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毕业设计（论文）翻译资料（外文及中文）专业：指导教师：学生姓名：班级-学号：年月Managing knowledge for quality assurance: an empiricalstudyAuthorJie Yang (Department of Management, College of Business and Public Administration, Kean University, Union, New Jersey, USA)Abstract(1)Purpose:The purpose of this paper is to examine the relationship between knowledge management and the quality of new product and identify different hidden patterns in which knowledge acquisition and dissemination affect the quality of new product.(2)Design/methodology/approach: Employs Additivity and Variance Stabilization (A V AS) analysis.(3)FindingsThe quality of new product is related to knowledge management significantly.(4)Research limitations/implicationsImplies that if organizations fail to understand the subtle ways by which different dimensions of knowledge acquisition and dissemination influence product quality, they may fail to harvest the full value of knowledgemanagement in quality assurance.(5)Originality/value:It is one of the first to assess the link between knowledge management and quality assurance.Keywords:Knowledgemanagement,Knowledgesharing,Productquality,Varian ce,Quality assurance.1 IntroductionKnowledge management has attracted an increasing attention to management scholars and practitioners in past decades. The management of knowledge and theeffective utilization of knowledge are becoming essential for businesses to remain profitable in new product development. There are five phases in the knowledge management process and each is very important in its own way: acquiring knowledge, codifying knowledge, disseminating knowledge, developing knowledge, and applying knowledge. When a firm develops new products, the process includes knowledge acquisition, knowledge innovation, knowledge protection, knowledge integration and knowledge dissemination. Various definitions exist for knowledge management, but the most acceptable one is a transformation process of tacit knowledge into explicit knowledge in order to facilitate flows of organizational knowledge. In this sense, organization, strategy, and people have become central issues in knowledge management. It has been recognized that IT implementation alone does not result in the success of knowledge management. In our thinking, knowledge management is a framework within which the organization views all its processes as knowledge processes. In line with the growing interests in knowledge management, researchers are paying attention to product quality assurance. Quality is crucial in measuring new product performance. Quality is defined as providing what the customer expects in products or services. For the firms carrying out new product development projects, products quality assurance has become a priority in their management agenda. However, these firms face significant problems in achieving their goals. First, evolving market needs make products obsolete quickly. Firms face more intensive competition than ever before. Second, competition via new product launches intends to enhance firms' performance in their own product endeavors, but it has the unintended effect of exposing consumers to newness on a widespread scale. As a result of this exposure, consumers are habituated to a continuous flow of new products and services and accept them more readily than in the past. Product development has become a dynamic capability of the firms, because of its ability to alter the resource configuration of the firms. Several studies have suggested that knowledge management is a potential strategy for these firms because it enablesthem to maintain dynamic capability and come up with the evolving marketexpectations aimed at achieving competitive advantage and continuous improvement in quality.To explore the impact of knowledge management on new product quality, it seems necessary to first discuss knowledge acquisition and dissemination whichare the starting and ending point respectively in knowledge management process2 Knowledge acquisitionKnowledge acquisition, as a fundamental and essential process of individual and organizational learning, refers to the process of learning, filtering new information, and storing it in memory; the activity of identifying knowledge in the organization's external environment and transforming it into a representation that can be internalized and used for knowledge generation or externalization; and a firm's capability to identify and acquire knowledge that is critical to its operations. In a review of literature, there are two processes through which an organization acquires information and knowledge, searching and organizational learning.3 Knowledge disseminationKnowledge dissemination is defined as managing knowledge sharing within an organization to encourage innovation or action, increase awareness of past good practice, and stimulate users to adopt better practices for future decision‐making processes.Formal presentations, work with external professionals, specialized training, and creative task forces have been considered as effective means to disseminate knowledge. The distinction between explicit knowledge and tacit knowledge has long been discussed in light of its transferability in an IT system. However, tacit knowledge is best shared through people. Dissemination of tacit knowledge is a social process. The more “valuable”the knowledge, the less sophisticated the technology that supports it. To show its commitment for sharing knowledge, an organization should foster the employee's willingness to share and contribute to the knowledge base.In the era of global competition and rapid product obsolescence, the primary value‐creating capability of the firm revolves around its ability to exploit itsintellectual capital. Madhavan and Grover viewed new product development as a knowledge management process. New product development literature has stressed the importance of knowledge management in creating value.This study seeks to examine the link between knowledge management and new product quality. In particular, two specific aspects of knowledge management, knowledge acquisition and knowledge dissemination, are considered for two reasons:1. Knowledge acquisition from customers plays a crucial role in quality assurance. Managing knowledge stock accumulation and knowledge process flow within organizations are elements of the success of knowledge management. In order to achieve continuous improvement in product quality, the firm should collect feedbacks while communicating with its customers in relation to inquires and product information. Such activities can actually accumulate the tacit knowledge embedded in customers regarding product preferences. Customer interaction is likely to lead to the design of new product features, which meet the customers' needs more closely and improve customer satisfaction.2. Participation of employees in knowledge dissemination is vital in quality assurance. Quality improvement is maximized through employee contributions and involvement.Within the organization, employee feedback should be encouraged in order to generate valuable information with which to run the company more effectively. Employee surveys, ongoing feedback and interviews are considered the most effective ways to improve product quality. Bell suggests that the most successful companies are those that invest in building high levels of employee engagement in product performance. Employee involvement is associated with improved performance of a new product, which results mainly in the quality of product. Theoretical background of the link between knowledge management and new product quality.Resource based view is employed as the theoretical basis in this study. Itstresses that a firm's resource endowment can be a source of profits as long as these resources are heterogeneous in the same industry.The resource based view offers interesting insights on the link between knowledge management and new product quality assurance. Penrose suggested that resources are valuable only to the extent that they can deliver valuable services. The most critical resources are no longer the traditional tangible assets, but intangible dynamic capability to achieve enhanced performance.One reason for the keen attention placed on product quality is because of its critical relevance for delivering superior customer value, which is the cornerstone of competitive advantage.Barneyemphasized the stock of knowledge as a potential source of sustained competitive advantage. In new product development, knowledge has become the most important intellectual capital. The effective management of knowledge is an intangible resource for new product quality assurance. Resource based view offers answers to the questions of why knowledge management can contribute to the new product quality assurance.Knowledge is the raw material of product development. For complex projects, knowledge from multiple technical and functional domains is a necessity (Curtis et al., 1988). Ideally, a product design team is staffed so that both the levels and the distribution of knowledge within the team match those required for the successful completion of the projects; as a result, the quality product can be developed. Successful product development is a function of the degree of information transferring and sharing between marketing and R&D (Moenaert and Souder, 1990). The logic is that effective participation of both marketing and R&D reduces decision‐making uncertainty and enhances product quality outcomes (Parry and Song, 1993; Gupta et al., 1985).This study aims to examine the form of the relationship between new product quality and knowledge acquisition and dissemination variables. In particular, it seeks to identify a potentially more accurate relationship between new product quality and knowledge acquisition and dissemination variables (i.e. the cost of knowledge acquisition, level of knowledge acquisition from customers, availabilityof knowledge dissemination facilities, and participation of employees in knowledge dissemination) by relaxing the model linearity assumption. I operationalize knowledge acquisition in two dimensions – the cost of knowledge acquisition and the level of knowledge acquisition from customers, and knowledge dissemination in another two dimensions – the availability of knowledge dissemination facilities and the participation of employees in knowledge dissemination. Three control variables are also included, including innovation‐marketing fit, process management, and competitive intensity, to account for internal and external factors respectively that may influence the relationship between knowledge acquisition and dissemination and new product quality. First, the fit between product innovation and market expectations enables the firms to develop highly innovative products and introduce products that meet customer needs more effectively. Second, effective process management is postulated to have an impact on quality performance through reduction of process variance where the quality knowledge is acquired and disseminated. Third, there exists a direct link between the level of competition in the marketplace and the level of new product failure.5 MethodsSample and data collection,Surveyed firms are from a wide variety of industries, with collected data coming through key informants. The key informant approach has been widely used in empirical studies because of the key informants' knowledge of the firm, access to strategic information, and familiarity with the environment of the firms. Top management responsible for the new product development project is the key informant in our study. Respondents were mailed a questionnaire and a letter explaining the purpose of this study and promised offering the research results if respondents returned the completed questionnaire. Following related studies, I used a 7‐point Likert scale for all items to ensure higher statistical variability among survey responses (Roth and Miller, 1992; Saraph et al., 1989;Schonberger, 1983). Thus, with the exception of those representing financial performance, items for all the constructs were measured on a7‐point scale (1=Strongly Disagree, 7=Strongly Agree). Once a draft of the questionnaire had been designed, two pretests were conducted to assess face validity of the measurement items. First, a draft questionnaire containing the measurement properties was administered to a total of nine expert judges (four academics and five executives) with knowledge of working experience with knowledge management concepts. They were asked to complete the questionnaire and point out any item that was either ambiguous or otherwise difficult to answer. Based on the detailed comments, some items were modified and others were eliminated. After completing the initial pretest, 47 executives provided input, representing a variety of industries in Hong Kong. In this phase, the scales for all the constructs were clearly marked and the experts were asked to critically evaluate each item relative to the assigned construct to provide a verification of the content validity of the scale. Second, the respondents were also asked to identify any item that was ambiguous or difficult to answer. At this phase of the process, very few concerns were noted with only minor suggested modifications, primarily in instructional content and wording.Following the two pretests, the complete survey was sent out to various firms. These firms represented a cross‐section of various industries in terms of product, process type and characteristics. Industries in the sample included manufacturing, consulting, high technology, container trading and finance. Information about each development project was collected from a number of respondents. In order to obtain measures of the variables of interest, information was obtained from project leaders who are responsible for the completion of the project. Some questionnaires were administered in one‐on‐one interview formats; others were distributed to key individuals who supervised their completion with designated respondents within each firm. After several rounds of follow‐up contacts, a total of 225 effective responses were obtained, for a response rate of 57 percent. The size of these firms ranges between 16 and 22,000 employees, with an average size of 410. The number of respondents of each firm is 2‐12 project leaders, the average is 6.Obviously, the responding firms exhibited significant diversity. These responses formed the basis for validating the scales for the knowledge management constructs.6 Measures and validationThe cost of knowledge acquisition was operationalized by measuring opportunity cost, knowledge transfer cost, and verification cost occurring during the knowledge acquisition activity . The level of knowledge acquisition from customers was judged by the degree of interaction with customers and the level of customer satisfaction.The availability of knowledge dissemination facilities was measured by the explicit knowledge transfer system. The participation of employees in knowledge dissemination was measured by the opportunity for tacit knowledge disseminationand reward systems for knowledge dissemination. In developing the research instrument, I followed the adapted paradigm for developing better measures of new product quality developed by Menon et al. and measures of three control variables with innovation‐marketing fit by Atuahene‐Gima, process management by Anderson and Mandal et al., and competitive intensity by Jaworski and Kohli. All measures had a Cronbach α above 0.70, this indicates a good evaluation of reliability of these measures.In examining the convergent and discriminant validity of the constructs, Table I shows the results of a confirmatory factor analysis on a measurement model. It shows that all measures loaded on the expected factors with standardized loadings over 0.50. The fit indices suggest a good fit for the model (χ2=465.43, p=0.00, GFI=0.88, CFI=0.90, RMSEA=0.05). This demonstrates strong convergent and discriminant validity. The relationships between new product quality and knowledge management variables were examined, having established the reliability and validity of the measurement model.7 Data analysisTo model the relationship between new product quality (Quality), and the costof knowledge acquisition (KACT), the level of knowledge acquisition from customer (KALV), the availability of knowledge dissemination facilities (KDA V), the participation of employee in knowledge dissemination (KDPT), innovation‐marketing fit (MINM), process management (MPRC), and competitive intensity (MCMP), Quality was regressed on KACT, KALV, KDA V, KDPT, MINM, MPRC, and MCMP using Ordinary Least Square (OLS) regression. The R2 of the regression model is 0.628 and all independent variables except for MPRC are insignificant at the 5 percent significance level (see Table II). One of the possible explanations might be that there exists a non‐linear relationship between knowledge management factors and product quality.One way to solve non‐linear problems is to use additive models that replace the linear function by a non‐linear function as follows:Equation 1 A popularly used tool to find non‐linear function fj is the Alternating Conditional Expectation (ACE) algorithm developed byBreiman and Friedman. ACE is a nonparametric tool using scatterplot smothers in an iterative fashion to find the maximally correlated transformation of variables. Additionally, this algorithm allows one to transform the response variable y at the same time. Thus the relationship between the dependent and independent variables are as follows: Equation 2Breiman and Friedman,De Veaux and Steele, and Sum et al. have demonstrated how ACE can be used to identify proper transformations needed for both dependent and independent variables. Although ACE is a useful exploratory tool for determining which of the response and the predictor variables are in need of non‐linear transformation and what type of transformation is needed, Tibshiraniargued that “it seems more suited for correlation analysis than regression”. This is because there is no guarantee that the errors in the ACE transformed model will be normally distributed with constant variance. Tibshirani provided good examples of this phenomenon. Therefore, I will not apply ACE in this study to avoid the unreliability.Additivity and Variance Stabilizing Transformation (A V AS) algorithmproposed by Tibshirani is similar to ACE in the sense that it tries to find transformations, θ(y) and fj(xj) to satisfy equation (2). However, A V AS differs from ACE in that it chooses θ(y) to achieve aspecial variance stabilizing feature –a balance between constant variance and maximal linear dependence. So A V AS is more suitable to find transformation for regression rather than correlation. It can alleviate many anomalies that ACE suffers with regression. Venables and Ripley also reported A V AS to be管理质量保证知识：一项实证研究作者：杨杰，美国新泽西市基恩大学联合会工商管理学院管理部。