BazEkon - Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie

BazEkon home page

Meny główne

Autor
Paulino Emmanuel P. (PLM Business Graduate School, Philippines)
Tytuł
Amplifying Organizational Performance from Business Intelligence: Business Analytics Implementation in the Retail Industry
Źródło
Journal of Entrepreneurship, Management and Innovation (JEMI), 2022, vol. 18, nr 2, s. 69-104, tab., rys., bibliogr. s. 95-102
Tytuł własny numeru
Quantitative Research in Economics and Management Sciences
Słowa kluczowe
Modelowanie równań strukturalnych, Wywiad gospodarczy, Analiza biznesowa, Przedsiębiorstwo
Structural Equation Modeling, Business intelligence, Business analysis, Enterprises
Uwagi
Klasyfikacja JEL: M15, O32
streszcz., summ.
Abstrakt
CEL: Koncepcja analityki biznesowej (BA) i business intelligence (BI) dopiero pojawia się na Filipinach. Ponieważ są to nowe koncepcje, ważne jest zbadanie ich wpływu na wydajność organizacji i wskaźniki wydajności w branży biznesowej. Celem jest zbadanie wpływu analityki biznesowej generującej business intelligence oraz jej wpływu na wydajność organizacji poprzez opracowanie modelu strukturalnego. W konsekwencji ustalono również wpływ wydajności organizacji na inne mierniki wydajności. METODYKA: Wykorzystano modelowanie metodą najmniejszych kwadratów - równania strukturalne. Zaproponowano model pokazujący, w jaki sposób business intelligence, generowany przez biznesowe BA, wpływa na wydajność organizacji, co w konsekwencji prowadzi do poprawy wydajności marketingowej, finansowej i procesów biznesowych. Przeprowadzono ankietę wśród analityków biznesowych i menedżerów wykonawczych firm detalicznych, które wdrażają BA już od co najmniej trzech lat. WYNIKI: Możliwości BA mają znaczący pozytywny wpływ na poziom BI. BI ma znaczący pozytywny wpływ na wydajność organizacji. Jednak wynik analizy moderacji wskazał, że poziom gotowości do wdrożenia BA nie może być uważany za czynnik moderujący związek między BI a wydajnością organizacji. IMPLIKACJE: Spośród różnych możliwości BA, system wspomagania decyzji i zarządzanie procesami biznesowymi okazały się najbardziej korzystnymi funkcjami w generowaniu BI. BI zwiększa wydajność organizacyjną, a w konsekwencji poprawia wydajność marketingu i procesów biznesowych w firmach detalicznych. Jednak gotowość do wdrożenia BA nie wpływa znacząco na to, jak BI poprawia wydajność organizacji. Ogólnie rzecz biorąc, zaleca się, aby w celu zwiększenia wydajności marketingu i procesów biznesowych, firmy detaliczne skupiły się na możliwościach BA systemu wspomagania decyzji i zarządzania procesami biznesowymi. ORYGINALNOŚĆ I WARTOŚĆ: jest to pierwsze badanie empiryczne na Filipinach, w którym oceniano wpływ analityki biznesowej i analizy biznesowej na wydajność organizacji. To badanie jest oryginalne w określaniu, jakie możliwości BA generują BI, co przekłada się na poprawę wydajności organizacji. Badanie to jest również wyjątkowe w określaniu, które kluczowe wskaźniki wydajności zostały znacznie ulepszone w wyniku jego wdrożenia. Może to służyć jako realne odniesienie dla innych badaczy zainteresowanych analityką biznesową i innymi technologiami dotyczącymi zarządzania danymi stosowanymi w operacjach biznesowych. (abstrakt oryginalny)

PURPOSE: The concept of business analytics (BA) and business intelligence (BI) is just emerging in the Philippines. Since these are new concepts, it is important to investigate their impact on organizational performance and the performance metrics in business industry. The aim of this study is to examine the impact of business analytics generating business intelligence and how it affects organizational performance by developing a structural model. Consequently, the impact of organizational performance on other performance metrics was also established. METHODOLOGY: The partial least squares - structural equation modeling was utilized, which proposed a model that shows how business intelligence, generated by business BA, affects organizational performance, which consequently leads to improved marketing, financial, and business process performance. A survey was conducted on business analysts and executive managers of retail companies that have already been implementing BA for at least three years. FINDINGS: BA capabilities have a significant positive effect on the level of BI. BI has a significant positive effect on organizational performance. However, the result of the moderation analysis indicated that the level of readiness for BA implementation could not be considered a moderating factor on the relationship between BI and organizational performance. IMPLICATIONS: Out of the different BA capabilities, the decision support system and business process management were found to be the most beneficial functions in generating BI. BI amplifies organizational performance and consequently improves the marketing and business process performance of retail firms. However, the readiness for BA implementation does not significantly affect how BI improves organizational performance. Overall, it is recommended that in order to enhance marketing and business process performance, retail firms should focus on the BA capabilities of decision support system and business process management. ORIGINALITY AND VALUE: This would be the first empirical study in the Philippines which has assessed how business analytics and business intelligence impact organizational performance. This study is original in determining what BA capabilities generate BI, which translates to improved organizational performance. This study is also unique in defining what key performance metrics are much improved as a result of its implementation. This may serve as a viable reference for other researchers interested in business analytics and other technology about data management applied in business operations. (original abstract)
Pełny tekst
Pokaż
Bibliografia
Pokaż
  1. Ajah, I.A., & Nweke, H.F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. Big Data Cognitive Computing, 3(32). https://doi.org/10.3390/bdcc3020032
  2. Aktera, S., Fosso, S., Angappa, W., Dubeyd, R., & Childee, S.J. (2016) How to improve firm performance using big data analytics capability and business strategy alignment. International Journal of Production Economics, 182, 113-131. https://doi.org/10.1016/j.ijpe.2016.08.018.
  3. Ashrafi, A., Ravasan, A.Z., Trkman, P., & Afshari, S. (2019). The role of business analytics capabilities in bolstering firms' agility and performance. International Journal of Information Management, 47, 1-15, https://doi.org/10.1016/j.ijinfomgt.2018.12.005
  4. Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of Business Research, 96, 228-237. https://doi.org/10.1016/j.jbusres.2018.11.028
  5. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108
  6. Barton, D., & Court, D. (2012). Making advanced analytics work for you. Harvard Business Review, 90(10), 79-83.
  7. Bedeley, R.T., Ghoshal, T., Iyer, L.S., & Bhadury, J. (2016). Business analytics and organizational value chains: A relational mapping. Journal of Computer Information Systems, 58(2), 151-161. https://doi.org/10.1080/08874417.2016.1220238
  8. Beltran, B. B. (2018, May 31). Big data, a good or bad omen for Philippine business. Business World. Retrieved from https://www.bworldonline.com/big-data-a-good-or-bad-omen-for-philippine-business/
  9. Carter, S.M., & Greer, C.R. (2013). Strategic leadership: Values, styles, and firm performance. Journal of Leadership & Organizational Studies, 20(4), 375-393. https://doi.org/10.1177/1548051812471724
  10. Chae, B.K., Yang, C., Olson, D., & Sheu, C. (2014). The impact of advanced analytics and data accuracy on operational performance: A contingent resource based theory (RBT) perspective. Decision Support Systems, 59, 119-126, https://doi.org/10.1016/j.dss.2013.10.012
  11. Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36, 1165-1188. https://doi.org/10.2307/41703503
  12. Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2013). IT capability and organizational performance: The roles of business process agility and environmental factors. European Journal of Information Systems, 23(3), 326-342. https://doi.org/10.1057/ejis.2013.4
  13. Chin, W., Thatcher, J., & Wright, R. (2012). Assessing common method bias: Problems with the ULMC technique. MIS Quarterly, 36(3), 1003-1019. https://doi.org/10.2307/41703491
  14. Chin, W. W., Kim, Y. J., & Lee, G. (2013). Testing the differential impact of structural paths in PLS analysis: A bootstrapping approach. In H. Abdi, W. W. Chin, V.Esposito Vinzi, G. Russolillo, & L. Trinchera (Eds.), New Perspectives in Partial Least Squares and Related Methods (pp. 221-229). New York: Springer. https://doi.org/10.1007/978-1-4614-8283-3_15
  15. Chin, W.W., Marcolin, B.L., & Newsted, P.R. (2003) A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2),189-217. https://doi.org/10.1287/isre.14.2.189.16018
  16. Cohen, L., Manion, L., & Morrison, K. (2017). Research Methods in Education (8th ed.). Abingdon, United Kingdom: Routledge.
  17. Côrte-Real, N., Oliveira, T., & Ruivo, P. (2017). Assessing business value of big data analytics in European firms. Journal of Business Research, 70, 379-390. https://doi.org/10.1016/j.jbusres.2016.08.011
  18. Creswell, J. W., & Creswell, D. J. (2018). Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (5th ed.). Thousand Oaks, California: SAGE Publications, Inc.
  19. Davenport, T.H., & Harris J.H., (2007). Competing on Analytics: The New Science of Winning. Boston, MA: Harvard Business School Press.
  20. Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186-195. https://doi.org/10.1016/j.jbusres.2018.05.013
  21. Digal, L.N. (2011). Market power in the Philippine retail and processed food industry. Journal of International Food & Agribusiness Marketing, 23(4), 289-309.https://doi.org/10.1080/08974438.2011.621818
  22. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly, 39(2), 297-316. https://doi.org/10.1080/08974438.2011.621818
  23. Elbashir, M. Z., Collier, P. A., & Davern, M. J. (2008). Measuring the effects of business intelligence systems: The relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135-153. https://doi.org/10.1016/j.accinf.2008.03.001
  24. Eriksson, M. (2014, November). The methodology of predictive design analysis. In ASME International Mechanical Engineering Congress and Exposition (IMECE2014-37141, V011T14A023). American Society of Mechanical Engineers. https://doi.org/10.1115/IMECE2014-37141
  25. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293-314. https://doi.org/10.1093/nsr/nwt032
  26. Foley, R., & Guillemette, M. G. (2010). What is business intelligence? Organizational Applications of Business Intelligence Management, 1(4), 52-75. https://doi.org/10.4018/978-1-4666-0279-3.ch005
  27. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312
  28. García-Gómez, B., Gutiérrez-Arranz, A. M., & Gutiérrez-Cillán, J. (2012). Exploring the influence of three types of grocery retailer loyalty programs on customer affective loyalty. The International Review of Retail, Distribution and Consumer Research, 22(5), 547-561. https://doi.org/10.1080/09593969.2012.711254
  29. Ghasemaghaei, M. (2019). Are firms ready to use big data analytics to create value? The role of structural and psychological readiness. Enterprise Information Systems, 13(5), 650-674. https://doi.org/10.1080/17517575.2019.1576228
  30. Glaister, K., Dincer, O., Tatoglu, E., Demirbag, M., & Zaim, S. (2008). A causal analysis of formal strategic planning and firm performance: Evidence from an emerging country. Management Decision, 46(3), 365-391. https://doi.org/10.1108/00251740810863843
  31. Grant, R.M. (1996): Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(S2), 109-122. https://doi.org/10.1002/smj.4250171110
  32. Grossmann, W., & Rinderle-Ma, S. (2015). Fundamentals of Business Intelligence. Berlin, Heidelberg: Springer. https://doi.org/10.1007/978-3-662-46531-8
  33. Grossman, R., & Siegel, K. (2014). Organizational models for big data and analytics. Journal of Organization Design, 1(3), 20-25. https://doi.org/10.7146/jod.9799
  34. Grover, V., Chiang, R.H.L., Liang, T., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423. https://doi.org/10.1080/07421222.2018.1451951
  35. Gruber, M., Heinemann, F., Brettel, M., & Hungeling, S. (2010). Configurations of resources and capabilities and their performance implications: An exploratory study on technology ventures. Strategic Management Journal, 31(12), 1337-1356. https://doi.org/10.1002/smj.865
  36. Gunasekaran, A., Papadopoulos, T., Dubey, R., Wamba, S.F., Childe, S.J., Hazen, B., & Akter, S. (2017). Big data and predictive analytics for supply chain and firm performance. Journal of Business Research, 70, 308-317. https://doi.org/10.1016/j.jbusres.2016.08.004.
  37. Gürdür D., El-khoury, J., & Törngren, M. (2019). Digitalizing Swedish industry: What is next?: Data analytics readiness assessment of Swedish industry, according to survey results. Computers in Industry, 105, 153-163. https://doi.org/10.1016/j.compind.2018.12.011.
  38. Hofmann, E., & Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: A conceptual analysis. The International Journal of Logistics Management, 29(2), 739-766. https://doi.org/10.1108/IJLM-04-2017-0088
  39. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2009). Multivariate Data Analysis. Hallbergmoos, Germany: Pearson.
  40. Hair, J.F., Sarstedt, M., Hopkins, L., & G. Kuppelwieser, V. (2014). Partial least squares structural equation modeling (PLS-SEM). An emerging tool in business research. European Business Review, 26(2), 106-121. https://doi.org/10.1108/EBR-10-2013-0128
  41. 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. https://doi.org/10.1007/s11747-011-0261-6
  42. Hair, J.R., Hult, G.T., Ringle C.M., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (2nd ed.). Thousand Oaks, CA: Sage Publications, Inc.
  43. Hair. J.F., Celsi, M.W., Ortinau, D.J., & Bush, R.P. (2017). Essentials of Marketing Research (2nd ed). New York, United States: McGraw Hill Education.
  44. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
  45. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen, D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common beliefs and reality about partial least squares: Comments on Rönkkö & Evermann. Organizational Research Methods, 17(2), 182-209. https://doi.org/10.1177/1094428114526928
  46. Hogan, S.J., & Coote, L.V. (2014). Organizational culture, innovation, and performance: A test of Schein's model. Journal of Business Research, 67(8), 1609-1621. https://doi.org/10.1016/j.jbusres.2013.09.007.
  47. Hoyle, R.H. (2012). Handbook of Structural Equation Modeling. New York: The Guilford Press.
  48. Jain, S., Narayanan, A., & Lee, Y. T. (2019). Infrastructure for model based analytics for manufacturing. In N. Mustafee, K.-H. G. Bae, S. Lazarova-Molnar, M. Rabe, C. Szabo, P. Haas, & Y.-J. Son (Eds.), Winter Simulation Conference (pp. 2037-2048). New York: IEEE Press. https://doi.org/10.1109/WSC40007.2019.9004893.
  49. Kaisler, S., Armour, F., Espinosa, J., & Money, W. (2013). Big data: Issues and challenges moving forward. In 46th Hawaii International Conference on System Sciences (pp. 995-1004). New York: IEEE. https://doi.org/10.1109/hicss.2013.645
  50. Kaplan, S., Schenkel, A., Krogh, G.V., & Weber, C. (2001). Knowledge-based theories of the firm in strategic management: A review and extension. International Journal of Project Management, 25, 143-158.
  51. Kowalczyk, M., & Buxmann, P. (2014). Big data and information processing in organizational decision processes: A multiple case study. Business and Information Systems Engineering, 5(1), 267-278. https://doi.org/10.1007/s12599-014-0341-5
  52. Kumar, R. (2011). Research Methodology: A Step-By-Step Guide for Beginners. Thousand Oaks, Canada: SAGE Publications.
  53. Kyriazos, T. A. (2018). Applied psychometrics: Sample size and sample power considerations in factor analysis (EFA, CFA) and SEM in general psychology. Scientific Research Publishing, 9, 2207-2230. https://doi.org/10.4236/psych.2018.98126
  54. Laerd Dissertation. (2012). Purposive Sampling. Retrieved from https://dissertation.laerd.com/purposive-sampling.php
  55. Larson, D., & Chang, V. (2016). A review and future direction of agile, business intelligence, analytics and data science. International Journal of Information Management, 36(5), 700-710. https://doi.org/10.1016/j.ijinfomgt.2016.04.013
  56. Laursen, G. H. N., & Thorlund, J. (2017). Business Analytics for Managers (2nd ed.). Hoboken, NJ, United States: Wiley.
  57. Leavy, P. (2017). Research Design: Quantitative, Qualitative, Mixed Methods, Arts-Based, and Community-Based Participatory Research Approaches. New York, NY: The Guilford Press.
  58. Lee, M. T., & Raschke, R. L. (2016). Understanding employee motivation and organizational performance: Arguments for a set-theoretic approach. Journal of Innovation & Knowledge, 1(3), 162-169. https://doi.org/10.1016/j.jik.2016.01.004
  59. Li, S., Ragu-Nathan, B., Ragu-Nathan, T.S., & Subba Rao, S. (2006). The impact of supply chain management practices on competitive advantage and firm performance. The International Journal of Management Science, 34(2), 107-124, https://doi.org/10.1016/j.omega.2004.08.002
  60. MacKenzie, S. B., & Podsakoff, P. M. (2012). Common method bias in marketing: Causes, mechanisms, and procedural remedies. Journal of Retailing, 88(4), 542-555. https://doi.org/10.1016/j.jretai.2012.08.001
  61. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute. Retrieved from https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/big-data-the-next-frontier-for-innovation
  62. Martin, W. E., & Bridgmon, K. D. (2012). Quantitative and Statistical Research Methods: From Hypothesis to Results (1st ed.). Hoboken, NJ, United States: Wiley.
  63. Mashingaidze, K., & Backhouse, J. (2017). The relationships between definitions of big data, business intelligence and business analytics: A literature review. International Journal of Business Information Systems, 26(4), 488-501. https://doi.org/10.1504/ijbis.2017.10008185
  64. McAfee, A., Brynjolfsson, E., Davenport, T. H., Patil, D. J., & Barton, D. (2012). Big data: The management revolution. Harvard Business Review, 90(10), 60-68.
  65. Min, H. (2016). Global Business Analytics Models: Concepts and Applications in Predictive, Healthcare, Supply Chain, and Finance Analytics. New Jersey, NJ: Pearson Education.
  66. Mishra, B.K., Hazra, D., Tarannum, K., & Kumar, M. (2016). Business intelligence using data mining techniques and business analytics. International Conference System Modeling & Advancement in Research Trends (SMART), 84-89, https://doi.org/10.1109/SYSMART.2016.7894496.
  67. Mithas, S., Ramasubbu, N., & Sambamurthy, V. (2011). How information management capability influences firm performance. MIS Quarterly, 35, 237-256. https://doi.org/10.2307/23043496
  68. Munoz, J. M., Raven, P.V., & Welsh, D. (2006). Retail service quality expectations and perceptions among Philippine small medium enterprises. Journal of Developmental Entrepreneurship, 11(2), 145-156. https://doi.org/10.1142/s1084946706000362
  69. Nemati, H., & Udiavar, A. (2012). Organizational readiness for implementation of supply chain analytics. Proceedings of the Eighteenth Americas Conference on Information Systems. https://doi.org/amcis2012/proceedings/DecisionSupport/26
  70. Nunnally, J. C., & Bernstein, J. H. (1994). Psychometric Theory (3rd ed.). New York, United States: McGraw-Hill.
  71. Nunnally, J. C. (1978). Psychometric Theory (2nd ed.). New York, United States: McGraw-Hill.
  72. Ordanini, A., & Rubera, G. (2010). How does the application of an IT service innovation affect firm performance? A theoretical framework and empirical analysis on e-commerce. Information & Management, 47(1), 60-67. https://doi.org/10.1016/j.im.2009.10.003
  73. Parra V.M., & Halgamuge M.N. (2018) Performance evaluation of big data and business intelligence open source tools: Pentaho and Jaspersoft. In N. Dey, A. Hassanien, C. Bhatt, A. Ashour, & S. Satapathy (Eds), Internet of Things and Big Data Analytics Toward Next-Generation Intelligence. Studies in Big Data, 30. Cham: Springer. https://doi.org/10.1007/978-3-319-60435-0_6
  74. Peterson, R. A., & Kim, Y. (2013). On the relationship between coefficient alpha and composite reliability. Journal of Applied Psychology, 98(1), 194-198. https://doi.org/10.1037/a0030767
  75. Prahalad, C., & Ramaswamy, V. (2004). Co-creating unique value with customers. Strategy & Leadership, 32(3), 4-9. https://doi.org/10.1108/10878570410699249
  76. Provost, F., & Fawcett, T. (2013). Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Sebastopol, California: O'Reilly Media, Inc.
  77. Rabo, J., & Ang, M. (2018). Determinants of customer satisfaction in a Philippine retail chain. [Conference Presentation]. DLSU Research Congress. Retrieved from https://www.dlsu.edu.ph/wp-content/uploads/pdf/conferences/research-congress-proceedings/2018/ebm-13.pdf
  78. Rahman, N. R. A., Othman, M. Z. F., Ab Yajid, M. S., Rahman, S. F. A., Yaakob, A. M., Masri, R., ...& Ibrahim, Z. (2018). Impact of strategic leadership on organizational performance, strategic orientation and operational strategy. Management Science Letters, 8, 1387-1398. https://doi.org/10.5267/j.msl.2018.9.006
  79. Ramanathan, R., Philpott, E., Duan, Y., & Cao, G. (2017). Adoption of business analytics and impact on performance: A qualitative study in retail. Production Planning & Control, 28(11-12), 985-998. https://doi.org/10.1080/09537287.2017.1336800
  80. Richland, L. E., Kornell, N., & Kao, L. S. (2009). The pretesting effect: Do unsuccessful retrieval attempts enhance learning? Journal of Experimental Psychology: Applied, 15(3), 243-257. https://doi.org/10.1037/a0016496
  81. Rothman, D. (2014). Lead Generation for Dummies (1st ed.). Hoboken, NJ, United States: Wiley.
  82. Kearns, G., & Sabherwal, R. (2007). Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcome, and consequences. Journal of Management Information Systems 23(3), 129-162. https://doi.org/10.2753/MIS0742-1222230306.
  83. Sabherwal, R., & Fernandez, I. B. (2011). Business Intelligence: Practices, Technologies, and Management. Hoboken, NJ, United States: Wiley.
  84. Santhanam, R., & Hartono, E. (2003). Issues in linking information technology capability to firm performance. MIS Quarterly, 27(1), 125-153. https://doi.org/10.2307/30036521
  85. Sharma, R., Reynolds, P., Scheepers, R., Seddon, P., & Shanks, G. (2010). Business analytics and competitive advantage: A review and a research agenda. Frontiers in Artificial Intelligence and Applications, 212, 187-198. https://doi.org/10.3233/978-1-60750-577-8-187.
  86. Stubbs, E. (2013). Delivering Business Analytics: Practical Guidelines for Best Practice. Hoboken, NJ, United States: Wiley.
  87. Tan, K. H. (2018). Managerial perspectives of big data analytics capability towards product innovation. Strategic Direction, 34(8), 33-35. https://doi.org/10.1108/sd-06-2018-0134
  88. Troilo, M., Bouchet, A., Urban, T. L., & Sutton, W. A. (2016). Perception, reality, and the adoption of business analytics: Evidence from north American professional sport organizations. Omega, 59, 72-83. https://doi.org/10.1016/j.omega.2015.05.011
  89. Ouahilal, M., El Mohajir, M., Chahhou, M., & El Mohajir, B.E. (2016). A comparative study of predictive algorithms for business analytics and decision support systems: Finance as a case study. In International Conference on Information Technology for Organizations Development (pp. 1-6). https://doi.org/10.1109/IT4OD.2016.7479258
  90. Visinescu, L. L., Jones, M. C., & Sidorova, A. (2016). Improving decision quality: The role of business intelligence. Journal of Computer Information Systems, 57(1), 58-66. https://doi.org/10.1080/08874417.2016.1181494
  91. Vossen, G. (2014). Big data as the new enabler in business and other intelligence. Vietnam Journal of Computer Science, 1(1), 3-14. https://doi.org/10.1007/s40595-013-0001-6
  92. Waljee, A. K., Higgins, P. D., & Singal, A. G. (2014). A primer on predictive models. Clinical and Translational Gastroenterology, 5(1), e44. https://doi.org/10.1038/ctg.2013.19
  93. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How 'big data'can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. https://doi.org/10.1016/j.ijpe.2014.12.031
  94. Williams, S. (2016). Business Intelligence Strategy and Big Data Analytics: A General Management Perspective. Amsterdam, Netherlands: Morgan Kaufmann. https://doi.org/10.1016/C2015-0-01169-8
  95. Wickramasinghe, N., & Lubitz, D. V. (2007). Knowledge-Based Enterprise: Theories and Fundamentals. Hershey, Pennsylvania: IGI Global.
  96. Wixom, B., Yen, B., & Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive, 12, 111-123.
  97. Yamada, J., Stevens, B., Sidani, S., Watt-Watson, J., & de Silva, N. (2010). Content validity of a process evaluation checklist to measure intervention implementation fidelity of the EPIC intervention. World views on Evidence-Based Nursing, 7(3), 158-164. https://doi.org/10.1111/j.1741-6787.2010.00182.x
  98. Zamanzadeh, V., Ghahramanian, A., Rassouli, M., Abbaszadeh, A., Alavi-Majd, H., & Nikanfar, A. R. (2015). Design and implementation content validity study: Development of an instrument for measuring Patient-Centered communication. Journal of Caring Sciences, 4(2), 165-178. https://doi.org/10.15171/jcs.2015.017^
Cytowane przez
Pokaż
ISSN
2299-7075
Język
eng
URI / DOI
https://doi.org/10.7341/20221823
Udostępnij na Facebooku Udostępnij na Twitterze Udostępnij na Google+ Udostępnij na Pinterest Udostępnij na LinkedIn Wyślij znajomemu