DOI QR코드

DOI QR Code

기술수용요인이 인지된 혜택을 매개로 농업드론 서비스 사용의도에 미치는 영향

The Effect of Technology Acceptance Factors on Behavioral Intention for Agricultural Drone Service by Mediating Effect of Perceived Benefits

  • 이정대 (호서대학교 벤처대학원 정보경영학과) ;
  • 허철무 (호서대학교 벤처대학원 정보경영학과)
  • Lee, Jung-Dae (Dept. of Management Information, Graduate School of Venture, Hoseo University) ;
  • Heo, Chul-Moo (Dept. of Management Information, Graduate School of Venture, Hoseo University)
  • 투고 : 2020.05.25
  • 심사 : 2020.08.20
  • 발행 : 2020.08.28

초록

본 연구는 농업드론 서비스의 사용의도에 미치는 영향 요인들을 살펴보고자 하였다. 농업 관련 종사인 324명의 설문결과를 SPSS v22.0 및 PROCESS macro v3.4를 사용하여 분석하였다. 통합기술수용이론에 의한 기술수용요인이 농업드론 서비스의 사용의도에 미치는 영향과 인지된 혜택의 매개효과를 분석하였다. 분석결과, 첫째, 기술수용요인은 인지된 혜택과 농업드론 서비스 사용의도에 정(+)의 영향을 미치는 것으로 나타났다. 둘째, 경제적 혜택은 성과기대를 제외한, 편의적 혜택은 사회적 영향을 제외한 기술수용요인과 농업드론 서비스 사용의도 간을 매개하는 것으로 나타났으나 실용적 혜택은 유의한 매개효과가 나타나지 않았다. 향후 농업 또는 드론 교육을 받은 사람이나 드론 자격소지자를 대상으로 추가 연구가 필요하다고 본다.

This study examined the factors affecting the behavioral intention for agricultural drone service. The survey results of 324 agricultural-related workers were analyzed using SPSS v22.0 and PROCESS macro v3.4. The effects of technology acceptance factors by UTAUT on the behavioral intention for agricultural drone service and the mediating effects of perceived benefits were analyzed. The results are as follows: First, the technology acceptance factors had positive (+) effects on perceived benefits and behavioral intention for agricultural drone service. Second, economics mediated between factors excluding performance expectancy and intention, convenience also mediated between factors excluding social influence and intention, and there was no significant mediating effect of practicality benefits. In the future, a further research is required for people trained in agriculture or drone or had a drone license.

키워드

참고문헌

  1. S. Klaus. (2016). The Fourth Industrial Revolution. Seoul : MegaStudy.
  2. MSIT. (2018.5.30). Cross-ministry innovations to driving the growth of innovation growth engine for a specific action in the field of a master plan. MSIT. http://www.msit.go.kr.
  3. COMPA. (2019). Drone Technology and Market Trends Report. Seoul : COMPA.
  4. Y. J. Kim, C. Y. Kang, M. G. Lee, J. Y. Park, Y. G. Park & S. M. Cho. (2018). A Study on the Agricultural and Rural Response Strategies in the Fourth Industrial Revolution. Jeollanam-do : KREI.
  5. iPET. (2019.9.19). Future agriculture keyword 'advanced robot'. Naver. https://blog.naver.com/ipet1002/221649804273
  6. K. H. Yeom & H. J. Jung. (2019). agricultural drone. Seoul : KISTEP.
  7. F. J. Rondan-Cataluna, J. Arenas-Gaitan & P. E. Ramirez-Correa. (2015). A comparison of the different versions of popular technology acceptance models: A non-linear perspective. Kybernetes, 44(5), 788-805. https://doi.org/10.1108/K-09-2014-0184
  8. Y. J. Shim. (2018). A Study on Factors Affecting to FinTech Service Adoption Using the UTAUT Model. Doctoral dissertation. Konkuk University, Seoul.
  9. G. Y. Kim. (2016). A Study on the Factors Influencing the Internet of Things(IoT) Technology Acceptance of SMEs: Applying Unified Theory of Acceptance and Use of Technology(UTAUT). Doctoral dissertation. Hansei University, Seoul.
  10. S. H. Kang. (2016). A Study on the Acceptance and Use of the Simple Payment Service based on the Integrated Technology Acceptance Theory(UTAUT): Focusing on the Moderating Effect of Innovation Resistance. Doctoral dissertation. Bugyeong University, Pusan.
  11. 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. https://doi.org/10.2307/30036540
  12. J. H. You & C. Park. (2010). A Comprehensive Review of Technology Acceptance Model Researches. Entrue Journal of Information Technology, 9(2), 31-50.
  13. H. C. Song. (2018). A Study of Factors Affecting on m-leaming Satisfaction based on UTAUT. Journal of Digital Convergence, 16(7), 123-129. https://doi.org/10.14400/JDC.2018.16.7.123
  14. J. O. Lee & Y. M. Kim. (2013). A Study on the Impact of the App-Book Purchasing Behavior of Smartphone Users in Korea. The Journal of Society for e-Business Studies, 18(3), 45-67. https://doi.org/10.7838/jsebs.2013.18.3.045
  15. S. S. Choi. (2018). A Study on the Factors Affecting the Intention to Use Drones Delivery Service. Doctoral dissertation. Soongsil University, Seoul.
  16. S. Y. Ham. (2017). A Study on Factors Affecting to the Acceptance Intention of Fintech Service. Doctoral dissertation. Soongsil University, Seoul.
  17. H. G. Lee. (2019). An Empirical Study on the Consumer Acceptance of Internet Primary Bank: The Application of UTAUT Model. Doctoral dissertation. Dankook University, Gyeonggi-do.
  18. T. Y. Lee & C. M. Heo. (2019). A Study on the Influence of Acceptance Factors of ICT Convergence Technology on the Intention of Acceptance in Agriculture: Focusing on the Moderating Effect of Innovation Resistance. Journal of Digital Convergence, 17(9), 115-126.
  19. V. Venkatesh & F. Davis. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
  20. V. Venkatesh, J. Y. L. Thong & X. Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly. 36(1). 157-178. https://doi.org/10.2307/41410412
  21. J. S. Kim. (2017). A Study on Factors Affecting the Intention to Accept Blockchain Technology. Doctoral dissertation. Soongsil University, Seoul.
  22. K. B. Kim. (2018). A Study on Factors Affecting Intention to Use Drone Technology Applying Extended Integrated Technology(UTAUT2) Model. Doctoral dissertation. Hoseo University, Seoul.
  23. 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.
  24. I. S. Park. (2012). A Study on the User Acceptance Model of Mobile Credit Card Service based on UTAUT. Doctoral dissertation. Kookmin University, Seoul.
  25. H. J. Son, S. W. Lee & M. H. Cho. (2014). Influential Factors of College Students Intention to Use Wearable Device: An Application of the UTAUT2 Model. Korean Journal of Communication & Information, 68, 7-33.
  26. J. P. Peter & J. C. Olson. (1994). Understanding Consumer Behavior. Burr Ridge. IL: R. D. Irwin.
  27. T. M. Lee & E. Y. Lee. (2005). A Study on the Determinants of Purchase Intention in Mobile Commerce: Focused on the Mediating Role of Perceived Risks and Perceived Benefits. Asia Pacific Journal of Information Systems, 15(2), 1-21. https://doi.org/10.1111/j.1365-2575.2005.00182.x
  28. S. B. Song, J. Y. Kang & S. G. Lee. (2013). Analyzing Impact Factors of User Resistance to Accepting Paid Mobile Application. Journal of the Korea Contents Association, 13(4), 361-375. https://doi.org/10.5392/JKCA.2013.13.04.361
  29. K. P. Gwinner, D. D. Gremler & M. J. Bitner. (1998). Relational benefits in service industries: The customer's perspective. Journal of the Academy of Marketing Science, 26(2), 106-107.
  30. M. J. Kim. (2017). A Study on the Continuance Intention of Delivery Application Service in the Food Industry: Based on Integrated Perspective of Value-Based Adoption Model and Resistance Factors. Doctoral dissertation. Kyunghee University, Seoul.
  31. S. H. Kim & H. S. Park. (2011). The Impact of Service Characteristics of Smartphone Application on Perceived Value, Satisfaction and Intention to Recommend. Korean Business Education Review, 26(6), 121-142.
  32. E. K. Choi. (2019). Effects of Perceived Benefit, Risk, Value, Attitude and Usage Attitude on Continuous Usage Intention toward Mobile Coupon. The Journal of Internet Electronic Commerce Research, 19(3), 173-199. https://doi.org/10.37272/JIECR.2019.06.19.3.173
  33. R. Batra & O. T. Ahtola (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 2(2), 159-170. https://doi.org/10.1007/BF00436035
  34. H. W. Hyun, J. K. Park & J. W. Yum. (2019). The effects of perceived benefits on usage intention in U.S market. Korean Academy of International Business, 30(3), 111-138.
  35. J. W. Jung & S. Y. Cho. (2018). Relationship among perceived benefit, perceived risk and continuous use of user' Internet primary bank: The mediation effects of trust. Journal of the Korea Convergence Society, 9(12), 195-205. https://doi.org/10.15207/JKCS.2018.9.12.195
  36. L. G. Brown. (1990). Convenience in Services Marketing. Journal of Service Marketing, 4(1), 53-59. https://doi.org/10.1108/EUM0000000002505
  37. J. H. Moon. (2016). A Study on Convenience of Smartphone Application on User's Response. Doctoral dissertation. Jeju National University, Jeju-do.
  38. H. T. Yoo. (2018). A Study on Intention to Use the drone delivery service Using TAM. Doctoral dissertation. Dankook University, Gyeonggi-do.
  39. I. Ajzen. (1991). The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. https://doi.org/10.1016/0749-5978(91)90020-t
  40. M. C. Lee. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130-141. https://doi.org/10.1016/j.elerap.2008.11.006
  41. Y. R. Kim, J. M. Lee & S. H. Woo. (2019). 5G era, changes in agriculture and rural areas. Jeollanam-do : KREI.
  42. KOTRA. (2019.12.19). 2020 Drone Major Market Report. KOTRA. https://news.kotra.or.kr.
  43. J. Y. Kim & K. H. Chu. (2014). The Role of Perceived Value on the Continuance Intention in Mobile Social Network Service. Journal of Digital Convergence, 12(10), 211-222. https://doi.org/10.14400/JDC.2014.12.10.211
  44. S. Y. Lee, H. R. Yim & H. S. Kim. (2019). A Study on Influence Relation of Membership Users Perceived Benefit, Sacrifice, Value and Continuous Use Intention by Using Theory of Value Based Adoption Model (VAM): Focused on 20s CJ Membership Service. Culinary Science & Hospitality Research, 25(6), 12-22.
  45. S. J. Park. (2016). The Influence of Health Applications Experience on Acceptance Intention for Wearable Devices: With a focus on Technology Acceptance and Innovation Resistance. Master's Thesis. Chung-Ang University, Seoul
  46. J. H. Park. (2018). The Influence of Pintech Characteristics on Users' Perception and Continuous Intentions for Use: Focused on the Moderating Effect of Innovation Resistance. Doctoral dissertation. Honam University, Gwangju.
  47. B. C. Song. (2018). A Study of User's Acceptance and Behavioral Intentions using the Medical Device Products based on the Unified Technology Theory of Acceptance. Doctoral dissertation. Bugyeong University, Pusan.
  48. E. G. Jeung & H. S. Park. (2017). An Empirical Study on the User Acceptance of Internet Primary Bank based on UTAUT2. The e-Business Studies, 18(3), 75-95. https://doi.org/10.15719/GEBA.18.3.201706.75
  49. J. J. Song. (2018). SPSS/AMOS Statistical Analysis Method. Gyeonggi-do : 21cbook.