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Opening a new horizon in green HRM practices with big data analytics and its analogy to circular economy performance: an empirical evidence

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Abstract

The study illustrates the impact of green HR management on circular economy performance along with the mediator role of green innovation and moderator roles of big data analytics and data-driven culture. The 438 survey questionnaires were collected from textile sector SMEs and evaluated through PLS-SEM functionality. The study outcomes deliberated that green HRM has shown a significant positive impact on circular economy performance. Similarly, green innovation and big data analytics sanctioned mediators and moderator roles by focusing on circular economy performance. Therefore, data-driven culture did not perform as a moderator task between green innovation and circular economy performance. The study developed a hypothetical distinctive connection of resource base view theorem and absorptive capacity theory that recognized a firm’s resources or capabilities as new value, externally generated knowledge, and its implementation to accomplish the competitive benefit in an outline of circular economy performance. The SMEs will acquire advantage from this study in the perspective of new business systems, changing consumption patterns, re-cycling, repair, re-use, re-manufacturing, product sharing, and modularization for sustainable performance. The study would be exceedingly valuable for the foundation of policy documents regarding developing an environmental strategic tool kit in the outline of a green HR environment, big data involvement, and enhancement of circular economy performance with sustainable environmental protection.

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References

  • Adegbile, A., Sarpong, D., & Meissner, D. (2017). Strategic foresight for innovation management: A review and research agenda. International Journal of Innovation Technology Management, 14(04), 1750019.

    Google Scholar 

  • Agyabeng-Mensah, Y., Ahenkorah, E., Afum, E., Agyemang, A. N., Agnikpe, C., & Rogers, F. (2020). Examining the influence of internal green supply chain practices, green human resource management and supply chain environmental cooperation on firm performance. Supply Chain Management: An International Journal, 25(5), 585–599.

    Google Scholar 

  • 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.

    Google Scholar 

  • Al Doghan, M. A., Abdelwahed, N. A. A., Soomro, B. A., & Ali Alayis, M. M. H. (2022). Organizational environmental culture, environmental sustainability and performance: The mediating role of green HRM and green innovation. Sustainability, 14(12), 7510.

    Google Scholar 

  • Albort-Morant, G., Henseler, J., Leal-Millán, A., & Cepeda-Carrión, G. (2017). Mapping the field: A bibliometric analysis of green innovation. Sustainability, 9(6), 1011.

    Google Scholar 

  • Albort-Morant, G., Leal-Rodríguez, A. L., & De Marchi, V. (2018). Absorptive capacity and relationship learning mechanisms as complementary drivers of green innovation performance. Journal of Knowledge Management, 22(2), 432–452.

    Google Scholar 

  • Ali, F., Rasoolimanesh, S. M., Sarstedt, M., Ringle, C. M., & Ryu, K. (2018). An assessment of the use of partial least squares structural equation modeling (PLS-SEM) in hospitality research. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-10-2016-0568

    Article  Google Scholar 

  • Arfi, W. B., Hikkerova, L., & Sahut, J.-M. (2018). External knowledge sources, green innovation and performance. Technological Forecasting Social Change, 129, 210–220.

    Google Scholar 

  • Armstrong, M., & Taylor, S. (2020). Armstrong’s handbook of human resource management practice. Kogan Page Publishers.

    Google Scholar 

  • Bag, S., Pretorius, J. H. C., Gupta, S., & Dwivedi, Y. K. (2021). Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities. Technological Forecasting Social Change, 163, 120420.

    Google Scholar 

  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

    Google Scholar 

  • Barney, J. B. (1996). The resource-based theory of the firm. Organization Science, 7(5), 469–469.

    Google Scholar 

  • Boiral, O., Ebrahimi, M., Kuyken, K., & Talbot, D. (2019). Greening remote SMEs: The case of small regional airports. Journal of Business Ethics, 154(3), 813–827.

    Google Scholar 

  • Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in numbers: How does data-driven decision-making affect firm performance? Available at SSRN 1819486.

  • Chen, G., Farh, J.-L., Campbell-Bush, E. M., Wu, Z., & Wu, X. (2013). Teams as innovative systems: Multilevel motivational antecedents of innovation in R&D teams. Journal of Applied Psychology, 98(6), 1018.

    Google Scholar 

  • Chen, Y.-S., Lai, S.-B., & Wen, C.-T. (2006). The influence of green innovation performance on corporate advantage in Taiwan. Journal of Business Ethics, 67(4), 331–339.

    Google Scholar 

  • Cheung, G. W., & Wang, C. (2017). Current approaches for assessing convergent and discriminant validity with SEM: Issues and solutions. Paper presented at the Academy of Management Proceedings.

  • Chien, F., Hsu, C. C., Moslehpour, M., Sadiq, M., Tufail, B., & Ngo, T. Q. (2023). A step toward sustainable development: The nexus of environmental sustainability, technological advancement and green finance: Evidence from Indonesia. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-023-03424-5

    Article  Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.

    Google Scholar 

  • Colasante, A., & D’Adamo, I. (2021). The circular economy and bioeconomy in the fashion sector: Emergence of a “sustainability bias.” Journal of Cleaner Production, 329, 129774.

    Google Scholar 

  • Conding, J., Habidin, N. F., Zubir, A. F. M., Hashim, S., & Jaya, N. (2012). The structural analysis of green innovation (GI) and green performance (GP) in Malaysian automotive industry. Research Journal of Finance Accounting, 3(6), 172–178.

    Google Scholar 

  • D’Adamo, I. (2019). Adopting a circular economy: Current practices and future perspectives. In (Vol. 8, pp. 328): Multidisciplinary Digital Publishing Institute.

  • de Burgos-Jiménez, J., Vázquez-Brust, D., Plaza-Úbeda, J. A., & Dijkshoorn, J. (2013). Environmental protection and financial performance: An empirical analysis in Wales. International Journal of Operations Production Management, 33(8), 981–1018.

    Google Scholar 

  • Del Giudice, M., Chierici, R., Mazzucchelli, A., & Fiano, F. (2020). Supply chain management in the era of circular economy: The moderating effect of big data. The International Journal of Logistics Management, 3, 119.

    Google Scholar 

  • Del Giudice, M., Soto-Acosta, P., Carayannis, E., & Scuotto, V. (2018). Emerging perspectives on business process management (BPM): IT-based processes and ambidextrous organizations, theory and practice. Business Process Management Journal, 24(5), 1070–1076.

    Google Scholar 

  • Dibia, C., Oruh, E., Anderson, M., & Dirpal, G. (2020). Human resource management and circular economy: a critical perspective. Paper presented at the British Academy of Management 2020 Conference: Innovation for a Sustainable Future.

  • Directive, E. (2008). Directive 2008/98/EC of the European Parliament and of the Council of 19 November 2008 on waste and repealing certain Directives. Official Journal of the European Union L, 312(3), 22.

    Google Scholar 

  • Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673–686.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., & Ali, S. S. (2015). Exploring the relationship between leadership, operational practices, institutional pressures and environmental performance: A framework for green supply chain. International Journal of Production Economics, 160, 120–132.

    Google Scholar 

  • Dubey, R., Gunasekaran, A., Childe, S. J., Bryde, D. J., Giannakis, M., Foropon, C., & Hazen, B. T. (2020). Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations. International Journal of Production Economics, 226, 107599.

    Google Scholar 

  • Dulock, H. L. (1993). Research design: Descriptive research. Journal of Pediatric Oncology Nursing, 10(4), 154–157.

    CAS  Google Scholar 

  • El-Kassar, A.-N., & Singh, S. K. (2019). Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices. Technological Forecasting Social Change, 144, 483–498.

    Google Scholar 

  • Fernandes, D., & Machado, C. (2022). Connecting ecological economics, green management, sustainable development, and circular economy: Corporate social responsibility as the synthetic vector. Green Production Engineering and Management. https://doi.org/10.1016/B978-0-12-821238-7.00001-4

    Article  Google Scholar 

  • Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting Social Change, 114, 254–280.

    Google Scholar 

  • Geissdoerfer, M., Savaget, P., Bocken, N. M., & Hultink, E. J. (2017). The circular economy—a new sustainability paradigm? Journal of Cleaner Production, 143, 757–768.

    Google Scholar 

  • Godfrey, K. R. (1980). Correlation methods. Automatica, 16(5), 527–534.

    Google Scholar 

  • Guerci, M., Longoni, A., & Luzzini, D. (2016). Translating stakeholder pressures into environmental performance—the mediating role of green HRM practices. The International Journal of Human Resource Management, 27(2), 262–289.

    Google Scholar 

  • Gupta, M., & George, J. F. (2016). Toward the development of a big data analytics capability. Information Management Science, 53(8), 1049–1064.

    Google Scholar 

  • Gupta, S., Chen, H., Hazen, B. T., Kaur, S., & Gonzalez, E. D. S. (2019). Circular economy and big data analytics: A stakeholder perspective. Technological Forecasting Social Change, 144, 466–474.

    Google Scholar 

  • Hafeez, M. H., Shariff, M. N. M., & Mad Lazim, H. (2013). Does innovation and relational learning influence SME Performance? An empirical evidence from Pakistan. Asian Social Science, 9(15), 204.

    Google Scholar 

  • Hair, J. F., Money, A. H., Samouel, P., & Page, M. (2007). Research methods for business. Education+ Training, 49(4), 336–337.

    Google Scholar 

  • Harkness, J., Pennell, B.-E., & Schoua-Glusberg, A. (2004). Survey questionnaire translation and assessment. Methods for Testing Evaluating Survey Questionnaires, 546, 453–473.

    Google Scholar 

  • Hart, S. L. (1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986–1014.

    Google Scholar 

  • Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009). The use of partial least squares path modeling in international marketing. Emerald Group Publishing Limited.

    Google Scholar 

  • Hindle, G. A., & Vidgen, R. (2018). Developing a business analytics methodology: A case study in the foodbank sector. European Journal of Operational Research, 268(3), 836–851.

    Google Scholar 

  • Holsapple, C., Lee-Post, A., & Pakath, R. (2014). A unified foundation for business analytics. Decision Support Systems, 64, 130–141.

    Google Scholar 

  • Hox, J. J., & Boeije, H. R. (2005). Data collection, primary versus secondary. Encyclopedia of Social Measurement. Elsevier.

    Google Scholar 

  • Jabbour, C. J. C., & de Sousa Jabbour, A. B. L. (2016). Green human resource management and green supply chain management: Linking two emerging agendas. Journal of Cleaner Production, 112, 1824–1833.

    Google Scholar 

  • Jabbour, C. J. C., de Sousa Jabbour, A. B. L., Sarkis, J., & Godinho Filho, M. (2019a). Unlocking the circular economy through new business models based on large-scale data: An integrative framework and research agenda. Technological Forecasting Social Change, 144, 546–552.

    Google Scholar 

  • Jabbour, C. J. C., Sarkis, J., de Sousa Jabbour, A. B. L., Renwick, D. W. S., Singh, S. K., Grebinevych, O., & Godinho Filho, M. (2019b). Who is in charge? A review and a research agenda on the ‘human side’of the circular economy. Journal of Cleaner Production, 222, 793–801.

    Google Scholar 

  • Jackson, S. E., Renwick, D. W., Jabbour, C. J., & Muller-Camen, M. (2011). State-of-the-art and future directions for green human resource management: Introduction to the special issue. German Journal of Human Resource Management, 25(2), 99–116.

    Google Scholar 

  • Jeble, S., Dubey, R., Childe, S. J., Papadopoulos, T., Roubaud, D., & Prakash, A. (2018). Impact of big data and predictive analytics capability on supply chain sustainability. The International Journal of Logistics Management, 29(2), 513–538.

    Google Scholar 

  • Jia, J., Liu, H., Chin, T., & Hu, D. (2018). The continuous mediating effects of GHRM on employees’ green passion via transformational leadership and green creativity. Sustainability, 10(9), 3237.

    Google Scholar 

  • Jr Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.

    Google Scholar 

  • Jr Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. J. E. B. R. (2014). Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review, 26(2), 106–121.

    Google Scholar 

  • Jr Hair, J. F., Sarstedt, M., Ringle, C. M., & Gudergan, S. P. (2017). Advanced issues in partial least squares structural equation modeling. Sage publications.

    Google Scholar 

  • Kamble, S. S., Belhadi, A., Gunasekaran, A., Ganapathy, L., & Verma, S. (2021). A large multi-group decision-making technique for prioritizing the big data-driven circular economy practices in the automobile component manufacturing industry. Technological Forecasting Social Change, 165, 120567.

    Google Scholar 

  • Kammerer, D. (2009). The effects of customer benefit and regulation on environmental product innovation: Empirical evidence from appliance manufacturers in Germany. Ecological Economics, 68(8–9), 2285–2295.

    Google Scholar 

  • Khalique, M., Isa, A. H. B. M., & Nassir Shaari, J. A. (2011). Challenges for Pakistani SMEs in a knowledge-based economy. Indus Journal of Management Social Sciences, 5(2), 7.

    Google Scholar 

  • Khan, R., Shaikh, A. S., & Masood, H. (2019). Impact of Pak–China free trade agreement (FTA) on trade and industry of Pakistan. Electronic Research Journal of Social Sciences Humanities, 1, 1–33.

    Google Scholar 

  • Kirchherr, J., Reike, D., & Hekkert, M. (2017). Conceptualizing the circular economy: An analysis of 114 definitions. Resources, Conservation Re-Cycling, 127, 221–232.

    Google Scholar 

  • Kiron, D., Ferguson, R. B., & Prentice, P. K. (2013). From value to vision: Reimagining the possible with data analytics. MIT Sloan Management Review, 54(3), 1.

    Google Scholar 

  • Kiron, D., Prentice, P. K., & Ferguson, R. B. (2012). Innovating with analytics. MIT Sloan Management Review, 54(1), 47.

    Google Scholar 

  • Kiron, D., & Shockley, R. (2011). Creating business value with analytics. MIT Sloan Management Review, 53(1), 57.

    Google Scholar 

  • Klein, N., Ramos, T. B., & Deutz, P. (2020). Circular economy practices and strategies in public sector organizations: An integrative review. Sustainability, 12(10), 4181.

    Google Scholar 

  • Kline, E., Wilson, C., Ereshefsky, S., Tsuji, T., Schiffman, J., Pitts, S., & Reeves, G. J. S. R. (2012). Convergent and discriminant validity of attenuated psychosis screening tools. Schizophrenia Research, 134(1), 49–53.

    Google Scholar 

  • Knights, D. (2017). Managing people: Contexts of HRM, diversity and social inequality. In D. Knights, & H. Willmott (Eds.), Introducing organizational behaviour and management (pp. 158–197). Andover, UK: Cengage Learning EMEA.

  • Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1–10.

    Google Scholar 

  • Kratzer, J., Meissner, D., & Roud, V. (2017). Open innovation and company culture: Internal openness makes the difference. Technological Forecasting Social Change, 119, 128–138.

    Google Scholar 

  • Kristoffersen, E., Aremu, O. O., Blomsma, F., Mikalef, P., & Li, J. (2019). Exploring the relationship between data science and circular economy: An enhanced CRISP-DM Process Model. Paper presented at the Conference on e-Business, e-Services and e-Society.

  • Lin, R.-J., Tan, K.-H., & Geng, Y. (2013). Market demand, green product innovation, and firm performance: Evidence from Vietnam motorcycle industry. Journal of Cleaner Production, 40, 101–107.

    Google Scholar 

  • Lubis, N. W. (2022). Resource based view (RBV) in improving company strategic capacity. Research Horizon, 2(6), 587–596.

    Google Scholar 

  • Melece, L. (2016). Challenges and opportunities of circular economy and green economy. Engineering for Rural Development, 25, 1162–1169.

    Google Scholar 

  • Merriam, S. B., & Grenier, R. S. (2019). Qualitative research in practice: Examples for discussion and analysis. John Wiley.

    Google Scholar 

  • Millsap, R. E. (2012). Statistical approaches to measurement invariance. Routledge.

    Google Scholar 

  • Mishra, R., Sarkar, S., & Kiranmai, J. (2014). Green HRM: Innovative approach in Indian public enterprises. World Review of Science, Technology Sustainable Development, 11(1), 26–42.

    Google Scholar 

  • Modgil, S., Gupta, S., Sivarajah, U., & Bhushan, B. (2021). Big data-enabled large-scale group decision making for circular economy: An emerging market context. Technological Forecasting Social Change, 166, 120607.

    Google Scholar 

  • Moktadir, M. A., Ahmadi, H. B., Sultana, R., Liou, J. J., & Rezaei, J. (2020). Circular economy practices in the leather industry: A practical step towards sustainable development. Journal of Cleaner Production, 251, 119737.

    Google Scholar 

  • Moslehpour, M., Chau, K. Y., Du, L., Qiu, R., Lin, C. Y., & Batbayar, B. (2023). Predictors of green purchase intention toward eco-innovation and green products: Evidence from Taiwan. Economic Research-Ekonomska Istraživanja, 36(2), 2121934.

    Google Scholar 

  • Moslehpour, M., Chau, K. Y., Tu, Y. T., Nguyen, K. L., Barry, M., & Reddy, K. D. (2022). Impact of corporate sustainable practices, government initiative, technology usage, and organizational culture on automobile industry sustainable performance. Environmental Science and Pollution Research, 29(55), 83907–83920.

    Google Scholar 

  • Muisyo, P. K., & Qin, S. (2021). Enhancing the FIRM’S green performance through green HRM: The moderating role of green innovation culture. Journal of Cleaner Production, 289, 125720.

    Google Scholar 

  • Munro, B. H. (2005). Statistical methods for health care research. Lippincott Williams.

    Google Scholar 

  • Nagati, H., & Rebolledo, C. (2012). The role of relative absorptive capacity in improving suppliers’ operational performance. International Journal of Operations Production Management, 32(5), 611–630.

    Google Scholar 

  • O’Donohue, W., & Torugsa, N. (2016). The moderating effect of ‘Green’HRM on the association between proactive environmental management and financial performance in small firms. The International Journal of Human Resource Management, 27(2), 239–261.

    Google Scholar 

  • Obeidat, S. M., Abdalla, S., & Al Bakri, A. A. K. (2023). Integrating green human resource management and circular economy to enhance sustainable performance: An empirical study from the Qatari service sector. Employee Relations: THe International Journal, 45(2), 535–563.

    Google Scholar 

  • Oliva, F. L., Semensato, B. I., Prioste, D. B., Winandy, E. J. L., Bution, J. L., Couto, M. H. G., & Santos, R. F. (2019). Innovation in the main Brazilian business sectors: characteristics, types and comparison of innovation. Journal of Knowledge Management, 23(1), 135–175.

    Google Scholar 

  • Olken, F., & Rotem, D. (1995). Random sampling from databases: A survey. Statistics Computing, 5(1), 25–42.

    Google Scholar 

  • POPOVIĆ, S. (2020). Green Economy-A HRM perspective. EUROBIT Publishing House, 30.

  • Rathi, R. (2018). Artificial intelligence and the future of hr practices. IJAR, 4(6), 113–116.

    Google Scholar 

  • Ren, S., Tang, G., & Jackson, S. E. (2018). Green human resource management research in emergence: A review and future directions. Asia Pacific Journal of Management, 35(3), 769–803.

    Google Scholar 

  • Renwick, D. W., Redman, T., & Maguire, S. (2013). Green human resource management: A review and research agenda. International Journal of Management Reviews, 15(1), 1–14.

    Google Scholar 

  • Rigdon, E. E. (2014). Rethinking partial least squares path modeling: Breaking chains and forging ahead. Long Range Planning, 47(3), 161–167.

    Google Scholar 

  • Ringle, C. M., Wende, S., & Will, A. (2005). SmartPLS 2.0 (beta). In: Hamburg.

  • Sarstedt, M., Henseler, J., & Ringle, C. M. (2011). Multi-group analysis in partial least squares (PLS) path modeling: Alternative methods and empirical results. In Measurement and research methods in international marketing: Emerald Group Publishing Limited.

  • Sassanelli, C., Rosa, P., Rocca, R., & Terzi, S. (2019). Circular economy performance assessment methods: A systematic literature review. Journal of Cleaner Production, 229, 440–453.

    CAS  Google Scholar 

  • Shah, N., & Soomro, B. A. (2023). Effects of green human resource management practices on green innovation and behavior. Management Decision, 61(1), 290–312.

    Google Scholar 

  • Singh, S. K., Del Giudice, M., Chierici, R., & Graziano, D. (2020). Green innovation and environmental performance: The role of green transformational leadership and green human resource management. Technological Forecasting Social Change, 150, 119762.

    Google Scholar 

  • Singh, S. K., & El-Kassar, A.-N. (2019). Role of big data analytics in developing sustainable capabilities. Journal of Cleaner Production, 213, 1264–1273.

    Google Scholar 

  • Sinkovics, R. R., Henseler, J., Ringle, C. M., & Sarstedt, M. (2016). Testing measurement invariance of composites using partial least squares. International Marketing Review, 33(3), 405–431.

    Google Scholar 

  • Sobaih, A. E. E., Hasanein, A., & Elshaer, I. (2020). Influences of green human resources management on environmental performance in small lodging enterprises: the role of green innovation. Sustainability, 12(24), 10371.

    Google Scholar 

  • Song, M., Cen, L., Zheng, Z., Fisher, R., Liang, X., Wang, Y., & Huisingh, D. (2017). How would big data support societal development and environmental sustainability? Insights and practices. Journal of Cleaner Production, 142, 489–500.

    Google Scholar 

  • Song, W., Yu, H., & Xu, H. (2020). Effects of green human resource management and managerial environmental concern on green innovation. European Journal of Innovation Management. https://doi.org/10.1108/EJIM-11-2019-0315

    Article  Google Scholar 

  • Stahel, W. R. (2016). The circular economy. Nature News, 531(7595), 435–438.

    CAS  Google Scholar 

  • Sun, L.-Y., Aryee, S., & Law, K. S. (2007). High-performance human resource practices, citizenship behavior, and organizational performance: A relational perspective. Academy of Management Journal, 50(3), 558–577.

    Google Scholar 

  • Tang, Z., & Tang, J. (2012). Stakeholder–firm power difference, stakeholders’ CSR orientation, and SMEs’ environmental performance in China. Journal of Business Venturing, 27(4), 436–455.

    Google Scholar 

  • Tavani, S. N., Sharifi, H., & Ismail, H. S. (2014). A study of contingency relationships between supplier involvement, absorptive capacity and agile product innovation. International Journal of Operations Production Management, 34(1), 65–92.

    Google Scholar 

  • Tsai, F. M., Bui, T.-D., Tseng, M.-L., Lim, M. K., & Hu, J. (2020). Municipal solid waste management in a circular economy: A data-driven bibliometric analysis. Journal of Cleaner Production, 275, 124132.

    Google Scholar 

  • Tseng, M.-L., Tan, R. R., Chiu, A. S., Chien, C.-F., & Kuo, T. C. (2018). Circular economy meets industry 4.0: Can big data drive industrial symbiosis? Resources, Conservation Re-Cycling, 131, 146–147.

    Google Scholar 

  • Tze San, O., Latif, B., & Di Vaio, A. (2022). GEO and sustainable performance: The moderating role of GTD and environmental consciousness. Journal of Intellectual Capital, 23(7), 38–67.

    Google Scholar 

  • Vidgen, R., Shaw, S., & Grant, D. B. (2017). Management challenges in creating value from business analytics. European Journal of Operational Research, 261(2), 626–639.

    Google Scholar 

  • Webster, K. (2015). Circular economy. Ellen Macarthur Foundatio.

    Google Scholar 

  • Weng, H.-H.R., Chen, J.-S., & Chen, P.-C. (2015). Effects of green innovation on environmental and corporate performance: A stakeholder perspective. Sustainability, 7(5), 4997–5026.

    Google Scholar 

  • Yu, W., Chavez, R., Jacobs, M. A., & Feng, M. (2018). Data-driven supply chain capabilities and performance: A resource-based view. Transportation Research Part e: Logistics Transportation Review, 114, 371–385.

    Google Scholar 

  • Yu, W., & Ramanathan, R. (2015). An empirical examination of stakeholder pressures, green operations practices and environmental performance. International Journal of Production Research, 53(21), 6390–6407.

    Google Scholar 

  • Yu, W., Ramanathan, R., & Nath, P. (2017). Environmental pressures and performance: An analysis of the roles of environmental innovation strategy and marketing capability. Technological Forecasting Social Change, 117, 160–169.

    Google Scholar 

  • Zeng, H., Chen, X., Xiao, X., & Zhou, Z. (2017). Institutional pressures, sustainable supply chain management, and circular economy capability: Empirical evidence from Chinese eco-industrial park firms. Journal of Cleaner Production, 155, 54–65.

    Google Scholar 

  • Zhao, R., Liu, Y., Zhang, N., & Huang, T. (2017). An optimization model for green supply chain management by using a big data analytic approach. Journal of Cleaner Production, 142, 1085–1097.

    Google Scholar 

  • Zhou, S., Zhang, D., Lyu, C., & Zhang, H. (2018). Does seeing “mind acts upon mind” affect green psychological climate and green product development performance? The role of matching between green transformational leadership and individual green values. Sustainability, 10(9), 3206.

    CAS  Google Scholar 

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Acknowledgements

The authors appreciate the cooperation of all the firms participating in the study.

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This research endeavor does not receive any financial support.

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The conceptualization and design of the study were collective efforts of all authors. Preparation of the materials, collection of data, and analysis were all carried out.

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Correspondence to Massoud Moslehpour.

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Appendix

Appendix

Sr. no

Constructs

Items

Adaption

1

Green HR Management (GHRM)

A lot of work goes into finding the proper person

Only hire people that care about the environment

Green personnel is given special consideration

Every employee is required to complete environmental training

The goal of environmental training is to increase employee competence in environmental matters

Employees will be required to employ environmental training in their jobs

Environmental performance is measured via performance appraisal

Events, responsibilities, issues, and policies in the surrounding environment are all taken into account when rating performance

Employee is rewarded for environmental stewardship

Employees are compensated for gaining certain environmental competencies

The company’s employees are working to improve their environmental track. Resolving environmental concerns through collaboration

Employees will hold team meetings to discuss environmental challenges

(Renwick et al., 2013; Sun et al., 2007)

2

Green Innovation (GI)

Green product Innovation

The materials used by the company are environmentally friendly

The company uses fewer energy and materials

The company will create an environmentally friendly product

The company is simple to recycle, reuse, and disintegrate

Green Process Innovation

The company’s industrial process is effectively reduced

The hazardous material is discarded by the company

Less use of coal, oil, electricity, and water

Less raw resources are used

Environmental Performance

Environmental actions have a substantial performance

Overall costs were reduced

Lead times have been reduced

Product and process quality has been improved

The company’s reputation has improved

Reduced waste across the value chain process

(Chen et al., 2006)

3

Big Data Analytics (BDA)

In business, BDA is used to strengthen the effectiveness of decisions

With the help of BDA, businesses have an easier time combining data from many sources

Data visualization is a common tool we employ to help users or decision-makers make sense of large amounts of data

In order to do root-cause analysis and prioritize continuous improvement, we can break down data using our dashboards

Leveraging allows for a shorter learning curve, quicker adaption to new situations, and reduced industrial waste

Through strategic leverage, the organization has improved resource utilization and made greater use of its assets

Leveraging has expanded recycling possibilities

More efficient resource utilization; quicker response time to fluctuations in energy supply; greater adaptation to demand curves

The BDA project is managed by professionals, and its deadline is scrupulously adhered to

The ever-changing nature of the corporate world necessitates regular reviews of BDA project objectives

(Dubey et al., 2020)

4

Data-Driven Culture (DDC)

I think it is important to have data, to understand it, and to put it to use

In light of recent developments, we welcome novel suggestions that seek to improve upon established procedures

Decisions are bolstered by insights gleaned from data

Insights gleaned from data are put to use in the development of brand new offerings

In order to make choices, people require information

(Kiron et al., 2012)

5

Circular Economy Performance (CEP)

The company is committed to minimizing workers’ efforts per product

The company is committed to cutting back on energy and material usage

The company has taken the initiative to increase the energy efficiency of their manufacturing machinery

Materials used for product packaging are recycled and reused

Chemicals used to clean machinery are recycled multiple times

The byproducts of one product’s production are used as building blocks for subsequent ones

Fabrication by-products are collected and reused

Consumer trash is processed for reuse

Garbage and recyclables are repurposed

Recycled materials are used in the production of brand-new goods

(Zeng et al., 2017)

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Chau, K.Y., Huang, T., Moslehpour, M. et al. Opening a new horizon in green HRM practices with big data analytics and its analogy to circular economy performance: an empirical evidence. Environ Dev Sustain 26, 12133–12162 (2024). https://doi.org/10.1007/s10668-023-03725-9

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