Skip to main content
SpringerLink
Log in

Human Capital in Public Research Laboratory: Towards an Alternative Evaluation and Prediction Method Based on Hybridization

  • Published:
Annals of Data Science Aims and scope Submit manuscript

Abstract

The alternative proposed method aims to combine management accounting, precisely the activity-based system, and statistical tools to develop a method of assessing and predicting human capital within research laboratory. Statistical tools are Standardized Mean Difference, Hierarchical Cluster Analysis and Discriminant Analysis. The first normalizes the activities of the laboratory; the second classifies the results obtained, while the third standardizes these results by expressing them in terms of probability. The standardized scores are used for the computation and the prediction of human capital in research laboratories via activity regrouping center. The originality of this work is to fill a research gap in the field of hybridization in calculation and prediction of human capital by integrating the two disciplines mentioned above. Likewise, the originality of this work lies in the use of an activity-based accounting architecture to process outputs (and not costs) related to intangible aspects. The proposed method has research and social implications since it allows making appropriate research policy, adequate management control and improves organizational relations within the laboratory concerned. The findings show, through an illustration, the applicability of the proposed method and the usefulness of the tools used to do this.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Source: Personal elaboration

Similar content being viewed by others

References

  1. Schultz TW (1961) Investment in human capital. Am Econ Rev 51(1):1–17

    Google Scholar 

  2. Penrose ET (1959) The theory of the growth of the firm. Wiley, New York

    Google Scholar 

  3. Drucker P (1954) The practice of management. Harper, New York

    Google Scholar 

  4. Flamholtz EG (1976) Toward a formal model of group dynamics in task performance relevant to accounting. J Bus Finance Account 3(4):3–25

    Article  Google Scholar 

  5. Dumay J, Guthrie J (2019) Reflections on interdisciplinary critical intellectual capital accounting research: multidisciplinary propositions for a new future. Account Audit Account J 32(8):2282–2306

    Article  Google Scholar 

  6. Charnes A, Colantoni C, Cooper WW, Kortanek KO (1972) Economic social and enterprise accounting and statistical models. Account Rev 47(1):85–108

    Google Scholar 

  7. Charnes A, Cooper WW, Kozmetsky G (1973) Measuring, monitoring, and modeling quality of Life. Manag Sci 19(10):1172–1188

    Article  Google Scholar 

  8. Colantoni CS, Cooper WW, Dietzer HJ (1974) Accounting and social reporting. In: Cramer JJ, Sorter GH (eds) Objectives of financial statements, vol. 2: selected papers. American Institute of Certified Public Accountants, New York

    Google Scholar 

  9. Charnes A, Colantoni C, Cooper WW (1976) A futurological justification for historical cost and multi-dimensional accounting. Acc Organ Soc 1(4):315–337

    Article  Google Scholar 

  10. Cooper WW, Ijiri Y (1977) From accounting to accountability: step to a corporate social reporting. In: Bedford NM (ed) Accountancy in the 1980s: some issues: proceedings of the arthur young professors roundtable. University of Illinois: Council of Arthur Young Professors, Champaign

    Google Scholar 

  11. Kaplan RS, Cooper R, Maisel L, Morrissey E, Oehm RM (1992) Implementing activity-based cost management: moving from analysis to action. Institute of Management Accountants, Montvale

    Google Scholar 

  12. Antos J, Brimson JA (1999) Driving value using activity-based budgeting. Wiley, New York

    Google Scholar 

  13. Becker GS (1964) Human capital: a theoretical and empirical analysis. Columbia University Press for the National Bureau of Economic Analysis, New York

    Google Scholar 

  14. Coccia M (2014) Structure and organisational behaviour of public research institutions under unstable growth of human resources. Int J Serv Technol Manag 20(4/5/6):251–266

    Article  Google Scholar 

  15. Barro RJ, Lee W (2013) A new data set of educational attainment in the world, 1950–2010. J Dev Econ 104:184–198

    Article  Google Scholar 

  16. Mevellec P (2017) Les systèmes de coûts dans les organisations. Edition la découverte, Paris

    Book  Google Scholar 

  17. Sims CA (1986) Are forecasting models usable for policy analysis? Fed Reserve Bank Minneap Q Rev 10(1):2–16

    Google Scholar 

  18. Koutsomanoli-Filippaki A, Mamatzakis E (2009) Performance and Merton-type default risk of listed banks in the EU: a panel VAR approach. J Bank Finance 33(11):2050–2061

    Article  Google Scholar 

  19. Naidenova L, Parshakov P (2013) Intellectual capital investments: evidence from panel VAR analysis. J Intellect Cap 14(4):634–660

    Article  Google Scholar 

  20. Benos N, Zotou S (2014) Education and economic growth: a meta-regression analysis. World Dev 64(C):669–689

    Article  Google Scholar 

  21. Verry DW, Layard PRG (1975) Cost functions for university teaching and research. Econ J 85:55–74

    Article  Google Scholar 

  22. Mincer J (1974) Schooling, experience and earnings. Columbia University Press, New York

    Google Scholar 

  23. Koenker R, Hallok KF (2001) Quantile regression. J Econ Perspect 15(4):143–156

    Article  Google Scholar 

  24. Justo WR, Alencar NdS, Alencar MOd, Alves DF (2018) Return on human capital: quantile regression evidence in Brazil 2003–2013. Int J Finance Account 7(5):153–159

    Google Scholar 

  25. Ding C (2006) Using regression mixture analysis in educational research. Pract Assess Res Eval 11:1–11

    Google Scholar 

  26. Asampana G, Nantomah KK, Tungosiamu EA (2017) Multinomial logistic regression analysis of the determinants of students’ academic performance in mathematics at basic education certificate examination. High Educ Res 2(1):22–26

    Google Scholar 

  27. Thani FN, Mirkamali SM (2018) Factors that enable knowledge creation in higher education: a structural model. Data Technol Appl 52(3):424–444

    Article  Google Scholar 

  28. United Nations (2016) Guide on measuring human capital. Retrieved from https://unstats.un.org/unsd/nationalaccount/consultationDocs/HumanCapitalGuide%20Global%20Consultation-v1.pdf. Accessed 8 Jan 2020

  29. Savall H, Zardet V (2011) The qualimetrics approach: observing the complex object. Research in management consulting. Information Age Pub (IAP) editor.

  30. Habersam M, Piber M, Skoog M (2018) Ten years of using knowledge balance sheets in austrian public universities: a retrospective and prospective view. J Intellect Cap 19(1):34–52

    Article  Google Scholar 

  31. Anderson SW, Hesford JW, Young SM (2002) Factors influencing the performance of activity based costing teams: a field study of ABC model development time in the automobile industry. Acc Organ Soc 27:195–211

    Article  Google Scholar 

  32. Xu J, Quaddus M (2013) Developing and implementing information systems. Managing information systems. Atlantis Press, Paris

    Google Scholar 

  33. Klein JT (2000) A conceptual vocabulary of interdisciplinary science. In: Weingart PS, Stehr N (eds) Practising interdisciplinarity. University of Toronto Press, Toronto, pp 3–24

    Chapter  Google Scholar 

  34. Najeeb AR, Salami MJE, Gunawan T, Aibinu AM (2016) Review of parameter estimation techniques for time-varying autoregressive models of biomedical signals. Int J Signal Process Syst 4:3

    Google Scholar 

  35. Berry P (2014) Starting with ABC and finishing with XYZ: what financial reporting model best fits a faculty and why? J High Educ Policy Manag 36(3):305–314

    Article  Google Scholar 

  36. Richard J (2017) Comptabilité et cogestion environnementale (le modèle CARE). Séminaire UMR 5600 EVS. Institut Henri Fayol de Mines Saint-Etienne, 1er décembre

  37. Cohen J (1988) Statistical power analysis for the behavioral sciences, 2nd edn. Erlbaum, Hillsdale

    Google Scholar 

  38. Laroche P, Soulez S (2012) La méthodologie de la méta-analyse en marketing. Rech Appl Mark 27(1):79–105

    Google Scholar 

  39. Aldenderfer MS, Blashfield RK (1984) Cluster analysis. Sage Publications, Newbury Park

    Book  Google Scholar 

  40. Bardos M (2001) Analyse discriminante—application au risque et scoring financier. Dunod, Paris

    Google Scholar 

  41. Soekardan D (2016) An analysis of activity based costing: between benefit and cost for its implementation. Int J Sci Technol Res 5(6):334–339

    Google Scholar 

  42. McChlery S, Jim McKendrick J, Rolfe T (2007) Activity-based management systems in higher education. Public Money Manag 27(5):315–322

    Article  Google Scholar 

  43. Lorino P (1991) Le contrôle de gestion stratégique. La gestion par les activités, Vuibert

    Google Scholar 

  44. Care E, Luo R (2016) Assesments of transversal competencies. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000246590. Accessed 8 Jan 2020

  45. Cropanzano R, Molina A (2015) Organizational justice. In: Wright JD (ed) International encyclopedia of the social and behavioral sciences, vol 17, 2nd edn. Elsevier, Oxford, pp 379–384

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Higher School of Technology, Mohammed I University, BP 473, Hay Al Qods, Oujda, Morocco

    Driss El Kadiri Boutchich

Authors
  1. Driss El Kadiri Boutchich
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Driss El Kadiri Boutchich.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

The classification analysis is performed on the sum of the activity scores. Even if the activities can be heterogeneous, the sum of their scores is valid since the same activities are counted for all the members of the laboratory. Cluster Quality is good, since the index of silhouette of cohesion and separation is close to 1 (Fig. 2).

Fig. 2
figure 2

Source: SPSS

Cluster quality.

Full size image

On the other hand, according to Table 9, the first two members are assigned to cluster 1, while the other members are affected to cluster 2. Thus, cluster 1 constitutes the standard or control sample (Table 9).

Table 9 Cluster membership.
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

El Kadiri Boutchich, D. Human Capital in Public Research Laboratory: Towards an Alternative Evaluation and Prediction Method Based on Hybridization. Ann. Data. Sci. 9, 1181–1200 (2022). https://doi.org/10.1007/s40745-020-00241-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40745-020-00241-9

Keywords

Search

Navigation