Abstract
Background: Tacit knowledge, which university lecturers draw on while teaching, is important to retain though, difficult to express in words. Factors that predict the retention and sharing of this knowledge had hitherto not been investigated in relation to Uganda’s public university’s unique setting.
Objectives: This study examined the extent to which 10 factors could be used as valid predictors of tacit knowledge retention and sharing (TKRS) within Uganda’s public universities.
Method: A quantitative survey was applied, and data were collected from 349 academics chosen using stratified random sampling. Data analysis was done using descriptive and complex factorial analysis with tools including STATA software Version 15 and SmartPLS software Version 4.1.0.9.
Results: A baseline theoretical factor model was developed and serves as a guide to support a TKRS information system. Four direct predictors and one indirect predictor with several mediator factors were confirmed. The most important direct predictor was the collaborative tacit knowledge management factor (β = 0.472, p = 0.000), followed by the individual personal disposition factor (β = 0.241, p = 0.000).
Conclusion: Collaborative tacit knowledge management is the most important factor in predicting the retention and sharing of tacit knowledge in public universities in the country.
Contribution: The study contributes to understanding the importance of each one of the predictor factors explored and their ideal logical combination in managing tacit knowledge in public universities in Uganda.
Keywords: higher education institutions; public universities; structural equation modelling; tacit knowledge management information system; tacit knowledge retention; tacit knowledge-sharing.
Introduction
This study contributes to the tacit dimension of knowledge management systems (KMS) in higher education institutions (HEIs). Al-Hawamdeh (2002) points out that much of the focus in information management is mostly on managing explicit knowledge. There is less emphasis on managing tacit knowledge. Dei and Van der Walt (2020) indicate that universities use knowledge to gain and sustain competitive advantages.
Although tacit knowledge is what we draw on while teaching (Polanyi 1966) and is important to manage (Abelein & Paech 2016), this knowledge is difficult to express in language. It is however important to retain it in HEIs, which equates to maintaining competitive advantage (El-Farr & Hosseingholizadeh 2019; Ngulube 2019).
Empirical knowledge of the various factors that predict tacit knowledge retention and sharing (TKRS) within the knowledge management information system (KMIS) of an academic institution is important.
Changes emanating from globalisation would demand universities to re-strategise in how they operate, so as to remain relevant in the 21st century. The main problem universities face is aligning their operations to be in tandem with global trends of developed countries. This calls for the need for HEIs to innovate in order to improve their way of working to make it relevant to the dynamic environment of the 21st century (Paudel 2022; Squicciarini & Loikkanen 2008). In Uganda’s case, rethinking how best public universities in the country ought to operate in supporting a TKRS information system, would require a theoretical model relating to factors that need to be considered in re-engineering the institutions’ business processes.
Based on this background, this study proposed a theoretical factor model universities could use as a guide to support the tacit knowledge management information system (TKMIS) that could best harness TKRS within a public university in the Ugandan context.
Research philosophy and theory
The study largely applied the positivism philosophy in relation to data management. The theoretical grounding of the study was the knowledge-based theory (KBT) of the firm or knowledge-based view (KBV) of the firm. The KBT theory adopts the objectivist perspective on knowledge which according to Hislop, Bosua and Helms (2018), is aligned with the positivism philosophical perspective.
The objectivist perspective on knowledge explains tacit knowledge as a commodity and as an asset. The first tenet of the objectivist perspective explains knowledge as a commodity. The second tenet relates to the organisation or institution possessing knowledge as a resource that is significant in relation to maintaining the competitive advantage of that institution. This theory was considered the most appropriate for this study since it specifically points out ‘knowledge’ as the primary resource to support a firm’s competitive advantage and considers it the most strategically significant resource by placing knowledge at the centre of the firm’s purpose and operations.
Other theories like the resource-based view (RBV) of the firm take a generic view of all valuable resources responsible for competitive advantage (Barney, Ketchen & Wright 2021; Mailani et al. 2024). The KBT theory on its part specifically argues that knowledge-based resources, for instance human resources, do provide a more sustainable advantage since they are difficult to copy.
The KBT theory also recognises the distinction between tacit and explicit knowledge, unlike other conventional theories. The theory supports the argument that managing tacit knowledge is crucial for innovation and problem solving.
Structure of the article
The article is structured as follows: The ‘Introduction’ presents the introduction and background. Section ‘Literature review and hypotheses’ reviews relevant literature together with hypotheses development. Section ‘Research methods and design’ discusses the methodology. Section ‘Results’ presents the research findings. Section ‘Discussion’ presents the discussion of the research findings. Section ‘Conclusion’ presents the conclusion. The ‘Acknowledgements’ section also presents the competing interests, authors’ contributions, funding information, data availability and disclaimer.
Literature review and hypotheses
Tacit knowledge retention and sharing
According to Hassan (2021), HEIs rely heavily on tacit knowledge-sharing (TKS). Kucharska and Erickson (2023) point out that for knowledge to have any impact or meaning in an organisation, it has to be shared. According to Amundsen, Ballam and Cosgriff (2019), tacit knowledge is known to be a competitive asset within the HEIs. The argument is that tacit knowledge held by individuals and by communities of practice in a university should be harnessed. Mentoring is one way to share tacit knowledge from senior subject specialists to their juniors. Johnson and Griffin (2024) argue that mentoring in an HEI should be intentional and deliberate. Coaching and good succession planning is one way of mentoring; and it supports the organisation in enhancing creativity and collaboration (Rothwell 2023). According to Chesbrough (2019), organisations should however, desist from the logic of closed innovation since it is no longer sustainable.
It is, therefore, better to use both internal and external ideas and allow knowledge to flow inbound (bringing in ideas from outside) and outbound (by securing knowledge transfer partnerships with entities outside your organisation).
Factors influencing tacit knowledge management in higher education institutions
Godfrey and Ejiri (2022) point out several factors which influence tacit knowledge management. Organisational culture and organisational structure are some of the factors. In addition, Alves and Pinheiro (2022) highlight, among other factors, the knowledge management strategy factor (KMSF).
Factors that were considered in this study are as follows: (1) The collaborative tacit knowledge management factor (CTKMF), (2) The leadership practice factor (LPF), (3) The organisational culture factor (OCF), (4) The individual personal disposition factor (IPDF), (5) The knowledge management strategy factor (KMSF), (6) The organisational environment factor (OEF), (7) The tacit knowledge management institutionalisation factor (TKMIF), (8) The human resource management practice factor (HRMPF), (9) The job design factor (JDF) and (10) The staff motivation factor (SMF).
Collaborative tacit knowledge management factor
The collaborative aspect of tacit knowledge management is supported by research from Kucharska and Erickson (2023); Sharma and Dey (2018) and by Mezghani, Exposito and Drira (2016).
In addition, Amundsen et al. (2019) also note that academic work often presumes collaboration among interdependent individuals. However, current tacit knowledge management models in HEIs hardly address the collaborative aspect. Based on these arguments, the study hypothesised as follows: Hypothesis 1 (H1). The CTKMF positively affects TKRS among academic staff in public universities in Uganda.
The leadership practice factor
Novitasari et al. (2021) point to a link between leadership practice and TKS in relation to charismatic leadership. However, extant tacit knowledge management models in HEIs hardly address the LPF. Other scholars have explored leadership styles, innovative work behaviour, organisational culture and organisational citizenship as influencing each other (Khan et al. 2020). Based on these arguments, the study hypothesised as follows: Hypothesis 2 (H2). The LPF positively affects TKRS among academic staff in public universities in Uganda.
The organisational culture factor
Several studies note a significant influence organisational culture has on TKS and in influencing employee attitudes towards knowledge behaviour, which stimulates scientific research (Horban et al. 2021; Ibrahim & Ali 2021).
Salta et al. (2022) suggest a communities of practice framework in HEIs to help promote TKS between learners and facilitators. These arguments led to the following hypothesis: Hypothesis 3 (H3). The OCF positively affects TKRS among academic staff in public universities in Uganda.
The individual personal disposition factor
Scholars like McClelland (1961), Shihabeldeen, Babiker and Ahmed (2020), Munns (2021), Chandra and Alammari (2021), Nicholls et al. (2022) as well as Enakrire and Smuts (2024) have investigated several aspects of the IPDF. This factor embodies personal needs such as desire for power, achievement and affiliation, as well as one’s attitude towards knowledge-sharing.
This factor, therefore, embodies individual personal traits and attitudes; as well as motivational and behavioural issues. With reference to these observations, the study hypothesised as follows: Hypothesis 4 (H4). The IPDF positively affects TKRS among academic staff in public universities in Uganda.
The knowledge management strategy factor
Alves and Pinheiro (2022) carried out a study in Brazilian universities and found that the knowledge management strategy strongly influenced TKS. Based on this argument, this study hypothesised that: Hypothesis 5 (H5). The KMSF positively affects TKRS among academic staff in public universities in Uganda.
The organisational environment factor
The organisational environment is a broad concept that can encompass internal and external factors (Akpoviroro & Owotutu 2018). Tanatova, Yudina and Korolev (2023), in their study, address the complex social environment in a university as part of the organisational environment. They highlight the importance of institutionalising tacit knowledge management. Bui and Tran (2023) also explain that the knowledge created by employees within an organisation is an internal environmental complexity factor, whereas information processing and digital transformation are considered as external environmental complexity factors.
In our study, the organisational environment was conceptualised to constitute the administrative structure, the operational structure and the social environment within the institution. This study, therefore, hypothesised that: Hypothesis 6 (H). The OEF positively affects tacit knowledge management institutionalisation in public universities in Uganda.
The tacit knowledge management institutionalisation factor
Nonaka and Takeuchi (1995), supporting institutionalising tacit knowledge management within the organisation or institution, developed the Socialisation, Externalisation, Combination, Internalisation (SECI) model for informal knowledge processes. The model suggests a four-step process through which tacit knowledge could be created and shared.
You (2017) also supports the institutionalisation of informal learning arguing that ‘social interaction provides useful explanations for tacit knowledge-sharing and creation’. Based on these observations, this study hypothesised that: Hypothesis 7 (H). The TKMIF positively affects the human resource management practice in public universities in Uganda.
The human resource management practice factor
Seeletse and Thabane (2016) as well as Hislop et al. (2018) endeavour to explain the influence human resource management has on staff retention and job design. Accordingly, job design can enhance employee satisfaction and motivation. In addition, effective human resource management practices create intrinsically interesting and challenging jobs that would encourage knowledge-sharing and utilisation.
Extant tacit knowledge management models in HEIs, however, hardly address the HRMPF. In light of these arguments, this study hypothesised that: Hypothesis 8 (H). The HRMPF positively affects job design in public universities in Uganda.
The job design factor
For job design, Hislop et al. (2018) explain that job design can support organisational knowledge management activities.
Liu et al. (2022) also note that there is a relationship between job characteristics and controlled motivation as well as autonomous motivation. However, existing tacit knowledge management models in HEIs, hardly address the JDF. The study, therefore, hypothesised that: Hypothesis 9 (H). The JDF positively affects staff motivation in public universities in Uganda.
Staff motivation factor
Borrowing Vroom’s valence expectancy theory, the argument is that people may want different things from an organisation, such as a steady salary, job security, advancement and challenging work (Vroom 1964).
Drawing ideas from Vroom’s theory, in relation to tacit knowledge management in public universities, Akosile and Olatokun (2020) as well as Hislop et al. (2018), argue that academic staff must be motivated to participate in tacit knowledge management activities within universities.
Staff members will collaborate in knowledge-sharing activities if they expect that the effort they expend on these tasks would lead to acceptable performance (expectancy). They must also believe that this effort will be handsomely, fairly and justly rewarded (instrumentality), and that the rewards themselves will be valuable (valence).
Akosile and Olatokun (2020) continue to argue that academic staff who are the source of much of the knowledge in HEIs need to be motivated in some way in order to share their knowledge. However, existing tacit knowledge management models in HEIs hardly address the SMF. The study, therefore, hypothesised: Hypothesis 10 (H10). The SMF positively affects collaborative tacit knowledge management in public universities in Uganda.
Proposed research model
The foregoing hypotheses are illustrated in the conceptual framework or research model presented in Figure 1, which has been adapted from Alves and Pinheiro (2022).
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FIGURE 1: The Proposed Research Model for Factors Predicting Tacit Knowledge Retention and Sharing within the Tacit Knowledge Management Information System in Uganda’s Public Universities. |
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Explanation of the proposed research model
Figure 1 represents the proposed research model and the hypothesised relationships between the factors. Five factors were hypothesised to have a direct and positive influence on TKRS in public universities. One factor, the OEF, was hypothesised to be mediated by a string of other factors in influencing TKRS.
Tacit knowledge-sharing in universities
Tacit knowledge-sharing in universities has been explored by a number of scholars like Fayda-Kinik (2022), who note a correlation between TKS and organisational commitment.
Hassan (2021) indicated that one of the mediators between organisational factors and TKS among academic staff in HEIs is the quality of the KMS.
Mazorodze and Mkhize (2022) explained that rewards, recognition, promotion and bonuses were significant motivators that promote TKS in universities. Yu and Zhou (2015) identified four types of TKS processes in education: peer review, learning communities, thumb-a-lift and academic conferences.
Conclusion and gap analysis
Literature indicates the value and importance of a TKMIS in HEIs and the factors that influence TKRS. We posit that it is prudent for public universities in Uganda to support a TKMIS by taking into consideration key factors that predict TKRS.
This study fills the gap where there was no theoretical factor model to guide public universities in Uganda to support the retention and sharing of tacit knowledge in these institutions by providing a theoretical factor model to serve as a guide.
Research methods and design
The data collection instrument was organised into a dozen sections (see Online Appendix 1). Initially, a consent statement and an introductory note explained the objective of the study. Questions in Section One related to the demographic characteristics of the respondents. The rest of the sections handled questions relating to the various factors that this study considered.
Research procedure
The procedure followed involved problem identification and definition. This was followed by formulating the research objectives and hypotheses. The main data collection instrument was designed, ethical approval was secured, data were collected and results were analysed. The process was finalised with model development, discussion, conclusion and results dissemination.
Research design
A quantitative survey and complex factorial analysis were used to understand the relationships between factors considered valid predictors of TKRS in Uganda’s public universities.
Data were collected from 349 academic staff in four public universities from November 2023 to February 2024 mainly using the Google Forms survey platform.
The study sampling strategy
Stratified random sampling was used to select respondents composed of academics at various levels in the four public universities selected. They were categorised into top-notch academics, middle-level academics and lower-level academic staff.
Administering the survey
Extant databases on targeted academic staff respondents in the four universities were applied to get email contacts of university academic staff. According to Tessler et al. (2019), survey research allows investigators to explore a large population and focus on relevant data.
Data collection
The main tool was pre-tested in the four universities and revised based on reviewers’ suggestions.
The final version was sent to 400 academic staff members primarily via email and to a lesser extent directly administered in print. The results were based on 349 well-completed responses.
Ethical considerations
Ethical clearance to conduct this study was obtained from the Research and Ethics Committee of the Uganda Christian University (No. UCUREC-2023-523) and the Uganda National Council of Science and Technology (UNCST) (No. SIR255ES).
Results
Demographic findings are presented in Table 1.
| TABLE 1: Demographic profile characteristics of respondents. |
Demographic distribution of respondents
In terms of sex, male respondents (67%) dominated the female respondents (33%). These demographics demonstrate the predominance of male academic staff among the public universities explored, implying a gender imbalance in academia. This imbalance would translate into the fact that results generated were more representative of the male perspective in relation to TKRS in the university.
In terms of the age bracket, those below 30 years of age comprised a mere 2% of the sample, while the ‘31–40’ year’s age bracket formed 29% of the sample. The ‘41–50’ year’s age bracket was the modal category contributing 42%. This was followed by the ‘51–60’ year’s age bracket, which comprised 24% of the sample, while those with ‘61–70’ years of age constituted 2% of the sample. None of the respondents were in the ‘Above 70’ age bracket.
In relation to the level of education attained, almost an equal number of respondents had qualifications of a master’s degree (41%) and PhD (44%), while those with only a bachelor’s degree (Teaching Assistants/Graduate Trainees) were 8%.
For academic rank, while assistant lecturer (41%) contributed the biggest percentage, lecturer (28%) contributed the second biggest percentage of the respondents. Senior lecturer constituted 16%, while teaching assistant was 8%. The lowest percentages included Associate Professor (4%) followed by Professor (3%). The finding indicates that academics from the rank of associate professor are the minority and should be specially motivated and encouraged to share and transfer their expertise to their juniors to support continuity in institutional knowledge and expertise in case the seniors departed.
In relation to years of teaching experience, this study observed that 12% of the respondents had taught at university level for up to 5 years, 17% had taught at the university for between 6 and 10 years, 23% had served between 11 and 15 years, 25% had served between 16 and 20 years and 23% above 20 years.
The implication is that a wider margin of staff possessed experiential knowledge accumulated over the years they taught at university. This suggests a tendency for staff to stay for decades in university employment. The modal age bracket of 41–50 years constitutes 42% with the 51–60 years bracket only 24% and the 61–70 year bracket taking only 2%. This points to the need to prioritise TKS by those more experienced and about to retire from academia so that their rich knowledge is tapped before they get separated from the institution.
Findings relating to suitability of data collected for factor analysis
The study assessed the final data collected to affirm their suitability for factor analysis. Data were first transformed. The Bartlett’s test of sphericity and the Kaiser-Meyer-Olkin (KMO) test of sampling adequacy were done as a preliminary check to ensure that the data collected were suitable for factor analysis. According to Bartlett (1951), a significant result (p < 0.05) of the test would indicate that the factors would be sufficiently correlated and thus suitable for factor analysis. The findings are presented in Figure 2.
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FIGURE 2: Test results for suitability of data collected for factor analysis. |
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The results presented in Figure 2 indicate a p = 0.000 of the Bartlett’s test. This implies that the variables were sufficiently inter-correlated and were, therefore, fit for factor analysis. In addition, the KMO test for sampling adequacy had a KMO value = 0.896, which is a value close to 1, indicating that the results were excellent for factor analysis.
Findings relating to normality of data collected
The study assessed whether data collected for each factor were normally distributed. The Shapiro-Wilk W test for normal data indicated p-values below 0.05 for data collected on all variables. This implied a non-normal distribution. This finding served as justification for applying SmartPLS software known to adequately handle non-normal distributions.
Findings relating to model assessment
To assess the measurement model and the structural model, the study applied exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The primary goal of both the analyses was to affirm whether a combination of the 10 factors hypothesised would be valid predictors of TKRS in public universities in Uganda. The study was more interested in the predictive ability of the intended model.
The study used the R-squared metrics and p-values to draw a conclusion. Findings indicate that all the hypothesised relationships were valid predictors of TKRS in Uganda’s public universities, except the KMSF.
Collinearity analysis for factor data collected
Variance inflation factor (VIF) values (typically above 5) would indicate multicollinearity (Hair, Ringle & Sarstedt 2019). The highest VIF value obtained in this study was 4.000, indicating the absence of multicollinearity among the predictor variables (see Table 2).
Assessing the measurement model for construct reliability and validity
Confirmatory factor analysis in partial least squares structural equation modelling (PLS-SEM) is a statistical technique used to assess measurement models (Hair et al. 2019).
Table 3 indicates internal consistency in terms of Cronbach’s alpha and composite reliability (CR) measures as well as results for convergent validity in terms of average variance extracted (AVE).
| TABLE 3: Construct reliability and convergent validity. |
In relation to Cronbach’s alpha and CR, values ranging between 0.40 and 0.708 may be removed only if removing indicators with such values would lead to an increase in the internal consistency reliabil-ity or convergent validity; otherwise, the indicator may remain. Since the majority of values in Table 3 for Cronbach’s alpha and CR were above 0.708, CR was confirmed.
According to Hair et al. (2021), AVE for constructs should be larger than 0.5. All the values for AVE in Table 3 were larger than 0.5; thus, AVE was satisfactory. Therefore, convergent validity was confirmed.
Assessing the measurement model for discriminant validity
Discriminant validity applied the heterotrait-monotrait (HTMT) test. Findings are presented in Table 4. According to Henseler, Ringle and Sarstedt (2015), HTMT values should at most be below 0.90. In our case, at least all the values in Table 4 were below 0.90; thus, discriminant validity was confirmed.
| TABLE 4: Discriminant validity – heterotrait-monotrait. |
Structural model assessment
For structural model assessment, R-squared values, path coefficients and p-values were used to evaluate the model. According to Hair et al. (2021), R-squared values are used to understand the variation of each independent variable (IV) with its dependent variable (DV). R-squared values, path coefficients and p-values are indicated in Figure 3.
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FIGURE 3: Theoretical Model for Factors Predicting Tacit Knowledge Retention and Sharing within the Tacit Knowledge Management Information System in Uganda’s Public Universities. |
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R-squared values in Figure 3 indicate that 89.3% of the variation in the dependent variable TKRS was explained by all the IVs combined, implying a high proportion of the variation in the DV being explained by the model. In our study, all hypotheses were proven true except one: there was no significant relationship between the KMSF and TKRS.
Discussion
The study revealed four main factors chiefly responsible for directly and significantly predicting TKRS within the TKRS information system in the context of Uganda’s public universities. These factors are: CTKMF: (β 0.472, p 0.000); IPDF: (β 0.241, p 0.000); LPF: (β 0.204, p 0.002) and OCF: (β 0.172, p 0.003).
The OEF was the fifth main factor in influencing TKRS indirectly via a number of mediator variables namely: TKMIF, HRMPF, JDF, SMF and CTKMF, as indicated in Figure 3.
The KMSF had no significant influence on TKRS: (β 0.024, p 0.467), in spite of earlier studies by scholars such as Alves and Pinheiro (2022), stating the contrary. This can be attributed to the fact that in our particular study, none of the universities explored had a well-formulated knowledge management strategy.
Discussion of results relating to each one of the factors
The findings for the CTKMF – an addition by this study to mainstream literature and notably the most important factor to predict TKRS – agree with Kucharska and Erickson (2023), Sharma and Dey (2018), Akanji et al. (2020) and Mezghani et al. (2016), who all acknowledge collaborative work as necessary in tacit knowledge management.
The findings for IPDF indicate that TKRS is directly affected by IPDF, as presented in Figure 3. This is in line with studies by Chandran and Alammari (2021) as well as Enakrire and Smuts (2024). The latter consider this factor to constitute openness in communication and interpersonal trust as significantly influencing the knowledge-sharing attitude. Our study confirms that interpersonal trust and willingness are necessary for staff to collaborate, communicate and share tacit knowledge.
Findings also agree with Munns (2021), who noted that uncertainty and mistrust – which in our study were part of IPDF – may prevail in an organisation and may cause a stumbling block in talent retention. Findings for the LPF agree with Novitasari et al. (2021), who explain that there is a direct link between leadership practice and TKS.
Findings for the OCF do agree with Ibrahim and Ali (2021), Horban et al. (2021), Chandran and Alammari (2021), Enakrire and Smuts (2024), Munns (2021) and Fullwood and Rowley (2018), who all acknowledge that the presence of a well-cultivated academic culture in HEIs facilitates easier and more effective sharing of tacit knowledge.
Our study contrasts with Fullwood and Rowley (2018) in the sense that whereas in their study organisational culture was noted to be the most important factor, in our study this factor was not found to be the most significant factor to predict TKRS.
In our case, the CTKMF stood out as the most significant. Our findings also disagree with Alves and Pinheiro (2022), who in their study noted organisational culture as the most significant factor affecting TKS. In our case, it was one of the most significant factors to affect TKRS in public universities in Uganda’s context as indicated in Figure 3.
The findings for the KMSF disagree with Alves and Pinheiro (2022), whose study was set in the Brazilian context. In the Ugandan context, none of the public universities surveyed had a well-articulated knowledge management strategy to which this study attributes our contrary findings.
The findings for the OEF were in agreement with Tanatova et al. (2023), who note that the social environment – as part of the organisational environment – is an essential component of the quality of education and the quality of work of teaching staff, employees and partners of a university. Our findings in this case do agree with Alves and Pinheiro (2022), who similarly got positive results in relation to their organisational structure factor, which in our case was part of the OEF.
The TKMIF was one of the additions by our study to mainstream literature on tacit knowledge management in universities. This factor adds an indirect value to the TKRS system in public universities through mediators such as HRMPF, JDF, SMF and CTKMF, as indicated in Figure 3.
The HRMPF was another addition by our study to existing literature (see Figure 3). This factor had an indirect positive significant influence on the SMF and an indirect positive significant influence on TKRS. The SMF in turn had a direct positive significant influence on the CTKMF. This finding supports claims by Hislop et al. (2018) that human resource management practices have a key role to play in knowledge management activities.
The JDF is an addition by our study to mainstream literature. Our finding aligns with Hislop et al. (2018), who argue that designing jobs that are intrinsically interesting and challenging encourages and motivates employees to utilise and share their tacit knowledge.
Our study demonstrates a significant relationship between the SMF and the CTKMF as noted in Figure 3. This finding agrees with Akosile and Olatokun (2020) and with Hislop et al. (2018), who argue that knowledge-sharing in HEIs depends on motivating members of staff who are the knowledge sources in the institution.
Conclusion
Retention and sharing of tacit knowledge within a public university in Uganda is supported by a logical combination of several factors within the institution, most notably the CTKMF. The p-values indicate that the direct relationship between CTKMF and TKRS was strongest (β = 0.472, p = 0.000) in predicting TKRS in public universities in Uganda. The effect is compounded by the organisational environment, the formalisation and institutionalisation of tacit knowledge management as well as the human resource management practices (like job design and staff motivation), which all support CTKMF in a public university in Uganda’s context. Second was IPDF (β = 0.241, p = 0.000) followed by LPF (β = 0.204, p = 0.002) and OCF (β = 0.172, p = 0.003), in that order of importance. The OEF was key in indirectly influencing TKRS via five mediating variables as illustrated in Figure 3. Future research should explore how all these factors ought to be operationalised in the institution.
Acknowledgements
The authors appreciate the Government of Uganda through the Makerere University Research and Innovation Fund (MakRIF) for sponsoring this research project. The authors thank the management and academic staff of the four public universities explored, who willingly took part in this study and provided the necessary information and other requirements.
This article is based on research originally conducted as part of Godfrey Luyimbazi’s doctoral thesis entitled, ‘Managing tacit knowledge in higher education institutions: A collaboration engineering approach’, submitted to the Department of Information Systems, School of Computing and Informatics Technology, Makerere University. The thesis is currently unpublished and not publicly available. The thesis was supervised by Annabella Ejiri Habinka. The manuscript has been revised and adapted for journal publication. The author confirms that the content has not been previously published or disseminated and complies with ethical standards for original publication.
Competing interests
The authors reported that they received funding from Government of the Republic of Uganda under the Makerere University Research and Innovation Fund (MakRIF) which may be affected by the research reported in the enclosed publication. The authors have disclosed those interests fully and have implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated University in accordance with its policy on objectivity in research.
Authors’ contributions
G.L. was responsible for conceptualisation and data management in the study, in addition to writing and editing the original draft. A.E.H. was responsible for administering and supervising the entire project, including funding acquisition as well as supervision of the editing of the original draft.
Funding information
This work was funded by the Government of the Republic of Uganda under the Makerere University Research and Innovation Fund (MakRIF).
Data availability
The data that support the findings of this study are available from the corresponding author, G.L., upon reasonable request.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. The article does not necessarily reflect the official policy or position of any affiliated institution, funder, agency or the publisher. The authors are responsible for this article’s results, findings and content.
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