디지털융복합연구 (Journal of Digital Convergence)

  • 제17권7호
  • /
  • Pages.73-87
  • /
  • 2019
  • /
  • 2713-6434(pISSN)
  • /
  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
권호 표지
DOI QR코드

DOI QR Code

인공지능이 적용된 온라인 구인정보 플랫폼의 품질 및 선호가 지속사용의도에 미치는 영향에 관한 탐색적 연구

An Exploratory Study on Artificial Intelligence Quality, Preference and Continuous Usage Intention: A Case of Online Job Information Platform

  • 안경민 (동국대학교 테크노경영협동과정) ;
  • 이영찬 (동국대학교 경주캠퍼스 경영학부)
  • An, Kyung-Min (The Cooperative Department of Techno-Management, Dongguk University) ;
  • Lee, Young-Chan (Department of Business Administration, Dongguk University Gyeongju)
  • 투고 : 2019.04.28
  • 심사 : 2019.07.20
  • 발행 : 2019.07.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 연구는 최근 빠르게 확산되는 인공지능의 지속적인수용에 관하여 탐색하고자 온라인 구인정보 플랫폼에 적용된 인공지능의 품질을 정의하고 인공지능의 선호, 지속사용의도 간의 구조적인 관계를 규명하였다. 인공지능 사용자를 대상으로 설문조사를 시행하였고 184개를 회수하였다. 실증분석결과 인공지능의 품질과 선호가 만족에 긍정적인 영향을 미치며, 인공지능의 만족이 지속사용의도에 통계적으로 유의한 수준에서 긍정적인 영향을 미치는 것으로 나타났다. 그러나 예상과는 달리 인공지능의 품질은 지속사용의도에 유의한 영향을 미치지 않는 것으로 나타났다. 이와 같은 결과는 향후 인공지능 기술을 제품이나 서비스에 적용하는데 있어 이론적, 실무적인 차원의 유용한 가이드라인을 제시할 수 있을 것으로 기대한다.

The purpose of this study is to clarify the continuous usage intention of artificial intelligence products and services. In this study, we try to define the artificial intelligence quality and preference on the online job information platform and investigate the effect of artificial intelligence on continues usage intention. A survey of artificial intelligence users was conducted and recalled 184. The empirical analysis shows that the artificial intelligence quality and preference have a positive effect on satisfaction, and that the satisfaction has significant effect on the intention of continuing use. but the artificial intelligence quality does not significantly affect the intention of continuing use. These results are expected to provide useful guidelines for artificial intelligence technology products or services in the future.

키워드

DJTJBT_2019_v17n7_73_f0001.png 이미지

Fig. 1. User information process framework

DJTJBT_2019_v17n7_73_f0002.png 이미지

Fig. 2. Research model

DJTJBT_2019_v17n7_73_f0003.png 이미지

Fig. 3. Result of hypothesis tests

Table 1. Operation definition and Measurement items

DJTJBT_2019_v17n7_73_t0001.png 이미지

Table 2. Exploratory factor analysis

DJTJBT_2019_v17n7_73_t0002.png 이미지

Table 3. Results of reliabilities and validity

DJTJBT_2019_v17n7_73_t0003.png 이미지

Table 4. Result of correlation matrix and discriminant validity

DJTJBT_2019_v17n7_73_t0004.png 이미지

Table 5. Second-order model test

DJTJBT_2019_v17n7_73_t0005.png 이미지

Table 6. Summary of hypothesis testing

DJTJBT_2019_v17n7_73_t0006.png 이미지

참고문헌

  1. T. Bondarouk, E. Parry & E. Furtmueller. (2017). Electronic HRM: four decades of research on adoption and consequences. The InTernaTIonal Journal of human resource management, 28(1), 98-131. DOI : 10.1080/09585192.2016.1245672
  2. P. van Esch, J. S. Black & J. Ferolie. (2019). Marketing AI recruitment: The next phase in job application and selection. Computers in Human Behavior, 90, 215-222. DOI :10.1016/j.chb.2018.09.009
  3. Oracle. (2018). HR Trends Report 2018. Oracle, p.1-15.
  4. W. H. Delone & E. R. McLean. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of management information systems, 19(4), 9-30. DOI : 10.1080/07421222.2003.11045748
  5. L. F. Pitt, R. T. Watson & C. B. Kavan. (1995). Service quality: a measure of information systems effectiveness. MIS quarterly, 19(2), 173-187. DOI : 10.2307/249687
  6. Y. Lee & K. A. Kozar. (2006). Investigating the effect of website quality on e-business success: An analytic hierarchy process (AHP) approach. Decision support systems, 42(3), 1383-1401. DOI : 10.1016/j.dss.2005.11.005
  7. D. Yoon & Kin Tong. (2009). A study of e-recruitment technology adoption in Malaysia. Industrial Management & Data Systems, 109(2), 281-300. DOI : 10.1108/02635570910930145
  8. K. Y. Tam & S. Y. Ho. (2006). Understanding the impact of web personalization on user information processing and decision outcomes. MIS quarterly, 30(4), 865-890. DOI : 10.2307/25148757
  9. J. Lee & J. N. Lee. (2009). Understanding the product information inference process in electronic word-of-mouth: An objectivity-subjectivity dichotomy perspective. Information & Management, 46(5), 302-311. DOI : 10.1016/j.im.2009.05.004
  10. A. Muthitcharoen, P. C. Palvia & V. Grover. (2011). Building a model of technology preference: The case of channel choices. Decision Sciences, 42(1), 205-237. DOI : 10.1111/j.1540-5915.2010.00306.x
  11. V. Venkatesh. (2006). Where to go from here? Thoughts on future directions for research on individual-level technology adoption with a focus on decision making. Decision Sciences, 37(4), 497-518. DOI : 10.1111/j.1540-5414.2006.00136.x
  12. T. Kowatsch & W. Maass. (2010). In-store consumer behavior: How mobile recommendation agents influence usage intentions, product purchases, and store preferences. Computers in Human Behavior, 26(4), 697-704. DOI : 10.1016/j.chb.2010.01.006
  13. Y. Zheng, K. Zhao & A. Stylianou. (2013). The impacts of information quality and system quality on users' continuance intention in information-exchange virtual communities: An empirical investigation. Decision Support Systems, 56, 513-524. DOI : 10.1016/j.dss.2012.11.008
  14. S. M. Tseng. (2015). Exploring the intention to continue using web-based self-service. Journal of Retailing and Consumer Services, 24, 85-93. DOI : 10.1016/j.jretconser.2015.02.001
  15. A. Bhattacherjee & C. Sanford. (2006). Influence processes for information technology acceptance: An elaboration likelihood model. MIS quarterly, 30(4), 805-825. DOI : 10.2307/25148755
  16. T. T. Kircher, C. Senior, M. L. Phillips, P. J. Benson, E. T. Bullmore, M. Brammer & A. S. David. (2000). Towards a functional neuroanatomy of self processing: effects of faces and words. Cognitive Brain Research, 10(1-2), 133-144. DOI : 10.1016/S0926-6410(00)00036-7
  17. V. Krishnaraju, S. K. Mathew & V. Sugumaran. (2016). Web personalization for user acceptance of technology: An empirical investigation of E-government services. Information Systems Frontiers, 18(3), 579-595. DOI : 10.1007/s10796-015-9550-9
  18. A. Levitin & T. Redman. (1995). Quality dimensions of a conceptual view. Information Processing & Management, 31(1), 81-88. DOI : 10.1016/0306-4573(95)80008-H
  19. J. J. Cronin Jr, M. K. Brady & G. T. M. Hult. (2000). Assessing the effects of quality, value, and customer satisfaction on consumer behavioral intentions in service environments. Journal of retailing, 76(2), 193-218. DOI : 10.1016/S0022-4359(00)00028-2
  20. H. H. Bauer, T. Falk & M. Hammerschmidt. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59(7), 866-875. DOI : 10.1016/j.jbusres.2006.01.021
  21. K. M. An & Y. C. Lee. (2018). Examining Success Factors of Online P2P Lending Service Using Kano Model and Fuzzy-AHP. Knowledge Management Review, 19(2), 109-132. DOI : 10.15813/kmr.2018.19.2.006
  22. Z. Yang, S. Cai, Z. Zhou & N. Zhou. (2005). Development and validation of an instrument to measure user perceived service quality of information presenting web portals. Information & management, 42(4), 575-589. DOI : 10.1016/j.im.2004.03.001
  23. Y. F. Kuo, C. M. Wu & W. J. Deng. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in human behavior, 25(4), 887-896. DOI : 10.1016/j.chb.2009.03.003
  24. C. Mang. (2012). Online job search and matching quality. Ifo Working Paper, 147.
  25. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald & E. Muharemagic. (2015). Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1. DOI : 10.1186/s40537-014-0007-7
  26. C. B. Stone, A. R. Neely & M. L. Lengnick-Hall. (2018). Human Resource Management in the Digital Age: Big Data, HR Analytics and Artificial Intelligence. In Management and Technological Challenges in the Digital Age, CRC Press, 13-42.
  27. N. Syam & A. Sharma. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135-146. DOI : 10.1016/j.indmarman.2017.12.019
  28. H. Chen, R. H. Chiang & V. C. Storey. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188. DOI : 10.2307/41703503
  29. J. R. Bettman, M. F. Luce & J. W. Payne. (1998). Constructive consumer choice processes. Journal of consumer research, 25(3), 187-217. DOI : 10.1086/209535
  30. J. Wang & A. Y. Lee. (2006). The role of regulatory focus in preference construction. Journal of Marketing research, 43(1), 28-38. DOI : 10.1509/jmkr.43.1.28
  31. H. H. Chang & Y. M. Liu. (2009). The impact of brand equity on brand preference and purchase intentions in the service industries. The Service Industries Journal, 29(12), 1687-1706. DOI : 10.1080/02642060902793557
  32. S. Lee & R. J. Koubek. (2010). The effects of usability and web design attributes on user preference for e-commerce web sites. Computers in Industry, 61(4), 329-341. DOI : 10.1016/j.compind.2009.12.004
  33. L. C. Cheng & H. A. Wang. (2014). A fuzzy recommender system based on the integration of subjective preferences and objective information. Applied Soft Computing, 18, 290-301. DOI : 10.1016/j.asoc.2013.09.004
  34. J. Nielsen & J. Levy. (1994). Measuring usability: preference vs. performance. Communications of the ACM, 37(4), 66-76. DOI : 10.1145/175276.175282
  35. J. R. Bettman & M. A. Zins. (1977). Constructive processes in consumer choice. Journal of Consumer Research, 4(2), 75-85. DOI : 10.1086/208682
  36. B. Lilly & R. Walters. (2000). An exploratory examination of retaliatory preannouncing. Journal of Marketing Theory and Practice, 8(4), 1-9. DOI : 10.1080/10696679.2000.11501875
  37. K. Z. Zhou & K. Nakamoto. (2007). How do enhanced and unique features affect new product preference? The moderating role of product familiarity. Journal of the Academy of Marketing Science, 35(1), 53-62. DOI : 10.1007/s11747-006-0011-3
  38. A. Bahrammirzaee. (2010). A comparative survey of artificial intelligence applications in finance: artificial neural networks, expert system and hybrid intelligent systems. Neural Computing and Applications, 19(8), 1165-1195. DOI : 10.1007/s00521-010-0362-z
  39. M. D. Fethi & F. Pasiouras. (2010). Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. European journal of operational research, 204(2), 189-198. DOI : 10.1016/j.ejor.2009.08.003
  40. G. Shafer. (1987). Probability judgment in artificial intelligence and expert systems. Statistical science, 2(1), 3-16. https://doi.org/10.1214/ss/1177013426
  41. Y. W. Lee, D. M. Strong, B. K. Kahn & R. Y. Wang. (2002). AIMQ: a methodology for information quality assessment. Information & management, 40(2), 133-146. DOI : 10.1016/S0378-7206(02)00043-5
  42. S. M. Choi & T. S. Moon. (2015). Impact of ICT Competence on Convergence Performance and the Moderating Effect of Convergence Capabilities. The Journal of Internet Electronic Commerce Resarch 15(1), 159-175.
  43. J. D. Lee, M. K. Rhee & M. R. Kim. (2018). Experiencing with Splunk, a Platform for Analyzing Machine Data, for Improving Recruitment Support Services in WorldJob+. Journal of Digital Convergence, 16(3), 201-210. DOI : 10.14400/JDC.2018.16.3.201
  44. K. H. Jeong & H. R. Kim. (2008). Quality Status Comparison and Analysis for the Service Development Direction of Domestic Job Information Site. Journal of The Korea Society of Computer and Information, 13(5), 211-218.
  45. I. Adjzen & M. Fishbein. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs NJ: Prentice Hall.
  46. F. D. Davis, R. P. Bagozzi & P. R. Warshaw. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003. DOI : 10.1287/mnsc.35.8.982
  47. H. Kim & L. S. Niehm. (2009). The impact of website quality on information quality, value, and loyalty intentions in apparel retailing. Journal of interactive marketing, 23(3), 221-233. DOI : 10.1016/j.intmar.2009.04.009
  48. N. J. Lightner. (2003). What users want in e-commerce design: effects of age, education and income. Ergonomics, 46(1-3), 153-168. DOI : 10.1080/00140130303530
  49. R. L. Oliver & J. E. Swan. (1989). Consumer perceptions of interpersonal equity and satisfaction in transactions: a field survey approach. Journal of marketing, 53(2), 21-35. DOI : 10.1177/002224298905300202
  50. S. O. Olsen. (2002). Comparative evaluation and the relationship between quality, satisfaction, and repurchase loyalty. Journal of the academy of marketing science, 30(3), 240-249. DOI : 10.1177/0092070302303005
  51. A. Parasuraman & C. L. Colby. (2015). An updated and streamlined technology readiness index: TRI 2.0. Journal of service research, 18(1), 59-74. DOI : 10.1177/1094670514539730
  52. A. Pearson. S. Tadisina & C. Griffin. (2012). The role of e-service quality and information quality in creating perceived value: antecedents to web site loyalty. Information Systems Management, 29(3), 201-215. DOI : 10.1080/10580530.2012.687311
  53. S. Sahadev & K. Purani. (2008). Modelling the consequences of e-service quality. Marketing Intelligence & Planning, 26(6), 605-620. DOI : 10.1108/02634500810902857
  54. V. Venkatesh & F. D. Davis. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204. DOI : 10.1287/mnsc.46.2.186.11926
  55. R. Y. Wang & D. M. Strong. (1996). Beyond accuracy: What data quality means to data consumers. Journal of management information systems, 12(4), 5-33. DOI : 10.1080/07421222.1996.11518099
  56. B. Xiao & I. Benbasat. (2007). E-commerce product recommendation agents: use, characteristics, and impact. MIS quarterly, 31(1), 137-209. DOI : 10.2307/25148784
  57. M. I. Choi. (2019). The effect of information seeking style and news literacy of card news users on recommendation intention: Focused on Technology Acceptance Model (TAM). Journal of Digital Convergence, 10(1), 141-148. DOI : 10.15207/JKCS.2019.10.1.141