Training and proprietary equipment: the bow and the arrow to shoot the target

Authors

DOI:

https://doi.org/10.1108/RAUSP-06-2023-0105

Keywords:

Operations strategy, Absorptive capacity, Proprietary equipment, New technologies, Trainin

Abstract

Purpose

This study aims to investigate the relationship between proprietary equipment and anticipation of new technologies in enhancing product innovativeness and competitive performance.

Design/methodology/approach

It used survey data collected in the fourth round of the High-Performance Manufacturing project (HPM), which comprised 270 plants in 15 countries across three industries. The relationships proposed in this study were analyzed through structural equation modeling, confirmatory factor analysis and endogeneity tests.

Findings

Results show that proprietary equipment alone does not directly impact performance but can still serve as as a source of advantage since the proper mechanisms are implemented.

Research limitations/implications

The theoretical underpinnings of the relationship among proprietary equipment, anticipation of new technologies and training provide a strong foundation for a better understanding of the integration of the structural and infrastructural elements of operations strategy and their benefits for competitive performance.

Practical implications

Results show how operations managers can capitalize on proprietary equipment to anticipate new technologies by developing training routines to absorb and apply new knowledge in the plant.

Social implications

This research contributes to the competitiveness of manufacturing firms by showing how knowledge can be created and disseminated in their operations to develop better-prepared employees.

Originality/value

This study advances the literature on world-class manufacturing by demonstrating that proprietary equipment per se has no direct impact on product performance and innovativeness, contrary to what previous literature has demonstrated.

Downloads

Download data is not yet available.

References

Ahmad, S., & Schroeder, R. G. (2003). The impact of human resource management practices on operational performance: Recognizing country and industry differences. Journal of Operations Management, 21(1), 19–43.

Amit, R., & Schoemaker, P. J. H. (1993). Strategic Assets and Organizational Rent. Strategic Management Journal, 14(1), 33–46.

Antonakis, J., Bendahan, S., Jacquart, P., & Lalive, R. (2010). On making causal claims: A review and recommendations. The Leadership Quarterly, 21(6), 1086–1120.

Arora, A., & Fosfuri, A. (2003). Licensing the market for technology. Journal of Economic Behavior and Organization, 52(2), 277–295. https://doi.org/10.1016/S0167-2681(03)00002-7

Barney, J. B. (1991). Firm Resources and Sustained Competitive Advantage. Journal of Management, 17(1), 99–120.

Becker, T. E. (2005). Potential Problems in the Statistical Control of Variables in Organizational Research: A Qualitative Analysis With Recommendations. Organizational Research Methods, 8(3), 274–289.

Bergkvist, L., & Rossiter, J. R. (2007). The Predictive Validity of Multiple-Item Versus Single-Item Measures of the Same Constructs. Journal of Marketing Research, 44(2), 175–184.

Boudreau, J., Hopp, W., Mcclain, J. O., & Thomas, L. J. (2003). On the Interface Between Operations and Human Resources Management. Manufacturing & Service Operations Management, 5(3), 179–202.

Calantone, R. J., Chan, K., & Cui, A. S. (2006). Decomposing product innovativeness and its effects on new product success. Journal of Product Innovation Management, 23(5), 408–421. https://doi.org/10.1111/j.1540-5885.2006.00213.x

Coeurderoy, R., & Durand, R. (2004). Leveraging the advantage of early entry: Proprietary technologies versus cost leadership. Journal of Business Research, 57(6), 583–590. https://doi.org/10.1016/S0148-2963(02)00423-X

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128–152.

Colarelli, S. M., & Mantei, M. S. (1996). Some Contextual Influences on Training Utilization. Journal of Applied Behavioral Science, 32(3), 306–322.

Cua, K. O., McKone, K. E., & Schroeder, R. G. (2001). Relationships between implementation of TQM, JIT, and TPM and manufacturing performance. Journal of Operations Management, 19(6), 675–694.

Finger, A. B., Flynn, B. B., & Paiva, E. L. (2014). Anticipation of new technologies: Supply chain antecedents and competitive performance. International Journal of Operations & Production Management, 34(6), 807–828.

Floyd, F., & Widaman, K. (1995). Factor analysis in the development and refinement of clinical assessment instruments. Psychological Assessment, 7(3), 286–299.

Flynn, B. B., & Flynn, J. E. (2004). An exploratory study of the nature of cumulative capabilities. Journal of Operations Management, 22(5), 439–457.

Flynn, B. B., Schroeder, R. G., & Flynn, J. E. (1999). World-class manufacturing: an investigation of Hayes and Wheelwright’s foundation. Journal of Operations Management, 17(3), 249–269.

Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Hair, J., Black, B., Babin, B., Anderson, R., & Tatham, R. (2009). Multivariate Data Analysis (6 ed.). Bookman.

Hayes, R. H., & Upton, D. M. (1998). Operations-Based Strategy. California Management Review, 40(4), 8–25.

Hayes, R. H., & Wheelwright, S. C. (1984). Restoring Our Competitive Edge: Competing Through Manufacturing. Wiley. New York.

Hayes, R. H., Wheelwright, S. C., & Clark, K. B. (1988). Dynamic Manufacturing: Creating the Learning Organization. Free Press. New York.

Hu, L., & 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.

Huselid, M. (1995). The impact of human resource management practices on turnover, productivity, and corporate financial performance. Academy of Management Journal, 38(3), 635–672.

Jin, Y., Vonderembse, M., & Ragu-Nathan, T. S. (2013). Proprietary technologies: Building a manufacturers flexibility and competitive advantage. International Journal of Production Research, 51(19), 5711–5727. https://doi.org/10.1080/00207543.2013.784407

Ketokivi, M., & McIntosh, C. N. (2017). Addressing the endogeneity dilemma in operations management research: Theoretical, empirical, and pragmatic considerations. Journal of Operations Management, 52, 1–14.

Kleinschmidt, E. J., & Cooper, D. R. (1991). The Impact of Product Innovativeness on Performance. Journal of Product Innovation Management, 8, 240–251.

Kline, R. B. (2011). Principles and Practice of Structural Equation Modeling (3 ed.). The Guilford Press. New York.

Lane, P. J., Koka, B. R., & Pathak, S. (2006). The reification of Absorptive Capacity: A critical review and rejuvenation of the construct. Academy of Management Review, 31(4), 833–863.

Laugen, B. T., Acur, N., Boer, H., & Frick, J. (2005). Best manufacturing practices: What do the best-performing companies do? International Journal of Operations & Production Management, 25(2), 131–150.

Lu, G., Ding, X. (David), Peng, D. X., & Hao-Chun Chuang, H. (2018). Addressing endogeneity in operations management research: Recent developments, common problems, and directions for future research. Journal of Operations Management, 64, 53–64.

Malhotra, M. K., & Grover, V. (1998). An assessment of survey research in POM: From constructs to theory. Journal of Operations Management, 16, 407–425.

Malhotra, M. K., Singhal, C., Shang, G., & Ployhart, R. E. (2014). A critical evaluation of alternative methods and paradigms for conducting mediation analysis in operations Management research. Journal of Operations Management, 32(4), 127–137.

Peng, D. X., Schroeder, R. G., & Shah, R. (2008). Linking routines to operations capabilities: A new perspective. Journal of Operations Management, 26(6), 730–748.

Peng, D. X., Schroeder, R. G., & Shah, R. (2011). Competitive priorities, plant improvement and innovation capabilities, and operational performance: A test of two forms of fit. International Journal of Operations & Production Management, 31(5), 484–510.

Peteraf, M. A. (1993). The Cornerstones of Competitive Advantage: A Resource-Based View. Strategic Management Journal, 14(3), 179–191.

Petrescu, M. (2013). Marketing research using single-item indicators in structural equation models. Journal of Marketing Analytics, 1(2), 99–117.

Podsakoff, P., MacKenzie, S., Lee, J., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

Pozzi, R., Rossi, T., & Secchi, R. (2023). The Management of Operations Industry 4.0 technologies: Critical success factors for implementation and improvements in manufacturing companies. Production Planning & Control, 34(2), 139–158.

Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.

Raj, A., Dwivedi, G., Sharma, A., & Jabbour, A. B. L. de S. (2020). Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective. International Journal of Production Economics, 224, 1–17.

Rungtusanatham, M. J., Miller, J. W., & Boyer, K. K. (2014). Theorizing, testing, and concluding for mediation in SCM research: Tutorial and procedural recommendations. Journal of Operations Management, 32(3), 99–113.

Saniuk, S., Dagmar, C., & Saniuk, A. (2023). Knowledge and Skills of Industrial Employees and Managerial Staff for the Industry 4.0 Implementation. Mobile Networks and Applications, 28, 220–230.

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research Methods for Business Students (5th ed.). Pearson Education Limited. New York.

Schroeder, R. G., Bates, K. a., & Junttila, M. a. (2002). A resource-based view of manufacturing strategy and the relationship to manufacturing performance. Strategic Management Journal, 23(2), 105–117.

Schroeder, R. G., & Flynn, B. B. (2001). High-Performance Manufacturing: Global Perspectives. Wiley. New York.

Schroeder, R. G., Shah, R., & Peng, D. X. (2011). The cumulative capability ‘sand cone’ model revisited: A new perspective for manufacturing strategy. International Journal of Production Research, 49(16), 4879–4901.

Sigov, A., Ratkin, L., Ivanov, L. A., & Da, L. (2022). Emerging Enabling Technologies for Industry 4.0 and Beyond. Information Systems Frontiers, 26(5), 1585-1595.

Staiger, D., & Stock, J. H. (1997). Instrumental Variables Regression With Weak Instruments. Econometrica, 65(3), 557–586.

Stank, T. P., Pellathy, D. A., In, J., Mollenkopf, D. A., & Bell, J. E. (2017). New Frontiers in Logistics Research: Theorizing at the Middle Range. Journal of Business Logistics, 38(1), 6–17.

Swink, M., & Hegarty, W. H. (1998). Core manufacturing capabilities and their links to product differentiation. International Journal of Operations & Production Management, 18(4), 374–396.

Tharenou, P., Saks, A. M., & Moore, C. (2007). A review and critique of research on training and organizational-level outcomes. Human Resource Management Review, 17, 251–273.

Thompson, D. V., Hamilton, R. W. & Rust, R. T. (2005). Feature Fatigue: When Product Capabilities Become Too Much of a Good Thing. Journal of Marketing Research, 42, 431–22.

Todorova, G., & Durisin, B. (2007). Absorptive Capacity: Valuing a Reconceptualization. Academy of Management Review, 32(3), 774–786.

Tracey, M., Vonderembse, M. A., & Lim, J.-S. (1999). Manufacturing technology and strategy formulation: Keys to enhancing competitiveness and improving performance. Journal of Operations Management, 17(4), 411–428.

Vandenberg, R. J., & Lance, C. E. (2000). A Review and Synthesis of the Measurement Invariance Literature: Suggestions, Practices, and Recommendations for Organizational Research. Organizational Research Methods, 3(1), 4–70.

Voss, C. A. (1995). Alternative paradigms for manufacturing strategy. International Journal of Operations & Production Management, 15(4), 5–16.

Wheelwright, S. C., & Clark, K. B. (1992). Competing Through Development Capability in a Manufacturing-Based Organization. Business Horizons, July-August, 29–43.

Wheelwright, S. C., & Hayes, R. H. (1985). Competing Through Manufacturing. Harvard Business Review, 63(1), 99–109.

Wu, S. J., Melnyk, S. A., & Swink, M. (2012). An empirical investigation of the combinatorial nature of operational practices and operational capabilities: Compensatory or additive? International Journal of Operations & Production Management, 32(2), 121–155.

Youndt, M. A., Snell, S. A., Dean, J. W., & Lepak, D. P. (1996). Human resource management, manufacturing strategy, and firm performance. The Academy of Management Journal, 39(4), 836–866.

Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203.

Zander, U., & Kogut, B. (1995). Knowledge and the Speed of the Transfer and Imitation of Organizational Capabilities: An Empirical Test. Organization Science, 6(1), 76–92.

Zhang, H., Liang, X., & Wang, S. (2016). Customer value anticipation, product innovativeness, and customer lifetime value: The moderating role of advertising strategy. Journal of Business Research, 69(9), 3725–3730.

Zheng, T., Ardolino, M., Bacchetti, A., & Perona, M. (2020). The applications of Industry 4.0 technologies in manufacturing context: A systematic literature review. International Journal of Production Research, 1–33.

Downloads

Published

2025-12-29

Issue

Section

Research Paper