Abstract: This study set out to investigate the critical factors that determine user intention to use a biometric system. We integrated previous research in the technology acceptance and biometric engineering literatures and identified six important factors: Perceived System Security, Perceived False Acceptance rates, Perceived False Rejection rates, Perceived System Invasiveness, System Trust and Facilitating Conditions. Eleven hypotheses were developed regarding the six factors to formulate direct and indirect influences of the six factors in a biometric intention-to-use model. The results support the eleven hypotheses and validate the model, which has an average variance explained of 31.1%. The contributions of the paper are discussed and future research directions are suggested.
Abstract: We study 56 subjects of varying knowledge in a preferential choice task, aided by one of two Decision Support Systems (DSS) of different restrictiveness: an eliminative tool (ELIM) and a parametric search tool (PS). Using a novel measure for performance based on Data Envelopment Analysis (DEA), we find that the gains due to effort are greater with the less restrictive DSS. Surprisingly knowledge has a negative effect on performance, an effect exacerbated with the less restrictive DSS. We interpret our results in terms of knowledge-effort substitution and the nature of knowledge relative to the restrictiveness of the DSS.
Abstract: We develop a Structural Equation Model (inspired from an exploratory Directed Acyclic Graph) from a dataset consisting of a cross section of 6,637 UK households in order to uncover the links among expenditures on various forms of gambling, income, and other household expenditures. We find evidence for the existence of latent variables that we refer to as hard gambling, soft gambling, and myopic behavior (myopia). Our central finding is that greater myopia increases hard gambling, but not soft gambling. We also find that a greater income has a strong and negative effect on myopia and hard gambling, but not on soft gambling. Greater spending on necessities, such as food and clothing, is associated with increased myopia, soft gambling and hard gambling. Finally our results reveal a moderate but significant positive association between soft gambling and hard gambling, providing some support for the âslippery slopeâ hypothesis. We discuss the results and new questions they suggest.
Abstract: User-customization is increasingly common in
electronic commerce, because both the buyer and seller
potentially benefit. The user interface to implement and the
influence of the interface on various process and outcome
measures, however, are not well understood.We developed a
Flow-based model consisting of seven hypotheses regarding
the user interface and its consequents. We conducted a field
experiment to test an attribute-based interface vs. a questionbased
interface on three variables (perceived control,
shopping enjoyment and choice satisfaction) as well as two
web site intentions: intention to return and intention to
purchase. Six of the seven hypotheses were supported in a
parsimonious model. Variance explained was 16.3% for
perceived control, 45.6% for shopping enjoyment, 59.3% for
choice satisfaction and 63.1% for web site intentions. The
main finding is that an attribute-based interface for retail eshopping
increases the shopperâs sense of control and feeling
of enjoyment in the process more than a question-based
interface, and thereby increases satisfaction with the outcome.
This combination of influences increases the intention
of the shopper to return to the web site and to purchase the
item. We discuss the results and suggest areas for future
research in user-customization, which may apply to many
different industries that engage in online commerce.
Abstract: How do we model and improve technical problem solving, such as network subnetting? This paper reports an experimental study that tested several hypotheses derived from Kolbâs experiential learning cycle and Huberâs problem solving model. As subjects solved a network subnetting problem, they mapped their mental processes according to Huberâs problem solving stages by tapping a keypad. Based on Kolbâs model, concrete and abstract representations of the subnetting problems were tested to determine whether the form of the problem representation improved performance. For subjects for whom full process data was available, nine of the ten hypotheses were supported. A partial least squares model was developed which explained 27.5 percent of the variance in performance with three predictors. Two of the three predictors for performance were from the Kolb side of the integrated model, whereas the third predictor was from the Huber side. We draw some implications for research and practice, based on the integrated model to explain performance. We conclude that technical problem solving can be modeled as an integration of Kolbâs experiential learning cycle and Huberâs stages of problem solving. Additional research is needed to extend Kolbâs cycle and Huberâs stages to other knowledge intensive problem solving domains and to a more diverse set of problem solvers.
Abstract: In the DSS literature, most studies have concentrated on the direct effects of DSS use and DSS design on decision outcomes and user performance in the workplace. Fewer DSS studies have integrated decision process variables, such as user beliefs and attitudes, in their models. In this paper, we examine the mediating role of decision process variables in the use of an online customer DSS. We do so through an experimental study of an alternative-based and an attribute-based DSS for product customization by online customers. Using Cognitive Fit and Flow theories, we develop a theoretical model with four mediating decision process variables (perceived usefulness, perceived ease of use, perceived enjoyment, and perceived control) and two of their antecedents: interface design (attribute-based vs. alternative-based) and task complexity (choice set size). Our results show that the impact of DSS interface design on behavioral intentions is fully mediated by perceived usefulness and perceived enjoyment, though not by perceived control. Specifically, we find that users of an attribute-based DSS express higher perceived usefulness and perceived enjoyment than users of an alternative-based one. In addition, we verify that task complexity has an interesting relationship with usefulness and enjoyment, both of which follow an inverted U-shaped curve as choice set size increases. Finally, we find that for users of the alternative-based DSS, perceived ease of use and perceived control decrease as task complexity increases. However, the attribute-based DSS alleviates that diminishment for both variables. Among other contributions, our results indicate the importance of including decision process variables when studying DSS as well as the complex effect of task complexity on those variables. Our study also provides some important guidelines for online companies that provide customer DSS on their websites, especially the danger of providing too many product choice options which can overwhelm customers and harm their shopping experience.
Abstract: The case examines the strategic positioning of BizE (disguised) an eCommerce start-up that serves small online businesses via an Internet portal. During an economic downturn BizE encounters difficulties with its new private-label strategy. With cash running dangerously low, executives reassess the new strategy and consider what they can do to ensure the companyâs survival.
Abstract: We offer a review of three software packages that estimate directed acyclic graphs (DAGs) from data. The three packages, MIM, Tetrad and WinMine, can help researchers discover underlying causal structure. Although each package uses a different algorithm, the results are similar. All three packages are free and easy to use. They are likely to be of interest to researchers who do not have strong theory regarding the causal structure in their data. DAG modeling is a powerful analytic tool to consider in conjunction with, or in place of, path analysis, structural equation modeling, and other second-generation statistical techniques.
Abstract: Shopping engines of different designs have been researched fruitfully as a means of convenient access to the goods and services on the Web. Some shopping engines are designed to function autonomously in one stage, searching to quickly maximize decision accuracy as a function of several parameters. Others are designed to strongly involve the user, searching in multiple stages to satisfy decision accuracy requirements. This study tested single-stage and multiple-stage shopping engines, designed with two approaches, termed QuickSearch and AdaptiveSearch, on 205 users who were trying to maximize accuracy while minimizing effort. The results show that the best performing shopping engine used two stages, first QuickSearch, and then AdaptiveSearch. The results imply that QuickSearch and AdaptiveSearch, although logically equivalent, have different impacts on shopping for differentiated, multi-attribute goods and services. In practice, the results of this study suggest that shopping engines should be designed to first save the shopper effort, and then provide attribute-focused support for examining the resulting set of items.
Abstract: Many online retailers try to assist consumers via parametric search engines xD;(PSEs), i.e., attribute-based search engines. The objective of this study is to model xD;the effectiveness of four PSEs in using search effort and domain knowledge to xD;increase decision quality, decision confidence, perceived ease of use and xD;perceived usefulness. Our model comprises a set of behavioral decision theory xD;antecedents to the technology acceptance model. We tested users with four PSEs xD;in a laboratory experiment and modeled the results with Partial Least Squares xD;(PLS) analysis. The main result of this paper is a PLS model showing that the xD;effects of search effort and domain knowledge are mediated through decision xD;quality and decision confidence to impact perceived ease of use and perceived xD;usefulness. The model explains the variance in decision quality (17.3%), decision xD;confidence (28.3%) and perceived usefulness (27.0%). Overall, this study shows xD;that input, process and outcome variables are important for predicting the xD;effectiveness of PSEs. Implications for research and practice are discussed.
Abstract: This study examines the impact of handheld computers on students in a first year Information Technology course (IT101), measuring IT attitudes, skills, and performance. Students in standard sections used their laptop computers and traditional textbooks. Students in intensive sections used both handheld computers and laptop computers, that is, multiple platforms. Both standard and intensive sections covered the same topics. Although students in both standard and intensive sections became more skilled during the semester, students in the intensive sections improved more in programming and Microsoft Excel skills. Students in intensive sections also scored significantly better than students in the standard sections on the common portion of the final exam. Finally, according to anecdotal evidence, students in the intensive sections gained significantly more skill-based confidence in IT unrelated to handheld computers compared to students in the standard sections. Seven of our ten expectations were supported. While students benefited from the intensive version of IT101, several factors could account for the improvements: a self-selection factor, an active learning vs. textbook learning factor, and possibly an instructor factor. Future research should shed light on the relative weights of these factors.
Abstract: Since technology adopters are sensitive to perceived ease of use, online shoppers who are novices to a product category may be particularly sensitive. We developed a predictive model of decision quality centered on perceived ease of use for online shoppers who are novices to a product category. The antecedents of perceived ease of use in this study are novice category knowledge, perceived time pressure and purchase involvement. We had undergraduate students interact with multiple-stage shopping engines (MSSEs) of varying effort-accuracy tradeoffs in our purpose-built Web store. Both novice category knowledge and perceived time pressure negatively impacted purchase involvement and perceived ease of use of the MSSEs. Perceived ease of use of the MSSEs positively impacted their perceived usefulness. Perceived usefulness positively impacted decision quality, which was measured objectively, with data envelopment analysis, and subjectively, with the weighted additive method. Six of the seven hypotheses were supported. Surprisingly, there were no significant differences among the MSSEs and purchase involvement did not positively impact perceived ease of use. The results, analyzed via partial least squares, demonstrate the potential value of giving multiple-stage shopping engines to online shoppers who are novices to a product or service category.
Abstract: Champions are critical to the success of an information system implementation. Research shows that success in implementing a large information system may hinge on there being more than one type of champion. This study investigates the types of champions used in federated inter-organizational systems (FIOS) in a state telemedicine context. Case studies were conducted in Georgia, Pennsylvania, Ohio, and Wisconsin to identify the network of champions in state telemedicine systems. We found that FIOS that relied on a network of champions, including a sponsorship champion at the state level, as well as a technical champion and user champion at site locations, were more successful than those that lacked such a network. We suggest that our model of champions in FIOS applies not only to state telemedicine, but also to any large-scale system implementation spanning a federation of loosely coupled organizations.
Abstract: How do we model and improve the learning of technical skills such as network subnetting? This paper reports an experimental study that tested three hypotheses, two derived from Kolbâs experiential learning cycle, which is concrete-based, and one derived from the advance organizer technique, which is abstraction-based. The two hypotheses following Kolbâs cycle were supported, and the one based on advance organizers was not supported. The results indicate that the learning of network subnetting starts concrete, becomes more abstract, and ends concrete. The lack of support for advance organizers indicates that they are not a good fit for technical problem solving, or that they need to be designed differently for it. We draw some implications for research and practice. We also call for additional research to extend Kolbâs cycle to other technical and scientific problem solving domains.
Abstract: Telemedicine networks bring together a range of medical facilities often governed by an external entity or task force. In order for a telemedicine network to be successful, it needs sufficient support from key individuals within the network. The collective skill set and logistical placement of these individuals, knows as champions, is critical to produce an effective telemedicine implementation. This study investigates the types of champions used in state telemedicine programs to determine the most effective champion mix. Research was conducted on Georgia, Pennsylvania, Ohio, and Wisconsin programs to identify the cadre of champions existing in state telemedicine systems. Data collected included interviews, as well as information related to each program reported in magazines, newspapers, medical journals, academic journals, web sites, state legislation records, minutes from state telemedicine task force meetings and internal evaluations. Triangulated qualitative analysis indicates that the telemedicine systems that relied on a network of champions that included a sponsorship champion at the state level, as well as a technical champion and user champion in each site were more successful than those that lacked such a network. Furthermore, the more efficacious implementations employed a greater overall number of technical and user champions at the participating sites.
Abstract: Prior efforts in personalization have focused primarily on modeling individual consumer's preferences so that products for which they have a higher likelihood of purchasing are presented. In this study we explore the potential of an approach to personalization focusing on customizing shopping tools based on a consumer's Product Category Knowledge. The low Product Category Knowledge user may not be able to use the shopping tools as well as the high Product Category Knowledge user, because lower Product Category Knowledge users allocate cognitive power to learning the product attribute space at the expense of using the tool effectively. Alternatively, shopping tools may effectively guide the decision making of low Product Category Knowledge users, but be perceived as too restrictive by high Product Category Knowledge users, thus diminishing their Decision Confidence. To determine which of these scenarios holds true we had sixty-six subjects interact with different decision tools of varying effort-accuracy tradeoffs in our purpose-built web store for purchasing a computer. We examine the impact of Product Category Knowledge on perceptions of ease of use and usefulness of the decision tools, and ultimately Decision Confidence, a key predictor of purchase likelihood. The results evidence the potential value of adapting tools to the degree of user Product Category Knowledge. High Product Category Knowledge users may require less restrictive decision tools to promote Decision Confidence, whereas low Product Category Knowledge users may require simpler tools and more decisional guidance
Abstract: In this study, we address the question of the effect of domain knowledge on performance with a decision tool in a preferential choice task. Specifically we explore the relationship between knowledge, effort and performance (accuracy), drawing on theories from both behavioral decision theory and cognitive science. Using a purpose-built web shopping simulation we study 56 subjects interacting with two different decision tools varying in effort and knowledge requirements; a lexicographic tool (LEX) and a parametric search tool (PS. Using a novel measure for accuracy based on Data Envelopment Analysis we find that effort positively influences accuracy, with a greater effect observed for the more powerful decision tool, PS. In conflict with theory and intuition we find knowledge has a negative effect on accuracy, and that this effect is greater with the more powerful decision tool. We interpret our results in terms of knowledge-effort substitution, the content and representation of knowledge, and the conceptual characteristics of the decision tool. Our results emphasize the importance and complexity of knowledge considerations in understanding performance with decision support tools. Pragmatically, it highlights the importance of considering who a decision support system is designed for, and how the use of that decision support may both constrain and enable effective performance.
