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A Study on the Continuous Use of Hospital Information Seeking Applications

병원정보탐색 어플리케이션의 지속적 이용에 관한 연구

  • 장정인 (성균관대학교 문헌정보학과) ;
  • 이용정 (성균관대학교 문헌정보학과)
  • Received : 2021.02.24
  • Accepted : 2021.03.19
  • Published : 2021.03.30

Abstract

The present study aims to identify the factors that affect the continuous use and discontinuance of the hospital information seeking applications(hospital apps thereafter) by employing the post acceptance model. The surveys were conducted with people who used the hospital apps from October 11 to 18, 2019. Researchers collected 125 valid data and analyzed them by using the structural equation model. The study found that the satisfaction and confirmation of expectation for the hospital apps users had significant effects on intention for continuous use and perceived usefulness, respectively. However, the perceived usefulness did not have a significant effect on the intention for continue use. The present study has identified the variables that influence the continuous use of these innovative technologies. The findings of the study confirmed the post acceptance model by observing the adoption and use of the hospital apps and extended the literature of the post acceptance model by discussing the unique characteristics of the hospital apps that satisfy the urgent help-seekers under emergency situations or the information needs emphasizing promptness. In addition, based on the benefits and limitations of hospital apps reported by consumers, the study provided practical implications for designing more user-friendly apps to hospital app developers or managers.

본 연구는 후기수용모델을 통해 병원정보탐색 어플리케이션(이하 병원 앱)의 지속적인 이용과 이용 중단에 영향을 미치는 요인을 파악하고자 하였다. 병원 앱을 사용한 경험이 있는 이용자를 대상으로 2019년 10월 11일부터 18일까지 설문조사를 실시하여 125개의 유효한 데이터를 수집하였고 구조방정식 모형을 사용하여 데이터를 분석하였다. 연구 결과, 병원 앱 사용자의 만족도와 기대일치도는 지속적 이용의도와 지각된 유용성에 각각 유의미한 영향을 미치는 것으로 나타났다. 하지만 지각된 유용성은 지속적 이용의도에 유의미한 영향을 미치지 않는 것으로 나타났다. 본 연구는 기존에 많이 조명되지 않았던 병원 앱에 관한 연구를 통해 이러한 혁신적 기술의 지속적 사용여부에 영향을 미치는 변인들을 파악하였다. 연구 결과는 병원앱의 수용과 이용을 관찰하여 후기수용모델을 재검증하였으며, 응급상황과 같은 긴급한 도움탐색이나 신속함이 강조되는 정보요구를 만족시키는 병원앱의 특수성을 논의함으로써 이론적 확장을 도모하였다. 또한 소비자가 보고한 병원 앱의 유익성과 한계점을 바탕으로 병원 앱 개발자나 운영자들에게 보다 이용자 친화적인 앱을 제공하기 위한 실질적인 시사점을 제공하였다.

Keywords

References

  1. Han, Haejin (2018. March 23). Care labs gooddak vs vibros spark. daily medi, Available: https://www.dailymedi.com/detail.php?number=828724
  2. Jung, You-Soo (2013). A study on a use-diffusion model of medical service websites and applications. Doctoral dissertation, Inje University, Business.
  3. Ki, Yeon-Su (2007). Research about factor affecting the continuous use of health app: Focusing on the moderating effect of sex and age. Master's thesis, Kookmin University, Department of Data Science.
  4. Kim, Sangwoo (2020. April 7). Corona 19 effect, explosive increase in app service utilization...gooddak, 1639%↑. Bridge Economy, Available: http://www.viva100.com/main/view.php?key=2020040700010002597
  5. Kim, Seong-Jin (2018). Influences of service convenience of medical service mobile application on behavioral intention: Focusing on intermediary effect of experience value and control effect of involvement. Doctoral dissertation, Gachon University, Business Administration.
  6. Lee, Jaeik (2018. June 11). Air-conditioning? hospital? pharmacy search apps have skyrocketed by more than 100,000 new installers. Digital Today, Available: http://www.digitaltoday.co.kr/news/articleView.html?idxno=200478
  7. Li, Jingshun (2017). Medical service information seeking of chinese who visit Korea: Medical tourists for cosmetic surgery. Master's thesis, Seoul National University, Nursing.
  8. Naver Knowledge Encyclopedia [n.d.]. National Science Museum - Internet of Things: O2O. Available: https://terms.naver.com/entry.nhn?docId=3386844&cid=58369&categoryId=58369
  9. Noh, Kyung-seop (2014). Well-written thesis statistical analysis SPSS & AMOS. Seoul: Hanbit Academy.
  10. Suh, Hyojung, Hong, Hyeonseok, Kim, Minjeong, Yoon, Wonjung, Lee, Taehoon, Jung, Jiyun, Hwang, Shinha, & Cho, Youngtae (2015). Mhealth apps: The current status of usage and the factors of continuous use. Journal of HCI Society of Korea, 10(1), 19-27. https://doi.org/10.17210/jhsk.2015.05.10.1.19
  11. Yi, Yong Jeong & Bae, Beom Jun (2017). An analysis of non-users of mobile healthcare applications: Based on diffusion of innovations theory. Journal of the Korean Society for Information Management, 34(1), 135-154. https://doi.org/10.3743/KOSIM.2017.34.1.135
  12. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS quarterly, 351-370.
  13. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. Management Information Systems Quarterly, 13(3), 319-340. https://doi.org/10.2307/249008
  14. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. https://doi.org/10.1287/mnsc.35.8.982
  15. Hsu, C. L. & Lin, J. C. C. (2019). Understanding continuance intention to use online to offline (O2O) apps. Electronic Markets, 1-15.
  16. Krebs, P. & Duncan, D. T. (2015). Health app use among US mobile phone owners: a national survey. JMIR mHealth and uHealth, 3(4), e101. https://doi.org/10.2196/mhealth.4924
  17. Lee, M. C. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
  18. Leung, L. & Chen, C. (2019). E-health/m-health adoption and lifestyle improvements: Exploring the roles of technology readiness, the expectation-confirmation model, and health-related information activities. Telecommunications Policy, 43(6), 563-575. https://doi.org/10.1016/j.telpol.2019.01.005
  19. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 173-191.
  20. Oh, In-gyu (2021. 2. 3). Healthcare services, leading the way in hospital paths. Medical Newspaper, Retrieved from http://www.bosa.co.kr/news/articleView.html?idxno=2143543
  21. Moscone, F., Tosetti, E., & Vittadini, G. (2012). Social interaction in patients' hospital choice: Evidence from Italy. Journal of the Royal Statistical Society: Series A (Statistics in Society), 175(2), 453-472. https://doi.org/10.1111/j.1467-985X.2011.01008.x
  22. Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the determinants of consumer satisfaction. Journal of Marketing. 60, 15-32. https://doi.org/10.2307/1251839
  23. Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810. https://doi.org/10.1016/j.ijhcs.2006.05.001
  24. Xiao, N., Sharman, R., Rao, H. R., & Upadhyaya, S. (2014). Factors influencing online health information search: An empirical analysis of a national cancer-related survey. Decision Support Systems, 57, 417-427. https://doi.org/10.1016/j.dss.2012.10.047