References
- Anderson, J. C. and D. W. Gerbing. 1988. Structural equation modeling in practice: A review and recommended two-step approach. Psyc. Bull. 103: 411-423.
- Barclay, D. W., C. Higgins, and R. Thompson. 1995. The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Tech. Stud. 2: 285-309.
- Bagozzi, R. P. and Y. Yi. 1988. On the evaluation of structural equation models. J. Acad. Marketing Sci. 16: 74-94. https://doi.org/10.1007/BF02723327
- Bhattacherjee, A. 2001. Understanding information systems continuance: An expectation-confirmation model. MIS Quart. 25: 351-370. https://doi.org/10.2307/3250921
- Chen, L., M. L. Gillenson, and D. L. Sherrell. 2000. Consumer acceptance of virtual stores : A theoretical model an critical success factors for virtual stores. Doctoral Thesis, The Univ. of Memphis 35: 8-31.
- Chin, W. W. 1998. The partial least squares approach to structural equation modeling, In G. A. Marcoulides (Ed.), Modern Methods for Business Research. Mahwah, NJ: Lawrence Erlbaum Associates Publishers: 295-336.
- Cohen, J. 1988. Statistical power analysis for the behavioral sciences(2ed.), Hillsdale, NJ: Lawrence Erlbaum Associates. Publishers.
- Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart. 13: 319-340. https://doi.org/10.2307/249008
- Fornell, C. and D. F. Larcher. 1981. Evaluating structural equation models with unobservable variables and measurement error. J. Marketing Res. 48: 39-50.
- Gefen, D., D. W. Straub, and M. C. Boudreau. 2000. Structural equation modeling and regression: Guidelines for research practice. Comm. Assoc. Inform. Systems 7: 1-78.
- Goodhue, D., W. Lewis, and R. Thompson. 2006. PLS, Small sample size, and statistical power in MIS research. Proceedings of the 39th Annual Hawaii International Conference on System Sciences 8: 202b.
- Hong, S. J. and Y. G. Kim. 2011. Study on the chinese cabbage producers' using patterns about a new variety of seed. CNU J. Agr. Sci. 38: 549-557.
- Jung, G. H., Y. C. Choe, H. D. Park, and I. H. Jang., 2010. Study on the relationship between factors of farmers' adoption and continuous use of innovative technology. J. Agr. Edu. Human Resour. Dev. 42: 109-137.
- Kim, J. D. 2012. PLS regression with SAS. Freeacademy.
- Lee, G. H. 2000. A study on consumer damages and efficient redress program for agricultural producers. Korea Consumer Agency.
- Lin, C. S., S. Wu, and R. J. Tsai. 2005. Integrating perceived playfulness into expectation-confirmation model for web portal context. Inform. Manag. 42: 683-693. https://doi.org/10.1016/j.im.2004.04.003
- Marcoulides, G. A. and C. Saunders. 2006. PLS: A silver bullet?. MIS Ouart. 30: 3-9.
- Ministry of Food, Agriculture, Forestry and Fisheries (MIFAFF). 2009. 2020 Government policy for promoting seed industry.
- Park, W. S., D. K. Suh, and S. Y. Lee. 2009. An empirical study on the determinant factors of new technology acceptance by farmhouse type. Kor. J. Agr. Manage. Pol. 36: 509-539.
- Taylor, S. and P. A. Todd. 1995. Understanding information technology usage: A test of competing models. Inform. Systems Res. 6: 144-176. https://doi.org/10.1287/isre.6.2.144
- Tenenhaus, M., V. E. Vinzi, Y. M. Chatelin, and C. Lauro. 2005. PLS path modeling. Computational Statis. Data Anal. 48: 159-205. https://doi.org/10.1016/j.csda.2004.03.005
- Venkatesh, V. and F. D. Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Manage. Sci. 46: 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
- Venkatesh, V., M. G. Morris, G. B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quart. 27: 425-478.