The affective response model: a theoretical framework of affective concepts and their relationships in the ICT context
01 Mar 2013-Management Information Systems Quarterly (Society for Information Management and The Management Information Systems Research CenterPUB1567Minneapolis, MN, USA)-Vol. 37, Iss: 1, pp 247-274
TL;DR: ARM is a theoretically bound conceptual framework that provides a systematic and holistic reference map for any ICT study that considers affect and has the power for explaining and predicting, as well as prescribing, potential future research directions.
Abstract: Affect is a critical factor in human decisions and behaviors within many social contexts. In the information and communication technology (ICT) context, a growing number of studies consider the affective dimension of human interaction with ICTs. However, few of these studies take systematic approaches, resulting in inconsistent conclusions and contradictory advice for researchers and practitioners. Many of these issues stem from ambiguous conceptualizations of various affective concepts and their relationships. Before researchers can address questions such as "what causes affective responses in an ICT context" and "what impacts do affective responses have on human interaction with ICTs," a theoretical foundation for affective concepts and their relationships has to be established.
This theory and review paper addresses three research questions: (1) What are pertinent affective concepts in the ICT context? (2) In what ways are these affective concepts similar to, or different from each other? (3) How do these affective concepts relate to or influence one another? Based on theoretical reasoning and empirical evidence, the affective response model (ARM) is developed. ARM is a theoretically bound conceptual framework that provides a systematic and holistic reference map for any ICT study that considers affect. It includes a taxonomy that classifies affective concepts along five dimensions: the residing, the temporal, the particular/general stimulus, the object/behavior stimulus, and the process/outcome dimensions. ARM also provides a nomological network to indicate the causal or co-occurring relationships among the various types of affective concepts in an ICT interaction episode. ARM has the power for explaining and predicting, as well as prescribing, potential future research directions.
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Cites background from "The affective response model: a the..."
...On the other hand, a contingent of IS scholars has advocated the study of emotions in IS research (see Ortiz de Guinea and Markus 2009; Zhang 2013), and researchers have begun to incorporate affect into established frameworks (Deng and Poole 2010; Sun and Zhang 2006a; Venkatesh 2000)....
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...Our work adds to a growing body of information systems research highlighting the role of emotions (Zhang 2013)....
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TL;DR: This article explored the effects of emotions embedded in a seller review on its perceived helpfulness to readers and found that over and above a well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is reader perceptions of reviewers' cognitive effort.
Abstract: This paper explores the effects of emotions embedded in a seller review on its perceived helpfulness to readers. Drawing on frameworks in literature on emotion and cognitive processing, we propose that over and above a well-known negativity bias, the impact of discrete emotions in a review will vary, and that one source of this variance is reader perceptions of reviewers' cognitive effort. We focus on the roles of two distinct, negative emotions common to seller reviews: anxiety and anger. In the first two studies, experimental methods were utilized to identify and explain the differential impact of anxiety and anger in terms of perceived reviewer effort. In the third study, seller reviews from Yahoo! Shopping web sites were collected to examine the relationship between emotional review content and helpfulness ratings. Our findings demonstrate the importance of examining discrete emotions in online word-of-mouth, and they carry important practical implications for consumers and online retailers.
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TL;DR: Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage.
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"The affective response model: a the..." refers background in this paper
...6.2 Attitude Chau and Hu 2001; Davis 1989; Davis et al. 1989; Venkatesh et al. 2003 An individual’s positive or negative feelings (evaluative affect) about performing the target behavior....
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TL;DR: In this article, the authors developed and validated new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance.
Abstract: Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions of these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited hgih convergent, discriminant, and factorial validity. Perceived usefulness was significnatly correlated with both self-reported current usage r = .63, Study 1) and self-predicted future usage r = .85, Study 2). Perceived ease of use was also significantly correlated with current usage r = .45, Study 1) and future usage r = .59, Study 2). In both studies, usefulness had a signficnatly greater correaltion with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecdent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.
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TL;DR: The Unified Theory of Acceptance and Use of Technology (UTAUT) as mentioned in this paper is a unified model that integrates elements across the eight models, and empirically validate the unified model.
Abstract: Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formulate a unified model that integrates elements across the eight models, and (4) empirically validate the unified model. The eight models reviewed are the theory of reasoned action, the technology acceptance model, the motivational model, the theory of planned behavior, a model combining the technology acceptance model and the theory of planned behavior, the model of PC utilization, the innovation diffusion theory, and the social cognitive theory. Using data from four organizations over a six-month period with three points of measurement, the eight models explained between 17 percent and 53 percent of the variance in user intentions to use information technology. Next, a unified model, called the Unified Theory of Acceptance and Use of Technology (UTAUT), was formulated, with four core determinants of intention and usage, and up to four moderators of key relationships. UTAUT was then tested using the original data and found to outperform the eight individual models (adjusted R2 of 69 percent). UTAUT was then confirmed with data from two new organizations with similar results (adjusted R2 of 70 percent). UTAUT thus provides a useful tool for managers needing to assess the likelihood of success for new technology introductions and helps them understand the drivers of acceptance in order to proactively design interventions (including training, marketing, etc.) targeted at populations of users that may be less inclined to adopt and use new systems. The paper also makes several recommendations for future research including developing a deeper understanding of the dynamic influences studied here, refining measurement of the core constructs used in UTAUT, and understanding the organizational outcomes associated with new technology use.
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