Evaluation of the Implications of Big Data Analytics with Organizational Performance in Small and Medium Enterprises and Its Associated Role of Knowledge Management
Keywords:
organizational performance, small enterprises, medium enterprises, knowledge management.Abstract
Introduction: Big data are distinguished by their quantity, speed, variety, and reliability. Big data presents opportunity for “small and medium-sized” businesses (SME), in addition to being a reality for giant firms. The term "big data" denotes to the fact that numerous varieties of data have become more easily accessible, and its consequences for various organizational types may vary from one another. SME can benefit greatly from creating and utilizing big data. Aim and objectives: The main purpose of the study is to assess the implications of “big data analytics” with organizational performance in small and medium enterprises and its associated role of knowledge management. Methods: This study's main goal was to develop a big data KM model for SME through an examination of several business situations. This study used the qualitative analysis of data methodology. The study gathered examples of large data for SME, which it then used to test a KM model. Data collection, coding, and analysis are the three main components of qualitative data analysis. Results: Big data collection is a business endeavor for SMEs. An SME needs to specify its big data plans and deal with all potential organizational difficulties. Big data must be strategically used in a way that is consistent with the SME's business strategy and embraces a long-term strategy for competitive sustainability. To tackle the big data challenge, IT solutions are chosen based on the definition of data requirements.
References
Abbas, Y. A., Al-Frijawy, J., Alrobaie, A. I. K., Al-Jazaeri, M. B. M., & Sabti, Y. M. (2023). Responsible business and corporate performance of private placements firms. South Asian Journal of Social Science and Humanities, 4(1), 163-176.
Abbas, Y. A., Sabti, Y. M., Al-Jazaeri, M. B. M., & Abu Khadher, H. A. H. (2023). Product quality and corporate financial performance of Bursa Malaysia. South Asian Journal of Social Science and Humanities, 4(1), 177-188.
Abrahams, A. S., Fan, W., Wang, G. A., Zhang, Z., & Jiao, J. (2015). An integrated text analytic framework for product defect discovery. Production and Operations Management, 24(6), 975-990.
Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and decision making in fashion industry. International Journal of Information Management, 42, 90-101.
Ackoff, R. L. (1989). From data to wisdom. Journal of applied systems analysis, 16(1), 3-9.
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.
Alaskar, T. H., Mezghani, K., & Alsadi, A. K. (2021). Examining the adoption of Big data analytics in supply chain management under competitive pressure: evidence from Saudi Arabia. Journal of decision systems, 30(2-3), 300-320.
Alavi, A.H. and Gandomi, A.H. (2017). Big data in civil engineering. Automation in Construction, 79, 1-2.
Alavi, M., & Leidner, D. E. (2001). Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS quarterly, 107-136.
Ali, Z., Gongbing, B., & Mehreen, A. (2019). Supply chain network and information sharing effects of SMEs’ credit quality on firm performance: do strong tie and bridge tie matter? Journal of Enterprise Information Management, 32(5), 714-734.
Almohri, H., Chinnam, R. B., & Colosimo, M. (2019). Data-driven analytics for benchmarking and optimizing the performance of automotive dealerships. International Journal of Production Economics, 213, 69-80.
Al-Saedi, S. H., Abbas, Y. A. (2023). Sustainability development and organizational performance in a developing economy: Evidence from Malaysia. South Asian Journal of Social Sciences and Humanities, 4(2), 91-105.
Amabile, S., Laghzaoui, S., Peignot, J., Peneranda, A., & Boudrandi, S. (2013). Business intelligence practices for exporting SMEs. International Business Research, 6(2), 101- 111.
Aqlan, F., & Al-Fandi, L. (2018). Prioritizing process improvement initiatives in manufacturing environments. International Journal of Production Economics, 196, 261-268.
Arunachalam, D., Kumar, N., & Kawalek, J. P. (2018). Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436.
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.
Azadeh, A., Zarrin, M., & Salehi, N. (2016). Supplier selection in closed loop supply chain by an integrated simulation-Taguchi-DEA approach. Journal of Enterprise Information Management, 29(3), 302-326.
Bagozzi, R. P. (1980). Causal Models in Marketing. John Wiley and Sons google schola, 2, 17- 30.
Barki, H., & Hartwick, J. (2001). Interpersonal conflict and its management in information system development. MIS quarterly, 195-228.
Beyer, M.A. and Laney, D. (2012). The importance of “big data”: A definition. Gartner, available at: www. gartner.com/doc/2057415/importance-big-data-definition (accessed 15 January 2020).
Biedenbach, T., & Müller, R. (2012). Absorptive, innovative and adaptive capabilities and their impact on project and project portfolio performance. International Journal of Project Management, 30(5), 621-635.
Blackburn, R., Lurz, K., Priese, B., Göb, R., & Darkow, I. L. (2015). A predictive analytics approach for demand forecasting in the process industry. International Transactions in Operational Research, 22(3), 407-428.
Bodnar, K. (2011). “HubSpot: small business case studies”, https://www.hubspot.com/ (accessed 15 January 2020).
Business News Daily (2020), available at: www.businessnewsdaily.com/6358-big-data solutions.html (accessed 15 January 2020).
Caputo, A., Borbély, A., & Dabic, M. (2019). Building theory on the negotiation capability of the firm: evidence from Ryanair. Journal of Knowledge Management, 23(2), 240-262.
Chan, J. O. (2014). Big data customer knowledge management. Communications of the IIMA, 14(3), 5.
Chao, C. A., & Chandra, A. (2012). Impact of owner's knowledge of information technology (IT) on strategic alignment and IT adoption in US small firms. Journal of Small Business and Enterprise Development, 19(1), 114-131.
Guarda, T., Santos, M., Pinto, F., Augusto, M., & Silva, C. (2013). Business intelligence as a competitive advantage for SMEs. International Journal of Trade, Economics and Finance, 4(4), 187.
H. Davenport, T. (2014). How strategists use “big data” to support internal business decisions, discovery and production. Strategy & leadership, 42(4), 45-50.
Huberman, A.M., & Miles, M.B. (2002). The Qualitative Researcher’s Companion. Sage, Thousand Oakes, CA.
Ilias, A., & Razak, M. Z. A. (2011). A Validation of the End-User Computing Satisfaction (EUCS) Towards Computerised Accounting System (CAS). Global Business & Management Research, 3(2).
Jamaluddin, A., & Dickie, C. (2011). Decision-making related to business growth: Malay small businesses in Selangor. International Journal of Business and management, 6(10), 284.
Karacan, H., & Şirin, E. (2017). A review on business intelligence and big data. International Journal of Intelligent Systems and Applications in Engineering, 4(5).
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2010). Big data, analytics and the path from insights to value. MIT Sloan Management Review.
Lee, B., Barua, A., & Whinston, A. B. (1997). Discovery and representation of causal relationships in MIS research: A methodological framework. MIS Quarterly, 109-136.
Lin, F. H., & Wu, J. H. (2004). An empirical study of end-user computing acceptance factors in small and medium enterprises in Taiwan: Analyzed by structural equation modeling. Journal of Computer Information Systems, 44(3), 98-108.
Markus, M. L., & Robey, D. (1988). Information technology and organizational change: Causal structure in theory and research. Management science, 34(5), 583-598.
Rydén, P., & Rootzén, H. (2021). Facilitating big data transformation in Danish SMEs: insights for managers. In Big Data in Small Business (pp. 226-247). Edward Elgar Publishing.
Sabti, Y.M., AL Hisnawi, S.M., Jaber AlMousawi, K.M., & Radhi, A.H. (2024). The effect of organizational virtuousness on employees’ innovative behavior: the role of moderating and mediating variables. South Asian Journal of Social Sciences and Humanities, 5(3), 240–253.
Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): a knowledge management model. Journal of Knowledge Management, 24(4), 881-897.
Chaudhry, S. S., Salchenberger, L., & Beheshtian, M. (1996). A small business inventory DSS: Design development, and implementation issues. Computers & Operations Research, 23(1), 63-72.
Chowdhury, M. F. (2015). Coding, sorting and sifting of qualitative data analysis: Debates and discussion. Quality & Quantity, 49(3), 1135-1143.
Christensen, C. M. (2001). Competitive advantage. MIT Sloan Management Review, 42(2), 105-109.
Coleman, S., Göb, R., Manco, G., Pievatolo, A., Tort‐Martorell, X., & Reis, M. S. (2016). How can SMEs benefit from big data? Challenges and a path forward. Quality and Reliability Engineering International, 32(6), 2151-2164.
D Macredie, R., & Mijinyawa, K. (2011). A theory-grounded framework of Open Source Software adoption in SMEs. European Journal of Information Systems, 20(2), 237-250.
Data.gov (2020), available at: www.data.gov/ (accessed 15 January 2020).
De Rijck, P. (2023). The impact of a company’s (big) data analytics capability on firm performance, decision-making and management control (Doctoral dissertation, University of Antwerp).
Del Vecchio, P., Di Minin, A., Petruzzelli, A. M., Panniello, U., & Pirri, S. (2018). Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management, 27(1), 6-22.
Del Vecchio, P., Mele, G., Ndou, V., & Secundo, G. (2018). Creating value from social big data: Implications for smart tourism destinations. Information processing & management, 54(5), 847-860.
Del Vecchio, P., Secundo, G., & Passiante, G. (2018). Analyzing Big Data through the lens of customer knowledge management: Evidence from a set of regional tourism experiences. Kybernetes, 47(7), 1348-1362.
Diakoulakis, I. E., Georgopoulos, N. B., Koulouriotis, D. E., & Emiris, D. M. (2004). Towards a holistic knowledge management model. Journal of knowledge management, 8(1), 32- 46.
Dibrell, C., Davis, P. S., & Craig, J. (2008). Fueling innovation through information technology in SMEs. Journal of small business management, 46(2), 203-218.
Fillis, I., & Wagner, B. (2005). E-business development: An exploratory investigation of the small firm. International small business journal, 23(6), 604-634.
Foote, A., & Halawi, L. A. (2018). Knowledge management models within information technology projects. Journal of Computer Information Systems, 58(1), 89-97.
Glaser, B.G. and Strauss, A.L. (1967), Qualitative Evaluation and Research Methods, 2nd ed., Aldine Publishing, New York, NY.