Knowledge management performance model
Definition
Knowledge management performance model is A particular nuclear knowledge management system model
Description
Research model
Figure 1 illustrates the basic elements of the conceptual model used in the research (adapted from Ref. [1]).
The elements of the research model include five main factors (i.e. theoretical construct variables):
- Support for knowledge management processes (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 processes’, 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 [2, 3] provide a useful categorization of KM processes 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 processes 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.
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 [4]; P.N. Rastogi [5]; and G. Probst [6]). 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 [1]):
- 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 [1]). 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 processes 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. [1].
References
[1] DE GROSBOIS, J., PhD Thesis: The Impact of Knowledge Management Practices on Nuclear Power Plant Organization Performance, Carleton University, Ottawa, Canada (2011).
[2] INTERNATIONAL ATOMIC ENRGY AGENCY, Knowledge Management for Nuclear Industry Operating Organizations, IAEA-TECDOC-1510, IAEA, Vienna (2006).
[3] INTERNATIONAL ATOMIC ENRGY AGENCY, Planning and Execution of Knowledge Management Assist Missions for Nuclear Organizations, IAEA-TECDOC-1586, IAEA, Vienna (2008).
[4] TANNENBAUM S.I., ALLIGER, G.M., Knowledge management: clarifying the key issues, ISBN 0967923913, IHRIM, (2000).
[5] RASTOGI, P.N., Knowledge management and intellectual capital — the new virtuous reality of competitiveness, Human Systems Management, 19, 1, (2000) 39–49.
[6] PROBST, G., Managing knowledge, building blocks for success, ISBN 0-471-99768-4, Wiley, West Sussex, United Kingdom (2002).
