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Research Article

Boosting the impact of knowledge management on innovation performance through industry 4.0 adoption

ORCID Icon, , , , ORCID Icon &
Pages 32-48 | Received 08 Apr 2022, Accepted 21 Jul 2022, Published online: 25 Aug 2022

References

  • Abubakar, A., Elrehail, H., Alatailat, M., & Elçi, A. (2019). Knowledge management, decision-making style and organizational performance. Journal of Innovation & Knowledge, 4(2), 104–114. https://doi.org/10.1016/j.jik.2017.07.003
  • Al-Emran, M., Mezhuyev, V., Kamaludin, A., & Shaalan, K. (2018). The impact of knowledge management processes on information systems: A systematic review. International Journal of Information Management, 43, 173–187. https://doi.org/10.1016/j.ijinfomgt.2018.08.001
  • Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
  • Appelbaum, S. H. (1997). Socio‐technical systems theory: An intervention strategy for organizational development. Management Decision, 35(6), 452–463. https://doi.org/10.1108/00251749710173823
  • Armstrong, J. S., & Overton, T. S. (1977). Estimating nonresponse bias in mail surveys. Journal of Marketing Research, 14(3), 396–402. https://doi.org/10.1177/002224377701400320
  • Bauer, M., & Leker, J. (2013). Exploration and exploitation in product and process innovation in the chemical industry. R&D Management, 43(3), 196–212. https://doi.org/10.1111/radm.12012
  • Bednar, P., & Welch, C. (2020). Socio-technical perspectives on smart working: Creating meaningful and sustainable systems. Information Systems Frontiers, 22(2), 281–298. https://doi.org/10.1007/s10796-019-09921-1
  • Bennett, R., & Gabriel, H. (1999). Organisational factors and knowledge management within large marketing departments: An empirical study. Journal of Knowledge Management, 3(3), 212–225. https://doi.org/10.1108/13673279910288707
  • Bhatia, M. S., & Kumar, S. (2020). Critical success factors of Industry 4.0 in automotive manufacturing industry. IEEE Transactions on Engineering Management. 69(5), 2439–2453. https://doi.org/10.1109/TEM.2020.3017004
  • Bonaglia, F., Goldstein, A., & Mathews, J. (2007). Accelerated internationalization by emerging markets’ multinationals: The case of the white goods sector. Journal of World Business, 42(4), 369–383. https://doi.org/10.1016/j.jwb.2007.06.001
  • Brislin, R. W. (1980). Translation and content analysis of oral and written materials. Methodology, 111(190), 389–444.
  • Brown, S., Lo, K., & Lys, T. (1999). Use of R2 in accounting research: Measuring changes in value relevance over the last four decades. Journal of Accounting and Economics, 28(2), 83–115. https://doi.org/10.1016/S0165-4101(99)00023-3
  • Cabrilo, S., & Dahms, S. (2018). How strategic knowledge management drives intellectual capital to superior innovation and market performance. Journal of Knowledge Management, 22(3), 621–648. https://doi.org/10.1108/JKM-07-2017-0309
  • Camisón, C., & López, A. V. (2010). An examination of the relationship between manufacturing flexibility and firm performance: The mediating role of innovation. International Journal of Operations and Production Management, 30(8), 853–878. https://doi.org/10.1108/01443571011068199
  • Cañas, H., Mula, J., Díaz-Madroñero, M., & Campuzano-Bolarín, F. (2021). Implementing Industry 4.0 principles. Computers & Industrial Engineering, 158, 107379. https://doi.org/10.1016/j.cie.2021.107379
  • Capestro, M., & Kinkel, S. (2020). Industry 4.0 and knowledge management: A review of empirical studies. In: Bettiol, M., Di Maria, E., Micelli, S. (eds.) Knowledge Management and Industry 4.0, Springer, Cham. 19–52.
  • Carayon, P. (2006). Human factors of complex sociotechnical systems. Applied Ergonomics, 37(4), 525–535. https://doi.org/10.1016/j.apergo.2006.04.011
  • Chang, C. W. (2022). Constructing an intelligent shoe production plant using a green supply chain and knowledge management. Knowledge Management Research & Practice, 20(1), 46–57. https://doi.org/10.1080/14778238.2021.1970488
  • Cheah, S., & Tan, C. (2021). Knowledge management, innovation capability, and manufacturing performance in the era of Industry 4.0: A proposed model. Global Business & Management Research, 13(1), 38–51.
  • Chen, F., Hope, O., Li, Q., & Wang, X. (2011). Financial reporting quality and investment efficiency of private firms in emerging markets. The Accounting Review, 86(4), 1255–1288. https://doi.org/10.2308/accr-10040
  • Chen, J., Zhaohui, Z., & Xie, H. (2004). Measuring intellectual capital. Journal of Intellectual Capital, 5(1), 195–212. https://doi.org/10.1108/14691930410513003
  • Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(1), 64–73. https://doi.org/10.1177/002224377901600110
  • Coakes, E. (2002). Knowledge management: A sociotechnical perspective. In: Coakes, E., Willis, D., Clarke, S. (eds.) Knowledge management in the sociotechnical world (pp. 4–14). Springer, London.
  • Concato, J., Peduzzi, P., Holford, T., & Feinstein, A. (1995). Importance of events per independent variable in proportional hazards analysis I. Background, goals, and general strategy. Journal of Clinical Epidemiology, 48(12), 1495–1501. https://doi.org/10.1016/0895-4356(95)00510-2
  • Cooper, R., & Foster, M. (1971). Sociotechnical systems. American Psychologist, 26(5), 467. https://doi.org/10.1037/h0031539
  • Darroch, J. (2003). Developing a measure of knowledge management behaviors and practices. Journal of Knowledge Management, 7(5), 41–54. https://doi.org/10.1108/13673270310505377
  • Darroch, J. (2005). Knowledge management, innovation and firm performance. Journal of Knowledge Management, 9(3), 101–115. https://doi.org/10.1108/13673270510602809
  • de Bem Machado, A., Secinaro, S., Calandra, D., & Lanzalonga, F. (2022). Knowledge management and digital transformation for Industry 4.0: A structured literature review. Knowledge Management Research & Practice, 20(2), 320–338. https://doi.org/10.1080/14778238.2021.2015261
  • Dragicevic, N., Ullrich, A., Tsui, E., & Gronau, N. (2019). A conceptual model of knowledge dynamics in the industry 4.0 smart grid scenario. Knowledge Management Research & Practice. https://doi.org/10.1080/14778238.2019.1633893
  • Du Plessis, M. (2007). The role of knowledge management in innovation. Journal of Knowledge Management, 11(4), 20–29. https://doi.org/10.1108/13673270710762684
  • Fabrigar, L., Wegener, D., MacCallum, R., & Strahan, E. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272. https://doi.org/10.1037/1082-989X.4.3.272
  • Fettermann, D., Cavalcante, C., Almeida, T., & Tortorella, G. L. (2018). How does Industry 4.0 contribute to operations management? Journal of Industrial and Production Engineering, 35(4), 255–268. https://doi.org/10.1080/21681015.2018.1462863
  • Finch, J., & West, S. (1997). The investigation of personality structure: Statistical models. Journal of Research in Personality, 31(4), 439–485. https://doi.org/10.1006/jrpe.1997.2194
  • Forcino, F. L. (2012). Multivariate assessment of the required sample size for community paleoecological research. Palaeogeography, Palaeoclimatology, Palaeoecology, 315, 134–141. https://doi.org/10.1016/j.palaeo.2011.11.019
  • Forcino, F. L., Leighton, L. R., Twerdy, P., & Cahill, J. F. (2015). Reexamining sample size requirements for multivariate, abundance-based community research: When resources are limited, the research does not have to be. PLoS One, 10(6), e0128379. https://doi.org/10.1371/journal.pone.0128379
  • Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
  • Franco, E., Ray, S., & Ray, P. K. (2011). Patterns of innovation practices of multinational-affiliates in emerging economies: Evidences from Brazil and India. World Development, 39(7), 1249–1260. https://doi.org/10.1016/j.worlddev.2011.03.003
  • Fugate, B. S., Stank, T. P., & Mentzer, J. (2009). Linking improved knowledge management to operational and organizational performance. Journal of Operations Management, 27(3), 247–264. https://doi.org/10.1016/j.jom.2008.09.003
  • Ganguly, A., Talukdar, A., & Chatterjee, D. (2019). Evaluating the role of social capital, tacit knowledge sharing, knowledge quality and reciprocity in determining innovation capability of an organization. Journal of Knowledge Management, 23(6), 1105–1135. https://doi.org/10.1108/JKM-03-2018-0190
  • Ghobakhloo, M. (2018). The future of manufacturing industry: A strategic roadmap toward Industry 4.0. Journal of Manufacturing Technology Management, 29(6), 910–936. https://doi.org/10.1108/JMTM-02-2018-0057
  • Gilchrist, A. (2016). Industry 4.0: The industrial internet of things. Apress.
  • Goldsby, T., Knemeyer, A., Miller, J., & Wallenburg, C. (2013). Measurement and moderation: Finding the boundary conditions in logistics and supply chain research. Journal of Business Logistics, 34(2), 109–116. https://doi.org/10.1111/jbl.12013
  • Goodwin, C. (2005). Research in Psychology: Methods and design. John Wiley & Sons, Inc.
  • Grandinetti, R. (2016). Absorptive capacity and knowledge management in small and medium enterprises. Knowledge Management Research and Practice, 14(2), 159–168. https://doi.org/10.1057/kmrp.2016.2
  • Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925
  • Hair, J., Black, W., Babin, B., & Anderson, R. (2014). Multivariate data analysis (11th ed.). Pearson New International .
  • Handzic, M. (2011). Integrated socio‐technical knowledge management model: An empirical evaluation. Journal of Knowledge Management, 15(2), 198–211. https://doi.org/10.1108/13673271111119655
  • Hassan, N., & Raziq, A. (2019). Effects of knowledge management practices on innovation in SMEs. Management Science Letters, 9(7), 997–1008. https://doi.org/10.5267/j.msl.2019.4.005
  • Hermann, M., Pentek, T., & Otto, B. (2016). Design principles for industrie 4.0 scenarios. Proceedings of the Annual Hawaii International Conference on System Sciences. 05-08 January 2016, Koloa, HI, USA
  • Herrmann, M., Pentek, T., & Otto, B. (2015). Design principles for Industry 4.0 scenarios: a literature review. Technische Universität Dortmund, Audi Foundation Professorship, Supply Net Order Management. http://www.snom.mb.tu-dortmund.de/cms/de/forschung/Arbeitsberichte/Design-Principles-for-Industrie-4_0-Scenarios.pdf (Accessed January 7 2022)
  • Hitt, M. A., Ahlstrom, D., Dacin, M. T., Levitas, E., & Svobodina, L. (2004). The institutional effects on strategic alliance partner selection in transition economies: China vs. Russia. Organization Science, 15(2), 173–185. https://doi.org/10.1287/orsc.1030.0045
  • Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
  • Inkinen, H. (2016). Review of empirical research on knowledge management practices and firm performance. Journal of Knowledge Management, 20(2), 230–257. https://doi.org/10.1108/JKM-09-2015-0336
  • Jabbour, A. B., Frascareli, F., Gonzalez, E., & Jabbour, C. J. (2021). Are food supply chains taking advantage of the circular economy? A research agenda on tackling food waste based on Industry 4.0 technologies. Production Planning & Control.
  • Jonsson, A. (2015). Beyond knowledge management–understanding how to share knowledge through logic and practice. Knowledge Management Research & Practice, 13(1), 45–58. https://doi.org/10.1057/kmrp.2013.28
  • Kolberg, D., Knobloch, J., & Zühlke, D. (2017). Towards a lean automation interface for workstations. International Journal of Production Research, 55(10), 2845–2856. https://doi.org/10.1080/00207543.2016.1223384
  • Kothari, C. (2004). Research methodology: Methods and techniques. New Age International.
  • Lasi, H., Fettke, P., Kemper, H. G., Feld, T., & Hoffmann, M. (2014). Industry 4.0. Business & Information Systems Engineering, 6(4), 239–242. https://doi.org/10.1007/s12599-014-0334-4
  • Lee, V., Leong, L., Hew, T., & Ooi, K. (2013). Knowledge management: A key determinant in advancing technological innovation? Journal of Knowledge Management, 17(6), 848–872. https://doi.org/10.1108/JKM-08-2013-0315
  • Lehaney, B., Clarke, S., Coakes, E., & Jack, G. (2004). Sociotechnical systems and knowledge management. In Beyond knowledge management (pp. 31–75). IGI Global.
  • Li, D., Fast-Berglund, Å., & Paulin, D. (2019). Current and future Industry 4.0 capabilities for information and knowledge sharing. The International Journal of Advanced Manufacturing Technology, 105(9), 3951–3963. https://doi.org/10.1007/s00170-019-03942-5
  • Liao, Y., Deschamps, F., Loures, E., & Ramos, L. (2017). Past, present and future of Industry 4.0-a systematic literature review and research agenda proposal. International Journal of Production Research, 55(12), 3609–3629. https://doi.org/10.1080/00207543.2017.1308576
  • Liao, S., & Wu, C. (2010). System perspective of knowledge management, organizational learning, and organizational innovation. Expert Systems with Applications, 37(2), 1096–1103. https://doi.org/10.1016/j.eswa.2009.06.109
  • Lu, Y. (2017). Industry 4.0: A survey on technologies, applications and open research issues. Journal of Industrial Information Integration, 6, 1–10. https://doi.org/10.1016/j.jii.2017.04.005
  • Lundvall, B. Å., & Nielsen, P. (2007). Knowledge management and innovation performance. International Journal of Manpower, 28(3/4), 207–223. https://doi.org/10.1108/01437720710755218
  • Marcon, É., Soliman, M., Gerstlberger, W., & Frank, A. (2021). Sociotechnical factors and Industry 4.0: An integrative perspective for the adoption of smart manufacturing technologies. Journal of Manufacturing Technology Management. 33(2), 259–286. https://doi.org/10.1108/JMTM-01-2021-0017
  • Mardani, A., Nikoosokhan, S., Moradi, M., & Doustar, M. (2018). The relationship between knowledge management and innovation performance. The Journal of High Technology Management Research, 29(1), 12–26. https://doi.org/10.1016/j.hitech.2018.04.002
  • Marodin, G., Chiappetta Jabbour, C. J., Godinho Filho, M., & Tortorella, G. L. (2022). Lean production, information and communication technologies and operational performance. Total Quality Management & Business Excellence, 1–18. https://doi.org/10.1080/14783363.2022.2035214
  • Marsh, H., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562. https://doi.org/10.1037/0033-2909.97.3.562
  • Massaro, M., Secinaro, S., Dal Mas, F., Brescia, V., & Calandra, D. (2021). Industry 4.0 and circular economy: An exploratory analysis of academic and practitioners’ perspectives. Business Strategy and the Environment, 30(2), 1213–1231. https://doi.org/10.1002/bse.2680
  • Mehrabani, S., & Shajari, M. (2012). Knowledge management and innovation capacity. Management Research, 4(2), 164.
  • Merat, A., & Bo, D. (2013). Strategic analysis of knowledge firms: The links between knowledge management and leadership. Journal of Knowledge Management, 17(1), 3–15. https://doi.org/10.1108/13673271311300697
  • Meyers, L., Gamst, G., & Guarino, A. (2006). Applied multivariate research. Sage Publications, Thousand Oaks.
  • Montgomery, D. (2013). Design and analysis of experiments. Wiley.
  • Narayanamurthy, G., & Tortorella, G. L. (2021). Impact of COVID-19 outbreak on employee performance–moderating role of industry 4.0 base technologies. International Journal of Production Economics, 234, 108075. https://doi.org/10.1016/j.ijpe.2021.108075
  • Nassif, A. (2007). National innovation system and macroeconomic policies: Brazil and India in comparative perspective (No.184). United Nations Conference on Trade and Development.
  • Neyer, A. K., Bullinger, A. C., & Moeslein, K. M. (2009). Integrating inside and outside innovators: A sociotechnical systems perspective. R&D Management, 39(4), 410–419. https://doi.org/10.1111/j.1467-9310.2009.00566.x
  • Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford university press.
  • Nunnally, J. C. (1979). Psychometric Theory (2nd ed.). Applied Psychological Measurement.
  • OECD. (2011). ISIC REV (Vol. 3). Technology Intensity Definition.
  • Olsen, T. L., & Tomlin, B. (2020). Industry 4.0: Opportunities and challenges for operations management. Manufacturing & Service Operations Management, 22(1), 113–122. https://doi.org/10.1287/msom.2019.0796
  • Pagliosa, M., Tortorella, G., & Ferreira, J. C. E. (2019). Industry 4.0 and lean manufacturing: A systematic literature review and future research directions. Journal of Manufacturing Technology Management, 32(3), 543–569. https://doi.org/10.1108/JMTM-12-2018-0446
  • Peduzzi, P., Concato, J., Feinstein, A. R., & Holford, T. R. (1995). Importance of events per independent variable in proportional hazards regression analysis II. Accuracy and precision of regression estimates. Journal of Clinical Epidemiology, 48(12), 1503–1510.
  • Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. https://doi.org/10.1037/0021-9010.88.5.879
  • Prajogo, D. I., & McDermott, C. M. (2011). The relationship between multidimensional organizational culture and performance. International Journal of Operations and Production Management, 31(7), 712–735. https://doi.org/10.1108/01443571111144823
  • Rahim, R., Mahmood, N., & Masrom, M. (2015). The role of knowledge management in facilitating innovation for sustainable SMEs performance. In 2015 International Conference on Technology, Informatics, Management, Engineering & Environment (TIME-E) (pp. 64–70). IEEE.
  • Rossini, M., Costa, F., Staudacher, A. P., & Tortorella, G. L. (2019). Industry 4.0 and Lean Production: An empirical study. IFAC-PapersOnLine, 52(13), 42–47. https://doi.org/10.1016/j.ifacol.2019.11.122
  • Rot, A., Sobińska, M., Hernes, M., & Franczyk, B. (2020). Digital transformation of public administration through blockchain technology. In Towards Industry 4.0—current challenges in information systems (pp. 111–126). Springer.
  • Ruggles, R. (2009). Knowledge management tools. Routledge.
  • Santos, L., Costa, M., Kothe, J., Benitez, G., Schaefer, J., Baierle, I., & Nara, E. (2020). Industry 4.0 collaborative networks for industrial performance. Journal of Manufacturing Technology Management, 32(2), 245–265. https://doi.org/10.1108/JMTM-04-2020-0156
  • Saunders, L. J., Russell, R. A., & Crabb, D. P. (2012). The coefficient of determination: What determines a useful R2 statistic? Investigative Ophthalmology & Visual Science, 53(11), 6830–6832. https://doi.org/10.1167/iovs.12-10598
  • Shujahat, M., Hussain, S., Javed, S., Malik, M., Thurasamy, R., & Ali, J. (2017). Strategic management model with lens of knowledge management and competitive intelligence: A review approach. VINE Journal of Information and Knowledge Management Systems, 47(1), 55–93. https://doi.org/10.1108/VJIKMS-06-2016-0035
  • Shujahat, M., Sousa, M., Hussain, S., Nawaz, F., Wang, M., & Umer, M. (2019). Translating the impact of knowledge management processes into knowledge-based innovation: The neglected and mediating role of knowledge-worker productivity. Journal of Business Research, 94, 442–450. https://doi.org/10.1016/j.jbusres.2017.11.001
  • Sony, M., Antony, J., Mc Dermott, O., & Garza-Reyes, J. A. (2021). An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector. Technology in Society, 67, 101754. https://doi.org/10.1016/j.techsoc.2021.101754
  • Sony, M., & Naik, S. (2020). Industry 4.0 integration with socio-technical systems theory: A systematic review and proposed theoretical model. Technology in Society, 61, 101248. https://doi.org/10.1016/j.techsoc.2020.101248
  • Spear, S. J. (2009). Chasing the rabbit: How market leaders outdistance the competition and how great companies can catch up and win, Foreword by Clay Christensen. McGraw Hill Professional.
  • Stentoft, J., Adsbøll Wickstrøm, K., Philipsen, K., & Haug, A. (2020). Drivers and barriers for Industry 4.0 readiness and practice: Empirical evidence from small and medium-sized manufacturers. Production Planning & Control, 1–18.
  • Tabachnik, B., & Fidell, L. (2007). Using multivariate statistics. Allyn and Bacon.
  • Toothaker, L. E., Aiken, L. S., & West, S. G. (1994). Multiple regression: Testing and interpreting interactions. The Journal of the Operational Research Society.
  • Tortorella, G. L., & Fettermann, D. (2018). Implementation of industry 4.0 and lean production in Brazilian manufacturing companies. International Journal of Production Research, 56(8), 2975–2987. https://doi.org/10.1080/00207543.2017.1391420
  • Tortorella, G. L., Fogliatto, F. S., Esposto, K. F., Vergara, A., Vassolo, R., Tlapa, D., & Narayanamurthy, G. (2022). Measuring the effect of healthcare 4.0 implementation on hospitals’ performance. Production Planning & Control, 33(4), 386–401. https://doi.org/10.1080/09537287.2020.1824283
  • Tortorella, G., Giglio, R., Fettermmann, D. C., & Tlapa, D. (2018). Lean supply chain practices: An exploratory study on their relationship. The International Journal of Logistics Management, 29(3), 1049–1076. https://doi.org/10.1108/IJLM-06-2017-0141
  • Tortorella, G. L., Giglio, R., & van Dun, D. H. (2019). Industry 4.0 adoption as a moderator of the impact of lean production practices on operational performance improvement. International Journal of Operations & Production Management, 39(6/7/8), 860–886. https://doi.org/10.1108/IJOPM-01-2019-0005
  • Tortorella, G. L., Marodin, G. A., Fogliatto, F. S., & Miorando, R. (2015). Learning organisation and human resources management practices: An exploratory research in medium-sized enterprises undergoing a lean implementation. International Journal of Production Research, 53(13), 3989–4000. https://doi.org/10.1080/00207543.2014.980462
  • Tortorella, G. L., Rossini, M., Costa, F., Portioli-Staudacher, A., & Sawhney, R. (2021b). A comparison on Industry 4.0 and lean production between manufacturers from emerging and developed economies. Total Quality Management & Business Excellence, 32(11–12), 1249–1270. https://doi.org/10.1080/14783363.2019.1696184
  • Tortorella, G. L., Saurin, T., Godinho Filho, M., Samson, D., & Kumar, M. (2021a). Bundles of lean automation practices and principles and their impact on operational performance. International Journal of Production Economics, 235, 108106. https://doi.org/10.1016/j.ijpe.2021.108106
  • Tortorella, G. L., Vergara, A., Garza-Reyes, J. A., & Sawhney, R. (2020). Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers. International Journal of Production Economics, 219, 284–294. https://doi.org/10.1016/j.ijpe.2019.06.023
  • Trist, E. (1981). The evolution of socio-technical systems (Vol. 2). Ontario Quality of Working Life Centre.
  • Tseng, S. M. (2008). The effects of information technology on knowledge management systems. Expert Systems with Applications, 35(1–2), 150–160. https://doi.org/10.1016/j.eswa.2007.06.011
  • Vassolo, R. S., Vergara, A. F., Tortorella, G. L., Fogliatto, F. S., Tlapa, D., & Narayanamurthy, G. (2021). Hospital investment decisions in Healthcare 4.0 technologies: Scoping review and framework for exploring challenges, trends, and research directions. Journal of Medical Internet Research, 23(8), e27571. https://doi.org/10.2196/27571
  • Vittinghoff, E., & McCulloch, C. E. (2007). Relaxing the rule of ten events per variable in logistic and Cox regression. American Journal of Epidemiology, 165(6), 710–718. https://doi.org/10.1093/aje/kwk052
  • Vlachos, I. (2021). Implementation of an intelligent supply chain control tower: A socio-technical systems case study. Production Planning & Control, 1–17.
  • Vogel-Heuser, B., & Hess, D. (2016). Guest editorial Industry 4.0–prerequisites and visions. IEEE Transactions on Automation Science and Engineering, 13(2), 411–413. https://doi.org/10.1109/TASE.2016.2523639
  • Walker, G. H., Stanton, N. A., Salmon, P. M., & Jenkins, D. (2008). A review of sociotechnical systems theory: A classic concept for new command and control paradigms. Theoretical Issues in Ergonomics Science, 9(6), 479–499. https://doi.org/10.1080/14639220701635470
  • World Bank (2021). World Bank Group Annual Reports 2021. https://thedocs.worldbank.org/en/doc/8d5e2ee0bac72a1d938d5cf94ceff751-0090012021/original/WBG-AR-2021-Executive-Summary-Presentation.pdf (accessed on January 4 2022)
  • Xu, L. D., Xu, E. L., & Li, L. (2018). Industry 4.0: State of the art and future trends. International Journal of Production Research, 56(8), 2941–2962. https://doi.org/10.1080/00207543.2018.1444806
  • Yamashita, G. H., Fogliatto, F. S., Anzanello, M. J., & Tortorella, G. L. (2022). Customized prediction of attendance to soccer matches based on symbolic regression and genetic programming. Expert Systems with Applications, 187, 115912. https://doi.org/10.1016/j.eswa.2021.115912
  • Yu, W., Chavez, R., Jacobs, M., & Wong, C. Y. (2020). Innovativeness and lean practices for triple bottom line: Testing of fit-as-mediation versus fit-as-moderation models. International Journal of Operations and Production Management, 40(10), 1623–1647. https://doi.org/10.1108/IJOPM-07-2019-0550
  • Zawislak, P. A., Fracasso, E. M., & Tello-Gamarra, J. (2018). Technological intensity and innovation capability in industrial firms. Innovation & Management Review, 15(2), 189–207. https://doi.org/10.1108/INMR-04-2018-012
  • Zhao, X., Flynn, B. B., & Roth, A. V. (2006). Decision sciences research in China: A critical review and research agenda—foundations and overview. Decision Sciences, 37(4), 451–496. https://doi.org/10.1111/j.1540-5414.2006.00135.x

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