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The Big Data Revolution: A Comprehensive Bibliometric Study on Management and Organizational Development with a Focus on Web of Science

Year 2024, Issue: 16, 328 - 356, 28.06.2024
https://doi.org/10.51531/korkutataturkiyat.1460092

Abstract

The rapid advancement of technology has accentuated the pivotal role of data, particularly with the emergence of big data across various sectors. This phenomenon has garnered substantial interest among researchers, practitioners, and decision-makers alike, prompting a concerted effort to elucidate the implications of big data on organizational dynamics and managerial strategies. This research undertakes a comprehensive review of the big data literature spanning from 2011 to 2023, aiming to dissect its multifaceted influences on management paradigms and organizational behaviour. Specifically, it scrutinizes the impact of big data on decision-making processes, organizational restructuring, and broader transformative initiatives. Additionally, the study explores how strategies, organizational structures, and cultural norms associated with big data utilization contribute to reshaping the organizational landscape. At its core, this investigation seeks to unravel the transformative effects of big data on organizational evolution. A nuanced understanding of the intricate interplay between big data and organizational strategies, structures, and cultures is imperative for deciphering the mechanisms driving these transformations. The insights garnered from this inquiry are poised to inform both academic scholarship and practical endeavours, laying the groundwork for future research endeavours and strategic planning initiatives. Critical to this discourse is an appraisal of the potential benefits, risks, and sector-specific ramifications of big data analytics. Furthermore, a discerning analysis of the impact of big data adoption on organizational cultures promises invaluable insights for industry leaders. In summation, this study represents a significant scholarly endeavour aimed at deepening our comprehension of the implications of big data for organizational transformation, thereby shedding light on pertinent research avenues.

References

  • Akter, S., Fosso Wamba, S., 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.
  • 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.
  • Avolio, B. J., Walumbwa, F. O., & Weber, T. J. (2009). Leadership: Current Theories, Research, and Future Directions. Annual Review of Psychology, 60, 421-449.
  • Bughin, J., & Chui, M. (2010). The Rise of The Networked Enterprise: Web 2.0 Finds Its Payday. Mckinsey Quarterly, (1), 3-8.
  • Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, Big Data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch. Mckinsey Quarterly, 56(1), 75-86.
  • Chaurasia, J. (2023, July 12). Data Governance Unlocks the Impact of Analytics: Data Strategy & Insights 2023. Retrieved from Https://Www.Forrester.Com/Blogs/Data-Governance-Unlocks-The-Impact-Of-Analytics-Data-Strategy-Insights-2023/ [Erişim tarihi: 11.01.2024].
  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: from Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
  • Chen, M. (2017). Entrepreneurial Leadership and New Ventures: Creativity in Entrepreneurial Teams. Creativity and Innovation Management, 26(3), 239-250.
  • Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT Capability and Organizational Performance: The Roles of Business Process Agility and Environmental Factors. European Journal of Information Systems, 23(3), 326-342.
  • Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64-72.
  • Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press.
  • Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, 90(10), 70-76.
  • Egghe, L. (2005). Power Laws in the Information Production Process: Lotkaian Informetrics. Elsevier.
  • Elo, S., & Kyngäs, H. (2008). The Qualitative Content Analysis Process. Journal of Advanced Nursing, 62(1), 107-115.
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and The Transformation of Marketing. Journal of Business Research, 69(2), 897-904.
  • Gahi, Y., Guennoun, Z., & Moussaid, L. (2016). Big Data Management: Challenges, Approaches, Tools, and Their Limitations. International Journal of Cloud Applications and Computing, 6(2), 1-20.
  • Gartner. (2023). Gartner Identifies the Top 10 Data and Analytics Trends for 2023. Retrieved from Https://Www.Gartner.Com/En/Newsroom/Press-Releases/2023-05-09-Gartner-Identifies-The-Top-Ten-Data-And-Analytics-Trends-For-20230 [Erişim tarihi: 03.01.2024].
  • 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 Organizational Performance. Journal of Business Research, 70, 308-317.
  • Gupta, M., & George, J. F. (2016). Toward The Development of a Big Data Analytics Capability. Information & Management, 53(8), 1049-1064.
  • Grant, A. M. (2016). Originals: How Non-Conformists Move the World. Viking.
  • Grant, R. M. (2016). Contemporary Strategy Analysis: Text and Cases Edition. John Wiley & Sons.
  • Henke, N., Libarikian, A., & Wiseman, B. (2016, October 28). Straight Talk About Big Data. Retrieved from Https://Www.Mckinsey.Com/Capabilities/Mckinsey-Digital/Our-Insights/Straight-Talk-About-Big-Data [Erişim tarihi: 23.12.2023].
  • Hopkins, B. (2016, March 9). Think You Want to Be “Data-Driven”? Insight Is the New Data. Retrieved from Https://Www.Forrester.Com/Blogs/16-03-09-Think_You_Want_To_Be_Data_Driven_Insight_Is_The_New_Data/ [Erişim tarihi: 24.01.2024].
  • Ismayilova, N. (2017). Bibliometric Analysis of Big Data Research. [Conference paper]. Retrieved from https://ict.az/uploads/konfrans/biq_data/1-14_Nigar_Ismayilova_Bibliometric_Analysis_of_Big_Data_ResearchSON.pdf [Erişim tarihi: 23.01.2024].
  • Jabareen, Y. (2009). Building A Conceptual Framework: Philosophy, Definitions, and Procedure. International Journal of Qualitative Methods, 8(4), 49-62.
  • Kalantari, A., Kamsin, A., Kamaruddin, H. S., et al. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4, 30.
  • Kallinikos, J., & Constantiou, I. D. (2015). New Games, New Rules: Big Data and The Changing Context of Strategy. Journal of Information Technology, 30(1), 44-57.
  • Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE.
  • 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.
  • Liu, X., Sun, R., Wang, S., & Wu, Y. J. (2020). The research landscape of big data: A bibliometric analysis. Library Hi Tech, 38(2), 367–384.
  • Lotka, A. J. (1926). The Frequency Distribution of Scientific Productivity. Journal of the Washington Academy of Sciences, 16(12), 317-323.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
  • Maxwell, J. A. (2004). Qualitative Research Design: An Interactive Approach (2nd Ed.). Sage Publications. Mcafee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60-68.
  • Neuman, W. L. (2006). Social Research Methods: Qualitative and Quantitative Approaches (6th Ed.). Allyn & Bacon.
  • Neuendorf, K. A. (2016). The Content Analysis Guidebook (2nd Ed.). Sage Publications.
  • O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  • Price, D. J. D. S. (1976). A General Theory of Bibliometric and Other Cumulative Advantage Processes. Journal of the American Society for Information Science, 27(5), 292-306.
  • Sahoo, S. (2022). Big Data Analytics in Manufacturing: A Bibliometric Analysis of Research in the Field of Business Management. International Journal of Production Research, 60(22), 6793–6821.
  • Schroeck, M., Romero-Morales, D., & Van Hillegersberg, J. (2016). Predictive Analytics in Information Systems Research. MIS Quarterly, 40(3), 553-572.
  • Sheng, J., Amankwah-Amoah, J., Wang, X., & Khan, Z. (2017). A Multidisciplinary Perspective of Big Data in Management Research. International Journal of Production Economics, 191.
  • Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical Analysis of Big Data Challenges and Analytical Methods. Journal of Business Research, 70, 263-286.
  • Solla Price, D. J. (1963). Little Science, Big Science. Columbia University Press.
  • Tabesh, P., Mousavidin, E., & Hasanzadeh, A. (2019). Big Data and Organizational Change: A Managerial Perspective. Journal of Organizational Change Management, 32(4), 397-416.
  • Teece, D. J. (2018). Business Models and Dynamic Capabilities. Long Range Planning, 51(1), 40-49.
  • Thusoo, A., & Sarma, J. S. (2017). Managing and Mining Big Data. Mcgraw-Hill Education.
  • Van Eck, N. J., & Waltman, L. (2010). Software Survey: Vosviewer, A Computer Program for Bibliometric Mapping. Scientometrics, 84(2), 523-538.
  • Verhoef, P. C., Kooge, E., & Walk, N. (2017). Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge.
  • Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84.
  • 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., Dubey, R., & Childe, S. J. (2017). Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research, 70, 356-365.
  • Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics, 176, 98-110.
  • Wang, Y., Kung, L., & Byrd, T. A. (2018). Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations. Technological Forecasting and Social Change, 126, 3-13.
  • Weill, P., & Woerner, S. L. (2015). Thriving in an Increasingly Digital Ecosystem. MIT Sloan Management Review, 56(4), 27-34.
  • Xu, Z., & Yu, D. (2019). A Bibliometrics analysis on Big Data Research (2009–2018). Journal of Data, Information and Management, 1, 3–15.
  • Zupic, I., & Cater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429-472.

Büyük Veri Devrimi: Web of Science Odaklı Yönetim ve Kurumsal Gelişim Üzerine Kapsamlı Bir Bibliyometrik Çalışma

Year 2024, Issue: 16, 328 - 356, 28.06.2024
https://doi.org/10.51531/korkutataturkiyat.1460092

Abstract

Teknolojinin hızlı ilerleyişi, özellikle de farklı sektörlerde büyük verinin ortaya çıkmasıyla birlikte, verinin kilit rolünü vurgulamıştır. Bu kavram, araştırmacılar, uygulamacılar ve karar vericiler arasında önemli bir ilgi uyandırmıştır. Bu durum, büyük verinin organizasyonel dinamikler ve yönetimsel stratejiler üzerindeki etkilerini aydınlatmaya yönelik kararlı bir çaba başlatmıştır. Bu araştırma, 2011’den 2023’e kadar uzanan büyük veri literatürünün kapsamlı bir değerlendirmesini yaparak, büyük verinin yönetim paradigmaları ve organizasyonel davranışlar üzerindeki çok yönlü etkilerini incelemeyi amaçlamaktadır. Özellikle, büyük verinin karar alma süreçleri, organizasyonel yeniden yapılanma ve daha geniş dönüşümsel girişimler üzerindeki etkilerini gözden geçirmektedir. Ayrıca, büyük veri kullanımıyla ilişkilendirilen stratejilerin, organizasyonel yapıların ve kültürel normların, organizasyonel peyzajı yeniden şekillendirmede nasıl katkı sağladığını keşfetmesi amaçlanmıştır. Bu araştırmanın temelinde, büyük verinin örgütsel evrim üzerindeki dönüştürücü etkilerini çözümlemek yer almaktadır. Büyük veri ile örgütsel stratejiler, yapılar ve kültürler arasındaki karmaşık etkileşimin nüanslı bir şekilde anlaşılması, bu dönüşümleri yönlendiren mekanizmaları çözümlemek için hayati öneme sahiptir. Bu araştırmadan elde edilen içgörüler, akademik çalışmaları ve pratik uygulamaları bilgilendirerek, gelecekteki araştırma çabalarının ve stratejik planlama girişimlerinin temelini oluşturacaktır. Bu tartışma için kritik olan, büyük veri analitiğinin potansiyel faydaları, riskleri ve sektöre özgü sonuçlarıdır. Ayrıca, büyük veri kullanımının organizasyonel kültürler üzerindeki etkilerinin dikkatli bir şekilde analizi, endüstri liderleri için değerli içgörüler sunmaktadır. Sonuç olarak, bu çalışma, büyük verinin organizasyonel dönüşüm üzerindeki etkilerini derinleştirme amacı taşıyan önemli bir akademik girişimi temsil etmektedir.

References

  • Akter, S., Fosso Wamba, S., 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.
  • 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.
  • Avolio, B. J., Walumbwa, F. O., & Weber, T. J. (2009). Leadership: Current Theories, Research, and Future Directions. Annual Review of Psychology, 60, 421-449.
  • Bughin, J., & Chui, M. (2010). The Rise of The Networked Enterprise: Web 2.0 Finds Its Payday. Mckinsey Quarterly, (1), 3-8.
  • Bughin, J., Chui, M., & Manyika, J. (2010). Clouds, Big Data, and Smart Assets: Ten Tech-Enabled Business Trends to Watch. Mckinsey Quarterly, 56(1), 75-86.
  • Chaurasia, J. (2023, July 12). Data Governance Unlocks the Impact of Analytics: Data Strategy & Insights 2023. Retrieved from Https://Www.Forrester.Com/Blogs/Data-Governance-Unlocks-The-Impact-Of-Analytics-Data-Strategy-Insights-2023/ [Erişim tarihi: 11.01.2024].
  • Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business Intelligence and Analytics: from Big Data to Big Impact. MIS Quarterly, 36(4), 1165-1188.
  • Chen, M. (2017). Entrepreneurial Leadership and New Ventures: Creativity in Entrepreneurial Teams. Creativity and Innovation Management, 26(3), 239-250.
  • Chen, Y., Wang, Y., Nevo, S., Jin, J., Wang, L., & Chow, W. S. (2014). IT Capability and Organizational Performance: The Roles of Business Process Agility and Environmental Factors. European Journal of Information Systems, 23(3), 326-342.
  • Davenport, T. H. (2013). Analytics 3.0. Harvard Business Review, 91(12), 64-72.
  • Davenport, T. H. (2018). The AI Advantage: How to Put the Artificial Intelligence Revolution to Work. MIT Press.
  • Davenport, T. H., & Patil, D. J. (2012). Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, 90(10), 70-76.
  • Egghe, L. (2005). Power Laws in the Information Production Process: Lotkaian Informetrics. Elsevier.
  • Elo, S., & Kyngäs, H. (2008). The Qualitative Content Analysis Process. Journal of Advanced Nursing, 62(1), 107-115.
  • Erevelles, S., Fukawa, N., & Swayne, L. (2016). Big Data Consumer Analytics and The Transformation of Marketing. Journal of Business Research, 69(2), 897-904.
  • Gahi, Y., Guennoun, Z., & Moussaid, L. (2016). Big Data Management: Challenges, Approaches, Tools, and Their Limitations. International Journal of Cloud Applications and Computing, 6(2), 1-20.
  • Gartner. (2023). Gartner Identifies the Top 10 Data and Analytics Trends for 2023. Retrieved from Https://Www.Gartner.Com/En/Newsroom/Press-Releases/2023-05-09-Gartner-Identifies-The-Top-Ten-Data-And-Analytics-Trends-For-20230 [Erişim tarihi: 03.01.2024].
  • 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 Organizational Performance. Journal of Business Research, 70, 308-317.
  • Gupta, M., & George, J. F. (2016). Toward The Development of a Big Data Analytics Capability. Information & Management, 53(8), 1049-1064.
  • Grant, A. M. (2016). Originals: How Non-Conformists Move the World. Viking.
  • Grant, R. M. (2016). Contemporary Strategy Analysis: Text and Cases Edition. John Wiley & Sons.
  • Henke, N., Libarikian, A., & Wiseman, B. (2016, October 28). Straight Talk About Big Data. Retrieved from Https://Www.Mckinsey.Com/Capabilities/Mckinsey-Digital/Our-Insights/Straight-Talk-About-Big-Data [Erişim tarihi: 23.12.2023].
  • Hopkins, B. (2016, March 9). Think You Want to Be “Data-Driven”? Insight Is the New Data. Retrieved from Https://Www.Forrester.Com/Blogs/16-03-09-Think_You_Want_To_Be_Data_Driven_Insight_Is_The_New_Data/ [Erişim tarihi: 24.01.2024].
  • Ismayilova, N. (2017). Bibliometric Analysis of Big Data Research. [Conference paper]. Retrieved from https://ict.az/uploads/konfrans/biq_data/1-14_Nigar_Ismayilova_Bibliometric_Analysis_of_Big_Data_ResearchSON.pdf [Erişim tarihi: 23.01.2024].
  • Jabareen, Y. (2009). Building A Conceptual Framework: Philosophy, Definitions, and Procedure. International Journal of Qualitative Methods, 8(4), 49-62.
  • Kalantari, A., Kamsin, A., Kamaruddin, H. S., et al. (2017). A bibliometric approach to tracking big data research trends. Journal of Big Data, 4, 30.
  • Kallinikos, J., & Constantiou, I. D. (2015). New Games, New Rules: Big Data and The Changing Context of Strategy. Journal of Information Technology, 30(1), 44-57.
  • Kitchin, R. (2014). The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. SAGE.
  • 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.
  • Liu, X., Sun, R., Wang, S., & Wu, Y. J. (2020). The research landscape of big data: A bibliometric analysis. Library Hi Tech, 38(2), 367–384.
  • Lotka, A. J. (1926). The Frequency Distribution of Scientific Productivity. Journal of the Washington Academy of Sciences, 16(12), 317-323.
  • Mayer-Schönberger, V., & Cukier, K. (2013). Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt.
  • Maxwell, J. A. (2004). Qualitative Research Design: An Interactive Approach (2nd Ed.). Sage Publications. Mcafee, A., & Brynjolfsson, E. (2012). Big Data: The Management Revolution. Harvard Business Review, 90(10), 60-68.
  • Neuman, W. L. (2006). Social Research Methods: Qualitative and Quantitative Approaches (6th Ed.). Allyn & Bacon.
  • Neuendorf, K. A. (2016). The Content Analysis Guidebook (2nd Ed.). Sage Publications.
  • O’Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.
  • Price, D. J. D. S. (1976). A General Theory of Bibliometric and Other Cumulative Advantage Processes. Journal of the American Society for Information Science, 27(5), 292-306.
  • Sahoo, S. (2022). Big Data Analytics in Manufacturing: A Bibliometric Analysis of Research in the Field of Business Management. International Journal of Production Research, 60(22), 6793–6821.
  • Schroeck, M., Romero-Morales, D., & Van Hillegersberg, J. (2016). Predictive Analytics in Information Systems Research. MIS Quarterly, 40(3), 553-572.
  • Sheng, J., Amankwah-Amoah, J., Wang, X., & Khan, Z. (2017). A Multidisciplinary Perspective of Big Data in Management Research. International Journal of Production Economics, 191.
  • Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical Analysis of Big Data Challenges and Analytical Methods. Journal of Business Research, 70, 263-286.
  • Solla Price, D. J. (1963). Little Science, Big Science. Columbia University Press.
  • Tabesh, P., Mousavidin, E., & Hasanzadeh, A. (2019). Big Data and Organizational Change: A Managerial Perspective. Journal of Organizational Change Management, 32(4), 397-416.
  • Teece, D. J. (2018). Business Models and Dynamic Capabilities. Long Range Planning, 51(1), 40-49.
  • Thusoo, A., & Sarma, J. S. (2017). Managing and Mining Big Data. Mcgraw-Hill Education.
  • Van Eck, N. J., & Waltman, L. (2010). Software Survey: Vosviewer, A Computer Program for Bibliometric Mapping. Scientometrics, 84(2), 523-538.
  • Verhoef, P. C., Kooge, E., & Walk, N. (2017). Creating Value with Big Data Analytics: Making Smarter Marketing Decisions. Routledge.
  • Waller, M. A., & Fawcett, S. E. (2013). Data Science, Predictive Analytics, and Big Data: A Revolution That Will Transform Supply Chain Design and Management. Journal of Business Logistics, 34(2), 77-84.
  • 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., Dubey, R., & Childe, S. J. (2017). Big Data Analytics and Firm Performance: Effects of Dynamic Capabilities. Journal of Business Research, 70, 356-365.
  • Wang, G., Gunasekaran, A., Ngai, E. W., & Papadopoulos, T. (2016). Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics, 176, 98-110.
  • Wang, Y., Kung, L., & Byrd, T. A. (2018). Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations. Technological Forecasting and Social Change, 126, 3-13.
  • Weill, P., & Woerner, S. L. (2015). Thriving in an Increasingly Digital Ecosystem. MIT Sloan Management Review, 56(4), 27-34.
  • Xu, Z., & Yu, D. (2019). A Bibliometrics analysis on Big Data Research (2009–2018). Journal of Data, Information and Management, 1, 3–15.
  • Zupic, I., & Cater, T. (2015). Bibliometric Methods in Management and Organization. Organizational Research Methods, 18(3), 429-472.
There are 55 citations in total.

Details

Primary Language English
Subjects Other Fields of Education (Other)
Journal Section Araştırma Makaleleri
Authors

Ecem Cemre Güleç Bilgili 0000-0003-1178-7438

Ayse Aslı Yılmaz 0000-0003-1784-7307

Publication Date June 28, 2024
Submission Date March 27, 2024
Acceptance Date May 30, 2024
Published in Issue Year 2024 Issue: 16

Cite

APA Güleç Bilgili, E. C., & Yılmaz, A. A. (2024). The Big Data Revolution: A Comprehensive Bibliometric Study on Management and Organizational Development with a Focus on Web of Science. Korkut Ata Türkiyat Araştırmaları Dergisi(16), 328-356. https://doi.org/10.51531/korkutataturkiyat.1460092