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Wen-Chin Chen, Pang-Lo Liu and Chih-Hung Tsai

An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance in Taiwan’s Industries Wen-Chin Chen Associate Professor Graduate Institute of Management of Technology Chung-Hua University 30 Tung-Shiang, HsinChu, Taiwan, ROC

Pang-Lo Liu

Chih-Hung Tsai

PhD Candidate and Instructor Professor, Department of PhD Candidate, Graduate Institute Industrial Engineering and of Management of Technology, Management Chung-Hua University Ta-Hwa Institute of Instructor, Department of Industrial Technology Engineering and Management 1 Ta-Hwa Road, Chung-Lin Ta-Hwa Institute of Technology Hsin-Chu, Taiwan, ROC E-mail: [email protected] E-mail: [email protected]

Abstract

summarizes, and discusses literature related to ERP, knowledge management, ERP knowledge management, the balanced scorecard (BSC), enterprise operating performance, and hi-tech industry, and measures the effects of ERP knowledge management implementation based on survey results using four BSC dimensions; finance, customer, internal business process, and learning and growth. The research results provide standards for enterprises that have introduced ERP knowledge management to measure enterprise operating performance, and a reference for industries developing ERP knowledge management.

Hi-tech industry plays a vital role in the Taiwanese economy, with products characterized by short product life cycle, great investment amount, fast market changes, and high rate of product upgrade. Under global competitive pressure, the hitech industry has adopted information technology to shorten the process, reduce costs, increase flexibility, improve product or service quality, and thus enhance the enterprise’s core capabilities to achieve perpetual growth. To reach these goals, enterprises are highly motivated to actively strengthen internal and external enterprise resource integration, introduce enterprise resource planning (ERP) and knowledge management (KM), and improve enterprise operating performance. Literature research shows that most researchers and scholars have focused only on ERP systems development and process improvement. Few studies have involved integrating the knowledge management concept to ERP systems and developing ERP knowledge management. This research studies ERP knowledge management, collects,

Keywords: ERP, Knowledge Management, Balanced Scorecard, Enterprise Operating Performance 1. Introduction The advancement of information technology expedites the pace of competitive globalization. Diversified commodities in small quantities have gradually replaced the economy of scale resulting from mass

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An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance

production and standardized manufacturing. In pursuit of improved efficiency and service quality, enterprises have developed tighter and more complicated cooperative upstream and downstream relationships. Enterprises have adopted enterprise resource planning (ERP), integrating the knowledge management concept (KM) into the ERP system. This resulted in a new ERP knowledge management model, and thus substantially improved operating performance. Since 1990, the Taiwan hi-tech industry has provided a primary production base for the world. In the competition for orders from large international firms, enterprises realized the importance of ERP knowledge management systems and actively implemented ERP knowledge management. Those enterprises that implemented ERP knowledge management found advantages in ERP knowledge management such as (1) shortened delivery time; (2) faster information acquisition; (3) more flexible production; (4) smoother coordination and integration. This research analyzes the performance resulting from implementing ERP knowledge management in the Taiwan hi-tech industry using a questionnaire survey and the four dimensions of the balanced scorecard (BSC). This research has the following three goals: (1) measuring the effect of ERP knowledge management implementation on enterprise operating performance in the Taiwan hi-tech industry using the four BSC dimensions; (2) exploring and analyzing the effect of ERP knowledge management implementation in the Taiwan hi-tech industry using statistical methodology; (3) and providing a reference for industries in implementing ERP knowledge management.

performance, and hi-tech industry topics will be introduced. 2.1 Enterprise Resource Planning (ERP) ERP is a system that effectively integrates all information required by the operating process functions including finance, accounting, human resources, production, material management, quality management, allocation and distribution, and sales by organization or process reengineering and information technology. ERP is an integrated information system that integrates enterprise internal function working processes, standardizes internal data processing procedures, combines the operational data generated by different functions so that the originally widely-spread individual databases can be utilized in a timely fashion to reflect the current status of enterprise internal resource use. This provides a good reference point for enterprise decision-making, and in turn increases enterprise competitive advantages and improves operating performance. The primary purpose for implementing ERP is to promptly capture the status of enterprise internal resource integration, reduce operational costs, and increase operating performance. However, in a rapidly changing industrial environment, the integration of enterprise internal and external resources becomes increasingly important. In other words, the concept of integration thus extends from the enterprise internal to the enterprise external environment and develops into the extended ERP concept (EERP). Future ERP will integrate supply chain management (SCM) to provide enterprise management more accurate information.

2. Literature Review 2.1.1. Motivation for ERP Implementation Bingi et al. (1999) stated that the ERP system provides enterprises a common language beneficial to the integration of

In the literature research ERP, knowledge management, ERP knowledge management, the BSC, enterprise operating

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global operational activities. The ERP system plays the role of central nervous system in promoting globalization of enterprise operations and shortening product lifecycle. Therefore, the ERP system has become popular. Li (1999) considered ERP enveloped software that provides various internal information for an enterprise using an efficient means that enables units at different organizational hierarchies to effectively make business or daily operational decisions. Stephen (2000) believed that ERP embraces the after-sales and sales support systems with considerable added value over and above the MRP and MRPII functions using an integrated correlated database.

reflection of market demand. Consequently, effective global planning and management is another enterprise operational focus. The ERP system came into being under this circumstance. In addition to MRP-II’s characteristic of integration across functions, the ERP system emphasizes on time sensibility across regions and across currencies and information integration to increase the enterprises’ responsiveness to any environmental change. Therefore, the ERP system integrates enterprise operational resources from the perspective of optimizing enterprise resources to optimize overall operating performance. 2.1.3 ERP Implementation Steps SAP (2001) proposed the phases involved in implementing ERP system as (1) project preparation phase; (2) business blueprint phase; (3) realization phase; (4) final preparation phase; (5) go live and support phase. Oracle (2001) proposed specialized application implementation methodology (AIM) whose implementation includes (1) implementation planning phase; (2) operational analysis phase; (3) solution planning phase; (4) final preparation phase.

2.1.2 Evolution of ERP System Since 1990, with the Internet technology becoming mature, enterprises have undertaken reforms in which information technology industrial applications have expedited the development of enterprise resource integration. The ERP system has therefore become the standard information system in recent years. Stephen (2000) divided the evolution of the ERP system into three stages: (1) Material Requirement Planning (MRP-I) Stage (1970s-1980s): Enterprises were facing a productionoriented market in which commodities produced in mass competed for market share by reducing price and management was focused on reducing manufacturing costs; (2) Manufacturing Resource Planning (MRP-II) Stage (1980s-1990s): MRP-II was an integrated manufacturing resource planning system that combined the traditional MRP system, financial analysis and business management in a customer-oriented market for diversified and high-quality products; (3) Enterprise Resource Planning (ERP) Stage (1990s-future): With the development of Internet technology, the operating model has become globalized and the focus of enterprise operations has shifted to prompt

2.1.4 Success Factors for ERP Implementation Laughlin (1999) believed that the success factor for ERP implementation is complete planning, which includes (1) definite vision; (2) successful reform management; (3) efficient and effective time management; (4) high-level strong support and promise; (5) good communication; (6) solution to related topics; (7) well-defined project scope; (8) early successful experience; (9) qualified project team; (10) excellent project management. Willcocks (2000) summarized the essential success factors for ERP implementation including (1) high-level management support and participation; (2) well-planned implementation time table; (3) commitment

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by all parties involved; (4) strong enterprise information technology capacity; (5) a great project team. Oracle (2001) listed the success factors for ERP system implementation as (1) highlevel management support and promise; (2) training for the personnel concerned; (3) successful software and hardware installation; (4) the project team’s knowledge about Oracle application; (5) inputs of the project team; (6) employees’ acceptance to changes; (7) solution test; (8) solution development; (9) confirmation of the final solution; (10) human resource inputs from the project team and units involved; (11) maintenance of document renewal; (12) user training; (13) various operational test; (14) coordination of live operation; (15) Oracle’s effective support to live operation. Based on the research and summary of the literature mentioned above, ERP system is considered in this research using eight dimensions; financial management, accounting management, human resource, production management, material management, quality management, distribution management, and sales management.

processes, and documents. According to Nonaka and Takeuchi (1995), knowledge is created by constant interaction of tacit and explicit knowledge. Four conversion models were developed using their theory: (1) Socialization is a process to convert tacit knowledge into other tacit knowledge, to accumulate knowledge and share experience by observation, emulation, experiment, or learning. This phase emphasizes on knowledge storing, sharing, and transmission. (2) Externalization is a process to convert tacit knowledge into explicit knowledge, to put tacit knowledge in writing, describe, and explain by analogy or metaphor. This phase emphasizes collection of thoughts and organization by code, deduction and a summary of the knowledge. (3) The combination process assesses and judges the value of new knowledge, filters and confirms explicit knowledge with added value, and conceptualizes it into personal knowledge. This phase emphasizes on knowledge filtering, selection, and judgment. (4) Internalization is a process that converts explicit knowledge to tacit knowledge. After experience is socialized, externalized, and combined, personal tacit knowledge is created consequently. The knowledge at this phase is called operational knowledge. Davenport (1998) analyzed 24 companies and summarized eight success factors from 31 projects on knowledge management implementation: (1) knowledge management combines enterprise operational strategies or competitive advantages; (2) knowledge management is the infrastructure for organizational structure and information technology; (3) the structure of knowledge management must be flexible and standardized; (4) enterprise culture encourages circulation, sharing, and innovation of knowledge; (5) knowledge management must be well defined with objectives in precise wording; (6) knowledge management should change employees’

2.2 Knowledge Management Davenport et al. (1998) defined knowledge management as management of knowledge as an important asset, whose activities and contents include establishing, defining, acquiring, organizing, sharing, and using knowledge asset. In short, knowledge management aims to accumulate and organize knowledge, take advantage of enterprise intelligence, and thus improve organizational performance, profitability, and competitiveness, functioning like a cycle from encouraging employees to share personal knowledge, promoting organized study, to systematically collecting, storing, transmitting, sharing, using, adding value to, and creating tacit and explicit experience and knowledge existing in employees, teams,

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behavior through instruction and incentives; (7) the organization should have various channels for knowledge transfer so that knowledge management can add value; (8) top management must support knowledge management orally, or by providing resources, or in implementing the project. Wiig (1995) defined knowledge management as a set of well-defined programs or methods that could be used to uncover important operations knowledge other than knowledge management, confirm a new product or strategy, strengthen human resource management, and therefore reach the business goals. Holtshouse (1998) conceptualized knowledge as a flowing element, meaning that knowledge can be communicated or exchanged in supply and demand fashion. Nona et al. (2000) reckoned that regardless of knowledge creation or innovation, the knowledge sharing behavior between people or communities is the origin point for the next knowledge spiral. The primary objective of knowledge management is knowledge creation, which allows member knowledge to absorb information and knowledge by socialization, externalization, combination, and internalization in the knowledge spiral. Interaction among the members strengthens the organizational competitive advantages and creates new knowledge out of existing knowledge through knowledge sharing and integration. Hendriks (1999) proposed that knowledge sharing was accomplished by knowledge transmission between the knowledge owner and knowledge user. An individual could play both roles at the same time, although the knowledge owner and user might have different motives. Hansen et al. (1999) pointed out that knowledge management is not a new thing. For hundreds of years the owners of family businesses have been handing business intelligence over to the next generation, artists have been tirelessly teaching students skills and craftsmanship, and workers have been exchanging thoughts

and techniques at work. However, business executives did not start talking about knowledge management until the 1990s. Davenport (1996) determined from his study of successful knowledge management cases that an enterprise’s knowledge management system includes a knowledge database of human talents and skills and on-line search assistance. Success in knowledge management cannot be achieved without cooperation between humans and technology. To summarize the literature mentioned above, knowledge management collects information and transmits information to users. Activities like knowledge obtaining, knowledge refining, knowledge storing, and knowledge sharing that can effectively add value to an organizational knowledge asset are called knowledge management. This research takes knowledge obtaining, knowledge refining, knowledge storing, and knowledge sharing of knowledge management as the main dimensions discussed in the “knowledge management” section. 2.3 ERP Knowledge Management Chen (2001) pointed out in his research on the correlation between ERP and the development of knowledge management that when governments, industries, and businesses are engaged in hot discussion of knowledge experience, 40% of the annual experience in the U.S. is attributable to “knowledge capital”. Knowledge is originated from data. ERP plays data collection role by keeping the transaction receipts, accounting for payment orders, delivery lists, and invoices for inventory in and out. When business data reaches a certain quantity, the data will be classified appropriately and organized into valuable information for management’s reference when making decisions. Importantly, all this information is post mortem information,

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which cannot provide a valid reference for management to determine future strategies. Therefore, this is the era of “doing the thing right”. Because the fuzzy theory is mature, and rapidly increased CPU speed, the computation and analysis that originally could only be done by mini computers can now be executed using personal computers quickly. This indicates that the era of business intelligence has arrived. With the advancement in technology, an analysis report that used to take an extended period can now be produced instantly. Various management theories have developed correspondingly. For example, benchmark management, activity-based cost (ABC), and the BSC, etc. The true goal of all of these theories is to “do the thing right”. “Do the thing right” requires knowledge. Only knowledge can produce decision-making intelligence because (1) enterprises have transformed from capital intensive to intelligence intensive; (2) demand changes rapidly and the market is unstable; (3) knowledge management allows enterprises to lead reform but not reform to lead enterprises; (4) only “knowledge” can survive; (5) “knowledge” plays a leading role in decision-making; (6) “knowledge” needs to be shared; (7) the competitors are not limited to companies in Taiwan any more. Consequently, combining knowledge management and ERP systems to create the ERP knowledge management concept has become an important substance in ERP. ERP knowledge management can be implemented by (1) analyzing the existing infrastructure; (2) integrating “ERP”, “knowledge management”, and “enterprise operational strategy”; (3) designing an infrastructure for knowledge management; (4) investigating current knowledge assets and systems; (5) establishing “ERP knowledge management organization”; (6) constructing knowledge management blueprint; (7) developing ERP knowledge management systems; (8)

equipping knowledge management systems and applying results-driven implementation models; (9) managing “change”, “culture”, and “incentive systems”; (10) measuring performance, evaluating return on investment (ROI), and adjusting the knowledge management system. The above ten steps are the operations in the ERP knowledge management establishment phase. Establishing ERP knowledge management is quite costly. Without knowing the specific business purposes, a great amount of money could be spent without achieving the intended results. Zheng (2001) pointed out in her research on ERP knowledge management application that with a high employee turnover rate, avoiding a situation in which ERP exits only when people are enforcing it and terminates when knowledgeable people are gone is a challenge to enterprise knowledge management. ERP must be maintained completely with continuous improvement. Time is also a critical issue. In recent years, most firms have spent tremendous efforts implementing ERP. A few firms have hesitated to do so because either ERP is too complex or hard to manage, or they are still cautiously planning for implementation based on other companies’ experiences. There are a large number of existing and potential clients in Taiwan. Care needs to be taken in ERP knowledge storage and management because ERP implementation requires a great amount of human resources and capital investment. Business and ERP processes must be monitored periodically. After employee turnover, ERP implementation must rely on an advisory consulting company or continuously modify the ERP software to maintain ERP system productivity. Various problems may arise from ERP implementation. If enterprises can promptly perform ERP knowledge management, the problems derived from ERP implementation will be easily solved. ERP is a gigantic, complex software set,

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making ERP knowledge management a daunting task. In selecting knowledge management tools, the focus should be placed on document management, ERP standard process maintenance, business process management and customized function selection. Even more importantly, the knowledge management tool should function as an e-learning tool to maintain business rules as advised and be applicable to employee turnover and training. ERP knowledge management should embrace business process integration, enterprise operational rules and ERP’s functions and data structure. The complete scope of ERP knowledge management, starting with ERP implementation and ending at ERP’s live enterprise-wide application, can be summarized using the following ten items and functions.

8. Business process and business planning change management. 9. ERP user’s manual and system management and other related document management. 10. MIS functional ERP system development and management, including ERP data dictionary (DD), ER modeling, ERP files and CRUD matrix, ERP standards, customized screen, tables, and specifications. With the above ten functions, ERP knowledge management can be implemented well and functions can be easily customized and localized. To enterprises, the most critical ROI of ERP knowledge management lies in the consulting fee, which reflects the cost of consultants during project implementation and after ERP goes alive. From years’ experience with ERP implementation projects, consulting resource duplication exists in many companies. In implementation, ERP consultants start with user education and training on operational patterns and planning on future business processes. They then conduct prototyping and modification, and finally develop the client’s on-line operational process and customized blueprint. The entire process seems simple, but it is a tight systematic course. It often happens that after education and training, users need ERP consultants for repeat advice on the same operation. If the documents and information obtained from every implementation process can be saved by a knowledge management tool, when users encounter the same problem, they can refer to historical records instead of relying on consultants. The primary function of ERP knowledge management is to resolve the customization project difficulty for the MIS department. MIS staff is busy solving hardware, software, and data problems for different departments every day. Among the

1. ERP implementation project management, which systematically maintains various project-related documents in Visio, Word, Excel, PowerPoint, and Project. 2. Complete records prior to business process reengineering (BPR), which is the old business process before ERP is implemented for BPR’s reference. 3. ERP software standard process, which provides a reference for project BPR and prototyping. 4. Complete BPR process records, which is the target business model for ERP implementation. 5. BPR process and ERP standard process gap analysis; a basis for subsequent project management customization. 6. Complete post-BPR records, that is, the final business model after ERP is implemented. 7. E-learning tool for employee training and turnover.

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numerous requests from ERP users, half are applications for requirement and data changes. Few requests involve applications for additional functions. If ERP customization project management integrates user requirements into ERP knowledge management, the MIS staff will not miss out on user requirement information and be able to propose appropriate recommendations and quickly resolve and satisfy user requirements by applying information provided by ERP knowledge management. The dependence on ERP consultants is reduced and the consultants will not be called until it is necessary. Therefore, after ERP knowledge management is applied, ERP implementation will not waste the investment. In fact, ERP knowledge management should be comprehensive function management applied to different departments but not restricted to the MIS department. Even though ERP knowledge management is most effective in the MIS department, if all department operating patterns can be effectively saved and adjusted through ERP knowledge management, the resulting benign interactions and convenient communications between departments through ERP knowledge management will replace the need for the MIS department to perform the integration for all other departments. Employee turnover and ISO process adjustment can be easily accomplished by ERP knowledge management in the departments where ERP is implemented. To summarize, this research defines the dimensions of ERP knowledge management as: (1) ERP system knowledge management; (2) ERP customization project knowledge management; (3) ERP business process reengineering knowledge management; (4) ERP employee training knowledge management.

2.4 The Balanced Scorecard (BSC) Kaplan and Notron introduced the BSC concept in 1992. The primary function of BSC is to provide future performance measures and complement the drawbacks of financial measures, which mainly evaluates past performance. The BSC objectives and measures are determined by organizational visions and strategies and intended to measure organizational performance using four dimensions; finance, customer, internal business process, learning and growth. BSC is a comprehensive structure that transforms strategies into actions and helps firms compete with their fundamental capabilities and innovation but not limited to tangible assets. With the advent of the information era, the basic assumptions about competitive advantages in industry are no longer accurate or profitable. Applying new technology to tangible assets can improve asset and liabilities management will not necessarily produce competitive advantages. Of the BSC’s four dimensions, one is a traditional financial performance measure and three involve non-financial performance measurement indexes; customer, internal business process, and learning and growth. These four dimensions integrate organizational visions and strategies into a new measurement system through objectives and measures that are the important factors in future competition. Chow and Haddad (1997) summarized the main features of BSC as it integrates organizational strategies, structure and visions, and helps convert objectives like long-term strategies and customer value into concrete internal and external organizational actions. The traditional performance measures focus on finance, and thus cannot combine organizational performance and strategies or

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act on organizational strategies. The four BSC future performance measures derived from the organizational vision structure including finance, customer, internal business process, and learning and growth, are aimed at realizing the firm’s strategies and visions.

customer base. Corporate values are the drivers of core customer result measures, which include customer satisfaction, customer life, new customer acquirement, and customer profitability.

(1) Financial Dimension

The objective of the internal business process dimension is stockholder satisfaction and achieving the objectives set forth in the customer dimension. Therefore, in determining the dimensional objectives and measures, the first step should be corporate value chain analysis. The old operating process should be adjusted to realize the financial and customer dimension objectives. A complete internal business process value chain that can meet current and future needs should then be constructed. A common enterprise internal value chain consists of three main business processes; innovation, operation and after-sale services. This model works in such a way that innovation is based on understanding customer needs, applied to new operating process design, the after-sales service process, and satisfying customers and stockholders through the internal value chain.

(3) Internal Business Process Dimension

Financial performance measures show whether a business strategy implementation contributes to improvement in net profit. Financial objectives are usually related to profitability, for instance, rapid growth in sales revenue or increase in cash flow. The measurement criteria are usually revenue, return on invested capital (ROIC), or economic value added (EVA). Enterprises have different financial objectives at different life stages. However, the business life cycle and financial topics involved in measurement strategies can be related. Business life cycle includes three phases; growth, maturity, and harvest. The business strategies at these three phases are all driven by three financial measurements: income growth and combination, cost reduction and productivity improvement, and assets and investment strategies. Firms at different life stages should analyze and develop appropriate performance measures corresponding to the financial topics following the business strategies.

(4) Learning and Growth Dimension This dimension stresses employee performance measurement. The employee growth is an intangible asset to enterprises that will contribute to business growth. The primary objective of this dimension is to provide the infrastructure for achieving the objectives for the other three dimensions. This creates the momentum for excellent results in the other three dimensions. To create long-term growth and progress, establish business infrastructure, business learning and growth come from three aspects: the human, system, and organizational procedures. In the other three dimensions there is often a gap between the

(2) Customer Dimension Customers are the source of business profits, hence, satisfying customer needs is the objective pursued by companies. In this dimension, management determines the expected target customers and market segments for operational units and monitors the performance of operational units in these target segments. Management helps enterprises clearly communicate corporate values to attract and maintain a target

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actual and target human, system, and procedure capabilities. Enterprises can shorten the gap through learning and growth. The measures for this dimension are employee satisfaction, continuity, training and skills. Based on the literature mentioned above, the BSC dimensions in this research are defined as finance, customer, internal business process, and learning and growth.

the indictor that is used most often by researchers to measure organizational results, for example, return on investment, sales revenue, and profitability, among which, sales revenue is the most popular. Chakravarthy (1986) divided operating performance and measures into four categories: (1) operating goals, which refer to the enterprise operating plan achievement in terms of the annual budget, increase in capital investment, factory expansion, joint venture, mergers and acquisitions; (2) productivity, which refers to the use of factory and facilities; (3) profit, which refers to the appropriate use of enterprise capital reflected in return on investment as measured by profit growth; (4) long-term advantage resources, which refers to the basis for perpetual enterprise operations with continuous growth. De Brentani (1989) studied the literature related to product innovation, service, and sales, investigating 115 enterprise in Canada, and found that performance measurements using differentiated services and product property are different.

2.5 Enterprise Operating Performance Enterprise operating performance can be evaluated in many aspects. Therefore, different opinions exist among scholars. The relevant literature is explained below. 2.5.1 Definition of Operating Performance For enterprises, performance measurement or performance evaluation refers to a system that measures or evaluates daily operating activities using quantitative standards or subjective judgment. Operating performance measurements can help enterprises determine if adopted strategies and organizational structure have reached the preset objectives. Venkatraman and Ramanujam (1986) proposed three conceptual performance areas: (1) financial performance, which refers to enterprise economic objectives that often use indicators such as after-tax profit, revenue, revenue growth rate, return on asset, and profit margin; (2) operational performance, which refers to operations performance in addition to financial performance, and non-financial indicators that include a product’s market share, product quality, added value and marketing effect etc.; (3) organizational effect, which is a broader view of operational performance, including resolving conflicts, satisfying the objectives of all parties involved and employee morale. According to Van de Ven and Ferry (1980), traditional financial performance is

2.5.2 Performance Evaluation Kaufman (1988) considered performance indicators as the measures that identify and prove whether the predetermined objectives were achieved. The criteria for measurement and evaluation are called performance criteria. Performance management is a management process about how to implement strategies to achieve preset goals. Predetermined overall organizational strategies and goals guide the process of accomplishing predetermined performance, and the macro organizational goals are realized by achieving the goals set for individual departments. These three performance indicators have causal relationships and the consistency in goals requires coordination and cooperation between departments. Fortuin (1988) used

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performance indicators as a variable to measure the efficiency or effectiveness of the overall organizational system or partial system to determine if the operating process satisfied the preset goals.

measures to understand if enterprise assets are used appropriately to increase shareholder value. Based on the above literature review, operating performance is defined in this research as (1) financial performance; (2) operational performance; (3) organizational performance; (4) longterm advantage resource, which comprise the four operating performance dimensions studied in this research.

2.5.3 Performance Indicators The tool for measuring performance is the performance evaluation indicators, categorized by the evaluation criteria or evaluated target as shown below:

2.6 Hi-Tech Industry

(1)Quantitative and Qualitative Indicators

Bleicher and Paul (1983) pointed out that the hi-tech industry is a capital- and technology-intensive industry that demands know how, research and development personnel training, and is characterized by high technology density, large economic scale, high risk, and high return. However, there is a great variety of definitions for the hi-tech industry, with no common standard accepted by all experts and scholars. The general definition of high technology is high technology that requires research and development. Gould and Keedle (1984) suggested that the hi-tech industry be measured by three indexes; the ratio of research and development expenditure to output, the speed of technological innovation, and the ratio of management staff to technical staff to research and development staff. According to Shanklin and Ryans (1984), the three conditions to qualify for hi-tech are that enterprises must have a strong science and technology foundation, that new technology can rapidly replace the existing one, and that the application of new technology can develop or change the market and demand. Rogers and Larson (1984) defined four requirements for the hi-tech industry as (1) great percentage of scientists and engineers; (2) fast industrial growth; (3) high percentage of revenue expended on research and development; (4) international product markets. Riggs (1985) considered hi-tech

MacArthur (1996) divided the targets under measurement into quantitative variables and qualitative variables. Quantitative variables such as preparation time can be evaluated using quantitative indicators directly. Qualitative variables, such as the degree (high, medium, or low) of satisfaction that the management has about preparation time, are measured using nonquantitative subjective judgment. These are referred to as qualitative indicators. However, some qualitative variables like employee morale can be measured using the quantitative employee turnover rate indicator or qualitative high, medium, or low degree of employee morale indicator set by the consulting firm. (2) Financial and Non-financial Indicators This is a further quantitative indicator classification. The quantitative indicators represented in dollars are financial indicators, otherwise they are non-financial. Although traditional accounts focused on financial measures, their importance is important and needs to be reiterated. Eccles and Pyburn (1992) believed that financial indicators play an important role in measuring enterprise overall performance. Under management’s control, financial indicators are the official and critical

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companies as those that employ technology as the main competitive strategy and tool, focus on research, produce short-lived commodities, bear high risk and experience rapid change. 2.6.1

Characteristics Industry

of

the

development history, the 1950s were the first time Taiwan implemented an import substitution policy to stress the development of labor-intensive light industry substituted by imports. The 1960s were a critical time for Taiwan to transition from an agricultural economy into an industrial economy. Through the export expansion policy, light industry was able to develop overseas markets with the relative international advantage of low salaries. The heavy industry-oriented second-time import substitution policy coupled with the export expansion policy was the trend in the 1970s. This approach emphasized the growth of the heavy chemical industry, and such strategic industries as machinery, information and electronics, the transition of the industrial product export structure from labor-intensive resource consumption to technologyintensive resource generation under the oil crisis, pressure from protectionism and weakened labor cost advantages and the corresponding adjustment of the industrial structure. Strategic industrial policies were adopted in the 1980s. Under three principles; forward looking, synchronized consideration of world technology development and market demand, and an international competition perspective, the emerging hitech industry was planned based on six principles; great market potential, high industrial correlation, great value added, high technical level, low pollution, and low dependence on energy. The hi-tech industries in Taiwan can be divided into six categories: (1) semiconductors; (2) computers and peripherals; (3) telecommunications; (4) opto-electronics; (5) high-precision machinery; (6) biological technology. Since 1990, the Taiwanese government has actively promoted the upgrade of traditional industries and hi-tech industrial policies focused on the development of ten emerging industries; telecommunications, information, consumer electronics, semiconductors, high-

Hi-Tech

Based on the definition of high technology, the primary difference between the hi-tech industry and traditional industries in Taiwan lies in the intensity of technology and capital. The commonly used factors indicative of hi-tech industry are technologyintensive, great value added, and capital intensive. The hi-tech industry in Taiwan is characterized by six great market potential principles, high industrial correlation, great added value, high technical level, low pollution and low dependence on energy. The development of hi-tech companies is closely correlated with rapid changes in technology, which requires a great deal of research and development human resources and capital investment in new product development based on market demand to sustain a competitive position. In response to environmental changes, the risk born by hitech industry is higher than any traditional industry, but it is rewarded with greater contribution to social economic development. Because of the impact of market scale, industrial environment, geographic location, and research and development technology level, the hi-tech industry in Taiwan shows slightly different characteristics. 2.6.2 Hi-Tech Industrial Development in Taiwan Taiwan is a narrow island with limited natural resources. In the past four decades, Taiwanese people have worked hard in labor-intensive industries making Taiwan one of the four Asian dragons in terms of economic size. In Taiwan industrial

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precision machinery and automation, aerospace, high-grade materials, special chemicals and pharmaceuticals, healthcare and pollution prevention. These industries are expected to become the major players in future industrial development. Overall, Taiwan is oriented toward hitech industries under planned support by the government and by following the direction of the industrial environment. Taiwan is expected to grow into a technology country that strategically centers on hi-tech industries driven by innovation in the future. In summary, the characteristics of the hi-tech industry in Taiwan are differentiated into six categories in this research. They are (1) semiconductor; (2) computer and peripheral industry; (3) telecommunication; (4) optoelectronics; (5) high-precision machinery; (6) biological technology.

3.

4.

3. Research Methodology 5. To understand the ERP knowledge management effect on enterprise operating performance in Taiwan, a questionnaire survey was conducted. The collected statistical data was analyzed using the four BSC dimensions of finance, customer, internal business process, learning and growth. The research structure is shown in Figure 1. 3.1 Research Variables and Measures

6.

1.

Knowledge Management: The four knowledge management dimensions: knowledge obtaining, knowledge refining, knowledge storing, and knowledge sharing were used for measuring knowledge management in this research. 2. ERP System: The ERP system dimensions used in this research were defined using eight ERP subsystems: (1) financial management; (2) accounting

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management; (3) human resource management; (4) production management; (5) material management; (6) quality management; (7) distribution management; (8) sales management. ERP Knowledge Management: The four ERP knowledge management dimensions were derived from integrating the four knowledge management dimensions with the ERP system. They are (1) ERP system knowledge management; (2) ERP customization engineering knowledge management; (3) ERP business process reengineering knowledge management; (4) ERP employee education and training knowledge management. The Balanced Scorecard: The measures that this research employed to evaluate the ERP knowledge management operating performance were the BSC four dimensions: (1) finance; (2) customer; (3) internal business process; (4) learning and growth. Industry and Company Position: This research targeted the hi-tech industry in Taiwan. Six industry groups were determined based on industrial characteristics, as described in the literature review. A Company’s size was determined by the amount of capital invested in the industry and its revenue. “Industrial characteristics” and “company’s size” were selected as the intermediate variables in this research. Enterprise Operating Performance Measurement: The indexes for measuring ERP knowledge management implementation were: (1) financial performance; (2) operational performance; (3) organizational performance; (4) long-term advantage resource.

An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance

Industry and Company’s Position

ERP System 1. Financial Management 2. Accounting Management 3. Human Resource Management 4. Production Management 5. Material Management 6. Quality Management 7. Distribution Management 8. Sales Management

1. Industrial Characteristics 2. Company’s Size

ERP Knowledge Management

Integration

Knowledge Management 1. Knowledge Obtaining 2. Knowledge Refining 3. Knowledge Storing 4. Knowledge Sharing

1. ERP System Knowledge Management 2. ERP Customization Engineering Knowledge Management 3. ERP Business Process Reengineering Knowledge Management 4. ERP Employee Education and Training Knowledge Management

H3a

H3b

Enterprise Operating Performance H1

1. Financial Performance 2. Operational Performance 3. Organizational Performance 4. Long-term Advantage Resource

H2b H2a

The Balanced Scorecard 1. Finance 2. Customer 3. Internal Business Process 4. Learning and Growth

Figure 1: Research Structure

H1-1: The stronger the ERP system knowledge management capacity, the more significant the effect on operating performance. The stronger the ERP H1-2: customization engineering knowledge management capacity, the more significant the effect on operating performance. H1-3: The stronger the ERP business process reengineering knowledge management capacity, the more significant the effect on operating performance. H1-4: The stronger the ERP employee education and training knowledge management capacity, the more significant the effect on operating performance.

3.2 Assumptions in Research Based on the literature review and theoretical analysis, the empirical research assumptions include: 1. The relationship between ERP knowledge management and enterprise operating performance H1: The stronger the ERP knowledge management capacity, the more significant the effect on enterprise operating performance.

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2. The relationship between the BSC and enterprise operating performance H2: Different BSC dimensions applied to measuring the enterprise operating performance have a significant effect on the operating performance. H2-1a: The financial dimension applied to enterprise operating performance measurement has a significant effect on the operating performance. H2-1b: When measuring the enterprise operating performance using the financial dimension, the stronger ERP knowledge management shows a more significant effect on the operating performance. H2-2a: The customer dimension applied to the enterprise operating performance measurement has a significant effect on the operating performance. H2-2b: When measuring the operating performance using the customer dimension, stronger ERP knowledge management shows a more significant effect on the operating performance. H2-3a: The internal business process dimension applied to the operating performance measurement has a significant effect on the operating performance. H2-3b: When measuring the enterprise operating performance using the internal business process dimension, stronger ERP knowledge management shows a more significant effect. H2-4a: The learning and growth dimension applied to enterprise operating performance measurement has a significant effect on the operating performance. H2-4b: When measuring the enterprise operating performance using the learning and growth dimension, stronger ERP knowledge management shows a more significant effect on the operating performance. 3. The relationship industry/company’s size and operating performance

H3: Industry and company size affects ERP knowledge management and enterprise operating performance. H3-1a: Industrial characteristics have a significant effect on the enterprise operating performance. With different industrial H3-1b: characteristics, stronger ERP knowledge management has a more significant effect on the enterprise operating performance. H3-2a: Company size has a significant effect on the enterprise operating performance. H3-2b: With different company sizes, stronger ERP knowledge management has a more significant effect on the enterprise operating performance. This research studied and analyzed the ERP knowledge management implementation effect in the hi-tech industry in Taiwan. The questionnaires were distributed in August 2003 and collected in September 2003. Well-known companies that had experience in ERP knowledge management implementation were selected for this survey. Six hundred questionnaires were sent out, with 112 questionnaires, 18.7%, collected. Of these, 106 questionnaires, 17.67%, were valid. 3.3 Research Target This research targeted hi-tech companies in Taiwan including (1) semiconductors; (2) computers and peripherals; (3) telecommunications; (4) opto-electronics; (5) high-precision machinery; and (6) biological technology. Related data were obtained from the questionnaires collected. To improve the validity of the questionnaires collected, the people that were selected to actually answer the survey questions included ERP project managers, experienced information planning personnel, experienced research and development managers, and knowledge management supervisors.

between enterprise

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An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance

3.4 Questionnaire Design and Measurement

3.5 Data Analysis Methodology A closed circle structured questionnaire design was adopted in this research. Based on relevant literature review and summary, analyses were performed using SPSS 10.0 for Windows. The statistical methods applied to the empirical analysis were Cronbach α validity analysis, t-test, and multiple regression analysis.

The questionnaire designed in this research consisted of six parts. The first five parts were measured using a Likert fivepoint scale. The first part is knowledge management including (1) knowledge obtaining; (2) knowledge refining; (3) knowledge storing; (4) knowledge sharing as measures in questionnaire design. The second part is ERP system including (1) financial management; (2) accounting management; (3) human resource management; (4) production management; (5) material management; (6) quality management; (7) distribution management; (8) sales management as measures in questionnaire design. The third part is ERP knowledge management including (1) ERP system knowledge management; (2) ERP customization engineering knowledge management; (3) ERP business process reengineering knowledge management; (4) ERP employee education and training knowledge management as measures in questionnaire design. The fourth part is the BSC including (1) finance; (2) customer; (3) internal business process; (4) learning ad growth as measures in questionnaire design. The fifth part is operating performance including (1) financial performance; (2) operational performance; (3) organization performance; (4) long-term advantage resource as measures in questionnaire design. The sixth part is the company’s fundamental data including industrial characteristics and the company’s position.

3.6 Reliability and Validity Test The survey questionnaire targeted hitech experts in Taiwan and was designed based on related literature review and research. The contents and wording were modified after the first empirical test. The validity of the content in the questionnaire was trustworthy. Cronbach α coefficients for all question items in every dimension were computed based on the scores for the answers given and the validity of every question item was tested. A greater Cronbach α value indicates greater correlation between the question items in the factorial dimension and higher internal consistency. Nunnally (1978) defined acceptable reliability as 0.7 and above in general basic research. The reliability shown in this research reached above 0.7. All measures and scales in this research are therefore reliable and internally consistent. The reliability values for all variables in this research are shown in Table 1.

Table 1: The Confidence Value for Each Variable Questionnaire Knowledge ERP Knowledge ERP System Management Management Criterion

Cronbach α

0.7997

0.7714

0.8689

The Balanced Enterprise Operating Scorecard Performance

0.7837

0.8315

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4. Research Analysis Results

that knowledge management has a vital impact on enterprise operating performance.

This research focused on empirical study of the effect of implementing ERP knowledge management in the Taiwan hitech industry on enterprise operating performance. The research results are analyzed below:

4.2 The Relationship between ERP System and Enterprise Operating Performance The impact that ERP system’s dimensions have on enterprise operating performance is shown in Table 3. In particular, financial management (p = 0.000), accounting management (p = 0.000), human resource management (p = 0.000), production management (p = 0.001), material management (p = 0.005), quality management (p = 0.000), distribution management (p = 0.003), and sales management (p = 0.000) all show statistical significance, indicating that ERP system has vital impact on enterprise operating performance.

4.1 The Relationship between Knowledge Management and Enterprise Operating Performance The impact that knowledge management’s dimensions have on enterprise operating performance is illustrated in Table 2. In particular, knowledge obtaining (p = 0.000), knowledge refining (p = 0.003), knowledge storing (p = 0.000), and knowledge sharing (p = 0.001) all show statistical significance, indicating

Table 2: The t-test of Knowledge Management on Enterprise Operating Performance Enterprise Operating Performance

Knowledge Management

t-value

p-value

Knowledge Obtaining

3.933

0.000***

Knowledge Refining

3.052

0.003***

Knowledge Storing

3.652

0.000***

Knowledge Sharing

1.087

0.001***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

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An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance

Table 3: The t-test of ERP System on Enterprise Operating Performance Enterprise Operating Performance t-value p-value

ERP System Financial Management

3.645

0.000***

Accounting Management

2.201

0.000***

Human Resource Management Production Management

0.111

0.000***

3.792

0.001***

Material Management

2.343

0.005***

Quality Management

0.066

0.000***

Distribution Management

0.069

0.003***

Sales Management

3.911

0.000***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

4.3

The Relationship between ERP Knowledge Management and Enterprise Operating Performance

process reengineering knowledge management (p = 0.005), and ERP employee education and training knowledge management (p = 0.001) are all statistically significant, indicating ERP knowledge management has vital impact on enterprise operating performance. Hence, hypothesis H1 cannot be rejected.

The impact that ERP knowledge management’s dimensions have on enterprise operating performance is highlighted in Table 4. In particular, ERP system knowledge management (p = 0.000), ERP customization engineering knowledge management (p = 0.000), ERP business

Table 4: The t-test of ERP knowledge Management on Enterprise Operating Performance Enterprise Operating Performance

ERP Knowledge Management

t-value

p-value

ERP System Knowledge Management

3.469

0.000***

ERP Customization Engineering Knowledge Management

3.087

0.000***

ERP Business Process Reengineering Knowledge Management ERP Employee Education and Training Knowledge Management Note: * p < 0.1, ** p < 0.05, *** p < 0.01

2.733

0.005***

3.445

0.001***

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4.4 The Relationship between the BSC’s Dimensions and Operating Performance

in Table 5. In particular, the finance, customer, internal business process, and learning and growth dimensions show statistical significance in measuring enterprise operating performance. Hence, hypothesis H2a is accepted.

The impact that the BSC’s dimensions have on operating performance is explained

Table 5: The t-test of the BSC on Enterprise Operating Performance Balanced Scorecard

Financial Performance t-value p-value

Enterprise Operating Performance Operational Organizational Long-term Performance Performance Advantage Resource t-value p-value t-value p-value t-value p-value

Finance

1.165

0.001***

1.301

0.005***

1.204

0.000***

1.065

0.000***

Customer

3.165

0.002***

2.281

0.000***

2.915

0.001***

3.168

0.002***

Internal 0.848 0.000*** 1.695 0.003*** Business Process Learning 3.093 0.003*** 2.347 0.000*** and Growth Note: * p < 0.1, ** p < 0.05, *** p < 0.01

1.089

0.005***

0.848

0.005***

2.880

0.003***

3.093

0.003***

4.5 The Relationship between the BSC and ERP Knowledge Management

customization engineering knowledge management, ERP business process reengineering knowledge management, and ERP employee education and training knowledge management all show statistical significance. Hence, hypothesis H2b is accepted.

Table 6 shows the results from measuring ERP knowledge management using the BSC dimensions. In particular, ERP system knowledge management, ERP

Table 6: The t-test of the BSC on ERP Knowledge Management ERP Knowledge Management ERP System ERP Customization Knowledge Engineering Balanced Management Knowledge Scorecard Management

ERP Business Process Reengineering Knowledge Management t-value p-value

ERP Employee Education and Training Knowledge Management t-value p-value

t-value

p-value

t-value

p-value

Finance

0.427

0.001***

1.844

0.005***

1.087 0.001***

0.699

0.005***

Customer

4.984

0.000***

1.268

0.003***

3.676 0.000***

2.210

0.000***

Internal 3.753 0.005*** 3.084 0.000*** Business Process Learning 4.148 0.000*** 3.641 0.001*** and Growth Note: * p < 0.1, ** p < 0.05, *** p < 0.01

0.323 0.005***

2.057

0.001***

3.520 0.001***

3.653

0.000***

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An Empirical Study on the Correlation between ERP Knowledge Management Implementation and Enterprise Operating Performance

4.6 The Impact That Industry and Company’s Position Have on Enterprise Operating Performance

performance (p = 0.000), organizational performance (p =0.000), and long-term advantage resources (p = 0.000). Company size shows statistical significance in measuring financial performance (p = 0.001), operational performance (p = 0.003), organizational performance (p =0.000), and long-term advantage resource (p = 0.000). Hence, hypothesis H3a is accepted.

Table 7 shows the results from measuring operating performance using industry and company position. The industrial characteristics shows statistical significance in measuring financial performance (p = 0.000), operational

Table 7: The t-test of Industry and Company’s Position on Enterprise Operating Performance Enterprise Operating Performance Industry and Financial Operational Organizational Long-term Company’s Performance Performance Performance Advantage Resource Position t-value p-value t-value p-value t-value p-value t-value p-value Industrial Characteristics Company’s Size

4.095

0.000***

2.716

0.000*** 3.702

0.000***

4.095

0.000***

4.079

0.001***

2.472

0.003*** 3.906

0.000***

3.079

0.000***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

4.7

The Impact that Industry and Company’s Position Have on ERP Knowledge Management Table 8 shows the results from measuring ERP knowledge management using industry and company position. The industrial characteristics show statistical significance in measuring ERP system knowledge management (p = 0.000), ERP customization engineering knowledge management (p = 0.001), ERP business process reengineering knowledge

management (p =0.000), and ERP employee education and training knowledge management (p = 0.001). Company size shows statistical significance in measuring ERP system knowledge management (p = 0.005), ERP customization engineering knowledge management (p = 0.000), ERP business process reengineering knowledge management (p =0.001), and ERP employee education and training knowledge management (p = 0.005). Hence, hypothesis H3b cannot be rejected.

Table 8: The t-test of Industry and Company’s Position on ERP Knowledge Management ERP Knowledge Management Industry and Company’s Position

ERP System Knowledge Management

ERP Employee ERP Customization ERP Business Process Reengineering Engineering Education and Training Knowledge Knowledge Knowledge Management Management Management

t-value p-value t-value p-value Industrial 1.585 0.000*** 1.198 0.001*** Characteristics Company’s Size 2.379 0.005*** 2.041 0.000***

t-value

p-value

t-value

p-value

4.884

0.000***

1.824

0.001***

4.427

0.001***

1.162

0.005***

Note: * p < 0.1, ** p < 0.05, *** p < 0.01

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4.8

Multiple Regression Analysis of Knowledge Management, ERP System, ERP Knowledge Management, BSC, Industry and Company’s Position against Enterprise Operating Performance

(3) The beta values for Model 3 are shown in Table 11. The model is y3=0.268x13+0.250x14+0.236x15+0.166x16+e x13 represents ERP system 3 (where knowledge management, x14 represents ERP customization engineering knowledge management, x15 represents ERP business process reengineering knowledge management, x16 represents ERP employee education and training knowledge management). All variables show a significant positive relationship to enterprise operating performance. The adjusted Rsquared is 0.805, implying great explanatory power for all variables. (4) The beta values for Model 4 are shown in Table 12. The model is y4=0.140x17+0.502x18+0.235x19+0.785x20+e 4 (where x17 represents financial dimension, x18 represents customer dimension, x19 represents internal business process dimension, and x20 represents learning and growth dimension). All variables show a significant positive relationship to enterprise operating performance. The adjusted Rsquared is 0.838, implying great explanatory power of all variables. (5) The beta values for Model 5 are shown in Table 13. The model is (where x21 y5=0.155x21+0.433x22+e5 represents industrial characteristics, and x22 represents company’s size). All variables show a significant positive relationship to enterprise operating performance. The adjusted R-squared is 0.798, implying great explanatory power for all variables. Therefore, in this research, knowledge management, ERP system, ERP knowledge management, the BSC’s dimensions, and industry and company’s position all show a strong significant positive relationship to enterprise operating performance improvement.

Multiple regression analysis intends to understand the linear relationship between multiple independent variables and one dependent variable. The multiple regression analysis conducted in this research is summarized in Tables 9, 10, 11, 12, and 13. The analysis results show positive significance in assumed B value, Beta value and t-value. The models are illustrated as follows: (1) The beta values for Model 1 (as shown in Table 9) are 0.151, 0.304, 0.415, and 0.180 respectively. The model is y1 = 0.101x1+0.188x2+0.216x3+0.466x4+e1 (where x1 represents financial management, x2 represents knowledge refining, x3 represents knowledge storing, and x4 represents knowledge sharing). All variables show a significant positive relationship to enterprise operating performance. The adjusted R-squared is 0.788, implying great explanatory power of all variables. (2) The beta values for Model 2 are shown in Table 10. The model is y2 = 0.138x5+0.501x6+0.233x7+0.687x8+0.236x9 +0.171x10+0.797x11+0.902x12+e2 (where x5 represents financial management, x6 represents accounting management, x7 represents human resource management, x8 represents production management, x9 represents material management, x10 represents quality management, x11 represents distribution management, and x12 represents sales management). All variables show a significant positive relationship to enterprise operating performance. The adjusted R-squared is 0.838, implying great explanatory power of all variables.

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Table 9: Multiple Regression Analysis of Knowledge Management against Enterprise Operating Performance Model 1 Variables

B

Std.E

Beta

t-value

Knowledge Obtaining x1

0.101

0.61

0.151

1.664

Knowledge Refining x2

0.188

0.159

0.304

3.210

Knowledge Storing x3

0.216

0.56

0.415

3.872

Knowledge Sharing x4 Adjusted R-Squared

0.466

0.54

0.180

1.746

0.788

Table 10: Multiple Regression Analysis of ERP System against Enterprise Operating Performance Model 2 Variables

B

Std.E

Beta

t-value

Financial Management x5

0.138

0.076

0.043

0.280

Accounting Management x6

0.501

0.087

0.096

0.518

Human Resource Management x7

0.233

0.052

0.373

0.856

Production Management x8

0.687

0.08

0.086

0.635

Material Management x9

0.236

0.050

0.107

0.846

Quality Management x10

0.171

0.043

0.155

1.427

Distribution Management x11

0.797

0.044

0.115

1.080

Sales Management x12

0.902

0.049

0.058

0.594

Adjusted R-Squared

0.838

Table 11: Multiple Regression Analysis of ERP Knowledge Management against Enterprise Operating Performance Model 3 Variables

B

Std.E

Beta

t-value

0.268

0.072

0.369

0.452

ERP Customization Engineering Knowledge 0.250 Management x14 ERP Business Process Reengineering Knowledge 0.236 Management x15 ERP Employee Education and Training Knowledge 0.166 Management x16 Adjusted R-Squared

0.059

0.442

3.709

0.050

0.444

3.248

0.052

0.279

3.209

ERP System Knowledge Management x13

0.805

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Table 12: Multiple Regression Analysis of the BSC against Enterprise Operating Performance Model 4 Variables

B

Std.E

Beta

t-value

Financial Dimension x17

0.140

0.081

0.042

0.285

Customer Dimension x18

0.502

0.092

0.086

0.615

Internal Business Process Dimension x19

0.235

0.056

0.108

0.655

Learning and Growth Dimension x20 Adjusted R-Squared

0.785

0.045

0.118

0.436

0.808

Table 13: Multiple Regression Analysis of Industry and Company’s Position against Enterprise Operating Performance

Model 5 Variables Industrial Characteristics x21 Company’s Size x22 Adjusted R-Squared

B

Std.E

Beta

t-value

0.155 0.433

0.188 0.287

0.089 0.083

0.295 0.313

0.798

4.9 The Impact that All Dimensions Have on Enterprise Operating Performance

dimension, 4.5802 in the internal business process dimension, and 4.5896 in the learning and growth dimension. The industry and company position value is 4.5961. In summary, all dimensions have a positive impact on enterprise operating performance. Therefore, the conclusion can be drawn from these research results that implementing ERP knowledge management and measuring enterprise operating performance using BSC will make material contributions and provide crucial help to enterprise operations.

Table 14 shows the impact that all dimensions have on enterprise operating performance. In particular, knowledge management, ERP system, and ERP knowledge management have a value of 4.5790 on enterprise operating performance. The impact of BSC on enterprise operating performance is 4.5896 in the financial dimension, 4.5506 in the customer

Table 14: Impact of All Dimensions on Enterprise Operating Performance Dimensions

Enterprise Operating Performance

Knowledge Management

4.5790

ERP System

4.5790

ERP Knowledge Management

4.5790

Financial Dimension

4.5896

Customer Dimension

4.5506

Balanced Scorecard Internal Business Process Dimension Learning and Growth Dimension

4.5802 4.5896

Industry and Company’s Position

4.5961

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5. Conclusions

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This research conducted empirical study of the effect that ERP knowledge management implementation has on enterprise operating performance improvement. Based on the theoretical and literature research and statistical analysis of survey results from the Taiwan hi-tech industry, ERP knowledge management implementation has a positive impact on operating performance. The hypotheses in research were tested and confirmed with statistical significance. The four BSC dimensions measured the effect of ERP knowledge management implementation in the Taiwan hi-tech industry on enterprise operating performance. The results show that in the financial, customer, internal business process, and learning and growth dimensions, positive effects exit. In the multiple regression analysis, the beta, t, and B knowledge management values against enterprise operating performance, ERP system against enterprise operating performance, ERP knowledge management against enterprise operating performance, and the BSC four dimensions against enterprise operating performance measurement all showed a positive relationship. The adjusted R-squared for all models shows high explanatory power. Based the above analysis and findings, BSC theory application to measuring ERP knowledge management implementation in the Taiwan hi-tech industry provides positive assistance to and has a positive effect on enterprise operating performance. References 1. Bingi, P., Maneesh K. Sharma, and Jayanth K. Godla, “Critical Issues Affecting an ERP Implementation,” Information Systems Management, pp.714, summer 1999.

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27. SAP Website, Http://www.sap.com 28. Shanklin, W.L. and Ryans, K.J., “Organizing for High-Tech Marketing,” Harvard Business Review, pp.164-171, 1984. 29. Stephen J. Mraz, “Keeping Up with ERP,” Machine Design, pp.56-60, July 20, 2000. 30. Thomas H. Davenport and David W. De Long, “Successful Knowledge Management Projects,” SMR, 1998. 31. Ven de Ven, Andrew H. and Diane L. Ferry, Measuring and Assessing Organizations, N.Y.: John Wiley & Sons, 1980. 32. Venkatraman, N. and Ramanujam, V., “Measurement of Business Performance on Strategy Research: A Comparison of Approach,” Academy of Management Review, 11(4), pp.801-814, 1986. 33. Willcocks, L., “The Role of the CIO and IT Function in ERP,” Communication of the ACM, Apr. 2000, pp.34-38. 34. Wiig, K.M., Knowledge Management Foundations-Thinking about ThinkingHow People and Organizations Create, Represent, and Use Knowledge, Schema Press Arlington, Texas, 1995.

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