Büyük veri analitiği yeteneği ve firma performansı ilişkisi: Firma büyüklüğünün düzenleyici rolü

Yıl 2022, Cilt: 6 Sayı: 2, 62 - 73, 09.03.2023

Cemal Zehir , Mahmut Bilgetürk

https://doi.org/10.14744/ysbed.2022.00019

Öz

Son dönemde çok çeşitli ve farklı kaynaklardan büyük miktarda veriyi hızlı bir şekilde elde etme olanağı sunan büyük veri analitiği (BVA) teknolojileri işletmelere yeni fırsatlar ve bakış açıları kazandırmıştır. BVA yeteneği, bir firmanın büyük veriye özgü kaynaklarını bir araya getirme, entegre etme ve dağıtma yeteneği olarak tanımlanır. Bilgi temelli bir dinamik yetenek olarak BVA yeteneği, büyük veri ortamında sürdürülebilir rekabet avantajı sağlayan önemli bir örgütsel yetenektir. Araştırmalar BVA yeteneği ile firma performansı arasında olumlu bir ilişki olduğunu belirtse de bu ilişkinin farklı bağlamsal koşullarda nasıl bir seyir izlediği üzerine yapılan araştırmalar sınırlı düzeydedir. Örneğin firma büyüklüğü gibi firmaların karar ve davranışlarını etkileme potansiyeli olan önemli bir örgüt içi faktörün bu ilişkide nasıl bir rolü olduğu yeterince araştırılmamıştır. Bu bağlamda çalışmanın amacı Bilgi Temelli Dinamik Yetenekler Görüşü bağlamında BVA yeteneği ile firma performansı arasındaki ilişkide firma büyüklüğünün düzenleyici rolünü araştırmaktır. Bu amaçla Türkiye’de faaliyet gösteren 252 KOBİ ve büyük ölçekli firmayı kapsayacak şekilde kesitsel bir alan araştırması gerçekleştirilmiştir. Araştırma bulgularına göre BVA yeteneği ile firma performansı arasında firma büyüklüğünün düzenleyici bir rolünün olduğu gözlenmiştir. Buna göre firma büyüklüğü arttıkça BVA yeteneğinin firma performansı üzerindeki etkisi de artmaktadır. Araştırma sonunda teorisyenlere ve uygulamacılara yönelik önerilerde bulunulmuş olup firmaların BVA’nın potansiyelini değerlendirebilmeleri açısından neler yapabilecekleri tartışılmıştır.

Anahtar Kelimeler

Büyük veri, büyük veri analitiği yeteneği, firma büyüklüğü

Big data analytics capability and firm performance: The moderator role of firm size

Öz

In recent times, big data analytics (BDA) technologies have provided firms new opportunities and perspectives with the ability to quickly acquire large amounts of data from various source. BDA capability is defined as a firm’s ability to aggregate, integrate, and deploy big data-specific resources. As a knowledge-based dynamic capability, BDA capability is an important organizational capability that provides sustainable competitive advantage in the big data environment. While research suggests a positive relationship between BDA capability and firm performance, studies on how this relationship manifests in different contexts are limited. For example, the role of an intra-organizational factor such as firm size, which has the potential to affect firms’ decisions and behaviors, in this relationship has not been sufficiently explored. In this context, the aim of this study is to investigate the moderating role of firm size in the relationship between BDA capability and firm performance, through the lens of the Knowledge-Based Dynamic Capabilities View. To this end, a cross-sectional field study was conducted on 252 SMEs and large-scale companies in Turkey. Results indicate that firm size plays a moderating role in the relationship between BDA capability and firm performance, with the effect of BDA capability on firm performance increasing as firm size increases. The study concludes with suggestions for theorists and practitioners, and a discussion on how companies can evaluate the potential of BDA.

Anahtar Kelimeler

Big data, big data analytics capability, firm size.

Kaynakça

• Akgün, A. E. [Ali E.], Keskin, H., & Byrne, J. (2012). Antecedents and Contingent Effects of Organizational Adaptive Capability on Firm Product Innovativeness. Journal of Product Innovation Management, 29, 171–189. https://doi.org/10.1111/j.1540-5885.2012.00949.x
• 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. https://doi.org/10.1016/j.ijpe.2016.08.018
• Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision, 57(8), 1993–2009. https://doi.org/10.1108/MD-07-2018-0754
• Barney, J. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
• BARON, R. M., & Da KENNY (1986). THE MODERATOR MEDIATOR VARIABLE DISTINCTION IN SOCIAL PSYCHOLOGICAL-RESEARCH - CONCEPTUAL, STRATEGIC, AND STATISTICAL CONSIDERATIONS. JOURNAL of PERSONALITY and SOCIAL PSYCHOLOGY, 51(6), 1173–1182.
• Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., & Venkatraman, N. (2013). Digital Business Strategy: Toward a Next Generation of Insights. MIS Quarterly, 37(2), 471–482. https://doi.org/10.25300/MISQ/2013/37:2.3
• 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
• 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. https://doi.org/10.1111/caim.12224
• Denford, J. S. (2013). Building knowledge: developing a knowledge‐based dynamic capabilities typology. Journal of Knowledge Management, 17(2), 175–194. https://doi.org/10.1108/13673271311315150
• 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. https://doi.org/10.1111/1467-8551.12355
• Field, A. (2009). Discovering statistics using SPSS (3rd ed.). Introducing statistical methods. London: SAGE.
• Fogelman-Soulié, F., & Lu, W. (2016). Implementing Big Data Analytics Projects in Business. In N. Japkowicz & J. Stefanowski (Eds.), Studies in Big Data. Big Data Analysis: New Algorithms for a New Society (Vol. 16, pp. 141–158). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-26989-4_6
• Gartner IT Glossary (2021, December 8). Definition of Big Data. Retrieved from https://www.gartner.com/en/information-technology/glossary/big-data
• Gobble, M. M. (2013). Big Data: The Next Big Thing in Innovation. Research-Technology Management, 56(1), 64–67. https://doi.org/10.5437/08956308X5601005
• 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
• Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049–1064. https://doi.org/10.1016/j.im.2016.07.004
• Gupta, S., Drave, V. A., Dwivedi, Y. K., Baabdullah, A. M., & Ismagilova, E. (2020). Achieving superior organizational performance via big data predictive analytics: A dynamic capability view. Industrial Marketing Management, 90, 581–592. https://doi.org/10.1016/j.indmarman.2019.11.009
• Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003
• Hair, J. F., Black, W. C., Babin, B. J., & Rolph, E. A. (2014). Multivariate Data Analysis (7. Edition, Pearson New International Edition). Harlow: Pearson Education Limited. Retrieved from https://elibrary.pearson.de/book/99.150005/9781292035116
• Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach / Andrew F. Hayes (Second edition). Methodology in the social sciences. New York: The Guilford Press.
• Hitt, M. A., Ireland, R. D., & Hoskisson, R. E. (2017). Strategic management: Competitiveness & globalization (12e). Canada: Cengage Learning.
• Johnson, J. S., Friend, S. B., & Lee, H. S. (2017). Big Data Facilitation, Utilization, and Monetization: Exploring the 3Vs in a New Product Development Process. Journal of Product Innovation Management, 34(5), 640–658. https://doi.org/10.1111/jpim.12397
• Kaur, V. (2022). Knowledge-based dynamic capabilities: a scientometric analysis of marriage between knowledge management and dynamic capabilities. Journal of Knowledge Management. Advance online publication. https://doi.org/10.1108/JKM-02-2022-0112
• Keskin, H., Akgün, A. E. [Ali Ekber], & Koçoğlu, İ. (2016). Örgüt teorisi. Nobel: no: 56. İstanbul: Nobel Yayıncılık.
• Kraaijenbrink, J., Spender, J.‑C., & Groen, A. J. (2010). The Resource-Based View: A Review and Assessment of Its Critiques. Journal of Management, 36(1), 349–372. https://doi.org/10.1177/0149206309350775
• 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. https://doi.org/10.1016/j.ijinfomgt.2014.02.002
• Lamba, H. S., & Dubey, S. K. (2015). Analysis of requirements for Big Data Adoption to maximize IT Business Value. In 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions) (pp. 1–6). IEEE. https://doi.org/10.1109/ICRITO.2015.7359268
• LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review, 52(2), 21–32. Retrieved from https://sloanreview.mit.edu/article/big-data-analytics-and-the-path-from-insights-to-value/
• Loebbecke, C., & Picot, A. (2015). Reflections on societal and business model transformation arising from digitization and big data analytics: A research agenda. The Journal of Strategic Information Systems, 24(3), 149–157. https://doi.org/10.1016/j.jsis.2015.08.002
• Mangla, S. K., Raut, R., Narwane, V. S., Zhang, Z., & priyadarshinee, P. (2021). Mediating effect of big data analytics on project performance of small and medium enterprises. Journal of Enterprise Information Management, 34(1), 168–198. https://doi.org/10.1108/JEIM-12-2019-0394
• Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Hung Byers, A. (2011). Big data: The next frontier for innovation, competition, and productivity.
• McAfee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60–68.
• Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities. Information & Management, 57(2), 103169. https://doi.org/10.1016/j.im.2019.05.004
• 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. https://doi.org/10.1007/s10257-017-0362-y
• Müller, O., Fay, M., & 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. https://doi.org/10.1080/07421222.2018.1451955
• 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. https://doi.org/10.1007/s10796-016-9720-4
• Rialti, R., Marzi, G., Ciappei, C., & Busso, D. (2019). Big data and dynamic capabilities: a bibliometric analysis and systematic literature review. Management Decision, 57(8), 2052–2068. https://doi.org/10.1108/MD-07-2018-0821
• Rialti, R., Zollo, L., Ferraris, A., & Alon, I. (2019). Big data analytics capabilities and performance: Evidence from a moderated multi-mediation model. Technological Forecasting and Social Change, 149, 119781. https://doi.org/10.1016/j.techfore.2019.119781
• Shamim, S., Zeng, J., Shariq, S. M., & Khan, Z. (2019). Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: A dynamic capabilities view. Information & Management, 56(6), 103135. https://doi.org/10.1016/j.im.2018.12.003
• Sheng, J., Amankwah-Amoah, J., & Wang, X. (2017). A multidisciplinary perspective of big data in management research. International Journal of Production Economics, 191, 97–112. https://doi.org/10.1016/j.ijpe.2017.06.006
• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. https://doi.org/10.1002/(SICI)1097-0266(199708)18:7<509::AID-SMJ882>3.0.CO;2-Z
• Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118–144. https://doi.org/10.1016/j.jsis.2019.01.003
• 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
• Wernerfelt, B. (1984). A resource-based view of the firm. Strategic Management Journal, 5(2), 171–180. https://doi.org/10.1002/smj.4250050207
• Zhang, Y., Hou, Z., Yang, F., Yang, M. M., & Wang, Z. (2021). Discovering the evolution of resource-based theory: Science mapping based on bibliometric analysis. Journal of Business Research, 137, 500–516. https://doi.org/10.1016/j.jbusres.2021.08.055
• Zheng, S., Zhang, W., & Du, J. (2011). Knowledge‐based dynamic capabilities and innovation in networked environments. Journal of Knowledge Management, 15(6), 1035–1051. https://doi.org/10.1108/13673271111179352