Journal of Industrial Convergence (산업융합연구)

DAEHAN Society of Industrial Management (대한산업경영학회)
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A Dynamic Investigation of iBeacon Adoption at Tourism Destination

관광지에서의 iBeacon 도입에 대한 동태적 분석

  • Received : 2018.04.19
  • Accepted : 2018.06.15
  • Published : 2018.06.30
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Abstract

The interconnectedness of all things is continuously expanding. For example, bluetooth low energy (BLE) beacons are wireless radio transmitters that can send an identifier to nearby receivers and trigger a number of applications, from proximity marketing to indoor location-based service. iBeacon technology which is one of the newest technologies in the smart tourism field, is reckoned as being very useful for travelers in enhancing the experience with visiting places. However, there is consequently not much existing research yet about the connection between iBeacon technology and tourism destination. Considering that, this study analyzes the adoption of iBeacon in tourism destination, this study examine the interrelationships and feedback structures of key factors in iBeacon adoption. To serve the purpose, this study used system dynamics approach to develop a model of iBeacon adoption in tourism destination. The analysis results showed that the concept of 'Social Influences' is one of the significant predictors for individual's intention behavior to accept iBeacon, and word of mouth (WOM), subjective norm, privacy concern, and perceived usefulness are key factors influencing the iBeacon adoption.

모든 사물들이 연결되고 있으며 계속 확장되고 있다. 예를 들어, BLE (Bluetooth low energy) 비콘은 인접한 수신기에 식별 신호를 보내고 근접 마케팅부터 실내형 위치기반서비스에 이르는 다양한 응용프로그램을 개발할 수 있는 무선통신기술이다. 스마트 관광 분야의 최신 기술 중 하나인 iBeacon은 관광지에서의 방문 경험을 향상시키는 데 매우 유용한 것으로 알려져 있으나 이와 관련한 학술연구는 많지 않다. 본 연구는 관광지에서 iBeacon이 채택되는 과정을 분석하기 위해 주요 영향요인의 상호 관계 및 피드백 구조를 조사하였다. 연구목적을 달성하기 위해 본 연구는 시스템 다이내믹스 방법을 이용하여 관광지에서 iBeacon 채택의 동태적 모형을 개발하였다. 분석 결과, '사회적 영향'의 개념이 관광객의 iBeacon 수용의도에 대한 중요한 예측 요인 중 하나이며, 구전효과, 주관적 규범, 프라이버시 그리고 인지된 유용성이 iBeacon 채택에 영향을 미치는 핵심 요소인 것으로 나타났다.

Keywords

References

  1. Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. https://doi.org/10.1007/s12525-015-0196-8
  2. Lamsfus, C., Martin, D., Alzua-Sorzabal, A., & Torres-Manzanera, E. (2015). Smart tourism destinations: An extended conception of smart cities focusing on human mobility. In Information and communication technologies in tourism 363-375. Springer, Cham.
  3. Ho, T. Y., Chen, C. H., Chien, S. F., Chen, Y. H., Liu, S. Y., & Bayona, J. S. (2015, August). BeaconPass: A Location Based APP Game for Traveler. In International Conference on Universal Access in Human-Computer Interaction, pp. 288-297. Springer, Cham.
  4. iBeacon Insider (1995). What is iBeacon? A guide to Beacons. Retrieved December 14th, 2015 from: http://www.ibeacon.com/what-is-ibeacon-a-guide-to-beacons/
  5. Barten, C. (2016). Smart Technologies in Tourism. Smart Technologies in Tourism Student Conference, April, 2016.
  6. Boes, K., Buhalis, D., & Inversini, A. (2015). Conceptualising smart tourism destination dimensions. In Information and communication technologies in tourism, 391-403.
  7. Buhalis, D., & Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. In Information and communication technologies in tourism, 377-389.
  8. Buhalis, D., & Amaranggana, A. (2013). Smart tourism destinations. In Information and communication technologies in tourism, 553-564.
  9. Tom Dieck, M. C., Jung, T., & Han, D. I. (2016). Mapping requirements for the wearable smart glasses augmented reality museum application. Journal of Hospitality and Tourism Technology, 7(3), 230-253. https://doi.org/10.1108/JHTT-09-2015-0036
  10. 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
  11. Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2(3), 173-191. https://doi.org/10.1287/isre.2.3.173
  12. Hein, D. W., & Rauschnabel, P. A. (2016). Augmented reality smart glasses and knowledge management: A conceptual framework for enterprise social networks. In Enterprise social networks (pp. 83-109). Springer Gabler, Wiesbaden.
  13. Kalantari, M., & Rauschnabel, P. (2018). Exploring the Early Adopters of Augmented Reality Smart Glasses: The Case of Microsoft HoloLens. In Augmented Reality and Virtual Reality,. 229-245. Springer, Cham.
  14. Westin, A. F. (1967). Privacy and freedom, atheneum. New York, 7.
  15. Bauer, R. A., & Cox, D. F. (1967). Risk taking and information handling in consumer behavior. Boston: Harvard University, 469-486.
  16. Konana, P., & Balasubramanian, S. (2005). The social-economic-psychological model of technology adoption and usage: an application to online investing. Decision Support Systems, 39(3), 505-524. https://doi.org/10.1016/j.dss.2003.12.003
  17. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley
  18. Ajzen, I., & Fishbein, M. (1988). Theory of reasoned action-Theory of planned behavior. University of South Florida.
  19. Forrester, J. W. (1961). Industrial Dynamics. Portland. Productivity Press.
  20. Sedarati, P., & Baktash, A. (2017). Adoption of Smart Glasses in Smart Tourism Destination: A System Thinking Approach. Tourism Travel and Research Association: Advancing tourism Research Globally, 13. 2017 ttra international conference, Quebec, Canada.
  21. Delgado-Maciel, J., Cortes-Robles, G., Alor-Hernandez, G., Alcaraz, J. G., & Negny, S. (2018). A comparison between the Functional Analysis and the Causal-Loop Diagram to model inventive problems. Procedia CIRP, 70, 259-264. https://doi.org/10.1016/j.procir.2018.03.235
  22. Sterman, J. D. (2000). Business dynamics: systems thinking and modeling for a complex world (No. HD30. 2 S7835 2000).
  23. Cazier, J. A., Jensen, A. S., & Dave, D. S. (2008). The impact of consumer perceptions of information privacy and security risks on the adoption of residual RFID technologies. Communications of the Association for Information Systems, 23(1), 14.
  24. Hofstede, G., & Minkov, M. (2010). Long-versus short-term orientation: new perspectives. Asia Pacific business review, 16(4), 493-504. https://doi.org/10.1080/13602381003637609
  25. Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism management, 29(3), 458-468. https://doi.org/10.1016/j.tourman.2007.05.011
  26. Van der Heijden, H., Verhagen, T., & Creemers, M. (2003). Understanding online purchase intentions: contributions from technology and trust perspectives. European journal of information systems, 12(1), 41-48. https://doi.org/10.1057/palgrave.ejis.3000445
  27. Ma'ruf, J. J., Mohamad, O., & Ramayah, T. (2005). Intention to purchase via the internet: a comparison of two theoretical models. Asian Academy of Management Journal, 10(1), 79-95.
  28. Malhotra, Y., & Galletta, D. (2005). A multidimensional commitment model of volitional systems adoption and usage behavior. Journal of Management Information Systems, 22(1), 117-151. https://doi.org/10.1080/07421222.2003.11045840
  29. Venkatesh, V., & Morris, M. G. (2000). Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS quarterly, 115-139.
  30. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation, Information systems research, 2(3), 192-222. https://doi.org/10.1287/isre.2.3.192
  31. Venkatesh, Viswanath, and Fred D. 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
  32. Devaraj, S., Easley, R. F., & Crant, J. M. (2008). Research note-how does personality matter? Relating the five-factor model to technology acceptance and use. Information systems research, 19(1), 93-105. https://doi.org/10.1287/isre.1070.0153
  33. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.
  34. Mun, Y. Y., Jackson, J. D., Park, J. S., & Probst, J. C. (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information & Management, 43(3), 350-363. https://doi.org/10.1016/j.im.2005.08.006
  35. H.L. Mu & Y.C.Lee. (2018). Analyzing the Adoption of Online Payment using System Dynamics.Journal of Industrial Convergence, 16(1), 1-14. DOI : 10.22678/JIC.2018.16.1.001
  36. Y. C. Lee, D. H. Seo & S. H. Song. (2017). Classification and Evaluation of Service Quality Factors of O2O Delivery Applications Using Kano Model. Journal of Industrial Convergence, 15(2), 27-36. DOI : 10.22678/JIC.2017.15.2.027