The effect of flow experience on reuse intention of mobile navigation apps: The mediating role of location-based mobile service quality
DOI:
https://doi.org/10.18089/tms.20240101Keywords:
Location-based services, Mobile navigation applications, Mobile service quality, Flow experience, Reuse intentionAbstract
This study aims to determine the effect of consumers' flow experience on their intention to reuse mobile navigation apps and reveal the mediating role of location-based mobile service quality in this effect. Data were collected through an online survey of 513 respondents who were actively using mobile navigation apps and analyzed using structural equation modeling and mediation analysis. The results reveal that flow experience affects the intention to reuse mobile navigation apps and that location-based mobile service quality mediates this effect. When the mediating role of location-based service quality was tested, the direct effect of flow experience on reuse intention became insignificant. Information, reliability, and design quality are the most significant dimensions of location-based service quality. Considering the limited number of studies on mobile navigation services, particularly user behavior, this study contributes substantially to services marketing literature. In addition, this study provides insights for practitioners on how to design service proposals and the realization processes of mobile navigation apps.
References
Akel, G., & Armağan, E. (2021). Hedonic and utilitarian benefits as determinants of the application continuance intention in location-based applications: the mediating role of satisfaction. Multimedia Tools and Applications, 80(5), 7103–7124. https://doi.org/10.1007/s11042-020-10094-2
Akter, S., Ray, P., & D’Ambra, J. (2013). Continuance of mHealth services at the bottom of the pyramid: The roles of service quality and trust. Electronic Markets, 23(1), 29–47. https://doi.org/10.1007/s12525-012-0091-5
Almaiah, M. A., & Al Mulhem, A. (2019). Analysis of the essential factors affecting of intention to use of mobile learning applications: A comparison between universities adopters and non-adopters. Education and Information Technologies, 24(2), 1433–1468. https://doi.org/10.1007/s10639-018-9840-1
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
Ayuning Budi, N. F., Adnan, H. R., Firmansyah, F., Hidayanto, A. N., Kurnia, S., & Purwandari, B. (2021). Why do people want to use location-based application for emergency situations? The extension of UTAUT perspectives. Technology in Society, 65(February), 101480. https://doi.org/10.1016/j.techsoc.2020.101480
Baek, H. (2022). Mobile Location-Based Services. International Journal of Mobile Computing and Multimedia Communications, 13(1), 1–17. https://doi.org/10.4018/IJMCMC.297967
Baldauf, A., Cravens, K. S., Diamantopoulos, A., & Zeugner-Roth, K. P. (2009). The Impact of Product-Country Image and Marketing Efforts on Retailer-Perceived Brand Equity: An Empirical Analysis. Journal of Retailing, 85(4), 437–452. https://doi.org/10.1016/j.jretai.2009.04.004
Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173
Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921
Blut, M. (2016). E-Service Quality: Development of a Hierarchical Model. Journal of Retailing, 92(4), 500–517. https://doi.org/10.1016/j.jretai.2016.09.002
Blut, M., Chowdhry, N., Mittal, V., & Brock, C. (2015). E-Service Quality: A Meta-Analytic Review. Journal of Retailing, 91(4), 679–700. https://doi.org/10.1016/j.jretai.2015.05.004
Chang, H. H. (2015). Which one helps tourists most? Perspectives of international tourists using different navigation aids. Tourism Geographies, 17(3), 350–369. https://doi.org/10.1080/14616688.2015.1016099
Chang, I. C., Liu, C. C., & Chen, K. (2014). The effects of hedonic/utilitarian expectations and social influence on continuance intention to play online games. Internet Research, 24(1), 21–45. https://doi.org/10.1108/IntR-02-2012-0025
Chen, C. C., Hsiao, K. L., & Li, W. C. (2020). Exploring the determinants of usage continuance willingness for location-based apps: A case study of bicycle-based exercise apps. Journal of Retailing and Consumer Services, 55(129), 102097. https://doi.org/10.1016/j.jretconser.2020.102097
Cheng, Y. M. (2014). Extending the expectation-confirmation model with quality and flow to explore nurses' continued blended e-learning intention. Information Technology & People, 27(3), 230–258. https://doi.org/10.1108/ITP-01-2013-0024
Chiu, C. L., Ho, H. C., Yu, T., Liu, Y., & Mo, Y. (2021). Exploring information technology success of Augmented Reality Retail Applications in retail food chain. Journal of Retailing and Consumer Services, 61(April), 102561. https://doi.org/10.1016/j.jretconser.2021.102561
Choi, S. (2018). What promotes smartphone-based mobile commerce? Mobile-specific and self-service characteristics. Internet Research, 28(1), 105–122. https://doi.org/10.1108/IntR-10-2016-0287
Constantiou, I. D., Lehrer, C., & Hess, T. (2014). Changing information retrieval behaviours: An empirical investigation of users' cognitive processes in the choice of location-based services. European Journal of Information Systems, 23(5), 513–528. https://doi.org/10.1057/ejis.2014.12
Csikszentmihalyi, M. (1975). Beyond Boredom and Anxiety: Experiencing Flow in Work and Play. Jossey-Bass Publishers.
Csikszentmihalyi, M. (2013). Flow: The Psychology of Happiness. Random House.
Dalton, C. M., & Thatcher, J. (2019). Seeing by the Starbucks: The Social Context of Mobile Maps and Users' Geographic Knowledges. Cartographic Perspectives, 2019(92), 24–42. https://doi.org/10.14714/CP92.1447
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Dhar, S., & Varshney, U. (2011). Challenges and business models for mobile location-based services and advertising. Communications of the ACM, 54(5), 121–128. https://doi.org/10.1145/1941487.1941515
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/10.2307/3151312
Gao, L., & Bai, X. (2014). An empirical study on continuance intention of mobile social networking services: Integrating the IS success model, network externalities and flow theory. Asia Pacific Journal of Marketing and Logistics, 26(2), 168–189. https://doi.org/10.1108/APJML-07-2013-0086
Girginkaya Akdağ, S., & Ergen, A. (2020). Role of location-based mobile apps in city marketing: Beşiktaş as a student-friendly district. Journal of Location Based Services, 14(2), 1–22. https://doi.org/10.1080/17489725.2020.1788184
Grace, D., & Weaven, S. (2011). An Empirical Analysis of Franchisee Value-in-Use, Investment Risk and Relational Satisfaction. Journal of Retailing, 87(3), 366–380. https://doi.org/10.1016/j.jretai.2010.06.001
Grönroos, C. (1984). A Service Quality Model and its Marketing Implications. European Journal of Marketing, 18(4), 36–44. https://doi.org/10.1108/EUM0000000004784
Gupta, A., & Dogra, N. (2017). Tourist adoption of mapping apps: a UTAUT2 perspective of smart travellers. Tourism and Hospitality Management, 23(2), 145–161. https://doi.org/10.20867/thm.23.2.6
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning EMEA.
Heo, J. Y., & Kim, K. J. (2017). Development of a scale to measure the quality of mobile location-based services. Service Business, 11(1), 141–159. https://doi.org/10.1007/s11628-016-0305-6
Hoffman, D. L., & Novak, T. P. (2009). Flow Online: Lessons Learned and Future Prospects. Journal of Interactive Marketing, 23(1), 23–34. https://doi.org/10.1016/j.intmar.2008.10.003
Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
Huang, E. Y., Lin, S. W., & Fan, Y. C. (2015). M-S-QUAL: Mobile service quality measurement. Electronic Commerce Research and Applications, 14(2), 126–142. https://doi.org/10.1016/j.elerap.2015.01.003
Huang, H., Gartner, G., Krisp, J. M., Raubal, M., & Van de Weghe, N. (2018). Location based services: ongoing evolution and research agenda. Journal of Location Based Services, 12(2), 63–93. https://doi.org/10.1080/17489725.2018.1508763
Hung, S. W., Cheng, M. J., & Chiu, P. C. (2019). Do antecedents of trust and satisfaction promote consumer loyalty in physical and virtual stores? a multi-channel view. Service Business, 13(1), 1–23. https://doi.org/10.1007/s11628-018-0364-y
James, L. R., Mulaik, S. A., & Brett, J. M. (2006). A Tale of Two Methods. Organizational Research Methods, 9(2), 233–244. https://doi.org/10.1177/1094428105285144
Jamshidi, D., Keshavarz, Y., Kazemi, F., & Mohammadian, M. (2018). Mobile banking behavior and flow experience: An integration of utilitarian features, hedonic features and trust. International Journal of Social Economics, 45(1), 57–81. https://doi.org/10.1108/IJSE-10-2016-0283
Joseph, S., & Namboodiri, V. (2019). Auxiliary Location-Based Services for Persons with Disabilities. The 21st International ACM SIGACCESS Conference on Computers and Accessibility, 621–623. https://doi.org/10.1145/3308561.3354631
Khalil, I., & Suharjito. (2019). Analysis of Factors That Affect the Reuse of Carsharing Mobile Application. Proceedings of 2019 International Conference on Information Management and Technology, ICIMTech 2019, 1(August), 18–23. https://doi.org/10.1109/ICIMTech.2019.8843791
Kim, E. (2021). In-store shopping with location-based retail apps: perceived value, consumer response, and the moderating effect of flow. Information Technology and Management, 22(2), 83–97. https://doi.org/10.1007/s10799-021-00326-8
Kim, J. (Sunny), Yoon, S., & Zemke, D. M. V. (2017). Factors affecting customers' intention to use of location-based services (LBS) in the lodging industry. Journal of Hospitality and Tourism Technology, 8(3), 337–356. https://doi.org/10.1108/JHTT-03-2017-0023
Kim, Y., Wang, Q., & Roh, T. (2021). Do information and service quality affect perceived privacy protection, satisfaction, and loyalty? Evidence from a Chinese O2O-based mobile shopping application. Telematics and Informatics, 56(August 2020), 101483. https://doi.org/10.1016/j.tele.2020.101483
Koohikamali, M., Mousavizadeh, M., & Peak, D. (2019). Continued Usage and Location Disclosure of Location-Based Applications: A Necessity for Location Intelligence. Proceedings of the 52nd Hawaii International Conference on System Sciences, 6, 1362–1372. https://doi.org/10.24251/HICSS.2019.166
Koufaris, M. (2002). Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior. Information Systems Research, 13(2), 205–223. https://doi.org/10.1287/isre.13.2.205.83
Lee, M. C. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers and Education, 54(2), 506–516. https://doi.org/10.1016/j.compedu.2009.09.002
Li, Y., Duan, Y., Fu, Z., & Alford, P. (2012). An empirical study on behavioural intention to reuse e-learning systems in rural China. British Journal of Educational Technology, 43(6), 933–948. https://doi.org/10.1111/j.1467-8535.2011.01261.x
Liébana-Cabanillas, F., Molinillo, S., & Ruiz-Montañez, M. (2019). To use or not to use, that is the question: Analysis of the determining factors for using NFC mobile payment systems in public transportation. Technological Forecasting and Social Change, 139(November 2018), 266–276. https://doi.org/10.1016/j.techfore.2018.11.012
Lu, Y., Zhang, L., & Wang, B. (2009). A multidimensional and hierarchical model of mobile service quality. Electronic Commerce Research and Applications, 8(5), 228–240. https://doi.org/10.1016/j.elerap.2009.04.002
Luo, X., & Bhattacharya, C. B. (2006). Corporate Social Responsibility, Customer Satisfaction, and Market Value. Journal of Marketing, 70(4), 1–18.
Malhotra, A., & Kubowicz Malhotra, C. (2013). Exploring switching behavior of US mobile service customers. Journal of Services Marketing, 27(1), 13–24. https://doi.org/10.1108/08876041311296347
Malhotra, N. K., Kim, S. S., & Patil, A. (2006). Common Method Variance in IS Research: A Comparison of Alternative Approaches and a Reanalysis of Past Research. Management Science, 52(12), 1865–1883. https://doi.org/10.1287/mnsc.1060.0597
Malhotra, N. K. (2020). Marketing Research: An Applied Orientation (7th Global). United Kingdom: Pearson.
Moon, H., Cheon, J., Lee, J., Banda, D. R., Griffin-Shirley, N., & Ajuwon, P. M. (2022). Factors influencing the intention of persons with visual impairment to adopt mobile applications based on the UTAUT model. Universal Access in the Information Society, 21(1), 93–107. https://doi.org/10.1007/s10209-020-00757-0
Nagaraj, A., & Stern, S. (2020). The Economics of Maps. Journal of Economic Perspectives, 34(1), 196–221. https://doi.org/10.1257/jep.34.1.196
Nambisan, P., & Watt, J. H. (2011). Managing customer experiences in online product communities. Journal of Business Research, 64(8), 889–895. https://doi.org/10.1016/j.jbusres.2010.09.006
Noh, M. J., & Lee, K. T. (2016). An analysis of the relationship between quality and user acceptance in smartphone apps. Information Systems and E-Business Management, 14(2), 273–291. https://doi.org/10.1007/s10257-015-0283-6
Obadă, D. R. (2014). Online Flow Experience and Perceived Quality of a Brand Website: InPascani.ro Case Study. Procedia - Social and Behavioral Sciences, 149, 673–679. https://doi.org/10.1016/j.sbspro.2014.08.252
Ozkara, B. Y., Ozmen, M., & Kim, J. W. (2017). Examining the effect of flow experience on online purchase: A novel approach to the flow theory based on hedonic and utilitarian value. Journal of Retailing and Consumer Services, 37(February), 119–131. https://doi.org/10.1016/j.jretconser.2017.04.001
Özer, A., Argan, M. T., & Argan, M. (2013). The Effect of Mobile Service Quality Dimensions on Customer Satisfaction. Procedia - Social and Behavioral Sciences, 99, 428–438. https://doi.org/10.1016/j.sbspro.2013.10.511
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1985). A Conceptual Model of Service Quality and Its Implications for Future Research. Journal of Marketing, 49(4), 41. https://doi.org/10.2307/1251430
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.
Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality. Journal of Service Research, 7(3), 213–233. https://doi.org/10.1177/1094670504271156
Park, E. (2020). User acceptance of smart wearable devices: An expectation-confirmation model approach. Telematics and Informatics, 47(October 2019), 101318. https://doi.org/10.1016/j.tele.2019.101318
Park, E., & Ohm, J. (2014). Factors influencing users' employment of mobile map services. Telematics and Informatics, 31(2), 253–265. https://doi.org/10.1016/j.tele.2013.07.002
Pavlou, P.A., Liang, H. & Xue, Y. (2007). Understanding and Mitigating Uncertainty in Online Exchange Relationships: A Principal-Agent Perspective. MIS Quarterly, 31(1), 105. https://doi.org/10.2307/25148783
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of Method Bias in Social Science Research and Recommendations on How to Control It. Annual Review of Psychology, 63(1), 539–569. https://doi.org/10.1146/annurev-psych-120710-100452
Pura, M. (2005). Linking perceived value and loyalty in location-based mobile services. Managing Service Quality, 15(6), 509–538. https://doi.org/10.1108/09604520510634005
Rabaa’i, A. A. (2022). What's for Dinner? Factors Contributing to the Continuous Usage of Food Delivery Apps (FDAs). Asia Pacific Journal of Information Systems, 32(2), 354–380. https://doi.org/10.14329/apjis.2022.32.2.354
Rareş, O. D. (2014). Measuring Perceived Service Quality Offline vs. Online: A New PeSQ Conceptual Model. Procedia Economics and Finance, 15(14), 538–551. https://doi.org/10.1016/S2212-5671(14)00505-X
Rauschnabel, P. A., Rossmann, A., & Tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Computers in Human Behavior, 76, 276–286. https://doi.org/10.1016/j.chb.2017.07.030
Santos, J. (2003). E‐service quality: a model of virtual service quality dimensions. Managing Service Quality: An International Journal, 13(3), 233–246. https://doi.org/10.1108/09604520310476490
Sarkar, S., & Khare, A. (2019). Influence of Expectation Confirmation, Network Externalities, and Flow on Use of Mobile Shopping Apps. International Journal of Human-Computer Interaction, 35(16), 1449–1460. https://doi.org/10.1080/10447318.2018.1540383
Schaupp, L. C. (2010). Web Site Success: Antecedents of Web Site Satisfaction and Reuse. Journal of Internet Commerce, 9(1), 42–64. https://doi.org/10.1080/15332861.2010.487414
Schwipper, S., Peche, S., & Schmitz, G. (2020). Mobile Location-Based Services' Value-in-Use in Inner Cities: Do a Customer's Shopping Patterns, Prior User Experience, and Sales Promotions Matter? Schmalenbach Business Review, 72(4), 511–564. https://doi.org/10.1007/s41464-020-00103-0
Shamdasani, P., Mukherjee, A., & Malhotra, N. (2008). Antecedents and consequences of service quality in consumer evaluation of self-service internet technologies. The Service Industries Journal, 28(1), 117–138. https://doi.org/10.1080/02642060701725669
Shao, Z., Li, X., Guo, Y., & Zhang, L. (2020). Influence of service quality in sharing economy: Understanding customers' continuance intention of bicycle sharing. Electronic Commerce Research and Applications, 40(December 2019), 100944. https://doi.org/10.1016/j.elerap.2020.100944
Sobolewski, M. (2021). Measuring consumer well-being from using free-of-charge digital services. The case of navigation apps. Information Economics and Policy, 56, 100925. https://doi.org/10.1016/j.infoecopol.2021.100925
Statista. (2023a). Number of smartphone mobile network subscriptions worldwide from 2016 to 2022, with forecasts from 2023 to 2028. Retrieved July 5, 2023, from https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/
Statista. (2023b). Number of available apps in the Apple App Store from 2008 to 2022. Retrieved July 5, 2023, from https://www.statista.com/statistics/268251/number-of-apps-in-the-itunes-app-store-since-2008/
Statista. (2023c). Number of available applications in the Google Play Store from December 2009 to June 2023. Retrieved July 5, 2023, from https://www.statista.com/statistics/266210/number-of-available-applications-in-the-google-play-store/
Tan, R., Tao, Y., Si, W., & Zhang, Y. Y. (2020). Privacy preserving semantic trajectory data publishing for mobile location-based services. Wireless Networks, 26(8), 5551–5560. https://doi.org/10.1007/s11276-019-02058-8
Trabelsi-Zoghlami, A., Berraies, S., & Ben Yahia, K. (2020). Service quality in a mobile-banking-applications context: do users' age and gender matter? Total Quality Management & Business Excellence, 31(15–16), 1639–1668. https://doi.org/10.1080/14783363.2018.1492874
Vaughan-Nichols, S. J. (2009). Will Mobile Computing's Future Be Location, Location, Location? Computer, 42(2), 14–17. https://doi.org/10.1109/MC.2009.65
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425–478. https://doi.org/10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
Wang, C., Teo, T. S. H., & Liu, L. (2020). Perceived value and continuance intention in mobile government service in China. Telematics and Informatics, 48(September 2019), 101348. https://doi.org/10.1016/j.tele.2020.101348
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 and Information Technology, 36(1), 2–10. https://doi.org/10.1080/0144929X.2016.1143033
Wang, W. T. T., Ou, W. M. M., & Chen, W. Y. Y. (2019). The impact of inertia and user satisfaction on the continuance intentions to use mobile communication applications: A mobile service quality perspective. International Journal of Information Management, 44(May 2018), 178–193. https://doi.org/10.1016/j.ijinfomgt.2018.10.011
Wang, W., & Li, H. (2012). Factors influencing mobile services adoption: a brand‐equity perspective. Internet Research, 22(2), 142–179. https://doi.org/10.1108/10662241211214548
Wangpipatwong, S., Chutimaskul, W., & Papasratorn, B. (2009). Quality Enhancing the Continued Use of E-Government Web Sites. International Journal of Electronic Government Research, 5(1), 19–35. https://doi.org/10.4018/jegr.2009092202
WeAreSocial. (2023). Digital 2023-Global Overview Report. Retrieved June 15, 2023 from https://wearesocial.com/digital-2023
Werner, C., & Schermelleh-Engel, K. (2010). Deciding between competing models: Chi-square difference tests. Introduction to structural equation modeling with LISREL, 1-3.
Wirtz, J., & Lovelock, C. (2018). Essentials of Services Marketing (3rd ed.). Pearson.
Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183–198. https://doi.org/10.1016/S0022-4359(03)00034-4
Wu, I. L., Chiu, M. L., & Chen, K. W. (2020). Defining the determinants of online impulse buying through a shopping process of integrating perceived risk, expectation-confirmation model, and flow theory issues. International Journal of Information Management, 52(February), 102099. https://doi.org/10.1016/j.ijinfomgt.2020.102099
Wu, X., Gursoy, D., & Zhang, M. (2021). Effects of social interaction flow on experiential quality, service quality and satisfaction: moderating effects of self-service technologies to reduce employee interruptions. Journal of Hospitality Marketing and Management, 30(5), 571–591. https://doi.org/10.1080/19368623.2021.1867284
Xu, H., & Gupta, S. (2009). The effects of privacy concerns and personal innovativeness on potential and experienced customers' adoption of location-based services. Electronic Markets, 19(2–3), 137–149. https://doi.org/10.1007/s12525-009-0012-4
Yang, H. L., & Lin, R. X. (2017). Determinants of the intention to continue use of SoLoMo services: Consumption values and the moderating effects of overloads. Computers in Human Behavior, 73, 583–595. https://doi.org/10.1016/j.chb.2017.04.018
Yoon, S., Kim, J. (Sunny), & Connolly, D. J. (2018). Understanding Motivations and Acceptance of Location-Based Services. International Journal of Hospitality and Tourism Administration, 19(2), 187–209. https://doi.org/10.1080/15256480.2017.1305316
Yu, J., Zo, H., Choi, M. K., & Ciganek, A. P. (2013). User acceptance of location-based social networking services: An extended perspective of perceived value. Online Information Review, 37(5), 711–730. https://doi.org/10.1108/OIR-12-2011-0202
Yun, H., Han, D., & Lee, C. C. (2013). Understanding the use of location-based service applications: Do privacy concerns matter? Journal of Electronic Commerce Research, 14(3), 220–230.
Zhou, T. (2011). Understanding mobile Internet continuance usage from the perspectives of UTAUT and flow. Information Development, 27(3), 207–218. https://doi.org/10.1177/0266666911414596
Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135–144.
Zhou, T. (2013a). An empirical examination of user adoption of location-based services. Electronic Commerce Research, 13(1), 25–39. https://doi.org/10.1007/s10660-013-9106-3
Zhou, T. (2013b). Examining continuous usage of location-based services from the perspective of perceived justice. Information Systems Frontiers, 15(1), 141–150. https://doi.org/10.1007/s10796-011-9311-3
Zhou, T. (2013c). Understanding continuance usage of mobile sites. Industrial Management and Data Systems, 113(9), 1286–1299. https://doi.org/10.1108/IMDS-01-2013-0001
Zhou, T. (2013d). Understanding the effect of flow on user adoption of mobile games. Personal and Ubiquitous Computing, 17(4), 741–748. https://doi.org/10.1007/s00779-012-0613-3
Zhou, T. (2016). Understanding location-based services continuance: An IS success model perspective. International Journal of Mobile Communications, 14(6), 553–567. https://doi.org/10.1504/IJMC.2016.079300
Zhou, T. (2017). Understanding location-based services users' privacy concern: An elaboration likelihood model perspective. Internet Research, 27(3), 506–519. https://doi.org/10.1108/IntR-04-2016-0088
Zhou, T., Li, H., & Liu, Y. (2010). The effect of flow experience on mobile SNS users' loyalty. Industrial Management & Data Systems, 110(6), 930–946. https://doi.org/10.1108/02635571011055126
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Tourism & Management Studies
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
The journal retains published articles’ copyrights, but they are simultaneously licensed under the Creative Commons Attribution License (CC BY-NC-ND), which allows individuals’ to share the relevant papers as long as authorship and publication in this journal are duly acknowledged.
