The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy: A Case Study in Kenya Lynus Ekuwam Ebenyo Nakiporo International School for Social and Business Studies, Slovenia ekuwamebenyo@gmail.com Kris M. Y. Law Deakin University, Australia kris.law@deakin.edu.au Purpose: The study aims to investigate the impact of knowledge sharing (KS) on institutional performance through healthcare outcomes at a public institution in Kenya. The case study is the Lodwar County Referral Hospital (LCRH) under the Turkana County Government. Study design/methodology/approach: The study adopted a quantitative approach using a case study survey method. Standardised and structured survey questionnaires collected quantitative data from healthcare workers. After piloting, a digital survey questionnaire using a 5-point Likert scale was used to collect data enabled by SurveyCTOCollect. Correlation and regression analyses were used to test the relationship between independent and dependent constructs. Findings: The results reveal that organisational culture (OC), leadership support and encouragement (LSE), clear communication channels (CCC), availability of technology tools (ATT), and knowledge sharing protocol (KSP) are the key drivers of institutional knowledge sharing culture at the LCRH. Moreover, the study uncovers that an individual’s knowledge-sharing behaviour directly impacts institutional performance through problem-solving effectiveness across healthcare workers at the LCRH. The study further reveals that knowledge-sharing practices strongly impact institutional performance at the LCRH. Originality/value: This study makes a remarkable contribution to the scientific body of knowledge (theory and practice), advancing the significance of KS in powering public sector transformation and accelerating service delivery improvements in Turkana County and Kenya. The invaluable and original insights provide the springboard for actionable, objective, and impactful KS programming in the public sector in Kenya. Introduction Many researchers have considered knowledge sharing (KS) to be an essential part of effective knowledge management. They consider KS as the core of a continuous improvement process, transforming an individual’s process improvements into actual learning (Yu et al., 2010). Irani et al. (2009) highlighted the relationship between organisational learning and KS, where organisational productivity and performance depend on the effectiveness of organisational knowledge across different units. Organisational learning contributes to increased organisational knowledge and oftentimes takes place via KS across organisational boundaries (Nordtvedt et al., 2008). KS tools are designed to facilitate interactions between senders and receivers to facilitate the sharing of their experiences and engagement in problem-solving (Sammour et al., 2008). De Angelis (2013) posited that knowledge-sharing activities may promote transparency in public administration, help release information more rapidly and make it available more widely to the public. Van der Meer (2014) stipulated that knowledge sharing can also contribute to improved employee performance, which translates into improved productivity levels and quality of work. Knowledge translation not only allows attention to be given to multiple International Journal of Management, Knowledge and Learning | ISSN 2232-5697 Volume 14 (2025) 67–77 | https://www.doi.org/10.53615/2232-5697.14.67-77 stakeholders across healthcare practices, including patients, consumers, and policymakers, but facilitates the transfer of high-quality research evidence into effective changes in health policy, clinical practice, or products (Mairs, K et al., 2013). While a significant body of studies has been done on the influence of knowledge sharing on organisational effectiveness and efficiency in the private sector, very little is recorded for the public sector, especially in Kenya. This study seeks to address this research gap by investigating the impact of knowledge sharing (KS) on institutional healthcare outcomes at the Lodwar County Referral Hospital (LCRH). Therefore, the study aims to investigate the impact of knowledge sharing (KS) on institutional performance through healthcare outcomes at a public institution in Kenya. The case study is the Lodwar County Referral Hospital (LCRH) under the Turkana County Government. This paper also intends to offer evidence-based insights to influence policy and strategy interventions and achieve sustainable quality healthcare outcomes at the LCRH. Theoretical background Mesmer-Magnus and de Church (2009) asserted that knowledge sharing can assist organisations to improve service delivery. Employees who share knowledge increase the resources of an organisation and reduce time wasted in trial-and-error exercises (Lin, 2007). Knowledge sharing improves customer response times, saves costs through process improvements, reduces workload, increases staff retention, and further improves innovations and developments (Van der Meer, 2014). Today, the success of an organisation depends on how well the organisational knowledge is shared and managed. Knowledge management (KM) involves providing the right knowledge to the right people at the right time. Most KM practices depend on how knowledge is effectively and efficiently shared within an organisation. As such, scholars who study KM have emphasised knowledge sharing as one of its critical components (Dikotla, MA., 2019). Sharing and integrating knowledge from various health and social care professions promotes a more holistic approach to patient care, suggesting that biopsychosocial needs are better met (Hall, 2005; Molyneux, 2001; Reeves, Lewin, Espin, & Zwarenstein, 2011). The evaluation, treatment, and follow-up of patients with mental disorders are more effective when front-line practitioners (e.g. general physicians and nurses) and specialists (e.g. psychiatrists) exchange information and expertise (Kates et al., 2011). The concept of knowledge management was initially introduced in the 80s. It is evident that sharing knowledge about research findings brings about a requisite qualitative innovation in the services and products of organisations, and it is indispensable for innovation (Kremer, Villamor & Aguinis, 2019). Most organisations perform well in creating and storing knowledge but fail to share it within organisations and ultimately confront failure in their outcomes. Moreover, apart from other factors, it is evident from studies that effective knowledge sharing within organisations is so important that it has persuaded the attainment of competitive advantage for the organisations in terms of employee and organisational performance (Kipkosgei, Kang & Choi, 2020). Knowledge sharing is such an important factor that it has played a key role in different phases of the developmental revolution and has been considered an influential source for the betterment of organisations (Mohajan, 2019). The Industrial Revolution, which brought enormous developmental changes to the world, was also influenced by KS (James, 2011). Cutting-edge knowledge-sharing practices are significantly different from the way we used to think about knowledge. These practices question one of our basic assumptions in knowledge Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 68 management: that we should create knowledge before someone requests or needs it (Dunn & Krob, 2014). Moreover, when the training and knowledge management teams work together on knowledge-sharing initiatives, significant improvements result (Dunn & Krob, 2014). By establishing common knowledge, practitioners can collaborate horizontally across professional boundaries to negotiate new working practices. The Change Laboratory workshops provided a crucial platform through which practitioners met physically, some for the first time, to reflect on their current practices and share core or specialist knowledge while at the same time ascertaining what others had to offer (Masilela & Olvitt, 2020). Participation in the decision-making process seems to promote knowledge sharing since healthcare professionals who are involved in decision-making about patient care have more opportunities to share information, expertise, experience, and methods to the benefit of all team members in providing better-integrated care. The evaluation, treatment, and follow-up of patients with mental disorders are more effective when front-line practitioners (e.g., general physicians and nurses) and specialists (e.g., psychiatrists) exchange information and expertise (Kates et al., 2011). Research Questions RQ1: What are the key drivers of the institutional knowledge-sharing culture at the LCRH? RQ2: Does knowledge-sharing behaviour impact institutional performance at the LCRH? Research Objectives RO1: To identify the factors influencing the knowledge-sharing culture at the LCRH. RO2: To evaluate the impact of knowledge-sharing practices on institutional performance at the LCRH. Figure 1: Research Model Knowledge Sharing (Tacit & Explicit) Organisational Culture Leadership Support & Encouragement Clear Communication Channels Reward & Recognition Training & Development Programs Availability of Technology Tools Knowledge Sharing Protocol Institutional Knowledge-Sharing Culture Individual Knowledge Sharing Behaviour Institutional Performance Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 69 Methodology Survey Design This is a quantitative survey study deploying both descriptive and inferential statistical tests. The survey used a structured questionnaire to test the relationship between independent and dependent constructs to answer the study questions and address the study objectives. A 5-point Likert scale was used to gather respondents’ ratings (1=Poor and 5=Excellent) and opinions (1=No Impact, while 5= Very Significant Impact), whether they agree or not with certain statements (1=Strongly disagree, while 5=Strongly agree), and whether they are satisfied or not satisfied with KS programs and suggestions (1=Very dissatisfied, while 5=Very satisfied). The research model consists of eleven constructs, as shown in Figure 1 above. The structured survey questionnaire comprised the demographics section with six (6) questions and the constructs section with thirty-three (33) questions. Each construct had three questionnaire items/factors, as shown in Table 1 below. Table 1: Survey Items Constructs Sub-level constructs Questionnaire items Knowledge Sharing (KS) Access to knowledge KS1-The overall rate of access to knowledge by healthcare workers at LCRH Knowledge sharing practices KS2-Frequently applied KS practices Participation in KS KS3-Level of engagement in KS Organisational Culture (OC) Leadership structure OC1-Effectiveness of the leadership structure Institutional norms OC2-Supportiveness of institutional norms Trust and openness OC3-Level of trust and openness Leadership Support and Encourageme nt (LSE) Visionary leadership LSE1-Impact of visionary leadership on institutional KS culture Collaboration and teamwork LSE2-The overall rate of collaboration and teamwork at LCRH Emotional support and well-being LSE3-Level of emotional support and well-being at LCRH Clear Communicatio n Channels (CCC) Formal communication channels CCC1-Effectiveness of formal communication channels Informal communication channels CCC2-Effectiveness of informal communication channels Cross-functional communication CCC3-Level of cross-functional communication among healthcare workers Reward and Recognition for KS (RR) Performance-based rewards RR1-Consistency in providing performance-based rewards Recognition programs RR2-Availability of recognition programs Opportunities for advancement RR3-Impact of opportunities for advancement on KS culture Training & Development Programs (TDP) Knowledge acquisition TDP1-Frequency of knowledge acquisition programs Skills development TDP2-Level of skills development Transfer of training TDP3-Impact of transfer of training Availability of Technology tools for KS (ATT) Access to digital platforms ATT1-Satisfaction with the level of access to digital platforms Communication and Collaboration tools ATT2-Effectiveness of communication and collaboration tools Knowledge management systems ATT3-Impact of knowledge management systems KS Protocol (KSP) Knowledge sharing guidelines KSP1-Effectiveness of formal KS guidelines on KS practices Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 70 Information classification and access control KSP2-Likelihood of adherence to formal information classification and access control procedures Documentation and knowledge capture KSP3-Elements of effective protocols and mechanisms for capturing, documenting, and disseminating knowledge within the LCRH Institutional KS Culture (IKSC) Open communication IKSC1-Influence of open communication on individual KS behaviour Learning and continuous improvement IKSC2-Influence of learning and continuous improvement culture on other employees' KS behaviour Knowledge sharing norms and standards IKSC3-Impact of KS norms and standards on overall KS behaviour among employees Individual KS Behaviour (IKSB) Willingness to share knowledge IKSB1-Impact of employee willingness to share knowledge Perceived benefits of knowledge sharing IKSB2-Impact of employee perceived benefits of KS Self-efficacy in knowledge sharing IKSB3-Impact of employee self-efficacy in KS on healthcare outcomes Institutional Performance (IP) Innovation and creativity IP1-Impact of employee KS behaviour on Innovation and creativity Decision-making quality IP2-Impact of employee KS behaviour on the decision-making quality Problem-solving effectiveness IP3-Impact of employee KS behaviour on Problem-solving effectiveness Sampling The sampling frame was four hundred (400) healthcare workers at the LCRH. A probability sampling strategy was deployed, first by dividing the sampling frame into fourteen (14) strata/departments (stratified sampling). Then, simple random sampling was applied to identify a representative study sample of 140 healthcare workers (ten healthcare workers per department). Probability sampling offers every study element a fair chance to participate in the study. Data Collection This study was conducted in August 2023 at the LCRH. After piloting with ten (10) healthcare workers, a digital survey questionnaire using a 5-point Likert scale was used to collect data enabled by SurveyCTOCollect (a digital data collection tool). The survey enlisted the support of seven (7) survey assistants (SAs) to support data collection using Android phones. The respondents took control of the Android phones, read through the digital survey questionnaire, and selected the appropriate responses independently. The SAs electronically submitted the completed questionnaires in real time to the SurveyCTO server maintained by the researcher. Finally, data management and quality assurance were performed before exporting the data to SPSS statistical software for data analytics. The respondents' privacy and anonymity were maintained at all times, as the survey questionnaires did not bear any known identity. The highest level of objectivity in the interviews, data analysis, discussions, and conclusions was strictly observed. Demographics The descriptive statistical tests covered the following demographic variables: age, gender, job position (JP), department, years of service (YoS), and highest academic level (HAL). The mean age and years of service for the respondents at LCRH were 32.30 and 5.71 years respectively. Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 71 The gender distribution ratio between male and female respondents was 58:42, respectively. There were more respondents aged 35, more males (1), more nurses (2), more respondents from the administration department (14), more respondents with 1 year of service, and finally, more respondents with a diploma qualification. Furthermore, there was more variability to the right of the mean values for age, job position, department, and years of service. The results of the data collection process by the SAs show the number of planned respondents (PRs) against actual respondents (ARs) across the 14 departments. Each SA was assigned two departments. A response rate of 98.6% was recorded, with 138 out of the planned 140 respondents participating. This also represents 34.5% of the study sampling frame of 400. Table 2: Respondents’ Details S/N Departments at the LCRH SAs PRs ARs 1 Accident and Emergency, Intensive Care & Renal Unit SA 1 10 09 2 Mother and Newborn Child Health SA 1 10 09 3 Pharmacy SA 2 10 10 4 Laboratory SA 2 10 10 5 Paediatrics SA 3 10 10 6 Medicine SA 3 10 10 7 Surgery SA 4 10 12 8 Obstetrics and Gynaecology SA 4 10 07 9 Ophthalmology SA 5 10 10 10 Medical Records SA 5 10 10 11 Rehabilitative Services and Public Health SA 6 10 09 12 Dental Unit SA 6 10 10 13 Radiology SA 7 10 08 14 Administration SA 7 10 14 Total 140 138 Results Reliability and Validity Tests Factor analysis revealed the factors that had the greatest influence on each of the eleven constructs since they had the highest mean values. The Kaiser-Meyer-Olkin (KMO) tests (were all greater than 0.5) indicating sampling adequacy and the appropriateness of data sets for factor analysis; Bartlett's Tests of Sphericity (BTS) were all significant at <0.001 significance level, while their associated probabilities were less than the p-value ( α) 0.05 suggesting stronger correlations among variables, the Total Variance Explained (TVE) revealed factors with the highest eigenvalues for each survey construct (the factors with greatest influence) and Cronbach’s alpha (CA) coefficient ( α ) revealed acceptable values for psychometric properties (reliability and validity) of the measurement scale for all the eleven constructs. The CA coefficients ranged from 0.547 to 0.891. The common notion of there being a threshold of acceptability for alpha values, if only as a rule of thumb (Plummer & Tanis Ozcelik, 2015), was not always seen as implying that lower values of alpha should be taken as indicating an unsatisfactory instrument. The authors used a wide range of different qualitative descriptors to interpret Cronbach's alpha values. So, alpha values were described as excellent (0.93–0.94), strong (0.91–0.93), reliable (0.84–0.90), robust (0.81), fairly high (0.76–0.95), high (0.73– 0.95), good (0.71–0.91), relatively high (0.70– 0.77), slightly low (0.68), reasonable (0.67– 0.87), adequate (0.64–0.85), moderate (0.61– 0.65), satisfactory (0.58–0.97), acceptable (0.45– 0.98), sufficient (0.45–0.96), not satisfactory (0.4–0.55) and low (0.11) Keith, S.T, (2018). Importantly, authors should interpret the value of Cronbach’s alpha reported in the context of their particular study, taking into account the expected dimensionally of what they are seeking Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 72 to measure and the total number of items included in the instrument or scale discussed (Keith, S.T, 2018). In the case of this survey study, the number of items per construct in the survey questionnaire was restricted to three. The critical factors with the greatest influence on each of the survey constructs, as confirmed by the factor analysis process, are detailed in Table 3 below; Table 3: Survey factors after factor analysis Research Model Constructs Survey Questionnaire Items/Factors Knowledge sharing (Tacit and Explicit) - KS KS2 - Frequently applied KS practices – Coaching. KS3 - Level of engagement in KS Organisational culture - OC OC1 - Effectiveness of the leadership structure Leadership support and encouragement - LSE LSE2 - The overall rate of collaboration and teamwork for KS Clear communication channels - CCC CCC1 - Effectiveness of formal communication channels Reward and recognition for knowledge sharing - RR RR2 - Availability of recognition programs Training and development programs - TDP TDP1 - Frequency of knowledge acquisition programs Availability of technology tools for knowledge sharing - ATT ATT1 - Satisfaction with the level of access to digital platforms Knowledge sharing protocol - KSP KSP3 - Defined roles and responsibilities. KSP3 - Training and development programs. KSP3 - Legal and ethical considerations. Institutional knowledge sharing culture - IKSC IKSC1 - Influence of open communication culture on individual's KS behaviour Individual knowledge sharing behaviour - IKSB IKSB3 – Impact of employee’s self-efficacy in KS on healthcare outcomes Institutional performance - IP IP3 – Impact of employee's KS behaviour on problem-solving effectiveness Regression Analysis The summary of the inferential statistical tests of correlation and regression analysis is provided in Table 4 below. It indicates the correlation between independent and dependent constructs, as illustrated in the research model (hypothesis). Table 4: Master Regression Results Regression Analysis Statistical Summary Regression Model Mean Std. Dev. N PC (r) Sig. DW ANOVA Mean Square ANOVA Sig. OC  IKSC 3.54 .929 138 .242 .002 1.872 5.689 .004 LSE  IKSC 3.81 .797 138 .280 <.001 1.858 7.632 <.001 CCC  IKSC 3.51 .890 138 .241 .002 1.900 5.641 .004 RR  IKSC 3.58 .965 138 .199 .010 1.897 3.838 .019 TDP  IKSC 3.60 1.000 138 .111 .097 1.850 1.204 .194 ATT  IKSC 3.72 .886 138 .361 <.001 1.874 12.671 <.001 KSP  IKSC 3.92 .855 138 .227 .004 1.897 5.694 <.001 IKSC  IKSB 3.76 .842 138 .394 <.001 1.734 10.979 <.001 IKSB  IP 3.90 .718 138 .558 <.001 1.805 21.104 <.001 KS  IP 3.43 .912 138 .316 <.001 1.860 4.071 <.001 Findings Pearson’s correlation (PC) is a widely recognised mechanism for measuring the relationship between independent and dependent variables. The test is also used to determine the size and direction of the correlations. Regression analysis was used to determine whether the Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 73 relationships were statistically significant. The Durbin-Watson (DW) test was used to measure autocorrelation (serial correlation) in residuals from regression analysis. The DW statistic ranges from 0 to 4, with a value of 2.0 indicating zero autocorrelation, while a value of less than 2 indicates a positive autocorrelation. The ANOVA (Analysis of Variance) statistical test was also used to report how well the regression equation fits the data (i.e., predicts the dependent variable); the significance (Sig) value should be less than the p-value <0.05. Considering the results in Table 4 above, a positive correlation (influence) was found between independent constructs: organisational culture (OC), leadership support and encouragement (LSE), clear communication channels (CCC), availability of technology tools (ATT), knowledge sharing protocol (KSP) and the dependent construct: institutional knowledge sharing culture (IKSC). They had PC coefficients greater than 0.2 i.e., r=0.242, r=0.280, r=0.241, r=0.361 and r=0.227 while statistically significant at p values =0.002, <0.001, 0.002, <0.001, and 0.004 respectively. A positive but irrelevant/unimportant correlation was found between independent constructs: reward and recognition for knowledge sharing (RR) and training and development programs (TDP) and IKSC (dependent construct) since they had PC coefficients of <0.2. i.e., 0.199 and 0.111 respectively. Therefore, RR and TDP do not influence the IKSC. A positive correlation was also established between IKSC (independent construct) and individual knowledge-sharing behaviour (IKSB)-dependent construct. The PC coefficient is r=0.394 while statistically significant at p-value = <0.001. Furthermore, a positive correlation was established between IKSB and institutional performance (IP) with the PC coefficient r=0.558 while statistically significant at p-value = <0.001. Additionally, a positive correlation was found between KS (independent construct) and IP (dependent construct) with PC coefficient r=0.316 while statistically significant at p-value = <0.001. Importantly, all the regression models with PC coefficient (r) values <0.2 also have p-values less than ≤ 0.05, denoting a statistically significant relationship between constructs. Consequently, the null hypothesis was rejected, and the alternative hypothesis was accepted. Table 4 above also shows a DW value of < 2 for all the regression models with (r) values >0.2. This reveals a positive autocorrelation of residuals/errors between the dependent and independent constructs. Similarly, the ANOVA significance values (sig) for the same regression models are <0.001 or 0.004, less than the p-value ≤ 0.05. Consequently, the regression models statistically significantly predict the outcomes/dependent constructs. Discussion The following objectives underpinned the survey study: (i) to identify the factors influencing KS culture at the LCRH, and (ii) to evaluate the impact of KS practices on institutional performance at the LCRH. Furthermore, the research aimed to investigate the impact of KS on institutional performance at the LCRH. The study findings are discussed under each study question below. RQ1: What are the key drivers of the institutional knowledge-sharing culture at the LCRH? The study model was tested using the inferential statistical test of regression analysis to determine the correlation (influence) between independent constructs (OC, LSE, CCC, RR, TDP, ATT, and KSP) and the dependent construct; institutional knowledge-sharing culture (IKSC). The results revealed that only the following constructs: organisational culture (OC), leadership support and encouragement (LSE), clear communication channels (CCC), availability of technology tools (ATT), and knowledge sharing protocol (KSP), had a positive and significant influence on institutional knowledge sharing culture. Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 74 This influence was supported by Pearson’s correlation coefficients r values >0.2, DW test values of less than <2, and the ANOVA significance values of less than p-value ≤ 0.05. Consequently, the key drivers of the institutional knowledge-sharing culture at the LCRH were empirically identified as organisational culture (OC), leadership support and encouragement (LSE), clear communication channels (CCC), availability of technology tools (ATT), and knowledge-sharing protocol (KSP). RQ2: Does knowledge-sharing behaviour impact institutional performance at the LCRH? The inferential statistical test (regression analysis) also revealed a positive and strong correlation/influence between individual knowledge-sharing behaviour (IKSB)-independent construct and institutional performance (IP)-dependent construct. This influence was reinforced by Pearson’s correlation coefficient r value >0.2, r=0.558, DW test value of less than <2, DW=1.805, and the ANOVA significance value of less than p-value ≤ 0.05, sig=<0.001. Consequently, empirical evidence confirmed that individual knowledge-sharing behaviour strongly impacts institutional performance at the LCRH in terms of problem-solving effectiveness. Additionally, regression analysis also established a positive and significant correlation between KS (independent construct) and institutional performance (IP)-dependent construct. This relationship was evidenced by Pearson’s correlation coefficient r value >0.2, r=0.316, a DW test value of less than <2, DW=1.860, and an ANOVA significance value of less than p-value ≤ 0.05, sig=<0.001. Accordingly, scientific evidence confirmed that KS practices strongly impact institutional performance at the LCRH. Specifically, coaching and the level of engagement in KS greatly impact performance at the LCRH. Conclusion The comprehensive analysis in the preceding discussion section scientifically leads to the following inferences: organisational culture (OC), leadership support and encouragement (LSE), clear communication channels (CCC), availability of technology tools (ATT), and knowledge sharing protocol (KSP) are the key drivers of institutional knowledge sharing culture at the LCRH. Certainly, an individual’s knowledge-sharing behaviour directly impacts institutional performance in terms of problem-solving effectiveness among healthcare workers. Emphatically, KS practices (coaching and level of engagement in KS) strongly impact institutional performance at the LCRH. Subsequently, the study’s aims and objectives have sufficiently been achieved, and the study questions have been answered. The leadership and management of the LCRH should, as a matter of strategy, invest in the most effective organisational culture that sustainably anchors KS, intentionally lead, support, and encourage objective KS programs and interventions, and robustly establish and continuously review and improve clear communication channels at the LCRH. The leadership should also provide effective, sufficient, user-friendly, and versatile technology tools for KS and install a clear and comprehensive KS protocol to guide institution-wide KS efforts and interventions at the LCRH for all stakeholders. These will have a triple effect: positive and progressive institutional KS culture, effective and efficient KS behaviour by healthcare workers, and sustainable improvements in the quality of healthcare outcomes and impacts at the LCRH for all stakeholders. Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 75 Granted, translating the study results into sustainable KM and KS practice can be a complex undertaking for management. Technically, this will involve a complete re-engineering and alignment of institutional KM and KS objectives with institutional vision and strategy, building and nurturing a KS culture propelled by a vision for change (people and culture), systematically capturing and sharing critical knowledge across the LCRH (process) and creating a unified knowledge network (technology) with dedicated resources and a monitoring, evaluation, accountability, and learning system. Management should consider investing in external technical assistance in the area of KM and KS systems. This study makes a remarkable contribution to the scientific body of knowledge (theory and practice), advancing the significance of KS in powering public sector transformation and accelerating service delivery improvements in Turkana County and Kenya. The study provides invaluable and original insights that provide the springboard for actionable, objective, and impactful KS programming in the public sector in Kenya. The study was not without some practical and operational limitations; the actual and planned respondents in some departments did not match. However, this was mitigated by adjusting the respondents in the other departments to achieve a near-target sample size, as shown in Table 2. The study was designed as a qualitative single case study, and the generalizability of study findings is not possible but specific to the LCRH domain. Budgetary implications restricted the scope of the study to LCRH as opposed to the entire government ecosystem. However, the results provide a foundational impetus for a broader study. Further research is therefore recommended to understand the impact of KS on the performance of the broader structure of the Turkana County Government since the study was limited to the LCRH under the Ministry of Health Services and Sanitation. References De Angelis, C. T. (2013). A knowledge management and organisational intelligence model for public administration. International Journal of Public Administration, 36(11), 807-819. Dikotla, M.A. (2019). Knowledge sharing in selected municipalities of Limpopo Province, South Africa. SA Jnl Libs & Info Sci, 85(1). Dunn, E & Krob, A. (2014). Partnering to Improve Time to Competency and Proficiency. Talent Development Magazine. Hall, P. (2005). Interprofessional teamwork: Professional cultures as barriers. Journal of Interprofessional Care, 19(1), 188–196. Irani, Z., Sharif, A. M., & Love, P. E. (2009). Mapping knowledge management and organisational learning in support of organisational memory. International Journal of Production Economics, 122(1), 200–215. James, O. (2011). Managing Citizens' Expectations of Public Service Performance: Evidence from Observation and Experimentation in Local Government. Public Administration, 89(4), 1419–1435. Kates, N., Mazowita, G., Lemire, F., Jayabarathan, A., Bland, R., Selby, P., ... & Audet, D. (2011). The evolution of collaborative mental health care in Canada: A shared vision for the future. Canadian Journal of Psychiatry, 56(5), I1. Kates, N., Mazowita, G., Lemire, F., Jayabarathan, A., Bland, R., Selby, P., ... & Audet, D. (2011). The evolution of collaborative mental health care in Canada: A shared vision for the future. Canadian Journal of Psychiatry, 56(5), I1. Keith S. Taber (2018). The Use of Cronbach’s Alpha When Developing and Reporting Research Instruments in Science Education. Res Sci Educ, 48:1273–1296. DOI 10.1007/s11165-016-9602-2. Kipkosgei, F., Kang, S. W., & Choi, S. B. (2020). A team-level study of the relationship between knowledge sharing and trust in Kenya: Moderating role of collaborative technology. Sustainability, 12(4), 1615. Kremer, H., Villamor, I., & Aguinis, H. (2019). Innovation leadership: Best-practice recommendations for promoting employee creativity, voice, and knowledge sharing. Business Horizons, 62(1), 65-74. Lin, H. F. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315–332. Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 76 Mairs K, McNeil H, McLeod J, Prorok J.C & Stolee P. (2013). Online strategies to facilitate health-related knowledge transfer: a systematic search and review. Health Info Libr J. 30(4), 261–277. Masilela, P & Olvitt, L. (2020). Transforming environmental health practitioners’ knowledge-sharing practices through inter-agency formative intervention workshops, Studies in Continuing Education, 42(2), 180-195. Mesmer-Magnus, J. R., & De Church, L. A. (2009). Information sharing and team performance: a meta- analysis. Journal of Applied Psychology, 94(2), 535. Mohajan, H. K. (2019). Knowledge sharing among employees in organisations. Journal of Economic Development, Environment, and People, 8(1), 52-61. Molyneux, J. (2001). Interprofessional teamworking: what makes teams work well? Journal of Interprofessional Care, 15(1), 29–35. Nakiporo, L.E.E, & Law, K.M.Y. (2024): How does Institutional Knowledge Sharing Culture Affect Individual’s Knowledge Sharing Behaviour in the Public Sector? MakeLearn & TIIM PIConf: Artificial Intelligence for Human-Technologies-Economy Sustainable Development. Lublin, Poland. PérezNordtvedt, L., Kedia, B. L., Datta, D. K., & Rasheed, A. A. (2008). Effectiveness and efficiency of crossborder knowledge transfer: An empirical examination. Journal of Management Studies, 45(4), 714-744. Plummer, J. D., & Tanis Ozcelik, A. (2015). Preservice teachers developing coherent inquiry investigations in elementary astronomy. Science Education, 99(5), 932–957. doi:10.1002/sce.21180. Reeves, S., Zwarenstein, M., Espin, S., & Lewin, S. (2011). Interprofessional teamwork for health and social care. Hugh Barr (Eds). John Wiley & Sons. Sammour, G., Schreurs, J., Zoubi, A. Y., & Vanhoof, K. (2008). Knowledge Management and eLearning in Professional Development. In The Open Knowledge Society. A Computer Science and Information Systems Manifesto: First World Summit on the Knowledge Society, WSKS 2008, Athens, Greece. Proceedings, 1, (178-183). Springer, Berlin Heidelberg. Van Der Meer, R. (2014). Knowledge sharing in inter-organisational collaborations. Unpublished PhD. Thesis, Deakin University. Yu, T., Lu, L & Liu, T. (2010). Exploring factors that influence knowledge sharing behavior via weblogs. Computers in Human Behavior, 26, 32. Ebenyo Nakiporo & Law | The Impact of Knowledge Sharing on Public Healthcare Delivery Strategy 77