Browse > Article
http://dx.doi.org/10.14400/JDC.2019.17.6.145

The Effect of Technical Characteristics of Smart Farm on Acceptance Intention by Mediating Effect of Effort Expectation  

Ahn, Mun Hyoung (Dept. of Management Information, Graduate School of Venture, Hoseo University)
Heo, Chul-Moo (Dept. of Management Information, Graduate School of Venture, Hoseo University)
Publication Information
Journal of Digital Convergence / v.17, no.6, 2019 , pp. 145-157 More about this Journal
Abstract
This study is to look at the influential factors associated with the acceptance intention of smart farm and suggest a proposal for spreading adoption of smart farms. The research questionnaire distributed to the farmers were used for the research analysis by statistical program SPSS v22.0 and Process macro v3.0. The technical characteristics of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on acceptance intention of smart farm and the mediating effect of effort expectation was observed. As a result, availability and economic efficiency have a positive(+) influence on acceptance intention and reliability have no influence on acceptance intention. And availability, reliability and economic efficiency have a positive(+) influence on effort expectation. Effort expectation mediates the relationship between the technical characteristics of smart farm and acceptance intention. The results of the study are expected to be utilized at the seeking direction of policy for potential adopters of smart farm, the training and consulting in actual field of smart farm.
Keywords
Smart Farm; Acceptance Intention; Technical Characteristics; Availability; Reliability; Economic Efficiency; Effort Expectation;
Citations & Related Records
Times Cited By KSCI : 10  (Citation Analysis)
연도 인용수 순위
1 M. Reid. & Levy, Y. (2008). Integrating trust and computer self-efficacy with TAM: An empirical assessment of customers' acceptance of banking information systems (BIS) in Jamaica. Journal of Internet Banking and Commerce, 13(3), 1-18.
2 S. C. Park & S. J. Kwon. (2011). A study on factors affecting intention to switch for using cloud computing: A case of google docs. Journal of Information Technology Services, 10(3), 149-166.   DOI
3 D. H. Kim, J. H. Lee & Y. P. Park. (2012). A study of factors affecting the adoption of cloud computing. Journal of Society for e-Business Studies, 17(1), 111-136.   DOI
4 G. G. Seo. (2013). Factor analysis of the cloud service adoption intension of Korean firms: Applying the TAM and VAM. Journal of Digital Convergence, 11(12), 155-160.   DOI
5 C. H. Cheong & S. H. Nam. (2014). Cloud computing acceptance at individual level based on extended UTAUT. Journal of Digital Convergence, 12(1), 287-294.   DOI
6 J. T. Kim & J. S. Han. (2017). Agricultural management innovation through the adoption of internet of things : Case of smart farm. Journal of Digital Convergence, 15(3), 65-75.   DOI
7 A. Benlian & T. Hess. (2011). Opportunities and risks of software-as-a-service: Findings from a survey of IT executives. Decision Support Systems, 52(1), 232-246.   DOI
8 J. H. Lee & H. J. Cho. (2013). Smart learning adoption in the workplace: The HRD manager perspective. Entrue Journal of Information Technology, 12(3), 107-119.
9 N. G. Yoon, J. S. Lee, G. S. Park & J. Y. Lee. (2017). Korea smart farm policy and technology development status. Rural Resources, 59(2), 19-27.
10 J. Y. Yoon & B. H. Lee. (2017). Implementation strategy and development methods for smart farms in Gangwon Province. Journal of Agricultural, Life and Environmental Sciences, 29, 137-151.
11 D. S. Suh & Y. J. Kim. (2016). A study on priority of policy for smart farming system using AHP approach. Journal of the Korea Academia-Industrial cooperation Society, 17(11), 348-354.   DOI
12 KREI. (2016). Analysis of smart farm status and success factors.
13 J. S. Kim. (2016). A study on factors affecting the intention to accept blockchain technology. Doctoral dissertation. Soongsil University, Seoul.
14 W. H. DeLone & E. R. Mclean. (2003). The DeLone and Mclean model of information systems success: a ten-year update. Jounal of Management Information Systems, 19(4), 9-30.   DOI
15 S. H. Kim & G. A. Kim. (2011). An empirical study on the factors affecting the adoption of mobile cloud and the moderating effect of mobile trust. The e-Business Studies, 12(1), 281-310.   DOI
16 J. H. Ryu, H. Y. Moon & J. H. Choi. (2013). Analysis of influence factors on the intention to use personal cloud computing. Journal of Information Technology Services, 12(4), 319-335.   DOI
17 V. Venkatesh, M. G. Morris, G. B. Davis. & F. D. Davis. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.   DOI
18 M. Alshehri, S. Drew, T. Alhussain & R. Alghamdi. (2012). The effects of website quality on adoption of e-government service: An empirical study applying UTAUT model using SEM. Australasian Conference On Information Systems.
19 S. H. Lee, J. Y. Ha, D. H. Kim & H. R. Lee. (2016). A Study on the export big data technology acceptance of information agricultural products. Tne Korea Society of Management Information Systems, Spring conference, 179-186.
20 Oliveira, T., Thomas, M. & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497-510.   DOI
21 D. H. Kim, I. T. Hwang & S. H. Lee. (2015). The relationship between adoption of innovation and diffusing intention for ICT convergency industry among farmers. Korean Society of Agricultural Extension, 22(1), 43-54.
22 D. J. Park & D. R. Bae. (2008). Factors accepting KMS and the moderating role of resistance in public sector. The Journal of Information Systems, 17(2), 73-94.   DOI
23 S. T. Nam, S. Y. Shin & C. Y. Jin. (2014). A Meta-analysis and review of external factors based on the technology acceptance model : Focusing on the journals related to smart phone in Korea. Journal of Korea Institute of Information and Communication Engineering, 18(4), 848-854.   DOI
24 S. H. Jeon, N. R. Park & C. C. Lee. (2011). Study on the factors affecting the intention to adopt public cloud computing service. Entrue Journal of Information Technology, 10(2), 97-112.
25 E. Y. Kim, J. H. Lee & D. U. Seo. (2013). A Study on the effect of organization's environment on acceptance intention for big data system. Journal of information technology applications & management, 20(4), 1-18.   DOI
26 W. S. Choi & S. B. Lee. (2013). Effects of the external variables of the RFID system for eco-friendly agricultural products on perceived value and behavioral intention: Applying an expanded TAM. The Korean Journal of Culinary Research, 19(2), 149-166.
27 S. J. Oh. (2017). A Design of intelligent information system for greenhouse cultivation. Journal of Digital Convergence, 15(2), 183-190.   DOI
28 G. I. Seong, B. W. Kim, H. G. Kim, M. H. Han & G. H. Park. (2015). Analyzing and countermeasure for smart livestock farming based on ICT. Ministry of Science, ICT and Future Planning.