Technology management methodologies and applications A literature ...

Technovation 25 (2005) 381–393 www.elsevier.com/locate/technovation

Technology management methodologies and applications A literature review from 1995 to 2003 Shu-hsien Liao* Department of Management Sciences and Decision Making, Tamkang University, No. 151, Yingjuan Road, Danshuei Jen, Taipei 251, Taiwan, ROC

Abstract Technology management is a process, which includes planning, directing, control and coordination of the development and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organization. This paper surveys technology management (TM) development using a literature review and classification of articles from 1995 to 2003 with keyword index in order to explore how TM methodologies and applications have developed in this period. Based on the scope of 546 articles of technology management methodologies, this paper surveys and classifies TM methodologies using the eight categories of: TM framework, General and policy research, Information systems, Information and communication technology, Artificial intelligence/expert systems, Database technology, Modeling, and Statistics methodology, together with their applications for different research and problem domains. Discussion is presented indicating future development for technology management methodologies and applications as follows: (1) TM methodologies tend to develop towards expert orientation, and TM applications development is a problemoriented domain. (2) Integration of qualitative and quantitative methods, and integration of TM technologies studies may broaden our horizons on this subject. (3) The ability to continually change and obtain new understanding is the power of TM methodologies and will be the subject of future work. q 2003 Elsevier Ltd. All rights reserved. Keywords: Technology management; Technology management methodology; Technology management application; Literature survey

1. Introduction Technology management is a process, which includes planning, directing, control and coordination of the development and implementation of technological capabilities to shape and accomplish the strategic and operational objectives of an organization (Task Force on Management of Technology, 1987). On the other hand, technology management includes: (1) planning for the development of technology capabilities; (2) identifying key technology and its related fields for development; (3) determining whether ‘to buy’ or ‘to make’, i.e. whether importation or selfdevelopment should be pursued; and (4) establishing institutional mechanisms for directing and coordinating the development of technology capabilities, and the design of policy measures for controls (Wang, 1993). Clearly, technology management should not only fulfill the management needs of a specific set of technologies within a domain * Tel: þ886-2-29472044; fax: þ886-2-29453007. E-mail address: [email protected] 0166-4972/$ - see front matter q 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.technovation.2003.08.002

and inter-domain relationship, but it should also develop the implementation strategies according to the available resources, current technologies, future markets, and socioeconomic environment (Linn et al., 2000). Therefore, how to manage technology has become an important issue in the past few decades, and the technology management (TM) community has developed a wide range of methodologies and applications for both academic research and practical applications. In addition, TM has attracted much effort to explore its nature, concepts, frameworks, architectures, theories, systems, models, tools, functions, and real world implementations in order to demonstrate TM methodologies and their applications. As a part of TM research, this paper focuses on surveying technology management development through a literature review and classification of articles from 1995 to 2002 in order to explore the TM methodologies and applications from that period. The reason for choosing this period is that the Internet was opened to general users in 1994 and this new era of information and communication

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technology has played an important role not only in electronic commerce, but also in technology management. The literature survey is based on a search for the keyword index ‘technology management’ on the Elsevier SDOS online database, from which 9253 articles were found on 31 July 2003. After topic filtering, there were 1626 articles related to the keyword ‘technology management application’ and 546 of these were connected to the keyword ‘technology management methodology’. Based on the scope of 546 articles on technology management methodology, this paper surveys and classifies TM methodologies using eight categories: TM framework, General and policy research, Information systems, Information and communication technology, Artificial intelligence/expert systems, Database technology, Modeling, and Statistics methodology, together with their applications on different research and problem domains. The rest of the paper is organized as follows. Sections 2 to 9, present the survey results of TM methodologies and applications based on the above eight categories. Section 10 presents discussion, with suggestions for future development of technology methodologies and applications. Finally, Section 11 contains a brief conclusion.

2. Technology management framework and its applications Since the 1980s, technology and the academic discipline management of technology, has received widespread attention from both practitioners of management and academics (Drejer, 1997). Researchers have developed a set of management definitions, concepts, activities, stages, circulations, and procedures, all directed towards dealing with objects in order to describe the framework of technology management as the TM methodology. Different TM working definitions, paradigms, frameworks, concepts, objects, propositions, perspectives, measurements, and impacts have been described for investigating the questions of: What is technology management? What are its methods and techniques? And what are its functions for supporting individual and organizations in managing the technology (Sarkis et al., 1995; Dey et al., 1996; Chan and Choi, 1997; Lopes and Flavell, 1998; Haas and Kleingeld, 1999; Garshnek et al., 2000; Pretorius and Wet, 2000; Sharratt and Choong, 2002; Wu, 2002; Hicks et al., 2002)? For example, the methodology of enterprise engineering methodology is an integrated socio-technical framework that addresses organizational, cultural, process, and technological issues (Sarkis et al., 1995). In 1996, Dey et al., proposed a conceptual framework for project control through risk analysis, contingency allocation and hierarchical planning models. In their article, risk analysis has been carried out through the analytic hierarchy process (AHP) due to the subjective nature of risks in construction projects

(Dey et al., 1996). From the business process reengineering perspective, the reasons for BPR failure have been categorized as the lack of understanding of and the inability to perform BPR; and new key concepts of BPR, such as fundamental, radical, dramatic, and process have been proposed as a conceptual and analytical framework (Chan and Choi, 1997). In addition, the methodology of project appraisal based on non-financial aspects of projects, has previously extended published guidance through interviews with a number of project-oriented organizations in the appraisal procedure (Lopes and Flavell, 1998). For strategy formulation and implementation, Haas and Kleingeld proposed a normative framework for multilevel design of diagnostic controls, i.e. performance measurement systems. Their framework is an attempt to synthesize a design theory from systems theory and cybernetics, using a composite of the goal-oriented model, the multipleconstituency model and the natural-systems model of organization (Haas and Kleingeld, 1999). In space technology, a scenario-based framework has been proposed to discuss and analyze the mitigation, management, and survivability of asteroid/ comet impact with earth (Garshnek et al., 2000). Furthermore, an assessment framework for new technology has been developed, providing suggestions for a 3-dimensional space structure of the business process in order to assess the relationship between technology and process on a manufacturing enterprise (Pretorius and Wet, 2000). In addition, the methodology of PERA (process environment risk assessment), has been presented for the assessment of business risks during the design of new processes. This methodology can be used as a project-centered risk assessment method that seeks potential problems along the overall supply chain (Sharratt and Choong, 2002). A framework for implementing an integrative approach based on a strategic perspective to business process reengineering has been discussed in an empirical study (Wu, 2002). On the other hand, a framework discusses data, information and knowledge, providing formal definitions and an understanding of the relations and limitations of these resources. This framework enables the development of better mechanisms and procedures for the capture and reuse of information and knowledge in engineering design (Hicks et al., 2002). In 2003, Liao presented a literature survey of knowledge management in order to explore the present and future development of KM (Liao, 2003). These methodologies offer technological frameworks and explore their content by broadening the research horizon with different perspectives on TM research issues. Some applications have been implemented using a TM framework, including computer integrated manufacturing, construction project management, business process reengineering, project appraisal, product design, space disaster management, technology assessment, process design, and engineering design. The methodology of technology

S.-h. Liao / Technovation 25 (2005) 381–393 Table 1 Technology management framework and its applications Technology management framework/Applications

Authors

Computer integrated manufacturing Construction project management Business process reengineering

(Sarkis et al., 1995) (Dey et al., 1996) (Chan and Choi, 1997; Wu, 2002) (Lopes and Flavell, 1998) (Haas and Kleingeld, 1999) (Garshnek et al., 2000) (Pretorius and Wet, 2000) (Sharratt and Choong, 2002) (Hicks et al., 2002) (Liao, 2003)

Project appraisal Product design Space disaster management Technology assessment Process design Engineering design Knowledge management

management framework and its applications are categorized in Table 1.

3. General and policy research and its applications Sometimes, government and enterprise technology policies, such as funding, regulations, limits, guidance, cooperation, and R&D, are significant factors. They can influence the public sector, enterprises and academic institutes for developing, managing, and implementing technology in organizations. Therefore, government and enterprise policy for technology management are good sources for observing different public sector and private sector technology policies and their decision-making processes. As policy research, this methodology could be helpful in exploring technology management in a global environment. In addition, general management is a methodology with qualitative methods, such as interviews, observations, classifications, logical induction, comparisons, reviews, and literature surveys. It is a methodology, which is part of not only social science, but also technology management. It explores findings or discoveries of nature in a specific problem domain. Some research using general and policy research methodologies on technology management issues are illustrated in the following. In policy research methodology, Hailey and McDonald propose a policy perspective for the assessment of diagnostic imaging technologies based on a synthesis of imperfect data in health care, presenting a list of attributes for consideration in the policy formulation process (Hailey and McDonald, 1996). Wood discusses the policy of the US Office of Technology Assessment (OTA) and proposes suggestions for increased drawing on OTA-like functions and other TA activities in the US and overseas (Wood, 1997). On the other hand, Tassey provides lessons on the methodology of economic impacts learned according to the experience of the US National Institute of Standards

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and Technology (NIST) and debates its R&D policy (Tassey, 1999). In 2001, Huang and Xia analyzed developments, advancements, challenges, and barriers associated with practices of water-quality management and policy. In addition, they examined a number of related methodologies, applications and policies presenting suggestions for waterquality policy in the future (Huang and Xia, 2001). In India, a government technology transfer policy has been implemented for commercialization of new technologies. An empirical study of technology institutions is described in (Kumar and Jain, 2003). In general research methodology, Chang and Hsu constructed a set of project management guidelines for a research institute taking over government R&D projects intended for commercial application in the industrial sector in Taiwan (Chang and Hsu, 1997). Kirk and Pine review papers, which describe particular innovative technological applications within the hospital industry, also presenting a thorough analysis of the appropriateness of technology and a detailed plan for the design and implementation of hospital technology is also presented (Kirk and Pine, 1998). Another diagnostic methodology is the management and organizational context of new product development, and McQuater et al., describe a new product development self- assessment method (McQuater et al., 1998). For the concept of Quality Function Development (QFD), Kim et al. suggest a method that constructs a decision path for information technology investment in Korea (Kim et al., 2000). Ratio ranking, a research methodology, is proposed for the risk ranking of projects in order to describe the use of a methodology for the risk ranking of projects undertaken by the Department of Contract and Management Services (CAMS) in Australia (Baccarini and Archer, 2001). There are also articles which implement a systematic approach for technology management. For example, Manzocco and Nicoli describe a predictive approach to food design, based on the systematic exploitation of the functional properties of each potential ingredient, using the formulation of syrups as a case study (Manzocco and Nicoli, 2002). Another example is a supply chain diagnostic methodology, and Naim et al. present a guide to conduct a supply chain business diagnostic method, Quick Scan. This is a systematic approach to the collection and synthesis of qualitative and quantitative data from case firms in order to determine the vector of change on the supply chain (Naim et al., 2002). Jacob and Kwak present a conceptual paper describing a new integrative evaluation approach for pharmaceutical R&D projects, which offers a significant improvement in project selection and resource allocation (Jacob and Kwak, 2003). In addition, Liu et al. propose an economic performance evaluation method for hydroelectric generating units (HGUs). Some concepts for evaluating the performance of HGUs, such as ideal performance, reachable performance, operational performance, overall efficiency and index of efficiency maintenance are proposed as a methodology for energy management (Liu et al., 2003).

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Table 2 General and policy research and its applications General and policy research/Applications

Authors

Patient management R&D management system Technology assessment Hospitality management New product development

(Hailey and McDonald, 1996) (Chang and Hsu, 1997) (Wood, 1997) (Kirk and Pine, 1998) (McQuater et al., 1998; Kim et al., 2000) (Tassey, 1999; Kumar and Jain, 2003) (Baccarini and Archer, 2001; Jacob and Kwak, 2003) (Huang and Xia, 2001) (Manzocco and Nicoli, 2002) (Naim et al., 2002) (Liu et al., 2003)

Government policy research Risk management Water-quality management and policy Food design Supply chain management Energy management

Some applications have been implemented using general and policy research methodologies such as patient management, R&D management system, technology assessment, hospitality management, new product development, government policy research, risk management, waterquality management and policy, food design, supply chain management, and energy management. The methodology of general and policy research and its applications are categorized in Table 2.

4. Information systems and its applications There are common questions and objectives of researchers using information systems as a methodology for technology management, including: Will information systems (IS) make technology management more efficient? How are information systems used and produced within an organization in order to manage technology? What must be done so that IS can earn its place as methodologies for technology management? What technology does IS support? And can be implemented IS in specific technology problem domains? Five main methodologies of IS which implement technology management can be seen as: decision support systems (DSS), object oriented method (OO), computer aided system engineering (CASE), knowledge-based systems (KBS) and database applications (DB). (Vadas et al., 1995; Recio et al., 1999; Lee et al., 2000; Quaglini et al., 2000; Bouter et al., 2001; Brand et al., 2002; Low et al., 1995; Chen, 1997; Lau et al., 1998; Lee et al., 1999; Lee and Yoo, 2000; Bhattacherjee et al., 2001; Mokdad and Probast, 2001; Shoval, 1995; Urwiler et al., 1995; Rupnik-Miklic and Zupancic, 1995; Ruland and Spindler, 1995; Linninger et al., 1996; Akoka and Comyn-Wattiau, 1996; Chatzoglou and Macaulay, 1997; Lovett et al., 2000; Liao, 2001; McMeekin and Ross, 2002; Tian et al., 2002;

Yap, 1995; Nakanishi et al., 2000; Song et al., 2001; Cho et al., 2002; Kim et al., 2003). In the case of a decision support system, GESMO is a DSS for defining water use policies and measuring monitor and control systems by employing telecom-detection and simulation of crop water needs, as well as another DSS methodology for water quality and quality assessment for wetland development (Vadas et al., 1995; Recio et al., 1999). In addition, DSS can design a distributed database on a local area network for file and workload allocation, as presented by Lee et al. (Lee et al., 2000). Rule-based reasoning is the basis of KBS, including database updating rules, process control rules, and data deletion rules for logical reference (Knight and Ma, 1997). A guideline-based care-flow system is one, which uses knowledge representation, model simulation, and implementation within a health care organization (Quaglini et al., 2000). DSS is also an example of a training tool to offer methods and techniques for multimedia prototyping (Bouter et al., 2001). STEEDS is a DSS that supports multi-criteria decision aid for transport energy problems (Brand et al., 2002). Object oriented (OO) is another kind of methodology for developing IS in a distributed environment (Low et al., 1995). ITMS, is an intelligent task management system created using OO technology on the net for achieving automatic task decomposition and assignment. Another example of an OO technology approach is to develop a collaborative environment for a computeraided concurrent net shape product and process development (Chen, 1997; Lau et al., 1998). In addition, OO hypermedia design methodology integration with Intranet technology also provides a methodology for IS (Lee et al., 1999). FORE is an OO modeling methodology for developing forms to recover semantics of the contents of business (Lee and Yoo, 2000). SUMMIT is a hybrid OO approach that was developed to build an enterprise-scale billing system for the cable industry (Bhattacherjee et al., 2001). An OO methodology integrating computational models has also been implemented for network and systems management (Mokdad and Probast, 2001). Some articles related to the concept of knowledge-based systems are presented as the methodology of IS. For example, a rule-based oriented KBS is a method for integrating design tasks and product design (Ruland and Spindler, 1995). Another design-aided KBS developed batch pharmaceutical process designs for waste management (Linninger et al., 1996). In information security, INFAUDITOR is a KBS for auditing computer and management information systems by implementing blackboard architecture (Akoka and Comyn-Wattiau, 1996). Human factors are also the basis for KBS to plan the requirements capture stage of a project (Chatzoglou and Macaulay, 1997), as well as applications of KBS on

S.-h. Liao / Technovation 25 (2005) 381–393 Table 3 Information systems and its applications Information systems/Applications

Authors

Water resources management

(Vadas et al., 1995; Recio et al., 1999) (Lee et al., 2000) (Quaglini et al., 2000) (Bouter et al., 2001) (Brand et al., 2002) (Low et al., 1995)

File and workload allocation Healthcare management Emergency management Environmental management Distributed system applications development Product data management Manufacturing information network Military Hypermedia design and model Legacy application System development in cable industry Network and systems management Product design Waste management Computer audit Project management System development in SMEs System design and development

Risk assessment Data acquisition Customer behavior Marketing

(Chen, 1997) (Lau et al., 1998). (Liao, 2001) (Lee et al., 1999; Kim et al., 2003) (Lee and Yoo, 2000) (Bhattacherjee et al., 2001) (Mokdad and Probast, 2001) (Ruland and Spindler, 1995) (Linninger et al., 1996) (Akoka and Comyn-Wattiau, 1996) (Chatzoglou and Macaulay, 1997; Tian et al., 2002) (Lovett et al., 2000) (Shoval, 1995; Urwiler et al., 1995; Rupnik-Miklic and Zupancic, 1995; Yap, 1995) (McMeekin and Ross, 2002) (Nakanishi et al., 2000) (Song et al., 2001) (Cho et al., 2002)

small-medium size enterprise and the military (Lovett et al., 2000; Liao, 2001). Database applications propose methodologies of system integration (Yap, 1995), object-oriented database (Nakanishi et al., 2000), data mining (Song et al., 2001; Cho et al., 2002), and data warehouse (Kim et al., 2003) for the technology management. Finally, computer aided system engineering technology is not only system design methodology, but is also a support for the implementation stage of systems development. Some CASE examples are presented for different application domains (Shoval, 1995; Urwiler et al., 1995; Rupnik-Miklic and Zupancic, 1995). Some of these applications which are implemented by information systems include the following: water resources management, file and workload allocation, healthcare management, emergency management, environmental management, distributed system application development, product data management, manufacturing information networks, hypermedia design, legacy application, system development in the cable industry, network and systems management, product design, waste management, computer auditing, project management, system development in SMEs, system design and development, data acquisition,

385

customer behavior, and marketing. The methodology of information systems and its applications are categorized in Table 3.

5. Information and communication technology and its applications In today’s information economy, rapid access to knowledge is critical to the success of many organizations. An information and communication technology (ICT) infrastructure provides a broad platform for exchanging data, coordinating activities, sharing information, supporting private and public sectors, and developing globalize commerce, all based on powerful computing and network technology. Information computing offers powerful information processing abilities, and the network provides standards and connectivity for digital integration. Internet is a kind of ICT that can combine with some other network technologies and services, such as Intranet, Extranet, virtual private network (VPN), and wireless web, to construct a digital environment to consistently create new knowledge, quickly disseminate it, and embody it in organizations. As the concept of sharing technology distribution, ICT enables technology management activities for collaborative communication, co-ordination, decision support, information sharing, consultation, data exchange, organizational learning, and organizational memory (Roy and Filiatrault, 1998; Dangelmaier et al., 1999; Ramesh and Tiwana, 1999; Carayannis, 1999; Rezayat, 2000; Huang and Mak, 2000; Robey et al., 2000; Liu et al., 2001; Pallaeala and Lun, 2001; Burkett, 2001; Balasubramanian et al., 2001; Standing, 2002; Xu et al., 2002; Dawood et al., 2002; Arch-int and Batanov, 2003). In addition, for technology management, intelligent software integrates information systems across multi-tier enterprises in the US auto industry in order to increase organizational flexibility (Olin et al., 1999). On the other hand, ontology is the knowledge integration of different representations of the same piece of technology knowledge at different levels of formalization. The experts who participate in the ontology process use domain terminology, facilitating knowledge integration with cooperative tools (Fernandez-Breis and Martinez-Bejar, 2000). Some applications are implemented by information and communication technology, such as decision support, new product development, organizational learning, organizational memory, knowledge integration, ontology, transport management, workflow management, supply chain, new product development, program generation, medical management, product data management, hypermedia design, electronic commerce, virtual enterprise, automatic system, and information system development. The methodology of

386


Table 4 Information and communication technology and its applications Information and communication technology/Applications

Authors

Decision support

(Ramesh and Tiwana, 1999; Hicks et al., 2002) (Ramesh and Tiwana, 1999; Huang and Mak, 2000) (Ramesh and Tiwana, 1999; Carayannis, 1999; Robey et al., 2000) (Ramesh and Tiwana, 1999; Robey et al., 2000) (Olin et al., 1999; Rezayat, 2000) (Roy and Filiatrault, 1998) (Fernandez-Breis and MartinezBejar, 2000) (Fernandez-Breis and MartinezBejar, 2000) (Dangelmaier et al., 1999) (Liu et al., 2001) (Pallaeala and Lun, 2001) (Burkett, 2001) (Balasubramanian et al., 2001) (Standing, 2002) (Xu et al., 2002) (Dawood et al., 2002) (Arch-int and Batanov, 2003)

New product development Organizational learning Organizational memory Supply chain Transport management Knowledge integration Ontology Workflow management Program generation Medical management Product data management Hypermedia design Electronic commerce Virtual enterprise Automatic system Information system development

information and communications, together with its applications are categorized in Table 4. 6. Artificial intelligence/Expert systems and its applications In 1994, Rubenstein pointed out that the research area with the most potential in technology management was the use of artificial intelligence (AI) in technology management (Rubenstein, 1994). In addition, expert systems (ES), an artificial intelligence method for capturing knowledge, are knowledge-intensive computer programs that capture the human expertise in limited domains of knowledge (Laudon and Laudon, 2002). For this, human knowledge must be modeled or presented in a way that a computer can process. Usually, expert systems capture human knowledge in the form of a set of rules, which the expert systems add to the organizational memory, or stored learning of the organization. An expert system can assist decision making by asking relevant questions and explaining the reasons for adopting certain actions. Expert systems of technology include knowledge base, rule-based systems, knowledge frames, expert system shell, inference engine, robots, and case-based reasoning (Dawood, 1996; Wei and Weber, 1996; Matsatsinis et al., 1997; Ben-Arieh, 1997; Lee and Hong, 1998; Brahan et al., 1998; Gilad and Karni, 1999; Lee, 2000; Noh et al., 2000; Jefferson and Nagy, 2002). Sometimes, expert systems are integrated with other AI methods, such as neural networks, cognitive science,

heuristic rules, and intelligent agents, using their functions of qualitative simulation, automated reasoning and machine learning (Clark and Mehta, 1997; Plant and Vayssieres, 2000; AI-Habaibeh et al., 2002; Boone and Roehm, 2002; Lee et al., 2002; Lang et al., 2002; Wang et al., 2002; Metaxiotis et al., 2003). On the other hand, object-oriented (OO) programming technology provides an approach to expert systems that combines knowledge and procedures into a single object. Traditional expert systems methods have treated knowledge and procedures as independent components. However, objects belonging to a certain class have knowledge of that class, and classes of objects in turn can represent knowledge and embed knowledge with OO programming architecture. This leads expert system developments toward fourth generation language and visual programming methods in order to provide a user-friendly structure and environment (Doyle et al., 1996; Wu et al., 1997; Nault and Storey, 1998; Shaalan et al., 1998; Chau et al., 2002). Some of the applications implemented by AI/ES including the following: agriculture, production management, waste management, electronic power programming, building management, financial management, task management, system maintenance, crime analysis and management, ergonomics design, aquaculture engineering, knowledge management, system design and milling, marketing, credit scoring, design methodology, catalog retrieval, energy management, education, and water resource management. The methodology of artificial intelligence/ expert systems and its applications are categorized in Table 5. Table 5 Artificial intelligence/Expert systems and its applications Expert systems/Applications

Authors

Agriculture

(Shaalan et al., 1998; Plant and Vayssieres, 2000) (Dawood, 1996) (Doyle et al., 1996) (Wei and Weber, 1996) (Nault and Storey, 1998; Noh et al., 2000) (Wu et al., 1997) (Clark and Mehta, 1997) (Matsatsinis et al., 1997) (Ben-Arieh, 1997) (Lee and Hong, 1998) (Brahan et al., 1998) (Gilad and Karni, 1999) (Lee, 2000) (AI-Habaibeh et al., 2002) (Boone and Roehm, 2002) (Lee et al., 2002) (Lang et al., 2002; Wang et al., 2002) (Jefferson and Nagy, 2002) (Metaxiotis et al., 2003) (Chau et al., 2002)

Production management Education Waste management Knowledge management Electronic power programming Building management Financial management Task management System maintenance Crime analysis and management Ergonomics design Aquaculture engineering System design and milling Marketing Credit scoring Design methodology Catalog retrieval Energy management Water resource management


7. Database methodology and its applications A database is a collection of data organized to efficiently serve many applications by centralizing the data and minimizing redundant data (McFadden et al., 2000). A database management system (DBMS) is the software that permits an organization to centralize data, manage it efficiently, and provide access to the stored data by application programs (Laudon and Laudon, 2002). However, some large databases make knowledge discovery computationally expensive because some domains or background knowledge, hidden in the database, may guide and restrict the search for important knowledge. Therefore, modern database technologies need to process large volumes of data, multiple hierarchies, and different data formats to discover in-depth experience or knowledge from large databases in order to manage technology. For example, multidimensional data analysis, on-line analytical processing, data warehouses, web and hypermedia databases (Koschel and Lockemann, 1998; Sokolov and Wulff, 1999; Huang et al., 2000; Wilkins and Barrett, 2000; Shafer and Agrawal, 2000). Furthermore, a hierarchical model learning approach for refining and managing concept clusters discovered from databases has been proposed. Its approach can be cooperatively used with other subsystems of decomposition based induction for knowledge refinement (Zhong and Ohsuga, 1996a; Zhong and Ohsuga, 1996b). One example is the domain knowledge used to guide to test the validity of the discovered knowledge (Owrang and Grupe, 1996). Recently, database and architecture design are other methodologies for implementing both ontology creation heuristics and intelligent agents into database conceptual modeling and knowledge repository domains (Sugumaran and Storey, 2002; Allsopp et al., 2002). Some of the applications implemented by database methodology are the following: hierarchical modeling, knowledge refinement, machine learning, error analysis, knowledge representation, knowledge discovery, ontology, database design, knowledge reuse, knowledge repository, geosciences, and web applications. These database technologies and their applications are categorized in Table 6.

8. Modeling methodology and its applications Modeling technology has become an interdisciplinary methodology of TM in order to build formal relationships with logical model design in different knowledge/problem domains. Quantitative methods for exploring the issues of theory, application, planning, learning, social studies, artificial intelligence algorithms, and decision support are the modeling technology of technology management. Some methodologies have been presented as examples of modeling methods and tools, including multi-objective programming, simulation, neural networks, genetic algorithms,

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Table 6 Database methodology and its applications Database technology/Applications

Authors

Hierarchical modeling Knowledge refinement Machine learning Error analysis Knowledge representation

(Zhong and Ohsuga, 1996a) (Zhong and Ohsuga, 1996a) (Zhong and Ohsuga, 1996a) (Zhong and Ohsuga, 1996b) (Zhong and Ohsuga, 1996b; Owrang and Grupe, 1996) (Zhong and Ohsuga, 1996a; Zhong and Ohsuga, 1996b; Owrang and Grupe, 1996) (Sugumaran and Storey, 2002) (Koschel and Lockemann, 1998; Huang et al., 2000; Sugumaran and Storey, 2002) (Allsopp et al., 2002) (Allsopp et al., 2002) (Sokolov and Wulff, 1999) (Sokolov and Wulff, 1999; Huang et al., 2000; Wilkins and Barrett, 2000; Shafer and Agrawal, 2000)

Knowledge discovery

Ontology Database design

Knowledge reuse Knowledge repository Geosciences Web applications

concurrent engineering, optimal tree search, knowledgebased modeling, stochastic modeling, forecasting, dynamic programming, optimization modeling, simulated annealing, mathematical programming, pricing model, fuzzy sets, data envelopment analysis, stochastic simulation, network equilibrium model, and algorithm architecture (Maimon and Dayagi, 1995; Jensen et al., 1996; Wang et al., 1996; Gong et al., 1996; Leu et al., 1996; Carstensen et al., 1997; Oehlmann et al., 1997; Spiliopoulos and Sofianopoulou, 1998; Demuynck et al., 1997; Cho et al., 1998; Young and Cabezas, 1999; Keller and Dungan, 1999; Sheu, 1999; Khan and Abbasi, 2000; Sonesson et al., 2000; Secomandi, 2000; Bick and Oron, 2000; Qin and Balendra, 2001; Horng and Cochran, 2001; Frederix, 2001; Rajaram, 2001; Choi et al., 2002; Chen et al., 2002a; Uddin and Shanker, 2002; Balkema et al., 2002; Dawood and Marasini, 2002; Tatsiopoulos et al., 2002; Chen et al., 2002b; Easton et al., 2002; Fleten et al., 2002; Lo and Szeto, 2002; Pekny, 2002; Laking et al., 2002; Rosen and Dincer, 2003; Ashok and Banerjee, 2003; Sigman and Liu, 2003). Some of the applications which have been implemented by modeling include production management, routing flexibility, marine engineering, waste water treatment, sequencing problem, water quality management, product development, flexible manufacturing systems, environmental management, business process reengineering, energy management, ecological management, traffic management, chemical process, vehicle routing, natural resource management, error analysis, job assignment, supply chain management, merchandise planning, telecommunication pricing, waste management, manufacturing process, stockyard layout design, capacity planning, purchasing evaluation, asset liability management, travel management, risk

388


Table 7 Modeling methodology and its applications

Table 8 Statistics methodology and its applications

Modeling/Applications

Authors

Data mining/Applications

Authors

Production management Routing flexibility Marine engineering Wastewater treatment

(Maimon and Dayagi, 1995) (Jensen et al., 1996) (Wang et al., 1996) (Gong et al., 1996; Balkema et al., 2002) (Leu et al., 1996) (Carstensen et al., 1997) (Oehlmann et al., 1997) (Spiliopoulos and Sofianopoulou, 1998) (Demuynck et al., 1997; Sonesson et al., 2000) (Cho et al., 1998; Tatsiopoulos et al., 2002) (Young and Cabezas, 1999; Ashok and Banerjee, 2003) (Keller and Dungan, 1999) (Sheu, 1999) (Khan and Abbasi, 2000) (Secomandi, 2000) (Bick and Oron, 2000) (Qin and Balendra, 2001) (Horng and Cochran, 2001) (Frederix, 2001) (Rajaram, 2001) (Choi et al., 2002) (Chen et al., 2002a) (Uddin and Shanker, 2002) (Dawood and Marasini, 2002) (Chen et al., 2002b) (Easton et al., 2002) (Fleten et al., 2002) (Lo and Szeto, 2002) (Pekny, 2002) (Laking et al., 2002) (Rosen and Dincer, 2003) (Sigman and Liu, 2003)

Manufacturing system Ecosystem management Safe and high efficiency operation Geology engineering Environmental and natural resources management

(Ip, 1997) (Norton, 1998) (Dai, 1998)

Sequencing problem Water quality management Product development Flexible manufacturing systems Environmental management Business process reengineering Energy management Ecological management Traffic management Chemical process Vehicle routing Natural resource management Error analysis Job assignment Supply chain management Merchandise planning Telecommunication pricing Waste management Manufacturing process Stockyard layout design Capacity planning Purchasing evaluation Asset liability management Travel management Risk management Healthcare management Thermal processes Design methodology

management, healthcare management, thermal processes, and design methodology. The methodology of modeling and its applications are categorized in Table 7.

9. Statistics methodology and its applications Statistical methodology is an interdisciplinary field that combines social science, computer science, engineering, natural science, data management, and mathematical algorithms. Given the enormous size of data, statistics is a methodology necessary for data analysis, providing different methods for decision-making, problem solving, analysis, planning, diagnosis, prediction, and learning (Ip, 1997; Norton, 1998; Dai, 1998; Liang et al., 1999; Varis and Kuikka, 1999). Some of the applications that have been implemented by statistical methodologies include the following:

(Liang et al., 1999) (Varis and Kuikka, 1999)

manufacturing system, ecosystem management, safe and high efficiency operation, geology engineering, environmental and natural resource management. The methodology of statistics methodology and its applications are categorized in Table 8.

10. Discussion and suggestions 10.1. Discussion Technology management methodologies and applications are a broad category of research issues on TM. Some specific methodologies and methods have been presented as examples in terms of exploring the suggestions and solutions to specific TM problem domains. Therefore, methodologies and applications of TM are attracting much attention and efforts, both academic and practical. From this literature review, we can see that TM methodologies and applications developments are diversified due to their authors’ backgrounds, expertise, and problem domains. This is why a few authors can appear in the literature of different methodologies and applications. On the other hand, some methodologies have common concepts, and types of technology, for example, information systems and database technology, or some modeling methods versus artificial intelligence/expert systems. However, there are a few authors who work in different methodologies and applications. This indicates that the trend of development on methodology is also diversified due to author’s research interests and abilities in the methodology and problem domain. These factors may direct development of TM methodologies toward expertise orientation. Furthermore, some applications have a high degree of overlap in different technologies. For example, production management, natural resource management, manufacturing systems, environmental management, business process reengineering, transport management, chemical management, industrial engineering, supply chain management, telecommunication planning, waste management, risk management, healthcare management, and energy management, are all topics of different methodologies, which implement TM in a common problem domain. This


indicates that those applications are the major trend of TM development, and many methodologies are focused on these problems. This may direct development of TM applications toward problem domain orientation. In this paper, most of the articles discussed were from the categories of agricultural and biological sciences, chemical engineering, chemistry, decision sciences, earth and planetary sciences, economics and finance, energy and power sciences, engineering and technology, physics and astronomy, environmental sciences, material sciences, mathematics, medical sciences, and social sciences journals on the Elsevier SDOS online database. We would make no presumption about TM methodologies and applications that are developed in other science fields. However, we would like to see more TM methodologies and applications of different research fields published in order to broaden our horizon of academic and practice works on TM. 10.2. Limitations Firstly, a literature review for the broad category of TM methodologies and applications is a difficult task due to the extensive background knowledge needed for studying, classifying, and comparing these articles. Although limited in background knowledge, this paper presents a brief literature review on TM from 1995 to 2002 in order to explore how TM methodologies and applications have developed in this period. Therefore, the first limit of this article is the author’s limited knowledge in presenting an overall picture of this subject. Secondly, some of the academic journals listed in the Engineering Index (EI), the Science Citation Index (SCI) and the Social Science Citation Index (SSCI), as well as other practical reports are not included in this survey. These would have provided more complete information to explore the development of TM technologies and applications. Thirdly, non-English publications are not considered in this survey, although they could help to determine the effects of different cultures on the development of TM methodologies and applications. We believe that TM methodologies and applications in addition to those discussed in this article have been published and developed in other areas. 10.3. Suggestions 1. Integration of qualitative and quantitative method. The qualitative and quantitative methods are different in both methodology and problem domain. Some articles present their variables, modeling, and system design without expert advice or considering human behavior from real world situations. These pertain to theoretical research and it is difficult to implement TM technology into individuals and organizations. On the other hand, some articles have presented their TM concepts without a scientific or systematic approach, which leads TM

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methodology to remain at the stage of discussion. Therefore, integration of qualitative and quantitative methods may be an important direction for future work on TM technologies and applications. 2. Integration of technologies. TM is an interdisciplinary research issue. Thus, future TM developments need integration with different technologies, and this integration of technologies and cross-interdisciplinary research may offer more methodologies to investigate TM problems. 3. Change is a source of development. The changes due to social and technical reasons may either enable or inhibit TM technologies and application development. This means that inertia, stemming from the use of routine problem-solving procedures, stagnant knowledge sources, and following past experience or knowledge may impede changes in terms of learning and innovation for individuals and organizations. Therefore, to continue creating, sharing, learning, and storing knowledge may also become a source of TM development.

11. Conclusions This paper is based on a literature review on technology management methodologies and applications from 1995 to 2003 using a keyword index search. We conclude that TM methodologies tend to develop towards expert orientation, and TM applications development is a problem-oriented domain. Different science methodologies are suggested to be implemented in TM. Integration of qualitative and quantitative methods, and integration of TM methodologies studies may broaden our horizon on this subject. Finally, the ability to continually change and obtain new understandings is the power of TM technologies, and will be the topic of future work.

References Akoka, J., Comyn-Wattiau, I., 1996. A knowledge-based system for auditing computer and management information systems. Expert Systems With Applications 11, 361–375. AI-Habaibeh, A., Zorriassatine, F., Gindy, N., 2002. Comprehensive experimental evaluation of a systematic approach for cost effective and rapid design of condition monitoring systems using Taguchi’s method. Journal of Materials Technology 124, 372–383. Allsopp, D.J., Harrison, A., Sheppard, C., 2002. A database architecture for reusable CommonKADS agent specification components. Knowledgebased Systems 15, 275–283. Arch-int, S., Batanov, D.N., 2003. Development of industrial information systems on the Web using business components. Computers in Industry 50, 231–250. Ashok, S., Banerjee, R., 2003. Optimal cool storage capacity for load management. Energy 28, 115–126. Baccarini, D., Archer, R., 2001. The risk ranking of projects: a methodology. International Journal of Project Management 19, 139– 145.

390


Balasubramanian, V., Bieber, M., Isakowitz, T., 2001. A case study in systematic hypermedia design. Information Systems 26, 295–320. Balkema, A.J., Preisig, H.A., Otterpohl, R., Lambert, F.J.D., 2002. Indicators for the sustainability assessment of wastewater treatment systems. Urban Water 4, 153–161. Ben-Arieh, D., 1997. Task management in a multi-robot environment. Computer Integrated Manfufacturing Systems 10, 123–131. Bhattacherjee, A., DeShazer, K., Gerlach, J.H., Rierden, B., 2001. A hybrid approach to OO development: the SUMMITrak project at TCI. The Journal of Systems and Software 57, 91 –98. Bick, A., Oron, G., 2000. Desalination technology for optimal renovation of saline groundwater in a natural reservoir. Desalination 131, 97– 104. Boone, D., Roehm, M., 2002. Retail segmentation using artificial neural networks. International Journal of Research in Marketing 19, 287– 3401. Bouter, V.W., Beroggi, G.E.G., Gent, D.V., 2001. Training-based evolutionary multimedia prototyping. Safety Science 39, 39–49. Brahan, J.W., Lam, K.P., Chan, H., Leung, W., 1998. AICAMS: artificial intelligence crime analysis and management system. Knowledge-Based Systems 11, 355–361. Brand, C., Mattarelli, M., Moon, D., Wolfler Calvo, R., 2002. STEEDS: A strategic transport-energy-environment decision support. European Journal of Operational Research 139, 416 –435. Burkett, W.C., 2001. Product data markup language: a new paradigm for product data exchange and integration. Computer-Aided Design 33, 489– 500. Carayannis, E.G., 1999. Fostering synergies between information technology and managerial and organizational cognition: the role of knowledge management. Technovation 19, 219–231. Carstensen, J., Vanrolleghem, P., Rauch, W., Reichert, P., 1997. Terminology and methodology in modeling for water quality management—a discussion starter. Water Science Technology 36, 157–168. Chan, S.L., Choi, C.F., 1997. A conceptual and analytical framework for business process reengineering. International Journal of Production Economics 50, 211–223. Chang, P.L., Hsu, C.W., 1997. A project management system (PMS) for research institutes applying for government R&D contracts in Taiwan. International Journal of Project Management 15, 165 –172. Chatzoglou, P.D., Macaulay, L.A., 1997. The importance of human factors in planning the requirements capture stage of a project. International Journal of Project Management 15, 39 –53. Chau, K.W., Chuntian, C., Li, C.W., 2002. Knowledge management system on flow and water quality modeling. Expert Systems With Applications 22, 321 –330. Chen, Y.M., 1997. Development of a computer-aided concurrent net shape product and process development environment. Robotics & ComputerIntegrated Manufacturing 13, 337–360. Chen, W.C., Chang, N.B., Chen, J.C., 2002aa. GA-based fuzzy neural controller design for municipal incinerators. Fuzzy Sets and Systems 129, 343–369. Chen, Z.L., Li, S., Tirupati, D., 2002bb. A scenario-based stochastic programming approach for technology and capacity planning. Computers & Operations Research 29, 781 –806. Cho, H.C., Kim, J.K., Kim, S.H., 1998. Role-based approach to business process simulation modeling and analysis. Computers in Industrial Engineering 35, 343 –346. Cho, Y.H., Kim, J.K., Kim, S.H., 2002. A personalized recommended system based on web usage mining and decision tree induction. Expert Systems With Applications 23, 329–342. Choi, H.W., Kim, I.J., Kim, T.S., 2002. Contingent claims valuation of optimal calling plan contracts in telephone industry. International Review of Financial Analysis 11, 433– 448. Clark, G., Mehta, P., 1997. Artificial intelligence and networking in integrated building management systems. Automation in Construction 6, 481 –498.

Dai, S.H., 1998. Methodology for predicting the occurrence of failure events for pressure vessels used in the process industry. International Journal of Pressure Vessels and Piping 75, 221 –228. Dangelmaier, W., Kress, S., Wenski, R., 1999. TelCow: telework under the co-ordination of a workflow management system. Information and Software Technology 41, 341 –353. Dawood, N.N., 1996. A strategy of knowledge elicitation for developing an integrated bidding/ production management expert system for the precise industry. Advances in Engineering Software 25, 225 –234. Dawood, N.N., Akinsola, A., Hobbs, B., 2002. Development of automated communication of system for managing site information using internet technology. Automation in Construction 11, 557 –572. Dawood, N.N., Marasini, R., 2002. Visualization of a stockyard layout simulator ‘SimStock’: a case study in precast concrete products industry. Automation in Construction 12, 113 –122. Demuynck, C., Bauwens, W., Pauw, N.D., Dobbelaere, I., Poelman, E., 1997. Evaluation of pollution reduction scenarios in a river basin: application of long term water quality simulations. Water Science Technology 35, 65–75. Dey, P.K., Tabucanon, M.T., Ogunlana, S.O., 1996. Petroleum pipeline construction planning: a conceptual framework. International Journal of Project Management 14, 231 –240. Doyle, M.D., Ang, C.S., Martin, D.C., Noe, A., 1996. The visible embryo project: embedded program objects for knowledge access, creation and management through the world wide web. Computerized Medical Imaging and Graphics 20, 423–431. Drejer, A., 1997. The discipline of management of technology, based on considerations related to technology. Technovation 17, 253–265. Easton, L., Murphy, D.J., Pearson, J.N., 2002. Purchasing performance evaluation: with data envelopment analysis. European Journal of Purchasing & Supply Management 8, 123– 134. Fernandez-Breis, J.T., Martinez-Bejar, R., 2000. A cooperative tool for facilitating knowledge management. Expert Systems With Applications 18, 315–330. Fleten, S.E., Hoyland, K., Wallace, S.W., 2002. The performance of stochastic dynamic and fixed mix portfolio models. European Journal of Operational Research 140, 37–49. Frederix, F., 2001. An extended enterprise planning methodology for the discrete manufacturing industry. European Journal of Operational Research 129, 317 –325. Garshnek, V., Morrison, D., Burkle, F.M., 2000. The mitigation, management, and survivability of asteroid/comet impact with Earth. Space Policy 16, 213– 222. Gilad, I., Karni, R., 1999. Architecture of an expert system for ergonomics analysis and design. International Journal of Industrial Ergonomics 23, 205 –221. Gong, N., Ding, X., Denoeux, T., Bertrand-Krajewski, J.L., Clement, M., 1996. STORMENT: A connectionist model for dynamic management of wastewater treatment plants during storm events. Water Science Technology 33, 247–256. Haas, M., Kleingeld, A., 1999. Multilevel design of performance measurement systems: enhancing strategic dialogue throughout the organization. Management Accounting Research 10, 233–261. Hailey, D., McDonald, I., 1996. The assessment of diagnostic imaging technologies: a policy perspective. Health Policy 36, 185–197. Hicks, B.J., Culley, S.J., Allen, R.D., Mullineux, G., 2002. A framework for the requirements of capturing, storing and reusing information and knowledge in engineering design. International Journal of Information Management 22, 263 –280. Horng, H.C., Cochran, J.K., 2001. Project surface regions: a decision support methodology for multitasking workers assignment in JIT systems. Computers and Industrial Engineering 39, 159 –171. Huang, G.Q., Mak, K.L., 2000. WeBid: A web-based framework to support early supplier involvement in new product development. Robotics and Computer Integrated Manufacturing 16, 169–179.

S.-h. Liao / Technovation 25 (2005) 381–393 Huang, G.Q., Shi, J., Mark, K.L., 2000. Synchronized system for ‘design for X’ guidelines over the WWW. Journal of Materials Processing Technology 107, 71– 78. Huang, G.H., Xia, J., 2001. Barriers to sustainable water-quality management. Journal of Environmental Management 61, 1– 23. Ip, W.H., 1997. Rule-based ARIMA models for FMA. Journal of Materials Technology 66, 240– 243. Jacob, W.F., Kwak, Y.H., 2003. In search of innovative techniques to evaluate pharmaceutical R&D projects. Technovation 23, 291–296. Jefferson, T.I., Nagy, T.J., 2002. A domain-driven approach to improving search effectiveness in traditional online catalogs. Information and Management 39, 559–570. Jensen, J.B., Malhotra, M.K., Philipoom, P.R., 1996. Machine dedication and process flexibility in a group technology environment. Journal of Operations Management 14, 19 –39. Keller, R.M., Dungan, J.L., 1999. Meta-modeling: a knowledge-based approach to facilitating process model construction and reuse. Ecological Modeling 119, 89–116. Khan, F.I., Abbasi, S.A., 2000. Cushioning the impact of toxic release from runway industrial accidents with greenbelts. Journal of Loss Prevention in the Process Industries 13, 109– 124. Kim, S.H., Jang, D.H., Lee, D.H., Cho, S.H., 2000. A methodology of constructing a decision path for IT investment. Journal of Strategic Information Systems 9, 17 –38. Kim, J., Suh, W., Lee, H., 2003. Hypermedia modeling for linking knowledge to data warehousing system. Expert Systems With Applications 24, 103–114. Kirk, D., Pine, R., 1998. Research in hospitality systems and technology. Hospitality Management 17, 203– 217. Koschel, A., Lockemann, P.C., 1998. Distributed events in active database systems: Letting the genie out of the bottle. Data and Knowledge Engineering 25, 11– 28. Knight, B., Ma, J., 1997. Temporal management using relative time in knowledge-based process control. Engineering Applications Artificial Intelligence 10, 269 –280. Kumar, V., Jain, P.K., 2003. Commercialization of new technologies in India: an empirical study of perceptions of technology institutions. Technovation 23, 113 –120. Laking, G.R., Price, P.M., Sculpher, M.J., 2002. Assessment of the technology for functional imaging in cancer. European Journal of Cancer 38, 2194–2199. Lang, S.Y.T., Dickinson, J., Buchal, R.O., 2002. Cognitive factors in distributed design. Computers in Industry 48, 89 –98. Lau, H.C.W., Tso, S.K., Ho, J.K.L., 1998. Development of an intelligent task management system in a manufacturing information network. Expert Systems With Applications 15, 165–179. Laudon, K.C., Laudon, J.P., 2002. Essential of Management Information Systems, Fifth ed., Prentice Hall Inc, New Jersey. Lee, H., Lee, C., Yoo, C., 1999. A scenario-based object-oriented hypermedia design methodology. Information and Management 36, 121–138. Lee, H., Yoo, C., 2000. A form driven objected-oriented reverse engineering methodology. Information Systems 25, 235– 259. Lee, H., Park, Y.K., Jang, G., Huh, S.Y., 2000. Designing a distributed database on a local area network: a methodology and decision support system. Information and Software Technology 42, 171– 184. Lee, J.K., Hong, J.S., 1998. A regenerative expert system approach for the maintenance of expert systems. Expert Systems With Applications 14, 313–321. Lee, P.G., 2000. Process control and artificial intelligence software for aquaculture. Aquaculture Engineering 23, 13–36. Lee, T.S., Chiu, C.C., Lu, C.J., Chen, I.F., 2002. Credit scoring using hybrid neural discriminant technique. Expert Systems With Applications 23, 245–254. Leu, Y.Y., Matheson, L.A., Rees, L.P., 1996. Sequencing mixed-model assembly lines with genetic algorithms. Computers in Industrial Engineering 30, 1027–1036.

391

Liang, R.Y., Nusier, O.K., Malkawi, A.H., 1999. A reliability based approach for evaluating the slope stability of embankment dams. Engineering Geology 54, 271–285. Liao, S.H., 2001. A knowledge-based architecture for implementing military geographical intelligence system on Intranet. Expert Systems With Applications 20, 313– 324. Liao, S.H., 2003. Knowledge technologies and applications—literature review from 1995 to 2002. Experts Systems with Applications 25, 155– 164. Linn, R.J., Zhang, W., Li, Z.Y., 2000. An intelligent management system for technology management. Computers and Industrial Engineering 38, 397– 412. Linninger, A.A., Ali, S.A., Stephanopoulos, G., 1996. Knowledge-based validation and waste management of batch pharmaceutical process designs. Computers Chemical Engineering 20, S1431–S1436. Liu, L., Pu, C., Han, W., 2001. An XML-enabled data extraction toolkit for web sources. Information Systems 26, 563 –583. Liu, Y., Ye, L., Benoit, I., Liu, X., Cheng, Y., Morel, G., Fu, C., 2003. Economic performance evaluation method for hydroelectric generating units. Energy Conversion and Management 44, 797–808. Lo, H.K., Szeto, W.Y., 2002. A methodology for sustainable traveler information services. Transportation Research Part B 36, 113–130. Lopes, M.D.S., Flavell, R., 1998. Project appraisal - a framework to assess non-financial aspects of projects during the project life cycle. International Journal of Project Management 16, 223 –233. Lovett, P.J., Ingram, A., Bancroft, C.N., 2000. Knowledge-based engineering for SMEs—a methodology. Journal of Materials Processing Technology 107, 384 –389. Low, G.C., Henderson-Sellers, B., Han, D., 1995. Comparison of objectoriented and traditional systems development issues in distributed environments. Information and Management 28, 327–340. Maimon, O., Dayagi, A., 1995. Nesting planning based on production priorities and technological efficiency. European Journal of Operational Research 80, 121 –129. Manzocco, L., Nicoli, M.C., 2002. Food design: from the methodological approach to the case study of low-calorie syrups. Trends in Food and Technology 13, 420 –427. Matsatsinis, N.F., Doumpos, M., Zopounidis, C., 1997. Knowledge acquisition and representation for expert systems in the field of financial analysis. Expert Systems With Applications 12, 247–262. McFadden, F.R., Hoffer, J.A., Prescott, M.B., 2000. Modern Database Management, Fifth ed., Prentice-Hall, New York. Metaxiotis, K., Kagiannas, A., Askounis, D., Psarrsa, J., 2003. Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Conversion and Management 44, 1525–1534. McQuater, R.E., Peters, A.J., Dale, B.G., Spring, M., Rogerson, J.H., Rooney, E.M., 1998. The management and organizational context of new product development. International Journal of Production Economics 55, 121–131. McMeekin, T.A., Ross, T., 2002. Predictive microbiology: providing a knowledge-based framework for change management. International Journal of Food Microbiology 78, 133 –153. Mokdad, A., Probast, W., 2001. The computational object approach for network and systems management. Telematics and Informatics 18, 211– 247. Naim, M.M., Childerhouse, P., Disney, S.M., Towill, D.R., 2002. A supply chain diagnostic methodology: determining the vector of change. Computers and Industrial Engineering 43, 135–157. Nakanishi, H., Emoto, M., Kojima, M., Ohsuna, M., Komada, S., 2000. Object-oriented data handling and OODB operation of LHD mass data acquisition system. Fusion Engineering and Design 48, 135– 142. Nault, B.R., Storey, V.C., 1998. Using object concepts to match artificial intelligence techniques to problem types. Information and Management 34, 19–31.

392


Noh, J.B., Lee, K.C., Kim, J.K., Lee, J.K., Kim, S.H., 2000. A cased-based reasoning approach to cognitive map-driven tacit knowledge management. Expert Systems With Applications 19, 249–259. Norton, B.G., 1998. Evaluation and ecosystem management: new directions needed? Landscape and Urban Planning 40, 185 –194. Oehlmann, R., Thoben, K.D., Weber, F., 1997. Capturing and assessing formal interaction at task level in product development. Computers in Industry 33, 179–189. Olin, J.G., Greis, N.P., Kasarda, J.D., 1999. Knowledge management across multi-tier enterprise: The promise of intelligent software in the auto industry. European Management Journal 17, 335–347. Owrang, M.M., Grupe, F.H., 1996. Using domain knowledge to guide database knowledge discovery. Expert Systems With Applications 10, 173– 180. Pallaeala, P.M.D.S., Lun, K.C., 2001. EMR based telegeriatric system. International Journal of Medical Informatics 61, 229– 234. Pekny, J.F., 2002. Algorithm architectures to support large-scale process systems engineering applications involving combinatorics, uncertainty, and risk management. Computers and Chemical Engineering 26, 239– 267. Plant, R.E., Vayssieres, M.P., 2000. Combining expert system and GIS technology to implement a state-transition model of oak woodlands. Computers and Electronics in Agriculture 27, 71– 93. Pretorius, M.W., Wet, G., 2000. A model for the assessment of new technology for the manufacturing enterprise. Technovation 20, 3–10. Qin, Y., Balendra, R., 2001. Concept of a design support system for form comparison. Journal of Materials Processing Technology 115, 245– 255. Quaglini, S., Stefanelli, M., Cavallini, A., Micieli Fassino, C., Mossa, C., 2000. Guideline-based careflow systems. Artificial Intelligence in Medicine 20, 5–22. Rajaram, K., 2001. Assortment planning in fashion retailing: methodology, application and analysis. European Journal of Operational Research 129, 186–208. Ramesh, B., Tiwana, A., 1999. Supporting collaborative process knowledge management in new product development teams. Decision Support Systems 27, 213–235. Recio, B., Rubio, F., Lomban, J., Ibanez, J., 1999. An econometric irrigated crop allocation model for analyzing the impact of water restriction policies. Agricultural Water Management 42, 47– 63. Rezayat, M., 2000. The enterprise-web portal for life-cycle support. Computer-Aided Design 32, 85–96. Robey, D., Boudreau, M.C., Rose, G.M., 2000. Information technology and organizational learning: a review and assessment of research. Accounting Management and Information Technologies 10, 125–155. Rosen, M.A., Dincer, I., 2003. Energy-cost-energy-mass analysis of thermal systems and processes. Energy Conversion and Management 44, 1633–1651. Roy, J., Filiatrault, P., 1998. The impact of new business practices and information technologies on business air travel demand. Journal of Air Transport Management 4, 77 –86. Ruland, D., Spindler, T., 1995. Integration of product and design data using a metadata—and a rule-based approach. Computer Integrated Manufacturing Systems 8, 211– 221. Rupnik-Miklic, E., Zupancic, J., 1995. Experiences and expectations with CASE technology—an example from Slovenia. Information and Management 28, 377– 391. Rubenstein, A.H., 1994. Trends in technology management revisited. IEEE Transaction on Engineering Management 41, 335–341. Sarkis, J., Presley, A., Liles, D.H., 1995. The management of technology within an enterprise engineering framework. Computers and Industrial Engineering 28, 497 –511. Secomandi, N., 2000. Comparing neuro-dynamic programming algorithms for the vehicle routing problem with stochastic demands. Computers and Operations Research 27, 1201–1225.

Shaalan, K., Rafea, M., Rafea, A., 1998. KROL: a knowledge representation object language on top of Prolog. Expert Systems With Applications 15, 33 –46. Shafer, J.C., Agrawal, R., 2000. Continuous querying in database-centric web applications. Computer Networks 33, 519 –531. Sharratt, P.N., Choong, P.M., 2002. A life-cycle framework to analyze business risk in process industry projects. Journal of Cleaner Production 10, 479–493. Sheu, J.B., 1999. A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data. Transportation Research Part A 33, 79–100. Shoval, P., 1995. Structured method for designing subsystems. Information and Software Technology 37, 79–85. Sigman, S., Liu, X.F., 2003. A computational argumentation methodology for capturing and analyzing design rationale arising from multiple perspective. Information and Software Technology 45, 113–122. Sokolov, A., Wulff, F., 1999. SwingStations: a web-based client tool for the Baltic environmental database. Computers and Geosciences 25, 863 –871. Sonesson, U., Bjorklund, A., Carlsson, M., Dalemo, M., 2000. Environmental and economic analysis of management systems for biodegradable waste. Resources, Conservation and Recycling 28, 29–53. Song, H.S., Kim, J.K., Kim, S.H., 2001. Mining the change of customer behavior in an internet shopping mall. Expert Systems With Applications 21, 157 –168. Spiliopoulos, K., Sofianopoulou, S., 1998. An optimal tree search method for the manufacturing systems cell formation problem. European Journal of Operational Research 105, 537 –551. Standing, C., 2002. Methodologies for developing Web applications. Information and Software Technology 44, 151–159. Sugumaran, V., Storey, V.C., 2002. Ontology for conceptual modeling: their creation, use, and management. Data and Knowledge Engineering 42, 251–271. Task Force on Management of Technology, 1987. Management of Technology: The Hidden Competitive Advantage. National Academy Press, Washington, DC, pp. 9. Tassey, G., 1999. Lessons learned about the methodology of economic impact studies: the NIST experience. Evaluation and Program Planning 22, 113–119. Tatsiopoulos, I.P., Panayiotou, N.A., Ponis, S.T., 2002. A modeling and evaluation methodology for E-commerce enabled BPR. Computers in Industry 49, 107 –121. Tian, Q., Ma, J., Liu, O., 2002. A hybrid knowledge and model system for R&D project selection. Expert Systems With Applications 23, 265 –271. Uddin, M.K., Shanker, K., 2002. Grouping of parts and machines in presence of alternative process routes by genetic algorithm. International Journal of Production Economics 76, 219–228. Urwiler, R., Ramarapu, N.K., Wilkes, R.B., Frolick, M.N., 1995. Computer-aided engineering: the determinates of an effective implementation strategy. Information and Management 29, 215– 225. Vadas, R.G., Carcia, L.A., Labadie, J.W., 1995. A methodology for water quality and quality assessment for wetland development. Water Science Technology 31, 293–300. Varis, O., Kuikka, S., 1999. Learning Bayesian decision analysis by doing: lessons from environmental and natural resources management. Ecological Modeling 119, 177 –195. Wang, H., 1993. Technology management in a dual world. International Journal of Technology Management 8, 108–120. Wang, J., Yang, J.B., Sen, P., Ruxton, T., 1996. Safety based design and maintenance optimization of large marine engineering systems. Applied Ocean Research 18, 13–27. Wang, L., Shen, W., Xie, H., Neelamkavil, J., Pardasani, A., 2002. Collaborative conceptual design—state of the art and future trends. Computer-Aided Design 34, 981 –996. Wei, M.S., Weber, F., 1996. An expert system for waste management. Journal of Environmental Management 46, 345–358.

S.-h. Liao / Technovation 25 (2005) 381–393 Wilkins, B., Barrett, J., 2000. The virtual site: a web-based teaching/ learning environment in construction technology. Automation in Construction 10, 169 –179. Wood, F.B., 1997. Lessons in technology assessment - methodology and management at OTA. Technological Forecasting and Social Change 54, 145–162. Wu, I.L., 2002. A model for implementing BPR based on strategic perspectives: an empirical study. Information and Management 39, 313–324. Wu, J.S., Lee, T.T., Tzeng, Y.M., Chen, C.S., 1997. Enhancement of an object-oriented expert system for contingency load transfer of distribution systems. Electronic Power Systems Research 42, 87–94. Xu, W., Wei, Y., Fan, Y., 2002. Virtual enterprises and its intelligence management. Computers and Industrial Engineering 42, 199–205. Yap, J.C.H., 1995. Rapid application development in small unit settings. International Journal of Bio-Medical Computing 40, 157 –163. Young, D.M., Cabezas, H., 1999. Designing sustainable process with simulation: the waste reduction (WAR) algorithm. Computers and Chemical Engineering 23, 1477–1491.

393

Zhong, N., Ohsuga, S., 1996a. A hierarchical model learning approach for refining and managing concept clusters discovered from database. Data and Knowledge Engineering 20, 227–252. Zhong, N., Ohsuga, S., 1996b. System for managing and refining structural characteristics discovered from databases. Knowledge-based Systems 9, 267 –279. Dr. Shu-hsien Liao is a professor in the Department of Management Sciences and Decision Making, Tamkang University, Taiwan. He received his PhD at the Operational Research/System Group of the Business School, University of Warwick, UK, in 1996. His publications have appeared in the European Journal of Operational Research, Expert Systems With Applications, Technovation and Space Policy. His current research interests are in information management, decision support systems, knowledge management, electronic commerce, and database management.