2015 EFFECT OF INTRA-FIRM INSTITUTIONALIZATION OF EXPLICIT KNOWLEDGE ON EMPLOYEE PERFORMANCE IN ENERGY SECTOR ORGANIZATIONS IN KENYA PhD ELIZABETH NEKESA SIRENGO KALEI DOCTOR OF PHILOSOPHY (Human Resource Management) KALEI, E. N. S. JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY 2015 Effect of Intra-Firm Institutionalization of Explicit Knowledge on Employee Performance in Energy Sector Organizations in Kenya Elizabeth Nekesa Sirengo Kalei A thesis Submitted in Partial Fulfilment for the Degree of Doctor of Philosophy in Human Resource Management in the Jomo Kenyatta University of Agriculture and Technology 2015 DECLARATION This thesis is my original work and has not been presented for a degree in any other university Signature________________________________ Date________________ Elizabeth Nekesa Sirengo Kalei This thesis has been submitted for examination with our approval as University Supervisors Signature_________________________________ Date_______________ Dr. Wario Guyo JKUAT, Kenya Signature__________________________________ Date________________ Dr. Willy Muturi JKUAT, Kenya - DEDICATION To Tom and our Kids - Elsie, Jeffrey and Duncan ACKNOWLEDGEMENT This thesis could not have been possible without due diligent guidance and supervision of Dr. Wario Guyo and Dr. Willy Muturi. I express my sincere gratitude to them. To my colleagues at JKUAT 2012/2013 cohort class, I sincerely appreciate your inspiration and encouragement as we hurdled through the doctoral course work. I am also indebted to my husband Tom Kalei for his patience as I worked long hours on this thesis including scheduled work trips to Addis Ababa, Ethiopia where I dedicated quality time in working on this thesis. I am also forever indebted to Francis Mugo and Peter Kimemia for their assistance and guidance in fine-tuning this thesis. To Janet Mokaya, Kelvin Munyasia, Philip Mutinda, Charles Itumo, Catherine Oduol, Betty Chebii, Gladys Achesa and Marie Kichamu, I just cannot thank you enough for the support you gave in data collection in various organizations. TABLE OF CONTENTS DECLARATION ------------------------------------------------------------------------------ ii DEDICATION -------------------------------------------------------------------------------- iii ACKNOWLEDGEMENT------------------------------------------------------------------ iv LIST OF TABLES -------------------------------------------------------------------------- viii LIST OF FIGURES -------------------------------------------------------------------------- x LIST OF APPENDICES------------------------------------------------------------------- xii LIST OF ACRONYMS AND ABBREVIATIONS ----------------------------------- xiii DEFINITION OF TERMS ----------------------------------------------------------------- xv ABSTRACT ---------------------------------------------------------------------------------- xix CHAPTER ONE ------------------------------------------------------------------------------ 1 INTRODUCTION ---------------------------------------------------------------------------- 1 1.1 Background of the Study ----------------------------------------------------------------- 1 1.2 Statement of the Problem ----------------------------------------------------------------- 6 1.3 General Objective -------------------------------------------------------------------------- 7 1.4 Statistical Hypotheses -------------------------------------------------------------------- 8 1.5 Justification of the Study ----------------------------------------------------------------- 8 1.6 Scope of the Study ----------------------------------------------------------------------- 10 1.7 Limitations of the Study ---------------------------------------------------------------- 10 CHAPTER TWO ---------------------------------------------------------------------------- 12 LITERATURE REVIEW ------------------------------------------------------------------ 12 2.1 Introduction ------------------------------------------------------------------------------- 12 2.2 Theoretical Framework ---------------------------------------------------------------- 12 2.3 Conceptual Framework ----------------------------------------------------------------- 26 2.4 Operationalization of Study Variables ----------------------------------------------- 29 2.5 Empirical Review ------------------------------------------------------------------------ 32 2.6 Critique of the Empirical Review ----------------------------------------------------- 46 2.7 Research Gaps ---------------------------------------------------------------------------- 48 CHAPTER THREE ------------------------------------------------------------------------- 49 RESEARCH METHODOLOGY --------------------------------------------------------- 49 3.1 Introduction -------------------------------------------------------------------------------- 49 3.2 Research Design -------------------------------------------------------------------------- 49 3.3 Target Population ------------------------------------------------------------------------ 50 3.4 Sample and Sampling Technique ----------------------------------------------------- 51 3.5 Data Collection --------------------------------------------------------------------------- 53 3.6 Pilot Study --------------------------------------------------------------------------------- 53 3.7 Data Analysis and Presentation -------------------------------------------------------- 55 CHAPTER FOUR --------------------------------------------------------------------------- 58 RESULTS AND DISCUSSIONS --------------------------------------------------------- 58 4.1 Introduction --------------------------------------------- Error! Bookmark not defined. 4.2 Background Information -------------------------------------------------------------------- 58 4.3 Demographic Profiles of Respondents -------------------------------------------------- 58 4.4 Descriptive Analysis for Independent Variables -------------------------------------- 61 4.5. Descriptive Analysis for Moderating Variable ---------------------------------------- 71 4.6 Descriptive Analysis for Dependent Variable ------------------------------------------ 74 4.7 Requisite Tests ------------------------------------------------------------------------------- 76 4.8 Correlation Analysis ------------------------------------------------------------------------ 81 4.9 Regression Analysis ------------------------------------------------------------------------ 86 4.10 Moderating Effect Test ------------------------------------------------------------------- 95 4.11 Optimal Model ------------------------------------------------------------------------------ 97 CHAPTER FIVE --------------------------------------------------------------------------- 100 SUMMARY, CONCLUSION AND RECOMMENDATIONS ------------------- 100 5.1 Introduction---------------------------------------------- Error! Bookmark not defined. 5.2 Summary of Major Findings -------------------------------------------------------------- 100 5.3 Conclusion ------------------------------------------------------------------------------------ 104 5.4 Recommendations: ------------------------------------------------------------------------- 106 5.5 Contribution of the study to the Body of Knowledge and Practice --------------- 107 5.5 Proposed Areas for Further Research-------------------------------------------------- 108 REFERENCES ----------------------------------------------------------------------------- 109 APPENDICES ------------------------------------------------------------------------------ 127 LIST OF TABLES Table 2.1: Tabulation of Independent Variables and their Specific Measures.......... 30 Table 2.2: Tabulation of Moderating Variable and its Specific Measures ................ 31 Table 2.3: Tabulation of Dependent Variable and its Specific Measures ................. 32 Table 3.1: Population - Energy Sector Organizations Employees ............. ………...50 Table 3. 2: Study Sample Size ................................................................................... 52 Table 4.1: Distribution of Respondents by Gender ................................................... 59 Table 4.2: Distribution of Respondents by Age ........................................................ 59 Table 4.3: Respondents’ Years of Service ............................................................... 60 Table 4.4: Level of Education of Respondents ......................................................... 60 Table 4.5: Categories of Energy Sector Organizations ............................................. 61 Table 4.6: Summary of Responses on Explicit Knowledge Acquisition .................. 62 Table 4.7: Effect of Explicit Knowledge Acquisition on Employee Performance ... 64 Table 4.8: Role of Explicit Knowledge Processing on Employee Performance ....... 67 Table 4.9: Contribution of Explicit Knowledge Dissemination on Employee Performance ............................................................................................................... 70 Table 4.10: Summary of Responses on Role of Knowledge Management Infrastructure .............................................................................................................. 72 Table 4.11: Overall ICT-set up rating in Organizations ............................................ 74 Table 4.12: Employee Performance Rating on Acquisition of Knowledge .............. 74 Table 4.13: Employee Performance Rating Agreements .......................................... 75 Table 4.14: Test for Normality .................................................................................. 76 Table 4.15: Durbin-Watson Test ............................................................................... 78 Table 4.16: Correlation Analysis of Independent Variable ....................................... 79 Table 4.17: Factor Analysis and Reliability Results ................................................. 80 Table 4.18: Reliability Statistics ............................................................................... 81 Table 4.19: Correlation Co-efficient Analysis on Explicit Knowledge Acquisition. 83 Table 4.20: Correlation Co-efficient Analysis on Explicit Knowledge Processing .. 84 Table 4.21: Correlation Co-efficient Analysis on Explicit Knowledge Dissemination ............................................................................................................. 86 Table 4.22: Model Summary for Explicit Knowledge Acquisition .......................... 87 Table 4.23: Analysis of Variance (ANOVA) for Explicit Knowledge Acquisition . 88 Table 4.24: Explicit Knowledge Acquisition Coefficient ......................................... 89 Table 4.25: Model Summary for Explicit Knowledge Processing ........................... 90 Table 4.26: Analysis of Variance (ANOVA) for Explicit Knowledge Processing ... 91 Table 4.27: Explicit Knowledge Processing Coefficient .......................................... 92 Table 4.28: Model Summary for Explicit Knowledge Dissemination ...................... 93 Table 4.29: Analysis of Variance (ANOVA) for Explicit Knowledge Dissemination ............................................................................................................. 94 Table 4.30: Explicit Knowledge Dissemination Coefficient .................................... 95 Table 4.31: Summary of Overall Moderating Effect Model ..................................... 96 Table 4.32: Analysis of Variance (ANOVA) for Moderation Model ....................... 97 Table 4.33: Moderation Model Coefficients ............................................................. 98 LIST OF FIGURES Figure 2.1: Nonaka & Takeuchi’s Knowledge Management Model ........................ 15 Figure 2.2: The SET Knowledge Management (KM) Model ................................... 17 Figure 2.3: Hedlund and Nonaka's Knowledge Management Model ....................... 18 Figure 2.4: Boisot's Knowledge Category Model ..................................................... 19 Figure 2.5: Kogut and Zander’s Knowledge Management Model ............................ 20 Figure 2.6: The Performance Management Model ................................................... 21 Figure 2.7: Conceptual Framework ........................................................................... 29 Figure 4.1: Normal QQ Plot of Employee Performance ........................................... 77 Figure 4.2: Correlation Results on Explicit Knowledge Acquisition........................ 82 Figure 4.3: Correlation Results on Explicit Knowledge Processing ......................... 83 Figure 4.4: Correlation Results on Explicit Knowledge Dissemination ................... 85 Figure 4.5: Regression Analysis on Explicit Knowledge Acquisition ...................... 87 Figure 4.6: Regression Analysis on Explicit Knowledge Processing ....................... 90 Figure 4.7: Regression Analysis on Explicit Knowledge Dissemination ................. 93 Figure 4.8: Validated Model of Effect of Intra-firm institutionalization of Explicit Knowledge on Employee Performance ...................................................................... 99 LIST OF EQUATIONS Determination of Study Sample Size……………………………………… ............. 51 Kunder-Richardson (K-R) 20 Formula…………………………………………. ..... 54 Reduced Regression Model………………………………………………................ 56 Full Regression Model.…………………………………………………………… .. 56 LIST OF APPENDICES Appendix I: Letter of Introduction......……………………………….………...…141 Appendix II: University Introductory Letter …...……………………………...…142 Appendix III: Questionnaire…………………………………...………………....143 Appendix IV: Energy Sector Organizations in Kenya …………………………...153 Appendix V: Factor Analysis and Reliability Results………………………........154 Appendix VI: Component Matrix Summary of Explicit Knowledge Acquisition..158 Appendix VII: Component Matrix Summary of Explicit Knowledge Processing 159 Appendix VIII: Component Matrix Summary of Explicit Knowledge Dissemination ……………………………………………………………...160 LIST OF ACRONYMS AND ABBREVIATIONS ANOVA Analysis of Variance ERC Energy Regulatory Commission GDC Geothermal Development Company Limited HR Human Resources Management HR Human Resources IC Intellectual Capital ICT Information and Communication Technology IEA Institute of Economic Analysis IKA Individual Knowledge Acquisition IPPS Independent Power Producers IT Information Technology IT Information Technology KBS Kenya Bureau of Statistics KenGen Kenya Electricity Generating Company Limited KETRACO Kenya Electricity Transmission Company Limited KM Knowledge Management KMI Knowledge Management Infrastructure KMS Knowledge Management System KNBS Kenya National Bureau of Statistics KNEB Kenya Nuclear Electricity Board KPC Kenya Pipeline Company Limited KPLC Kenya Power & Lighting Company Limited MoE Ministry of Energy and Petroleum NEP National Energy Policy OK Organizational Knowledge OL Organization Learning OM Organization Memory OMS Organizational Management System RD&D Research, Development & Dissemination REA Rural Electrification Authority RoK Republic of Kenya SPSS Statistical Packages for Social Scientists WEF World Economic Forum DEFINITION OF TERMS Explicit Knowledge This is Knowledge that has been communicated or documented and is therefore available for use. Explicit knowledge can be easily expressed in words and/or numbers, shared via discussion, documents, specifications, manuals etc., and, where documented can be organized for searching and re-use. (Hall & Paradice, 2005). Externalization This is the process of articulating tacit knowledge into explicit knowledge. This is the tacit to explicit step in the SECI process: the process that transforms knowledge into a tangible form through documentation and/or discussion. (Nonaka & Takeuchi, 1995). Human Capital These are Knowledge, competencies, and skills of people in an organisation and one of the components that make up Intellectual capital. It is owned by the individuals who have it, rather than the organisation. It is the sum of everything that everybody in the company knows which gives a competitive edge in the market place(Sandhawalia & Dalcher, 2011). Intangible Assets They are non-physical resources and rights of an organization that are not traditionally accounted for in the financial balance sheets of an organization - assets such as brands, patents, copyrights, knowledge, know-how and customer loyalty (Beveren, 2002). Intellectual Assets This is knowledge related to assets of an organization and subset of its Intangible assets that relate directly to knowledge - such as intellectual property, know-how, best practice and customer knowledge(Argyris, 1993). Intellectual Capital This is the value or potential value of the intellectual assets of an organization such as knowledge, information, Intellectual property and experience that can be put to use to create wealth. Intellectual capital is often defined as the combination of the three broad categories: Human capital - the skills, knowledge and expertise of people; Structural capital - captured knowledge such as intellectual property and good practice Customer capital - relationships and knowledge of the customers (Ipe, 2003). Internalization It is the process of embodying explicit knowledge into tacit knowledge and is the term used for the explicit to tacit link of Knowledge conversion in the SECI process. The process gives rise to operational knowledge that can then be put into use. Documenting experience is one route to internalize knowledge to make sense of it and turn it into useful tacit knowledge(Nonaka & Takeuchi, 1995). Institutionalization of knowledge In this context, institutionalization refers to having organizational practices and technological infrastructure that make possible knowledge acquisition; processing and dissemination to create and sustain competitive advantage (Cheruiyot, Jagongo, & Owino, 2012). Knowledge Acquisition Knowledge Acquisition is a process through which the required knowledge is actually obtained by individuals acting as knowledge carriers (Beveren, 2002). Knowledge Assets This is Knowledge that is relevant to an organization’s strategy and operation and are high-value Knowledge creation, dissemination and application actions undertaken within an organization. Knowledge assets can be human (the individual people and teams, networks and communities) and structural (the codified knowledge that can be found in strategies, processes and procedures etc.) (Hall & Paradice, 2005). Knowledge Based Economy This refers to an economy in which the generation and exploitation of knowledge plays the predominant part in the creation of wealth (Demerest, 2007). Knowledge Knowledge is the validated platform for action with a mix of framed experiences, values, and expert insights that are grounded in intuition that provide an environment and framework for evaluating and incorporating new experience and information. (Bhatt, 2001). Knowledge Processing Knowledge processing is the activity of collecting, analyzing, synthesizing, storing, manipulating, displaying, and transmitting knowledge (Davenport & Prusak, 2000). Knowledge Dissemination This is the transfer of knowledge within and across settings, with the expectation that the knowledge will be "used" conceptually (as learning, enlightenment, or the acquisition of new perspectives or attitudes) or instrumentally, (in the form of modified or new practices).The knowledge dissemination process ensures knowledge acquired and internalized by individuals, groups, or teams is spread to other departments of a company (Jelenic, 2011). Knowledge Management Infrastructure This is a system that is made up of processes, content, people and tools used to deliver Knowledge Management within an organization (Grant, 2006). Know-how This is capability derived from knowledge and experience and is usually used to refer to knowledge and experience that has been recorded in context so that it can be used to make decisions and solve problems efficiently. Know-how may also refer to skills (Mclnerney, 2002). Knowledge Worker These are professionals, whose role lies in their ability to find, synthesize, communicate and apply knowledge. It is a generic term used to describe any worker who uses knowledge within their work which consists largely of converting information into knowledge (Smith , 2011) Learning Organization This is an organization that is skilled at creating, acquiring, interpreting, and retaining knowledge; then modifying its behavior to reflect new knowledge and insights. It is an organization with the necessary practices, culture and systems to promote the continuous sharing of experience and lessons learned. (Argyris, 2003) ABSTRACT Knowledge has become the basis of every organization in creating and sustaining competitive differentiation.There are howeverno studies which explain whether organizations in the energy sector in Kenya institutionalize knowledge for better employee performance. This study aimed at determining the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations in Kenya. The study was guided by the following objectives: to determine the effect of explicit knowledge acquisition, processing and dissemination on employee performance. The study adopted explanatory research design using both qualitative and quantitative research design. The target population was 22,173 employees in 9 Energy Sector Organizations in Kenya. Out of 320 employees that were targeted, 242 responses were received as valid constituting 76% response rate. Survey data was collected by use of a structured questionnaire. Data reliability was done using Cronbach’s Alpha formula and factor analysis was used to determine data validity. Normality test for the dependent variable was done using Kolmogorov- Smirnov Test. Descriptive statistics, correlation analysis and multiple regression models were used to test whether explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination had any effect on employee performance. It was found that institutionalization of explicit knowledge had a positive significant effect on employee performance since all the identified factors were significant predictors of employee performance. Combined, the three independent variables without the moderating variable explain 73% variation in employee performance and 86% with moderating variable. The study therefore concluded that since intra-firm institutionalization of explicit knowledge is a positive significant predictor of employee performance, employers should give attention to this area in order to enhance the performance of their employees. The study established that employee performance is affected by quality of work, job knowledge, adaptability and reliability on the job. This is one area where Human Resource professional can play an important role in helping line managers design jobs that effectively enable employees measure the quality of their work, understand the job and be able to measure their adaptability and reliability on the job. CHAPTER ONE INTRODUCTION 1.1 Background of the Study This study focused on determining the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations in Kenya. In the fast changing business environment, knowledge has become the basis of every organization in creating and sustaining competitive differentiation (Chan & Chau, 2005). Many organizations have put forth additional efforts to meet or adjust to the pressures of their customers, players, shareholders and supervisory bodies (Cheruyoit, Jagongo, & Owino, 2012). Business executives have embraced knowledge as one of the organization’s most important asset and that its quality and availability can facilitate them to face pressures, challenges and remain competitive (Bhatt, 2001). Organizations are therefore, mounting knowledge strategies that tackle organizational development, retrenchment, amalgamation and internal reorganization (O’Dell & Hubert, 2011). Organizations are building up knowledge management (KM) capabilities that are institutionalized and embedded in their business operations and practices (Sandhawalia & Dalcher, 2011). According to Amy Javenick (2008) approximately 40% of engineering design and construction organizations have had a knowledge management strategy and 41% planned to have it within one year. 1.1.1 Knowledge Management in an organization According to Davenport and Prusak (2008) most knowledge management projects aim to make knowledge visible and show the role of knowledge in an organization; develop a knowledge-intensive culture that facilitates knowledge sharing (as opposed to hoarding) and proactively seeking and offering knowledge; build a knowledge infrastructure-not only a technical system, but a web of connections among people given space, time, tools, and encouragement to interact and collaborate. To be competitive and successful, experience shows that enterprises must create and sustain a balanced intellectual capital portfolio (Wiig, 2007). While technology and environmental circumstances vary, intellectual capital in the form of knowledge possessed by an organization is distinctive and hard to duplicate (Silvi & Cuganesan, 2006). Knowledge has been acknowledged as a vital competitive asset and many enterprises are embracing it (Ajmal, Helo & Kekale, 2010). Furthermore, the key source of sustainable competitive advantage in an ever more unstable business environment is knowledge (Ling, et al, 2009). Making people knowledgeable brings innovation and continued ability to create and deliver products and services of the highest quality (Wiig, 2007). The organizational capability to create, recognize, disseminate widely, and embody knowledge in new products and technologies is critical when faced with shifting markets, rapid product obsolescence, hyper competition and financial upheavals (Abbas & Yaqoob, 2013)). This requires that the company develops effective knowledge harnessing, reuse, and learning from prior knowledge. 1.1.2 Institutionalization of Knowledge In contemporary organizations, there is unquestionable urge for knowledge management practices in the workplace to enable managers to press forward knowledge sharing, attainment and preservation of knowledge (Sunassee & Sewry, 2003). While, Sandhawalia and Dalcher (2011), argues that organizations should develop Knowledge Management (KM) capabilities into a state where KM practices are institutionalized and rooted into its business processes, institutionalization of knowledge management is all about having organizational practices and technological infrastructure that make possible continuous knowledge creation and use to create and sustain competitive advantage. According to Edwards (2001), one appealing way to conceive of KM activities in an organization is in the form of a life cycle of knowledge. This includes various activities of knowledge creation, refinement, storage, transfer and use. In some contexts, it also includes knowledge acquisition (from outside the organization) as an alternative to an internal process of knowledge creation. In a Knowledge-based economy, the recognition is that advanced economies derive a high propotion of their economic wealth from creation, exploitaton and distribution of konlwedge and information (Roberts, 2009). According to the Organization for Economic Co-operation and Development (OECD) in advanced economies, knowledge is valued more as an economic resource and can be accounted for in economic terms. The study argues that there is much potential for the benefits of knowledge and with the distribution of some basic information resources, those currently excluded from the knowledge economy can quickly develop the necessary skills to particpate in the consumption and production of knowledge (OECD, 2009). This argument is in line wth the United Kingdom’s Economic and Social Research Council (ESRC) which pointed out that knowledge economy is the economic structure emerging in the global information society in which economic success is increasingly depending on the effective utilization of intangible assets such as knowledge, skills and innovative potential (ESRC, 2014). The key components of a knowledge economy thus include a greater reliance on intellectual capabilities than on physical inputs or natural resources, combined with efforts to integrate improvements in every stage of the production process (Alavi & Leidner, 2001). 1.1.3 Knowledge-Intensive Sector Equally, Knowledge Intensive Sector (KIS) is also concerned with providing knowledge-intensive inputs to business processes and Miles et al. (2005) identified principal characteristics of KIS as relying heavily on professional knowledge and being primary sources of information and knowledge. The term KIS has been used to refer to service firms that are characterised by their high knowledge intensity and the orientation of their services to other firms and organisations, services that are predominantly non-routine including the energy sector (Connor, 2002) The Energy Architecture Performance Index (EAPI) ranked 105 countries globally on how well their energy systems deliver economic growth and development, environmental sustainability and energy security access, Norway was ranked 1st globally with an overall score of 0.95 (WGI, 2013). Sweden was ranked 2nd with a mean score of 0.71 where world powers like the United Kingdom and the United States of America were ranked number 10 and number 55 with an overall score of 0.67 and 0.56 respectively. South Africa is the only highly ranked African country at number 56 with an overall score of 0.55 with Namibia, Senegal, Cameroon and Nigeria being ranked 79, 80, 81 and 89 with overall scores of 0.47, 0.46, 0.46 and 0.44 respectively. The report ranks Kenya as number 93 with an overall score of 0.43 albeit ahead of other african countries like Zambia at 97 with an overall score of 0.42; Mozambique number 102 with overall score of 0.39; Tanzania number 104 with overall score of 0.37 and Ethiopia at number 105 with overall score of 0.36 (WGI, 2013). Kenya has the potential of doing well in the global ranking in the energy sector. The energy sector in kenya has diversified with distinctions in each energy field and has some of the qualified employees in the economy (PIEA, Energy Policies of IEA Countries, 2005). These employees and their knowledge thus have to be preserved to ensure the sectors performs well. Managing knowledge, not just in the energy sector, has therefore become a mainstream business objective considered as the process through which organizations generate value from their intellectual capital and knowledge-based assets. Usually, the value is obtained by finding what employees, partners and customers know, and sharing information with employees, departments and even with other companies, in order to find best practices (Beveren, 2002). Knowledge Management therefore is an important managerial tool, which promotes creation of new knowledge and it’s sharing, through the corporate values, thereby increasing the effectiveness of decision-making processes, including the level of operational efficiency, flexibility, commitment and involvement of employees (Jelenic, 2011). 1.1.4 Employee Performance Performance management is a process that bring together people management practices including learning and development. It is a process which contributes to the effective management of individuals and teams in order to achieve improved level of individual and organizational performance (Armstrong, 2009). Performance management is about establishing a culture where individuals and teams take responsibility for continuous improvement of service delivery and of their own skills, behaviour and contributions (Schwartz, 2009). It is a strategic process, long term in nature, aimed at development of an appropriate culture linking people management, service issues and long term goals. Its not a once off quick fix process (Zwell, 2000). Performance management is a tool to ensure effective management which results in individuals and teams knowing and understanding what is expected of them; having the skills and ability to deliver on these expectations; having individuals and teams that are supported by organisations in developing the capacity to meet these expectations; individuals and teams that are given feedback on their performance and individuals who have the opportunity to discuss and contribute to individual and team aims and objectives (Risher, 2003). 1.1.5 Energy Sector Organizations Kenya The public energy sector in Kenya comprises of four sub-sectors namely: Biomass, Fossil Fuels, Electricity and other renewable energy sources (RoK, 2013). The Kenya National Bureau of Statistics (KNBS) shows that commercial energy sector is dominated by three main sources namely wood fuel (68%), petroleum (22%), electricity (9%) and others including coal and solar (1%) (RoK, 2013). The Energy Sector Legal and Institutional frameworks are stipulated in the Energy Act No. 12 of 2006 while the policy framework is anchored in Sessional Paper No. 4 of 2004 on Energy. Pursuant to these two documents, a new institutional structure has been set up over the years with the creation of new state agencies with varied mandates. In these arrangements, the oversight agencies include the Ministry of Energy, Energy Regulatory Commission and the Energy Tribunal. The Ministry of Energy is responsible for policy formulation and overall energy planning. The sector has ten (10) organizations referred to as State Corporations or parastatals summarized as Kenya Power & Lighting Company Limited (KPLC); Kenya Electricity Generating Company Limited (KenGen); Geothermal Development Company (GDC); Rural Electrification Authority (REA); Kenya Electricity Transmission Company (KETRACO); Kenya Pipeline Company (KPC); National Oil Corporation of Kenya (NOCK); Kenya Nuclear Electricity Board (KNEB); (MoE, 2013) The Kenya Vision 2030 has identified energy as one of the infratructure enablers of it’s economic pillar with sustainable, competitive, affordable and reliable energy for all citizens being a key factor in realization of the vision (RoK, 2013). The energy sector is recognized as development enabler with the objective of expanding and upgrading the infrastructure; mobilising finanicial resources for expansion of services to meet demand; diversifying sources of supply in a cost effective manner and increasing energy access to all. 1.2 Statement of the Problem Energy is inevitable for human life and a secure and accessible supply of energy is crucial for the sustainability of modern societies (Smith, 2011). In Kenya, Vision 2030 has identified energy as the key element of Kenya’s sustained economic growth and transformation that is supposed to spur an annual GDP of at least 10% (RoK, 2013). However, the energy sector has been noted to be performing poorly due to challenges of improving the quantity, quality and reliability of energy supply, (RoK, 2013). These challenges have resulted in the sector’s inability to offer sustainable supply of energy to meet the growing demand (KIPPRA, 2013). The RoK (2013) reiterates that if not adequately addressed, these challenges are a drawback on Kenya’s Vision 2030. The increased level of demand for quality service delivery, therefore, has triggered concern for improved employee performance (Linda, 2013). The change in work processes resulting from new technological breakthroughs also require that employee functionality in line with such changes is given premium attention (Okoth, 2012). In an evaluation report of performance of the public agencies for the financial year 2010/2011 where 178 state corporations were evaluated, no energy sector state corporation was ranked among the top ten most sustained state corporations. The Ministry of Planning, National Development and Vision 2030 was ranked highest at 2.1055 mark (OPM, 2012). According to the report on implementation of performance contracting in the public service that was in its 7th year, only one state corporation achieved ‘Excellent’ grade representing 0.6 per cent of the 178 state corporations that were evaluated. One Hundred and Fifteen (115) state corporations achieved ‘Very Good’ representing 64.6 percent while 53 corporations representing 29.8% achieved ‘Good’ grade. Nine (9) corporations representing 5.0 percent achieved ‘Poor’ grade with no organizations achieving ‘Fair’ grade (OPM, 2012). Through a critical analysis of different conceptualizations on employee performance, Melford (2006) came to a conclusion that the changing times and context have diminished the potency of the existing conceptual /theoretical framework that once served managers in ensuring high employee performance. The implication is that there is a dare challenge for further assertion of workplace action either strategic or tactical that will ensure improved employee performance for contemporary customer expectation (Prusak 2006). Edward (2008), goes further to suggest that challenges in energy sector can be addressed from a knowledge management perspective through institutionalization of knowledge. This was acknowledged by Morrisey (2005) that institutionalization of knowledge contributes to greater competitive advantage of an organization through enhanced employee performance. The today organization is no doubt increasing its guest for intangible asset like knowledge for continued survival and employees are central to this in view of the fact that knowledge is a resource for strategic innovative actions that enhances operational actions (Cheruyoit, Jagongo, & Owino, 2012). Nonetheless, the study noted that there are there are no studies that have been done to explain whether organizations in the energy sector in Kenya institutionalize knowledge for better employee performance (Cheruyoit, Jagongo, & Owino, 2012). Since the transfer and conversion of tacit knowledge is difficult and hard to access, as it is often not known to others (Brun, 2005), it is for this reason that this study seeks to analyse the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations in Kenya. 1.3 General Objective The general objective of this study was to determine the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations in Kenya. 1.3.1 Specific Objectives The specific objectives were: i. To establish the effect of explicit knowledge acquisition on employee performance in energy sector organisations in Kenya; ii. To determine the effect of explicit knowledge processing on employee performance in energy sector organisations in Kenya; iii. To investigate the effect of explicit knowledge dissemination on employee performance in public energy sector organisations in Kenya; iv. To analyse the moderating effect of knowledge management infrastructure on explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance in energy sector organizations in Kenya. 1.4 Statistical Hypotheses The study was guided by the following hypothetical statements: i. H0: There is no significant effect of explicit knowledge acquisition on employee performance in energy sector organizations in Kenya. ii. H0: There is no significant effect of explicit knowledge processing on employee performance in energy sector organizations in Kenya. iii. H0: There is no significant effect of explicit knowledge dissemination on employee performance in energy sector organizations in Kenya. iv. H0: There is no significant moderating effect of Knowledge management infrastructure on explicit knowledge acquisition, processing, dissemination and employee performance in energy sector organizations in Kenya. 1.5 Justification of the Study Contrary to the industrial era, in this era of Information Technology (IT), the value of physical ‘intangible’ resources has significantly increased and ‘intangible’ assets are becoming a major source of competitive advantage (Jelenic, 2011). Companies that care about their high business performance have realized that the market value of their property increases with greater participation of ‘intangibles’ (intellectual) resources in relation to tangible property (Sarvary, 2009). Therefore, knowledge is considered a strategic company’s resource, the source of competitive advantage and business success. Consequently, it is anticipated that the findings of this study will benefit the following organizations: 1.5.1 Organizations in the Energy Sector Organization in the Public Energy Sector in Kenya shall benefit from the insights of this study on the effect of intra-firm institutionalization of explicit knowledge on employee performance and the value addition that comes with this venture. From the findings of this study, energy sector organizations now understand how knowledge is acquired, processed and disseminated including formulation of policies to encourage these activities to enhance employee performance. More specifically the study benefitted the following stakeholder. 1.5.2 Human Resources Practitioners Human Resources Practitioners being the facilitators of Knowledge Management in organizations shall gain from insights of implementing policies on knowledge management. It is envisioned that most organization in the energy sector will now adopt institutionalization of explicit knowledge which will lead to improved employee performance and preserve institutional memory. 1.5.3 Employees in energy sector organizations Employees are negatively or positively affected, directly or indirectly, by knowledge management initiatives in an organization. From the insights of this study, employees will now understand various forms of knowledge management initiatives and thus help their organizations in knowledge management initiatives as well ability to effectively utilize their various knowledge for improved performance. 1.5.4 Research and Academic Community Researchers will benefit from both theoretical literature reviewed and the findings of this study, which aim to determine the effect of intra-firm institutionalization of explicit knowledge on employee performance. If any areas of further research are established, other researchers will have an opportunity to carry out further research and grow knowledge in intra-firm institutionalization of explicit knowledge as well as employee performance. 1.6 Scope of the Study The study focused on analyzing the effect of intra-firm institutionalization of explicit knowledge in energy sector organizations in Kenya. The focus was on explicit knowledge because it is easily explained by individuals even though some effort and forms of assistance at times is required to help individuals articulate what they know. The explicit knowledge approach considered that useful knowledge of individuals in an organization can be articulated and made explicit. The study also focused on determining how explicit knowledge acquisition which is how employees capture, learn and develop knowledge affect employee performance; How explicit knowledge processing which is how the explicit knowledge is organized, analyzed and stored contribute to employee performance. The focus on explicit knowledge dissemination was on how knowledge that is accessed, used and transferred affects employee performance. While looking at this, the moderating role of knowledge management infrastructure which entailed organization culture, organization structure and organizational IT set-up was evaluated to determine how it moderates explicit knowledge acquisition, processing and dissemination on employee performance. To determine employee performance, the quality, Job knowledge, adaptability and reliability of employees was evaluated. 1.7 Limitations of the Study This study focused on determining the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations. The target population of study was energy sector organizations and the respondents were employees in these organizations. The geographical spread of these organizations inhibited the researchers’ access to the respondents who mitigated this factor by organizing b flexible sessions that suited the respondents’ schedules. Knowledge management is also a wide and new area of study and was not clear to the study population. The definition of knowledge management and explicit knowledge were wide and varied as the respondents interpreted them differently. For example, what one employee knew to be knowledge management was not the same to another. To mitigate this limitation, the study simplified the terminologies used to enable the respondents clearly understand the issue under study. Getting information on Human Resources issues such employee performance and knowledge management posed a challenge as such issues were considered confidential. To mitigate this challenge, the study approached respective Human Resources Managers in the public energy sector organizations and got support in availing such information. The study also assured the respondents of confidentiality in handling the information provided. CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This chapter reviewed theoretical and empirical literature relevant to institutionalization of knowledge and performance management as articulated by various scholars. The study focused on theories and models of knowledge management and performance management, developed a conceptual framework on institutionalization of knowledge variables and performance management. The chapter also reviewed empirical evidence from past studies on knowledge management and performance management. The chapter ended with a summary of identified research gaps explored in the study as well as a summary on the chapter-outlay. 2.2 Theoretical Framework This section reviewed theoretical propositions that have relevance to the understanding of the subject matter under study. Bacharach (2009) defined a theory as a statement of relations among concepts within a boundary assumptions and constraints. It is a coherent description, explanation and presentation of observed or experienced phenomena (Gioia & Gooley, 2011). Thus, a theory explains what a phenomenon is and how it works. The framework guided the formulation and direction of the research study. 2.2.1 Theories and Models related to the study There are many theories relating to the subject of study. This section discusses the following theories and models which are the most relevant to the study; Human Capital Theory, Nonaka and Takeuchi (1995) Knowledge Management Model, Hedlund and Nonaka (1993) Knowledge Management Model explain the independent variables while the Resource-based view of the firm theory and Boisot’s Knowledge Category Model relate to the dependent variable. Human Capital Theory The human capital theory acknowledges the value that people can contribute to an organization and regards peoples as assets stressing that investment by organizations in people will generate worthwhile returns (Bigge, 2004). Human capital theory is associated with resource-based view of the firm developed by Barney in 1991 which proposes that sustainable competitive advantage is attained when a firm has a human resource pool that can not be imitated or substituted by its rivals. Becker et al (2001), argues that human capital theory helps determine the impact of people on the business and their contribution to shareholder value; demonstrates that Human Resources (HR) practices produce value for money in terms return on investment; provides guidance on future HR practices and business strategies and data that will inform strategies and practices designed to improve effectiveness of people management in organizations (Becker, Huselid, & Ulrich, 2001) According to Armstrong (2009), individuals generate, retain and use knowledge and skills (human capital) to create intellectual capital. Their knowledge is enhanced by the interactions between themselves (social capital) and this generates the institutionalized knowledge possessed by an organization as organizational capital (Armstrong, 2009). He argues that Human capital consists of knowledge, skills and abilities of the people employed in an organization. In the context of this study, it is indeed the knowledge, skills and abilities of individuals that create value, which is why the focus has to be on means of attracting, retaining, developing and maintaining the human capital they represent. Resource-based View Theory The resource-based view of the firm (Barney, 1991; Peteraf, 1993) examines the manner in which organizational resources are applied and combined, the causes, which determine the attainment of a sustainable competitive advantage and the nature of rents generated by organizational resources. On the basis of this theory, the firm is viewed as the accumulation of unique resources of a diverse nature (Peteraf, 1993). Resources are defined as assets of different types which enable the firm to conceive and implement strategies leading the firm to improve its efficiency and effectiveness, and generate an increase in its competitiveness (Amit and Schoemaker, 1993; Grant, 2006). In order for organizational resources to become a source of sustainable competitive advantage, certain characteristics must be present. Barney (1991) argues that these resources must be rare, valuable, without substitutes and difficult to imitate. Which clearly define explicit knowledge for this matter. Researchers (Prahalad and Hamel, 1990; Barney, 1991) note that these resources cannot be commercialized, as they are developed and accumulated within the company; display a strong intrinsic character as well as social complexity. In the context of this study and on combining these characteristics and employee knowledge, Perez and de Pablos (2003) developed a framework, which quotes high value and uniqueness of human capital as core human capital that is the source for the competitive advantage. Thus, firms are more likely to create it through focused internal development and improving extendibility characteristics of intangible resources in which core competencies are created from the various fields' skills and knowledge (Lepak & Snell, 1999; Prahalad & Hamel, 1990). Performance Management Theories Buchner (2007) identified three theories as underpinning performance management. Goal theory, as developed by Latham and Locke (1979), highlights four mechanisms that connect goals to performance outcomes: they direct attention to priorities; they stimulate effort; they challenge people to bring their knowledge and skills to bear to increase their chances of success; and the more challenging the goal, the more people will draw on their full repertoire of skills. This theory underpins the emphasis in performance management on setting and agreeing objectives against which performance can be measured and managed. Control theory by Power (1998) focuses attention on feedback as a means of shaping behaviour. As people receive feedback on their behaviour they appreciate the discrepancy between what they are doing and what they are expected to do and take corrective action to overcome the discrepancy. Feedback is thus recognized as a crucial part of performance management processes. Social cognitive theory was developed by Bandura (1986). It is based on his central concept of self-efficacy. This suggests that what people believe they can or cannot do powerfully impacts on their performance. Developing and strengthening positive self- belief in employees is therefore an important performance management objective. Nonaka and Takeuchi (1995) Knowledge Management Model Nonaka’s Knowledge Management Model is an attempt at giving a high-level conceptual representation of Knowledge Management and essentially considers KM as a knowledge creation process. According to the model, knowledge is considered as consisting of Tacit and Explicit elements. Tacit knowledge is defined by Polanyi (1996) as non-verbalized, intuitive and unarticulated. Explicit or articulated knowledge is specified as being in writing, drawings, and computer programs (Hedlund, 1994). The model assumes that tacit knowledge can be transferred through a process of socialization into tacit knowledge and that tacit knowledge can become explicit knowledge through a process of Externalization (Nonaka I. , 1994). The model as shown in Figure 2.1 also assumes that explicit knowledge can be transferred into tacit knowledge through a process of internalisation, and that explicit knowledge can be transferred to explicit knowledge through a process of combination. The transforming processes are assumed as socialisation (everyday comradeship); externalization (formalising a body of knowledge); internalisation (translating theory into practice) and combination (combining existing theories) (Nonaka & Takeuchi, 1995). This model is thus relevant in this study because it defines the explicit type of knowledge to be studied which is basically tacit knowledge that has been formalized. To Tacit Explicit From Tacit Socialization Externalization Explicit Internalization Combination Figure 2.1: Nonaka & Takeuchi’s Knowledge Management Model The SET Knowledge Management (KM) Model Choo (1998) argues that ‘knowing organizations’ are those which use information strategically in the context of three arenas, namely sense making; knowledge creation and decision making. These three highly interconnected processes play a strategic role as to the unfoldment of the organization’s knowledge vision, it’s potential to knowledge creation and its commitment into taking knowledge creation to the utmost consequences (Alavi & Leidner, 2001). Concerning sense making, this is a long term goal that organizations will adapt and continue to prosper in a dynamic and complex environment through activities of prospecting and interpretation of relevant information enabling it to understand changes, trends and scenarios about clients, suppliers, competitors and other external environment actors (Ambrect & Johnes, 2001). Knowledge creation is a process that allows an organization to create or acquire, organize and process information in order to generate new knowledge through organizational learning (Choo, 1998). The new knowledge generated, in its turn, allows the organization to develop new abilities and capabilities, create new products and new services, improve the existing ones and redesign its organizational processes (Argyris, 2003). This process reveals the organization ‘potential to act’. The third component of Choo’s (1998) model involves decision-making. Thus, the organization must choose the best option among those that are plausible and presented and pursue it based on the organization’s strategy (Ball, 2002). In the decision making theory, Choo (1998) lists a few of them: (i) the decision making process is driven by the search for alternatives that are satisfactory or good enough, rather than seeking for the optimal solution; (ii) the choice of one single alternative implies in giving up the remaining ones and concomitantly in the emergence of trade-offs or costs of opportunity; (iii) a completely rational decision would require information beyond the capability of the organization to collect, and information processing beyond the human capacity to execute. The decision-making process results in the organization commitment for action. Figure 2.2 shows that knowing what to do is not enough (Pfeiffer and Sutton, 2002) as the firm must turn its knowledge into action. Thereafter, the environmental conditions may be translated into the Japanese concept of ba (Nonaka & Konno, 1998, Von Krogh & Voelpel, 2006). Therefore, ba is the bridge that links strategy to action and this re- defines the role of leadership of middle-managers in the means of knowledge enablers or knowledge activists. Knowledge Organizations (uses Knowledge in 3 Areas) Knowledge Creation Decision Making Strategy Sense Making Figure 2.2: The SET Knowledge Management (KM) Model Hedlund and Nonaka (1993) Knowledge Management Model This model is a more elaborate version of Nonaka's model and assumes there are four different levels of ‘carriers’ or agents of knowledge in organizations namely the individual, the small group, the organization and the inter-organizational domain (Hedlund & Nonaka, 1993). While the model a shown in Figure 2.3 is helpful in that it relates the carriers to the types of knowledge, it remains problematic in that it assumes the carriers, like the knowledge, can be simply segregated. An analogy is drawn with the management competency movement which assumes a simplistic desegregation of management tasks rather than a more representative holistic approach (Hedlund, 1994). The relevance of this model to the study is about the individual who is the carrier or agent of knowledge in an organizations. The individual is also the subject of study in this matter. Individual Group Organization Inter- organizational Domain Articulated/Explicit Knowledge Knowing Calculus Quality Circle’s documented analysis of performance Organization Chart Supplier’s Patents and documented practices Tacit Knowledge Cross Cultural Negotiation Skills Team Coordination in Complex Work Corporate Culture Customer’s attitudes to products and expectations Figure 2.3: Hedlund and Nonaka's Knowledge Management Model Boisot’s Knowledge Category Model Boisot's model considers knowledge as either codified or uncodified and as diffused or undiffused, within an organization (Boisot, 1987). Boisot uses the term ‘codified’ to refer to knowledge that can be readily prepared for transmission purposes. The term ‘uncodified’ refers to knowledge that cannot be easily prepared for transmission purposes. The term ‘diffused’ refers to knowledge that is readily shared while ‘undiffused’ refers to knowledge that is not readily shared. From the model, if knowledge is categorized as both codified and undiffused, then the knowledge is referred to as proprietary knowledge (Allee, 1997). In this case, as shown in Figure 2.4 knowledge is prepared for transmission but is deliberately restricted to a selectively small population, on a ‘need to know’ basis (Beijerse, 2000). Boisot’s Model proposes 2 key points: The more easily data is converted to information the more easily it is diffused and the less the data is structured requires a shared context for its diffusion, the more diffusible it becomes (Demerest, 2007). Boisot KM model links the content, information and knowledge management in an effective way and maps the organizational knowledge assets to social learning cycle which other KM models do not directly address. The relevance of this model is all about knowledge that is codified and diffused. This is the knowledge that is held by employees in an organization and is the subject matter of the study. Codified Proprietary Knowledge Public Knowledge Uncodified Personal Knowledge Common Sense Undiffused Diffused Figure 2.4: Boisot's Knowledge Category Model Kogut and Zander’s Knowledge Management Model Kogut and Zander (1992) are among the first researchers who established the foundation for the knowledge-based theory of the firm when emphasizing the strategic importance of knowledge as a source of competitive advantage. Their work is focused on the idea that ‘what firms do better than markets is the creation and transfer of knowledge within the organization’ (Kogut & Zander, 1992). Knowledge, which consists of information and know-how, is not only held by individuals but is also expressed in regularities by which members cooperate in a social community (Lubit, 2001). Firms as social communities act as ‘a repository of capabilities’ determined by the social knowledge embedded in enduring individual relationships structured by organizing principles (Kogut & Zander, 1992). The organizing principles refer to as ‘the organizing knowledge that establishes the context of discourse and coordination among individuals with disparate expertise and that replicates the organization over time in correspondence to the changing expectations and identity of its members’ (Kogut & Zander, 1996). This view was further articulated and empirically tested in Kogut and Zander (1993). They assert that (i) firms are efficient by which knowledge is created and transferred (ii) a common understanding is developed by individuals and groups in a firm through repeated interaction to transfer knowledge from ideas into production and markets (iii) what a firm does is not depending on the market’s failure rather the efficiency in the process of transformation relative to other firms, and (iv) the firm’s boundary is determined by the difference in knowledge and the embedded capabilities between the creator and the users (possessed with complementary skills) and not market failure. Kogut and Zander (1996) further extend their discussion as shown in Figure 2.5 on the concept of identity by asserting that individuals are ‘unsocial sociality’ where they have both a desire to become a member of community and at the same time also have a desire to retain their own individuality. As firms provide a normative territory to which members identify costs of coordination, communication and learning within firms is much lower which allow more knowledge to be shared and created within firms. Knowledge Creation Knowledge Transfer Process and Transformation of Knowledge Efficient Firms Competitive Advantage Knowledge Capabilities Individual Unsocial Sociality Figure 2.5: Kogut and Zander’s Knowledge Management Model Performance Management Model Performance management is an integral part of effective human resource management and development strategy (Hellriegel, 2004). Performance management is an on-going and joint process where the employee, with the assistance of the employer ‘strives to improve the employee’s individual performance and his contribution to the organization’s wider objectives’ (Hellriegel, 2004). Amos (2004) defines performance management as ‘the process that begins with translating the overall strategic objective of the organization into clear objectives for each individual employee’. Accordingly, it is also the process that incorporates all aspects of human resource management that are designed to progress and/or develop the effectiveness and efficiency of both the individual and the organization as first class performance management begins and develops with the employee’s understanding of the organization’s expectations (Hendrey, 2005) It takes the form of a continuous self-renewing cycle: performance and development agreement; managing performance throughout the year; and performance review and assessment (Armstrong, 2009). The Booz Allen performance management model includes four key human resources processes (planning, performing, evaluating, and rewarding) and a number of components that focus on designing an integrated system. The model as shown in Figure 2.6 highlights the need for the system to be integrated with the organization’s overarching strategy to ensure alignment between organizational goals and individual performance (Abbas & Yaqoob, 2013). A well- designed system promotes clarity of expectations, increases trust among supervisors and employees, and increases fairness and transparency in the system — setting the foundation for a high-performance culture in which employees and managers can focus on individual and organizational development (Akpotu & Lebari, 2014). Performance management diagram Figure 2.6: The Performance Management Model 2.2.2 Knowledge Acquisition Knowledge management as a process involves acquisition, refinement, storage, transfer and sharing of knowledge within organizations thus representing a dynamic competitive resource as espoused in the knowledge base view of Gilsby,(2007). It has also been defined by Scaborough (2008) that it is the process of creating, acquiring, capturing and sharing knowledge whenever it is found. Egbu (2001) had considered strongly that in all of these processes, the need to acquire knowledge is a strong concern for all members of the organization. Acquiring knowledge according to the author is a candidly initiated effort to strategically alter attempt at competitiveness with a view to ensuring dominance among competitors. Billa (2006) opined that knowledge seeking firms are operational string to the extent that they sufficiently share through structural flexibility and infrastructure that facilitate sharing. These positions suggest firms acquire knowledge for all purposes. Prahlad and Hammel (2002) relying on the knowledge based view had noted that competencies are seen as the basis for a company’s ability to acquire competitive advantage. They had further observed that employees improved work action in relation to assigned responsibilities is not a function of tangible or extrinsic incentives or the conducive work environment rather, the todays worker characteristically acquire knowledge which constitute the asset that reengineer all work processes towards goals. As earlier noted, the acquisition component of the entire knowledge management process is fundamental as it precedes other activities in the entire knowledge management spectrum. Renderick (2008) had considered knowledge spread among employees as being a significant practice especially within explicit classification of knowledge. The argument put forward here is that since knowledge is in built within the organization, it will require germane organizational platform created to help in its acquisition. To achieve this, Renderick (2008) believed that a continuous interaction platform that will help in knowledge sharing and transfer is important for knowledge acquisition and sharing. This is in addition to the authors thinking that the sources from which the individual acquire knowledge and share it is also imperative for quality knowledge that meets the goals of building intellectual capital that is sustainable for competitiveness (Ryu, 2003). Ewang (2006) noted that to generate employee support for organizational success, knowledge acquisition provides the strategic leverage that is empowering both in psychological and practical context of work. The willingness to acquire and strengthen what is eventually shared is the link between knowledge acquisition and several work outcomes. While these links are empirically biased they have been contextualized within the functions and levels of work structure especially when viewed in the light of responsibility variance that may result from different levels of the organization Renderick (2008). Knowledge acquisition, therefore, is a fundamental and essential process of individual and organizational learning (Hergenhahn & Olson, 2006; Nonaka, 1994). It incorporates the process of capturing (learning), filtering new information, and storing it in memory (Sternberg, 2008). It is the activity of identifying knowledge in the organization's external environment and transforming it into a representation that can be internalized and used for knowledge generation or externalization (Holsapple and Singh, 2001); Knowledge acquisition is also considered as a firm's capability to identify and acquire knowledge that is critical to its operations (Zahra & George, 2002). 2.2.3 Knowledge Processing Knowledge processing is considered as organization, analysis and application of knowledge in decision making (Argyris, 2003). Once the relevant critical knowledge has been identified, a firm must develop processes and tools in order to organize, analyze and apply such knowledge and information (E., 2009). Regardless of the tools and processes involved, the critical knowledge must be stored in a location and format, which can be easily found and accessed by users i.e. Employees and other stakeholders (Buckley & Carter, 2002). The idea that firms should categorize their knowledge assets is not a new one (Horvath 2000, Bukowitz & Williams 1999). In order to determine what resources they have at their disposal and to pin point strengths and weaknesses, organizations need to organize their knowledge into something manageable (Cheruyoit, Jagongo, & Owino, 2012). Knowledge organization involves activities that "classify, map, index, and categorize knowledge for navigation, storage, and retrieval" (Botha et al. 2008). Markus (2001) assigns the role of preparing, sanitizing, and organizing this knowledge to a "knowledge intermediary". In order for knowledge to be shared, it must be prepared in such a way that it can be identified, retrieved, and understood by the knowledge user (Mclnerney, 2002). It is generally encouraged in e-organization as a means of organizing and retrieving (Gamble & Blackwell 2001, Botha, 2008). IT based systems use taxonomies and ontologies to classify and organize knowledge and information (Bali, 2009). These are categorization methods that create a logical, hierarchical knowledge map, allowing the user to navigate by category (Botha et al 2008). 2.2.4 Knowledge Dissemination Knowledge dissemination is defined as managing knowledge sharing within an organization to encourage innovation or action, increase awareness of past good practice and stimulate users to adopt better practices for future decision-making processes (Awad & Gharizi, 2007). Formal presentations, work with external professionals, specialized training, and creative task forces have been considered as effective means to disseminate knowledge (Falkenberg, 2002). To show its commitment for sharing knowledge, an organization should foster the employee's willingness to share and contribute to the knowledge base (O’Dell & Hurbert, 2011). Knowledge Management systems must also facilitate the sharing of relevant knowledge between users. Thus even if a firm has successfully collected, stored and organized knowledge, potential users of such knowledge must be made aware of its existence and encouraged to contribute and use knowledge within the firm’s knowledge repository (Argyris, 2003). Bukowitz and Williams (1999) posit that successful explicit knowledge sharing is determined by various criteria; articulation which is the ability of the user to define what he needs; Awareness of the knowledge available; access to knowledge; guidance where knowledge managers are considered key in the build-up of a knowledge sharing system. IT has been identified as a key component of this type of knowledge sharing, facilitating and lowering the cost of the storage, access, retrieval, and variety of explicit knowledge (Davenport & Prusak 2000, Gamble & Blackwell 2001). 2.2.5 Knowledge Management Infrastructure Knowledge Management Infrastructure reflects long-term foundations for information and knowledge management. In an organizational context, the infrastructure includes three major components: Organization Culture; Organization Structure; Organization’s Information Technology (IT) set-up (Becerra-Fernandez & Sabherwal, 2010). Organization culture reflects the norms and beliefs that guide the behaviour of the organization's member as it is an important enabler of knowledge management in organizations (Bigge, 2004). Attributes of enabling organizational culture include understanding the value of knowledge management practices, managing support for knowledge management at all levels, incentives that reward knowledge sharing and encouragement of interaction for the creation and sharing of knowledge (Ambrect et al, 2001). Organizational structure determines the manner and extent to which roles, power, and responsibilities are delegated, controlled and coordinated and how information flows between levels of management (Ball, 2002). The most common organization structures are: hierarchical, centralized and decentralized, flat and tall (Lambe, 2006). A traditional hierarchical structure of the organization defines each employee's role within the organization and greatly affects with whom each individual mainly and frequently interacts, and share knowledge as most important decisions in organizations with a traditional hierarchical structure are usually taken by senior management (Morrisey, 2005). In a decentralized structure, the decision making power is distributed and the departments and divisions have varying degrees of autonomy (Lambe, 2006). Knowledge management is also facilitated by the organization's Information Technology Infrastructure, developed to support the organization's information systems needs (Peres & Pablos, 2003). The Information Technology Infrastructure is the combination of data processing, storage, and communication technologies and systems and the processes that make it all work (Javenick, 2008). It comprises the entire spectrum of organization's information systems, including transaction processing systems and management information systems which consist of databases and data warehouses, as well as enterprise resource planning systems (Trinic, 2008). 2.3 Conceptual Framework Knowledge is a valuable intangible resource that should be managed dynamically by any organization seeking to gain competitive advantages (Birknshaw & Sheehan 2002). Knowledge acquisition, according to Gamble & Blackwell, (2001) is one of the most difficult and error-prone tasks that a knowledge expert does while building a knowledge-based system. The cost and performance of the application depends directly on the quality of the knowledge acquired (Ochara, 2008). During this process, one must determine where the organization knowledge exists, how to capture it and how to disseminate this knowledge throughout the enterprise (Trinic, 2008). In the traditional approach to acquiring knowledge, a knowledge expert consults reference materials, databases, and human experts where explicit knowledge is acquired through printed material (Awad & Gharizi, 2007). Several other sources of explicit knowledge include company policy manuals and regulations, reports, memos, published books, journal articles, programs, source code and database stored procedures are also used (Sternberg, 2008). Then how does this process impact employee performance. This review led us to the following hypothesis; There is no significant effect of explicit knowledge acquisition on employee performance in energy sector organizations in Kenya…………………….Hypothesis 1 In Knowledge processing, Nonaka (1995) divides the KM process into four modes: First, the socialization that involves sharing and distributing the ideas and the interaction of tacit knowledge with tacit knowledge. In this phase, the members discuss about what is more important and use the others’ thoughts. Organizations gain new knowledge from outside their boundaries such as interacting with customers, suppliers and stack holders (Beijerse, 2000). The second model is externalization (tacit to explicit), which focuses on tacit to explicit knowledge linking. Externalization requires the expression of tacit knowledge and its translation into comprehensible forms that can be understood by others as this helps in creating new knowledge as tacit knowledge comes out of its boundary and be-comes collective group knowledge (Lubit, 2001). The next model is combination, where the explicit knowledge, in the form of different collections of knowledge, already exchanged, distributed, and documented or discussed during meetings and sessions, is processed and categorized in order to create new knowledge. It is easily documented and distributed, when the knowledge is explicit and evident (Hoe & McShane, 2010). The fourth model is internalization (explicit to tacit) which involves the process of converting the explicit knowledge to tacit knowledge. But how does this process affect employee performance? Based on this review; the following hypothesis was formulated; There is no significant effect of explicit knowledge processing on employee performance in energy sector organizations in Kenya. …………………….Hypothesis 2 Knowledge dissemination refers to the transfer of knowledge within and across settings, with the expectation that the knowledge will be "used" conceptually as learning, enlightenment, or the acquisition of new perspectives/attitudes or instrumentally in the form of modified or new practices (Blair, 2002)). Knowledge dissemination also has legitimate outcomes which include: increased awareness; ability to make informed choices among alternatives and the exchange of information; materials or perspectives with key dimensions being knowledge accessibility, knowledge usage and knowledge transfer (Botha, 2008). Then how does this process affect employee performance. Based on this review, the following hypothesis was formulated: There is no significant effect of explicit knowledge dissemination on employee performance in energy sector organizations in Kenya. ……………………Hypothesis 3 There are different approaches in defining knowledge management infrastructure. Lambe (2006) notes that knowledge and information infrastructure "mean all the things that combine to facilitate the flow of information and knowledge in support of the myriad tasks and actions and decisions that comprise organizational activity. Knowledge Management Infrastructure reflects long-term foundations for information and knowledge management which includes three major components: Organization Culture; Organization Structure; Organization’s Information Technology (IT) set-up (Becerra-Fernandez & Sabherwal, 2010). So how does this process moderate the effect of knowledge acquisition, processing and dissemination on employee performance? Based on this review, the following hypothesis was formulated: Knowledge management infrastructure does not moderate the effect of explicit knowledge acquisition, processing, dissemination and employee performance in energy sector organizations in Kenya…………………………………….…Hypothesis 4 Figure 2.7 below shows the conceptual framework of the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations. The independent variable is intra-firm institutionalization of explicit knowledge, which comprises of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination. The moderating variable is knowledge management infrastructure and the dependent variable is employee performance. It is perceived that when employees of an institution make good use of its explicit knowledge acquisition, processing and dissemination, they gain more knowledge pertaining to their work and this greatly improves their performance. However, if the knowledge management infrastructure in not well organized, kept or maintained, then it is likely to deter employees from getting knowledge freely and this affects their performance. The above review of literature has resulted into the formulation of presumed effect between the variables under investigation and is illustrated in the hypothetical model in Figure 2.7. Institutionalization of Explicit Knowledge Knowledge Management Infrastructure .Organization Culture .Organization Structure .Organization IT Set- up Explicit Knowledge Acquisition .Capturing .Learning .Development Explicit Knowledge Processing .Organization .Analysis .Storage .Retrieval Employee Performance .Quality of Work .Job Knowledge .Adaptability .Reliability Explicit Knowledge Dissemination .Access .Usage .Transfer Independent Variables Moderating Variable Dependent Variable Figure 2.7: Conceptual Framework 2.4 Operationalization of Study Variables Operationalization refers to the process of developing indicators or items for measuring a research construct (Cresswell, 2004). Literature reviewed has identified various variables for this study. Independent variables for assessment of intra-firm institutionalization of employee explicit knowledge is composed of knowledge acquisition, knowledge processing and knowledge dissemination as detailed in table 2.1. The moderating variable of this study is knowledge management infrastructure, which comprises of organization culture, organization structure and organization Information Technology (IT) set-up detailed in table 2.2. The dependent variable is intra-firm institutionalization of employee explicit knowledge. 2.4.1 Independent Variables An independent variable is a variable that the researcher manipulates in order to determine the effect or influence on another variable (Mugenda & Mugenda, 2003). In this study, independent variables are explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination. Table 2.1 illustrates how these variables will be measured. Table 2.1: Tabulation of Independent Variables and their Specific Measures Independent Constructs Sub-constructs General Items Explicit Knowledge Acquisition Capturing i. Learning and Development environment ii. Knowledge requirement in area of specialty iii. Do they have sufficient knowledge about their work Learning iv. How does learning takes place v. Where learning takes place Development vi. Who facilitates development vii. How development is facilitated Explicit Knowledge Processing Organization i. How knowledge is structured ii. Who facilitates knowledge organization iii. Why knowledge organization Analysis iv. Understanding of knowledge analysis v. Who does knowledge analysis vi. When is knowledge analyzed Storage vii. How knowledge is stored viii. Why is knowledge stored Explicit Knowledge Dissemination Access i. How knowledge is access ii. What knowledge is accessed iii. Who facilitates knowledge access Usage i. How knowledge is applied ii. When knowledge is applied Transfer i. How knowledge is shared ii. What knowledge is shared iii. Whom knowledge is shared with 2.4.2 Moderating Variable A moderating variable represents a process or a factor that alters the impact of an independent variable X on a dependent variable Y (Olsen, 2012). The moderating variable in this study is knowledge management infrastructure. Table 2.2 identifies the sub-variables for knowledge management infrastructure and the measures to be used. Table 2.2: Tabulation of Moderating Variable and its Specific Measures Independent Constructs Sub-constructs General Items Knowledge Management Infrastructure Organization Culture i. What organization culture is in organization ii. How organization cultures promotes knowledge acquisition iii. How organization culture facilitate knowledge processing iv. How organization culture drives knowledge dissemination Organization Structure i. How organization structure promotes knowledge acquisition ii. How organization structure facilitate knowledge processing iii. How organization structure drives knowledge dissemination Organization IT Set-up i. How IT set-up facilitates knowledge acquisition ii. When organization IT set-up facilitates knowledge processing iii. How organization IT Set-up drives knowledge dissemination 2.4.3 Dependent Variables If a variable depends upon or is a consequence of other variables, it is termed as a dependent variable (Kothari, 2004). In this study, the dependent variable is employee performance. In the existing framework, it’s dependent on the three independent variable of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination and will measured as illustrated in Table 2.3. Table 2.3: Tabulation of Dependent Variable and its Specific Measures Dependent Variable (DV) Measureable Sub- variables of Dependent Variables Specific Measure Employee Performance Quality of Work i. Standards of work output ii. Ability to discharge assigned duties Job Knowledge iii. Understanding of Job requirements iv. Ability to interpret Job requirements Adaptability v. Adjustment to environment vi. Flexibility in adjusting to work environment Reliability vii. Dependability to give required output viii. Ability to work under minimal supervision 2.5 Empirical Review Various studies have investigated the concepts of knowledge management and performance. However, most of the studies carried out have been done in the west with little or no attention being paid to developing world (Bray & Konsynki, 2012). Although, these studies have been done in the west, the results and findings of these studies indicate that knowledge management initiatives have positive effect both on organizational and employee performance (Resatch & Faisst, 2013) Studies and business practice reveal that knowledge management affects employee performance and contribute to the competitive advantage of organizations (Ball, 2002). The following studies have specifically examined the effect of knowledge acquisition, knowledge processing and knowledge dissemination on employee performance. 2.5.1 Institutionalization of Knowledge Awad and Ghaziri (2007) defined knowledge as ‘understanding gained through experience or study’. It is know-how or familiarity with how to do something that enables a person to perform a specialized task. According to Davenport and Prusak (2000), knowledge is a fluid mix of framed experience, values, contextual information and expert insight that provide a framework for evaluating and incorporating new experiences and information. It originates and is applied in the minds of the knowers. In organizations, it becomes embedded not only in documents or repositories but also in organizational routines, processes, practices and norms a definition the study concurs with. O’Sullivan (2010) acknowledges that in the progressively more competitive and global marketplace, organizations are eager to integrate, leverage, capitalize, and monetize employee knowledge and make it available when and where it is needed throughout the enterprise. He argues that institutionalization of knowledge requires the identification and assessment of all internal and external knowledge touch points and their impact and potential change to the organization’s core processes and business applications. According to Nonaka (1994), there are two types of knowledge; tacit and explicit knowledge. Sunassee and Sewry (2003) argues that explicit knowledge is knowledge that is easy to articulate, capture and distribute in different formats, since it is formal and systematic. Explicit knowledge is codified, recorded and available in books, journal articles, databases, in corporate internets and intellectual property portfolios (Kemboi, 2011). Explicit knowledge comes in the form of documents, formulas, contracts and process diagrams as well as manuals (O’Dell & Hubert 2011) and is not useful without the context provided by experience. American Productivity and Quality Centre (APQC) posit that institutionalization of knowledge is a systematic efforts to enable information and knowledge to grow, flow and create value. It is the process of identifying, growing and effectively applying an organization’s existing knowledge in order to achieve the organization’s goals, while creating an organizational culture that permits further knowledge creation. According to Bhatt (2011) institutionalization of knowledge is a comprehensive process of knowledge creation, knowledge validation, knowledge presentation, knowledge distribution and knowledge application. Advanced organizations build transform, organize, deploy and use knowledge assets effectively (Wiig, 2000). Institutionalization of knowledge, according to Dalkir (2013) is supported by the process of knowledge management which comprises various process of generating new knowledge; accessing knowledge from external sources; representing knowledge in documents and databases; embedding knowledge in processes, products and services; transferring existing knowledge around an organization; using accessible knowledge in decision making; facilitating knowledge growth through culture and incentives and measuring the value of knowledge. This coordination is achieved through creating, sharing and applying knowledge as well as through feeding the valuable lessons learned and best practices into corporate memory in order to foster continued organizational learning (Kemboi, 2011) 2.5.2 Knowledge Acquisition A study by Hoe and McShane ( 2010) on structural knowledge acquisition and dissemination in organizational learning in Australia, found out that informal knowledge acquisition and dissemination have, significant paths to market knowledge use, where structural knowledge acquisition and dissemination have a weak association with market knowledge use. The study showed that informal knowledge process are at least as important as structural knowledge processed in market-based organizational learning. The study adopted a model of organizational learning that incorporates both structural and informal knowledge acquisition and dissemination constructs with three predictors of organizational learning constructs. In Thailand, Paracharapha and Ractham (2012) in a study on knowledge acquisition; the role of perceived value of knowledge content and source found out that organizations desiring to institutionalize staff’s knowledge acquisition should focus on factors influencing Individual knowledge acquisition influenced by perceived compatibility. Thus, the perception that knowledge is consistent with needs and experiences. The study adopted a survey method and examined hypotheses by applying the structural equation method where unit of analysis was an individual. Rasula et al., (2012) was interested in the impact of knowledge management on organizational performance. The study aimed to show that through creating, accumulating, organizing and utilizing knowledge, organizations can enhance organizational performance. By using structural equation modelling on sample of 329 companies both in Slovenia and Croatia with more than 50 employees, the impact of knowledge management practices on performance was empirically tested and from the study it was evident that knowledge management practices measured through information technology, organization and knowledge positively affect organizational performance. In Kenya, Cheruiyot, Jagongo and Owino (2012) investigated institutionalization of knowledge management in manufacturing enterprises in Kenya. The main objective of the study was to examine factors that influence institutionalization of Knowledge Management (KM) in manufacturing enterprises in Kenya. By using a cross-sectional descriptive design, which used stratified random sampling to identify 60 heads and deputy heads of department, from the manufacturing enterprises to participate in the study, the study established that there are a number of reasons as to why organizations are embracing knowledge management in their business operations. Among them is to avoid costly mistakes and ill-informed decisions as well as to capture and retain employee knowledge. The study also established that organizational practices and technological infrastructure are the key factors that influence institutionalization of knowledge. In a study by Zaied, Hussein and Hassan (2012) that investigated the role of knowledge management in enhancing the organizational performance in some Egyptian organizations Results of correlation analysis showed significant relationship between knowledge management elements and performance improvement measures, which in turn represented the quality of organizational knowledge that was utilized in a wide variety of decision - makings in the firm. Thus, if the quality of organizational knowledge is good, it can be conclude that management performance improves significantly. The study findings shed light on the following points. First, besides providing empirical evidence to the correlation between knowledge management and organizational performance, the study showed high positive correlation between the following couple of elements and measures: technology and market share; culture and profitability; structure and market share; human resource and innovativeness; acquisitions and profitability; conversions and sales growth; applications and innovativeness; protections and profitability and storing and sales growth. These results were consistent with findings of previous research by Chang and Chuang (2009). Other factors like organization type and size affect level of adopting knowledge management; whereas factor like sector type affects the role of knowledge management in enhancing the organizational performance. From the findings of the study, many organizations still view knowledge management as launching some software programs without adequate consideration of their organizational characteristics, this study brings to attention the importance of focusing on creating a knowledge environment that is made up of appropriate technology; Cultural; structural and human resources (Zaid, 2012). Findings of a study by Akpotu and Lebari (2014) that investigated the relationship between knowledge acquisition practices and performance of administrative employees in tertiary educational institutions in South-South Nigeria showed a significant relationship between knowledge acquisition and administrative employee performance in the studied institutions. The study had relied on a structured survey instrument adapted from existing literature The result from the analysed data found that the knowledge acquisition influence the functional capability of administrative employees in higher educational institutions and knowledge acquisition has a high predictive capability for administrative employee performance (R = .736). 2.5.3 Knowledge Processing In a study by Bray and Konsynski (2012) on Improved Organizational Performance by Knowledge Management in distributed E-Government and E-Business Organizations in the United States of America, four theoretical propositions relating to use of a knowledge management system to improved performance in either the public or private sectors were found to cumulatively motivate employees to believe the benefits to knowledge exchange behaviors exceed the costs. The theoretical perceptions of (1) formal incentives, (2) normative values, (3) inter- employee trust, and (4) enabling knowledge technologies represented an organization’s motivational attributes, which indirectly determine the net effect knowledge management has on organizational performance. These motivational attributes influence adoption of an organization’s knowledge processes as supported by information systems. Favorable perceptions translate into successful adoption of an organization’s knowledge processes. In turn, employee process adoption directly influences the contribution of knowledge management to improved organizational performance, to include organizational efficiency and organizational responsiveness. Although the focus of this research was on organizational performance, its influence is determined by employee perception and by extension employee performance. Lara (2008) investigated the effects of knowledge management on organizations in Spain. The main objective of the study was to demonstrate that in the future the only sustainable competitive advantage will be the creation of collective and explicit knowledge. The study realized that employee satisfaction is positively influenced by the intrategic directive competencies while sales and profits are influenced by the strategic planning processes and by personal directive competencies. On the other hand, productivity is directly related to knowledge management factors, such as management measurement systems and knowledge flows such as training. Qiu, Chui and Helander, (2008) undertook a study on cognitive understanding of knowledge processing in Singapore. The study aimed at improving the cognitive understanding of knowledge processing and provide a cognitive knowledge modelling method in product design. From the study, it was found that there is often a fundamental mismatch between the way human process knowledge and the way it is processed by technology. According to the study, it is necessary to develop tools, methods and technology, which integrate seamlessly as they affect overall employee performance. In Iran Rahimi, Navidian and Razieh, (2012) conducted a study on the analysis of knowledge conversion processes in the university and its effect on psychological empowerment among faculty members. The main purpose of this research was the analysis of knowledge conversion process in the university and its effect on empowerment among faculty members. Four dimensions of knowledge conversion process (Socialization, Combination, Externalization, and Internalization) were analysed and psychological empowerment based on demographic variables. The findings of the study revealed that there is a positive and significant effect between knowledge conversion processes and psychological empowerment. However, the study did not observe any significant difference between faculty members’ knowledge conversion processes considering the variables of gender, and field of study, neither was there any significant difference among faculty members’ psychological empowerment considering scientific degree, and employment status. Jantunen (2006) on knowledge-processing capabilities and innovative performance found out that it is not only the firm’s knowledge stock but also its knowledge flows that are crucial for sustaining innovative performance. According to the study knowledge, utilization capabilities were reflected in the firm’s innovative performance. Knowledge-based assets and organizational learning capabilities are recognized to be critical for a firm’s innovation activities. The process of creating knowledge thus requires acquiring useful data and information, utilizing it effectively in its internal innovation activities. The study emphasizes the importance of the firm’s ability to utilize and renew its knowledge base effectively. In order for firms to utilize externally generated knowledge, firms need the ability to internalize it and combine the information and new insights with the existing knowledge base. The study was based on a large-scale survey comprising of 217 Finnish firms from seven different sectors. A study by Mosoti and Mesheka (2010) that focused on knowledge management in organizations in Nairobi, Kenya found out that most of the challenges experienced organizations in Nairobi in knowledge management practices are how to create and implement knowledge management as part of organizational culture, organizational strategy and organizational leadership. They established that 45 organizations representing 65 percent of the sampled organizations experienced significant resistance when implementing knowledge management practices. A study by Maingi (2007) on knowledge management in a competitive economy in Kenya, brought into focus the need to develop knowledge management as supplementary measure of organizational profitability, sustainability and continuity outside the usual measures that include financial statement analysis such as profit and loss accounts and balance sheets. The study concluded that many people were still unaware of what knowledge management is all about. Mararo (2013) researched on knowledge management practices as a competitive tool in insurance companies in Kenya. The main objective of the study was to examine the effect of knowledge management practices as a tool for competitive advantage in Insurance Companies in Kenya. Using a survey of 45 registered insurance companies, the findings of the study revealed that organizations are using knowledge management in their business functions to enhance competitive advantage. However, the study reiterated that most managers do not frequently examine knowledge for errors/mistakes, and that their company structure do not facilitates the transfer of new knowledge across structural boundaries and they do not have standardized reward system for sharing knowledge. Besides, it was also observed that majority of the respondent companies do not design processes to facilitate knowledge exchange across functional boundaries and that organization have processes for generating new knowledge from existing knowledge. The research recommended that knowledge distribution systems be availed and organizations hold brain storming meetings for ideas and that the organization culture will enhance competitiveness through mentoring and training staff, creation of open door policy where employee can reach all levels of management. Further still, the study also recommends that to clearly interlink knowledge management and competitive advantage, staff should be involved in the process of knowledge management instituting knowledge management practices. To build the public confidence in business integrity these companies must create strong knowledge systems that protect data from inappropriate use from inside and outside the company. 2.5.4 Knowledge Dissemination In Britain, a Study by Hatami et al., (2005) on exploring the impact of Knowledge re- use and organizational memory on the effectiveness of strategic decisions, found that organizations relying on knowledge on average make higher-quality decisions on business strategies for better future performance. Thus in competitive environments, the manner in which corporations learn from past performances and manage knowledge, impacts future decisions. From the study, effective decision-making depends on the use of quality information, including systems that capture lessons learned from past decisions and performances. The study concluded that key to success is the ability to capture organizational learning, to effectively re-use the knowledge through efficient means, and to synthesize these into a more intelligent problem recognition, strategic analysis and choices in strategic decisions. Hau and Kim (2011) investigated the effects of individual motivations and social capital on employees’ tacit and explicit knowledge sharing intentions in South Korea. The study aimed at developing an integrated model to understand key factors of employee knowledge sharing intentions through constructs prescribed by two established knowledge management research streams, namely, those concerning individual motivations and social capital. The research showed that the proposed model significantly explains the variance of employees’ tacit and explicit knowledge sharing intentions. In particular, the findings reveal that organizational rewards have a negative effect on employees’ tacit knowledge sharing intentions but a positive influence on their explicit knowledge sharing intentions. Besides, it was also evident from the study that reciprocity, enjoyment, and social capital contribute significantly to enhancing employees’ tacit and explicit knowledge sharing intentions. Additionally, these factors have more positive effects on tacit than on explicit knowledge intentions. Rashed, Azeem and Halim (2010) studied the effect of information and knowledge sharing on supply chain performance in Bangladesh. The aim of the study was to identify the combined effect of information and knowledge sharing on supplier’s operational performance. From the study, it was evident that information sharing with key supplier does not affect the supplier’s operational performance. This is due to the fact that few companies understand how to turn operational or knowledge based information sharing into a competitive advantage. It was also found out that information sharing with supplying firm has a very weak linkage with supplier-buyer relationship, due to the inaccuracy, late response of relevant information, and that information sharing with supplier promotes knowledge sharing. However, the study noted that knowledge sharing with key supplier does not have a strong linkage with supplier-buyer relationship. The reason for this sort of outcome indicates that the supplying firms are not capable to utilize the knowledge based information effectively and efficiently. It was also documented that knowledge sharing with the supplier has a weak positive effect with supplier’s operational performance and that the buyer- supplier relationship has a strong influence on supplying firm’s performance. Ha, Okigbo and Igboaka (2008) studied knowledge creation and dissemination in sub- Saharan Africa. This was an experimental study aimed at examining the effects of using broadband internet technology for creating and disseminating agricultural knowledge in Nigeria. By conducting a pre- and post-test interviews of a panel of female farmers who were conducted before and after the establishment of a free broadband internet access service centre and experimental web site, the study established that farmers who visited the experimental web site evaluated the site positively as giving them relevant knowledge. The broadband facility was unanimously rated by the users as a great place for socializing and learning from other fellow farmers. Thus customized information and socialization functions in a knowledge creation program are important. A study by Abbas and Yaqoob (2009), coaching, training and development, empowerment, participation and delegation were found to influence employee performance with 50%. Sahinidis and Bouris (2008) noted that employees may not feel motivated and lack commitment due to insufficient knowledge and skills which can be imparted to them through training. This insufficiency may result into conflict with organizational goal achievement and eventually affecting organizational performance. Okemwa (2006) investigated knowledge management in a research organization i.e. the International Livestock Research Institute (ILRI). The study looked at how knowledge is generated in ILRI, how it is shared, how it is transferred and how it is integrated into the day-to-day operations of the Institute. The study noted that there are no known, standards or techniques for auditing knowledge in research organization. It was also observed that the demand for knowledge and information far outstrips the organization’s ability to meet all the requirements, there are not many people who have been trained in knowledge management and in information technology in particular, and that knowledge management programme does not enjoy an enormous budget allocation in organizations. 2.5.5 Knowledge Management Infrastructure According to Jalaldeen, Karim, and Mohamed (2009) KM infrastructure includes KM supportive organizational culture, structure, and supportive Information Technology edifice. These preconditions, on which KM resides, have been defined as KM infrastructures in the KM literature (Becerra-Fernandez, Gonzales & Sabherwa, 2004; Gold, Malhotra & Segars, 2001). To adopt KM processes in an organization, specified structural, physical, and logical changes are required in their conduct of operation. Several authors have stated these factors as the main contributing factors for adoption of KM processes, though they have termed them differently. For example, KM enablers Lee and Choi (2003), KM critical success factors Al-Alawi, Al-Marzooqi and Mohammed (2007); Hung, Huang, Lin and Mei-Ling-Tsai (2005); Wong (2005), influencing factors on KM Holsapple and Joshi (2000) and KM initiatives (Kulkarni, Ravindran & Freeze, 2007). Various literatures also points out to different HR-related parameters that can act as a knowledge management infrastructure within an organization. According to Acton and Golden (2003); Cohen and Backer (1999), a well-engineered training initiatives can aid in retention of knowledge within the organization. Moreover, employee involvement which describes how all employees can contribute effectively to meeting the organization's objectives is another key factor in successful KM implementation. According to Bartlett and Ghoshal (2002) the nature of knowledge creation and sharing is unthinkable without employee involvement. Greengard (2008) adds that the transformation to a knowledge-based organization requires peer-to-peer collaboration, that is, teamwork is an essential source of the knowledge generation process. Creating teams allows organizations to apply diverse skills and experiences towards its processes and problem-solving. An organization's members must work together and build on each other's ideas and strengths. Anyone who has knowledge and interest in a problem should be included on the team. 2.5.6 Employee Performance Performance either at the employee or organizational levels has had a robust discourse in management literature (Ghalayani & Noble, 1996; Kaplan & Norton, 2001; Robins, 2003; Chenhall, 2005). It is importantly discussed with the organizational ideology of an entity with goal focus therefore the imperative need to assess if such goals are being met or are progressively achieved. A common point arising from the multi positions in literature is the agreement on the multi dimensions that are applicable in expressing performance at the employee and organizational levels (Pannel & Wright, 1993; Dennison & Mishra, 1995; Peter & Crawford, 2004; Lee, 2005; Nagho, 2009). At the employee level of analysis, which is the focus in this study, employee performance according to Yakok, (2008) is the expected positive input required at all functional levels which convert to overall organizational performance. It simply means that there is considered and contextualized employee behaviour or input in relation to tasks and responsibilities that are strategically desired by organizations that aptly describe performance for employees. Philemon, (2009) has espoused that in measuring performance at the employee level, much of the attention is on social behaviour and in this vein is assessed with creativeness and quality output that is expected based on available volume of knowledge that serves the competencies needed to undertake tasks. Armstrong, (2009) posits that performance management is a systematic process for improving organizational performance by developing the performance of individuals and teams. It is a means of getting better results by understanding and managing performance within an agreed framework of planned goals, standards and competency requirements. As Weiss and Hartle (2007) posited, performance management is: ‘A process for establishing a shared understanding about what is to be achieved and how it is to be achieved, an approach to managing people that increases the probability of achieving success’. Armstrong (2009), outlines the main concerns of performance management as aligning individual objectives to organizational objectives and encouraging individuals to uphold corporate core values; enabling expectations to be defined and agreed in terms of role responsibilities and accountabilities (expected to do), skills (expected to have) and behaviour (expected to be); providing opportunities for individuals to identify their own goals and develop their skills and competencies. Performance management is a planned process of which five primary elements are agreement, measurement, feedback, positive reinforcement and dialogue (Risher, 2003). It is concerned with measuring outcomes in the shape of delivered performance compared with expectations expressed as objectives. It thus focuses on targets, standards and performance measures or indicators based on the agreement of role requirements, objectives and performance improvement and personal development plans (Hendrey, 2005). It provides the setting for ongoing dialogues about performance, which involves the joint and continuing review of achievements against objectives, requirements and plans. Performance is also concerned with inputs and values. The inputs are the knowledge, skills and behaviour required to produce the expected results (Hellriegel, 2004). Developmental needs are identified by defining these requirements and assessing the extent to which the expected levels of performance have been achieved through the effective use of knowledge and skills and through appropriate behaviour that upholds core values (Weiss & Hartle, 2007). Performance management is not just a top-down process in which managers tell their subordinates what they think about them, set objectives and institute performance improvement plans. It is not something that is done to people and as Buchner (2007) emphasizes, performance management should be something that is done for people and in partnership with them. Performance management is a continuous and flexible process that involves managers and those whom they manage acting as partners within a framework that sets out how they can best work together to achieve the required results. It is based on the principle of management by contract and agreement rather than management by command. It relies on consensus and cooperation rather than control or coercion (Armstrong, 2009). People are an organization’s greatest assets and an organization depends on its people (Temple, 2002) . The role of human resource is absolutely critical in raising performance in an organization ( (Armstrong, 2009). Ultimately it is the performance of any individuals which culminates in the performance of an organization, or the achievement of goals in an organizational context. Employee Performance means employee productivity and output because of employee development and ultimately affects organizational effectiveness (Hameed & Waheed, 2011). Performance is a major multidimensional construct aimed at achieving results and has a strong link to strategic goals of an organization (Mwita, 2000). Employee development leads to employee performance as Hameed and Waheed (2011) observed that employee development is of great influence on employee performance. Individual Performance of an employee will lead to the organizational effectiveness (Milkovich & Boudreau, 2004). Markos and Sridevi, (2010) realised that employee engagement is stronger predictor of positive organizational performance clearly showing the two-way effect between employer and employee compared job satisfaction, employee commitment and organizational citizenship behaviour. In this study therefore, employee performance shall be measured by organizational effectiveness, which refers to the achievement of overall organizational goals (Milkovich & Boudreau, 2004). 2.6 Critique of the Empirical Review The empirical literature reviewed could not find studies that are directly concerned with the effect of intra-firm institutionalization of employee explicit knowledge on employee performance. However, there are some studied that focused on institutional knowledge management which has relevance to institutionalization of knowledge in this study. A study by Cheruiyot, Jagongo and Owino (2012) on institutionalization of knowledge management in manufacturing enterprises in Kenya recognizes knowledge as a firm’s core asset that is central to organizational performance. This not only improves organizational performance but is also a pointer to the fact that such knowledge should be institutionalized and preferred. As Mosoti and Mesheka (2010) found out, most of the challenges, experienced organizations in Nairobi face in knowledge management practices are how to create and implement knowledge management as part of organizational culture, organizational strategy and organizational leadership. Indeed this is true since knowledge is still not known among many organizations and employees as confirmed by Maingi, (2007). His emphasis on developing knowledge management as a supplementary measure for organization profitability, sustainability and continuity is supported by Zyngier (2006) who argues that knowledge is a valuable intangible resource that should be managed dynamically by any organization seeking to gain competitive advantage. The findings by Ogare, Jalango and Othieno (2010) organizations should strive to convert tacit knowledge into explicit knowledge to ensure that relevant information is available to users of and retained in institutional memory confirms that indeed institutionalization of knowledge is important to avoid knowledge loss when employees leave the organization. From the study, it can also be adduced that the type of technology applied determines the response of the recipients as demonstrated in the study by Okigbo and Igboaka, (2008) where the broadband technology was successful in dissemination of knowledge among users thus creating knowledge for them. The technology used in the study is however different from the engineering sector that the study intends to review. This not withstanding means that knowledge acquisition is possible since it was successful in emerging rural communities. IT especially the intranet and web-technology were found to be central infrastructure of knowledge based organizations by Lee and Hong, (2009). Technology changes rapidly and organizations should monitor the trend of new technology in order to recognize new applications which may provide competitive advantage. With the development of advanced IT as well as global competition, the way of doing business and the level of knowledge requirements for organizations have been and will continue to change (Liao, 2009) Paracharapha and Ractham (2012) findings that organizations desiring to institutionalize staff’s knowledge acquisition should focus on factors influencing individual knowledge acquisition influenced by perceived compatibility, affirms that in the workplace, although acquiring knowledge from others seems to be a personal agenda, the study reveals the value signal concept in which management can become involved. However, data collected in the study was from organizations that were willing to participate in the study and not randomly selected. There is a possibility that the samples were a typical of a more generalized population. Findings by Ari Jantunen, (2006) in order to sustain innovativeness in a dynamic environment, the firm must have the ability to renew its knowledge base are quite logical. Although data used in this study was cross-sectional, the results represent only a snapshot view of fundamentally dynamic phenomena. It does not also invalidate the logic argument that in a dynamic environment, firms need processes for re-configuring their asset structures and that proficiency in knowledge processing capabilities is reflected in innovative performance (Bhatt, 2001). Hoe and McShane (2010) study, provides that foundation for better understanding of informal knowledge and structural knowledge processes. The study developed new measures for informal knowledge acquisition and informal knowledge dissemination. 2.7 Research Gaps From the above review, it is evident that the existing body of empirical studies has not sufficiently explained the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector with Kenya as an example. The closest study on institutionalization of knowledge by Cheruiyot, Jagongo and Owino (2012) focused on institutionalization of knowledge in the manufacturing sector which does not lead to generalization in the energy sector whose operating environment is different. The level of explicit knowledge acquisition in energy sector organisations has not been adequately explored. As a result, the effect of explicit knowledge, acquisition processing and dissemination on employee performance is not well understood. Consequently, organizations especially in Kenya are failing to make use of and exploit knowledge held by employees as a resource. Since modern organizations are knowledge-driven, knowledge management infrastructure is a critical foundation for intra-firm institutionalization of explicit knowledge, however its one of the least understood and studied management component in current management of organizational knowledge. The importance of these aspects on intra-firm institutionalization of explicit knowledge is acknowledged in the literature reviewed. However, the variables contribution to intra-firm institutionalization in the industry especially the energy sector have not been adequately studied. The Energy Sector also is a prime candidate for institutionalization of explicit knowledge, as it is human and intellectual capital- intensive sector. The sector is highly competitive and this study therefore bridges the gap by examining the effect of intra-firm institutionalization of explicit knowledge on employee performance in energy sector organizations in Kenya. CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter describes the methodology that was used in the study. The chapter describes the research design, study population, sampling frame, sample size determination and sampling techniques, data collection instruments and pilot testing. The chapter also discusses the type of data collected, data collection techniques and methods of data analysis. The statistical measurement model to be used in the analyses and the tests for hypotheses are also described in this chapter. 3.2 Research Design 3.2.1 Research Philosophy Saunders et al (2009), defines research philosophy as a term relating to the development of knowledge and the nature of that knowledge. There are four pillars of research philosophy i.e. positivism which tries to uncover the truth about how things are – at least what we focus on. Realism, which is about objects existing independently from knowledge. Interpretivism, which focuses on understanding the difference between humans and their role as social actors. Pragmatism, which argues that it is possible to work within both positivism and interpretivism (Saunders, Lewis, & Thornhill, 2009). The study adopted a positivism philosophy that there can be a quantitative and qualitative approach to investigating a phenomenon. Positivism is based upon values of reason, truth and validity and focuses purely on facts discovered through direct observation, experience or measured empirically using methods like surveys ((Patton, 2002). The philosophy assumes that an objective reality exists independent of human behaviour and is therefore not a creation of human mind (Kothari, 2004). Predictions can be made based on previously observed and explained realities and their inter- relationships. This position presumes that the social world exists objectively and externally, that knowledge is valid only if it is based on observations of this external reality. Universal or general laws and theoretical models can be developed that are generalized and which lend themselves to predicting outcomes to explain this cause and effect relationship. 3.2.2 Research Design The study adopted an explanatory research design. According to Kothari (2004), explanatory design is used where a researcher wants to gain familiarity with a phenomenon or to achieve new insights into it. Explanatory research studies also known as formulative research emphasises on the discovery of new ideas and insights. This study aims at gaining new ideas and insights on intra-firm institutionalization of employee explicit knowledge and hence the appropriateness of explanatory design. 3.3 Target Population According to Sekaran (2006), a population is the total collection of elements about which inferences are made and refers to all possible cases, which are of interest for the study. It is thus the entire group of individuals, events or objects having common observable characteristics. The target population of this study was the energy sector organizations in Kenya. According to the list obtained from the Ministry of Energy & Petroleum, there are nine (9) Energy Sector Organizations also known as parastatals or state corporations. The study population were the employees of these energy sector organizations totalling to 22,173 as shown in Table 3.1. Table 3.1: Population - Energy Sector Organizations Employees Organization No. of Employees Kenya Power & Lighting Company Limited (KPLC) 10,584 Kenya Electricity Generation Company Limited (KenGen)) 5,798 Kenya Electricity Transmission Company Limited (KETRACO) 2,223 Kenya Pipeline Company Limited (KPC) 1,719 Rural Electrification Authority (REA) 356 Geothermal Development Company Limited (GDC) 416 Energy Regulatory Commission (ERC) 176 National Oil Corporation of Kenya (NOCK) 717 Kenya Nuclear Electricity Board (KNEB) 184 Total 22,173 3.4 Sample and Sampling Technique A sample is a portion or part of the population of interest. The purpose of sampling is to gain an understanding about some features or attributes of the whole population based on understanding about some features or attributes of the whole population based on the characteristics of the sample (Sekaran, 2008). When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey. A sample frame is a list, directory or index of cases from which a sample can be selected (Neuman, 2006). Mugenda and Mugenda (2003) recommends that for a small population, a sample size of 30 is statistically significant. Sekaran (2008) points out that for populations lying between 30 and 35 units, a sample size of between 20 and 30 is adequate to represent the population of 10 organizations. To select a representative sample, a researcher must first have a sampling frame. The sampling frame for this study was the Integrated Human Resource Data bases for all the nine (9) energy sectors organizations in Kenya from which information on all employees totalling 22,173 (see table 3.2) was obtained. The lists were sourced from respective Human Resources Managers of these organizations. The study investigated 320 Employees randomly selected using statistical method through the determination of the sample size formula by Mugenda & Mugenda (2003) as follows: ..= ..2.... ..2 Determination of Study Sample Size………………………………………Equation 1 Where: n = the desired sample size (if the target population is greater than 10,000) z = the standard normal deviate at the required confidence level for example 95% confidence level, the Standard deviation value of 1.96 p = the proportion in the target population estimated to have characteristics being measured, taken to be 50% in this study q = 1-p d = the level of statistical significance set at 4% (0.04) This was calculated as follows: ..= (2.0537)2 (0.50)(0.50) (0.04)2 =...... The 320 respondents were proportionally determined using the appropriate ratio. This determination of the sample size using the formula given agrees well with the Krejcie and Morgan (1970) method of calculating the sample which gives a sample size of 379 for population size of between 20,000 and 30,000. Therefore, the study’s sample size of 320 is still within the acceptable range of the required sample size. Table 3. 2: Study Sample Size State Corporation No. of Employees Sample Size Kenya Power & Lighting Company Limited (KPLC) 10,584 152 Kenya Electricity Generation Company Limited (KenGen)) 5,798 84 Kenya Electricity Transmission Company Limited (KETRACO) 2,223 32 Kenya Pipeline Company Limited (KPC) 1,719 25 Rural Electrification Authority (REA) 356 5 Geothermal Development Company Limited (GDC) 416 6 Energy Regulatory Commission (ERC) 176 3 National Oil Corporation of Kenya (NOCK) 717 10 Kenya Nuclear Electricity Board (KNEB) 184 3 22,173 320 This study adopted stratified and simple random sampling techniques. According to Johnson and Christensen (2010), stratified sampling technique produces estimates of overall population parameters with greater precision. Kothari (2004) argues that simple random sampling gives each and every item in the population an equal chance of inclusion in the sample and each one of the possible samples, in case of finite universe, has the same probability of being selected. These methods have also been preferred because the study population is homogenius and the issue of intra-firm institutionalization of explcit knowledge is an issue that any employee can respond to or deliniate across the board. The numbers obtained in each organization were proportional to the total number of employees in the target population. The use of this method reduced bias and achieve high levels of representation (Mugenda & Mugenda, 2003; Saunders, Lewis, & Thornhill, 2009). 3.5 Data Collection Data collection method refers to the instruments, which a study needs to collect the necessary information (Mugenda & Mugenda, 2003). Kothari, 2004 emphasizes that while deciding about the method of data collection to be used for the study, the study should keep in mind two types of data: primary and secondary. The study collected primary data through use of a questionnaire. The questionnaire had structured and semi-structured questions which is ideal and easy to use to collect quantitative data from a large sample within a reasonably short time (Mugenda & Mugenda, 2003). According to Babbie (1990), questionnaires avoid the embarrassment of direct questioning and hence enhance the validity of the responses. The questionnaire was designed based on thorough literature review to identify key constructs of the study variables. The questionnaire had various parts with Part A & B having questions on respondent’s bio data and organizational information respectively. Part C had questions on Knowledge Acquisition; Part D had questions on Knowledge Processing; Part E on Knowledge Dissemination and Part F on Knowledge Management Infrastructure. Part F had general questions on Employee Performance. 3.6 Pilot Study Pilot testing is used to test design and instruments prior to carrying out research (Mitchell, 2006). It also helps to show the adequacy of whether research instruments and research protocol are realistic and workable (Mugenda & Mugenda, 2003). It helps to ascertain the validity (extent to which data produced truly measures what it purports to measure and reliability (consistency of data collected) according to Yin (2004). It also helps to establish if the sampling frame and techniques are effective and to identify logistical problems that might occur in the course of a study. According to Sekaran (2006), the size of the pilot sample varies according to time, cost and practicability. A pilot study was carried out on the questionnaire to ensure that all the items were clearly understood by the respondents, test the relevance of the questionnaire, ease of interpretation and ability to address the study objectives. The aim of the pilot study was to give an idea of approximately how long it took to complete the questionnaire for purposes of planning on the administration of the questionnaire. The Pilot was done on 10% equivalent of the study sample (32 respondents). 3.6.1 Reliability Testing To test for reliability, Cronbach’s Co-efficient Alpha correlation which is a measure of internal consistency and average correlation which ranges between 0 and 1 (Jeane, 2009) was used. The Cronbach’s alpha results ranged from 0.7 and above correlative for each content was acceptable. According to Mugenda and Mugenda (2003), a high co-efficient implies that items correlate highly among themselves; i.e. there is consistency among the items in measuring the concept of interest. Cronbach’s alpha which is a general form of the Kunder- Richardson (K-R) 20 formula is the most commonly used measure of coefficient of internal consistency and is computed as (Mugenda & Mugenda, 2003); ....20= (..)(..2-S..2) (..2)(..-1) Kunder-Richardson (K-R) 20 Formula………………………………………….Equation 2 Where: KR20 = Reliability of co-efficient of internal consistency K = Number of items used to measure the concept S2 = Variance of all scores s2 = Variance of Individual Items 3.6.2 Validity of Data Shadish et al, (2002) defines validity as approximate truth of an inference or knowledge claim of the relationship between evidence that supports the inference as being true or correct. Validity analysis was conducted by use of factor analysis. This is a data reduction method to enable the management of data by reducing it for easier management and meaning derivation. Factor analysis was therefore conduced and those variable that were found to have a factor loading of 0.4 and above were retained for further analysis. Factor analysis therefore added the researcher with only items that correspond to the subject dependent on their factor loadings. 3.7 Data Analysis and Presentation LeCompte and Schensul (2009) defines data analysis as the process a researcher uses to reduce data to a story and interpretation. It is the process of reducing large amounts of data collected to make sense of them. In line with Jean (2009) posits, during data analysis, data was organized; reduced through summarization and categorization of patterns and themes in data were identified and linked. Data obtained from the field in raw form is difficult to interpret and must be cleaned, coded, key-punched into a computer and analysed (Kothari, 2004), since it is from the results of such analysis that researchers are able to make sense of the data. Qualitative data was converted into quantifiable forms by coding all relevant data followed by systematic assembly. Statistcal Package for Science (SPSS) was used for anlyzing the data to generate summative staistics like mean, medium, mode variance and standard deviation. SPSS also assistaed in generating table and graphs. Quantitative data analysis was done through description and summarization of the data using descriptive statistics. According to Babbie (2007), descriptive statics provide methods of reducing large amounts of data to manageable summaries permitting easy understanding and interpretation. The purpose of descriptive statistics is also to enable the researcher to meaningfully describe a distribution of score of measures using few indices of statistics ((Kothari, 2004). Descriptive statistics was applied to quantitative data in order to generate percentages, means, median, mode and standard deviation and variance on both dependent and independent variable. Correlation analysis was done using Pearson’s correlation to determine the strength and direction of relations between the independent variables (knowledge acquisition, processing and dissemination) and the dependent variable (employee performance). The correlation coefficients that were found to be less than 0.2 were not considered for subsequent analysis. A Coefficient of 0.5 and above was considered to have met the threshold. Regression analysis was used to test whether the independent variables had any effect on employee engagement in energy sector organizations. Other statistical tests were also applied in data analysis. Normality test was used to test the normality of all variables. Analysis of variance (ANOVA) test was also be used to analyse respodents characteristics ralating to age, gender, level of education, experience and current organisation. Mugenda (2008) observes that analysis of variance is useful because it make use of test in terms of sum of squares effect over sum of squares residual. Table have be used to summarize background information while graphs have been used to present descriptive statistics of the study variables. 3.7.1 Model Specification Multiple linear regression analyses was used to determine the statistical significance of the effect between the independent and the dependent variables that is explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance. The study used the following model to test how the dependent variable of intra-firm institutionalization of explicit knowledge, which comprises of knowledge acquisition, processing and dissemination, affects employee performance. Y=ί0 + ί1X1 + ί2X2 + ί3X3 + . Reduced Regression Model……………………………………………...Equation 3 To test the moderating role of Knowledge Management Infrastructure on explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance, the study used the following full model: Y= ί0 + ί1X1 + ί2X2 + ί3 X3 + ί4X4+ . Full Regression Model.……………………………………………………………………..Equation 4 i. Where X = the independent variables Y = the dependent variable .= error term ii. Where ί0 = Intercept ί1- ί4 = Slopes coefficients representing the influence of the associated independent variables over the dependent variable. iii. Where X1 = Explicit Knowledge Acquisition X2 = Explicit Knowledge Processing X3 = Explicit Knowledge Dissemination X4 = Knowledge Management Infrastructure Y = Employee Performance iv. ίo is a constant, which is the value of dependent variable when all the independent variables are 0. v. ί1-n is the regression coefficients or change induced by X1, X2, X3 and X4 on Y. It determines how much each (i.e. X1, X2, X3 and X4) contribute to Y. The study applied coefficient of determination (R2) to test the moderating effect of knowledge management infrastructure on intra-firm institutionalization of explicit knowledge and employee performance. 3.7.2 Hypothesis Testing A t-test was used to test whether independent variables had a significance effect on the dependent variable. The calculated t-test was compared with the critical t-values and the significnce levels noted whether within significance levels or not. A p-value of less than 0.005 was used to confirm the significance. CHAPTER FOUR RESULTS AND DISCUSSIONS 4.1 Introduction This chapter discusses research findings for data collected from 242 respondents in nine (9) Energy Sector Organizations in Kenya. The chapter is divided into six sections covering: response rate, data reliability and validity, factor analysis, background information on the respondents and Energy Sector Organizations in Kenya, descriptive and inferential analysis of the dependent variable which is Employee Performance, the three dimensions of institutionalization of explicit knowledge ( explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination) which is the independent variable and the moderating variable which is Knowledge Management Infrastructure. 4.2 Background Information 4.2.1 Response Rate The data was collected from Nine (9) Energy Sector Organizations. A total of 320 questionnaires were administered and 242 were received as completed and therefore, all of them were viable for consideration. This translated to 76% percent response rate. The response was considered appropriate according to Sekaran (2008) who argues that any response above 70% is classified as very good. This is further affirmed by Kothari (2004) who notes that percentage response in survey-type research studies tend to be low, even as low as 20 to 30% but are representative of cross-sectional studies used to gather data from relatively large number of cases. 4.3 Demographic Profiles of Respondents 4.3.1 Gender Distribution Gender-distribution response rate was considered in this study and results show there was a fair balance of gender participation in the study. The results in Table 4.1 show majority of the respondents (55.4%) were Male while (44.6%) of the respondents were Female. This was a good distribution which depicted a fair balance of gender. Since majority of the responses for this study relied on the perceptual measure of the respondents, this gender distribution is expected to accommodate the opinions and views from both sides of the gender divide. Equally, the balance in gender in energy sector organizations may also be evidence of successful gender mainstreaming campaigns in these organizations. Table 4.1: Distribution of Respondents by Gender Gender Percentage of respondents Male 55.4 Female 44.6 Total 100 4.3.2 Age Distribution of Respondents The respondents age was sought and table 4.2 shows that majority of the respondents were aged between 25-35 year at 48.5% followed by those aged between 36 and 35 years of age at 30.8%. Respondents who were below the age of 25 years were 2.5% while those who ere more that 55 years were 0.4%. A simple majority of 17.8% of the respondents were aged between 46 and 55 years. This distribution shows that majority of the respondents were aged between 25 and 55 years which is a good aged distribution that targets respondents who can articulate themselves when it comes to responding to issues of the subject under study. Table 4.2: Distribution of Respondents by Age Gender Percentage of respondents Below 25 years 2.5 25 to 35 years 48.5 36 to 45 years 30.8 46 to 55 years 17.8 Over 55 Years 0.4 Total 100 4.3.3 Working Experience of Respondents The number of years each respondent has worked with the organization was also sought. Findings in Table 4.3 show Majority (40.9) of the respondents have a working experience of between 6 to 10 years, 19.4% have less than 5 years, 28.1|% have between 11 to 15 years, 9.1% have between 16 to 20 years and 2.5% have more than 20 years. This means that the respondents have adequate working experience in the energy sector organizations and therefore possess the requisite knowledge and information which was considered useful for this study. Table 4.3: Respondents’ Years of Service Years of Service Percentage of respondents Less than 5 years 19.4 6 to 10 years 40.9 11 to 15 years 28.1 16 to 20 years 9.1 More than 20 years 2.5 Total 100 4.3.4 Level of Education of Respondents Respondents’ level of education was sought and majority (33.1%) of the respondents indicated they hold at least tertiary level of education (degree and/or postgraduate studies), while a sizeable (29.3%) hold diploma level of education, (28.5%) possess Craft/Artisan tertiary level of education and (9.1%) have only secondary level of education as shown in Table 4.4. This means that the response level of education was acceptable and the respondent were well disposed to respond to knowledge management issues. Table 4.4: Level of Education of Respondents Level of Education Percentage Secondary Level 9.1 Tertiary – Craft/Artisan Level 28.5 Tertiary – Diploma Level 29.3 Tertiary- Degree/Postgraduate Level 33.1 Total 100 4.3.5 Energy Sector Organizations Response The study focused on Energy sector organizations and findings in Table 4.5 show that the sample of this study was representative. Respondents were sourced from Energy Sector organizations as follows: Kenya Power & Lighting Company Limited (58%), Kenya Electricity Generating Company Limited (18%), Kenya Electricity Transmission Company Limited (6%), Kenya Pipeline Company Limited (10%), Rural Electrification Authority (2%), Geothermal Development Company Limited (2%), National Oil Corporation of Kenya (2%), Kenya Nuclear Electricity Board 1%), Energy Regulatory Commission (1%). This was a good distribution as it was based on various organizations in the Energy Sector in Kenya which made the sample more representative and eased the generalizability of the research findings. Table 4.5: Categories of Energy Sector Organizations Categories Percentage of Respondents Kenya Power & lighting Company Limited (KPLC) 58 Kenya Electricity Generation Company Limited (KenGen) 18 Kenya Electricity Transmission Company Limited (KETRACO 6 Kenya Pipeline Company Limited (KPC) 10 Rural Electrification Authority (REA) 2 Geothermal Development Company Limited (GDC) 2 National Oil Corporation of Kenya (NOCK) 2 Kenya Nuclear Electricity Board (KNEB) 1 Electricity Regulatory Commission (ERC) 1 Total 100 4.4 Descriptive Analysis for Independent Variables 4.4.1 Descriptive Analysis for Explicit Knowledge Acquisition The study sought to establish the effect of explicit knowledge acquisition on employee performance in energy sector organisations in Kenya. To achieve this objective, employees’ opinion was sought on whether they have ever acquired any additional knowledge since they joined their current organizations. Majority of the respondents at 67.8% agreed that they have acquired additional knowledge while 32.2% disagreed. This means that there are initiatives in energy sector organizations to enable employees acquire additional knowledge. On further probing if organizations provide a conducive environment for acquisition of additional knowledge, majority of the respondents at 73.1% were of the opinion that their organizations provided a conducive environment for acquisition of additional knowledge while 26.9% disagreed. This confirms that organizations in the energy sector have also created an environment for additional knowledge acquisition for employees. Respondents’ views were also sought if development is facilitated in their organization. And majority of the respondents at 59.1% agreed that development is facilitated in their organizations while 49.9% disagreed. Thus, energy sector organizations thus has also put in measures to facilitate further development of their employees. Table 4.6: Summary of Responses on Explicit Knowledge Acquisition Statement Yes No Have your ever acquired any additional Knowledge since you joined your current organization? 67.8 32.2 Does your organization provide a conducive environment for acquisition of additional knowledge? 73.1 26.9 Is employee development facilitated in your organization 59.1 40.9 Respondents were also asked on how additional knowledge in their organizations is acquired. Majority of the respondents at 40.9% indicated that additional knowledge in their organizations is acquired through in-house trainings, 36.0% through educational courses and 23.1% considered acquisition of additional knowledge through external trainings. This means that energy sector organizations recognizes the need for development of their employees through in-house trainings. Further the respondents were asked when they considered themselves as having acquired additional knowledge. Majority of the respondent at 44.2% indicated that they considered having acquired additional knowledge when they are able to carry out tasks independently while 30.2% indicated when they are able to undertake and accomplish new tasks. A sizeable 25.6% indicated they considered having acquired additional knowledge when they offer advise to co-workers on tasks. This means energy sector organization employees highly regard acquisition of additional knowledge since it enables them improve their performance. On how development is facilitated in their organizations, majority of the respondents at 40.5% indicated it was through self-sponsorship, 36.0% indicated through company sponsorship while 23.6% considered it was done through on-the-job trainings as per appendix VI attached. This corroborated with earlier findings where majority of the respondents at 40.9% acquire additional knowledge through in-house training and hence have to strive for self-sponsorship development. Explicit Knowledge Acquisition was also further tested using a five point Likert scale of 1-5 where 1 was Strongly disagree, 2- Disagree, 3-Neutral, 4-Agree and 5-Strongly Agree. The findings in Table 4.7 show that majority of the respondents at 63.3% considered external sources like seminars, conferences and educational course as enabling them to obtain new knowledge. Equally, a sizeable 30.5% indicated that employees in their organizations built on each other’s knowledge and also create their own knowledge to improve their performance. Majority of the respondents at 47.5% agreed that the knowledge they have gained enables them perform their duties well while 50.8% rely on written sources of information like documented organizational procedures to gain new knowledge. The results are a confirmation of the finding of Rasula and Stemberger (2012) that knowledge management affects organization performance. The findings also corroborate findings by Cheruiyot, Jagongo and Owino (2012) that organizations that are embracing knowledge management in their business operations do so by organizing seminars, conferences and other eternal trainings for their employees. Table 4.7: Effect of Explicit Knowledge Acquisition on Employee Performance (%) Statement Strongly Disagree (1) Disagree (2) Neutral (3) Agree (4) Strongly Agree (5) Mean Median Mode External Sources like seminars, conference and educational courses do not enable me obtain new knowledge 31.4 21.9 31.4 11.6 3.7 2 2 1 Employees in my organization do not build on each other’s knowledge but create their own knowledge to boost their performance 16.5 24.0 20.2 24.8 14.5 3 3 4 In my work, I rely on information and knowledge gained from external sources to enable me perform my duties well. 5.8 17.8 28.9 34.7 12.8 3 3 4 In my work, I rely on written sources of information like documented organizational procedures to gain new knowledge. 5.8 17.4 26.0 32.6 18.2 3 4 4 4.4.2 Descriptive Analysis for Explicit Knowledge Processing The study sought to determine the extent to which explicit knowledge processing contribute to employee performance in energy sector organisations in Kenya. To achieve this objective, respondents were asked if knowledge in their organization is stored as documented or undocumented. Majority of the respondents at 45.0% indicated that knowledge in their organization is stored both as documented and undocumented. A sizeable number 43.8% indicated that knowledge in their organization is un- documented while 11.2% indicated knowledge in their organization is documented. This means that energy sectors organization indeed have initiatives in place to institutionalize knowledge in their organizations through proper storage of knowledge. This information corroborates finding by Buckley & Carter 2002 that critical knowledge must be stored in location and format which can be easily found and accessed by users. Further, respondents were asked to indicate who facilitates knowledge organization in their companies. Majority (43.8%) indicated it was facilitated by the Human Resource Function, 35.1% indicated by Company Libraries while 21.1% indicated knowledge organization was facilitated by the ICT Department. The respondents were also asked to indicate how knowledge analysis is done in their organizations. Majority of the respondents at 43.0% indicated it was done through Training Needs Analysis while 30.2% indicated it was through Performance Management Process. A sizeable 26.9% indicated it was through self-assessment process. Thus, the Human Resource function has a critical role in knowledge management in an organization, which corroborates findings by Mclenerney (2002) on the role of Human Resource in Knowledge Management. Respondents were also asked to indicate how knowledge analysis is done their organizations. Majority of the respondents at 37.6% indicated knowledge analysis was done when an occupational accident occurs while 31.4% indicated it was when an employee is being transferred from one station to another. A sizeable 31.0% indicated that knowledge analysis is done when a new work method is being introduced. This means that organizations in the energy sector organizations recognize the importance of knowledge management in their performance to avoid occupational accidents, which affects their employees’ performance. Further probing on how knowledge is stored in their organizations, Majority of the respondents at 37.2% indicated it was in accessible through digital formats while 36.8% indicated it was through company libraries and depositories. A sizeable 26.0% indicated it was stored in work instructions manuals as per appendix VII attached. This means that organizations in the energy sector too have embraced technology in their knowledge management initiatives aimed at improving their employee’s performance. This finding corroborates findings by Botha et al. (2005) that knowledge must be classified, mapped, indexed and categorized for navigation, storage and ease of retrieval. The role of explicit knowledge processing on employee performance was also measured on a five-point Likert Scale of 1-5 where 1 was Strongly disagree, 2- Disagree, 3-Neutral, 4-Agree and 5-Strongly Agree. Majority of the respondents at 48% agreed that there is inclination to cooperation and exchange of knowledge in energy sector organization. A sizeable 23.2% were not in agreement while 28.9% were neutral. Majority of the respondents at 48.8% however agreed that the general leadership of their organization does not promote exchange of knowledge and information among employees while 20.7 were neutral. A sizeable 30.4% indicated that general leadership of their organisations supported exchange of knowledge and information among employees. Majority of the respondents at 64.9% also agreed that employees in their organization trust each other in their work and rely on knowledge and information of their co- workers while 19.0% were neutral. A sizeable 16.1% did not agree. Majority of the respondents at 62.9% agreed that the general management/leadership motivates employees to engage in formal education systems to achieve a higher level of knowledge while 24.8% were neutral and while 12.4% disagreed. Majority of the respondents at 69.4% indicated that there is support in their organizations for exchange of information and knowledge among organizational work units while 20.7% were neutral with a sizeable 9.9% disagreeing. Overall, the findings in table 4.8 below show that explicit knowledge processing has a role to play in Employee Performance in Energy Sector organization is given that the mean of responses is four (4). Table 4.8: Role of Explicit Knowledge Processing on Employee Performance (%) Statement Strongly Agree (1) Disagree (2) Neutral (3) Agree (4) Strongly Agree (5) Mean Median Mode In my organization, there is no inclination to cooperation and exchange of knowledge and information among employees 17.8 30.2 28.9 17.8 5.4 3 3 2 The general leadership of my organization does not promote exchange of knowledge and information among employees 11.2 19.4 20.7 33.1 15.7 3 3 4 Employees in my organization generally trust each other in their work and rely on knowledge and information of their co-workers 4.5 11.6 19.0 42.6 22.3 4 4 4 The general management/leadership motivates employees to engage in formal education systems to achieve a higher level of knowledge 5.8 6.6 24.8 33.1 29.8 4 4 4 In my organization we support the exchange of information and knowledge among organizational work units 4.5 5.4 20.7 40.5 28.9 4 4 4 4.4.3 Descriptive Analysis for Explicit Knowledge Dissemination The study sought to investigate how explicit knowledge dissemination affects employee performance in public energy sector organisations in Kenya. To achieve this objective, respondents’ opinion were sought on how documented knowledge is accessed in their organizations. Majority of the respondents at 38.4% indicated it was through Intranet Sharing while 37.2% indicated it was through digital public folders. A sizeable 24.4% indicated it was through use of library facilities. This corroborates with findings by Argyris (2003) that if a firm has successfully collected, stored and organized knowledge, potential users of such knowledge must be made aware of its existence and encouraged to contribute and use knowledge within the firm’s knowledge repository. Respondents’ views were also sought on how often respondents use their knowledge in their organizations and majority of the respondents at 52.5% indicated that often knowledge is used in decision making while 39.7% indicated in routine work. A sizeable 7.9% indicated in sharing with co-workers. Further probing of respondents on what type of knowledge is commonly shared, majority of the respondents at 35.5% indicated organizational knowledge while 34.7% indicated general knowledge. A significant 29.8% indicated technical knowledge. This means that employees in the energy sector organization are keen on sharing organizational knowledge with each other e findings that corroborate with findings by Bulkowitz and William (1999) that successful explicit knowledge sharing is determined by various criteria of articulation, awareness of knowledge available and types of knowledge. Respondents were also required to indicate how the shared knowledge is managed in their organization and majority of the respondent at 45.5% indicated it was through categorization while 33.5% indicated through organization. A substantial 21.1% indicated through sharing. This means that energy sector organization have indeed put measure in place to manage knowledge in their organizations, findings that corroborate with findings by Rashed, Azeem and Halim (2010) that affirms how knowledge is shared in organizations. The effect of knowledge dissemination on employee performance was also measured on a five-point Likert scale of 1-5 where 1 was Strongly disagree, 2- Disagree, 3- Neutral, 4-Agree and 5-Strongly Agree. Majority of the respondents at 43.3% did not agree that employees in their organizations are not motivated to share insights and knowledge about their professions while 52.0% agree that creativity and new knowledge is valued in their organizations. A sizeable 64.5% agree that important rules and standards are identified and stored well in their organizations while 69.9% are encouraged to transfer their professional knowledge to less experienced and new employees. Another sizeable 75.6% agree that knowledge sharing with others help one to perform better in their work while 75.2% agree that information in their organizations is organized clearly and regularly shared to support decision-making. This means that energy sector organizations should enhance their knowledge management initiatives to improve employees’ performance. Overall, the findings in Table 4.9 show that majority of the respondents were of the opinion that explicit knowledge processing has effect on employee performance in energy sector organizations in Kenya given that the mean of the responses was 4. Table 4.9: Contribution of Explicit Knowledge Dissemination on Employee Performance (%) Statement Strongly Disagree (1) Disagree (2) Neutral (3) Agree (4) Strongly Agree (5) Mean Median Mode In my organization, employees are not motivated to share with each other insights and knowledge about their profession. 16.5 36.8 22.3 16.9 7.4 3 2 2 Creativity and new knowledge are valued n my organizations 5.4 12.4 30.2 38.8 13.2 3 4 4 In my organization, important rules and standards are identified and stored well. 2.9 10.3 22.3 34.7 29.8 4 4 4 Staff are encouraged to transfer their professional knowledge to less experience and new employees. 1.2 11.2 17.8 33.5 36.4 4 4 5 Knowledge sharing with others help one to perform better in their work. 4.5 8.3 11.6 43.8 31.8 4 4 4 Information in my organization is organized clearly and regularly shared to support decision making 2.5 5.8 16.5 38.4 36.8 4 4 5 4.5. Descriptive Analysis for Moderating Variable 4.5.1 Knowledge Management Infrastructure on Additional Knowledge Acquisition The study sought to determine the moderating role of knowledge management infrastructure on explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance in energy sector organizations in Kenya. To achieve this objective respondents’ opinions were sought on whether organizational culture in their companies facilitate self development, on- the-job training, staff interactions, write-ups in areas of speciality and formal topical presentations. Majority of the respondents were in agreement – Self development (54.1%), on-the-job training (65.5%), staff interactions (69.4%), write-ups in areas of speciality (63.2%) and presentations on formal Topical presentations (50.4%). A sizeable respondents were not in agreement - Self development (45.9%), on-the-job training (33.5%), staff interactions (30.6%), write-ups in areas of speciality (36.8%) and presentations on formal Topical presentations (49.6%). The role of organization culture in knowledge management cannot thus be understated. Respondents’ opinions were also sought on whether organization culture encourages restrictions on knowledge sharing, ease access of required information and/or free flow of information. Majority of the respondents were in agreement – does not restrict on knowledge sharing (58.3%), facilitates ease access of required information (69.4%) and free flow of information (65.3%). The rest of the respondents were not in agreement - restricts knowledge sharing (41.7%), does not ease access of required information (30.6%) and free flow of information (34.7%). Overall organization culture is thus an important factor in moderating knowledge management and employee performance. Majority of the respondents were also in agreement that the Organization structure encouraged documentation of available knowledge (67.4%), analysis of available knowledge (70.7%) and/or proper storage of the documented knowledge (64.5%). The rest of the respondents were not in agreement - documentation of available knowledge (32.6%), analysis of available knowledge (29.3%) and/or proper storage of the documented knowledge (35.5%). Further probing of the respondents also indicated that majority were in agreement that their organization structures facilitated top down knowledge sharing (72.3%), Down- top Knowledge Sharing (63.2%) as well as Horizontal knowledge sharing (73.1%). The rest of the respondents were not in agreement top down knowledge sharing (27.7%), Down-top Knowledge Sharing (36.8%) as well as Horizontal knowledge sharing (26.9%). The moderating role of organization structure in this case is thus important. Majority of the respondents were also in agreement that their organization’s ICT Set- up promoted easy storage of information (72.3%), easy access of information (69.8%) and easy retrieval of information (70.7%). The rest of the respondents were not in agreement - easy storage of information (27.7%), easy access of information (30.2%) and easy retrieval of information (29.3%). The role of organization ICT set-up in this case is paramount. Table 4.10: Summary of Responses on Role of Knowledge Management Infrastructure Statement Yes No Does the organization culture in your company facilitate self development 54.1 45.9 Does the organization culture in your company facilitate on-the-job training 66.5 33.5 Does the organization culture in your company facilitate staff interactions 69.4 30.6 Does the organization culture in your company facilitate write-ups in areas of specialty 63.2 36.8 Does the organization culture in your company facilitate formal topical presentations 50.4 49.6 Does your organization culture encourage restrictions on knowledge sharing 58.3 41.7 Does your organization culture encourage ease access of required information 69.4 30.6 Does your organization culture encourage free flow of information 65.3 34.7 Does your organization's structure encourage documentation of available knowledge 67.4 32.6 Does your organization's structure encourage analysis of available knowledge 70.7 29.3 Does your organization's structure encourage proper storage of documented knowledge 64.5 35.5 Does your organization's structure facilitate top-down knowledge sharing 72.3 27.7 Does your organization's structure facilitate down-top knowledge sharing 63.2 36.8 Does your organization's structure facilitate horizontal knowledge sharing 73.1 26.9 Does your organization's ICT set up promote easy storage of information 72.3 27.7 Does your organization's ICT set up promote easy access of information 69.8 30.2 Does your organization's ICT set up promote easy retrieval of information 70.7 29.3 4.5.2 Knowledge Management Infrastructure and Organization’s IT Set-up At the same time, respondents’ views were also sought on a three-point scale of high (1), medium (2) and low (3) on the overall organization’s ICT set-up and the responses were as indicated in Table 4.11. Majority of the respondents rated their organization’s ICT Set-up medium because of the mean score of 2. This means the Energy Sector organizations have a good ICT facilities. Table 4.11: Overall ICT-set up rating in Organizations (%) Statement High (1) Medium (2) Low (3) Mean Median Mode How would you rate the overall ICT setup of your organizations on availability of IT Systems 64.5 29.8 5.8 1 1 1 How would you rate the overall ICT setup of your organizations on accessibility of IT Systems 36.0 48.3 15.7 2 2 2 How would you rate the overall ICT setup of your organizations on user- friendliness of IT Systems 19.4 47.5 33.1 2 2 2 4.6 Descriptive Analysis for Dependent Variable 4.6.1 Employee Performance based on Additional Knowledge Acquired Respondents were asked to rate their performance in specific areas based on Knowledge acquired since joining their current organizations on a closed scaled of Excellent (5), Very Good (4), Good (3), Fair (2) and Poor (1). Majority of the respondents rated themselves as very good on quality of their work, knowledge of the job, adaptability and reliability since the mean score was 4. Table 4.12: Employee Performance Rating on Acquisition of Knowledge (%) Statement Poor (1) Fair (2) Good (3) Very Good (4) Excellent (5) Mean Median Mode Quality of your work 12.0 15.3 19.4 47.5 5.8 3 4 4 Knowledge of the Job 5.0 11.2 23.6 41.7 18.6 4 4 4 Adaptability 7.0 6.6 29.3 40.5 16.5 4 4 4 Reliability 3.7 8.7 26.4 40.1 21.1 4 4 4 4.6.2 Employee Performance based on Work Out Assessment Results Employee performance was further rated on a five-point Likert Scale of Strongly Agree (5), Agree (4), Neutral (3), Disagree (2) and Strongly Disagree (1). The findings in Table 4.13 below show that majority of the employees were in agreement that knowledge gained in their organizations has enabled them give quality results, independently undertake tasks, easily adapt and have initiatives in their work since the mean score was 4. Table 4.13: Employee Performance Rating Agreements (%) Statement Strongly Disagree (1) Disagree (2) Neutral (3) Agree (4) Strongly Agree (5) Mean Median Mode The knowledge I have gained in the organizations enables me give quality results. 7.4 8.3 15.7 43.4 25.2 4 4 4 The Knowledge I have gained in the organization enables me to undertake my tasks independently. 6.2 7.4 19.8 33.1 33.5 4 4 5 The knowledge I have gained in the organization enables me to easily adapt to my work 1.7 7.9 17.8 36.8 36.0 4 4 4 The knowledge I have gained in the organization has boosted my initiative to commence work on my own .8 6.6 15.3 41.7 35.5 4 4 4 4.7 Requisite Tests 4.7.1 Test for Normality of Independent The test for normality of the independent and dependent variables was done using Kolmogorov-Smirnov test. In this study, the level of significance (a) was se at 5%. The rule was to reject H0 if p-value is less than a or else fail to reject H0, where data is normal and H1: where data is not normal. Table 4.14 indicates that using Kolmogorov- Smirnov test of normality, employee performance data is normal since the p-value at 0.152 is above 0.005 and thus we fail to reject the null hypothesis (H0). The study therefore concluded that employee performance variable is normal in distribution and hence subsequent analysis could be carried out. Table 4.14 further shows that employee performance is normally distributed with a mean of 15.9139, standard deviation of 5.25498 with 242 respondents represented by N=242. The study required that the dependent variable should be normally distributed because the study was using multiple linear regression model, where the condition of normality must be satisfied (Lapan, et al, 2012). Data Analysis methods including t-test, ANOVA and linear regression depend on the assumption that data is sampled from Gaussian distribution (Indiana, 2011). Table 4.14: Test for Normality One-Sample Kolmogorov-Smirnov Test Employee Performance N 242 Normal Parametersa Mean 15.9139 Std. Deviation 5.25498 Most Extreme Differences Absolute .073 Positive .060 Negative -.073 Kolmogorov-Smirnov Z 1.135 Asymp. Sig. (2-tailed) .152 a. Test distribution is Normal 4.7.2 Quantile-quantile plot for Employee Performance To determine the condition of normality for employee performance, quantile-quantile (QQ) plot was used. Quantile-quantile plot determines whether the proportion of the observed scores fall below any one score. According to Shenoy and Pant (1994), for a variable to be normally distributed most of the points should lie on the theoretical quantile line. The theoretical quantile line of the data is fitted and from the normal QQ plot, it indicates that the observed values versus the expected normal values are randomly distributed along the line of best fit indicating that the dependent variable is normally distributed. In case the dependent variable is not normally distributed, then normality has to be sought for before proceeding to check whether the dependent variable is influenced by the other independent variables. Figure 4.1 shows the normal quantile-quantile (QQ) plot for the condition of employee performance which is normally distributed. Figure 4.1: Normal QQ Plot of Employee Performance 4.7.3 Autocorrelation The auto-correlation in the dependent variable in this study was tested using Durbin- Watson test. Durbin-Watson Test is used to check serial correlation among variables. When error terms from different, usually adjacent time periods or cross-section observations are correlated, we say that the error term is serially correlated. Serial correlation will not affect the biasness or consistency of ordinary least squares (OLS) estimators, but it does affect their efficiency. Therefore in using a linear model the dependent variable must be independent (Montgomery, Peck, & Vinning, 2006). This means there should be no serial correlation among observations. In auto-correlation where Ho: y= 0, the residuals are independent and where H1: z> 0, the residuals ate inter-dependent and where p> 0.05, we fail to reject the null hypothesis. Results in table 4.15 indicate that p is >0.05, thus we accept fail to reject the null hypothesis that residuals are independent and thus conclude that there is no serial correlation among variables under study and linear model is justified. Table 4.15: Durbin-Watson Test Test Statistics (Durbin-Watson) P-Value 2.090 0.0885 4.7.4 Multicollinearity Multicollinearity refers to the case in which two or more explanatory variables in the regression model are highly correlated, making it difficult or impossible to isolate their individual effects on the dependent variables. Items that did not meet a minimum criterion of having a primary factor loading of 0.64 or above were eliminated. Three separate Pearson correlation coefficients r’s were computed in order to determine the degree to which the three independent variables of the study, explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination were correlated. The results revealed that they were significantly correlated since their p-values were < 0,05. However for one to conclude that there is multicollinearity, the correlations should be above, say 0.8. Tabachnick and Fidell (2011) recommends an inspection of correlation matric for evidence of coefficients greater than 0.3 and rule of thumb of +.80 as a limiting value. If the correlation coefficient between the two variables was much less than the limiting value, there was no problem but if it got closer to +.80, there was concern. From the results displayed in Table 4.16, since the highest correlation coefficient of 0.309 was much less than 0.80, the study concluded that no significant multicollinearity problems were indicated as to cause concern. Table 4.16: Correlation Analysis of Independent Variable Employee Performance Explicit Knowledge Acquisition Explicit Knowledge Processing Explicit Knowledge Dissemination Employee Performance Pearson Correlation 1 .151* .182** .235** Sig. (2- tailed) .019 .005 .000 N 242 242 242 242 Explicit Knowledge Acquisition Pearson Correlation .151* 1 .288** .257** Sig. (2- tailed) .019 .000 .000 N 242 242 242 242 Explicit Knowledge Processing Pearson Correlation .182** .288** 1 .309** Sig. (2- tailed) .005 .000 .000 N 242 242 242 242 Explicit Knowledge Dissemination Pearson Correlation .235** .257** .309** 1 Sig. (2- tailed) .000 .000 .000 N 242 242 242 242 *Correlation is significant at the 0.05 level (2-tailed). **Correlation is significant at the 0.01 level (2-tailed). 4.7.5 Factor Analysis To determine whether findings of this study would explain the effect of institutionalization of explicit knowledge on employee performance in Energy Sector Organizations in Kenya, factor analysis was conducted in order to develop factors that help in explaining the role of the construct in employee performance. A loading factor of 0.64 and above was considered acceptable and has been used by other studies such Watanabe, Benton and Senoo (2011). Cheruyoit, Jagongo and Owino also (2012) used the same method which has been widely accepted as reliable. The results in Table 4.17 show that most of the factors relating explicit knowledge acquisition, explicit knowledge processing, and explicit knowledge dissemination and employee performance were found to have a factor loading of 0.64 and above. Therefore they were used in subsequent analysis and those that did not meet this threshold were discarded. According to Field (2005), factor analysis is an exploratory tool used to help the researcher make decisions on whether the variables under investigation explain the dependent variable. Kothari (2004) terms factor analysis as the most often used multivariate technique of research studies that pertains to social and behavioral sciences. Table 4.17: Factor Analysis and Reliability Results Statement Summary Component Component Matrix on Explicit Knowledge Acquisition Sources of Additional Knowledge .840 Use of Knowledge to perform duties well .840 Acquisition of knowledge through formal procedures .776 Building on each other’s knowledge .682 Knowledge as organizational asset .643 Component Matrix on Explicit Knowledge Processing Reliance on co-worker knowledge .822 Employees’ motivation to engage in formal education .792 Leadership’s promotion of knowledge exchange .752 Exchange of knowledge among work units .652 Component Matrix on Explicit Knowledge Dissemination Knowledge sharing and work performance .832 Transfer of professional knowledge .810 Identification of important rules and standards .790 Knowledge application in decision making .718 Component Matrix on Employee Performance Adaptability .857 Knowledge of the Job .847 Reliability .809 Quality of Work .650 Work adoption .865 Work Results .875 Tasks Independence .871 Work Commencement Initiatives .825 4.7.6 Results for the Pilot Study (Validity & Reliability) Internal consistency reliability in this study was investigated using Cronbach Alpha which is the basic method for determining reliability based on internal consitence (Brown, 2006). According to Nunnally (2008) and Marshalls (2006) the standard minimum value is a=0.7. Thus the values in Table 4.18 of Knowledge Acquisition a =.803, Knowledge Processing a=.792 and Knowledge Dissemination a=.748 are sufficient confirmation of data reliability for the three independent variables and employee performance a=.831. These findings further confirm the reliability of the survey on impact of knowledge management on organizational performance ( (Jelena, Bosilj, & Indihar, 2012). Internal consistency reliability is the most common used psychometric measure assessing survey instruments and skills (Zhang, Wasnik & Wijingaard, 2000). Table 4.18: Reliability Statistics Variable Cronbach’s Alpha Number of Items Knowledge Acquisition 0.803 6 Knowledge Processing 0.748 5 Knowledge Dissemination 0.792 6 Employee Performance 0.831 8 4.8 Correlation Analysis 4.8.1. Scatter Plot between Explicit Knowledge Acquisition and Employee Performance Correlation Analysis was used to determine the significance effect of Explicit Knowledge Acquisition on Employee Performance. The scatter diagram in Figure 4.2 shows that there is a positive significant effect between explicit knowledge acquisition and employee performance based on the pattern of the scatter plots dotted within the entire diagram area. This means that explicit knowledge acquisition is a significant positive predictor of employee performance and organizations in energy sector should strive to ensure that their employees acquire additional knowledge. This finding corroborates with findings by Zaied, Hussein and Hassan (2012) which showed significant relationship between knowledge management elements and performance improvement measures, which in turn represented quality of organizational knowledge that was utilized in a wide variety of decision-makings in the firm. Figure 4.2: Correlation Results on Explicit Knowledge Acquisition 4.8.2 Correlation Co-efficient Analysis between Explicit Knowledge Acquisition and Employee Performance To gauge the effect of explicit knowledge acquisition on employee performance, Pearson correlation co-efficient analysis was used where the results indicated that explicit knowledge acquisition has a significant positive effect on employee performance. This was indicated by Table 4.19 which shows p-value =0.019 and this meets the threshold since p<0.05. The positive effect was represented by correlation coefficient of 0.151 with 242 respondents. This means that explicit knowledge acquisition is a positive factor in consideration of factors that affect employee performance and should be enhanced in energy sector organizations. The results corroborate with the findings of Akpotu and Lebari (2014) that showed a positive significant effect between Knowledge Acquisition and Administrative Employee Performance in Nigerian Universities. The results confirmed that knowledge acquisition positively influences the functional capability of Administrative Performance of Employees. Table 4.19: Correlation Co-efficient Analysis on Explicit Knowledge Acquisition Employee Performance Knowledge Acquisition Employee Performance Pearson Correlation 1 .151* Sig. (2-tailed) .019 N 242 242 * Correlation is significant at 0.05 level (2-tailed) 4.8.3 Scatter Plot between Explicit Knowledge Processing and Employee Performance Correlation Analysis was also used to determine the significance effect of explicit knowledge processing on employee performance. The scatter diagram in Figure 4.3 shows that there is a positive significant effect between explicit knowledge processing and employee performance based on the pattern of the scatter plots dotted within the entire diagram area. This means that explicit knowledge processing is a significant positive predictor of employee performance and organizations in energy sector should ensure knowledge processing is enhanced within their organizations as it positively affects employee performance. This finding corroborates with findings by Lara (2008) that employee productivity is enhanced by knowledge management factors such as knowledge flows and management measurement systems. Figure 4.3: Correlation Results on Explicit Knowledge Processing 4.8.4 Correlation Co-efficient Analysis between Explicit Knowledge Processing and Employee Performance Pearson Correlation Co-efficient was also used to gauge the effect of explicit knowledge processing on employee performance. The results indicate that explicit knowledge processing has a significant positive effect on employee performance. This was indicated by Table 4.17 which shows p-value =0.005 and this meets the threshold since p is <0.05. The positive effect was represented by correlation coefficient of 0.182 with 242 respondents. This to means that knowledge processing is a positive factors in consideration of factors that affect explicit knowledge processing and should be enhanced by energy sector organizations. The results corroborate with the findings by Nickols (2012) that confirmed that if Knowledge Management (KM) initiatives are to yield any benefit in an organization, they must affect employee performance. The findings also corroborated findings by Mararo (2013) that recommended that to interlink knowledge management and competitive advantage, staff should be involved in the knowledge management process of institutionalizing knowledge management practices. Table 4.20: Correlation Co-efficient Analysis on Explicit Knowledge Processing Employee Performance Knowledge Processing Employee Performance Pearson Correlation 1 .182** Sig. (2-tailed) .005 N 242 242 ** Correlation is significant at the 0.01 level (2-tailed). 4.8.5 Scatter Plot between Explicit Knowledge Dissemination and Employee Performance To determine the significance effect of explicit knowledge dissemination on employee performance, correlation analysis was also used. The scatter diagram in Figure 4.4 shows that there is a positive significant effect between explicit knowledge dissemination and employee performance based on the pattern of the scatter plots dotted within the entire diagram area. This means that explicit knowledge dissemination is a significant positive predictor of employee performance and organizations in energy sector should enhance explicit knowledge sharing among employees in their organizations as this positively affects employee performance. This finding corroborates with findings by Hatami et al (2005) that organizations relying on knowledge on average make higher quality decisions on business strategies for future better performance. Figure 4.4: Correlation Results on Explicit Knowledge Dissemination 4.8.6 Correlation Co-efficient Analysis between Explicit Knowledge Dissemination and Employee Performance Pearson Correlation Co-efficient was also used to gauge the effect of explicit knowledge dissemination on employee performance and the results indicated that explicit knowledge dissemination has a significant positive effect on employee performance. This is indicated in Table 4.18 which shows p-value =0.000 and this meets the threshold since p<0.05. The positive effect was represented by correlation coefficient of 0.235 with 242 respondents. This means explicit knowledge dissemination is a positive predictor of employee performance and too should be enhanced in energy sectors organizations in Kenya. The results corroborate with the findings of Jelenic (2011) which indicate that knowledge dissemination affects employee performance. The results also corroborate findings by Ha, Okigbo and Igboaka (2008) where information sharing through broadband facility was unanimously rated as a great form of learning and enhancing output. Table 4.21: Correlation Co-efficient Analysis on Explicit Knowledge Dissemination Employee Performance Knowledge Dissemination Employee Performance Pearson Correlation 1 .235** Sig. (2-tailed) .000 N 242 242 **Correlation is significant at the 0.01 level (2-tailed). 4.9 Regression Analysis To establish statistical significance between independent variables of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on the dependent variable of employee performance, regression analysis was used. According to Marshall and Rossman (2006), regression analysis is a statistical process of estimating the relationship between variables. Regression analysis helps in generating equations that describe the statistical effect between one or more predictor variables and the response variable. The regression analysis results were presented using scatter diagrams, analysis of variance (ANOVA) tables, regression model summary tables and beta coefficients tables. 4.9.1 Regression Line for Explicit Knowledge Acquisition and Employee Performance A scatter plot diagram was used to determine the significance effect of explicit knowledge acquisition on employee performance and figure 4.5 presents the line of best fit that is increasing positively upwards implying there is a positive linear effect between explicit knowledge acquisition and employee performance. This linear positive effect consequently means that explicit knowledge acquisition positively affects employee performance which also corroborates findings by Paracharapha and Ractham (2012) that organizations desiring to institutionalize staff’s knowledge should focus on factors influencing individual knowledge acquisition. Figure 4.5 Regression Analysis on Explicit Knowledge Acquisition 4.9.2 Goodness of Fit for regression line between Explicit Knowledge Acquisition and Employee Performance The coefficient of multiple determination indicates that 0.23% of the variation in employee performance is explained by explicit knowledge acquisition as shown in table 4.22. This implies that since explicit knowledge acquisition explains only 23% variation in employee performance, there exists other factors that explain 77% of employees which in this study include explicit knowledge processing and explicit knowledge dissemination. This finding confirm that explicit knowledge acquisition has a positive significant effect between explicit knowledge acquisition and employee performance. The findings corroborate findings by Cheruiyot, Jagongo and Owino (2012) which found that there are various factors that affect employee performance among them explicit knowledge acquisition. Table 4.22: Model Summary for Explicit Knowledge Acquisition Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .151a .23 .19 5.20552 a. Predictors: (Constant) – Explicit Knowledge Acquisition 4.9.3 ANOVA for regression line between Explicit Knowledge Acquisition and Employee Performance The analysis of variance (ANOVA) results shown in Table 4.23 confirmed that the linear model fit was appropriate for this data since p-value of 0.019 is less than 0.05. This confirmed that the effect between explicit knowledge acquisition (X1) and employee performance could be quantified using the linear model Y= ί0 + ί1X1+ .. Table 4.23: Analysis of Variance (ANOVA) for Explicit Knowledge Acquisition ANOVAb Model Sum of Squares df Measure Square Sig. 1 Regression 151.790 1 151.790 .019a Residual 6503.388 240 27.097 Total 6655.177 241 a. Predictors: (Constant) – Explicit Knowledge Acquisition b. Dependent Variable: Employee Performance 4.9.5 Model Parameter Estimate of regression line between Explicit Knowledge Acquisition and Employee Performance The results further indicate that explicit knowledge acquisition has positive and significant effect on employee performance as shown in table 4.24. The fitted model Y=13.942+0.208*X1 implies that a unit change in explicit knowledge acquisition will increase employee performance by the rate of 0.208. This means that even when there is no explicit knowledge acquisition, employee performance is still positive at 13.942 indicating that there are other drivers of employee performance including explicit knowledge processing and explicit knowledge dissemination. Table 4.24: Explicit Knowledge Acquisition Coefficient Coefficientsa Model Unstandardized Coefficients Standardized Coefficients Sig. B Std. Error Beta 1 (Constant) 13.942 .898 .000 Explicit Knowledge Acquisition .208 .088 .151 .019 a. Dependent Variable: Employee Performance In terms of significant associations found between explicit knowledge acquisition and employee performance null hypothesis 1: ‘There is no significant effect of explicit knowledge acquisition on employee performance’ was rejected and alternative hypothesis ‘There is significant effect of explicit knowledge acquisition on employee performance’ accepted. This finding corroborates findings by Akpotu and Lebaru (2014) that showed a significant positive between Knowledge acquisition and Administrative Employee Performance in Nigerian Universities. 4.9.1 Regression Line for Explicit Knowledge Processing and Employee Performance To determine the significance effect of explicit knowledge processing on employee performance, a scatter plot diagram was used and figure 4.6 presents the line of best fit that is increasing positively upwards. This implies that there is a positive linear effect between explicit knowledge processing and employee performance. The linear positive effect means that explicit knowledge processing positively affects employee performance which corroborates findings by Qiu, Chui and Helander (2008) that recommends development of knowledge management processes, tools, methods, technology which integrate seamlessly and affect overall employee performance. Figure 4.6 Regression Analysis on Explicit Knowledge Processing 4.9.2 Goodness of Fit for regression line between Explicit Knowledge Processing and Employee Performance The coefficient of multiple determination indicates that 0.33% of the variation on employee performance is explained by explicit knowledge processing as shown in Table 4.25. This means explicit knowledge processing affects employee performance since it explains variation in employee performance by 33% although there are other factors such as explicit knowledge acquisition and explicit knowledge dissemination which could explain the difference of 67%. This finding corroborates findings by Bray and Konsynski (2012) which found that use of knowledge management systems improved employee performance in both public and private sectors and cumulatively motivated employees to performance well. Table 4.25: Model Summary for Explicit Knowledge Processing Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .182a .33 .29 5.17830 a. Predictors: (Constant) – Explicit Knowledge Processing 4.9.3 ANOVA for regression line between Explicit Knowledge Processing and Employee Performance The analysis of variance (ANOVA) results shown in Table 4.26 confirmed that the linear model was appropriate for this data since p-value of 0.005 is less than 0.05. This confirmed that the effect between explicit knowledge processing (X2) and employee performance could be quantified using the linear model fit Y= ί0 + ί2X2+ .. Table 4.26: Analysis of Variance (ANOVA) for Explicit Knowledge Processing ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 219.617 1 219.617 8.190 .005a Residual 6435.560 240 26.815 Total 6655.177 241 a. Predictors: (Constant) – Explicit Knowledge Processing b. Dependent Variable: Employee Performance 4.9.5 Model Parameter Estimate of regression line between Explicit Knowledge Processing and Employee Performance The results further indicate that explicit knowledge processing has positive and significant effect on employee performance as shown in Table 4.27. The fitted model was Y=12.762+0.260*X2 implies that a unit change in explicit knowledge processing will increase employee performance by the rate of 0.260. Even when there is no explicit knowledge processing, employee performance is still positive at 12.762 indicating that there are other drivers of employee performance including explicit knowledge acquisition and explicit knowledge dissemination. The results thus confirm that explicit knowledge processing has positive significant effect on employee performance. Table 4.27: Explicit Knowledge Processing Coefficient Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 12.762 1.150 11.093 .000 Explicit Knowledge Processing .260 .091 .182 2.862 .005 a. Dependent Variable: Employee Performance In terms of significant associations found between explicit knowledge processing and employee performance null hypothesis 1 ‘There is no significant effect of explicit knowledge processing on employee performance’ is rejected and alternative hypothesis ‘there is significant effect of explicit knowledge processing on employee performance’ accepted. The findings corroborate with the existing research by Zaied, Hussein and Hassan (2012) that all elements of knowledge management capabilities including knowledge processing have a positive significant effect with all measures of performance at 1% level of significance. This means that there is a great correlation between knowledge management and employee performance which leads to overall organization performance. Therefore we can conclude that explicit knowledge processing positively influences employee performance. 4.9.1 Regression Line for Explicit Knowledge Dissemination and Employee Performance Regression analysis was also used to determine the significance effect of explicit knowledge dissemination on employee performance and figure 4.7 illustrates scatter plot diagram of regression analysis results of significance of explicit knowledge dissemination versus employee performance. The figure presents the line of best fit that is increasing positively upwards. This implies that there is a positive linear effect between explicit knowledge dissemination and employee performance. The findings corroborate findings by Bennet and Bennet (2003) that emphasizes that the goal of knowledge management is for an organization to be aware f individual and collective knowledge so that it can make the most effective use of the knowledge it has. Figure 4.7: Regression Analysis on Explicit Knowledge Dissemination 4.9.2 Goodness of Fit for regression line between Explicit Knowledge Dissemination and Employee Performance The coefficient of multiple determination indicates that 0.55% of the variation on employee performance is influenced by explicit knowledge dissemination as shown in table 4.28. This implies that there exists a positive significant effect between explicit knowledge dissemination and employee performance where explicit knowledge dissemination explains 55% variation in employee performance and the difference of 45% is explained by other factors such as explicit knowledge acquisition and explicit knowledge dissemination. This finding corroborates findings by Abbas and Yaqoob (2009) which also found factors such as coaching, training, development, empowerment among others to influence employee performance by 50%. Table 4.28: Model Summary for Explicit Knowledge Dissemination Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .235a .55 .51 5.11796 a. Predictors: (Constant) – Explicit Knowledge Dissemination 4.9.3 ANOVA for regression line between Explicit Knowledge Dissemination and Employee Performance The analysis of variance results shown in Table 4.29 indicate that the linear model was appropriate for this data since p-value of 0.000 is less than 0.05. This confirmed that the effect between explicit knowledge dissemination (X3) and employee performance could be quantified using the linear model fit Y= ί0 + ί3X3+ .. Table 4.29: Analysis of Variance (ANOVA) for Explicit Knowledge Dissemination ANOVAb Model Sum of Squares df Mean Square F Sig. 1 Regression 368.743 1 368.743 14.078 .000a Residual 6286.434 240 26.193 Total 6655.177 241 a. Predictors: (Constant) – Explicit Knowledge Dissemination b. Dependent Variable: Employee Performance 4.9.5 Model Parameter Estimate of regression line between Explicit Knowledge Processing and Employee Performance The results further indicate that explicit knowledge dissemination has positive and significant effect on employee performance as shown in Table 4.30. The fitted model was Y=12.452+0.395*X3. This implies that a unit change in explicit knowledge dissemination will increase employee performance by the rate of 0.395. Even when there is no explicit knowledge dissemination, employee performance is still positive at 12.452 indicating that there are other drivers of employee performance including explicit knowledge acquisition and explicit knowledge processing. Table 4.30: Explicit Knowledge Dissemination Coefficient Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 12.452 980 12.712 .000 Explicit Knowledge Dissemination .395 .105 .235 3.752 .000 a. Dependent Variable: Employee Performance In terms of significant associations found between explicit knowledge dissemination and employee performance null hypothesis I ‘There is no significant effect of explicit knowledge dissemination on employee performance’ is rejected and alternative hypothesis ‘There is significant effect of explicit knowledge dissemination on employee performance’ accepted. The findings corroborate with the existing research by Hsu, (2014) that organizational knowledge sharing improves employee and organizational performance to achieve competitive advantage. According to the findings, organizational knowledge sharing are able to encourage and facilitate knowledge sharing and are hypothesized as having a positive effect on employee performance. Therefore we can conclude that explicit knowledge dissemination positively influences employee performance. 4.10 Moderating Effect Test This study sought to determine the moderating effect of knowledge management infrastructure on explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance. To determine the combined effect of the independent variables on the dependent variable without the moderating variable, the fitted model 1: Y=ί0 + ί1X1 + ί2X2 + ί3X3 + . explained 0.73 of the variations in employee performance. This means that explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination explained 73% of the variation in employee performance as shown in Table 4.28. To determine the effect of independent variables on the dependent variable with the moderating variable, the fitted model 2: Y=ί0 + ί1X1 + ί2X2 + ί3X3 + ί4X4 . explained .086 of the variations in employee performance. This means that the knowledge management infrastructure moderating variable explained 13% of the variation in employee performance at 86%. Thus, 13% variation in effect of explicit knowledge acquisition, processing and dissemination on employee performance is attributable to the knowledge management infrastructure moderating variable. Table 4.31: Summary of Overall Moderating Effect Model Model R R Square Adjusted R Square Std. Error of the Estimate 1 .271a .73 .62 5.09034 2 .293b .86 .70 5.06698 a. Predictors: Explicit Knowledge Dissemination; Explicit Knowledge Processing; Explicit Knowledge Acquisition; To determine the model fit for independent variables and dependent variable without knowledge management infrastructure moderating variable, analysis of variance was done using fitted model 1 : Y=ί0 + ί1X1 + ί2X2 + ί3X3 + . which shows the model fit is significant at p=0.000, F=6.281 with 238 degree of freedom. This means that the independent variables of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination have a significant effect on employee performance without moderating variable of knowledge management and could be quantified using the linear model 1 : Y=ί0 + ί1X1 + ί2X2 + ί3X3 + . as shown in Table 4.32. The analysis of variance for independent variables and dependent variable with knowledge management infrastructure moderating variable using the fitted model 2: Y=ί0 + ί1X1 + ί2X2 + ί3X3 + ί4X4 . shows the model is significant at p=0.000, F=5.554 with 237 degree of freedom. This confirmed that the effect of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance could be quantified using the linear model 2: Y=ί0 + ί1X1 + ί2X2 + ί3X3 + ί4X4 .. Table 4.32: Analysis of Variance (ANOVA) for Moderation Model Model Sum of Squares df Mean Square F Sig. 1 Regression 488.227 3 162.742 6.281 .000a Residual 6166.950 238 25.912 Total 6655.177 241 2 Regression 570.373 4 142.593 5.554 .000 Residual 6084.804 237 25.674 Total 6655.177 241 a. Predictors: Explicit Knowledge Acquisition, Knowledge Processing and Knowledge Dissemination 4.11 Optimal Model Table 4.33 shows that explicit knowledge acquisition and explicit knowledge processing are insignificant on employee performance at p= 0.267; 0.126 respectively while explicit knowledge dissemination is highly significant at p= 0.006. The fitted model was Y=0.092X1+0.096X2+0.112X3 With the knowledge management infrastructure moderating variable, explicit knowledge acquisition and explicit knowledge processing were insignificant at p= 0.225 and p=0.142 respectively. Explicit knowledge dissemination was highly significant at p=0.004 with the moderating variable itself being slightly insignificant at 0.075. The fitted model was Y=0.91X1+0.096X2+0.112X3 +0.1.009X4. This means that independent variables of explicit knowledge acquisition and explicit knowledge processing do not have any effect on employee performance with the moderating variable of knowledge management infrastructure. On there own however, these variables on their own have effect on the dependent variable of employee performance. Table 4.33: Moderation Model Coefficients Unstandardized Coefficients Standardized Coefficients Sig. Model B Std. Error Beta t 1 (Constant) 10.443 1.353 7.718 .000 Knowledge Acquisition .102 .092 .074 1.113 .267 Knowledge Processing .148 .096 .103 1.535 .126 Knowledge Dissemination .309 .112 .184 2.764 .006 2 (Constant) 8.270 1.813 4.561 .000 Knowledge Acquisition .111 .091 .080 1.216 .225 Knowledge Processing .141 .096 .099 1.472 .142 Knowledge Dissemination .326 .112 .194 2.916 .004 Knowledge Management Infrastructure 1.805 1.009 .112 1.789 .075 a. Dependent Variable: Employee Performance Theses findings corroborate findings by Guyo (2012) that the role of knowledge management infrastructure as a moderator on performance, reward, training, developing and mentoring in facilitating sharing of tacit knowledge was not supported. Knowledge management infrastructure was noted not to have any clear role of moderating human resource management practices in an enabling social environment that mediates knowledge sharing. Therefore, failure by the results to support the theoretical framework surrounding the effect of knowledge management infrastructure in the model was an indication that knowledge management infrastructure does not play any fundamental role in moderating the effect of explicit knowledge acquisition and processing on employee performance. It can thus be concluded that all the independent variables are significant and significantly affect employee performance. However, the moderating effect of Knowledge management infrastructure was minimal in affecting only one independent variable of knowledge dissemination. Based on this finding, the original model was modified and summarized in the optimal model Institutionalization of Explicit Knowledge Explicit Knowledge Acquisition . Capturing . Learning . development Employee Performance . Quality of Work . Job Knowledge . Adaptability . Reliability Explicit Knowledge Processing . Organization . Analysis . Storage . Retrieval Explicit Knowledge Dissemination . Access . Usage . Transfer Knowledge Management Infrastructure . Organization Culture . Organization Structure . Organization IT Set- up Independent Variables Dependent Variable Moderating Variable Figure 4.8: Validated Model of Effect of Intra-firm institutionalization of Explicit Knowledge on Employee Performance KEY Path Insignificant Path Significant N/B: All the Variables were Significant CHAPTER FIVE SUMMARY, CONCLUSION AND RECOMMENDATIONS 5.1 Introduction This chapter presents a summary of major findings of the study, relevant discussions, conclusions and makes necessary recommendations and suggestions for further research based on the findings of the study. The study sought to investigate the effect of intra-firm institutionalization of explicit knowledge on employee performance in Energy Sector Organizations in Kenya. Specifically the study aimed at determining the effect of explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance in energy sector organizations in Kenya. The following are the specific breakdown of the summaries of major findings based on the output of descriptive and inferential statistical analyses which guided to answer the four hypothetical statements of the study. 5.2 Summary of Major Findings 5.2.1 Effect of Explicit Knowledge Acqusition on Employee Performance The findings of the study indicate that there is a positive significant effect between explicit knowledge acqusition and employee performance based on correlation analysis and regression analyses done. Correlation analysis for explicit knowledge acquisition indicated that there is a positive significant effect between explicit knowledge acquisition and employee performance with the positive effect represented by 0.151 against the number of respodents at 242. Regression analysis conducted to determine the significance of explicit knowledge acquisition and employee performance indicated that there was a positive linear effect between explicit knowledge acquisition and employee performance with the co-efficient of multiple determination indicating that 23% of the variation on employee performance was influenced by explicit knowledge acquisition. The study thus rejected the null hypothesis and accepted the alternative hypothesis that there is significant effect of explicit knowledge acquisition on employee performance. This means new knowledge in energy sector organizations is acquired through external sources like seminars, conferences and educational course. Employees in this sector also create their own knowledge to boost performance and rely on information from external sources to enhance their performance. The organizations also rely on written sources of documented organizational procedures to gain new knowledge and consider their knowledge as organizational asset and not their own source of strength. The findings also support the SET Knowledge Management model that new knowledge generated or acquired allows organizations to develop new abilities and capabilities, create new products and new services, thus improving the existing ones, redesign its organizational processes, and improve employee performance. The results and findings of the study therefore concluded that there was a positive signifcant effect between explicit knowledge acqusition and employee performance. 5.2.2 Effect of Explicit Knowledge Processing on Employee Performance The study found out that there is a positive significant effect between explicit knowledge processing and employee performance based on correlation analysis and regression analyses done. Correlation analysis for explicit knowledge processing indicated that there is a positive significant effect between explicit knowledge processing and employee performance with the positive effect represented by 0.182 against the number of respodents at 242. Regression analysis conducted to determine the significance of explicit knowledge procesing and employee performance indicated that there was a positive linear effect between explicit knowledge acquisition and employee performance with the co-efficient of multiple determination indicating that 33% of the variation on employee performance was influenced by explicit knowledge processing. The study thus rejected the null hypothesis and accepted the alternative hypothesis that there is significant effect of explicit knowledge processing on employee performance. The three dimesnions of knowledge processing studied were: Knowledge Organization, Knowledge Analysis and Knowledge Storage. Thus, for explicit knowledge processing to have effect on employee performance, this knowlede has to be documented, organized, analysed and sored in work instruction manuals, accesible digital formats as well as company libraries and repositories. There also has to be inclination and cooperation for exchange of this knolwdge and information among employees. The general leadership of the organization should also promote exhange of knowledge and information among employees who should also trust each other in their work and rely on knowledge of their co-workers to enhance performance. Organizational leadership should also motivate employees to engage in formal education systems to achieve higher levels of knowledge. Organizations should also support exchange of knowledge and informtion among organizational work units. The results and findings of the study therefore concluded that there was a positive signifcant effect between explicit knowledge processing and employee performance. 5.2.3 Effect of Explicit Knowledge Dissemination on Employee Performance The study found out that there is a positive significant effect between explicit knowledge dissemination and employee performance based on correlation analysis and regression analyses done. Correlation analysis for explicit knowledge dissemination indicated that there is a positive significant effect between explicit knowledge processing and employee performance with the positive effect represented by 0.235 being the highest among the three independent variables against the number of 242 respodents. Regression analysis conducted to determine the significance of explicit knowledge procesing and employee performance indicated that there was a positive linear effect between explicit knowledge acquisition and employee performance with the co- efficient of multiple determination indicating that 55% of the variation on employee performance was influenced by explicit knowledge dissemation. The study thus rejected the null hypothesis and accepted the alternative hypothesis that there is significant effect of explicit knowledge dissemination on employee performance. Thus explicit knolwdge dissemination affects employee performance through access of documened knowledge, use of knowledge in rouine work, decision making and sharing wih co-workers. Organizations also share technical knowledge, geral knowledge as well as organizational knowledge. In such circumstances, employees are motivated to share insights and knowledge about their profession, creativity and new knowledge is valued and employees are encouraged to transfer their professional knowledge to less experienced and new employees. They also acknowledge that knowledge sharing helps them perform better in their work. Such knowledge is clearly organized and regularly shared to support decision making. The results and findings of the study concluded that there was a positive significant effect between explicit knowledge dissemination and employee performance. Three dimesnions of knowledge dissemination studied were: Knowledge Access, Knowledge Usage and Knowledge Transfer which confirmed that indeed knowledge dissemination significantly affects employee performance. 5.2.4 Moderating Effect of Knowledge Management Infrastructure The study also aimed at finding out the overall moderating effect of Knowledge Management Infrastructure on explicit knowledge acquisition, explicit knowledge processing and explicit knowledge dissemination on employee performance. The study found out that all the independent variables are significant and significantly affect employee performance. However, the mediating effect of knowledge management infrastructure was minimal affecting only one independent variable of knowledge dissemination. The study thus rejected the alternative hypothesis and accepted the null hypothesis that knowledge management infrastructure has no significant moderating effect between explicit knowledge acquisition, explicit knowledge processing except explicit knowledge dissemination on employee performance. This means that organizational culture structure and organizational ICT set-up does not facilitate explicit knowledge acquisition and processing. However, explicit knowledge dissemination is supported by these structures through top-down, down top and horizontal knowledge sharing, easy storage, access and retrieval of information. This also means that there is high availability and accessibility of various It systems in energy sector organizations which are user-friendly. The effect of explicit knowledge acquisition and explicit knowledge processing without the moderating variable of knowledge management infrastructure confirm the validity of the Human Capital Theory that people can contribute to an organization through knowledge when they are regarded as assets stressing that investment in people by organizations generate worthwhile returns . Thus, it is the knowledge, skills and abilities of individuals that create value as a means of attracting, retaining, developing and maintaining the human capital they represent. 5.3 Conclusion Based on the findings of this study, it can be concluded that; 1. Institutionalization of explicit knowledge has a positive significant effect on employee performance. The study found out that explicit knowledge acquisition and explicit knowledge processing have a direct positive effect on employee performance without the moderating effect of knowledge management infrastructure. 2. The study also found out that explicit knowledge dissemination has a positive significant effect on employee performance with the support of the moderating variable of knowledge management infrastructure. This means that through a supportive organizational climate and modern information technology, knowledge, as a form of capital, must be exchangeable among employees. The goal of knowledge management is for an organization to be aware of individual and collective knowledge so that it can make the most effective use of the knowledge it has. 3. It is all about getting the right information to the right people (dissemination of knowledge) at the right time. Knowing who knows what, who needs to know what, and how to transfer that knowledge is critical as organizations use of most of available knowledge in decision making and this can only be effectively done where knowledge management infrastructure such as organization culture, organization structure and organization IT-Set up support availability of such knowledge. 4. Knowledge acquisition is a personalized individual matter and with or without knowledge management infrastructure, individuals will still acquire additional knowledge. In an organization where an organization’s culture, structure or IT-Set up does not support acquisition of knowledge, learning and/or development, employees will always find ways of acquiring additional knowledge on their own as well and ensure they learn and develop themselves. Thus knowledge management infrastructure may not be so much of a critical factor in this process. 5. Knowledge processing too does not seem to require the moderating effect of knowledge management infrastructure because organization, analysis and storage of information that has not been acquired nor disseminated may not add any value to the organization. Thus an organization’s culture, structure and/or IT-Set up may not support explicit knowledge processing unless such knowledge is acquired and is intended for dissemination. 6. Knowledge dissemination which is all about how knowledge in an organization is accessed, used and transferred from one person to another has a positive significant effect on employee performance and must be shared to be effective. That is why this study focused on explicit knowledge which is documented unlike tacit knowledge which is personal to persons. 7. Knowledge Management Infrastructure as a moderating variable focused on organization culture, organization structure and organization IT-set up which was found to moderate only explicit knowledge dissemination. The understanding here that explicit knowledge is a significant positive predictor of employee performance and can only be effective is shared and transferred from one person to another and this is only through knowledge management infrastructure like organization culture, organization structure and organization IT Set-up. 8. The study also concluded that organizations should enable access of documented explicit knowledge in organizations and encourage its employees to apply explicit knowledge acquired in routine work, decision making as well as sharing with co-workers. Sharing of explicit knowledge should not be limited to organizational knowledge alone but should also be on technical and general knowledge. Therefore, overall institutionalization of explicit knowledge has positive significant effect on employee performance through explicit knowledge acquisition, processing and dissemination and should be enhanced by energy sector organizations. 5.4 Recommendations: Based on the findings of this study, explicit knowledge acquisition positively affect employees performance, organizations in the energy sector should strive to create conducive environment where employees are able to acquire additional knowledge as well as develop themselves. This will enhance institutionalization of explicit knowledge among energy sector organizations and eventually improve employee performance. This can be through development of a Knowledge Management Policy which no organization in the energy sector currently has. Organizations should also consider inter-organizational exchange programmes for exchange of knowledge as well as having a centralized source of information for each employee to access explicit knowledge. From the study, obstacles that undermine institutionalization of knowledge, are lack of proper definition of each employee’s role in institutionalization of knowledge and this should be enhanced through more training programmes in the organization. This can be clearly defined in organization’s Training Policy manuals. Organizations have also to recognize that knowledge constitutes a valuable intangible asset for creating and sustaining competitive advantages. The sharing of knowledge constitutes a major challenge in the field of knowledge management because some employees tend to resist sharing their knowledge with the rest of the employees in organization. Knowledge sharing activities should therefore be generally supported by knowledge management systems like technology which constitutes only one of the many factors that affect the sharing of knowledge in organizations. 5.5 Contribution of the study to the Body of Knowledge and Practice This study contributes to the body of knowledge both in methodology, theory and practice. In order to derive more valuable and broader conclusions, the methodology adopted in this reasearch involved administering questionaires across energy sector organizations in order to increase the generalizability of the results. As employee performance is of paramount interest to all employers, this study is of scholarly interest as it has further uncovered factors that lead to enhanced employee performance. This is equally important for finding out the effect of intra-firm institutionalization of explicit knowledge and emplooyee performance. In the context of aspiring to bring out suplemental factors that enhance employee performance, other studies have ignored to examine what according to employee’s perceptions are the variables that contribute and enhance institutionalization of knwoledge in their organizations. The study has also established that the main drivers for employee performance are explicit knowledge dissemination, explicit knowledge processing and explicit knowledge acquisition. Further the study has established that employee performance is affected by quality of work, job knowledge, adaptability and reliability on the job. This is one area where Human Resource professional can play an important role in helping line managers design jobs that effectively enable employees measure the quality of their work, understand the job and be able to measure their adaptability and reliability on the job. The results and findings of this study also suggest that organizations in the Energy Sector in Kenya should institutionalize explicit knowledge as it is an important factor in employee performance. In addition explicit knowledge dissemination and explicit knowledge processing should be given priorty in management as explicit knowledge acqusition without processing and/or dissemination has a slightly lower impact on employee performance. Further, the findings of this research present an opportunity to Human Resource professional not just to manage employee performance, but to also look at the enablers of employee performance such as knowledge management. In the contemporary business environment, the competitive position of companies among others is influenced by its capability to create new knowledge which in return results in the creation of a competitive advantage. Organizational learning is an integrative characteristic of most companies although not all of them are able to utilize it for the creation of improved performance. 5.5 Proposed Areas for Further Research Despite the contributions made by this study, it highlights a few aspects to be considered by future studies. Firstly, the proportion put forward in this study emphasize intra-firm institutionalization of explicit knowledge. The study focused on energy sub-sector organizations in the public sector. Explicit knowledge acquisition, processing and dissemination of knowledge, were foundto have a positive significant effect on employee performance. Subsequent studies should consider replicating this study in differen sector and preferably a privae sector to establish how knowledge acquisition, processing and dissemination impacts employee performance in such sector and organizations. 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APPENDICES APPENDIX I: LETTER OF INTRODUCTION Dear Sir/Madam I am a student for a Doctor of Philosophy (PhD) degree in Human resources Management at the Jomo Kenyatta University of Agriculture & Technology (JKUAT), School of Human Resource Development. I am currently conducting a research in the area of Human Resources Management. My research Topic is: The Effect of Intra-firm Institutionalization of Explicit Knowledge on Employee Performance in Energy Sector Organizations in Kenya. The purpose of this letter is to request you to respond to the attached questionnaire. The information given will be treated in strict confidentiality and at no time will your name or that of your organization be referred to directly as the information given is for academic purposes only. Thank in advance for your time and cooperation. Elizabeth Kalei Supervisors PhD Student – JKUAT ………………………… Dr. Wario Guyo: PhD ……………………………….. Dr. Willy Muturi: PhD ……………………………… APPENDIX II: UNIVERSITY INTRODUCTORY LETTER C:\Users\kpl14102\Pictures\2015-03-15 JKUAT RESEARCH INTRODUCTORY LETTER\JKUAT RESEARCH INTRODUCTORY LETTER 001.jpg APPENDIX III: QUESTIONNAIRE EFFECT OF INTRA-FIRM INSTITUTIONALIZATION OF EXPLICIT KNOWLEDGE ON EMPLOYEE PERFORMANCE IN ENERGY SECTOR ORGANIZATIONS IN KENYA Please read each question carefully and follow the instructions given. Then, kindly answer the questions by ticking (v) in the box that best describes your answer or write your answers in the space provided where applicable. The answers provided are for ACADEMIC PURPOSES only and will be treated with utmost confidentiality. PART A: YOUR BACKGROUND INFORMATION 1. Please indicate your gender Male [ ] Female [ ] 2. Please indicate your age Below 25 years [ ] Between 25 and 35 years [ ] Between 36 and 45 years [ ] Between 46 and 55 years [ ] Over 55 years [ ] 3. Please indicate your highest level of education (Please tick only one appropriate answer) Primary [ ] Tertiary- Diploma [ ] Secondary [ ] Tertiary – Degree/Postgraduate [ ] Tertiary- Craft/Artisan [ ] Other [ ] PART B: YOUR ORGANIZATION’S BACKGROUND INFORMATION 1. Name of your Organization . Please tick only one appropriate answer Kenya Power & Lighting Company Limited (KPLC) [ ] Kenya Electricity Generation Company Limited (KenGen)) [ ] Kenya Electricity Transmission Company Limited (KETRACO) [ ] Kenya Pipeline Company Limited (KPC) [ ] Rural Electrification Authority (REA) [ ] Geothermal Development Company Limited (GDC) [ ] Energy Regulatory Commission (ERC) [ ] National Oil Corporation of Kenya (NOCK) [ ] Kenya Nuclear Electricity Board (KNEB) [ ] 2. Length of service with your current organization Please tick only one appropriate answer Less than 5 years [ ] 6 to 10 years [ ] 11 to 15 years [ ] 16 to 20 years [ ] More than 20 years [ ] PART C: EXPLICIT KNOWLEDGE ACQUISITION Knowledge acquisition refers to one’s capability to identify and capture knowledge that is critical to an organization’s operation. It also entails employee learning and development. Based on this understanding, please tick (v) the statement that describe your response appropriately in the space provided. 1. Have you ever acquired any additional knowledge since you joined your current organization? Yes [ ] No [ ] 2. Does your organization provide a conducive environment for acquisition of additional knowledge Yes [ ] No [ ] 3. How is additional knowledge acquired in your organization? Educational Courses [ ] In-House Trainings [ ] External Trainings [ ] Any other (please specify)……………………………………………… 4. When do you consider yourself as having acquired additional knowledge? When I am able to undertake and accomplish a new task [ ] When I am able to carry out tasks independently [ ] When I am able to offer advise to my co-workers on tasks [ ] Any other (please specify)……………………………………………… 5. Is employee development facilitated in your organization? Yes [ ] No [ ] 6. How is employee development facilitated in your organization? Company sponsorship [ ] Self-Sponsorship [ ] On-the Job Trainings [ ] Any other (please specify)……………………………………………… 7. By ticking the appropriate box, please indicate the extent to which you agree with the following statements related to knowledge acquisition in your organization Strongly Agree 5 Agree 4 Neutral 3 Disagree 2 Strongly Disagree 1 External sources like seminars, conferences and educational courses do not enable me obtain new knowledge. Strongly Agree 5 Agree 4 Neutral 3 Disagree 2 Strongly Disagree 1 Employees in my organization do not build on each other’s knowledge but create their own knowledge to boost their performance. In my work, I rely on information, and knowledge gained from external sources to enable me perform my duties well. In my work, I rely on written sources of information like documented organizational procedures to gain new knowledge. In my organization, employees share their knowledge through formal procedures like reports and company publications. Employees in my organization consider their knowledge as an organizational asset and not their own source of strength. 8. Please give any suggestions that would contribute to additional knowledge acquisition in your organization. ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… ……………………… PART D: EXPLICIT KNOWLEDGE PROCESSING Knowledge processing is considered as organization, analysis and application of knowledge in decision making. This knowledge has to be shared and must be identified, retrieved and understood by knowledge user. Based on this understanding, please tick (v) the statement that describe your response appropriately in the space provided. 1. How is knowledge in your organization organized? Documented Knowledge [ ] Un-documented Knowledge [ ] Documented & Undocumented Knowledge [ ] Any other (please specify)……………………………………………… 2. Who facilitates knowledge organization in your company? Human Resource function [ ] Company’s Libraries [ ] ICT Department [ ] Any other (please specify)……………………………………………… 3. How is knowledge analysis done in your organization? Through Performance Management process [ ] Through Training Needs Analysis process [ ] Through Individual Self Assessment process [ ] Any other (please specify)……………………………………………… 4. When is knowledge analysis done in your organization? When a new work method is being introduced [ ] When an occupational accident occurs [ ] When one is being transferred [ ] Any other (please specify)……………………………………………… 5. How is knowledge in your organization stored? In Work Instruction manuals [ ] In accessible digital formats [ ] In Company Libraries and repositories [ ] Any other (please specify)……………………………………………… 6. By ticking in the appropriate box, please indicate the extent to which you agree with the following statements. Strongly Agree 5 Agree 4 Neutral 3 Disagree 2 Strongly Disagree 1 In my organization, there is no inclination to cooperation and exchange of knowledge and information among employees. The general leadership of my organization does not promotes exchange of knowledge and information among employees. Employees in my organization generally trust each other in their work and rely on knowledge and information of their co- workers. The general management Strongly Agree 5 Agree 4 Neutral 3 Disagree 2 Strongly Disagree 1 /leadership motivates employees to engage in formal education systems to achieve a higher level of knowledge. In my organization we support the exchange of information and knowledge among organizational work units. 7. Please give any suggestions that would contribute to knowledge processing in your organization. ………………………………………………………………………………………… ………………………………………………………………………………………… ………………………………………………………………………………………… ……………………… PART E: EXPLICIT KNOWLEDGE DISSEMINATION Knowledge dissemination is considered as managing and sharing knowledge within an organization to encourage action, increase awareness of good practices and stimulate users to adopt better practices for future decision-making process. Based on this understanding, please tick (v) the statement that describe your response appropriately in the space provided. 1. How do you access documented knowledge in your organization? Use of Library facilities [ ] Computer public folders [ ] Intranet Sharing [ ] Any other (please specify)……………………………………………… 2. How do you often use your knowledge in your organization? In my routine work [ ] In decision-making [ ] In sharing with my co-workers [ ] Any other (please specify)……………………………………………… 3. What type of knowledge is commonly shared in your organization? Technical Knowledge [ ] General Knowledge [ ] Organizational Knowledge [ ] Any other (please specify)……………………………………………… 4. How is shared knowledge in your organization managed? Organization [ ] Categorization [ ] Sharing [ ] Any other (please specify)……………………………………………… 5. By ticking in the appropriate box, please indicate the extent to which you agree with the following statements; Strongly disagree 5 Disagree 4 Neutral 3 Agree 2 Strongly Agree 1 In my organization, employees are motivated to share with each other insights and knowledge about their profession Strongly disagree 5 Disagree 4 Neutral 3 Agree 2 Strongly Agree 1 Creativity and new knowledge are valued in my organization In my organization, important rules and standards are identified and stored well Staff are encouraged to transfer their professional knowledge to less experienced and new employees Knowledge sharing with others helps one to perform better in their work. Information in my organization is organized clearly and regularly shared to support decision making 6. Please give any suggestions that would contribute to knowledge dissemination in your organization. …………………………………………………………………………………… …………………………………………………………………………………… …………………………………………………………………………………… ……………………………… PART F: KNOWLEDGE MANAGEMENT INFRASTRUCTURE Knowledge Management infrastructure refers to long-term foundations of information and knowledge management in an organization and include the organization culture, organization structure and organization ICT set-up. Based on this understanding, please tick (v) the statement that describe your response appropriately in the space provided. 1. Does the organization culture in your company facilitate the following? Yes No Self Development [ ] [ ] On-the-job Training [ ] [ ] Staff interactions [ ] [ ] Write-ups in areas of specialty [ ] [ ] Formal Topical Presentations [ ] [ ] 2. Does your organization culture encourage the following? Yes No Restrictions on knowledge sharing [ ] [ ] Ease access of required Information [ ] [ ] Free flow of information [ ] [ ] 3. Does your organization’s structure encourage the following? Yes No Documentation of available knowledge [ ] [ ] Analysis of available knowledge [ ] [ ] Proper storage of documented knowledge [ ] [ ] 4. Does your organization’s structure facilitate the following? Yes No Top - down Knowledge Sharing [ ] [ ] Down - top Knowledge Sharing [ ] [ ] Horizontal Knowledge Sharing [ ] [ ] 5. Does your organization’s ICT-set up promote the following? Yes No Easy storage of information [ ] [ ] Easy access of information [ ] [ ] Easy retrieval of information [ ] [ ] 6. How would you rate the overall ICT-Setup of your organization in the following areas? High Medium Low Availability of various IT Systems [ ] [ ] [ ] Accessibility of various IT Systems [ ] [ ] [ ] User-friendly IT Systems [ ] [ ] [ ] 7. What other form of knowledge management infrastructure should your organization put in place to enhance knowledge management? …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………… PART G: EMPLOYEE PERFORMANCE Employee performance refers an individual’s strive to improve his/her work output and overall contribution to the organization’s wider objectives. It takes the form of a self-renewing cycle of performance and development agreement; managing performance throughout the year; performance review and assessment. Based on this understanding; 1. Please indicate how you would be rated in your performance in the following areas based on the Knowledge you acquired since joining your current organization. The rating scale is as follows: Excellent (5) Very Good (4) Good (3) Fair (2) Poor (1) Quality of your work Knowledge of the Job Adaptability Reliability 2. By placing a tick in the appropriate box, please indicate the extent to which you agree with the following statements on performance. . Strongly Agree 5 Agree 4 Undecided 3 Disagree 2 Strongly Disagree 1 The knowledge I have gained in the organization enables me to give quality results The knowledge I have gained in the organization enables me to undertake my tasks independently The knowledge I have gained in the organization enables me to easily adapt to my work The knowledge I have gained in the organization has boosted my initiative . Strongly Agree 5 Agree 4 Undecided 3 Disagree 2 Strongly Disagree 1 to commence work on my own 3. In your opinion, what are some of the obstacles that undermine the institutionalization of knowledge in your organization? …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………… 4. From your experience, how can institutionalization of knowledge be enhanced in your organization? …………………………………………………………………………………………… …………………………………………………………………………………………… …………………………………………………………………………………………… ……………………… Thanks a lot for taking your time to respond to this questionnaire APPENDIX IV: ENERGY SECTOR ORGANIZATIONS IN KENYA 1. Kenya Power and Lighting Company Limited (KPLC) 2. Kenya Electricity Generation Company Limited (KenGen) 3. Kenya Electricity Transmission Company Limited (KETRACO) 4. Kenya Pipeline Company Limited (KPC) 5. Rural Electrification Authority (REA) 6. The Geothermal Development Company Limited (GDC) 7. The Energy Regulatory Commission (ERC) 8. National Oil Corporation (NOCK) 9. Kenya Nuclear Electricity Board(KNEB) APPENDIX V: Factor Analysis and Reliability Results 1. Knowledge Acquisition Component Matrix of Knowledge Acquisition Statement Component 1 In my work, I rely on written sources of information like documented organizational procedures to gain new knowledge .840 In my work, I rely on information and knowledge gained from external sources to enable me perform my duties well. .828 In my organization, employees share their knowledge through formal procedures like reports and company publications .776 Employees in my organization do not build on each other's knowledge but create their own knowledge to boost their performance. .682 Employees in my organization consider their knowledge as an organizational asset and not their own sources of strength .643 External sources like seminars, conferences and educational courses do not enable me obtain new knowledge .494 Reliability Statistics Cronbach’s Alpha No. of Items .803 8 2. Knowledge Processing Component Matrix of Knowledge Processing Statement Component 1 Employees in my organization generally trust each other in their work and rely on knowledge and information of their co-workers .822 The general management/leadership motivates employees to engage in formal education systems to achieve a higher level of knowledge .792 The general leadership of my organization does not promote exchange of knowledge and information among employees .752 In my organization we support the exchange of information and knowledge among organizational work units .652 In my organization, there is no inclination to cooperation and exchange of knowledge and information among employees .492 3. Reliability Statistics Cronbach’s Alpha No. of Items .748 5 4. Knowledge Dissemination Component Matrix of Knowledge Dissemination Statement Component 1 Knowledge sharing with others helps one to perform better in their work .832 Staff are encouraged to transfer their professional knowledge to less experienced and new employees .810 In my organization, important rules and standards are identified and stored well .790 Information in my organization is organized clearly and regularly shared to support decision making .718 Creativity and new knowledge are valued in my organization .556 In my organization, employees are motivated to share with each other insights and knowledge about their profession .483 5. Reliability Statistics Cronbach’s Alpha No. of Items .792 6 6. Employee Performance Component Matrix of Employee Performance 1 – Performance on Joining organization Statement Component 1 Adaptability .857 Knowledge of the Job .847 Reliability .809 Quality of your work .750 Reliability Statistics Cronbach’s Alpha No. of Items .831 4 Component Matrix of Employee Performance 2 – Performance with Additional Knowledge Statement Component 1 The Knowledge I have gained in the organization enables me to easily adapt to my work .895 The Knowledge I have gained in the organization enables me to give quality results .875 The Knowledge I have gained in the organization enables me to undertake my tasks independently .871 The Knowledge I have gained in the organization has boosted my initiative to commence work on my own .825 Reliability Statistics Cronbach’s Alpha No. of Items .887 4 APPENDIX VI: Component Matrix Summary of Explicit Knowledge Acquisition 1. How is additional knowledge acquired in your organization Response Frequency Percentage Educational Courses 87 36.0 In-house Trainings 99 40.9 External Trainings 56 23.1 Total 242 100.0 2. When do you consider yourself as having acquired additional knowledge? Response Frequency Percentage When I am able to undertake and accomplish a new task 73 30.2 When I am able to carry out tasks independently 107 44.2 When I am able t offer advise to my co-worker on tasks 62 25.6 Total 242 100.0 3. How is employee development facilitated in your organization? Response Frequency Percentage Company sponsorship 87 36.0 Self-sponsorship 98 40.5 On-the-job Trainings 57 23.6 Total 242 100.0 APPENDIX VII: Component Matrix Summary of Explicit Knowledge Processing 1. How is Knowledge in your company organized? Response Frequency Percentage Documented Knowledge 27 11.2 Undocumented Knowledge 106 43.8 Documented and Un- documented Knowledge 109 45.0 Total 242 100.0 2. Who facilitated Knowledge Organization in your company? Response Frequency Percentage Human Resource Function 106 43.8 Company Libraries 85 35.1 ICT Department 51 21.1 Total 242 100.0 3. How is Knowledge Analysis done in your organization? Response Frequency Percentage Through Performance Management process 73 30.2 Through Training Needs Process 104 43.0 Through Individual Self Assessment Process 65 26.9 Total 242 100.0 4. When is knowledge analysis done in your organizations? Response Frequency Percentage When a new work method is being introduced 72 31.0 When an occupational accident occurs 91 37.6 When one is being transferred 76 31.4 Total 242 100.0 5. How is knowledge in your organization stored? Response Frequency Percentage In Work Instruction Manuals 63 26.0 In accessible digital formats 90 37.2 In Company Libraries and repositories 89 36.8 Total 242 100.0 APPENDIX VIII: Component Matrix Summary of Explicit Knowledge Dissemination 1. How do you access documented knowledge in your organization? Response Frequency Percentage Use of Library Facilities 59 24.4 Digital Public Folders 90 37.2 Intranet Sharing 93 38.4 Total 242 100.0 2. How do you often use your knowledge in your organization? Response Frequency Percentage In my routine work 96 39.7 In decision making 127 52.5 In sharing with my co- workers 19 7.9 Total 242 100.0 3. What type of knowledge is commonly shared in your organization? Response Frequency Percentage Technical Knowledge 72 29.8 General Knowledge 84 34.7 Organizational Knowledge 86 35.5 Total 242 100.0 4. How is shared knowledge in your organization managed? Response Frequency Percentage Organization 51 21.1 Categorization 110 45.5 In sharing with my co- workers 81 33.5 Total 242 100.0