J.J. Po-An Hsieh

Explaining employees' Extended Use of complex information systems

Investments in complex information systems by organizations reached a record high of U.S.

A two-stage architecture for stock price forecasting by integrating self-organizing map and support vector regression

26.7 billion in 2004. Yet, organizations seldom use these systems to the fullest extent and attain the expected return on investment. This paper addresses the issue of system underutilization by investigating Extended Use, which refers to using more system features to support ones tasks. Extended Use was examined in the nomological networks of the IS Continuance (ISC) Model and Technology Acceptance Model (TAM). A field survey was conducted in a large manufacturing firm that had successfully implemented a popular enterprise resource planning solution for more than 2 years. All paths in both ISC and TAM were statistically significant. A synthesized model was later proposed and examined in a post hoc analysis. The results indicate that the synthesized model, as compared to ISC and TAM, explained slightly higher variances in Extended Use, Perceived Usefulness (PU), and Satisfaction. Specifically, both Perceived Ease of Use (PEOU) and PU both affected Extended Use. Interestingly, Satisfaction has no direct impact on Extended Use in the presence of PU and PEOU. In contrast to most technology acceptance research, PEOU has a stronger behavioral impact than that of PU. This research provides a framework that explains Extended Use and is one of the few studies that investigates IS use behavior that exceeds simple, shallow, and routine use.

Public IT Policies in Less Developed Countries: A Critical Assessment of the Literature and a Reference Framework

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.

Consuming information systems: An economic model of user satisfaction

Abstract How well do extant difwion models originating in developed countries explain adoption of information technologies in less developed countries? m a t is the current status of the literature with respect to public ITpolicies? The authors explore the literature on public ITpolicies to answer these questions. Findings indicate that, due to diferences in environmental factors, existing models may not be readily applicable to less developed countries without careful consideration of the structural differences between developed countries -where most models originated- and less developed countries. Within extant studies of public IT policies, this article identifies typical research characteristics - e.g., case study methodologies, single country selection, single project scope, and little theory development. Finally, an integrative framework for the rationalization of existing models is proposed.

The Bumpy Road to Universal Access: An Actor-Network Analysis of a U.S. Municipal Broadband Internet Initiative

This paper has two major objectives. The first objective intends to answer the following question which is of significant interest to information system (IS) researchers and practitioners: How does user satisfaction (satisfaction) respond to changes in system use and system attributes? The second and more ambitious objective is to promote the application of economic theories in user behavior research. In contrast to prior research that conceived the development of user satisfaction as an information valuation and integration process, we consider such development to be embedded in the IS consumption process, that is, users gain utility (satisfaction) from consuming (using) the system. This perspective enables us to re-conceptualize user satisfaction as a proxy of utility and apply utility research in economics to study user satisfaction. An economic model of user satisfaction was developed. Two empirical studies were conducted to examine the research model. The findings confirmed the consumptive nature of user satisfaction. Apart from enriching our understanding of user satisfaction, this research demonstrates the usefulness of economic theories in user behavior research.

Cross-National Differences in Individual Knowledge-Seeking Patterns: A Climato-Economic Contextualization

Universal high-speed Internet access can productively transform a nations economy. However, many municipalities in the United States have been left behind in terms of Internet penetration. Some municipal governments have tried to address this by launching initiatives that aim at offering citywide, universal broadband access. Unfortunately, most of these initiatives either have been discontinued or have ended in failure. Drawing on actor-network theory, we conducted a 3-year study to investigate the evolution of the Internet TV initiative in LaGrange, Georgia, in the United States. The results reveal distinct interpretations of the initiative by different actor groups (the government, the service providers, socioeconomically advantaged residents, and socioeconomically disadvantaged residents), at different stages of implementation, pointing to tensions among the various groups. These tensions reflect the structural problems embedded in the macro political, economic, and societal context. The findings offer insights for policymakers who intend to achieve universal broadband access.

Understanding Intention to Explore Business Intelligence Systems: The Role of Fit and Engagement

Electronic Knowledge Repository (EKR) is one of the most commonly deployed knowledge management technologies, yet its success hinges upon employees’ continued use and is further complicated in today’s multinational context. We integrate multiple theoretical linkages into a research model, conceptualizing knowledge-seeking as an instrumental behavior, adopting the technology acceptance model to characterize the individual-level continued EKR knowledge-seeking behavioral model, and drawing on the climato-economic theory to explain cross-national behavioral differences. Using hierarchical linear modeling (HLM), we test the model with data from 1352 randomly sampled knowledge workers across 30 nations. We find that two national-level factors, climate harshness and national wealth, interactively moderate the individual-level relationship between perceived usefulness (PU) and behavioral intention (BI) to continue seeking knowledge from EKR, such that the difference in the strength of this relationship is larger between poor-harsh and poor-temperate nations than between rich-harsh and rich-temperate nations. We find similar cross-level cross-national differences for the link between perceived ease of use (PEOU) and PU but not for the link between PEOU and BI. Implications for research and practice are discussed.

Understanding digital inequality: comparing continued use behavioral models of the socio-economically advantaged and disadvantaged

This paper explores how user engagement affects users’ intention to explore business intelligence system (BIS) and how user engagement is promoted by the cognitive fit between BIS interface and tasks and the regulatory compatibility between BIS interface and personal characteristics, such as style of information processing. Results from the lab experiment suggest that the cognitive fit and the regulatory compatibility could both influence users’ engagement experience, which in turn affected users’ intention to explore BIS. This study may contribute to the extant information systems (IS) literature by uncovering the impacts of engagement experience on intention to explore and responding to the call for investigation of the BIS context where rich visualizations of the systems influence users’ engagement experience.