Structural equation modeling (SEM) is a multivariate statistical framework that is used to model complex relationships between directly observed and indirectly observed (latent) variables. SEM is a general framework that involves simultaneously solving systems of linear equations and encompasses other techniques such as regression, factor analysis, path analysis, and latent growth curve modeling. Recently, SEM has gained popularity in the analysis of complex genetic traits because it can be used to better analyze the relationships between correlated variables (traits), to model genes as latent variables as a function of multiple observed genetic variants, and to assess the association between multiple genetic variants and multiple correlated phenotypes of interest. Though the general SEM framework only allows for the analysis of independent observations, recent work has extended SEM for the analysis of data on general pedigrees. Here, we review the theory of SEM for both unrelated and family data, describe the available software for SEM, and provide examples of SEM analysis.

Structural Equation Modeling

Integrating TTF and UTAUT to explain mobile banking user adoption

House of Quality

Full length articleMobile payment: Understanding the determinants of customer adoption and intention to recommend the technology

Retaining users and facilitating their continuance usage are crucial for mobile payment service providers. Drawing on the information systems success model and flow theory, this research identified the factors affecting continuance intention of mobile payment. We conducted data analysis with structural equation modeling. The results indicated that service quality is the main factor affecting trust, whereas system quality is the main factor affecting satisfaction. Information quality and service quality affect flow. Trust, flow and satisfaction determine continuance intention of mobile payment. The results imply that service providers need to offer quality system, information and services in order to facilitate users' continuance usage of mobile payment. Highlights? Service quality is the main factor affecting trust. ? System quality is the main factor affecting satisfaction. ? Information quality and service quality affect flow. ? Trust, flow and satisfaction determine continuance usage of mobile payment.

An empirical examination of continuance intention of mobile payment services

The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services

Towards an understanding of the behavioral intention to use 3G mobile value-added services

Importance–performance analysis (IPA) is a simple and effective technique that can be applied to guide quality-based marketing strategies and can assist practitioners in prioritising attributes when enhancing service quality and customer satisfaction. As several studies have shown, the relationship between customer's perception of attribute performance and overall satisfaction is sometimes nonlinear. These findings make the application of IPA questionable. Furthermore, competition analysis is necessary for a firm's management to attain competitive advantage. Therefore, this study presents a revised IPA which integrates three-factor theory and benchmarking. An illustrative case demonstrates the revised IPA and identifies any shortcomings produced by applying the traditional IPA.

Revised importance–performance analysis: three-factor theory and benchmarking

This study uses Kano’s two-way quality model to categorize web-community service quality dimensions and their elements and understand the demands of users. The results show that, in terms of overall samples, among the 29 service quality elements, 6 are attractive quality, 5 are one-dimensional quality, 11 are must-be quality, and 7 are indifferent quality. In the six service quality dimensions, there is 1 attractive quality, 2 one-dimensional quality, and 3 must- be quality. The satisfaction increment index and dissatisfaction decrement index of service quality of the web-community, based on the CQM (1993) quality index, are also calculated in this study. Through these two indexes, we can find the increase of satisfaction and the decrease of dissatisfaction in the analysis of improvement on certain quality elements, and use the results as a reference for future improvements on services provided to the web-community. Future research can incorporate Kano’s model with a Quality Function Deployment (QFD) to i...

Integrating Kano’s Model into Web- community Service Quality

This study proposes the IPA–Kano model as a new tool for categorising and diagnosing service quality attributes and providing specific strategies for attributes in each category. The IPA–Kano model avoids the limitation of the Kano model in neglecting the attribute performance and importance and eliminates the weakness of the IPA model in considering only one-dimensional qualities. The proposed model can enable managers to easily grasp accurate user perceptions of a quality attribute and corresponding coping strategies. The effectiveness of this model implementation is later presented with an example of mobile value-added service quality. By applying the proposed IPA–Kano model, the appropriate action strategy for each service quality attribute can be acquired in any service encounters and thus enables service industry managers to improve service quality and to achieve a competitive advantage.

https://ir.nuk.edu.tw/bitstream/310360000Q/10730/2/IPA_Kano+model.pdf

Ying-Feng Kuo

Papers

The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services

Towards an understanding of the behavioral intention to use 3G mobile value-added services

Revised importance–performance analysis: three-factor theory and benchmarking

Integrating Kano’s Model into Web- community Service Quality

IPA–Kano model: A new tool for categorising and diagnosing service quality attributes