A new management approach to knowledge-creating ...

Int. J. Management and Enterprise Development, Vol. 10, No. 4, 2011

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A new management approach to knowledge-creating strategic decision-making in organisations Mostafa Jafari, Peyman Akhavan, Roozbeh Hesamamiri* and Atieh Bourouni Department of Industrial Engineering, Iran University of Science and Technology (IUST), Hengham St., Tehran, Iran E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] E-mail: [email protected] *Corresponding author Abstract: The latest researches depict that there is a gap in the literature on the role of knowledge creation in higher-level strategic decision-making process. The purpose of this paper is to develop a conceptual model in order to conceptualise the relationship between these two important concepts. Due to the subjective nature of this situation, soft systems methodology is used to incorporate and conceptualise the related principles. The proposed conceptual model provides advantages for managers, decision-makers and consultants to understand the role of creating new strategic and competitive knowledge from their current decision-making processes. Keywords: strategic decision-making; KM; enterprise development; SD; system dynamics.

knowledge

management;

Reference to this paper should be made as follows: Jafari, M., Akhavan, P., Hesamamiri, R. and Bourouni, A. (2011) ‘A new management approach to knowledge-creating strategic decision-making in organisations’, Int. J. Management and Enterprise Development, Vol. 10, No. 4, pp.291–314. Biographical notes: Mostafa Jafari is an Assistant Professor in the Department of Industrial Engineering at Iran University of Science and Technology (IUST), Tehran, Iran, with BE in Mechanical, ME in Productivity and PhD in Industrial Engineering from IIT, Delhi. Working in area of strategic planning, BPR, knowledge management, with more than 30 research paper and 5 books in area of industrial engineering. Peyman Akhavan received his MSc and PhD degrees in Industrial Engineering from Iran University of Science and Technology, Tehran, Iran. His research interests are in business process reengineering, knowledge management, information technology and strategic planning. He has published 1 book and has more than 30 research papers in different conferences and journals.

Copyright © 2011 Inderscience Enterprises Ltd.

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M. Jafari et al. Roozbeh Hesamamiri received his BE in Industrial Engineering from Iran University of Science and Technology, Tehran in 2007, and currently, he is a PhD student in the same university. His research interests are in knowledge management, information technology, artificial intelligence, systems modelling and human resource management. He has published more than 20 papers in different conferences and journals. Atieh Bourouni received her BE in Industrial Engineering from Iran University of Science and Technology, Tehran in 2007, and currently, she is a PhD student in the same university. Her research interests are in knowledge management, agent-based simulation, systems modelling and human resource management. She has published more than 15 papers in different conferences and journals.

1

Introduction

Knowledge management (KM) is increasingly becoming regarded as the most important source of lasting competitive advantage for firms (Drucker, 1993; Grant, 1996a; LeonardBarton, 1992, 1995; Nelson, 1991; Nonaka, 1990, 1991, 1994; Nonaka and Takeuchi, 1995; Quinn, 1992; Sveiby, 1997). KM can also constitute an emerging discipline aiming to support enterprises in new business environments where the notion of economics of ideas seems to be an important prerequisite for success and viability (Wiig, 1997). The goal of KM is to improve organisational capabilities through better use of individuals and collective knowledge resources. These resources are skills, capabilities, experiences, routines, norms and technologies. Furthermore, based on the recent researches, KM has a major role in organisational decision-making process (Bennett, 1998; Brockmann, 2002; Meso et al., 2002; Nicolas, 2004). Higher-level policy makers in corporations usually make strategic decisions based on their implicit knowledge and mental models. This kind of knowledge-based decisionmaking (KBDM) is mainly applied to firms’ strategic concerns. Yim et al. (2004) propose the application of knowledge and its management to business decision-making and strategic planning along with an approach to KBDM using system dynamics (SD). SD (Forrester, 1961) offers a methodology to assist businesses and government organisations in strategy development, analysis of policy options and analysis of dynamic processes where capturing information flow and feedback are important considerations. This type of analysis can help decision-makers in understanding a complex and interrelated system. SD can explain individual’s experiences through archetypes and underlying structures in behaviour, which is indeed one useful insight about a system (Senge, 1990). Moreover, organisational knowledge creation (KC) theory (Nonaka, 1991) is one of the first KM theories. This theory explains a process of making available and amplifying what individuals come to know and connecting this knowledge to a knowledge system (Nonaka et al., 2006). KC process is needed to be considered as a mediator in relationship between new venture strategy and firm’s performance (Tsai and Li, 2007). This theory has been used to explain phenomena in many fields, including those of organisation theory (Osterloh and Frey, 2000), organisation behaviour (Peterson, 2002), human resource management and leadership (Ranft and Lord, 2000), innovation and technology management (Nonaka et al., 1996), strategic management (Choo and Bontis, 2002),

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public administration (Larsen and Pedersen, 2001), management information systems (Scott, 1998), strategy making (Salmador and Bueno, 2007) and public policy making (Kim, 2004). Knowledge flow and interactions always take place in strategic decision-making processes. Managers in high velocity and emerging environments conceptualise strategy making as a double-spiral process of KC (Salmador and Bueno, 2005, 2007). Thus, it is important to highlight the importance and implications of knowledge involved in decision-making processes. Yahya and Goh (2002) examined the linkage between different aspects of decision-making and KM. They calculated the correlations between issues in decision-making and KM activities (such as acquire, document, transfer, creation and apply). The correlation between ‘company strategy’ and ‘KC’ is depicted as 0.29. This implies that based on their empirical research, these concepts are related in a positive manner. This means that the need for better ‘company strategy’ for decision-making causes in the need for better ‘KC’. This paper argues that practitioners in higher-level strategic decision-making could improve their decisions in an iterative manner by considering KC and decision-making process conceptually connected. To depict this relationship, a conceptual model is proposed. The aim of this model is to consider the use of KC concepts in order to find, develop and utilise organisation’s capability to enhance strategic decisions. Thus, this process would dynamically use effective and new competitive knowledge. This paper is structured as follows. Firstly, a literature review on KC and SD methodology is performed. Secondly, we present explanations about research methodology. Thirdly, we proceed to conceptualise the relationship between KC and organisational higher-level strategic decision-making using SD methodology. Finally, Section 8 includes issues of further research and managerial implications.

2

Literature review

2.1 Knowledge-based perspective of the firm The knowledge-based view of the firm is originated from the strategic management literature. It builds upon and extends the resource-based view of the firm (Barney, 1986a,b,c, 1988, 1991a,b; Penrose, 1959). In this theory, knowledge is being acknowledged as the most important and strategic resource of the firm. This knowledge is embedded and carried through multiple entities including organisational culture and identity, policies, routines, documents, systems and employees. Other researchers expanded knowledge-based theory afterwards (Conner, 1991; Grant, 1996a,b; Kogut, 2000; Kogut and Zander, 1992; Nonaka and Takeuchi, 1995; Spender, 1996). This theory is a basis for different KM researches (e.g. Alavi and Leidner, 2001; Hüseyin, 2005; Jafari et al., 2008, 2009, 2010; Malhotra and Galletta, 2003). According to Grant (1996a), specialised knowledge can be coordinated using the following mechanisms: 1

rules and directives which include etiquette, social norms and procedures

2

sequencing, i.e. each specialist’s input occurs independently in own time slot

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routines that can support a high level of simultaneity of individuals performing their own specialised tasks

4

group problem solving and decision-making.

2.2 Organisational dynamic KC Nonaka and Takeuchi (1995) proposed a model of KC process describing the dynamic nature of KC. This model consists of three main parts which are socialisation, externalisation, combination and internalisation (SECI), ‘ba’ and knowledge assets. These elements dynamically interact with each other enabling the knowledge assets of an organisation to be shared in ‘ba’, whereas the individuals’ tacit knowledge is converted and amplified by the spiral of knowledge through the SECI model.

2.2.1 The SECI model Nonaka (1994) has argued that KC is a spiral process of interactions between explicit and tacit knowledge. These interactions can lead to the creation of new knowledge. Figure 1 shows the characteristics of four steps in the knowledge conversion process which can be understood as processes of self-transcendence. In this figure, i, g and o stand for individual, group and organisation. Figure 1

Spiral evolution of knowledge conversion and self-transcending process

Source: Nonaka and Konno (1998).

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Socialisation includes the sharing of tacit knowledge between individuals. This term is used to emphasise that experiences, ideas, images, mental models and technical skills are exchanged through joint activities, such as being together, observation, imitation, practice and spending time and living in the same environment rather than through different written or verbal instructions. Externalisation refers to conversion of tacit knowledge to explicit knowledge. This process is about the expression of hidden and tacit knowledge as ideas, concepts, visuals, metaphors, analogies, etc. into comprehensible forms that can be understood by others. The individuals’ ideas become integrated with the groups’ mental models. There are two important supporting factors for externalisation: 1

articulation of tacit knowledge

2

translation of tacit knowledge into readily understandable forms.

Combination includes the conversion of explicit knowledge into more complex sets of explicit knowledge. Here, the key factors are communication and diffusion processes, and systemisation and codification of knowledge. Finally, internalisation involves the conversion of explicit knowledge into tacit knowledge. This step has two important dimensions; firstly, explicit knowledge has to be embodied in action and practice. Secondly, there is a process of embodying the explicit knowledge by using simulation or experiments to trigger learning by doing processes.

2.2.2 The concept of ‘ba’ Learning does not occur in a vacuum, though it needs a platform. The concept of ‘ba’, which almost translates as ‘space’, was originally introduced by Japanese philosopher Kitaro Nishida. Nonaka and Toyama (2002) defined ‘ba’ as “a shared context in motion, in which knowledge is shared, created and utilized, ‘ba’ is a place where information is given meaning through interpretation to become knowledge, and new knowledge is created out of existing knowledge through the change of the meanings and contexts y ....”

This concept is different from ordinary human interactions, because it emphasises on the KC. Thus, ‘ba’ is considered as a shared space that serves as a foundation for KC, meaning creation and knowledge conversion. It can be thought of as a shared space for emerging relationships which can be physical (such as an office or dispersed business space), virtual (such as e-mails and teleconferences), mental (such as shared experiences, ideas and ideals) or any combination of them.

2.3 System dynamics As one of the first responses to the shortcomings of operations research and other management science techniques for complex problems, such as large number of variables and non-linearity, J.W. Forrester introduced SD in the 1960s at the Massachusetts Institute of Technology. With background knowledge of electric circuits, servomechanism theory and feedback control theory, he developed a powerful method and a set of tools to model problems in complicated situations. These models and tools

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were based on those used by control engineers to analyse the stability of mechanical and electrical control systems that was first suggested by Tustin (1953). SD considers systems as ‘feedback processes’ which can describe specific and orderly structures. In his book, Industrial Dynamics (Forrester, 1961), he showed how models of the structure of a human system and the policies used to control it could help to more understanding of its operation and future behaviour. This methodology is based on the theory of information feedback and control to evaluate a real-world problem. In SD terms, the complex relationships between variables to predict and control the behaviour of a system can be described as levels and rates.

2.3.1 SD methodology SD prides itself on combining human mind and the power of computers to overcome the barrios to learning, such as dynamic complexity, limited information of problem situation, confounding variables and ambiguity, bounded rationality, flawed cognitive maps, erroneous inferences about dynamics and judgemental errors (Sterman, 2000). SD is a three-step process: 1

understand the problem situation: in this key step, the purpose is to clearly identify the problem, its factors that most appear to be causing it and the relationships between them

2

explicit conceptual model and simulation model building: a causal diagram is drawn to develop the understanding of influences between the variables. Explicit concepts of SD, such as flows, levels and auxiliary, are used in simulations model building process

3

simulation and gathering the results: after building the simulations model, it is now possible to analyse different scenarios for different policies and decisions.

Supplying managers with a set of tools, SD provides a powerful methodology for systemic knowledge investigation. Wolstenholme (1990) introduced two phases to implement SD methodology (Table 1), these two phases are: 1

qualitative SD

2

quantitative SD.

The qualitative SD phase is about creating ‘cause and effect’ diagrams. These system maps are essential to analysis of the system. From Wolstenholme’s point of view, it is very important to consider and include different actors of the system in this phase (Wolstenholme, 1990). Recent works by Vennix et al. (1990, 1992, 1993, 1997), Luna-Reyes et al. (2006), Andersen and Richardson (1997), Andersen et al. (1997), Rouwette et al. (2002) and Visser (2007) suggest involving managers, decision-makers and other stakeholders through the SD methodology in a process of group model building.

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SD – a subject summary

Qualitative SD (diagram construction and analysis phase)

Quantitative SD (simulation phase) Stage 1

Stage 2

To create and examine feedback loop structure of systems using resource flows, represented by level and rate variables and information flows, represented by auxiliary variables To provide a qualitative assessment of relationship between system processes (including delays), information, organisational boundaries and strategy

To examine the quantitative behaviour of all system variables overtime

To design alternative systems structures and control strategies based on

To estimate system behaviour and to postulate strategy design changes to improve behaviour

To examine the validity To optimise the behaviour of and sensitivity of specific system variables system behaviour to changes 1

in information structure

2

strategies

3

delays/uncertainties

1

intuitive ideas

2

control theory analogies

3

control theory algorithms, in terms of non-optimising robust policy design

Source: Adapted from Wolstenholme (1990).

2.3.2 Group model building in SD To improve performance in tackling strategic and messy problems, Vennix (1996) has suggested building SD models with teams. In complex problems, it is obvious that every individual can only have a limited view of nature and causes of the problem. Group model building makes the natural tendency people to think in terms of causal processes to systematically elicit and integrate the limited individual mental models into a more holistic view of the problem. As the result is a shared SD model, this can then be used to explore the dynamics of the holistic view. The client is involved throughout the model building process. The first step is to construct a preliminary SD model based on individual interviews of participants or the study of research reports and policy documents. This model is then further refined, in consultation with the individuals involved, before being presented at a group session. During the group session, the team seeks to elaborate the model to bring it to a point where the dynamic complexity of their view of the problem situation can be explored. This process depends crucially upon the facilitator. This facilitator needs a thorough knowledge of SD and must exhibit the right attitudes, skills and tasks. ‘Group model building’, as discussed above, is about identifying networks of related variables rather than simple causal chains. It is a process in which team members exchange their perceptions of a problem and explore questions such as: 1

What is the problem exactly?

2

How did this problematic situation originate?

3

What might be the most important underlying causes?

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How can we effectively tackle the problem?

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2.3.3 Different ways of using SD models Writing about validation in SD, Lane (1995) has argued that SD can be put to use in four different ways. These ways are 1

ardent SD

2

qualitative SD

3

discursive SD

4

theoretical SD.

On the other hand, these ways of using SD models have different aspects. These aspects are 1

appreciation of the situation

2

communicated conceptual model

3

formal model

4

policy insights or recommendations.

Lane captured these aspects and ways of using SD in the idea of a folding star, i.e. an extension of a simpler tetrahedron model proposed by Oral and Kettani (1993), and it can be imagined as a tetrahedron whose sides have been unfolded. This folding star is shown in a simplified form in Figure 2. Figure 2

Lane’s folding star

Source: Lane (1995).

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Appreciation of the situation stems from efforts to collect data from, and to reflect on, the ‘world’. Thus, it is a conceptualisation which provides data that will be used later. When the star is folded into a tetrahedron, the three identically named vertices coincide. Communicated conceptual model is the expression of the appreciation of the situation within some ordered framework so that it can be communicated to, and understood by, other people. Formal model is a representation of the communicated conceptual model, probably using a computer package. It may be used for further experimentation. Policy insights or recommendations are the results, in qualitative or quantitative terms, of the use of a SD model. The faces emphasise the fact that SD may be put to a range of different uses, only some of which are properly interpretive. Ardent SD links the left-hand trio of vertices and represents the use of a formal model, captured in computer software to develop a set of recommendations for change in the real world. Qualitative SD links the top three vertices and represents a mode of use in which SD provides a language that allows people to discuss their views of the systems under consideration. In a limited sense, this is, therefore, an interpretive mode of use. Discursive SD links the right-hand three vertices and represents the use of a formal model to help in understanding and to develop learning. This, too, has some interpretive features. Theoretical SD might be thought to imply the worst kind of speculative modelling in which there is no concern whatsoever with a real system. Lane uses it to describe work done for the SD community to demonstrate the power of the approach, or models for which there is no obvious client or user. This has some interpretive features.

3

Research methodology

In this study, two methodologies are discussed. Soft systems methodology (SSM) is considered as research methodology and SD as decision-making methodology. Both of these methodologies consist of their corresponding techniques, steps and methods. Due to the subjective nature of this problem, in this study, the authors adopted SSM (Checkland, 1988; Checkland and Scholes, 1990) which can be described as an iterative methodology that focuses and accommodates various stakeholders’ perspective in the design of the solution. It can also involve learning about a complex problematical human situation, and lead to finding accommodations and taking purposeful action in the situation aimed at improvement. In this research, the problem objectives are unclear and there are several perceptions about the problem. Indeed, it was felt that some improvements could be achieved by building a knowledge-creating platform for SD. This problem is not aimed to be solved in one fell swoop, but making incremental improvements is desired. There are two representations of SSM, which are depicted in Figures 3 and 4.

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M. Jafari et al. The first ‘seven-step’ representation of SSM

Source: Checkland (2001). Figure 4

A representation of older SSM

Source: Checkland (2001).

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Figure 3 shows that SSM has seven stages as below: 1

problem situation unstructured

2

the problem situation expressed

3

root definitions of relevant systems

4

deriving conceptual models

5

comparing conceptual models with the ‘real’ world

6

defining feasible, desirable changes

7

taking action.

Among these stages, stages (1), (2), (5), (6) and (7) are about working in real world, whereas stages (3) and (4) are considered systems thinking about the real world. Stages (1) and (2) are the start of a dialogue on this problem area that would lead into the development of a shared language. The authors conducted deep interviews and used the appreciative influence diagrams as rich pictures (Avison and Wood-Harper, 1990; Checkland and Scholes, 1990). The results allowed commentaries and discusses to be made allowing the problem situation to become more structured. In stage (3), ‘root definitions’ are constructed for the relevant systems identified in the previous stages. It should include the emergent properties of the system in question. To define these properties, CATWOE is used as follows: x

C: customer (These are the immediate beneficiaries or victims of what the system does. It can be an individual, several people, a group or groups).

x

A: actor (In any system, there are people who carry out one or more of the activities in the system, these are the actors).

x

T: transformation (This is the core of the system in which some definite input is converted into some output and then passed on to the customers).

x

W: Weltanschauung (This is often taken for granted outlook or world view that makes sense of the root definition being developed).

x

O: ownership (This is the individual or group responsible for the proposed system in the sense that they have the power to modify it or even to close it down).

x

E: environment (All systems operate within some constraints imposed by their external environment).

In stage (4), conceptual models are drawn based on ‘root definitions’. The conceptual model must be derived from the root definition alone. It is an intellectual model and must not be affected by the knowledge of the ‘real’ world. Stages (5) and (6) identify which activities need to be included in that particular situation. Finally, stage (7) is the implementation phase.

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Non-algorithmic representation of SSM is presented in Figure 4, Checkland (2001) proposes five broad ‘stages’ that are not necessarily sequential as a guide to implementation of SSM: 1

finding out

2

formulating root definitions

3

building conceptual models

4

using models, defining changes

5

taking action.

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Knowledge-creating strategic decision-making

SD plays a supportive role for strategy implementation in organisations and its modelling can be helpful for strategy implementation in regards to both strategy refinement and transfer of insights and understanding underlying the strategy forming (Snabe and Grobler, 2006). Forrester (1991) argues that management is an ongoing circular environment like Figure 5 in which action is based on current conditions, such as actions affect conditions, and the changed conditions become the basis for future action. As a result, policy option analysis must be an iterative process, which continuously uses the previous results of actions and produces new policies. To build a conceptual model, SSM is used as research methodology. Three steps of SSM, which are finding out, formulating root definitions and building conceptual model, are depicted to propose some improvements in policy making system of organisations. Figure 5

Closed-loop structure of the world

Source: Forrester (1991).

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4.1 Expressing the problem situation SD models can organise, clarify and unify knowledge to give people a more effective understanding about an important system that has previously exhibited puzzling or controversial behaviour. Different policies/strategies cause different results, which are recognisable in the system. These results help managers or decision-makers to gain much more knowledge about the problem situation. This knowledge can be divided into tacit and explicit. Explicit or codified knowledge refers to knowledge that is easily transmittable in formal, systematic language (Nonaka and Taguchi, 1995). This kind of knowledge could be gained by quantitative analysis and researches. Tacit knowledge is non-verbalisable, intuitive and unarticulated (Polanyi, 1962). It is learned through collaborative experience and is difficult to articulate, communicate and formalise (Nonaka and Takeuchi, 1995; Polanyi, 1962, 1966). Tacit knowledge about the new problem situation after a new policy or strategy largely depends on the skills, past experiences and schemes of experts. Tacit knowledge affects experts’ mental models. It might cause a new insight about the problem. SD models include mental models and tacit knowledge accordingly by using interviews, brainstorming and focused group interviews. Figure 6 shows these affects by arcs between SD modelling, tacit and explicit models, expert-based mental models and results of applying policies and strategies. Yim et al. (2004) proposed a five-step KBDM approach using SD as depicted in Table 2. In this approach, KM concepts are used to facilitate decision-making rather than just identifying, storing and disseminating process-related knowledge in an organised manner. This kind of KBDM has a dynamic decision environment for strategic issues and problems. It supports three steps of Nonaka’s SECI model, which are externalisation, internalisation and combination. Figure 6

Causal loop diagram for knowledge amplification

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The KC process needs socialisation as well. It also needs an important platform, which is known as ‘ba’. These aspects of KC are mainly concern with organisation’s culture. This is a platform that makes SECI model, a KC process. Thus, Yim’s KBDM needs a knowledge-creating platform to be considered as a knowledge-creating process. According to different aspects of using SD, Figure 7 shows influence relationships between these aspects and the two kinds of knowledge. In this figure, arcs from tail (e.g. explicit knowledge) to head (e.g. mental models) means that the tail concept influences the head concept (explicit models effect mental models). Table 2

Five phase of KBDM application

Phase Tasks/activities

Output

Technique

1

Define strategic issues and identify sources of knowledge

Strategic issues, scope and Interview, brainstorming and knowledge document analysis

2

Conceptualisation and integration of knowledge

CLD, problem statements Focused group interview

3

Formulation of knowledge model

SFD decision requirements Focused group interview and feedback analysis

4

Decision-making support

Simulation test

5

Applying

Feedback

Simulation test and focused group interview

Note: CLD: causal loop diagram and SFD: stock and flow diagram. Source: Adapted from Yim et al. (2004). Figure 7

Influence diagram for aspects of implementing SD and KC

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4.2 Formulating root definitions A root definition is the stimulus for the parsimonious selection of a set of verbs. These verbs can model the human activity system previously defined and provide the subprocesses that may be the domain of action for improvement. Checkland (1985) proposed that a root definition is well formulated, if it covers the elements in the mnemonic CATWOE. x

Customers: Who are the system’s victims or beneficiaries?

x

Actors: Who would do these activities?

x

Transformation process: What input is turned into what output?

x

Weltanschauung: The world view that makes this definition meaningful.

x

Owners: Who could abolish this system?

x

Environmental constraints: What does this system take as given?

One way of understanding CATWOE and the idea of Weltanschauung is captured in Figure 8. Alternatively, the root definition can be formulated from the components of the CATWOE mnemonic. Either way, the root definition will be a short paragraph, which will contain all the necessary information to describe the system. Figure 8

CATWOE as an input–output system

Source: Pidd (2003).

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The CATWOE of the strategic knowledge-creating decision-making is depicted in Table 3. Alternatively, we might capture this in the following sentences: “A system owned by the company and managed by its top managers to transfer available knowledge to strategies. It must be effective in the real world ambiguity about situations. The system is needed so that the company can be supplied with up to date and effective policies.” Table 3

CATWOE analysis

CATWOE

In the context of creating policy making knowledge

Customers

The company

Actors

Company’s personnel (top managers)

Transformation

Knowledge to strategies

Weltanschauung

The company needs to be supplied with the most effective strategies

Ownership

Company

Environmental constraints

We do not have a clear knowledge of current situation and future. There are always ambiguities about the problem situations that make strategy option analysis a complex process

4.3 Developing a conceptual model After formulating root definition of relevant systems, to avoid purely problem structuring, a conceptual model (Wilson, 2001) is intended to be developed. This conceptual model depicts what a system must include to meet root definitions. In this stage, conceptual models are not intended to refer to any particular implementation within any particular organisation. At this stage, we are in the realm of systems thinking and not in the real-world domain. In the terms of SSM, a conceptual model is a diagrammatic representation of the interconnections of the activities that must be present for the root definition to make sense. Checkland and Scholes (1990) suggest that a conceptual model should contain between five and nine activities, any more than that and some aggregation is needed, any less and the model is too simple to be of use. Thus, the models are validated against systems concepts and not, at this stage, against any notion of ‘reality’. Based on the above discussions about the relationships between different forms of knowledge and the process of implementing the SD methodology, it appears that supporting SD with a knowledge-creating platform is an important concern. As a foundation for the development of traditional SD implementation and based on the previously defined root definition for strategic knowledge-creating decision-making, a conceptual model is proposed.

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Figure 9 shows the proposed conceptual model as well as its different layers. This model has three layers: x

knowledge-creating platform layer (KCPL)

x

knowledge-creating SD layer (KCSDL) which is enhanced by KC concepts

x

SD methodology layer (SDML).

KCPL is intended to make possible and enhance the process of KC. As discussed before, learning needs a platform and it does not happen in vacuum. Here, the integration of SECI model and ‘ba’ in an organisation could create new competitive knowledge. This process is iterative and it should be seen as a cycle. In this cycle, some activities such as socialisation, externalisation, combination and internalisation should be considered in an organisational shared space named ‘ba’. ‘Ba’ is one of the most important basic parts of the KC process. SECI activities might not necessarily make new knowledge by themselves. Figure 9

A conceptual model for knowledge-creating strategic decision-making

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SDML is the policy analysis layer. In this layer, the organisation seeks to find a good policy by implementing all of its available knowledge (such as tacit and explicit). SD methodology has some steps, which was introduced before as appreciation of the situation, building communicated conceptual model, formulating formal model and finally, finding policy insights or recommendation to be implemented in the real world. This process is also a cyclic process. It starts from understanding the situation and tries to find a good policy, after implementing that policy, new understanding about the situation is achieved. The core layer of this conceptual model is KCSDL. This layer tries to relate the other two functional layers of the model (KCPL and SDML). These two cyclic processes should be done in related to each other. Thus, the KCSDL integrates these layers in the way that the learning process is applied on the policy option analysis cycle. With the use of KCSDL, SD methodology cycle’s knowledge about the problem situation would be amplified by using KCPL. According to the theory of absorptive capacity, KCSDL has a limitation in absorbing strategic knowledge (Cohen and Levinthal, 1990). This model shows that how can an organisation try to integrate its KM principles and systems approaches, which is used to learn about the problems. With the benefit of this model, an organisation can create new competitive knowledge in the field of its policy option analysis.

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Implications for management practice

Salmador and Bueno (2007) argued the main theoretical and practical implications of understanding strategy making as a knowledge-creating process. In their experience, strategy making is conceptualised by a double-spiral process of KC that is proposed to identify, document and analyse the processes of strategic action, strategic reflection-onaction and strategic imagination. In this research, a theoretical construct is proposed which considers the process of KC from strategic decision-making using a systems methodology (SD). In terms of practice, in SSM, after building-related conceptual model based on the root definition, the last two steps should be considered which are: 1

using models, defining changes

2

taking action.

In this step (which is also shown in Figure 4), the conceptual model should be compared with the real-world situation and any discrepancies should be identified. Then, based on these discrepancies, some feasible or desirable changes might be recognised by management. Finally, after processing the desired changes, some actions are implemented to decrease discrepancies. To perform the comparison, the following methods are proposed: (Checkland, 1981) x

general discussion and observation

x

question generation

x

testing in practice

x

model overlay.

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Considering the above discussion about the implementation of actions based on the proposed conceptual model, managers and practitioners in higher-level strategic decisionmaking can apply this conceptual model to organisations. They must first identify the basic discrepancies between their strategic decision-making process as a KC practice and the suggested conceptual model. Then, prepare a list of feasible and desirable changes to improvements and finally implement these actions. Managers can also develop their own comprehensive models based on the proposed conceptual model. These comprehensive models can be customised in different organisations.

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Limitations

There are three criteria for evaluating conceptual models, which are: 1

Efficacy (Will the system do what is required? Will it work?).

2

Efficiency (What resources will be required to enable the system to work?).

3

Effectiveness (Is this the best way to meet the higher-level goals within which the system must operate).

In this study, authors have used a logical analysis to find out about the problem situation and conceptualise the relationship between strategic decision-making using SD and KC. Based on this analysis, the designed system (conceptual construct) would definitely transfer available knowledge of organisation to strategies and because of this transformation, new strategic knowledge for further transformation is created. This is a cyclic process and should be considered as a kind of group knowledge-creating modelbuilding process. The limitation of this research origins the efficiency and effectiveness of the suggested conceptual model. The efficiency of the model cannot be exactly identified because it is theoretical in nature and needs empirical studies, but with using different aspects of using SD, authors have tried to minimise the concepts, which should be used in this conceptual model. In terms of effectiveness, the main question is that if the conceptual model is best way for this purpose. This question should be answered by further practical and theoretical researches in this field. Furthermore, according to the theory of absorptive capacity (Cohen and Levinthal, 1990), there is always a limit to the rate or quantity of scientific or technological information that a firm can absorb. There is also a limit to the capacity of knowledge absorption and thus KC in higher strategic decision-making process. Distinct dimensions to absorptive capacity are acquisition, assimilation, transformation and exploitation (Zahra and George, 2002). Firms should expand their absorption capacity to create new knowledge dynamically from their existing knowledge and strategic decisions.

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Further research

With respect to existing strategic decision-making practices, almost very little consideration has been given to the value of strategic KC in organisations. This conceptual model is the first step in developing more detailed models of the potential relationship between KC and higher lever policy analysis. Different organisations can try to learn this model by interactive development of their own comprehensive models. Researchers should then use these models in order to study their further implications. Further researches are also possible in determining and improving absorption capacity of knowledge to develop this process of KC in the field of higher-level strategic decisionmaking and open to practitioners in different areas.

8

Conclusion

We have argued in this paper that KC theory and practice have much to offer SD. They provide a platform of KC to assist SD to avoid possible risks in modelling complex systems. In terms of a conceptual model, the proposed model helps to clarify the potential role of KC in the analysis of current organisations’ policies and the exploration of alternatives. The value of this conceptual model is that it simplifies the importance of KC and reduces knowledge-creating decision-making to manageable and understandable components. This simplicity will enable managers to focus their attention on all three layers and not just SD methodology. This is very important because the role of KC is often underestimated in policy option analysis or decision-making process. Future research by the authors will focus on more detailed and systematic approach that enables organisations to be knowledge creating in the field of choosing the best decisions and policies.

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