Organization of this dissertation

The remaining part of this dissertation is arranged as follows. In chapter 2 we review the literature on knowledge management regarding innovation, new product development and the customer in the market place, as well as data mining application in market segmentation. Then in chapter 3 we propose an integrated KM model to link CKM with an innovative NPD project, details of the implementation process are also described. In chapter 4 we report the application of the integrated KM model in a Telematics NPD project, and then present an empirical study and the result.

The outcome that meets the evaluation criteria will confirm the feasibility of this model in the real world business environment. It enables the application of customer knowledge in making product variants for different target market segments, in reducing

project risk, and in meeting custimers’ satisfaction, so as to make the business a success. In chapter 5 we will present the discussion and compare the results of these three data mining methods, and finally in chapter 6 we draw our conclusion and indicate the direction for further research.

CHAPTER 2 LITERATURE REVIEW

2.1 THE EVOLUTION OF NEW PRODUCT DEVELOPMENT STUDIES

It is well recognized that to substantiate a company’s success the market offerings whatever products or services must meet customers’ requirement or even more, to exceed their expectation. So the importance of new product development cannot be underestimated.

To explore the best practice how to bring out successful products, industry and academic community have made efforts to study the theoretical foundation and real world case by means of domestic investigation and international survey.

In U.S.A. a systematic study for product development management practice has been done to report that the basic product development processes should be: new product strategy, exploration, screening, business analysis, development, testing and commercialization (Booz 1982). Brown organized the within-ten-years empirical research literatures on product development into three main streams: the ‘rational plan approach’

emphasized determinants of financial performance of the product, the ‘communication web approach’ dealt with communication effect on project result, and the ‘disciplined problem solving approach’ concentrated on factors that bring product into being – team, suppliers, project leader (Brown 1995). He also proposed an ‘integrative model for product development’ by taking product effectiveness and process performance as the most influential points for the financial success of the product developed. Eleven large-scale surveys sponsored by Product Development and Management Association, U.S.A., have been conducted from 1990 to 1996. The results were summarized to conclude a consistent clue that multi-functional team, rational process/resources and considerate strategy are crucial part for success; however, best practices may be context-specific (Griffin 1997).

Another article evaluated product development literatures in the past ten years to assess the relationships between project performance and several specific product development characteristics: product development process, product definitions, organization context and cross-function teaming. The positive impact factors found are: the use of overlapping/interaction activities by cross-function teaming, employment of integrated tools and formal methods, and the organizational influence of team leaders (Gerwin 2002).

Other school of study was focused to the successful practices in specific industry of certain countries. Teams of scholars at Massachusetts Institute of Technology, Harvard University and the University of Michigan have identified a number of strengths in Japanese automobile industry (Lynn 2002). For instance, technology fusion by convergence of different technologies from separate disciplines (Kodama 1992), integrating of suppliers’ technology capability in development stage (Kamath 1994), set-based concurrent engineering (Ward 1995, Liker 1996), technology integration by incremental improvement (Iansiti 1997), and organizational mechanism (Sobeck 1998).

The taxonomy and evolution of technology strategies of Taiwan’s high technology-based firms was reported too (Hung 2003). However, methodology down to the product structure level is relatively seldom to be studied, except researches about product modularization by several scholars (Baldwin 1997, Diaz 1998, Sanchez 1999, Schilling 2000, Mikkola 2003) to emphasize the design strategies aiming at less lead time and less cost for introducing new product variants for multiple market segments.

Recent studies in the NPD literatures look into new issues such as sustainable development, innovative problem solving theory, and knowledge management. The Earth Summit held in Rio de Janeiro, Brazil in 1992, produced a conference document: Agenda 21, which is regarded as “a blueprint for action for global sustainable development into 21^st century”. From then on, sustainable development become a new concept for environment

protection by collaboration among governments, industries and education institutes.

Therefore, many environment assessment tools were developed to measure the impact on environment by industrial products, for instance, life cycle analysis (LCA), cumulative energy expenditure (KEA), material input per service input (MIPS) and so on.

However, some firms may see the approach of sustainable development as a constraint undermining their economic interest, they indicates that financial resources assigned by firms for environment sustainability must be rewarded by market success for the sustainability of firms. Innovative technology development and management is a key success factor in sustainability of both environment and firm. Through technological collaboration and value chain management in 3R (reduce, reuse and recycle) countermeasures for product and process design, a competitive advantage can be gained in an innovative new product development project. WBCSD (World business council for sustainable development) has identified seven major eco-efficiency elements to guide companies in developing eco-friendly products for reducing environment impacts (Desimone 1997): (1) reduce the material intensity of its goods and services, (2) reduce the energy intensity of its goods and services, (3) reduce the dispersion of any toxic materials, (4) enhance the re-cyclability of its materials, (5) maximize the sustainable use of renewable resources, (6) extend the durability of its products, (7) increase the service intensity of its goods and services.

In February of 2003, European Community announced the proposal for two directives:

(1) WEEE, a directive on Waste Electrical and Electronic Equipment, (2) RoHS, a directive on ‘the Restriction of the use of certain Hazardous Substance’ in electrical and electronic equipment. The registration of environment protection measure will certainly influence the future NPD program.

On the other hand, innovation in NPD has already been well studied, however, merely in the level of conceptual frame (Lynn 1996, Goel 1998, Thomke 2002, Rice 2002, McDermott 2002, and Chapman 2004). A tool to help engineers to tackle systematic incompatibility and technical contradiction problem in an innovative NPD project has drawn attention in recent years. It is TRIZ (an acronym in Russian: Theory of Inventive Problem Solving), a methodology developed in 1946 by Genrich Altshuller by investigating the intellectual property contained in 200,000 patents (Smith 2003) . The core of TRIZ are the 40 principles and a matrix of contradictions with 39 parameters to reduce creativity to an exact science. It has been proven to be a very powerful method for creating new NPD solution by providing a new and more problem-solving-oriented way of creation of innovative conceptual design solution (Orloff 2003, Bariani 2004). The TRIZ function analysis helps in coping with design problems involves some kinds of conflict condition such as: (1) a product should be stronger but lighter—the technical contradiction, (2) a product should be of higher quality but lower cost—the management contradiction.

Therefore, researcher has tried using TRIZ to deal with eco-innovative NPD problems:

achieving material reduction, energy reduction, toxicity reduction in a more durable, better service product (Chang 2004).

The most significant contribution of TRIZ is the conversion of tacit knowledge (Polanyi 1966) hoarded in the world’s finest inventive and innovative minds into a codified and comprehensive form, and becomes the explicit product knowledge to facilitate the innovative new product development.

2.2 KNOWLEDGE MANAGEMENT

2.2.1 Technological Innovation and Knowledge

Peter Drucker defined innovation as “The effort to create purposeful, focused change in an enterprise’s economical or social potential”. He indicated that most successful innovation stemmed from seven areas of opportunities and ‘industry and market change’,

‘changes in perception’ as well as ‘new knowledge’ are three among those seven areas.

Knowledge-based innovation requires not only one kind of knowledge but many, and the innovation that creates new users and new markets should be carefully aimed at the specific application (Drucker 1998). Betz (2003) defined technological innovation as:

“Technological innovation is both the invention of a new technology and its introduction into the marketplace as a new high-technology product, process, or service.” Technological innovation allows us to cope with increasingly intensive competition in a rapidly changing marketplace. Most companies should use their knowledge to promote their competitive advantage in product innovation, by enhancing their capability in managing that knowledge so as to convert it into useful products and services. In the past decade, knowledge has been recognized as one of the most valuable asset in organization as indicated by Peter Drucker: “The most important contribution management needs to make in the 21^st century is to increase the productivity of knowledge work and knowledge worker” (Drucker 1999).

Among many examples on definition about knowledge given by researchers, Davenport’s pragmatic one is “The most valuable form of contents in a continuum starting at data, encompassing information, and ending at knowledge” (Davenport 1996).

Researchers, a long time ago, defined the knowledge category using the concept of explicit knowledge and tacit knowledge (Polanyi 1966). Then, Nonaka used a SECI model shown in Figure 1 to identify knowledge creation as a spiral process of interaction between

explicit knowledge and tacit knowledge. The ‘externalization’ step which takes place at

‘interaction ba’ plays an important role in knowledge creation, it is supported by two key factors: (1) converting tacit knowledge into explicit knowledge and (2) translating the tacit knowledge of experts or customers into comprehensible forms (Nonaka 1998).

Figure 1. The SECI Model (Nonaka 1998)

Investment in knowledge work can lead to innovation efforts such as the discovery and the development of new technologies, new products, and new production processes according to Carneiro (2000). Danneels (2002) offered an important insight when he argued that product innovation for a company must link technological competence such as engineering and process know-how with customer competence such as knowledge of customer needs and communication channels.

Therefore, it is worthwhile to manage the knowledge a company desires to have and it is also imperative to institutionalize the knowledge management process for transforming knowledge into company’s competitive advantage. Different, however similar approaches have been proposed by researchers in this field: Davenport again pointed out that the knowledge process lies somewhere between information and a company’s products and services, and the knowledge process consists of three sub-processes: knowledge generation, knowledge codification, and knowledge transfer / realization (Davenport 2001). Bhatt (2000) defined the knowledge development cycle as four phases: (1) knowledge creation, (2) knowledge adoption, (3) knowledge distribution and (4) knowledge review and revision, and addressed that appropriate strategies should be required for each phase to organize knowledge into the development cycle. His viewpoint is largely concurred with Kakabadse’s four distinct stages of knowledge institutionalization: (1) knowledge creation, (2) knowledge sharing, (3) knowledge application, and (4) knowledge acquisition from outside (Kakabadse 2001).

2.2.2 Knowledge Management on New Product Development

The importance of knowledge work for a company has been well recognized.

However, justifying this knowledge as being valuable is a must for a company in order to qualify the knowledge as an intangible asset. “Knowledge assets underpin competence, and the competence in turn underpins the company’s products and services offering in the market.” (Styre 2002). Indeed, the success of a knowledge-conscious company relies on its efficiency in creating knowledge, and its effectiveness in applying that knowledge to products and services that offer a deliverable value to customers thereby generating a profit for the company. Researchers argued that the challenge for a new product development task is to design and create organizational context for the knowledge work. Therefore, they

proposed a conceptual model of the NPD organization as a knowledge enterprise, the model constitutes four high-level constructs that shapes up the knowledge system: (1) contextual organizational elements such as information quality, participants from boundary spanning structure, and etc., (2) knowledge work behaviors such as linking knowledge sources and knowledge users, creating opportunities for producing new knowledge, and etc., (3) knowledge outcomes such as effective knowledge use and new knowledge generation, (4) knowledge effectiveness such as organizational performance in quality, innovation, customer focus, knowledge and productivity (Mohrman 2003). A cross-functional collaborative KM model in NPD teams is also proposed to create internal knowledge repositories by developing IT-based KM tools to store NPD process knowledge, as a successful company should be able to continuously create new knowledge, quickly disseminate knowledge and embody knowledge in new products (Ramesh 1999). Other researchers have emphasized the use of KM for reducing the risk in NPD, by collecting data from internal and external sources and then extracting relevant information in order to prevent product failure. The internal problems affecting product failure are: being unable to meet performance, reliability, or cost requirement, while the external problems are:

unsuccessful reception in the market, changing regulations, and so on (Cooper 2003).

Today the role that KM plays in the NPD activities is better understood. However, it only makes a contribution to within-organization NPD outcomes such as product/service quality, cost, and deliverables-to-market. In fact these NPD outcomes should link with market outcomes like products sales, customer satisfaction, and return on investment, and to be jointly assessed in order to evaluate the business success. Therefore, customer knowledge is an important attribute of any NPD project.

2.2.3 Knowledge Management on The Customers in Marketplace

Li (1998) suggested that market knowledge competence in NPD is composed of three processes: (1) a customer knowledge process; (2) a competitor knowledge process; and (3) the interface between marketing research and R&D. Based on his work, Campell (2003) proposed a conceptual framework of “customer knowledge competence” to create and integrate customer knowledge within an organization. It is composed of four processes: (1) a customer information process, (2) a market-IT interface, (3) senior management involvement, and (4) employing evaluation and reward system. He explained that a customer information process is a set of activities that generate customer knowledge about customers’ current and potential needs for products and services. As far as market-IT interface is concerned, data about customers can be available through a customer relationship management (CRM) database. However, data needs to be converted into information and the company can then integrate the information to develop customer knowledge. Another researcher further opined that CRM cannot take place without KM, because in order to deliver products and services to delight customers, knowledge from customer must be well managed to make sure that the deliverables a company offers meet customers’ satisfaction (Plessis 2004).

CRM, with the support of IT, has already been recognized as a contemporary management tool in the digital economy for managing the relationship with customers. It does so by taking advantage of on-line data analysis, data mining and a database management system to assist a company in its management decisions. In order to maintain a good relationship with customers, it is crucial that a company communicates and interacts with its customers in a satisfactory manner, and provides market offerings that the customers want. This requires the deliberate management of different categories of

‘customer knowledge’ shown in Table 2 (Davenport 2001, Garcia-Murillo 2002, Gebert 2003).

Table 2. The categories of customer knowledge

(1) Knowledge ‘for’ customers satisfies customers’ requirement for knowledge about products, the market, and other relevant items.

(2) Knowledge ‘about’ customers captures customers’ background, motivation, attitude, and preference for products or services.

(3) Knowledge ‘from’ customers understands customers’ needs pattern and/or consumption experience of products and/or services.

In this regard, customer knowledge obtained via a CRM system is a valuable intellectual asset for a company to develop or improve products and services in order to meet or even exceed customers’ expectations. CRM systems that collect information for customer knowledge are classified into three main categories shown in Table 3 (Dyche 2002, Gebert 2003).

Table 3. The categories of CRM systems

(1) Operational CRM systems enhances the efficiency of a CRM process through service-center management and marketing-automation like database marketing.

(2) Analytical CRM systems evaluates knowledge of an individual customer’s attitude, needs, and values for cluster analysis.

Data mining is a typical technique in this category.

(3) Collaborative CRM systems synchronizes customer communication time through channels such as e-mail, the Internet, and/or the telephone.

In the literature most studies on KM and CRM are treated in separate research domains. However, lately their mutual synergy potential has drawn the attention of researchers in the field by employing KM in an effort to help CRM to transcend from its original technology-driven and data-oriented approach into a more people-oriented

‘customer knowledge management’ model or CKM model, it has already invoked a convergence of the two (Davenport 2001, Garcia-Murillo 2002). The CKM model emphasizes a bi-directional communication channel. This interaction with customers, and customer knowledge management set up strategies for how a company can develop attractive innovative products, or improve its service to win customers satisfaction.

2.3 DATA MINING FOR GENERATING CUSTOMER KNOWLEDGE 2.3.1 Market Segmentation to Support NPD

To secure market acceptance, customer knowledge on customer group of the same propensity is very important for a company in deciding which product variant to develop, to satisfy or delight customers in that market segment. Extracting knowledge of customers behavior in various segments for making decisions in product variants development is critical to the success of marketing efforts, as shown in Figure 2. Heinrichs (2003) stipulated prerequisites for company to sustain competitive advantage, one of them is the use of leading edge information technology tools for effective knowledge management, for example, web-based data mining tools to build up specific capacities in information presentation, knowledge discovery and analytical capabilities.

Product

Figure 2. Product variants for various segments

2.3.2 Market Segmentation to Form Customer Clusters

The concept of market segmentation was presented along with the concept of product differentiation by Wendell R. Smith to describe the demand-supply condition in imperfect competition market (Smith 1956). Since then, segmentation concept dominated marketing research literature and practice. Major considerations involved in segmentation research studies are the selection of the bases of segmentation -- a set of variables to allocate potential customers into homogeneous groups. Variables employed as bases (the dependable variable) or descriptors (the independent variable) can be divided into two categories: ‘general’ customer characteristics and ‘situation specific (product specific)’

customer characteristics (Wind 1978). Kamakura (2000) further classified variables into

‘observable’ ones (i.e., be measured directly) and ‘unobservable’ ones (i.e., inferred). For example, geographic and demographic variables are ‘general-observable’, situations and usage frequency variables are ‘product specific-observable’, psychographics and life-style variables are ‘general-unobservable’, and benefits, preferences variables are ‘product specific-unobservable’.

‘Benefits’ has been regarded as a preferred segmentation base for understanding a market and for making decision about market positioning and new product development (Wind 1978). Haley (1985) argued that ‘causal segmentation schemes’ (segments based on benefits, experiences, beliefs) are more likely to discover potential responsive subgroups and more attractive targets, when compared with ‘descriptive segmentation schemes’

(segments based on demographic and volumetric characteristics). Because demographic segmentation only describes customers behavior without explaining it, and the change in the perception of benefits delivered by brand/product can alter customers’ attitude – the tacit customer knowledge. Today, concerning the base for segmenting consumer market, customer characteristics such as geographic, demographic, and psychographic variables, are one of two broad groups of variables for segmenting the market. The other group is the response customers towards benefits/needs, situation and brand. Six criteria are employed to evaluate the effectiveness of different segmentation bases: identifiability, substantiality, accessibility, stability, actionability and responsiveness (Kotler 2003). ‘Benefit-based’ or

‘needs-based’ segmentation scheme is ranked as the most appropriate one if taking into account the overall performance (Kamakura 2000). Using market survey instrument to collect data that represent customers’ response towards product benefits or product features enables data mining technique to contribute in performing cluster analysis to form several target customer segments.

Generating customer knowledge in each segment requires a precedent segmentation task. There are two principal segmentation frameworks for segmentation studies (Green 1977, Wind 1978) and a hybrid two-stage segmentation method (Punj 1983):

Generating customer knowledge in each segment requires a precedent segmentation task. There are two principal segmentation frameworks for segmentation studies (Green 1977, Wind 1978) and a hybrid two-stage segmentation method (Punj 1983):