https://scholars.lib.ntu.edu.tw/handle/123456789/472576
標題: | Robustness testing of PLS, LISREL, EQS and ANN-based SEM for measuring customer satisfaction | 作者: | Hsu S.-H. WUN-HWA CHEN Hsieh M.-J. |
關鍵字: | ANN; EQS; LISREL; PLS; Robustness | 公開日期: | 2006 | 卷: | 17 | 期: | 3 | 起(迄)頁: | 355-372 | 來源出版物: | Total Quality Management and Business Excellence | 摘要: | Researchers have shown the Customer Satisfaction Index (CSI) can serve as a predictor for companies' profitability and market value. To measure a CSI model, we have to use a Structure Equation Model (SEM) technique. There are two types of SEM techniques - covariance-based (e.g. LISREL, EQS or AMOS) and component-based SEM techniques (e.g. Partial Least Square). With the growing importance of a CSI model, we must determine which SEM technique can better measure a CSI model. In addition, with the increasing complexity of a theoretical model (e.g. non-linear relations between variables), researchers have called for new SEM techniques that could address this issue. Hackle & Westlund (2000) contended that the Artificial Neural Network (ANN)-based SEM technique could be superior to traditional SEM techniques because it can measure non-linear relations by using different activity functions and layers of hidden nodes. Thus, this study extends previous research in several directions. First, we conduct the robustness testing of both covariance-based (LISREL and EQS) and component-based (PLS) SEM techniques, not only on a simulated CSI-like model but also on a real-life CSI model. Second, we explore the feasibility of an ANN-based SEM technique. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/472576 | ISSN: | 14783363 | DOI: | 10.1080/14783360500451465 |
顯示於: | 工商管理學系 |
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