Skip to main navigation menu Skip to main content Skip to site footer
##common.pageHeaderLogo.altText## ASEAN Journal on Hospitality and Tourism
Institut Teknologi Bandung

Articles

Vol. 19 No. 3 (2021)

What Determines Consumers’ Intention for Hotel Bookings through Smartphone Apps?

DOI
https://doi.org/10.5614/ajht.2021.19.3.01
Submitted
November 15, 2020
Published
2022-01-18

Abstract

In the emerging market, the usage of smartphone apps is playing an imperative role and supporting travelers in their online hotel bookings. This study aims to examine the various determinants that affect travelers’ behavioral intention regarding hotel booking through smartphone apps. Data was collected from 379 hotel guests who used smartphone apps for hotel bookings. The respondents for this study are taken from Delhi. Further, the hypotheses of this study were validated with the help of structural equation modeling (SEM) using partial least squares (PLS). The results of this study found all dimensions significant except for effort expectancy, facilitating conditions, and habits. A newly added dimension, perceived trust was also found a significant predictor of consumers’ behavioral intentions for hotel booking through smartphone apps. The study provides implications for hotel managers that the information provided on apps must be accurate, up-to-date, and reliable regarding hotel services. By incorporating relevant information in the system, travelers’ will feel the hotels are trustworthy and thus their tendency to use smartphone apps for hotel bookings will increase.  As well as hotel apps must be easy to operate which reduces travelers' extra efforts and time while using them.

References

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  2. Akg1/4l, Y., Yaman, B., Gegil, G., & Yavuz, G. (2019). The Influencing Factors for Purchasing Intentions in Social Media by Utaut Perspective. In Structural Equation Modeling Approaches to E-Service Adoption (pp. 254-267). IGI Global. DOI: 10.4018/978-1-5225-8015-7.ch013
  3. Alalwan, A. A. (2020). Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse. International Journal of Information Management, 50, 28-44. https://doi.org/10.1016/j.ijinfomgt.2019.04.008
  4. Alam, M. Z., Hu, W., Kaium, A., Hoque, R., & Alam, M. M. D. (2020). Understanding the determinants of mHealth apps adoption in Bangladesh: A SEM-Neural network approach. Technology in Society, 101255. https://doi.org/10.1016/j.techsoc.2020.101255
  5. Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 101293. https://doi.org/10.1016/j.techsoc.2020.101293
  6. Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020). Developing a general extended UTAUT model for M-payment adoption. Technology in Society, 101293. https://doi.org/10.1016/j.techsoc.2020.101293
  7. Arora, N., & Lata, S. (2020). YouTube channels influence on destination visit intentions. Journal of Indian Business Research. https://doi.org/10.1108/JIBR-09-2019-0269
  8. Assaker, G. (2020). Age and gender differences in online travel reviews and user-generated-content (UGC) adoption: extending the technology acceptance model (TAM) with credibility theory. Journal of Hospitality Marketing & Management, 29(4), 428-449. https://doi.org/10.1080/19368623.2019.1653807
  9. Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Kizgin, H., & Patil, P. (2019). Consumer use of mobile banking (M-Banking) in Saudi Arabia: Towards an integrated model. International Journal of Information Management, 44, 38-52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
  10. Barney, J. B., & Hansen, M. H. (1994). Trustworthiness as a source of competitive advantage. Strategic management journal, 15(S1), 175-190.
  11. BCG (2017), Boston Consultant Group: Transforming the client experience https://www.bcg.com/en-in/
  12. Bhatiasevi, V., & Yoopetch, C. (2015). The determinants of intention to use electronic booking among young users in Thailand. Journal of Hospitality and Tourism Management, 23, 1-11. https://doi.org/10.1016/j.jhtm.2014.12.004
  13. Buettner, R. (2017). Predicting user behavior in electronic markets based on personality-mining in large online social networks. Electronic Markets, 27(3), 247-265. https://doi.org/10.1007/s12525-016-0228-z
  14. Cheung, C. M., Lee, M. K., & Rabjohn, N. (2008). The impact of electronic word-of-mouth: The adoption of online opinions in online customer communities. Internet Research: Electronic Networking Applications and Policy, 18(3), 229-247.
  15. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern methods for business research, 295(2), 295-336.
  16. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information systems research, 14(2), 189-217.
  17. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
  18. 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.
  19. DeA man, M. (2015). Modeling the acceptance of e-learning in mandatory environments of higher education: The influence of previous education and gender. Computers in human behavior, 49, 272-281.
  20. Dhir, A., Kaur, P., & Rajala, R. (2020). Continued use of mobile instant messaging apps: A new perspective on theories of consumption, flow, and planned behavior. Social Science Computer Review, 38(2), 147-169. https://doi.org/10.1177/0894439318806853
  21. Dickinson, J. E., Ghali, K., Cherrett, T., Speed, C., Davies, N., & Norgate, S. (2014). Tourism and the smartphone app: Capabilities, emerging practice and scope in the travel domain. Current issues in tourism, 17(1), 84-101. https://doi.org/10.1080/13683500.2012.718323
  22. Dickinson, J. E., Ghali, K., Cherrett, T., Speed, C., Davies, N., & Norgate, S. (2014). Tourism and the smartphone app: Capabilities, emerging practice and scope in the travel domain. Current issues in tourism, 17(1), 84-101. https://doi.org/10.1080/13683500.2012.718323
  23. Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store information on buyers' product evaluations. Journal of marketing research, 28(3), 307-319. https://doi.org/10.1177/002224379102800305
  24. Eurostat statistics database, 2015 https://ec.europa.eu/eurostat/data/database
  25. Fatima, J. K., Ghandforoush, P., Khan, M., & Mascio, R. D. (2019). Mobile learning adoption for tourism education in a developing country. Current issues in Tourism, 22(4), 420-427. https://doi.org/10.1080/13683500.2018.1434135
  26. Fishbein, M. leek Ajzen (1975). Belief, attitude, intention and behavior: An introduction to theory and research, 181-202.
  27. Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4), 161.
  28. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics.
  29. Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS quarterly, 27(1), 51-90.
  30. Gruzd, A., Staves, K., & Wilk, A. (2012). Connected scholars: Examining the role of social media in research practices of faculty using the UTAUT model. Computers in Human Behavior, 28(6), 2340-2350. https://doi.org/10.1016/j.chb.2012.07.004
  31. Gunasinghe, A., Abd Hamid, J., Khatibi, A., & Azam, S. F. (2019). The adequacy of UTAUT-3 in interpreting academician's adoption to e-Learning in higher education environments. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-05-2019-0020
  32. Gupta, K., & Arora, N. (2019). Investigating consumer intention to accept mobile payment systems through unified theory of acceptance model. South Asian Journal of Business Studies. https://doi.org/10.1108/SAJBS-03-2019-0037
  33. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
  34. Hashim, H. S., & Hassan, Z. B. (2015). Factors that influence the users' adoption of cloud computing services at Iraqi Universities: An empirical study. Australian Journal of Basic and Applied Sciences, 9(27), 379-390.
  35. Hew, J. J., Lee, V. H., Ooi, K. B., & Wei, J. (2015). What catalyses mobile apps usage intention: an empirical analysis. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-01-2015-0028
  36. Hew, J. J., Lee, V. H., Ooi, K. B., & Wei, J. (2015). What catalyses mobile apps usage intention: an empirical analysis. Industrial Management & Data Systems. https://doi.org/10.1108/IMDS-01-2015-0028
  37. Higgins, J. P. (2016). Smartphone applications for patients' health and fitness. The American journal of medicine, 129(1), 11-19. https://doi.org/10.1016/j.amjmed.2015.05.038
  38. Hosizah, H., & Tamzil, F. (2020). Security system testing on electronic integrated antenatal care (e-iANC). International Journal of Electrical and Computer Engineering, 10(1), 346.
  39. How mobile app benefits" , 2015 https://www.goodworklabs.com/how-mobile-app-benefits-travel-and-tourism-industry/
  40. Huang, K. Y., & Chuang, Y. R. (2017). Aggregated model of ttf with utaut2 in an employment website context. Journal of Data Science, 15(2), 187-204.
  41. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: A review of four recent studies. Strategic management journal, 20(2), 195-204.
  42. Humbani, M., & Wiese, M. (2019). An integrated framework for the adoption and continuance intention to use mobile payment apps. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-03-2018-0072
  43. Islam, T., Ahmad, U. N. B. U., Ali, G., Ahmed, I., & Bowra, Z. A. (2013). Turnover intentions: The influence of perceived organizational support and organizational commitment. Procedia-Social and Behavioral Sciences, 103, 1238-1242.
  44. ITU (2015) https://www.itu.int/en/ITU-D/Statistics/Documents/facts/ICTFactsFigures2015.pdf
  45. Jamal, S., & Habib, M. A. (2020). Smartphone and daily travel: How the use of smartphone applications affect travel decisions. Sustainable Cities and Society, 53, 101939. https://doi.org/10.1016/j.scs.2019.101939
  46. Jeon, H. M., Ali, F., & Lee, S. W. (2019). Determinants of consumers' intentions to use smartphones apps for flight ticket bookings. The Service Industries Journal, 39(5-6), 385-402. https://doi.org/10.1080/02642069.2018.1437908
  47. KaliniA, Z., MarinkoviA, V., Djordjevic, A., & Liebana-Cabanillas, F. (2019). What drives customer satisfaction and word of mouth in mobile commerce services? A UTAUT2-based analytical approach. Journal of Enterprise Information Management. https://doi.org/10.1108/JEIM-05-2019-0136
  48. Kamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018). Examining branding co-creation in brand communities on social media: Applying the paradigm of Stimulus-Organism-Response. International Journal of Information Management, 39, 169-185. https://doi.org/10.1016/j.ijinfomgt.2017.12.001
  49. Kang, M., Liew, B. Y. T., Lim, H., Jang, J., & Lee, S. (2015). Investigating the determinants of mobile learning acceptance in Korea using UTAUT2. In Emerging issues in smart learning (pp. 209-216). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44188-6_29
  50. Kang, S. (2014). Factors influencing intention of mobile application use. International Journal of Mobile Communications, 12(4), 360-379. https://doi.org/10.1504/IJMC.2014.063653
  51. Karanasios, S., Burgess, S., & Sellitto, C. (2012). A classification of mobile tourism applications. In Global hospitality and tourism management technologies (pp. 165-177). IGI Global. DOI: 10.4018/978-1-61350-041-5.ch011
  52. Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology. https://doi.org/10.1111/bjet.12905
  53. Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of Bank Marketing. https://doi.org/10.1108/IJBM-03-2015-0025
  54. Lau, L. S., Choong, Y. O., Wei, C. Y., Seow, A. N., Choong, C. K., Senadjki, A., & Ching, S. L. (2020). Investigating nonusers' behavioural intention towards solar photovoltaic technology in Malaysia: The role of knowledge transmission and price value. Energy Policy, 144, 111651. https://doi.org/10.1016/j.enpol.2020.111651
  55. Lei, S. I., Wang, D., & Law, R. (2019). Perceived technology affordance and value of hotel mobile apps: A comparison of hoteliers and customers. Journal of Hospitality and Tourism Management, 39, 201-211. https://doi.org/10.1016/j.jhtm.2019.02.006
  56. Leong, L. Y., Ooi, K. B., Chong, A. Y. L., & Lin, B. (2013). Modeling the stimulators of the behavioral intention to use mobile entertainment: does gender really matter?. Computers in Human Behavior, 29(5), 2109-2121. https://doi.org/10.1016/j.chb.2013.04.004
  57. Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International journal of information management, 31(3), 252-260. https://doi.org/10.1016/j.ijinfomgt.2010.07.006
  58. Lu, J., Yu, C. S., Liu, C., & Wei, J. (2017). Comparison of mobile shopping continuance intention between China and USA from an espoused cultural perspective. Computers in Human Behavior, 75, 130-146. https://doi.org/10.1016/j.chb.2017.05.002
  59. Machado, G. C., Pinheiro, M. B., Lee, H., Ahmed, O. H., Hendrick, P., Williams, C., & Kamper, S. J. (2016). Smartphone apps for the self-management of low back pain: a systematic review. Best Practice & Research Clinical Rheumatology, 30(6), 1098-1109.
  60. https://doi.org/10.1016/j.berh.2017.04.002
  61. Madan, K., & Yadav, R. (2016). Behavioural intention to adopt mobile wallet: a developing country perspective. Journal of Indian Business Research. https://doi.org/10.1108/JIBR-10-2015-0112
  62. Mafe, C. R., Blas, S. S., & Tavera" Mesas, J. F. (2010). A comparative study of mobile messaging services acceptance to participate in television programmes. Journal of service Management. https://doi.org/10.1016/j.sbspro.2013.10.452
  63. Mahfuz, M. A., Khanam, L., & Hu, W. (2016, September). The influence of culture on m-banking technology adoption: An integrative approaches of UTAUT2 and ITM. In 2016 Portland International Conference on Management of Engineering and Technology (PICMET) (pp. 824-835). IEEE.
  64. Malik, A., Suresh, S., & Sharma, S. (2019). An empirical study of factors influencing consumers' attitude towards adoption of wallet apps. International Journal of Management Practice, 12(4), 426-442. https://doi.org/10.1504/IJMP.2019.102534
  65. Martins, C., Oliveira, T., & PopoviA , A. (2014). Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13. https://doi.org/10.1016/j.ijinfomgt.2013.06.002
  66. Maruping, L. M., Bala, H., Venkatesh, V., & Brown, S. A. (2017). Going beyond intention: Integrating behavioral expectation into the unified theory of acceptance and use of technology. Journal of the Association for Information Science and Technology, 68(3), 623-637. https://doi.org/10.1002/asi.23699
  67. Mehta, A., Morris, N. P., Swinnerton, B., & Homer, M. (2019). The influence of values on E-learning adoption. Computers & Education, 141, 103617. https://doi.org/10.1016/j.compedu.2019.103617
  68. Miladinovic, J., & Hong, X. (2016). A study on factors affecting the behavioral intention to use mobile shopping fashion apps in Sweden.
  69. Moorthy, K., Chun T'ing, L., Chea Yee, K., Wen Huey, A., Joe In, L., Chyi Feng, P., & Jia Yi, T. (2019). What drives the adoption of mobile payment? A Malaysian perspective. International Journal of Finance & Economics. https://doi.org/10.1002/ijfe.1756
  70. Morosan, C., & DeFranco, A. (2016). Modeling guests' intentions to use mobile apps in hotels. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-07-2015-0349
  71. Muda, M., Mohd, R., & Hassan, S. (2016). Online purchase behavior of generation Y in Malaysia. Procedia Economics and Finance, 37(16), 292-298. doi: 10.1016/S2212-5671(16)30127-7
  72. Nair, P. K., Ali, F., & Leong, L. C. (2015). Factors affecting acceptance & use of ReWIND. Interactive Technology and Smart Education. https://doi.org/10.1108/ITSE-02-2015-0001
  73. Nghia, H. T., Olsen, S. O., & Trang, N. T. M. (2020). Shopping value, trust, and online shopping well-being: a duality approach. Marketing Intelligence & Planning. https://doi.org/10.1108/MIP-08-2019-0411
  74. Nysveen, H., Pedersen, P. E., & Thorbj",rnsen, H. (2005). Explaining intention to use mobile chat services: moderating effects of gender. Journal of consumer Marketing. https://doi.org/10.1108/07363760510611671
  75. Ozturk, A. B., Nusair, K., Okumus, F., & Hua, N. (2016). The role of utilitarian and hedonic values on users' continued usage intention in a mobile hotel booking environment. International Journal of Hospitality Management, 57, 106-115. https://doi.org/10.1016/j.ijhm.2016.06.007
  76. Palau-Saumell, R., Forgas-Coll, S., Sanchez-Garca, J., & Robres, E. (2019). User acceptance of mobile apps for restaurants: An expanded and extended UTAUT-2. Sustainability, 11(4), 1210. https://doi.org/10.3390/su11041210
  77. Patil, P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020). Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management, 54, 102144. https://doi.org/10.1016/j.ijinfomgt.2020.102144
  78. Rafique, H., Almagrabi, A. O., Shamim, A., Anwar, F., & Bashir, A. K. (2020). Investigating the acceptance of mobile library applications with an extended technology acceptance model (TAM). Computers & Education, 145, 103732. https://doi.org/10.1016/j.compedu.2019.103732
  79. Ramrez-Correa, P., Rondan-Cataluna, F. J., Arenas-Gaitan, J., & Martn-Velicia, F. (2019). Analysing the acceptation of online games in mobile devices: An application of UTAUT2. Journal of Retailing and Consumer Services, 50, 85-93. https://doi.org/10.1016/j.jretconser.2019.04.018
  80. Raza, S. A., Shah, N., & Ali, M. (2019). Acceptance of mobile banking in Islamic banks: evidence from modified UTAUT model. Journal of Islamic Marketing. https://doi.org/10.1108/JIMA-04-2017-0038
  81. Raza, S. A., Shah, N., & Ali, M. (2019). Acceptance of mobile banking in Islamic banks: evidence from modified UTAUT model. Journal of Islamic Marketing. https://doi.org/10.1108/JIMA-04-2017-0038
  82. Rondan-Cataluna, F. J., Arenas-Gaitan, J., & Ramrez-Correa, P. E. (2015). A comparison of the different versions of popular technology acceptance models. Kybernetes. https://doi.org/10.1108/K-09-2014-0184
  83. San Martn, H., & Herrero, . (2012). Influence of the user's psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341-350.
  84. Santos-Vijande, M. L., Lopez-Sanchez, J. ., & Pascual-Fernandez, P. (2018). Co-creation with clients of hotel services: the moderating role of top management support. Current Issues in Tourism, 21(3), 301-327.
  85. Sheppard, B. H., Hartwick, J., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of consumer research, 15(3), 325-343.
  86. Singh, N., Sinha, N., & Liebana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191-205. https://doi.org/10.1016/j.ijinfomgt.2019.05.022
  87. Slade, E. L., Dwivedi, Y. K., Piercy, N. C., & Williams, M. D. (2015). Modeling consumers' adoption intentions of remote mobile payments in the United Kingdom: extending UTAUT with innovativeness, risk, and trust. Psychology & Marketing, 32(8), 860-873. https://doi.org/10.1002/mar.20823
  88. Slade, E. L., Williams, M. D., & Dwivedi, Y. K. (2014). Devising a research model to examine adoption of mobile payments: An extension of UTAUT2. The Marketing Review, 14(3), 310-335. https://doi.org/10.1362/146934714X14024779062036
  89. Statista (2018), India- Statistics & Facts https://www.statista.com/topics/1164/social-networks/
  90. Statista (2020), Social Media- Statistics & Facts https://www.statista.com/topics/1164/social-networks/
  91. Statistics (2019), Ministry of tourism http://tourism.gov.in/sites/default/files/Other/India%20Tourism%20Statistics%20at%20a%20Glance%202019.pdf
  92. Suki, N. M., & Suki, N. M. (2017). Determining students' behavioural intention to use animation and storytelling applying the UTAUT model: The moderating roles of gender and experience level. The International Journal of Management Education, 15(3), 528-538. https://doi.org/10.1016/j.ijme.2017.10.002
  93. Suki, N. M., & Suki, N. M. (2017). Flight ticket booking app on mobile devices: Examining the determinants of individual intention to use. Journal of Air Transport Management, 62, 146-154. https://doi.org/10.1016/j.jairtraman.2017.04.003
  94. Talwar, S., Dhir, A., Khalil, A., Mohan, G., & Islam, A. N. (2020). Point of adoption and beyond. Initial trust and mobile-payment continuation intention. Journal of Retailing and Consumer Services, 55, 102086.
  95. https://doi.org/10.1016/j.jretconser.2020.102086
  96. Tan, E., & Lau, J. L. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers. https://doi.org/10.1108/YC-07-2015-00537
  97. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 2158244013503837. https://doi.org/10.1177/2158244013503837
  98. Tan, P. J. B. (2013). Applying the UTAUT to understand factors affecting the use of English e-learning websites in Taiwan. Sage Open, 3(4), 2158244013503837. https://doi.org/10.1177/2158244013503837
  99. Tarhini, A., Alalwan, A. A., Shammout, A. B., & Al-Badi, A. (2019). An analysis of the factors affecting mobile commerce adoption in developing countries. Review of International Business and Strategy. https://doi.org/10.1108/RIBS-10-2018-0092
  100. Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers' acceptance and use of internet banking in Lebanon. Information Technology & People. https://doi.org/10.1108/ITP-02-2014-0034
  101. Tavares, J., & Oliveira, T. (2016). Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. Journal of medical Internet research, 18(3), e49.
  102. Tenenhaus, M., Vinzi, V. E., Chatelin, Y. M., & Lauro, C. (2005). PLS path modeling. Computational statistics & data analysis, 48(1), 159-205.
  103. Teo, A. C., Tan, G. W. H., Ooi, K. B., & Lin, B. (2015). Why consumers adopt mobile payment? A partial least squares structural equation modelling (PLS-SEM) approach. International Journal of Mobile Communications, 13(5), 478-497. https://doi.org/10.1504/IJMC.2015.070961
  104. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS quarterly, 125-143. DOI: 10.2307/249443
  105. TosuntaAY, Až. B., KaradaAY, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education, 81, 169-178. https://doi.org/10.1016/j.compedu.2014.10.009
  106. Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2020). Would you like to shop via mobile app technology? The technology acceptance model, social factors and purchase intention. Australasian Marketing Journal (AMJ). https://doi.org/10.1016/j.ausmj.2020.01.002
  107. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478. DOI: 10.2307/30036540
  108. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 157-178. DOI: 10.2307/41410412
  109. Viviani, L., Halder, M., Gruber, M., Bruckner, L., Cussler, K., Sanyal, G., ... & da Silva, A. L. L. (2020). Global harmonization of vaccine testing requirements: Making elimination of the ATT and TABST a concrete global achievement. Biologicals, 63, 101-105. https://doi.org/10.1016/j.biologicals.2019.10.007
  110. Wang, Y. S., Li, H. T., Li, C. R., & Zhang, D. Z. (2016). Factors affecting hotels' adoption of mobile reservation systems: A technology-organization-environment framework. Tourism Management, 53, 163-172. https://doi.org/10.1016/j.tourman.2015.09.021
  111. World Bank Database (2015) https://data.worldbank.org/indicator/ST.INT.ARVL
  112. World Bank Group (2018), https://openknowledge.worldbank.org/handle/10986/30326
  113. Wu, J., & Liu, D. (2007). The effects of trust and enjoyment on intention to play online games. Journal of electronic commerce research, 8(2).
  114. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013