References
- Vassakis K, Petrakis E, Kopanakis I. Big data analytics: applications, prospects and challenges. In: Big data analytics: Applications, prospects and challenges. Mobile big data. Springer; 2018 3-20.
- Big data analytics adoption soared in the enterprise in 2018 Forbes.com: Forbes
- Getting started with advanced analytics is as much about changing mindset and culture as it is about tools and skills. Gartner: Gartner
- 5 fixes for your failing big data initiatives Forbes.com: Forbes
- Companies are failing in their efforts to become data-driven Harvard Business Review
- Big data project success-a meta analysis. Pacis; date. 376.
- Brancheau JC, Wetherbe JC. The adoption of spreadsheet software: testing innovation diffusion theory in the context of end-user computing. Inf Syst Res. 1990;1(2):115–43. doi:https://doi.org/10.1287/isre.1.2.115.
- Morris MG, Venkatesh V. Age differences in technology adoption decisions: implications for a changing work force. Pers Psychol. 2000;53(2):375–403. doi:https://doi.org/10.1111/j.1744-6570.2000.tb00206.x.
- Eley R, Fallon T, Soar J, Buikstra E, Hegney D. Barriers to use of information and computer technology by australia’s nurses: A national survey. J Clin Nurs. 2009;18(8):1151–58. doi:https://doi.org/10.1111/j.1365-2702.2008.02336.x.
- Limsarun T, Anurit P. The different perspective of managerial and operational level toward customer relationship management practice inthailand. J Manage Res. 2011;3:1.
- Wasko W, Faraj F. Why should iI share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Q.2005;29(1):35–57. doi:https://doi.org/10.2307/25148667.
- A risk and benefits behavioral model to assess intentions to adopt big data. Proceedings of the 10th International Conference on Intellectual Capital, Knowledge Management and Organisational Learning: ICICKM 2013, Washington, DC.
- Challenges to the organisational adoption of big data analytics: A case study in the south african telecommunications industry. Proceedings of the 2015 annual research conference on South African institute of computer scientists and information technologists: ACM. 27. Cape Town, South Africa.
- Soon K, Lee C, Boursier P. A study of the determinants affecting adoption of big data using integrated technology acceptance model (tam) and diffusion of innovation (doi) in malaysia. Int J Appl Bus Econ Res. 2016;14:17–47.
- Brock V, Khan HU. Big data analytics: does organizational factor matters impact technology acceptance? J Big Data. 2017;4(1):21. doi:https://doi.org/10.1186/s40537-017-0081-8.
- Shahbaz M, Gao C, Zhai L, Shahzad F, Hu Y. Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change. J Big Data. 2019;6(1):6. doi:https://doi.org/10.1186/s40537-019-0170-y.
- Weerakkody V, Kapoor K, Balta ME, Irani Z, Dwivedi YK. Factors influencing user acceptance of public sector big open data. Prod Plan Control. 2017:28(11–12):891–905.
- Ajibade P Technology acceptance model limitations and criticisms: Exploring the practical applications and use in technology-related studies, mixed-method, and qualitative researches. Technology 2018.
- Chuttur MY. Overview of the technology acceptance model: origins, developments and future directions. Working Papers Inf Syst. 2009;9:9–37.
- Benbasat I, Barki H. Quo vadis tam? J Assocr Inf Syst. 2007;8(4):7. doi:https://doi.org/10.17705/1jais.00126.
- Tornatzky LG, Fleischer M, Chakrabarti AK Processes of technological innovation. Lexington books.
- Goodhue DL, Thompson RL. Task-technology fit and individual performance. MIS Q. 1995;19(2):213–36.
- Lee C-C, Cheng HK, Cheng H-H. An empirical study of mobile commerce in insurance industry: task–technology fit and individual differences. Decis Support Syst. 2007;43(1):95–110. doi:https://doi.org/10.1016/j.dss.2005.05.008.
- Sun Y, Bhattacherjee A, Ma Q. Extending technology usage to work settings: the role of perceived work compatibility in ERP implementation. Inf Manage. 2009;46(6):351–56. doi:https://doi.org/10.1016/j.im.2009.06.003.
- Leem CS, Kim BW, Yu EJ, Paek MH Information technology maturity stages and enterprise benchmarking: an empirical study. Industrial Management & Data Systems 2008.
- Goodhue DL. Understanding user evaluations of information systems. Manage Sci. 1995;41(12):1827–44. doi:https://doi.org/10.1287/mnsc.41.12.1827.
- Lee YW, Strong DM, Kahn BK, Wang RY. Aimq: A methodology for information quality assessment. Inf Manage. 2002;40(2):133–46. doi:https://doi.org/10.1016/S0378-7206(02)00043-5.
- Levitin AV, Redman TC. Data as a resource: properties, implications, and prescriptions. MIT Sloan Manage Rev. 1998;40:89.
- Seddon PB. A respecification and extension of the delone and mclean model of is success. Inf Syst Res. 1997;8(3):240–53. doi:https://doi.org/10.1287/isre.8.3.240.
- Nelson RR, Todd PA, Wixom BH. Antecedents of information and system quality: an empirical examination within the context of data warehousing. J Manage Inf Syst. 2005;21(4):199–235. doi:https://doi.org/10.1080/07421222.2005.11045823.
- Cappiello C, Francalanci C, Pernici B. Time-related factors of data quality in multichannel information systems. J Manage Inf Syst. 2003;20(3):71–92. doi:https://doi.org/10.1080/07421222.2003.11045769.
- Cai L, Zhu Y. The challenges of data quality and data quality assessment in the big data era. Data Sci J. 2015;14(0):2 doi:https://doi.org/10.5334/dsj-2015-002.
- McGilvray D. Executing data quality projects: Ten steps to quality data and trusted information (TM). Elsevier.
- Owais SS, Hussein NS. Extract five categories cpivw from the 9v’s characteristics of the big data. Int J Adv Comput Sci Appl. 2016;7:254–58.
- Kwon O, Lee N, Shin B. Data quality management, data usage experience and acquisition intention of big data analytics. Int J Inf Manage. 2014;34(3):387–94. doi:https://doi.org/10.1016/j.ijinfomgt.2014.02.002.
- Kim G, Shin B, Kwon O. Investigating the value of sociomaterialism in conceptualizing it capability of a firm. J Manage Inf Syst. 2012;29(3):327–62. doi:https://doi.org/10.2753/MIS0742-1222290310.
- Erdoğmuş İE, Eren E Knowledge management and database marketing applications. 2004.
- Al-Mamary YH, Shamsuddin A, Hamid A, Aziati N. The role of different types of information systems in business organizations: A review. Int J Res (IJR). 2014;1(7):1279–1286.
- Bughin J, Livingston J, Marwaha S. Seizing the potential of ‘big data’. McKinsey Q. 2011;4;103–109
- Janssen M, van der Voort H, Wahyudi A. Factors influencing big data decision-making quality. J Bus Res. 2017;70:338–45 doi:https://doi.org/10.1016/j.jbusres.2016.08.007.
- Miller HG, Mork P. From data to decisions: A value chain for big data. IT Prof. 2013;1:57–59.
- Jagadish H, Gehrke J, Labrinidis A, Papakonstantinou Y, Patel JM, Ramakrishnan R, Shahabi C. Big data and its technical challenges. Commun ACM. 2014;57(7):86–94. doi:https://doi.org/10.1145/2611567.
- Pinsonneault A, Kraemer KL. Middle management downsizing: an empirical investigation of the impact of information technology. Manage Sci. 1997;43(5):659–79. doi:https://doi.org/10.1287/mnsc.43.5.659.
- Thompson SM, Altay N, Green III WG, Lapetina J. Improving disaster response efforts with decision support systems. Int J Emergency Manage. 2006;3(4):250. doi:https://doi.org/10.1504/IJEM.2006.011295.
- Asemi A, Safari A, Zavareh AA. The role of management information system (MIS) and decision support system (DSS) for manager’s decision making process. Int J Bus Manage. 2011;6(7):164–73. doi:https://doi.org/10.5539/ijbm.v6n7p164.
- De Alwis SMG, Higgins SE Information as a tool for management decision making: A case study of singapore. 2002.
- Ghasemaghaei M, Ebrahimi S, Hassanein K. Data analytics competency for improving firm decision making performance. J Strateg Inf Syst. 2018;27(1):101–13. doi:https://doi.org/10.1016/j.jsis.2017.10.001.
- Hernández MA, Stolfo SJ. Real-world data is dirty: data cleansing and the merge/purge problem. Data Min Knowl Discov. 1998;2(1):9–37. doi:https://doi.org/10.1023/A:1009761603038.
- Nagle T, Redman TC, Only SD. 3% of companies’ data meets basic quality standards. Harv Bus Rev. 2017;95:2–5.
- Very fast estimation for result and accuracy of big data analytics: the earl system. 2013 IEEE 29th International Conference on Data Engineering (ICDE): IEEE; date. 1296-1299, Seoul, South Korea.
- Kambatla K, Kollias G, Kumar V, Grama A. Trends in big data analytics. J Parallel Distrib Comput. 2014;74(7):2561–73. doi:https://doi.org/10.1016/j.jpdc.2014.01.003.
- Li EY. Perceived importance of information system success factors: A meta analysis of group differences. Inf Manage. 1997;32(1):15–28. doi:https://doi.org/10.1016/S0378-7206(97)00005-0.
- Pugna IB, Duțescu A, Stănilă OG. Corporate attitudes towards big data and its impact on performance management: A qualitative study. Sustainability. 2019;11(3):684. doi:https://doi.org/10.3390/su11030684.
- Greeff G, Ghoshal R. Practical e-manufacturing and supply chain management. Burlington (MA): Oxford; 2004.
- Chen DQ, Preston DS, Swink M. How the use of big data analytics affects value creation in supply chain management. J Manage Inf Syst. 2015;32(4):4–39. doi:https://doi.org/10.1080/07421222.2015.1138364.
- Venkatesh V, Bala H. Adoption and impacts of interorganizational business process standards: role of partnering synergy. Inf Syst Res. 2012;23(4):1131–57. doi:https://doi.org/10.1287/isre.1110.0404.
- Iacovou CL, Benbasat I, Dexter AS. Electronic data interchange and small organizations: adoption and impact of technology. MIS Q. 1995;19(4):465–85.
- Rouse M. Guide to NoSQL databases: How they can help users meet big data needs, TechTarget, 2014, https://searchdatamanagement.techtarget.com/essentialguide/Guide-to-NoSQL-databases-How-they-can-help-users-meet-big-data-needs. Accessed 10 May. 2020.
- Lee SM, Lee Z, Lee J. Knowledge transfer in work practice: adoption and use of integrated information systems. Ind Manage Data Syst. 2007;107(4):501–18. doi:https://doi.org/10.1108/02635570710740661.
- Kim G, Shin B, Kim KK, Lee HG. It capabilities, process-oriented dynamic capabilities, and firm financial performance. J Assocr Inf Syst. 2011;12(7):487. doi:https://doi.org/10.17705/1jais.00270.
- Ravichandran T, Lertwongsatien C, Lertwongsatien C. Effect of information systems resources and capabilities on firm performance: A resource-based perspective. J Manage Inf Syst. 2005;21(4):237–76. doi:https://doi.org/10.1080/07421222.2005.11045820.
- Mata FJ, Fuerst WL, Barney JB. Information technology and sustained competitive advantage: A resource-based analysis. MIS Q. 1995:19(4):487–505.
- Comparing data science project management methodologies via a controlled experiment. Proceedings of the 50th Hawaii International Conference on System Sciences. Waikoloa Village, HI, USA.
- Saltz J, Shamshurin I, Connors C. Predicting data science sociotechnical execution challenges by categorizing data science projects. J Assoc Inf Sci Technol. 2017;68(12):2720–28. doi:https://doi.org/10.1002/asi.23873.
- The ambiguity of data science team roles and the need for a data science workforce framework. 2017 IEEE International Conference on Big Data (Big Data): IEEE. 2355–61. Boston, MA, USA.
- Delone WH, McLean ER. The delone and mclean model of information systems success: A ten-year update. J Manage Inf Syst. 2003;19:9–30.
- Hosseinioun P, Shayeghi R, Rostam GG. Organizational decision based on business intelligence. Int J Ind Syst Eng. 2012;6:841–43.
- Watad MM, Perez-Alvarez C. Managers’perceptions of the role of it in organizational change. J Manage Inf Decis Sci. 2006;9:21.
- Chin WW. How to write up and report pls analyses. In How to write up and report pls analyses. Handbook of partial least squares. Springer, Berline, Heidelberg. 2010. p. 655–690
- Fornell C, Larcker DF. Structural equation models with unobservable variables and measurement error: algebra and statistics. J Marketing Res. 1981;18(3):382–88 doi:https://doi.org/10.1177/002224378101800313.
- Hulland J. Use of partial least squares (pls) in strategic management research: A review of four recent studies. Strateg Manage J. 1999;20(2):195–204 doi:https://doi.org/10.1002/(SICI)1097-0266(199902)20:2<195::AID-SMJ13>3.0.CO;2-7.
- Chin WW. The partial least squares approach for structural equation modeling. In: Marcoulides GA, editor. The partial least squares approach for structural equation modeling. Modern methods for business research. Mahwah (NJ): Lawrence Erlbaum Associates Publishers. 1998. p. 259–336
- Chin WW. Partial least squares is to lisrel as principal components analysis is to common factor analysis. Technol Stud. 1995;2:315–19.
- Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis. In: Multivariate data analysis. Vol. 6. Upper Saddle River (NJ): Pearson Prentice Hall; 2006.
- Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited; 2009
- Goh -T-T, Sun S. Exploring gender differences in islamic mobile banking acceptance. Electron Commer Res. 2014;14(4):435–58. doi:https://doi.org/10.1007/s10660-014-9150-7.
- Applying software engineering processes for big data analytics applications development. 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC): IEEE, Las Vegas, NV, USA. 1–7.
- Business analytics and competitive advantage: A review and a research agenda. DSS; date. 187-198.
- Hossain MA, Quaddus M Mandatory and voluntary adoption of RFID. In. Mandatory and voluntary adoption of RFID. Encyclopedia of business analytics and optimization: IGI Global; 2014.
- Gallivan MJ. Organizational adoption and assimilation of complex technological innovations: development and application of a new framework. ACM SIGMIS Database: DATABASE Adv Inf Syst. 2001;32(3):51–85. doi:https://doi.org/10.1145/506724.506729.
- Rawstorne P, Jayasuriya R, Caputi P An integrative model of information systems use in mandatory environments. ICIS 1998 Proceedings 1998: 32. Helsinki, Finland.