Araştırma Makalesi
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İK ANALİTİĞİ LİTERATÜRÜNÜN BİBLİYOMETRİK ANALİZİ

Yıl 2022, Cilt 21, Sayı 83, 1147 - 1169, 01.07.2022
https://doi.org/10.17755/esosder.950426

Öz

İnsan kaynakları analitiği (İK analitiği) araştırması son yıllarda popüler hale gelmiş ve yeni ortaya çıkan bir araştırma alandır. Bu alanda yapılan çalışmaların hangi çerçevede ilerlediğini görmek, ileride bu alanda yapılacak yeni çalışmalara ışık tutacaktır. Bu çalışma, İK analitiğinin entelektüel çerçeve temelinde nasıl ilerlediğini incelemektedir. Bu araştırma, çalışmaların referansları, yazarları, konuları, atıfları ve dergileri incelenerek literatüre katkı sağlamayı amaçlamaktadır. Bu amaçla, 2010 ile 2021 yılları arasında yayınlanan 178 makaleyi incelerek bibliyometrik analiz tekniği kullanılmıştır. İK analitiği konularını ele alan ve bu makaleleri yayınlayan dergilerde çok çeşitli disiplinler kullanıldı görülmüştür. Makalelerde toplanan ana temalar, büyük veri, yetenek yönetimi ve işgücü analitiği kavramları etrafındadır. Çalışma sonuçları, İK analitiğine yönelik araştırma ilgisinin son yıllarda arttığını göstermektedir. İK profesyonellerinin yetkinlikleri, veri kalitesi, teknolojik gelişmeler, BT departmanı ile işbirliği ana başlıklar iken, literatürün etik konusunu ihmal ettiği görülmüştür.

Kaynakça

  • Andersen, M. K. (2017). Human capital analytics: the winding road. Journal of Organizational Effectiveness: People and Performance.
  • Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
  • Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913-931.
  • Bag, S., & Pretorius, J. H. C. (2020). Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework. International Journal of Organizational Analysis.
  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
  • Bassi, L., & McMurrer, D. (2016). Four lessons learned in how to use human resource analytics to improve the effectiveness of leadership development. Journal of leadership studies, 10(2), 39-43.
  • Ben-Gal, H. C. (2019). An ROI-based review of HR analytics: practical implementation tools. Personnel Review.
  • Berk, L., Bertsimas, D., Weinstein, A. M., & Yan, J. (2019). Prescriptive analytics for human resource planning in the professional services industry. European Journal of Operational Research, 272(2), 636-641.
  • Bohlouli, M., Mittas, N., Kakarontzas, G., Theodosiou, T., Angelis, L., & Fathi, M. (2017). Competence assessment as an expert system for human resource management: A mathematical approach. Expert Systems with Applications, 70, 83-102.
  • Boudreau, J. W., & Ramstad, P. M. (2007). Beyond HR: The new science of human capital. Harvard Business Press.
  • Bowen, D. E. (1996). Market-focused HRM in service organizations: Satisfying internal and external customers. Journal of Market-Focused Management, 1(1), 31-47.
  • Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International journal of information management, 46, 93-103.
  • Calvard, T. S., & Jeske, D. (2018). Developing human resource data risk management in the age of big data. International Journal of Information Management, 43, 159-164.
  • Carmeli, A., & Tishler, A. (2004). The relationships between intangible organizational elements and organizational performance. Strategic management journal, 25(13), 1257-1278.
  • Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: definition, theory, and practice. Human Resource Management Review, 31(1), 100698.
  • Chenthamarakshan, V., Dixit, K., Gattani, M., Goyal, M., Gupta, P., Kambhatla, N., ... & Visweswariah, K. (2010). Effective decision support systems for workforce deployment. IBM Journal of Research and Development, 54(6), 5-1.
  • Chittiprolu, V., Singh, S., Bellamkonda, R. S., & Vanka, S. (2020). A text mining analysis of online reviews of Indian hotel employees. Anatolia, 1-14.
  • Choi, Y., & Choi, J. W. (2020). A study of job involvement prediction using machine learning technique. International Journal of Organizational Analysis.
  • Culnan, M. J. (1987). Mapping the intellectual structure of MIS, 1980-1985: A co-citation analysis. Mis Quarterly, 341-353.
  • Danvila-del-Valle, I., Estévez-Mendoza, C., & Lara, F. J. (2019). Human resources training: A bibliometric analysis. Journal of Business Research, 101, 627-636.
  • Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard business review, 88(10), 52-58.
  • De Laat, M., & Schreurs, B. (2013). Visualizing informal professional development networks: Building a case for learning analytics in the workplace. American Behavioral Scientist, 57(10), 1421-1438.
  • De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management, 54(5), 807-817.
  • Deloitte, (2017), “Redesigning performance management Deloitte insights”, available at: https://www2.deloitte.com/insights/us/en/focus/human-capital-trends/2017/redesigning-performance-management.html, access date: 20.02.2021.
  • Deloitte, (2019), “2019 Global Human Capital Trends Report”, available at: https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2019-interactive.html, access date: 20.02.2021.
  • Deloitte, (2020), “2020 Global Human Capital Trends Report”, available at: https://www2.deloitte.com/cn/en/pages/human-capital/articles/global-human-capital-trends-2020.html, access date: 20.02.2021.
  • Dulebohn, J. H., & Johnson, R. D. (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23(1), 71-83.
  • Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction and equity prices. Journal of Financial Economics, 101(3), 621–640. https://doi.org/ 10.1016/j.jfineco.2011.03.021.
  • Edwards, M. R., & Edwards, K. (2019). Predictive HR analytics: Mastering the HR metric. Kogan Page Publishers.
  • Falletta, S. V., & Combs, W. L. (2020). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management.
  • Fernandez, V., & Gallardo-Gallardo, E. (2020). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal.
  • Frederiksen, A. (2017). Job satisfaction and employee turnover: A firm-level perspective. German Journal of Human Resource Management, 31(2), 132-161.
  • Galbreath, J. (2005). Which resources matter the most to firm success? An exploratory study of resource-based theory. Technovation, 25(9), 979-987.
  • Garcia-Arroyo, J., & Osca, A. (2019). Big data contributions to human resource management: a systematic review. The International Journal of Human Resource Management, 1-26.
  • Gelbard, R., Ramon‐Gonen, R., Carmeli, A., Bittmann, R. M., & Talyansky, R. (2018). Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5), e12289.
  • Ghasemaghaei, M., & Calic, G. (2019). Does big data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research, 104, 69-84.
  • Gibbons, J. M., & Woock, C. (2007). Evidence-based human resources: A primer and summary of current literature. Conference Board, Incorporated.
  • Gobble, M. M. (2017). The datification of human resources. Research-Technology Management, 60(5), 59-63.
  • González-Torres, A., García-Peñalvo, F. J., Therón-Sánchez, R., & Colomo-Palacios, R. (2016). Knowledge discovery in software teams by means of evolutionary visual software analytics. Science of Computer Programming, 121, 55-74.
  • Green, D. (2017). The best practices to excel at people analytics. Journal of Organizational Effectiveness: People and Performance.
  • Gubbins, C., & Rousseau, D. (2015). Embracing translational HRD research for evidence-based management: Let’s talk about how to bridge the research-practice gap. Human Resource Development Quarterly, 26(2), 109-125.
  • Gubbins, C., Harney, B., van der Werff, L., & Rousseau, D. (2018). Enhancing the Trustworthiness and Credibility of HRD: Evidence-based Management to the Rescue?. Human Resource Development Quarterly, 29(3), 193-202.
  • 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.
  • Hall, R. (1992). The strategic analysis of intangible resources. Strategic management journal, 13(2), 135-144.
  • Hall, R. (1993). A framework linking intangible resources and capabiliites to sustainable competitive advantage. Strategic management journal, 14(8), 607-618.
  • Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.
  • Hatch, N. W., & Dyer, J. H. (2004). Human capital and learning as a source of sustainable competitive advantage. Strategic management journal, 25(12), 1155-1178.
  • Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue.
  • Huselid, M. A., & Barnes, J. E. (2003). Human capital measurement systems as a source of competitive advantage. Retrieved November, 30, 2010.
  • Hwang, J., Bai, K., Tacci, M., Vukovic, M., & Anerousis, N. (2016). Automation and orchestration framework for large-scale enterprise cloud migration. IBM Journal of Research and Development, 60(2-3), 1-1.
  • Iqbal, N., Ahmad, M., Allen, M. M., & Raziq, M. M. (2018). Does e-HRM improve labour productivity? A study of commercial bank workplaces in Pakistan. Employee Relations.
  • Iyamu, T., & Mgudlwa, S. (2018). Transformation of healthcare big data through the lens of actor network theory. International Journal of Healthcare Management, 11(3), 182-192.
  • Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: measures that drive performance. Harvard business review, 83(7), 172.
  • Kapoor, B., & Sherif, J. (2012). Human resources in an enriched environment of business intelligence. Kybernetes.
  • Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: being mindful of employees. Journal of General Management, 42(2), 57-66.
  • Kim, S., Wang, Y., & Boon, C. (2021b). Sixty years of research on technology and human resource management: Looking back and looking forward. Human Resource Management, 60(1), 229-247.
  • King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487-495.
  • Koriat, N., & Gelbard, R. (2018). Knowledge sharing motivation among external and internal IT workers. Journal of Information & Knowledge Management, 17(03), 1850026.
  • Kremer, K. (2018). HR analytics and its moderating factors. Vezetéstudomány-Budapest Management Review, 49(11), 62-68.
  • Kryscynski, D., Reeves, C., Stice‐Lusvardi, R., Ulrich, M., & Russell, G. (2018). Analytical abilities and the performance of HR professionals. Human Resource Management, 57(3), 715-738.
  • Langford, L., & Haynes, B. (2015). An investigation into how corporate real estate in the financial services industry can add value through alignment and methods of performance measurement. Journal of Corporate Real Estate.
  • Larsson, A. S., & Edwards, M. R. (2021). Insider econometrics meets people analytics and strategic human resource management. The International Journal of Human Resource Management, 1-47.
  • Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics–uses and impacts. Human Resource Planning Journal, 27(4), 27-35.
  • Lengnick-Hall, M. L., Neely, A. R., & Stone, C. B. (2018). Human resource management in the digital age: Big data, HR analytics and artificial intelligence. Management and technological challenges in the digital age, 13-42.
  • Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685-700.
  • Levenson, A., & Fink, A. (2017). Human capital analytics: too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness: People and Performance.
  • Liu, D., & Lee, G. (2015). What are the Most Critical HR Capabilities and Competencies that are Emerging for the Future?.
  • Liu, L., Akkineni, S., Story, P., & Davis, C. (2020, April). Using HR analytics to support managerial decisions: a case study. In Proceedings of the 2020 ACM Southeast Conference (pp. 168-175).
  • Liu, X., Van Jaarsveld, D. D., Batt, R., & Frost, A. C. (2014). The influence of capital structure on strategic human capital: Evidence from US and Canadian firms. Journal of Management, 40(2), 422-448.
  • Malisetty, S., Archana, R. V., & Kumari, K. V. (2017). Predictive Analytics in HR Management. Indian Journal of Public Health Research & Development, 8(3).
  • Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
  • Marler, J. H., & Fisher, S. L. (2013). An evidence-based review of e-HRM and strategic human resource management. Human resource management review, 23(1), 18-36.
  • Marler, J. H., Cronemberger, F., & Tao, C. (2017). HR analytics: Here to stay or short lived management fashion?. In Electronic HRM in the Smart Era. Emerald Publishing Limited.
  • Martín-de-Castro, G., Delgado-Verde, M., López-Sáez, P., & Navas-López, J. E. (2011). Towards ‘an intellectual capital-based view of the firm’: origins and nature. Journal of business ethics, 98(4), 649-662.
  • Martinez, V., Zhao, M., Blujdea, C., Han, X., Neely, A., & Albores, P. (2019). Blockchain-driven customer order management. International Journal of Operations & Production Management.
  • McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American society for information science, 41(6), 433-443.
  • McCartney, S., Murphy, C., & Mccarthy, J. (2020). 21st century HR: a competency model for the emerging role of HR Analysts. Personnel Review.
  • McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397-407.
  • Minbaeva, D. B. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713.
  • Mirski, P., Bernsteiner, R., & Radi, D. (2017). Analytics in human resource management the openskimr approach. Procedia computer science, 122, 727-734.
  • Nasar, N., Ray, S., Umer, S., & Mohan Pandey, H. (2020). Design and data analytics of electronic human resource management activities through Internet of Things in an organization. Software: Practice and Experience.
  • Nienaber, H., & Sewdass, N. (2016). A reflection and integration of workforce conceptualisations and measurements for competitive advantage. Journal of Intelligence Studies in Business, 6(1).
  • Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2), 687-698.
  • Papoutsoglou, M., Mittas, N., & Angelis, L. (2017, August). Mining people analytics from stackoverflow job advertisements. In 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 108-115). IEEE.
  • Pejic-Bach, M., Bertoncel, T., Meško, M., & Krstić, Ž. (2020). Text mining of industry 4.0 job advertisements. International journal of information management, 50, 416-431.
  • Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290.
  • Pirola, F., Cimini, C., & Pinto, R. (2019). Digital readiness assessment of Italian SMEs: a case-study research. Journal of Manufacturing Technology Management.
  • Pitt, C. S., Botha, E., Ferreira, J. J., & Kietzmann, J. (2018). Employee brand engagement on social media: Managing optimism and commonality. Business Horizons, 61(4), 635-642.
  • Ramamurthy, K. N., Singh, M., Davis, M., Kevern, J. A., Klein, U., & Peran, M. (2015, November). Identifying employees for re-skilling using an analytics-based approach. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 345-354). IEEE.
  • Rangone, A. (1999). A resource-based approach to strategy analysis in small-medium sized enterprises. Small business economics, 12(3), 233-248.
  • Roy, V., Silvestre, B. S., & Singh, S. (2020). Reactive and proactive pathways to sustainable apparel supply chains: Manufacturer's perspective on stakeholder salience and organizational learning toward responsible management. International Journal of Production Economics, 227, 107672.
  • Ryan, J. C. (2020). Retaining, resigning and firing: bibliometrics as a people analytics tool for examining research performance outcomes and faculty turnover. Personnel Review.
  • Safarishahrbijari, A. (2018). Workforce forecasting models: A systematic review. Journal of Forecasting, 37(7), 739-753.
  • Sengupta, A., Mittal, S., & Sanchita, K. (2020). How do mid-level managers experience data science disruptions? An in-depth inquiry through interpretative phenomenological analysis (IPA). Management Decision.
  • Sharma, A., & Sharma, T. (2017). HR analytics and performance appraisal system. Management Research Review.
  • Shukla, A., Chaturvedi, S., & Simmhan, Y. (2017). Riotbench: An iot benchmark for distributed stream processing systems. Concurrency and Computation: Practice and Experience, 29(21), e4257.
  • Simón, C., & Ferreiro, E. (2018). Workforce analytics: A case study of scholar–practitioner collaboration. Human Resource Management, 57(3), 781-793.
  • Sohrabi, B., Vanani, I. R., & Abedin, E. (2018). Human resources management and information systems trend analysis using text clustering. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 9(3), 1-24.
  • Surwase G., Sagar, A., Kademani, B. S., & Bhanumurthy, K. (2011). Co-citation analysis: an overview.
  • Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21(3), 525-547.
  • Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224-247.
  • Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What's next for HR?. Human Resource Management Review, 25(2), 188-204.
  • Ulrich, D., Kryscynski, D., Ulrich, M., & Brockbank, W. (2017). Competencies for HR professionals who deliver outcomes.
  • Van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of Organizational Effectiveness: People and Performance.
  • Van der Laken, P., Bakk, Z., Giagkoulas, V., van Leeuwen, L., & Bongenaar, E. (2018). Expanding the methodological toolbox of HRM researchers: The added value of latent bathtub models and optimal matching analysis. Human Resource Management, 57(3), 751-760.
  • van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness: People and Performance.
  • Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22), 3046-3067.
  • Verma, S., Singh, V., & Bhattacharyya, S. S. (2020). Do big data-driven HR practices improve HR service quality and innovation competency of SMEs. International Journal of Organizational Analysis.
  • Villalonga, B. (2004). Intangible resources, Tobin’sq, and sustainability of performance differences. Journal of Economic Behavior & Organization, 54(2), 205-230.
  • Wang, L., & Cotton, R. (2018). Beyond Moneyball to social capital inside and out: The value of differentiated workforce experience ties to performance. Human Resource Management, 57(3), 761-780.
  • White, H. D., & McCain, K. W. (1997). Visualization of literatures. Annual review of information science and technology (ARIST), 32, 99-168.
  • Wu, D. D., Kapoor, B., & Sherif, J. (2012). Human resources in an enriched environment of business intelligence. Kybernetes.
  • Xu, H., Yu, Z., Yang, J., Xiong, H., & Zhu, H. (2018). Dynamic talent flow analysis with deep sequence prediction modeling. IEEE Transactions on Knowledge and Data Engineering, 31(10), 1926-1939.
  • Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1-15.
  • Yassine, N., & Singh, S. K. (2020). Sustainable supply chains based on supplier selection and HRM practices. Journal of Enterprise Information Management.
  • Zhang, D., Ma, Y., Zhang, Y., Lin, S., Hu, X. S., & Wang, D. (2018, April). A real-time and non-cooperative task allocation framework for social sensing applications in edge computing systems. In 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 316-326). IEEE.
  • Zhou, Y., Liu, G., Chang, X., & Wang, L. (2021). The impact of HRM digitalization on firm performance: investigating three‐way interactions. Asia Pacific Journal of Human Resources, 59(1), 20-43.
  • Ziebell, R. C., Albors-Garrigos, J., Schoeneberg, K. P., & Marin, M. R. P. (2019). Adoption and success of e-HRM in a cloud computing environment: a field study. International Journal of Cloud Applications and Computing (IJCAC), 9(2), 1-27.

BIBLIOMETRIC ANALYSIS OF HR ANALYTICS LITERATURE

Yıl 2022, Cilt 21, Sayı 83, 1147 - 1169, 01.07.2022
https://doi.org/10.17755/esosder.950426

Öz

Human resource analytics (HR analytics) research has been popular in recent years and is a newly emerging research area. Seeing in which frame the work done in this field is progressing will shed light on new future studies in the field. This study examines how HR analytics work is built on the basis of the intellectual framework. This research aims to contribute to the literature by examining the references, authors, topics, citations and journals of the studies. For this purpose, bibliometric techniques were used to examine 178 articles published between 2010 and 2021. A wide variety of disciplines have been used in the journals that publish these articles to address the issues of HR analytics. Main themes gathered in the articles are around the concepts of big data, talent management and workforce analytics. The study results show that research interest in HR analytics has increased in recent years. While the competencies of HR professionals, data quality, technological developments, cooperation with the IT department are the main topics, the literature seems to neglect the issue of ethics.

Kaynakça

  • Andersen, M. K. (2017). Human capital analytics: the winding road. Journal of Organizational Effectiveness: People and Performance.
  • Angrave, D., Charlwood, A., Kirkpatrick, I., Lawrence, M., & Stuart, M. (2016). HR and analytics: why HR is set to fail the big data challenge. Human Resource Management Journal, 26(1), 1-11.
  • Aral, S., Brynjolfsson, E., & Wu, L. (2012). Three-way complementarities: Performance pay, human resource analytics, and information technology. Management Science, 58(5), 913-931.
  • Bag, S., & Pretorius, J. H. C. (2020). Relationships between industry 4.0, sustainable manufacturing and circular economy: proposal of a research framework. International Journal of Organizational Analysis.
  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of management, 17(1), 99-120.
  • Bassi, L., & McMurrer, D. (2016). Four lessons learned in how to use human resource analytics to improve the effectiveness of leadership development. Journal of leadership studies, 10(2), 39-43.
  • Ben-Gal, H. C. (2019). An ROI-based review of HR analytics: practical implementation tools. Personnel Review.
  • Berk, L., Bertsimas, D., Weinstein, A. M., & Yan, J. (2019). Prescriptive analytics for human resource planning in the professional services industry. European Journal of Operational Research, 272(2), 636-641.
  • Bohlouli, M., Mittas, N., Kakarontzas, G., Theodosiou, T., Angelis, L., & Fathi, M. (2017). Competence assessment as an expert system for human resource management: A mathematical approach. Expert Systems with Applications, 70, 83-102.
  • Boudreau, J. W., & Ramstad, P. M. (2007). Beyond HR: The new science of human capital. Harvard Business Press.
  • Bowen, D. E. (1996). Market-focused HRM in service organizations: Satisfying internal and external customers. Journal of Market-Focused Management, 1(1), 31-47.
  • Božič, K., & Dimovski, V. (2019). Business intelligence and analytics for value creation: The role of absorptive capacity. International journal of information management, 46, 93-103.
  • Calvard, T. S., & Jeske, D. (2018). Developing human resource data risk management in the age of big data. International Journal of Information Management, 43, 159-164.
  • Carmeli, A., & Tishler, A. (2004). The relationships between intangible organizational elements and organizational performance. Strategic management journal, 25(13), 1257-1278.
  • Cheng, M. M., & Hackett, R. D. (2021). A critical review of algorithms in HRM: definition, theory, and practice. Human Resource Management Review, 31(1), 100698.
  • Chenthamarakshan, V., Dixit, K., Gattani, M., Goyal, M., Gupta, P., Kambhatla, N., ... & Visweswariah, K. (2010). Effective decision support systems for workforce deployment. IBM Journal of Research and Development, 54(6), 5-1.
  • Chittiprolu, V., Singh, S., Bellamkonda, R. S., & Vanka, S. (2020). A text mining analysis of online reviews of Indian hotel employees. Anatolia, 1-14.
  • Choi, Y., & Choi, J. W. (2020). A study of job involvement prediction using machine learning technique. International Journal of Organizational Analysis.
  • Culnan, M. J. (1987). Mapping the intellectual structure of MIS, 1980-1985: A co-citation analysis. Mis Quarterly, 341-353.
  • Danvila-del-Valle, I., Estévez-Mendoza, C., & Lara, F. J. (2019). Human resources training: A bibliometric analysis. Journal of Business Research, 101, 627-636.
  • Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard business review, 88(10), 52-58.
  • De Laat, M., & Schreurs, B. (2013). Visualizing informal professional development networks: Building a case for learning analytics in the workplace. American Behavioral Scientist, 57(10), 1421-1438.
  • De Mauro, A., Greco, M., Grimaldi, M., & Ritala, P. (2018). Human resources for Big Data professions: A systematic classification of job roles and required skill sets. Information Processing & Management, 54(5), 807-817.
  • Deloitte, (2017), “Redesigning performance management Deloitte insights”, available at: https://www2.deloitte.com/insights/us/en/focus/human-capital-trends/2017/redesigning-performance-management.html, access date: 20.02.2021.
  • Deloitte, (2019), “2019 Global Human Capital Trends Report”, available at: https://www2.deloitte.com/us/en/insights/focus/human-capital-trends/2019-interactive.html, access date: 20.02.2021.
  • Deloitte, (2020), “2020 Global Human Capital Trends Report”, available at: https://www2.deloitte.com/cn/en/pages/human-capital/articles/global-human-capital-trends-2020.html, access date: 20.02.2021.
  • Dulebohn, J. H., & Johnson, R. D. (2013). Human resource metrics and decision support: A classification framework. Human Resource Management Review, 23(1), 71-83.
  • Edmans, A. (2011). Does the stock market fully value intangibles? Employee satisfaction and equity prices. Journal of Financial Economics, 101(3), 621–640. https://doi.org/ 10.1016/j.jfineco.2011.03.021.
  • Edwards, M. R., & Edwards, K. (2019). Predictive HR analytics: Mastering the HR metric. Kogan Page Publishers.
  • Falletta, S. V., & Combs, W. L. (2020). The HR analytics cycle: a seven-step process for building evidence-based and ethical HR analytics capabilities. Journal of Work-Applied Management.
  • Fernandez, V., & Gallardo-Gallardo, E. (2020). Tackling the HR digitalization challenge: key factors and barriers to HR analytics adoption. Competitiveness Review: An International Business Journal.
  • Frederiksen, A. (2017). Job satisfaction and employee turnover: A firm-level perspective. German Journal of Human Resource Management, 31(2), 132-161.
  • Galbreath, J. (2005). Which resources matter the most to firm success? An exploratory study of resource-based theory. Technovation, 25(9), 979-987.
  • Garcia-Arroyo, J., & Osca, A. (2019). Big data contributions to human resource management: a systematic review. The International Journal of Human Resource Management, 1-26.
  • Gelbard, R., Ramon‐Gonen, R., Carmeli, A., Bittmann, R. M., & Talyansky, R. (2018). Sentiment analysis in organizational work: Towards an ontology of people analytics. Expert Systems, 35(5), e12289.
  • Ghasemaghaei, M., & Calic, G. (2019). Does big data enhance firm innovation competency? The mediating role of data-driven insights. Journal of Business Research, 104, 69-84.
  • Gibbons, J. M., & Woock, C. (2007). Evidence-based human resources: A primer and summary of current literature. Conference Board, Incorporated.
  • Gobble, M. M. (2017). The datification of human resources. Research-Technology Management, 60(5), 59-63.
  • González-Torres, A., García-Peñalvo, F. J., Therón-Sánchez, R., & Colomo-Palacios, R. (2016). Knowledge discovery in software teams by means of evolutionary visual software analytics. Science of Computer Programming, 121, 55-74.
  • Green, D. (2017). The best practices to excel at people analytics. Journal of Organizational Effectiveness: People and Performance.
  • Gubbins, C., & Rousseau, D. (2015). Embracing translational HRD research for evidence-based management: Let’s talk about how to bridge the research-practice gap. Human Resource Development Quarterly, 26(2), 109-125.
  • Gubbins, C., Harney, B., van der Werff, L., & Rousseau, D. (2018). Enhancing the Trustworthiness and Credibility of HRD: Evidence-based Management to the Rescue?. Human Resource Development Quarterly, 29(3), 193-202.
  • 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.
  • Hall, R. (1992). The strategic analysis of intangible resources. Strategic management journal, 13(2), 135-144.
  • Hall, R. (1993). A framework linking intangible resources and capabiliites to sustainable competitive advantage. Strategic management journal, 14(8), 607-618.
  • Hamilton, R. H., & Sodeman, W. A. (2020). The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources. Business Horizons, 63(1), 85-95.
  • Hatch, N. W., & Dyer, J. H. (2004). Human capital and learning as a source of sustainable competitive advantage. Strategic management journal, 25(12), 1155-1178.
  • Huselid, M. A. (2018). The science and practice of workforce analytics: Introduction to the HRM special issue.
  • Huselid, M. A., & Barnes, J. E. (2003). Human capital measurement systems as a source of competitive advantage. Retrieved November, 30, 2010.
  • Hwang, J., Bai, K., Tacci, M., Vukovic, M., & Anerousis, N. (2016). Automation and orchestration framework for large-scale enterprise cloud migration. IBM Journal of Research and Development, 60(2-3), 1-1.
  • Iqbal, N., Ahmad, M., Allen, M. M., & Raziq, M. M. (2018). Does e-HRM improve labour productivity? A study of commercial bank workplaces in Pakistan. Employee Relations.
  • Iyamu, T., & Mgudlwa, S. (2018). Transformation of healthcare big data through the lens of actor network theory. International Journal of Healthcare Management, 11(3), 182-192.
  • Kaplan, R. S., & Norton, D. P. (1992). The balanced scorecard: measures that drive performance. Harvard business review, 83(7), 172.
  • Kapoor, B., & Sherif, J. (2012). Human resources in an enriched environment of business intelligence. Kybernetes.
  • Khan, S. A., & Tang, J. (2016). The paradox of human resource analytics: being mindful of employees. Journal of General Management, 42(2), 57-66.
  • Kim, S., Wang, Y., & Boon, C. (2021b). Sixty years of research on technology and human resource management: Looking back and looking forward. Human Resource Management, 60(1), 229-247.
  • King, K. G. (2016). Data analytics in human resources: A case study and critical review. Human Resource Development Review, 15(4), 487-495.
  • Koriat, N., & Gelbard, R. (2018). Knowledge sharing motivation among external and internal IT workers. Journal of Information & Knowledge Management, 17(03), 1850026.
  • Kremer, K. (2018). HR analytics and its moderating factors. Vezetéstudomány-Budapest Management Review, 49(11), 62-68.
  • Kryscynski, D., Reeves, C., Stice‐Lusvardi, R., Ulrich, M., & Russell, G. (2018). Analytical abilities and the performance of HR professionals. Human Resource Management, 57(3), 715-738.
  • Langford, L., & Haynes, B. (2015). An investigation into how corporate real estate in the financial services industry can add value through alignment and methods of performance measurement. Journal of Corporate Real Estate.
  • Larsson, A. S., & Edwards, M. R. (2021). Insider econometrics meets people analytics and strategic human resource management. The International Journal of Human Resource Management, 1-47.
  • Lawler III, E. E., Levenson, A., & Boudreau, J. W. (2004). HR metrics and analytics–uses and impacts. Human Resource Planning Journal, 27(4), 27-35.
  • Lengnick-Hall, M. L., Neely, A. R., & Stone, C. B. (2018). Human resource management in the digital age: Big data, HR analytics and artificial intelligence. Management and technological challenges in the digital age, 13-42.
  • Levenson, A. (2018). Using workforce analytics to improve strategy execution. Human Resource Management, 57(3), 685-700.
  • Levenson, A., & Fink, A. (2017). Human capital analytics: too much data and analysis, not enough models and business insights. Journal of Organizational Effectiveness: People and Performance.
  • Liu, D., & Lee, G. (2015). What are the Most Critical HR Capabilities and Competencies that are Emerging for the Future?.
  • Liu, L., Akkineni, S., Story, P., & Davis, C. (2020, April). Using HR analytics to support managerial decisions: a case study. In Proceedings of the 2020 ACM Southeast Conference (pp. 168-175).
  • Liu, X., Van Jaarsveld, D. D., Batt, R., & Frost, A. C. (2014). The influence of capital structure on strategic human capital: Evidence from US and Canadian firms. Journal of Management, 40(2), 422-448.
  • Malisetty, S., Archana, R. V., & Kumari, K. V. (2017). Predictive Analytics in HR Management. Indian Journal of Public Health Research & Development, 8(3).
  • Marler, J. H., & Boudreau, J. W. (2017). An evidence-based review of HR Analytics. The International Journal of Human Resource Management, 28(1), 3-26.
  • Marler, J. H., & Fisher, S. L. (2013). An evidence-based review of e-HRM and strategic human resource management. Human resource management review, 23(1), 18-36.
  • Marler, J. H., Cronemberger, F., & Tao, C. (2017). HR analytics: Here to stay or short lived management fashion?. In Electronic HRM in the Smart Era. Emerald Publishing Limited.
  • Martín-de-Castro, G., Delgado-Verde, M., López-Sáez, P., & Navas-López, J. E. (2011). Towards ‘an intellectual capital-based view of the firm’: origins and nature. Journal of business ethics, 98(4), 649-662.
  • Martinez, V., Zhao, M., Blujdea, C., Han, X., Neely, A., & Albores, P. (2019). Blockchain-driven customer order management. International Journal of Operations & Production Management.
  • McCain, K. W. (1990). Mapping authors in intellectual space: A technical overview. Journal of the American society for information science, 41(6), 433-443.
  • McCartney, S., Murphy, C., & Mccarthy, J. (2020). 21st century HR: a competency model for the emerging role of HR Analysts. Personnel Review.
  • McIver, D., Lengnick-Hall, M. L., & Lengnick-Hall, C. A. (2018). A strategic approach to workforce analytics: Integrating science and agility. Business Horizons, 61(3), 397-407.
  • Minbaeva, D. B. (2018). Building credible human capital analytics for organizational competitive advantage. Human Resource Management, 57(3), 701-713.
  • Mirski, P., Bernsteiner, R., & Radi, D. (2017). Analytics in human resource management the openskimr approach. Procedia computer science, 122, 727-734.
  • Nasar, N., Ray, S., Umer, S., & Mohan Pandey, H. (2020). Design and data analytics of electronic human resource management activities through Internet of Things in an organization. Software: Practice and Experience.
  • Nienaber, H., & Sewdass, N. (2016). A reflection and integration of workforce conceptualisations and measurements for competitive advantage. Journal of Intelligence Studies in Business, 6(1).
  • Pape, T. (2016). Prioritising data items for business analytics: Framework and application to human resources. European Journal of Operational Research, 252(2), 687-698.
  • Papoutsoglou, M., Mittas, N., & Angelis, L. (2017, August). Mining people analytics from stackoverflow job advertisements. In 2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) (pp. 108-115). IEEE.
  • Pejic-Bach, M., Bertoncel, T., Meško, M., & Krstić, Ž. (2020). Text mining of industry 4.0 job advertisements. International journal of information management, 50, 416-431.
  • Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290.
  • Pirola, F., Cimini, C., & Pinto, R. (2019). Digital readiness assessment of Italian SMEs: a case-study research. Journal of Manufacturing Technology Management.
  • Pitt, C. S., Botha, E., Ferreira, J. J., & Kietzmann, J. (2018). Employee brand engagement on social media: Managing optimism and commonality. Business Horizons, 61(4), 635-642.
  • Ramamurthy, K. N., Singh, M., Davis, M., Kevern, J. A., Klein, U., & Peran, M. (2015, November). Identifying employees for re-skilling using an analytics-based approach. In 2015 IEEE International Conference on Data Mining Workshop (ICDMW) (pp. 345-354). IEEE.
  • Rangone, A. (1999). A resource-based approach to strategy analysis in small-medium sized enterprises. Small business economics, 12(3), 233-248.
  • Roy, V., Silvestre, B. S., & Singh, S. (2020). Reactive and proactive pathways to sustainable apparel supply chains: Manufacturer's perspective on stakeholder salience and organizational learning toward responsible management. International Journal of Production Economics, 227, 107672.
  • Ryan, J. C. (2020). Retaining, resigning and firing: bibliometrics as a people analytics tool for examining research performance outcomes and faculty turnover. Personnel Review.
  • Safarishahrbijari, A. (2018). Workforce forecasting models: A systematic review. Journal of Forecasting, 37(7), 739-753.
  • Sengupta, A., Mittal, S., & Sanchita, K. (2020). How do mid-level managers experience data science disruptions? An in-depth inquiry through interpretative phenomenological analysis (IPA). Management Decision.
  • Sharma, A., & Sharma, T. (2017). HR analytics and performance appraisal system. Management Research Review.
  • Shukla, A., Chaturvedi, S., & Simmhan, Y. (2017). Riotbench: An iot benchmark for distributed stream processing systems. Concurrency and Computation: Practice and Experience, 29(21), e4257.
  • Simón, C., & Ferreiro, E. (2018). Workforce analytics: A case study of scholar–practitioner collaboration. Human Resource Management, 57(3), 781-793.
  • Sohrabi, B., Vanani, I. R., & Abedin, E. (2018). Human resources management and information systems trend analysis using text clustering. International Journal of Human Capital and Information Technology Professionals (IJHCITP), 9(3), 1-24.
  • Surwase G., Sagar, A., Kademani, B. S., & Bhanumurthy, K. (2011). Co-citation analysis: an overview.
  • Tonidandel, S., King, E. B., & Cortina, J. M. (2018). Big data methods: Leveraging modern data analytic techniques to build organizational science. Organizational Research Methods, 21(3), 525-547.
  • Tursunbayeva, A., Di Lauro, S., & Pagliari, C. (2018). People analytics—A scoping review of conceptual boundaries and value propositions. International Journal of Information Management, 43, 224-247.
  • Ulrich, D., & Dulebohn, J. H. (2015). Are we there yet? What's next for HR?. Human Resource Management Review, 25(2), 188-204.
  • Ulrich, D., Kryscynski, D., Ulrich, M., & Brockbank, W. (2017). Competencies for HR professionals who deliver outcomes.
  • Van den Heuvel, S., & Bondarouk, T. (2017). The rise (and fall?) of HR analytics: A study into the future application, value, structure, and system support. Journal of Organizational Effectiveness: People and Performance.
  • Van der Laken, P., Bakk, Z., Giagkoulas, V., van Leeuwen, L., & Bongenaar, E. (2018). Expanding the methodological toolbox of HRM researchers: The added value of latent bathtub models and optimal matching analysis. Human Resource Management, 57(3), 751-760.
  • van der Togt, J., & Rasmussen, T. H. (2017). Toward evidence-based HR. Journal of Organizational Effectiveness: People and Performance.
  • Vargas, R., Yurova, Y. V., Ruppel, C. P., Tworoger, L. C., & Greenwood, R. (2018). Individual adoption of HR analytics: a fine grained view of the early stages leading to adoption. The International Journal of Human Resource Management, 29(22), 3046-3067.
  • Verma, S., Singh, V., & Bhattacharyya, S. S. (2020). Do big data-driven HR practices improve HR service quality and innovation competency of SMEs. International Journal of Organizational Analysis.
  • Villalonga, B. (2004). Intangible resources, Tobin’sq, and sustainability of performance differences. Journal of Economic Behavior & Organization, 54(2), 205-230.
  • Wang, L., & Cotton, R. (2018). Beyond Moneyball to social capital inside and out: The value of differentiated workforce experience ties to performance. Human Resource Management, 57(3), 761-780.
  • White, H. D., & McCain, K. W. (1997). Visualization of literatures. Annual review of information science and technology (ARIST), 32, 99-168.
  • Wu, D. D., Kapoor, B., & Sherif, J. (2012). Human resources in an enriched environment of business intelligence. Kybernetes.
  • Xu, H., Yu, Z., Yang, J., Xiong, H., & Zhu, H. (2018). Dynamic talent flow analysis with deep sequence prediction modeling. IEEE Transactions on Knowledge and Data Engineering, 31(10), 1926-1939.
  • Yasmin, M., Tatoglu, E., Kilic, H. S., Zaim, S., & Delen, D. (2020). Big data analytics capabilities and firm performance: An integrated MCDM approach. Journal of Business Research, 114, 1-15.
  • Yassine, N., & Singh, S. K. (2020). Sustainable supply chains based on supplier selection and HRM practices. Journal of Enterprise Information Management.
  • Zhang, D., Ma, Y., Zhang, Y., Lin, S., Hu, X. S., & Wang, D. (2018, April). A real-time and non-cooperative task allocation framework for social sensing applications in edge computing systems. In 2018 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) (pp. 316-326). IEEE.
  • Zhou, Y., Liu, G., Chang, X., & Wang, L. (2021). The impact of HRM digitalization on firm performance: investigating three‐way interactions. Asia Pacific Journal of Human Resources, 59(1), 20-43.
  • Ziebell, R. C., Albors-Garrigos, J., Schoeneberg, K. P., & Marin, M. R. P. (2019). Adoption and success of e-HRM in a cloud computing environment: a field study. International Journal of Cloud Applications and Computing (IJCAC), 9(2), 1-27.

Ayrıntılar

Birincil Dil İngilizce
Konular İşletme
Bölüm Araştırma Makalesi
Yazarlar

Merve VURAL ALLAHAM> (Sorumlu Yazar)
İSTANBUL GELİŞİM ÜNİVERSİTESİ
0000-0002-3735-3008
Türkiye

Yayımlanma Tarihi 1 Temmuz 2022
Başvuru Tarihi 10 Haziran 2021
Kabul Tarihi 17 Nisan 2022
Yayınlandığı Sayı Yıl 2022, Cilt 21, Sayı 83

Kaynak Göster

APA Vural Allaham, M. (2022). BIBLIOMETRIC ANALYSIS OF HR ANALYTICS LITERATURE . Elektronik Sosyal Bilimler Dergisi , 21 (83) , 1147-1169 . DOI: 10.17755/esosder.950426

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                                                   Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences),  Creative Commons Atıf-GayriTicari 4.0 Uluslararası Lisansı ile lisanslanmıştır.

                                              ESBD Elektronik Sosyal Bilimler Dergisi (Electronic Journal of Social Sciences), Türk Patent ve Marka Kurumu tarafından tescil edilmiştir. Marka No:2011 119849.