Author Guidelines for 8 - Open Access Brunelbura.brunel.ac.uk/bitstream/2438/11456/1/Fulltext.docx · Web viewinvest ment decision-making processes, to better understand the often

Proceedings of the 47th Hawaii International Conference on System Sciences - 2014

Visualisation of Knowledge Mapping for Information Systems Evaluation:A Manufacturing Context

Amir SharifBrunel University

Brunel Business [email protected]

Muhammad M. KamalBrunel University


Zahir IraniBrunel University


Abstract

Information Systems (IS) are primary enablers in instigating organisational change, increasing organisational responsiveness and decreasing supply chain overheads. This paper aims to contribute through exploring and visualising Knowledge Mapping (KM) from the perspective of Information Systems Investment Evaluation (ISIE). Complexity of IS evaluation process increases with the increase in intricacy of IS. A number of interrelating factors (e.g. costs, benefits and risks) contribute towards the intricacy of IS evaluation. There seems to be a growing need to evaluate the IS investment decision-making processes, to better understand the often far reaching repercussions related with IS implementation and interconnected Knowledge Components (KC). In seeking to edify the often vague IS evaluation process, this paper attempts to, emphasise the increase of knowledge and learning through the application of a Fuzzy Cognitive Mapping (FCM) technique. The resulting FCM determines the intricate and developing behaviour of causal relationships within the knowledge management area.

1. Introduction

In today’s global economic environment, the world-wide business organisational ambience has significantly transformed which contributed to today’s competition in many sectors of industry, commerce and government [1]. In such a hyper-competitive state, organisations need to be aware that their operations would abruptly cease to function should the technology that underpins their activities and help to automate organisational workflows, ever come to a halt. Thus, business organisations need to constantly explore state-of-the-art ways to re-orchestrate their products and delivery of services. In recent years, enterprise IS have played a significant role in supporting organisational agility, dealing with improbability in decision-making, supplementing their competitiveness, and coordinating information in the supply chain [22, 46]. According to Stockdale and Standing [44] a substantial growth in enterprise IS investments has enforced a number of business organisations to focus on the usefulness and assessment of their business processes and approaches. The latter argument is supported by

Sharif et al., [38] who state that in essence, enterprise IS evaluation is a decision-making process, which enables an organisation in defining costs, benefits, risks and more importantly, in highlighting the repercussions of investing in IS. According to Hedman and Borell [10] the evaluation of enterprise IS is essentially based upon knowledge of the organisation and strategic, tactical and operational prerequisites. Such enterprise IS support organisations in capturing and storing knowledge of human experts and then imitating human reasoning and decision-making in the design, production and delivery of manufactured goods [22, 44].

Regardless of using any approach in an organisation, the evaluation process aims to ascertain a link between the anticipated value of an investment and analysis [often quantitative] of the costs, benefits and risks [13]. To address the requisite for an organised evaluation instrument for supporting the top management in better understanding the human, organisational and technical repercussions of their investment decisions, academics and practitioners have approached investment decision-making from a variety of perspectives. For example, Expert Systems (ES) perform a number of tasks that are carried out by humans with certain knowledge and experience. Assessing performance necessitates an understanding of human expert performance and in the way it can be assessed. The knowledge and investigational learning that is a prerequisite within a decision-making process, is thus essential to the ending result. Kim et al., [20] points here that sharing and managing knowledge in all its forms requires being stable and controlled to make the most of its effect.

The motivation for this paper is to attempt to map out and visualise the range and aspects of knowledge that are relevant to the ISIE process in the manufacturing context, based upon the extant literature and managerial, operational, organisational, technological and strategic aspects of an organisation’s strategy. As such, the motivation for this research rests on understanding what aspects of the relevant expert knowledge ultimately drive this knowledge-intensive evaluation task, thereby highlighting some of the dynamic inter-relationships inherent within the field and in a practical context. This paper therefore seeks to investigate and map factors influencing the decision-making process for ISIE and their pertinent KC. The mapping of ISIE factors and related KCs is achieved by using a FCM

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technique, resulting in exploring the inter-relationships and intricacies of decision-making factors in a manufacturing context. FCM was selected to explore the cognitive perspective of ISIE factors – the focus is to examine the link based context, not the agent based context. Moreover, the authors are not limiting or neglecting the usefulness of other techniques, instead we are seeking to apply FCM as it has limited application to the ISIE related context as compared to agent based modelling.

2. Research Design

A robust research design (based on four steps) was constructed and proposed, which acted as a blueprint for the research process. Each step in this process acts as a basis for the next step: Step 1 is about investigating factors that define

ISIE in the manufacturing sector. This was achieved by studying the extant general IS and manufacturing literature in-depth. This research exercise facilitated the authors in understanding of ISIE practices in manufacturing organisations and as a result, supported the identification and defining of 15 influential factors. These factors are classified according to the proposed ‘MOOTS’ dimensions – Managerial, Organisational, Operational, Technological, and Strategic.

Step 2 is about identifying and correlating KC with the relevant ISIE factor. These KCs are identified using the proposed five-step Pairwise IS Theory Equivalence (PIE) framework (Figure 1). The proposed PIE process is further divided into five sub-steps. For example, for each ISIE factor a supposition is developed, then, two relevant IS theories are identified for each ISIE factor – this allowed more flexibility in extracting a relevant KC. Then a rationale is developed that supports the identification of the dependent and independent constructs relevant to each IS theory. From these constructs only those are selected that clearly associate the ISIE factor with the two chosen IS theories. After identifying the constructs, a relevance check is conducted – this sub-step is to ensure the whole process is moving rightly, resulting in identifying the gap. This void is then translated into a single KC for each ISIE related factor.

Step 3 details the process by which the MOOTS and the PIE classification approach is combined with expert knowledge to construct a matrix (of ISIE factors. Through pairwise comparison – the so-called Field Anomaly Relaxation (FAR) as stated by Rhynne [37] – these factors then determine the scope of the knowledge to be mapped. Each of these factors is then assigned fuzzy weightings using a range of positive to

negative values. A directed graph can be constructed of these pairwise fuzzy values – which ultimately becomes the FCM.

Step 4 involves the algorithmic process of the FCM simulation. This needs several simulation scenarios to be identified. These scenarios are effectively vectors which denote the initial states of the ISIE factors from Step 3. These vectors are details of expert knowledge encoded into numerical fuzzy values per factor. These vectors are, in turn, fed into the simulation algorithm where the successive nodal states of each factor in the directed graph are updated from the preceding nodal state until equilibrium is achieved. The output values for each node, hence the ISIE factor, are plotted against iterative steps. Then updated FCM is created through calculating the inverse of the fuzzy weight matrix and the final ISIE nodal values.

3. Information Systems Investment Evaluation

Information systems constitute a substantial financial investment for organisations [12], thus, they should be justified, evaluated and managed with caution [4]. To invest in new IS and or enhance the efficiency of existing IS and technological infrastructure, managements are required to consider investment risks and payoffs and acquire knowledge of existing IS inefficiencies, respectively [21]. In this context, evaluating IS investments becomes a requisite for management, for the purpose that enterprise IS implementation influence the way organisations operate and impact their strategies, tactics and operational decisions. Evaluation is vital to rationalise higher IS investment costs, improbability of returns from IS investments and act as a control and management mechanism [4]. However, Stockdale and Standing [44] argue here that in evaluating IS, a crucial task is to develop frameworks that are effectively standardised and can be applied to a broad range of applications but at the same time thoroughly detailed to offer effective support. In this context, systematic but equally coherent methodologies are a prerequisite to determine IS justification concerns emerging from the complexity of topical technologies [8]. Though, successfully pursuing ISIE can lead an organisation to sustain its corporate viability and success.

3.1 ISIE in Manufacturing Context

To improve business operations and productivity of the supply chain, manufacturing organisations have developed themselves as responsive industrial entities by implementing pertinent IS. The latter argument is supported by Mondragon et al., [26] who report that the operations of agile organisations require the existence of

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efficient IS within their supply chains. Wang et al., [47] report that information systems are vital for (a) instigating transformational changes within and between manufacturing organisations and (b) the efficient functioning of their operations i.e. design, production, and delivery of manufactured goods. According to Coronado et al., [5] IS implementation enhances the agility of manufacturing organisations and enables the development of resilient and collaborative relations. Despite the significance of IS, there are a number of cases where manufacturing organisations have been unsuccessful in completing their IS projects. This has resulted in manufacturing organisations failing to gratify their key stakeholders. For example Irani et al., [16] in their study on SME manufacturing enterprises focused on gaining insights SME IS implementation failures. The reasoning concluded from their IS failure was failing to realise the human and organisational factors influencing the evaluation/implementation process. Perera and Costa [33] also alleged that most of the IS investments by manufacturing organisations have not reaped the desired returns. This is because selecting an appropriate IS is primarily complicated and vital decision for manufacturing organisations [2].

Such commentaries on IS performance show that while manufacturing organisations have benefited from IS, many of them have been less than satisfied. One of the apparent reasons for this discontentment is reported by Small [41] who states that inapt IS investment justification practices can result in organisations not able to differentiate the benefits that manufacturing IS may be capable of conferring, whilst, there is a lack of evidence on returns on IS investment as managements have failed to prove the tangible returns on the resources deployed to plan, develop, implement and operate IS. Advocates claim that ISIE when managed and pursued effectively can have a positive impact on organisational performance and productivity [9]. Thus, a formal justification proposal must be prepared and accepted by decision-makers, prior to IS investments [14].

3.2 MOOTs Classification of ISIE Factors

In Table 1, the authors present 15 factors (based on the MOOTS dimension) that define ISIE in the manufacturing context. This list of factors may not be comprehensive, nevertheless, these 15 factors are identified based on the literature specifically focusing on IS, ISIE, manufacturing organisations and supply chain management. These factors also directly related to the context of this research.

4. From Knowledge to Knowledge Management

Knowledge has long been deliberated as a vital organisational asset supporting the top management in their decision-making process and augmenting

organisational competitiveness [34]. Advocates highlight that its effective management is essential for organisational success [7]. Knowledge is a blend

Dimension ISIE Factors Ref.

ManagerialManagement Commitment (MC) [29]

Management Style (MS) [29]Managerial Capability (MC*) [6]

OrganisationalOrganisational Cultural (OC) [19]

Organisational Performance (OP) [45]Organisational Size (OS) [25]

Operational

Employee Commitment (EC) [27]Training and Education (TE) [3]

Information Systems & Manufacturing Agility (ISMA) [31]

Technological

Enterprise Integration in Manufacturing (EIM) [31]Information Systems & Organisational Fit (ISOF) [15]

Information Systems Quality Output & Performance (ISQOP) [36]

StrategicStrategic Information Systems Impact (SISI) [11]Business Strategy & IS Alignment (BSISA) [36]Strategic IS Business Partnership (SISBP) [48]

Table 1: MOOTS Classification of Factors Defining ISIE

of experience, values, circumstantial information and professional understanding that support the evaluation and incorporation of new experiences and information. Researchers have thus studied Knowledge Management (KM) so as to define its involvement in managing and leveraging organisational knowledge [28]. Organisations that focus on KM tools and techniques recognise the significance of exchanging knowledge, as this sharing of exchange increases efficiency and sustained competitiveness. Such use of knowledge tools and techniques to generate economic benefits is one facet of knowledge economy [42]. According to Powell and Snellman [35], the key component of knowledge economy is a higher dependence on intellectual competencies of human resources than contribution from other physical or natural resources. From the economic perspective, Shin [42] claims that employing KM tools and techniques for knowledge sharing should not be assumed on a simple conjecture that organisational competitiveness is positively interrelated with knowledge sharing. Shin [42] further argues that knowledge sharing has a negative link with competitiveness and a positive one. Considering both the knowledge-sharing aspects, the economic viewpoint may offer a way to explore how to curtail barriers and promote enablers so as to acquire knowledge sharing benefits in organisations.

Thus, three important aspects can be drawn from the above discussion i.e., People (organisational and cultural aspects of the use of knowledge); Process (methods and techniques for managing the flow of knowledge); and Technology (tools and infrastructure that provide access to knowledge). From these three key aspects, a rational theme has been to relate explicit and tacit forms of knowledge together [17].

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In the context of organisational knowledge, Nonaka [30] reported that this level of knowledge is created through a series of socialisation, externalisation, combination and internalisation that transform knowledge between the tacit and explicit modes. In line with this dynamic process of knowledge creation, the process of transmitting tacit knowledge to explicit (and the other way round), is a collective act, where knowledge is relocated and shared with other individuals in the organisation. Thus, two key points can be extracted from the aforesaid knowledge conceptualisation: First, knowledge is in a reformed mode;

nevertheless, to make an individual’s knowledge beneficial for others, it must be conveyed effectively and efficiently and that it is understandable and open to others. This can be attained by using expert systems – the fundamental idea is that knowledge from the human mind is stored in the computer and users call upon the computer for specific advice as and when necessary. The computer can make extrapolations and reach to a decision. Then like a human expert, the computer offers suggestions and explains, if need be, the rationality behind the suggestion. Expert systems offer an effective and flexible means of exploring solutions to problems that often cannot be dealt with by other, more orthodox approaches.

Second, hoard of information is of limited value to organisations – it is only when this information is dynamically processed in an individual’s mind through a process of discussion and learning that it can be effective.

At this point in time, KM plays a vital role by performing as a meticulous process for managing intellectual and knowledge assets to meet organisational aim and objectives. KM aims to make knowledge approachable and re-utilisable for and by the organisation. Therefore, knowledge not only exists in documents and repositories, but it becomes rooted in individuals’ minds over time and is revealed through their actions and behaviours. Likewise, in an organisational context, decision makers and their decision-making processes are influenced by the knowledge that is generated as a result of evaluating organisational IS investments [18]. Kulkarni et al., [24] highlight that knowledge is entrenched and streams across various units within an organisation. For example, experts with specific domain capability, categorical best practice measures, or lessons learned from related experiences, documents, daily operational practices, and IS. It is, therefore, important to understand the different types of KCs so as to uncover its likely contribution to the performance of an organisation [32].

4.1 Process of Identifying ISIE related KC

In an attempt to determine the KC resulting from ISIE, a five-step Pairwise IS Theory Equivalence (PIE) process is followed (Figure 1). Each ISIE factor defined under MOOTS classification will follow the same process in order to identify related knowledge components. As highlighted in Figure 1, the steps include (with an example of how a KC for ‘management commitment’ factor is identified from Step 1 to Step 5 in Table 2) the following: Step 1 is about identifying the

assumptions/starting point – For each ISIE factor an assumption is developed. The assumption is divided into the ‘Focus’ and ‘Dependence’ of the ISIE factor. For example, Focus signifies the central theme i.e. the management is committed to ISIE – this indicates the Focus of management; whereas, Dependence shows the state of being determined, influenced or controlled by something else i.e. MC is dependent on the availability and utilisation of resources. From this assumption (i.e., dependence), the keywords extracted are availability, utilisation, resources, and evaluation.

Step 2 is about identifying the relevant IS theories, models or frameworks – In this step, the authors identified two relevant IS theories for each ISIE factor. The decision to select two appropriate IS theories, models, or framework was made on the understanding that it would allow flexibility in extracting a relevant KC. For example, for the ‘management commitment’ factor, the resource-based view and contingency theory were considered relevant based on their dependent and independent constructs.

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Figure 1: A Three-Step PIE Process for Identifying ISIE related KC

Step 3 is about developing the rationale – Identifying the main dependent and independent constructs relevant to an IS theory, model or a framework. However, from these available constructs, only those constructs were selected that associate an individual ISIE factor with the two relevant IS theories, models or frameworks. For example, the constructs that were deemed relevant from the two IS theories, models or frameworks are assets, resources, efficiency, capabilities and organisational performance.

Step 4 is about conducting a relevance check – Identifying the link between Step 1 and Step 3. This relevance check enabled the authors to relate the keywords extracted from the assumption to the dependent and independent constructs of each IS theory, model or framework to identify a gap.

Table 2: Example of Managerial Dimension ISIE Factor (Management Commitment) and related KC

Step 5 is about identifying a gap as a result of Step 4. Based on the relevance check of keywords and dependent/independent constructs a KC is extracted. The authors assert that a KC is based upon relevant IS theories, models or frameworks that, in turn, support that KC. For example, for the ‘management commitment’ factor, the knowledge component identified is Effective Use of Resources.

A similar process for extracting the relevant KCs is followed for each of the other 14 ISIE factors. The remaining ISIE factors and their related KCs are presented in Table 3.

Table 3: All ISIE Factors and their related KCs

5. Visualising the Knowledge Map

The 15 ISIE knowledge components presented in Tables 2 and 3 were grouped into six key thematic areas as shown as follows, i.e., constructed to be within a morphological field of factors [37]. ICT – Actual Use of IS, Use of System. MGMT – Past Management Experience, Better

Management of Resources. PERF – Improved Performance and

Management of Resources, Effective Organisational Benchmarks and Performance,

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MANAGERIAL DIMENSION

STE

P 1

–A

ssum

ptio

ns

(a) FOCUS: Management is committed to evaluating their IS investments.(b) DEPENDENCE: MC is dependent on the availability and effective utilisation of financial and other organisational resources e.g. if there is enough investment to implement and evaluate IS, management will be committed towards promoting / pursuing the evaluation.

STE

P 2

–IS

The

orie

s

ResourceBasedView

ContingencyTheory

STE

P 3

– M

ain

Dep

ende

nt&

Inde

pend

ent C

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r Se

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The

orie

s

Inde

pend

ent

Assets Capabilitie

s Resources

Strategy Technology Task Organisational

Size Structure &

Culture

Dep

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nt

Competitive Advantage

Organisational Performance

Efficiency Organisational

Performance

STE

P 4

–R

elev

ance

Che

ck

Rel

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t C

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sfr

om S

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Assets and Resources Efficiency Capabilities Organisational Performance

Rel

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Ste

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Availability Utilisation Resources Evaluation

STE

P 5

–K

Cre

late

d to

M Effective Use of Resources

ISIE Factors Knowledge Components

Man

ager

ial

Dim

ensi

on

MC Effective Use of Resources

MS Past Management Experience

MC* Improved Performance & Management of Resources

Org

anis

atio

nal

Dim

ensi

on

OC Effective Organisational Benchmarks & Performance

OP Performance ManagementMetrics

OS Better Management of Resources

Ope

ratio

nal

Dim

ensi

on

EC Actual Use of IS

TE Skills Identification & System Training

ISMA Consistent Information Output

Tec

hnol

ogic

alD

imen

sion EIM Radical Transformation

ISOF Use of System

ISQOP Quality Production & Performance Measurement

Stra

tegi

cD

imen

sion

SISI Enhancing Organisational Competitiveness

BSISA Strategic Alignment/Fit Procedures

SISBP Development & Effectiveness of Relationship


Performance Management Metrics, Consistent Information Output, Radical Transformation, & Quality Production, Performance Measurement.

RES – Effective Use of Resources, Development and Effectiveness of Relationship.

SKILLS – Skills Identification & Systems Training.

STRAT – Enhancing Organisational Competitiveness, Strategic Alignment/Fit Procedures.

By doing so, the authors wished to carry out a pairwise analysis to determine and remove any redundant / duplicated factors. This approach has been successfully used before [39]. In comparing any and every two sets of factors, a reduced morphological field was generated leading to an 83% reduction of ISIE constructs (i.e. from 90 to 15). The method for doing so was based on identifying those ISIE pairwise combinations where four or more similar dependent / independent sub-constructs existed. The resulting reduced morphological field of ISIE factors is therefore shown in Table 4.

ICT MGMT PERF

Actual Use of IS

Past Management Experience

Effective Organisational Benchmarks & Performance

Better Management of

Resources

Radical Transformation

Table 4: ISIE Factors Following Pairwise Analysis

This set of ISIE factors was then used as the basis for constructing the fuzzy cognitive map. Causal relationships were developed by the authors based

upon the PIE construct, to yield the fuzzy weight matrix in Table 5. Subsequently the FCM in Figure 2 was constructed as a directed graph, where the strength of causality between each node (henceforth USEIT – TRANSF in the weight matrix) was determined by the thickness of the line connecting each factor. A thicker line / thinner line denotes stronger / weaker causal relationships, respectively, and a value of 0 or no line indicates no relationship.

Figure 2: FCM of ISIE Factors

USEIT PAST MGMT RES PERF TRANSF

Actual Use of IS

Past Management Experience

Better Management of Resources

Effective Organisational Benchmarks & Performance

Radical Transformation

USEIT Actual Use of IS 0 0.333 0.667 0 1PAST

MGMTPast Management

Experience 1 0 -0.333 -0.333 0

RES Better Management of Resources 0.333 0 0 0.333 0.667

PERF Effective Organisational Benchmarks & Performance 0.333 0 1 0 1

TRANSF Radical Transformation -0.667 0.333 0 0.667 0

Table 5: Fuzzy Weight Matrix for the ISIE Factors

5.1 Knowledge Mapping / Simulation of the FCM

The mapping and simulation of the FCM follows the technique defined by Kosko [23] and as denoted by Sharif and Irani [39]. The authors as a result used the TAPE framework [40] to identify two scenarios in manufacturing ISIE (with user’s and manager’s perspective of evaluating an IS). These were used as ‘seed’ factors in the FCM – wherein each of the FCM nodes were classified into explicit or tacit KCs.

5.2 Analysis

In both scenarios (Figures 3 & 7) the overall results show that the dynamics are founded on high to low causal responses ranging from TRANSF, PERF, RES, PAST MGMT through to USEIT. First scenario, there is a major shift in terms of the negative causal response related to USEIT and RES; with opposite causal responses from other factors. Second scenario, there is a constant large negative reduction in the USEIT variable (from 0.93 to 0.09

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showing little or no causal effect); and a corresponding fall in causal effect for RES and TRANSF. But, there are causal increases for PAST MGMT and PERF. This is more prominent when the starting and ending nodal values are plotted for each scenario (Figures 5, 6, 9 and 10), respectively.

Figure 3: FCM Results for Scenario 1

USEIT

PAST MGMT

RES

PERF

TRANSF

(++++)

(++++)

(++++)

(----)

(--)

(--)

(---)

(--) (----)

(----)

(++++)

(----) (----)

(----)

(----)

(++++)

(---)

(--)

(----)

(----)

(----)

Figure 4: Resulting FCM for Scenario 1

Figure 5: Starting Nodal Values for Scenario 1

Figure 6: End Nodal Values for Scenario 1

Figure 7: FCM Results for Scenario 2

Figure 8: Resulting FCM for Scenario 2

Figure 9: Starting Nodal Values for Scenario 2

Figure 10: End Nodal Values for Scenario 2

Plotting the separate responses on a polar and/or Cartesian scale, magnifies the respective scenario results further as shown in Figures 11 and 12. Here, across both scenarios there appear to be two key dynamic factors which dominate the results – namely the interactions between nodes PERF and RES which show an out-of-phase relationship with one another (highlighting that as the causality of organisational benchmarks and performance increases, the causality of better management of resources decreases (and vice versa). Underlying all of these factors, the dynamic of the past management experience node (PAST MGMT) appears to have a stabilising effect on all of the other nodes – i.e., when this nodal

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response stabilises, all the other nodes stabilise soon afterwards as well. Hence, in mapping the knowledge involved in ISIE, the PAST MGMT factor appears to have a tacit controlling impact over other factors. But, analysing the FCM results which at last encode the knowledge within the ISIE process in this vein reveals that this is only part of the overall picture.

Figure 11: Scenario 1 Phase Plot

Figure 12: Scenario 2 Phase Plot

Figures 4 and 8 show the respective reconstructed FCM diagrams as following each of the simulation runs. The redrawn FCM in both cases were constructed through matrix manipulation of the computed results, scenario vectors and the original fuzzy weight matrix. This yielded the computed fuzzy eight matrices in Tables 6 and 7 for scenarios 1 and 2, respectively. The FCMs were once again drawn and constructed based upon the pairwise relationship between each node and the strength of each of the causal links determined the thickness of the lines connecting nodal points. Ultimately for both scenarios and FCMs, these diagrams show that each scenario involves an inherent range of inter-relationships belying the initial FCM.


USEIT 0.000 0.956 0.915 0.977 0.842PAST

MGMT -0.948 0.000 -0.445 -0.445 -0.671

RES -0.742 -0.552 0.000 -0.742 -0.859

PERF -0.897 -0.809 -0.972 0.000 -0.972TRANS

F -0.915 -0.988 -0.977 -0.994 0.000

Table 6: Final Fuzzy Weight Matrix for Scenario 1


USEIT 0.000 -0.883 -0.938 -0.785 -0.968

PAST MGMT -0.546 0.000 0.617 0.617 0.369

RES -0.561 -0.293 0.000 -0.561 -0.748

PERF 0.729 0.851 0.253 0.000 0.253TRANS

F 0.938 0.620 0.785 0.372 0.000

Table 7: Final fuzzy weight matrix for Scenario 2

First, analysing scenario 1 through the redrawn FCM (Figure 4) shows that strong causal relationships continue to exist between PERF – RES and USEIT-TRANSF, a range of others have either strengthened, weakened significantly or new relationships have emerged (as shown in Figure 13). The resulting FCM shows a unique set of internal ‘knowledge loops’ arising from the USEIT node supplemented by inputs into the PERF component also. The strength of these causal inter-relationships appear to suggest that factors of USEIT, PERF and RES are given more prominence by users of IT in any IS evaluation, based upon how they may perceive the utility and benefit of IS in the work that they do. In this FCM it is also interesting to note that there are considerably weak causal relationships in the ‘outer loop’ of relationships (between PERF-PAST MGMT and PERF-RES especially) that reveal the challenges that management faces in overcoming the ‘benefit of hindsight’ effect concerning past experiences of IT benefits and risks. This is consistent with literature where tactical/operational considerations within the IS evaluation process shared alike relationship [16].

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Figure 13: Summary of Causal RelationshipsSecond, analysing scenario 2 through the redrawn

FCM in Figure 8 shows that there are a range of strong causal inter-relationships principally emanating from the PERF and TRANSF nodes. These knowledge loops subsequently jointly feed into the USEIT and PAST MGMT nodes (with the latter having a strong reinforcing loop back to PERF). Here, it is evident that while there may be implicit and weak causal links between the impact of resources applied to IS and the impact that IS has on organisational performance, it is interesting to note that the majority of the negative causal loops in the resulting FCM are related to those factors which implicitly involve the interactions between users and how resources may be used to derive benefit from IT. As identified within Figure 13, even though the initial FCM started off with a greater proportion of positive/strong to negative/weak causal links (i.e. 12 vs. 3) between the scenarios explored, there are 88% more weak causal links that emerged overall as a result of each of each of the FCM simulations. There are almost three times as many weak relationships as strong relationships in scenario 1 (i.e. 17 vs. 5), although there is a better balance between these in scenario 2 (i.e. 10 vs. 9). This shows that ultimately a mapping of the knowledge inherent in these IS evaluation scenarios gives an indication of the dynamics of just how difficult and complex it is to overcome organisational culture and technology adoption factors (encapsulated by the explicit knowledge PERF component) as well as integrated and effective efficiency strategies (encapsulated by the tacit knowledge TRANSF component).

6. CONCLUSIONS

This paper sought to extend the view and understanding of knowledge by seeking to apply a cognitive technique to explore knowledge-based decisions involved in evaluating IS investments. This has also been with a view to researching into how strategic IS is used in manufacturing where a paucity of research into the breadth and impact of how knowledge is used in such a context has previously been identified [7]; and, how knowledge may be represented in knowledge-intensive environments [41]. In light of the examination of the literature, this paper presented a MOOTS classification of factors defining ISIE in the manufacturing organisations. Therefore, the authors identified the key KCs related to each ISIE factor, using the five-step PIE framework. Subsequently, the MOOTS and the PIE classification approach is pooled with expert knowledge to construct a matrix of ISIE factors – this step led towards the development of the FCM of ISIE factors. The latter process allowed the authors to identify the inter-relationships and intricacies of decision-making constructs in this research case.

The FCM approach used sought to identify those knowledge constructs which are relevant to the ISIE. This paper has been able to present this technique as a means of exploring and therefore mapping the knowledge inherent in IS evaluation from both user and managerial perspectives. The resulting knowledge mapping through the application of an FCM has shown the intricate and developing behaviour of causal relationships within the knowledge area. The main relationships and knowledge within ISIE have been shown to be driven by a mixture of managerial and user perspectives. These are eventually balanced by strong (and weak) driving elements centring around: the actual (intended) usage of IS and the instant operational / tactical benefits this can provide from a user perspective (FCM scenario 1); and a clear set of relationships based upon how organisational culture, technology adoption and the integration of IS for the benefit of the organisation from a manager’s perspective (FCM scenario 2).

7. References

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Author Guidelines for 8 - Open Access Brunelbura.brunel.ac.uk/bitstream/2438/11456/1/Fulltext.docx · Web viewinvest ment decision-making processes, to better understand the often