Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 2, Article No: 23912
https://doi.org/10.55267/iadt.07.14677

Published Online: 23 Apr 2024

Views: 196 | Downloads: 138

Making informed decisions and improving organizational performance are crucial in the modern, data-driven environment. These processes are significantly shaped by a number of variables, including Data Imaging, Interaction Systems, Decision Support Systems, IT Infrastructure, and Technology Readiness. Interaction Systems enable communication and teamwork, Data Imaging translates complex data into visual insights, and Decision Support Systems offer cutting-edge analytics. The IT infrastructure serves as the foundation of technology, and technology readiness measures how ready people and universities are to adopt new technologies. This research aims to explore the interplay between these variables within the context of organizational change theory and their impact on organizational performance and decision-making. Additionally, it examines the moderating effect of Technology Readiness and the mediating role of IT Infrastructure in the organizational change process. Structural Equation Modeling (SEM) in AMOS is used to do this study quantitatively. A total of 450 professionals from various fields are surveyed using reliable questionnaires to compile this data. Within the context of organizational change theory, this study provides insights into the complex interactions between these factors and their combined impact on organizational performance and decision-making. It offers insightful information about how university management can use technology and human resources to improve decision-making procedures and overall performance results. This study adds to both practical and theoretical knowledge, providing concrete recommendations for firms trying to thrive in a technologically driven society. It also increases theoretical understanding by offering a comprehensive framework and putting light on the roles of IT Infrastructure, and Technology Readiness in the decision-making and performance improvement of universities.

Data Imaging Interaction Systems Decision Support Systems IT Infrastructure Technology Readiness Informed Decision-Making Performance Enhancement

• Ahmad, N. S., Bahri, S., & Fauzi, A. (2023). Does Mobile Instant Messaging (MIM) affect power redistribution? Evidence from a Malaysian school management organization. Social Sciences & Humanities Open, 7(1), 100495.
• Ahmed, N., Assadi, M., Ahmed, A. A., & Banihabib, R. (2023). Optimal design, operational controls, and data-driven machine learning in sustainable borehole heat exchanger coupled heat pumps: Key implementation challenges and advancement opportunities. Energy for Sustainable Development, 74, 231-257.
• Alkosha, H. M. A., El Adalany, M. A., Elsobky, H., Zidan, A. S., Sabry, A., & Awad, B. I. (2022). Flexion/Extension cervical magnetic resonance imaging: A potentially useful tool for decision-making in patients with symptomatic degenerative cervical spine. World Neurosurgery, 164, e1078-e1086.
• Anejionu, O. C. D., Thakuriah, P. (Vonu), McHugh, A., Sun, Y., McArthur, D., Mason, P., & Walpole, R. (2019). Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics. Future Generation Computer Systems, 98, 456-473.
• Anthony, B. (2019). Green information system integration for environmental performance in organizations: An extension of belief-action-outcome framework and natural resource-based view theory. Benchmarking, 26(3), 1033-1062.
• Argyroudis, S. A., Mitoulis, S. A., Chatzi, E., Baker, J. W., Brilakis, I., Gkoumas, K., . . . Linkov, I. (2022). Digital technologies can enhance climate resilience of critical infrastructure. Climate Risk Management, 35, 100387.
• Atadil, H. A., & Green, A. J. (2020). An analysis of attitudes towards management during culture shifts. International Journal of Hospitality Management, 86, 102439
• Barcelona, J. M., Castelli, D. M., Duncan Cance, J., Pitt Barnes, S., & Lee, S. (2021). Presidential youth fitness program implementation: An antecedent to organizational change. Evaluation and Program Planning, 86. https://doi.org/10.1016/j.evalprogplan.2021.101919
• Benhenneda, R., Brouard, T., Charousset, C., & Berhouet, J. (2023). Can artificial intelligence help decision-making in arthroscopy? Part 2: The IA-RTRHO model—A decision-making aid for long head of the biceps diagnoses in small rotator cuff tears. Orthopaedics & Traumatology: Surgery & Research, 103652.
• Beriro, D., Nathanail, J., Salazar, J., Kingdon, A., Marchant, A., Richardson, S., . . . Nathanail, P. (2022). A decision support system to assess the feasibility of onshore renewable energy infrastructure. Renewable and Sustainable Energy Reviews, 168, 112771.
• Bernard Bracy, J. M., Bao, K. Q., & Mundy, R. A. (2019). Highway infrastructure and safety implications of AV technology in the motor carrier industry. Research in Transportation Economics, 77. https://doi.org/10.1016/j.retrec.2019.100758
• Blanquer, I., Brasileiro, F., Brito, A., Calatrava, A., Carvalho, A., Fetzer, C., . . . Silva, F. (2020). Federated and secure cloud services for building medical image classifiers on an intercontinental infrastructure. Future Generation Computer Systems, 110, 119-134.
• Bolton, M., Raven, R., & Mintrom, M. (2021). Can AI transform public decision-making for sustainable development? An exploration of critical earth system governance questions. Earth System Governance, 9, 100116.
• Breyton, M., Smith, A. Ben, Rouquette, A., & Mancini, J. (2021). Cancer information overload: Association between a brief version of the CIO scale and multiple cancer risk management behaviours. Patient Education and Counseling, 104(5), 1246-1252.
• Caldwell, M. (2020). An investigation into the perceptions of Japanese University educators on the use of ICT in an EFL tertiary setting. CALL-EJ, 21(2), 1-16.
• Chen, A., Zhu, L., Zang, H., Ding, Z., & Zhan, S. (2019). Computer-aided diagnosis and decision-making system for medical data analysis: A case study on prostate MR images. Journal of Management Science and Engineering, 4(4), 266-278.
• Chen, C. M., Li, M. C., & Chen, T. C. (2020). A web-based collaborative reading annotation system with gamification mechanisms to improve reading performance. Computers & Education, 144, 103697.
• Choi, S. H., & Park, K. W. (2022). Cloud-BlackBox: Toward practical recording and tracking of VM swarms for multifaceted cloud inspection. Future Generation Computer Systems, 137, 219-233.
• Chou, J. S., & Ongkowijoyo, C. S. (2019). Hybrid decision-making method for assessing interdependency and priority of critical infrastructure. International Journal of Disaster Risk Reduction, 39, 101134.
• Chou, Y. T., Lin, C. T., Chang, T. A., Wu, Y. L., Yu, C. E., Ho, T. Y., . . . Kuang-Sheng Lee, O. (2023). Development of artificial intelligence-based clinical decision support system for diagnosis of meniscal injury using magnetic resonance images. Biomedical Signal Processing and Control, 82, 104523.
• Davidson, H. (2022). The Assisted Decision-Making (Capacity) Act 2015: Interrogating the guiding principles for a person with dementia. International Journal of Law and Psychiatry, 84, 101819.
• Dev, N. K., Shankar, R., & Swami, S. (2020). Diffusion of green products in industry 4.0: Reverse logistics issues during design of inventory and production planning system. International Journal of Production Economics, 223, 107519.
• Duffy, R. D., Allan, B. A., England, J. W., Blustein, D. L., Autin, K. L., Douglass, R. P., . . . Santos, E. J. R. (2017). The development and initial validation of the decent work scale. Journal of Counseling Psychology, 64(2), 206-221.
• Ebel, M., Jaspert, D., & Poeppelbuss, J. (2022). Smart already at design time—Pattern-based smart service innovation in manufacturing. Computers in Industry, 138. https://doi.org/10.1016/j.compind.2022.103625
• Fan, B., & Pan, T. (2023). Does information technology–organizational resource interaction affect E-government performance? Moderating roles of environmental uncertainty. Government Information Quarterly. https://doi.org/10.1016/j.giq.2023.101830
• Fix, G. M., Rikkerink, M., Ritzen, H. T. M., Pieters, J. M., & Kuiper, W. A. J. M. (2021). Learning within sustainable educational innovation: An analysis of teachers’ perceptions and leadership practice. Journal of Educational Change, 22(1), 131-145.
• Greenwood, M., Wrogemann, J. M., Schmuch, R., Jang, H., Winter, M., & Leker, J. (2022). The Battery Component Readiness Level (BC-RL) framework: A technology-specific development framework. Journal of Power Sources Advances, 14. https://doi.org/10.1016/j.powera.2022.100089
• Gundogan, S. (2022). The relationship of COVID-19 student stress with school burnout, depression and subjective well-being: Adaptation of the COVID-19 student stress scale into Turkish. Asia-Pacific Education Researcher. https://doi.org/10.1007/S40299-021-00641-2
• Hajializadeh, D., & Imani, M. (2021). RV-DSS: Towards a resilience and vulnerability-informed decision support system framework for interdependent infrastructure systems. Computers & Industrial Engineering, 156, 107276.
• Hasan, S., Ali, M., Kurnia, S., & Thurasamy, R. (2021). Evaluating the cyber security readiness of organizations and its influence on performance. Journal of Information Security and Applications, 58, 102726.
• Hoelscher, J., & Mortimer, A. (2018). Using Tableau to visualize data and drive decision-making. Journal of Accounting Education, 44, 49-59.
• Hu, A., Chaudhury, A. S., Fisher, T., Garcia, E., Berman, L., Tsao, K., . . . Raval, M. V. (2022). Barriers and facilitators of CT scan reduction in the workup of pediatric appendicitis: A pediatric surgical quality collaborative qualitative study. Journal of Pediatric Surgery, 57(11), 582-588.
• Hunte, M. R., McCormick, S., Shah, M., Lau, C., & Jang, E. E. (2021). Investigating the potential of NLP-driven linguistic and acoustic features for predicting human scores of children’s oral language proficiency. Assessment in Education: Principles, Policy and Practice, 28(4), 477-505.
• Islam, M. N., Furuoka, F., & Idris, A. (2021). Mapping the relationship between transformational leadership, trust in leadership and employee championing behavior during organizational change. Asia Pacific Management Review, 26(2), 95-102.
• Jacobsson, S., Arnäs, P. O., & Stefansson, G. (2020). Automatic information exchange between interoperable information systems: Potential improvement of access management in a seaport terminal. Research in Transportation Business & Management, 35, 100429.
• Joshi, A., Benitez, J., Huygh, T., Ruiz, L., & De Haes, S. (2022). Impact of IT governance process capability on business performance: Theory and empirical evidence. Decision Support Systems, 153, 113668.
• Kierner, S., Kucharski, J., & Kierner, Z. (2023). Taxonomy of hybrid architectures involving rule-based reasoning and machine learning in clinical decision systems: A scoping review. Journal of Biomedical Informatics, 144, 104428.
• Kollias, D., Arsenos, A., & Kollias, S. (2023). A deep neural architecture for harmonizing 3-D input data analysis and decision making in medical imaging. Neurocomputing, 542, 126244.
• Kucuksari, S., Pamucar, D., Deveci, M., Erdogan, N., & Delen, D. (2023). A new rough ordinal priority-based decision support system for purchasing electric vehicles. Information Sciences, 647, 119443.
• LaForett, D. R., & De Marco, A. (2020). A logic model for educator-level intervention research to reduce racial disparities in student suspension and expulsion. Cultural Diversity and Ethnic Minority Psychology, 26(3), 295-305.
• Lee, B. J. (2022). Enhancing listening comprehension through kinesthetic rhythm training. RELC Journal, 53(3), 567-581.
• Lee, S., Kang, M. J., & Kim, B. K. (2022). Factors influencing entrepreneurial intention: Focusing on individuals’ knowledge exploration and exploitation activities. Journal of Open Innovation: Technology, Market, and Complexity, 8(3). https://doi.org/10.3390/joitmc8030165
• Lex, S. W., Calì, D., Koed Rasmussen, M., Bacher, P., Bachalarz, M., & Madsen, H. (2019). A cross-disciplinary path to healthy and energy efficient buildings. Technological Forecasting and Social Change, 142, 273-284.
• Liao, H., He, Y., Wu, X., Wu, Z., & Bausys, R. (2023). Reimagining multi-criterion decision making by data-driven methods based on machine learning: A literature review. Information Fusion, 100, 101970.
• Liu, H., Lin, C. H., & Zhang, D. (2017). Pedagogical beliefs and attitudes toward information and communication technology: A survey of teachers of English as a foreign language in China. Computer Assisted Language Learning, 30(8), 745-765.
• Liu, J., Stewart, H., Wiens, C., Mcnitt-Gray, J., & Liu, B. (2022). Development of an integrated biomechanics informatics system with knowledge discovery and decision support tools for research of injury prevention and performance enhancement. Computers in Biology and Medicine, 141, 105062.
• Lovell, K., Watson, J., & Hiteva, R. (2022). Infrastructure decision-making: Opening up governance futures within techno-economic modelling. Technological Forecasting and Social Change, 174, 121208.
• Luo, J., Xu, J., Aldosari, O., Althubiti, S. A., & Deebani, W. (2022). Design and implementation of an efficient electronic bank management information system based data warehouse and data mining processing. Information Processing & Management, 59(6), 103086.
• Mahroof, K. (2019). A human-centric perspective exploring the readiness towards smart warehousing: The case of a large retail distribution warehouse. International Journal of Information Management, 45, 176-190.
• Mohamad, A., Zainuddin, Y., Alam, N., & Kendall, G. (2017). Does decentralized decision making increase company performance through its Information Technology infrastructure investment? International Journal of Accounting Information Systems, 27. https://doi.org/10.1016/j.accinf.2017.09.001
• Mushore, R., & Kyobe, M. (2022). Aligning tasks, technology, people, and structures to leverage the value of big data analytics. Procedia Computer Science, 203, 739-744.
• Ninan, J., Hertogh, M., & Liu, Y. (2022). Educating engineers of the future: T-shaped professionals for managing infrastructure projects. Project Leadership and Society, 3. https://doi.org/10.1016/j.plas.2022.100071
• Parasuraman, A. (2000). Technology Readiness Index (TRI) a multiple-item scale to measure readiness to embrace new technologies. Journal of service research, 2(4), 307-320.
• Pratt, L., Bisson, C., & Warin, T. (2023). Bringing advanced technology to strategic decision-making: The Decision Intelligence/Data Science (DI/DS) Integration framework. Futures, 152, 103217.
• Priester, A., Fan, R. E., Shubert, J., Rusu, M., Vesal, S., Shao, W., . . . Sonn, G. A. (2023). Prediction and mapping of intraprostatic tumor extent with artificial intelligence. European Urology Open Science, 54, 20-27.
• Raei, E., Reza Alizadeh, M., Reza Nikoo, M., & Adamowski, J. (2019). Multi-objective decision-making for green infrastructure planning (LID-BMPs) in urban storm water management under uncertainty. Journal of Hydrology, 579, 124091.
• Ramaswamy Govindan, A., & Li, X. (2023). Fuzzy logic-based decision support system for automating ergonomics risk assessments. International Journal of Industrial Ergonomics, 96, 103459.
• Ree, I. M. C., van ’t Oever, R. M., Jansen, L., Lopriore, E., de Haas, M., & van Klink, J. M. M. (2021). School performance and behavioral functioning in children after intrauterine transfusions for hemolytic disease of the fetus and newborn. Early Human Development. https://doi.org/10.1016/j.earlhumdev.2021.105381
• Renzi, E., & Trifarò, C. A. (2023). Knowledge and Digitalization: A way to improve safety of Road and Highway Infrastructures. Procedia Structural Integrity, 44, 1228-1235.
• Salim, T. A., El Barachi, M., Mohamed, A. A. D., Halstead, S., & Babreak, N. (2022). The mediator and moderator roles of perceived cost on the relationship between organizational readiness and the intention to adopt blockchain technology. Technology in Society, 71, 102108.
• Senocak, A. A., & Guner Goren, H. (2023). Three-phase artificial intelligence-geographic information systems-based biomass network design approach: A case study in Denizli. Applied Energy, 343. https://doi.org/10.1016/j.apenergy.2023.121214
• 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
• Singh, S., Singh, H., Bueno, G., Deniz, O., Singh, S., Monga, H., . . . Pedraza, A. (2023). A review of image fusion: Methods, applications and performance metrics. Digital Signal Processing, 137. https://doi.org/10.1016/j.dsp.2023.104020
• Somasekar, J., Ramesh, G., Ramu, G., Dileep Kumar Reddy, P., Eswara Reddy, B., & Lai, C. H. (2019). A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images. Data in Brief, 27. https://doi.org/10.1016/j.dib.2019.104643
• Song, C., Diessner, N. L., Ashcraft, C. M., & Mo, W. (2021). Can science-informed, consensus-based stakeholder negotiations achieve optimal dam decision outcomes? Environmental Development, 37. https://doi.org/10.1016/j.envdev.2020.100602
• Steininger, D. M., & Gatzemeier, S. (2019). Digitally forecasting new music product success via active crowdsourcing. Technological Forecasting and Social Change, 146, 167-180.
• Struckell, E., Ojha, D., Patel, P. C., & Dhir, A. (2022). Strategic choice in times of stagnant growth and uncertainty: An institutional theory and organizational change perspective. Technological Forecasting and Social Change, 182, 121839.
• Taherdoost, H., & Madanchian, M. (2022). Employment of technological-based approaches for creative E-learning; Teaching management information systems. Procedia Computer Science, 215, 802-808.
• Tengilimoglu, O., Carsten, O., & Wadud, Z. (2023). Infrastructure requirements for the safe operation of automated vehicles: Opinions from experts and stakeholders. Transport Policy, 133, 209-222.
• Veluchamy, S., Mahesh, K. M., Bharathi A., P., & Sheeba, P. T. (2023). DeepDrive: A braking decision making approach using optimized GAN and Deep CNN for advanced driver assistance systems. Engineering Applications of Artificial Intelligence, 123. https://doi.org/10.1016/j.engappai.2023.106111
• Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
• Vilda, F. G., Yagüe Fabra, J. A., & Torrents, A. S. (2019). Person-based design: A human-centered approach for lean factory design. Procedia Manufacturing, 41, 445-452.
• Villalón-Fonseca, R. (2022). The nature of security: A conceptual framework for integral-comprehensive modeling of IT security and cybersecurity. Computers & Security, 120. https://doi.org/10.1016/j.cose.2022.102805
• Vink, L. S. (2022). A new methodology for assessing human contributions to occurrences (MAHCO) in Air Traffic Management utilising a Bayesian hierarchical predictive coding approach to the brain, and the benefits for just culture. Transportation Research Procedia, 66, 201-213.
• Wendong, W., Hanhao, L., Menghan, X., Yang, C., Xiaoqing, Y., Xing, M., & Bing, Z. (2020). Design and verification of a human-robot interaction system for upper limb exoskeleton rehabilitation. Medical Engineering & Physics, 79, 19-25.
• Wu, K. J., Tseng, M. L., Ali, M. H., Xue, B., Chiu, A. S. F., Fujii, M., . . . Bin, Y. (2021). Opportunity or threat in balancing social, economic and environmental impacts: The appearance of the Polar Silk Road. Environmental Impact Assessment Review, 88. https://doi.org/10.1016/j.eiar.2021.106570
• Xie, X., Wu, Y., Palacios-Marqués, D., & Ribeiro-Navarrete, S. (2022). Business networks and organizational resilience capacity in the digital age during COVID-19: A perspective utilizing organizational information processing theory. Technological Forecasting and Social Change, 177, 121548.
• Yu, W., Jin, D., Cai, W., Zhao, F., & Zhang, X. (2022). Towards tacit knowledge mining within context: Visual cognitive graph model and eye movement image interpretation. Computer Methods and Programs in Biomedicine, 226, 107107.
• Yun, Y., Ma, D., & Yang, M. (2021). Human-computer interaction-based decision support system with applications in data Mining. Future Generation Computer Systems, 114, 285-289.
• Zarghami, S. A., & Zwikael, O. (2022). Measuring project resilience—Learning from the past to enhance decision making in the face of disruption. Decision Support Systems, 160, 113831.
• Zhu, X., & Li, Y. (2023). The use of data-driven insight in ambidextrous digital transformation: How do resource orchestration, organizational strategic decision-making, and organizational agility matter? Technological Forecasting and Social Change, 196, 122851.

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