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Wu W.-W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

Enterprise Resource Management (ERP) systems are viewed as a promising and powerful information technology solution for dealing with the impact of competition advancements and enabling corporations to improve productivity and to operate more efficiently. Although implementations of ERP are complex and costly, corporations may actively adopt and engage in such ERP implementations if perceived benefits exceed perceived risks and costs. A number of studies have contributed to discussion of important factors related to ERP introduction or implementation. Other studies have listed various potential benefits which may be obtained when implementing ERP systems. However, few studies attempt to deepen the analyses of the ERP users' perceived benefits in order to gain meaningful findings for promoting ERP implementations. Typically, elements of a set of ERP benefits do not necessarily share the same importance. Moreover, a given ERP benefit may be accorded a variety of very different levels of importance by different corporations. This paper attempts to segment the ERP users into two subgroups according to the notion of Herzberg's Motivation-Hygiene theory, and further, to uncover imperative perceived benefits for distinct subgroups of ERP users employing the rough set theory. The results of this study should provide better understanding and knowledge of strategic implications for both ERP system adopters and vendors, and thus advance the scope of ERP implementations. © 2010 Elsevier Ltd. All rights reserved. Source

Liu P.-L.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

The rapid development of information technology and the emergence of the Internet have created a borderless business environment and intensified market competition. Riding on the globalization trend, high-tech companies have been gradually leveraging information technology in order to shorten their manufacturing processes, enhance productivity with lower costs and prompt delivery to meet the customers' needs. To achieve these targets and maintain competitive advantages, companies have been introducing enterprise resource planning (ERP) and knowledge management (KM). This paper finds, via literature review, that most scholars focus only on the deployment of ERP systems and improvement of flows. Few have introduced the KM concept into ERP systems. This paper collated the literature relevant to ERP and KM and integrates the findings to introduce the ERP KM concept. The most important thing is to establish a detailed introduction plan and a prior understanding of the critical success factors (CSFs) for ERP KM introduction. This paper summarizes the CSFs for ERP KM introduction via literature review and examines the influence of these CSFs on management performance. A questionnaire survey is conducted to collect the relevant data and SPSS 10.0 (statistics software) is run for statistical and multiple regression analyses. Among these CSFs, support from senior managers, corporate vision, reengineering of corporate flows and project management, selection of appropriate consulting firms and software suppliers, the identification of suitable employees to take part in ERP introduction and the proper training and education programs have positive influences on management performance. In the multiple regression analysis, all of the individual constructs are positively and significantly correlated. The explanatory power of individual variables is high. It is hoped that the research finding can serve as a reference for ERP KM introduction to corporations. © 2011 Published by Elsevier Ltd. Source

Wu W.-W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2011

Among several types of "cloud services", the Software as a Service (SaaS) solution is promising. The Technology Acceptance Model (TAM) and its modified versions have been popularly utilized for examining how users come to accept a new technology, but have not yet been employed to handle issues regarding SaaS adoption. This paper attempts to develop an explorative model that examines important factors affecting SaaS adoption, in order to facilitate understanding with regard to adoption of SaaS solutions. An explorative model using partial least squares (PLS) path modeling is proposed and a number of hypotheses are tested, which integrate TAM related theories with additional imperative constructs such as marketing effort, security and trust. Thus, the findings of this study can not only help enterprise users gain insights into SaaS adoption, but also help SaaS providers obtain inspiration in their efforts to discover more effective courses of action for improving both new product development and marketing strategy. © 2011 Elsevier Ltd. All rights reserved. Source

Hsieh C.-S.,Ta Hwa University of Science and Technology
Automatica | Year: 2010

In this paper, a globally optimal filtering framework is developed for unbiased minimum-variance state estimation for systems with unknown inputs that affect both the system state and the output. The resulting optimal filters are globally optimal within the unbiased minimum-variance filtering over all linear unbiased estimators. Globally optimal state estimators with or without output and/or input transformations are derived. Through the global optimality evaluation of this research, the performance degradation of the filter proposed by Darouach, Zasadzinski, and Boutayeb [Darouach, M., Zasadzinski, M., & Boutayeb, M. (2003). Extension of minimum variance estimation for systems with unknown inputs. Automatica, 39, 867-876] is clearly illustrated and the global optimality of the filter proposed by Gillijns and De Moor [Gillijns, S., & De Moor, B. (2007b). Unbiased minimum-variance input and state estimation for linear discrete-time systems with direct feedthrough. Automatica, 43, 934-937] is further verified. The relationship with the existing literature results is addressed. A unified approach to design a specific globally optimal state estimator that is based on the desired form of the distribution matrix from the unknown input to the output is also presented. A simulation example is given to illustrate the proposed results. © 2010 Elsevier Ltd. All rights reserved. Source

Wu W.W.,Ta Hwa University of Science and Technology
Expert Systems with Applications | Year: 2010

Causal knowledge based on causal analysis can advance the quality of decision-making and thereby facilitate a process of transforming strategic objectives into effective actions. Several creditable studies have emphasized the usefulness of causal analysis techniques. Partial least squares (PLS) path modeling is one of several popular causal analysis techniques. However, one difficulty often faced when we commence research is that the causal direction is unknown due to the lack of background knowledge. To solve this difficulty, this paper proposes a method that links the Bayesian network and PLS path modeling for causal analysis. An empirical study is presented to illustrate the application of the proposed method. Based on the findings of this study, conclusions and implications for management are discussed. © 2009 Elsevier Ltd. All rights reserved. Source

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