Counterintuitive, Yet Essential: Taking Stock of Organizational Unlearning Research Through a Scientometric Analysis (1976-2019)

ABSTRACT

With the growth in the literature on organisational unlearning (OU), there is a commensurate increase in the diversity of its knowledge base. Past studies have carried out narrative and systematic reviews to synthesise the research on OU. But none of them have specifically addressed the questions about leading indicators like most influential articles, authors, and journals of unlearning publications. Moreover, previous studies have not studied the inter-relationship of unlearning with other concepts/disciplines. This study addresses these shortcomings and it is also the first attempt to perform a scientometric analysis of OU. In addition, to establish the linkages between unlearning and other concepts/disciplines, co-occurrence of keyword analysis is used. Unlearning shares an intense association with organisational learning, knowledge management, organisational change, innovation, and forgetting. Later, we discuss several implications that can help improve the present arguments and simultaneously shape the future research of OU.

KEYWORDS:

• Unlearning
• organisational unlearning
• organisational learning
• knowledge management
• scientometrics
• co-occurrence analysis

Acknowledgment

An earlier version of this paper was presented at the 79th Annual Meeting of the Academy of Management held in Boston, Massachusetts, in August 2019. The authors acknowledge the inputs received from the participants of the session.

In addition, the authors gratefully acknowledge the three anonymous reviewers for their valuable suggestions and constructive comments for this paper. Furthermore, the authors would like to thank Prof. Thomas Jackson, Editor-in-Chief, Knowledge Management Research & Practice journal.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. This derivation relates to Appendix 1 where Lotka's Law of Author Productivity is explained.The derivation is as follows: Dividing both sides of equation 1 by ∑ Y_X:

$\frac{Y_x}{\sum Y_x}=\frac{c}{\sum Y_x}\ast \frac{\sum Y_x}{x^n}$

Solving further,

$\frac{Y_x}{\sum Y_x}=c\ast \frac{1}{x^n}$

Taking ∑ i.e., total sample of authors, we get:

$\frac{\sum Y_x}{\sum Y_x}=c\ast \sum \frac{1}{x^n}$

or,

$c=\frac{1}{\left(\sum 1/x^n\right)}.$

Additional information

Funding

This work was supported by the Indian Institute of Technology Roorkee [IIT/GIC-12/2019/10]; Indian Council of Social Science Research [CON/178/2019-ICS]; University Grants Commission Junior Research Fellowship Scheme in Sciences, Humanities and Social Sciences [1071/(NET-DEC. 2015)].