Factors Impacting the Perceived Strategic Value, Evaluation, And Adoption of Big Data Analytics - A Case Study of The Top Ten Revenue Share Contractor Companies in Bangkok, Thailand
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Purpose: The purpose of the research was to clarify the causal correlation among Big data analytic adoption environment of Top ten revenue-shared contractors company located in Bangkok with an aimed to arrange an alternative extensively and potentially ensures that the study's findings are valid and reliable, offering valuable insights for organizations, decision-makers and to explore the factors that impact the intention to use perceived strategic value of big data analytics. Research design, data and methodology: This study uses the questionnaire survey and quantitative method to collect data from target groups. distributed questionnaires to workers who worked in the Top 10 revenue construction company. Results: The relationships between Casual correlation among big data analytic adoption environment were clearly defined toshape the conceptual framework. This research gathers data from organizations experienced with big data analytics. The research outcomes confirmed the theories and relationships between the factors impacting big data analytics adoption. Conclusions: The conclusion of research provide valuable insights into the factors that influence big data analytics adoption, highlighting the importance of managing complexity, ensuring compatibility, fostering organizational readiness, securing top management support, and conducting thorough evaluations to realize the strategic value of big data analytics. These insights can guide organizations in developing strategies to effectively adopt and utilize big data analytics for improved performance and competitive advantage. In order to maximize the highest utility, organizations need to ensure that new analytics technologies are compatible with existing IT infrastructure and business processes. This involves conducting thorough compatibility assessments before adoption and making necessary adjustments to current systems to facilitate seamless integration. Compatibility ensures that big data analytic tools can be effectively utilized without disrupting existing operations.
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References
Adom, E., Awuah, K. B., & Agyei, J. (2018). The role of perceived service quality on perceived value and loyalty intention in the public transport sector in Ghana. International Journal of Research in Business and Social Science, 7(4), 1-11.
https://doi.org/10.21444/ijrbss.2018.7040
Akter, S., & Wamba, S. F. (2016). Big data analytics in e-commerce: a systematic review and agenda for future research. Electronic Markets, 26(2), 173-194. https://doi.org/10.1007/s12525-016-0219-0
Al-Qirim, N. (2006). The adoption of eCommerce communications and applications technologies in small businesses in New Zealand. Conference Paper Electronic Commerce Research and Applications, 6(1), 462-473. 10.1109/AICCSA.2006.205194
Alsetoohy, O., Ayoun, B., Arous, S., Megahed, F., & Nabil, G. (2019). Intelligent agent technology: what affects its adoption in hotel food supply chain management?. Journal of Hospitality and Tourism Technology, 10(3), 286-310.
Babar, M. A., & Stamelos, I. (2022). Assessing the Impact of Evaluation Methods on Big Data Analytics Adoption in Information Systems. Information Systems Frontiers, 24(3), 559-580. https://doi.org/10.1007/s10796-021-10159-4
Boonmee, C., Nartklaew, S., & Kaewkasi, C. (2021). The application of big data analytics for construction project management: A systematic review. Advanced Engineering Informatics, 49(101314). https://doi.org/10.1016/j.aei.2021.101314
Chan, F., & Chong, A. (2013). What drives the adoption of the mobile news application? The roles of perceived threat, perceived cost, perceived benefit, and perceived quality. Journal of Computer Information Systems, 53(3), 85-94. https://doi.org/10.1080/08874417.2013.11645474
Chen, C. L. P., & Zhang, C.-Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. 10.1016/j.ins.2014.01.015
Chen, D. Q., & Preston, D. S. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4-39. https://doi.org/10.1080/07421222.2015.1138364
Chen, T., & Zhang, J. (2017). The Impact of Mathematics Anxiety on Academic Performance A Meta-Analysis. Journal of Educational Psychology, 42, 567-589.
Davenport, T. H., Barth, P., & Bean, R. (2012). How 'big data' is different. MIT Sloan Management Review, 54(1), 22-24.
Dowgiallo, D. (2020). Technological advancements in the construction industry: Global trends and implications for Thailand. Journal of Construction Engineering and Management, 46(3), 233-248. 10.1061/(ASCE)CO.1943-7862.0001735
El-Gohary, N. M., & Aziz, Z. (2014). A framework for automated construction safety risk identification using BIM and text mining. American Society of Civil Engineers.
Fornell, C., & Larcker, D. F. (1981). Structural Equation Models with Unobservable Variables and Measurement Error: Algebra and Statistics. Journal of Marketing Research, 18, 382-388. http://dx.doi.org/10.2307/3150980
Gangwar, H. (2018). Understanding the determinants of big data adoption in India: an analysis of the manufacturing and services sectors. Information Resources Management Journal, 31(4), 1-22.
Ghobakhloo, M., Arias-Aranda, D., & Benitez-Amado, J. (2011). Adoption of e-commerce applications in SMEs. Industrial Management & Data Systems, 111(8), 1238-1269.
GlobalData. (2020). Construction in Thailand - Key Trends and Opportunities to 2024. https://www.globaldata.com/store/report/gdcn20tr-p018-construction-in-thailand-key-trends-and-opportunities-to-2024/
Grover, V. (1993). An Empirically Derived Model for the Adoption of Customer-Based Interorganizational Systems. Decision Sciences, 24(3), 603 - 640. 10.1111/j.1540-5915.1993.tb01295.x
Gupta, S., & Sarker, S. (2021). The role of compatibility in the adoption of big data analytics: A technology-organization-environment (TOE) framework. Information Systems Frontiers, 23(1), 95-115. https://doi.org/10.1007/s10796-019-09945-0
Holt, D. T., Armenakis, A. A., Feild, H. S., & Harris, S. G. (2007). Readiness for organizational change: The systematic development of a scale. The Journal of Applied Behavioral Science, 43(2), 232-255. https://doi.org/10.1177/0021886306295295
Ismail, S. A., Bandi, S., & Maaz, Z. N. (2018). An Appraisal into the Potential Application of Big Data in the Construction Industry. International Journal of Built Environment and Sustainability, 5(2), 145-154. 10.11113/ijbes.v5.n2.274
Katal, A., Wazid, M., & Goudar, R. H. (2013). Big data: Issues, challenges, tools and good practices. In 2013 Sixth International Conference on Contemporary Computing (IC3), 404-409. 10.1109/IC3.2013.6612229
Kaur, H., & Singh, M. (2020). Application of artificial intelligence in the construction industry: A review. Journal of Building Engineering, 31(101225). https://doi.org/10.1016/j.jobe.2020.101225
Lai, F., Zmud, R. W., & Chen, G. (2018). The effects of big data analytics capabilities on supply chain agility: An empirical study in the high-tech industry. International Journal of Production Economics, 205, 193-206. https://doi.org/10.1016/j.ijpe.2018.09.006
LaValle, S., Lesser, E., & Shockley, R. (2020). Evaluating the Impact of Big Data Analytics on Technology Adoption: An Empirical Study. Information & Management, 57(7). https://doi.org/10.1016/j.im.2020.103284
Limsakul, C., & Saithanu, K. (2019). A big data analytics framework for enhancing the decision-making process in Thai construction projects. Journal of Industrial Information Integration, 15, 9-18. https://doi.org/10.1016/j.jii.2019.04.002
Maroufkhani, P., Wagner, R., Wan Ismail, W. K., & Baroto, M. B. (2019). Big Data Analytics and Firm Performance: A Systematic Review. Information (Switzerland), 10(7), 226. 10.3390/info10070226
Marsh, H. W., Hau, K.-T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320-341. https://doi.org/10,1207/s15328007sem1103_2
Mikalef, P., Lekakos, G., Boura, M., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98, 261-276. 10.1016/j.jbusres.2019.01.044
Nayir, F. (2013). Algılanan örgütsel destek ölçeğinin” kısa form geçerlik güvenirlik çalışması[“Perceived Organizational Support Scale- Short Form Validity-Reliability Study]. Mehmet Akif Ersoy Üniversitesi Eğitim Fakültesi Dergisi, 28, 89-106.
Pacharawanit, P., Pongpeng, J., & Chinda, T. (2019). Factors affecting the adoption of Building Information Modeling (BIM) in Thailand's construction industry. Engineering, Construction and Architectural Management, 26(10), 2303-2320.
1108/ECAM-09-2018-0406
Pedroso, R., Zanetello, L., Guimaraes, L., Pettenon, M., Goncalves, V., Scherer, J., Kessler, F., & Pechansky, F. (2016). Confirmatory factor analysis (CFA) of the crack use relapse scale (CURS). Archives of Clinical Psychiatry, 43(3), 37-40.
Phruksaphanrat, B., Taneerananon, P., & Chotibhongs, R. (2020). Predictive maintenance in construction industry: A review and case study. Journal of Building Engineering, 31(101422). https://doi.org/10.1016/j.jobe.2020.101422
Premkumar, G., & Roberts, M. (1999). Adoption of new information technologies in rural small businesses. Omega-International Journal of Management Science, 27(4), 467-484.
Premkumar, G. (2003). A meta-analysis of research on information technology implementation in small business. Journal of organizational computing and electronic commerce, 13(2), 91-121.
Priyadarshinee, P., Raut, R. D., Jha, M. K., & Kamble, S. (2017). A cloud computing adoption in Indian SMEs: scale development and validation approach. The Journal of High Technology Management Research, 28(2), 221-245.
Raguseo, E., & Vitari, C. (2018). Investments in big data analytics and firm performance: an empirical investigation of direct and mediating effects. International Journal of Production Research, 56(15), 1-16.
Rajabifard, A., Bishop, I. D., & Escobar, F. (2017). The role of Geographic Information Systems in the Thai construction industry. ISPRS International Journal of Geo-Information, 6(12), 393.
Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). Free Press.
Sahin, I. (2006). Detailed review of Rogers' diffusion of innovations theory and educational technology-related studies based on Rogers' theory. Turkish Online Journal of Educational Technology, 5(2), 14-22.
Sharma, G. P., Verma, R. C., & Pathare, P. (2005). Mathematical modeling of infrared radiation thin layer drying of onion slices. Journal of Food Engineering, 71(3), 282-286.
Sharma, R. (2020). Big data analytics adoption in organizations: A technology-organization-environment framework approach. Information Systems Frontiers, 22(3), 667-685. https://doi.org/10.1007/s10796-019-09940-6
Sills, J., & Song, J. (2002). The effect of customer-vendor relationship on new technology adoption: A case study of electronic commerce. Journal of Computer Information Systems, 42(2), 48-57. https://doi.org/10.1080/08874417.2002.11645398
Singh, R. P., & Rathi, R. (2018). Big data analytics and technology adoption: An integrated model. Journal of Business Research, 86(1), 269-280. https://doi.org/10.1016/j.jbusres.2018.01.045
Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286. 10.1016/j.jbusres.2016.08.001
Sornchuer, P., & Charoenngam, C. (2021). Green construction management practices in Thailand: A framework for effective implementation. Journal of Cleaner Production, 278(123699). https://doi.org/10.1016/j.jclepro.2020.123699
Sutrisna, M., Cooper-Cooke, B., & Goulding, J. S. (2018). Emerging technologies for construction delivery. Engineering, Construction and Architectural Management, 25(2), 141-157. 10.1108/ECAM-03-2017-0057
Thong, J. Y. L. (1999). An Integrated Model of Information Systems Adoption in Small Businesses. Journal of Management Information Systems, 15(4), 187-214. 10.1080/07421222.1999.11518227
World Bank. (2020). World Development Indicators. https://databank.worldbank.org/reports.aspx?source=world-development-indicators
Wu, J., & Wang, Y. (2006). The impact of knowledge management on organizational performance in the information technology industry. Journal of Computer Information Systems, 46(5), 41-50. https://doi.org/10.1080/08874417.2006.11645851
Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science and Technology, 67(1), 134-147. https://doi.org/10.1002/asi.23362
Zhu, K., Dong, S., Xu, S., & Kraemer, K. (2006). Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies. European Journal of Information Systems, 15(6), 601-616.