Impact of Knowledge Management Practices on NPP Organizational Performance – Results of Global Survey - document as published

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Introduction

Background

Nuclear power plant (NPP) organizations have been dealing with knowledge management (KM) related issues and knowledge processes from the outset. However, while some NPPs have adopted KM practices and have been proactive in implementing strategic company-wide KM programmes, many other NPPs do not view or manage these activities from a strategic KM perspective, nor yet see any need to do so. While the concepts of KM are beginning to be understood in the nuclear industry, they have yet to be widely applied and the benefits are difficult to measure. There has been little prior research on KM in NPP organizations.

Objectives

The objective of this report is to summarize the findings of research that was conducted as a thesis to explore the link between KM practices and their impact on NPP organizational performance. In general, the issue has not been extensively researched and is not well understood. Little or no prior empirical research has been done on this topic in the specific context of NPP operations. The report also summarizes the findings from the research on the importance of a supportive organizational culture, how it is influenced by KM practices, and how it impacts organizational knowledge processes and performance. Finally, the report summarizes the research findings on what specific knowledge management practices have proven effective in NPPs and what benefits have been achieved in terms of organizational effectiveness.

Scope

This report summarizes the results of empirical research that directly investigated the relationship between KM practices in NPPs, their impact on the quality of organizational knowledge processes, and the resulting effects on NPP organizational effectiveness. It presents the basic findings of the IAEA Global KM Survey of NPPs conducted in 2010.

Structure

Section 2 is a brief introduction to the knowledge management context and provides some theoretical perspective. Section 3 discusses some of the unique characteristics of the nuclear industry that present additional challenges to knowledge management with respect to nuclear power plants. Section 4 discusses nuclear power plant organizations from a knowledge management perspective and provides additional context for the research. Section 5 describes the research approach taken including the research questions, research model, and key constructs used. Section 6 describes the survey distribution method and response. Section 7 provides descriptive statistics. Section 8 summarizes the results of the statistical data analysis. Section 9 summarizes the study limitations. Section 10 provides the conclusions of the report. In addition, the Appendices I–VII provide (respectively) the survey instrument, a list of participating NPPs, descriptive data for each of the construct variables, bivariate scatterplots for constructs, descriptive data for each indicator measure in the study, demographic data, and the detailed results of the multiple regression analysis. Appendix VIII summarises several recommended revisions to improve the survey instrument for use in future.

Target audience

This report was prepared primarily for NPP managers and other KM practitioners and stakeholders in nuclear operating facilities. It may also be useful to other nuclear facility owners and operators, nuclear design and support organizations, nuclear R&D organizations, nuclear regulators, academia, and government policy makers.

Knowledge management context

Knowledge exists in different forms and at different levels in an organization. Tacit knowledge is experiential knowledge or ‘know how’ in the minds of individuals that typically cannot easily be easily expressed, captured or transferred. An example of tacit knowledge would be the know-how of an experienced maintenance engineer that allows him/her to arrive at a rapid and accurate diagnosis of problems with complex plant equipment such as a turbine. Explicit knowledge is knowledge that has been recorded or codified in some form such as manuals, procedures, databases, or electronic media. It is important to recognize knowledge in organizations exists at an individual level, at a group level, at a department level, and at an organizational level. Further, the level of abstraction and form of knowledge may range from detailed facts, to organized information, to interpretations and analysis, to conceptualizations, to theoretical models, or even wisdom. Knowledge can be considered a resource (i.e. an input), it may be embedded in work methods (i.e. part of a process) or it can be a product (i.e. an output). Knowledge may often be time dependent or contextual, and must be maintained and renewed. In the literature, authors such as N.T. Pham and F.W. Swierczek [1] describe the mechanisms by which knowledge is accumulated, disseminated and stored in organizations and many refer to these as knowledge processes. There are many different definitions of knowledge processes used in the literature. This research classified the more widely used and accepted definitions into one of five primary knowledge processes, shown below in Figure 1. The primary knowledge processes are defined as [2]:

  1. Knowledge acquisition and adoption;
  2. Knowledge generation and validation;
  3. Knowledge sharing and transfer;
  4. Knowledge retention and storage; and
  5. Knowledge utilization and application.

Knowledge processes can be viewed as the means by which organizations build, maintain and apply the tacit and explicit knowledge in all its various forms.

FIG. 1. The primary knowledge processes (see Ref. [2]).


Knowledge management has been described by leading authors such as G.F. Hedlund [3] and D. Andriessen [4] as those practices (i.e. activities, initiatives or actions initiated or supported by management) that can influence and improve organizational knowledge processes. The goals of KM cited in the literature by authors like A. Jantunen [5], D. Carluccii and G. Schiuma [6], and J. Darroch [7] are to improve organizational learning, to build and maintain an effective organizational knowledge base, and to enable effective knowledge utilization. All of these goals are argued to help achieve organizational objectives. Authors like Y. Malhotra [8], J.M. Firestone and M.W. McElroy [9], S.G. Chang and J.H. Ahn [10], and G.F. Hedlund (see Ref. [3]) all contend that organizations having quality knowledge processes (i.e. they are aligned with business needs and priorities, and are efficient and effective) will be higher performing organizations.

Knowledge management challenge in an NPP

NPPs operate in a highly regulated environment with stringent requirements. Effective management systems must be in place to ensure compliance with a number of regulatory and operating licence requirements including, for example: nuclear safety, environmental controls, equipment reliability and qualification, nuclear quality assurance, nuclear security, nuclear waste management and safeguards, radiation protection and monitoring, operating experience feedback and corrective action programmes, work management and control, outage planning and management, and design basis configuration management. All of these are knowledge intensive processes that involve knowledge management considerations. Knowledge management in the NPP context presents many challenges and issues and these stem from many factors such as:

  • A complex technology base and infrastructure;
  • Lengthy technology and plant life-cycles;
  • Highly capital-intensive plant assets;
  • A reliance on multi-disciplinary technologies and expertise;
  • Competing operational objectives (i.e. safety, economics, and production);
  • Potentially high hazards that must be systematically managed to demonstrably low tolerable risks; and
  • An organization that is a complex socio-technical system.

There is an on-going need in NPPs for coordination and alignment of often inter-dependent knowledge processes. There is also a frequent need for risk-informed technical decision making, both from a design basis management perspective and from an operations and maintenance perspective. Nuclear plant organizations are heavily knowledge-dependent and their operational needs demand a high level of expertise and knowledge-based infrastructure. Knowledge is embedded in humans, the underlying plant technology, and work processes and methodologies. The terms ‘knowledge-worker’ and ‘knowledge organization’ are all the more relevant to the multi-disciplinary environment of NPP organizations. For these reasons, NPP managers are interested in understanding and influencing the factors that affect not only the building and retention of the corporate knowledge base, but its effective utilization. The KM issues and priorities will vary in each NPP organization and this will depend on both internal organizational factors, and factors such as the national industry and infrastructure issues. Many NPPs have started to manage knowledge and knowledge processes on a corporate-wide level as part of an integrated strategic KM programme. There are many reasons for this trend. For example, as existing plants have aged, there have been many hard lessons learned about the need for accurate maintenance of plant design basis information to ensure the continued safe and economic operation of each NPP (i.e. this information must be kept up to date, accurate and correct). Another reason is that many NPPs are under pressure to achieve improvements in economics, and this is driven by factors such as ownership consolidation and fleet management, deregulation and competition, rising operating costs, and opportunities arising from new technology. As a result, some plants are reducing staff by outsourcing more maintenance and design services, and this creates additional risks and dependencies on outside firms to maintain essential knowledge. There are also several reasons why KM issues may become a priority in nuclear organizations. For example, in some Member States, the nuclear industry is a maturing industry and NPPs are experiencing high attrition rates due to retirements. This has highlighted their vulnerability to the loss of experts and their highly specialized and (difficult to replace) knowledge. In other Member States, there are aggressive plans underway for new builds and critical skills shortages have become a problem. Some Member States are experiencing both problems simultaneously, and further, need to staff upcoming refurbishment or decommissioning projects. Finally, there is concern in the industry over the ‘pipeline’ of adequately skilled new graduates due to the lack of university level nuclear engineering and science programmes. It takes typically months of formal in-house training and many more years of on-the-job training to build up the competencies and experience needed for many specialized NPP staff roles. Any of these factors may contribute to a shortage of critical technical competencies in nuclear organizations and may have a direct impact on safety, production, and economics. Pro-active measures aimed at knowledge building, retention and transfer have been needed.

Knowledge management view of an NPP organization

Basic management theory suggests that organizations, in order to be effective, must fulfil the goals of core business processes (i.e. work processes, procedures and methods), using plant and equipment (i.e. base technology), people (i.e. human competencies), and information technology infrastructure (i.e. supporting technology). All of these factors, it is generally recognized, need to be aligned to organizational objectives (and in NPPs, that means the safe and reliable production of electricity) to achieve organizational performance. Organizational theory further predicts that organizational performance will be enhanced by a supportive culture that promotes organizational learning. Figure 2 illustrates these relationships and they are assumed to apply in the context of any NPP organization.

FIG. 2. Aligning to organizational objectives (see Ref. [2]).

From the literature, it is predicted that KM may play a significant role in achieving this alignment and in improving organizational performance. However, there is little consensus in the literature as to just how and why this may occur. This research hypothesizes that KM practices, by creating and enabling quality knowledge processes, help achieve and support this synergistic alignment, thus enabling and enhancing organizational effectiveness and ultimately performance. It is argued that quality knowledge processes promote the building and maintenance of a more integrated and shared organizational knowledge base, enhance organizational learning, and result in better knowledge-based decisions and action. The literature (e.g. [11]) suggests that a supportive organizational culture will also play an important role in these relationships, in that it promotes excellence in actions and decisions by motivating employees to be pro-active and to strive for continuous improvement. The net effect is hypothesized to be greater organizational effectiveness that will in turn improve overall operations, maintenance and administration (OM&A) of the organization. Figure 3 illustrates these relationships.


FIG. 3. KM links to improved operations, maintenance and administration (see Ref. [2]).


Many NPP organizations have invested heavily in information technology infrastructure as a way to improve efficiency and achieve cost reduction. In most operating NPPs, the information technology infrastructure is quite complex. There are typically a large number of systems. Figure 4 illustrates some of the more typical information systems and technology (IST) and operational support system (OSS) found in NPPs. The figure helps to convey the concept of an integrated and shared organizational knowledge-base (K-base) supported by these systems. Examples of these systems include computer aided design (CAD) models and drawings, operations and maintenance (O&M) history databases, outage planning systems, equipment reliability systems, and others.

Basic information systems theory predicts that collectively, these systems (if properly implemented) should support work and effective decision processes. This in turn, it is argued, should enable the tacit knowledge of plant staff to be leveraged and fully utilized in the day- to-day operation of the facility. The predicted end result being the more effective implementation of organizational policy, practices, and procedures, to achieve the objectives of the organization’s OM&A strategies (see Fig. 4). Information systems technology support then, it is argued, when viewed from a KM perspective, is essentially another, though perhaps quite distinct, way to enhance the quality of knowledge processes.

FIG. 4. Typical information systems & technology infrastructure in NPPs [12].

In summary, it is hypothesized that effective KM practices will have a direct impact on the quality of knowledge processes, and these in turn should improve overall NPP organizational effectiveness. The information technology infrastructure of the organization is also expected to play an important role by enabling and supporting these quality knowledge processes. Finally it is expected that the extent of KM practices and the effectiveness of the IT infrastructure will both have a positive impact on the level of supportive organizational culture, and all these factors will have a positive effect on the quality of knowledge processes. Finally, it is believed that quality knowledge processes and a supportive organizational culture will directly and positively impact organizational effectiveness.

Research questions and approach

The preceding discussion provides some useful insights into the hypothesized role and influence of knowledge management practices, knowledge processes, and the knowledge base in NPP organizations, and specifically with respect to work-processes and organizational learning. However, although there is an abundance of literature that conceptually supports knowledge management practices as important and beneficial, very little empirical research has been done to back up these claims. It is difficult for managers to know what KM practices are being applied in NPPs today, whether or not they are beneficial, and to what extent. Many factors in an organization will impact performance, and KM practices may be just one of them. Basic questions such as whether NPP organizations that implement KM practices realize any real measurable performance benefits remain unanswered. To address these questions, an empirical survey of the total global population of NPPs was conducted to explore and investigate these issues further in detail. The main research questions which drove the design of the survey were (see Ref. [2]):

  • To what extent do NPPs have specific knowledge management practices supported and in use by managers?
  • To what extent do NPPs have a supportive organizational culture?
  • To what extent do NPPs have quality knowledge processes?
  • To what extent do NPP organizations consider themselves effective?
  • To what extent does support for knowledge management practices impact on and help create a supportive organization culture?
  • To what extent does support for knowledge management practices impact the quality of knowledge processes?
  • To what extent does the level of technology support (i.e. in terms of the effectiveness of information systems and information technologies) impact the quality of knowledge processes?
  • To what extent does the quality of knowledge processes impact organizational effectiveness?
  • To what extent does the degree of supportive organizational culture impact the quality of knowledge processes?
  • To what extent does the degree of supportive organizational culture impact the organizational effectiveness?
  • To what extent does the quality of knowledge processes impact organizational effectiveness?

The answers to these questions are of interest to NPP owners and operators. Little if any management research has been done on nuclear plant organizations in general, and none on this specific issue, perhaps due to their being less accessible to researchers. Figure 5 illustrates the basic elements of the conceptual model used in the research (adapted from Ref. [2]).

FIG. 5. The ‘KM Performance Model’ relationships (adapted from Ref. [2]).

The elements of the research model include five main factors (i.e. theoretical construct variables):

  • Support for knowledge management practices (i.e. degree to which management is supporting those practices that are known to influence employee behaviour and action to positively affect knowledge processes), (independent variable);
  • Level of organizational technology support, (independent variable);
  • The quality of knowledge processes (i.e. the extent to which knowledge processes effectively meet the requirements of the organization’s business processes), (an intermediate variable);
  • The degree of supportive organizational culture (an intermediate variable); and
  • Organizational effectiveness (i.e. the degree to which the organizational goals, including production and safety, are achieved), (dependent variable).

As with any social sciences, organizational studies research requires careful consideration and design of a meaningful measurement model. This should be based on prior theory and established measures where possible. Three of the main constructs in the research model were defined with well-defined sub-constructs. Measures were developed (in the form of survey questions) for each of the constructs (and sub-constructs) and included in the NPP survey. The basis for each of the construct measures is summarized below, and this includes the sub- constructs identified. The first construct, ‘support for KM practices’, measures the extent of perceived organizational support for KM, where KM is assumed to be the collective set of actions/practices implemented by management to influence the quality of knowledge processes and represents the upper part of the left-hand side of the research model. The IAEA KM Guidelines [13, 14] provide a useful categorization of KM practices that have been adapted for use in the survey:

  • KM strategy and planning — the extent to which corporate wide KM policy and strategy has been established and the planning to implement it has been put in place;
  • Support for organizational learning — the extent to which management provides sufficient resources and enables various mechanisms for individual, group, or institutional level learning;
  • Process management practices — the extent to which management establishes and maintains effective knowledge-based business processes (e.g. process-oriented KM practices);
  • Information management practices — the extent to which effective information management practices have been implemented (i.e. that support knowledge processes);
  • Organizational performance management practices — the extent to which knowledge- based performance management practices have been put in place;
  • Training related practices — the extent to which best practices for training have been put in place and address KM related issues of training;
  • Human resource (HR) related practices — the extent to which HR related KM practices such as competency development and knowledge retention have been put in place.

The second construct, ‘technology support’ measures the level of organizational support for the effective use of information systems and technology, including advanced operational (decision) support systems. It is comprised of two sub-constructs: one measuring conventional application of information systems and technology (IST) (i.e. the effectiveness of the enterprise IS and IT); and the other measuring support for advanced operational support systems (OSS) (i.e., measures how effectively advanced NPP-specific decision support systems are utilized). Together, these sub-constructs represent the information management infrastructure supporting the organization’s integrated and shared knowledge base as shown in Figures 3 and 4. Operational support systems might include, for example: advanced decision support systems such as refuelling software; probabilistic ‘production risk’ models for equipment reliability (used for maintenance and outage planning); real-time probabilistic ‘safety risk’ models for operator evaluation and awareness of plant safety (i.e. ‘safety monitors’); system health monitors (e.g. predictive maintenance tools such as vibration, acoustic, thermal, or other monitors); advanced model-based monitoring and diagnostics (e.g. physics, chemistry, boiler, feed water and thermal hydraulics models); advanced information exchange (e.g. hand-held computers, plant-wide equipment status monitoring, wireless communications); electronic (i.e. graphical) road-maps of business and decision processes or work-flows (e.g. operational flow-sheets with links to supporting procedures or related resource documents); and automated field data collection (i.e. smart instruments, field-bus, radio frequency identification (RFID) tagging, data logging, equipment monitors).

The third construct, quality of knowledge processes, is based on five key knowledge processes. Several authors agree that the accumulation and use of knowledge and core competencies in organizations are enabled by effective knowledge processes (e.g. S.I. Tannembaum and G.V. Alliger [15]; P.N. Rastogi [16]; and G. Probst [17]). Authors use different terms and definitions to describe knowledge processes; however, they can be summarized as five basic knowledge processes that are found frequently in the literature, and for the purposes of this research were defined as follows (see Ref [2]):

  • Quality of knowledge acquisition and adoption processes (KA) — the process of obtaining and adopting new external knowledge (whether tacit or explicit) into the organization. This is interpreted to include knowledge identification and selection processes for the purpose of acquisition;
  • Quality of knowledge sharing and transfer processes (KS) — the exchange of knowledge within the organization (directly or indirectly) and including processes of knowledge conveyance and distribution;
  • Quality of knowledge generation and validation processes (KG) — the creation of new knowledge, typically by incremental knowledge development, and its validation within the organization. It may also include knowledge identification and selection processes associated with internal knowledge generation processes;
  • Quality of knowledge retention and storage processes (KR) — the process of keeping knowledge (whether tacit or explicit) within the organization and maintaining its availability and relevance for future use. It incorporates the related concepts of knowledge capture, preservation, storage, retrieval, accessibility, identification and protection in the context of internal organizational knowledge retention;
  • Quality of knowledge utilization and application processes (KU) — the concept of internal organizational knowledge use (whether tacit or explicit) and including the process of adapting or interpreting it in a problem context.

Much of the literature on organizational culture, safety culture, and knowledge sharing culture describes similar factors of trust, leadership, rewards, shared vision and goals, personal responsibility, support for learning, a questioning attitude, and communication (see Ref [2]). In the context of KM, an organizational culture that promotes effective knowledge processes and thus supports and enables organizational learning is seen as playing an important role in organizational effectiveness and overall performance. The research model posits that from a knowledge management practice and knowledge process perspective, a ‘supportive organizational culture’ (SOC) enhances the effect of KM practices on the quality of knowledge processes in an organization. It is also expected to enhance the subsequent effect that the quality of knowledge processes will have on organizational effectiveness and performance. Thus Figure 5 includes the construct ‘supportive organizational culture’ as part of the model to indicate its important influence. Measures for SOC were adapted from prior research on organizational culture (there are many established measures in the literature) and included existing measures of safety culture as an important component of organizational culture in an NPP context.

Finally, there is a significant body of literature on the topic of organizational effectiveness, the construct on the right hand side of the model, and the dependent variable. The study focused specifically on relevant measures from the nuclear industry related to NPPs, and adapted them as appropriate. Measures for the construct ‘organizational effectiveness’ were based on three general areas: well-accepted top level management objectives for NPPs; prior research on the fundamentals of NPP operational excellence (including operations, engineering, maintenance, radiological protection, chemistry, and training); and high-level organizational effectiveness measures that focus specifically on NPP operational effectiveness. The exact measures used in the survey can be found in Appendix I. Additional explanation of the research methodology can be found in Ref. [2].

Survey distribution and response

The NPP KM survey was distributed and responses collected between April and September 2010. E-mail invitations were sent to NPP site interface officers asking their station senior operations manager(s) to participate by completing the survey with input as required from members of the plant management team. Surveys were downloadable from the IAEA web-site in four languages: English, Chinese, Russian, and French. In cases where contacts with senior NPP operations managers were established, direct invitations to participate were e-mailed to the identified individuals. A total of 118 individual survey responses were received. Three of these could not be used, therefore 115 completed responses were considered. The respondents identified in many cases that the response represented multiple reactor units. In a few cases the response was a ‘fleet’ response reporting on multiple stations, all of which were claimed to have similar ‘standardized’ management practices. This resulted in a total of 124 station ‘site organizations’ (i.e. slightly higher than the total number of survey responses) being represented out of a total of 204 organizations in the total global population, or 60.8%. NPP stations range from single unit to eight unit stations. On average there are two units per station. In a few cases, there were multiple stations at a single site. When considering the total number of units at each participating site, the responses represented a total of 253 reactor units or 57.9% of all 437 operating reactors. A total of 50 different operating organizations were represented in the response. The following sections provide a summary and analysis of the survey findings (for additional detail, see Ref. [2]). Survey response data was treated as confidential and only aggregate findings are reported.

Descriptive statistics

This section summarizes basic descriptive data to characterize the total population of NPPs, followed by a summary of the basic demographics of survey response data. Table 1 summarizes the number of plants by reactor type in each country for the entire global population of NPPs at the time of the survey. The various plant reactor types include:

  • AGR — advanced gas reactor;
  • BWR — boiling water reactor;
  • FBR — fast breeder reactor;
  • GCR — gas cooled reactor;
  • LWCGR — light water cooled gas reactor;
  • PHWR — pressurized heavy water reactor;
  • PWR — pressurized water reactor;
TABLE 1. SUMMARY OF ALL NPPs BY COUNTRY AND REACTOR TYPE (see Ref. [2])
Country Reactor type
AGR BWR FBR GCR LWCGR PHWR PWR Total
Armenia 0 0 0 0 0 0 1 1
Belgium 0 0 0 0 0 0 7 7
Brazil 0 0 0 0 0 0 2 2
Bulgaria 0 0 0 0 0 0 2 2
Canada 0 0 0 0 0 18 0 18
China 0 0 0 0 0 2 9 11
Czech Republic 0 2 0 0 0 0 6 8
Finland 0 2 0 0 0 0 2 4
France 0 0 0 0 0 0 58 58
Germany 0 6 0 0 0 0 11 17
Hungary 0 0 0 0 0 0 4 4
India 0 2 0 0 0 16 0 18
Japan 0 30 0 0 0 0 24 54
South Korea 0 0 0 0 0 4 16 20
Lithuania 0 0 0 0 1 0 0 1
Netherlands 0 0 0 0 0 0 1 1
Romania 0 0 0 0 0 3 1 4
Russian Federation 0 0 1 0 15 0 15 31
Slovakia 0 0 0 0 0 0 4 4
Slovenia 0 0 0 0 0 0 1 1
South Africa 0 0 0 0 0 0 2 2
Spain 0 2 0 0 0 0 6 8
Sweden 0 7 0 0 0 2 3 12
Switzerland 0 2 0 0 0 0 3 5
Taiwan, China 0 4 0 0 0 0 2 6
United Kingdom 14 0 0 4 0 0 1 19
Ukraine 0 0 0 0 0 0 15 15
USA 0 35 0 0 0 0 69 104
Total 14 92 1 4 16 45 265 437


Table 2 summarizes the response data by country with respect to the total NPP population and the NPPs included in the set of responding stations.

TABLE 2. RESPONDING NPPs BY PERCENT OF POPULATION AND COUNTRY (see Ref. [2])



Country
Total NPP population NPPs in sample response


Frequency


Percent


Frequency
Percent of total NPPs in survey response Percent country NPP
population
Percent of global NPP
population
Armenia 1 0.2 0 0 0 0
Belgium 7 1.6 7 2.8 39.5 1.6
Brazil 2 0.5 2 0.8 39.5 0.5
Bulgaria 2 0.5 2 0.8 39.5 0.5
Canada 18 4.1 17 6.7 37.3 3.9
China 11 2.5 9 3.6 32.3 2.1
Czech Republic 8 1.8 2 0.8 9.9 0.5
Finland 4 0.9 4 1.6 39.5 0.9
France 58 13.3 18 7.1 12.3 4.1
Germany 17 3.9 10 4.0 23.3 2.3
Hungary 4 0.9 4 1.6 39.5 0.9
India 18 4.1 2 0.8 4.4 0.5
Japan 54 12.4 20 7.9 14.6 4.6
South Korea 20 4.6 18 7.1 35.6 4.1
Lithuania 1 0.2 1 0.4 39.5 0.2
Netherlands 1 0.2 1 0.4 39.5 0.2
Romania 4 0.9 2 0.8 19.8 0.5
Russian Federation 31 7.1 3 1.2 3.8 0.7
Slovakia 4 0.9 4 1.6 39.5 0.9
Slovenia 1 0.2 1 0.4 39.5 0.2
South Africa 2 0.5 2 0.8 39.5 0.5
Spain 8 1.8 7 2.8 34.6 1.6
Sweden 12 2.7 7 2.8 23.1 1.6
Switzerland 5 1.1 5 2.0 39.5 1.1
Taiwan, China 6 1.4 6 2.4 39.5 1.4
United Kingdom 19 4.3 19 7.5 39.5 4.3
Ukraine 15 3.4 9 3.6 23.7 2.1
USA 104 23.8 71 28.1 27.0 16.2
Total 437 100 253 100 n/a 57.9


USA had a high count of NPPs represented (To check for non-response bias, an independent samples t-test comparison of respondents versus non-respondents was done to see if there was any difference in NPP operational performance using 3-year unit Capacity Factor (CF) (see Ref [2]). There was a significant (i.e. to P < 0.005 level) difference in means between the two groups with responding units having a 3.79% higher mean 3-year Unit Capacity Factor (UCF). The number of US responses in the study may have contributed to this difference. Although not large in magnitude, this difference does indicate a bias in the response towards higher performing plants). Figure 6 shows NPP units by output (in MWe).

FIG. 6. Breakdown of responding NPPs by plant output rating (see Ref. [2]).

Table 3 summarizes the responses by country and reactor type for stations responding.

TABLE 3. RESPONDING NPPs BY COUNTRY AND REACTOR TYPE (see Ref. [2])

Country
Count by reactor type
AGR BWR GCR LWCGR PHWR PWR Total
Belgium 0 0 0 0 0 7 7
Brazil 0 0 0 0 0 2 2
Bulgaria 0 0 0 0 0 2 2
Canada 0 0 0 0 17 0 17
China 0 0 0 0 2 7 9
Czech Republic 0 0 0 0 0 2 2
Finland 0 2 0 0 0 2 4
France 0 0 0 0 0 18 18
Germany 0 4 0 0 0 6 10
Hungary 0 0 0 0 0 4 4
India 0 0 0 0 2 0 2
Japan 0 13 0 0 0 7 20
South Korea 0 0 0 0 4 14 18
Lithuania 0 0 0 1 0 0 1
Netherlands 0 0 0 0 0 1 1
Romania 0 0 0 0 2 0 2
Russian Federation 0 0 0 0 0 3 3
Slovakia 0 0 0 0 0 4 4
Slovenia 0 0 0 0 0 1 1
South Africa 0 0 0 0 0 2 2
Spain 0 1 0 0 0 6 7
Sweden 0 4 0 0 0 3 7
Switzerland 0 2 0 0 0 3 5
Taiwan, China 0 4 0 0 0 2 6
United Kingdom 14 0 4 0 0 1 19
Ukraine 0 0 0 0 0 9 9
USA 0 26 0 0 0 45 71
Total 14 56 4 1 27 151 253

Figure 7 shows the number of units by each responding operator within the sample (with operator identification numbers being assigned alphabetically).

FIG. 7. Number of responding NPPs by operator (see Ref. [2]).

For readers that are interested in more detailed descriptive statistics of the response data, please refer to Appendices III–V. Detailed descriptive data (figures and tables) are provided that can be used as benchmark data. The data specifically answers the following basic research questions:

  • To what extent are KM practices supported and in use by managers in operating NPPs?
  • To what extent do NPP organizations have a supportive organizational culture?
  • To what extent do NPP organizations have quality knowledge processes?
  • To what extent do NPP organizations consider themselves to be effective?

Appendix III provides descriptive data and histograms for each of the construct variables in the study. Appendix IV provides individual bivariate scatterplots between each the construct and sub-construct variables (all possible combinations) in the study. A simple bivariate scatterplot allows the visualization of the relationship (or lack thereof) between the various constructs and sub-constructs in the research model. Appendix V summarizes descriptive data for individual indicator measures used for each construct or sub-construct in the study, in the form of histograms. Appendix VI provides additional descriptive data from Section G (Demographic Data) of the survey (see Appendix I). Appropriate procedures for data entry and preparation, data quality and screening (including removal of outliers), handling of missing data, missing value analysis, and reliability screening of measures (construct reliability analysis) were followed and are described in Ref. [2]. The study was based on the use of constructs and sub-constructs, each comprised of several Likert-scale measures. Construct values for each respondent were calculated based on simple averaging of the construct’s measures. Construct reliability analysis was performed to ensure the integrity of each construct. The measures considered unreliable were removed from the data set and statistical analysis (see Appendix VII). Improvements to these measures are planned for future versions of the survey and these are summarized in Appendix VIII.

Summary of regressions and findings

This chapter summarizes the results of the statistical analysis that was done to address the following basic research questions:

  • To what extent do knowledge management practices impact on and help create a supportive organization culture?
  • To what extent do knowledge management practices impact the quality of knowledge processes?
  • To what extent does the level of technology support (i.e. in terms of effective information systems technologies or operational support systems) impact the quality of knowledge processes?
  • To what extent does a supportive organizational culture impact on quality of knowledge processes?
  • To what extent does a supportive organizational culture impact on organizational effectiveness?
  • To what extent does the quality of knowledge processes impact on organizational effectiveness?

One of the challenges of this type of organizational study is that both theory and prior research predict that all of our variables (the constructs and sub-constructs), though independent, will have some degree of covariance. Of interest is the relative effect size and the amount of variance explained by these relationships when they are considered together in each many-to-one relationship (i.e. to determine which sub-construct covariates are explaining the variance of the dependent variable in each case). Thus in order to discriminate, we ideally need a method to examine their effects simultaneously.

As an initial investigation of these relationships, a statistical analysis based on a series of independent multiple regressions was performed. A summary of the findings is provided in this section. Detailed results of each of the regressions are summarized in Appendix VII. For readers interested in a more advanced analysis, please see Ref. [2] for a full description of a statistical analysis using Path Analysis methodology. In terms of the significant relationships identified, the results of the two analyses are quite similar, with two exceptions: the first being the link between organizational performance management (OPM) related KM practices and the quality of knowledge generation and validation processes (KG); the second being the link between supportive organizational culture (SOC) and the quality of knowledge sharing and transfer (KS). Both these relationships were not found to be significant in the path analysis (see Ref. [2]). The differences may be due to simultaneous effects, indirect effects, the possible effects of collinearity, or possible limitations in the measures used. Only the results of the multiple regressions are reported here for simplicity.

Multiple regressions can help to explore and understand the nature and strength of the dominant relationships between the various constructs. This section summarises the results of a systematic piece-wise multiple regression analysis to examine what significant associations exist between the constructs and sub-constructs. It is important to recognize that this approach is limited in that it does not account for simultaneous or indirect interactions among all the factors in a full model analysis. However, as there is no prior empirical research to draw upon, it does provide a useful method to identify the more important relationships and forms a basis for further analysis or research. Note that when a variable is eliminated from a multiple regression model, it does not necessarily mean it has no effect whatsoever, rather, it should be interpreted that the variable is not explaining much of the variance of the dependent variable in the presence of the other independent variables in the model.

A backward elimination multiple regression procedure was used to explore all possible direct main-effect relationships between constructs (i.e. specific knowledge management practices, organizational technology support, quality of knowledge processes, supportive organizational culture, and organizational effectiveness). This was done to the sub-construct level. Significance levels of 0.05 were used as a cut-off. Significance results of interest are discussed in the interpretations. Appendix VII provides the results of each detailed regression model.

In a backwards elimination regression procedure, all the independent variables included in the model are regressed on the dependent variable. If any variables are not statistically significant, the one making the smallest contribution is dropped. Then the remaining variables are regressed on the dependent variable, and again if any variables are not statistically significant, the one making the smallest contribution is dropped. The procedure continues until all remaining variables are statistically significant.

Recall that in multiple regression, the objective is to determine whether the coefficients (slopes) of the independent variables are different from zero (i.e. if they are having a real effect on the dependent variable), or if different from zero, they are not just due to random chance. The null hypothesis is that each independent variable has no effect (i.e. B = 0) and evidence is needed to reject this hypothesis. The criteria, is that the P-value, the probability that the observed result occurred randomly, is lower than the predetermined cut-off (i.e. the significance level). See Appendix VII for further explanations.

In multiple regression, the size of the coefficient (i.e. B) for each independent variable is the size of the effect that variable has on the dependent variable, and the sign on the coefficient (positive or negative) is the direction of the effect. The coefficient (i.e. B) tells you how much a given dependent variable is expected to increase when the corresponding independent variable increases by one unit, holding all the other independent variables constant. The findings are summarized below. All findings reported were statistically significant results at the P < 0.05 level or better.

The first finding from the piece-wise regressions is that specific knowledge management practices and technology support sub-constructs positively impacted specific knowledge processes. The following sets of relationships (see Sections VII.2–VII.6) were found to be significant:

  • Organizational performance management related KM practices (OPM, B = 0.415), human resource related KM practices (HRP, B = 0.29), and advanced operational support systems (OSS, B = 0.207) have a positive direct influence on the quality of knowledge acquisition and adoption processes (KA);
  • Human resource related KM practices (HRP, B = 0.295), information management related KM practices (IMP, B = 0.418), and support for organizational learning related KM practices (SOL, B = 0.404) all have a positive and direct impact on the quality of knowledge sharing and transfer processes (KS);
  • Human resource related KM practices (HRP, B = 0.355) and training related KM practices (TRP, B = 0.409) have a positive and direct impact on the quality of knowledge retention and storage processes (KR);
  • Operational performance management related KM practices (OPM, B = 0.571) and knowledge management strategy and planning related practices (KMS, B = 0.255) all have a positive and direct impact on quality of knowledge generation and validation processes (KG); and
  • Information management related KM practices (IMP, B = 0.419), human resource related KM practices (HRP, B = 0.235), and information systems and technology support (IST, B = 0.224) all have a positive and direct impact on the quality of knowledge utilization and application processes (KU).

The second finding from the piece-wise regressions is that specific knowledge management practices and technology support sub-constructs positively impacted the construct supportive organizational culture (SOC). The following sets of relationships (see Section VII.7) were found to be significant:

  • Information management related KM practices (IMP, B = 0.168), human resource related KM practices (HRP, B = 0.156), effective use of information systems and technology (IST, B = 0.097), support for organizational learning related KM practices (SOL, B = 0.405), and support for KM strategy and planning (KMS, B = 0.169) all have a positive and direct impact on the supportive organizational culture (SOC).

Although training related practices and operational support systems were expected to play a role, this was not supported by the data. The third finding from the piece-wise regressions is that a supportive organizational culture has a strong, direct, and significant effect on all of the quality of knowledge processes. The following specific relationships (see Section VII.8) were significant:

  • Supportive organizational culture (SOC, B = 0.628) had a positive and direct impact on the quality of knowledge acquisition and adoption processes (KA);
  • Supportive organizational culture (SOC, B = 0.572) had a positive and direct impact on the quality of knowledge generation and validation processes (KG);
  • Supportive organizational culture (SOC, B = 0.753) had a positive and direct impact on the quality of knowledge sharing and transfer processes (KS);
  • Supportive organizational culture (SOC, B = 0.538) had a positive and direct impact on the quality of knowledge utilization and application processes (KU); and
  • Supportive organizational culture (SOC, B = 0.616) had a positive and direct impact on the quality of knowledge retention and storage processes (KR).

The fourth finding from the piece-wise regressions (see Section VII.9) is that:

  • Supportive organizational culture (SOC, B = 0.60) has a strong, direct, and significant effect on organizational effectiveness (OE).

The fifth finding from the piece-wise regressions is that there are several important inter- relationships among the quality of knowledge processes. Using piece-wise regression, each of the quality of knowledge processes was regressed against the other remaining four quality of knowledge process constructs. As discussed earlier, the causal direction of these relationships has not been determined or assumed. The following specific relationships (see Sections VII.10.1–VII.10.5) were found to be significant:

  • The quality of knowledge generation and validation processes (KG, B = 0.672) and the quality of knowledge sharing and transfer processes (KS, B = 0.239) had a positive and direct impact on quality knowledge acquisition and adoption processes (KA);
  • The quality of knowledge generation and validation processes (KG, B = 0.312), the quality of knowledge retention and storage processes (KR, B = 0.502) and the quality of knowledge acquisition and adoption processes (KA, B = 0.262), had a positive and direct impact on the quality of knowledge sharing and transfer processes (KS);
  • The quality of knowledge utilization and application processes (KU, B = 0.316) and the quality of knowledge sharing and transfer processes (KS, B = 0.452) had a positive and direct impact on the quality of knowledge retention and storage processes (KR);
  • The quality of knowledge utilization and application processes (KU, B = 0.212), the quality of knowledge acquisition and adoption processes (KA, B = 0.428), and the quality of knowledge sharing and transfer processes (KS, B = 0.181) had a positive and direct impact on the quality of knowledge generation and validation processes (KG); and
  • The quality of knowledge retention and storage processes (KR, B = 0.328) and the quality of knowledge generation and validation processes (KG, B = 0.341) had a positive and direct impact on the quality of knowledge utilization and application processes (KU).

The sixth finding from the piece-wise regressions is that there are important findings on the relationships between the quality of knowledge management processes and organizational effectiveness. Using piece-wise regression, all of the quality of knowledge process constructs were regressed against organizational effectiveness. The following specific relationships (see Section VII.11) were found to be significant:

  • The quality of knowledge retention and storage processes (KR, B = 0.361) and the quality of knowledge utilization and application processes (KU, B = 0.385) have a positive and direct impact on organizational effectiveness (OE).

Figure 8 illustrates the relationships among the quality of knowledge processes constructs and organizational effectiveness. They are an important finding in that they establish the knowledge process mechanisms by which organizational effectiveness is impacted. Multiple regression does not prove a causal relationship (i.e. the direction must be interpreted based on theory and more advanced statistical methods) and the literature is not conclusive on the direction of these inter-relationships. For this reason they are shown with a ‘dotted line’ link to indicate the causal nature of the relationship is not determined and it could be causal in either direction. However, these relationships help to understand that significant inter- relationships do exist, and when combined with theory, guide the selection of feasible causal path links for further research. The links KU to OE and KR to OE have been established empirically in the literature by authors such as A. Jantunen (see Ref. [5]) and J.D. McKeen et al. [18] respectively and therefore are shown as unidirectional ‘solid lines’ to indicate they are assumed to be causal in nature. The path analysis (not described in this report, see Ref. [2]) confirmed these relationships and established causality among most of the quality of knowledge process constructs.

FIG. 8. Links among knowledge processes and to organizational effectiveness (adapted from Ref. [2]).

The seventh finding from the piece-wise regressions was obtained from the regression of all possible sub-constructs (i.e., the full model, which included all the knowledge management practices, both organizational technology support sub-constructs, supportive organizational culture, and all the quality of knowledge process sub-constructs) on organizational effectiveness. This test was to examine whether any direct relationships were more significant than the hypothesized KMPM relationships. The following set of relationships (see Section VII.12.) were found to be significant:

  • The quality of knowledge utilization and application (KU, B = 0.367), KM strategy and planning (KMS, B = 0.083), supportive organizational culture (SOC, B = 0.215), and quality of knowledge retention and storage (KR, B = 0.193) were found significant with OE at 0.05 level.

All other constructs dropped out of the model as not significant. Although KM strategy and planning (KMS) had a significant direct relationship with organizational effectiveness (OE), the effect size is small. The findings agree with the other regression findings and support the hypothesized KMPM relationships. They show clearly that the mechanism by which the KM practices influence organizational effectiveness is not direct and is primarily through their effect on a supportive organizational culture and on the quality knowledge processes. Figure 9 shows the combined results from all of the regressions in Appendix VII. Only the statistically significant relationships (i.e. the arrows) are shown, and these represent the links found by the multiple regressions between the factors. The links between the quality of knowledge process constructs are shown as two-way arrows to indicate the causal direction is not determined by regression and cannot be assumed. The link from KM strategy and planning (KMS) to organizational effectiveness (OE) is shown as a dotted line to emphasize it is the only significant (though small in effect size) direct link found between the KM practices or organizational technology support sub-constructs and OE. The link between organizational performance management practices (OPM) and quality of knowledge generation and validation (KG) and the link between supportive organizational culture (SOC) and quality of knowledge sharing and transfer (KS) are also shown as dotted lines to indicate these links were significant in the multiple regressions but were not supported in the path analysis (see Ref. [2]). All the remaining links were found to be significant positive direct relationships.


FIG. 9. Significant links between all constructs and sub-constructs (adapted from Ref. [2]).

In summary, the findings from the linear regressions substantially agree with the findings of the path analysis and support the Knowledge Management Performance Model (KMPM), see Ref. [2]). They provide evidence of specific and meaningful direct effect relationships, all to a significance of P = 0.05 or better. Standard multiple linear regression techniques allow many- to-one relationships to be examined and provide valuable insights into the data, however, the findings must be interpreted with appropriate care. Some of the limitations of the study are discussed in the following section.

Study limitations

A challenge of organizational studies research is the validation of developed theory with empirical results. Latent construct research models, which are essentially abstract conceptual frameworks that represent and help to explain organizational factors (i.e. influences, processes, behaviours or phenomena) in a theoretical context, must be supported by meaningful measures that can be applied to obtain reliable data. In many such studies, the researcher must try to identify and contend with many practical limitations. As with any such study, there are several sources of potential error. Independent multiple regressions can help identify the significant variables that may explain the variance in the dependent variable in each model but they cannot simultaneously consider the whole set of variables as system. Indirect and simultaneous effects cannot be evaluated. Careful consideration of this limitation when interpreting the results is necessary. Another limitation may occur when there is collinearity between two independent variables in a model. Linear regression is sensitive to the effects of high collinearity and unreliable findings may be produced in some cases, such as negative coefficients occurring when all correlations are positive. Although tests for collinearity and multi-collinearity were performed and levels considered reasonable, it is possible that collinearity is influencing some regression findings. In addition, there is always a question of possible weaknesses in the measures used. For instance, unexpected links that were found to be significant may be legitimate, but may also be related to measurement limitations. As an example, the link between operational support systems and quality of knowledge acquisition and adoption processes was not expected and should be interpreted with caution. It may be due to the perception by managers of recent acquisitions of these systems themselves as a knowledge (i.e. technology) acquisition process, which was not the intent of the measure. Another potential weakness in the study is bias. Self-report bias, individual response bias and non-response bias are common problems in empirical social sciences research. To minimize self-report bias, reverse coding of some questions was used. To minimize individual bias, cases where multiple responses were received were averaged. To check for non-response bias, an independent samples t-test comparison of respondents versus non-respondents was performed and results indicated a slight bias in the response towards higher performing plants (see Ref. [2]). A further limitation of the study is small sample size. Although a high percentage of the total population responded, it was not possible to obtain an adequate model fit using Structural Equation Model (SEM) techniques with a sample size of only 124 station organizations. If the study is repeated in future and a higher response is achieved, SEM methods would be recommended. A small sample size also makes the study more vulnerable to influence of outliers, reliability issues, etc.

Finally, it should be noted that the results of the path analysis (see Ref. [2]) were similar and reconfirmed the regression findings. The strength (effect size) of specific relationships vary somewhat in the path analysis but this is expected as the method is able to analyse all the modelled relationships as a system of linear equations, and indirect and simultaneous effects are considered. However, the same significant relationships were observed, with the two exceptions (discussed in Section 8). In these cases, simultaneous or indirect effects, possible effects of collinearity, and/or the possible effects of weaknesses in the construct measures may be a factor and should be considered in future research.


Conclusions

The research represents the first comprehensive empirical study of NPP organizations on the topic of KM and its links to organizational effectiveness. The findings show the levels to which KM practices have been applied in NPPs and provide clear evidence NPPs that have implemented KM practices obtain significant measurable benefits. The research provides new insights for managers on how and why KM practices are effective at improving organizational effectiveness, and explains the mechanisms by which this occurs. The findings will hopefully help NPP managers to better understand and achieve the benefits of KM practices in future.

KM practices are well recognized in the literature as important enablers of organizational performance. The empirical findings of this study strongly support this and reconfirm other research showing a link between knowledge processes (that enable organizational learning) and firm performance. The findings help understand why KM is an important strategic issue for NPPs. However, KM remains difficult and challenging and NPP managers often have difficulty assessing the benefits realized from their efforts. In this respect, the findings also provide useful justification for allocating resources to implement KM programmes and practices. This research clearly shows that management support for KM practices is an important determinant of organizational effectiveness in the context of NPPs.

In general, the findings show that NPP organizations with higher levels of support for KM practices have higher levels of organizational effectiveness (measured across a range of performance measures that include safety, economic, operations, and maintenance indicators). The research findings were statistically significant and strongly support the relationships hypothesized in the Knowledge Management Performance Model (KMPM). These relationships are of interest to NPP managers and include:

  1. KM practices and organizational technology support have a strong collective positive effect on the extent of supportive organizational culture in NPPs;
  2. KM practices and organizational technology support have a strong collective and positive effect on the quality of knowledge processes;
  3. The five quality of knowledge process constructs have a strong collective and positive effect on organizational effectiveness. This effect happens ultimately through the quality of knowledge utilization and application construct and the quality of knowledge retention and storage construct (but it occurs via a specific mechanism, i.e. pattern of interactions, among the other quality of knowledge processes); and
  4. The extent of a supportive organizational culture has a strong positive effect on the quality of knowledge processes and organizational effectiveness.

When the full model (i.e. all the sub-constructs) was regressed simultaneously on organizational effectiveness, only the following three relationships were found to be significant and have a meaningful (i.e. large) effect size: supportive organizational culture, the quality of knowledge utilization and application, and the quality of knowledge retention and storage. Although KM strategy and planning was found to be significant, it had a relatively small effect size (B = 0.083). This finding further supports the validity of the KMPM model as explaining the nature and mechanics of the relationships among the sub- constructs. The findings clearly support the main research hypothesis, i.e. that the mechanism by which the seven KM practice constructs and the two extent of technology support constructs influence organizational effectiveness is not direct: it is primarily through their effect on the intermediate variables of a supportive organizational culture and the five quality of knowledge process constructs.

Finally, the data from the study and the subsequent analysis findings provides a useful industry benchmark. This may help to better understand where and how NPPs may improve current KM practices and programmes and realize additional benefits. As the study represents the first of its kind, further research is recommended in this area and it is hoped will build on these findings. The IAEA Global NPP KM Survey may be repeated by the IAEA in future and if so, the data can be used to see important trends and develop measures for improvement at an industry level.

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