Derleme
BibTex RIS Kaynak Göster

Strategic Alignment and Data-Oriented Culture in Effective Management of Big Data

Yıl 2020, Güz, 63 - 76, 18.12.2020
https://doi.org/10.21733/ibad.717117

Öz

Towards the end of the 2000s, the interest in big data has also increased in the management field as in many other areas. The widespread use of big data has been accepted as a major revolution in the management field. Many businesses around the world have aimed to increase their performance by investing in big data. According to the researches carried out in this direction, although some enterprises have become succesful in big data initiatives, the majority of the enterprises have failed in big data investments. Studies conducted on a global scale have shown that supporting large data investments only with technological infrastructure and highly qualified employees are not enough to achieve high performance. The main critical point that brings success to businesses in big data initiatives is the ability of companies in managing big data. In the literature reviews, it is undesrtood that the failure to integrate big data strategies with business strategies and the lack of a data-oriented culture are the two biggest causes of failure in big data initiatives of companies. In this study, a road map is proposed with an emphasis on the importance of harmonizing big data strategies with business strategies and creating a data-oriented culture in order to achieve high success in big data investments. Also, some suggestions were given to companies to achieve strategic and cultural harmony based on the studies in the literature.

Kaynakça

  • Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. In Industry 4.0: Managing the digital transformation (pp. 61-94). Springer, Cham.
  • 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.
  • Amankwah-Amoah, J. (2015). A unified framework for incorporating decision making into explanations of business failure. Industrial Management & Data Systems.
  • Amankwah-Amoah, J., & Adomako, S. (2019). Big data analytics and business failures in data-Rich environments: An organizing framework. Computers in Industry, 105, 204-212.
  • Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07.
  • Andrews, K. R. (1971). The Concept of Corporate Strategy, Homewood, Illinois, Dow Jones-Irwin, Inc., l97l.
  • Aral, S., & Weill, P. (2007). IT assets, organizational capabilities, and firm performance: How resource allocations and organizational differences explain performance variation. Organization science, 18(5), 763-780.
  • Baldwin, H. (2015, January 22). When big data projects go wrong. Forbes. Available at http://www.forbes.com/sites/ howardbaldwin/2015/01/22/when-big-data-projects-go-wrong/#671a28642736
  • Barney, J. B., & Clark, D. N. (2007). Resource-based theory: Creating and sustaining competitive advantage. Oxford University Press on Demand.
  • Barton, D. and D. Court (2012). ‘Making advanced analytics work for you’, Harvard Business Review, 90, pp. 78–83.
  • Bean, R. A. N. D. Y., & Kiron, D. (2013). Organizational alignment is key to big data success. MIT Sloan Management Review, 54(3), 1-6.
  • Berndtsson, M., Forsberg, D., Stein, D., & Svahn, T. (2018). Becoming a data-driven organisation.
  • Brands, K. C. M. A. (2014).Big data and business intelligence for management accountants. Strategic Finance,95,64–65.
  • Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35.
  • Cao, G., & Duan, Y. (2014). A path model linking business analytics, data-driven culture, and competitive advantage.
  • Chandler Jr., A. D. (1977). The Visible Hand. London: The Belknap Press of Harvard University Press.
  • Chiera, B. A., & Korolkiewicz, M. W. (2017). Visualizing big data: Everything old is new again. In Big data management (pp. 1-27). Springer, Cham.
  • Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57.
  • Daft, R. L., & Lane, P. (2005). The leadership experience (3rd). Mason, OH: Thomson-Southwestern.
  • Davenport, T. H. 2006. “Competing on Analytics”. Harvard Business Review. 84: 98–107.
  • Davenport, T. H., Barth, P., & Bean, R. (2012). How ‘big data’is different. MIT Sloan Management Review, 54, 43–46.
  • Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013, May). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 48-55). IEEE.
  • Demirbag, M., K. W. Glaister and E. Tatoglu (2007). ‘Institutional and transaction cost influences on MNEs’ ownership strategies of their affiliates: evidence from an emerging market’, Journal of World Business, 42, pp. 418–434.
  • Dubey, R., A. Gunasekaran, S. J. Childe, T. Papadopoulos, B. Hazen, M. Giannakis and D. Roubaud (2017). ‘Examining the effect of external pressures and organizational culture on shaping performance measurement systems (PMS) for sustainability benchmarking: some empirical findings’, International Journal of Production Economics, 193, pp. 63–76.
  • Dumbill, E.: Making sense of big data (editorial). Big Data. 1(1), 1–2 (2013)
  • Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
  • Forrester, 2012. The Big Deal About Big Data for Customer Engagement Business: Leaders Must Lead Big Data Initiatives to Derive Value.
  • Gahi, Y., Guennoun, M., & Mouftah, H. T. (2016, June). Big data analytics: Security and privacy challenges. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 952-957). IEEE.
  • Garmaki, M., Boughzala, I., & Wamba, S. F. (2016, June). The effect of Big Data Analytics Capability on Firm Performance. In PACIS (p. 301).
  • Gartner. (2013, April 18). Big data. Glossary 2012. Retrieved from http://www.gartner.com/itglossary/big-data/
  • Gartner. (2015, September 15). Gartner says business intelli-gence and analytics leaders must focus on mindsets and culture to kick start advanced analytics. Available at http://www.gartner.com/newsroom/id/3130017
  • George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research.
  • Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
  • Henderson, J. C., & Venkatraman, H. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM systems journal, 38(2.3), 472-484.
  • Henderson, J. C., Venkatraman, N., & Oldach, S. (1996). Aligning business and IT strategies. Competing in the information age: Strategic alignment in practice, 21-42.
  • Hofer, C. W., & Schendel, D. (1978). Strategy Formulation: Analytical Concepts, St. Paul. MN. West.
  • Kates, A., & Galbraith, J. R. (2010). Designing your organization: Using the STAR model to solve 5 critical design challenges. John Wiley & Sons.
  • Kaur, N., & Sood, S. K. (2017). Dynamic resource allocation for big data streams based on data characteristics (5Vs). International Journal of Network Management.
  • 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.
  • Lakoju, M., & Serrano, A. (2017). Framework for aligning Big-Data strategy with organizational goals.
  • 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.
  • Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.
  • Liu, H., W. Ke, K. K. Wei, J. Gu and H. Chen (2010). ‘The role of institutional pressures and organizational culture in the firm’s intention to adopt Internet-enabled supply chain management systems’, Journal of Operations Management, 28, pp. 372–384.
  • Liu, Y. (2014). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40.
  • Mandal, S. (2018). Exploring the influence of big data analytics management capabilities on sustainable tourism supply chain performance: the moderating role of technology orientation. Journal of Travel & Tourism Marketing, 35(8), 1104-1118.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
  • Marshall, A., Mueck, S., & Shockley, R. (2015). How leading organizations use big data and analytics to innovate. Strategy & Leadership.
  • 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.
  • 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.
  • Mitra, A., Gaur, S. S., & Giacosa, E. (2019). Combining organizational change management and organizational ambidexterity using data transformation. Management decision.
  • O'Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99.
  • Oracle, 2012. Big Data for the Enterprise. Redwood Shores, CA: Oracle
  • Peteraf, M. A. (1993). The cornerstones of competitive advantage: a resource‐based view. Strategic management journal, 14(3), 179-191.
  • Rathnam, R. G., Johnsen, J., & Wen, H. J. (2005). Alignment of business strategy and IT strategy: a case study of a fortune 50 financial services company. Journal of Computer Information Systems, 45(2), 1-8.
  • Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard business review, 91(12), 90-98.
  • Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.
  • Santhanam, R., & Hartono, E. (2003). Issues in linking information technology capability to firm performance. MIS quarterly, 125-153.
  • Schein, E. H. (1990). Organizational Culture: What it is and How to Change it. In Human resource management in international firms (pp. 56-82). Palgrave Macmillan, London.
  • Seddon, J. J., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307.
  • Shamim, S., J. Zeng, S. M. Shariq and Z. Khan (2018). ‘Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: a dynamic capabilities view’, Information & Management, https://doi.org/10.1016/j.im.2018.12.003.
  • Sirmon DG, Hitt MA, Ireland RD, Gilbert BA (2011) Resource orchestration to create competitive advantage: Breadth, depth, and life cycle effects. J of Manag, 37(5):1390-1412
  • Teece, D. J. (2015). ‘Intangible assets and a theory of heterogeneous firms’. In A. Bounfour and T. Miyagawa (eds), Intangibles, Market Failure and Innovation Performance, pp. 217–239. New York: Springer.
  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
  • Van Der Zee, J. T. M., & De Jong, B. (1999). Alignment is not enough: integrating business and information technology management with the balanced business scorecard. Journal of management information systems, 16(2), 137-158.
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28.
  • 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.
  • Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
  • Wang N, Liang H, Zhong W, Xue Y, Xiao J (2012) Resource structuring or capability building? An empirical study of the business value of information technology. J of Manag Inf Syst, 29(2):325-367
  • Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., ... & Zheng, C. (2014, February). Bigdatabench: A big data benchmark suite from internet services. In 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA) (pp. 488-499). IEEE.
  • Watson, J., "The Requirements for Being an Analytics-Based Organization", Business Intelligence Journal, 2012, pp. 4-6.
  • Yong, K. T. and L. S. Pheng (2008). ‘Organizational culture and TQM implementation in construction firms in Singapore’, Construction Management and Economics, 26, pp. 237–248.
  • Zeng, J., & Glaister, K. W. (2018). Value creation from big data: Looking inside the black box. Strategic Organization, 16(2), 105-140.
  • Zu, X., T. L. Robbins and L. D. Fredendall (2010). ‘Mapping the critical links between organizational culture and TQM/Six Sigma practices’, International Journal of Production Economics, 123, pp. 86–106.

Büyük Verinin Etkin Yönetiminde Stratejik Uyum ve Veri Odaklı Kültür

Yıl 2020, Güz, 63 - 76, 18.12.2020
https://doi.org/10.21733/ibad.717117

Öz

2000’li yılların sonlarına doğru büyük veriye ilgi her alanda olduğu gibi yönetim alanında da artmıştır. Büyük veri kullanımının yaygınlaşması yönetim alanında büyük bir devrim olarak kabul edilmiştir. Dünya çapında birçok işletme büyük veri yatırımları yaparak performanslarını arttırmayı amaçlamıştır. Bu doğrultuda yapılan araştırmalara göre bazı işletmeler büyük veri girişimlerinde başarılı olurken işletmelerin büyük çoğunluğu büyük veri yatırımlarından planladıkları sonucu elde edemeyerek başarısız olmuştur. Küresel ölçekte yapılan çalışmalar, büyük veri yatırımlarını sadece teknolojik altyapıyla ve analitik bilgisi yüksek nitelikli çalışanlarla desteklemenin yüksek performans elde etmek için yeterli olmadığını göstermiştir. İşletmelere büyük veri girişimlerinde başarıyı getiren asıl kritik husus ise şirketlerin büyük veriyi yönetebilme kabiliyetleridir. Yapılan literatür incelemelerinde, büyük veri stratejilerinin işletme stratejileriyle entegrasyonunun sağlanamaması ve veri odaklı kültürün olmayışı şirketlerin büyük veri girişimlerinin başarısızlıkla sonuçlanmasında etkili olan en büyük iki unsur olarak yer almaktadır. Bu çalışmada işletmelerin büyük veri yatırımlarında yüksek başarı elde edebilmesi için büyük veri stratejilerinin işletme stratejileriyle uyumlaştırılması ve veri odaklı bir kültür oluşturulmasının önemine vurgu yapılarak bir yol haritası önerilmiştir. Ayrıca, literatürdeki çalışmalardan hareketle işletmelerin stratejik ve kültürel uyumu gerçekleştirebilmesi için nelere dikkat etmesi gerektiğiyle ilgili önerilerde bulunulmuştur.

Kaynakça

  • Akdil, K. Y., Ustundag, A., & Cevikcan, E. (2018). Maturity and readiness model for industry 4.0 strategy. In Industry 4.0: Managing the digital transformation (pp. 61-94). Springer, Cham.
  • 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.
  • Amankwah-Amoah, J. (2015). A unified framework for incorporating decision making into explanations of business failure. Industrial Management & Data Systems.
  • Amankwah-Amoah, J., & Adomako, S. (2019). Big data analytics and business failures in data-Rich environments: An organizing framework. Computers in Industry, 105, 204-212.
  • Anderson, C. (2008). The end of theory: The data deluge makes the scientific method obsolete. Wired magazine, 16(7), 16-07.
  • Andrews, K. R. (1971). The Concept of Corporate Strategy, Homewood, Illinois, Dow Jones-Irwin, Inc., l97l.
  • Aral, S., & Weill, P. (2007). IT assets, organizational capabilities, and firm performance: How resource allocations and organizational differences explain performance variation. Organization science, 18(5), 763-780.
  • Baldwin, H. (2015, January 22). When big data projects go wrong. Forbes. Available at http://www.forbes.com/sites/ howardbaldwin/2015/01/22/when-big-data-projects-go-wrong/#671a28642736
  • Barney, J. B., & Clark, D. N. (2007). Resource-based theory: Creating and sustaining competitive advantage. Oxford University Press on Demand.
  • Barton, D. and D. Court (2012). ‘Making advanced analytics work for you’, Harvard Business Review, 90, pp. 78–83.
  • Bean, R. A. N. D. Y., & Kiron, D. (2013). Organizational alignment is key to big data success. MIT Sloan Management Review, 54(3), 1-6.
  • Berndtsson, M., Forsberg, D., Stein, D., & Svahn, T. (2018). Becoming a data-driven organisation.
  • Brands, K. C. M. A. (2014).Big data and business intelligence for management accountants. Strategic Finance,95,64–65.
  • Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4(1), 24-35.
  • Cao, G., & Duan, Y. (2014). A path model linking business analytics, data-driven culture, and competitive advantage.
  • Chandler Jr., A. D. (1977). The Visible Hand. London: The Belknap Press of Harvard University Press.
  • Chiera, B. A., & Korolkiewicz, M. W. (2017). Visualizing big data: Everything old is new again. In Big data management (pp. 1-27). Springer, Cham.
  • Constantiou, I. D., & Kallinikos, J. (2015). New games, new rules: big data and the changing context of strategy. Journal of Information Technology, 30(1), 44-57.
  • Daft, R. L., & Lane, P. (2005). The leadership experience (3rd). Mason, OH: Thomson-Southwestern.
  • Davenport, T. H. 2006. “Competing on Analytics”. Harvard Business Review. 84: 98–107.
  • Davenport, T. H., Barth, P., & Bean, R. (2012). How ‘big data’is different. MIT Sloan Management Review, 54, 43–46.
  • Demchenko, Y., Grosso, P., De Laat, C., & Membrey, P. (2013, May). Addressing big data issues in scientific data infrastructure. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 48-55). IEEE.
  • Demirbag, M., K. W. Glaister and E. Tatoglu (2007). ‘Institutional and transaction cost influences on MNEs’ ownership strategies of their affiliates: evidence from an emerging market’, Journal of World Business, 42, pp. 418–434.
  • Dubey, R., A. Gunasekaran, S. J. Childe, T. Papadopoulos, B. Hazen, M. Giannakis and D. Roubaud (2017). ‘Examining the effect of external pressures and organizational culture on shaping performance measurement systems (PMS) for sustainability benchmarking: some empirical findings’, International Journal of Production Economics, 193, pp. 63–76.
  • Dumbill, E.: Making sense of big data (editorial). Big Data. 1(1), 1–2 (2013)
  • Fisher, D., DeLine, R., Czerwinski, M., & Drucker, S. (2012). Interactions with big data analytics. interactions, 19(3), 50-59.
  • Forrester, 2012. The Big Deal About Big Data for Customer Engagement Business: Leaders Must Lead Big Data Initiatives to Derive Value.
  • Gahi, Y., Guennoun, M., & Mouftah, H. T. (2016, June). Big data analytics: Security and privacy challenges. In 2016 IEEE Symposium on Computers and Communication (ISCC) (pp. 952-957). IEEE.
  • Garmaki, M., Boughzala, I., & Wamba, S. F. (2016, June). The effect of Big Data Analytics Capability on Firm Performance. In PACIS (p. 301).
  • Gartner. (2013, April 18). Big data. Glossary 2012. Retrieved from http://www.gartner.com/itglossary/big-data/
  • Gartner. (2015, September 15). Gartner says business intelli-gence and analytics leaders must focus on mindsets and culture to kick start advanced analytics. Available at http://www.gartner.com/newsroom/id/3130017
  • George, G., Osinga, E. C., Lavie, D., & Scott, B. A. (2016). Big data and data science methods for management research.
  • Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information & Management, 53(8), 1049-1064.
  • Henderson, J. C., & Venkatraman, H. (1993). Strategic alignment: Leveraging information technology for transforming organizations. IBM systems journal, 38(2.3), 472-484.
  • Henderson, J. C., Venkatraman, N., & Oldach, S. (1996). Aligning business and IT strategies. Competing in the information age: Strategic alignment in practice, 21-42.
  • Hofer, C. W., & Schendel, D. (1978). Strategy Formulation: Analytical Concepts, St. Paul. MN. West.
  • Kates, A., & Galbraith, J. R. (2010). Designing your organization: Using the STAR model to solve 5 critical design challenges. John Wiley & Sons.
  • Kaur, N., & Sood, S. K. (2017). Dynamic resource allocation for big data streams based on data characteristics (5Vs). International Journal of Network Management.
  • 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.
  • Lakoju, M., & Serrano, A. (2017). Framework for aligning Big-Data strategy with organizational goals.
  • 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.
  • Lee, I. (2017). Big data: Dimensions, evolution, impacts, and challenges. Business Horizons, 60(3), 293-303.
  • Liu, H., W. Ke, K. K. Wei, J. Gu and H. Chen (2010). ‘The role of institutional pressures and organizational culture in the firm’s intention to adopt Internet-enabled supply chain management systems’, Journal of Operations Management, 28, pp. 372–384.
  • Liu, Y. (2014). Big data and predictive business analytics. The Journal of Business Forecasting, 33(4), 40.
  • Mandal, S. (2018). Exploring the influence of big data analytics management capabilities on sustainable tourism supply chain performance: the moderating role of technology orientation. Journal of Travel & Tourism Marketing, 35(8), 1104-1118.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., & Byers, A. H. (2011). Big data: The next frontier for innovation, competition, and productivity.
  • Marshall, A., Mueck, S., & Shockley, R. (2015). How leading organizations use big data and analytics to innovate. Strategy & Leadership.
  • 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.
  • 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.
  • Mitra, A., Gaur, S. S., & Giacosa, E. (2019). Combining organizational change management and organizational ambidexterity using data transformation. Management decision.
  • O'Leary, D. E. (2013). Artificial intelligence and big data. IEEE Intelligent Systems, 28(2), 96-99.
  • Oracle, 2012. Big Data for the Enterprise. Redwood Shores, CA: Oracle
  • Peteraf, M. A. (1993). The cornerstones of competitive advantage: a resource‐based view. Strategic management journal, 14(3), 179-191.
  • Rathnam, R. G., Johnsen, J., & Wen, H. J. (2005). Alignment of business strategy and IT strategy: a case study of a fortune 50 financial services company. Journal of Computer Information Systems, 45(2), 1-8.
  • Ross, J. W., Beath, C. M., & Quaadgras, A. (2013). You may not need big data after all. Harvard business review, 91(12), 90-98.
  • Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.
  • Santhanam, R., & Hartono, E. (2003). Issues in linking information technology capability to firm performance. MIS quarterly, 125-153.
  • Schein, E. H. (1990). Organizational Culture: What it is and How to Change it. In Human resource management in international firms (pp. 56-82). Palgrave Macmillan, London.
  • Seddon, J. J., & Currie, W. L. (2017). A model for unpacking big data analytics in high-frequency trading. Journal of Business Research, 70, 300-307.
  • Shamim, S., J. Zeng, S. M. Shariq and Z. Khan (2018). ‘Role of big data management in enhancing big data decision-making capability and quality among Chinese firms: a dynamic capabilities view’, Information & Management, https://doi.org/10.1016/j.im.2018.12.003.
  • Sirmon DG, Hitt MA, Ireland RD, Gilbert BA (2011) Resource orchestration to create competitive advantage: Breadth, depth, and life cycle effects. J of Manag, 37(5):1390-1412
  • Teece, D. J. (2015). ‘Intangible assets and a theory of heterogeneous firms’. In A. Bounfour and T. Miyagawa (eds), Intangibles, Market Failure and Innovation Performance, pp. 217–239. New York: Springer.
  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.
  • Van Der Zee, J. T. M., & De Jong, B. (1999). Alignment is not enough: integrating business and information technology management with the balanced business scorecard. Journal of management information systems, 16(2), 137-158.
  • Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28.
  • 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.
  • Wamba, S. F., Gunasekaran, A., Akter, S., Ren, S. J. F., Dubey, R., & Childe, S. J. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356-365.
  • Wang N, Liang H, Zhong W, Xue Y, Xiao J (2012) Resource structuring or capability building? An empirical study of the business value of information technology. J of Manag Inf Syst, 29(2):325-367
  • Wang, L., Zhan, J., Luo, C., Zhu, Y., Yang, Q., He, Y., ... & Zheng, C. (2014, February). Bigdatabench: A big data benchmark suite from internet services. In 2014 IEEE 20th International Symposium on High Performance Computer Architecture (HPCA) (pp. 488-499). IEEE.
  • Watson, J., "The Requirements for Being an Analytics-Based Organization", Business Intelligence Journal, 2012, pp. 4-6.
  • Yong, K. T. and L. S. Pheng (2008). ‘Organizational culture and TQM implementation in construction firms in Singapore’, Construction Management and Economics, 26, pp. 237–248.
  • Zeng, J., & Glaister, K. W. (2018). Value creation from big data: Looking inside the black box. Strategic Organization, 16(2), 105-140.
  • Zu, X., T. L. Robbins and L. D. Fredendall (2010). ‘Mapping the critical links between organizational culture and TQM/Six Sigma practices’, International Journal of Production Economics, 123, pp. 86–106.
Toplam 73 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Derleme Makaleler
Yazarlar

Tuğba Karaboğa 0000-0003-3830-3536

Cemal Zehir 0000-0003-2584-4480

Yayımlanma Tarihi 18 Aralık 2020
Kabul Tarihi 23 Nisan 2020
Yayımlandığı Sayı Yıl 2020 Güz

Kaynak Göster

APA Karaboğa, T., & Zehir, C. (2020). Büyük Verinin Etkin Yönetiminde Stratejik Uyum ve Veri Odaklı Kültür. IBAD Sosyal Bilimler Dergisi(8), 63-76. https://doi.org/10.21733/ibad.717117

IBAD'da yayımlanan makaleler, Creative Commons Attribution-NonCommercial (CC-BY-NC) 4.0 lisansı altındadır. Makalede kullandıkları materyaller için gerekli izinlerin alınması yazarların sorumluluğundadır. Makalelerin bilimsel ve hukuki mesuliyeti yazarlarına aittir.