of mobile telephony. Information Technology & People, 24(1), 46–63.CrossRefGoogle Scholar
  14. Di Benedetto, A., Calantone, R., & Zhang, C. (2006). International technology transfer: Model and exploratory study in the People's Republic of China. International Marketing Review, 4(4), 446–462.CrossRefGoogle Scholar
  15. Dimara, E. D. (2003). Adoption of agricultural innovations as a two-stage partial observability process. Agricultural Economics, 28, 187–196.CrossRefGoogle Scholar
  16. Dong, H., Wang, Y.Q. (2018). Analysis of the status and problems concerning the development of farmer professional cooperatives in China and possible countermeasures. Journal of Yunnan Minzu University (Social Sciences), 35(2), 106–109 (Chinese).  https://doi.org/10.13727/j.cnki.53-1191/c.2018.02.015.
  17. Fishbein M, Ajen I. (1975). Theory and Research[M]. MA: Belief Attitude Intention and Behavior: An Introduction to Addison-Wesley.Google Scholar
  18. Fornell, C., & Larcker, D. F. (1982). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  19. Gao, L. L., & Waechter, K. A. (2017). An examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19(3), 525–548.CrossRefGoogle Scholar
  20. Gholami, R., Molla, A., Goswami, S., & Brewster, C. (2018). Green information systems use in social enterprise: The case of a community-led eco-localization website in the west midlands region of the UK. Information Systems Frontiers, 20, 1345–1361.  https://doi.org/10.1007/s10796-016-9733-z.CrossRefGoogle Scholar
  21. Gorla, N., Chiravuri, A., & Chinta, R. (2017). Business-to-business e-commerce adoption: An empirical investigation of business factors. Information Systems Frontiers, 9, 645–667.CrossRefGoogle Scholar
  22. Hair, J. F., Jr., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A primer on partial least squares structural equation modelling. Thousand Oaks: Sage Publications.Google Scholar
  23. Higgins, C. (1995). The partial least squares (pls) approach to causal modeling: Personal computer adoption and use an illustration. Technology Studies, 2, 284–324.Google Scholar
  24. Hong, K. K., & Kim, Y. G. (2002). The critical success factors for ERP implementation: An organizational fit perspective. Information & Management, 40(1), 25–40.CrossRefGoogle Scholar
  25. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.  https://doi.org/10.1016/S0016-5085(12)63267-3.CrossRefGoogle Scholar
  26. Hossain, M. A., & Quaddus, M. (2015). Radio frequency identification (RFID) adoption: A cross-sectional comparison of voluntary and mandatory contexts. Information Systems Frontiers, 17, 1057–1076.CrossRefGoogle Scholar
  27. Hossain, M. A., Quaddus, M., & Islam, N. (2016). Developing and validating a model explaining the assimilation process of RFID: An empirical study. Information Systems Frontiers, 18, 645–663.CrossRefGoogle Scholar
  28. Iacobucci, D. (2010). Structural equation model: Fit indices, sample size and advanced topics. Journal of Consumer Psychology, 20, 90–98.CrossRefGoogle Scholar
  29. Iacovou, C. L., Benbasat, I., & Dexter, A. S. (1995). Electronic data interchange and small organizations: Adoption and impact of technology. MIS Quarterly, 19(4), 465–485.CrossRefGoogle Scholar
  30. Idemudia, E. C., Raisinghani, M. S., & Samuel-Ojo, O. (2018). The contributing factors of continuance usage of social media: An empirical analysis. Information Systems Frontiers, 20, 1267–1280.  https://doi.org/10.1007/s10796-016-9721-3.CrossRefGoogle Scholar
  31. Jarvenpaa, S. L., & Ives, B. (1991). Executive involvement and participation in the Management of Information Technology. MIS Quarterly, 15(2), 205–227.CrossRefGoogle Scholar
  32. Karahanna, E., Steaub, D. W., & Chervany, N. (1999). Information technology adoption across time: A cross sectional comparison of pre-adoption and post-adoption beliefs. MIS Quarterly, 23(2), 183–213.CrossRefGoogle Scholar
  33. Kim, S., & Garrison, G. (2009). Investigating mobile wireless technology adoption: An extension of the technology acceptance model. Information Systems Frontiers, 11, 323–333.CrossRefGoogle Scholar
  34. Kim, K., & Prabhakar, B.. (2000). Initial Trust, Perceived Risk, and the Adoption of Internet Banking. Twenty First International Conference on Information Systems. Association for Information Systems. https://dl.acm.org/citation.cfm?id=359809
  35. King, J. L., & Gurbaxani, V. (1994). The institutional factors in information technology innovation. Information Systems Research, 5(2), 139–169.CrossRefGoogle Scholar
  36. Lei, L. (2015). An Empirical Study of the Impact of the Local Government Behavior upon the Development of Regional Brands. Journal of Lanzhou University (Social Sciences), 43(01), 112–119 (Chinese).  https://doi.org/10.13885/j.issn.1000-2804.2015.01.015.
  37. Lian J W, Yen D C, Wang Y T (2014). An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital.Google Scholar
  38. Liang, H. (2009). Innovation and transformation of agricultural science and technology based on farmers' professional cooperatives. Social Scientist, 02, 69–72 (Chinese).Google Scholar
  39. Lin, D., Lee, C. K. M., & Lin, K. (2016). Research on effect factors evaluation of internet of things (IOT) adoption in Chinese agricultural supply chain. IEEE International Conference on Industrial Engineering & Engineering Management.  https://doi.org/10.1109/IEEM.2016.7797948.
  40. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test mediation and other intervening variable effects. Psychological Methods, 7, 83–104.CrossRefGoogle Scholar
  41. Madhu, K. (2001). Sequential adoption of site-specific technologies and its implications for nitrogen productivity: A double selectivity model. American Journal of Agricultural Economics, 83(1), 35–51.CrossRefGoogle Scholar
  42. Mizruchi, M. S., & Fein, L. C. (1999). The social construction of organizational knowledge: A study of the uses of coercive, mimetic, and normative isomorphism. Administrative Science Quarterly, 44(4), 653–683.CrossRefGoogle Scholar
  43. Nohria, N., & Gulati, R. (1997). What is the optimum amount of organizational slack? A study of the relationship between slack and innovation in multinational firms. European Management Journal, 15(6), 603–611.CrossRefGoogle Scholar
  44. Rana, N. P., Dwivedi, Y. K., Lal, B., Williams, M. D., & Clement, M. (2017). Citizens' adoption of an electronic government system: Towards a unified view. Information Systems Frontiers, 19(3), 549–568.CrossRefGoogle Scholar
  45. Rau, D., & Haerem, T. (2010). Applying an organizational learning perspective to new technology deployment by technological gatekeepers: A theoretical model and key issues for future research. Information Systems Frontiers, 12, 287–297.CrossRefGoogle Scholar
  46. Rogers, E. M. (1983). Diffusion of innovations [M]. New York: The Free Press.Google Scholar
  47. Scupola, A. . (2004). Adoption of E-commerce in small and medium size Enterprises in Australia. Americas Conference on Information Systems. DBLP. https://aisel.aisnet.org/amcis2004/49/.
  48. Seeman, E., & Gibson, S. (2009). Predicting acceptance of electronic medical records: Is the technology acceptance model enough? SAM Advanced Management Journal, 74(4), 21–26 http://web.a.ebscohost.com/ehost/pdfviewer/pdfviewer?vid=5&sid=3135bcc7-ec06-42f7-9379-95e624c62d33%40sessionmgr4009.
  49. Segars, A. H., & Grover, V. (1998). Strategic information systems planning success: An investigation of the construct and its measurement. MIS Quarterly, 22(2), 139–164.CrossRefGoogle Scholar
  50. Shalini, C., & Nanda, K. K. (2018). Exploring factors influencing organizational adoption of augmented reality in e-commerce: Empirical analysis using technology-organization-environment model. Journal of Electronic Commerce Research, 19(3), 237–265.Google Scholar
  51. Thomas, M., Costa, D., & Oliveira, T. (2016). Assessing the role of IT-enabled process virtualization on green IT adoption. Information Systems Frontiers, 18, 693–710.CrossRefGoogle Scholar
  52. Thong, J. Y. L. (1999). An integrated model of information system adoption in small business. Journal of Management Information System, 15(4), 187–214.CrossRefGoogle Scholar
  53. Thong, J. Y. L., Yap, C. S., & Raman, K. S. (1996). Top management support, external expertise and information systems implementation in small businesses. Information Systems Research, 7(2), 248–267.CrossRefGoogle Scholar
  54. Tian, Z. F. (2013). Application of information Technology in Agriculture in developed countries and its enlightenment. World agriculture, 06, 45–48 (Chinese).  https://doi.org/10.13856/j.cn11-1097/s.2013.06.013.CrossRefGoogle Scholar
  55. Tornatzky, L. G., & Fleischer, M. O. (1990). The process technological innovation [M]. Lexington Mass: Lexington books.Google Scholar
  56. Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273–315.CrossRefGoogle Scholar
  57. 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.CrossRefGoogle Scholar
  58. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 45(2), 186–204.CrossRefGoogle Scholar
  59. 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.CrossRefGoogle Scholar
  60. Xian, J., & Zhaoyou, L. (2008). The basic connotation and behavior of Enterprise technology innovation adoption. Science and Technology Management Research, 6, 13–15 (Chinese).Google Scholar
  61. Xu, S., Zhu, K., & Gibbs, J. (2004). Global technology, local adoption: A cross-country investigation of internet adoption by companies in the United States and China. Electronic Markets, 14(1), 13–24.CrossRefGoogle Scholar
  62. Zhang, N., Guo, X., & Chen, G. (2011). Why adoption and use behavior of it/is cannot last? —Two studies in China. Information Systems Frontiers, 13(3), 381–395.  https://doi.org/10.1007/s10796-010-9288-3.CrossRefGoogle Scholar
  63. Zhao, C., Yang, X., Li, B., Li, M., & Yan, H. (2018). The retrospect and Prospect of agricultural information Technology in China. Agricultural Science and Engineering in China, 30(04), 3–7 (Chinese).  https://doi.org/10.19518/j.cnki.cn11-2531/s.2018.0083.CrossRefGoogle Scholar
  64. Zheng, S., Chen, Q., & Wang, Z. (2018). Scale of land, enrollment of agricultural cooperatives and adoption of unmannered aerial vehicle. Agricultural Technology Economy, 6, 92–105 (Chinese).  https://doi.org/10.13246/j.cnki.jae.2018.06.008.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Agricultural equipment of Jiangsu UniversityZhenjiang CityChina
  2. 2.School of Management of Jiangsu UniversityZhenjiang CityChina

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