Critical factors influencing the adoption of data warehouse technology: a study of the banking industry in Taiwan
Introduction
With keener and stronger competition, enterprises are much more eager in getting immediate and accurate information to make better decisions. Furthermore, with the rapidly growing need for large amounts of information, enterprises' traditional database is incapable of effectively handling the demands of increasing online information retrieval, access, update, and maintenance. This inability greatly impacts businesses in a way that the management level cannot utilize internal data efficiently and effectively to assist reliable decision-making in a timely manner. As a result, it is such a critical issue for every business to seek for ways and/or means to access, store, maintain, and utilize the massive data efficiently.
Businesses constantly require a database environment with high flexibility, better adaptability, and good support to make a decision. During the past years, academia and industry have continuously offered different solutions to create such an aforementioned environment. One of the possible alternatives is to adopt data warehouse technology. According to Inmon [21], data warehouse technology was described as, “Collecting data from several dispersed sources to build a central data warehouse. Then users can use appropriate data-analyzing tools to store and analyze needed data.” The main application of data warehouse is to process every dimension analysis in internal data by utilizing data survey technology, and it can be a reference of decision-making for enterprises. For example, online analytical processing (OLAP) capability can be a convenient and effective way to query data immediately and to provide various information using multidimensional querying to match with users' demands.
The banking industry is categorized as one industry with high information demands. Information technology is taken as a key tool to improve the quality of service and to gain a competitive advantage. The Taiwanese banking industry has showed a great interest in adopting data warehouse technology and has started to undertake the challenges of implementing it in order to manage the existing internal and external heterogeneous database. According to the recent research of Wen et al. [45], the adoption of data warehouse technology, which requires huge capital spending and also consumes a good deal of development time, has a very high possibility of failure. Even most of the companies adopting data warehouse technology have successfully accomplished the expected goals established before adoption, and 20% of companies still fail [30]. As a result, it will be critical and essential to have a detailed understanding of critical success factors to ensure the successful adoption of data warehouse technology. Unfortunately, in the area of the adoption of data warehouse technology, most of the research available focused on the technological and operational aspects, and there is very little research to consider the factors in the managerial and strategic levels.
In addition, there is a little prior research to study the key factors affecting adoption of data warehouse technology, especially for the enterprises in Asia. Asia, including Singapore, Taiwan, and mainland China, is a fast-growing market. Many western banks have great interest in establishing Asian branches in cities such as Shanghai, Singapore, Hong Kong, and Taipei. To this end, this study, which reports valuable empirical data on the adoption of data warehouse for the banking industry in Taiwan, can be easily applied to mainland China, Singapore, Hong Kong, and Taiwan, since these are all with the same culture and speak the same language. It is obvious that the results, findings, and conclusions drawn from this study can be a good reference for global banks to establish and develop operational strategies for their eastern branches.
With a systematic examination about the existing situation and future development of data warehouse technology in the banking industry of Taiwan, this study also investigates the factors affecting the adoption of data warehouse technology. The results presented in this manuscript can certainly help those banks, which might wish to adopt data warehouse technology by overcoming potential obstacles, and hence reducing the high risk of failure during implementation. For other industries intending to adopt data warehouse technology, the results and findings from this study can be used as a case study for the adoption of data warehouse technology in the near future. Furthermore, academia can use the findings of this study as a basis to initiate other related studies in the data warehouse area.
Section snippets
Definitions and characteristics of data warehouse technology
The concept of data warehouse technology was initially introduced by Devlin and Murphy [7]. They suggested the construction of a read-only database that stores historical datum for operating and offers integration tools for users to query and search what they want for decision supporting and analyzing. Inmon [20] perceives that data warehouse technology mainly includes four essential characteristics, “subject-oriented,” “integrated,” “time-variant,” and “non-volatile.” “Subject-oriented”
Research problem and objectives
Given the previous discussion, the investment on data warehouse technology is very expensive and the failure is pretty high compared with other information technologies. Consequently, this study is important for the corporations and/or businesses to understand the critical factors, which will affect the adoption and/or implementation of the data warehouse technology. Furthermore, since there is little research focusing on the managerial and strategic aspects and discussing the banking industry
Design of the questionnaire
In line with the research model, the design of the questionnaire used in this study also includes the results of the conversation with bank's top management and the findings from other related research questionnaires. According to Kwon and Zmud's [25] classification scheme of the technology-adopting situations in the industry, this study categorizes the current situations to adopting data warehouse technology in the Taiwanese banking industry into four types: adopted, implementing, considering
Data analysis
Fifty questionnaires were mailed to chief information officers in domestic banks, and a final of thirty-two responded to the questionnaires via a second effort of mailing to solicit a response. After the deletion of two incomplete ones, a total of 30 valid survey responses were included in this study, and the resulting response rate was 60%. To ensure the full representation of samples, this study takes a chi-square test to demonstrate their homogeneity. The result shows that all parts in the
Findings and observations in the organizational dimension
Prior researches indicate that the larger the size of the organization, the more resources and capital can be secured to adopt the new information technology [8], [26]. The capital spending and number of employees are used to measure the size of the bank in this study. From the studies of these aforementioned researches, the strategies to adopt information technologies are affected by factors—the size and industrial category of the enterprise. This study focused on a single industry; the result
Important observation/contribution of this study
Given the fact that a number of businesses jumped on board to use the data warehouse technology, the users need to be careful that this technology may not be a perfect solution for all different organizations to resolve every data-related problem. Consequently, many adoption-related factors must be carefully and fully evaluated before any adoption attempt is made. The adoption of data warehouse technology is not just a simple activity to purchase the required hardware and software, but rather a
Uncited references
[5]
[35]
[40]
[41]
[44]
Hsin-Ginn Hwang is an Associate Professor of MIS at National Chung Cheng University. He received his PhD from the University of Texas at Arlington. His research interests include Group Support Systems, Decision Support Systems, Healthcare Information Systems, and Electronic Medical Records. His published works have appeared in Information and Management, Journal of International Information Systems, and International Journal of Information and Management Sciences.
References (48)
- et al.
Planning and Designing the Data Warehouse
(1997) Supporting the information technology champion
MIS Quarterly
(1991)Interfirm adoption of capital goods innovations
Review of Economics and Statistics
(1984)Data minefields
PC Week
(1996 (March))- et al.
Information technology implementation research: a technological diffusion approach
Management Science
(1990) Firm size and the characteristic of computer usage
MIS Quarterly
(1981)- et al.
An architecture for a business and information system
IBM Systems Journal
(1988) - et al.
The adoption of radical and incremental innovations: an empirical analysis
Management Science
(1986) - et al.
Discrepancy model of end-user computing involvement
Management Science
(1989) - et al.
Organizational context and the success of management information systems
Management Science
(1978)
Building the data warehouse
Communications of the ACM
Technology diffusion: an empirical test of competitive effects
Journal of Marketing
The initiation, adoption, and implementation of telecommunications technologies in U.S. organizations
Journal of Management Information Systems
Multivariate Data Analysis
Market concentration and the diffusion of new technology in the banking industry
Review of Economics and Statistics
Explaining the role of user participation in information system use
Management Science
Data warehousing: bringing it all together
Information Systems Management
Building the Data Warehouse
Managing the Data Warehouse
Issues in building a successful data warehouse
Information Strategy: The Executive's Journal
The Data Warehouse Toolkit
Organizational size and the structural perspective: a review, critique, and proposal
Administrative Science Quarterly
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Hsin-Ginn Hwang is an Associate Professor of MIS at National Chung Cheng University. He received his PhD from the University of Texas at Arlington. His research interests include Group Support Systems, Decision Support Systems, Healthcare Information Systems, and Electronic Medical Records. His published works have appeared in Information and Management, Journal of International Information Systems, and International Journal of Information and Management Sciences.
Cheng-Yuan Ku was born in Taipei, Taiwan, on October 14, 1965. He received his BS degree in control engineering from National Chiao Tung University, Taiwan, in 1987 and his MS and PhD degrees in electrical engineering from Northwestern University in 1993 and 1995, respectively. From 1995 to 1999, he was with the Department of Information Engineering, I-Shou University. Since 1999, he joined the Department of Information Management, National Chung Cheng University. His current research interests include decision support systems, cryptography, and network management. He is a member of INFORMS, IEEE, and IEICE.
David C. Yen is a professor of MIS and chair of the Department of Decision Sciences and Management Information Systems at Miami University. He received a PhD in MIS and Master of Sciences in Computer Science from the University of Nebraska. Professor Yen is active in research; he has published two books and over 70 articles, which have appeared in Communications of the ACM, Information and Management, International Journal of Information Management, Journal of Computer Information Systems, Interface, Telematics and Informatics, Computer Standards and Interfaces, and Internet Research among others. He was also one of the co-recipients for a number of grants such as Cleveland Foundation (1987–1988), GE Foundation (1989), and Microsoft Foundation (1996–1997).
Chi-Chung Cheng received his MS degree in Information Management from National Chung Cheng University, Taiwan. His research interest is customer relationship management, data mining, and data warehouse.
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