Work experience influence on the knowledge sharing barriers perceived significance by higher educational institutions administrative employees

Abstract

The article describes the influence of the work experience of the administrative employees of the higher educational institutions on the perceived significance of knowledge sharing barriers faced by them in their work activities. The methodology suggests testing the relationship between the work experience and the 12 knowledge sharing barriers perceived significance (estimated from the positions of the respondent as a knowledge requester and a knowledge holder, thus giving 24 equations) by linear regression modeling. The equations with statistically significant work experience coefficient contain the perceived significance of the knowledge sharing barrier of requested knowledge sensitivity for its holder, both for the respondents' knowledge requester and holder position. On the basis of the educed results, recommendations are given to managers of the higher educational institutions administrative subdivisions regarding mitigating the influence of the barrier of the requested knowledge importance for its holder, especially considering its influence on the employees with smaller work experience. Several recommendations to business practitioners outside of the higher educational institutions are also given, regarding collaboration with such institutions' administrative subdivisions. This article develops the inquiry into the knowledge sharing barriers problem in the higher educational institutions administrative subdivisions, that have not been a specific object of research in knowledge sharing barriers before the research project including the research presented in the current article. A number of further research directions is also suggested.

The onlinezation influence on knowledge sharing for corporate innovation during theCoViDcrisis

“…The supplementary activities, in their turn, are more onlinezation‐friendly in every company or industry, even if its core business technologies and business model do not obligatory imply it. The importance of supplementary activities onlinezation is truly demonstrated by these results, with a notable exception of significant coefficient lack for ; an explanation for this lack could be that the perceived speed of knowledge sharing is tending to be more dependent on personal relationships of a respondent with colleagues rather than the organizational variables (Blagov et al, 2018 ; Szulanski, 1996 ).…”

“…Positive influence of on and , in addition to the same overall positive influence bias or bias of humane proneness to self‐praise, can be explained either by an increase of intrinsic motivation for knowledge sharing as a reaction on an onlinezation stress (Chedid et al, 2020; Nguyen et al, 2019), or by greater online instruments convenience for knowledge sharing (Kinnunen & Georgescu, 2020; Razif et al, 2020) in terms of both knowledge speed and fullness (but not adequacy, for which face‐to‐face tacit knowledge sharing could be more sensitive and influential (Blagov et al, 2020)).…”

“…Efficiency and effectiveness of sharing knowledge between the requester and holder can be assessed by several criteria, most of which can be generally divided into knowledge sharing speed and quality (Blagov et al, 2018 , 2020 ; Ismail & Yusof, 2010 ; Rumanti et al, 2018 ; Wang et al, 2018 ). While knowledge sharing speed, that is, the speed of the requested knowledge transfer from the knowledge holder to the requester, is a considerably clear and homogeneous category, knowledge sharing quality is much more complicated and can be divided into several subcategories.…”

“…While knowledge sharing speed, that is, the speed of the requested knowledge transfer from the knowledge holder to the requester, is a considerably clear and homogeneous category, knowledge sharing quality is much more complicated and can be divided into several subcategories. A considerable amount of structures of such subcategories has been suggested in the literature (Cabrera & Cabrera, 2002 ; Rumanti et al, 2018 ; Witherspoon et al, 2013 ); one of the possible high‐level classifications of knowledge quality criteria divides these into, for example, knowledge sharing fullness (i.e., does the knowledge holder transfer all the volume of the requested knowledge item) and adequacy (i.e., does the knowledge holder react to the knowledge sharing request by transferring exactly that knowledge item that is being requested) (Blagov et al, 2020 ; Doronin et al, 2020 ; Gavrilova et al, 2012 ; Younis & Adel, 2020 ).…”

“…Using the classification developed in Blagov et al ( 2018 , 2020 )), it has been decided that such parameters should be knowledge sharing speed, fullness and adequacy.…”