Knowledge management has assumed an important place in organisations today due to the fact that the major competitive advantage of organisations lies on attracting and retaining corporate knowledge [1]. This presupposes that for organisations to become excellent, they have to treat knowledge well since it contributes to their core competencies, just as they would do to any other strategic irreplaceable assets. Therefore, managing knowledge involves the leveraging of intellectual assets to enhance organisational performance [2-5]. 

The goal of knowledge management is to ensure that organisations are aware of the knowledge at their disposal. They need to find out their strengths, weaknesses, opportunities and threats so as to operate profitably. This will help them to make the most efficient and effective use of their knowledge resources to enhance performance. To this end, knowledge management remains one of the many strategies that successful organisations use to enhance employee performance and organisational competitiveness. Therefore, knowledge management is largely concerned with developing, depositing, extracting, and sharing knowledge, for subsequent retrieval. This is needed to facilitate quick decision-making for organisational growth and development [6]. In fact, it has been acknowledged that knowledge management contributes substantially to better decisions [7-10].

Organisations can only have good results if they are doing the right things at the right time. With the advancement in information communication technology (ICT), knowledge has become a vital resource for organisations to gain competitive advantage and improve their performance.  Performance is the accumulated end results of the organisation’s work processes and activities. It is about how effectively an organisation transforms inputs into outputs and comprises the actual outputs or results as measured against its intended outputs. In the view of Richard, Davinny and Johnson [11], organisational performance covers financial performance, product market performance, and shareholders’ return.

However, a look at the performance of Nigeria’s banking sector raises some concerns. Recent observations reveals an upsurge in employee turnover arising from corporate restructuring including casualisation of workers, outright retrenchment of permanent staff and subsequent recruitment of contract staff by deposit money banks. For instance, Ecobank Nig plc sacked 1,200 employees in July 2019. As noted by Nairametric [12] “While in most cases some of the banks described the exercise as right sizing, but the concept could be confusing as they recruit more staff shortly after the exercise”. This study believes that this can lead to loss of invaluable knowledge resources and decrease in organisational performance. Sadlly, management of banks are yet to give this issue the thought it deserves.  Therefore, knowledge management gap exists in the sector [13]. As a result, further investigation into the nexus between knowledge management activities and performance of deposit money banks is warranted. 

In the light of the foregoing, the questions this study seeks to address are: What is the influence of knowledge acquisition as a dimension of knowledge management on performance? Is there a significant relationship between knowledge conversion dimension of knowledge management and performance? What is the combined impact of knowledge acquisition and conversion on performance? Specifically, the objectives of the paper are to: determine the influence of knowledge acquisition on performance, examine the relationship between knowledge conversion and performance, and investigate the joint impact of knowledge acquisition and conversion on performance from the standpoint of deposit money banks in Nigeria. Hypotheses are formulated and tested along these objectives. 

Review of Related Literature        

In this section of the paper, we present some documented evidence on knowledge management and organisational performance. Siretea and Gritorita [14] examined perspective of knowledge management in Jordanian Universities. The study discovered that universities can accomplish their missions as learning organisations through knowledge management, aiming to acquire knowledge needed to improve organisational performance and improving efficiency, effectiveness, and innovation. 

Mills and Smith [15] evaluated the impact of specific knowledge resources on organisational performance using survey data from 189 managers in Britain and adopting structural equation modeling. The study showed that some knowledge resources are directly related to organisational performance, while others, though important pre-conditions for knowledge management are not directly related.

Absel, Gawater, and Mohammad [16] investigated the role of knowledge management in enhancing organisation’s performance among Egyptian companies. Questionnaire was used to collect the required information. The result showed that knowledge management capabilities have a positive significant relationship with performance at 1 percent level of significance. This means there is a high correlation between knowledge management capabilities and organisational performance. 

William, John, and Peter [17]  surveyed 973 Australian organisations to investigate knowledge identification practices. The survey findings showed that while organisations do perceive identification to be important, the practice of identification has not reached mainstream adoption yet. The study also revealed two opposing approaches organisations take in practicing knowledge identification namely: proactive knowledge identification and reactive knowledge identification. 

Martin, Nakhchian, and Kashami [18] examined the influence knowledge acquisition strategies has on performance of some selected small and medium scale enterprises in Iran. Both quantitative and qualitative data were employed. The study revealed four discrete knowledge acquisition strategies. (Low-key, mid-range, focus, and explorer) and showed that strategies differ in their relation to company performance. This was as a result of their design of knowledge acquisition activities and the type of knowledge acquired.   

Ahmad, Mohamad, and Ibrahim [19] employed a survey method in finding out the relationship between individual’s absorptive capacity and knowledge acquisition behaviors by engineers in electrical and electronics sector in Malaysia. There were 305 responses from the survey. Partial least square properties of structural equation model were used to measure the relationship between variables. They found that individual absorptive capacity has partial influence on employees’ knowledge acquisition.

Mahamad, Mehrdad, Salma, and Noruzy [20] investigated the influence of knowledge management practices on organisational performance in small and medium scale enterprises (SMEs) in Iran. Structural equation model was used. Senior managers from these enterprises were chosen using simple random sampling technique. The findings showed that knowledge acquisition, storage, creation, and implementation of knowledge have a significant influence on knowledge management. They concluded that productivity and financial performance, staff performance, innovation, work relationships and customer satisfaction have significant relationship with organisational performance. The results suggest that knowledge management activities directly influence the performance of SMEs. 

Alnaweigah [21] discussed the impact of knowledge management functions on organisation using staff of University of Al-Taif, Kuwait. The study assessed the reality of knowledge management at the university and explored its impact on increasing excellence of its employees. The findings showed a statistically significant impact of knowledge management dimensions on organisational excellence among the staff of the university and a statistically significant difference in their evaluation of the level of knowledge management which was attributed to their functional and demographic characteristics.

Akpotu and Lebari [22] investigated the link between knowledge acquisition practices and performance of administrative employees of educational institutions in South-South Nigeria. The findings revealed a significant relationship between knowledge acquisition and administrative employees’ performance.  Chweya, Ochieng, and Riwo-Abudho [23] did a study in Kenya to determine the extent of knowledge creation, sharing and acquisition practices in commercial banks in Kisumu City. Correlation analysis showed that over 45 percent of the respondents’ approved effective staff development tools to support management practices while less than 13 percent respondents disapproved. Also, 65 percent agreed that their banks had formal mechanism in place which guaranteed the sharing of best practices in work activities.

Nnabuife, Onwuka, and Ojukwu [24] carried out a study on selected commercial banks in Awka, Nigeria on knowledge management and organisational performance. Thirty-five (35) respondents were involved and Pearson’s product moment correlation coefficient was used to analyze the data generated. The findings revealed that knowledge identification contributes to organisational performance and that knowledge acquisition significantly effect organisational performance. 

Lekhanath and Santosh [8] examined knowledge management, employee satisfaction and performance of some financial institutions in Nepal. They reported that knowledge acquisition and protection have significant effect on employee satisfaction and performance.In the same vein, Gangaram (2018) investigated knowledge management and employee job performance in Nepalese banking sector. A descriptive research design was adopted and it was reported that knowledge acquisition, conversion, and transfer have significant effects on employee job performance.

Hayfa and Abdullah [5] examined how knowledge management impacts on organisational performance in Pakistani banks. Using survey method of research and primary data generated by means of questionnaire, the study applied the ordinary least square framework to analyse the data. It reported that knowledge acquisition, application and protection are vital to organisational performance.

Data and Methods

Survey research approach was used to conduct this research. Of the sixteen (16) listed banks on the Nigerian Stock Exchange as at October 2019, five banks were selected for this study. The banks and their respective number of employees are: Eco-bank (19,142), First Bank (7,616), Access bank (3,192), Diamond bank (4,731) and Polaris bank (2,804). Together, this gives a total of 37,485 employees (Annual Accounts of sampled banks, 2019). These banks represent the biggest five banks in total assets of the deposit money banks. Applying Taro Yamane formula to the 37,485 employees gives a sample size of 400. The sampled employees were selected from senior management staff of each bank.

Questionnaire was administered to the respondents to generate the primary data needed for analysis. The questionnaire contained structured questions placed on the modified Likert five-point scale. The response scoring weights were: strongly agree 5 points, agree 4 points, disagree 3 points, strongly disagree 2 points, and undecided 1 point.

Table 1: Reliability Test

Source: Authors’ Output

Model Specification

The models formulated to test the hypotheses are: 

 (1)

 (2)

 (3)

,

• Kacq: Knowledge Acquisition  operationalised as the extracting, structuring and organising knowledge from employee expertise so that it can be captured and transferred into readable form

• Kcon: Knowledge conversion is operationalised as how existing knowledge is made useful to an organisation

• Perf: Organisational performance is operationally defined as the end result of business activities

Reliability Test of the Instrument 

To ensure the reliability of the instrument, a pilot study was conducted. A total of 46 copies of questionnaire were used and the Cronbach Alpha test result shows a mean value of 0.81 as presented in Table1. This high value shows that the instrument is reliable and usable for generating the needed data.

Data Presentation, Analysis and Discussion of Results

Data Presentation: The data presented and analysed in this study includes: the descriptive analysis of responses, correlation matrix, variance inflation factor test, and regression analysis to test the hypotheses formulated for the study. Four hundred (400) copies of questionnaire were administered. Three hundred and nine (309) copies were retrieved, nine copies of the questionnaire were rendered void therefore not usable because of unanswered questions, while three hundred (300) copies of questionnaire (75%) were used for analysis in the study. To illuminate the data, the descriptive statistics is presented in Table 2.

Table 2 shows the descriptive properties of the data set used for the analysis. The constructs have a maximum value of 5 indicating that the respondents strongly agree at some points to all the questions asked, while the minimum value ranges from 1.4 to 1.8 indicating that some respondents were undecided and strongly disagree at some points. The mean values for knowledge acquisition (kacq), knowledge conversion (kcon), and organisational performance (perf) are respectively 3.930667, 3.924, and 3.982667. These values are approximately 4 (the score weight for agree) which means that on the average the respondents agree to the questions/statements. The standard deviation values which measure the dispersion of the individual observation from the mean value are 0.7393341, 0.9093112, and 0.8812993 respectively for kacq, kcon, and perf. These values are quite low, signifying that the observations cluster around the mean and normally distributed. 

Table 2: Descriptive Statistics 

Source: Authors’ Output

To further provide insight into the data, the correlation matrix is presented in Table 3.

Correlation matrix shows the correlation coefficient among the variables. The result in Table 3 reveals that there is a positive correlation between dimensions of knowledge management and organisational performance. Specifically, the correlation between knowledge acquisition and organisational performance is 0.9042 while the value is 0.9516 between knowledge conversion and performance. These correlations are not only positive, but show that there is high correlation. Accordingly, the dependent and independent variables are strongly related.

Table 3: Correlation Matrix 

Source: Authors’ Output

Regression Diagnostic Test 

To estimate model 3, that is, the multiple equation model, it is important to ascertain if multicollinearity is present in the independent variables. This test is carried out using Variance Inflation Factor (VIF) and the result presented in Table 4.

The result in Table 4 shows the mean VIF value is 5.13 which is less than the benchmark value of 10. This indicates the absence of multicollinearity. Hence, we can proceed to use the variables to test the hypotheses.

Table 4: Result of VIF Test

Source: Authors’ Output

Test of Hypotheses

• H_01:  Knowledge acquisition does not have significant impact on organisational performance

• This is tested using model (1) and the result presented in Table 5

The regression result in Table 5 shows that knowledge acquisition (kacq) has positive influence on organisational performance as shown by the value of the coefficient 1.077838. The statistical significance of this coefficient measured by the probability value of the t-statistics (0.000) is less than 0.05 implying that it significant at 1%. Therefore, knowledge acquisition has a significant positive impact on performance. This result is further supported by the value of the coefficient of determination, R² (0.8176), which shows that the explanatory power of knowledge acquisition is 81.76%. On the basis of this statistical evidence, the null hypothesis of no significant impact is rejected.

Table 5: Simple Regression (Performance and Knowledge Acquisition)

Number of Obs = 300.  R2 = 0.8176.   Adj. R2 = 0.8170.  F (1, 298) = 1335.78.   Pro>F = 0.0000

Source: Authors’ Output

• H_02: There is no significant relationship between knowledge conversion and organisational performance

• To test this hypothesis, model 2 is implemented and result presented in Table 6

The result in Table 6 shows that the parameter for knowledge conversion is 0.922248. This estimate is positive; signifying that a positive relationship exists between knowledge conversion and organisational performance. This is not at variance with the apriori expectation that  The statistical significance of this estimate judging from the probability value of the t-stat is 0.000.  Given that this value is less than the 5 % level of significance, then knowledge conversion is significant in explaining organisational performance. Again, this result is supported by the value of R² which is 0.9055. This implies that knowledge conversion explains 90.55% of the variations in organisational performance. Hereby, the null hypothesis of no significant relationship between knowledge conversion and organisational performance is not accepted.

Table 6: Simple Regression (Performance and Knowledge Conversion)

Number of Obs = 300.  R2 = 0.9055.   Adj. R2 = 0.9052.  F (1, 298) = 2854.41.   Pro>F = 0.0000

Source: Authors’ Output

The frontier of the analysis is extended to test the joint impact of knowledge acquisition and conversion on organisational performance. This is done by implementing model (3) with the result presented in Table 7.

From Table 7, the estimates for the independent variables, knowledge acquisition and knowledge conversion, are respectively 0.3080307 and 0.6975061.  These   values   are  positive, meaning that they both have positive impact on organisational performance. The t-value that measures the statistical significance of the coefficients has probability of 0.000. As this is less than the 5% level of significance, then the estimates are statistically significant at the 1% level. Therefore, the two variables are significant in explaining organisational performance. The strength of the association of the variables with performance measured by R² is 0.9185. Implicitly, 91.85% change in the dependent variable (organisational performance) is brought about by the independent variables (knowledge acquisition and conversion). The F-statistic and its associated p-value of 0.000 shows that the model is statistically significant at 1% level. This means that the regression model is valid and well fitted. Clearly, knowledge acquisition and conversion jointly impact on organisational performance.

Table 7: Multiple Regression Result

Number of Obs = 300.  R2 = 0.9185.   Adj. R2 = 0.9179.  F (2, 298) = 1673.02.   Pro>F = 0.0000

Source: Authors’ Output

The simple equation regression results shows that knowledge acquisition has a significant positive impact on organisational performance ( = 1.077838 (0.000)). This finding aligns with theoretical expectation that acquired knowledge increases organisation’s stock of intellectual asset which strengthens its ability to make timely decisions essential for superior performance. Clearly, this result is in consonance with previous findings (Martin et al., 2012; Akpotu & Lebari, 2014; Felix & Guillermo, 2017, Salman & Sumaiya, 2017; Gangaram, 2018; Stojanovic-Aleksic, Eric-Nielsen, & Boskovic, 2018) that knowledge acquisition has a significant effect on organisational performance.

The test of the influence of knowledge conversion on organisational performance shows that it has a significant positive effect on organisational performance ( = 0.922248 (0.000)). This did not contradict apriori expectation that when organisations make existing learning helpful to others, their expertise and efficiency is improved as knowledge is distributed to where it is needed, and will consequently raise organisational performance. 

In addition to ascertaining the influence of each explanatory variable on the dependent variable, the study also measured the ability of the combination of knowledge acquisition and conversion to impact on performance. The coefficient of determination (R²) of the estimated multiple linear model shows the explanatory ability of the two independent variables to be 91.85%. This shows that knowledge acquisition and conversion are significant knowledge management dimensions that have positive impact on organisational performance. These findings are consistent with the earlier submissions by Mahamad et al. [20] Absel et al. [16], and Hayfa and Abdullah [5] that knowledge management dimensions have significant positive impact on organisational performance.

In a knowledge driven economy, organisations that use their knowledge in the right way, or manage it effectively to gain strategic advantage are more likely to be successful than others. Identifying and leveraging the individual and collective knowledge in an organisation to support the organisation to become more competitive is the essence of knowledge management. Given that employees in the workplace are drivers; their knowledge should be managed and pooled together so that the organisation can use it to build unique knowledge to enhance the organisation’s activities. This study reports that knowledge management activities in the form of knowledge acquisition and knowledge conversion positively and significantly influence organisational performance.

In the light of the findings of this study, it is recommended that knowledge acquisition and conversion practices should be encouraged in deposit money banks so that their performances can improve. This means that both management and employees should work together as a group of people having common identity and professional interest to undertake, share, participate and establish a fellowship. This interaction with one another will improve the use of information to enrich their knowledge and expertise for proper organisational development. This is necessary to influence employees to participate in knowledge management activities.

1. Lee, C.C. and Choi, T. "Knowledge management enablers, processes and organisational performance: An integrative view and empirical examination." Journal of Management Information Systems, vol. 20, no. 1, 2006, pp. 179-228.

2. Ahmed, H.M. and Mohamed, M.S. "The effect of knowledge management critical success factors on knowledge management effectiveness and performance: An empirical research in Egyptian banking sector." Business and Management Review Journal, vol. 9, no. 2, 2017, pp. 201-211.

3. Akram, K. and Hilman, H. "Effect of knowledge management activities and dynamic capabilities on employee performance in the banking sector: Empirical evidence from Pakistan." Studies in Business and Economics Journal, vol. 13, no. 2, 2018, pp. 41-60.

4. Felix, F.G. and Guillermo, L.S. "Explicit and tacit knowledge conversion effects in software engineering undergraduate students." Knowledge Management Research Practice Journal, vol. 10, no. 15, 2017, pp. 336-345.

5. Hayfa, Y.A. and Abdullah, A.M. "The impact of knowledge management on organisational performance." International Journal of Advanced Computer Science and Application, vol. 9, no. 4, 2018, pp. 204-208.

6. Bamgboye-Ayodele, A. and Ellis, L. "Knowledge management and the Nigerian culture – a round peg in a square hole?" The African Journal of Information, vol. 7, no. 1, 2015, pp. 50-88.

7. Holsapple, C.W. and Wu, J. "An elusive antecedent of superior firm performance: The knowledge management factors." Decision Support Systems, vol. 52, no. 1, 2011, pp. 271-283.

8. Lekhanath, K. and Santosh, R.P. "Knowledge management, employee satisfaction and performance: Empirical evidence from Nepal." Saudi Journal of Business and Management Studies, vol. 2, no. 10, 2017, pp. 82-91.

9. Onyango, R.A. "Effects of knowledge management practices in financial institutions: A case of Icea Lion Asset Management Limited in Nairobi, Kenya." Thesis, University of Nairobi, 2018. http://hdl.handle.net/11295/103339

10. Poston, R. and Speier, C. "Knowledge management system usage: Rating scheme validity and the effort accuracy trade off." Journal of Organisational and End User Computing, vol. 20, no. 1, 2008, pp. 45-60.

11. Richard, P. et al. "Measuring organisational performance: Towards methodological best practices." Journal of Management, vol. 35, no. 3, 2009, pp. 718-804.

12. Nairametrics. "CBN: Banks no longer allowed to sack more than five staff without approval." February 22, 2020. https://nairametrics.com

13. Ugurlu, O.Y. "A comparative analysis of knowledge management in banking sector." European Journal of Business and Management, vol. 1, no. 5, 2013, pp. 13-19.

14. Siretea, N. and Gritorita, M. "Perspectives of knowledge management in universities." 2007. http://www.researchgate.net/publication/228216173

15. Mill, A.M. and Smith, T.A. "Knowledge management and organisational performance: A decomposed view." Journal of Knowledge Management, vol. 15, no. 1, 2011, pp. 156-171.

16. Absel, N. et al. "The role of knowledge management in enhancing organisational performance in information engineering and electronic engineering." Business Journal, vol. 5, no. 6, 2012, pp. 27-38.

17. Williams, N.H.T. et al. "How organizations know what they know: A survey of knowledge identification methods among Australian organisations." Paper presented at the 23rd Australasian Conference on Information Systems, Geelong, 3-5 December 2012.

18. Martin, E.K. et al. "Effect of employees entrepreneurial orientations on knowledge management in small and medium enterprises in Iran." Journal of Basic and Applied Scientific Research, vol. 3, no. 3, 2012, pp. 608-617.

19. Ahmad, F. et al. "Knowledge acquisition amongst engineering section in MNCs independent." Journal of Management and Production, vol. 4, no. 1, 2013, pp. 20-25.

20. Mohammad, H. et al. "Investigating the influence management practice on organisational performance: An empirical study." Acta Polytechnia Hungarica, vol. 10, no. 2, 2013, pp. 1-13.

21. Alnaweigah, A.B. "The impact of knowledge management functions on the organisational excellence from perspective of the University of Al-Taif staff." Interdisciplinary Journal of Contemporary Research in Business, vol. 5, no. 3, 2013, pp. 170-192.

22. Akpotu, C. and Labari, E.D. "Knowledge acquisition and administrative employee performance in Nigerian universities." Journal of Management and Sustainability, vol. 4, no. 14, 2014, pp. 116-124.

23. Chweya, K. et al. "The effect of knowledge conversion and knowledge application on performance of commercial banks in Kenya." Journal of Knowledge Management, vol. 9, no. 3, 2014, pp. 64-82.

24. Nnabuife, E. et al. "Knowledge management and organisation performance in selected commercial banks in Awka, Anambra State, Nigeria." IOSR Journal of Business and Management, vol. 17, no. 8, 2015, pp. 25-32.