Factors affecting the adoption of Big Data analytics in companies
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Abstract
With the total quantity of data doubling every two years, the low price of computing and data storage, make Big Data analytics (BDA) adoption desirable for companies, as a tool to get competitive advantage. Given the availability of free software, why have some companies failed to adopt these techniques? To answer this question, we extend the unified theory of technology adoption and use of technology model (UTAUT) adapted for the BDA context, adding two variables: resistance to use and perceived risk. We used the level of implementation of these techniques to divide companies into users and non-users of BDA. The structural models were evaluated by partial least squares (PLS). The results show the importance of good infrastructure exceeds the difficulties companies face in implementing it. While companies planning to use Big Data expect strong results, current users are more skeptical about its performance.
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References
Afonso, C., Gonzalez, M., Roldán, J., & Sánchez-Franco, M. (2012). Determinants of user acceptance of a local eGovernment Electronic Document Management System (EDMS). In Proceedings of the 12th European Conference on e-Government, ECEG (pp. 19-28), Barcelona: Academic Publishing International Limited.
Agrawal, D., Bernstein, P., & Bertino, E. (2011). Challenges and opportunities with Big Data 2011-1. Proceedings of the VLDB Endowment (pp. 1-16). Retrieved from http://dl.acm.org/citation.
cfm?id=2367572%5Cnhttp://docs.lib.purdue.edu/cctech/1/
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211. doi:10.1016/0749-5978(91)90020-T
Akerkar, R. (2014). Analytics on big aviation data: Turning data into insights. International Journal of Computer Science and Applications,11(3), 116-127.
Al-Gahtani, S. S., Hubona, G. S., & Wang, J. (2007). Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management, 44(8), 681-691. doi:10.1016/j.im.2007.09.002
Alharbi, S. T. (2014). Trust and acceptance of cloud computing: A revised UTAUT model. Proceedings - 2014 International Conference on Computational Science and Computational Intelligence, CSCI 2014,
(Mm) (pp. 131-134). doi:10.1109/CSCI.2014.107
Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on Big Data in marketing: A text mining and topic modeling based literature analysis. European Research on Management and Business Economics, 24(1), 1-7. doi:10.1016/j.iedeen.2017.06.002
Arenas-Gaitán, J., Peral-Peral, B., & Villarejo-Ramos, A.-F. (2016). Grupos de mayores en la banca electrónica. Segmentación de clases latentes con PLS-POS. In Congreso Marketing AEMARK. Madrid, Spain.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (PLS) approach to causal modelling: Personal computer adoption and use as an illustration. Technology Studies, 2(2), 285-309.
Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013). Discovering unobserved heterogeneity in structural equation models to avert Validity threats. MIS Quarterly, 37(3), 665-694. doi:10.25300/MISQ/2013/37.3.01
Berg, P., Leinonen, M., Leivo, V., & Pihlajamaa, J. (2002). Assessment of quality and maturity level of R&D. International Journal of Production Economics, 78(1), 29-35. doi:10.1016/S0925-5273(00)00166-3
Bhattacherjee, A., & Hikmet, N. (2007). Physicians’resistance toward healthcare information technology: A theoretical model and empirical test. European Journal of Information Systems, 16(6), 725-737. doi:10.1057/palgrave.ejis.3000717
Bozan, K., Parker, K., & Davey, B. (2016). A closer look at the social influence construct in the UTAUT Model: An institutional theory based approach to investigate health IT adoption patterns of the elderly. Proceedings of the Annual Hawaii International Conference on System Sciences, 2016-March (pp. 3105-3114). doi:10.1109/HICSS.2016.391
Brown, S. A., & Venkatesh, V. (2005). Model of adoption of
technology in households: A baseline model test and extension incorporating household life cycle. MIS Quarterly, 29(3), 399-426.doi:10.2307/25148690
Brünink, L. (2016). Cross-functional Big Data integration: Applying the UTAUT model. University of Twente (The Netherlands).
Burton-Jones. (2009). Minimizing method bias through programmatic research. MIS Quarterly, 33(3), 445-471. doi:10.2307/20650304
Cabrera-Sánchez, J.-P., & Villarejo-Ramos, Á.-F. (2018). Factores que afectan a la adopción del Big Data como instrumento de marketing en las empresas españolas. In XXVIII Jornadas Luso-Espanholas de Gestâo Científica, At Guarda (Portugal).
Chauhan, S., & Jaiswal, M. (2016). Determinants of acceptance of ERP software training in business schools: Empirical investigation using UTAUT model. International Journal of Management Education, 14(3),248-262. doi:10.1016/j.ijme.2016.05.005
Chiang, R. H. L., Grover, V., Liang, T.-P., & Zhang, D. (2018). Special issue: strategic value of Big Data and business analytics. Journal of Management Information Systems, 35(2), 383-387. doi:10.1080/07421222.2018.1451950
Chin, W. W., & Dibbern, J. (2010). An introduction to a permutation based procedure for multi-group PLS analysis: Results of tests of differences on simulated data and a cross-cultural analysis of the sourcing of information system services between Germany and the USA. In V. E.
Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares (pp. 171-193). doi:10.1007/978-3-540-32827-8
Claudy, M. C., Garcia, R., & O’Driscoll, A. (2015). Consumer resistance to innovation: A behavioral reasoning perspective. Journal of the Academy of Marketing Science, 43(4), 528-544. doi:10.1007/s11747-014-0399-0
Davis, F. (1985). A technology acceptance model for empirically testing new end-user information systems. Massachusetts Institute of Technology, Sloan School of Management (December).
Demoulin, N. T. M., & Coussement, K. (2018). Acceptance of text-mining systems: The signaling role of information quality. Information & Management. Advanced online publication. doi:10.1016/j.im.2018.10.006
Ducange, P., Pecori, R., & Mezzina, P. (2018). A glimpse on big data analytics in the framework of marketing strategies. Soft Computing, 22(1), 325-342. doi:10.1007/s00500-017-2536-4
Duyck, P., Pynoo, B., Devolder, P., Voet, T., Adang, L., Ovaere, D., & Vercruysse, J. (2010). Monitoring the PACS implementation process in a large university hospital-discrepancies between radiologists and physicians. Journal of Digital Imaging, 23(1), 73-80. doi:10.1007/s10278-008-9163-7
Escobar-Rodríguez, T., & Carvajal-Trujillo, E. (2014). Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model. Tourism Management, 43, 70-88. doi:10.1016/j.tourman.2014.01.017
Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. Akron, OH: University of Akron Press.
Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451-474. doi:10.1016/S1071-5819(03)00111-3
Fishbein, M., & Ajzen, I. (1975). Belief attitude, intention and behavior. An introduction to theory and research. Reading, MA: Addison-Wesley.
Gargallo López, B., Suárez Rodríguez, J., & Almerich Cerveró, G. (2006). La influencia de las actitudes de los profesores en el uso de las nuevas tecnologías. Revista Espanola de Pedagogia, 64(233), 45-66.
Gefen, D., Rigdon, E. E., & Straub, D. (2011). An update and extension to SEM guidelines for administrative and social science research. MIS Quarterly, 35(2), 3-14. doi:10.1016/j.lrp.2013.01.001
Gibson, C.F. (2004). IT-enabled business change: An approach to understanding and managing risk. MIT Sloan Working Paper No. 4520-04; CISR Working Paper No. 346. doi:10.2139/ssrn.644922
Gupta, V. K., Huang, R., & Niranjan, S. (2010). A longitudinal examination of the relationship between team leadership and performance. Journal of Leadership & Organizational Studies, 17(4), 335-350. doi:10.1177/1548051809359184
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414-433. doi:10.1007/s11747-011-0261-6
Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115- 135. doi:10.1007/s11747-014-0403-8
Hsieh, P. J. (2015). Healthcare professionals’ use of health clouds: Integrating technology acceptance and status quo bias perspectives. International Journal of Medical Informatics, 84(7), 512-523. doi:10.1016/j.ijmedinf.2015.03.004
Huang, T. C. K., Liu, C. C., & Chang, D. C. (2012). An empirical investigation of factors influencing the adoption of data mining tools. International Journal of Information Management, 32(3), 257-270.
doi:10.1016/j.ijinfomgt.2011.11.006
Hung, Y. H., Wang, Y. S., & Chou, S. C. T. (2007). User acceptance of e-government services. PACIS 2007 roceedings, 97. Retrieved from http://aisel.aisnet.org/pacis2007
Ives, B., & Olson, M. H. (2008). User involvement and MIS success: A review of research. Management Science, 30(5), 586-603. doi:/10.1287/mnsc.30.5.586
Khatibian, N., Hasan gholoi pour, T., & Abedi Jafari, H. (2010).Measurement of knowledge management maturity level within organizations. Business Strategy Series, 11(1), 54-70. doi:10.1108/17515631011013113
Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: The role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. doi:10.1016/j.dss.2007.07.001
Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567-582. doi:10.2307/20650309
Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: An empirical investigation. Decision Support Systems, 43(1), 111-126. doi:10.1016/j.dss.2005.05.009
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of E-Collaboration, 11(4), 1-10. doi:10.4018/ijec.2015100101
Kock, N., & Lynn, G. S. (2012). Lateral collinearity and misleading results in variance-based SEM : An illustration and recommendations. Journal of the Association for Information Systems, 13(7), 546-580.
Kwon, O., Lee, N., & Shin, B. (2014). Data quality management, data usage experience and acquisition intention of big data analytics. International Journal of Information Management, 34(3), 387-394. /doi:10.1016/j.ijinfomgt.2014.02.002
Lai, Y., Sun, H., & Ren, J. (2017). Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. International Journal of Logistics Management, 29(2), 676-703. doi. 10.1108/IJLM-06-2017-0153
Lapointe, L., & Rivard, S. (2007). A triple take on information system implementation. Organization Science, 18(1), 89-107. doi:10.1287/orsc.1060.0225
Laumer, S., Maier, C., Eckhardt, A., & Weitzel, T. (2016). User personality and resistance to mandatory information systems in organizations: A theoretical model and empirical test of dispositional resistance to change. Journal of Information Technology, 31(1), 67-82. doi:10.1057/jit.2015.17
Lee, J. H., & Song, C. H. (2013). Effects of trust and perceived risk on user acceptance of a new technology service. Social Behavior and Personality: An International Journal, 41(4), 587-597. doi:10.2224/sbp.2013.41.4.587
Liu, X., Shin, H., & Burns, A. C. (2019). Examining the impact of luxury brand’s social media marketing on customer engagement: Using big data analytics and natural language processing. Journal of Business Research. Advanced online publication. doi: 10.1016/j.jbusres.2019.04.042
Martins, C., Oliveira, T., & Popovič, A. (2014). Understanding the internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management, 34(1), 1-13. doi:10.1016/j.ijinfomgt.2013.06.002
McAfee, A., & Brynjolfsson, E. (2012). Big Data: The management revolution. Harvard Business Review, 90(10), 61-68. https://doi.org/10.1007/s12599-013-0249-5
MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly,35(2), 293-334. doi:10.2307/23044045
Norzaidi, M. D., Salwani, M. I., Chong, S. C., & Rafidah, K. (2008). A study of intranet usage and resistance in Malaysia’s port industry. Journal of Computer Information Systems, 49(1), 37-47. doi:10.1080/08874417.2008.11645304
Nunnally, J. C. (1978). Psychometric theory (2nd edit.) Hillsdale, NJ:McGraw-hill.
Palmatier, R. W., & Martin, K. D. (2019). Understanding and valuing customer data. In R. W. Palmatier & K. D. Martin, The Intelligent Marketer’s Guide to Data Privacy (pp. 73-92). Palgrave Macmillan, Cham. doi:10.1007/978-3-030-03724-6
Paulk, M. C., Curtis, B., Chrissis, M. B., & Weber, C. V. (1993).Capability maturity model, version 1.1. IEEE Software, 10(4), 18-27. doi:10.1109/52.219617
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. doi:10.1037/0021-9010.88.5.879
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539-569.
doi:10.1146/annurev-psych-120710-100452
Poon, E. G., Blumenthal, D., Jaggi, T., Honour, M. M., Bates, D.W., & Kaushal, R. (2004). Overcoming barriers to adopting and implementing computerized physician order entry systems in U.S. hospitals. Health Affairs, 23(4), 184-190. doi:10.1377/hlthaff.23.4.184
Rahman, N. (2016). Factors affecting Big Data technology adoption, 0–29. Student Research Symposium. Retrieved from http://pdxscholar.library.pdx.edu/studentsymposium%5Cnhttp://pdxscholar.library.pdx.edu/studentsymposium/2016/Presentations/10
Rehman, M. H. U., Chang, V., Batool, A., & Wah, T. Y. (2016). Big data reduction framework for value creation in sustainable enterprises. International Journal of Information Management, 36(6), 917-928.
doi:10.1016/j.ijinfomgt.2016.05.013
Ringle, C. M., Wende, S., & Becker, J. M. (2015). SmartPLS. “SmartPLS 3.” Boenningstedt: SmartPLS GmbH. Retrieved from http://www.smartpls.com
Roldán, J. L., & Sánchez-Franco, M. J. (2012). Variance-based structural equation modeling: Guidelines for using partial least squares in information systems research. In M. Mora, O. Gelman, A.L. Steenkamp & M. Raisinghnani, Research methodologies, innovations and philosophies in software systems engineering and information systems. IGI-Global. doi:10.4018/978-1-4666-0179-6.ch010
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. https://doi.org/10.1016/j. jbusres.2016.08.001
Straub, D., Boudreau, M., & Gefen, D. (2004). Validation guidelines for IS positivist research. Communications of the Association for Information Systems, 13(24), 380-427. doi:10.17705/1CAIS.01324
Sun, Z., Sun, L., & Strang, K. (2018). Big Data analytics services for enhancing business intelligence. Journal of Computer Information Systems, 58(2), 162-169. doi:10.1080/08874417.2016.1220239
Urwiler, R., & Frolick, M. N. (2008). The IT value hierarchy: Using Maslow’s hierarchy of needs as a metaphor for gauging the maturity level of information technology use within competitive organizations. Information Systems Management, 25(1), 83-88.
doi:10.1080/10580530701777206
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39 (2), 273-315.
doi:10.1111/j.1540-5915.2008.00192.x
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field
studies. Management Science, 46(2), 186-204. doi:10.1287/
mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. doi:10.2307/30036540
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.
doi:10.1017/CBO9781107415324.004
Verma, S., Bhattacharyya, S. S., & Kumar, S. (2018). An extension of the technology acceptance model in the big data analytics system implementation environment. Information Processing and Management, 54(5), 791-806. doi:10.1016/j.ipm.2018.01.004
Wedel, M., & Kannan, P. K. (2016). Marketing analytics for datarich environments. Journal of Marketing, 80(November), 97-121.doi:10.1509/jm.15.0413
Watson, H. J. (2019). Update tutorial: Big Data analytics: Concepts,technology, and applications. Communications of the Association for Information Systems, 44(1), 364–379. doi:10.17705/1CAIS.04421
Wright, L. T., Robin, R., Stone, M., & Aravopoulou, D. E. (2019). Adoption of Big Data technology for innovation in B2B marketing. Journal of Business-to-Business Marketing. Advanced online publication. doi:
1080/1051712X.2019.1611082
Wu, Y. L., Tao, Y. H., & Yang, P. C. (2007). Using UTAUT to explore the behavior of 3G mobile communication users. IEEM 2007: 2007 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 199-203). doi:10.1109/IEEM.2007.4419179
Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018). Influence of big data adoption on manufacturing companies’ performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change, 137(March), 199-210.
doi:10.1016/j.techfore.2018.07.043
Yaqoob, I., Hashem, I. A. T., Gani, A., Mokhtar, S., Ahmed, E., Anuar, N. B., & Vasilakos, A. V. (2016). Big data: From beginning to future. International Journal of Information Management, 36(6), 1231-1247.
doi:10.1016/j.ijinfomgt.2016.07.009
Yu, C.-S. (2012). Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of Electronic Commerce Research, 13(2), 104–121.
Zicari, R. (2014). Big data: Challenges and opportunities. In R. Akerkar (Ed.), Big Data Computing (pp. 103-128). Boca Raton, FL: CRC Press. doi:10.1201/b16014-5
