Integrating Regulatory Capital into Knowledge Capital Measurement Model (KCMM) - Evidence from Ghanaian Service Firms

The measurement of knowledge capital has become a cornerstone of organizational research, yet existing models typically emphasize human, structural, and relational capital without fully accounting for institutional and regulatory contexts. This study develops and validates a Knowledge Capital Measurement Model (KCMM) tailored to Ghanaian enterprises, extending the classical triad with a fourth dimension, regulatory capital. Drawing on the knowledge-based view of the firm and absorptive capacity theory, we collected survey data from 210 service-sector firms in Accra, encompassing telecommunications, banking, ICT, consulting, and logistics. Using confirmatory factor analysis (CFA) and structural equation modeling (SEM), we evaluated reliability, validity, and invariance across firm size and tenure.
The results demonstrate that the KCMM is psychometrically sound: Cronbach’s alpha and composite reliability exceeded 0.80 for all constructs, average variance extracted (AVE) values surpassed 0.50, and model fit indices were within recommended thresholds (χ²/df = 2.15; CFI = .943; RMSEA = .052; SRMR = .041). Regulatory capital emerged as a distinct and dominant construct, with the highest standardized loading onto the higher-order knowledge capital factor (β = .85, p < .001), surpassing human (β = .78), structural (β = .74), and relational (β = .69) capital. Multi-group analyses confirmed configural, metric, and scalar invariance across SMEs versus large firms and younger versus older organizations, with latent mean differences indicating stronger knowledge capital stocks among larger and more mature firms.
These findings extend intellectual capital theory by empirically validating regulatory capital as a fourth pillar of intangible assets in regulatory-intensive economies. They also highlight the contextual embeddedness of knowledge management: while relational capital dominates in global studies, Ghanaian firms prioritize compliance-driven knowledge as a strategic resource. Theoretically, this supports a contingent view of the knowledge-based firm, in which the salience of knowledge domains varies by institutional environment. For managers, the KCMM provides a validated instrument for diagnosing and balancing knowledge portfolios, while policymakers can design regulations as learning enablers rather than compliance burdens. Future research should test the KCMM longitudinally, incorporate objective performance indicators, and extend comparative analyses across African economies.

Knowledge Capital; Human Capital; Structural Capital; Relational Capital; Regulatory Capital;...

1. Alegre, J., & Chiva, R. (2013). Linking entrepreneurial orientation and firm performance: The role of organizational learning capability and innovation performance. Journal of Small Business Management, 51(4), 491–507. https://doi.org/10.1111/jsbm.12005 [Google Scholar] [Crossref]

2. Andreeva, T., & Kianto, A. (2012). Does knowledge management really matter? Linking knowledge management practices, competitiveness and economic performance. Journal of Knowledge Management, 16(4), 617–636. https://doi.org/10.1108/13673271211246185 [Google Scholar] [Crossref]

3. Byrne, B. M. (2016). Structural equation modeling with AMOS (3rd ed.). Routledge. [Google Scholar] [Crossref]

4. Chen, F. F. (2007). Sensitivity of goodness of fit indexes to lack of measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 14(3), 464–504. https://doi.org/10.1080/10705510701301834 [Google Scholar] [Crossref]

5. Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement invariance. Structural Equation Modeling: A Multidisciplinary Journal, 9(2), 233–255. https://doi.org/10.1207/S15328007SEM0902_5 [Google Scholar] [Crossref]

6. Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152. https://doi.org/10.2307/2393553 [Google Scholar] [Crossref]

7. Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. https://doi.org/10.1037/0021-843X.112.4.558 [Google Scholar] [Crossref]

8. Donate, M. J., & de Pablo, J. D. S. (2020). The role of knowledge-oriented leadership in knowledge management practices and innovation. Journal of Business Research, 108, 186–193. https://doi.org/10.1016/j.jbusres.2019.11.044 [Google Scholar] [Crossref]

9. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 [Google Scholar] [Crossref]

10. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage. [Google Scholar] [Crossref]

11. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 [Google Scholar] [Crossref]

12. 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 [Google Scholar] [Crossref]

13. Idrees, H., Xu, J., Haider, S. A., & Tehseen, S. (2023). A systematic review of knowledge management and new product development projects: Trends, issues, and challenges. Journal of Innovation & Knowledge, 8(2), Article 100350. https://doi.org/10.1016/j.jik.2023.100350 [Google Scholar] [Crossref]

14. Grant, R. M. (1996). Toward a knowledge-based theory of the firm. Strategic Management Journal, 17(Winter Special Issue), 109–122. https://doi.org/10.1002/smj.4250171110 [Google Scholar] [Crossref]

15. Kianto, A., Sáenz, J., & Aramburu, N. (2017). Knowledge-based human resource management practices, intellectual capital and innovation. Journal of Business Research, 81, 11–20. https://doi.org/10.1016/j.jbusres.2017.07.018 [Google Scholar] [Crossref]

16. Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press. [Google Scholar] [Crossref]

17. Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101 [Google Scholar] [Crossref]

18. Kusa, R., Suder, M., Duda, J., Czakon, W., & Juárez-Varón, D. (2024). Does knowledge management mediate the relationship between entrepreneurial orientation and firm performance? Journal of Knowledge Management, 28(11), 33–61. https://www.emerald.com/insight/content/doi/10.1108/JKM-11-2023-1090/full/html [Google Scholar] [Crossref]

19. Little, T. D. (2013). Longitudinal structural equation modeling. Guilford Press. [Google Scholar] [Crossref]

20. Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114–121. https://doi.org/10.1037/0021-9010.86.1.114 [Google Scholar] [Crossref]

21. MacKenzie, S. B., Podsakoff, P. M., & Podsakoff, N. P. (2011). Construct measurement and validation procedures in MIS and behavioral research: Integrating new and existing techniques. MIS Quarterly, 35(2), 293–334. https://doi.org/10.2307/23044045 [Google Scholar] [Crossref]

22. McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144 [Google Scholar] [Crossref]

23. Mohaghegh, F., Zaim, H., Dzenopoljac, V., Dzenopoljac, A., & Bontis, N. (2024). Analyzing the effects of knowledge management on organizational performance through knowledge utilization and sustainability. Knowledge and Process Management. Advance online publication. https://doi.org/10.1002/kpm.1777 [Google Scholar] [Crossref]

24. Muthén, B. O. (1994). Multilevel covariance structure analysis. Sociological Methods & Research, 22(3), 376–398. https://doi.org/10.1177/0049124194022003006 [Google Scholar] [Crossref]

25. Ndlovu, N., & Ngwenya, S. (2020). Institutional factors and knowledge acquisition in South African SMEs. South African Journal of Economic and Management Sciences, 23(1), a3305. https://doi.org/10.4102/sajems.v23i1.3305 [Google Scholar] [Crossref]

26. Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company: How Japanese companies create the dynamics of innovation. Oxford University Press. [Google Scholar] [Crossref]

27. OECD. (2021). OECD Science, Technology and Innovation Outlook 2021: Times of crisis and opportunity. OECD Publishing. https://doi.org/10.1787/75f79015-en [Google Scholar] [Crossref]

28. OECD/Statistics Canada. (2004). Measuring knowledge management in the business sector: First steps. OECD Publishing. https://doi.org/10.1787/9789264100282-en [Google Scholar] [Crossref]

29. Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Journal of Applied Psychology, 97(5), 879–903. https://doi.org/10.1037/a0029481 [Google Scholar] [Crossref]

30. Preacher, K. J., Zyphur, M. J., & Zhang, Z. (2010). A general multilevel SEM framework for assessing multilevel mediation. Psychological Methods, 15(3), 209–233. https://doi.org/10.1037/a0020141 [Google Scholar] [Crossref]

31. Probst, G. (2008). Managing knowledge: Building blocks for success. Wiley. [Google Scholar] [Crossref]

32. Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: The state of the art and future directions for psychological research. Developmental Review, 41, 71–90. https://doi.org/10.1016/j.dr.2016.06.004 [Google Scholar] [Crossref]

33. Reise, S. P. (2012). The rediscovery of bifactor measurement models. Multivariate Behavioral Research, 47(5), 667–696. https://doi.org/10.1080/00273171.2012.715555 [Google Scholar] [Crossref]

34. Rhemtulla, M., Brosseau-Liard, P. É., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological Methods, 17(3), 354–373. https://doi.org/10.1037/a0029315 [Google Scholar] [Crossref]

35. Stewart, T. A. (1997). Intellectual capital: The new wealth of organizations. Doubleday/Currency. [Google Scholar] [Crossref]

36. Westland, J. C. (2010). Lower bounds on sample size in structural equation modeling. Electronic Commerce Research and Applications, 9(6), 476–487. https://doi.org/10.1016/j.elerap.2010.07.003 [Google Scholar] [Crossref]

37. Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3), 450–463. https://doi.org/10.5465/amj.2005.17407911 [Google Scholar] [Crossref]

38. Sveiby, K. E. (1997). The new organizational wealth: Managing & measuring knowledge-based assets. Berrett-Koehler. [Google Scholar] [Crossref]

39. Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40–49. https://doi.org/10.1016/j.lrp.2017.06.007 [Google Scholar] [Crossref]

40. 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. https://doi.org/10.1177/109442810031002 [Google Scholar] [Crossref]

41. Wu, W., Li, Z., & Surangkana, T. (2024). Mediation effect of knowledge management on the impact of IT capability on firm performance: Exploring the moderating role of organizational culture management. Frontiers in Psychology, 15, 1344330. https://doi.org/10.3389/fpsyg.2024.1344330 [Google Scholar] [Crossref]

42. Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393 [Google Scholar] [Crossref]

43. Zahra, S. A., & George, G. (2002). Absorptive capacity: A review, reconceptualization, and extension. Academy of Management Review, 27(2), 185–203. https://doi.org/10.5465/amr.2002.6587995 [Google Scholar] [Crossref]

• The Impact of Ownership Structure on Dividend Payout Policy of Listed Plantation Companies in Sri Lanka
• Urban Sustainability in North-East India: A Study through the lens of NER-SDG index
• Performance Assessment of Predictive Forecasting Techniques for Enhancing Hospital Supply Chain Efficiency in Healthcare Logistics
• The Fractured Self in Julian Barnes' Postmodern Fiction: Identity Crisis and Deflation in Metroland and the Sense of an Ending
• Impact of Flood on the Employment, Labour Productivity and Migration of Agricultural Labour in North Bihar