Knowledge Management Research (지식경영연구)

The Knowledge Management Society of Korea (한국지식경영학회)
권호 표지
DOI QR코드

DOI QR Code

Motivational Factors Affecting Intention to Use Mobile Health Apps: Focusing on Regulatory Focus Tendency and Privacy Calculus Theory

모바일 헬스 앱 사용의도 동기요인: 조절초점성향과 프라이버시계산이론을 중심으로

  • So, Hyeon-jeong (Graduate School of Business IT, Kookmin University) ;
  • Kwahk, Kee-Young (College of Business Administration/Graduate School of Business IT, Kookmin University)
  • 소현정 (국민대학교 비즈니스IT전문대학원) ;
  • 곽기영 (국민대학교 경영대학/비즈니스IT전문대학원)
  • Received : 2020.10.29
  • Accepted : 2020.12.14
  • Published : 2021.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

Use of mobile apps being extended, privacy concern on the side of the users is increased while they are willing to provide the private information to use the apps. In this study, we tried to identify the motivating elements that influence the users' intention to use the apps, based on the tendency towards regulatory focus and the privacy calculus theory. To verify the study model, we collected data from 151 adults who use health apps throughout the country, and analyzed the data using the PLS-SEM method. According to the result of the study, it was turned out that tendency towards promotion focus had negative impact on privacy concern and privacy danger, and tendency towards prevention focus had positive impact on privacy concern. Privacy concern had negative impact on the intention to use the mobile apps, and privacy benefit and privacy knowledge had positive impact on the intention to use the mobile apps. Finally, the intention to use the mobile apps had positive impact on the intention to continue to use the mobile apps. In this study, we identified different impacts of two types of tendency towards regulatory focus on privacy concern, and identified different influences on the intention to use the mobile apps accordingly.

모바일 앱 사용이 확대되면서 사용자 프라이버시 침해에 대한 염려는 증가되고 있지만 사용자들은 앱을 사용하기 위해 개인정보를 기꺼이 공개하고 있다. 본 연구는 조절초점성향과 프라이버시계산이론을 바탕으로 사용자의 앱 사용의도에 영향을 미치는 동기요인을 제시한다. 제시한 연구모델을 검증하기 위해 헬스 앱을 사용하는 전국의 성인 151명을 대상으로 설문 데이터를 수집하였으며, PLS-SEM 기법을 이용하여 분석을 진행하였다. 연구결과에 따르면 조절초점 두 성향 중 향상초점성향은 정보프라이버시염려와 프라이버시위험에 부의 영향을 미쳤으며, 예방초점성향은 정보프라이버시 염려에 정의 영향을 주었다. 정보프라이버시염려는 모바일 앱 사용의도에 부의 영향을 미쳤으며 프라이버시이익과 프라이버시지식은 모바일 앱 사용의도에 정의 영향을 미쳤다. 마지막으로 모바일 앱 사용의도는 모바일 앱 지속사용의도에 정의 영향을 미쳤다. 본 연구는 앱 사용자의 조절초점성향이 정보프라이버시염려에 미치는 영향관계의 차이를 규명하였으며 이에 따른 모바일 앱 사용의도의 영향을 확인하였다.

Keywords

Acknowledgement

이 논문은 2018년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임 (NRF-2018S1A3A2075114).

References

  1. 김기호, 이형용 (2017). 조절 초점에 따른 개인 특성이 스마트 결제시스템 수용에 미치는 영향. 지식경영연구, 18(4), 213-235. https://doi.org/10.15813/kmr.2017.18.4.009
  2. 김미량, 김재운, 황선환 (2010). 진지한 여가척도(SLIM)의 단일문항측정 적용검토. 한국여가레크리에이션학회지, 34(4), 147-156.
  3. 김병수 (2012). 모바일 소셜네트워크서비스 환경에서 지속사용 의도의 선행 요인에 관한 연구: 신뢰와 프라이버시 우려의 역할. 지식경영연구, 13(4), 83-100. https://doi.org/10.15813/KMR.2012.13.4.006
  4. 김은진, 박재진 (2016). 조절초점 이론과 TAM 모델의 결합을 통한 해외직구 소비자 행동 연구. 사회과학연구, 27(4), 47-66.
  5. 박지영, 곽기영 (2019). SNS 사용자의 자기노출 행동 동기요인. 경영학연구, 48(2), 561-587.
  6. 서봉군, 박도형 (2020). 온라인 메신저 서비스에서의 이모티콘 사용 동기: 조절초점 성향을 중심으로. 지식경영연구, 21(2), 101-118. https://doi.org/10.15813/KMR.2020.21.2.006
  7. 오선주 (2014). 사용자 유형에 따른 모바일 앱 구매요인에 관한 연구. Information Systems Review, 16(1), 73-88. https://doi.org/10.14329/isr.2014.16.1.073
  8. 유재호, 이애리, 김경규 (2016). 페이스북에서의 프라이버시 패러독스 현상 연구: 듀얼팩터이론을 중심으로. 지식경영연구, 17(1), 17-47.
  9. 이승호, 김창식, 곽기영 (2016). 인스타그램 고객의 지속적 사용의도 결정요인: 성별의 조절효과를 중심으로. 디지털산업정보학회지, 12(2), 61-72.
  10. 이애리, 안효영 (2016). 핀테크 사용에 대한 정보프라이버시 염려와 이용자 저항에 대한 연구: 조절초점성향과의 상호작용 효과 고찰. 정보보호학회논문지, 26(1), 209-226. https://doi.org/10.13089/JKIISC.2016.26.1.209
  11. 차훈상 (2012). 스마트폰 위치기반 어플리케이션의 이용의도에 영향을 미치는 요인: 프라이버시 계산 모형의 적용. Asia Pacific Journal of Information Systems, 22(4), 7-29.
  12. Androulidakis, I., & Kandus, G. (2011, April). Mobile phone security awareness and practices of students in budapest. In Proceedings of the 6th International Conference on Digital Telecommunications, 17-22.
  13. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74-94. https://doi.org/10.1007/BF02723327
  14. Beinat, E. (2001). Privacy and location-based services: Stating the policies clearly. GeoInformatics, 4, 14-17.
  15. Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS Quarterly, 25(3), 351-370. https://doi.org/10.2307/3250921
  16. Buck, C., Horbel, C., Kessler, T., & Christian, C. (2014). Mobile consumer apps: Big data brother is watching you. Marketing Review St. Gallen, 31(1), 26-35. https://doi.org/10.1365/s11621-014-0318-2
  17. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295-336.
  18. Clarke, R. (2001). Person location and person tracking-Technologies, risks and policy implications. Information Technology & People, 14(2), 206-231. https://doi.org/10.1108/09593840110695767
  19. Crowe, E., & Higgins, E. T. (1997). Regulatory focus and strategic inclinations: Promotion and prevention in decision-making. Organizational Behavior and Human Decision Processes, 69(2), 117-132. https://doi.org/10.1006/obhd.1996.2675
  20. 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
  21. Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80. https://doi.org/10.1287/isre.1060.0080
  22. Dogruel, L., Joeckel, S., & Vitak, J. (2017). The valuation of privacy premium features for smartphone apps: The influence of defaults and expert recommendations. Computers in Human Behavior, 77, 230-239. https://doi.org/10.1016/j.chb.2017.08.035
  23. Egele, M., Kruegel, C., Kirda, E., & Vigna, G. (2011, February). PiOS: Detecting privacy leaks in iOS applications. In NDSS, 177-183.
  24. Enck, W., Gilbert, P., Han, S., Tendulkar, V., Chun, B. G., Cox, L. P., ... & Sheth, A. N. (2014). TaintDroid: An information-flow tracking system for realtime privacy monitoring on smartphones. ACM Transactions on Computer Systems(TOCS), 32(2), 1-29.
  25. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.1177/002224378101800104
  26. Freitas, A. L., Liberman, N., & Higgins, E. T. (2002). Regulatory fit and resisting temptation during goal pursuit. Journal of Experimental Social Psychology, 38(3), 291-298. https://doi.org/10.1006/jesp.2001.1504
  27. George, D., & Mallery, M. (2003). Using SPSS for Windows step by step: A simple guide and reference. Pearson Education India.
  28. Gutierrez, A., O'Leary, S., Rana, N. P., Dwivedi, Y. K., & Calle, T. (2019). Using privacy calculus theory to explore entrepreneurial directions in mobile location-based advertising: Identifying intrusiveness as the critical risk factor. Computers in Human Behavior, 95, 295-306. https://doi.org/10.1016/j.chb.2018.09.015
  29. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152. https://doi.org/10.2753/MTP1069-6679190202
  30. Higgins, E. T. (1997). Beyond pleasure and pain. American Psychologist, 52(12), 1280-1300. https://doi.org/10.1037/0003-066X.52.12.1280
  31. Higgins, E. T., Idson, L. C., Freitas, A. L., Spiegel, S., & Molden, D. C. (2003). Transfer of value from fit. Journal of Personality and Social Psychology, 84(6), 1140-1153. https://doi.org/10.1037/0022-3514.84.6.1140
  32. Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1-2), 45-71. https://doi.org/10.1023/A:1019104520776
  33. Jiang, Z., Heng, C. S., & Choi, B. C. (2013). Research note-privacy concerns and privacy-protective behavior in synchronous online social interactions. Information Systems Research, 24(3), 579-595. https://doi.org/10.1287/isre.1120.0441
  34. Jozani, M., Ayaburi, E., Ko, M., & Choo, K. K. R. (2020). Privacy concerns and benefits of engagement with social media-enabled apps: A privacy calculus perspective. Computers in Human Behavior, 107, 106260. https://doi.org/10.1016/j.chb.2020.106260
  35. Jung, E. J., McKnight, D. H., Jung, E., & Lankton, N. K. (2011). The surprising lack of effect of privacy concerns on intention to use online social networks. In AMCIS.
  36. Kline, P. (2000). Test construction: Factor analytic and item analytic methods. In Handbook of psychological testing (2nd ed., pp. 161-181). London: Routledge.
  37. Krasnova, H., & Veltri, N. F. (2010, January). Privacy calculus on social networking sites: Explorative evidence from Germany and USA. In 2010 43rd Hawaii International Conference on System Sciences, IEEE, 1-10.
  38. Nagy, M. S. (2002). Using a single-item approach to measure facet job satisfaction. Journal of Occupational and Organizational Psychology, 75(1), 77-86. https://doi.org/10.1348/096317902167658
  39. Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22-42. https://doi.org/10.1111/j.1540-4560.1977.tb01880.x
  40. Lee, A. Y., & Aaker, J. L. (2004). Bringing the frame into focus: the influence of regulatory fit on processing fluency and persuasion. Journal of Personality and Social Psychology, 86(2), 205-218. https://doi.org/10.1037/0022-3514.86.2.205
  41. Li, H., Sarathy, R., & Xu, H. (2010). Understanding situational online information disclosure as a privacy calculus. Journal of Computer Information Systems, 51(1), 62-71.
  42. Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers' decision to disclose personal information to unfamiliar online vendors. Decision Support Systems, 51(3), 434-445. https://doi.org/10.1016/j.dss.2011.01.017
  43. Lockwood, P., Jordan, C. H., & Kunda, Z. (2002). Motivation by positive or negative role models: Regulatory focus determines who will best inspire us. Journal of Personality and Social Psychology, 83(4), 854-864. https://doi.org/10.1037/0022-3514.83.4.854
  44. McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359. https://doi.org/10.1287/isre.13.3.334.81
  45. Mosteller, J., & Poddar, A. (2017). To share and protect: Using regulatory focus theory to examine the privacy paradox of consumers' social media engagement and online privacy protection behaviors. Journal of Interactive Marketing, 39, 27-38. https://doi.org/10.1016/j.intmar.2017.02.003
  46. Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460-469. https://doi.org/10.2307/3150499
  47. Pentina, I., Zhang, L., Bata, H., & Chen, Y. (2016). Exploring privacy paradox in information-sensitive mobile app adoption: A cross-cultural comparison. Computers in Human Behavior, 65, 409-419. https://doi.org/10.1016/j.chb.2016.09.005
  48. PWC (2014). Wearable technology future is ripe for growth-most notably among millennials. Available at: http://www.pwc.com/us/en/press-releases/2014/wearable-technology-future.html
  49. Robins, R. W., Hendin, H. M., & Trzesniewski, K. H. (2001). Measuring global self-esteem: Construct validation of a single-item measure and the Rosenberg Self-Esteem Scale. Personality and Social Psychology Bulletin, 27(2), 151-161. https://doi.org/10.1177/0146167201272002
  50. Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: Measuring individuals' concerns about organizational practices. MIS Quarterly, 20(2), 167-196. https://doi.org/10.2307/249477
  51. Venkatesh, V., & Davis, F. D. (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
  52. Wang, E. S. T., & Lin, R. L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2-10.
  53. Wang, J., & Lee, A. Y. (2006). The role of regulatory focus in preference construction. Journal of Marketing Research, 43(1), 28-38. https://doi.org/10.1509/jmkr.43.1.28
  54. Wang, T., Duong, T. D., & Chen, C. C. (2016). Intention to disclose personal information via mobile applications: A privacy calculus perspective. International Journal of Information Management, 36(4), 531-542. https://doi.org/10.1016/j.ijinfomgt.2016.03.003
  55. Warren, S. D., & Brandeis, L. D. (1890). The right to privacy. Harvard Law Review, 4(5), 193-220. https://doi.org/10.2307/1321160
  56. Wottrich, V. M., Van Reijmersdal, E. A., & Smit, E. G. (2018). The privacy trade-off for mobile app downloads: The roles of app value, intrusiveness, and privacy concerns. Decision Support Systems, 106, 44-52. https://doi.org/10.1016/j.dss.2017.12.003
  57. Xu, H., & Teo, H. H. (2004). Alleviating consumers' privacy concerns in location-based services: A psychological control perspective. ICIS 2004 Proceedings, 64.
  58. Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual's privacy concerns: Toward an integrative view. ICIS 2008 Proceedings, 6.
  59. Xu, H., Gupta, S., Rosson, M. B., & Carroll, J. M. (2012). Measuring mobile users' concerns for information privacy. 33rd International Conference on Information Systems, 1-16.
  60. Xu, H., Luo, X. R., Carroll, J. M., & Rosson, M. B. (2011). The personalization privacy paradox: An exploratory study of decision making process for location-aware marketing. Decision Support Systems, 51(1), 42-52. https://doi.org/10.1016/j.dss.2010.11.017
  61. Xu, H., Teo, H. H., Tan, B. C., & Agarwal, R. (2009). The role of push-pull technology in privacy calculus: The case of location-based services. Journal of Management Information Systems, 26(3), 135-174. https://doi.org/10.2753/MIS0742-1222260305