Ontology-based knowledge management for enterprise systems

Companies face the challenges of expanding their markets, improving products, services and processes, and exploiting intellectual capital in a dynamic network. Therefore, more companies are turning to an Enterprise System (ES). Knowledge management (KM) has also received considerable attention and is continuously gaining the interest of industry, enterprises, and academia. For ES, KM can provide support across the entire lifecycle, from selection and implementation to use. In addition, it is also recognised that an ontology is an appropriate methodology to accomplish a common consensus of communication, as well as to support a diversity of KM activities, such as knowledge repository, retrieval, sharing, and dissemination. This paper examines the role of ontology-based KM for ES (OKES) and investigates the possible integration of ontology-based KM and ES. The authors develop a taxonomy as a framework for understanding OKES research. In order to achieve the objective of this study, a systematic review of existing research was conducted. Based on a theoretical framework of the ES lifecycle, KM, KM for ES, ontology, and ontology-based KM, guided by the framework of study, a taxonomy for OKES is established.

Perception of prescription medicine sample packs among Australian professional, government, industry, and consumer organizations, based on automated textual analysis of one-on-one interviews

Background Prescription medicine samples provided by pharmaceutical companies are predominantly newer and more expensive products. The range of samples provided to practices may not represent the drugs that the doctors desire to have available. Few studies have used a qualitative design to explore the reasons behind sample use. Objective The aim of this study was to explore the opinions of a variety of Australian key informants about prescription medicine samples, using a qualitative methodology. Methods Twenty-three organizations involved in quality use of medicines in Australia were identified, based on the authors' previous knowledge. Each organization was invited to nominate 1 or 2 representatives to participate in semistructured interviews utilizing seeding questions. Each interview was recorded and transcribed verbatim. Leximancer v2.25 text analysis software (Leximancer Pty Ltd., Jindalee, Queensland, Australia) was used for textual analysis. The top 10 concepts from each analysis group were interrogated back to the original transcript text to determine the main emergent opinions. Results A total of 18 key interviewees representing 16 organizations participated. Samples, patient, doctor, and medicines were the major concepts among general opinions about samples. The concept drug became more frequent and the concept companies appeared when marketing issues were discussed. The Australian Pharmaceutical Benefits Scheme and cost were more prevalent in discussions about alternative sample distribution models, indicating interviewees were cognizant of budgetary implications. Key interviewee opinions added richness to the single-word concepts extracted by Leximancer. Conclusions Participants recognized that prescription medicine samples have an influence on quality use of medicines and play a role in the marketing of medicines. They also believed that alternative distribution systems for samples could provide benefits. The cost of a noncommercial system for distributing samples or starter packs was a concern. These data will be used to design further research investigating alternative models for distribution of samples.

Dynamics of industry architecture and firm’s capability development : role of knowledge integration within product innovation

Aligned with the decline of Marshalian view of industry as constituting homogeneous set of firms, the new perspective is emerging by concentrating more on dynamics of sectors as the building block of industrial changes. Based on new assumptions, much of the action in terms of strategy, technology, and knowledge development does not happen either among firms within a stable industry, or through the growth or decline of certain sectors compared to others. Instead, the action happens in terms of the definition, redefinition, drawing, and redrawing of the very nature of these sectors. Technology does not progress and develop within a sector; rather it shapes (and is shaped by) the encompassing architecture of multiple sectors.

Enhancing the learning experiences through Industry-Based Learning from Indonesian university perspective

This paper investigates how students’ learning experience can be enhanced by participating in the Industry-Based Learning (IBL) program. In this program, the university students coming into the industry to experience how the business is run. The students’ learning media is now not coming from the textbooks or the lecturers but from learning by doing. This new learning experience could be very interesting for students but at the same time could also be challenging. The research involves interviewing a number of students from the IBL programs, the academic staff from the participated university who has experience in supervising students and the employees of the industry who supported and supervised the students in their work placements. The research findings offer useful insights and create new knowledge in the field of education and learning. The research contributes to the existing knowledge by providing a new understanding of the topic as it applied to the Indonesian context.

Realisation of University-Industry Collaboration through Industry-Based Learning at Indonesian Higher Education

The collaboration between universities and industries has become increasingly important for the development of Science and Technology. This is particularly more prominent in the Science Technology Engineering and Mathematics (STEM) disciplines. Literature suggest that the key element of University-Industry Partnership (UIP) is the exchange of knowledge that is mutually beneficial for both parties. One real example of the collaborations is Industry-Based Learning (IBL) in which university students are coming into industries to experience and learn how the skills and knowledge acquired in the classroom are implemented in work places. This paper investigate how the University-Industry Collaboration program is implemented though Industry-Based Learning (IBL) at Indonesian Universities. The research findings offer useful insights and create a new knowledge in the field of STEM education and collaborative learning. The research will contribute to existing knowledge by providing empirical understanding of this topic. The outcomes can be used to improve the quality of University-Industry Partnership programs at Indonesian Universities and inform Indonesian higher education authorities and their industrial partners of an alternative approach to enhance their IBL programs.

The impact of unique characteristics of projects and project-based organisations on knowledge transfer

Knowledge has been recognised as an important organisational asset that increases in value when shared; the opposite to other organisational assets which decrease in value during their exploitation. Effective knowledge transfer in organisations helps to achieve and maintain competitive advantage and ultimately organisational success. So far, the research on knowledge transfer has focused on traditional (functional) organisations. Only recently has attention been directed towards knowledge transfer in projects. Existing research on project learning has recognised the need for knowledge transfer within and across projects in project-based organisations (PBOs). Most projects can provide valuable new knowledge from unexpected actions, approaches or problems experienced during the project phases. The aim of this paper is to demonstrate the impact of unique projects characteristics on knowledge transfer in PBO. This is accomplished through review of the literature and a series of interviews with senior project practitioners. The interviews complement the findings from the literature. Knowledge transfer in projects occurs by social communication and transfer of lessons learned where project management offices (PMOs) and project managers play significant roles in enhancing knowledge transfer and communication within the PBO and across projects. They act as connectors between projects and the PBO ‘hub’. Moreover, some project management processes naturally facilitate knowledge transfer across projects. On the other hand, PBOs face communication challenges due to unique and temporary characteristics of projects. The distance between projects and the lack or weakness of formal links across projects, create communication problems that impede knowledge transfer across projects. The main contribution of this paper is to demonstrate that both social communication and explicit informational channels play important role in inter-project knowledge transfer. Interviews also revealed the important role organisational culture play in knowledge transfer in PBOs.

From the consciousness industry to the creative industries : consumer-created content, social network markets, & the growth of knowledge

In studies of media industries, too much attention has been paid to providers and firms, too little to consumers and markets. But with user-created content, the question first posed more than a generation ago by the uses & gratifications method and taken up by semiotics and the active audience tradition (‘what do audiences do with media?’), has resurfaced with renewed force. What’s new is that where this question (of what the media industries and audiences did with each other) used to be individualist and functionalist, now, with the advent of social networks using Web 2.0 affordances, it can be re-posed at the level of systems and populations as well.

Operationalizing the resource based view for nascent and young firms : developing scales for resource advantages and disadvantages

Principal Topic: It is well known that most new ventures suffer from a significant lack of resources, which increases the risk of failure (Shepherd, Douglas and Shanley, 2000) and makes it difficult to attract stakeholders and financing for the venture (Bhide & Stevenson, 1999). The Resource-Based View (RBV) (Barney, 1991; Wernerfelt, 1984) is a dominant theoretical base increasingly drawn on within Strategic Management. While theoretical contributions applying RBV in the domain of entrepreneurship can arguably be traced back to Penrose (1959), there has been renewed attention recently (e.g. Alvarez & Busenitz, 2001; Alvarez & Barney, 2004). This said, empirical work is in its infancy. In part, this may be due to a lack of well developed measuring instruments for testing ideas derived from RBV. The purpose of this study is to develop a measurement scales that can serve to assist such empirical investigations. In so doing we will try to overcome three deficiencies in current empirical measures used for the application of RBV to the entrepreneurship arena. First, measures for resource characteristics and configurations associated with typical competitive advantages found in entrepreneurial firms need to be developed. These include such things as alertness and industry knowledge (Kirzner, 1973), flexibility (Ebben & Johnson, 2005), strong networks (Lee et al., 2001) and within knowledge intensive contexts, unique technical expertise (Wiklund and Shepard, 2003). Second, the RBV has the important limitations of being relatively static and modelled on large, established firms. In that context, traditional RBV focuses on competitive advantages. However, newly established firms often face disadvantages, especially those associated with the liabilities of newness (Aldrich & Auster, 1986). It is therefore important in entrepreneurial contexts to expand to an investigation of responses to competitive disadvantage through an RBV lens. Conversely, recent research has suggested that resource constraints actually have a positive effect on firm growth and performance under some circumstances (e.g., George, 2005; Katila & Shane, 2005; Mishina et al., 2004; Mosakowski, 2002; cf. also Baker & Nelson, 2005). Third, current empirical applications of RBV measured levels or amounts of particular resources available to a firm. They infer that these resources deliver firms competitive advantage by establishing a relationship between these resource levels and performance (e.g. via regression on profitability). However, there is the opportunity to directly measure the characteristics of resource configurations that deliver competitive advantage, such as Barney´s well known VRIO (Valuable, Rare, Inimitable and Organized) framework (Barney, 1997). Key Propositions and Methods: The aim of our study is to develop and test scales for measuring resource advantages (and disadvantages) and inimitability for entrepreneurial firms. The study proceeds in three stages. The first stage developed our initial scales based on earlier literature. Where possible, we adapt scales based on previous work. The first block of the scales related to the level of resource advantages and disadvantages. Respondents were asked the degree to which each resource category represented an advantage or disadvantage relative to other businesses in their industry on a 5 point response scale: Major Disadvantage, Slight Disadvantage, No Advantage or Disadvantage, Slight Advantage and Major Advantage. Items were developed as follows. Network capabilities (3 items) were adapted from (Madsen, Alsos, Borch, Ljunggren & Brastad, 2006). Knowledge resources marketing expertise / customer service (3 items) and technical expertise (3 items) were adapted from Wiklund and Shepard (2003). flexibility (2 items), costs (4 items) were adapted from JIBS B97. New scales were developed for industry knowledge / alertness (3 items) and product / service advantages. The second block asked the respondent to nominate the most important resource advantage (and disadvantage) of the firm. For the advantage, they were then asked four questions to determine how easy it would be for other firms to imitate and/or substitute this resource on a 5 point likert scale. For the disadvantage, they were asked corresponding questions related to overcoming this disadvantage. The second stage involved two pre-tests of the instrument to refine the scales. The first was an on-line convenience sample of 38 respondents. The second pre-test was a telephone interview with a random sample of 31 Nascent firms and 47 Young firms (< 3 years in operation) generated using a PSED method of randomly calling households (Gartner et al. 2004). Several items were dropped or reworded based on the pre-tests. The third stage (currently in progress) is part of Wave 1 of CAUSEE (Nascent Firms) and FEDP (Young Firms), a PSED type study being conducted in Australia. The scales will be tested and analysed with a random sample of approximately 700 Nascent and Young firms respectively. In addition, a judgement sample of approximately 100 high potential businesses in each category will be included. Findings and Implications: The paper will report the results of the main study (stage 3 – currently data collection is in progress) will allow comparison of the level of resource advantage / disadvantage across various sub-groups of the population. Of particular interest will be a comparison of the high potential firms with the random sample. Based on the smaller pre-tests (N=38 and N=78) the factor structure of the items confirmed the distinctiveness of the constructs. The reliabilities are within an acceptable range: Cronbach alpha ranged from 0.701 to 0.927. The study will provide an opportunity for researchers to better operationalize RBV theory in studies within the domain of entrepreneurship. This is a fundamental requirement for the ability to test hypotheses derived from RBV in systematic, large scale research studies.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Electronic tendering: an industry perspective

The construction industry is categorised as being an information-intensive industry and described as one of the most important industries in any developed country, facing a period of rapid and unparalleled change (Industry Science Resources 1999) (Love P.E.D., Tucker S.N. et al. 1996). Project communications are becoming increasingly complex, with a growing need and fundamental drive to collaborate electronically at project level and beyond (Olesen K. and Myers M.D. 1999; Thorpe T. and Mead S. 2001; CITE 2003). Yet, the industry is also identified as having a considerable lack of knowledge and awareness about innovative information and communication technology (ICT) and web-based communication processes, systems and solutions which may prove beneficial in the procurement, delivery and life cycle of projects (NSW Government 1998; Kajewski S. and Weippert A. 2000). The Internet has debatably revolutionised the way in which information is stored, exchanged and viewed, opening new avenues for business, which only a decade ago were deemed almost inconceivable (DCITA 1998; IIB 2002). In an attempt to put these ‘new avenues of business’ into perspective, this report provides an overall ‘snapshot’ of current public and private construction industry sector opportunities and practices in the implementation and application of web-based ICT tools, systems and processes (e-Uptake). Research found that even with a reserved uptake, the construction industry and its participating organisations are making concerted efforts (fortunately with positive results) in taking up innovative forms of doing business via the internet, including e-Tendering (making it possible to manage the entire tender letting process electronically and online) (Anumba C.J. and Ruikar K. 2002; ITCBP 2003). Furthermore, Government (often a key client within the construction industry),and with its increased tendency to transact its business electronically, undoubtedly has an effect on how various private industry consultants, contractors, suppliers, etc. do business (Murray M. 2003) – by offering a wide range of (current and anticipated) e-facilities / services, including e-Tendering (Ecommerce 2002). Overall, doing business electronically is found to have a profound impact on the way today’s construction businesses operate - streamlining existing processes, with the growth in innovative tools, such as e-Tender, offering the construction industry new responsibilities and opportunities for all parties involved (ITCBP 2003). It is therefore important that these opportunities should be accessible to as many construction industry businesses as possible (The Construction Confederation 2001). Historically, there is a considerable exchange of information between various parties during a tendering process, where accuracy and efficiency of documentation is critical. Traditionally this process is either paper-based (involving large volumes of supporting tender documentation), or via a number of stand-alone, non-compatible computer systems, usually costly to both the client and contractor. As such, having a standard electronic exchange format that allows all parties involved in an electronic tender process to access one system only via the Internet, saves both time and money, eliminates transcription errors and increases speed of bid analysis (The Construction Confederation 2001). Supporting this research project’s aims and objectives, researchers set to determine today’s construction industry ‘current state-of-play’ in relation to e-Tendering opportunities. The report also provides brief introductions to several Australian and International e-Tender systems identified during this investigation. e-Tendering, in its simplest form, is described as the electronic publishing, communicating, accessing, receiving and submitting of all tender related information and documentation via the internet, thereby replacing the traditional paper-based tender processes, and achieving a more efficient and effective business process for all parties involved (NT Governement 2000; NT Government 2000; NSW Department of Commerce 2003; NSW Government 2003). Although most of the e-Tender websites investigated at the time, maintain their tendering processes and capabilities are ‘electronic’, research shows these ‘eTendering’ systems vary from being reasonably advanced to more ‘basic’ electronic tender notification and archiving services for various industry sectors. Research also indicates an e-Tender system should have a number of basic features and capabilities, including: • All tender documentation to be distributed via a secure web-based tender system – thereby avoiding the need for collating paperwork and couriers. • The client/purchaser should be able to upload a notice and/or invitation to tender onto the system. • Notification is sent out electronically (usually via email) for suppliers to download the information and return their responses electronically (online). • During the tender period, updates and queries are exchanged through the same e-Tender system. • The client/purchaser should only be able to access the tenders after the deadline has passed. • All tender related information is held in a central database, which should be easily searchable and fully audited, with all activities recorded. • It is essential that tender documents are not read or submitted by unauthorised parties. • Users of the e-Tender system are to be properly identified and registered via controlled access. In simple terms, security has to be as good as if not better than a manual tender process. Data is to be encrypted and users authenticated by means such as digital signatures, electronic certificates or smartcards. • All parties must be assured that no 'undetected' alterations can be made to any tender. • The tenderer should be able to amend the bid right up to the deadline – whilst the client/purchaser cannot obtain access until the submission deadline has passed. • The e-Tender system may also include features such as a database of service providers with spreadsheet-based pricing schedules, which can make it easier for a potential tenderer to electronically prepare and analyse a tender. Research indicates the efficiency of an e-Tender process is well supported internationally, with a significant number, yet similar, e-Tender benefits identified during this investigation. Both construction industry and Government participants generally agree that the implementation of an automated e-Tendering process or system enhances the overall quality, timeliness and cost-effectiveness of a tender process, and provides a more streamlined method of receiving, managing, and submitting tender documents than the traditional paper-based process. On the other hand, whilst there are undoubtedly many more barriers challenging the successful implementation and adoption of an e-Tendering system or process, researchers have also identified a range of challenges and perceptions that seem to hinder the uptake of this innovative approach to tendering electronically. A central concern seems to be that of security - when industry organisations have to use the Internet for electronic information transfer. As a result, when it comes to e-Tendering, industry participants insist these innovative tendering systems are developed to ensure the utmost security and integrity. Finally, if Australian organisations continue to explore the competitive ‘dynamics’ of the construction industry, without realising the current and future, trends and benefits of adopting innovative processes, such as e-Tendering, it will limit their globalising opportunities to expand into overseas markets and allow the continuation of international firms successfully entering local markets. As such, researchers believe increased knowledge, awareness and successful implementation of innovative systems and processes raises great expectations regarding their contribution towards ‘stimulating’ the globalisation of electronic procurement activities, and improving overall business and project performances throughout the construction industry sectors and overall marketplace (NSW Government 2002; Harty C. 2003; Murray M. 2003; Pietroforte R. 2003). Achieving the successful integration of an innovative e-Tender solution with an existing / traditional process can be a complex, and if not done correctly, could lead to failure (Bourn J. 2002).