Caractérisation des échanges des informations et des connaissances dans les groupements d’entreprises : mise en œuvre d’un système de gestion des connaissances pour le cas du cluster de l’industrie artisanale Thaïlandaise Pradorn Sureephong

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Pradorn Sureephong. Caractérisation des échanges des informations et des connaissances dans les groupements d’entreprises : mise en œuvre d’un système de gestion des connaissances pour le cas du cluster de l’industrie artisanale Thaïlandaise. Gestion et management. Université Lumière - Lyon II, 2009. Français. ￿NNT : 2009LYO20065￿. ￿tel-01540277￿

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École Doctorale Informatique et Mathématiques Lyon (InfoMaths) Laboratoire d'Informatique pour l'Entreprise et les Systèmes de Production (LIESP)

Université de Chiang Mai, Thaïlande

College of Arts, Media and Technology

Caractérisation des échanges des informations et des connaissances dans les groupements d’entreprises : mise en œuvre d’un système de gestion des connaissances pour le cas du cluster de l’industrie artisanale Thaïlandaise Par Pradorn SUREEPHONG

thèse de doctorat en Informatique

Sous la direction d’Abdelaziz BOURAS et Nopasit CHAKPITAK

Présentée et soutenue publiquement le 15 octobre 2009

Membres du jury : Abdelaziz BOURAS, Professeur des universités, Université Lyon 2 Nopasit CHAKPITAK, Maître de conférences, université de Chiang Mai Sebti FOUFOU, Professeur des universités, Université de Bourgogne Matteo SAVINO, Professeur d’université, Sannium university Yacine OUZROUT, Maître de conférences, Université Lyon 2 Gilles NEUBERT, Maître de conférences Université Lyon 2 Benoit EYNARD, Maître de conférences HDR, Université de Troyes Ahmed LBATH, Professeur des universités, Université Grenoble 1 Contrat de diffusion

Ce document est diffusé sous le contrat Creative Commons « Paternité – pas d’utilisation commerciale - pas de modification » : vous êtes libre de le reproduire, de le distribuer et de le communiquer au public à condition d’en mentionner le nom de l’auteur et de ne pas le modifier, le transformer, l’adapter ni l’utiliser à des fins commerciales. [Dédicace]

A mes parents, à mon épouse, à toute ma famille et à tous ceux qui me sont chers Remerciements

Ces travaux de thèse ont été réalisés conjointement au sein de l’équipe Lyon 2, du Laboratoire d’Informatique pour l’Enterprise et les Système de Production (LIESP), et en cotutelle au sein du College of Arts, Media and Technology, de l’Université de Chiang Mai (Thaïlande). Je remercie Abdelaziz Bouras et Nopasit Chakpitak, Professeurs à l’Université Lumière Lyon2 et à l’Université de Chiang Mai (Thaïlande), d’avoir accepté de codiriger cette thèse. Tout deux ont su me faire partager leur enthousiasme et leurs connaissances. Ainsi que le soutien, les conseils et le temps qu’ils m’ont consacrés m’ont permis de mener à bien ce travail. Je tiens à remercier Yacine Ouzrout et Gilles Neubert, Maître de Conférences à l’Université Lumière Lyon 2 et Ecole Supérieure de Commerce de Saint-Etienne d’avoir co-encadré cette thèse. Leurs conseils techniques m’ont permis de valider la méthodologie et l’application informatique concernant ces travaux. Je remercie Madame et Messieurs les membres du jury qui ont supervisés mon travail et participés à ma soutenance. Je remercie tout particulièrement Sebti Foufou et Matteo Savino, Professeurs à l’Université de Bourgogne et à l'Université de Sannio, Benevento (Italie), d’avoir accepté de rapporter cette thèse et du temps consacré à sa lecture. J’adresse également mes remerciements à Benoît Eynard et Ahmed LBATH, Professeurs à l'Université de Technologie de Compiègne et l'Université Joseph Fourier, pour m’avoir fait l’honneur de participer au jury de thèse en cotutelle. Je tiens à remercier aussi les personnes que j’ai côtoyées à l’IUT Lumière, au College of Arts, Media and Technology, et dans le laboratoire LIESP, pour leur soutien, aides et conseils. Un grand merci enfin à ma famille et ma femme pour leurs encouragements, leur soutien et les sacrifices qu’ils ont du faire pour moi tout le long de cette thèse. Sans eux, ce travail n’aurait jamais pu voir le jour. Résumé

Certains facteurs (terrain, travail et capital) étaient considérés comme accessibles, suffisants et à la base même de la compétitivité économique. La connaissance cependant n'avait pas retenu beaucoup d'attention. Actuellement, c'est l’ère de l'économie basée sur la connaissance qui est impactée par l'usage croissant des technologies de l’information. Les facteurs de la production cités auparavant s’avèrent ainsi insuffisants pour maintenir l'avantage compétitif de l’entreprise et la connaissance est sensée jouer un rôle clé dans ce contexte. L'étude de Yoong et Molina montre que le seul moyen de survivre dans un environnement de plus en plus concurrentiel est que les PMEs forment des alliances stratégiques ou des fusions avec d’autres entreprises semblables ou complémentaires. Les résultats de leur étude prônent le concept du «cluster industriel», proposé initialement par Michael Porter en 1990. Ainsi, les gouvernements essayent de soutenir ces PMEs via des moyens financiers, des stratégies politiques ou encore des promotions à l’import/export. Cependant, un grand nombre de PMEs n’arrive pas à survivre dans le contexte de concurrence du marché mondial. Des recherches antérieures effectuées autour de modèles semblables à celui du cluster industriel ont montré que par rapport à ces PMEs, celles qui réussissent davantage sont celles qui en sont membres. Les nouveaux facteurs clés du succès, mis en évidence par ces recherches, sont principalement le partage des connaissances et la collaboration au sein du cluster. Ces connaissances sont répertoriées en tant que connaissances tacites et explicites chez les experts et les différentes organisations/entreprises du cluster. Ainsi, l’application d’une approche de gestion des connaissances pour le développement du cluster devrait permettre à ces entreprises d’acquérir plus d’efficacité dans la réalisation de leurs objectifs. Afin d’aider les PMEs à mieux collaborer et partager leurs connaissances à travers le déploiement d’un système de gestion des connaissances pour le cluster industriel, nous nous focalisons sur les trois critères suivants :

• Quel genre des connaissances l’entreprise désire t-elle partager à travers le cluster et dans quelles conditions ? Sachant que les relations entre les organisations dans un cluster sont de différents types (des relations de type client-fournisseur comme dans une chaîne logistique; des relations de concurrence…). Par conséquent, la volonté de partager la connaissance ainsi que les conditions de partage représentent l’un des problèmes majeurs de notre recherche.

• Comment aider le cluster à créer, représenter et partager ses connaissances ? Sachant qu’un cluster industriel est composé essentiellement de PMEs qui ne peuvent pas disposer de moyens pour investir dans la mise en place d’un système d’information coûteux dans l’optique de faciliter le partage des informations et des connaissances (Dans le cadre de l’industrie Thaïlandaise de la céramique, les moyens couramment utilisés consistent en des lettres mensuelles, téléphone, emails et réunions mensuelles).

• Quelles infrastructures et architectures, générales et spécifiques, nécessaires pour une meilleure caractérisation des informations et des connaissances, et l’aboutissement à un système de gestion des connaissances répondant aux besoins d’un tel contexte ? Pour répondre à la problématique générale de ce travail de thèse, nous avons organisé notre travail en cinq chapitres: Le premier chapitre est dédié à la description du contexte dans lequel évoluent les travaux présentés ainsi qu’a la problématique d’origine. Dans la partie contextuelle, nous décrivons la situation économique actuelle des PMEs et la nécessité de promouvoir la collaboration sous forme de cluster. Nous présentons aussi les caractéristiques du cluster et les facteurs clé de réussite pour le développement des clusters industriels. Ensuite, nous présentons les recherches relatives au développement du cluster. Nous avons analysé différents modèles et cadres que nous avons comparés au modèle artisanal thaïlandais, qui représente notre cadre d’étude. Nous présentons également des méthodes développées dans différents pays (par exemple la France, les USA, Taiwan, la Thaïlande, etc.). Enfin, nous avons défini l’hypothèse et la problématique principales ; ce qui nous permet de spécifier notre recherche dans deux domaines : la collaboration et le partage des connaissances. Le deuxième chapitre est divisé en trois parties : la première partie de ce chapitre établit un état des processus de la gestion des connaissances (i.e. créer, représenter, partager, et réutiliser) qui aidera à proposer une approche pour l’amélioration de la collaboration et du partage des connaissances au sein du cluster. La deuxième partie de ce chapitre a pour but d’étudier l’intégration du Système de Gestion de la Connaissance (KMS ou Knowledge Management System) appliqué à notre contexte. Nous avons tenu compte des différents points de vue présents dans le KMS (tels que rôles et technologie) afin de concevoir un système KMS qui convient au contexte du cluster. Cette conception représente une tâche complexe mais essentielle dans le cycle de vie du cluster et nécessite une analyse pointue. La dernière partie est dédiée à la méthodologie support de l’Ingénierie de la Connaissance (KE ou Knowledge Engineering), qui permettra entre autre de concevoir le système de connaissance adapté et de capturer la connaissance des experts du cluster. Nous avons soumis à l’étude différentes méthodologies (i.e. MOKA, SPEDE, AKEM et CommonKADS) afin de pouvoir en sélectionner la plus appropriée à notre contexte. Le troisième chapitre est principalement consacré à la méthodologie proposée et qui repose sur l’adaptation du concept de « suite de modèles » de CommonKADS. Elle est décomposée en quatre niveaux (schéma 1). Tout d’abord le niveau contextuel qui se concentre sur le modèle du cluster, son modèle organisationnel et le modèle des tâches induites. Le second niveau, dit conceptuel, est axé sur le modèle de connaissance et de collaboration. Ensuite, le niveau de conception se concentre sur la conversion des exigences en architecture, scenarios et spécifications. Enfin, le niveau relatif à la mise en œuvre a pour but de développer le système KMS en tenant compte des spécificités données dans le niveau précédant.

Figure 1 : méthodologie proposée

Le quatrième chapitre présente les résultats obtenus dans les trois premiers niveaux de la suite de modèles à partir d’une étude de cas. Le résultat fondamental obtenu à partir du Niveau Contextuel est la formalisation du réseau physique du cluster, de la liste des tâches et l’intensité des connaissances échangées, ainsi que l’analyse de l’organisation du cluster. La faisabilité du projet a été étudiée à ce niveau. Le résultat obtenu à partir du Niveau Conceptuel peut être divisé en deux parties : • La première partie traite l’extraction des connaissances des divers experts du cluster. Ces connaissances sont représentées sous forme de « carte sémantique » (basée sur l’idée de carte de connaissances développée dans la thèse de L. Buzon) et sont considérées comme le noyau, support de la connaissance dans cette tâche.

• La seconde partie se concentre sur le modèle de collaboration pour la communication et l’échange des connaissances parmi les membres du cluster. Ce modèle permet de mieux appréhender les caractéristiques de la collaboration, du niveau de partage de la connaissance, et les activités du cluster. Nous avons opté pour le Niveau Design pour une approche Logiciel d’Ingénierie (SE) afin de faciliter la création du concept du système. Les résultats, obtenus à partir de ce niveau concernent l’architecture, les scenarios et la spécification du KMS qui sera prêt à être mis en œuvre au niveau suivant. Le cinquième chapitre concerne le développement du KMS. De ce fait, plusieurs types de technologies de l’information ont été choisis avec un objectif essentiel : remplir les conditions énoncées par le cluster (celui de l’ industrie de la céramique en Thaïlande dans notre cas ). L’architecture KMS proposée (schéma 2) est divisée en trois niveaux de service i.e. le service de connaissance, le service de collaboration et le service de présentation. Le service de connaissance est le cœur du système puisqu’il permet aux membres du cluster de représenter, partager, et réutiliser la connaissance à travers le KMS. L’infrastructure de ce service est basée sur la technologie FLEX, qui permet de créer un GUI (Graphique User Interface) avec les utilisateurs et donne l’accès à un service web sur un serveur. Le concept de carte de connaissance a été pris en compte en tant que moyen permettant d’échanger la connaissance dans ce niveau. Nous avons développé un service de collaboration dans l’intention de renforcer les activités collaboratives du cluster industriel. Ces cartes ont aussi pour mission de faire circuler la connaissance de l’expert aux utilisateurs, et ceci, au bon moment et au bon endroit. De ce fait, nous avons développé ce niveau avec les technologies PHP et Ajax. Finalement, le service de présentation s’attache à visualiser et personnaliser tous les services fournis par le KMS pour les utilisateurs de manière optimale. L’objectif principal de ce service est d’intégrer harmonieusement le KMS dans les activités du cluster. Nous avons donc développé un outil d’informatique de connaissance (Knowledge Widget) qui est en fait une application légère destinée au client pour personnaliser le KMS dans le rôle qu’il doit jouer pour chaque groupe. Enfin, nous faisons une démonstration à partir de trois scenarios qui présentent l’interaction entre le KMS et l’activité du cluster.

• Le premier scenario est basé sur l’activité collaborative des membres. Il montre comment le comité du cluster organise une réunion par le biais d’un module de conférence virtuelle. Il montre également que les trois modes de communication (entre homologues, à l’intérieur d’un groupe, la communication de masse) ont été renforcés par le KMS.

Figure 2 : Architecture système du KMS

• Le deuxième scenario se penche sur le processus de gestion de la connaissance. Il montre comment les experts, à l’intérieur du cluster, peuvent représenter et partager leurs connaissances au moyen du module de carte de connaissance collaborative. Ensuite, nous avons utilisé un moteur d’inférence pour extraire les connaissances du système.

• Le dernier scénario présente l’intégration commune du service de la connaissance et de celui de la collaboration. Le résultat de cette assimilation permet aux utilisateurs de la connaissance de l’extraire du KMS n’ importe où et n’importe quand, à partir de leur téléphone mobile. En conclusion , les résultats explicites de cette étude peuvent être divisés en deux parties. La première concerne la méthodologie d’évaluation du KMS pour le cluster. Cette méthodologie peut aussi être généralisée à d’autres clusters dans différents domaines. La seconde partie des résultats concerne le KMS qui a été spécifiquement conçu pour le cluster de l’industrie de la céramique en Thaïlande, qui a fait l’objet de notre étude de cas. Il a été prouvé que le KMS est capable de renforcer la collaboration et le partage de connaissance, et que cet état de fait à l’intérieur du cluster va favorablement impacter la compétitivité de cette industrie. Mots Clés : Système de Gestion des Connaissances (KMS), Ingénierie des Connaissances, Echange d’Informations, Collaboration, Cluster, Industrie de la céramique en Thaïlande. CommonKADS, FLEX. Abstract

The knowledge-based economy forces SMEs to form strategic alliances or merge with other similar or business companies in order to compete in the world markets. In the meantime, the concept of the industry cluster was widely implemented in many developing and developed countries for improving the competitiveness of their industries. Although many clusters have successful to develop their competitiveness, large numbers of them have failed. The study of Ecotec [DTI 05] which focuses on the key success factors of the cluster development stated that the two factors of the cluster development are collaboration and knowledge sharing among the cluster members. Thus, the principle of this study is to enhance the cluster by adopting the notion of the knowledge management which focuses on creation, representation, sharing, and reusing the knowledge. Therefore, Knowledge Management System (KMS) architecture for the cluster is proposed in this study in order to support these processes. We propose a “Model Suite” for analyzing and designing the KMS which comprises four levels: Firstly, the context level focuses on the cluster organizational analysis, which will present a global view of the actors, knowledge assets, and knowledge-intensive tasks of the specific cluster. Secondly, the concept level focuses on the knowledge itself by modeling the experts’ knowledge into the explicit form. Moreover, it also examines the collaboration model of the cluster. Thirdly, the design level aims at extracting cluster’s requirements into the system specification in order to be transferred to the implementation level . In this level, the information system is generated from different technologies for supporting the needs of the cluster organization. The KMS which is an outcome of the Model Suite is applied to a handicraft cluster in Thailand as our case study. It allows cluster members to create, share, and reuse the knowledge via a collaborative knowledge card module which is proposed in our study. The scenarios are presented in order to demonstrate how the KMS improves the collaboration and knowledge sharing among the members. Moreover, we also present the integration of the knowledge and collaboration services of the system in order to facilitate the knowledge users to retrieve the knowledge from the system via mobile device anywhere and anytime. Keywords : knowledge management system (KMS), knowledge engineering (KE), knowledge exchanging, collaboration, industry cluster, ceramic cluster in Thailand, CommonKADS, FLEX Abbreviation

AFCS Adobe Flash Collaboration Service AJAX Asynchronous JavaScript and XML AKEM Application Knowledge Engineering Methodology CDA Cluster Development Agent CK-Card Collaborative Knowledge Card CoP Community of Practice EDI Electronic Data Interchange GUI Graphic User Interface HTTP HyperText Transfer Protocol ICT Information and Communication Technology KADS Knowledge Acquisition and Design System KBE Knowledge-Based Engineering KE Knowledge Engineering KM Knowledge Management KMAP Knowledge Map KMS Knowledge Management System MOKA Methods and tools Oriented Knowledge Acquisition OM Organization Model PSTN Public Switched Telephone Network RSS Really Simple Syndication RTMP Real Time Messaging Protocol SIP Session Initiation Protocol SMEs Small and Medium Enterprises SPEDE Structured Process Elicitation Demonstrations Environment TM Task Model TTS Text-To-Speech UML Unified Modeling Language VoIP Voice over Internet Protocol VXML Voice eXtensible Markup Language XML eXtensible Markup Language Introduction

In the past, the three production factors (Land, Labor and Capital) were abundant, accessible and were considered as the reason for economic advantage, knowledge did not get much attention. Nowadays, the knowledge-based economy era shows that a small country which has less resources and higher labor cost, is still able to compete in the world market. Thus, previous production factors are currently no longer enough to sustain a firm’s competitive advantage; knowledge is being called on to play a key role. The study of Yoong and Molina shows that one way of surviving in today’s turbulent business environment for SMEs is to form strategic alliances or merge with other similar or complementary business companies. The conclusion of their study supports the idea of « industry cluster », which was proposed by Prof. Michael E. Porter in 1990. Thus, governments try to support these clusters of SMEs in various ways such as financial support, government policy, import/export promotion, etc. However, large number of SMEs still cannot survive in the strong competition in the world market. The previous research on cluster revealed that the successful companies gain benefits from being a member of the cluster. And, the major key success factors are knowledge sharing and collaboration within the cluster. This knowledge was collected in form of tacit and explicit knowledge in expert and institutions within the cluster. Thus, this work aims at applying the knowledge management practice through the cluster development which will create competitiveness to the industry cluster. In order to help SMEs cluster to have better collaboration and knowledge sharing by implementing knowledge management system for the industry cluster, we focus on three problematic as follow.

• Firstly, what kind of knowledge are firms willing to share among the cluster and in what sharing condition ? Since the relationships of organization in the cluster are not necessarily of the same type. We can either find in the same cluster collaborator-like relationships as in the supply chain or competitor-like relationships. Maintaining both relationships at the same time makes the cluster members feel uneasy to share their knowledge. Hence, these criteria are one of the major problems in our research.

• Secondly, how to help the cluster members to create, represent, share and reuse their knowledge? These activities are the fundamental of the knowledge management practice. This research will focus on the methodology to support these activities in the industry cluster context.

• Lastly, what are the general and specific infrastructures and architectures which are needed to achieve a specific collaborative knowledge management system for this specific field? We propose a specific architecture for especially improving the collaboration and knowledge exchange of the industry cluster. In order to response to the general problematic of this thesis work, we have organized our work into five chapters: The first chapter is dedicated to the description of the context in which the research work has been conducted presented. In the relative context part, we describe the present economical situation of SMEs and the need for developing collaboration as an industry cluster. We also introduce the characteristic of the cluster and the key success factors of its development. Then, we present the related researches. Different models and frameworks are analyzed and compared with the handicraft cluster in Thailand which is our case study. We also introduce the cluster development methods from various countries (e.g. France, USA, Taiwan, Thailand, etc.) in order to understand the specific characteristics of the cluster. Finally, the principal hypothesis and problematic are defined. These specify our research work in two domains: collaboration and knowledge sharing between the cluster members. The second chapter focuses on the knowledge management context and is divided into three parts. The first part describes the state of the art of the knowledge management and its processes (i.e. create, represent, share, and reuse). The intention of this part is to propose an approach to improve the collaboration and knowledge sharing of the cluster. Then, the second part of this chapter aims at studying the integration of Knowledge Management Systems (KMS) concept to our context. Different point of views of the KMS such as roles, technology, and architecture are taken into account. However, designing the KMS to suit the industry cluster context is a sophisticated work which requires a deep analysis. Therefore, the final part will concentrate on the Knowledge Engineering (KE) methodology which helps us to design the knowledge system and also to capture the knowledge from experts. In this study, several KE methodologies (i.e. MOKA, SPEDE, AKEM, and CommonKADS) are investigated in order to select an appropriate one to be applied in our study. The third chapter mainly focuses on the proposed methodology for designing a KMS for the industry cluster. We adapt the notion of CommonKADS methodology to match with our context. The proposed methodology is separated into four levels, called the model suite. Firstly, the context level focuses on the global view of the industry cluster which is composed of the cluster model, the organization model, and the task model. The second level is called concept level which focuses on the knowledge and collaboration model of the cluster members. Then, the design level focuses on converting the requirements into architecture, scenarios, and specifications. Finally, the implementation level aims at developing KMS architecture with respect to the specifications of the previous level. The fourth chapter presents the results of the first three levels of the proposed model suite applied to the ceramic industry cluster in Thailand. The fundamental result from the context level shows the physical network of the cluster, the list of knowledge-intensive tasks, the knowledge assets, and the analysis of the cluster organization. The feasibility of the project was also examined in this level. The result from the concept level can be divided into two parts.

• The first part is the knowledge models which are captured from the experts by using provided knowledge templates in the methodology. The modeled knowledge is represented using semantic map and stored in the knowledge base of the KMS. This knowledge is considered as the task’s body of knowledge.

• The second part focuses on the collaboration model for communication and exchanging the knowledge among the cluster members. This model gives better understanding about the characteristics of collaboration, the level of knowledge sharing, and the activities of the cluster. This information helps in defining the requirement for our collaborative system. Finally, the design level adopts the Software Engineering (SE) approach to facilitate the system’s design generation. The outcomes from these levels are an architecture, scenarios, and specification of the KMS which is ready to be implemented in the next level. The fifth chapter concentrates on the development of the KMS itself. Thus, various types of information technologies are adopted to fulfill the requirements of the cluster. The proposed KMS architecture is separated into three level of service: knowledge service, collaboration service, and presentation service. Knowledge service is comparable to the heart of the system since it makes possible for cluster members to represent, share, and reuse their knowledge via the KMS. The infrastructure of this service is based on FLEX technology which allows us to create attractive GUI for the users and also to enable web services on the server side. Moreover, the concept of the Collaborative Knowledge Card is taken into account as a medium of knowledge exchange in this service. Then, the collaboration service is developed with the intention of supporting collaborative activities of the industry cluster. Besides, it aims at conveying the knowledge from the expert to the knowledge user on the right place and at the right time. This level is developed on the concept of collaborative tools which is based on PHP and AJAX technologies. Finally, the presentation service concentrates on visualizing and personalizing all provided services on the KMS to the cluster members in the appropriate way. The main objective is to harmonically integrate the KMS into the cluster activities. Thus, we propose a knowledge widget which is a small application on the client-side in order to personalize the KMS to suit the role of each group. Lastly, we demonstrate three scenarios which present the interaction between KMS and the activity of the cluster. The first scenario is based on the collaborative activity of the members. It demonstrates how the cluster committee organizes meetings via virtual conference module. It also proves that three modes of communication (i.e. peer-to-peer, group, and mass communication) are supported in the KMS. The second scenario focuses on the knowledge management process. It shows how the experts in the cluster are able to represent and share their knowledge via the Collaborative Knowledge Card module. Then, the application of the inference engine is presented to demonstrate the knowledge retrieval from the KMS. The last scenario presents the value-added service of the KMS by integrating the collaboration and knowledge service together. The result of the assimilation allows knowledge users to retrieve knowledge from KMS anywhere and anytime via their mobile device. The explicit results from this study can be divided into two parts. The first part is the methodology for assessing the cluster, modeling the knowledge, and designing the KMS for the industry cluster. This methodology can also be generalized to industry clusters in different domains. The second part of the results is related to the KMS which is specifically designed for the ceramic cluster in our case study. These results prove that the KMS is able to support collaboration and knowledge sharing of the cluster which will enhance of competitiveness of the industry. Chapter I: Context and Problematic

I.1. SMEs and Industry Cluster

Small and Medium Enterprises (SMEs) have been pillar of the economies of developing and developed countries for a long time. However, in the last decade, change of rivalry in the global market and also knowledge-based economy drive SMEs to adapt themselves to the new economic paradigm. For this reason, knowledge becomes one of the new production factors from traditional production factors. Young and Molina give the motive of altering that, in the past, three production factors (i.e. land, labor and capital) were abundant, accessible and were considered as the reason of economic advantage, knowledge did not get much attention [Young 03]. Thus, the enterprises that are capable to utilize their knowledge and shift their business into new economy will survive in the global market competition. Not only the large enterprises require the alteration but also SMEs are trying to use available information and knowledge to gain more competitive advantages in their market. The study also assumed that one way of surviving in today’s turbulent business environment for business organizations is to form strategic alliances or mergers with other similar or complementary business companies. The assumption of Young and Molina’s study supports the idea of industry cluster which is proposed by Prof. Michael E. Porter [Porter 90]. The concept of the industry cluster was implemented in many continents over the world. The main objective of the industry cluster is to improve the competitive advantages of their industry and country. Even if many clusters have a great success and became the major industry of the country such as Silicon Valley in USA, electronic industry in Taiwan and leather industry in Italy, number of established clusters could not develop themselves as the competitive cluster. In this chapter, we will describe about the global view of SMEs, problem of the industry, the support from their governments and the force that group them together as industry cluster. The second part of this chapter focuses on the behavior of the industry cluster, the development of the cluster, the best practices and the key success factors of the cluster development. In the last part, related researches and methodologies that are used in the industry cluster development are reviewed and compared with cluster development in Thailand which is our case study. The objective of this study is positioned to improve the three key success factors of cluster development i.e. knowledge sharing, collaboration and the CDA (cluster development agent). The details of these factors will be described later.

I.1.1. Small and Medium Enterprises

SMEs are recognized as playing an essential role in regional economic growth and sustainability for the country. UNIDO (United Nations Industrial Development Organization) endorsed that their contribution to employment generation, poverty reduction, and wider distribution of wealth and opportunities represents a major window of opportunity for most developing countries [Dowson 03]. For these reasons, governments tried to support the SMEs by both fiscal policy and monetary policies. An in-depth view is that the SME is the business in the small to medium size that was driven from a unique skill product using a local raw material that is flexible and feasible to the local people way of life and the world market demand [Lertwongsatien 05]. The mentioned business includes manufacturing, agriculture and service industry which are embedded in the skill of local people that live near by the business establishment. Thus, SMEs generate income to its natives and bring the advantages to the region which will be a shock absorber to the fluctuation of the national economy. The definition of the SME may be differed by the policy of the government and the size of economy in each country. The criteria that are commonly used to classify small, medium and large enterprise are number of employees (headcount) and amount of investment (turnover). The examples of definition of SME in various countries are compared in table I.1.

Table I.1: Definition of SMEs in each region

Headcount Turnover Category USA UK EU Thailand USA UK EU Thailand Large >500 >250 >250 >200 >50M. $ >22.8M. £ >50M. € >100M.B Medium <500 <250 <250 <200 ≤50M. $ ≤22.8M. £ ≤50M. € ≤100M.B Small <100 <50 <50 <50 <6.5 M. $ ≤5.6 M. £ ≤10M. € ≤20M. B Micro - - <10 - - - ≤2M. € - The European Union (EU) seems to put more focus on the SMEs than other continents. From the statistic of 2008, EU comprises 23 million of SMEs, providing around 75 million jobs and representing 99% of all enterprises [EC 03]. These SMEs are the major source of entrepreneurial skills, innovation and employment in this region. In 2005, EU has adopted new recommendations regarding the SME definition to promote the micro enterprises . This helps the new investors to access to capital, start-up their business and improve to the SMEs level. These criteria are also used to classify the range of enterprises which are able to obtain the support from the government. The objective of government support is to improve the capability of SMEs in their country through fiscal and monetary policies. Thus, many policies are custom issued for the SMEs. For example, in USA, there is a Tax Credit for the investment in the venture capital or Local Seed Capital in states level, and offering on Capital Gain Tax Cut is indexed by inflation of SMEs. In UK, there are many supporting policies such as encouraging an investment in venture capital and unlisted stocks SME, tax exemption for the dividend from venture capital, exemption on capital gain tax and allow to deduct loss from income, and allow a tax relief as a percent of the investment [Lertwongsatien 05]. In term of financial support, many specific financial institutes were established to support SMEs’ business activities such as SME banks and regional funds, etc. These implied the effort of government to increase the competitiveness of the SMEs in the country or region. However, only fiscal and monetary policies from the government itself are no longer sufficient to maintain the economical competitiveness of the nation in the present competition. SMEs also required developing their competitiveness through the product and service innovation. The innovation is the driving force behind the long-term competitive advantage of the country [Bornemann 03] which requires the knowledge and collaboration between the members of SMEs in the industry. The following section will describe the development of SMEs in Thailand and their competitiveness improvement model.

I.1.2. SMEs in Thailand

SMEs have long been a leader of Thai economy. They create a large portion of national economy in terms of output, employment and effective utilization of regional resources. From national statistics, more than 90% of the total numbers of establishments in the manufacturing sector in Thailand are SMEs, which are scattered around Bangkok metropolitan and regional areas. In the past, Thailand aimed at being an industrialized country by developing its schemes based on the large foreign investment, labor-intensive production and advanced technology while neglecting the skills and know-how of local people. Since the severe economic crisis happened in Thailand in 1997 (known as Tom Yum Kung crisis), numbers of large enterprises closed down or relocated to other regions. Foreign capital and technology were transferred to new production bases. Meanwhile, SMEs also suffered from the impact of the crisis. Although many of them are closed, many of them were adaptable to the change and grew during the crisis. Hence, SMEs were the main mechanism for Thailand to pass through the crisis. Moreover, this phenomenon brought the Thai government to realize that SMEs are an important function of the country to survive and compete in the globalize world [Intrapairot 03]. Since then, Thailand’s government has attempted to support the SMEs in various ways such as establishing a SME development bank, the office of SME promotion, the SME training center, etc. With the support from the government and capability of the SMEs, the economy of Thailand has grown up step by step. In 2006, there were 2,274,525 SMEs, 99.5 % of the total enterprises in Thailand and engaged 76.7% of the work force. The structure of enterprises in Thailand is classified into four sectors: manufacturing, commerce and maintenance and service sector, as shows in table I.2 [OSMEP 08].

Table I.2: Statistics of established SMEs in Thailand in 2006 [OSMEP 08]

From the table, we see that 40% of SMEs are in the commerce and maintenance sector and the most employment (39.4%) is in the manufacturing sector. These SMEs have grown up by domestic demand and cost-focused products in the world market. They bring wealth and sustainability to the villages and regions in where they are located. In recent years, the emergence of two new factors has severely affected the SMEs’ markets.

• The first factor is the alteration of world’s economic paradigm. This influenced three elements (i.e. knowledge, innovation and competitiveness) which became the significant factors in SMEs’ business.

• The second factor is the entrance of new, lower cost, competitors in the global market. These factors impelled SMEs to realize that they could no longer rely on cost-focus strategy alone. The competitive development model concerned both public and private sectors. Thus, the concept of industry cluster was seriously implemented in Thai SMEs. The details of this concept will be discussed in the next part. With the support of many organizations, neighboring SMEs in the same industry have been regrouped to form industry clusters. One interesting example is the ceramic industry. This industry was established in Thailand more than a hundred years ago, and became a competitive industry for Thailand since then due to the fine quality of clay in the country and the high skill of craftsmanship. It had proved its performance by being one of few industries that grew during the economic crisis. The largest ceramic manufacturing network is situated in Lampang province. Lampang province is located in the middle of northern Thailand which has the highest density of ceramic industry (43.46% of Thailand’s total ceramic industry) [MIT 08]. It is the source of several positive factors for ceramic business such as fine quality of white clay, a raw material for ceramic production, low labor cost but good quality, skilled, craftsmanship. Moreover, supportive factors such as logistic convenience, availability of a ceramic exposition center, etc. have been key success factors for rapid growth of ceramic SMEs in Lampang in the last two decades. The ceramic industry in Thailand can be classified into 2 categories by using type of product i.e. traditional ceramic and new ceramic [MTEC 08]. 1 Traditional ceramics : tile, mosaic, sanitary-ware, tableware, souvenirs & decorative items, and electric insulators. 2 New ceramic : structural ceramics (for high temperature resistance) and functional ceramics (for electronic application). Most numbers of the ceramic industries in northern Thailand are traditional ceramic producers. The total export value of traditional ceramics in 2007 was 30,129.14 million baht [TCD 08] which had grown from 2003 (21,833.7 million baht) by about 37%.

I.1.3. Forces behind the industry cluster

Since the Thai economic crisis in 1997, numbers of ceramic SMEs became bankrupt and closed down, but many still survive by maintaining their comparative advantages such as a focus on lower cost of production, and the quality of the products slowly moved from “C” to “B” class (low to medium range product). However, the price and quality of products meant that these latter were able to compete in domestic and international markets. Nowadays, the comparative advantage itself is not enough to help them survive in the new economy. The SMEs are facing a new crisis from lower-cost product from neighboring countries such as China and Vietnam, the former having greater comparative advantage since it entered the World Trade Organization (WTO) in 2001 [Untong 05]. Part of the domestic market segment was taken by lower priced products from neighboring countries. The report of the Department of Industrial Promotion of Thailand [DIP 08] confirmed that ceramic enterprises in Lampang province tended to close their business down rather than set up new factories in recent years. The rest are focusing on the accessing to the new international market. Another problem for this industry is a lack of Research and Development (R&D) on their products. From the results of questionnaires in this research, we found that 55% of these companies are Original Equipment Manufacturers (OEM). About 33.67% of manufacturers are Original Design Manufacturers (ODM) and only 11.33% are Original Brand Manufacturers (OBM). The designing process has mostly been done by customers from foreign countries such as the United States, Japan, France, etc. Due to this, SMEs focused on keeping the cost of production as low as possible, to maintain their advantage for a long time, without focusing on any product development. This is the cause of deficiency in developing product and process innovation, and enables easy copying by other manufacturers. Accordingly, branding and marketing processes were also done by overseas customers, resulting in a lacking of experience in competition in foreign markets. This also created considerable problems for SMEs in the present situation, due to the demand for ceramic products in the country being less than the supply in the last few years [DIP 08]. This circumstance pushed SMEs to search for market opportunities in the global market by themselves. The lack of knowledge in the international markets made them lose out in the strong competition. These three problems are driving forces that lead to alterations in the ceramic industry in Thailand. The ceramic producers are aware that they are losing their comparative advantages in the domestic and global markets. This aroused national and local government to pay more attention to the competitiveness of ceramic SMEs, which used to be a good potential industry of the nation. Hence, the competitiveness creating model was considered by the government and academic institutes in order to build the competitiveness of the industry in Thailand. The concept of the model is illustrated in figure I.1.

Figure I.1: Knowledge as a basis of competitive advantage

From the model above, it is clear that improving the competitiveness of the industry requires developing of innovation and the knowledge of the industry. Actually, innovation could mean renewal and change, but in today’s business, it has come in particular to mean the development of new corporate services, products, processes and structures. To cope with innovation, organizations must become more flexible, and one certain way for them to do so is to strengthen their potential to learn as organizations [Bornemann 03]. Thus, the term “knowledge ” becomes an essential driver and a key factor in value creation in each organization. Creating the knowledge-based organization can be done either by learning from others (such as colleagues, partners, third party content, etc.) or creating new knowledge through innovation. Both processes help secure sustainable competitive advantage. In brief, the competitiveness of the SMEs can be improved by combining the expertise of cooperative partners to generate new knowledge and innovation which are sources of competitiveness. From this perception, the Thai government adopted the concept of “industry cluster” proposed by Porter [Porter 90] and officially applied it to the ceramic industry in 2002, in order to improve their competitive advantage. Moreover, the local government in Lampang also put the ceramic cluster development agenda into the provincial strategy. The goal of this strategy is to promote Lampang province as “ the Asian Ceramic center in 2012 ”. Hence, many government projects have been proposed to support these SMEs, such as establishing ceramic showrooms and distribution centers, ceramic development centers and ceramic competence centers for labor skill development [Untong 05]. Grouping as the industry cluster seems to be a good solution for the Lampang ceramic industry in order to enhance its competitiveness and shift the industry to the new economic paradigm. The next part will depict the general view of the industry cluster, characteristic, and the methodology used in the development and the implementation in the Thai economy. I.2. Industry Cluster

The concept of industry cluster is in the focus of many countries since it was proposed by Porter in his book named “Competitive Advantages of Nations” in 1990 [Porter 90]. A few years later, industry cluster became an economical development policy of many nations which tried to improve the competitiveness of their industry and nation. These policies represent a major shift from traditional economic development programs, which focused on individual firm oriented policies. Cluster policies, on the other hand, are based on the recognition that firms and industries are inter-related in both direct and indirect ways [Cumbers 98]. However, there are considerable debates regarding the definition of an industry cluster, how to identify industry clusters, and what factors drive the development of an industry cluster. Doeringer and Terkla have affirmed that there is no single definition of the industry cluster. From the most simplistic view, an industry cluster is “geographical connections of industries that gains performance advantages through co- location” [Doeringer 95]. Rosenfeld has enlarged the connections to those companies that also provide complementary services, including consultants, education and training providers, financial institutions, professional associations and government agencies. He defined the industry cluster as “a geographically bounded concentration of similar, related or complementary businesses, with active channels for business transactions, communications and dialogue that share specialized infrastructure, labor markets and services, and that are faced with common opportunities and threats” [Rosendfel 97]. Also, Porter defined the industry cluster later in 1998 in his book named “On Competition” as “a geographically proximate group of companies and associated institutions in a particular field, linked by commonalities and complementarities” [Porter 98b]. In this study, we adopted the definition by Thailand’s government that defined industry cluster as “a geographically proximate group of companies in a particular field that linked by commonalities and complementarities, faced with common opportunities and threats, and include service providers, financial institutes, supporting industries and government agents ” [NESDB 04]. From this definition, a cluster map can be illustrated as in figure I.2. Figure I.2: Outline of the cluster map

The cluster map implies that the industry cluster includes core cluster, which is firms or enterprises that are the key players in the industry. Support cluster means government agencies, associations, academic and financial institutes, up-stream and down-stream industries which provides support to the core cluster in terms of knowledge, finance, opportunity, etc. Although the definition of the industry cluster differs due to the economical environments in each country and continent, the objectives and characteristics of the cluster are similar. Thus, it is necessary to understand the objective, networking, environment of the core cluster in the different industry cluster. The following section will analyze the specific attributes of the industry cluster, especially in the core cluster, the key players in the cluster.

I.2.1. Objective of the cluster development

Actually, the main objective of the industry cluster development is to improve national competitiveness which will enhance people’s welfare is affected by increasing of productivity of the industries. From Porter’s theory [Porter 98a], the objective of the development is divided into 2 levels from different points of view.

• Macro-economic level (Government’s view) : The objective is improving the competitiveness of the country in the world market. The important factors in this level are government policy, industrial promotion, law and regulation. • Micro-economic level (Enterprise’s view) : The objective is improving the innovation and productivity of the enterprise. The fundamentals of manufacturing, commerce and service business are the key factors of this level. Although the objective of the industry cluster development is clearly defined in both levels, the objective of clustering of the SMEs could not be obviously defined. The objective of the SMEs to participate in the industry cluster is different and is depending on the expectation of the enterprise. From literature review [Sureephong 06], there are many objectives that attract SMEs in the industry to group together as an industry cluster. We classified these objectives into four categories i.e. connection, collaboration, competition and collective efficiency. 3 Connection : participants in a cluster are connected in both vertical and horizontal ways to share information and knowledge between Enterprises, Education, Research and Development, Financial Institutes, Government, and Private sector e.g. members expected to gain advantages from networking in the cluster. 4 Collaboration : participants are able to have collaboration not only in opportunity but also threat e.g. members shared their opportunities and tried to solve the problem together. 5 Competition : to improve positive competition such as new product, product design or production process e.g. members initiate joint research to improve the innovation of product and service. 6 Collective Efficiency : not only information and knowledge can be shared between participants within the cluster, but other issues such as resources or negotiating power are able to be shared e.g. members try to aggregate demand to reduce cost on production, marketing, human resource development, infrastructure, and etc. In a single industry cluster, we can find more than one objective/expectation of the enterprise in the collaboration. These objectives can be noticed in term of the activities that are usually done by the members of the cluster. The more activity supports these objectives, the more value-added factors become available for the members of the cluster. These collaborations are also indicators for the growth of the industry cluster. The acquired benefit of the enterprises who participated in the cluster differed by the objective of the particular collaborations and activities in which they participated. However, the global benefit of the cluster can be explained by the characteristics of the industry cluster. Many researches [Keeble 00] [Storper, 97] pointed out that transaction cost savings alone is insufficient to explain the growth and persistence of the clusters. One explanation is that large firms try to expand their business in horizontal and vertical ways. They are able to do so because they have economies of scale [Patrice 05]. But, SMEs are limited in their access to specialized resources and intelligent capital. Taylor and McRae-Williams [Taylor 05] posit that clustering simulates large firm behavior, e.g., when small firms are not in a position to internalize externalities through economies of scale, they cluster to access resources, to reduce costs, to compete with larger firms, and to innovate. In other words, by networking and sharing knowledge, small firms are able to compete and access specialized resources and information as well as internalize competencies and assets that typically are internalized by large firms with economies of scale [Tayler 05]. Clustering hence provides SMEs benefits that would be unavailable (or available at a greater cost) to non-clustering members. While value-added factors and activities such as R&D, access to a global client base and advanced business services/production are clearly major contributing factors for small business clustering, the need for access to localized explicit and tacit knowledge networks has proven to be a central driver for clustering [Keeble 00]. As we mentioned, the collaboration in the industry cluster is the key for the SMEs to gain the benefits from being a member of the cluster. However, the collaboration in the cluster comprises various types of network. Some types of network stimulate the collaboration in the cluster, but some types of networks obstruct the collaboration. The next section will describe each type of network in the industry cluster.

I.2.2. Networking in the industry cluster

As the concept of the industry cluster has become increasingly popular, and used to pursue a wide variety of objectives, so the definition of network in the cluster has diversified. Some emphasize the key objective that drives the cluster e.g. trade-driven and knowledge-driven [Johnston 03]. However, the most common way to define the network in the cluster is physical topology of cluster. Porter [Porter 90] stated that there are two types of networking in the industry cluster i.e. vertical and horizontal clustering.

• Vertical clustering is made up of industries that are linked through buyer-seller relationships as can be found in a supply chain.

• Horizontal clustering include industries which might share a common market for the end products, use a common technology, labor force skills, or similar resources, likes competitor-like relationship . Anderson [Anderson, 1994] had emphasized the importance of the relationship within a cluster using three categories: 7 Buyer-Seller Relationship concentrates on vertical interactions between the core production processes and the inputs and distribution of the goods and services. 8 Competitor and Collaborator Relationships exist because competitors frequently share information about product and process information and may, in fact, formally collaborate to develop such innovations. 9 Shared-Resource Relationship identifies horizontal relations stemming from shared technology, labor force or information, even among companies that may produce unrelated goods or services. The main objective of networking in the cluster is to build the co-operation on the competition , called “ co-opetition ” [NESDB 04], by jointly creating a core objective, strategy, exchanging information and knowledge, and resources between members of a cluster for improving collective efficiency/productivity. Moreover, clustering also facilitates the knowledge distribution between organizations which will create innovation in the industry. Fostering the linkages between the members of a cluster is one of the most important elements of any cluster development strategy [DTI 05]. It is the key to growth for SMEs within a cluster to gain strength through the competition and collaboration by utilizing the formal and informal networks [OECD 96]. The critical point of networking is that all members gain some benefits from their participation. From the study of DTI, the successful clusters seem to have strongly embedded networks and relationship systems [DTI 05]. The size of network can vary from a small group of companies working together on collaborative ideas to associations with a large number of members. However, size of network is less important than the fact that they serve a purpose and there are benefits to membership. Some networks are highly specialized, whilst others cover many different topics. Trust and interpersonal relationships are highly developed, providing the cluster with a strong degree of social capital. From the definition of cluster networking, we can see that the cluster comprises the concept of supply chain, virtual enterprise and extended enterprise together in the network. The study of Ron Johnston [Johnston 03] described that industry cluster is an alternative way to create the value chain in SMEs. However, there are debatable arguments about the similarity and dissimilarity between these concepts and the industry cluster. The next section will provide the comparison and illustration on this issue. I.2.3. Industry cluster and manufacturing network

Today’s manufacturing, companies are trying to re-invent their businesses and maintain their competitive advantage through collaboration. This collaborative practice can be seen as supply chain, value chain, extended enterprise, virtual enterprise or industry clusters. However, these manufacturing collaborations are becoming commonplace. One confusing issue is the objective of the collaboration. The collaboration in the manufacturing network such as supply chain, virtual enterprise and extended enterprise aims at increasing utilization and synchronization of the chain, resulting in tangible benefits for each participating company [Anand 00]. Within this context, it could be seen as a collaborative network of organizations working together to maximize the value of a product to the end consumers. To be able to reduce conflict within this complex system, companies need to have common goals, clearly defined domains and especially a uniform understanding of situations. This collaboration increases the ability of the network to make rapid decisions [Davenport 98] in order to decrease the cycle time and increase the flexibility to respond to the change of customer’s demand [Romano 03]. In this way, enterprises share knowledge to improve the global value carried out by the supply chain [Larsson, 98]. In fact, they are focusing on the same objective but in a different point of view. Industry cluster is created in the area of dense manufacturing networks. The objective of the cluster is to improve the competitiveness of the enterprises in the network by integrating government agencies, financial and academic institutes, associations, and supporting industries in the network of industry cluster in order to create innovation and enhance the knowledge in the supply chain [Sureephong 08]. Although supply chain and industry cluster have common objectives, there are differences in the characteristics of the collaboration which can be compared in table I.3.

Table I.3: Characteristic of supply chain and industry cluster network [Rosenfeld 97]

Supply chain Industry Cluster ● Restricted Membership ● Open membership ● Based on cooperation ● Based o cooperation and competition ● Have common business goals ● Collective vision ● Formal or Informal agreements ● Social norms, trust and reciprocity ● Accesses specialized serves at lower ● Attract specialized services to the cost region ● Enhances ability to build complex ● Generates more firms with similar and products related capabilities Another view to differentiate the industry cluster from the manufacturing network is physical network topology. In general, these manufacturing networks are integrated in the core cluster. Chain topology represents the relationship as a supply chain. Star topology represents firms that work on shared resources likes extended enterprise. Peer-to-peer is firms working together as partners to complete some task/project. Figure I.3 demonstrates the general view of the integration of supply chains and extended enterprises in an industry cluster [Jordan 00].

Figure I.3: Industry cluster and supply chain network

Industry cluster and supply chain are in the different levels of management, but focus on common objectives. However, there are many benefits from the industry cluster that can enhance supply chain activities as follows;

• Networking in the cluster can improve the capability of company in the supply chain to search and select their partners.

• Collaboration in the cluster will improve knowledge and information sharing between partners in the supply chain.

• The cluster supporter, such as an academic or financial institute could improve the knowledge and innovation in the supply chain. The comparison gives us better understanding about the general requirements for developing the industry cluster on the relationship likes supply chain or extended enterprise. However, developing the industry cluster in the different phase requires distinct intervention in each phase. Thus, the industry cluster lifecycle will be analyzed below section. I.2.4. Cluster Lifecycle

Clusters are dynamic and have a recognizable lifecycle. The interventions that appropriate at an early stage in the lifecycle of a cluster are likely to differ from those suitable for later stages. The lifecycle is often described in different ways but can be represented simply as a cyclical process containing four stages [DTI 04] (illustrated in figure I.4):

Figure I.4: Industry cluster lifecycle model [DTI 04]

• Embryonic clusters – are clusters at the early stages of growth.

• Established clusters – are clusters that are perceived as having room for further growth.

• Mature clusters – are clusters that are stable, or will find further growth difficult.

• Declining clusters – are clusters that have reached their peak and are failing or declining. Clusters at this stage are sometimes able to reinvent themselves and enter the cycle again. DTI’s research suggested that different interventions are likely to be appropriate at different stages of the cluster lifecycle. In embryonic clusters, government and cluster development organization are important in encouraging collaboration and acting as information brokers, a role that may not be needed at a later stage. They do not only help to maintain the competitiveness of the clusters, but also act as starting point for promoting the development of new industries. The cluster development organization is the key success factor in these stages. However, many clusters failed to develop after the embryonic stage because they get used to support from the government. Thus, the appropriate interventions in the lifecycle are required to support the cluster development. However, the type of required support is different in each stage of the lifecycle. For example, the intervention in the established cluster required amounts of collaboration and knowledge in the cluster in order to develop the competitiveness to their cluster than the earlier stage. Moreover, developing from the established stage to the mature stage requires an abundance of high degree on collaboration and knowledge sharing among the members of the cluster. This study will focus on the cluster which is in the established stage. The key success factors of cluster development in this stage will be explained in the next part.

I.2.5. Key success factors of cluster development

Although many clusters achieved their goal to develop the competitiveness of their industry, many clusters have failed. Most of them broke down after the establishing stage when the support of initiators or government declined. Thus, this stage is the critical point of development of the cluster. It requires many factors to maintain the collaboration of the industry cluster [Rosenfeld 02]. For this reason, we have studied many case studies to identify the key success factors for the industry cluster development. The study of DTI in 2005 [DTI 05] stated that the critical success factors in the cluster development were collaboration in networking partnership and knowledge creation for innovative technology. It mentioned that about 78% and 74% of articles cited that knowledge and collaboration in the cluster were the success criteria. These two factors are the internal factors of the cluster. The study of the cluster initiative green book [Sölvell 03] stated that a Cluster Development Agent (CDA) is one of key success factors of the cluster. From statistics, about 89% of the successful clusters have a dedicated facilitator in the cluster. Thus, CDA is the external factor that critically affects cluster development. Thus, to achieve the intention of this study which is supporting and improving the development of industry cluster; these 3 factors i.e. (1) Collaboration (2) Knowledge sharing and (3) CDA, will be considered as the major domain of the research. The details of 3 key success factors will be explained in the following part. I.2.5.1. Collaboration in the cluster

In the manufacturing domain, there have been rapid developments in term of collaborative network in the last two decades and involvement in networks contributing to knowledge and productivity to the industry. Collaboration in the industry cluster concerned obtaining sustainable competitive advantage from the maximization of value added benefits from working collaboratively. However, firms are often reluctant to share information and knowledge formally for fear of their competitive position being undermined. This dilemma was affected by the special relationship in the cluster, in which cooperator and competitor are in the same situation [Levy 03]. It has been observed that the cluster can collaborate in the co- opetition relationship as long as the common objectives of collaboration remained. The goal of collaboration as an industry cluster accomplishes the collective objectives of the members; however the expected benefits to firms from the collaboration differ in the objective of participation. The table I.4 shows the reviewed objectives of firms from the collaboration in the industry cluster. These benefits not only attract SMEs, but also large enterprises to participate in the collaboration.

Table I.4: Review of firms’ objectives for the collaboration in the cluster

Objectives References to increase their market share [Lewis 90] to increase asset utilization [Lewis 90] to enhance customer service – reduction in lead times, customer [Lewis 90] complaints, etc. to share and reduce the cost of product development [Lewis 90]; [Parker 00], [Horvath 01]; [McLaren 00] to reduce time in product development [Lewis 90]; [Parker 00]; [McCarthy 02], [McLaren 00] to decrease risk of failure of product development [Parker 00] to increase quality of product [Lewis 90] to enhance skill and knowledge [Lewis 90] to have technological gain [Lewis 90] ;[Parker 00] to achieve economies of scale in production [Lewis 90] to reduce inventory – in the face of increasing technological [Parker 00]; [Holton 01] complexity and rapid rate product development and obsolescence to gain access to markets [Parker 00]; [Gogilic 03] The table above implies the benefits that members of the cluster are able to acquire from collaboration in the cluster. However, the acquired benefits also depended on the quality of activity and degree of collaboration. Nowadays, the internet era, Information and Communications Technology (ICT) brought a shift in the phenomenon of clusters subjugating the importance of proximity and location by virtual proximity . Additionally, ICT fosters interregional collaboration, so the boundaries between regions important to regional clusters are no longer as distinct as they once were. It also accelerates the degree of collaboration in the cluster in term of frequency, quality of information, time, etc. Hence, ICT is a critical driver of integration and co-operation since it enables businesses to integrate activities and functions otherwise not possible [Levy 03]. In this study, collaboration, which is one of the most important key success factors for industry cluster is analyzed in order to understand the characteristic of collaboration and information sharing. Moreover, ICT used in the cluster is considered as an impact factor in the collaboration. The results of the analysis were used for designing a collaborative knowledge management system for the industry cluster [Jacobs 96].

I.2.5.2. Knowledge sharing in the cluster

The collaboration that generates formal and informal flows of knowledge and information throughout a cluster creates the success for the industry over time. Accessing this knowledge creates collective learning and more competitive performance for firms in the cluster. Both formal and informal networks enable a transfer of knowledge around the cluster. The knowledge dissemination, such as informal collaboration and extensive contact networks, can create a ‘ knowledge community ’ within the cluster which is a source of competitiveness of a cluster [Huxham 96]. With the concept of knowledge networks, we entered a new era by accepting to assess not only internal values but, equally, values external to the enterprise. This requires two factors. Firstly firms must have the appropriate tools and capacity to communicate and to stock external knowledge according to its needs. Secondly, they must have in place appropriate processes to assure access to, and management of, this knowledge in order to exploit it and so generate added value. In other words, firms looking to be involved in collaborative networks need to ‘ develop a new knowledge management process’ [EC 03]. The knowledge sharing through networks and partnerships can be achieved through ways which are direct (face-to-face) or indirect (through persons or information system). Information technology has advanced significantly in this respect, and cluster practitioners are using interactive cluster portals to facilitate networking, and share information about the cluster as well as using them for actual business to business interactions. Moreover, it is one of the effective tools for creating direct (e.g. chat) or indirect (e.g. discussion board) sharing.

Figure I.5: Direct (A) and indirect (B) knowledge sharing

Although there is a consensus in many studies [OEDC 96] [Sölvell 03] [Porter 98a] [DTI 05] about the significance of knowledge sharing in the cluster, no study has proposed any methodology to create, share or maintain the knowledge in the cluster. Most of the researches depict that the knowledge sharing process is embedded in the process of collaboration. Firms will share their knowledge when they work together as partners. This study focused on improving the knowledge sharing process of the industry cluster. Moreover, the processes of knowledge creating, representing and utilizing were taken into account for enhancing competitiveness of the industry cluster.

I.2.5.3. Cluster Development Agent

Cluster Development Agent (CDA) is one of the external key success factors for cluster development. In practice, CDA can be person(s) or organization who acts as the cluster facilitator by conceptualizing the overall developmental strategy for a cluster and initiating implementation. He is also the facilitator between the various cluster players and the target cluster. From the cluster initiative green book [Sölvell 03] indicated the importance of CDA to the successful cluster. Almost all Cluster Initiatives (89%) have a dedicated facilitator, and many (68%) have some sort of permanent office and cluster facilitators tend to have an industrial background. A guide to cluster development of Ukraine SMEs [GFA 06] described the characteristics of a CDA as follows. The primary role of a CDA is to help the cluster formation process and motivate potential cluster members to commit to joint activities. The typical goal of CDA is enhancing local economic growth by bringing together businesses in strategic grouping with a focus on developing collaborative engagement at cluster level. The facilitator will facilitate growth of existing firms and their support infrastructure, including government, educational resources, and research and development facilities. In the developing phase, CDA takes very important roles in achievement of a cluster by facilitating information exchange among members of the core cluster, between core cluster and support cluster, and improving collaboration between all players. Moreover, CDA is such a good catalyst for two internal success factors by motivating the members to share their knowledge and participate in the collaboration. Although many studies claimed that these activities are necessary for a developing cluster, there is no study that proposed tools or methodology to support the activities. Thus, our study tries to support CDA in order to facilitate the collaboration and information sharing within the industry cluster. The proposed framework will include a set of services for assisting CDA in the architecture. The next part will give a brief review of methodology used and a case study in the cluster development in global view.

I.3. Related research on the cluster development

Since the concept of the industry cluster was popularized by Michael Porter in 1990, it has rapidly attracted attention from many governments, consultants, and academics. Porter had proposed the first model of cluster development which is known as Porter’s diamond model [Porter 90]. The model relies on qualitative methods. This objective of the model is to determine competitiveness within an industry and illustrates how those forces are related. This model was widely used for analyzing the industry clusters in many nations. The overview of this model will be presented in the next part. Afterwards, many researches were initiated in order to improve the potential of the industry cluster. One of the famous frameworks that aims to expand the Porter’s model in term of quantitative methods is called the HHH framework, which proposed by the Hubert H. Humphrey Institute of Public Affairs, USA in 1998 [HHH 98]. This model focused on applying economical analysis such as location quotient and shift-share analysis for identifying the key industry for future development. However, the goal of both models is in the cluster initiation phase (embryonic stage). The methodology for developing the established cluster is still ambiguous for cluster initiators. The first comprehensive study of cluster initiatives around the world was reported in the “Cluster Initiative Greenbook ” published by Örjan Sölvell et al. in 2003 [Sölvell 03]. This report provides a clear view of the industry cluster development approach in various countries. The report provided better understanding about the key success factors for the cluster development for cluster facilitators. Moreover, it was used as the guide book for the government in many countries in order to initiate the government policy to support the development of the cluster after the establishing stage. This research has studied about 500 clusters in developed countries around the world. So far the results of the study have been extended to 1400 clusters in many countries. In the mean time, the United Nations Industrial Development Organization (UNIDO) has proposed the model for industry cluster development which focused on SMEs in developing countries such as Mexico, Morocco, India and Indonesia. UNIDO believe that productivity can be created by network and collaboration among the partners. This model relies on the Porter’s model in the beginning phase and collaboration between CDA and members after the clusters are formed. Hence, UNIDO’s model is generally used in developing countries because of the suitability of the model to the social and economical environments. Brief details of these related researches are as follows.

I.3.1. Related Research

I.3.1.1. Porter’s diamond model

In practical analysis, Porter relied on both qualitative and quantitative methods to analyze the industry cluster. The quantitative approach towards identifying industry clusters is generally regarded as a critical component of a cluster analysis. This type of analysis provides an initial tool for identifying potential clusters and indicates the relative presence of different industries in the local region. The four corners of the diamond (figure I.6) include factor conditions, demand conditions, industry strategy/rivalry, and related and supporting industries.

Figure I.6: Porter’s Diamond Model [Porter 98b]

• Factor Conditions : The situation in a country regarding production factors, like skilled labor, infrastructure, etc., which are relevant for competition in particular industries.

• Home Demand Conditions : The more demanding the customers in an economy, the greater the pressure facing firms to constantly improve their competitiveness via innovative products, high quality, etc.

• Related and Supporting Industries : Competitive supplying industries will reinforce innovation and internationalization in industry at later stages in the value system.

• Firm Strategy, Structure, and Rivalry : The conditions in the country that determine how companies are established, organized and managed and that determine the characteristics of domestic competition. The results from the model provide an initial analysis which illustrates the overview of the industry in terms of competition in the industry. This information is often used by government agencies for selecting potential clusters (from a set of clusters in the nation) to be promoted and supported.

I.3.1.2. HHH framework

The Hubert H. Humphrey Institute of Public Affairs proposed the “HHH Framework” [HHH 98] to identify the key industry by using only quantitative analysis. The objects of this framework are for determining which industries are growing and which are declining; the importance of an industry to the economy relative to its importance nationally, and competitiveness of regional industries compared with their counterparts nationally. The framework is composed of 8 steps, as follows: • Step 1: Share of local employment

• Step 2: Change in employment

• Step 3: Location quotients

• Step 4: Change in location quotients

• Step 5: Shift-share analysis

• Step 6: Analysis of payroll data

• Step 7: Analysis of earnings data

• Step 8: Analysis of firm data An example of the formula in this framework is Location Quotient (LQ). LQ is mostly used by many researches in the cluster initiation phase. The result of the analysis shows the sufficiency to satisfy the local demand.

• LQ =1.0] local production in the industry is assumed to be just sufficient to satisfy local demand, and the industry is assumed to contain no basic employment

• LQ < 1.0] local production in the industry is assumed to be insufficient to satisfy local demand, and require products to be “ imported”

• LQ > 1.0] local production in the industry is specified and is assumed to exceed local demand, allowing the excess products to be “ exported” Many industry cluster analysts in the United States relied on this framework. The advantage of this framework is representing the importance and characteristic of industry in a mathematical model. The empirical result from calculation provides economical information for governments to make decisions. However, completing these 8 steps requires amounts of economical data from many sources. Thus, this framework is not popular in developing countries which lack complete data. I.3.1.3. Cluster Green Book

The Cluster Green Book [Sölvell 03] introduces information about 250 cluster initiatives from 500 surveyed clusters around the world for Global TCI Conference at Gothenburg, Sweden in September 2003. The objectives of this study are identifying characteristics, patterns, vision, processes, and driving forces of cluster development in the global view. This study also proposed Cluster Initiative Performance Model (CIPM) (figure I.7) which considered fundamental elements such as social view, political view and economics of country, objective, and process of development. These elements are considered as key factors that will affect the clustering process.

Figure I.7: Cluster Initiative Performance Model (CIPM) [Sölvell 03]

The report has shown that each cluster is developed in a different environment. There is significant dissimilarity between developed and developing countries, competitive and weak locations. The purposes of development for cluster can be classified into 6 reasons i.e. network research and development, government policy, collaborative commerce, resource, innovation and technology, and extending network collaboration. The interesting statistics from CIPM are displayed below:

• Over 50% of the clusters are concentrated in an area within 1 hour traveling time of each other, are open for general members, and usually do not include multi-national companies or very small companies.

• 89% of the clusters have full-time coordinators (CDAs)

• 68% of the clusters have their own offices • 95% of the clusters have group leader and more than 10 members in the committee.

I.3.1.4. UNIDO’s Model

The United Nations Industrial Development Organization (UNIDO) is an organization that is well known in knowledge and human resource development. The study of UNIDO focused on enhancing network of small and medium enterprises in developing countries. Thus, the idea of industry cluster was applied to the study. UNIDO believed that productivity can be created by network and collaboration among the partners. This required trust, knowledge, understanding, and learning together is facilitated by a broker [Dowson 03]. UNIDO recommended using external cluster development agents (CDA) with no financial stake in the cluster. These CDAs were supposed to establish coordination and trust among the members. The members jointly define the vision, mission and strategic plan for competitiveness enhancement. The initial phase was to create understanding and analyze the enterprises, survey the group’s requirements and collect cluster data by using local consultants who could later become CDAs. The data were analyzed under the guidance of an external consultant to ensure confidence among cluster members. The research also included analysis of the linkages of the cluster components based on the Diamond Model. Then, the CDAs worked to develop the cluster map as well as the vision, mission and strategic plan. UNIDO assisted with expenses for meetings, site visits, dissemination of knowledge as well as hiring CDA and supporting activities according to the strategic plan of the cluster. The level of assistance decreased after the cluster had developed and generated more reliable income [KIASIA 06].

Figure I.8: UNIDO’s cluster development model [Dowson 03]

UNIDO’s model was divided into 5 steps: diagnosis, trust building, identification of an action plan, implementation activities, and monitoring and evaluation. This model aims at providing a guideline for CDA in order to develop the industry cluster from the beginning to the end of the cluster lifecycle.

Table I.5: Comparison of related researches on the cluster development

Researches Area of research Method Critique Porter’s Model Japan, Taiwan, - Qualitative Analysis This model is useful in the cluster Canada, U.S., etc. (Diamond Model) initiating phase but the method of - Quantitative Analysis development is not cleared defined. Economical Analysis HHH Framework North America Economic-based Analysis This model tries to emphasize the (8 steps) dimension of quantitative analysis of Porter’s model. This model is recommended for government in order to select suitable industry. Cluster Green Book Europe, New Zealand, CIPM (Cluster Initiative This research aims to guide the Australia, etc. Performance Model) developing cluster with key success factors obtained from successful clusters all over the world. UNIDO’s Model Mexico, Morocco, - Cluster Map, This research proposed a model to India, Indonesia, etc. - CDA develop the SMEs cluster by - Collaboration focusing on CDA’s activities. The studies presented were used as a guide by cluster practitioners in order to initiate and develop their cluster. However, the implementation depended on the economical and social context of each region. The table I.5 shows the comparison of four methodologies for the industry cluster development. The next section will introduce the cluster developments in the global view and a case study of implementation of these cluster development model in the context of Thai economy. I.3.2. Cluster Development in Global View

The initiation of cluster development can be categorized into 2 types. Private Initiative : is a bottom-up approach. Core businesses of a cluster are grouped together to create their own objective, strategy and development policy, and then request support from government and related organizations. Public Initiative : is a top-down approach. Governments select competitive industry for supporting and stimulate collaboration from the private sector. Government acts as the facilitator of the cluster and assesses the development of clusters in a global view. However, cluster projects in these countries typically get support through local government in the form of budget and policy. Successful stories of clusters in many countries were reviewed in order to understand the best practice of cluster development in a global view.

I.3.2.1. Rhone-Alpes Cluster - France

The history of cluster building in Rhône-Alpes region (France) started around 200 years ago as humble collaboration of textile processing companies Although that to complex structures of today, this first cluster made pioneering brick-laying in the wall of regions current structure. Nowadays, the main objective of region Rhône-Alpes is to reinforce the competitiveness of the vehicles industry cluster. The cluster development in the region is divided into three phases: Firstly, establishing a common view on the challenges that the companies are facing. Secondly, the creation of shared vision which means that companies’ demands are toughly analyzed and international differences is mapped. Finally, the action lines, which are meant to implement the defined strategies into real process. The action lines proposed are: international alliances, international expansion, collaborative development and sophisticated demand. The Lyon Chamber of Commerce and Industry (CCI) acts as cluster facilitator in the region and was founded 300 years ago. It represents 52,600 companies today [CCI 09], of which the majority is SMEs. Today there are a total of 12 CCIs in the region. The CCI acts as information provider on various topics, facilitator for training and education centers, and stimulator for business start ups, acquisitions and internationalizations. Above all, the CCI offers individual customized support to companies in the region. In the case study of the French cluster, we considered that the initiation type of this cluster is a top-down approach. The CCI in each regions act as Cluster Development Agent (CDA) by trying to facilitate their clusters, distribute information and knowledge, and perform as the interface between core cluster and support cluster for the dialog. Moreover, CCIs help SMEs in technology transferring activities within the “Presénce Rhône Alpes” initiative, where the 12 CCIs in the region collaborate to mediate contacts between support organizations and companies. The initiative helped increase transparency between supply and demand of innovation support services to one of the highest levels in Europe [ACENET 03].

I.3.2.2. Silicon Valley – USA

In the mid- to late 1990s, several successful computer technology related companies emerged in Silicon Valley in California. This led anyone who wished to create a startup company to do so in Silicon Valley. The surge in the number of Silicon Valley startups led to a number of venture capital firms relocating to or expanding their Valley offices. This in turn encouraged more entrepreneurs to locate their startups there. In other words, venture capitalists (sellers of finance) and dot-com startups (buyers of finance) "clustered" in and around a geographical area. The cluster effect in the capital market also led to a cluster effect in the labor market. As an increasing number of companies started up in Silicon Valley, programmers, engineers etc realized that they would find greater job opportunities by moving to Silicon Valley. This concentration of technically skilled people in the valley meant that startups around the country knew that their chances of finding job candidates with the proper skill-sets were higher in the valley, hence giving them added incentive to move there. This in turn led to more high-tech workers moving there [KIASIA 06]. The model of clustering in Silicon Valley is a bottom-up approach. The private sector and academic institutes are initiators of the cluster. The academic institutes (e.g. Stanford University) act as the CDA behind the collaboration of the members of the cluster. The success of the initiation brought great competitiveness to the electronic and software industries in this region. With the support from national government, this cluster has become one of the core industries of the U.S [Sternberg 91].

I.3.2.3. Textile Cluster - Italy

A textile cluster was started in Italy after 2 nd World War by a group of manufacturers, large enterprises but with products which were not of such high quality. While the economy of Italy was growing, many small enterprises became linked with the cluster. Now, the Prato industrial zone is 700 sq. km. in size, employs 45,000 laborers, includes 8,000 enterprises and accounts for 60% of exports of the country. The geographic location of Prato supports this cluster in terms of logistics. Moreover, many famous academic institutes such as the Tullio Buzzi Textile Institution and the University of Florence are situated in this area. Financial institutions also provided short term loans for SMEs. Furthermore, government policy supports and motivates investors to invest in this area. This textile industry cluster in Italy is one of the best examples of the private initiated cluster in term of knowledge sharing. The universities located in the area provide fresh knowledge, new technology, human resources, etc. to the cluster. These elements are the key factors that create competitiveness for the textile industry in Prato. Nowadays, Italy is one of the world leaders in textile fashions.

I.3.2.4. Cluster in Latin America

Cluster development in Latin America tends to be initiated in all levels of manufacturing i.e. micro enterprise, small and medium enterprise and large enterprise. The development is categorized into three types [KIASIA 06]:

• Survival Cluster : micro to small enterprises which need to be strengthened in order to survive in the strong competition of their market segment. This category includes small labor-intensive businesses producing low-quality products. Promotion of co- operation among such enterprises has usually proven to have limited success.

• More Advanced and Differentiated Cluster : that can adapt quickly to meet global competition. Government intervention usually takes the form of creating an enabling environment for new knowledge, technology and innovation development, as well as eliminating regulatory restrictions, encouraging entrepreneurs and trade associations to be more self-reliant.

• Trans-national Corporation Clusters : the government provides investment assistance for business expansion of the local medium to large enterprises in the supply chain to promote technology transfer. Most projects have consultants who act as coordinators. The outcome of these development efforts often depend on the level of understanding that members have in cluster development strategy and process. Both the top-down and bottom-up approaches can be found in cluster initiation models in Latin America. The top-down approach is the potential clusters that meet requirements in obtaining support and opportunity from the government. The bottom-up approach is the survival cluster that faced difficulties and grouped together to survive in their market.

I.3.2.5. Electronic Cluster - Taiwan

The electronics industry in Taiwan is one of the best examples of cluster development in Asia. It was developed from OEM (Original Equipment Manufacturer) to innovative electronic producers in recent years. The competitiveness of the private sector (i.e. skilled and low cost labor) and the strategy of the public sector (major technology based program) reinforced this country to become the “Asia Pacific Operation Centre”. In 1995, the successful of HSIP (Hsinchu Science-based Industrial Park) cluster attracted many foreign enterprises to invest in it. Taiwan has mapped six specific industries in accordance with its national industrial policy. The mapping is based on information on the income, number of laborers, number of establishments and location quotient to analyze the concentration and expertise of the industry and compare the data with that from other areas, as well as national averages. In addition data from the Input-Output table will be used to evaluate the strength of the linkages and study the industrial expertise in the area. After the cluster map at the company and organization level is created, Taiwan’s government provides support to these clusters. The model of cluster development in this country is heavily based on quantitative analysis.

I.3.2.6. Ceramic Cluster - Thailand

The concept of network industry has long been recognized in Thai historyOne good example of a clustering model in Thailand is the “Thai Food Market”. Many well known markets in Thailand originated from a couple of mobile kiosks which sell food in an area. Over time, more and more mobile kiosks moved into the geographical area, which created variety of choices for customers. The entry of a new mobile kiosk creates competitiveness to the market in term of variety of food. Customers prefer to buy food at the market rather than from restaurants, because of the variety of food in the same area and competitive prices which satisfy both sellers and customers. With the same concept, this culture created many industrial networks of small to medium firms in every area of Thailand. These SMEs have played an important role in the Thai economy for a long time. Although there are many government policies to support individual SMEs, a policy that supported SMEs as an industry cluster has never existed. Fortunately, the cluster development in Thailand was officially initiated by the government in 2004 [NESDB 04]. It was set as a policy for developing Thailand’s social and economic structure which is in the charge of Thailand’s National Competitiveness Committee under the National Economic and Social Development Board (NESDB). Hence, 11 partners from the public and private sectors signed a Memorandum of Understanding (MoU) to create networks of cluster developments in the country. Thus, cluster analysis in the national view has been done to comprehend the present situation of clusters in Thailand, and assess the competitiveness of each cluster. In the beginning, the cluster analysis in Thailand adopted Porter’s Model. The government of Thailand allocated a budget to analyze the competitiveness of their industries. In the pilot project, 5 key industries: fashion, automotive, agriculture, tourism and electronics were chosen to be analyzed by The Competitiveness Institute which was conducted by Prof. Michael E. Porter. Later, 152 clusters in Thailand were chosen and analyzed by NESDB Thailand by using secondary data and quantitative analysis. However, the HHH framework, which could be useful in this phase, was neglected due to a lack of complete and up to date economic data in Thailand. Then, 60 of the 152 clusters were selected for in-depth analysis. This analysis was conducted based on Porter’s Model i.e. cluster mapping, supply chain and value chain analysis, diamond model and SWOT analysis. The result from this study is presented in Annex A. Finally, 20 highly competitive clusters were selected to be initiated and supported by the government. In the cluster development process, both cluster green book and UNIDO’s study were considered. The Thai government tends to rely on UNIDO’s model due to suitability in terms of methodology. However, the cluster green book is used for decision making in terms of cluster supporting policy. The ceramic industry in Lampang is one of the twenty highly competitive clusters in Thailand. The origin of this cluster is the establishment of small ceramic factories in the region more than 60 years ago. “White clay”, which is the raw material for ceramics, was found in the area of Lampang province [Untong 05]. The first product was “chicken-painted bowl” (figure I.9) which now is a symbol of this province. Then, other products such as Chinese tea set and oval bowl were produced by mixing another type of clay with white clay from this region to suit the form of the product. Since then, the ceramic products from Lampang have become varied and famous. Nowadays, the ceramic industry in Lampang is including 200 manufacturers. Most of them are original equipment manufacturers which mostly receive orders from Europe, the United States and Japan. OEMs in Lampang are well known by foreign traders due to high skill manufacturing and low production cost. However, changes in the world economy and the entry of new competitors (i.e. China and Vietnam) in ceramic markets are major forces that have pushed ceramic manufacturers to group together as an industry cluster.

Figure I.9: Examples of Lampang’s ceramic products

The Lampang ceramic cluster is a privately initiated (bottom-up) cluster established by SMEs in a geographical area similar to many successful clusters in foreign countries. In the past, ceramic manufacturers focused mainly on reducing the cost of products rather than product quality development. The prices of ceramic products decreased due to a price war between manufacturers. Thus, manufacturers tried to solve this problem together by establishing the Lampang ceramic association. This collaboration helped manufacturers to survive the strong competition. Later, the government established a ceramic centre in this area which supported information, knowledge and technology for manufacturers and product development. In 2002, this ceramic cluster generated revenue of about 4,000 million baht (approx. 90 million euros). The strengths of the Lampang ceramic cluster development are: (1) Initiation by the private sector which is a core cluster that tries to solve problems together and creates commitment between partners to develop their industry. (2) The Government that supports and facilitates the cluster by providing knowledge, technology and opportunity. (3) The support cluster creates collaboration between manufacturers [NESDB 04]. Even though this cluster was well formed and obtained support from external organizations, it is still getting lost in between the co-operation and the competition of the cluster. Moreover, this cluster is facing external problems such as increasing labor costs and decreasing raw material in the area. These problems revealed that manufactures could no longer rely on low-cost product. Therefore, the collaboration of this cluster was extended to maintain their competitiveness in areas such as order sharing, cost sharing, problem solving, etc. From our initial investigation into the Lampang ceramic cluster, we found that the cluster still has problems on collaboration and knowledge sharing, which are the key success factors of cluster development. The members of the ceramic cluster agreed that the cluster needs to develop its collaboration in the aspect of communications e.g. the frequency, coverage, quality and completeness of information shared in the cluster is insufficient. Information about the cluster’s activity is a fundamental of the collaboration in the cluster. Because the structure of cluster organization is flat, distributing information about cluster activities requires an effective means of communication to satisfy all cluster members. Moreover, they realized that the degree of knowledge sharing in the cluster is at an unsatisfactory level. This problem could be explained by the special characteristics of the industry cluster. The relationship among the members in the cluster comprises both cooperation and competition. Two contrasting, concurrent relationships cause the members of cluster to be uneasy in sharing their knowledge face-to-face (direct sharing). Some knowledge has been transferred from expert to knowledge worker unintentionally (knowledge spill-over). Sometimes, knowledge has been requested through the middle man (usually the CDA) and delivered to the knowledge worker (indirect sharing). Knowledge spill-over and indirect sharing methods would help members to exchange their knowledge, but the quality of the knowledge is reduced. In this context, ‘quality of knowledge’ means delivering the right knowledge to the right person at the right place, at the right time and in the right format. More details of these problems will be discussed in the organization model in Chapter 4. From these reasons, the problematic of this study were set to improve the collaboration and knowledge sharing of the industry cluster in the co-opetition situation. The next section will position our research problems and our concern.

I.4. Problematic

For a century, the ceramic industry has been one of the strongest SME industries of Thailand. However, changes in foreign and domestic markets, the entry of new competitive manufacturers from neighboring countries, and a lack of knowledge in the value chain has diminished the competitiveness of the industry. Many ceramic SMEs have closed due to this crisis. Fortunately, with the assistance from the government, institutes, consultants, and related industries, the ceramic enterprises in Lampang formed into a cluster a few years ago. They hoped that this collaboration could create competitiveness in the ceramic industry and would help them out of the crisis. Although the ceramic enterprises and supporting organizations are linked together as an industry cluster, they are still facing the problems of collaboration and knowledge sharing within the industry cluster, as we described in the previous part. These problems may decelerate the innovation and competitiveness creation processes of the cluster. The literature [Sölvell 03] [DTI 05] insisted that collaboration, knowledge sharing and Cluster Development Agent (CDA) are key success factors for sustaining the industry cluster. The results of the studies also displayed that these problems were not unique to Thai ceramic cluster, but that many clusters all over the world were facing the same difficulties in developing effective collaboration and exchanging knowledge within the cluster. The initial investigation of this study into the problems in the industry cluster found that the relationship as “co-opetitor” makes the collaboration in the cluster unique. It also makes members feel uneasy to collaborate or share their knowledge in the cluster. Hence, in order to analyze and propose the solution for these problems, knowledge management which is the discipline that helps spread individual or group knowledge across organizations [Levy 03], seems to be an appropriate methodology to implement in this study. In order to achieve this, we considered the Knowledge Management System (KMS), which is a system for managing knowledge in the organizations. This KMS could help CDA as a tool for managing and improving knowledge sharing and collaboration in the cluster. The details about knowledge management will be explained in the next chapter. As we mentioned, the collaboration and knowledge sharing model in the industry cluster are unique. Implementing a KMS in the industry cluster required analysis and knowledge sharing model of the cluster. Moreover, there is no study about the collaboration and knowledge sharing model in the industry cluster. Thus, in order to propose a KMS for supporting the SMEs cluster, these are our research problems: 10 What kinds of knowledge are firms willing to share within the cluster, and what are the conditions of sharing? Since the organizational relationship in the cluster is unique, we can either find buyer-seller relationships, as in the supply chain, or competitor-like relationships in the same cluster. Although competitor relationships in the cluster may create difficulty for the collaboration, these relationships also create innovation to the cluster via positive competition among the members. Analyzing this willingness to share knowledge and these conditions of sharing (the knowledge structure model of the cluster) is a challenge in our research. 11 How to help the cluster to create, represent and share their knowledge? These activities are the principle of knowledge management, and also the main objective of collaboration within the cluster. The competitive advantage of industry is a consequence of innovation process which is created from the knowledge of members in the cluster. Proposing the knowledge creating, representing, and sharing method is the main objective of the study. 12 How can the knowledge management system help CDA to improve the cluster? CDA is the external key success factor of the industry cluster. However, many CDAs do not comprehend their cluster. Moreover, there is no specific tool or information system that helps CDA to manage the collaboration or exchanging of knowledge in their cluster. Thus, this study also tries to propose the knowledge management system which assists CDA to manage the main activities of the cluster. 13 What general and specific infrastructures and architectures are needed to achieve a specific collaborative knowledge management system for the ceramic cluster? The particular characteristics of an industry cluster that we mention earlier imply that the proposing system requires specific infrastructure and architecture that answer requirements of the cluster. At the end of this study, we try to propose and develop a specific KMS to apply to the general industry cluster. This proposed system will be adapted from a general knowledge management system and the specific requirements obtained from the analysis of the industry cluster. Chapter II: Knowledge Management Context

II.1. Introduction

The success of the industry cluster in the developing phase relies heavily on the degree of knowledge sharing and collaboration among the members of the cluster. These two factors create “fresh knowledge” which is the essential ingredient for creating competitiveness of the cluster. For this reason, knowledge management is the influential mechanism to achieve this. Thus, the first part of this chapter will clarify knowledge management, taxonomy of knowledge and knowledge management processes in the industry cluster domain. As we declared that the KMS is a key for improving knowledge sharing and collaboration within the cluster. It is a set of information technologies that are custom designed, configured or developed to support the knowledge activities in the organization. In the second part of this chapter, the studies about knowledge management system, its architecture and the information technology usually implemented in knowledge management projects will be reviewed and compared. Finally, the last part of this chapter aims to close the gap between knowledge management processes and the knowledge management system with knowledge engineering methodology. Knowledge engineering is always used for designing, developing and maintaining the knowledge-based system in the knowledge management projects. The content in this chapter will mainly focus on selecting the appropriate knowledge engineering methodology for the industry cluster context. Suitable methodology will be implemented in our study.

II.2. Knowledge Management

In the past, the three production factors (Land, Labor and Capital) were abundant, accessible and were considered as the reasons for economic advantage. Knowledge did not get much attention [Young 03]. Nowadays, the knowledge-based economy era is affected by the increasing use of information technologies. Thus, previous production factors are currently no longer enough to sustain a firm’s competitive advantage; knowledge is being called on to play a key role [Romer 86]. The knowledge-based economy is based on the production, distribution and use of available knowledge and information [Porter 90] to gain more competitive advantages over others. To this purpose, Knowledge Management (KM) is the discipline that helps spread knowledge of individuals or groups across organizations in ways that directly affect performance. Moreover, it envisions getting the right information, within the right context, to the right person, at the right time, for the right business purpose [Schreiber 99]. However, the discipline of KM has changed very fast during last decade. We can separate KM practice into two generations. First-generation KM sought to enhance the integration of existing organizational knowledge through strategies such as knowledge capture and sharing. Second-generation KM strives to improve knowledge integration, too, but it also seeks to improve knowledge production. To illustrate this concept, the study of Dave [Dave 07] has distinguished two generations of knowledge management in term of value proposition, strategy, content format and model as shown in table II.1.

Table II.1: Generations of knowledge management [Dave 07]

1st Generation KM 2nd Generation KM 1995 – 2005 since 2005 KM Value Reduce cost, Improve customer Find and contact with people more effectively, Propositions: relationships, Accelerate employee Tap the wisdom of crowds learning, Improve technology ROI, Facilitate virtual collaboration, Increase employee retention Improve the context and understandability of information, Understand why things are the way they are, Improve knowledge worker effectiveness KM Users contribute best practices to Stories and conversation automatically canvassed from Strategy: large central repositories for re-use shared personal repositories for learning and discovery to reduce costs KM Content Mostly text, organized by subject Graphic and multimedia, organized by application Format: (taxonomy) (ontology) KM Model Acquire, store, add value, Connect, canvass, synthesize (just in time) disseminate (just in case) From the table above, we can see that the discipline of KM these days is more complicated than before. It requires a multi-disciplinary view of knowledge to achieve goals such as management, information technology, organizational learning, etc. In summary, the new generation of knowledge management is managing knowledge in a collaborative environment . The following sections will give a clear view of KM in the industry cluster. II.2.1. What is Knowledge

The formal definition of “knowledge” is defined by Webster’s dictionary and implies that knowledge extends beyond information. It gives the following description:

Knowledge-N. 1. applies to facts or ideas acquired by study, investigation, observation, or experience 2. rich in the knowledge of human nature 3. Learning applies to knowledge acquired especially through formal, often advanced, schooling 4. a book that demonstrates vast learning. However, data , information and knowledge are three often-encountered words that are close together, seem to have slightly different meanings, yet are often used interchangeably as synonyms, leading to continuing confusion [Schreiber 99]. There are many ways to classify them such as context/domain and characteristic of information. Knowledge very much depends on the context. O ne person’s knowledge can be just another person’s information . For example, company ‘A’ , expert in ceramic design domain tried to explain their processes, best practices or cautions to company ‘B’ who is working in ceramic marketing domain. Although the information that company ‘A’ transferred to company ‘B’ is essential knowledge, company ‘B’ cannot transform that knowledge into action. Thus, knowledge from company ‘A’ is just information for company ‘B’. Hence, borderlines between data, information and knowledge are not sharp, because they relate to the context of use. Another distinguishing aspect of knowledge is the characteristic of information that transferred from actor to actor in the same domain. Schreiber et al. [Schreiber 99] have proposed characteristics and examples of data, information, and knowledge as shown in the following table.

Table II.2: Distinctions between data, information and knowledge [Schreiber 99]

Characteristic Example Data Un-interpreted, Raw …---… Information Meaning attached to data S O S Knowledge Attach purpose and Emergency alert competence to information, → Potential to generate action Start rescue operation • Data are the uninterrupted signal s that reach our senses every minute by the zillions, a red, green, or yellow light at an intersection is one example. Computers are full of data: signals consisting of numbers, characters, and other symbols that are blindly and mechanically handled in large quantities. • Information is data equipped with meaning . For a car driver, a red traffic light is not just a signal by some colored object, rather, it is interpreted as an indication to stop or go. For example, a red light signal in an interaction is just data in traffic systems, but for drivers it brings information for them to stop their cars.

• Knowledge is the whole body of data and information that people bring to bear on practical use in action , in order to carry out tasks and create new information. Knowledge adds two distinct aspects: first, a sense of purpose , since knowledge is the “intellectual machinery” used to achieve a goal; second, a generative capability , because one of the major functions of knowledge is to produce new information. Simply defined, knowledge is “actionable information” [Schreiber 99]. Actionable refers to the notion of relevance and being available in the right place at the right time, in the right context, and in the right way so that users can bring it to bear on decisions, unlike information which simply gives us the facts. According to Nonaka [Nonaka 95], knowledge is justified belief (i.e. information) that increases an entity’s capacity for effective action, while information is the flow of messages or meaning which may add to knowledge. In that sense, information is raw material for the production of knowledge and information transforms to knowledge in the context of actions. This observation concerning the context dependence of knowledge is found, in different terminology, across different study fields of knowledge [Schreiber 99]. The definitions of knowledge have shown that there is a thin line between knowledge and information. In order to extract the knowledge from the experts in the ceramic cluster, the type of knowledge, location of knowledge and extracting process are necessary. The next section gives details about the taxonomy of knowledge shared in the industry cluster.

II.2.2. Taxonomy of Knowledge

In order to understand the knowledge exchange model of the industry cluster, the type of exchanged knowledge should be considered. From a review of the literature, knowledge can be taxonomically categorized in different point of views. Polanyi [Polanyi 66] classifies knowledge into two types: tacit and explicit knowledge, by using complexity of knowledge . Tacit knowledge tends to have more complexity than explicit knowledge. Accordingly, sharing tacit knowledge is more complicated than explicit knowledge. In the domain of collaboration, knowledge is classified by how knowledge is created i.e. individual and social knowledge [Nonaka 94]. Individual knowledge is created by individuals; this type of knowledge is a personal expertise which can be developed from learning-by-doing. Social knowledge is created by collective actions of a group of people. Another view point of knowledge categorization is how knowledge is used . The taxonomy of these knowledge types i.e. know-who, know-what, know-how, know-where, know-when, and know-with. The definitions and examples of the taxonomy are summarized in the table II.3.

Table II.3: Examples of the knowledge category

Approach Knowledge types Definition Examples

Tacit Knowledge is rooted in Unstructured knowledge such as actions, experience, and surgery skill. Complexity of involvement is specific Knowledge context [Polanyi 66] Explicit Articulated, generalized Codified knowledge such as knowledge diagnostic skill Individual Created by and inherent in Individual’s belief on cause and Creation of the individual effect such as crafting skill Knowledge Social Created by and inherent in Norm for inter-group [Nonaka 94] collective actions of a group communication such as best practice Networking Know-Who How can I "know who knows"? e.g. yellow pages Declarative Know-What What method is appropriate for shipping products? Use of Procedural Know-How How to solve production problem? Knowledge Conditional Know-When When the seminar will take place? [Lundvall 94] Locational Know-Where Where is the conference about product design? Relational Know-With Understanding how the problem relates to another problem. Causal Know-Why Why does this problem occur? Polanyi first described two types of knowledge i.e. tacit and explicit knowledge in 1966 [Polanyi 66]. Tacit knowledge is knowledge that is impossible to articulate (e.g. how to ride a bicycle). Explicit knowledge , on the other hand, is knowledge that can be articulated (e.g. a bicycle has two wheels). Tacit knowledge is often thought of as knowledge of how to do things (i.e. procedural knowledge). Then, Nonaka and Takeuchi [Nonaka 95] argued that “the key of knowledge creation lies in the mobilization conversion of tacit knowledge”. They give another definition for tacit and explicit knowledge as follows. Tacit knowledge is comprehension within an expert's mind and which cannot be directly expressed by data or knowledge representations. It is commonly referred to as unstructured knowledge (e.g. knowledge embedded in the human mind through experience and jobs). Explicit knowledge is knowledge which can be directly expressed by knowledge representations (e.g. knowledge that is codified and digitized in books, documents, reports, memos, etc.) which is known as structured knowledge. Nonaka stated that converting explicit knowledge to tacit knowledge is closely related to ‘learning by doing’. This process was defined in the internalization process in Nonaka’s SECI model [Nonaka 94] as shown in Annex B. The knowledge spiral is a model for creating and transferring knowledge in the organization. The spiral processes aim at creating competitiveness of organization from the fresh knowledge of members. As in the industry cluster, competitiveness was developed from the collaboration of members in core and supporting clusters. The new knowledge was jointly created from cluster activities such as marketing, product development, training, etc.

(a) Use of Knowledge

The knowledge can be categorized into 7 types of knowledge according to how the knowledge is used [Lundvall 94]. These types of knowledge are often used for understanding characteristics of exchanged knowledge in each activity in the organization. Know-Who is the networking knowledge. It has been claimed to be the most necessary knowledge in the organization due to limits of individual knowledge, one person is an expert only in their domain. Thus, the Chief Knowledge Officer (CKO) needs this knowledge to find the right person to participate in the task. Know-What is the basic sense of knowing. This is especially true in complex situations in where experience is required. For example, the expert instinctively knows what “firing” is in the context of ceramic production Know-How is the knowledge of how to get things done. Some of this knowledge is made explicit in organization procedures, i.e. codified knowledge. However, in practice, knowledge is still in tacit form. Acquiring know-how depends on skill and practice that takes time to develop and refine. This type of knowledge is always transferred within a particular community/domain. Know-When is the knowledge involved with time domain. Since time is always a condition of every task, know-when is required by experts to acquire opportunity or solve problems. In our study, Know-When is mostly transferred when a new opportunity is offered to the members of the cluster, such as when should we contact a government agency for a tax reduction. Know-Where is a type of knowledge which concerns strategic location. However, the communications technology has changed the nature of geography in the business world such as e-commerce. The strategic location is still a key success factor of organizations, especially in the logistic domain. A company gains the competitive edge over another from knowing where the strategic location is. However, this knowledge is shared in the industry cluster in terms of indicating the location of the opportunity, such as where the ceramic trade fair will take place this year. Know-With is the knowledge that describes how things relate to other things. This knowledge helps experts reuse their knowledge with similar problems. This knowledge is represented in the form of ontology . Understanding the relationship between two things (objects) helps expert to reuse their knowledge. For example, if an expert knows that ceramic clay type A has property B it will create a problem crack in the finished product. Thus, experts can reuse their knowledge by presuming that ceramic clay type Z, which has the same property as B, will create the same problem. Know-Why is a backward reasoning of the knowledge. Mostly, this type of knowledge is used to discover the cause of the problem. Many times, the ontology is used for reasoning the cause of a problem. If the expert found a problem, such as a crack in a finished product, he is able deduce that this may be caused by a property of the clay. In this study, we will adopt this taxonomy of knowledge in order to represent the knowledge exchanging model in the industry cluster. This issue will be referred to in the collaboration model in our proposed methodology in Chapter 3. Another factor that separates knowledge from information is place of knowledge (called domain of knowledge). In the KM practice, domain knowledge can be broken down into small communities of practices. The concept of this issue will be explained in the next section.

II.2.3. Community of Practice (CoP)

Community of Practice (CoP) is a small group of people who have worked together over a period of time. People in CoP can perform the same job or collaborate on a shared task or work together on a product [Lesser 00]. What holds them together is a common sense of purpose and a real need to know what each other knows (knowledge sharing). CoP is often used as a learning tool in organizations to create a collaborative culture and provide a knowledge sharing environment. Thus, CoP has become a fundamental of many knowledge management systems. The main objective of the CoP is facilitating a group of people to share their experience over a particular subject of common interest. This shared experience helps each person to solve day to day problems and update themselves with fresh knowledge in the area of their interest. In a CoP, there are three elements present: domain, community and practice. The study of the DTI [DTI 05] insisted that “ cluster networks need to be more than simply opportunities to meet. Networks will ideally form “Communities of Practice” (CoP), with many such networks present in each cluster, associated with different interests. What causes one company or interest to join a cluster will not be the same for everyone and their needs are likely to shift in time. It is likely that networks will continuously form and reform as membership and needs change ”.

• Domain is the area or the subject of interest which binds all the members together. This can be our profession, or any other area of our interest. A domain keeps the point of discussion focused. The members of the ceramic cluster are in the same domain - the ceramic business. As we discussed about the definition of knowledge, one person’s knowledge can be just information to another. The common interest stimulates members to share useful knowledge with each other.

• Community concerns the interaction, trust, relationship, and structure of community. Forming a community requires appropriate frequency of the interaction. A one time interaction could not create community. Trust and the good relationship of members will sustain the community. The structures of industry clusters vary due to the environment of collaboration. The formal interaction, such as cluster meetings, was held once a month. Thanks to internet technology, the informal interaction such as e- mail, chat, and web board can be established between formal interactions. Trust and relationship are concerned in our study, with the special relationship as “ coopetition ” (cooperation and competition in the same environment) making a cluster unique. In this study, we analyze the cluster’s relationship characteristics in order to propose a suitable knowledge management system.

• Practice binds the members with some benefits of collaboration. A ceramic producer and a financial institution can not join the same community of ceramic manufacturing. They must be active in their area to get benefit out of the interaction. This implies that a single domain comprises many practices. However, the practice depends on the common interest of cluster members. The practice also guides experts to share the specific knowledge in the right places, which makes knowledge retrieval easy. An example of CoPs in a ceramic cluster domain is illustrated in figure II.1.

Figure II.1: An example of Community of Practices in a ceramic cluster

The development of CoP could help companies to share ideas, trade or innovate with new ideas. They can also operate across the clusters in such areas as training or workshops. Communities of interest are likely to be a feature of clusters of the future, and indeed many industries are taking steps to encourage this process through the creation of virtual enterprise networks or centers of industrial collaboration [DTI 05]. Organization culture and its knowledge management system play an important role in promoting community interaction. These will be discussed further in our methodology.

II.2.4. Knowledge Management Processes

In order to transform knowledge into a valuable organizational asset, knowledge, experience and expertise must be formalized, distributed, shared, and applied. Knowledge management is considered a key part of the strategy to use expertise to create a sustainable competitive advantage in today’s business environment [Beckman 98]. These processes are variously defined in the different study as shown in table II.4.

Table II.4: Comparing knowledge management processes

DiBella and Holzner and Marx, Ruggles, 1997 O’Dell, 1996 Nevis,1998 1979 1. Acquire 1. Creation 1. Generation 1. Identify 2. Disseminate 2. Storage/Retrieval Creation 2. Collect 3. Utilize 3. Transfer Acquisition 3. Adapt 4. Application Synthesis 4. Organize Fusion 5. Apply Adaptation 6. Share 2. Codification 7. Create Capture Representation 3. Transfer However, the simple idea of each model is (1) creating, (2) representing, (3) sharing and (4) utilizing the knowledge identically. These processes are considered as the major activities for improving the competitiveness of organization in terms of knowledge. The details of each process will be described in the following sections.

(a) Knowledge Creation

Knowledge creation involves developing new content or replacing existing content within the organization’s tacit and explicit knowledge [Pentland 95]. Creating (or acquisition) refers to the activity of identifying knowledge in the organization’s environment and transforming it into a representation that can be internalized, and/or used within an organization [Holsapple 99]. Nonaka has proposed the knowledge creation model (called SECI model as shown in figure II.2) to explain how the knowledge is created in the organization [Nonaka 95].

Figure II.2: Knowledge processes and SECI Model [Nonaka 95]

The model implies that the knowledge is created when converted from tacit knowledge into explicit knowledge and combined with explicit knowledge from others. Then, the new tacit knowledge is obtained when it was internalized by a learning process. The new knowledge is created in the socialization mode in the model. Knowledge creation also refers to ability to combine new knowledge with experience in order to create new tacit knowledge. This process mostly occurred inside human. There is no effective information technology that supports creating tacit knowledge in humans directly, but the collaborative tools such as telephone, live chat, discussion board, etc. could support knowledge workers to create new knowledge as well.

(b) Knowledge Representation

Knowledge representation may be called by different names such as organizing, customizing or codifying knowledge [Schreiber 99]. The objective of this process is representing the knowledge to suit the knowledge selection. Comparing this process with SECI model, knowledge representation is transforming tacit knowledge into explicit (codified) knowledge which is equivalent to the externalization mode in the SECI model. In the past, representing the knowledge was done by writing, drawing or coding the knowledge into a codified format (e.g. procedure). Nowadays, the emergence of multimedia and information technology means that knowledge can be represented in various forms and formats such as sound, picture, video, concept map, 3-D model, database, etc. These technologies make it possible for experts to articulate their tacit knowledge into explicit knowledge, which was nearly impossible in the past.

(c) Knowledge Sharing

Knowledge sharing (known as knowledge transferring) is disseminating personal knowledge in the explicit form over the organization through a specific medium. The sharing of knowledge constitutes a major challenge in the field of knowledge management, without this process the new knowledge could not be created. Thus, most studies give an important role to this process. Knowledge sharing could be mapped with the combination mode in SECI model. This mode aims to disseminate the explicit knowledge within the community of practice. In order to achieve this, internet technology is the key that accelerates knowledge sharing today. We can see a number of best practices about knowledge sharing in many specific communities over the internet. (d) Knowledge Utilization

Knowledge utilization is integrating the knowledge into the organization. In the knowledge engineering domain, this process refers to providing the right knowledge to the right person at the right time, right place and right format. Thus, knowledge storage and retrieval are considered as a key technology that supports knowledge workers to complete their task. This process supports the internalization mode in SECI model. It helps knowledge workers to internalize the explicit knowledge and transform to tacit knowledge when needed. Knowledge retrieval is extraction of knowledge from identified knowledge resources (i.e. document, computer system, an employee) [Holsapple 99]. This technique is often related to the representation mode of the knowledge. Then, there are a lot of interdependencies between representation mode and retrieval mode. One of the main objectives of the present work is to improve the knowledge processes in the industry cluster. Hence, the knowledge management system which is a set of information technologies is the mechanism to support this cluster. The details of knowledge management system will be described in the next part.

II.3. Knowledge Management System

Knowledge management is the discipline that helps spread knowledge of individuals or groups across organizations in ways that directly affect performance. It envisions getting the right information within the right context to the right person at the right time for the right business purpose [Klung 01]. Accordingly, Knowledge Management System (KMS) concerns a set of Information Systems (IS) that are applied to manage organizational knowledge by supporting and enhancing the processes of knowledge creation, transfer, storage/retrieval, and representation. To achieve this, the design of information systems should be rooted in, and guided by, an understanding of the nature and characteristic of organizational knowledge. Alavi and Leidner [Alavi 01] defined a KMS as “IT (Information Technology)-based systems developed to support and enhance the organizational processes of knowledge creation, storage/retrieval, transfer, and application”. Maier (2002) expanded on the IT concept for the KMS by calling it an ICT (Information and Communication Technology) system that supported the functions of knowledge creation, construction, identification, capturing, acquisition, selection, valuation, organization, linking, structuring, formalization, visualization, distribution, retention, maintenance, refinement, evolution, accessing, search, and application. Technology cannot provide a perfect substitute for face-to-face contact, which is crucial for building a culture of knowledge sharing [Robert 00]. Neither can it replace human social interaction in affording rich interactivity among individuals necessary for knowledge creation [Fahey 98]. Nonetheless, technology is able to overcome the barriers of time and space that would otherwise be limiting factors in KM activities. As we mentioned the difficulty of knowledge sharing in the industry cluster in the last chapter, the members of the cluster feel uneasy to share their knowledge in face-to-face conditions due to part of their relationship being competitive. In this case, sharing knowledge via KMS seems to be an appropriate solution for the industry cluster context. In addition, KMS also serves as a repository in which knowledge can be reliably stored and efficiently retrieved. The key is to understand how technology is most appropriately deployed and aligned to the knowledge activities in the organization.

II.3.1. Human roles in the KMS development

It is important to identify the roles that humans play in the knowledge management processes. From the study of Schreiber et al. [Schreiber 99], six roles are considered for developing the KMS for the organization. However, the number of roles could be less in smaller projects, and other roles such as project manager could be added to the processes in larger project. The correlation and responsibility of each role are illustrated in figure II.3 and briefly explained. Knowledge manager addresses the knowledge needs of the enterprise, researching to understand what knowledge is needed to make decisions and enable actions, and establishes the knowledge management policies at enterprise level. In a simple project, the knowledge manager could act as project manager in order to manage and facilitate knowledge engineer and knowledge system developer. Knowledge engineer (or knowledge analyst) works on such areas as data and information representation, repository storage and retrieval, workflow management, information technologies, etc. The roles of knowledge engineers would most likely be researching the technologies needed to meet the enterprise's knowledge management requirements, establishing the processes by which knowledge requests are examined, information assembled, and knowledge returned to the inquirer. The role of knowledge manager and knowledge engineer are often used alternately, which leads to misunderstanding. The main difference seems to be that the knowledge manager establishes the direction the process should take, whereas the knowledge engineer develops the means to accomplish that direction.

Figure II.3: Human roles in knowledge engineering [Schreiber 99]

Knowledge provider (or expert) is the owner of the knowledge who plays an important role in the process. One important problem for a knowledge engineer is to find the ‘real’ expert [Schreiber 99]. Fake experts are very harmful for the knowledge management project. The two major responsibilities of the experts in the processes are providing useful knowledge to the knowledge engineer and validating knowledge in the knowledge system. Knowledge user (or knowledge worker) makes use, directly or indirectly, of a knowledge system. Involving knowledge users from the beginning is even more important than in regular software engineering projects [Schreiber 99]. The two major responsibilities of knowledge users in the processes are providing requirements to knowledge engineers and use of knowledge system for making decisions to complete their knowledge tasks. Knowledge system developer is actually a software designer. In a small project with fewer complexes in software implementation, this process was often done by the knowledge engineer. The responsibilities of a knowledge system developer are acquiring system specifications from the knowledge engineer, and designing and implementing the knowledge system.

II.3.2. Knowledge Management System Architecture

Developing a KMS is a complex task and requires a careful planning before selecting the tools for supporting the knowledge processes. The designed system architecture should suit the organizational culture and business needs. KMS can be as simple as a file folder until a complex business intelligence system which uses an advanced data visualization and artificial intelligence. Thus, we have studied several KMS architectures which aim to support knowledge management processes and collaboration in the organization. We found that even if there are differences between architectures in term of functions and services, the major components of architecture are comparable. The general KMS architecture is proposed by Tiwana [Tiwana 02]. He pointed out that the KMS should comprise four major components: repository, collaborative platform, network, and culture. 14 Repository holds explicated formal and informal knowledge, such as declarative knowledge, procedural knowledge, causal knowledge, and context. This component acts as the core of KMS which aims to store and retrieve knowledge for future use. 15 Collaborative platform supports distributed work and incorporates pointers, skills databases, expert locators, and informal communications channels. 16 Network means both physical and social networks that support communication and conversation. Physical network is a ‘hard’ network such as intranet, shared space, and back bone. Social network is a ‘soft’ network such as Communities of Practice (CoP), associations, and working groups. 17 Culture is the enabler to encourage sharing and use of the KMS. Research has revealed that the greatest difficulty in KM is ‘‘changing people’s behavior,’’ and the current biggest impediment to knowledge transfer is ‘‘culture’’. These four components are considered as the basis elements for each knowledge management system. However, other tools could be integrated to enhance the quality of services of the system. Tiwana also proposed seven-layer KMS architecture [Tiwana 02] which is the integration of these four components and their supportive information technologies.

Figure II.4: Seven layers KMS architecture [Tiwana 02]

Actually, seven layer KMS architecture is just a reflection of OSI model (Open Systems Interconnection basic reference model). This model tries to represent the functions and tools of KMS in terms of layer that the knowledge passed though. This architecture might suit with complex systems which require network and data manipulation. Chua [Chua 04] has proposed a simple architecture called three-tiered KMS architecture which is composed of three services i.e. infrastructure services , knowledge services , and presentation services . These services aim at supporting knowledge processes and communication in the organization. This system emphasizes technologies that help creating, sharing, and storing knowledge. Figure II.5 illustrated Chua’s three-tiered KMS architecture

Figure II.5: The three-tiered architecture [Chua 04]

• The first tier in this model comprises the infrastructure services which focus on storage and communication technology. Storage technology is a part of a repository in the general model, and is typically the basis for supporting KM processes, particularly knowledge creation and knowledge reuse. The communication technology makes possible for the KMS to support knowledge transferring activity among the users.

• The second tier is the knowledge services which focus on technologies for creating, sharing, and reusing of knowledge in the system. The technology for knowledge creation helps users to convert their tacit knowledge in to codified (explicit) knowledge. Knowledge sharing technology refers to the flow of knowledge from one part of the organization to other parts. The knowledge reuse helps users to retrieve required knowledge from the system when needed.

• The third tier is presentation services which mainly focus on displaying the suitable information for users to support their decision-making. Technologies that provide presentation services are primarily concerned with enhancing the interface between the user and the information/knowledge sources. This part is related to the culture of knowledge usage of the organization by visualizing and personalizing all services in the KMS to suit the organizational culture. However, to design these services, the organizational analysis is required. One of the technical perspectives of KMS architecture was proposed by Meso and Smith [Meso 00], as shown in figure II.6, which consists of three components: technology, function and knowledge. This model involves the processes for acquiring or collecting, organizing, disseminating or sharing knowledge among people in an institution. Meso and Smith’s model relies on four functions of knowledge processes: using, finding, creating, and packaging knowledge. These four functions are supported by various information technologies (such as messaging, web browsing, data mining, intelligent agents, etc.) that aim to facilitate the knowledge process. The objective of this model is to enhance each type of knowledge in different taxonomy i.e. know-how, know-what, know-why, self- motivated creativity, personal tacit, cultural tacit, organizational tacit, and regulatory assets. Alavi [Alavi 99] supports this concept by specifying that KMS refer to a class of information system applied to managing organizational knowledge and support knowledge processes. The reviews show that there is no single solution for neither designing, nor best practice of the KMS architecture. Each system is designed to fit with the different culture, activity, strategy, and objective of each organization.

Figure II.6: A technical perspective of KMS architecture [Meso 00]

They also showed that KMS is composed of three common applications: (1) the coding and sharing of best practices, (2) the creation of corporate knowledge directories, and (3) the creation of knowledge networks. The next section will present a list of information technology generally used in KM projects and compare their function with the knowledge processes. II.3.3. Technology in Knowledge Management

The most valuable role of technology in KM is broadening the reach and enhancing the speed of knowledge transference. Technology plays three key roles in KM domain. Firstly, it facilitates communication among the experts and knowledge users in the organization. Secondly, it provides the infrastructure for storing codified and explicated knowledge. Lastly, it assists with mapping dispersed bits and pieces of tacit and explicit knowledge to establish and maintain intricate interdependencies among them [Tiwana 03]. Due to the fast growth of the information technologies during recent years, KMS became the major tool to manage knowledge in the organizations. However, the critical aspect of building a KMS is determining the best combination of available tools and integrating them into a coherent architecture. Considering the overview of available technologies, and the activities they support, can be very useful in the KM implementation processes. From our literature review, many different technologies are proposed to support knowledge management activities for different purposes. Table II.5 shows a list of the information technologies which are generally used to support the KM processes.

Table II.5: List of IT generally used in KM projects

Technology Purpose Create Represent Transfer Utilize Data Mining Identifying and extracting from any knowledge source s data patterns that are X X [Becker 99] interesting and meaningful regarding a particular knowledge issue. Collaborative System Aim at improving quality of communication between users in the community. X X [Tiwana 03] It is also used as a tool to transfer information/knowledge. Expert System, To capture a portion of the expert’s decision -making knowledge, codify it in a X X Rule Based System way that preserves it, and that make it processed by an inference engine that [Huntington 99] can use “backward chaining” or “forward chaining Case Based Reasoning To use capabilities of memorization and similarities retrieval processes to face X X [Tiwana 03] new problems by comparing them to old archived ones. Database To support the management of persist ent data by providing facilities to X [Tiwana 03] create, remove, update and access to pieces of these data. Data Warehouse To enable systematic archiving of operational data to enable potential future X [Sena 99] analysis. Semantic Network To prov ide a conceptualization of a given area of interest, by defining X [Becker 99] consistent categorization of all the concepts identified into this area and by linking them through meaningful relationships. Ontology To provide complete and meaningful rep resentation of all the required X [Gruber 93] concepts pertaining to a given area of interest. Ontology oriented retrieval To improve the relevancy of expected results of a document retrieval attempt X [Dieng 00] by providing symmetrical conventions addressing both annotation definition and query formulation. These conventions are supported by the ontology. Wiki Web To make possible collaborative content management which allow user to X [Nastase 08] create, share and modify the content collectively. Technology Purpose Create Represent Transfer Utilize Search Engine To store and to classify web pages according to their content and level of X [Kim 07] interest. Electronic Document Providing facilities to support complete document management from the X Management Systems creation to the archiving. [Dieng 00] Workflow system Providing supports for monitoring and execution of a part of all the business X management processes involved in an enterprise organization. [Azarian 99] Groupware Supporting the efforts of teams and other paradigms wh ich require people to X [Coleman 99] work together, even though they may not actually be together, in either time or space. Groupware maximizes human interaction while minimizing technology interference. Multi agent system To distribute the Knowledge mana gement system architecture through X [Baek 97] different proactive components able to collaborate and to achieve some identified task on behalf of their human users. Push/Pull Technology Push technology distribution and deliver knowledge to their aud ience after X X [Tiwana 03] filtering it through highly customized filter. Requires a user to actively seek information when they need it. This system does not distract user by unwanted updates but requires user initiative. Discussion Board Help users to request information and respond to the issue. It could be used X [Yang 05] as a tool for gathering solutions from different points of view. Topic (Concept) Map Aim at representing the concepts in term of semantic maps. It is often used as X [Wang 07] a tool for organizing personal and organization knowledge. Knowledge Card Is integrating the concept of topic map and wiki web together to make it X X [Buzon 03] possible for users to represent and share their knowledge in the organization. The table shows the examples of technologies that were used in many KM projects. Although many KMS initiatives are relying on IT as an important enabler, designing greatly relies on the non-technical side. Organizational culture is a key factor in selecting tools and technologies to support the KMS application. Thus, the organizational analysis is called to play an important role in designing the KMS. The details of the analysis will be discussed in the next section.

II.3.4. Designing a Knowledge Management System

In recent years the term knowledge management has been used to describe the efforts of organizations to capture, store, represent and share knowledge. It helps an organization to gain competitive advantage and effective work through sharing and re-use of knowledge in an organization. Most current knowledge management activities rely on database and web-based technology, document management system, artificial intelligence, etc. However, few organizations have a systematic process for capturing knowledge, as distinct from data [Preece 00]. Thus, knowledge engineering, which is an aspect of systems engineering, was applied to deal with knowledge management in an organization. Knowledge management practices significantly under utilize knowledge engineering technologies [Preece 00] as it was viewed as being equivalent to transferring knowledge from an expert into a knowledge base. Knowledge engineering has often been concerned only with knowledge acquisition processes (to capture structured knowledge systematically), and knowledge representation processes (to store the knowledge, preserve important relationships), which often failed [Studer 03]. Actually, the role of knowledge engineering in knowledge management is supporting the knowledge engineer to deal with information in each stage of knowledge management. The processes of knowledge engineering can be described as follows:

• Analyzing the role of knowledge skills and expertise in an organization.

• Understanding how knowledge supports the organization in meeting its objectives.

• Identifying knowledge sources, knowledge flows, knowledge bottlenecks and where the organization is vulnerable to knowledge attrition through staff losses.

• Determining the scope of improving performance through knowledge acquisition, knowledge utilization, knowledge communication and knowledge representation.

• Implementing the systems in the organization necessary to achieve the improved performance and business effectiveness. These processes help knowledge engineer to understand the culture of knowledge usage and characteristics of the knowledge in the organization, which are crucial elements for designing the KMS for the organization. Moreover, the KM project often needs change management to enhance the competitiveness of knowledge workers in the organization. Knowledge engineering could provide the consensus between the members of an organization in the KM project. Hence, in this study, knowledge engineering methodology was adopted for designing the KMS for the industry cluster. The models and examples of knowledge engineering methodology will be discussed in the following part.

II.4. Knowledge Engineering

Knowledge engineering refers to the designing, developing and maintaining of knowledge- based systems in the knowledge management project. It has a great deal in common with software engineering, and is related to many computer science domains such as artificial intelligence, databases, data mining, expert systems and decision support systems. Knowledge engineering is also related to cognitive science and socio-cognitive engineering, where the knowledge is produced by socio-cognitive aggregates (mainly humans) and is structured according to our understanding of how human reasoning and logic works [Schreiber 99]. Zhao [Zhao, 2005] defined knowledge engineering as “a process of creating such a semantic system” . Similar to software development, it includes such tasks as scoping, modeling, integration, deployment and maintenance within the methodology. It emphasizes the formal and methodical process of deductive problem solutions and aims at optimization of solutions. Knowledge modeling is based on guidelines and heuristics tools (e.g card sorting, mind mapping, etc.) in an inductive process of finding appropriate solutions in the context of conflicting interests and requirements. Knowledge engineering was in a field of artificial intelligence in the past. But since the last decade, knowledge engineers have developed their principles to improve the process of knowledge acquisition [Chua 04]. These principles are used to apply knowledge engineering in many actual environment issues. Firstly, there are different types of knowledge which are defined as “know what” and “know how” [Levy 03] or “explicit” and “tacit” knowledge from Nonaka’s definition [Nonaka 95]. Secondly, there are different type of experts and expertise. Thirdly, there are many ways to represent knowledge and use of knowledge. Finally, there is the use of structured method to relate the difference together to perform knowledge oriented activity [Shadbolt 99]. These created several knowledge engineering techniques to solve different problems in various domains such as diagnosis of bacterial infections, advice on mineral exploration, assessment of electronic circuit designs or financial analysis. In this study, knowledge engineering methodologies (i.e. MOKA, SPEDE, AKEM, and CommonKADS) were reviewed and compared in order to propose a methodology for applying a selected technique in the industry cluster development domain. The next section, knowledge-based engineering lifecycle (which is a general model of knowledge engineering), will be analyzed for benchmarking and selecting a suitable methodology.

II.4.1. Knowledge-Based Engineering Lifecycle

Knowledge engineering differs from conventional software engineering mainly at the early stages of the lifecycle, when user requirements and functional methods (or knowledge) are being acquired. The tools for implementation, user interface design, testing, maintenance and updating systems may differ, but the principles which govern all software systems are the same. Therefore, although the early stages of knowledge acquisition will involve a knowledge engineer and one or more domain experts, later stages will involve software engineers for implementation/integration. One well known Knowledge-based Engineering (KBE) lifecycle was proposed by Preston [Preston 05]. It focused on six critical phases i.e. identify, justify, capture, formalize, package, and activate as shown in figure II.7.

Figure II.7: Knowledge-Based Engineering application lifecycle [Preston 05]

• Identify aims at identifying the driving factors of the project. The main activities of this step are studying industrial needs and technical feasibility of the project.

• Justify aims to motivate and ensure the relevance of the project. It involves estimation of resource requirements, costs, profit, risk, and development of a project plan.

• Capture aims to collect all knowledge related to the application that is to be created. The raw knowledge is sorted and structured into an informal model.

• Formalize aims to develop a formal model from captured knowledge. The output from the capture step will be structured into standardized form.

• Package refers to implementing the formal model into KBE-platform, i.e. programming. This step mainly focuses on application specification and development.

• Activate is the process of populating the finished application. This focuses on dissemination and maintenance. KBE application lifecycle is usually used for describing the significant processes from the beginning to the end of the project. Thus, most knowledge engineering methodology provides tools that support the KE project lifecycle. In this study, we will use KBE lifecycle as one of the criteria for selecting knowledge engineering methodology. The next section will give a brief review of knowledge engineering methodologies i.e. MOKA, SPEDE, AKEM and CommonKADS, and select a suitable methodology for implementing in this study.

II.4.2. Knowledge engineering Methodologies

II.4.2.1. MOKA

MOKA stands for Methodology and tools Oriented to Knowledge based engineering Applications. It supports structuring and modeling knowledge about engineering design [Stroke 01]. Moreover, it also provides a standard way of storing knowledge that makes re- use and maintenance of the knowledge assets more feasible. The MOKA methodology focuses on two levels of knowledge representation: informal model and formal model . These models support the means of recording the structure behind the knowledge, not only things about the product and design process, but also the design rationale as well. The informal model is assembled from five categories of knowledge types, described on forms known as ICARE forms ( Illustrations, Constraints, Activities, Rules and Entities). These forms are linked together as shown in figure II.8, and provide a comprehensive and straightforward approach for placing structure upon the raw knowledge. This is done by introducing the knowledge to an appropriate form, depending upon its type, and then defining the links between the individual knowledge elements. The outputs of structure are informal models which can be stored and used for building the formal model.

Figure II.8: MOKA ICARE Forms

The formal model is associated with the formalized step. This step is composed of two sub- models i.e. product model and process model as shown in figure II.9. The product model concerns five views of the product: structure, function, behavior, technology, and representation views. The process model describes the activities and tasks of production. Activities include description, input, output, constraint and method of production. Tasks are types of activities described by a set of activities and their sequence.

Figure II.9: MOKA formal model

This tool allows the user to create instances of the underlying meta-models. The knowledge stored in the informal model is transferred to the formal model by manual process, assisted by the MOKA tool until the contents of each ICARE form have been dealt with. This methodology is widely used in the aerospace and automotive industries. It is one of the most well known methodologies for knowledge acquisition in the industrial domain. The use of ICARE forms in the informal model, and the product and design process formal models, provides the user with templates specifically suited for the product design process, unlike more generic knowledge acquisition methodologies [Stroke 01]. We realized that MOKA is a concrete methodology which mainly focuses on manufacturing context. However, the methodology did not give much attention to network and culture of organization.

II.4.2.2. SPEDE

SPEDE stand for Structured Process Elicitation Demonstrations Environment. SPEDE is funded by both industry (i.e. Rover, Rolls-Royce and Computer Vision) (NB: I think that Rover no longer exists and I don’t know the 3 rd name) and academia (i.e. the Universities of Leeds, Nottingham and Warwick). It presents a set of widely applicable methods and software tools to assist and guide the business process engineer in the task of Business Process Improvement (BPI); mainly Business Process Reengineering (BPR), but including other improvement approaches. The SPEDE methodology is a combination of principles, techniques and tools taken from knowledge engineering and adapted to knowledge management. It provides an effective means to capture, validate and communicate vital knowledge to provide business benefit [Shadbolt 99]. The concept of the methodology is designing new business process in order to improve the organizational knowledge by using knowledge engineering technique. This was made possible by breaking the activities into generally acknowledged high level BPR/BPI stages and providing a sequence at that level. In addition, it also provides core procedures for capturing the information created by the activities of each stage. The concept of this methodology is called ‘Swim Lane’ diagram as shown in figure II.10. These processes help knowledge engineer to understand the context of the organization, such as business processes, people, resources, etc. Each step of the swim lane diagram is assisted by the ‘swim lane flowcharts’. They describe the project team’s activities for a BPI project sponsor, project manager, project team, and process modeling/simulation specialist [Shadbolt 99].

Figure II.10: SPEDE swim lane diagram.

For the knowledge acquisition process, SPEDE provides the Knowledge Requirement Templates (KRTs), which is a part of General Process Ontology (GPO), to facilitate and assist the knowledge elicitation process. KRT provides definition of the required knowledge to make role, agent, organization, and location associated with the ‘activities’ within a process. The GPO supports knowledge acquisition by enabling the definition of the structure of process knowledge prior to acquisition. Table II.6 shows an example of a KRT.

Table II.6: An example of KRT of activities

Ontology Relationship

Concept ID: ...

Name: ...

Attributes: Start Time, Finish Time, Duration

Hierarchy Relations: Has Sub-Activity, Has Parent Activity

Sequence Relations: Ends Before Starts, Starts Before Ends, Starts Before Starts, Ends Before Ends, Starts After Ends, Ends After Starts, Starts After Starts, Ends After Ends, Meets, Contains

Resource Relations: Uses, Produces, Consumes, Releases

Data Relations: Has Data Input, Has Data Output

Other Relations: Is Performed By, Has Location, Has Result

In summary, SPEDE is structured methodology which mainly focuses on improving business processes within a company. The knowledge engineering methodology was adapted to enable the integration of process modeling and information modeling to facilitate rapid deployment of reengineered business processes. SPEDE focuses on the context of the organization only in the process aspect. The network and culture of members in the organization is omitted in this methodology. In terms of knowledge processes, this methodology more focuses on knowledge storage and retrieval in order to generate, simulate, and analyze business improvement options, rather than assisting knowledge workers in the organization to achieve their tasks. This methodology is recommended for organizational KM project which focus on the business process of organization.

II.4.2.3. AKEM

AKEM stands for Application Knowledge Engineering Methodology. It is a knowledge engineering methodology practiced and evolved in FF POIROT project. AKEM is a collection of strategies and heuristics in knowledge capture, representation and application. A key principle of its development is the ease of practice and adaptation with emphasis on low ceremony and agility, considering the features of knowledge engineering and its dynamic contexts [Zhao 05]. AKEM lifecycle model is summarized in figure II.11. It organizes knowledge engineering projects through four phases: inception, elaboration, construction, and transition. Each phase is one or more iteration of 8 activities with different degrees of emphasis and intensity: problem determination, scoping, analysis, development, deployment, test and validation, documentation, and control. It recognizes the importance of knowledge management in knowledge engineering and stresses the facility to trace back to the scope specification and conceptual context in order to recapture or re-examine the previous modeling decisions. It also builds traceability into deliverables in AKEM to enable links among stories, knowledge analysis, ontology, and deployment specification [Zhao 05].

Figure II.11: AKEM lifecycle model

Problem determination : is the activity for determining the nature of problems to solve and examine the cost-effectiveness and feasibility of a knowledge-based approach to solutions. The problem space is examined to identify and characterize the problem in the current application and system context.

• Scoping : the scoping activity in the domain perspective in AKEM produces two main deliverables: knowledge resources (documents, interview protocols) and stories (knowledge use cases).

• Analysis activity : produces the knowledge constituent model and task hierarchy. The knowledge constituent model consists of the knowledge breakdown and the elaboration of each constituent. The knowledge breakdown seeks to modularize knowledge in a hierarchical structure, and the knowledge elaboration provides the description of each constituent in a program specification language to capture the concerned business logic.

• Deployment : is modeling the ontology underlying the story and knowledge constituent analysis. The knowledge is not developed only for business logic and rules, but also the underlying meta-knowledge in the form of lexicons (context-term-role relations). The application specific constraints and rules are the special commitments to the lexons. The purpose is to maximize the reusability and versatility of knowledge resources over different applications, time and versions. Ontology is extracted from knowledge resources, such as regulations, requirements specification, abstracted into term-role tuples and organized into an architecture reflecting the knowledge structure of the expert of the subject. • Extraction : is a linguistic work in terms of its input and output. It works on natural language texts selected and generated in knowledge scoping and analysis: knowledge resources, stories and knowledge constituent analysis In summary, the AKEM methodology is mainly focusing on extracting knowledge from experts and users in the organization for the purpose of developing a suitable knowledge system. The deliverables from each stage of the AKEM’s framework are useful to the knowledge engineer to control, plan, and develop the knowledge system project. However, this framework is just a mirror of a software lifecycle development. The key characteristics of this methodology are application-oriented and ontology-based knowledge management system. Ontology modeling processes, i.e. extraction, abstraction, and organization, were exploited for modeling organizational knowledge. However, the method for modeling knowledge for this methodology is still ambiguous. It did not provide the concrete method than linguistic operation, key word highlighting, and paraphrasing. In terms of the knowledge management activities, this methodology concerns all knowledge processes, but mainly focuses only on storage/retrieval and representation. Knowledge creation and transfer are briefly mentioned in the framework. AKEM methodology is suitable for a knowledge management project that relies on ontology and is metadata application-oriented.

II.4.2.4. CommonKADS

CommonKADS stand for Common Knowledge Acquisition and Design System which is a present version of KADS. The method has been developed since 1984 through two major CEC Esprit funded research projects. The methodology aims to support structured knowledge engineering. It indicates the opportunities and bottlenecks in the organizations, distributes and applies their knowledge resources, and so gives tools for corporate knowledge management. It also provides the methods to perform a detailed analysis of knowledge-intensive tasks and processes. CommonKADS supports the development of knowledge systems that support selected parts of the business process [Schreiber 99]. CommonKADS methodology offered a structured approach to break down and structure knowledge engineering process. It provided CommonKADS model suite for creating requirements specifications for knowledge system as shown in figure II.12. The method enabled a top-down approach and provided handles for quality control and feasibility assessment. Figure II.12: CommonKADS model suite

• Context level analyzes the organizational environment and the corresponding critical success factors for a knowledge system.

− Organization model supports the analysis of the major features of an organization, in order to discover problems and opportunities for the knowledge system, establish their feasibility, and assess the impacts on the organization of intended knowledge actions.

− Task models are the relevant subparts of a business process. The task model analyzes the global task layout, its input and outputs, preconditions and performance criteria, as well as needed resources and competences skills.

− Agent models are the agents who are the executors of a task. It describes the characteristics of agents, in particular their competences, authority to act, and constraints in this respect. Furthermore, it lists the communication links between agents in carrying out a task.

• Concept level yields the conceptual description of problem-solving functions and data that were handled and delivered by a knowledge system.

− Knowledge model explicates in detail the type and structures of the knowledge used in performing a task. It provides an implementation-independent description of the role that different knowledge components play in problem-solving, in a way that is understandable by humans. − Communication model shows transaction between the agents involved in a conceptual and implementation-independent way, just as with the knowledge model.

• Artifact level integrates the above levels together in the design model in order to construct the requirements specification for the knowledge system.

− Design model gives the technical system specifications in term of architecture, implementation platforms, software modules, representational constructs, and computational mechanisms needed to implement the functions laid down in the knowledge and communication models. In terms of knowledge processes, CommonKADS methodology provides knowledge templates for supporting the knowledge modeling process, which constitute predefined reusable knowledge models and which have proven to work in the past. The details of knowledge templates will be described in the next chapter. This methodology concerns all knowledge activities i.e. create, store/retrieve, share, and representation. However, knowledge sharing methods did get much attention in this methodology. The notion of network and organizational culture are analyzed in organizational model in the context level. Although this method did not provide a concrete process for handling a specific task, it is detailed enough to be able to apply in any knowledge intensive task. This methodology is recommended for KM projects that concern the knowledge exchange between agents in intra- and inter-organization.

II.4.3. Knowledge Engineering Methodology Selection

In order to select the most suitable methodology to be applied to solve problems in the industry cluster (a macro-economic domain), many well known knowledge engineering methodologies (i.e. MOKA, SPEDE, AKEM, and CommonKADS) were analyzed. Although, these methodologies were successfully implemented in many domains such as product design, CAD/CAM, or cost/time reduction, there is no empirical study that applied knowledge engineering technique to solve industry cluster problem. Each technique has its own advantages and disadvantages. Moreover, the way for applying the technique in the case study is adaptable to the environment and characteristics of the organization. Thus, in this study, we propose three criteria for comparing and selecting knowledge engineering methodology in the industry cluster, as follows: 18 Coverage of framework for the KM project 19 Provided tools in the methodology 20 Adaptability of methodology to the case study

II.4.3.1. Coverage of framework for the KM project

In order to evaluate knowledge engineering methodology in term of coverage of framework, the KBE application lifecycle represents the significant processes from the beginning till the end of the project, is concerned as a benchmarking tool. Thus, our reviewed KE methodologies will be compared with KBE application lifecycle in purpose of analyzing the completeness of the methodology. The suitable methodology should provide tools that support the KE project from beginning of lifecycle until end of lifecycle. The result of the comparison was shown in table. II7.

Table II.7: Comparing KE methodologies with KBE Lifecycle

KE Project Methodology Identify Justify Capture Formalize Package Activate Management Informal Formal MOKA ICARE Model - - - - Model Model Swim Lane Understand Understand Design the Evaluate the Communicate SPEDE - Framework the project the project Process New Process Process AKEM Problem AKEM Scoping Analysis Deployment Extraction - Lifecycle Model Determination CommonKADS Context Context Concept Concept Artifact CommonKADS - Model Suite Level Level Level Level Level From the comparison, we found that none of knowledge engineering methodologies provided a tool to support activating process, due to the fact that the initiating and maintaining application did not get much attention in the knowledge engineering methodology. The application maintaining mostly depends on infrastructure of the knowledge-based system. Nevertheless, most of knowledge engineering methodologies provide tools to support from identifying to packaging process, except MOKA. It mainly focuses on capturing and formalizing process by using informal and formal models which are the core of the methodology [Stroke 01]. The project identification and justification could be done separately from the methodology. Moreover, the main objective of MOKA project is to improve manufacturing process. II.4.3.2. Provided tools in methodology

The core of most knowledge engineering methods is capturing and formalizing the organizational knowledge. Therefore, each knowledge engineering methodology provides the knowledge elicitation and representation tools in order to support the knowledge engineer in these stages of a lifecycle. Table II.8 shows a comparison of the reviewed knowledge engineering techniques.

Table II.8: Comparing KE methodologies with provided tools

KE Project Knowledge Knowledge Methodology Knowledge Engineering Tool(s) Management Elicitation Representation

MOKA ICARE Model MOKA Modeling Language (MML) Yes Yes

Knowledge Requirement Templates SPEDE Swim Lane Framework Yes Yes (KRTs)

AKEM AKEM Lifecycle Model Ontology modeling - Yes

CommonKADS Model CommonKADS KADS Templates Yes Yes Suite The comparison shows that each methodology provides knowledge engineering tools to assist the knowledge engineer in handling the organizational knowledge. Although AKEM methodology relies on ontology modeling to represent the knowledge, the knowledge elicitation process is still vague. This methodology concentrates more on KE project management than handling the knowledge from the experts. Actually, AKEM is a general methodology which is adaptable to any KM project. We realized that this methodology is could not be recommended for a project that contains complex knowledge, such as problem solving or procedural knowledge. However, it is very useful and easy for handling less complex knowledge.

II.4.3.3. Adaptability of methodology

The difference between these tools and techniques depends on the originality and objective of the methodology. For example, MOKA methodology was developed by a project in the manufacturing domain. Thus, this methodology provides the complete tools to capture, store, represent and retrieve knowledge about manufacturing, but culture and network of organization are omitted. Thus, adaptability of the methodology to our case study is one of the critical criteria for the selection. From the results of comparison with the first two criteria, the methodologies that seem to be suitable for our case study are SPEDE and CommonKADS methodology. These two methodologies provide adequate KE project management framework and KE tools for conducting the project identically. However, if we take a look into the details of each methodology, there are big differences between the two methodologies in many aspects. In terms of the objective of the methodology, SPEDE and CommonKADS are focusing on the different levels of problems in the organization. SPEDE focuses on business process improvement and reengineering in the organization by using the advantages of knowledge engineering technique. The objective of this methodology is enhancing performance of the organization at the process level by utilizing the knowledge requirement templates (KRTs). On the other hand, CommonKADS is focusing on structuring knowledge in the organization. The objective of this methodology is enhancing knowledge intensive tasks in the organization at the practice level by using CommonKADS knowledge templates. In the supporting tool aspect, tools that are provided in the methodology are the key success factor for the knowledge engineering method; they assist the knowledge engineer in knowledge elicitation and representation processes. Different functions of tools extract different types of knowledge from the experts. SPEDE provides KRTs which is a part of General Process Ontology (GPO) for extracting knowledge from the expert. These templates are well-defined method concerning the process ontology. Similarly, CommonKADS provided models and templates for each level of the framework. These templates support the knowledge engineer for knowledge elicitation in two types of knowledge task, i.e. analytic tasks and synthetic tasks. Due to these knowledge templates being flexibly defined, the knowledge engineer is able to apply this technique to different types of knowledge problems. For this reason, we believe that the CommonKADS is suitable to be adapted in our case study. In summary, from comparison with three criteria: coverage of framework of KM project, provided tools, and adaptability of methodology, we agreed that CommonKADS is the suitable methodology for applying in our context. Therefore, the knowledge engineering processes in this study will be based on CommonKADS methodology. However, the CommonKADS methodology cannot be straightforwardly applied to the industry cluster. In the next chapter, we will propose adapted CommonKADS methodology for an industry cluster. The problems and solutions of applying will be discussed. Chapter III: Proposed Methodology

III.1. Introduction

Although there is ambiguity about methodology for the industry cluster development, the fundamentals of the development are consensual. According to Rosenfeld [Rosenfeld 97], “cluster development can be done in various ways, depending on characteristics of the cluster and propriety. Participants gain benefits and advantages from being members of the cluster through collaboration” . The study of DTI [DTI 05] confirmed this statement that “the critical factor for successful cluster development is the formal and informal flows of knowledge that is generated from the network” . Accessing the knowledge can support collective learning and enhance the competitive performance of the industry cluster. These factors imply that the foundation of cluster development is managing the knowledge and collaboration of cluster members. Hence, the purpose of our methodology is aimed at applying the knowledge management practice to the industry cluster development context. Actually, the transferring of the knowledge through networks and partnerships can be achieved by two methods, direct and indirect sharing. Direct sharing (e.g. face-to-face contact or synchronous communication) allows cluster members to transfer their knowledge to the knowledge user accurately. Although direct sharing seems to be the most effective method for transferring the knowledge, there are many limitations in this method (e.g. place, time, characteristic of relationship, level of trust, etc.). On the other hand, indirect sharing (e.g. a synchronous communication or sharing knowledge via an information system) is a less effective method in terms of quality of knowledge. However, this method enables knowledge sharing, while direct sharing is impracticable. As we described the “ co-opetition ” relationship among the cluster members, this makes the enterprises which are competitors feel uneasy to share their knowledge in the direct way. This situation obstructs knowledge sharing, which will decelerate the cluster development. Hence, the indirect method was used to overcome this obstacle. Cluster practitioners used the advantages of technology, such as interactive cluster portals, to facilitate networking, and share information about the cluster as well as using them for actual business to business interactions [DTI 05]. The Knowledge Management System (KMS) is considered as a solution for enabling the knowledge flow and managing the knowledge of the industry cluster. The KMS brings various benefits to the industry cluster. It enables the members to have ready access to the knowledge base of the cluster. It supports the knowledge management activities e.g. creation, representation, sharing and retrieval of the knowledge. KMS can also reduce the limits of collaboration among members by using the advantages of information technology. Although there are some studies about the industry cluster in the knowledge context [Bornemann 03] [Levy 03] [Malmberg 04], the methodology of the knowledge management (or KMS) for the industry cluster currently does not exist. Although there are many information systems which are claimed as organizational knowledge management systems, they could not be directly applied to the industry cluster context due to several factors: 21 The differences in the type of relationships within a single industry cluster such as collaborator, competitor and the shared-resource relationship. This separates sharing of knowledge in the cluster into many levels regarding trust. Most organizational knowledge management systems do not address this matter. 22 An industry cluster is a loose network, unlike a supply chain. The knowledge shared among the members is dependent on the strengths, weaknesses, opportunities and threats in each cluster. It does not only depend on the product itself, but also the business environment. For this reason, the process-based knowledge management system has failed in the industry cluster; even though it has had great success in the manufacturing context. 23 The industry cluster includes a variety of groups of users (e.g. enterprises, government agents, academic institutes, supporting industries, etc.) which take different roles in the cluster. Each group offers and requires a different type of knowledge from the system. Developing a KMS for the industry cluster thus requires a prudential analysis to suit each cluster. The KMS architectures which were proposed in chapter 2 revealed that there is no exact architecture. The specification of KMS depends on the characteristics of the organization. Thus, in this chapter, we will propose the methodology for developing a KMS for the cluster with the Knowledge Engineering (KE) method. In the study, we adopted the CommonKADS which is knowledge engineering methodology for designing a KMS for the industry cluster. Knowledge engineering methodology helps us to analyze the requirements of cluster members. Moreover, it places strong emphasis on the conceptual modeling of the knowledge- intensive activities. The output of the knowledge engineering methodology provides us the feasibility analysis, knowledge models and specifications of the KMS. CommonKADS was used for developing the knowledge management system in various domains. For examples, it was applied to the financial domain for developing mortgage assessment systems [Schreiber 99]; to the manufacturing domain for improving product manufacturing processes and to the automation domain for developing mobile robot control. Nevertheless, the CommonKADS methodology itself could not be straightforwardly deployed in the industry cluster context, due to many reasons which will now be described. Thus, the following part depicts why and how we adapted the CommonKADS methodology within the industry cluster context. Afterwards, the research framework will give details of each level of the proposed methodology, with examples. Finally, the assimilation of the methodology will be depicted.

III.2. Adapting CommonKADS to the industry cluster context

CommonKADS provides a “ CommonKADS Model Suite ” as a core of the methodology. The model suite was developed from a need to build industry quality knowledge-based systems on a large scale in a structured, controllable and repeatable way. The CommonKADS model suite consists of three groups of models, each with its own focus, due to the fact that there are several different aspects that need to be investigated. Although CommonKADS became the European de facto standard for knowledge analysis and knowledge-intensive system development, it was developed for well defined, formalized, “hard” domain(s) [Sandberg 96]. However, the industry cluster development is loosely defined and does not provide well- established theories or methods. From our investigation, although CommonKADS appears to be the most suitable methodology for the industry cluster which is a “semi-soft” domain, some modifications in its model suite are required to match with the organizational context and environment. The CommonKADS model suite provides six model templates as we described in the previous chapter, which highlight the different aspects of the design space i.e. organization, task, agent, knowledge, communication, and design model. However, in the industry cluster context, some CommonKADS models could not be promptly applied. Thus, in this part we will describe why and how we customized the CommonKADS methodology in the industry cluster context. From our initial investigation into the industry cluster, it is clear that there are some difficulties in applying agent model and communication model in the industry cluster context. Thus, we have replaced these models with appropriate models that will be proposed in the following section. Moreover, the design model of the CommonKADS methodology is not defined in a concrete manner. This might lead to misinforming requirements and specifications from the knowledge engineer to the system developer. Therefore, we have modified the design model in order to emphasize and clarify the output from the methodology. Hence, the modification of the CommonKADS models is shown in figure III.1. The dashed outline boxes represent the models which required the modification or replacement.

Figure III.1: CommonKADS model suite

The agent model involves collecting lists of executors of a task and describing the characteristics of the agent. The objectives of this model are specifying the list of tasks that are performed by the agent, lists of other agents communicated with, the knowledge items possessed by the agent, competence of the agents, and responsibilities and constraints. However, in the industry cluster context, agents (known as cluster members) were not well- defined, which differs from usual organizations. Thus, we adapted a method called cluster mapping for extracting the list of agents from the industry cluster. So, the agent model would be replaced by the cluster model which will be briefly described in the proposed methodology section. The communication model focuses on modeling the communicative transactions between the agents involved. A transaction tells what information objects are exchanged between what agents and what tasks. Thus, the objective of this model is structuring the information exchanging of the organization. However, the characteristics of communication in the industry cluster also differ from the intra-organizational communication. Due to the structure of cluster organization being flat, communication among the members is difficult to structure. Besides, the behavior of the industry cluster in exchanging information is more complex. Thus, the communication model would instead be replaced with the collaboration model in order to analyze the characteristic of collaboration in the industry cluster. In order to represent the communication model of the industry cluster, the scenario model was used to analyze behavior of communication in the cluster. In order to develop an accurate KMS for the industry cluster, the design model takes an important role for interpreting requirements into the system specification. This model not only allows knowledge engineers to verify the system with cluster members, but also to convey the blue-print of the system to system developers. Unfortunately, this model was lightly defined in the CommonKADS methodology. Moreover, the output of the design model relies on some specific software tools. Therefore, we have intensified and generalized the design level with the software engineering concept, which comprises three modules, system specification, scenario and architecture. The outputs from these modules are system specification documents which are ready to hand over to system developers for the developing process. Hence, this study has proposed a new methodology for designing the knowledge system for the industry cluster by adapting from the concept of CommonKADS methodology, as shown in figure III.2. The model suite combines the cluster analyzing technique; CommonKADS knowledge engineering methodology and software engineering concept together, in order to support knowledge engineers to analyze the organizational context, capture the knowledge concept, and design the knowledge system for the industry cluster. The proposed model suite was divided into four levels: context, concept, design, and implementation level. The model suite presents our knowledge engineering methodology, which is a core of our framework for designing and implementing the knowledge management system for the industry cluster. The thickly outlined boxes indicate the modules modified from the original methodology. The advantage of this framework is that it does not concern only the ‘hard’ side (e.g. structure, process, task, etc.) but also culture, behavior, and opportunity which are the ‘soft’ side. The hierarchic and complementary nature of the models in terms of model- refinement and viewpoints, functions particularly well for soft domain. This is because it provides complementary descriptions of the acquired or constructed aspects of a knowledge- based system, which gives a good handle on consistency and completeness checking [Sandberg 96].

Figure III.2: Proposed methodology for developing KMS for the industry cluster

In the following sections, we will explain how to conduct the knowledge system development project within this framework. The details, techniques and tools of each model in this research framework will also be briefly described. Finally, the integration of these models in terms of input and output will be clarified.

III.3. Research Framework

A knowledge-based system must be managed by learning from the experiences in a controlled “spiral ” way, because knowledge is too rich and too difficult to understand to fit into a rigid approach [Schreiber 99]. The spiral method is repeating and revising the different steps in the cycle of development. The spiral model approach was originally devised by Barry W. Boehm [Boehm 88]. This model of development unites the features of the prototyping model with an iterative approach of system development, combining elements of design and prototyping-in- stages. The spiral model represents the evolutionary approach of IT project system development and carries the same activities over a number of cycles in order to elucidate system requirements and its solutions. This model attempts to combine the good features of the prototyping model and the waterfall model approaches. The prime difference between the waterfall model and the spiral model is that the project system development cycle moves towards eventual completion in both the models, but in the spiral model the cycles go back several times over earlier stages, in a repetitive sequence. The spiral model approach was illustrated in figure III.3.

Figure III.3: Spiral model approach [Boehm 88]

The four quadrants indicate recurring and structured steps of project management activity. The project management cycle consists of four activities (determine objectives, identify and resolve risk, development and test, and plan the next iteration) that recur in every cycle of the project. Accordingly, the model suite in this study also requires the repeating and revising of the levels of methodology. The context level generates organizational requirements for the knowledge management project. Then, the concept level produces the detailed requirement of the system. Afterward, the design level converts requirements into detailed design. Finally, implementation level develops and tests the system before releasing the KMS. However, feedback from users is necessary for revising and improving the knowledge system to suit the organization. The comparison of the spiral model approach with our research framework is illustrated in figure III.4.

Figure III.4: The proposed methodology in the spiral approach

The diagram above shows the procedures of conducting the knowledge management project. The spiral loop enables us to improve the knowledge system from the users’ feedback. However, it is not necessary to repeat the context level again, due to it giving only the concept of the requirements and the fact that it already has consensus from the members of the organization. On the other hand, the concept level contributes the detailed requirements i.e. the knowledge model and collaboration model for the system. In this section, the details of elements in each phase will be described, while the results from applying the proposed methodology in the industry cluster will be demonstrated in Chapter 4. Then, output from the design level (requirements and specifications from the cluster) will be used to create detailed design and integration of information technology for KMS, which will be presented in Chapter 5.

III.3.1. Context Level

Context level is comparable to the initial investigation phase of the spiral model. This phase is very important for the project management. It introduces better understanding and deals properly with the wider organization context. This level supports not only the knowledge engineer to comprehend the environment of the organization dealt with, but also knowledge managers, knowledge users and experts to achieve consensus for the required knowledge in their organization. Many failures in knowledge system development have resulted, not from problems with the technology itself, but from the lack of concern for social and organizational factors [Schreiber 99]. CommonKADS methodology called this phase “ knowledge orientation ” the goals of which at this level can be explicated as follows: 24 Identify problems and opportunities which knowledge management system can provide added value to the organization, and the risk in the project. 25 Decide about solutions and feasibility of the project in terms of expected cost, benefits, technological feasibility, and needed resources and commitments within the organization. 26 Improve task and task-related knowledge by analyzing the required knowledge of the task in a selected business process, and what improvements can be achieved in this respect. 27 Plan for organizational changes needed by investigating the impacts from deploying knowledge management system to the organization. The context level comprises 3 models: cluster model, organization model, and task model. Each model concerns a different organizational context in order to achieve the knowledge management project. It provides the concept of requirement for the knowledge engineer, which will be used as the input for the concept level. Thus, it is very important to follow the order of the model in this framework.

III.3.1.1. Cluster Model

The cluster model was proposed to replace the agent model of CommonKADS for analyzing the participants in the industry cluster. Identifying the network, collaboration, potential and strategy of the industry cluster are important tasks which have to be done before developing a cluster. Thus, there are many qualitative and quantitative methods that support this model. Porter [Porter 98a] recommended the cluster map and the diamond model as fundamental tools for the initial analysis of the industry cluster. The cluster map aims at visualizing the participants in the industry cluster in the layout of a map (such as group or geography), while the diamond model aims at analyzing the economical environment of the cluster. Both tools are used for initial investigation in most of the cluster development projects.

(a) Cluster Mapping The cluster map was originally adapted from the Social Network Analysis (SNA) theory. It was popularly used for mapping and measuring of relationship and flow between people, groups, organizations, computers, web sites, and other information/knowledge processing entities. However, these techniques are depending on the objective of the analysis and required outputs. To understand the industry cluster network, we evaluated the location of participants in the network. Measuring the network location involves finding position of nodes in the network. These measurements give us insight into the various roles and groupings in a network. From the given definition in the first chapter about the industry cluster, the cluster map can be separated into 5 main groups of the members in the cluster: core cluster, government agencies and associations, financial and academic institutions, supporting industries, and downstream industries. The position of each group is displayed in figure III.5.

Figure III.5: The outline of the cluster map

In order to extract a cluster map from the industry cluster network, one of the knowledge elicitation techniques, called ‘structured interview’, has been applied. The structured interviewing comprises a set of questions. The “10 Questions Technique”, proposed by UNIDO [Dawson 03], was used to analyze the location of participants in the industry cluster network. The set of questions are listed below: Q1. What companies do similar business to yours? Q2. What companies produce and supply raw materials for you? Q3. What companies provide these services for you: logistics, warehousing, maintenance, financial, training, consulting, advertising, product distributing, marketing and communications? How importantly do those companies affect your competitiveness? Q4. What companies sell equipment for production for (to?) you? Q5. What academic institutions provide manpower meeting your company’s requirements? Q6. What institutions support you in terms of research and development? Q7. Of what associations or specific institutions are you a member? Q8. What government offices are involved with your company the most? Q9. How does a government department affect/influence your ability to be competitive? Q10. Does your company need financial support from financial institutions? If so, what kind of finance source do you expect from them? One result from interviewing is a list of the participants involved in the industry cluster, which are categorized into five main groups: core cluster (Q1), supporting industry (Q2-Q3), downstream industry (Q4), academic institute & financial institutes (Q5-Q7), and association and government agencies (Q8-Q10). The number of inquiries repeated depends on the quality and quantity of the answers obtained. However, it is not necessary to complete questioning of all the participants in the cluster map in the beginning of the analysis. New entities can be added to the cluster map later to update it. The results from each inquiry are combined by using union (U) operation as shown in the equation below.

Figure III.6: Cluster maps combining

The cluster map above represents two types of information. Firstly, the arrows in the cluster map represent the inter-connection of the cluster members. This information is useful for doing the social network analysis. Secondly, the group of organizations in the cluster map represents the role of each member in the cluster. This type of information was used for understanding the responsibility (e.g. information, activity, etc.) of the organization to the cluster. This map is mostly used for representing the physical model of the industry cluster. The list of grouped members in this map will be referred by other models. Another technique usually used for analyzing environment of the industry cluster is the diamond model, which will be presented in the following part.

(b) Diamond Model Analysis

The “Diamond Model” [Porter 90] is a well-known technique which is used for analyzing the economical environment of the industry cluster (e.g. potential, strategy, chance, etc.) as showed in figure I.6. The model consists of 5 main elements: government, factor conditions, demand conditions, related and supporting industries, and firm strategy, structure and rivalry. Table III.1 shows the list of criteria of the diamond model with descriptions from the handicraft cluster context.

Table III.1: The outline of Porter’s diamond model

Criteria Analysis Government Factors from government Firm strategy, structure and rivalry Factors from competition in the industry Factor condition Factors from condition of the industry Related and supported industries Factors from related industry Demand condition Factors from the demand-side The interpretation of the diamond model provides the macro-economical view of the handicraft cluster. Information from this model is mostly concerns the government or cluster development organization in order to give suitable support for the industry cluster. However, considering the macro-view of the industry also helps the knowledge engineer to identify external factors of the industry cluster which may affect the knowledge management project. Due to diamond model analysis being fundamental to the industry cluster development, there was much secondary data from earlier studies. For example, in Thailand, there was a study supported by the Thai government to analyze all potential clusters in Thailand [NESDB 04] and diamond model was one of the tools used in that analysis.

III.3.1.2. Organization Model

The organization model aims at investigating and explaining the problems and opportunities, general context, and potential solutions that the introduction of the KMS would bring. This model also includes functionality for investigating organizational structure, processes, impacts and feasibility. The CommonKADS methodology provides five worksheets (OM-1 to OM-5) for analyzing the organizational environment and the corresponding critical success factors for the knowledge system. An overview of the organization model is illustrated in figure III.7.

Figure III.7: Organization model processes and worksheets

The worksheets OM-1 to OM-5 are used for interviewing knowledge decision makers in organizations. Then, the outputs from the model are the list of the knowledge intensive tasks, assets and agents which are related to each task. Finally, the feasibility of the knowledge management project was analyzed to see if the project was feasible in terms of business, technique, project and solution. It serves as a decision support for an economical, technical and project feasibility study, in order to select the most promising focus area and target solution. The details of each worksheet are presented as follows: The OM-1 worksheet investigates the circumstance of the industry cluster from a micro economical point of view. It explains the various aspects to consider, and helps in specifying the organization. In this study, this worksheet was used for interviewing key staff members such as the cluster leader, CDA, or responsible government agency. In addition, some information in this worksheet could be obtained from the diamond model.

Table III.2: The outline of Organization Model worksheet 1 (OM-1)

Organizational model (OM-1) Problems and Opportunities Problems and opportunities Problems: Opportunities: Organizational context Vision: Missions: Strategies: Supply chain: Solution Proposed solutions: The OM-2 worksheet is derived from OM-1 but concentrates upon the focused area of the organization. The worksheets contains information regarding the structure of the organization, the process in focus, the people involved, the type of resources used, the knowledge (as a needed resource used within the process in focus), and the culture and power which pays attention to the different “invisible” factors such as unwritten rules. In the general view, this worksheet was extended from OM-1 but focused on the in-depth analysis within the organization. This worksheet could be used for interviews with the same group as OM-1.

Table III.3: The outline of Organization Model worksheet 2 (OM-2)

Organizational model (OM-2) Variant Aspect Structure Organization structure Process Processes, tasks, activities People Members, responsible, stakeholders Resource Information system, equipment, technology Knowledge Knowledge in organization Culture and power Unwritten rules, social context The OM-3 worksheet provides a break-down of the processes specified in the OM-2 worksheet. It indicates what the knowledge intensive process is and what knowledge is used. The document also provides us with information on the knowledge worker who is performing the task, list of resources used and how significant the task is. This worksheet was used for interviewing experts or process managers in each process declared in OM-2.

Table III.4: The outline of Organization Model worksheet 3 (OM-3) Organizational model Process Breakdown (OM-3) No. Task Performed By Where Knowledge Asset Intensive Significance no. task name People, system Location List of knowledge Boolean Significance of (from process in OM- (from people and (from resources (yes/no) the knowledge 2) resource in OM-2) structure in OM-2) … … … … … … … The OM-4 worksheet is broken-down and investigates the knowledge assets that were identified in OM-3. Along with the aid of the previous worksheet, it specifies the person who is the owner, what knowledge assets are used, and where it is used. OM-4 also provides information regarding if the knowledge used is in the right form, right place, right time and right quality. This worksheet was used for interviewing the experts who performed the tasks, and users who use the knowledge assets.

Table III.5: The outline of Organization Model worksheet 4 (OM-4)

Organization Model: Knowledge Assets OM-4 Knowledge Right Right Right Right Possessed By Used In Process Asset Form? Place? Time? Quality? Knowledge name Agent Task Boolean Boolean Boolean Boolean (from OM-3) (from OM-3) (from OM-3) (yes/no) (yes/no) (yes/no) (yes/no) … … … … … … … The OM-5 worksheet is a feasibility and decision supporting document. The worksheet summarizes information derived from OM-1 to OM-4. It focuses on feasibility in terms of business, technique, and project. Then, the proposed actions are specified from the managerial commitment and decision making. This worksheet is important to give consensus to the knowledge project between the knowledge engineer, managers, experts and knowledge users.

Table III.6: The outline of Organization Model worksheet 5 (OM-5)

Organizational model (OM-5) Checklist for Feasibility Decision Document Business Feasibility Problem and opportunity in business view (e.g. added value, change management, etc.) Technical Feasibility Problem and opportunity in technical view (e.g. technology, complexity, etc.) Project Feasibility Problem and opportunity in project view (e.g. commitment, resource, communication, etc.) Proposed Actions Focus, target, expected results, actions, risks In order to understand the global view of Organization Model, figure III.8 illustrated the inter- connection diagrams of the worksheets. Each worksheet in this model is correlated to each other. The output from one worksheet was transferred to another worksheet.

Figure III.8: Inter-connection diagram of organization model worksheets

The output that transferred from this model to the task model is a list of knowledge-intensive tasks which have consensus from the cluster members. The result of these worksheets will be presented in the next chapter.

III.3.1.3. Task Model

The task model is a refinement of knowledge intensive tasks identified in the organization model. For investigating a task, three viewpoints are concerned in this model. The functional view divides a task into subtasks: input and output. The s tatic information structure view is a description of the information content and structure of objects that are handled in the task. The control view (or dynamic view) provides understanding about triggering events, decision- making points, and other knowledge about the time aspects. The task model comprises two worksheets, TM-1 and TM-2, as shown in figure III.9.

Figure III.9: Task models roadmap (TM-1 and TM-2)

The task analysis will be performed if the knowledge project is feasible (from OM-5). Each task from the list of selected knowledge intensive tasks would be analyzed in this model. The worksheet TM-1 aims at refining the task within the target process. The three views of tasks are addressed by this worksheet. The basic criteria of the task analysis are presented in table III.7.

Table III.7: The outline of Task Model worksheet 1 (TM-1)

Task Model (TM–1) Task Analysis Worksheet Task Task ID and name (from OM-3) Organization Cluster organization involved Goal and Value Objective and value of the task Dependency and Flow Input tasks, Output tasks Objects Handled Input objects, Output objects, internal objects Timing and Control Frequency, duration, control, constraints Agents Agents involved Knowledge and List of knowledge involved (from OM-4) Competence Resources Resource in the cluster involved (from OM-2) Quality and Performance List of quality and performance for measuring task In the complex or structured task, for example the production processes, the UML diagram is an appropriate tool for representing each view. For instance, functional view can be represented by an activity diagram, static information structure view can be represented by a class diagram, and control view can be represented by state diagram. All of the tasks analyzed in this worksheet will be arranged for reviewing in TM-2 in view of quality of the knowledge. The TM-2 worksheet is a specification of the knowledge employed for a task, and possible bottlenecks and areas for improvement. Actually, TM-2 is a refinement of OM-4. This worksheet is very important for the analysis, as it concerns the bottleneck and improvement of the knowledge in the organization. The TM-2 worksheet is shown in table III.8.

Table III.8: The outline of Task Model worksheet 2 (TM-2)

Task Model (TM-2) Knowledge Item Worksheet ID: Knowledge ID (from OM-4) Name: Knowledge name Possessed by: Member of cluster Used In: Task ID and name (from OM-3) Domain: Domain of knowledge used Nature of the knowledge To be Improved Formal, rigorous Boolean (yes/no) Boolean (yes/no) Empirical, quantitative Heuristic, rule of thumb Highly specialized, domain-specific Experience-based Action-based Incomplete Uncertain, may be incorrect Quickly changing Hard to verify Tacit, hard to transfer Form of the knowledge Mind Paper Electronic Action skill Other Availability of the knowledge Limitation in time Limitation in space Limitation in access Limitation in quality Limitation in form From this TM-2 worksheet, a set of the knowledge is analyzed in terms of nature, form and availability of the knowledge in the industry cluster. The outcome of this model is a guide for the knowledge engineer in order to carry on the knowledge model in the next level.

III.3.2. Concept Level

The concept level is comparable with the requirement generation phase in the KMS development framework, which is used to obtain the required information for problem solving in the industry cluster. One common way for knowledge elicitation is to directly obtain information from the domain expert, called the direct method . It involves directly questioning a domain expert on how they do their job. However, for this to be achieved, the domain expert has to be reasonably articulate and willing to share information. Another way is the indirect method which is used in case the information from an expert cannot be easily expressed directly. The tools which support this method are: card sorting, 20 questions, document analysis, etc. This level consists of two main processes: knowledge modeling and collaboration analysis. The knowledge modeling aims at extracting the knowledge from the experts and repositories. The knowledge model represents the knowledge shared between the participants in the cluster. In contrast, the collaboration analysis aims at understanding the approach for sharing the knowledge in the cluster. The collaboration model specifies the type of knowledge, method, and willingness to share the knowledge in the organization. The details of these models will be discussed as follows:

III.3.2.1. Knowledge Model

The CommonKADS knowledge model consists of three types of knowledge: domain knowledge, inference knowledge, and task knowledge, each of which captures a related group of knowledge structures (called knowledge category). Domain knowledge specifies the domain-specific knowledge and information types mentioned in the KMS. An example of this type of knowledge is the action for solving a specific problem. Inference knowledge describes how to make use of domain knowledge. It gives a primitive reasoning step for a knowledge model. For example, selecting ceramic products for international trade fair exhibitions of the cluster members, “Match” inference could be used for matching class, attributes and features of objects to meet the goal of classification. Task knowledge describes goals and strategies which were used for realizing goals. Task knowledge can be divided into several sub-tasks. This task knowledge is required by cluster members for achieving the knowledge intensive tasks in the industry cluster. Figure III.10 gives a brief overview of the three knowledge categories, as well as an example of knowledge elements.

Figure III.10: Overview of knowledge categories and the knowledge model

The diagram in the figure above shows an example of the connection between each element of knowledge. The task in this knowledge is about selecting ceramic products for an international trade fair, while the domain knowledge is the attributes of ceramic products appropriate to the task. The inference provides the relationship between the task and the domain elements. In order to extract three types of knowledge, CommonKADS provides a set of templates, which are the core of this knowledge engineering methodology, for eliciting the knowledge from the experts. Task templates form a common type of a reusable combination of model elements, which is a partial knowledge model [Schreiber 99]. It was designed for dealing with two groups of tasks in the system, analytical and synthetic tasks. Analytic task concerns the pre-existing artifacts in the system such as classification, diagnosis, monitoring task, etc. On the contrary, synthetic task concerns non-existing artifacts in the system such as design, planning, scheduling, etc. The overview of task types is presented in table III.9.

Table III.9. Overview of analytic and synthetic task types

Task type Input Output Knowledge Type

Analytic Task System observation System characterization System model Classification Object features Object class Feature-class associations Diagnosis Symptoms / Faults category Model of system behavior Complaints Assessment Case description Decision class Criteria, norms Monitoring System data Discrepancy class Normal system behavior Prediction System data System state Model of system behavior Synthetic Task Requirements System structure Elements, constraints, preferences Design Requirements Artifact description Components, constraints, preferences Configuration Requirements Artifact description Components, skeletal designs, design constraints, preference Assignment Two object sets, Mapping set 1 to set 2 Constraints, preferences requirements Planning Goals, requirements Action plan Actions, constraints, preferences Scheduling Job activities, Schedule = activities Constraints, preferences resources, time slots, allocated to time slot of requirements resources Modeling Requirements Model Model elements, template models, constraints, preference The process of the knowledge elicitation could be divided into 3 main tasks: interviewing, transcription, and modeling. Firstly, the knowledge engineer selects the knowledge template that suits the task type. Then, the interview complies with the objects in the knowledge template. The result from the interview is the transcript of the knowledge. In this stage, the knowledge engineer tries to repeat the interviewing process in order to totally complete the transcript. Finally, the transcript obtained will be represented in format of knowledge model. For instance, the classification template is one of the simplest templates for capturing knowledge in the analytic task. It is concerned with establishing the correct class for the object, which is based on characteristics of the object. This type of template is used for extracting knowledge model in the “Product selection for exporting” task, which is illustrated in figure 11. The goal of this task is selecting ceramic products for export. The Object is the object for cauterizing which is a set of ceramic products. Class is the category of exhibition for ceramic products such as an international trade fair, road show, domestic market, local trade fair, etc. The Attribute is the characteristics of ceramic products that are usually defined in the cluster such as grading A, B, and C or ‘art product’, ‘theme product’, etc. Feature is an attribute- value pairing that applies to a certain object e.g. “international trade fair = ‘art product’ and ‘theme product’ which has factory grade = ‘A’ only”. The truth value is a categorized product that is matched to a required class.

Figure III.11: The knowledge modeling processes

The diagram above gives an example of the knowledge modeling processes from the experts in the industry cluster. The processes comprise three parts: interviewing the expert by using the knowledge template, explicating the expert’s knowledge into the transcript, and modeling the knowledge model from the transcript. In the example, the knowledge about ceramic product selection for export was the focal point Product selection is classifying the ceramic objects’ features to match with the objective, which is an analytic task. Thus, the classification template was applied in the knowledge elicitation process. The outcome of this process is a set of structured knowledge that was represented in the format of knowledge transcripts. These transcripts are comparable with semi-final knowledge model of the task. The advantage of the transcript is that it facilitates the knowledge engineer in manipulating the knowledge. Afterward, the transcripts are converted into the knowledge models in task/inference/domain format. Finally, the modeled knowledge will be validated by the experts who are the owners of the knowledge. In the example, we represented the knowledge model with a Unified Modeling Language (UML) class diagram. However, there are many methods for modeling the knowledge such as topic maps, semantic networks, mind map, etc. In the knowledge modeling/visualizing stage, UML was proposed as a standard notation for CommonKADS methodology. It comprises the activity diagram, state diagram, class diagram and use-case diagram. However, the methodology is not dependent only on UML. The “ topic map ”, which is a standard for the representation and interchange of knowledge, can be used in the methodology. The topic map becomes the ISO standard and is formally known as ISO/IEC 13250:2003. The advantage of the topic map is that it is easier to read and understand by humans than the UML diagram. The inference class is transformed to the relationship between task node and domain node. Combining this with the topic map provides the semantic relationship to the knowledge model. Figure III.12 presents comparisons of knowledge representation by UML diagram and semantic topic map.

Figure III.12. Knowledge model in UML diagram and semantic map

Actually, topic maps are a form of semantic web technology (in the wider sense) and some work has been undertaken on interoperability between the W3C's RDF/OWL/SPARQL family of semantic web standards and the ISO's family of topic maps standards. Topic maps are also similar to concept maps and mind maps in many respects, though only the topic maps are standardized in this respect [Wang 07] [Natase 08]. The result from applying the knowledge model to the handicraft cluster will be presented in chapter 4 in UML class diagram format. Then, these knowledge maps will be stored in the knowledge-base of the KMS for further retrieval. The integration of the knowledge model in the knowledge management system will be illustrated in chapter 5. III.3.2.2. Collaboration Model

The collaboration model specifies the information exchanged between the different members of the cluster. The collaboration model in this study is considerably different from CommonKADS communication model. The CommonKADS communication model focuses on the transaction of information exchange between agents working on the same task. However, due to the flat structure of the industry cluster and the loose relationship between the members, extracting the communication model of the cluster would be worthless. The communication in the cluster is varied by the characteristic of activity. In addition, in the “co- petition” relationship, cluster members share their knowledge depending on the level of the trust. Therefore, our collaboration model aims at analyzing the characteristic of collaboration in the cluster, and also modeling the knowledge sharing model of the industry cluster. It relies on two knowledge eliciting techniques, the in-depth interview, which is qualitative method, and the questionnaire which is quantitative method. Interviewing consists of asking the domain expert questions about the domain of interest and how they perform their tasks. Interviews can be unstructured, semi-structured, or structured. Some interview methods are used to build a particular type of model of the task. The model is built by the knowledge engineer based on information obtained during the interview and then reviewed with the domain expert. In some cases, the models can be built interactively with the expert, especially if there are software tools available for model creation [Cooke 94]. The interviewing could be classified into three categories as follow. An unstructured interview is a general discussion of the domain, designed to provide a list of topics and concepts. A s tructured interview is concerned with a particular concept within the domain, a particular problem-solving skill or small group of skills. A s emi-structured interview is in between these two types. It is recommended to start interviewing with an unstructured interview for collaborative model identification. Then, the structured interviewing techniques, such as a problem-solving interview or dialogue, will be used for specifying collaboration model. In the final stage, the last interview attempts to validate the collaboration model of the organization. In this study, the interviewing and questionnaire are considered as knowledge elicitation tools for modeling the collaboration of the industry cluster. It not only supports the collaboration modeling but also characterizes the collaboration, level of trust, etc. The objective of this model aims at answering the following questions: 28 What are the critical activities of the industry cluster? This question aims at analyzing the physical collaboration of the cluster members in various aspects. 29 What kinds of the information/knowledge are exchanged in the collaboration? This question concerns the type of knowledge shared in the collaboration, and also the willingness of knowledge sharers at different levels of trust in the cluster. 30 What are the internal and external factors that impact the cluster’s collaboration? A set of questions are used to analyze the characteristics of collaboration in the industry cluster. From the objectives of the model, the collaboration analysis could be divided into three main parts: activity (physical collaboration), information sharing and organizational characteristics. However, the organizational information is included in the questionnaire for analyzing the global view of the ceramic cluster. The diagram of the collaboration model is presented in the figure III.13.

Figure III.13: The structure of collaboration model analysis

Accordingly, the outline of the questionnaire consists of four parts, as follows. A sample of questionnaire is showed in Annex C. The first part aims at acquiring the organizational information about the industry cluster. The second part focuses on analyzing the critical activities in the cluster collaboration. The third part aims at modeling the information and knowledge exchanging model of the members. Finally, the fourth part aims at analyzing the internal and external factors that affect the collaboration, such as information and communication technology, literacy or the environment of the collaboration. The detail of each part is described as follows: Part I: Organizational information aims at acquiring general information such as characteristics of the organization, their products and markets. Although this part does not provide the concrete requirement collaboration modeling, it provides primary statistical data about the industry cluster. The information extracted from this part was represented by using UML diagram as shown in the figure III.14.

Figure III.14: The UML model of organizational information analysis

The diagram above presents required data mentioned in the questionnaire. The results from this part give a better understanding to the knowledge engineer about the members of the industry cluster. Therefore, this analysis was included in the questionnaire. Part II: Physical collaboration aims at analyzing the main activities of the members of the cluster. The physical collaboration analysis concerns the different point of views on expectations, satisfaction, frequency, and impact. The list of activities can be obtained from the Organization Model 2 (OM-2) worksheet in the context level, or the interviewing of members. Then, the expectation and satisfaction from the collaboration will be compared with the intention of evaluating the value of the collaboration from the members’ point of view. This value can be determined from the difference between the value of expectation and satisfaction. If the value of satisfaction is higher than expectation, this means that the collaborator gains benefit from the collaboration. On the other hand, if the expectation is higher than satisfaction, the members feel that this type of collaboration requires improvement. The total of differences also indicates the satisfaction of the members from participating in the industry cluster in the global view. Another objective is analyzing the main activities performed in the cluster itself from the members’ point of view. The evaluation concerns the frequency of collaboration and the expected impact of the activity on the industry cluster. The results from this analysis represent the value of the group of activities to the industry cluster. The details of activity analysis will be discussed in the next chapter, while the model of analysis is shown in figure III.15.

Figure III.15: The UML model of activity analysis

Part III: Willingness to share information/knowledge aims at analyzing the collaboration of the industry cluster from the information point of view. Thus, the objective of this part is modeling the structure of information and knowledge sharing in the cluster. The information structure modeling relies on the knowledge taxonomy which was explained in chapter 2. The questionnaire and interview intends to acquire shared information which matches with each type of knowledge. Moreover, the level and willingness of sharing are concerned for the modeling. The result of this part is the information model of the industry cluster which will be used for designing the collaboration services in the next level.

Figure III.16: The UML model of information sharing analysis

Part IV: Characteristic of collaboration aims at examining internal and external factors that affect the collaboration such as the technology aspect, problem solving techniques, strength of relationships, etc. These factors will be the criteria for designing the knowledge system. For example, the Information and Communication Technology (ICT) literacy of the cluster members will be used to specify the level of technology of the system. The conflict solving techniques and relationships are used for indicating the strength of the relationship. The key success factors of collaboration aim to analyze the present situation of collaboration by using 20 success factors of collaboration [Bruce 07]. The results give a better understanding about the collaboration in the particular industry cluster. Figure III.17 shows the criteria used in this part.

Figure III.17: The criteria for analyzing the characteristics of collaboration

In summary, the goal of this collaboration model is analyzing the critical activities of the members, the information sharing model, and the factors that affect the industry cluster. The models proposed in this study are an example of the collaboration analysis. However, the methods of analysis are not limited to these models. Thus, the interview is necessary for designing the questionnaire, while the questionnaire itself builds a consensus on the collaboration model.

III.3.3. Design Level

This level is comparable to the detailed design phase, which concerns the requirements, specification and architecture of the knowledge system. This level is necessary for transferring the responsibility from the knowledge engineer to the knowledge system developer. Although CommonKADS is also concerned with the design level in the methodology, it provides few explanations and is not concrete. Thus, this study emphasizes this level by adopting the software engineering theory in our methodology. In this level, we considered three views of the system development: specification view, scenario view and architecture view. Each view provides requirements and specifications from the knowledge engineering process to the knowledge system development process from the different aspects. The details of each view will be discussed as follows.

III.3.3.1. System Architecture

There are several models related to KMS architectures, as discussed in Chapter 2. Although there are differences in the function of each model, the fundamentals are similar. In this study, we adopted the concept of “three-tier KMS architecture” [Chua 04], which is the simplest architecture but concerns both knowledge management activities and collaboration. The model comprised three distinct services that are supported by knowledge management technologies. The architecture of the three-tiered model is shown in figure III.18. It is the assembling of three main services: knowledge services, collaboration services, and presentation services.

Figure III.18: The overview of three-tiered model

1 Knowledge services : intended to help cluster members achieve their goals of knowledge management. Three primary objectives are proposed to promote the process of generating new knowledge, encourage the flow of knowledge among cluster members, and ensure the ease of access to the knowledge repository [Martin 00].

• Knowledge creation : is the capability to capture and codify knowledge held by experts in the industry cluster. This process will be carried out by the domain experts or the knowledge engineer with knowledge elicitation techniques that are provided in CommonKADS.

• Knowledge sharing : is an important goal of KM technology that supports the knowledge sharing process which is collaborative tools, such as shared spaces, wiki, calendaring, workflow management service, etc.

• Knowledge reuse : is a synonym for “information retrieval” in the information management literature. The emerging technology aims at enhancing search capabilities as users require, and automatic generation of meta-data [Marwick 01]. An example of technology that supports the knowledge reuse process is semantic search. 2 Collaboration services : refer to the basic technology platform and features needed to implement KM. The purpose of these services is facilitating the collaboration among the cluster members. The two main infrastructures provided by technology are storage and communication.

• Storage : known as knowledge repository such as drawings, audio, video or multimedia documents. The knowledge server which allows users to build content, create references and establish links among documents is technology that supports KM processes, particularly knowledge creation and knowledge reuse. This meaning of storage also extends to the database which is required for managing the knowledge behind the system.

• Communication : supports the collaboration and information sharing activities within the cluster. These services are designed with regard to requirements from the output of collaboration model. The communication service enhances the quality of communication of the cluster member on the focused activities. 3 Presentation services : concerns the interface between the user and the information /knowledge source in the KMS. It aims at visualizing the required knowledge to suit the knowledge user’s preference.

• Personalization: involves gathering user-information and delivering the appropriate content and service to meet the specific needs of a user [Bonett 01]. This service refers to the rule that determines how users and content are matched, based on their attributes and values.

• Visualization : helps users better understand the information and knowledge available by making subject-based browsing and navigation easier [Marwick 01]. This service seeks to represent the knowledge in the right format for the usage.

III.3.3.2. System Scenario

Unified Modeling Language (UML) is a useful tool in order to create the system model. For breaking down system architecture in to different views, “4+1 Model” [Kruchten 95] is a famous approach for modeling a complex system. The 4+1 model (figure III.19) depicts five views with UML: logical view, process view, physical view, development view, and use case view. Each view presents one aspect of the system and each has a particular kind of UML diagram associated with it.

Figure III.19: The 4+1 model [Kruchten 95]

• The logical view shows the parts that comprise the system, as well as their interaction. The UML diagrams that show the logical view include: class diagram, state diagram, object diagram, sequence diagram and communication diagram.

• The process view describes a system’s processes. UML activity diagram represents the process view.

• The physical view models the system’s execution environment, which explains how to map software in to the hardware system. UML deployment diagram is used to model the physical view of a system.

• The development view describes the system’s modules, or components, including packages, sub-systems and class libraries. UML diagram that shows the development view includes: component diagram and package diagram.

• The use case view ( scenario ) shows the system functionality. It captures user goals and scenarios. This view is very helpful in defining and explaining the structures and functionality in the other four views. The UML use case diagram provides the use case view. The 4+1 view model allows various stakeholders to perceive what they need in the system architecture. The knowledge engineer can approach it first from the physical view, then the process view; cluster members, experts, and CDA can approach it from the logical view; and project managers and system developers can approach it from the development view [Kruchten 95]. III.3.3.3. System Specification

In this stage, Software Requirement Specification (SRS) which is IEEE standard (IEEE-830) has been used, in order to create a knowledge management system specification. SRS is a comprehensive description of the intended purpose and environment for software under development. It fully describes what the software will do and how it will be expected to perform. The core of SRS comprises five documents: requirement specification, functional specification, design specification, system specification, and test specification as shown in figure III.20.

Figure III.20: Structure of Software Requirement Specification (SRS)

These documents are required for KMS development in the next level. They provide better understanding of the needs and constraints of the system to system developers and stakeholders. Requirement specification provides users’ viewpoints for the system. Functional specification describes the requested behavior of the system. Design specification describes the organization of the functions/modules over the user interface. System specification describes hardware, software and environment that are required for operating the system. Test specification describes how to test each module, scenario and feed back for the system developer. These documents act like the medium for the knowledge engineer and system developer in order to develop the knowledge system in the next level. III.3.4. Implementation Level

This phase is actually the KMS development process, also known as coding process. It aims at translating requirements and specifications into software product. Actually, the complexity of implementation phase varies on the defined specification in the previous section. The knowledge system implementation could be done by the simplest approach e.g. configuring existing system to suit with the user’s requirement, until a more complex approach. The details of the implementation level will be described in chapter 5. After the prototype of knowledge system is created, the verification and validation of the system are required.

III.3.5. Verification and Validation

System verification and validation are essential for knowledge system development. A primary purpose is confirming that the developed system is matching with the specification and user requirements. Moreover, it aims at detecting software failures so that defects can be uncovered and corrected. The method for verifying the system is obtainable from the test specification in the design level. The test specification relies on three methods of system testing: Demonstration Test (DT), Functional Test (FT) and Operational Test (OT). The demonstration test focuses on testing the system by the role of user (e.g. anonymous user, support cluster, core cluster, CDA, and administrator). This kind of testing aims at verifying that the user requirements were supported by the developed system. The functional specification seeks to test the system function by function, which is indicated in the functional specification. This verification aims at detecting software failures in each function. Lastly, operational test focuses on testing the system with the example test cases. It aims at verifying the system from the beginning to the end of operation in order to detect the errors between functions. Moreover, it also validates if the system complies with the industry cluster scenarios. In general, test specification (which is functional testing) can be used to detect functional failure of the system. It provides the feedback about functional testing results to the system developer. Furthermore, the test scenario (which is non-functional testing) is also useful to check if the system is corresponding to the users’ requirements. If provides the feedback in terms of suitability of the system for users’ requirements to the system developer and knowledge engineer. These testing results are fed back to the requirement generation phase in order to modify the system specification and users’ requirements. Finally, the revised system can be released for knowledge workers and experts in the industry cluster.

III.4. Methodology Implementation

The proposed methodology is the integration of knowledge engineering methodology, software engineering and cluster development technique. The combination of these three domains supports the knowledge engineer to develop the knowledge system for the industry cluster. The CommonKADS knowledge engineering methodology strongly supports the analyzing of organization, requirement and knowledge modeling, but less in knowledge system design and development. The software engineering methodology, such as the waterfall and spiral models, is widely used for managing and developing the software project. Cluster development tools such as Porter’s diamond model, cluster mapping and collaboration analysis were used to overcome some limitations of the knowledge engineering methodology. The core models of the methodology were separated into 4 levels, called model suites: context level, concept level, design level and implementation level. These 4 levels provide a step by step guide for the knowledge engineer from analyzing, modeling and designing until the development of the KMS. The model suite was illustrated in figure III.2. The objective of the model suite is constructing the KMS for the industry cluster. Each level focuses on extracting information from the experts in different aspects. Context level aims to provide better understanding about the context of the cluster, knowledge intensive tasks and archetype of the industry cluster. As a result, this level provides the analyzed organization worksheets, task worksheets and the cluster map. Concept level aims at modeling the required knowledge, type of knowledge, pattern of sharing, and characteristic of collaboration in a particular cluster. The modeled knowledge and information sharing model are the result of this level. Design level aims to convert the results from previous models into requirements and specifications for the knowledge system. The output from this level is UML diagram, system architecture and specifications for system development process. Implement level is selecting information systems to meet the requirements and specifications that are defined in the design level. The final result of the model suite is the knowledge management system that complies with the organizational context, collaboration behavior, requirements and conditions of the industry cluster.

Figure III.21: The structure of the proposed methodology

This model suite has been tested with “Cera Cluster”, which is the largest ceramic cluster in Thailand. The initial analysis was initiated at the core of the Cera Cluster level and the CDA. The results from the context and concept level will be described in the next chapter. Then, these results are considered as the criteria for generating requirements and specification for the knowledge system. Finally, the outcomes from design and implementation levels will be presented in chapter 5. Chapter IV: Analysis and Results

IV.1. Introduction

This chapter mainly focuses on the analysis and results from applying the research framework to a ceramic cluster in Thailand. It concerns the first three levels of the framework: context, concept and design. The first part of this chapter will present the results from the context analysis. The investigation has been carried out with the members of the Cera Cluster e.g. core cluster, CDA, academic institute, government agency, etc. The second part of the chapter focuses on the concept level, which aims at eliciting the knowledge model from the experts regarding a list of knowledge-intensive tasks that is derived from the previous level. In addition, it also concerns the collaboration model of the cluster e.g. how the members exchanged their knowledge, what kind of knowledge is shared in the cluster, etc. The last part of this chapter will propose the design and specification of the knowledge system for this ceramic cluster. A set of software engineering documents are used as protocol between the knowledge engineer and system developer. The following figure presents the structure of chapter 4 and 5 with regard to the proposed framework.

Figure IV.1: The organization of chapters

IV.2. Context Level

The main objective of the context level in our framework is to analyze the characteristic of the industry cluster in different aspects e.g. physical network, characteristic of cluster, tasks, knowledge assets, etc. Although this level does not analyze the knowledge itself, it provides a global view of the organization regarding the knowledge management project. The context level comprises three models: cluster model, organization model, task mode. The cluster model is the first analysis that focuses on physical network of the members and stakeholders of the industry cluster. The organization model focuses on the feasibility of the knowledge management project. Finally, the task model aims at analyzing the processes which were broken down from the knowledge intensive tasks of the ceramic cluster. This model also analyzes the quality of the knowledge itself in terms of nature, form, and availability of the knowledge. In order to visualize the interconnection within the context level, the input/output model of the context level is presented as follows:

Figure IV.2: Input/Output model of the context level

IV.2.1. Cluster Model

The objective of cluster modeling is to identify the competitive strengths, weaknesses, and organization of particular industry clusters. Two techniques that are widely used for cluster modeling are diamond model and cluster map analysis. Diamond model [Porter 90] mainly focuses on the cluster analysis in the business aspects such as the present situation of the industry, demand from the markets, value chain, strategy and infrastructure of the industry cluster. In contrast, cluster map focuses on collaboration and organization of the industry cluster. It aims at examining the network around the core cluster and accessibility to supporting organizations. These approaches are necessary for the initial stage of developing a KMS for the industry cluster. The integration of these two techniques provides a better understanding of both aspects for the knowledge engineer and government agency involve in cluster development. The following parts will present the results obtained from applying these techniques to the ceramic cluster in Thailand.

IV.2.1.1. Diamond Model Analysis

The National Economic and Social Development Board of Thailand (NESDB) have studied the competitiveness of this ceramic cluster in 2004. One of the selected tools in this study is the diamond model. The results from this study showed that this cluster is one of twenty potential industry clusters in Thailand. It was promoted as the most competitive ceramic cluster in Thailand. The summary of the diamond model analysis by NESDB is presented in the table IV.1. The analysis gives two aspects of the results, the positive forces (+) which were factors created advantages to the cluster, and negative forces (–) which may created disadvantages to the cluster.

Table IV.1: Diamond model analysis of Lampang’s ceramic cluster [NESDB 04]

Criteria Analysis

Government (+) Local government set provincial strategy as the ceramic center of Asian. (–) Lack of continuous supports from government to improve product, design and market. Firm Strategy, (+) Most are SMEs which manufacture by OEM and ODM. Structure and (+) Large enterprises focus on exporting to client’s order. Rivalry (+) Much supports from organizations to develop dynamic and sustainable cluster. (+) Focus on niche market rather than mass market. (+) Cross-linked between food, hotel and ceramic industry. (+) Establishment of National Ceramic Center in Lampang. (–) Strong rivalry in small enterprises by cutting price and copying designs. (–) OEMs are not motivated to develop their designs. (–) Lack of own branding in ceramic industry (–) Production costs such as raw material, logistics and fuel are increasing. (–) SMEs lack of knowledge and experience in export field. Factor Conditions (+) Largest source of high quality white clay in the country. (+) Availability of LPG factory in the area, which is the main fuel for production. (+) Availability of local highly-skilled craftsmen and designer. (+) Located in the middle of northern Thailand, connected to many provinces which provide advantages in terms of logistics. (+) 17 electrical substations are in service for the factories. (+) Most raw materials can be purchased in the province. (–) Most machines have to be imported from foreign countries. (–) Lack of labor responsibility in their tasks. (–) Entrepreneurs’ lack of awareness in industrial and production standard. Criteria Analysis

Related and (+) The correlation between related industries exists, e.g. tourism, handicrafts, Supported construction and decoration. Industries (+) Availability of ceramic association and ceramic center in the province. (+) Supported by National Innovation Agent (NIA) to create own branding. (+) Distributed production process to the competent factories in the cluster. (–) Low degree of relations with academic institutes. (–) Lack of industry and academic institute to develop technology and machine for production in the supply chain. (–) Weakness of linkage of supply chain in ceramic industry. (–) Clustering in ceramic enterprises in Lampang still lacks strong collaboration. Demand Condition (+) Demanding in the country still has good trends, major market is in Bangkok. (+) Medium to high level customers, and foreign customers, emphasizes importance of product quality. (+) Large enterprises keep track of the preference of customers via many channels. (–) Foreign market is effect from the termination of GSP (Generalized System of Preferences) privilege by European Union (EU). (–) Small enterprises could not access the information about trend and preference of customers. (–) Domestic customers do not appreciate the quality of the product; feels that Lampang’s ceramic products are low to medium quality. The results from examining the impact of government on the industry cluster revealed that national and local government give importance to the ceramic industry, especially Lampang’s ceramic industry cluster by providing infrastructure and support in terms of finance and policy. This is a significant advantage for this cluster. However, continuality of supporting from the government is still deficient. Rivalry of the cluster can be separated into two obvious levels. The first level is the competition in the large enterprises. The main markets of this group are EU, Japan, USA and East-Asia. These companies have experience in foreign markets, continuous order from customers, and their own brands. Thus, the competition in this level is product development and human resources. Another level of rivalry is competition in the SMEs level, which presents the major portion of manufacturers in the ceramic cluster. In this level, there are many factors in competition, e.g. price, design, market, etc. There are many positive factors which supports this cluster. Negative factors are technologies, standards, and human resources. Although there are many industries which are supporting this cluster, the linkage between cluster and supporting industries is required. The domestic demand is on a good trend but ranges of product are only low to medium quality. Moreover, although there is an opportunity for Thai ceramic products in the global market, SMEs still lacks of the knowledge to access these markets. In brief, the analysis provided us with an overview about the positive and negative forces from both inside and outside the industry cluster. It revealed that even though Lampang’s ceramic cluster is facing the problems about accessing to the local and global markets, there are many positive factors which support the manufacturers to be able to compete in the markets.

IV.2.1.2. Cluster Mapping

In order to visualize the industry cluster network, one of the knowledge elicitation techniques (called structured interview ) is introduced to this study. The interview comprises 10 questions as described in chapter 3. In this study, we start from the ceramic cluster committee, then extended to supportive organizations, CDA, and so on. The complete cluster map is generated by combining different viewpoints from cluster members.

• The first cluster map is generated from interviewing the president of the ceramic cluster committee. The primary result gives us a list of 60 organizations in the ceramic cluster, 27 organizations in the core cluster and 33 organizations in the cluster support. The map obtained is a kind of preliminary view of the core of the cluster due to this map indicates the key persons of the cluster development. Then, the next interview will be repeated with one of the enterprises from this map.

• The second cluster map is generated from interviewing the leader of a former ceramic cluster in Lampang, called “Trust Group”. The result gives a list of 61 organizations, 26 in the core cluster and 35 in the cluster supporter. From the second cluster map, there are 26 new defined organizations which were comparable to 46% of the cluster map. These elements will thus be appended to the previous cluster map.

• The third map is generated from interviewing the cluster’s association. The interview with the president of Lampang Ceramic Association shows 68 organizations. From this list, there are 15 new defined organizations. Thus, about 22% of the total organization in the cluster map is appended to the third map.

• The forth map is generated from interviewing peoples in the supplier side of the ceramic cluster. The interview with one of the biggest white clay suppliers in this cluster provided 65 organizations, but only 6 new organizations are defined. Thus, we finished the interview at the fourth mapping due to the percentage of new defined organization being less than 10%. In practice, the cluster mapping can be continued again later on in order to build up the list of users and member of the system. The results from these four interviews are combined and visualized as follows:

Figure IV.3: Cluster map of Lampang’s ceramic cluster

The ceramic cluster map above shows that there are at least 105 organizations which influence the development of this cluster. The elements of the cluster map are categorized into 7 groups. The first group, located on the middle of the map is the core cluster. This group comprises 45 ceramic manufacturers in Lampang and nearby areas. The second group, located on the left side of the map is supporting industries (or up-stream industries). It is composed of 26 enterprises which supplies raw materials and services to the cluster. The third group (down-stream industries) is located on the right side of the map. It is composed of 8 enterprises involved with the end of the ceramic supply chain. The fourth group is composed of government agencies and is located on the top-left of the map. This group represents 5 agencies which have direct impacts on the ceramic cluster. The fifth group, which is cluster’s association, is located on the top-right of the map. This group indicated 7 associations that always provide support to the cluster. The sixth group, financial institutes, is located on the bottom-left of the map, comprises 6 institutions that provide financial support for the ceramic cluster. Finally, the seventh group (bottom-right of the map) is academic institutions who support the ceramic cluster in terms of fresh knowledge and innovation. In this state, the cluster model provides two primary views of the ceramic cluster. The first view is a conceptual view which is examined by diamond model analysis. This view presents both positive and negative forces which affect the ceramic cluster from various aspects. On the other hand, the cluster map analysis provides a physical view of the ceramic cluster. It reveals the agents who are involved and also their roles in the system. Both views are not only essential for the knowledge engineer to get ‘the big picture’ about the particular cluster, but also important for the analyzing in subsequent models. The list of actors in the ceramic cluster map will be referred by the organization model in the next part.

IV.2.2. Organization Model

The organization model is he core model of this level. There are two main objectives for implementing the organization model in this study. The first one is to provide better comprehension about the ceramic cluster to the knowledge engineer. The second one is to explicate the cluster members about how the knowledge management project will improve the cluster. The process of organization model analysis was divided into five parts. Each part is supported by different worksheets which are provided within the CommonKADS methodology. The processes of the organization model analysis are illustrated in the following figure.

Figure IV.4: Five steps of the organization model analysis

The first worksheet (OM-1) focuses on a global view of the ceramic cluster. In this study, we use this worksheet for interviewing the cluster committee in order to investigate problems, solutions, and the context of the ceramic cluster. Then, the second worksheet (OM-2) provides deeper view of the ceramic cluster by focusing inside the cluster on aspects such as structure, process, people, resource, and knowledge. The elements in this worksheet were examined and designated with unique identity codes for ease of reference by other worksheets. These elements are considered as the basis component of the ceramic cluster. Then, the output from OM-2 will be analyzed in two aspects, process aspect (OM-3) and knowledge aspects (OM-4). The OM-3 worksheet brakes down main processes of the ceramic cluster into tasks and subtasks. The objective of this worksheet is to identify and rank the knowledge-intensive tasks within the ceramic cluster by using the specified criteria. On the other hand, OM-4 worksheet aims at analyzing knowledge assets of the ceramic cluster in term of place, time, form, and quality of the knowledge. The results from this worksheet tell us how the knowledge assets can be improved. Finally, OM-5 worksheet provides the knowledge engineer with a feasibility decision checklist in different points of view i.e. business, technical, and project feasibility. This worksheet is filled in by the knowledge engineer in order to propose the actions for the knowledge management project of the ceramic cluster. It is considered as a decision support document for the stakeholders to continue or to terminate this project. In this section, we will present the results from applying each worksheet in the ceramic cluster.

IV.2.2.1. Problems and opportunities worksheet (OM-1)

The first part of the organization model focuses on problems and opportunities in the wider view of the organizational context. Then, it focuses onto specifies of the organization such as vision, mission, strategy, value chain, etc. At the end, the potential solutions from the industry cluster point of view are examined in order to get a real and explicit understanding of the ceramic cluster context. In this study, we used this worksheet for interviewing the ceramic cluster committee in order to get the perspective of the cluster. The results from the interview are displayed in the following table

Table IV.2: Problems and opportunities worksheet (OM-1)

Organization Problems and Opportunities Worksheet Model: OM-1 Problems and Problems Opportunities 1. Supply in the country exceeds demand, causing negative rivalry in small enterprises by cutting prices and copying designs. 2. Invasion of low cost products from neighboring countries. 3. Lack of own branding and designs in the SMEs. 4. Lack of knowledge and experience about the global market. 5. Production costs are increasing, causing pricing to become less competitive. 6. Weakness of collaboration in the supply chain. 7. Small enterprises could not access necessary information, such as global ceramic trends. Opportunities 1. Support from the government in terms of finance and policy. 2. Wide variety of targeted customers, from high-end to low-end consumers. 3. Location in the center of ceramic production of Thailand. 4. Availability of the largest source of high quality white clay in the area. 5. Most raw material suppliers are situated in the area. 6. Availability of local highly-skilled craftsmen and designers.

Organizational Vision Context “Lampang Ceramic City” Asian ceramic hub in 2012 Mission 1. Develop strength of cluster organizations in order to create dynamic and sustainable conditions for the ceramic industry cluster. 2. Enhance quality, design, innovation and branding of the product to reach standards recognized in the global market. 3. Support and create market opportunities for the cluster. Strategy 1. Improve collaboration among the people responsible in the area by developing integrated collaboration between B2B, B2S, B2C, B2G, etc. 2. Revolution of strategy of enterprises and supporting organizations from old style business e.g. cost-focused products, to high value-added product. This can be done by concentrating on quality development, designing, branding, and marketing using e- commerce, and trade fair and exhibition. 3. Enhance network of supporting organizations in the area such as ceramic producers’ association, academic institute, government agencies, etc. These will develop beneficial activities and add fresh knowledge to the business strategy. Solutions 1. Exchange information/knowledge and enhancing of the business network. 2. Consult and solve the problems together. 3. Increase the opportunity to acquire support from government. 4. Reduce production costs, using aggregate purchase quantity and negotiations with the supplier. 5. Reduce marketing costs, using co-investment in ceramic trade fairs and exhibitions. 6. Reduce Research and Development (R&D) costs, using co-investment in developing new formulae of ceramic products. 7. Reduce Human Resource Development (HRD) costs, by organizing training together. 8. Reduce costs of building the infrastructure by requesting supporting investment from the government. 9. Improve innovation for products and services together.

From the analysis, this cluster has many positive factors for cluster development. However, weakness of collaboration and lack of knowledge seem to be the major obstacles of the development. Thus, the main strategies of the ceramic cluster were set for improving the networking and business practices of the members. The solution proposed by the cluster committee can be considered as planned activities for supporting the strategy. The next worksheet will present the elements inside the ceramic cluster.

IV.2.2.2. Variant aspects worksheet (OM-2)

The second part of organization model concentrates on specific aspects of the ceramic cluster. This worksheet comprises six aspects: structure, process, people, resources, knowledge, and culture and power. The structure aspect focuses on the department, unit, or group involved in the core activity of the ceramic cluster. The process aspect focuses on business process which is relevant to the value chain of the cluster. The people aspect indicates the actors who are involve with process mentioned. The groups of actors in the cluster are derived from the cluster map in the previous model. The resource aspect can be any information system, equipment, or technology which is used within the ceramic cluster. The knowledge aspect represents the knowledge element which is required to accomplish the task. Finally, culture and power pay the attention to the “unwritten rules” of the cluster organization. The results from the analysis in these aspects are showed in table IV.3. It describes the basic elements of the organization and is considered as an outline for the following parts. These components are identified by particular codes in order to be referred to accurately by other worksheets. Table IV.3: Variant aspects worksheet (OM-2)

Organization Variant Aspects Worksheet Model: OM-2

Structure 1. Core Cluster 2. Government Agency 3. Association 4. Financial Institute 5. Academic Institute 6. Supporting Industry 7. Downstream Industry 8. Cluster development agent (CDA)

Process First Phase [P-1] Obtain information about new global trends [P-2] Design new products Second Phase [P-3] Find market opportunities [P-4] Contact customers [P-5] Product developments [P-6] Manufacturing [P-7] Logistics and Exporting (Shipping) Third Phase [P-8] After sales service [P-9] Inventory Clearance (Sales mosaic, B and C grade products)

(See one-year cycle of ceramic business in Lampang in Annex A)

Organization Variant Aspects Worksheet Model: OM-2 Peoples [A-1] Core Cluster [A-2] Government Agency [A-3] Association [A-4] Financial Institute [A-5] Academic Institute [A-6] Supporting Industry [A-7] Downstream Industry [A-8] Cluster development agent (CDA)

See details in the Cluster Map (Figure IV.3)

Resource 1. Ceramic cluster website and e-commerce system 2. Mail, E-mail, Fax, Telephone 3. Cluster development agent (CDA) 4. Facilities at Ceramic Center

Knowledge [K-1] Accessing global trends [K-2] Product development [K-3] Accessing new market opportunities [K-4] Customer Relationship Management (CRM) [K-5] Ceramic Manufacturing [K-6] Logistics and Exporting [K-7] Acquiring support from the government [K-8] Ceramic Research and Development (R&D) [K-9] Human Resource Development (HRD) [K-10] Ceramic Branding Culture 1. Trust in the ceramic cluster is in the low to medium level. Thus, we can see many and Power small groups of enterprises in a single industry cluster, such as IFCT cluster which includes 5 small groups called trust , active , believe , harmonize and sila Lampang group . These small groups contain 5-10 enterprises from upstream to downstream industries. Trust within the group is at a high level. They tend to share proprietary knowledge and some business secrets among each other. However, this knowledge and information are exchanged less often in the cluster level. 2. Structure of cluster organization is flat. The cluster committee and CDA are selected for facilitating the cluster. However, they often face problems when decision making is required. This is caused by uncover (uneven?) information sharing; e.g. some member did not obtain the same information as the cluster committee. Sometimes, it is apparent that the committee lacks experience and knowledge to make the right decisions. Trial and error on the part of the committee creates disagreements and diminishes trust in the cluster. 3. Direct sharing of knowledge and information in the cluster is still at a low level. The special collaboration between competitors makes the members uneasy to enquire about and share knowledge when they are face to face. Thus, the CDA who is the cluster facilitator, has sometimes taken responsibility as ’the middle man’ by transferring knowledge from one member to another. This process, called Indirect sharing, may improve the knowledge exchange in the cluster, but the quality of knowledge may be distorted. With the limitations of the CDA, this method of sharing may be unsustainable.

The details of each group or people were described in the cluster map in the previous part. The process aspect showed the major business activities of Lampang’s ceramic industry in a one-year cycle. The activity starts from obtaining design information until inventory clearance which can be divided into nine processes. Then, the resource aspect shows the available facilities within the cluster. From this aspect, we can see that the ceramic cluster in this case study does not rely on high technology or equipment for their development due to this cluster is composed of SMEs in the handicraft domain. The knowledge aspect shows a list of required knowledge for achieving the goal of each process. Lastly, the culture and power aspect describes specific characteristic of the industry cluster which impact cluster development but have never been discussed. From the interviewing CDA, three major issues (trust, structure of organization, and direct sharing) are concerned. These issues will be discussed again in the collaboration model. The defined processes in this worksheet will be broken down in the OM-3 worksheet, whilst the list of knowledge assets will be examined in the OM-4 worksheet. IV.2.2.3. Process breakdown worksheet (OM-3)

The defined processes from the previous part are specified in more details within this worksheet. The business process is broken down into smaller tasks for classifying knowledge- intensive task from general task. In order to do this, a consensus from cluster members on the significance of each task should be obtained. However, there are no hard rules for assessing task significance [Schreiber 99]. Methods such as ordinal scale can be applied for acquiring a consensus among the cluster members. In this study, we propose a table of criteria for evaluating the significance of the task, as shown in the following table.

Table IV.4: Criteria for accessing task significance

Criteria (F)requency (I)mpact (M)ission (C)ommon (R)isk 1 Yearly No Impact Supporting Process Specific No Risk 2 Half-Yearly Low Impact Supporting Strategy Narrow Use Low Risk 3 Quarterly Moderate Impact Main Process Common Moderate Risk 4 Monthly High Impact Main Strategy Wide Use High Risk 5 Daily Very High Impact Core Competency Universal Very High Risk This table, designed for assessing the tasks in the ceramic cluster, was composed of five criteria: frequency, impact, mission, commonality, and risk. These criteria were valued with scores from 1 to 5, meaning that the task with the highest score is the most significant task. The method for allocating these scores can be done by allowing the cluster committee to evaluate each task. Then, the average score will portray the significance of each task. The resulting from assessments are showed as follows:

Table IV.5: Process breakdown worksheet (OM-3)

Organization Process Breakdown Worksheet Model: OM-3 No. Task Performed By Where Knowledge Asset Intensive Significance P-1 Obtain [A-1] Core Cluster Cluster [K-1] Accessing global No (12) information [A-2] Government Level trends F=2; I=3; Agency [K-7] Acquiring about new [A-3] Association support from the M=2; C=2; global trend [A-5] Academic Institute government R=3 [A-7] Downstream Industry [A-8] CDA P-2 Design new [A-1] Core Cluster Factory [K-1] Accessing global Yes (17) product [A-3] Association trends F=2; I=5; [A-5] Academic Institute [K-2] Product development M=2; C=5; [K-8] Ceramic R&D R=3 [K-9] Human Resource Development P-3 Accessing [A-1] Core Cluster Trade Fair [K-1] Accessing global Yes (20) market [A-2] Government and trend F=3; I=5; opportunity Agency Exhibition [K-2] Product M=4; C=4; [A-3] Association development [A-4] Financial Institute [K-3] Accessing new R=4 [A-6] Supporting market opportunities Industry [K-7] Acquiring [A-8] CDA support from the government [K-10] Ceramic Branding P-4 Contact [A-1] Core Cluster Factory [K-4] Customer Yes (11) customer [A-5] Academic Institute Relationship F=5; I=2; [A-7] Downstream Management (CRM) Industry M=1; C=1; R=2 P-5 Product [A-1] Core Cluster Cluster [K-1] Accessing global Yes (15) development [A-3] Association and trends F=3; I=3; [A-4] Financial Institute [K-2] Product [A-5] Academic Institute Factory development M=4; C=2; [A-6] Supporting [K-8] Ceramic R&D R=3 Industry [K-9] Human [A-7] Downstream Resource Industry Development [A-8] CDA P-6 Manufacturing [A-1] Core Cluster Cluster [K-1] Accessing global Yes (18) [A-3] Association and trends F=5; I=3; [A-4] Financial Institute [K-2] Product [A-5] Academic Institute Factory development M=4; C=3; [A-6] Supporting [K-5] Ceramic R=3 Industry Manufacturing [A-8] CDA [K-8] Ceramic R&D [K-9] Human Resource Development P-7 Shipping/ [A-1] Core Cluster Factory [K-6] Logistics and Yes (18) Exporting [A-3] Association Exporting F=4; I=3; [A-4] Financial Institute [A-6] Supporting M=3; C=4; Industry R=4 P-8 After sales [A-1] Core Cluster Factory [K-9] Human Resource Yes (9) service [A-7] Downstream Development F=3; I=3; Industry [K-10] Ceramic Branding M=1; C=1; R=1 P-9 Sales mosaic, [A-1] Core Cluster Cluster [K-7] Acquiring support No (10) B and C grade [A-2] Government from the government F=2; I=3; Agency products [A-3] Association M=3; C=3; [A-4] Financial Institute R=2 [A-8] CDA The two factors considered for assessing the task in OM-3 are knowledge-intenseness and the significance of the task. In this worksheet, each task is matched with corresponding elements: participate who perform tasks, places where task were executed, and the knowledge required for achieving the task. The intensiveness of knowledge is examined whether the task relied on the knowledge or not. In order to clarify this statement, the difference between a knowledge intensive task and non-knowledge intensive task will be described. The significance column shows the scores of significance of the tasks which have consensus among the cluster committee. The range of scoring varies from 5 to 25 points. As an illustration, a task which has the highest score (25) means that this task is performed every day; has a very high impact on the cluster; is the core competency of the industry; can be applied along with others; and carries a high risk if not well managed. In our case study, the most significant task in this ceramic cluster is “accessing the market opportunity ” task. The cluster members agreed that this knowledge-intensive task is critically required for the ceramic cluster under current economic situation. Therefore, the following models will use this task as a case study.

IV.2.2.4. Knowledge assets worksheet (OM-4)

This worksheet focuses on the knowledge elements in the ceramic cluster. It provides an overview of the knowledge in terms of form, place, time, and quality. The main objective of this part is to identify which knowledge assets can be improved in different perspectives such as form, accessibility, time, space, or quality. This analysis is not only significant for knowledge system development but also knowledge management action. The results from analysis of each knowledge asset are presented in table IV.6.

Table IV.6: Knowledge assets worksheet (OM-4)

The table above implies that none of the knowledge assets have complete attributes. Most s assets are in the right place with the right quality, but few of them are in the right form and accessible at the right time. For example, in the case of knowledge assets for ceramic manufacturing, Lampang Ceramic Center was established for supporting manufacturers to solve their production problems. Thus, knowledge users know where to acquire this knowledge when it is needed. The knowledge is also of the right quality, which means that retrieved knowledge is good enough for solving those problems However, most knowledge about ceramic manufacturing is available in tacit form (experts’ experience) and thus accessible whenever it is needed. Thus, this knowledge needs to be improved in terms of form and availability

IV.2.2.5. Feasibility decision worksheet (OM-5)

This worksheet contains checklist for producing the feasibility decision document. It focuses on four dimensions: business aspect, technical aspect, project aspect, and proposed actions. Each aspect aims at analyzing the effect of knowledge management project and organization in different dimensions. For example, business feasibility mainly focuses on costs and benefits of the project to the cluster. The technical feasibility focuses on desired technologies for solving the problem. The project feasibility concerns analyzing project risks in term of time, budget, equipment, commitment, etc. Finally, a set of recommendation is proposed to cluster committee for improving the knowledge management in the ceramic cluster. The proposed solutions from the analysis are presented in the following table.

Table IV.7: Feasibility decision document (OM-5)

Organization Model: Checklist for Feasibility Decision Document OM–5 Business Feasibility 1. The KMS will improve the quality of knowledge sharing and collaboration among the experts and knowledge workers in the cluster. This will improve the competitiveness of the ceramic industries which make products of global standard quality. 2. Sharing knowledge and experience about new market opportunities, global markets and exporting will support SMEs to find new channels for their markets. This will relieve the price war in the domestic market. 3. The proposed system may reinforce collaboration in the ceramic supply chain. 4. The proposed system will create equality of obtaining information in the cluster.

Technical Feasibility 1. The system might change the way of collaboration of cluster members from face-to-face to virtual communication. The communication via system can be stored as a knowledge base for future use. However, some conservative enterprises may not change their ways of communication. 2. The proposed system should not require advanced technology and specifications, as SMEs cannot afford to invest in additional software or hardware for communicating with the cluster. 3. The system must utilize open standard protocol for communications so that large and small enterprises can communicate with the same system. 4. All experts and knowledge users of the system are in the commercial domain. So knowledge should be shared and represented in human comprehensible format and be effortless.

Project Feasibility 1. The project may be interesting and useful for cluster members, but it still lacks commitment from the participants and stakeholders. 2. The project requires low budget and resources to achieve the objective. However, the processes are time consuming. 3. Some knowledge is not available in the cluster. The CDA and KE should acquire the knowledge from external sources to fulfill the requirement. 4. The project is realistic. The effect of the project may impact macro-economical competency of the country in the long run. Members of the cluster expect to use the system to support their activities. 5. The project organization is well communicated at the beginning of the project (requirement phase). 6. The only risk of the project is lack of acceptance by the cluster. The knowledge in the system may not be adequate in the beginning. CDA is a key person to motivate experts to supply their knowledge to the system.

Proposed Action The project should have the following criteria; Focus : Collaboration and knowledge sharing within the cluster Target solution s: 1. Exchange information/knowledge and enhance business network 2. Support the cluster to find new market opportunities 3. Create a place to consult and solve problems together. Expected result s: Members of the cluster have better quality communications; knowledge workers are able to acquire the required knowledge from the system, and the CDA facilitates the cluster by using the KMS as a tool. Risk : The communication via KMS may require changing. The knowledge model may need time to be completed.

The information in this table is a kind of initial investigative report of the knowledge management project which will be used for making decisions by the project owner (cluster committee). Business feasibility analysis showed how the knowledge management project will bring opportunities to the cluster members. It also addressed whether or not the proposed project complies with cluster activities and strategy. Afterwards, the technical feasibility analysis revealed that the proposed knowledge system should exploit the open standard and be easily operated. In addition, the project feasibility concerned the commitment of cluster members and the availability of knowledge. Lastly, the proposed actions were introduced for enhancing the cluster’s situation and noted the criteria required. In summary, the cluster committee has a consensus to initiate the knowledge management project in the ceramic cluster by focusing on 2 points: supporting collaboration and enabling knowledge sharing among cluster members. The knowledge concerning accessing market opportunities will be selected as our case study. In the next model, characteristics of task and knowledge assets will be analyzed in detail.

IV.2.3. Task Model

This part of the methodology focuses on two issues which are tasks and knowledge assets. The TM-1 (task analysis worksheet) mainly focuses on the analysis of the knowledge- intensive tasks which are selected in OM-3 worksheet. Then, TM-2 (knowledge items worksheet) focuses on the knowledge assets which are used for achieving the task. In this study, we have made use of task model worksheets as the outline for interviewing with the experts in each task. The result from the analysis will be presented in the following part. IV.2.3.1. Task analysis worksheet (TM-1)

This worksheet concentrates on the selected tasks in OM-3. Thus, the interviews have been done with the experts of each specific task. The first part of the worksheet aims at acquiring the overview of the task. Then, it decomposes the focused task into subtasks for analyzing the activities. The final part, the agents, knowledge, and resource are examined as additional factors. In this worksheet, the knowledge-intensive task about “accessing market opportunity” is analyzed in details. Results from the analysis are presented in the following table.

Table IV.8: Task analysis worksheet (TM-1)

Task Model: Task Analysis Worksheet TM–1-P3

Task [P-3] Finding Market Opportunity

Organization [A-1] Core Cluster [A-2] Government Agency [A-3] Association [A-4] Financial Institute [A-6] Supporting Industry [A-8] CDA

Goal and Value Goal: Accessing new markets via trade fairs and exhibitions Values: Create opportunities for enterprises to sell their product in the global market. Decrease the pricing war in the domestic market. Motivate enterprises to develop their own competency.

Dependency Input Tasks: None and Flow Output Tasks: None

Objects Handled Input objects: New opportunities such as information about the trade fair Lessons learned from experienced enterprises Information and support from the government Financial support from financial institute Output objects: Decision support for enterprises to achieve at trade fair

Timing and Frequency: About 3-4 times a year. Control

Agents See Organization above

Knowledge and [K-1] Accessing global trends Competence [K-2] Product development [K-3] Accessing new market opportunities [K-7] Acquiring support from the government [K-10] Ceramic Branding

Resources 1. Ceramic cluster website and e-commerce system 2. Mail, E-mail, Fax, Telephone 3. Cluster development agent (CDA) 4. Facilities at Ceramic Center

Quality and Purchase order from new clients after the trade fair. Performance

The expert claimed that supporting accessing new market will bring many advantages to the cluster e.g. create opportunity for enterprise to sales their product in the global market, decrease pricing war in the domestic market, and also motivate enterprise to develop their own competency. We classified the composition of task into particular groups rather than decomposing this task into structured subtasks. The details of this task will be illustrated in the knowledge model section. In the next worksheet, knowledge which is involved with this task will be inspected.

IV.2.3.2. Knowledge item worksheet (TM-2)

This part of the analysis mainly focuses on knowledge items and competence of the task. It constitutes a refinement of the result from OM-4 on the knowledge asset. This worksheet aims at analyzing the bottleneck and improvement relating to specific areas of knowledge which comprises three main parts. These are: nature of knowledge, form of knowledge, and availability of knowledge. It allows knowledge engineer to assess the present situation of each knowledge asset and considers if the knowledge asset need to be improved in specific point. In this study, six knowledge assets involved with the specified task were analyzed in table IV.9. The displayed result was combined from both the knowledge provider and user point of view. The results in the table are considered as a benchmark for designing the knowledge system for improving these knowledge assets. It also implied that the nature of the knowledge asset varies the domain of knowledge. For example, knowledge asset in manufacturing domain (e.g. product development) trend more to be in an explicit form than business domains (e.g. accessing global trends or ceramic branding). In contrast, completeness and accessibility of the knowledge in business domain are better. In terms of availability of the knowledge, cluster members claimed that even though knowledge is in the right form and right quality, accessing the knowledge when and where they need to do so is still limited. This may be affected by two causes, level of trust, and the communication approach. The level of trust in the cluster level is still lower than CoP level. Moreover, the characteristics of the relationships between the members also have a great influence upon the level of trust. Thus, knowledge users experienced that accessing knowledge from another domain or from the experts who were competitors was limited. Another factor that affects knowledge sharing is the communication approach. Cluster members revealed that sometimes they could not access to the required knowledge wherever and whenever they want because of competitor-like relationship. It makes cluster members feel uneasy to acquire/share the knowledge in a direct way. Instead of acquiring the knowledge from the expert directly, they preferred to inquire the knowledge through the CDA who is the cluster facilitator. Although this approach might facilitate knowledge sharing between the competitors in the same cluster, it distorted the quality and also decreased availability of the knowledge. Improvement of this situation is required to achieve a successful knowledge management project for this ceramic cluster.

Table IV.9: Knowledge item worksheet (TM-2)

Task Model: Knowledge Item Worksheet TM-2 ID [K-1] [K-2] [K-3] [K-6] [K-7] [K-10] Name Access global Products New Market Logistic/Export Govern. Ceramic Possessed trend develop . Refer. OM-4 Refer. O~M-4 Support Branding by Refer. OM-4 Refer. OM-4 P: 3 P:7 Refer. OM-4 Refer. OM-4 Used In P: 1,2,3,5 and P: 2,3,5 and 6 Marketing Sales P: 1,3 and 9 P: 3 and 8 Domain 6 Manufacturing Management Marketing Design Nature of the To be To be To be To be To be To be

knowledge Improved Improved Improved Improved Improved Improved Formal, X X X X rigorous Empirical, X X quantitative Heuristic, rule of X thumb Highly specialized, X X X X X domain- specific Experience- X X X X X based Action- X X X X X based Incomplete X X X Uncertain, may be X X X incorrect Quickly X X X changing Hard to X X X X verify Tacit, hard X X to transfer Form of the

knowledge Mind X X X X Paper X X X X X Electronic X X X X X X Action skill X X X Other X X Availability of

the knowledge Limitation X X X X X X in time Limitation X X X X X X in space Limitation X X in access Limitation X in quality Limitation X X X X in form In summary, the results from the models in this level provide a great opportunity to understand the industry cluster context. The analysis has been done from the broadest view and drilled down to the specific views. The investigation was separated into three models.

• The first model (called cluster model) is aimed at identifying the stakeholders (knowledge providers, knowledge users, and decision-makers) in the ceramic cluster context. Besides, it provides a macro view (external and internal force) of the cluster.

• The second analysis aimed at examining the organization model of the cluster. The empirical result from this model composed of cluster’s organizational context, list of knowledge-intensive task, list of required knowledge, and feasibility study report. If the project is feasible, then the next analysis will be initiated.

• The last analysis in this level aimed at examining the task model of the cluster. It focuses on the scope of knowledge-intensive tasks and knowledge assets which are derived from the organization model. The outcome of these models provided us with a clear idea about the required knowledge assets for achieving the task and improvement points of the knowledge. In the next level, the knowledge model and collaboration model of the cluster will be investigated in order to extract the body of knowledge from expert and also the characteristic of knowledge sharing in the cluster. These models are essential for designing the knowledge system which will support the collaboration and knowledge activities in the industry cluster.

IV.3. Concept Level

The concept level is comparable to the core of the proposed methodology. The objective of this level is to extract the knowledge from the experts in the industry cluster and modeled characteristic of information sharing of the cluster members. Therefore, two models (knowledge model and collaboration model) are adopted for supporting the objective. The knowledge model is set for capturing the experts’ knowledge from tacit into explicit form. The captured knowledge will be organized and stored in the knowledge base in order to be used in the knowledge system. In contrast, collaboration model focuses on the flow of information and knowledge within the ceramic cluster. It aims at modeling the communication structure of the cluster. The details of both models will be presented as follows.

IV.3.1. Knowledge Model

The goal of the knowledge model is to extract knowledge from the experts in the ceramic cluster. In order to achieve this, the knowledge elicitation templates which are proposed in the CommonKADS model, were adopted to deal with different type of knowledge intensive tasks in the cluster. In our study, the knowledge modeling was divided into 3 main processes i.e. eliciting, transcribing, and modeling. An example of these processed were illustrated in figure III.11. In order to depict the knowledge modeling process, we have extracted the knowledge of the task “ Finding market opportunity ” [P-03] from the ceramic cluster. Hence, eight knowledge elicitation meetings were organized for interviewing different participants involved in this task. The details of each meeting are displayed in table IV.10. The table described the ID of the meeting, experts who attended the meeting, domain knowledge involed, focused topic, and the obtained knowledge model which is the result of the knowledge modeling process.

Table IV.10: Summary of the knowledge elicitation meetings

Meeting Experts Knowledge Domain Knowledge Topic Obtained ID Knowledge Model M1-T-P03 CeraCluster Committee [K-3] Accessing new Method T-P03 [Core Cluster ] market I-P03-014 I-P03-007 I-P03-010 M2-T-P03 CeraCluster Committee [K-3] Accessing new Lesson learned I-P03-004 [Core Cluster ] market I-P03-020 M3-T-P03 Department of Export [K-7] Acquiring support Acquiring Support I-P03-015 Promotion from government from DEP [Government Agency ] M4-T-P03 Office of Product Value [K-2] Product development Global trend I-P03-021 Development [Government Agency ] M5-T-P03 Lampang Ceramic [K-3] Assessing new Booth decoration I-P03-016 Association market and management I-P03-017 [Association ] M6-T-P03 Export and Import [K-3] Accessing new Acquiring financial I-P03-019 (EXIM) Bank market support [Financial Institute ] M7-T-P03 Market Intelligence [K-3] Accessing new Online marketing I-P03-011 [Supporting Industry ] market and ceramic I-P03-012 [K-10] Ceramic branding branding M8-T-P03 Cera Cluster CDA [K-3] Accessing new Repository and I-P03-005 [Cluster Development market Contacts I-P03-006 Agent ] From the table above, the outcomes of these knowledge elicitation meetings are a set of knowledge maps required for completing the specific task. The first meeting has been done with the ceramic cluster committee in order to acquire an overview of the task: “Accessing to new market opportunity”. The knowledge model of this task is presented in the map ID: T- P03. This map contains task, sub-tasks and inferences which involved with the focused task. Task and sub-task elements are represented by hexagon shape, while the inference elements are represented with the rectangle shape. The knowledge map about “accessing to new market opportunity” (T-P03) is illustrated in the figure below.

Figure IV.5: Knowledge model of accessing to new market task.

This knowledge model is the first model which is obtained from the experts in the cluster committee. The content of the knowledge is about the overview concept of the task (i.e. accessing to new market). The process of the knowledge modeling was explained in the chapter 3. The parent node of the knowledge model is the name of the task. Then, the child nodes in this model imply that the task assessing to new market should concern at least four sub-tasks: international trade fair, domestic trade fair, road show, and soft marketing. Then, the inference elements are connected with these nodes in order to describe the inference concept of each node. The knowledge map in the inference level is displayed in the same concept. The inference element is set as the parent node of the map. Then, the domain knowledge is represented with oval shape and connected to the inference element in order to describe the knowledge about specific concept. An example of the knowledge model of the “product selection for international trade fair” (I-P03-014) is displayed in the following figure.

Figure IV.6: Knowledge model of the product selection concept.

The knowledge models above show the first group of the knowledge models which is extracted from the experts in the ceramic cluster committee by using the planning template. It implies the methods for accessing to the new market of this cluster. The complete knowledge models about this task are displayed in Annex D. In this part, the knowledge assets which involve with the focused knowledge-intensive tasks are modeled into an appropriate format. Actually, there are many possible methods for representing the knowledge (e.g. rule base, semantic map, etc.). Thus, it is a judgment of the knowledge engineer to decide on knowledge representation method that best fit with the context. However, In this study, we represent the knowledge model in form of the semantic map due to it provides many advantages to our knowledge system e.g. readable by human and machine, give better search result, makes it easy to manipulate the knowledge, compatibility with the inference engine, etc. The application of these semantic knowledge maps will be described in chapter 5. The next section will concentrate on the knowledge exchange model of the ceramic cluster. Moreover, the characteristics of collaboration of the cluster members will be analyzed in order to investigate the circumstances of the “ co-opetition ” relationship. IV.3.2. Collaboration Model

The collaboration model of the industry cluster will be analyzed by both qualitative and quantitative methods. The interviews and questionnaires have been applied to members of the industry cluster in order to examine the characteristic and environment of the collaboration. Moreover, the results of analysis will be used to confirm the hypotheses and statements that obtained from experts in the context level. The analysis was separated into 4 parts i.e. expectation and satisfaction of member, activities in the cluster, information and knowledge exchange, and characteristic of collaboration. The outline of the questionnaire can be found in Annex C. We have analyzed questionnaire from 50 enterprises (45 SMEs and 5 large enterprises) in the ceramic cluster, which is about 25% of total registered ceramic company in Lampang province [Untong 05]. The presented information in this part was acquired and analyzed in March 2008. The interviews already taken place with the entrepreneurs or mangers of the enterprises. The results of the analysis are illustrated and described as follow.

IV.3.2.1. Expectation and satisfaction of cluster members

This part of the questionnaire refers to the proposed solutions by the cluster committee which are described in OM-1. The defined solutions are considered as the main activities of the ceramic cluster in order to improve the competitiveness of the ceramic cluster. The analysis aims at evaluating the expectation of the member for participating in the ceramic cluster. Besides, it also focuses on the satisfaction that they gained from being a member of the cluster. The result of the analysis will help us to understand the objective of the collaboration and also indicate the strength and weakness points of collaboration in the cluster, as showed in the following table.

Table IV.11: Expectation and satisfaction of SMEs

The graph above represents the expectations (solid line) and satisfaction (dash line) of the SMEs members in the ceramic cluster. The expectations line shows the degree of benefit that they expected to acquire from being cluster members. The satisfaction line shows the degree of benefit that they are obtaining from the cluster at the present. Then, the degree of satisfaction is the difference between expectation and satisfaction of the members. This graph implies that the SME members of the cluster expected to obtain these benefits from the cluster: improving business network, consulting and solving problems together, increasing opportunities to acquire support from the government, and reducing marketing costs. The results from the graph imply many interesting issues:

• Firstly, the SMEs have high expectation to exchange the information and knowledge between each others in order to solve their problems. However, they feel that the benefits that obtained from the cluster are unable to fulfill their expectation. This disappointment could be explained by referring to the OM-2 worksheet in the organization model. The direct sharing of knowledge and information in the cluster is still at a low level due to the special relationship as collaborator and competitor in the same time. This makes the members feel uneasy to inquire and share knowledge when they are face-to-face.

• Secondly, these enterprises gain the most satisfaction in reducing production cost, because at the beginning, they did not expect to be able to reduce costs by being cluster members. However, they gained many benefits from the collaboration within the cluster, such as aggregating demand to purchase common raw materials, sharing the orders between partners, and learning new techniques to reduce production costs.

• Lastly, reducing marketing costs is the second objective of SMEs participating in the ceramic cluster. They expected to gain opportunities to access new markets such as foreign markets, international trade fairs and ceramic road shows. However, many enterprises claimed that they did not receive such information from the cluster, nor from government agencies. Part of this problem is due to the structure of the organization which explained in OM-2 worksheet that the cluster organization structure is flat. Thus, sometimes the information may not throughout the cluster members. In the global view, the benefits from the ceramic cluster seem to satisfy the SME members. However, if we focus on the heights expectations from the cluster, we find that members still require better information sharing in order to solve their problems, and also better collaboration to access to the opportunities available from their membership, which are the potential success factors of the cluster. In contrast, if we analyze the expectation and satisfaction levels of the large enterprises in the ceramic cluster, we find that these enterprises are focusing on different benefits from the SMEs in some aspects. The expectation and satisfaction lines are presented in table IV.12.

Table IV.12: Expectation and satisfaction of large enterprises

Although large enterprises are in the minority in the Lampang ceramic cluster in term of quantity, they are the pillars of the cluster. The graph above implies that some objectives of the large enterprises have commonalities with those of SMEs, but some objectives are different. Compared with the SMEs’ expectation, these enterprises focus less on marketing but more on human resource development and improving the innovation of products and services. This part of the questionnaire gave us a better understanding about the objectives of the cluster members in their participation. It also indicates the gap between expectations and satisfaction of the members which will help the knowledge engineer to select the suitable tools to be integrated in the KMS. In the next part, these objectives will be divided into the activity level in order to analyze the degree of collaboration and the impact of each activity on the collaboration of the cluster.

IV.3.2.2. Activities in the cluster

This part focuses on the activities already carried out by the members of the cluster. The main objective of this part is to validate the information from the cluster committee about the joint activities carried out to improve Lampang’s ceramic cluster. Moreover, the questionnaire aims to evaluate the percentage of participation and the impact of each activity on the ceramic cluster. The questionnaire is composed of 14 main activities of the ceramic cluster that were declared in the OM-1 worksheet, as shown in table IV.13.

Table IV.13: Degree of participation and impact of activities on the cluster

The table above presents the percentage of participation of the members in each activity which implies the degree of collaboration of the activity. It also represents the impact of the activity to the cluster development from the members’ point of view. From the result in the table above, we can classify these activities into 4 categories as shown in table IV.14.

Table IV.14: Four groups of activities in the ceramic cluster

The first group is the activity which has high impact and a high degree of collaboration in the ceramic cluster. The activities in this group are used as the main actions to develop the ceramic cluster. These activities confirmed the results from expectations and satisfaction of the SMEs in part II of the questionnaire. Thus, this study will consider these types of activities as the core activities of the industry cluster. The second group is the activity which has a high impact on cluster development but still has a low degree of collaboration from the members. These activities required improvements by the cluster committee or CDA. Comparing with the results in the part IV.3.2.1, improving the collaboration in these activities will increase the satisfaction of cluster members. We will also take these activities into account as a part of the requirements for the knowledge system. The third group is the activity which has low impact but a high degree of collaboration. These activities have usually been done in order to improve the collaboration of the cluster. They may not give direct impact to the core business of the factory, but may help to sustain the collaboration of the cluster and will indirectly impact other activities. The forth group is the activity which has low impact and a low degree of collaboration. These activities have been done by the small group of companies in the cluster. Although some activities will give direct impact on companies, there are many specific constraints in order to achieve the goal of collaboration. Thus, these activities are not considered as important activities for developing the ceramic cluster. Hence, the proposed knowledge system for SMEs cluster will adopt the activities in the first and second groups as hard requirements and the activities in the third group as the soft requirements of the system. The analysis in parts I and II of the questionnaire implied the characteristic of collaboration in the Lampang ceramic cluster in terms of activity. This information will support the knowledge engineer in the design level of the proposed methodology, which will be explained at the end of this chapter. IV.3.2.3. Willingness to share information

The objective of this part is to comprehend the information and knowledge sharing model of the cluster, which will help us in the designing the collaboration service of the KMS. This part attempts to answer the questions about what kinds of knowledge are companies are willing to share in the cluster and what are the conditions of sharing, which is one of our research questions. This part of the questionnaire will help us to examine the willingness to share information and knowledge within the cluster. From the in-depth interviews, we realized that the cluster tends to share more complex knowledge within the cluster than outside. Thus, we designed the questionnaire by using the taxonomy of knowledge which is defined in chapter 2 (see more detail in table II.3). The level of networking is also concerns as the conditions of sharing i.e. anonymous level (sharing to outside the cluster), cluster level (sharing to support cluster) and core cluster level (sharing to core cluster member). The results of analysis are represented in the following table.

Table IV.15: Willingness to share information among cluster members

The results from analysis confirm our hypothesis that cluster members are willing to share their different types of knowledge in the different levels of the networking. The condition of knowledge sharing in this cluster is the “ level of trust ” which is mentioned in OM-2 worksheet. The level of trust in the network tends to have an effect on the complexity of shared knowledge. From the table, we can see that all members are willing to share know- who knowledge (which is the least complex knowledge) to anyone inside and outside the cluster. However, about one-fourth of members seem to be unwilling to share more complex knowledge (know-when, know-where and know-what) to anonymous outside the cluster. These types of knowledge concerned the opportunities in the ceramic industry such as where to get them, when to access them and the repository of the ceramic industry. The last groups of knowledge i.e. know-how, know-with and know-why tend to be shared primarily in the core cluster level and some in the cluster level. This group of knowledge concerns problem solving knowledge in the ceramic cluster. In addition, members of the cluster claimed that some of these types of knowledge which involved proprietary knowledge (which may be called ‘business secrets’) may not be able to be shared even in the core cluster level, because they could affect the core competency of the enterprise. One example given was a list of clients of the enterprise. Although the list of clients is know-who knowledge which is claimed can be shared to anonymous parties, this knowledge creates competitiveness in the enterprise. However, they can be shared under very specific conditions, such as exchanging proprietary knowledge between the strategic partners in the same supply chain, or sharing business secrets such as financial data to banks or government agencies. Thus, this study will consider that this level of knowledge can be shared under specific conditions, and the topic will not be taken into account in the KMS. From the results of the analysis, we proposed the structure of knowledge sharing in the ceramic cluster called “info-structure”. The info-structure comprise four levels of information: contact information (global level), opportunity (cluster level), problem solving (CoP level) and business secrets (company level). These levels involved with different types of knowledge can be represented in figure IV.7.

Figure IV.7: Info-structure of knowledge sharing in the industry cluster

This info-structure will be considered in our KMS in terms of characteristics of shared information in the cluster. Moreover, this could be used for designing authentication level of users for accessing knowledge/information in each level of the system. This will assure the experts that their knowledge will be stored in the right place for the right user. In the next part, we will analyze present situation of collaboration of the ceramic cluster. IV.3.2.4. Characteristics of collaboration in the industry cluster

This part aims at analyzing the present situation of collaboration of the ceramic cluster. The criteria for analyzing are adapted from 20 success factors of collaboration [Bruce 07]. These factors are separated into 6 groups: environment, membership characteristic, process and structure, communication, purpose, and resource. It also indicates whether the characteristic of collaboration in the cluster is suitable and sustainable or not. The results of the analysis imply the present situation of collaboration in Lampang ceramic cluster which are showed as follows.

Table IV.16: Characteristic of collaboration in the ceramic cluster

The result from the table above implies that the members of this cluster agreed that the collaboration of the cluster is in good condition. However, we could see some weak points which will be described as follows:

• The members agree that this collaboration is quite new to this industry. The average duration of membership in this cluster is only 3.23 years. This might affect some activities of the cluster in terms of experience. However, they do not feel that this factor will affect their collaboration in the long term. • Another weak point is the lack of clear roles and policy guidelines of the cluster. Most members are still confused about the future roles and guidelines of the cluster. Most activities within the cluster are initiated by local or central government. In addition, this cluster is in the newly developing stage. The sharing of vision and information from the cluster committee to members is absolutely vital.

• The weak point in terms of communication within the cluster is the lack of open and frequent communication. This disadvantage has a consensus of opinion among members that the collaboration needs to be more open and more frequent. At current situation, the Lampang ceramic cluster organizes official monthly meetings with the cluster committee. However, only 15-20 members participate in the meeting. The members who did not attend the cluster meeting would receive information from the cluster committee from time to time. Improving this flaw may also amend the weak point about the lack of clear vision mentioned earlier.

• The last disadvantage shown by the analysis is the lack of sufficient funds, staff, material, and time to collaborate in the cluster. The benefits from collaboration in the cluster may not be promptly financial, but are actually opportunities to develop the businesses. Also, it should be noted that there is no permanent support from any government agency for the collaboration. Government support was given to the project; however, cluster members agree that the unstable nature of the support from government may be the cause of unsustainable collaboration. In conclusion, we could see that the environmental factors are suitable for the collaboration. Most of the members agreed to develop the collaboration of the cluster. The process and structure of the cluster are flexible and support the development. Also, the members have a consensus on the vision and purpose of the collaboration. However, there are two main points that require enhancement to create sustainable collaboration in the cluster: communication in the cluster and support in terms of resources from the government agencies and enterprises. Thus, this study will address the problem of communication among the members by proposing KMS to assist the cluster to have a better quality of communications among the member. IV.4. Design Level

This level is a transition phase between the knowledge engineer who performed context and concept level, and the knowledge system developer who performs the implementation level. The main objective of this part is to convey the exact information from previous levels to the implementation level in format of system requirements and specifications to the knowledge system developer. Moreover, these requirements and specifications are comparable to the protocol between the knowledge engineer and system developer. Thus, we have enlarged the CommonKADS design model by adopting the theory of software engineering in order to clarify the design model. Software engineering is always be used for transforming organizational requirements into software specifications and managing the software development project. For this reason, the design level in our model was composed of three parts: system architecture, scenario and specification. Each part aims at explicating the requirements of the ceramic cluster from different point of views which will be described in this section. Although the order of the processes is not significant, we recommend starting from the global view of the system (i.e. system architecture) to more specific view (i.e. system specification).

IV.4.1. System Architecture

From the review of the KMS architectures in chapter 2, the proposed KMS architecture for the industry cluster was adapted from three-tier KMS architecture [Chua 04] which identified three distinct services supported by knowledge management technologies: knowledge , collaboration and presentation . Each service is designed for solving particular problems in the industry cluster. In this part, we will specify the system architecture in the functional point of view. The consensus among the cluster members and knowledge engineer is essential in this model. Lack of agreement over the system architecture may leads to incorrect system specification. In our case study, the consensus of the ceramic cluster committee, CDA and the knowledge engineer is achieved. The proposed KMS architecture for the ceramic cluster was illustrated in the following figure.

Figure IV.8: The functional view of the KMS architecture

In the KMS architecture, the knowledge service aims at supporting knowledge creation, sharing and reusing. These activities were facilitated by the concept of Collaborative Knowledge Card (CK-Card) which will be discussed in detail in the next chapter. This technology is proposed and developed in this research so members could mutually manage the knowledge of the cluster.

• The collaboration service aims at assisting the communication and information storing of the cluster. The collaborative technologies which support this service are a kind of information and communication technology over the internet. The simplest examples of the collaborative technologies are live chat and discussion board. However, selecting the appropriate technology should cover many issues:

− the type of knowledge to be supported,

− cluster’s activities to be supported,

− organizational context,

− IT infrastructure,

− basic knowledge of the members on particular technology, etc. Neglecting these issues could lead to failure in integrating the knowledge system with the cluster [Malhotra 04]. From the proposed architecture, various types of collaborative technologies are selected for supporting the sharing of different types of knowledge. For examples, the cluster map which is a kind of ‘cluster’s address book’, is proposed for sharing the knowledge about know-who. The Push/Pull News and collaborative calendar (c-calendar) are considered as tools for sharing the knowledge about know-where and know-when . Whereas live chat, Voice over Internet Protocol (VoIP) and video conference are integrated to support the communication among the cluster for exchanging more complex types of knowledge.

• Finally, the presentation service aims at personalizing and visualizing the amount of information and knowledge on the knowledge system to suit each knowledge user. In this study, we proposed the widget - which is a small client-side application - as a tool for customizing the user’s view. This widget also allows knowledge users to acquiring the collaboration and knowledge services directly from their desktop. In practice, the architecture model is very important to the KMS development project because it provides a common-view among the members of the cluster who are not IT professionals, and the knowledge engineer. The functional view of system architecture shows the available services which are the benefits that they would obtain from the system. Moreover, the services and technologies provided could be considered as the outline for defining the system specification. In this part, we gave a general idea about the KMS architecture and their functions. However, the details of each function will be described in detail in the next chapter.

IV.4.2. System Scenario

In order to create a scenario model for the knowledge system, the 4+1 model [Kruchten 95] is adopted for describing the scenario model of the system. The system scenario aims at illustrating the interaction between objects and processes in the ceramic cluster. As we depicted about the 4+1 model in chapter 3, four views (i.e. logical, development, process, and physical view) are required for designing the fifth view which is scenario view. These views were acquired from the system architecture and cluster interviews in the previous levels. In this chapter, we will present an example of scenario model with the UML use case diagram and sequence diagram. The complete system scenario model of the ceramic cluster can be found in Annex E. The UML use case diagram was generated from the requirement of the system architecture. It presents a graphical overview of the functionality provided by the system in terms of the actors, their goals (represented as use cases ) and dependencies between those use cases. The use case diagram illustrated the actors (e.g. CDA, core cluster, support cluster, etc.) and their roles in the KMS.

Figure IV.9: Use case diagram of the KMS and cluster members

The use case diagram above implies that there are four types of participants in the knowledge management system, the CDA, support cluster, core cluster and anonymous user. The goal of each type of user is different.

• The CDA acts as the administrator of the system. The roles of the CDA to the system are facilitating the virtual collaboration of the system, providing opportunities to members, and distributing information to all users.

• The core cluster is composed of the experts and knowledge users. This group of users is the primary actor of the KMS. It is allowed to access most of services on the system except the administration module.

• The support cluster is the knowledge provider, a representative of a government agency, financial and academic institution, supporting industry and/or association. The relationship among these participants is generalization. Thus, they have the same roles in the system i.e. sharing their knowledge, collaborating with the core cluster, and providing opportunities from their part to the ceramic cluster.

• The anonymous user is the participant who is not a stakeholder of the ceramic cluster, but is interested in the information from the cluster. Regarding the security of the system, an anonymous user could acquire some types of knowledge from system (i.e. know-who, know-where and know-when), but the domain knowledge may be prohibited. The extended use cases represent the function of the knowledge system that the participants could perform. The sequence diagram extends the requirement of the system in terms of interaction among the actors. It shows how the processes operate with one another and in what order. This diagram was used to model the message, information or knowledge that exchanged from one actor to another in the system. In this part, we will demonstrate the sequence diagram for knowledge sharing in the ceramic cluster by using a case study of a ceramic trade fair , as shown in figure IV.10. From our initial investigation into the knowledge sharing in the ceramic cluster, we found that there is no explicit system for sharing the knowledge in the cluster. Moreover, the procedure of knowledge acquisition is also vague. There is only a meeting of cluster members before the ceramic trade fair in order to prepare for the trading. Knowledge about the trade fair from the experienced members was shared to define the strategy of the exposition. However, the shared knowledge came from the memories of the experts and was mostly incomplete. Moreover, the members of the cluster admitted that the meeting after the trade fair was frequently neglected. Thus, this sequence diagram shows the scenario of the knowledge sharing in the cluster via the KMS.

Figure IV.10: Sequence diagram of knowledge sharing activities

The sequence diagram above was divided into three periods: before the trade fair, after it, and next the trade fair. Assume that there is no knowledge about the ceramic trade fair in the knowledge system. The process was initiated by the CDA sending a request to the KMS for inviting members to share knowledge about “ trade fair ” via KMS. The invitation message will be sent to all members in the ceramic cluster by the system. Then, the CDA receives a message from the KMS to inform that the invitation is completed. As soon as the members have read the message from CDA, the experts or experienced members could access to the KMS via the link provided in the message. They can jointly create the knowledge card about the ceramic trade fair, similar to the wiki concept. Hence, a new knowledge card about the ceramic trade fair was created and ready to be acquired by the knowledge users interested in contributing to the ceramic trade fair in the future. After the ceramic trade fair, the CDA sent another invitation to the cluster members who participated in the exhibition to share their experience and knowledge about it. On this occasion, the experienced members could share their knowledge, different points of view and lessons learned warnings, best practice, etc. This new knowledge will be appended in order to enlarge the knowledge of the ceramic trade fair. These knowledge maps will be stored in the KMS and await retrieval by the knowledge users in the next trade fair. At this point, we already have architecture and the views of the system which is a kind of system requirements from the ceramic cluster. However, these requirements are in the format of diagrams, which are not precise and unsuitable for the system development. Therefore, the next section will address the transformation of these diagrams into the explicit form to reduce ambiguity.

IV.4.3. System Specification

Creating the system specification is the fundamental of software engineering theory, known as System Requirement Specification (SRS). It is not only the medium communication between the system designer and system developer but also the guideline for software development project. In this chapter, we will present a these specification in brief. The full details of system specification could be found in Annex C. The SRS approach comprises five specification documents: requirement, function, design, system and test. The Requirement Specification (RS) of our knowledge system is elicited from the analysis from the previous models and the proposed system architecture. The table below briefly presents a list of requirements for developing the KMS for a ceramic cluster.

Table IV.17: Requirement specification

RS-1 Support Knowledge Creation RS-1.1 Provide opportunity (Refer to FS-1.2 and FS-5.3) RS-1.2 Create knowledge card (Refer to FS-2.1) RS-1.3 Add contact (Refer to FS-1.1) RS-2 Support Knowledge Sharing RS-2.1 Push/Pull news system (Refer to FS-4) RS-2.2 Collaborative calendar system (Refer to FS-5.1 and FS-5.2) RS-2.3 Display collaborative knowledge card (Refer to FS-2.1 and FS-2.2) RS-2.4 Display cluster map (Refer to FS-3.1) RS-3 Support Knowledge Reuse RS-3.1 Search opportunity (Refer to FS-4, FS-5.1 and FS-5.2) RS-3.2 Search knowledge card (Refer to FS-2.2 and FS-10) RS-3.3 Search contact (Refer to FS-3.2) RS-4 Support information storage (Refer to FS-1) RS-5 Support Communication RS-5.1 Text communication (Refer to FS-6) RS-5.2 Voice and video communication (Refer to FS-7) RS-5.3 Support mobile device integration (Refer to FS-8) RS-6 Support Users’ Personalization (Refer to FS-9.1, FS-9.2 and FS-9.3) RS-7 Support Users’ Visualization (Refer to FS-9.4 and FS-9.5) PS-8 Support Knowledge System Management (Refer to FS-1) Functional Specification (FS) describes the needs by the system such as technique, material or service, which is referred by the requirement specification. This type of specification helps avoid duplication and inconsistencies, allow for accurate estimates of necessary work and resources. Moreover, it also helps for verifying that all the hard requirements of the organization are supported in the system. The verification can be done by linking the requirements from table IV.17 to the functional specification, as shown in table IV.18.

Table IV.18: Functional specification

FS -1 Administrative Functions FS-1.1 Manage User: Cluster map manipulation FS-1.2 Manage News: Push/Pull information FS-1.3 Manage Opportunity: Collaborative calendar FS-1.4 Manage Virtual Collaboration: Collaborative platform FS-2 Collaborative Knowledge Card Functions : FS-2.1 Collaborative Knowledge Card FS-2.2 Knowledge Map Search FS-3 Cluster Map Functions: Collaborative platform FS-3.1 Cluster Map Visualization FS-3.2 Cluster Map Visualization FS-4 Push/Pull News Functions: Display news and events from the news database FS-5 Collaborative Calendar Functions: Display list of events from c-calendar FS-5.1 Display Week calendar FS-5.2 Display Month Calendar: display on the calendar page FS-5.3 Add New Event to Calendar FS-6 Live Chat Functions: Allow user to send text message to online users FS-7 Video Conference Functions: Allow user to create virtual meeting room FS-7.1 Broadcast audio to online users FS-7.2 Broadcast video to online users FS-7.3 Display online users in the conference room FS-8 Voice over Internet Protocol (VoIP) Functions: Allow us to communicate to mobile devices FS-8.1 Web-based VoIP Phone FS-8.2 VoIP Server FS-9 Widget Functions: Allow user to personalize information from the KMS FS-9.1 Customizable knowledge map search FS-9.2 Customizable news view FS-9.3 Customizable calendar view FS-9.4 Customizable cluster map view FS-9.5 Customizable collaborative system FS-10 Advanced Search Functions: Allow user to search over the collaborative knowledge card system FS-10.1 Knowledge card search FS-10.2 Wiki content search FS-10.3 Forward inference search FS-10.4 Backward inference search Design Specification (DS) shows the characteristic of the KMS structure. It aims at describing the detailed design of the system architecture. This type of specification also implies the level of access and features of the system that each level of user can manipulate. Each feature of the system should be referred to the requirement and function specifications in order to justify the purpose and technique used behind the feature. Our design specification was separated into 4 levels regarding the info-structure that was proposed in the collaboration model. The descriptions of features and screens of each level of user are shown in the table IV.19.

Table IV.19: Design specification

DS-1 Area Level 0: Global Level DS-1.1 Top Menu Frame · Home, Cluster Map, Calendar DS-1.2 Main Content Frame · Portal (Refer to RS-2:FS-3) · Cluster Map (Refer to RS-2:FS-3) · Calendar (Refer to RS-2:FS-5) DS-2 Area Level 1: Cluster Level DS-2.1 Top Menu Frame · Home, Cluster Map, Calendar, Knowledge Card, Advanced Search DS-2.2 Main Content Frame (Extended from DS-1.2) · Knowledge Card (Refer to RS-1.2:FS-2) · Advance Search (Refer to RS-3.2:FS-10) DS-3 Area Level 2: CoP Level DS-3.1 Top Menu Frame · Home, Cluster Map, Calendar, Knowledge Card, Advanced Search DS-3.2 Main Content Frame (Extended from DS-2.2) · CoP Knowledge Card (Refer to RS-2.3:FS-2) DS-3.3 Widget · Search (Refer to RS-6 and RS-7:FS-9.1) · News (Refer to RS-6:FS-9.2) · Events (Refer to RS-6:FS-9.3) · Knowledge Card (Refer to RS-7:FS2) · Collaborative System (Refer to RS-5.1 and RS-5.2:FS-7 and FS-8) · VoIP Phone (Refer to RS-5.3:FS-8) DS-4 Area Level 3: Administrator Level DS-4.1 Top Menu Frame · Home, Cluster Map, Calendar, Knowledge Card, Advanced Search, Administrator Control Panel DS-4.2 Main Content Frame (Extended from DS-3.2) · News Management (Refer to RS-1.1:FS-1.2) · Subscriber Management (Refer to RS-1.1:FS-1.2) · Cluster Map Management (Refer to RS-1.3:FS-1.1) · Collaborative Platform Management (Refer to RS-8:FS-1.4) System Specification (SS) is the software, hardware, or resource requirements for implementing the developing system. It defines two sets of system requirement: recommendation and minimum. For our KMS, the system specification concerns server and client specifications. The system specification is shown in table IV.20.

Table IV.20: System specification

SS -1 Hardware Specification SS-1.1 Server Specification · Processor: Intel Pentium4 3.0 GHz minimum · Memory (RAM): 2048 MB recommended · Etc. SS-1.2 Client Specification · Processor: Intel Pentium4 1.0 GHz minimum · Memory (RAM): 512 MB recommended SS-3 Software Specification SS-3.1 Server Specification SS-3.1.1 KMS Server ( Linux distribution Debian) · Apache Web Server · MySQL · Red5 flash server SS-3.1.2 VoIP Server (Linux distribution Debian) · Asterisk 1.4.0 or compatible · Festival TTS · Mbrola TTS Voice Pack SS-3.2 Client Specification · Microsoft Internet Explorer 6.0 or Mozilla Firefox 3.0 · Adobe Flash Player 9.0 plug-in · Adobe AIR Installer Test Specification (TS) provides the detailed summary of what scenarios will be tested and how they will be tested for a given feature. Actually, there are several ways for testing the system. In this study, we propose three types of test specifications which are Demonstration Test (DT), Function Test (FT) and Operational Test (OT). The demonstration test aims at testing the system in each view of users who manipulate the system. The functional test intends to verify the correction of each function. Lastly, operational test is about assessment the knowledge system in the scenario view. The table V.21 shows an outline of functional test specification for our case study. The complete test specification can be found in Annex E.

Table IV.21: Functional test specification

Issue Proper Fair Poor Comment FT -1 Administrative Functions … FT -2 Collaborative Knowledge Card Functions: … FT -3 Cluster Map Fu nctions: … FT -4 Push/Pull News Functions … FT -5 Collaborative Calendar Functions: … FT -6 Live Chat Functions: … FT -7 Video Conference Functions … FT -8 Voice over Internet Protocol (VoIP) Functions … FT -9 Widget Functions … FT -10 Advanced Search Functions … From this point, all the requirements from the ceramic cluster were explicated and transformed into the system software specification and made ready for the knowledge system development. The results from this level would be handed over to the system developer in order to continue the system development and implementation process. The KMS for the ceramic cluster which is an outcome of the implementation level will be discussed in the next

chapter.

IV.5. Conclusion

In summary, this chapter shows the obtained results from applying the model suite to Lampang’s ceramic cluster in Thailand. It concentrates mainly on the first three levels of the model. The results from the context level are composed of three models: cluster, organization, and task model. The cluster model implies the physical network of the ceramic cluster. The organization model reveals the present organizational context of the ceramic cluster e.g. level of trust, structure, and knowledge sharing approach. And, the task model focuses on selecting the knowledge-intensive tasks and knowledge assets for our case study. In this level, we selected the “ finding market opportunity ” and its knowledge required as the instance. Then, the concept level is composed of two models (i.e. knowledge and collaboration model). The knowledge model aims at extracting the expert’s knowledge and represent in the semantic knowledge map. The collaboration model concentrates on the approach for exchanging the information within the cluster. Finally, the design level aims at interpreting the requirements from previous levels into system architecture, scenarios, and specifications. The outcome from this chapter is the specification documents for developing the KMS for the ceramic cluster. These documents are also considered as the medium between knowledge engineer and system developer. Chapter V: Application and Scenarios

V.1. Introduction

In this chapter we aim at developing the Knowledge Management System for the ceramic cluster regarding the requirements and specification which are obtained from the processes in chapter 4. The organization of this chapter will follow the provided services in the system architecture: collaboration, knowledge, and presentation. In the first part of this chapter, the complete system architecture will be presented in order to provide the integrated view of the system in terms of services and inter-connection between each service. After that, the system architecture will be broken down into class level of each function. Particular functions that support the service will be depicted by database diagram, class diagram, and also the user interface. Finally, the application of the KMS will be demonstrated by using different scenarios of ceramic cluster activities. Lastly, we demonstrate three scenarios which present the interaction between KMS and the activity of the cluster. The first scenario is based on collaborative activity of the members. It demonstrates how the cluster committee organizes a meeting via the virtual conference module. It also proves that three modes of communication: individual, group, and mass communication were supported in the KMS. The second scenario focuses on the knowledge management process. It shows how experts in the cluster are able to represent and share their knowledge via the Collaborative-Knowledge Card module. Then, the inference engine was used for retrieving the knowledge from the system semantically. The last scenario presents the integration of collaboration and knowledge service together. The result of the assimilation allows knowledge users to retrieve knowledge from KMS anywhere and anytime via their mobile device. V.2. The proposed KMS architecture

In this section, the system architecture of the KMS for the ceramic cluster will be illustrated in order to visualize the subsystem, functions, services, tools, and interface between each element in the system. The proposed system architecture in the design level of the framework will be ameliorated in order to present functionality of the service. The functional view of the system is illustrated in figure V.1. Furthermore, the connection aspect of system architecture was designed in order to demonstrate the interface between the modules in the system. The system interface diagram shows the principle modules of the system with required protocols for interfacing with each specific module. The proposed system architecture was designed on the web services concept which supports the interoperability between machine-to-machine interactions over a network. This concept is quite useful to our knowledge system in order to retrieve the information from another web service server and present it to the cluster member. For example, the collaborative calendar service in the knowledge system exchanges information with Yahoo’s server by using iCalendar standard (RFC 2445). Another advantage of the web service is that the cluster members do not need to invest on new information system infrastructure.

Figure V.1: KMS system architecture for the ceramic cluster

The architecture above shows the proposed functions which support the activities in each service. The knowledge service level is fundamental to the knowledge system. It aims at supporting the knowledge activities of the ceramic cluster: knowledge creation, representation, sharing, and retrieval. This service was supported by two modules: knowledge card module and ontology search module. The second level is a collaboration service which focuses on improving the collaboration of the ceramic cluster. This service is divided into two levels: communication and collaboration. The communication level (light green) comprises the VoIP module and virtual conference module. These modules aim at enhancing the quality and facilitating communication among the cluster members. The collaboration level (dark green) includes cluster map, push/pull news and collaborative calendar. These modules focus on the collaborative tools which support the critical activity of the ceramic cluster. The details of each tool will be described in the next section. The top level introduces the presentation services. The objective of this level is delivering information and knowledge from the knowledge system to the knowledge users at the right place, time, format and quality. Moreover, it allows the users to personalize and visualize the knowledge to suit their preferences. The presentation service comprises two modules: the KMS portal and the cluster widget. In order to gain better comprehension about the system architecture, the system interface diagram is illustrated in figure V.2. Figure V.2: The interface model of the system architecture

The interface model demonstrates the inter-connection between each module in our KMS. The left-hand side of vertical dashed line denotes the server side, while the right-hand side is the client machine. The fundamental of the system is composed of three types of server: KMS, collaborative, and VoIP.

Server Side

The KMS server is the centre of the system. It includes knowledge services, collaborative functions, and KMS portal of presentation services. The principal databases of the system are located within this server. MySQL database was selected for storing data of the system. The SQL (Structured Query Language) and XML (eXtensible Markup Language) are the major approaches which are used for retrieving data from the database. The collaboration module handles the information sharing of the cluster members. Moreover, it also takes care of the connection between users and collaborative server via the protocol RTMFP (Real Time Media Flow Protocol). Then, the knowledge card module was built to support knowledge activities of the cluster. The data structure of this module is based on XML format to ease exchanging between different platforms. The KMS Portal was developed on PHP 5 and AJAX (Asynchronous JavaScript and XML) technology which permits the cluster member to browse and access all the provided services on the KMS via client web browser via HTTP (HyperText Transfer Protocol). Furthermore, this module facilitates the access to the VoIP server by encapsulating HTTP within RTMP (Real Time Messaging Protocol). The protocol encapsulation will be explained in VoIP function in the next section. The collaborative server is implemented with the intention of enhancing the quality of collaboration of the industry cluster. This server allows users to collaborate via virtual conferencing. This means that members are able to send/receive text, voice, and video to each other in real time. In order to achieve this goal, this study adopted the AFCS platform which is short for Adobe Flash Collaboration Service. The AFCS is a platform-as-a-service [AFCS 09] that allows systems to add real-time social capabilities to the Rich Internet Application (RIAs). The examples of available services on this platform are roster, chat, webcam, etc. The features and architecture of this plat form will be depicted in the virtual conference section in this chapter. Another service that aims to ameliorate the communication of the members is VoIP (Voice over Internet Protocol). This service enables members to make a phone call (deliver voice) over IP network without any additional cost. In our study, we have integrated Asterisk® which is an open source telephone engine that allows us to establish VoIP service in the KMS server. It also provides many benefits to cluster members and their businesses. A user could call other cluster members who are connecting with KMS server without any telephone costs. Furthermore, it can be used for calling customers in long distance with lower rate than landline telephone. From the diagram in figure V.2, it is implied that there are three methods to communicate with VoIP server. The common communication protocol for VoIP service is Session Initiation Protocol (SIP). SIP provides us an approach to transmit voice and video over the internet. It seems that more and more mobile phones support this protocol which provides an alternative way for users rather than GSM service providers. The second method is connecting from ordinary Public Switched Telephone Network (PSTN). This type of connection requires special hardware to interface with normal telephone lines. However, this connection allows users to make a call from VoIP phone to ordinary telephone sets. Lastly, the alternative way is connecting via the web service that provided in the KMS portal. Cluster members can access the VoIP service without installing any software in their machines. Therefore, the RTMP protocol encapsulation, mentioned in the previous paragraph, is required. Client Side

On the client side, users have two ways to communicate with the KMS server. The first approach is accessing via web browser in their machine. While cluster members logged in to the KMS portal, KMS server will handle the connection to all provided services. The requested services will be encapsulated in HTTP and sent back to the user. An alternative approach is accessing via the knowledge widget. The knowledge widget is a small desktop application which was developed in our study. The widget provides the Graphic User Interface (GUI) look which allows cluster members to personalize their information, and also access all KMS services from one application without protocol encapsulation. The details of knowledge widget for industry cluster will be described in the widget section in this chapter.

V.3. Knowledge Management services

This level of the service is the fundamental of the knowledge system, which aims at facilitating knowledge management activities of the industry cluster (refer to our proposed system architecture; the main knowledge services that are supported in the knowledge system are knowledge creation, representation, sharing, and reusing). These activities are considered as the heart of the knowledge management. Actually, the most important objective of these services is to transfer the required knowledge from the experts to the knowledge users at the right place and on the right time. In order to improve these activities, the role of KMS and technology which support the activity were considered.

• Knowledge representation may be called by different names such as organizing, customizing or codifying knowledge. The objective of this process is representing the knowledge to suit the knowledge selection. In the past, representing the knowledge was done by writing, drawing or coding the knowledge into a codified format (e.g. procedure). Nowadays, thanks to the emergence of multimedia and information technology, knowledge can be represented in various forms and formats such as sound, picture, video, concept map, 3-D model, database, etc. These technologies make it possible for experts to articulate their tacit knowledge into explicit knowledge, which has been almost impossible in the past. • Knowledge sharing (also known as knowledge transferring) is disseminating personal knowledge in the explicit form over the organization through a specific medium. This process is a core of the KMS which allows knowledge users in the industry cluster to acquire the required knowledge from the experts via the KMS.

• Knowledge utilization is integrating the knowledge from the KMS to the organization. In the knowledge engineering domain, this process refers to providing the right knowledge to the right person at the right time, right place and right format. Thus, knowledge storage and retrieval are considered as a key technology that supports the knowledge workers to complete their tasks. In this study, the mobile service was proposed to decrease the limitations in terms of place and time. It allows the knowledge user to acquire the knowledge from the knowledge-base in KMS via mobile technology such as telephone, soft-phone or web services. The details of mobile technology will be explained in the following section. In order to combine these services into our knowledge system, two major modules are integrated into the system: knowledge card [Buzon 03] and knowledge search module. The knowledge card module supports knowledge representation and the knowledge sharing activity of the cluster members. It allows the cluster’s experts to express their knowledge in different modes such as text content, semantic map, audio and video stream, etc. Moreover, it also allows experts in the same Community of Practice (CoP) to jointly share knowledge on the same task. The explicated knowledge is stored in the knowledge system in the knowledge card format. The details of the knowledge card will be described in the next section. Then, the knowledge search module will be used to retrieve the knowledge from the knowledge system. This module allows knowledge workers to reuse the knowledge which has been accumulated within the knowledge system whenever they need to do so. Therefore, this section will focus on both modules. An overview of the components of knowledge services is illustrated by a UML diagram, as shown in figure V.3.

Figure V.3: The components of knowledge services

The diagram above implies that knowledge services are composed of two main modules. The first module is the knowledge card which contains 4 components: metadata, knowledge map, wiki, and links. The metadata component provides the card’s name and metadata of the knowledge card. The knowledge map provides the knowledge model that was derived from the knowledge elicitation process. The wiki component allows experts to describe their knowledge model in descriptive approach. The links component shows the links of specified knowledge to the relative cards. The second module is the search module. This module allows the knowledge worker to browse through the knowledge card via two methods, wiki search and ontology search. The details of these search methods will be described in the knowledge search section.

V.3.1. Knowledge cards

In order to make use of the knowledge maps, the notion of knowledge card [Buzon 03] was adopted in our knowledge system as the medium of exchange. The knowledge card allows knowledge users to retrieve the knowledge map, information, and metadata of each task in a single card. A knowledge card represents one concept about a particular task and is linkable to other cards. Thus, this concept permits the experts in the cluster to create their knowledge card and attach to others on the same topic. Supplementing new knowledge to the system will improve the completeness of the knowledge for achieving the knowledge intensive task. In this part, we will depict the overview of the knowledge card and its structure. Then, the knowledge map which is the core of the knowledge card will be described. Finally, the system architecture of the knowledge card module from the points of view of data, class, and user interface will be presented.

V.3.1.1. Knowledge card’s outline

The concept of knowledge card was adopted in this study in order to present the knowledge about a specific task in the form of a card. In a single card, all necessary information will be displayed to the reader. The information displayed in the card is metadata, body of knowledge, description, and links. The elements in the knowledge card are structured as shown in figure V.4.

Figure V.4: An outline of the knowledge card

A knowledge card comprises five elements: card’s metadata, knowledge map, wiki, repository and links. These elements help knowledge workers to comprehend and search for required knowledge through the KMS.

• Card’s Metadata shows general information about the knowledge card such as author, version, last modified date, etc. This part notifies the reader about the popularity, version, and permission of the user on the specific card.

• Knowledge Map displays concept of a knowledge card in the form of a semantic map. This is the core of the knowledge card because it allows machine and human to browse over the cards. It also aims at representing the experts’ knowledge into semantic map form in order to facilitate knowledge sharing, and reuse. The details of this will be discussed in the following part.

• Wiki module displays the collaborative knowledge base that is created and modified by experts in the same community of practice. This part allows users to share their knowledge which could not be represented by the knowledge map module.

• Repository displays a list of documents (files, databases, images, videos, etc.) that supports or is related to the knowledge card.

• Links part displays the incoming, outgoing, and popular links which are obtained from the metadata of knowledge cards. With these elements, experts in the cluster are able to explicate their knowledge and information in various methods. They are able to manipulate (i.e. create and modify) the knowledge card concurrently via knowledge representation module in KMS. Knowledge cards are linked to each other by the inference engine and stored in the knowledge base for further modification or acquisition. The inference engine automatically links the domain knowledge with equivalent knowledge cards in the knowledge base. The advantage of the knowledge card is that it conveys brief and concrete knowledge about a single task to the reader. A knowledge card signifies one task of the industry cluster. There are two methods to reuse the knowledge cards in the knowledge system. The first method is accessing via the knowledge representation module. Knowledge users should start browsing from the first card of the community of practice. This card will illustrate all possible tasks which are involved with particular practice. Then, users are able to browse from the general task to the more specific tasks via the knowledge map module. The second method is accessing the required knowledge card via the search module. This method allows knowledge users to precisely reuse their knowledge from the knowledge base. Moreover, it also facilitates knowledge retrieval from other applications. The details of the knowledge map component will be explained in the following part.

V.3.1.2. Knowledge map component

The knowledge map module is based on the concept of Task-Inference-Domain which was proposed in the CommonKADS methodology [Schreiber 99]. This module aims at representing expert’s tacit knowledge into explicit knowledge in the format of knowledge model. The knowledge model comprises three elements (task, inference and domain knowledge), each capturing a related group of knowledge structures.

• Task knowledge describes what goal(s) an application pursues, and how these goals can be realized through the decomposition into subtasks and inference.

• Inference knowledge describes the basic inference steps required for making use of the domain knowledge. The inference level is also employed by the inference engine for retrieving required knowledge.

• Domain knowledge specifies the domain-specific knowledge and information focused on the particular task. The knowledge modeling allows experts in the industry cluster to present their knowledge in graphical form, also called the knowledge map. Regarding the CommonKADS methodology, the knowledge map is notated as UML class diagram (without operation) which permits the experts and knowledge engineers to clarify and manipulate the structure of knowledge intensive task. Although the UML class diagram is suitable representation for the inference engine, it is not convenient for the knowledge user who is not in the computer domain to comprehend the knowledge map. Thus, this study adopted the concept of semantic concept map which is the integration of CommonKADS knowledge model and semantic map concept. So, the modeled knowledge is more comprehensible to the knowledge user and still readable to the machine. Another advantage is enabling the relationship between two concepts in both directions (i.e. forward and backward). The defined relationships help the user to read the knowledge map semantically and also allow the machine to do semantic search in the knowledge map. Figure V.5 shows an example of modeling experts’ personal knowledge into semantic concept map format.

Figure V.5: Personal knowledge and the knowledge model

The figure above demonstrates an approach for representing experts’ knowledge in the semantic map format by using the notion of Task-Inference-Domain. The knowledge about “technical ceramic classification” was presented on the left hand side as an example. During this process, the knowledge engineer employed the knowledge elicitation technique which was described in chapter 3 to extract the body of knowledge from the expert. Many knowledge elicitation methods such as interview, questionnaire, or focus group, were proposed for achieving this process. Then, the body of knowledge (i.e. task, inference, and domain knowledge) was notated and modeled into semantic map format as shown on the right hand side in the figure. The task knowledge was symbolized by the hexagonal shape. This type of knowledge was modeled as the parent node of the knowledge model. Consequently, the domain knowledge was symbolized by the oval shape and linked with their parent concept node. Finally, the relationships between task and domain knowledge were designated as the inference. These processes will be repeated until all domain knowledge about the specified task is modeled. The semantic map makes it possible for the experts to explicate their knowledge in graphical form. The nature of a semantic map structure allows cluster experts to easily add new knowledge concepts onto other’s knowledge. The shared knowledge is focused on a single task which is easy for the practice. The major advantage of a semantic map to the knowledge system is enabling the search module to acquire the required knowledge from the knowledge system. Each knowledge concept is linked to each other with semantic relationships, i.e. forward and backward. These relationships support humans to comprehend the interaction between the concepts, and also assist the machine to search for the required knowledge via the semantic search module. The forward relationship is visible in the knowledge card for reading by the human. In contrast, the backward relationship is concealed from the knowledge card, but employed by the machine for backward inference. The semantic map was stored in the format of XML, based on the topic map standard (ISO/IEC 13250:2003). Complying with this standard provides interoperability to the knowledge system for exchanging the knowledge with other systems. For a better understanding about the semantic concept map, figure V.6 shows a simple semantic knowledge map with both forward and backward relationships and the map’s structure in the XML format.

Figure V.6: Semantic concept map with XML code

The figure above shows the knowledge map compared to its XML form. The knowledge is map composed of two nodes, task and domain concept. Each node is identified by node id, as shown in the XML. The forward relationship is used for specifying the relationship from task concept (parent node) to the domain concept (child node). Conversely, the backward relationship is used for specifying the relationship from the child node back to the parent node. These relationships are employed by the search module in order to retrieve the knowledge from the knowledge base, which will be explained in the search module section. In this study, there are two methods for creating the knowledge card, knowledge elicitation and collaborative knowledge sharing. Knowledge elicitation is utilizing the elicitation method in order to extract knowledge from the experts in the cluster. CommonKADS knowledge engineering methodology was adopted for converting experts’ tacit knowledge into the knowledge model. The elicitation processes were described in the knowledge model section in chapter 3. Another method is allowing experts in the industry cluster to share their knowledge collaboratively. This method is comparable to a card sorting technique but in the virtual way. This means that the knowledge card system allows experts to add their knowledge (index card) to others’ knowledge on a focused topic. Then, experts provide the relationship between their concept and relative concepts. Although the obtained knowledge card from this approach is not in well-structured form, it gives a possible method for the experts in the industry cluster to create and represent their knowledge with less effort. One advantage of the knowledge model is that it is easy to manage the concept. Experts can share their ideas on the same topic by using less effort than for editing text or wiki content. However, experts are able to describe more ideas about particular knowledge in the knowledge-based wiki section, which will provide the complete information. This part explained the methodology for enabling knowledge sharing for the industry cluster. The knowledge card was considered as the medium for transferring the knowledge from the experts to knowledge users. In the next section, knowledge card architecture will be used for explaining, from the global view to class view, the knowledge service level. V.3.1.3. Knowledge card architecture

In order to get a better understanding about the knowledge card module, the architecture of knowledge card will be described in this section. The base engine of the knowledge card is wiki engine. In this study, we have employed PhPWiki [PhPWiki 08] as a core of these modules. It is an open source wiki engine which allows cluster members to collaboratively manipulate the content pages through an HTML form. Linking the wiki pages is done automatically on the server side; all pages are stored in the database. Due to this engine being developed on open source agreement, it allows us to modify the source code in order to add the knowledge card module over the wiki system. The database of the system was customized to suit our knowledge system. The database schema of the knowledge card is illustrated in figure V.7.

Figure V.7: Database schema of knowledge card system

The knowledge card module includes five main database tables: Wiki, WikiScore, HitCount, Kmap, and Links. The primary key of these tables is the page name (or card name). Wiki table contains metadata and wiki content of the card. WikiScore and HitCount are used by the wiki engine to provide recommended card or popular card to the reader. Then, Kmap table contains knowledge map in XML format. Lastly, Links table stores incoming and outgoing links associated with a particular card. From the database point of view, it provides us with an overview of exchanged data in the module. However, the complete database is presented in Annex G. In order to depict the structure of knowledge card system, the UML class diagram of the system is illustrated in figure V.8.

Figure V.8: UML class diagram of knowledge card system

The diagram is composed of two modules, knowledge map module and wiki module. Wiki module is the center of this system. The lowest level of the system is database connection (DB_Connection) class. This class is handling all connections between the knowledge system and SQL database. The Transform class is in charge of transforming wiki content from the DB_Connection into HTML content. On the contrary, it is also be used for transforming HTML input from user into wiki content in order to store in the database. The EditPage class is a module that allows experts to edit wiki content in the knowledge card. The Wiki and WikiConfig modules concern the user interface of the wiki content in the knowledge card. The objective of these classes is displaying formatted wiki content on users’ HTML browser. Finally, the information from Metadata, Wiki, Links and Kmap class are integrated in the knowledge card class so as to display the required knowledge to knowledge workers. Knowledge map module is an important element in the knowledge card system. It provides the body of knowledge about the task to the knowledge worker at first glance, without him having to read the content inside the wiki module. Moreover, it was employed by the inference engine for enabling ontology search in the knowledge system. The structure of knowledge map module is composed of two main classes, MapRenderer and RelationshipMapper. MapRenderer handles the connection with database in wiki module. The knowledge map in XML format is manipulated in this class. RelationshipMapper aims at visualizing knowledge map and its control panel on the knowledge card. A relationship manager comprises three elements, control panel, nodes, and links. Control panel is rendered by MapEditor class which allows the user to edit, zoom in/out, position map, etc. Nodes are rendered by NodeRenderer class which aims at representing task and domain knowledge in graphical format. Each node comprises node id, node name, and node type. RelationshipRenderer focuses on rendering inference knowledge on the knowledge map. This class draws an arrow between two nodes in order to indicate the forward relationship from parent node to child node. Although backward relationship is invisible to the reader, it is used by the inference engine for browsing the knowledge map. In order to emphasize the structure of the knowledge map, user interfaces of the knowledge map will be presented in this section. The captured screen of the main screen of knowledge map is illustrated in figure V.9.

Figure V.9: User interface of knowledge map module

Actually, the knowledge map will be displayed within the knowledge card. However, the figure above focuses on the knowledge map component only. The knowledge map component can be divided into three sections: map area (1), control panel (2), and menu (3). The map area is a section for displaying knowledge map in the format of a semantic map. Users can drag each node or move the view pane easily. The control panel section allows readers to rearrange the map style, zoom-in/out, or enable the edit mode of the map. When the edit mode is enabled, the menu would popup on the map area. This menu enables the reader to add, edit, or delete nodes from the knowledge map. In addition, it is able to alternate node type between task and domain node. The updated map will be stored in the database when the Save button in the control panel is clicked. In summary, this section presented major components of the knowledge card, the basic element for representing and sharing knowledge among the cluster members. Thus, the notion of knowledge card was considered as a medium for sharing the task intensive knowledge in this study. Moreover, the knowledge creation and representation techniques were proposed in order to create the knowledge model for the knowledge card. Finally, the user interface was illustrated for demonstrating knowledge card manipulation. In the next section, we will demonstrate the usage of the knowledge card in the system.

V.3.2. Knowledge Search

The knowledge search is a module that focuses on reusing the knowledge which is stored in the knowledge system. The objective of this module is to provide requested knowledge from the knowledge system to the applicant when needed. Compared with knowledge management activity, this module supports the knowledge reuse/retrieval facility. In this section, we will describe the user interface of the search module, which is composed of two views. Then, an algorithm of knowledge module will be depicted. Next, two knowledge acquisition techniques, which are the fundamental of this module, will be described along with knowledge acquisition scenario. These will give a better understanding about the usage of the knowledge. In order to describe the overview of the knowledge search module, the user interface of search module is illustrated in figure V.10. The user interface of this module is separated into two parts, list view and map view . Both views provide an alternative for knowledge users to display the retrieved knowledge in two formats. List view is displaying the search result in a format of listed items, while map view displays search results as the knowledge map concept. For example, the list view in figure x shows the result from searching with the keyword “Purchase”. The result sets were listed with links to the knowledge card. The map view shows the same result in the different way from list. The map view in the figure implies that one knowledge card which contained the keyword is found which is represented by a rectangular symbol. And, it also found a group of concepts which relate to the keyword, represented by an oval symbol. In order to view the detail, the user is able to click on the transparent square button. Then, the second level of the knowledge map will be displayed.

Figure V.10: Search results in the List and Map view

The list view is effective when the result set contains large amounts of search results. This view aims at providing all search results and supportive information within a single page. The result list is composed of four columns: topic, relationship, content, and page name. The first column (topic) shows the list of matched concepts. The second column shows the relationship between the topic and the knowledge content - which is displayed in the third column. The last column shows the page name (or card name). The user is able to click on the page name which will link to the knowledge card. In this view, the knowledge worker is allowed to customize their search results by re-sorting or eliminating unwanted results from the list. The map view displays retrieved knowledge in the graphic mode. The map view is divided into two levels. The first level of map view shows groups of concepts that have the same topic, and also the card name that exactly matches with the search keyword. From this level, the user is able to access directly to the matched knowledge card (if found), or open the second level that will display domain knowledge about the selected topic. In this level, the user can acquire knowledge about a particular topic in the form of semantic map. Moreover, the user is allowed to jump to the knowledge card that contains particular domain knowledge by clicking on the map link. This view is suitable when the search results are not too large. In order to describe the searching process, the algorithm of search engine is illustrated in the form of a flow chart, as shown in figure V.11. The flow chart comprises two parts, querying and displaying. The beginning of the flow chart concerns the querying method. Then, the final part concerns displaying the retrieved knowledge in list or map view. From the flow chart, we can see that the knowledge acquisition process commences when the user sends a search keyword to the system. There are two type of querying process, querying via database connection module (content search), or querying via the inference engine. As soon as the result set was received from the database, the displaying process is initiated. If the search keyword could not be found in the database, the returned result set is null. Then, the system sends a message to the user that the requested page was not found, and then terminates the search process. But if the result set is not null, it will be sent to the displaying module which has two viewing options, list view and map view. The user is able to choose their search display option according to their preference.

Figure V.11: Flowchart of knowledge search module

As we mentioned earlier, the user is able to choose one from two types of search method, content search and semantic search. Content search is an ordinary text search. It allows the user to search for the knowledge card that contains the search keyword anywhere in the card. The matched cards which include the keyword in their card will be accumulated in the result. Another type of knowledge search is searching over the concepts on the knowledge map, called a semantic search. This type of search is adapted from the concept of the ontology search which makes it possible for the inference engine to search through the semantic relationship between the concepts. Semantic search gives fewer number result sets, but more precise search results than content search. The key factor that enhances capability of the search engine is the semantic relationship within the knowledge map.It facilitates the acquisition by integrating search results from various knowledge cards, and generates a new semantic concept map, called the result map. In order to generate the result map, the two techniques of forward and backward reasoning are adopted in the semantic search module. Forward reasoning allows the knowledge user to search over the domain knowledge with a keyword and inference in the knowledge card. The result from the search engine is a knowledge map that contains the domain knowledge elements which relate with the keyword. In algorithmic view, the inference engine browses all knowledge cards in the system and searches for the domain knowledge which matches with the keyword. Then, the related nodes and their relationship from different cards are integrated in order to create a new knowledge card which explains the keyword. Figure V.12 demonstrates an example of knowledge acquisition by using the forward reasoning method. On the other hand, backward reasoning is an inference method which intends to find the consequent from the list of goals. For example, the knowledge user searches with a set of keywords. Then, the inference engine looks for possible nodes which relate to the search keywords. Finally, the node that exactly matches with the keywords will be placed on the top of the list while the partial matches will be listed later on.

Figure V.12: Knowledge card searching and the result map

From the given example, a knowledge user acquires the knowledge about “white clay” from the knowledge system. Consequently, the inference engine found four knowledge cards (i.e. KMAP- 1 to 4) that contain the search keyword. The result set implied that the search module found one knowledge map which exactly matched with the search keyword (KMAP-1), and three knowledge cards which contain the search keyword in their domain concept (KMAP-2 to 4). The matched concepts were represented with a bold border. Then, it browsed over the corresponding nodes via forward and backward inference. The corresponding concepts were represented with dash borders. An example of forward inference is the knowledge model in KMAP-1. This map explains the composition of the white clay, which is composed of Kaolinite, Halloysite, and Illite. Conversely, the backward inference was used in KMAP-2 to 4. The inference engine employed the backward relationship to indicate the link between matched node and corresponding node. For example, the result obtained from employing backward inference on KMAP-2 could be “White clay is a factor of product strength”. The result map is composed of a link to the knowledge card that exactly matches with the keyword and the corresponding concepts. The links allow the user to access a specific knowledge card directly. The related concepts were attained for generating the result map. From the example above, the result map was represented in semantic concept map format, which could be read by the human as “White clay comprises Kaolinite, Halloysite, and Illite. It indicates the ceramic product strength, can be found in Lampang province, and was used in bone china products” . The result map was also translated into XML format which makes it possible for the knowledge system to exchange information with other application. To demonstrate the application of the knowledge card, the application section will describe how to make use of the knowledge in the system, and how we enhance the knowledge acquisition module with the mobile technology. V.3.3. Knowledge Exchange Scenario

In the present situation of the ceramic cluster in our case study, there are neither tools nor systems that support knowledge management in the organization. However, the development of the industry cluster was maintained by the capability of the facilitators, support from the government, and the opportunities inside the industry cluster. Although these factors kept the members of the group together, they still faced the problems of knowledge sharing among members. For example, the ceramic trade fair exhibition is one of the most important business activities of this cluster due to it provides a great opportunity to the companies to access new markets and new customers. In order to achieve this, various type of knowledge are required for making decisions about this event such as product selection for the exhibition, logistics planning, exporting, acquiring support from the government, booth preparation, etc. Thus, the ceramic cluster had organized the meeting to share information and make decisions about this activity. Unfortunately, the required knowledge and information never satisfied the members due to many reasons. For instance, the expert was absent from the meeting, the knowledge and experience of the previous trade fair were not in explicit form, the documents had been mislaid, etc. Another critical problem of knowledge sharing in the cluster is the competitor-like relationship. This relationship sometimes makes experts feel uneasy to share their knowledge with their competitors. These are the obstacles to the potential achievements of the industry cluster. Hence, the proposed KMS is considered as a solution for solving these problems. This part of the research will clarify how the knowledge system will ameliorate the problematic situations in the ceramic cluster. The UML sequence diagram in figure V.13 demonstrates a complete scenario of the cluster members from requesting, sharing, and reusing knowledge. The ceramic trade fair event was selected as a case study of knowledge sharing. This scenario was divided into three periods of time, before, during and after the trade fair.

Figure V.13: UML sequence diagram of knowledge sharing scenario

In the beginning of the scenario, a government agency offered an opportunity for SMEs in the ceramic cluster to join the international trade fair in the form of some subsidies from the government. Collaboration services were used to present this opportunity to the cluster via the calendar module. After that, CDA sent a message to all members, inviting experts to create a new knowledge base in this CoP. The process was done by Push News system on the KMS. Cluster members received an invitation, with URL link, via e-mail in order to contribute to the new knowledge card. Experts who had experience about the international ceramic trade fair got access to the knowledge card module and shared their knowledge in the format of knowledge card. The advantage of this system was in allowing experts to represent their knowledge over other knowledge. This helps experts to put less effort into sharing their knowledge, and focus only on their domain of expertise. This knowledge sharing process is illustrated in figure V.14.

Figure V.14: Knowledge sharing via Knowledge card module

The figure above demonstrates that experts from different domains can contribute their knowledge on a common field via the knowledge card system. In our scenario, these experts were invited to share their knowledge on the knowledge system. An expert from the core cluster logged into the system and provided the body of knowledge about the “ international trade fair ”. Then, an expert from the Department of Export Promotion (a Thai government agency) accessed the system. He appended his knowledge about “ exporting product ” to the previous knowledge. Finally, an academic institute added a new knowledge card about “trading contracts ” to the system. The knowledge system automatically linked these knowledge cards together with the inference engine. This process portrayed how the knowledge base was constructed in the KMS. During the trade fair, a knowledge worker at the exhibition required knowledge about “trading contracts”. He searched with the specified keyword in order to request knowledge through the search engine. The inference engine searches for a knowledge card which matched with “trading contracts”. Then, the result set was returned to the inquirer in the format of knowledge card. The procedure of searching was previously described in the knowledge acquisition section. The result set obtained from searching in the knowledge card above is displayed in figure V.15.

Figure V.15: User interface of search result module

The search result obtained can be displayed in two views, list view and map view. From the figure above, the top side shows the search result in list form. List view displays complete information about the trading contract. Conversely, map view displays search results in the knowledge map model. The outcome from this page can be read as follow; “A Trading Contract is required for exporting product. It is composed of Invoice and Purchase order” . After the trade fair, the CDA is able to send invitations to cluster members who exhibited to share their experiences. In this process, the specific knowledge such as lessons learned, warnings, and advice will be added to the knowledge base.

V.3.4. Maintaining the Knowledge Base

In this part, we will describe how the knowledge base is maintained in our KMS. In order to maintain sustain the reliability of the knowledge system, new knowledge should be added to the system carefully. Thus, in this study, we propose two approaches to achieve this. The overview the knowledge base maintaining is presented in the figure V.16. The first approach is oriented to the knowledge elicitation processes from the knowledge sources in the industry cluster directly. The second approach is oriented to the ontology module which is integrated to the collaborative knowledge card module. Both methods allow the experts in the cluster to share their knowledge in the explicit form, as shown in the following diagram.

Figure V.16: Knowledge-base maintaining

The first approach is capturing the knowledge from the different sources in the cluster (e.g. repository, experts, codified knowledge, etc.) by using the KE templates which is performed by the knowledge engineer. The idea of approach is to repeat the knowledge model of the model suite which has been depicted in Chapter 3. The particular knowledge about a task is transcribed and represented into the structured knowledge model and add to the knowledge base directly via the knowledge acquisition module. This module is accessible only by the administrator, CDA or knowledge engineer, which is the main module for maintaining the knowledge. The second approach is transferring the knowledge directly from the cluster members to the knowledge base via the KMS. This method allows the experts in the cluster to share their knowledge to the cluster liberally. However, the ontology module is implemented to the system in order to maintain the consistency of the system. The general objective of the ontology module is that it checks the relationships between the related concepts. It ensures that the backward relationship is appropriate with the defined forward relationship. The objective of the task ontology module is to verify the new knowledge concept whether it is existing in the knowledge base. If the existing concept is Task knowledge (is a name of knowledge card), it will change the symbol of new concept to “Inference” symbol. But if the existing concept is Domain knowledge, it will link new concept with the group. Thus, the inference engine is able to browse over the concept during the searching process. The objective of the domain ontology module is to provide the common vocabulary about a specific domain. For example, CDA and Cluster Development Agent are defined as the same topic in the ceramic cluster domain. Thus, the inference engine is able to show the combined result map from the result sets of both concepts.

V.4. Supporting Services

The main objective of these supporting services aims at sustaining knowledge management activities of the industry cluster. Although these services are not focusing on the knowledge itself, they permit the cluster members to exchange the knowledge within the cluster with different approaches. From the study of [Malhotra 04], many KMS have failed to integrate in the organization due to neglecting this issue. Thus, in this study, we propose two types of services i.e. presentation and collaboration service in order to support knowledge management activities in the ceramic cluster. These services will be added to the KMS in order to enhance the functionality of the system.

V.4.1. Presentation services

The presentation service was designed with the intention of visualizing and personalizing all services provided on the KMS to the users in an appropriate way. The main objective is to harmoniously integrate the KMS into cluster activities. Moreover, it was set to facilitate cluster members to share and retrieve knowledge with less effort. This service aims at integrating the knowledge system’s services with the activities of cluster members. The failure of many knowledge systems are caused by neglecting the integration of a developed system with the actual activity of the organization. Experts may need to double their efforts in order to share their knowledge with the knowledge workers in the workplace, and also the knowledge system. This situation will decrease willingness to share organizational knowledge. The benefit of the presentation service is in allowing users to personalize their view for presenting the information or services from the knowledge system. In order to achieve these goals, we have created two applications for supporting presentation services, KMS portal and Widget . The details of both applications will be described in the following Figure V.17 which illustrates how the KMS portal and knowledge widget were integrated in the KMS server.

Figure V.17: Presentation services and KMS server integration

The architecture revealed the integration of presentation services in the knowledge system. The modules for presentation service were annotated using blue, while the KMS system was annotated in red. The presented architecture implied that there are two manners of application that support the service, server side and client side. The KMS portal is a server-side application which allows users to access all provided services on the KMS via web browser on the client side section (e.g. Internet Explorer, Mozilla Firefox, etc.). The KMS portal is a server-side application which aims at presenting services from diverse modules (e.g. knowledge card, collaborative tools, communication, etc.) in a unified way. Portal also handles HTTP connection from web browser on the users’ machines. Another application is the knowledge widget, which was developed for this knowledge system. Adopting the notion of widget in the system will facilitate cluster members to get direct access to all available services on the system. Besides, it makes it possible for knowledge system to push information to all users, and conversely allows users to pull information from the knowledge system. Although these modules are mainly concerned with the user interface, it is a key achievement of the knowledge system. Cluster members learn the benefits from the knowledge system via KMS portal and knowledge widget. The following parts will give explanations on both modules.

V.4.1.1. Knowledge Management System Portal

The KMS portal provides a single point access to a variety of contents and services on the knowledge system, and ideally offers a single sign-on point. The portal often organizes information into channels, customizable page containers where specific information or an application appears. Another major benefit of the KMS portal is that it offers an access point for users in the same community to collaborate. Since the concept of second generation of web development and design (called Web 2.0) was popularized in recent years, every web portal has tried to facilitate communication, secure information sharing, interoperability, and collaboration on the World Wide Web. The difference between Web 1.0 and 2.0 is that Web 1.0 is about linking documents, but Web 2.0 is about linking people together. Thus, knowledge management practice has taken an advantage from Web 2.0 by supporting experts and knowledge workers in the organization to collaborate and exchange their knowledge on the particular practices. Although there is no defined architecture, the KMS portal is designed with respect to the organization requirements and provided services of the system. Thus, the site map of KMS portal in our study is proposed in figure V.18 for presenting the overview of portal’s architecture.

Figure V.18: KMS portal page and site map

The architecture of proposed KMS portal is composed of six sections: portal, cluster map, calendar, virtual conference, knowledge card, and administrator. The portal’s screen is illustrated on the left hand side of the figure. It comprises two parts i.e. menu and content part. The menu part is located on the top of the screen which allows users to access to the provided services on the knowledge system. Content part is the main panel of the screen. Each content module was placed according to the significance of the information and preference of the users. The example shows the knowledge card page on the KMS portal. The site map implies that users are able to access other services of the knowledge system via the top menu. The cluster map menu gives access to the cluster map and address book service. The calendar menu shows full function of the collaborative calendar service. Then, the collaborative tools menu allows users to employ all virtual conference services and VoIP web services via the web browser. Knowledge card page contains two main services of the knowledge system, knowledge card and knowledge search. Lastly, the administrator menu links to a secure page that allows the CDA to manage the KMS portal and underlying services. In order to access the services over the KMS portal, users have to identify themselves via login module. An anonymous user is permitted to access only the home page and cluster map. Referring to Info-Structure in chapter 4 (figure IV.7), cluster members agreed to share this information outside the cluster. Identified users are allowed to access all services on the knowledge system. However, some modules were personalized in regard to their roles in the industry cluster. For example, a user from the core cluster will be connected with “Ceramic Production CoP” automatically when they logon to the system, but a user from a government agency will be connected with Export Promotion CoP instead. This concept also refers to the Info-Structure that users are more willing to share specific knowledge if they are in the same community of practice. CDA has the privilege of accessing some exclusive services such as Push News, back-office, user management, etc. The advantage of the KMS portal is providing an access point for cluster members to use all provided services on the knowledge system without installing any special software on the client side. However, the KMS portal is limited by HTTP service and web browser. Thus, the next section will propose the knowledge widget, which is a client side application. It makes it easy for cluster members to access all services on the knowledge management server from their own desktop.

V.4.1.2. Knowledge Widget

The notion of widget was well-known in recent years, due to the emergence of redundant information on the internet. In the past, knowledge management practice faced a problem about lacking of information to achieve the task. Nowadays, information is available anywhere on the internet. A newly emerging challenge is how to retrieve a minimum of information, yet enough to accomplish the task. Therefore, the concept of widget is adopted for retrieving information from different locations and presenting it to the user in an appropriate format via GUI. Widget is mostly used for displaying information which is often used. In this study, we create a widget for retrieving information from the different servers (knowledge server, collaboration server, and communication server), called the knowledge widget. Moreover, it is also designed for facilitating cluster members to collaborate with others from a single point. In order to get a big picture of the knowledge widget, the structure of the widget is illustrated in figure V.19. From the structure presented, the concept of widget was clarified with UML class diagram, as shown in figure V.20. The knowledge widget is composed of eleven classes. These classes included three service classes: Knowledge, Collaboration and Communication service, which was designed for handling the connection and authentication to the service provider. The main class that takes control of these service classes is Cluster Widget.

Figure V.19: Architecture of the knowledge widget

The Cluster Widget class is in charge of user authentication. As soon as the authentication is successful, the widget_config, which stored parameters for personalizing the widget will be retrieved from the Config classThen, three associated service classes (knowledge, collaboration, and communication) logon to the servers and acquire permission to get access to the provided services. Three knowledge services (news, event, and knowledge search) were provided by the knowledge server directly. Collaboration services displayed cluster map from the knowledge server and connected with AFCS server.

Figure V.20: Knowledge Widget’s UML class diagram

Finally, communication service class gets access to the VoIP phone web service on the knowledge server in order to allow users to connect with the communication server. With the intention of clarifying the functions of the widget, the captured screens of each service are illustrated in figure V.21.

Figure V.21: User interface of the knowledge widget

The user interface of knowledge widget showed three main services of the knowledge system. The first screen that will be displayed to the user after logged in is knowledge panel . This panel handles three types of service from the KMS server i.e. pull news, c-calendar, and knowledge search. Pull news module allows users to retrieve update information from the cluster. The c-calendar is represented with the event module which allows users to view specific type of event (e.g. seminar, meeting, training, etc.) in the specific period (e.g. today, this week, etc.). Finally, the knowledge search module allows user to reuse the knowledge from the knowledge system. The knowledge widget provides two ways of search i.e. map and voice. If user selected map search approach, the search result will be transmitted to the user in the semantic knowledge map format via the web browser (which was explained in the previous section). But if user selected voice approach, the knowledge map will be synthesized into voice format. Then, the search result in voice form will be transmitted to the user via communication panel of this widget. The details of these processes will be described at the end of this chapter. The collaboration panel allows user to connect to two services i.e. cluster map and virtual conference. The cluster map module provides the personal information of each actor in the ceramic cluster. Moreover, it also allows users to communicate by peer-to-peer approach. On the other hand, the conference tab facilitates the cluster member to collaborate in group. The details of these services will be described in the collaborative services section. These captured screens proved that user is able to access all provided services of the knowledge system via the knowledge widget. However, the next section will describe the usage of these collaboration services and their benefits to the cluster.

V.4.2. Collaboration services

This level of the service in the system architecture aims at enhancing quality of collaboration and communication of the cluster members. The analysis from collaboration model in chapter 4 found that the activities of the cluster member which have high degree of collaboration and high impact to the industry cluster could be categorized into three major activities i.e. (1) sharing opportunity (such as seminar, trade fair exhibition, workshop, etc.), (2) joint working in specific topic (such as joint exhibit in the trade fair, joint research, aggregate demand, etc.), and (3) solving problem together. In order to support these activities, three levels of collaboration are concerned i.e. mass, group, and individual collaboration. Each level of collaboration aims at supporting the collaboration of the cluster members in different aspects. In our study, the collaboration service model can be represented as in the figure V.22. It shows a group of collaborative tools which are selected to support each level. Figure V.22: Collaboration service model

The model implies the three modes of communication that are supported by the KMS server: mass, group, and individual (peer-to-peer). The knowledge system makes it possible for cluster member to broadcast their messages to all members via push/pull news system. This type of communication enhances the coverage of information sharing within the cluster. The virtual conference tools allows cluster member to interact within a restricted group of members. This type of communication improves frequency of collaboration due to it eliminates the barrier of collaboration in term of place and time. The final mode is the peer-to- peer communication which enables members to exchange information to each other individually. The objective of this service is to reduce cost of collaboration of the industry cluster by adopting VoIP technology in the system. In order to provide a general view of the collaboration services of the KMS, the UML class diagram in the figure V.23 shows the elements and inter-connection between classes for these services. The diagram comprises three groups of classes. The first group is placed on the top- left of the diagram, called c-calendar. The second group is the push/pull news which is placed on the top-right of the diagram. Finally, a group of cluster map classes is placed on the bottom. In development aspect, the collaboration services can be divided into five modules i.e. cluster map, push/pull news, collaborative calendar, virtual conference and VoIP. The cluster map is considered as the center of these modules. It is accountable for managing cluster members’ information, level of security, and authentication to other modules. Push/Pull news and collaborative calendar aim at improving sharing of information and opportunity in the cluster, while virtual conference and VoIP focus on ameliorating the communications among the members. The VoIP module will be described at the end of this chapter. We will first examine the cluster map which is the central module. V.4.2.1. Mass Collaboration

The mass collaboration makes possible for cluster members to broadcast their message to everyone in the cluster or only to the focused group. The benefit of supporting this type of collaboration is that it improves the coverage of the information distribution in the ceramic cluster. Moreover, it is used for sharing some types of knowledge which are not in the complex form e.g. know-who, who-where, know-when, etc. Therefore, this part will focus on the collaborative tools which are designed to support these goals.

Figure V.23: Structure of the collaboration services in UML

(a) Cluster map module

The main objective of the cluster map is to provide the information of the industry cluster network in the visualized view. Moreover, this module contains all important information of the members for the system such as authentication information, user ID, e-mail, role, etc. Thus, this module became the center of collaboration level. The database of the cluster map module comprises four tables (Member, Group, VoIP_Auth, and Conf_Auth). The databases model can be found in Annex G. The fields in member table were customized from the ontology for vCards [vCard 08] to match with cluster context. The benefit of complying with the vCard ontology is that it provides the capability to share members’ information with other systems. An example of a simple vCard is presented as follow.

This vCard is generated from the cluster map database and exported into XML format. The group table contains the group of member in the cluster (e.g. core cluster, academic institute, government agency). VoIP_Auth and Conf_Auth contain the required authentication information of the user for connecting with other servers. For a better comprehension, the class diagram in figure V.23 shows their interrelationship, attribute, and operation of the classes. The class diagram showed that the cluster map was composed of two modules: user interface module and administrator module. The administrator module focuses on managing information of each table in the databases, while the user interface module focuses on displaying information from databases to the users. It visualizes the members of the cluster by categorizing into different views such as group, roles, or CoP. The figure V.24 show cluster map which is categorized with the group view.

Figure V.24: Cluster map in group view with user’s profile

The cluster map (left-hand side) shows an example of the groups of members involved with the ceramic cluster. It also allows users to export viewing result into vCard format for introducing to other applications such as Microsoft Outlook, Palm contact, etc. Moreover, users are able to view the complete information about each member by clicking on the member’s image. The information of the user will show up as illustrated on the right hand side of the figure. It provides knowledge about the cluster network, called know who . The question about “ how do I know who knows?” is a good example of accessing to the cluster map.

(b) Push/Pull News

One of the critical problems of the ceramic cluster is the thoroughness of information distribution. As we mentioned earlier that the cluster organization is flat, sometimes the delivered information may not go thorough the cluster, whilst sometimes CDA does not receive that information. The Push and Pull approach is adopted to resolve this kind of problems. Push News allows CDA to ‘narrowcast’ or rather to ‘broadcast’ personalized news directly to readers. It also empowers readers by letting them specify what content they want delivered, via which channel, as well as how often. Information and communication technology offers various channels to convey news to cluster members such as e-mail, voice mail, short messages, etc. On the other hand, The Pull technology is a style of network communication where the initial request for data originates from the client, and then is then responded to by the server. Pull News allow cluster members to access all news from the server. News feed is an example of pull technology. It is published news on the server in RSS (Really Simple Syndication) format which end users can register with an aggregator program (e.g. feed reader or news reader). The database schema of push/pull news is showed in Annex G. It shows two tables that are required in this service. The News table contains information about the news such as topic, content, image, etc.; which was designed according to the RSS 2.0 format [RSS 09]. The Subscriber table stores e-mails of the subscribers who are not a member of the ceramic cluster, but desired to receive information from the cluster. In order to visualize the usability of these modules, the GUI of these classes is illustrated in figure V.25.

Figure V.25: User interfaces of Push and Pull News module

The Push News is displayed on the left-hand side, allows only CDA for narrowcasting news to a specific group of member. The Pull News can be accessed by cluster members and anonymous users in order to obtain latest information from the industry cluster. The left pane (1) displays a list of recent news in the system. It also allows the CDA to edit or delete out-of- date news. The right pane shows the form for adding new news. The CDA is able to add topic (2), content of news (3), and images (4) to be attached to the news. The radio boxes at the bottom of the pane provide the options for distributing particular news to the members. The Pull News interface (6) shows the screen that readers will see when they get access to the news system. The example in figure x demonstrates a scenario that the CDA would like to push information about “Lampang ceramic cluster plan” to the members in the core cluster. As soon as the CDA has clicked on the submit button, the news distribution class will send the topic, content, image link, and news link to all ceramic producers in this industry cluster. At the same time, this news is stored in the news system and waits for Pulling by the reader. This system ensures information sharing, and every member will receive the exact same information. Next, we will present a tool that supports opportunity sharing for the ceramic cluster. (c) Collaborative Calendar

The collaborative calendar service is designed for enabling cluster members to manage and coordinate their opportunities in terms of place and time. In a theoretical aspect, the exchanged opportunity in the cluster is a type of knowledge, also known as know-where and know-when. However, the opportunity varies according to the characteristics of the industry cluster. The examples of shared opportunity in the ceramic cluster are cluster’s appointment, meeting of cluster committee, seminar from academic institutes, trade fair exhibition from government agencies, low interest rate loans, etc. These opportunities are the main criteria that attract the members with the ceramic cluster. Thus, the collaborative calendar (c- calendar) is proposed to support this information sharing. A calendar standard called iCalendar (RFC 2245) is adopted in order to allow the knowledge system to send the events and tasks to the other systems via email, or sharing files with an “.ics” extension. The basic elements of iCalendar (iCal) are: event, start date, and end date. Here is a simple example of an iCalendar object, “ Cluster Yearly Meeting event which occurs on July 14, 2009 (UTC) ”.

BEGIN: VCALENDAR VERSION: 2.0 PRODID: -//Apple Computer\, Inc//iCal 1.5//EN BEGIN: VEVENT DTSTART: 20090714T000000Z DTEND: 20090715T000000Z SUMMARY: Cluster Yearly Meeting END: VEVENT END: VCALENDAR The code above shows the basic syntax of iCal format. The texts in red color represent the value of the object, while the black color (e.g. VEVENT) represents the predefined syntax. Actually, there are many different types of components which can be used in iCal standard, depending on the purpose of usage. In general view, the collaborative calendar service can be separated into two main parts: user interface part and iCal handler part, as shown in figure V.23. We will first focus on the engine of the service. The ICS_FileHandler class manages reading and writing the ICS files in the server. Then, iCalConnetion read stream file from previous class and stores in the memory. The iCal_Parser aims at converting iCal format to event variables for file reading operation. Conversely, append_iCal convert events variables to iCal format for file writing operation. PhpCalendar put all events from iCal_Parser into created table which is defined with CSS (Cascading Style Sheets) format, while the category of event obtained from ICS filename via Get_iCalFile class. The user interface part comprises c- calendar, control panel, and RSS classes. The c-calendar class generates graphic interface and also control panel to the user. Finally, RSS class allow user to export event(s) into ICS or RSS feed format in order to exchange with other system or software application. For better comprehension, the graphical interface of collaborative calendar was presented in figure V.26.

Figure V.26: User interface of the collaborative calendar

The c-calendar interface shows the events for the ceramic cluster by month. The main section (1) displays categorized event with different colors. This pane also allows a cluster member to create multi-day events or modify events by using a drag-and-drop approach. In the control panel section, first three event categories (2): seminar, trade fair and training are ICS files on the server, while holidays events (3) are imported from Yahoo’s iCal web service. Lastly, the bottom part of the control panel (4) allows the user to add new events to the calendar system. An example of opportunity sharing is that an academic institute gets access to c-calendar service in order to announce a seminar event on “web marketing”. Two type of knowledge required in this module are where and when to get this opportunity. After that, the cluster member who is interested in this seminar could acquire further information about this opportunity from the description provided. Next, we will demonstrate how KMS can improve the quality of collaboration and facilitate knowledge sharing in the direct approach via group collaboration approach. V.4.2.2. Group Collaboration

The group collaboration aims at supporting the cluster member to communicate or exchange their knowledge in a specific Community of Practice (CoP). This mode of communication allows experts to share more complex knowledge (e.g. know-how, know-what, know-why, etc.) to the knowledge users. The examples of this type of collaboration in the ceramic cluster are cluster meeting, group discussion, etc. Thus, the virtual conference module is designed to support this type of collaboration within the ceramic cluster.

(a) Virtual Conference

The virtual conference service aims at improving the quality of communication of the cluster member. This service allows members to communicate via text, voice or video through the KMS server. From the analysis, although a cluster meeting is organized monthly, less than 10% of the members of the ceramic cluster participated in this meeting. The main obstacles are place and time which are not convenient to the other members. Thus, this service tries to reduce these barriers and also increase frequency of communication at a lower cost. The main element of this service is the collaborative server (named AFCS) which was introduced in the beginning of this chapter.The platform is composed of five major components: session and authentication, shared manager, shared model components, collaboration user interface component, and pods. The organization of this platform is displayed in figure V.27.

Figure V.27: AFCS architecture [AFCS 09]

• Session and authentication component handles connection and authentication of the conference members. Besides, it also controls the synchronization of the data between users’ applications and the server.

• Shared model component involves the shared state of the application classes. This component manages user queue, shared property and permission of users in the conference room.

• Shared managers provide four pillars of every AFCS application.

− RoomManager controls access to the room state, privacy setting, bandwidth, etc.

− UserManager controls users inside the room, promotes/demotes users, shows a list of users, and accepts or denies users.

− FileManager give access to the files associated with the room, including publishing new files on the server.

− StreamManager provides access to the audio and video stream associated with the application.

• Collaboration User Interface components allow the developer to reuse the UI for the application and pods.

• Pods are high-level, mini applications consisting of (and providing the functionality of) AFCS platform such as web camera, file sharing or whiteboard. As soon as the authentication processes are completed, the user is allowed to get access to the provided services on the collaboration server. The platform provides six Pods i.e. roster, chat, file sharing, note, web cam, and whiteboard as default services. However, the system developer is able to create or customize Pods to match with the organization’s requirements. In this study, we have customized the provided Pods for our virtual conference service. Therefore, all provided services are integrated in one desktop application, as shown in figure V.28. This application is the main module which handles the connection of each Pod.

Figure V.28: User interface of virtual conference modules

The figure above shows screen capture of the virtual conference application. The main window (1) is the first screen that will be displayed to the users when they have logged in to the collaboration server. It provides the chat module which allows cluster members to communicate with text messages. From this window, the user can get access to other services via the control panel (2) at the bottom of the application. The control panel allows the user to enable each Pod from the client side. The Roster (3) button enables a list of online users in the conference room. File sharing (4) makes it possible for users to share their digital files with conference members. The Note module (5) is used for creating the conference note collectively. Then, Webcam window (6) displays multiple video streams from users’ web camera. Finally, the Whiteboard module (7) allows the cluster members to express their information/knowledge to the conference members in the graphical view. These Pods were implemented in our virtual conference service in order to facilitate the collaboration among the cluster members. In the next part, we will focus on the collaboration in peer-to-peer approach. V.4.2.3. Individual Collaboration

The objective of the individual collaboration service is to transfer information or knowledge between the members in the direct way. There are two major benefits of this type of service to the industry cluster. The first benefit is that it increases the frequency and quality of the collaborations within the cluster. The second benefit is that it allows the experts to share their knowledge to the knowledge users directly via KMS. In order to achieve these, the VoIP server is implemented for improving the functionality of the proposed KMS. The diagram in figure V.29 shows the necessary elements for the implementation.

Figure V.29: System architecture of VoIP server and KMS server

The diagram above describes the inter-connection between the KMS server and the VoIP server. The VoIP web service on the KMS server side is developed on Flex technology. The user interface of this application was displayed in figure V.21 (communication). For example, if the cluster leader wants to communicate with one of the cluster committee via this service, he can make a call promptly via the web service on the KMS or the Widget. Then, the request from the KMS server will be encapsulated in the RTMP protocol and send to Red5 Plug-in. This Plug-in is integrated to the system in order to convert RTMP protocol (supported by Flash application) into SIP protocol (supported by VoIP server). Then, the VoIP server will handle the connection between the web service and SIP phone. Integrating VoIP server not only brings the benefit to the ceramic cluster in term of communication aspect but also extend the capability of the knowledge retrieval of the KMS. An application of this integration will be discussed in the next section. V.5. Mobile Knowledge Retrieval

In previous sections, we have presented scenarios of collaboration, knowledge sharing, and knowledge retrieval of the industry cluster via the KMS. These scenarios proved that the proposed KMS is able to enhance the collaboration and knowledge of members which will create competitiveness in the industry cluster. Although this system makes it possible for the cluster to create, share, and reuse knowledge liberally (which is the fundamental of the knowledge management theory), there are limitations in terms of place and time. This means that users are able to retrieve their knowledge only if the internet connection is available. Moreover, the web browser application is required for displaying or requesting knowledge. These restrictions sometimes obstruct knowledge workers from acquiring the knowledge from the system when needed. Thus, in order to eliminate such limitations of the proposed KMS, this section will demonstrate an application of knowledge retrieval by combining the advantages of knowledge service and collaboration service. The fundamental of the application is combining the VoIP service with the knowledge query module. The objective of this integration is to support knowledge workers to reuse the knowledge from the knowledge base via their mobile device. Then, the required knowledge is transformed into the appropriate format (text, voice, image, or video) which suits the mobile device. The benefit of the system is allowing the knowledge worker to request or retrieve the required knowledge to immediately solve problems on their task, even in the workplace. The important technology which allows us to traverse over the barrier of place and time is mobile technology. We cannot deny that the mobile technology is becoming a part of our business in these days. These technologies make the cost of collaboration among the cluster members lower and affordable. Moreover, broadening of internet bandwidth makes VoIP become one of standard protocol for voice communication over the internet in this era. The VoIP application has been integrated in many mobile devices (e.g. SIP phone, soft-phone). In order to enable VoIP communication in the industry cluster, a VoIP server is required for handling the connections from the clients. Nowadays, there are wide ranges of software and hardware that allow users to establish a VoIP server, even for home-use. In this study, we use Asterisk® [Asterisk 09], which is an open source server for VoIP applications. This server lets us manage the connection of the Session Initiation Protocol (SIP), which is a widely used signaling protocol for multimedia communication sessions, such as voice and video over the internet. Clients are able to connect to their server via their mobile device or desktop application in order to make a connection with other users. With the intention of exchanging information between VoIP server and KMS server, the VoiceXML [VXML 09] is considered as standard format between both servers. This standard is designed for creating audio dialogs that feature synthesized speech, digitized audio, recognition of key input, etc. Its major goal is to bring the advantages of Web-based development and content delivery to interactive voice response applications. Therefore, the result map on the KMS server has to be re-formatted into VXML format. Then, the VXML content will be interpreted by the voice browser such as VXI* [VXI* 09]. The voice browser module is integrated into the system for reading VXML and converts into text content with the instructions. It also consents to insert scripts and commands into the content in order to support the input from the user. Next, the content and instructions will be transferred to speech synthesis module. This module is the artificial production of human speech. Hence, Text-To-Speech (TTS) software is integrated in the system for rendering normal language text into digital audio format (wave file). Finally, the audio file will be played when the connection to the inquirer is accomplished. Actually, there are many open source TTS modules that are compatible with Asterisk server such as Festival, Flite, Mbrola, etc. In this study, we applied Festival as voice wrapper. Then, Mbrola module was integrated in order to enhance quality of the voice in TTS module. The architecture of VoIP server and its modules are illustrated in figure V.30.

Figure V.30: Knowledge acquisition via VoIP server

The figure above illustrates knowledge acquisition via VoIP server. The main components on VoIP servers are query module, VXML Browser, and TTS module. In order to retrieve the knowledge from the KMS server, the knowledge worker sends the request via a mobile device. The request is the search keyword in text format, which is encapsulated in SIP protocol. Then, VoIP server extracts the search keyword and converts to a query string which complies with KMS’s format. Next, VoIP server sends the HTTP request to the KMS server via the knowledge acquisition module. The knowledge map from the inference engine will be converted into VXML standard and sent back to VoIP server. Then, VXML browser and TTS module convert the retrieved knowledge into a voice (speech) file. Lastly, VoIP calls the knowledge worker’s mobile device via SIP protocol in order to play the search result in voice form. Without this system, users require a web browser to request and retrieve the knowledge from the system.

Figure V.31: Interconnection diagram between KMS and VoIP server

The diagram above implies that there are two approaches for reusing the knowledge from the system. The first approach is acquisition via the desktop application. This approach is direct knowledge retrieval from the knowledge system which requires web browser and internet connection. In this study, we create a small desktop application called the knowledge widget , for retrieving real-time information from the KMS server. The retrieved knowledge will be transferred back to the knowledge workers via widget. The second approach is acquiring knowledge via the mobile device. In this approach, the request will be transferred to the KMS server through the VoIP server. Mobile devices can be categorized into two types i.e. standard telephones and IP phone. A “standard telephone” means mobile device that requires PSTN (Public Switched Telephone Network) in order to connect to VoIP server. IP Phone (Internet Protocol Phone) is mobile devices that support internet protocol at their hardware or firmware level. The knowledge request will be send to VoIP server via SIP protocol. Then, the request is encapsulated and forwarded to the KMS server. The search result will be send back to the requester in voice format. From the communication model in figure V.31, the arrow head indicates the direction of communication with the protocol used for conveying information. The knowledge acquisition process is initiated when the client side sends the inquiry keyword to the server. IP Phone can send a keyword over SIP protocol to the VoIP server. Similarly, a mobile phone is able to send Short Message Service (SMS) through PSTN. An optional way to send a query without a messaging service is to call to VoIP server and press the keypad, following the alphabet layout. The VoIP captures DTMF and converts to a query string. The query module sends the query string to KMS server. However, desktop applications can send query strings to a KMS server directly via web service. The inference engine searches for results from the knowledge cards in the knowledge base. The search results are re-formatted to VXML format and transferred to VoIP server via VXML browser module. The interpreted VXML is converted into digital audio (wave) format by TTS module. Finally, VoIP server makes a phone call and plays search results in audio format to the user. In order to summarize the processes, figure x demonstrates the knowledge acquisition in terms of transferred data. A scenario that demonstrates the knowledge acquisition of the cluster member via a mobile device is illustrated in figure V.32.

Figure V.32: The knowledge acquisition process in data point of view

The process of this knowledge acquisition will be depicted along with the scenario from the ceramic cluster in our case study. Form the example in the diagram above, a knowledge worker on the ceramic glazing process found that the glaze on the ceramic product was rippled. In order to comprehend the cause of this fault, he sent a search keyword (“rippling of glaze”) to the KMS server via his mobile phone. Then, the search keyword was modified into a query string by adding essential parameters for the inference engine. As soon as the inference engine received the query string, it connected to the knowledge base in order to retrieve the knowledge required. The result set from the inference engine (which is in the form of semantic map) is translated into VXML format by the knowledge representation module. (The output from the KMS server in VXML format allows the VXML browser to extract the knowledge content and commands from the script.) Next, the content ids converted into synthesized speech by TTS module. Finally, the knowledge worker receives a phone call from VoIP server. The VoIP handler played the retrieved knowledge “rippling of glaze caused by too heavy an application of glaze” . The knowledge obtained from the system helps the knowledge worker to understand the cause of the fault and knowledge related to this fault. Therefore, the problem could be solved immediately. Although there are many advantages from implementing this system in the ceramic cluster, there are some limitations and improving points which will be discussed.

V.6. System Validation

The proposed KMS is applied to a ceramic cluster in Lampang province of Thailand in order to improve the cluster development by focusing on two issues i.e. the knowledge sharing and the collaboration of the cluster members. Our knowledge system is introduced to the cluster committee and CDA for testing. In this process the test specification which was designed in the design level (chapter 4) is used for assessing the functional ability of the proposed KMS. Thus, a scenario of “trade fair preparation” which is one of the key activities of this ceramic cluster; is used for validating our system. This scenario is concern with the BIG+BIH (Bangkok International Gift & Bangkok International Houseware) Fair in 2009. The UML diagram in figure V.33 shows how the cluster members can make use of the provided knowledge and collaboration service on the KMS to achieve their goal in the trade fair. The sequence diagram comprises two main actors i.e. cluster member and CDA, and two services in the KMS i.e. knowledge service and collaboration service.

Figure V.33: Sequence diagram of the international trade fair preparation

For this trade fair, this ceramic cluster obtains some subsidization from the government to join this event. Thus, cluster members have to plan their strategy for accessing new markets during this fair. In the past, this cluster did not have any system for supporting these activities. There were only a few meetings before the trade fair which can not satisfy the cluster members. Moreover, the knowledge and experience were not shared among the members. Thus, this scenario will show how the cluster members adopt the KMS for improving this situation, which is divided into five parts as follows.

(a) First part: Mass collaboration

The starting point of this scenario is that the CDA create an event on the c-calendar module in order to inform all members to participate in the virtual meeting via the KMS. Moreover, the invitation is sent to experts in the ceramic cluster by push news module in order to motivate them to share their knowledge about the international trade fair. The invitation message contains the information about the required knowledge and link to the knowledge card. An example of the push news and mass collaboration model are displayed in the figure V.34.

Figure V.34: Mass collaboration model

This type of collaboration is useful for the CDA or the cluster leader to communicate with the cluster members in mass. It also ensures that the information will be distributed throughout the ceramic cluster. After received the message from the CDA, the experts who want to share their knowledge will continue in the second part.

(b) Second part: Knowledge representation and sharing

The experts who are willing to share their knowledge about the international trade fair are able to access the CoP via the provided link in the message. In this part, the knowledge service in the KMS will facilitate the knowledge representation and sharing. The shared knowledge will be stored in form of the knowledge card. An example of knowledge sharing is illustrated in the following figure.

Figure V.35: Knowledge sharing scenario

The figure above demonstrates the knowledge sharing scenario of the experts in the ceramic cluster. For instance, the first expert gets access to the CoP and shares his knowledge about exporting product which is a related task after the international trade fair. Then, the second expert realizes that he has some knowledge about the customs which is necessary for exporting the product. Accordingly, he creates new knowledge card about the trading contract. Finally, the third expert shares his knowledge by extending from the previous knowledge. The knowledge about the EDI system for the customs is added into the system. Then, each knowledge card will be linked together automatically by the inference engine. With this concept, the new knowledge could be added to the knowledge base liberally.

(c) Third part: Group collaboration

In the meeting, the virtual conference module is initialized by the CDA who logged in as the host of the meeting room. Then, the cluster members and anonymous users can join the meeting room with the different privileges. The model of the group collaboration via the KMS is illustrated in the figure V.36.

Figure V.36: Group collaboration model

The figure implies that there are three types of privilege on the KMS i.e. room manager (host), member (participant), and guest (audience). The benefit of the virtual conference module allows cluster members to organize the meeting from the distance. The knowledge and information about the trade fair preparation is shared in this stage.

(d) Forth part: Individual collaboration

After cluster leader assign the task to the member from the meeting in the previous stage, the individual collaboration service is considered for supporting the communication exterior the meeting. This VoIP module is not only allows the cluster members to communicate via the VoIP web service on the knowledge widget but also via their mobile devices. This will support the communication among the members of the cluster as shows in the following model. Figure V.37: Individual collaboration model

The model above shows that the VoIP module supports the communication between the members in peer-to-peer approach. The details information which is unable to be shared in the meeting will be shared in this stage. The cluster members exchange their information (e.g. progress of preparation, urgent problems, etc.) before the next meeting via this service.

(e) Fifth part: Knowledge retrieval

In this stage, the search module in the knowledge service is responsible for the knowledge retrieval. The knowledge user is able to send a request for searching the required knowledge from the cluster knowledge base. The knowledge retrieval model of our proposed KMS is showed in the figure V.38.

Figure V.38: Knowledge retrieval via KMS

The model above shows the process of knowledge retrieval from the KMS. For example, a knowledge user requires the knowledge about “EDI System” from the KMS. The keyword is sent to the search module. Then, the inference engine browses over the knowledge base in order to query the knowledge cards which match with the keyword. Finally, the search result in knowledge card format is delivered to the knowledge user in an appropriate form. V.7. Conclusion

In summary, this chapter showed how the design model is transformed into the KMS. Each tool in the system architecture is selected with regard to the cluster’s requirements in both knowledge and collaboration aspects. Concerning these requirements the knowledge service is introduced to the system architecture for managing the knowledge activities of the cluster members (i.e. representing, sharing, and reusing). Two major modules that facilitate these activities are knowledge card and inference engine. The notion of knowledge card was adopted for representing and sharing the knowledge of experts in the cluster. On the other hand, the inference engine was designed for searching the knowledge through the semantic knowledge model in the knowledge card. These services make it possible for cluster members to share their knowledge through an indirect approach and still retain the quality of knowledge. Collaboration service is supported by collaboration and communication tools (such as c- calendar, push/pull news, virtual conference, etc.) with the purpose of enhancing the coverage of information and degree of collaboration. In the knowledge management point of view, the major categories of exchanged knowledge on this level are know-who, know-where, and know-when. Cluster members gain knowledge about other persons from the cluster map, and opportunities from calendar and push/pull news. However, deeper level of knowledge (e.g. know-how, know-what, know-with) can be obtained via the virtual conference or the VoIP service. The obtained result from our investigation (presented in chapter 4) confirmed that cluster members feel uneasy to request/share their in-depth knowledge in the direct approach. They preferred to exchange their knowledge via CDA. Therefore, this situation indicated that CDA is an important element for the industry cluster development. Although, this method is able to facilitate the knowledge sharing in the cluster, the quality of knowledge is decreased and the knowledge could not be reused in the future request. The last group of service which was developed for the system is the presentation service. The main objective of this group is to present the provided services on the KMS to the users in the appropriate form. Many studies [Pentland 95] [Schreiber 99] [Malhotra 04] consented that one of the failures of knowledge system development is neglecting specific characteristic of the organization. Thus, this presentation services aimed at introducing the KMS to the organization in regard to cluster’s preference. It allows cluster members personalizing the KMS to suit with their role. In addition, it provides the possibility for the users to interface with KMS in three ways i.e. server-side application, client-side application, and mobile device. In order to support this, KMS portal, knowledge widget, and VoIP services are implemented in the system to increase the usability of the KMS. At the end of the chapter, three scenarios of KMS usage were demonstrated in order to verify the application of the KMS in the industry cluster.

Conclusion and Perspectives

The ultimate goal of this research is to improve the competitive advantage of the industry cluster by focusing on the collaboration and sharing of knowledge among the members which are the key success factors of the industry cluster. Therefore, our study is organized into five parts (chapters). Each chapter focused on different point of views of the study. The explicit results from each chapter are described as follows. The first chapter aims at analyzing the industry cluster context and the need for knowledge management in order to improve the competitiveness of the cluster. We found that the innovation is a driving force behind the long-term competitive advantage of the industry cluster. Moreover, in order to develop the innovation of the cluster, knowledge and collaboration are the foundation of the development. The literature review related to the clusters development in many countries also confirmed that these elements are the key success factors of the cluster development. However, we found that the main obstacle of the development is the “co-petition” relationship. This type of relationship in the industry cluster context is more intense than supply chain, extended enterprise or other organization networks. Thus, the critical problems of this study are set to understand the characteristics of information and knowledge sharing of the cluster, and to propose the architecture to improve this situation. The second chapter concentrates on the application of the organizational knowledge management methodology to the cluster context. We found that among the cases that have been studied; none of them relied on the knowledge management approach. Thus, we propose the KMS for facilitating and improving the industry cluster development. The knowledge engineering methodology is adopted for supporting analyzing, designing, implementing the KMS for a specific context. This chapter compares various approaches of knowledge engineering methodology (i.e. MOKA, SPEDE, AKEM, and CommonKADS) in order to select an appropriate method for to be applied in the industry cluster context. The result of the analysis shows that CommonKADS methodology is the most suitable for our case study. However, it cannot be directly employed without a certain adaptation. Thus, the third chapter concentrates on adapting the methodology to suit with our context. In our methodology, the “model suite” is proposed to design the KMS. It is composed of four levels i.e. context, concept, design, and implementation. The context level is proposed to analyze the organizational context of the cluster in terms of actors, processes, knowledge-intensive tasks, and knowledge assets. The concept level is defined for extracting the knowledge from the experts and also collaboration model of the cluster. The design level was added to the model suite of convert the cluster’s requirements into a system specification by using software engineering approach. Finally, the implementation level is oriented to the KMS development process. The fourth chapter shows the obtained results from the application of the model suite to a ceramic cluster in Thailand. This chapter concentrates mainly on the first three levels of the model suite. The results from the context level are composed of three models i.e. cluster, organization, and task. The cluster model implies the physical network of the ceramic cluster. The organization model reveals the present organizational context of the ceramic cluster e.g. level of trust, structure, and knowledge sharing. The task model focuses on selecting the knowledge-intensive tasks and knowledge assets for our case study. In this level, we selected a task about “ finding market opportunity ” as our instance. The cluster committee has consensus that this task is the most significant task in the ceramic cluster in the present situation. The concept level is composed of two models i.e. knowledge model and collaboration model. The knowledge model is used for extracting the expert’s knowledge about the specified task and then converts it into semantic knowledge map. The collaboration model concentrates on the approach for exchanging the information within the cluster. Finally, the design level interprets the requirements from the previous levels into system scenarios, specifications, and architecture. The outcome from this chapter is the specification documents to develop the KMS. These documents are also considered as the medium between the knowledge engineers and the system developers. The fifth chapter concentrates on the development and the implementation of KMS for the specific case of ceramic cluster. The KMS architecture is separated into three levels of services: collaboration, knowledge, and presentation. Each level aims at supporting the industry cluster in the different aspects. The collaboration services comprise the collaborative tools such as push/pull news, collaborative calendar, and virtual conference. The objective of these tools is to improve the collaboration of the cluster in terms of frequency and quality of collaboration. The knowledge services are supported by two main modules i.e. the knowledge card and the inference engine. The knowledge card module is developed in this study in order to facilitating knowledge creation and sharing of the experts in the cluster. The advantages of this concept are related to the possibility for experts to express their knowledge in different ways (semantic map or wiki) and the possibility to append their knowledge to other’s knowledge. The main technology behind these benefits is the semantic map module. It provides many advantages to our knowledge system e.g. readability by both human and machine, simplicity to manipulate the knowledge, compatibility with the inference engine, which supports the users to reuse the knowledge in the system. It provides better mining results than the ordinary content search due to the result set generated by semantic relationship of the concept (in the semantic map module). The implementation scenarios are also presented in this chapter in order to depict how the KMS can improve the situation of the ceramic cluster. The first scenario shows the collaboration possibilities via the KMS. It proved that our knowledge system supports the three types of communication (i.e. peer-to-peer, group, and mass). The second scenario demonstrates the possibilities in term of knowledge creation, sharing and retrieving via the KMS. This scenario shows that major knowledge activities of the cluster members are supported by the proposed architecture. Finally, the last scenario shows the integration between the knowledge service and the collaboration service in order to eliminate the barriers of knowledge retrieval in term of place and time. In this scenario, the mobile device is used as the tools for acquiring the knowledge from the server. This allows the users to reuse the knowledge from anywhere and anytime, even their workplace (production lines, logistic sites, exhibition halls, etc.). From our perspective , although this study showed the ability of the KMS for supporting the industry cluster development, it can be improved in two aspects: Methodological aspect and Technical aspect. The methodological improvement concerns the knowledge engineering methodology which is the principle of this study. The proposed methodology is based on CommonKADS methodology which is considered as more suitable for applying in the industry cluster context than the others (MOKA, SPEDE, and AKEM). However, it requires an enhancement in term of knowledge elicitation (knowledge modeling) process. The knowledge model is provided in the CommonKADS is quite subjective to the engineering domain which makes uneasy to knowledge engineer to apply the proposed methodology in the soft domain. Thus, enhancing this part of our methodology will help in generalize the methodology into various domains. The validation of the methodology in the medium-term is one of the critical points in our perspective. The methodology validation has been done in our study only in event-based aspect (short-term) due to the limit of time. However, the evolution of the KMS during the first business activity cycle of the cluster (medium-term) is highly important for the knowledge management project. Thus, the CDA is required to motivate and facilitate the cluster member to collaborate and share their knowledge via the system. The study of evolution of KMS and the cluster behavior in medium-term will help us to improve the methodology. In the technical point of view, the KMS architecture itself can be improved in two points. The first point is the collaboration services which are the fundamental of the information and knowledge exchange of the proposed architecture. The architecture of these services can be enhanced by the concept of Service-Oriented Architecture (SOA) which brings ability to the KMS to exchange data with other systems. Another point is the knowledge services level which allows experts to create, represent, and share their knowledge. The concept of ontology can be applied in the Collaborative Knowledge Card system in order to formalize the knowledge map into the structured form. Moreover, this concept make possible for the knowledge engineer to maintain the knowledge map in the KMS. The last perspective is the extensibility of the methodology and the platform into other industry clusters in the different domains. We are interested in generalizing our methodology to the domain of tourism cluster which seem to have similar characteristics as the ceramic cluster. The proposed KMS would help cluster members to exchange their knowledge and collaborate on the tourism business activities. Moreover, the knowledge users which are tourist are able retrieve the required knowledge from the system.

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A.1. General Information

General Information about Cluster Location Lampang Province Upper Northern Thailand Economic Sector Ceramic Industry Product Tableware, Decorative Item and Souvenir Supporter Ceramic Center Funding National Innovation Agency (NIA) of Thailand Potential Group 4 : High Potential Groups 1) Ceramic cluster in Lampang 2) Ceramic cluster in Chiang Mai 3) Ceramic cluster in Rachabury 4) Ceramic cluster in Om-Noi 5) Ceramic cluster in Sarabury

A.2. Diamond Analysis

Analysis Criteria Government (+) Local government set provincial strategy as the ceramic center of Asian. (–) Lack of continuous supporting from government to improve product, design and market. Firm strategy, (+) Most are SMEs which manufacture by OEM and ODM. Structure and (+) Large enterprises focus on exporting by client’s order. Rivalry (+) Many supports from organizations to develop dynamic and sustainable cluster. (+) Focus on niche market than mass market. (+) Cross-linked between food, hotel and ceramic industry. (+) Establishment of National Ceramic Center in Lampang. (–) Strong rivalry in small enterprises by cutting price and copying the design. (–) OEMs are not motivated to develop their design. (–) Lack of own branding in ceramic industry (–) Production cost such as raw material, logistic and fuel are increasing. (–) SMEs lack of knowledge and experience in exportation. Factor Condition (+) Largest source of high quality white clay in the country. (+) Availability of LPG factory in the area, which is the main fuel for production. (+) Availability of local high-skilled craftsmen and designer. (+) Located in the middle of the northern Thailand, connected to many provinces which provide the advantage in term of logistic. (+) 17 electrical substations are in service for the factory. (+) Most of raw materials can be purchased in the province. (–) Most of machines have to import from foreign country. (–) Labors lack of responsibility in their task. (–) Entrepreneur lack of awareness in industrial and production standard.

Criteria Analysis

Related and (+) The correlation between related industries is existed e.g. tourism, handicraft, Supported construction and decoration. Industries (+) Availability of ceramic association and ceramic center in the province. (+) Supported by National Innovation Agent (NIA) to create own branding. (+) Distributed production process to the competent factory in the cluster. (–) Low degree of relation with academic institutes. (–) Lack of industry and academic institute who develop technology and machine for production in the supply chain. (–) Weakness of linkage of supply chain in ceramic industry. (–) Clustering in of ceramic enterprises in Lampang still lack of strong collaboration. Demand Condition (+) Demanding in the country still has good trend, major market is in Bangkok. (+) Medium to High level customer and foreign customer give important to quality of product. (+) Large enterprises keep track the preference of customers via many channels. (–) Foreign market affect from termination of GSP (Generalized System of Preferences) privilege by European Union (EU). (–) Small enterprises could not access to information about trend and preference of customers. (–) Domestic customers do not concern the quality of the product; feel that Lampang’s ceramic is low to medium quality.

A.3. Ceramic cluster’s supply chain

Figure A.2: Supply chain of Lampang’s ceramic cluster

A.4. Business cycle of Lampang’s ceramic cluster Figure A.3: One-year cycle of ceramic business in Lampang

A.5. Cluster Map

Figure A.1: Ceramic cluster map

Annex B: SECI Model

B.1. Nonaka’s Sprial Model

Nonaka and Takeuchi [Nonaka 95] propose a model of the knowledge creating process to understand the dynamic nature of knowledge creation and to manage such a process effectively which consists of 3 elements: 31 SECI (Socialization, Externalization, Combination, and Internalization) 32 Ba (a Japanese word: translated as a place or a shared context in which knowledge is shared, created and utilized through interaction) 33 Knowledge Assets (company-specific resources that are indispensable to create values for the firm) These 3 elements interact with each other organically and dynamically. The knowledge assets of an organization are mobilized and shared in 'Ba' whereas the tacit knowledge held by individuals is converted and amplified by the spiral of knowledge through:

Figure B.1: Nonaka’s SECI model [Nonaka 94 ]

Organizational knowledge transfer is occurred when members exchange their knowledge. The individual knowledge is able to transform to social knowledge in two modes i.e. socialization and externalization. Accordingly, social knowledge can be transformed to individual knowledge by combination and internalization. The details of each mode in the SECI model are described below:

• Socialization : is the process of sharing experiences, thereby creating tacit knowledge such as mental models and technical skills. Tacit knowledge can be obtained without the use of language (i.e. through observation, imitation and practice);

• Externalization : is the process of articulating tacit knowledge in the form of explicit concepts such as metaphors, analogies, hypotheses and models;

• Combination : is the process of systemizing concepts into a knowledge system by combining different bodies of explicit knowledge. Explicit knowledge is transferred through media such as documents, meetings, e-mail and phone conversations. Categorizing this knowledge can lead to the generation of new knowledge;

• Internalization : is the process of converting explicit knowledge to tacit knowledge and is closely related to learning by doing. The knowledge spiral is a model for creating and transferring knowledge in the organization. These four processes aim at creating competitiveness of organization from fresh knowledge of members. As same as the industry cluster, the competitiveness was developed from the collaboration of members in core and supporting cluster. The new knowledge was jointly created from cluster activities such as marketing, product development, training, etc. Annex C: Questionnaire Outline

Part 3: Information and communication technology that used in the SMEs 7. Is your company using these technology and how often of usage? (Please mark √ in the box below(

Technology Very often Frequently Seldom Never Technology for collaboration 7.1 Telephone/Fax/Mail 7.2 E-mail 7.3 Messaging Tools 7.4 Web-based Application Technology for designing 7.5 CAD/CAM 7.6 Design Automation Technology for production 7.7 Material Management 7.8 Process Automation 7.9 Accounting Technology for marketing 7.10 Website 7.11 e-Commerce/Online Store Technology for CRM 7.12 Customer Database 7.13 Delivery 7.14 After Sales Technology for HRM 7.15 Training 7.16 Evaluation 7.17 Resource Development Part 4: Willing to share information and knowledge of the company Definition for level of collaboration Core Cluster: Ceramic producers Cluster: Ceramic producers + Government agents + Academic institutes + Supportive industries Anonymous: People, Company or Organization that are not related to your cluster 8. Do you willing to share these information or knowledge to each level of collaboration below (Please mark √ in each level of collaboration)

Anonymous Cluster Core Cluster Topics Strong Medium Low Strong Medium Low Strong Medium Low 3 2 1 3 2 1 3 2 1 8. General Information, such as contact address, e-mail, capability of company 8.2 Business opportunity, such as details of exhibition, low interest loan, training 8.3 Techniques and Experience, such as product selection for international fair Part 5: Characteristic of collaboration in the cluster 9. How do you think about your cluster for characteristic of collaboration below?

Strongly Not Not Strongly Topics Agree agree sure agree not agree Environment 9.1 Long history of collaboration in the community. 9.2 The collaborative group is seen as a legitimate leader in the community. 9.3 A favorable political and social climate. Membership Characteristics 9.4 Mutual respect, understanding, and trust for members and their respective organizations. 9.5 An appropriate cross section of members. 9.6 An appropriate cross section of members. 9.7 Your company sees the benefit from collaboration. 9.8 In case of any problem about collaboration between you and your partner, you and your partner solve the problem together. 9.9 In case of any problem about collaboration between you and your partner, you and your partner try to avoid the conflict. Processes and Structure 9.10 Members share a stake in process and outcome. 9.11 Multiple layers of participation. 9.12 Flexibility in both structure and methods. 9.13 Development has clear policy guidelines. 9.14 Members understand their roles. 9.15 Adaptability or the ability of the collaborative group to sustain itself in the midst of changes. 9.16 You can abort the collaboration without losses. 9.17 It is easy to stop the collaboration and start a new one. 9.18 Cost of establishing the collaboration would be high. 9.19 Partner has a good collaboration and co-development. 9.20 Member actively seeking for suggestions for improvement from partner. Communication 9.21 Open and frequent communication. 9.22 Communication is complete and reliable. 9.23 Communication is exact and on time. 9.24 Established informal and formal communication links. 9.25 Partner keeps informing useful information to each other. 9.26 Partner share information about them to each other. 9.27 You share some confidential information among the cluster. Purpose 9.28 Clear attainable goals and objectives that are communicated to all partners and can be realistically attained. 9.29 Cluster has the unique purpose. 9.30 The collaboration achieves the objective. 9.31 Member in the cluster have same vision. Resource 9.32 Cluster has sufficient funds, staff, materials, and time to maintain the collaboration. 9.33 Cluster has skilled leadership to take care of the collaboration. Annex D: Knowledge Models

In order to build the Knowledge Management System, 8 meetings with the ceramic cluster members were organized. The knowledge from the experts in each process in the specific task was represented in form of the knowledge models which is presented below. Meeting : M1-T-A01 Topic : The methodology for accessing to the new market opportunity for SMEs Template selected : 1. Planning template 2. Classification template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Planning template Accessing to new market T-A01 Accessing to new the market opportunity Classification template Products selection I-A01-014 Product selection for international trade fair I-A01-007 Product selection for domestic trade fair I-A01-010 Product selection for domestic road show

Figure D.1: Accessing to new the market (T-A01)

Figure D.2: Product selection for international trade fair (I-A01-014)

Figure D.3: Product selection for domestic trade fair (I-A01-007)

Figure D.4: Product selection for domestic road show (I-A01-010)

Meeting : M2-T-A01 Topic : Lesson learned from international trade fair (case study of UAE) Template selected : 1. Diagnosis template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Diagnosis Template Lesson learned from I-A01-004 Lesson learned from international trade fair in international trade fair UAE Recommendation for I-A01-020 Recommendation for UAE market UAE market

Figure D.5: Lesson learned from international trade fair (I-A01-004)

Figure D.6: Recommendation for UAE market (I-A01-020)

Meeting : M3-T-A01 Topic : Acquiring support from the government Template selected : 1. Classification template 2. Diagnosis template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Classification template Acquiring support from I-A01-015 Acquiring support and Diagnosis template the Department of Export from the government Promotion

Figure D.7: Acquiring support from the government (I-A01-015)

Meeting : M4-T-A01 Topic : Acquiring support from the government Template selected : 1. Classification template Derived Knowledge Model : Template used Knowledge Elicitation Map ID Map Topic Classification template Acquiring global trend I-A01-021 Global trend from department of export promotion

Figure D.8: Global trend (I-A01-021)

Meeting : M5-T-A01 Topic : Booth Decoration and Management Template selected : 1. Classification template 2. Diagnosis template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Diagnosis template Booth Decoration I-A01-016 Booth decoration Classification template Booth Management I-A01-017 Booth management

Figure D.9: Booth decoration (I-A01-016)

Figure D.10: Booth management (I-A01-017)

Meeting : M6-T-A01 Topic : Acquiring financial support from financial institute Template selected : 1. Diagnosis template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Diagnosis template Financial support for trade fair I-A01-019 Acquiring financial support from exhibitor financial institute

Figure D.11: Acquiring financial support from financial institute (I-A01- 019)

Meeting : M7-T-A01 Topic : Web Marketing / Ceramic Branding Template selected : 1. Diagnosis template Derived Knowledge Model : Template used Knowledge Elicitation Map ID Map Topic Diagnosis template Web Marketing I-A01-011 Web Marketing Diagnosis template Ceramic Branding I-A01-012 Ceramic Branding

Figure D.13: Web marketing (I-A01-011)

Figure D.14: Ceramic Branding (I-A01-012)

Meeting : M8-T-A01 Topic : Repository and Contacts Template selected : 1. Classification template 2. Diagnosis template Derived Knowledge Model :

Template used Knowledge Elicitation Map ID Map Topic Classification template International trade fair repository I-A01-005 Repository Classification template International trade fair contacts I-A01-006 Contacts

Figure D.15: Repository (I-A01-005)

Figure D.16: Contact (I-A01-006)

Annex E: KMS Specification

E.1. Requirement Specification

User Requirement Specification for the Cluster Knowledge Management System

RS-1 Support Knowledge Creation

RS-1.1 Provide opportunity (Refer to FS-1.2 and FS-5.3) • Add new event on collaborative calendar

• Add new news on the push/pull news system

RS-1.2 Create knowledge card (Refer to FS-2.1) • Add new knowledge card to CK-Card system

RS-1.3 Add contact (Refer to FS-1.1) • Add new user to the cluster map

RS-2 Support Knowledge Sharing RS-2.1 Push/Pull news system (Refer to FS-4) • Manage news system

• Push news to subscribers/users/user groups

RS-2.2 Collaborative calendar system (Refer to FS-5.1 and FS-5.2) • Display events from the calendar

RS-2.3 Display collaborative knowledge card (Refer to FS-2.1 and FS-2.2) • Display selected knowledge card

• Browse over the knowledge card

• Search knowledge card from the keyword

RS-2.4 Display cluster map (Refer to FS-3.1) • Visualize cluster map

RS-3 Support Knowledge Reuse

RS-3.1 Search opportunity (Refer to FS-4, FS-5.1 and FS-5.2) • Display events from C-Calendar

• Display news • Display knowledge map

RS-3.2 Search knowledge card (Refer to FS-2.2 and FS-10) • Knowledge card search

• Advanced knowledge search over the knowledge card

RS-3.3 Search contact (Refer to FS-3.2) • Search contact in the cluster map

RS-4 Support information storage (Refer to FS-1) • Cluster map database

• C-Calendar database

• Push/Pull news database

• Knowledge card database

RS-5 Support Communication

RS-5.1 Text communication (Refer to FS-6) • Live chat system (asynchronous)

RS-5.2 Voice and video communication (Refer to FS-7) • Video conferencing system (synchronous)

RS-5.3 Support mobile device integration (Refer to FS-8) • Voice over Internet Protocol (VoIP) system

RS-6 Support Users’ Personalization (Refer to FS-9.1, FS-9.2 and FS-9.3) • Customizable knowledge map search

• Customizable news view

• Customizable calendar view

RS-7 Support Users’ Visualization (Refer to FS-9.4 and FS-9.5) • Customizable cluster map view

• Customizable collaborative system

PS-8 Support Knowledge System Management (Refer to FS-1) • Manage user information

• Mange user group

• Manage user’s VoIP account E.2. Functional Specification

Functional Specification for cluster knowledge management system

FS-1 Administrative Functions

FS-1.1 Manage User: Cluster map manipulation • Manage user information

• Manage user group

• Manage user VoIP account

FS-1.2 Manage News: Push/Pull information • Add/Delete news subscribers

• Mange push/pull news

FS-1.3 Manage Opportunity: Collaborative calendar • Manage events

• Manage events categories

FS-1.4 Manage Virtual Collaboration: Collaborative platform • Enable/Disable collaborative system

FS-2 Collaborative Knowledge Card Functions:

FS-2.1 Collaborative Knowledge Card • Knowledge card metadata

• Semantic Knowledge map

• Wiki content

• Knowledge card links

FS-2.2 Knowledge Map Search • Knowledge card search

• Wiki content search

FS-3 Cluster Map Functions: Collaborative platform

FS-3.1 Cluster Map Visualization • Display User by group

FS-3.2 Cluster Map Visualization • Search User in the cluster map FS-4 Push/Pull News Functions: Display news and events from the news database • Display news topic

• Display news content and image (if available)

FS-5 Collaborative Calendar Functions: Display list of events from c-calendar

FS-5.1 Display Week calendar • Display only this week event for displaying on the portal page

FS-5.2 Display Month Calendar: display on the calendar page • Display all events in the selected month for displaying on the calendar page

FS-5.3 Add New Event to Calendar • Add a new event to the calendar system

FS-6 Live Chat Functions: Allow user to send text message to online users • Display online users on the KMS

• Send text message to all users who is online on the KMS

• Send personal text message to a user who is online on the KMS

• Send text message to admin (CDA)

FS-7 Video Conference Functions: Allow user to create virtual meeting room

FS-7.1 Broadcast audio to online users • Enable/Disable audio broadcasting

FS-7.2 Broadcast video to online users • Enable/Disable video broadcasting

• Display video from users’ webcam (if enabled)

FS-7.3 Display online users in the conference room • Show list of online users

FS-8 Voice over Internet Protocol (VoIP) Functions: Allow use to communicate to mobile devices

FS-8.1 Web-based VoIP Phone • VoIP phone (web service)

FS-8.2 VoIP Server • Manage users VoIP account

• Allow connection to PSTN (Public Switched Telephone Network)

• Handle all VoIP connections

FS-9 Widget Functions: Allow user to personalize information from the KMS FS-9.1 Customizable knowledge map search • Search engine connection: allow user to connect to advanced search engine on the KMS from their client application.

• Customizable search result

FS-9.2 Customizable news view • Display news by (all new / today / this week / this month)

FS-9.3 Customizable calendar view • Display events by (today / this week / this month)

• Display events by category

FS-9.4 Customizable cluster map view • Display contact by group of user

• Display online/offline contact

FS-9.5 Customizable collaborative system • Enable live chat function on client application

• Enable video conference function on client application

• Enable VoIP function on client application

FS-10 Advanced Search Functions: Allow user to search over the collaborative knowledge card system

FS-10.1 Knowledge card search • Search knowledge card name which match with the search keyword

FS-10.2 Wiki content search • Search wiki content which contains the search keyword

FS-10.3 Forward inference search • Search domain knowledge which related with the search keyword

FS-10.4 Backward inference search • Search domain knowledge which related with the search keyword

E.3. Design Specification

Design Specification for the Cluster Knowledge Management System

DS-1 Area Level 0: Global Level

DS-1.1 Top Menu Frame • Home

• Cluster Map

• Calendar

DS-1.2 Main Content Frame • Portal : display week calendar, push/pull news and subscribing box (Refer to RS-2:FS- 3)

• Cluster Map: display visualized cluster map and provide XML feed for cluster contact or contact list (Refer to RS-2:FS-3)

• Calendar: display events within the selected month and represent by categories. (Refer to RS-2:FS-5)

DS-2 Area Level 1: Cluster Level

DS-2.1 Top Menu Frame • Home

• Cluster Map

• Calendar

• Knowledge Card

• Advanced Search

DS-2.2 Main Content Frame (Extended from DS-1.2) • Knowledge Card: display knowledge card and enable knowledge card browsing (Refer to RS-1.2:FS-2)

• Advance Search: display knowledge card search input box and represent search result in format of list or map (Refer to RS-3.2:FS-10)

DS-3 Area Level 2: CoP Level

DS-3.1 Top Menu Frame • Home

• Cluster Map

• Calendar

• Knowledge Card

• Advanced Search

DS-3.2 Main Content Frame (Extended from DS-2.2) • CoP Knowledge Card: display knowledge card for particular community of practice (Refer to RS-2.3:FS-2)

DS-3.3 Widget • Search: allow customizable search (Refer to RS-6 and RS-7:FS-9.1)

• News: allow customizable news display (Refer to RS-6:FS-9.2)

• Events: allow customizable events display (Refer to RS-6:FS-9.3)

• Knowledge Card: allow user to view knowledge card from client application (Refer to RS-7:FS2)

• Collaborative System: allow customizable collaborative platform (Refer to RS-5.1 and RS-5.2:FS-7 and FS-8)

• VoIP Phone: allow user to connect to VoIP server via client application (Refer to RS- 5.3:FS-8)

DS-4 Area Level 3: Administrator Level

DS-4.1 Top Menu Frame • Home

• Cluster Map

• Calendar

• Knowledge Card

• Advanced Search

• Administrator Control Panel

DS-4.2 Main Content Frame (Extended from DS-3.2) • News Management: allow admin to add new news and distribute information to users (Refer to RS-1.1:FS-1.2)

• Subscriber Management: allow admin to manage news subscribers (Refer to RS- 1.1:FS-1.2)

• Cluster Map Management: allow admin to mange users information and cluster map (Refer to RS-1.3:FS-1.1)

• Collaborative Platform Management: allow admin to enable/disable live chat and conference room (Refer to RS-8:FS-1.4) E.4. System Specification

System Specification for the Cluster Knowledge Management System

SS-1 Hardware Specification

SS-1.1 Server Specification • Processor: Intel Pentium4 3.0 GHz minimum • Memory (RAM): 2048 MB recommended

• Hard drive space: minimum 50 MB

• Network Interface Card 100 Mbit/sec

SS-1.2 Client Specification • Processor: Intel Pentium4 1.0 GHz minimum • Memory (RAM): 512 MB recommended

• Hard drive space: minimum 20 MB

• Network Interface Card 100 Mbit/sec

SS-2 Network Specification • Wired or Wireless Network

• Domain Controller Service

• Fixed IP address

SS-3 Software Specification

SS-3.1 Server Specification SS-3.1.1 KMS Server ( Linux distribution Debian) • Apache Web Server

• MySQL

• PHP 5.0

• Red5 flash server

SS-3.1.2 VoIP Server (Linux distribution Debian) • Apache Web Server

• PHP 5.0

• Asterisk 1.4.0 or compatible

• Festival TTS • Mbrola TTS Voice Pack

SS-3.2 Client Specification • Microsoft Internet Explorer 6.0 or Mozilla Firefox 3.0

• Adobe Flash Player 9.0 plug-in

• Adobe AIR Installer

E.5. Test Specification

Test Specification for the Cluster Knowledge Management System

E.5.1. Demonstration Test Specification

Test No. DT-01 2. Test Name: Demonstration Test 3. Test Description:

- Demonstration by ceramic Anonymous user, support cluster, core cluster and cluster development agent. 4. Assumption:

- Attendants are experts who have domain knowledge about case study.

5. Test Case Examples : Exporting ceramic product 6. Feed Back:

Issue Proper Fair Poor Comment DT-1 Anonymous user View events View push/pull news Subscribe news View cluster map DT-2 Support cluster member View events View push/pull news Subscribe news View cluster map Browse knowledge card Search knowledge card DT-3 Core cluster member View events View push/pull news Subscribe news View cluster map Issue Proper Fair Poor Comment Browse knowledge card Search knowledge card Search knowledge from widget View news from widget View events from widget View knowledge card from widget Collaborate via text, voice and video from widget Call user mobile device via VoIP server from widget DT-4 Cluster development agent Add new news to system Distribute news to all/selected users Manage subscriber list Manage user information Manage collaboration platform 7. Questions: N/A 8. Logging System: Video Recorder

E.5.2. Functional Test Specification

1. Test No.: FT-01 2. Test Name: Functional Test 3. Test Description:

- Functional Test done by administrator, core cluster and support cluster

4. Assumption:

- Attendants are expert and decision maker for ceramic cluster

5. Test Case Examples: Functional Specification Testing

6. Feed Back:

Issue Proper Fair Poor Comment FS-1 Administrative Functions FS-1.1 Manage User FS-1.2 Manage News FS-1.3 Manage Opportunity FS-1.4 Manage Virtual Collaboration FS-2 Collaborative Knowledge Card Func. FS-2.1 Collaborative Knowledge Card FS-2.2 Knowledge Map Search FS-3 Cluster Map Functions FS-3.1 Cluster Map Visualization FS-3.2 Cluster Map Visualization FS-4 Push/Pull News Functions FS-4.1 Display news Issue Proper Fair Poor Comment FS-5 Collaborative Calendar FS-5.1 Display Week calendar FS-5.2 Display Month Calendar FS-5.3 Add New Event to Calendar FS-6 Live Chat Functions FS-6.1 Live Chat FS-7 Video Conference FS-7.1 Broadcast audio to online users FS-7.2 Broadcast video to online users FS-7.3 Display online users in the conference room FS-8 Voice over Internet Protocol (VoIP) Functions FS-8. Web-based VoIP Phone FS-8.2 VoIP Server FS-9 Widget Functions FS-9.1 Customizable knowledge search FS-9.2 Customizable news view FS-9.3 Customizable calendar view FS-9.4 Customizable cluster map view FS-9.5 Customizable collaborative system FS-10 Advanced Search Functions FS-10.1 Knowledge card search FS-10.2 Wiki content search FS-10.3 Forward inference search FS-10.4 Backward inference search 7. Questions: N/A 8. Logging System: Video Recorder

E.5.3. Operational Test Specification

1. Test No.: OT-01 2. Test Name: Operational Test 3. Test Description:

- Operational Test done by cluster members on real environment.

4. Assumption:

- Attendants are CDA, Expert and Knowledge worker who participated in each activity.

5. Test Case Examples: Ceramic trade faire exhibition - Pre-trade faire Preparation (Preparation, Meeting, Follow up) - Problem solving during trade faire (Preparation, Meeting, Follow up) - Post-trade faire meeting (Decision, Problem Solving) (Follows the sequence diagram ) 6. Feed Back:

Issue Proper Fair Poor Comment OT-1 Pre-trade faire preparation …

OT-2 Problem solving during trade faire …

OT-3 Post-trade faire meeting …

7. Questions: N/A 8. Logging System: Video Recorder Annex F: Technologies

F.1. Flex-based application

In the technological point of view, the knowledge management system in this study relied on three major technologies i.e. Adobe Flex, PHP and AJAX. Flex is a useful tool which allows the knowledge system developer to create the Rich Internet Applications. It also provides an ability to design and implement human-centered user interface without worrying about browser limitation [AFCS 09]. Flex application is based on MXML and ActionScript language. MXML is an XML-based markup language that is primarily used to layout application display elements and GUI. ActionScript is an ECMAScript-compliant object- oriented programming language that is primarily used for application logic. The generic concept of Flex application was shown in figure F.1.

Figure F.1: The concept of Flex-based application implementation [AFCS 09]

The diagram implied that Flex is a client-side technology that is rendered by Flash Player or Adobe AIR. Flex can also work with JavaScript on the client machine. However, for communicating with the server, Flex requires a server-side technology (like ColdFusion, LiveCycle Data Services, Java, ASP.NET or PHP) to provide it with real-time data. In this study, PHP which is a core technology of KMS server was taken into account for interfacing between Flex application and web server. PHP is an interpreted scripting language that allows a web server to create dynamic web pages or web services. It is one of the most well-known back-end server technologies. With PHP, both application logic and layout execute on the server, which returns HTML to the browser. Another advantage of PHP to Flex-based application is PHP can talk directly with a database server, and therefore can be used to write web services. Flex does not communicate with the database directly; however, it can consume data from a web service and display it. Figure F.2 show the interfacing between PHP and Flex.

Figure F.2: The interfacing between PHP and Flex [AFCS 09]

Finally, AJAX (Asynchronous Javascript And X ML ) was integrated in KMS in order to retrieve data from web server asynchronously in background without interfering with display or behavior of the existing page. It allows application to use XMLHttpRequest which is widely used in many dynamic base applications such as Google Maps, Windows’ Live, or Facebook. Annex G: Database and Class Models

G.1. Database Models

Figure G.1: Knowledge card’s data model

Figure G.2: Cluster map’s data model

Figure G.3 Push/Pull News’ data model

G.2. Class models

Figure G.4: Knowledge map’s class model

Figure G.5: Knowledge Widget’s class model

Figure G.6: Cluster map’s class model

Figure G.7: Push/Pull News’ class model

Figure G.8: Collaborative calendar’s class model