Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 2, Article No: 24148
https://doi.org/10.55267/iadt.07.14747

Published Online: 24 Apr 2024

Views: 277 | Downloads: 126

This study examines how big data-driven decision support and mobile technology interact to improve information integration and user experience. The research studies big data for digital decision-making and provides theoretical and practical suggestions to assist organizations in overcoming its challenges. This study used mixed method analysis to find the relationship between big data-driven user experience and mobile-integrated technology. Businesses require sophisticated decision support tools to navigate the digital landscape of massive data. Big data-driven decision support is examined to determine how information integration and user experience affect mobile-integrated technologies. A rigorous quantitative technique examines data volume and decision precision. Although big data volumes may have diminishing returns, decision-making generally improves. The study emphasizes the delicate balance between data volume, quality, velocity, diversity, and governance. Beyond quantitative analysis, the study examines complex decision-making. Information integration methods and user experience affect decision-making time, with more data offering strategic options. Agile integration and user-centric design boost efficiency and decision-making. The research highlights the change in mobile integrated technology. The title fits the research since mobile technology increases information integration and user experience. According to the study, mobile technology's user-friendly gadgets, quick internet connectivity, security safeguards, and app functionality boost user contentment, productivity, and decision-making accuracy. The report also emphasizes big data governance in decision quality. Decision support systems need big data governance for data access, accuracy, security, and compliance. Finally, this study provides theoretical insights into big data-driven decision support and practical suggestions for organizations navigating it. The study uses data, technology, user experience, and governance to improve business decision-making. This provides them with digital-era precision, agility, and strategic edge.

Big Data Decision Support Information Integration User Experience Mobile Integrated Technology

• Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42, 90-101.
• Al Hamdani, D. S. (2013). Mobile learning: A good practice. Procedia-Social and Behavioral Sciences, 103, 665-674.
• Ali, U., Shamsi, M. H., Bohacek, M., Purcell, K., Hoare, C., Mangina, E., & O’Donnell, J. (2020). A data-driven approach for multi-scale GIS-based building energy modeling for analysis, planning and support decision making. Applied Energy, 279, 115834.
• Austin, S. F., Frøsig, A., Buus, N., Lincoln, T., von Malachowski, A., Schlier, B., . . . Simonsen, E. (2021). Service user experiences of integrating a mobile solution (IMPACHS) into clinical treatment for psychosis. Qualitative Health Research, 31(5), 942-954.
• Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data analytics capability and decision-making: The role of data-driven insight on circular economy performance. Technological Forecasting and Social Change, 168, 120766.
• Biswas, S., & Sen, J. (2017). A proposed architecture for big data driven supply chain analytics. ICFAI University Press (IUP) Journal of Supply Chain Management, XIII[3(2016)], 7-34.
• Blackwell, C. (2013). Teacher practices with mobile technology integrating tablet computers into the early childhood classroom. Journal of Education Research, 7(4), 1-25.
• Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2021). A review of data-driven decision-making methods for industry 4.0 maintenance applications. Electronics, 10(7), 828.
• Bunterm, T., Srisawasdi, N., & Pondee, P. (2018). Preparing pre-service teachers to integrate mobile technology into science laboratory learning: an evaluation of technology-integrated pedagogy module. International Journal of Mobile Learning and Organisation, 12(1), 1-17.
• Calza, F., Sorrentino, A., & Tutore, I. (2023). Combining corporate environmental sustainability and customer experience management to build an integrated model for decision-making. Management Decision, 61(13), 54-84.
• Chen, C. H., Jong, M. S. Y., & Tsai, C. C. (2022). A comparison of in-service teachers’ conceptions of barriers to mobile technology-integrated instruction and technology-integrated instruction. Australasian Journal of Educational Technology, 35-50.
• Chen, J. S., Tsou, H. T., Chou, C. Y., & Ciou, C. H. (2020). Effect of multichannel service delivery quality on customers’ continued engagement intention: A customer experience perspective. Asia Pacific Journal of Marketing and Logistics, 32(2), 473-494.
• Chen, S. C., Liu, M. L., & Lin, C. P. (2013). Integrating technology readiness into the expectation-confirmation model: an empirical study of mobile services. Cyberpsychology, Behavior, and Social Networking, 16(8), 604-612.
• Chen, T., Guo, W., Gao, X., & Liang, Z. (2021). AI-based self-service technology in public service delivery: user experience and influencing factors. Government Information Quarterly, 38(4), 101520.
• Churchill, D., Chiu, T., & Gu, N. J. (2016). Mobile learning, MOOCs and 21st century learning. In Proceedings of the International Mobile Learning Festival 2015. Retrieved from http://eprints.um.edu.my/14253/1/IMLFProceeding2015.pdf
• Chylinski, M., Heller, J., Hilken, T., Keeling, D. I., Mahr, D., & de Ruyter, K. (2020). Augmented reality marketing: A technology-enabled approach to situated customer experience. Australasian Marketing Journal, 28(4), 374-384.
• Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412-421.
• Gerea, C., Gonzalez-Lopez, F., & Herskovic, V. (2021). Omnichannel customer experience and management: An integrative review and research agenda. Sustainability, 13(5), 1-24.
• Harricharan, M., Gemen, R., Celemín, L. F., Fletcher, D., De Looy, A. E., Wills, J., & Barnett, J. (2015). Integrating mobile technology with routine dietetic practice: The case of mypace for weight management. Proceedings of the Nutrition Society, 74(2), 125-129.
• Heilig, L., Stahlbock, R., & Voß, S. (2020). From digitalization to data-driven decision-making in container terminals. Operations Research/Computer Science Interfaces Series, 125-154.
• Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356-365.
• Jarvenpaa, S. L., & Lang, K. R. (2005). Managing the paradoxes of mobile technology. Information Systems Management, 22(4), 7-23.
• Jeble, S., Kumari, S., & Patil, Y. (2018). Role of big data in decision making. Operations and Supply Chain Management, 11(1), 36-44.
• Keengwe, J., Pearson, D., & Smart, K. (2009). Technology integration: Mobile devices (iPods), constructivist pedagogy, and student learning. AACE Journal, 17, 333-346.
• Khlaif, Z. (2018). Teachers’ perceptions of factors affecting their adoption and acceptance of mobile technology in K-12 settings. Computers in the Schools, 35(1), 49-67.
• Khrais, L. T., & Alghamdi, A. M. (2021). The role of mobile application acceptance in shaping E-customer service. Future Internet, 13(3), 1-13.
• Lawless, K. A., & Pellegrino, J. W. (2007). Professional development in integrating technology into teaching and learning: Knowns, unknowns, and ways to pursue better questions and answers. Review of Educational Research, 77(4), 575-614.
• Li, C., Chen, Y., & Shang, Y. (2022). A review of industrial big data for decision making in intelligent manufacturing. Engineering Science and Technology, an International Journal, 29, 101021.
• Lu, J., Liu, A., Song, Y., & Zhang, G. (2020). Data-driven decision support under concept drift in streamed big data. Complex and Intelligent Systems, 6(1), 157-163.
• Maja, M. M., & Letaba, P. (2022). Towards a data-driven technology roadmap for the bank of the future: Exploring big data analytics to support technology roadmapping. Social Sciences And Humanities Open, 6(1), 100270.
• Montrieux, H., Vanderlinde, R., Schellens, T., & De Marez, L. (2015). Teaching and learning with mobile technology: a qualitative explorative study about the introduction of tablet devices in secondary education. PLoS ONE, 10(12), 1-17.
• Nxele, S. R., Moetlhoa, B., Kgarosi, K., & Mashamba-Thompson, T. (2023). A scoping review protocol on integration of mobile-linked POC diagnostics in community-based healthcare: User experience. PLoS ONE, 18(2 February), 1-8.
• Peeples, M. M., Iyer, A. K., & Cohen, J. L. (2013). Integration of a mobile-integrated therapy with electronic health records: Lessons learned. Journal of Diabetes Science and Technology, 7(3), 602-611.
• Peng, M. Y. P., Xu, Y., & Xu, C. (2023). Enhancing students’ English language learning via M-learning: Integrating technology acceptance model and SOR model. Heliyon, 9(2). https://doi.org/10.1016/j.heliyon.2023.e13302
• Picciano, A. G. (2012). The evolution of big data and learning analytics in American higher education. Journal of Asynchronous Learning Network, 16(3), 9-20.
• Polese, F., Troisi, O., Grimaldi, M., & Romeo, E. (2019). A big data-oriented approach to decision-making: a systematic literature review. In 22nd international conference proceedings (pp. 472-496). Retrieved from https://sites.les.univr.it/eisic/wp-content/uploads/2019/11/31-Polese-Troisi-Grimaldi-Romeo-1.pdf
• Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59.
• Rossit, D. A., Tohmé, F., & Frutos, M. (2019). A data-driven scheduling approach to smart manufacturing. Journal of Industrial Information Integration, 15, 69-79.
• Saheb, T. (2020). An empirical investigation of the adoption of mobile health applications: Integrating big data and social media services. Health and Technology, 10(5), 1063-1077.
• Santos, P. A., Madeira, R. N., & Correia, N. (2021). Applications across co-located devices: User Interface distribution, state management and collaboration. ACM International Conference Proceeding Series, 602-613.
• Saritas, O., Bakhtin, P., Kuzminov, I., & Khabirova, E. (2021). Big data augmentated business trend identification: The case of mobile commerce. Scientometrics, 126(2), 1553-1579.
• Shamim, S., Zeng, J., Khan, Z., & Zia, N. U. (2020). Big data analytics capability and decision making performance in emerging market firms: The role of contractual and relational governance mechanisms. Technological Forecasting and Social Change, 161. https://doi.org/10.1016/j.techfore.2020.120315
• Shi, C., Pei, Y., Li, D., & Wu, T. (2021). Influencing factors of catering o2o customer experience: An approach integrating big data analytics with grounded theory. Tehnicki Vjesnik, 28(3), 862-872.
• Sinha, M., Fukey, L., Balasubramanian, K., Kunasekaran, P., Ragavan, N. A., & Hanafiah, M. H. (2021). Acceptance of consumer-oriented health information technologies (Chits): Integrating technology acceptance model with perceived risk. Informatica (Slovenia), 45(6), 45-52.
• Sousa, M. J., Pesqueira, A. M., Lemos, C., Sousa, M., & Rocha, Á. (2019). Decision-making based on big data analytics for people management in healthcare organizations. Journal of Medical Systems, 43(9). https://doi.org/10.1007/s10916-019-1419-x
• Tekiner, F., & Keane, J. A. (2013, October). Big data framework. In 2013 IEEE International Conference on Systems, Man, and Cybernetics (pp. 1494-1499). https://doi.org/10.1109/SMC.2013.258
• Uzunboylu, H., Hürsen, Ç., Özütürk, G., & Demirok, M. (2015). Determination of Turkish University students’ attitudes for mobile integrated EFL classrooms in North Cyprus and scale development: ELLMTAS. Journal of Universal Computer Science, 21(10), 1283-1296.
• Vecchio, P. Del, Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information Processing and Management, 54(5), 847-860.
• Yang, Y., Gong, Y., Land, L. P. W., & Chesney, T. (2020). Understanding the effects of physical experience and information integration on consumer use of online to offline commerce. International Journal of Information Management, 51, 102046.
• Yu, W., Wong, C. Y., Chavez, R., & Jacobs, M. A. (2021). Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture. International Journal of Production Economics, 236, 108135.
• Zanfardino, M., Castaldo, R., Pane, K., Affinito, O., Aiello, M., Salvatore, M., & Franzese, M. (2021). MuSA: A graphical user interface for multi-omics data integration in radiogenomic studies. Scientific Reports, 11(1), 1-13.
• Zhang, Y. [Yingfeng], Ren, S., Liu, Y., Sakao, T., & Huisingh, D. (2017). A framework for big data driven product lifecycle management. Journal of Cleaner Production, 159, 229-240.
• Zhang, Y. [Yongheng], Zhang, R., Wang, Y., Guo, H., Zhong, R. Y., Qu, T., & Li, Z. (2019). Big data driven decision-making for batch-based production systems. Procedia CIRP, 83, 814-818.

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.