Social Media, Mobile Payment, and Mobile Gaming for Intentional and Behavioral Recommendations

References

• Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. ACM SIGMOD Record, 22(2), 207–216. https://doi.org/10.1145/170036.170072
   Google Scholar
• Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules [Paper presentation]. The Proceedings of the 20th International Conference on Very Large Database, United States (Vol. 20, pp. 487–499).
   Google Scholar
• Alike Audience. (2023). Data targeting: How to reach the right audience in programmatic advertising. Accessed on Aug 23, 2023. https://www.alikeaudience.com/data-targeting-how-to-reach-the-right-audience-in-programmatic-advertising/
   Google Scholar
• Atiqi, M. A., Chang, S., & Deguchi, H. (2022). Simulating the influence of Facebook fan pages on individual attitudes toward vaccination using agent-based modelling. Systems Research and Behavioral Science, 40(3), 595–610. https://doi.org/10.1002/sres.2889
   Web of Science ®Google Scholar
• Bae, J., Park, J., Choi, J., & Bum Soh, S. (2023). A recommending system for mobile games using the dynamic nonparametric model. Journal of Business Research, 167(7), 114079. https://doi.org/10.1016/j.jbusres.2023.114079
   Web of Science ®Google Scholar
• Berson, A., & Smith, S. (1997). Data warehousing, data mining, and OLAP. McGraw-Hill.
   Google Scholar
• Boldi, A., Rapp, A., & Tirassa, M. (2022). Playing during a crisis: The impact of commercial video games on the reconfiguration of people’s life during the COVID-19 pandemic. Human–Computer Interaction, 1–42. https://doi.org/10.1080/07370024.2022.2050725
   Web of Science ®Google Scholar
• Bui, S., Kettinger, W. J., & Park, I. (2022). Identity sharing and adaptive personalization influencing online repurchases. Journal of Computer Information Systems, 62(4), 778–789. https://doi.org/10.1080/08874417.2021.1919939
   Web of Science ®Google Scholar
• Campos, L. M., Fernández-Luna, J. M., & Huete, J. F. (2023). Use of topical and temporal profiles and their hybridisation for content-based recommendation. User Modeling and User-Adapted Interaction, 33(4), 911–937. https://doi.org/10.1007/s11257-022-09354-7
   Google Scholar
• Chang, T.-W. (2023). The entertainment and gaming industry is on the rise every year! What are the changes in the market due to the rise of the new generation of gamers? Future Commerce. Accessed on Feb 21, 2023. https://fc.bnext.com.tw/articles/view/2745
   Google Scholar
• Chen, Z., Zou, H., Yu, H., Zheng, S., & Gao, S. (2023). Relevance meets calibration: Triple calibration distance design for neighbour-based recommender systems. Journal of Information Science. https://doi.org/10.1177/016555152311820
   Web of Science ®Google Scholar
• Chung, J., Hong, S., Kang, S., & Kim, C. (2022). Sequential UI behaviour prediction system based on long short-term memory networks. Behaviour & Information Technology, 41(6), 1258–1269. https://doi.org/10.1080/0144929X.2021.1871954
   Web of Science ®Google Scholar
• Dhawan, S., Singh, K., Rabaea, A., & Batra, A. (2022). ImprovedGCN: An efficient and accurate recommendation system employing lightweight graph convolutional networks in social media. Electronic Commerce Research and Applications, 55, 101191. https://doi.org/10.1016/j.elerap.2022.101191
   Web of Science ®Google Scholar
• Dinulescu, C. C., & Prybutok, R. (2022). In authority, or peers we trust? Reviews and recommendations in social commerce. Behaviour & Information Technology, 41(13), 2887–2904. https://doi.org/10.1080/0144929X.2021.1957016
   Web of Science ®Google Scholar
• Du, Y., Lubniewski, K., Price, L., Breslin, G., Thomson, P., Jinadasa, N., & Soni, N. (2023). “They Can’t Believe They’re a Tiger”: Insights from pediatric speech-language pathologist mobile app users and app designers. International Journal of Language & Communication Disorders, 58(5), 1717–1737. https://doi.org/10.1111/1460-6984.12898
   PubMed Web of Science ®Google Scholar
• Gan, M., Xu, G., & Ma, Y. (2023). A multi-behavior recommendation method exploring the preference differences among various behaviors. Expert Systems with Applications, 228(10), 120316. https://doi.org/10.1016/j.eswa.2023.120316
   Web of Science ®Google Scholar
• Gonçalves, D., Pais, P., Gerling, K., Guerreiro, T., & Rodrigues, A. (2023). Social gaming: A systematic review. Computers in Human Behavior, 147(5), 107851. https://doi.org/10.1016/j.chb.2023.107851
   Web of Science ®Google Scholar
• Guo, J., Holmes, P., & Altanlar, A. (2020). Is herding spurious or intentional? Evidence from analyst recommendation revisions and sentiment. International Review of Financial Analysis, 71(21), 101539. https://doi.org/10.1016/j.irfa.2020.101539
   Web of Science ®Google Scholar
• Gustriansyah, R., Ermatita, E., & Rini, D. P. (2022). An approach for sales forecasting. Expert Systems with Applications, 207(1), 118043. https://doi.org/10.1016/j.eswa.2022.118043
   Web of Science ®Google Scholar
• Hjorth, L., & Silva, A. S. (2022). Playing with place: Location-based mobile games in post-pandemic public spaces. Mobile Media & Communication, 11(1), 52–58. https://doi.org/10.1177/20501579221126959
   Web of Science ®Google Scholar
• Hosseini, S., & Sardo, S. R. (2021). Data mining tools - A case study for network intrusion detection. Multimedia Tools and Applications, 80(4), 4999–5019. https://doi.org/10.1007/s11042-020-09916-0
   Web of Science ®Google Scholar
• Hsieh, P. H. (2020). Exploratory analysis of grocery product networks. Journal of Management Analytics, 9(2), 169–184. https://doi.org/10.1080/23270012.2022.2072779
   Web of Science ®Google Scholar
• Jeon, G., Kim, S., & Lee, S. (2023). Interactive feedback loop with counterfactual data modification for serendipity in a recommendation system. International Journal of Human–Computer Interaction, 1–17. https://doi.org/10.1080/10447318.2023.2238369
   Web of Science ®Google Scholar
• Kashef, R., & Pun, H. (2022). Predicting l-CrossSold products using connected components: A clustering-based recommendation system. Electronic Commerce Research and Applications, 53(4), 101148. https://doi.org/10.1016/j.elerap.2022.101148
   Web of Science ®Google Scholar
• Khademizadeh, S., Nematollahi, Z., & Danesh, F. (2022). Analysis of book circulation data and a book recommendation system in academic libraries using data mining techniques. Library & Information Science Research, 44(4), 101191. https://doi.org/10.1016/j.lisr.2022.101191
   Web of Science ®Google Scholar
• Kim, M. K., Kim, H. Y., & Kang, Y. A. (2019). Influence of gift-giving in social network games on social capital: Relationship types, gift-giving types, and the big five factors of personality. International Journal of Human–Computer Interaction, 35(1), 38–52. https://doi.org/10.1080/10447318.2018.1426234
   Web of Science ®Google Scholar
• Liébana-Cabanillas, F., Alcántara-Pilar, J. M., Singh, N., & Pavluković, V. (2023). Overview of the adoption of online food ordering services in Spain and India. An analytical approach based on the stimulus-organism-response model. International Journal of Human–Computer Interaction, 1–15. https://doi.org/10.1080/10447318.2023.2201550
   Web of Science ®Google Scholar
• Li, Q., Ma, H., Zhang, R., Jin, W., & Li, Z. (2023). Dual-scale contrastive learning for multi-behavior recommendation. Applied Soft Computing, 144(13), 110523. https://doi.org/10.1016/j.asoc.2023.110523
   Web of Science ®Google Scholar
• Li, X., Zhu, X., Lu, Y., Shi, D., & Deng, W. (2023). Understanding the continuous usage of mobile payment integrated into social media platform: The case of WeChat Pay. Electronic Commerce Research and Applications, 60(4), 101275. https://doi.org/10.1016/j.elerap.2023.101275
   Web of Science ®Google Scholar
• Liao, S. H., Widowati, R., & Hsieh, Y. C. (2021). Investigating online social media users’ behaviors for social commerce recommendations. Technology in Society, 66(3), 101655. https://doi.org/10.1016/j.techsoc.2021.101655
   Web of Science ®Google Scholar
• Liao, S. H., & Ho, C. H. (2021). Mobile payment and mobile application (App) behavior for online recommendations. Journal of Organizational and End User Computing, 33(6), 1–26. https://doi.org/10.4018/JOEUC.20211101.oa2
   Web of Science ®Google Scholar
• Liu, X., Holopainen, J., & Wagner, C. (2023). A design strategy for Geo AR mobile game sustainable success emphasizing game completeness. Entertainment Computing, 46(3), 100569. https://doi.org/10.1016/j.entcom.2023.100569
   Web of Science ®Google Scholar
• Manzerolle, V., & Daubs, M. (2021). Friction-free authenticity: Mobile social networks and transactional affordances. Media, Culture & Society, 43(7), 1279–1296. https://doi.org/10.1177/01634437219999
   Web of Science ®Google Scholar
• Mobarak, M. A., Dakrory, I., Elsotouhy, M., Ghonim, A., & Khashan, A. (2022). Drivers of mobile payment services adoption: A behavioral reasoning theory perspective. International Journal of Human–Computer Interaction, 1–14. https://doi.org/10.1080/10447318.2022.2144122
   Web of Science ®Google Scholar
• Murthy, D., Sudarshan, S., Lee, J. A., Ghosh, C., Shah, P., Xiao, W. J., Arora, I., Unger, C., & Acker, A. (2020). Understanding the meaning of emoji in mobile social payments: Exploring the use of mobile payments as hedonic versus utilitarian through skin tone modified emoji usage. Big Data & Society, 7(2), 205395172094956. https://doi.org/10.1177/2053951720949
   Web of Science ®Google Scholar
• Nilashi, M., Asadi, S., Minaei-Bidgoli, B., Ali Abumalloh, R., Samad, S., Ghabban, F., & Ahani, A. (2021). Recommendation agents and information sharing through social media for coronavirus outbreak. Telematics and Informatics, 61(6), 101597. https://doi.org/10.1016/j.tele.2021.101597
   PubMed Web of Science ®Google Scholar
• Nilashi, M., Ali Abumalloh, R., Samad, S., Minaei-Bidgoli, B., Hang Thi, H., Alghamdi, O. A., Yousoof Ismail, M., & Ahmadi, H. (2023). The impact of multi-criteria ratings in social networking sites on the performance of online recommendation agents. Telematics and Informatics, 76(1), 101919. https://doi.org/10.1016/j.tele.2022.101919
   Web of Science ®Google Scholar
• Rexer, K., Gearan, P., Allen, H. (2021). Data science survey. Rexer analytics. Accessed November 23 2021. https://www.rexeranalytics.com/assets/rexer_analytics_2015_data_miner_survey_summary_report.pdf
   Google Scholar
• Srivastava, P. R., Sharma, S., & Kaur, S. (2020). Data mining-based algorithm for assortment planning. Journal of Management Analytics, 7(3), 443–457. https://doi.org/10.1080/23270012.2020.1725666
   Web of Science ®Google Scholar
• Tahir, R., & Wang, A. I. (2022). Evaluating the effectiveness of game-based learning for teaching refugee children Arabic using the integrated LEAGUÊ-GQM approach. Behaviour & Information Technology, 43(1), 110–138. https://doi.org/10.1080/0144929X.2022.2156386
   Web of Science ®Google Scholar
• Tan, S. N., & Ng, K. H. (2022). Gamified mobile sensing storytelling application for enhancing remote cultural experience and engagement. International Journal of Human–Computer Interaction, 1–14. https://doi.org/10.1080/10447318.2022.2144121
   Web of Science ®Google Scholar
• The International Market Analysis Research and Consulting Group. (2023). Gaming market: Global industry trends, share, size, growth, opportunity and forecast 2023–2028. IMARC Services Private Limited. Accessed on Jan 31, 2023. https://www.gii.tw/report/imarc1206848-gaming-market-global-industry-trends-share-size.html
   Google Scholar
• Tsai, A. (2023). Growth against the odds! Taiwan becomes the world’s fifth largest mobile game market. Accessed on Feb 7, 2022. https://www.inside.com.tw/article/30656-google-paly-2023-3-strategy-of-gaming
   Google Scholar
• Tsuei, H. J., Tsai, W. H., Pan, F. T., & Tzeng, G. H. (2020). Improving search engine optimization (SEO) by using hybrid modified MCDM models. Artificial Intelligence Review, 53(1), 1–16. https://doi.org/10.1007/s10462-018-9644-0
   Web of Science ®Google Scholar
• Verkijika, S. F., & Neneh, B. N. (2021). Standing up for or against: A text-mining study on the recommendation of mobile payment apps. Journal of Retailing and Consumer Services, 63(6), 102743. https://doi.org/10.1016/j.jretconser.2021.102743
   Web of Science ®Google Scholar
• Verma, N., Malhotra, D., & Singh, J. (2020). Big data analytics for retail industry using MapReduce-Apriori framework. Journal of Management Analytics, 7(3), 424–442. https://doi.org/10.1080/23270012.2020.1728403
   Web of Science ®Google Scholar
• Wang, T., & Ge, D. (2023). Research on recommendation system of online Chinese learning resources based on multiple collaborative filtering algorithms (RSOCLR). International Journal of Human–Computer Interaction, 1–11. https://doi.org/10.1080/10447318.2023.2171536
   Web of Science ®Google Scholar
• Williams, N. (2022). What role has social media played in COVID-19? Accessed on July 27, 2022. https://www.news-medical.net/health/What-Role-has-Social-Media-Played-in-COVID-19.aspx
   Google Scholar
• Wu, D., Zhao, Y., Wang, X., Song, S., & Lian, J. (2023). Digital hoarding in everyday hedonic social media use: The roles of fear of missing out (FoMO) and social media affordances. International Journal of Human–Computer Interaction, 1–16. https://doi.org/10.1080/10447318.2023.2233139
   Web of Science ®Google Scholar
• Xu, L., & Hyman, R. (2023). How and when ranking-based recommendations induce higher willingness-to-pay. Journal of Marketing Communications, 1–23. https://doi.org/10.1080/13527266.2023.2175894
   Google Scholar
• Xu, L., Roy, A., & Niculescu, M. (2023). A dual process model of the influence of recommender systems on purchase intentions in online shopping environments. Journal of Internet Commerce, 22(3), 432–453. https://doi.org/10.1080/15332861.2022.2049113
   Web of Science ®Google Scholar
• Yang, T., Yang, F., & Men, J. (2023). Understanding consumers’ continuance intention toward recommendation vlogs: An exploration based on the dual-congruity theory and expectation-confirmation theory. Electronic Commerce Research and Applications, 59(3), 101270. https://doi.org/10.1016/j.elerap.2023.101270
   Web of Science ®Google Scholar