The effect of positive mood on intention to use computerized decision aids

While psychology research indicates that positive mood enhances cognition and behavior, MIS researchers have largely ignored the potential effects of positive mood on user acceptance of new information technologies (IT). Using two cognitive theories about mood and memory, positive mood theory and the affect infusion model (AIM), this study develops hypotheses about users’ acceptance of new IT under two mood conditions and two levels of uncertainty. These hypotheses are investigated via a lab experiment using a computerized decision aid. The lab experiment found that positive mood increased acceptance, as compared to a control group, under both levels of uncertainty. These results held for both induced and naturally occurring positive mood. The results for the high uncertainty condition along with results of two post-hoc tests are consistent with positive mood theory, but not with the AIM. These results indicate that mood is an important focus for future MIS acceptance research, which should be based on positive mood theory rather than the AIM

UNDERSTANDING THE EFFECTS OF CONSUMERS’ VALUETECHNOLOGY FIT ON A MOBILE SHOPPING WEBSITE：THE CASE OF RAKUTEN ICHIBA

Mobile shopping is very popular nowadays. Many mobile shopping websites were founded in recent years, such as Yahoo! and Rakuten Ichiba. Most previous articles focused on theory of planned behavior, trust, flow, perceived usefulness, consumer usability preference, consumer shopping experience and decision-making, integrating technology acceptance model and perceived value. There are few studies to discuss benefits of matching discounts/bargains with a mobile shopping website, as well as how much affective reaction and flow affect continuance intention of using a mobile shopping website. The aim of this study investigates the factors (value-technology fit, affective reaction, flow) influencing continuance intention of the mobile shopping website, and mediation effects of affective reaction and flow. The results of this study show that users’ value-technology fit significantly affects their affective reaction, flow, and continuance intention to use the mobile shopping website. Affective reaction and flow significantly affect users’ continuance intention to use the mobile shopping website. Moreover, affective reaction and flow partially mediate relationship between value-technology fit and users’ continuance intention to use the mobile shopping website, Rakuten Ichiba. The research findings have suggestions for the mobile shopping managers and future research studies

PREDICTING LISTENER'S MOOD BASED ON MUSIC GENRE: AN ADAPTED REPRODUCED MODEL OF RUSSELL AND THAYER

Mood has presently received growing consideration as an interesting technique for organizing and accessing music. Stress which changes individual mood is a major physical and psychological problem of individuals today. Many researches have been conducted based on this study of mood, particularly in the U.S.A, Canada, Europe, and some part in Asia.  However, while these studies are important, and help to solve the problem of mood change, still, researchers were unable to look into this important aspect in one of the 25 rapid growth markets in the world-Malaysia. In solving this problem, this study suggested using music genre as an influence mechanism to predict mood and again identify what kind of classified musical genre that can be used to predict certain mood. This study adapts and reproduces a model of Russell and Thayer to categorize moods. A total population of 245 university students of both sexes, aged from 18-56 and above, married and single, different educational level, race, and religions were used to achieved the objective of this study. The data was analyzed using SPSS version 20. The analysis results were presented based on majority and popularity of respondents. The findings indicate that the result of this study is 60%-80% percent positive on both part A and Part B due to the higher population respondents of the investigation. Hence, based on the findings, the study clearly interprets and presents an encouraging methodology that predicts the mood of the listener's with a positive outcomes.

Can a Reasonable Time Limit Improve the Effective Usage of a Computerized Decision Aid?

This study examines the impact of a reasonable time limit on the effective usage of a computerized decision aid. Using current decision making models, a theoretical argument about decision aid usage is developed. This argument is then investigated via two lab experiments. The first experiment determines a reasonable time limit for the task used in the study. The second experiment investigates users’ behavior and heart rate variability under this time limit. The results of our study indicate that the reasonable time limit determined in the first study improved effective utilization of the computerized decision aid. The analysis of heart rate variability provides evidence that the given time constraint improved users’ cognitive coherence

Moods and Their Relevance to Systems Usage Models within Organizations: An Extended Framework

Traditionally, information systems (IS) usage models have examined user behavior within a cognitive framework, that is, these models suggest that a user’s cognition influences his/her IS usage behavior. Research over the past three decades has shown that mood, one’s global feeling state at a given time, can significantly impact a person’s cognitive processes. Mood effects on cognition are particularly relevant to organizational settings. Because moods are pervasive, they provide a stable context for cognitive processes that influence behavior at work; therefore, the inclusion of mood in individual IS usage models that support organizational tasks is both relevant and necessary. Because positive mood can enhance performance under certain circumstances, mood management is also relevant to IS usage models. Thus, we highlight how moods can be managed via IS and propose a model that takes into account users’ moods at the time they work with a system. This model provides an extended framework for incorporating relevant mood literature into current IS usage behavioral models. With this model, researchers can examine certain aspects of the model (such as how IS design can influence user feeling states or how users’ moods can impact their behavior), or conduct more comprehensive research using the entire model. This model can contribute to theory by providing a more complete picture of user behavior, and contribute to practice by helping mangers plan for desired outcomes

Taking “Fun and Games” Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM)

Hedonic-motivation systems (HMS)—systems used primarily to fulfill users’ intrinsic motivations—are the elephant in the room for IS research. Growth in HMS sales has outperformed utilitarian-motivation systems (UMS) sales for more than a decade, generating billions in revenue annually; yet IS research focuses mostly on UMS. In this study, we explain the role of intrinsic motivations in systems use and propose the hedonic-motivation system adoption model (HMSAM) to improve the understanding of HMS adoption. Instead of a minor, general TAM extension, HMSAM is an HMS-specific system acceptance model based on an alternative theoretical perspective, which is in turn grounded in flow-based cognitive absorption (CA). The HMSAM extends van der Heijden’s (2004) model of hedonic system adoption by including CA as a key mediator of perceived ease of use (PEOU) and of behavioral intentions to use (BIU) hedonic-motivation systems. Results from experiments involving 665 participants confirm that, in a hedonic context, CA is a more powerful and appropriate predictor of BIU than PEOU or joy, and that the effect of PEOU on BIU is fully mediated by CA sub-constructs. This study lays a foundation, provides guidance, and opens up avenues for future HMS, UMS, and mixed-motivation system research

The impact of affect on assessment of group decision support systems

This thesis discusses the role of affect in users' assessment of a collaborative system. A web-based multi-attribute group decision support system (iMade) is developed that is characterized by both its adaptability for a variety of decision-making situations, and the resulted utility due to applying design science principles in constructing this artifact. We present findings of a controlled experiment designed to assess influence of affect and various other factors on adoption of information systems using a research model based on Technology Acceptance Model (TAM). Although many researchers have extended the TAM model and studied various antecedents of using that system, no one has systematically studied the effect of group members' behavior and their interaction with one another on the evaluation of the system that they are engaged in. It is hypothesized that the system features by themselves are not the sole factors that affect the users' perception and their intention to use it; but, group interactions play an important role in the user's perceptions of the system (Etezadi-Amoli and Kersten, 2008, Etezadi, 2010). To test this phenomenon, a group decision problem was developed and an experiment was carried out using the iMade system. Thirty subjects assumed the role of three groups of stakeholders and negotiated along with two experimenters as a group, with the task of purchasing a fleet of taxicab; the experimenters were required to induce either positive or negative affect within the group. Analysis of the data clearly shows that group affect significantly influences users evaluation of the syste

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic

The Role of Cognitive Effort in Decision Performance Using Data Representations :;a Cognitive Fit Perspective

A major goal of Decision Support (DSS) and Business Intelligence (BI) systems is to aid decision makers in their decision performance by reducing effort. One critical part of those systems is their data representation component of visually intensive applications such as dashboards and data visualization. The existing research led to a number of theoretical approaches that explain decision performance through data representation\u27s impact on users\u27 cognitive effort, with Cognitive Fit Theory (CFT) being the most influential theoretical lens. However, available CFT-based literature findings are inconclusive and there is a lack of research that actually attempts to measure cognitive effort, the mechanism underlying CFT and CFT-based literature. This research is the first one to directly measure cognitive effort in Cognitive Fit and Business Information Visualization context and the first one to evaluate both self-reported and physiological measures of cognitive effort. The research provides partial support for CFT by confirming that task characteristics and data representation do influence cognitive effort. This influence is pronounced for physiological measures of cognitive effort while it minimal for self-reported measure of cognitive effort. While cognitive effort was found to have an impact on decision time, this research suggests caution is assuming that task-representation fit is influencing decision accuracy. Furthermore, this level of impact varies between self-reported and physiological cognitive effort and is influenced by task complexity. Research provides extensive cognitive fit theory, business information visualization and cognitive effort literature review along with implications of the findings for both research and practic