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Using neural network-based tools for building learning organisations
Accting., Mgmt. & Info. Tech. 8 (1998) 211–226
Using neural network-based tools for building learning organisations Walter Baetsa,*, Leon Brunenbergb,1, Michiel van Wezelc,2 a
Nijenrode University, The Netherlands Business School, Straatweg 25, 3621 BG Breukelen, The Netherlands b Molenaar en Lok Consultancy, Oude Utrechtseweg 24, 3743 KN Baarn, The Netherlands c Centrum voor Wiskunde en Informatica, Postbus 94079, 1090 GB Amsterdam, The Netherlands Received 30 May 1997; received in revised form 15 January 1998; accepted 31 January 1998
Abstract During the last decade, most European countries have institutionalised a series of measures in order to protect the environment and to provide better public services. Such measures have also been adopted in The Netherlands by Rijkswaterstaat (RWS), part of the ministry of communications and responsible for roads and waterways. One of the current RWS objectives is to provide a quality motorway of high impact to the Dutch road network. In recent years, RWS has committed itself wholeheartedly to Total Quality Management (TQM). It is in the process of making fundamental changes in motorway construction management. For instance, it is considering giving the responsibilities for the quality of road maintenance to sub-contractors. Managers involved in the TQM project feel the need to develop the attitudes and qualityconsciousness of all parties involved in the management of the construction and maintenance of the motorway. In an initial stage, it is essential to study the current attitudes to quality in general and to policies related to ‘providing a high-quality road’ of all parties involved. The research project described below is a step in this direction. The research project took place within the broader framework of RWS’s interest in ‘organisational learning’. The research project capitalised on the ability of Artificial Neural Networks (ANNs) to study human behaviour and integrated this with current RWS needs. More particularly, ANNs were used to visualise stakeholder perceptions and to monitor changes in these perceptions. As a result of the research project, a software tool was developed which is currently used in order to support the organisational change process. 1998 Published by Elsevier Science Ltd. All rights reserved.
* Corresponding author. E-mail: [email protected] 1 [email protected] 2 [email protected] 0959-8022/98/$19.00 1998 Published by Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 9 - 8 0 2 2 ( 9 8 ) 0 0 0 1 0 - 1
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Keywords: Organisational change; Organisational learning; Artificial neural networks; Adaptive systems; Quality management
1. Introduction: on learning, perceptions and knowledge The concept of ‘organisational learning’ is attracting increasing interest in management literature (De Geus, 1988; Senge, 1990; Leonard-Barton, 1992; Morecroft, 1992). According to these authors, an open attitude to learning within an organisation, and to learning of the organisation itself, needs to be based on a process of knowledge generation, monitoring and exploitation. Most theories on the learning organisation are somewhat vague in this particular key aspect of learning (Vennix et al., 1992). We would therefore like to discuss briefly the interaction between learning, perceptions and knowledge, as we understand it. Further detail can be found in Baets (1998b). Learning in itself is a complex issue and can only be introduced briefly here. Kolb (1984) defines learning as the process whereby knowledge is created through the transformation of experience. This definition of learning relates to the ‘know-how’ and ‘know-why’ definition which Kim (1993) gave more recently. According to Kolb’s definition, learning takes place in a cycle: from experience; to making observations and reflections on that experience; to forming abstract concepts and generalisations based on these reflections; to testing these ideas in a new situation which gives new experiences. Kim (1993) calls this cyclical learning loop the OADI-cycle (Observe; Assess; Design; Implement). Based on the OADI-cycle, Kim proposes a simple model of Individual Learning where he links up individual learning with individual mental models (left upper corner of Fig. 1). Fig. 1 shows a double-loop learning process: double in the sense that it includes both learning based on external impulses (OADI-cycle) and the connection of this learning with the individual’s mental models. The reason so much emphasis is put on mental models is that these contain the building blocks for what later becomes organisational knowledge (both know-how and know-why) in the human brain (Kim, 1993). On an organisational level, a comparable double-loop learning model can be designed (see bottom left in Fig. 1). Of lesser importance to the process of organisational learning, but still important to the concept of learning per se, individual, single-loop learning, which is not indicated in Fig. 1, could be added. This individual, single-loop learning links the implementation phase of the OADI-cycle to an individual action, which in turn creates an environmental response. This response gives new input to the OADI cycle. The organisational level is introduced in Fig. 1 in two different ways. Comparable to the single-loop learning in the individual model, each individual action can be part of an organisational action (bottom right in Fig. 1) which in turn causes an
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Fig. 1.
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An integrated model of organizational learning (Baets, 1998b, after Kim, 1993).
additional environmental response. Organisational double-loop learning takes place when the individual mental models are brought into relation via different means which causes them to form shared mental models (shared on a corporate or group level), which in turn have an influence on the individual mental models. In Fig. 1, shared mental models are pictured having two facets: the ‘weltanschauung’ (vision of the world) and the organisational routines. Improved learning via knowledge creation and management takes place, to a large extent, in the organisational doubleloop learning as displayed in the left-hand bottom corner of Fig. 1. Explicit shared models (explicit ‘weltanschauung’ and organisational routines) improve the learning ability of an organisation. What we attempt to incorporate within a tool for organisational learning is the organisational model, the routines, the ‘weltanschauung’: the shared mental model. This shared mental model should then be able to learn and be adapted to the management process itself. As Fig. 1 suggests, and in order to identify the shared mental model, we have to go via the individual mental models and via experiences which have built the individual’s mental models. Connectionist approaches and more specifically Artificial Neural Networks (ANNs) are designed, trained and used in order to simulate particularly this kind of behaviour. 2. Artificial neural networks for visualising learning behaviour 2.1. What are neural networks ANNs attempt to model the architecture of biological neural systems. Biological neural networks (e.g. the human brain) are made of simple, tightly interconnected processing elements called neurones. The interconnections are made by the outgoing
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branches, the ‘axons’, which again form several connections (‘synapses’) with the other neurones. When a neurone receives a number of stimuli and when the sum of the received stimuli exceeds a certain threshold value, it will fire and transmit the stimulus to adjacent neurones. The aim of ANNs is to extract concepts from the biological networks from which new powerful computational methodologies can be developed. The power of neural computing comes from connecting artificial neurones into artificial neural networks. The simplest network is a group of neurones arranged in a layer. Multilayer networks may be formed by simply cascading a group of single layers. The numbers of layers and neurones and the weights to be attached to the connections from neurone to neurone can be decided so as to give the best possible fit to a set of data. Different types of neural network models have been developed in current literature, each having different properties and applications. For this particular application, two types of neural networks have been selected, as will be described below. ANN models are characterised by their properties, viz. the structure of the network (topology), how and what the network computes (computational property) and how and what the network learns to compute (learning or training property). Here, learning means the process by which a set of input values is presented sequentially to the input of the networks and where the network weights are adjusted so that similar inputs give the same output. Learning strategies are categorised as supervised and unsupervised. In pattern recognition type applications, unsupervised learning algorithms are used. In unsupervised learning, the training set consists of input vectors only. The output is determined by the network during the course of training. The unsupervised learning procedures construct internal models that capture regularities in their input values without receiving any additional information. 2.2. Relevance of artificial neural networks for managerial problems There has recently been an increase in management research into the ‘chaotic’ behaviour of companies and markets (Arthur, 1990; Boisot, 1995; Nilson, 1995). While, on the one hand, Decision Support Systems were failing to perform adequately, they were becoming at the same time more and more necessary in order to cope with the complex behaviour of markets and companies (Genelot, 1992; Stacey, 1992). New routes needed to be explored. Research on dynamic non-linear behaviour has some interesting applications in business (Langton, 1989; Maturana & Varela, 1984; Goonatilake & Treleaven, 1995; Zahedi, 1993; Baets, 1995, 1998a). Most relates to the change management function and the way a company deals with change (Merry, 1995). Furthermore, it has been observed that ‘knowledge’ is not stored, but rather that it is re-created each time it is required by means of a densely connected network of simple elements: the human brain (Nicolis & Prigogine, 1989; Langton, 1989; Maturana & Varela, 1984). Similar to the operation of the human brain, knowledge management and change within companies could be based on a densely connected net-
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work of people, working closely together and supported by an adequate IT network. It appears to be logical, therefore, to adopt network or connectionist approaches in order to support change processes. Change cannot be imposed on a company or on a number of people for the simple reason that knowledge cannot even be taught, let alone imposed. Knowledge can be created when a number of experienced people share views. Change based on knowledge can therefore only be incremental. Incremental change in self-organising systems is what works with people. In order to simulate human behaviour, a ‘change support system’ should be able to show incremental learning behaviour itself. Just as people continuously learn during the execution of a job as well as during a change process, so should any system which supports this change process. Since people work with their pre-judgements, their perceptions and feelings, and therefore their mind-sets, these play a critical role in building human knowledge networks. Similarly, such notions should also play a role in change support (or decision support) systems. Knowledge cannot be encapsulated in a system of rules. Perceptions and experience form the basis on which human beings take decisions. Positive feedback loops reinforce decision-making. Just as any economic system behaves in a dynamic and non-linear way and learns from positive feedback, so do change processes and any support system dealing with change processes. Compared to other methods which also deal with perceptions for organisational learning with the aim of making mind sets explicit (e.g. cognitive mapping, systems dynamics, soft systems methodology), neural networks seem to have distinctive features. Unlike neural networks, most other methods are limited in their applicability. These aim at establishing a consensus in a specific and limited group at a specific time. Larger groups are almost impossible to deal with and the same group at a different time would probably result in a different consensus. This makes it extremely difficult to create general validity and acceptance of these methods. Secondly, in so far as these methods do not deliver a ‘numeric’ representation, it is a little more difficult to use them in ‘simulations’. As a result of such insights acquired over recent years alongside the apparent success of connectionist methods in real business applications (Zahedi, 1993; Goonatilake & Treleaven, 1995), it appeared an innovative but realistic choice to opt for neural networks as a basis for this ‘change support process’. 2.3. Network algorithms used: Sanger network and SAMANN network Different algorithms exist in unsupervised learning, which is used for pattern recognition applications, of which many are based on Kohonen networks (Kohonen, 1989; Kangas, 1990). More recent developments have illustrated the potential of Sammon’s non-linear projection (SAMANN) and the Sanger network, particularly for pattern recognition and exploratory data analysis (Mao & Jain, 1995). The Sanger network is based on Principle Component Analysis. In a second stage, Sanger maps the data on the first two principle components, using linear mapping. Sanger attempts to retain variance in data as much as possible. The Samann network (for Sammon’s projection method) attempts to minimise an
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error function which quantifies the topology of the mapping. The Sammon’s stress factor is a measure of how well the interpattern distances are preserved when the patterns are projected from a high dimensional space to a lower dimensional space. Sammon used a gradient descent algorithm to find a configuration of ‘n’ patterns in the m-dimensional space that minimises the ‘stress factor’ (Sammon, 1969). A common attribute of both networks is that they employ adaptive learning algorithms which makes them suitable in some environments where the distribution of pattern in feature space changes with respect to time. Moreover, the SAMANN network offers the generalisation ability of projecting new data, which is not present in the original Sammon’s projection algorithm. SAMANN focuses on ‘small distances’, which provokes a non-linear mapping. With the purpose of data-visualisation, this approach is more appropriate than using networks which attempt to maximise the included variance. The latter type of network would be better, for example, for data compression with the aim of using expensive data transmission technologies. Mao & Jain (1995) have compared different neural networks for feature extraction and data projection on eight different data sets. The networks compared are: the SAMANN network, a linear discriminant analysis (LDA) network, a network for nonlinear projection (NP–SOM) based on Kohonen, a nonlinear discriminant analysis (NDA) network, Sanger network based on Principle Components Analysis (PCA). They conclude: 1. The NP–SOM network has good performance for data visualisation. It reveals the cluster tendency in the multivariate data very well due to the role of the distance image; 2. The SAMANN and PCA networks preserve the data structure, cluster shape and interpattern distances better than the LDA, NDA and NP–SOM networks; 3. The NDA network is superior to all the other networks for classification purposes, especially when the data are not linearly-separable, but it severely distorts the structure of the data and the interpattern distances; 4. There is no general conclusion on whether the PCA or the LDA is better for preserving the nearest-neighbour category information; 5. The LDA network outperforms the PCA, SAMANN and NP–SOM networks in the sense of nearest mean classification error; 6. Feature extraction can help to reduce or eliminate the ‘curse of dimensionality’; 7. It is often necessary to use more than one method (network) in order to reveal various properties of multivariate data; 8. Knowledge of the structure of the data set obtained from the projection maps can guide in choosing proper classification and clustering tools. Based on these conclusions, we have chosen both Sanger and SAMANN as the two algorithms to be used during the research project and later in the software. 3. Objectives of the research project from the RWS point of view RWS is committed to TQM and is introducing changes in order to achieve this. In the first instance, RWS is exploring the attitudes of people involved in all aspects
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of the road construction/maintenance project. Gauging these will enable RWS to make the right decisions regarding, for instance, the selection of sub-contractors to carry out road operation and maintenance and related quality-control activities. Hence RWS’s concern to visualise the attitudes of the different stakeholders engaged in the relevant processes. The objectives of the research project which have been set by RWS are the following: 앫 To study the quality-consciousness of all parties involved in motorway maintenance (e.g. regional authorities, sub-contractors, central authorities, RWS studyservices). 앫 To study the attitudes of RWS employees in general with respect to RWS’s present sub-contracting policy as well as to the envisaged change in policy. 앫 To develop a neural network-based tool/system for creating a response map based on the quality perceptions of parties involved in motorway management. This response map could be used for decision support concerning the organisational action necessary for developing/improving quality-consciousness. 앫 To test the tool within the boundary considered and monitor the improvement in quality-consciousness of employees. RWS wants to visualise learning about this subject amongst its employees. In order to improve understanding and to increase the communicative power of the tool, it was stressed that the desired ‘quality system’ in general should be characterised by an internal coherence, a certain level of quantification and classification according to the level of abstraction, importance and sequence of decisions. Different groups of qualities were identified by RWS: objective, subjective (e.g. comfort), composed (e.g. homogeneity), user-dependent (e.g. security) and geometrical. The expected outcome of the research project should provide insight into the issue of TQM with respect to this particular business process. In a broader sense, it should give insight into the possible use of neural networks for supporting organisational learning and change, as well as for measuring that learning and/or change. To be more specific, the research project aimed at producing the following: 앫 A quality-consciousness response map which could be used by RWS to decide on an appropriate strategy for developing/improving the quality-consciousness of the people involved in the project, and particularly of the employees. 앫 A benchmark for quality perception of people involved in motorway management and maintenance. This benchmark could be used by RWS in the future in order to monitor the TQM programme in general. 앫 An organisational learning tool (in this case, a Neural Network-based tool) which would ultimately help RWS in learning about people’s perceptions and the relationship between these perceptions and the RWS strategy in this respect. For the future, this learning would help RWS in the development of a suitable strategy for the creation of quality-consciousness (in general).
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4. RWS involvement in the project In a first stage, it was necessary to study the business processes that take place in motorway construction/maintenance management in order to select those relevant to this particular case. RWS documentation was therefore used, and key people involved in the different processes were consulted. In a second stage, interviews were planned with parties involved in the selected business processes in order to record their quality perceptions/attitudes with respect to the relevant business process. Afterwards, a conceptualisation of ‘quality perception’ into a framework which can then be used to guide management action needed to be agreed upon with RWS. Field research could then take place. During the development phase of the neural network system prototypes and their supporting software environment, contact was maintained with RWS, but no formal involvement of RWS took place. Eventually, for testing and implementation of the neural network system and in order to discuss the design of the software tool, close co-operation with RWS was established. It was decided by RWS to involve six groups of different stakeholders, both from inside and outside RWS. In the following table, the groups are given with the number of people involved (Table 1). These people took part in the field research stage of the project.
5. The research proposition: visualisation of perceptions on quality management in order to support change processes This section of the paper discusses how the project was executed, from the researchers’ point of view. Major change processes have to do with changing the perceptions of stakeholders, who all have, in general, different aims and experiences. An attempt is made to visualise a change process via the perceptions of the different stakeholders. In a second stage, a software tool is constructed which allows people to observe the different perceptions and to play around with these ‘knowledge maps’. Table 1 Composition of the sample Stakeholders
Number of people involved
Board and Central direction (RWS) Quality services (RWS) Engineering consultants Sub-contractors Material suppliers Regional directions (of RWS) Total
5 10 10 20 10 150 (around 40 in 4 regions) 205 people
The list of names was produced by RWS. Data-gathering was undertaken by the research team.
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The aim of the tool and of this approach in general is to support a learning process. When different stakeholders use the software tool, they can observe different attitudes and learn from them. The tool allows stakeholders to view how other stakeholders perceive the problem. All this takes place in a non-confrontational environment and the approach can take into account large numbers of people and stakeholder groups. Since the outcome is a software tool, it can also be put at the disposal of many people via a computer network. The research questions related to this purpose are formulated as follows: can a software tool or a support system of any kind be built which is able to do the following: 1. Contain all the tacit knowledge which the different stakeholders (including ‘clients’) have about the process to be changed. 2. Picture (visualise) a situation and its evolution via the perceptions of all stakeholders. 3. Retain in the picture as much of the initial complexity as possible, as well as attempt to keep as much variety of perceptions as possible. 4. Visualise this network of mutually connected opinions and positions in a way that the different stakeholders can ‘play’ with each others perceptions and ‘learn’ from this. 5. Create a procedure of continuous adaptation of the picture of the process via the stakeholders who question the system. The system should learn from the questions of the stakeholders and the system should evolve on the basis of the learning behaviour of the users. 6. Enable the use of adaptive (learning) qualities to identify which rules are of real importance. Allow the tool to suggest what can be learned from this particular process and what could be of use in other comparable situations. Allow people to learn from each other’s experience. This approach aims for learning and adaptive behaviour, based on the changing perceptions and the learning of the stakeholders themselves (e.g. clients, personnel, suppliers, etc.). The system adapts gradually according to the learning processes of the people who use it. The system learns from the users and hence the system does not force the users to think the way the application does.
6. The research procedure and the results In a first step, existing material was consulted describing the project and the process from any possible point of view. The material included internal reports, interviews, studies, etc. The aim at this stage was only to identify as many issues as possible, not to identify the relationships between the issues. The list of issues identified in the first stage was validated by a number of stakeholders. It is important to make sure that the different stakeholders use the same terminology before launching a widespread questionnaire. This is a rather technical matter but a necessary procedure in order to guarantee comparability of results. Based
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on the validated list of issues, a formal questionnaire was produced. A total of 140 issues was selected as being representative. Different media can be used in order to distribute and process the questionnaires. In this research, a paper-based distribution was used, which of course imposed a limit on the number of possible respondents. One could easily design an electronic questionnaire, which would make both distribution and processing easier and faster. It would allow the project to use much more input. In this particular case, the company did not prefer this alternative, due to the fact that many outside stakeholders would have had to be involved. The company feared that this would lower the response rate. The questionnaire was filled out by the 205 participants selected for the field research who were mentioned earlier. The questionnaire asks for ‘recognition’ of issues. A typical question would be “how much do you recognise issue 1 as being important in this process?”. Measurement takes place on a 1 to 10 Likert-type scale. The same kind of procedure is used for the definitions. ‘A priori’ definitions are avoided, e.g. of ‘quality’, since any given definition immediately excludes all other views on the same subject. Instead of making the process clearer, in many cases it divides the camp into two. Furthermore, it is arguable whether clear definitions do exist for such concepts as ‘quality’. In a change process where the aim is to gather stakeholder views, it would be counterproductive to work with strict definitions. Instead, a number of statements about quality are given and the respondents are asked to score how recognisable they are. Eventually, after training of the neural network, this approach will produce a networked version of statements which incorporates the definition of quality without really specifying it. It is not a straightforward definition. It gives all important aspects of ‘quality’ and it also gives the interrelationship between the different aspects. The next step consists in training neural networks by using the data (perceptions) gathered from the field research. The aim is to identify structure. In this particular case, both Sanger and SAMANN networks were used. Any representation (or a number of different representations) will allow a structure to be identified which is deducted from observations and perceptions, rather than from any predetermined mental map. In this process, no mental model is imposed onto any of the stakeholder groups. All different structures are, ‘a priori’, equally possible. Only the practical use of the different pictures will allow discrimination of the one in favour of the other. Whereas Sanger and SAMANN (based on Kohonen maps) are ‘unsupervised’ learning algorithms aiming to identify structure, the picture can be detailed further by ‘supervised’ learning algorithms. For the purpose of visualisation and representation of the results, and this mainly for reasons of simplicity and comprehension, and without claiming that this method can be applied generally, we have opted in this case for a graphen representation. Figs. 2–4 give three different cases. Each figure represents the case of one particular person. The figures only represent 20 of the 140 issues (the software allows to change the number of issues represented). Each axis is a representation on a Likert 1–10 scale. The middle of the ‘wheel’ equals 0 for each of the issues. Comparing Figs. 2–4 illustrates how far perceptions of different stakeholders can be different from each other.
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Fig. 2.
Case 1.
Fig. 3.
Case 2.
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All 205 cases could be represented as in Figs. 2–4. Using the neural networks, an attempt is made to identify patterns in the data: this means identifying groups of people whose perceptions of the issues are similar. Neural network analysis identified from among the 205 stakeholders, 5 virtual stakeholder groups. A virtual stakeholder group is a group of people (who are members of different original stakeholder groups) who have perceptions that are similar. These are the groups of people with whom RWS could work in order to achieve progress fast and easily. The following figures compare each case (Figs. 2–4) to its nearest ‘virtual group’. Figs. 5–7 show
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Fig. 4.
Fig. 5.
Case 3.
Case 1 to its closest group (group 2).
optimal fits. One can observe that the two representations in their respective figures (the original one in Figs. 2–4 and the corresponding virtual group representation) are close; of course, they will never be exactly the same. Fig. 5 gives case 1 (Fig. 2) compared to its virtual group (group 2). One immediately observes those issues on which person 1’s perception approximates the virtual group perception and those where person 1’s perception is distant. Most work should be concentrated on these latter issues (e.g. 11, 10 and 1). Fig. 6 shows case 2 in comparison to its closest virtual group (which is group 4) and Fig. 7 shows case 3 in comparison with its closest virtual group (which is again group 2).
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Fig. 6.
Fig. 7.
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Case 2 to its closest group (group 4).
Case 3 to its closest group (group 2 again).
The software tool also allows comparison between any case and any arbitrarily chosen virtual group. Fig. 8 compares case 1 to virtual group 4, which is not its closest virtual stakeholder group. Comparison with Fig. 5 shows that it would be more difficult to try and bring person 1 closer to group 4 than to group 2. Finally Fig. 9 shows person 25 (not yet used in any of the figures above) in comparison to the (in this case non-optimal) virtual stakeholder group 2. Comparing this figure with Fig. 5 and Fig. 7 shows the difference between trying to bring case 1 and 3 closer to the closest virtual group, compared to trying to bring person 25 close to group 2. The software tool allows any combination of the above given kind. What it does
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Fig. 8.
Case 1 compared to virtual group 4.
Fig. 9.
Case 25 compared to virtual group 2.
is that it visualises the perceptions of the different people, compared to those of any virtual stakeholder group desired. It visualises difference in perception, visualises common vision, indicates shortest ‘learning paths’ and indicates difficult paths of learning and change. The pattern recognition capacities of ANNs are put to optimum use in this application. They perform better than any other quantitative grouping method, with which these kind of ‘data’ could not be tackled (Venugopal & Baets, 1994a, b; Mao & Jain, 1995). The stakeholders can ‘see’ how the different groups ‘perceive’, without meeting the other stakeholders. It allows stakeholders to gain much greater insight into the problem, by making use of the perceptions of all the different stakeholders. One can
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hardly imagine any other approach which not only visualises perceptions, but also allows comparison and representation. This software tool allows individuals to play and to learn. Of course, it remains each individual’s responsibility to be open and willing to learn. Group sessions can use this tool as a pedagogical support for the discussion.
7. Long term relevance of the project for RWS The project, and particularly the software which is now in use, provides a trained neural network in which 5 virtual groups were identified. For each of these groups, the characteristics/issues are known and a test-print (see figures above) has been made in order to reveal them. The software can be further improved in the following ways: 앫 Out of the 140 issues and statements, any combination of 20 should be possible. The groups could be labelled better and clearer. 앫 In a next phase, it should be made possible to ‘guide the change’. In practice this means that a desired path of development can be identified and the difference between the present state and the desired path can be visualised. 앫 The neural network which now exists will constantly be updated by any new query to the system. Each query is considered as a new input and the auto-adaptive character of the neural network causes continuous learning and updating. As was understood from the beginning of the project, it is the aim of RWS to direct the change process in order to fit better the aims which RWS has given itself for the coming years with respect to quality control. Eventually, the proof of the pudding is also in this case, in the eating. Extensive use of the software tool, both by individuals and by groups, validates the tool. At a later stage, the use of the software tool will show evidence of the possibility, not only to visualise perceptions (which can already be done), but also to show change. For reasons of corporate policy, this tool is currently only in use by RWS employees in its first stage. However, there is no reason other than for political considerations, not to have other stakeholders experiment with it. In a later stage, and in order to magnify the test, groupware can be used for group sessions. For budgetary reasons, this has not yet been considered. At a later stage, evaluation can take place on whether the tool learns and shows adaptive behaviour. The evaluation should be twofold. First, an analysis needs to be made of whether the people who have worked with the tool have changed or adapted their mind-set, at least, again, according to their own perception. However, given the auto-adaptive nature of the tool, a second way of testing exists. It is also possible to check how much the original tool differs from the one which evolved after multiple use. If the final tool differs from the first tool, this will show indirectly that some learning has taken place, both by the people involved and by the system itself.
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References Arthur, B. (1990). Positive Feedbacks in the Economy. Scientific American, Febr., 92–99. Baets, W. (1995). Artificial Neural Networks: visualisation of change processes and measurement of learning (Dutch language). Handboek effektief opleiden, 4.131. Delwel. Baets, W. (1998a). The corporate mind-set as a precursor for business process (re-)engineering: About knowledge, perceptions and learning. In R. Galliers & W. Baets (Eds.), Information Technology and Organizational Transformation: Innovation for the 21st Century Organisation. Wiley. Baets, W. (1998b). Organizational learning and knowledge technologies in a dynamic environment. Kluwer Academic Publishers. Boisot, M. (1995). Information Space: A framework for learning in organisations, institutions and culture. Routledge. De Geus, A. (1988). Planning as learning. Harvard Business Review, March/April, 70–74. Genelot, D. (1992). Manager dans la complexite´. Insep Editions. Goonatilake, S., & Treleaven, P. (Eds.). (1995). Intelligent Systems for Finance and Business. Wiley. Kangas, J. A. (1990). Variants of Self-Organizing Maps. IEEE Transactions on Neural Networks, 1(1), March. Kim, D. (1993). The link between Individual and Organizational Learning. Sloan Management Review, Fall, 37–50. Kohonen, T. (1989). Self-Organization and Associative Memory. Springer Series in Information Sciences, Springer Verlag. Kolb, D. (1984). Problem Management: Learning from Experience. In Suresh Srivastva and Associates, The Executive Mind: New Insights on Managerial Thought, Jossey-Bass Ltd. Langton, C. (Ed.). (1989). Artificial life. In Proceedings of Santa Fe Institute Studies in the Sciences of Complexity Vol. 6. Redwood City: Addison-Wesley. Leonard-Barton, D. (1992). The Factory as a Learning Laboratory. Sloan Management Review, Fall, 34(11), 23–38. Mao, J., & Jain, A. (1995). Artificial Neural Networks for Feature Extraction and Multivariate Data Projection. IEEE Transactions on Neural Networks, 6(2), March. Maturana, H., & Varela, F. (1984). The tree of knowledge, The Biological Roots of Human Understanding. Scherz Verlag. Merry, U. (1995). Coping with uncertainty: Insights from the new sciences of chaos, self organization and complexity. Praeger. Morecroft, J. (1992). Executive knowledge, models and learning. European Journal of Operational Research, 59(1), 9–27. Nicolis, G. & Prigogine, I. (1989). Exploring Complexity. Freeman. Nilson, T. (1995). Chaos Marketing: How to win in a turbulent world. McGraw Hill. Sammon, J. W. (1969). A non-linear mapping for data structure analysis. IEEE Transactions in Computing, C-18. Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. NY: Doubleday. Stacey, R. (1992). Managing Chaos. Kogan Page. Vennix, J., Andersen, D., Richardson, G., & Rohrbaugh, J. (1992). Model building for group decision support: Issues and alternatives in knowledge elicitation. European Journal of Operational Research, 59(1), 28–41. Venugopal, V., & Baets, W. (1994). Neural Networks and Statistical Techniques in Marketing Research: A conceptual comparison. Marketing Intelligence and Planning, 12(7), 30–38. Venugopal, V., & Baets, W. (1994b). Neural Networks and their Applications in Marketing Management. The Journal of Systems Management, September, 16–23. Zahedi, F. (1993). Intelligent Systems for Business: Expert Systems with Neural Networks. Wadsworth Publishing.
Using neural network-based tools for building learning organisations Walter Baetsa,*, Leon Brunenbergb,1, Michiel van Wezelc,2 a
Nijenrode University, The Netherlands Business School, Straatweg 25, 3621 BG Breukelen, The Netherlands b Molenaar en Lok Consultancy, Oude Utrechtseweg 24, 3743 KN Baarn, The Netherlands c Centrum voor Wiskunde en Informatica, Postbus 94079, 1090 GB Amsterdam, The Netherlands Received 30 May 1997; received in revised form 15 January 1998; accepted 31 January 1998
Abstract During the last decade, most European countries have institutionalised a series of measures in order to protect the environment and to provide better public services. Such measures have also been adopted in The Netherlands by Rijkswaterstaat (RWS), part of the ministry of communications and responsible for roads and waterways. One of the current RWS objectives is to provide a quality motorway of high impact to the Dutch road network. In recent years, RWS has committed itself wholeheartedly to Total Quality Management (TQM). It is in the process of making fundamental changes in motorway construction management. For instance, it is considering giving the responsibilities for the quality of road maintenance to sub-contractors. Managers involved in the TQM project feel the need to develop the attitudes and qualityconsciousness of all parties involved in the management of the construction and maintenance of the motorway. In an initial stage, it is essential to study the current attitudes to quality in general and to policies related to ‘providing a high-quality road’ of all parties involved. The research project described below is a step in this direction. The research project took place within the broader framework of RWS’s interest in ‘organisational learning’. The research project capitalised on the ability of Artificial Neural Networks (ANNs) to study human behaviour and integrated this with current RWS needs. More particularly, ANNs were used to visualise stakeholder perceptions and to monitor changes in these perceptions. As a result of the research project, a software tool was developed which is currently used in order to support the organisational change process. 1998 Published by Elsevier Science Ltd. All rights reserved.
* Corresponding author. E-mail: [email protected] 1 [email protected] 2 [email protected] 0959-8022/98/$19.00 1998 Published by Elsevier Science Ltd. All rights reserved. PII: S 0 9 5 9 - 8 0 2 2 ( 9 8 ) 0 0 0 1 0 - 1
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Keywords: Organisational change; Organisational learning; Artificial neural networks; Adaptive systems; Quality management
1. Introduction: on learning, perceptions and knowledge The concept of ‘organisational learning’ is attracting increasing interest in management literature (De Geus, 1988; Senge, 1990; Leonard-Barton, 1992; Morecroft, 1992). According to these authors, an open attitude to learning within an organisation, and to learning of the organisation itself, needs to be based on a process of knowledge generation, monitoring and exploitation. Most theories on the learning organisation are somewhat vague in this particular key aspect of learning (Vennix et al., 1992). We would therefore like to discuss briefly the interaction between learning, perceptions and knowledge, as we understand it. Further detail can be found in Baets (1998b). Learning in itself is a complex issue and can only be introduced briefly here. Kolb (1984) defines learning as the process whereby knowledge is created through the transformation of experience. This definition of learning relates to the ‘know-how’ and ‘know-why’ definition which Kim (1993) gave more recently. According to Kolb’s definition, learning takes place in a cycle: from experience; to making observations and reflections on that experience; to forming abstract concepts and generalisations based on these reflections; to testing these ideas in a new situation which gives new experiences. Kim (1993) calls this cyclical learning loop the OADI-cycle (Observe; Assess; Design; Implement). Based on the OADI-cycle, Kim proposes a simple model of Individual Learning where he links up individual learning with individual mental models (left upper corner of Fig. 1). Fig. 1 shows a double-loop learning process: double in the sense that it includes both learning based on external impulses (OADI-cycle) and the connection of this learning with the individual’s mental models. The reason so much emphasis is put on mental models is that these contain the building blocks for what later becomes organisational knowledge (both know-how and know-why) in the human brain (Kim, 1993). On an organisational level, a comparable double-loop learning model can be designed (see bottom left in Fig. 1). Of lesser importance to the process of organisational learning, but still important to the concept of learning per se, individual, single-loop learning, which is not indicated in Fig. 1, could be added. This individual, single-loop learning links the implementation phase of the OADI-cycle to an individual action, which in turn creates an environmental response. This response gives new input to the OADI cycle. The organisational level is introduced in Fig. 1 in two different ways. Comparable to the single-loop learning in the individual model, each individual action can be part of an organisational action (bottom right in Fig. 1) which in turn causes an
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Fig. 1.
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An integrated model of organizational learning (Baets, 1998b, after Kim, 1993).
additional environmental response. Organisational double-loop learning takes place when the individual mental models are brought into relation via different means which causes them to form shared mental models (shared on a corporate or group level), which in turn have an influence on the individual mental models. In Fig. 1, shared mental models are pictured having two facets: the ‘weltanschauung’ (vision of the world) and the organisational routines. Improved learning via knowledge creation and management takes place, to a large extent, in the organisational doubleloop learning as displayed in the left-hand bottom corner of Fig. 1. Explicit shared models (explicit ‘weltanschauung’ and organisational routines) improve the learning ability of an organisation. What we attempt to incorporate within a tool for organisational learning is the organisational model, the routines, the ‘weltanschauung’: the shared mental model. This shared mental model should then be able to learn and be adapted to the management process itself. As Fig. 1 suggests, and in order to identify the shared mental model, we have to go via the individual mental models and via experiences which have built the individual’s mental models. Connectionist approaches and more specifically Artificial Neural Networks (ANNs) are designed, trained and used in order to simulate particularly this kind of behaviour. 2. Artificial neural networks for visualising learning behaviour 2.1. What are neural networks ANNs attempt to model the architecture of biological neural systems. Biological neural networks (e.g. the human brain) are made of simple, tightly interconnected processing elements called neurones. The interconnections are made by the outgoing
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branches, the ‘axons’, which again form several connections (‘synapses’) with the other neurones. When a neurone receives a number of stimuli and when the sum of the received stimuli exceeds a certain threshold value, it will fire and transmit the stimulus to adjacent neurones. The aim of ANNs is to extract concepts from the biological networks from which new powerful computational methodologies can be developed. The power of neural computing comes from connecting artificial neurones into artificial neural networks. The simplest network is a group of neurones arranged in a layer. Multilayer networks may be formed by simply cascading a group of single layers. The numbers of layers and neurones and the weights to be attached to the connections from neurone to neurone can be decided so as to give the best possible fit to a set of data. Different types of neural network models have been developed in current literature, each having different properties and applications. For this particular application, two types of neural networks have been selected, as will be described below. ANN models are characterised by their properties, viz. the structure of the network (topology), how and what the network computes (computational property) and how and what the network learns to compute (learning or training property). Here, learning means the process by which a set of input values is presented sequentially to the input of the networks and where the network weights are adjusted so that similar inputs give the same output. Learning strategies are categorised as supervised and unsupervised. In pattern recognition type applications, unsupervised learning algorithms are used. In unsupervised learning, the training set consists of input vectors only. The output is determined by the network during the course of training. The unsupervised learning procedures construct internal models that capture regularities in their input values without receiving any additional information. 2.2. Relevance of artificial neural networks for managerial problems There has recently been an increase in management research into the ‘chaotic’ behaviour of companies and markets (Arthur, 1990; Boisot, 1995; Nilson, 1995). While, on the one hand, Decision Support Systems were failing to perform adequately, they were becoming at the same time more and more necessary in order to cope with the complex behaviour of markets and companies (Genelot, 1992; Stacey, 1992). New routes needed to be explored. Research on dynamic non-linear behaviour has some interesting applications in business (Langton, 1989; Maturana & Varela, 1984; Goonatilake & Treleaven, 1995; Zahedi, 1993; Baets, 1995, 1998a). Most relates to the change management function and the way a company deals with change (Merry, 1995). Furthermore, it has been observed that ‘knowledge’ is not stored, but rather that it is re-created each time it is required by means of a densely connected network of simple elements: the human brain (Nicolis & Prigogine, 1989; Langton, 1989; Maturana & Varela, 1984). Similar to the operation of the human brain, knowledge management and change within companies could be based on a densely connected net-
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work of people, working closely together and supported by an adequate IT network. It appears to be logical, therefore, to adopt network or connectionist approaches in order to support change processes. Change cannot be imposed on a company or on a number of people for the simple reason that knowledge cannot even be taught, let alone imposed. Knowledge can be created when a number of experienced people share views. Change based on knowledge can therefore only be incremental. Incremental change in self-organising systems is what works with people. In order to simulate human behaviour, a ‘change support system’ should be able to show incremental learning behaviour itself. Just as people continuously learn during the execution of a job as well as during a change process, so should any system which supports this change process. Since people work with their pre-judgements, their perceptions and feelings, and therefore their mind-sets, these play a critical role in building human knowledge networks. Similarly, such notions should also play a role in change support (or decision support) systems. Knowledge cannot be encapsulated in a system of rules. Perceptions and experience form the basis on which human beings take decisions. Positive feedback loops reinforce decision-making. Just as any economic system behaves in a dynamic and non-linear way and learns from positive feedback, so do change processes and any support system dealing with change processes. Compared to other methods which also deal with perceptions for organisational learning with the aim of making mind sets explicit (e.g. cognitive mapping, systems dynamics, soft systems methodology), neural networks seem to have distinctive features. Unlike neural networks, most other methods are limited in their applicability. These aim at establishing a consensus in a specific and limited group at a specific time. Larger groups are almost impossible to deal with and the same group at a different time would probably result in a different consensus. This makes it extremely difficult to create general validity and acceptance of these methods. Secondly, in so far as these methods do not deliver a ‘numeric’ representation, it is a little more difficult to use them in ‘simulations’. As a result of such insights acquired over recent years alongside the apparent success of connectionist methods in real business applications (Zahedi, 1993; Goonatilake & Treleaven, 1995), it appeared an innovative but realistic choice to opt for neural networks as a basis for this ‘change support process’. 2.3. Network algorithms used: Sanger network and SAMANN network Different algorithms exist in unsupervised learning, which is used for pattern recognition applications, of which many are based on Kohonen networks (Kohonen, 1989; Kangas, 1990). More recent developments have illustrated the potential of Sammon’s non-linear projection (SAMANN) and the Sanger network, particularly for pattern recognition and exploratory data analysis (Mao & Jain, 1995). The Sanger network is based on Principle Component Analysis. In a second stage, Sanger maps the data on the first two principle components, using linear mapping. Sanger attempts to retain variance in data as much as possible. The Samann network (for Sammon’s projection method) attempts to minimise an
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error function which quantifies the topology of the mapping. The Sammon’s stress factor is a measure of how well the interpattern distances are preserved when the patterns are projected from a high dimensional space to a lower dimensional space. Sammon used a gradient descent algorithm to find a configuration of ‘n’ patterns in the m-dimensional space that minimises the ‘stress factor’ (Sammon, 1969). A common attribute of both networks is that they employ adaptive learning algorithms which makes them suitable in some environments where the distribution of pattern in feature space changes with respect to time. Moreover, the SAMANN network offers the generalisation ability of projecting new data, which is not present in the original Sammon’s projection algorithm. SAMANN focuses on ‘small distances’, which provokes a non-linear mapping. With the purpose of data-visualisation, this approach is more appropriate than using networks which attempt to maximise the included variance. The latter type of network would be better, for example, for data compression with the aim of using expensive data transmission technologies. Mao & Jain (1995) have compared different neural networks for feature extraction and data projection on eight different data sets. The networks compared are: the SAMANN network, a linear discriminant analysis (LDA) network, a network for nonlinear projection (NP–SOM) based on Kohonen, a nonlinear discriminant analysis (NDA) network, Sanger network based on Principle Components Analysis (PCA). They conclude: 1. The NP–SOM network has good performance for data visualisation. It reveals the cluster tendency in the multivariate data very well due to the role of the distance image; 2. The SAMANN and PCA networks preserve the data structure, cluster shape and interpattern distances better than the LDA, NDA and NP–SOM networks; 3. The NDA network is superior to all the other networks for classification purposes, especially when the data are not linearly-separable, but it severely distorts the structure of the data and the interpattern distances; 4. There is no general conclusion on whether the PCA or the LDA is better for preserving the nearest-neighbour category information; 5. The LDA network outperforms the PCA, SAMANN and NP–SOM networks in the sense of nearest mean classification error; 6. Feature extraction can help to reduce or eliminate the ‘curse of dimensionality’; 7. It is often necessary to use more than one method (network) in order to reveal various properties of multivariate data; 8. Knowledge of the structure of the data set obtained from the projection maps can guide in choosing proper classification and clustering tools. Based on these conclusions, we have chosen both Sanger and SAMANN as the two algorithms to be used during the research project and later in the software. 3. Objectives of the research project from the RWS point of view RWS is committed to TQM and is introducing changes in order to achieve this. In the first instance, RWS is exploring the attitudes of people involved in all aspects
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of the road construction/maintenance project. Gauging these will enable RWS to make the right decisions regarding, for instance, the selection of sub-contractors to carry out road operation and maintenance and related quality-control activities. Hence RWS’s concern to visualise the attitudes of the different stakeholders engaged in the relevant processes. The objectives of the research project which have been set by RWS are the following: 앫 To study the quality-consciousness of all parties involved in motorway maintenance (e.g. regional authorities, sub-contractors, central authorities, RWS studyservices). 앫 To study the attitudes of RWS employees in general with respect to RWS’s present sub-contracting policy as well as to the envisaged change in policy. 앫 To develop a neural network-based tool/system for creating a response map based on the quality perceptions of parties involved in motorway management. This response map could be used for decision support concerning the organisational action necessary for developing/improving quality-consciousness. 앫 To test the tool within the boundary considered and monitor the improvement in quality-consciousness of employees. RWS wants to visualise learning about this subject amongst its employees. In order to improve understanding and to increase the communicative power of the tool, it was stressed that the desired ‘quality system’ in general should be characterised by an internal coherence, a certain level of quantification and classification according to the level of abstraction, importance and sequence of decisions. Different groups of qualities were identified by RWS: objective, subjective (e.g. comfort), composed (e.g. homogeneity), user-dependent (e.g. security) and geometrical. The expected outcome of the research project should provide insight into the issue of TQM with respect to this particular business process. In a broader sense, it should give insight into the possible use of neural networks for supporting organisational learning and change, as well as for measuring that learning and/or change. To be more specific, the research project aimed at producing the following: 앫 A quality-consciousness response map which could be used by RWS to decide on an appropriate strategy for developing/improving the quality-consciousness of the people involved in the project, and particularly of the employees. 앫 A benchmark for quality perception of people involved in motorway management and maintenance. This benchmark could be used by RWS in the future in order to monitor the TQM programme in general. 앫 An organisational learning tool (in this case, a Neural Network-based tool) which would ultimately help RWS in learning about people’s perceptions and the relationship between these perceptions and the RWS strategy in this respect. For the future, this learning would help RWS in the development of a suitable strategy for the creation of quality-consciousness (in general).
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4. RWS involvement in the project In a first stage, it was necessary to study the business processes that take place in motorway construction/maintenance management in order to select those relevant to this particular case. RWS documentation was therefore used, and key people involved in the different processes were consulted. In a second stage, interviews were planned with parties involved in the selected business processes in order to record their quality perceptions/attitudes with respect to the relevant business process. Afterwards, a conceptualisation of ‘quality perception’ into a framework which can then be used to guide management action needed to be agreed upon with RWS. Field research could then take place. During the development phase of the neural network system prototypes and their supporting software environment, contact was maintained with RWS, but no formal involvement of RWS took place. Eventually, for testing and implementation of the neural network system and in order to discuss the design of the software tool, close co-operation with RWS was established. It was decided by RWS to involve six groups of different stakeholders, both from inside and outside RWS. In the following table, the groups are given with the number of people involved (Table 1). These people took part in the field research stage of the project.
5. The research proposition: visualisation of perceptions on quality management in order to support change processes This section of the paper discusses how the project was executed, from the researchers’ point of view. Major change processes have to do with changing the perceptions of stakeholders, who all have, in general, different aims and experiences. An attempt is made to visualise a change process via the perceptions of the different stakeholders. In a second stage, a software tool is constructed which allows people to observe the different perceptions and to play around with these ‘knowledge maps’. Table 1 Composition of the sample Stakeholders
Number of people involved
Board and Central direction (RWS) Quality services (RWS) Engineering consultants Sub-contractors Material suppliers Regional directions (of RWS) Total
5 10 10 20 10 150 (around 40 in 4 regions) 205 people
The list of names was produced by RWS. Data-gathering was undertaken by the research team.
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The aim of the tool and of this approach in general is to support a learning process. When different stakeholders use the software tool, they can observe different attitudes and learn from them. The tool allows stakeholders to view how other stakeholders perceive the problem. All this takes place in a non-confrontational environment and the approach can take into account large numbers of people and stakeholder groups. Since the outcome is a software tool, it can also be put at the disposal of many people via a computer network. The research questions related to this purpose are formulated as follows: can a software tool or a support system of any kind be built which is able to do the following: 1. Contain all the tacit knowledge which the different stakeholders (including ‘clients’) have about the process to be changed. 2. Picture (visualise) a situation and its evolution via the perceptions of all stakeholders. 3. Retain in the picture as much of the initial complexity as possible, as well as attempt to keep as much variety of perceptions as possible. 4. Visualise this network of mutually connected opinions and positions in a way that the different stakeholders can ‘play’ with each others perceptions and ‘learn’ from this. 5. Create a procedure of continuous adaptation of the picture of the process via the stakeholders who question the system. The system should learn from the questions of the stakeholders and the system should evolve on the basis of the learning behaviour of the users. 6. Enable the use of adaptive (learning) qualities to identify which rules are of real importance. Allow the tool to suggest what can be learned from this particular process and what could be of use in other comparable situations. Allow people to learn from each other’s experience. This approach aims for learning and adaptive behaviour, based on the changing perceptions and the learning of the stakeholders themselves (e.g. clients, personnel, suppliers, etc.). The system adapts gradually according to the learning processes of the people who use it. The system learns from the users and hence the system does not force the users to think the way the application does.
6. The research procedure and the results In a first step, existing material was consulted describing the project and the process from any possible point of view. The material included internal reports, interviews, studies, etc. The aim at this stage was only to identify as many issues as possible, not to identify the relationships between the issues. The list of issues identified in the first stage was validated by a number of stakeholders. It is important to make sure that the different stakeholders use the same terminology before launching a widespread questionnaire. This is a rather technical matter but a necessary procedure in order to guarantee comparability of results. Based
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on the validated list of issues, a formal questionnaire was produced. A total of 140 issues was selected as being representative. Different media can be used in order to distribute and process the questionnaires. In this research, a paper-based distribution was used, which of course imposed a limit on the number of possible respondents. One could easily design an electronic questionnaire, which would make both distribution and processing easier and faster. It would allow the project to use much more input. In this particular case, the company did not prefer this alternative, due to the fact that many outside stakeholders would have had to be involved. The company feared that this would lower the response rate. The questionnaire was filled out by the 205 participants selected for the field research who were mentioned earlier. The questionnaire asks for ‘recognition’ of issues. A typical question would be “how much do you recognise issue 1 as being important in this process?”. Measurement takes place on a 1 to 10 Likert-type scale. The same kind of procedure is used for the definitions. ‘A priori’ definitions are avoided, e.g. of ‘quality’, since any given definition immediately excludes all other views on the same subject. Instead of making the process clearer, in many cases it divides the camp into two. Furthermore, it is arguable whether clear definitions do exist for such concepts as ‘quality’. In a change process where the aim is to gather stakeholder views, it would be counterproductive to work with strict definitions. Instead, a number of statements about quality are given and the respondents are asked to score how recognisable they are. Eventually, after training of the neural network, this approach will produce a networked version of statements which incorporates the definition of quality without really specifying it. It is not a straightforward definition. It gives all important aspects of ‘quality’ and it also gives the interrelationship between the different aspects. The next step consists in training neural networks by using the data (perceptions) gathered from the field research. The aim is to identify structure. In this particular case, both Sanger and SAMANN networks were used. Any representation (or a number of different representations) will allow a structure to be identified which is deducted from observations and perceptions, rather than from any predetermined mental map. In this process, no mental model is imposed onto any of the stakeholder groups. All different structures are, ‘a priori’, equally possible. Only the practical use of the different pictures will allow discrimination of the one in favour of the other. Whereas Sanger and SAMANN (based on Kohonen maps) are ‘unsupervised’ learning algorithms aiming to identify structure, the picture can be detailed further by ‘supervised’ learning algorithms. For the purpose of visualisation and representation of the results, and this mainly for reasons of simplicity and comprehension, and without claiming that this method can be applied generally, we have opted in this case for a graphen representation. Figs. 2–4 give three different cases. Each figure represents the case of one particular person. The figures only represent 20 of the 140 issues (the software allows to change the number of issues represented). Each axis is a representation on a Likert 1–10 scale. The middle of the ‘wheel’ equals 0 for each of the issues. Comparing Figs. 2–4 illustrates how far perceptions of different stakeholders can be different from each other.
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Fig. 2.
Case 1.
Fig. 3.
Case 2.
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All 205 cases could be represented as in Figs. 2–4. Using the neural networks, an attempt is made to identify patterns in the data: this means identifying groups of people whose perceptions of the issues are similar. Neural network analysis identified from among the 205 stakeholders, 5 virtual stakeholder groups. A virtual stakeholder group is a group of people (who are members of different original stakeholder groups) who have perceptions that are similar. These are the groups of people with whom RWS could work in order to achieve progress fast and easily. The following figures compare each case (Figs. 2–4) to its nearest ‘virtual group’. Figs. 5–7 show
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Fig. 4.
Fig. 5.
Case 3.
Case 1 to its closest group (group 2).
optimal fits. One can observe that the two representations in their respective figures (the original one in Figs. 2–4 and the corresponding virtual group representation) are close; of course, they will never be exactly the same. Fig. 5 gives case 1 (Fig. 2) compared to its virtual group (group 2). One immediately observes those issues on which person 1’s perception approximates the virtual group perception and those where person 1’s perception is distant. Most work should be concentrated on these latter issues (e.g. 11, 10 and 1). Fig. 6 shows case 2 in comparison to its closest virtual group (which is group 4) and Fig. 7 shows case 3 in comparison with its closest virtual group (which is again group 2).
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Fig. 6.
Fig. 7.
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Case 2 to its closest group (group 4).
Case 3 to its closest group (group 2 again).
The software tool also allows comparison between any case and any arbitrarily chosen virtual group. Fig. 8 compares case 1 to virtual group 4, which is not its closest virtual stakeholder group. Comparison with Fig. 5 shows that it would be more difficult to try and bring person 1 closer to group 4 than to group 2. Finally Fig. 9 shows person 25 (not yet used in any of the figures above) in comparison to the (in this case non-optimal) virtual stakeholder group 2. Comparing this figure with Fig. 5 and Fig. 7 shows the difference between trying to bring case 1 and 3 closer to the closest virtual group, compared to trying to bring person 25 close to group 2. The software tool allows any combination of the above given kind. What it does
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Fig. 8.
Case 1 compared to virtual group 4.
Fig. 9.
Case 25 compared to virtual group 2.
is that it visualises the perceptions of the different people, compared to those of any virtual stakeholder group desired. It visualises difference in perception, visualises common vision, indicates shortest ‘learning paths’ and indicates difficult paths of learning and change. The pattern recognition capacities of ANNs are put to optimum use in this application. They perform better than any other quantitative grouping method, with which these kind of ‘data’ could not be tackled (Venugopal & Baets, 1994a, b; Mao & Jain, 1995). The stakeholders can ‘see’ how the different groups ‘perceive’, without meeting the other stakeholders. It allows stakeholders to gain much greater insight into the problem, by making use of the perceptions of all the different stakeholders. One can
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hardly imagine any other approach which not only visualises perceptions, but also allows comparison and representation. This software tool allows individuals to play and to learn. Of course, it remains each individual’s responsibility to be open and willing to learn. Group sessions can use this tool as a pedagogical support for the discussion.
7. Long term relevance of the project for RWS The project, and particularly the software which is now in use, provides a trained neural network in which 5 virtual groups were identified. For each of these groups, the characteristics/issues are known and a test-print (see figures above) has been made in order to reveal them. The software can be further improved in the following ways: 앫 Out of the 140 issues and statements, any combination of 20 should be possible. The groups could be labelled better and clearer. 앫 In a next phase, it should be made possible to ‘guide the change’. In practice this means that a desired path of development can be identified and the difference between the present state and the desired path can be visualised. 앫 The neural network which now exists will constantly be updated by any new query to the system. Each query is considered as a new input and the auto-adaptive character of the neural network causes continuous learning and updating. As was understood from the beginning of the project, it is the aim of RWS to direct the change process in order to fit better the aims which RWS has given itself for the coming years with respect to quality control. Eventually, the proof of the pudding is also in this case, in the eating. Extensive use of the software tool, both by individuals and by groups, validates the tool. At a later stage, the use of the software tool will show evidence of the possibility, not only to visualise perceptions (which can already be done), but also to show change. For reasons of corporate policy, this tool is currently only in use by RWS employees in its first stage. However, there is no reason other than for political considerations, not to have other stakeholders experiment with it. In a later stage, and in order to magnify the test, groupware can be used for group sessions. For budgetary reasons, this has not yet been considered. At a later stage, evaluation can take place on whether the tool learns and shows adaptive behaviour. The evaluation should be twofold. First, an analysis needs to be made of whether the people who have worked with the tool have changed or adapted their mind-set, at least, again, according to their own perception. However, given the auto-adaptive nature of the tool, a second way of testing exists. It is also possible to check how much the original tool differs from the one which evolved after multiple use. If the final tool differs from the first tool, this will show indirectly that some learning has taken place, both by the people involved and by the system itself.
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