An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32845
An Empirical Investigation of Big Data Analytics: The Financial Performance of Users versus Vendors

Authors: Evisa Mitrou, Nicholas Tsitsianis, Supriya Shinde

Abstract:

In the age of digitisation and globalisation, businesses have shifted online and are investing in big data analytics (BDA) to respond to changing market conditions and sustain their performance. Our study shifts the focus from the adoption of BDA to the impact of BDA on financial performance. We explore the financial performance of both BDA-vendors (business-to-business) and BDA-clients (business-to-customer). We distinguish between the five BDA-technologies (big-data-as-a-service (BDaaS), descriptive, diagnostic, predictive, and prescriptive analytics) and discuss them individually. Further, we use four perspectives (internal business process, learning and growth, customer, and finance) and discuss the significance of how each of the five BDA-technologies affect the performance measures of these four perspectives. We also present the analysis of employee engagement, average turnover, average net income, and average net assets for BDA-clients and BDA-vendors. Our study also explores the effect of the COVID-19 pandemic on business continuity for both BDA-vendors and BDA-clients.

Keywords: BDA-clients, BDA-vendors, big data analytics, financial performance.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.7787147

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 500

References:


[1] Abbasi, A.; Sarker, S.; and Chiang, R.H. Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17, 2 (February 2016), i–xxxii.
[2] AI-Business. (2020). Descriptive, Predictive & Prescriptive Analytics: What are the differences? Retrieved from https://aibusiness.com/author.asp?section_id=796&doc_id=763806
[3] AIM. (2020). How Amazon Is Using AI To Better Understand Customer Search Queries. Retrieved from https://analyticsindiamag.com/how-amazon-is-using-ai-to-better-understand-customer-search-queries/
[4] Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, 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.
[5] Ali, K., Hamilton, M., Thevathayan, C., & Zhang, X. (2021). Big Social Data as a Service (BSDaaS): A Service Composition Framework For Social Media Analysis.
[6] AnalytixLabs. (2021). What Are Different Types of Business Analytics? Retrieved from https://www.analytixlabs.co.in/blog/types-of-business-analytics/
[7] Andersen, D. L., Ashbrook, C. S. A., & Karlborg, N. B. (2020). Significance of big data analytics and the internet of things (IoT) aspects in industrial development, governance and sustainability. International Journal of Intelligent Networks, 1, 107-111.
[8] Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. K. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44. doi:10.1016/j.accinf.2017.03.003
[9] Asghari, S., & Navimipour, N. J. (2018). Nature inspired meta-heuristic algorithms for solving the service composition problem in the cloud environments. International Journal of Communication Systems, 31(12). doi:ARTN e370810.1002/dac.3708
[10] Attaran, M., & Woods, J. (2019). Cloud computing technology: improving small business performance using the Internet. Journal of Small Business & Entrepreneurship, 31(6), 495-519.
[11] Ayaburi, E. W. Y., Maasberg, M., & Lee, J. (2020). Decision Framework for Engaging Cloud-Based Big Data Analytics Vendors. Journal of Cases on Information Technology (JCIT), 22(4), 60-74.
[12] Bani, J. (2020). Looking Towards The Sky: Cloud Computing from a Conceptual to an IT Industry Game Changer. Mountain Plains Journal of Business and Technology, 21(2), 7.
[13] Basole, R.C.; Seuss, C.D.; and Rouse, W.B. IT innovation adoption by enterprises: Knowledge discovery through text analytics. Decision Support Systems, 54, 2 (January 2013), 1044–1054.
[14] Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information Development, 36(1), 78-96. doi:10.1177/0266666918811394
[15] Bhatt, S. K. (2020). Survey on Big Data Analytics: Domain Areas and Features. Paper presented at the 2020 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN).
[16] Brandt, T., Wagner, S., & Neumann, D. (2021). Prescriptive analytics in public-sector decision-making: A framework and insights from charging infrastructure planning. European Journal of Operational Research, 291(1), 379-393.
[17] BusinessBlog. (2021). Top 10 benefits of cloud computing. Retrieved from https://www.future-processing.com/blog/top-10-benefits-of-cloud-computing/
[18] CCG. (2020). Customer Data Analytics and Customer Analysis: The Definitive Guide. Retrieved from https://www.customer.com/blog/retail-marketing/customer-data-analytics-analysis/
[19] Chiang, R., Grover V., Liang, T.P., and Zhang, D (2018) Special Issue: Strategic Value of Big Data and Business Analytics, Journal of Management Information Systems, 35:2, 383-387.
[20] Chen, Y. S., Wu, C. W., Chu, H. H., Lin, C. K., & Chuang, H. M. (2018). Analysis of performance measures in cloud-based ubiquitous SaaS CRM project systems. Journal of Supercomputing, 74(3), 1132-1156. doi:10.1007/s11227-017-1978-x
[21] CI. (2021). Diagnostic Analytics determining "root cause" of certain performance outcomes. Retrieved from https://www.ci-advantage.com/analytics/diagnostic-analytics/
[22] CIO. (2019). What is predictive analytics? Transforming data into future insights. Retrieved from https://www.cio.com/article/3273114/what-is-predictive-analytics-transforming-data-into-future-insights.html
[23] Côrte-Real, N.; Oliveira, T.; and Ruivo, P. Assessing business value of big data analytics in European firms. Journal of Business Research. 70, 4 (2016), 379–390
[24] Dataversity. (2017). Fundamentals of Descriptive Analytics. Retrieved from https://www.dataversity.net/fundamentals-descriptive-analytics/#
[25] Davenport, T.H., and Harris, J.G. Competing on Analytics: The New Science of Winning. Boston: Harvard Business Press, 2007.
[26] Delen, D., & Zolbanin, H. M. (2018). The analytics paradigm in business research. Journal of Business Research, 90, 186-195. doi:10.1016/j.jbusres.2018.05.013
[27] Demirkan, H., & Delen, D. (2013). Leveraging the capabilities of service-oriented decision support systems:Putting analytics and big data in cloud. Decision Support Systems, 55(1), 412–421.
[28] Dezyre. (2021). Types of Analytics: descriptive, predictive, prescriptive analytics. Retrieved from https://www.dezyre.com/article/types-of-analytics-descriptive-predictive-prescriptive-analytics/209?_gl=1* 1r2ekpl*_ga*ODMzNTc1OTk4LjE2MjkyOTU4NDc.*_ga_X4LWJBRP5X*MTYyOTI5NTg0Ni4xLjEuMTYyOTI5NjEzOC4w
[29] Dubey, R., Gunasekaran, A., Childe, S. J., Blome, C., & Papadopoulos, T. (2019). Big Data and Predictive Analytics and Manufacturing Performance: Integrating Institutional Theory, Resource-Based View and Big Data Culture. British Journal of Management, 30(2), 341-361. doi:10.1111/1467-8551.12355
[30] Elashkar, E., Aldeek, F., & Shoukry, A. (2020). Business predictive analysis from business insurance data using business strategic planning techniques. Knowledge Management Research & Practice, 1-10.
[31] Ellul, L., & Buttigieg, R. (2021). Benefits and challenges of applying data analytics in government auditing.
[32] Ezdatamunch. (2019). Financial Reporting and Analysis dashboard and KPIs.
[33] Forbes. (2019). How Much Money Can Businesses Save by Moving to the Cloud? Retrieved from https://www.forbes.com/sites/quora/2019/12/20/how-much-money-can-businesses-save-by-moving-to-the-cloud/?sh=40c0cc32ef43
[34] GCDCP. (2020). Tools and Skills required for Diagnostic Analysis. Retrieved from https://www.gcdcp.org/tools-and-skills-required-for-diagnostic-analysis/
[35] Gorry, G.A., and Scott Morton, M.S. A Framework for Management Information Systems. Cambridge, MA: Massachusetts Institute of Technology, 1971.
[36] Grover, V., Chiang, R. H., Liang, T.-P., & Zhang, D. (2018). Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), 388-423.
[37] Gupta, S., Justy, T., Kamboj, S., Kumar, A., & Kristoffersen, E. (2021). Big data and firm marketing performance: Findings from knowledge-based view. Technological Forecasting and Social Change, 171, 120986.
[38] Gov UK (2022) doi:https://find-and-update.company-information.service.gov.uk/search?q=
[39] Gangwar, H. (2020) Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model. The Electronic Journal of Information Systems Evaluation. 23(1) pp. 45-64. Doi: www.ejise.com
[40] Holmlund, M., Van Vaerenbergh, Y., Ciuchita, R., Ravald, A., Sarantopoulos, P., Ordenes, F. V., & Zaki, M. (2020). Customer experience management in the age of big data analytics: A strategic framework. Journal of Business Research, 116, 356-365. doi:10.1016/j.jbusres.2020.01.022
[41] Hossain, M. A., Akter, S., & Yanamandram, V. (2020). Customer analytics capabilities in the big data spectrum: a systematic approach to achieve sustainable firm performance. In Technological Innovations for Sustainability and Business Growth (pp. 1-17): IGI Global.
[42] Huang, C. K., Wang, T. W., & Huang, Z. Y. (2020). Initial Evidence on the Impact of Big Data Implementation on Firm Performance. Information Systems Frontiers, 22(2), 475-487. doi:10.1007/s10796-018-9872-5
[43] InfoWorld. (2019). Can the cloud save you money? These companies say yes. Retrieved from https://www.infoworld.com/article/3445206/can-the-cloud-save-you-money-these-companies-say-yes.html
[44] Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management.
[45] Jiang, P. H. W., & Wang, W. Y. C. (2022). Comparison of SaaS and IaaS in cloud ERP implementation: the lessons from the practitioners. Vine Journal of Information and Knowledge Management Systems.
[46] Jigsaw Academy. (2021). Diagnostic Analytics: An Overview in 3 Easy Points.
[47] Kamble, S. S., & Gunasekaran, A. (2020). Big data-driven supply chain performance measurement system: a review and framework for implementation. International Journal of Production Research, 58(1), 65-86. doi:10.1080/00207543.2019.1630770
[48] Kaplan, R. S., & Norton, D. P. (1996). Using the balanced scorecard as a strategic management system. Harvard business review, 74(1), 75-&.
[49] Kaplan, R. S., & Norton, D. P. (2005). The balanced scorecard: measures that drive performance. Harvard business review, 83(7), 172.
[50] Kathuria, A., Mann, A., Khuntia, J., Saldanha, T. J. V., & Kauffman, R. J. (2018). A Strategic Value Appropriation Path for Cloud Computing. Journal of Management Information Systems, 35(3), 740-775. doi:10.1080/07421222.2018.1481635
[51] Khade, A. A. (2017). Prescriptive analytics for customer recommendation system-A Review.
[52] Khayer, A., Bao, Y. K., & Nguyen, B. (2020). Understanding cloud computing success and its impact on firm performance: an integrated approach. Industrial Management & Data Systems, 120(5), 963-985. doi:10.1108/Imds-06-2019-0327
[53] Kunc, M., & O'Brien, F. A. (2019). The role of business analytics in supporting strategy processes: Opportunities and limitations. Journal of the Operational Research Society, 70(6), 974-985. doi:10.1080/01605682.2018.1475104
[54] Lal, P., & Bharadwaj, S. S. (2020). Understanding the Drivers of Cloud-Based Service Adoption and Their Impact on the Organizational Performance: An Indian Perspective. Journal of Global Information Management, 28(1), N.PAG-N.PAG. doi:10.4018/JGIM.2020010104
[55] LaValle, S.; Lesser, E.; Shockley, R.; Hopkins, M.S.; and Kruschwitz, N. Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52, 2 (Winter 2011), 21–31.
[56] Lee, S. G., Chae, S. H., & Cho, K. M. (2013). Drivers and inhibitors of SaaS adoption in Korea. International Journal of Information Management, 33(3), 429-440. doi:10.1016/j.ijinfomgt.2013.01.006
[57] Lee, C. S., Cheang, P. Y. S., & Moslehpour, M. (2022). Predictive analytics in business analytics: decision tree. Advances in Decision Sciences, 26(1), 1-29.
[58] Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics: Literature review and research challenges. International Journal of Information Management, 50, 57-70. doi:10.1016/j.ijinfomgt.2019.04.003
[59] Loon, L. K., & Peing, L. C. (2019). Big Data and Predictive Analytics Capabilities: A Review of Literature on Its Impact on Firm’s Financial Performance. KnE Social Sciences, 1057–1073-1057–1073.
[60] Mahmood, Z., Vistro, D. M., Iftikhar, W., Ashfaq, U., & Aziz, H. I. T. (2020). Data Analytics for Customer Retention Using Machine Learning Techniques. Journal of Critical Reviews, 7(9), 1636-1643.
[61] Malafronte, I. (Producer). (2018, 25th August, 2021). Financial Performance Management.
[Lecture slides for RBP020L062A]
[62] Maroufkhani, P., Tseng, M. L., Iranmanesh, M., Ismail, W. K. W., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54. doi:ARTN 10219010.1016/j.ijinfomgt.2020.102190
[63] Mckinsey. (2021). Prediction: The future of CX. Retrieved from https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/prediction-the-future-of-cx
[64] Mckinsey & Company Global Banking Report, 2018
[65] Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics capabilities and innovation: the mediating role of dynamic capabilities and moderating effect of the environment. British Journal of Management, 30(2), 272-298.
[66] Mikalef, P., Pappas, I. O., Krogstie, J., & Giannakos, M. (2018). Big data analytics capabilities: a systematic literature review and research agenda. Information Systems and e-Business Management, 16(3), 547-578. doi:10.1007/s10257-017-0362-y
[67] Mohini, P., & Srivastav, M. R. K. (2021). Big data analytics in banking and financial services sector. EPRA International Journal of Research and Development, 6(3), 92-96.
[68] Müller, O., Fay, M. and vom Brocke, J. (2018) The Effect of Big Data and Analytics on Firm Performance: An Econometric Analysis Considering Industry Characteristics, Journal of Management Information Systems, 35:2, 488-509
[69] Netsuite. (2021). Descriptive Analytics Defined: Benefits & Examples. Retrieved from https://www.netsuite.com/portal/resource/articles/erp/descriptive-analytics.shtml
[70] Ng, W., & Poquet, O. (2020). Exploratory study of analytics-based technologies used for corporate learning and development. Retrieved from
[71] Nielsen, S. (2018). Reflections on the applicability of business analytics for management accounting–and future perspectives for the accountant. Journal of Accounting & Organizational Change.
[72] Nielsen, S. (2022). Business analytics: an example of integration of TD-ABC and the balanced scorecard. International Journal of Productivity and Performance Management. doi:10.1108/Ijppm-05-2020-0244
[73] Oliveira, C., Martins, A., Camilleri, M. A., & Jayantilal, S. (2021). Using the Balanced Scorecard for strategic communication and performance management. In Strategic corporate communication in the digital age: Emerald Publishing Limited.
[74] Packt. (2018). Big data as a service (BDaaS) solutions: comparing IaaS, PaaS and SaaS. Retrieved from https://hub.packtpub.com/big-data-as-a-service-bdaas-solutions-comparing-iaas-paas-and-saas/
[75] Pakath, R. (2015). Competing on the cloud: A review and synthesis of potential benefits and possible pitfalls. Journal of Organizational Computing and Electronic Commerce, 25(1), 1–27.
[76] Poornima, S., & Pushpalatha, M. (2020). A survey on various applications of prescriptive analytics. International Journal of Intelligent Networks, 1, 76-84.
[77] Popovič, A., Hackney, R., Tassabehji, R., & Castelli, M. (2018). The impact of big data analytics on firms’ high value business performance. Information Systems Frontiers, 20(2), 209-222.
[78] Pospieszny, P. (2017). Software estimation: towards prescriptive analytics. Paper presented at the Proceedings of the 27th international workshop on software measurement and 12th international conference on software process and product measurement.
[79] Praxidia. (2020). AI Analytics.
[80] Psarras, A., Anagnostopoulos, T., Tsotsolas, N., Salmon, I., & Vryzidis, L. (2020). Applying the Balanced Scorecard and Predictive Analytics in the Administration of a European Funding Program. Administrative Sciences, 10(4). doi:ARTN 10210.3390/admsci10040102
[81] Raffoni, A., Visani, F., Bartolini, M., & Silvi, R. (2018). Business Performance Analytics: exploring the potential for Performance Management Systems. Production Planning & Control, 29(1), 51-67. doi:10.1080/09537287.2017.1381887
[82] 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), 5206-5221.
[83] Rajabion, L., Shaltooki, A. A., Taghikhah, M., Ghasemi, A., & Badfar, A. (2019). Healthcare big data processing mechanisms: The role of cloud computing. International Journal of Information Management, 49, 271-289. doi:10.1016/j.ijinfomgt.2019.05.017
[84] Reis, J., Amorim, M., Melão, N., Cohen, Y., & Rodrigues, M. (2019). Digitalization: A literature review and research agenda. Paper presented at the International Joint conference on Industrial Engineering and Operations Management.
[85] Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58.
[86] Schütze, C., Cleophas, C., & Tarafdar, M. (2020). Revenue management systems as symbiotic analytics systems: insights from a field study. Business Research, 13(3), 1007-1031.
[87] Selerity. (2019). How does diagnostic analytics aid in decision making. Retrieved from https://seleritysas.com/blog/2019/04/11/how-does-diagnostic-analytics-aid-in-decision-making/
[88] Sivarajah, U., Kamal, M. M., Irani, Z. and Weerakkody, V. (2017) Critical analysis of Big Data challenges and analytical methods. Journal of Business Research. 70 pp. 263-286. doi: http://dx.doi.org/10.1016/j.jbusres.2016.08.001
[89] Sigma. (2021). Descriptive, Predictive, Prescriptive, and Diagnostic Analytics: A Quick Guide. Retrieved from https://www.sigmacomputing.com/blog/descriptive-predictive-prescriptive-and-diagnostic-analytics-a-quick-guide/
[90] Sisense. (2021). What is Descriptive Analytics?
[91] Surendro, K. (2019). Predictive Analytics for Predicting Customer Behavior. Paper presented at the 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT).
[92] Tambe P. Big data investment, skills, and firm value. Management Science, 60, 6 (June 2014), 1452–1469
[93] TechTarget. (2015). Boost employee retention through descriptive analytics. Retrieved from https://searchcustomerexperience.techtarget.com/tip/Boost-employee-retention-through-descriptive-analytics
[94] Udoh, E., Patterson, B., & Cordle, S. (2014). A Performance Analysis of Cloud Computing Using the Balanced Scorecard Approach. 2014 Annual Global Online Conference on Information and Computer Technology, 11-16. doi:10.1109/Gocict.2014.8
[95] Ukhalkar, P. K., Phursule, D. R. N., Gadekar, D. D. P., & Sable, D. N. P. (2020). Business Intelligence and Analytics: Challenges and Opportunities. International Journal of Advanced Science and Technology, 29, 2669-2676.
[96] UNSW. (2020). Descriptive, Predictive & Prescriptive Analytics: What are the differences? Retrieved from https://studyonline.unsw.edu.au/blog/descriptive-predictive-prescriptive-analytics
[97] VerticalLeap. (2018). Data science for marketers (part 2): Descriptive v diagnostic analytics. Retrieved from https://www.vertical-leap.uk/blog/data-science-for-marketers-part-2-descriptive-v-diagnostic-analytics/
[98] Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information systems research, 11(4), 342-365.
[99] Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
[100] Vitari, C., & Raguseo, E. (2020). Big data analytics business value and firm performance: linking with environmental context. International Journal of Production Research, 58(18), 5456-5476. doi:10.1080/00207543.2019.1660822
[101] Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2018). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365. doi:10.1016/j.jbusres.2016.08.009
[102] Wang, C. H., Cheng, H. Y., & Deng, Y. T. (2018). Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries. Computers & Industrial Engineering, 115, 486-494. doi:10.1016/j.cie.2017.12.003
[103] Wassouf, W. N., Alkhatib, R., Salloum, K., & Balloul, S. (2020). Predictive analytics using big data for increased customer loyalty: Syriatel Telecom Company case study. Journal of Big Data, 7(1), 1-24.
[104] Whitelock, V. (2018). Business analytics and firm performance: role of structured financial statement data. Journal of business analytics, 1(2), 81-92.
[105] Wu, I. L., & Chang, C. H. (2012). Using the balanced scorecard in assessing the performance of e-SCM diffusion: A multi-stage perspective. Decision Support Systems, 52(2), 474-485. doi:10.1016/j.dss.2011.10.008
[106] Ying, S., Sindakis, S., Aggarwal, S., Chen, C., & Su, J. (2021). Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance. European Management Journal, 39(3), 390-400.
[107] Yu, C.-C. (2016). A value-centric business model framework for managing open data applications. Journal of Organizational Computing and Electronic Commerce, 26(1–2), 80–115
[108] Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. Transportation Research Part E: logistics and transportation review, 114, 371-385.
[109] Zendesk. (2020). Better customer analytics. Retrieved from https://www.zendesk.co.uk/blog/3-types-customer-analytics/
[110] Zhu, X., & Yang, Y. (2021). Big Data Analytics for Improving Financial Performance and Sustainability. Journal of Systems Science and Information, 9(2), 175-191.doi:10.21078/JSSI-2021-175-17
[111] Zulaikha, S., H. Mohamed, M. Kurniawati, S. Rusgianto, and S. A. Rusmita. 2020. Customer predictive analytics using artificial intelligence. The Singapore Economic Review:1-12.