Showing papers by "Paul Jen-Hwa Hu published in 2010"

Modeling Citizen Satisfaction with Mandatory Adoption of an E-Government Technology

Abstract: While technology adoption is a major stream of research in information systems, few studies have examined the antecedents and consequences of mandatory adoption of technologies. To address this gap, we develop and test a model of mandatory citizen adoption of an e-government technology. Based on a framework that outlines the key stages associated with the launch of technology products, we identify various external factors as antecedents of four key technology adoption variables from the unified theory of acceptance and use of technology (UTAUT), i.e., performance expectancy, effort expectancy, social influence, and facilitating conditions, which ultimately impact citizen satisfaction. The four stages of technology launch and the salient antecedents in each stage are: market preparation stage - awareness; targeting stage - compatibility and self-efficacy; positioning stage - flexibility and avoidance of personal interaction; and execution stage - trust, convenience, and assistance. We test our model in a two-stage survey of 1,179 Hong Kong citizens, before and after they were issued a mandatory smart card to access e-government services. We find that the various factors tied to the different stages in launching the technology predict key technology adoption variables that, in turn, predict citizen satisfaction with e-government technology. We discuss the theoretical and practical implications for governments implementing technologies whose use by citizens is mandated.

The Impact of Service System Design and Flow Experience on Customer Satisfaction in Online Financial Services

Abstract: Prior research examines customer satisfaction in retailing and e-commerce settings, yet online financial services have received little research attention. To understand customer satisfaction with t...

Is stickiness profitable for electronic retailers

Abstract: Introduction Is stickiness the Holy Grail for e-tailing? In general, stickiness refers to the amount of time a person spends on a Web site during a visiting session (such as, session stickiness) or over a specified time period (such as, site stickiness). Zauberman equates stickiness and "within-site lock-in" and uses it to approximate visitors' loyalty to a Web site. The conventional wisdom suggests that stickiness is crucial and can contribute to e-tailers' bottom lines considerably. However, the direct economic impacts of stickiness have not been duly examined empirically, particularly from the perspective of consumers' within-session visiting behaviors. E-tailing offers an exciting global virtual channel for marketing and exchanges. According to the U.S. Department of Commerce, the e-retailing industry has grown at a 29% compounded annual rate between 2000 and 2004, amounting to $81 billion in sales in 2005 and projected to reach $144 billion by 2010. Accompanied by this impressive growth is the increasingly fierce market competition that results from greatly reduced search costs, diminished product/service differentiation, and rapidly eroding customer loyalty. To survive and excel in this highly competitive market, e-tailers must be effective in converting their Web site visitors into paying customers. Such conversions are challenging, as manifested by the dismal conversion rate (for example, estimated at 2.3% by e-tailing.com) that is likely to continue to decline. Stickiness serves as a common indicator of customer loyalty to e-tailers. Accordingly, firms have focused on effective Web site design and business strategies to "lock in" visitors by making their Web sites increasingly sticky. Despite the salient beliefs about the business value of stickiness in e-tailing, empirical evidence of its direct economic impacts is surprisingly limited. Moe and Fader discuss the importance of stickiness for business profits and advocate the use of visiting behaviors to investigate the relationship. Straub et al. also highlight the importance of analyzing prominent visitors' behaviors and particularly suggest the use of click-stream data to examine the effects of essential visiting behaviors on business outcomes, such as online purchases. However, few (if any) studies have examined these effects empirically. In this study, we use within-session visiting behaviors, recorded by a designated client-side monitoring program, to examine the relationship between stickiness and conversion, an outcome metric directly affecting e-tailers' bottom lines. We respond to the call by Moe and Fader by examining session stickiness through analyses of visitors' within-session behaviors. We measure session stickiness using both visiting session duration and total number of pages accessed during a visiting session. We empirically test whether stickiness significantly affects conversion, and further investigate whether product category moderates the focal stickiness--conversion relationship.

A Web-based personalized recommendation system for mobile phone selection: Design, implementation, and evaluation

Abstract: Recommendation systems that provide appropriate solutions to users to reduce their decision complexity have become popular in the Internet world. Designing and evaluating such systems remain essential challenges to researchers and practitioners. Toward that end, a critical task is how to obtain user preferences. Mobile phones have become indispensable in everyday life, yet fierce market competition, characterized by rapid introductions of different models with novel designs and advanced features, have made consumers' purchase decision making increasingly complex. As a well-established, multiple criteria decision technique, analytic hierarchy processing (AHP) provides an intuitive model of a hierarchical structure capable of supporting complex product comparisons and evaluations by consumers. In this paper, we illustrate the application of an AHP-based mechanism to develop a Web-based recommendation system and empirically evaluate the prototype by conducting a controlled experiment with 244 mobile phone users, focusing on both content and system satisfaction. Our evaluation includes benchmark systems built on rank-based analysis and an equal weight-based system as comparative baselines. Overall, the results suggest the viability and value of using AHP to construct effective recommendation systems. Subjects appear satisfied with the recommendations by the AHP-based system, though its relatively demanding input requirements may need mitigation and adequate interface designs. This study contributes to research and practice in recommender systems in general and helps develop mobile phone recommendation systems for online stores and consumers in particular.

Examining Gender Effects in Technology Acceptance by Arabian Workers: A Survey Study

Abstract: While information technology is increasingly ubiquitous globally, the pace at which the technology has disseminated varies in different regions. We study technology acceptance by working individuals in the Arabian region, which has recorded substantial growths in technology infrastructure and deployments. We focus on gender because the Arabian region has a long-standing cultural tradition and entrenched social norms that distinctly define the gender roles. We develop a factor model, premised on the theory of planned behavior and the technology acceptance model, which explains the focal technology acceptance phenomenon. We test the model and the hypotheses with the responses from 1,088 Arabian workers from 56 firms that participate in our survey voluntarily. The model accounts for a significant portion of the variances in the workers’ intentions to use computer technology. We find that gender moderates the effect of subjective norms on intention (significantly stronger for males than for female workers) and the influence of perceived usefulness on attitude (significantly stronger for male than for female workers). However, the moderating role of gender appears insignificant on other relationships we hypothesized. Our findings have several important implications for both research and practice, which we will discuss in this paper.

Agency satisfaction with electronic record management systems: A large-scale survey

Abstract: We investigated agency satisfaction with an electronic record management system (ERMS) that supports the electronic creation, archival, processing, transmittal, and sharing of records (documents) among autonomous government agencies. A factor model, explaining agency satisfaction with ERMS functionalities, offers hypotheses, which we tested empirically with a large-scale survey that involved more than 1,600 government agencies in Taiwan. The data showed a good fit to our model and supported all the hypotheses. Overall, agency satisfaction with ERMS functionalities appears jointly determined by regulatory compliance, job relevance, and satisfaction with support services. Among the determinants we studied, agency satisfaction with support services seems the strongest predictor of agency satisfaction with ERMS functionalities. Regulatory compliance also has important influences on agency satisfaction with ERMS, through its influence on job relevance and satisfaction with support services. Further analyses showed that satisfaction with support services partially mediated the impact of regulatory compliance on satisfaction with ERMS functionalities, and job relevance partially mediated the influence of regulatory compliance on satisfaction with ERMS functionalities. Our findings have important implications for research and practice, which we also discuss. © 2010 Wiley Periodicals, Inc.

Examining Effects of Technology-Assisted Learning on Learning Effectiveness and Satisfaction: A Quasi-Experimental Study

Abstract: Examining students’ learning effectiveness and satisfaction is critical to the ultimate success of technology-assisted learning that has been deployed at a fast-growing pace. The accumulated results from prior research are mostly equivocal. Based on how technologyassisted learning may influence students’ learning process, we analyze technology-assisted learning and synthesize relevant prior research, and propose a factor model that explains learning effectiveness and satisfaction. We empirically test that model with a quasiexperiment that involves 212 university students, observing their learning of Adobe Photoshop. We test the hypothesized effects of technology-assisted learning and its moderating role in influencing students’ learning effectiveness and satisfaction. According to our results, the use of technology-assisted learning adversely affects student engagement. This, in turn, negatively influences their learning effectiveness and satisfaction. Student engagement in learning activities appears to mediate the impact of technology-assisted learning on learning effectiveness. Furthermore, the influence of technology-assisted learning on learning satisfaction is mediated by both student engagement and learning effectiveness. Technology-assisted learning shows no significant moderating effects on learning effectiveness or satisfaction. Our empirical results have several important implications for technology-assisted learning research and practice.

Supporting Acute Appendicitis Diagnosis: A Pre-Clustering-Based Classification Technique

Abstract: Service quality and cost containment represent two critical challenges in healthcare management. Toward that end, acute appendicitis, a common surgical condition, is important and requires timely, accurate diagnosis. The diverse and atypical symptoms make such diagnoses difficult, thus resulting in increased morbidity and negative appendectomy. While prior research has recognized the use of classification analysis to support acute appendicitis diagnosis, the skewed distribution of the cases pertaining to positive or negative acute appendicitis has significantly constrained the effectiveness of the existing classification techniques. In this study, we develop a pre-clustering-based classification (PCC) technique to address the skewed distribution problem common to acute appendicitis diagnosis. We empirically evaluate the proposed PCC technique with 574 clinical cases of positive and negative acute appendicitis obtained from a tertiary medical center in Taiwan. Our evaluation includes tradition support vector machine, a prevalent resampling classification technique, Alvarado scoring system, and a multi-classifier committee for performance benchmark purposes. Our results show the PCC technique more effective and less biased than the benchmark techniques, without favoring the positive or negative class.