Post on 08-Jan-2023
MISQ Forthcoming Zhang, ARM
1
The Affective Response Model: A Theoretical Framework of Affective Concepts and Their Relationships in the ICT Context
This is a pre-publication version of the paper as of 2012.05.01. Suggested citation is:
Zhang, P. (Forthcoming), The Affective Response Model: A Theoretical Framework of Affective Concepts and their Relationships in the ICT Context, MIS Quarterly.
Ping Zhang
School of Information Studies, Syracuse University, Syracuse, NY 13244 U.S.A.
pzhang@syr.edu
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 constructs 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
MISQ Forthcoming Zhang, ARM
2
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.
Keywords: Affect, emotion, mood, affective response, affective evaluation, affective quality,
individual reactions toward ICT, theory, Affective Response Model, ARM
MISQ Forthcoming Zhang, ARM
3
Introduction Affect is conceived of as an umbrella term for a set of more specific concepts that includes
emotions, moods, and feelings (Bagozzi et al. 1999; Liljander and Mattsson 2002; Russell 2003).
Affect is a fundamental aspect of being human, playing an integral role in human motivation
(Reeve 2005), influencing reflexes, perceptions, cognitions, social judgments, and impacting
various behaviors (Brief 2001; Forgas 1995; Forgas and George 2001). Evidence in
organizational behavior, marketing, social psychology, management, and information systems
has confirmed affect as a strong determinant of job satisfaction (Weiss et al. 1999), decision-
making behavior (Mittal and Ross 1998), consumer shopping behavior (Childers et al. 2001),
creative problem-solving (Isen et al. 1987), and attitude change or persuasion (Petty et al. 2001).
These studies suggest that affect can explain a significant amount of variance in one’s cognition
and behavior, and can even have more explanatory power than cognition under certain
circumstances. Contrary to the widespread assumption that affective reactions are inherently
subjective, contextually labile, and thus unreliable, studies find that people often exhibit greater
similarity in affective reactions than in reason-based or cognitive assessments (Pham et al. 2001).
As such, understanding affect, its causes, and its effects, is important both for its own sake, and
for understanding other mainstream concepts and issues studied in various disciplines, including
those concerned with ICTs. A robust understanding of affect may also have practical
implications for design, acceptance, use, and management of ICTs.
Affect-related phenomena and concepts have been studied since the early days of the
information systems (IS) discipline. Examples include attitude and satisfaction, which are still of
interest to scholars (Hess et al. 2006; Zhang and Sun 2009). However, the intensity of interest in
affective concepts and phenomena has fluctuated over the decades, largely due to inconsistent
MISQ Forthcoming Zhang, ARM
4
and inconclusive results, echoing similar patterns in psychology and other disciplines. In recent
years, IS and human-computer interaction (HCI) studies have broadened the coverage of
affective concepts in studying human interaction with ICTs1. For example, Norman has shifted
from usability-centric design (1992; 1993; 1983; 1988) to emotional design (2002; 2004;
Norman et al. 2003). Studies have focused on a number of affective topics including emotional
usability (Kim et al. 2003), affect in organizational communication behavior (Te'eni 2001),
affective reactions toward information technologies (Zhang and Li 2004; Zhang and Li 2005),
emotion on information technology use (Beaudry and Pinsonneault 2010), mood in IS usage
(Loiacono and Djamasbi 2010), affective user interface (Johnson and Wiles 2003; Lisetti and
Nasoz 2002), computer playfulness (Webster and Martocchio 1992), flow in computer mediated
environments (Finneran and Zhang 2003; Finneran and Zhang 2005; Ghani 1995; Koufaris
2002), cognitive absorption (Agarwal and Karahanna 2000; Zhang et al. 2006), fun (Carroll and
Thomas 1988) and funology (Blythe et al. 2005), hedonic quality (Hassenzahl 2001), among
others (Sun and Zhang 2006a; Thatcher and Perrewe 2002; Venkatesh 2000).
As ICT use has grown beyond organizational and work contexts into nearly every facet of
our lives, interest in affect is particularly timely and relevant as we seek to understand
individuals’ use and exploration behaviors. Users’ choices of ICTs have grown tremendously,
with many competing ICT products and services available for similar purposes. For example,
when choosing a mobile phone, one considers more than just usability, functionality, and
reliability. The final decision is likely based on both cognitive factors (such as price, service,
features, and usability) and affective factors (how cute the product is, how unique it is from
others, or how it makes one feel). Such technological changes have prompted researchers to
1 In this paper, we consider ICT broadly, including technologies for personal, organizational, and societal communication, information processing, learning, entertaining, and other purposes.
MISQ Forthcoming Zhang, ARM
5
challenge the cognitive-dominant paradigm in studying individual reactions toward ICTs
(Agarwal and Karahanna 2000; Beaudry and Pinsonneault 2010; Loiacono and Djamasbi 2010;
Sun and Zhang 2006a; Zhang and Li 2004; Zhang and Li 2005).
The majority of affect-related empirical studies within the ICT context have focused on the
causes and/or effects of affect (for reviews, see Brave and Nass 2003; Sun and Zhang 2006a).
Yet, many studies showed inconsistent and inconclusive findings. As we will demonstrate in
greater detail in this paper, there is a lack of agreement on the names, labels, meanings,
connotations, and even measures of affective concepts. Also realized is the practice of adding
various measures (such as affective and emotional) to certain ICT phenomenon but lacking
specific theories tailored for affect/emotion in the ICT context (Bagozzi 2007). Although
affective studies in psychology have established certain consensus, the ICT literature is largely
disconnected from this consensus and exhibits a lack of systematic examination of affective
phenomena that may involve relationships among affective concepts. Compared to the broad
coverage of the cognitive and behavioral aspects of ICT interaction, only a small number of
studies consider more than one affective construct in the same study (see Appendix A for detail),
and an even smaller number of studies provide examinations of the relationships among affective
constructs (see Appendix B for detail). All these gaps indicate a lack of systematic and
theoretical examination of affective phenomena, concepts and their relationships in the ICT
context. Such lack of research attention may be a major contributing factor to the inconclusive
findings. As a result, research progress is hindered, and future research directions become
indecisive. Given the importance of, and increased interest in the affective dimension of human-
ICT interaction, theoretically-grounded systematic analyses of affective concepts must be
established before one can investigate the causes or impacts of affective responses in the ICT
MISQ Forthcoming Zhang, ARM
6
context.
In this paper, our grand research question is: What are the affective concepts in the ICT
context? This question can be further examined along three detailed aspects:
1. What are pertinent affective concepts (not just terms or labels) 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?
Answers to these questions are timely, interesting and important to the IS community. With
an increasing interest in affect in the ICT context, scholars need to be aware of the issues in the
current literature, and be guided with theoretically bounded foundations, frameworks and
research directions. The Affective Response Model developed in this paper provides a
theoretically bounded framework to understand the intrinsic meanings of all types of affective
concepts, their similarities and differences, and how they may relate or influence each other in an
ICT interaction episode. As a framework, ARM explains and predicts certain findings in the
literature, depicts gaps and holes, synthesizes and unifies existing studies on particular affective
concepts and their relationships, and prescribes future research efforts and directions.
To remain focused, this study’s scope is set by the following considerations. First, since the
ICT literature has extensively covered cognitive reactions, this paper mainly covers affective
concepts, although at times we discuss cognitive and behavioral concepts briefly to better explain
affective concepts. Second, we avoid detailed discussions about the causes and consequences of
affect unless such discussions help explain what affective concepts are. Third, as we are
interested in one’s direct experience with an ICT, we rule out studies or situations that may focus
on managers or executives who express satisfaction or other affective responses to ICTs (such as
ERP implementation) that are not based on direct or hands-on experience with ICTs.
MISQ Forthcoming Zhang, ARM
7
The paper proceeds as follows. In the next section, we introduce briefly a set of basic
affective concepts from the psychology and social sciences literature to provide a foundation for
ICT-specific affective concepts that appear in the Affective Response Model (ARM). In the
following section, we focus on the first part of ARM: a taxonomy of affective concepts. Based
on extensive literature reviews of empirical studies that include affective constructs in the ICT
context, as well as theoretical reasoning and justification, the taxonomy contains five dimensions
for conceptualizing various types of affective responses. The taxonomy section is followed by
the section on the second part of ARM where a set of propositions that describe the relationships
among various types of affective concepts, resulting in a nomological network. We conclude the
paper with discussions, limitations, theoretical and practical implications, and future research
directions.
Basic Affective Concepts in Psychology & Social Sciences Despite diverse perspectives, approaches, theories and findings on affect and related
phenomena, recent developments in psychology and social sciences have achieved a level of
consensus on both the meanings and structures of various affective concepts (Barrett and Russell
1999; Rosenberg 1998; Russell 2003; Russell 2009; Watson and Clark 1994; Watson et al. 1999;
Weiss and Cropanzano 1996). In general, it is widely recognized that the term affect is an
umbrella term that represents a set of concepts that can be very different from each other (Russell
2003) and that affect-related phenomenon is much broader than emotions (Russell 2009). In this
paper, we do not intend to and cannot provide a comprehensive review of all views and works on
affect in psychology and social sciences. Rather, we plan to build our framework on several
pertinent concepts and theories in the psychology and social sciences literature. Specifically, we
introduce the two most fundamental concepts, core affect and stimulus, and then a set of basic
MISQ Forthcoming Zhang, ARM
8
affective concepts such as perceived affective quality, affective quality, affective cue, mood,
temperament, emotion, and attitude. We will also briefly introduce the structure of affect as it is
used to define what concept is considered an affective concept.
Core Affect and Stimulus: the Fundamentals
Among all affective concepts, the most fundamental is core affect (Barrett et al. 2007;
Russell 2003; Russell 2009). Supported by neuroanatomical evidence, core affect is considered
an intrinsic aspect of consciousness (Barrett et al. 2007). It is a neurophysiological state
consciously accessible as a simple, non-reflective feeling that is an integral blend of hedonic or
valence value (pleasure–displeasure, the extent to which one is generally feeling good or bad)
and arousal or activation value (sleepy–activated, the extent to which one is feeling engaged or
energized) (Russell 2003). As consciously experienced, core affect is mental but not cognitive or
reflective (Russell 2009; Zajonc 2000). It is primitive, universal, and ubiquitous, existing without
being labeled, interpreted, or attributed to any cause (that is, Object free or free-floating). An
analogy of core affect’s existence is one’s felt body temperature: you can notice it whenever you
want (Russell 2003). An analogy of core affect as a single feeling is color, where dimensions of
hue, saturation, and brightness combine in an integral fashion to form one unified sensation of
any particular color (Russell 2009). To varying degrees, core affect is the core of all emotion-
laden events, is involved in most psychological events, and is what makes any event “hot” (i.e.,
emotional or affective) (Russell 2003). Core affect was referred to by other names in the past,
such as affect or basic affect (Batson et al. 1992; Watson et al. 1988) and mood (Morris 1989,
based on early conceptualization of mood). These references thus should be read carefully in
light of the new conceptual consensus.
Like body temperature, core affect can change due to individual genetic differences, internal
MISQ Forthcoming Zhang, ARM
9
factors (e.g., activities of immune cells and hormone changes), and external factors (e.g., a bear
caused Alice’s core affect to change from tranquility to distress) (Russell 2003; Russell 2009).
Change in core affect can be caused by drugs (e.g., stimulants and depressants), or by a
combination of events. Some causes are obvious and yet sometimes one may go through a
change in core affect without knowing why. The key is that people have either no direct access to
these causal connections or limited ability to track complex causal connections. Instead, a person
makes attributions. Through attribution, core affect can become directed at an Object/a stimulus
(Russell 2003).
Stimulus is defined as something or some event in one’s environment that a person reacts or
responds to. Stimulus is a psychological representation; it can be real, imagined, fictitious ,
remembered, in the future or anticipated, or in other forms of virtual reality (Russell 2003).
Stimuli enter consciousness being affectively interpreted (Russell, 2003). This leads to a number
of affective concepts that concern the subject (the person) or/and the object (the stimulus). These
are briefly introduced below.
Basic Affective Concepts
Table 1 provides a summary of the fundamental and basic concepts in the psychology
literature that play a role in both the development of other affective concepts and the
relationships among affective concepts in the ICT context. Each basic affective concept is
discussed in detail below.
**** Insert Table 1about here ****
Perception of affective quality (PAQ) is an individual’s perception of a stimulus’ ability to
change his or her core affect. It is a perceptual process that estimates the affective quality of the
stimulus. It begins with a specific stimulus and remains tied to that stimulus (Russell 2003), but
MISQ Forthcoming Zhang, ARM
10
it varies by individual. Different people may perceive the same stimulus to have different
affective qualities (thus the notion: beauty is in the eye of the beholder). Perception of affective
quality is considered a ubiquitous and elemental process (Cacioppo et al. 1999; Russell 2003;
Zajonc 1980). It is important to note that perception of an affective quality per se is a “cold”
(meaning cognitive/logical) perceptual process (made “hot” [meaning affective/emotional]
through its linkage to core affect) that fixes a belief about the affective consequences of
something without having to undergo those consequences; it is the anticipation (or
representation), rather than the experience of a change in core affect. For example, a change in
core affect is not needed in order to know that a garden is lovely (Russell 2003). The concept of
perception of affective quality was called by other names in the past, such as evaluation,
automatic evaluation, affective judgment, appraisal, affective reaction, and primitive emotion
(Russell 2003). These names all touched upon some aspects of PAQ but may also introduce
confusions when being referenced without considering the new consensus in conceptualization.
Closely related to but different from perception of affective quality is the concept of affective
quality. Affective quality is the affective property of a stimulus that has the ability to cause a
change in a person’s core affect (Russell 2003). Whereas core affect exists as a feeling inside
oneself (it is Alice who feels upset), affective quality exists in the stimulus (it is the bear that is
upsetting) (Russell 2003). Objects and events all have their own affective qualities (regardless
who perceive them), just as they have other function or performance related qualities that can be
perceived and judged as being relative to other similar stimuli. For example, films and role
playing are frequently used to elicit certain feelings in people because of these stimuli’s strong
affective qualities.
Affective cues are specific features or characteristics of a stimulus that can manifest the
MISQ Forthcoming Zhang, ARM
11
affective quality of the stimulus. Continue the examples from the last paragraph: it could be the
size or the look of the bear that makes it upsetting. Thus size, look, and other features of a
stimulus function as affective cues that manifest the stimulus’ affective quality. Affective cues
have been studied as environmental cues or signals containing affective information that can
influence emotions (Valdez and Mehrabian 1994) and cognitive processing strategies (Soldat et
al. 1997). In the literature, colors (Soldat et al. 1997), music (Scherer 2004), images (Chowdhury
et al. 2008), and visual presentations (Park et al. 2008) are found to be distinctive affective cues.
Mood is defined as prolonged core affect that has an unclear or unknown stimulus (Russell
2003), thus is not tied to a specific event, situation, or behavior (Lazarus 1991; Moore and Isen
1990; Watson and Clark 1994). Temperament is characteristic of a habitual inclination or mode
of affective response that can be a result of affective traits (a stable individual difference in the
tendency to experience a corresponding mood state) (Watson and Clark 1994).
One of the most complex affective concepts is emotion. Put simply, emotions are induced
affective states (Clore and Schnall 2005), or core affect attributed to stimuli (Barrett et al. 2007;
Russell 2003). Emotions typically arise as reactions to situational events in an individual’s
environment that are appraised to be relevant to his/her needs, goals, or concerns. Once
activated, emotions generate subjective feelings (such as anger or joy), generate motivational
states with action tendencies, arouse the body with energy-mobilizing responses that prepare the
body for adapting to whatever situation one faces, and express the quality and intensity of
emotionality outwardly and socially to others (Damasio 2001; Izard 1993; Reeve 2005). Emotion
is thus formally defined as “an episode of interrelated, synchronized changes in the states of all
or most of the five organismic subsystems in response to the evaluation of an external or internal
stimulus event as relevant to major concerns of the organism” (Scherer 2005, p697). Emotional
MISQ Forthcoming Zhang, ARM
12
episodes have been modeled to understand various elements of emotions and emotional
experiences (Russell 2003; Scherer 2005; Weiss and Cropanzano 1996). This is a concept we
will revisit and build on later in the paper.
A very special affective concept is attitude. Attitude is one of the most studied concepts on
judgments and behaviors in social psychology, and thus has numerous definitions from many
social science disciplines, including Information Systems. To remain focused, this study uses the
generally agreed upon definition of attitude as a summative evaluation of a stimulus that may
help guide behavior toward the stimulus (Cacioppo and Berntson 1994; Crites et al. 1994; Fazio
1986; Petty et al. 1997; Zanna and Rempel 1988). Attitude can be considered as either a
multidimensional construct comprised of cognitive, affective, and behavioral components, or a
two dimensional construct with instrumental (mostly cognitive) and experiential (mostly
affective) aspects (Ajzen and Fishbein 2005). Similar to some of the previous studies that treat
attitudes as possibly an affect (Bagozzi et al. 1999), we consider attitude a special type of
affective concept by focusing on its affective component, at the same time, note its summative
nature. This is largely consistent with many conceptual treatments of attitude in the IS literature
(e.g., the TAM studies, Davis 1993; Davis et al. 1989).
The Structure of Affect
Besides conceptualization, the structure and measurement of affect also has emerging
consensus. It is generally agreed that affect can be represented by a circumplex model (Russell
1980; Schlosberg 1941; Schlosberg 1952; Watson et al. 1999; Yik et al. 1999), one of the most
widely studied affect models (Barrett and Russell 1999; Remington et al. 2000). This model
includes two independent dimensions: the pleasure dimension (or the valence dimension), which
ranges from one extreme (e.g., misery, agony) to another (e.g., ecstasy), and the arousal
MISQ Forthcoming Zhang, ARM
13
dimension (or the activation dimension), which ranges from sleepiness/deactivation to activation
(Russell 2003). Figure 1 depicts the affect structure, which can be used as a base to measure all
affective constructs including emotions, moods, temperaments, and affective evaluations.
Variations of the circumplex model have been investigated and tested empirically, further
validating the basic structure of affect (Scherer 2005; Watson and Clark 1984; Watson et al.
1999).
**** Insert Figure 1 about here ****
A Taxonomy of Affective Concepts in the ICT Context We now attend to the ICT literature to review disconnection between the efforts and
findings in the ICT context to the consensus in the psychology and social sciences literature and
to review some inconsistent definitions and treatments of affective concepts (Norman 2002; Sun
and Zhang 2006a). Such a review will lead to the development of a taxonomy where
fundamental and basic affective concepts introduced earlier will guide the understanding of ICT-
specific affective concepts. We consider a concept to be affective if it touches on any of the two
dimensions of the circumplex model. We are particularly interested in two types of stimuli: ICT
(as an object), and interacting with ICT (as a behavior). To illustrate the main ideas of various
ICT-specific affective concepts, we start this section with one of many possible scenarios of
humans interacting with ICT. The scenario will be revisited throughout the paper to illustrate
various concepts and ideas. In each of the following subsections, we first review the ICT
literature to point out certain gaps, then introduce one dimension of ARM to address the gaps.
The ARM taxonomy is finally summarized in the last sub section.
MISQ Forthcoming Zhang, ARM
14
A Scenario
Imagine a person named Alex. Alex usually does not get easily over-excited about novel
things in his surroundings. He does not like playing computer games in general, yet tends to like
colorful things. One day, while passing by an electronics store in a calm mood, Alex was
attracted to a set of sharp, colorful and dynamic screen displays of a game. He said to himself,
“Wow, that is cool!” He stepped in and started exploring the game. Soon, he felt engaged,
stimulated, playful, and overall was having a lot of fun. Alex was really enjoying himself without
realizing the passing of time. Once he finished the exploration, he was thinking: “Playing this
game was really engaging and enjoyable.” As a result of this experience, Alex concluded that
“This is a really cool game that is well designed, and I liked playing it.” Then, he wondered that
maybe “playing computer games is not such a bad idea.”
The scenario can be considered an emotional episode. It contains various categories of
affective concepts that can be represented by the ARM taxonomy, which consists of five
dimensions to compare and differentiate various affective concepts: the residing, the temporal,
the particular/general stimulus, the object/behavior stimulus, and the process/outcome
dimensions.
The Residing Dimension
Most studies that contain affective constructs in the ICT literature are concerned with ICT-
related stimuli, such as computers, software applications, websites, GSS (group support
systems), mobile data services, e-vendors, and instant messengers, as well as behaviors on such
objects including viewing, using, interacting, and working with them. For example, in our
scenario, stimuli are “computer games” and “playing computer games.” In the literature,
“microcomputer playfulness” is defined as the degree of cognitive spontaneity in microcomputer
MISQ Forthcoming Zhang, ARM
15
interactions (Webster and Martocchio 1992, p211). It emphasizes a user’s feelings that arise
while interacting with computers. The meaning of this concept is tied to both the person and
“computer interactions.” Here, “computer interactions” cause the person to feel a certain way,
thus the stimulus.
Some affective constructs do not concern a particular stimulus. For example, “negative
affectivity” is defined as the general experience of negative emotions such as guilt or shame
regardless of the situation (Thatcher and Perrewe 2002). Thus, “negative affectivity” occurs
without stimuli. “Perceived mood” of a feedback giver (Ang et al. 1993) is measured with the
following: “looked as if he had had a bad day,” “seemed to be in a good mood today,” and
“looked as if he did not want to be disturbed.” Thus perceived mood is not concerned with a
particular stimulus.
When studying affective concepts, similarly named constructs have been found to differ in
referring to stimuli or not. For example, the above mentioned “perceived mood” (Ang et al.
1993) has no stimulus, while “positive mood” (Webster and Martocchio 1992) relates to how
participants feel during a (computer) training session. In this case, the training session is the
stimulus because positive mood is induced by or reflective of the training session. Another study
defines “mood” as a state variable that refers to how people feel when they are engaged in any
number of activities (Venkatesh and Speier 1999). In this case, its cause is clearly identified
(“engaging in a number of activities”). Such different conceptualizations need to be examined on
whether a stimulus is involved or not when an affective concept is concerned.
Categorizing Affective Concepts along the Residing Dimension
The need to differentiate where the meanings reside can go back to some of the heated
philosophical debates on subjectivity and objectivity in human perception and interaction with
MISQ Forthcoming Zhang, ARM
16
objects in the environment. Such debates can be found in many disciplines and on various
concepts such as perception, consciousness, affect and aesthetics, to name a few. On the affect
front, the related debate is concerned with the distinction between subject (the person) and object
(the stimulus). Fortunately, the debate has reached certain consensus in that certain concepts are
about subject (the person) due to their insistent self-reference implicit in feeling (thus the states
of the subject such as “no pleasure - extreme pleasure”). Other concepts are about measures of
hedonic value of a stimulus (such as “pleasing – displeasing”) (Batra and Ray 1986; Mehrabian
and Russell 1974). It is beyond the scope of this paper to go deeper into these debates and
consensus. In ARM, we propose that affective concepts can be examined by where their
meanings exist and grouped into three categories accordingly. Table 2 outlines these categories
and lists relevant affective concepts according to the residing dimension. Core affect is not listed
in the table because it is the basis to all affective concepts and is something that makes a concept
affective in nature. For this reason, core affect is not treated in parallel with other affective
concepts, and thus is not listed in Table 2 (and some other tables).
**** Insert Table 2 about here ****
1. Residing within a person without a stimulus
The meaning of an affective concept can reside within a person, either without a clear
stimulus or regardless of stimuli. For example, in our scenario, “Alex usually does not get overly
excited easily about novel things in his surroundings” is a temperament. Its value has little to do
with any stimuli. Another example would be moods, which are specific affective states without
any stimuli (especially ICT related stimuli), the stimulus cannot be clearly identified. In general,
moods and temperaments are not dedicated to specific objects: cheerful moods and cheerful
temperaments may make things seem positive in general (Clore and Schnall 2005, pp. 438).
MISQ Forthcoming Zhang, ARM
17
2. Residing within an ICT related stimulus
An affective concept can be purely about an ICT-related stimulus, regardless of who
perceives or interprets it. In this case, it is an objective attribute or property of the stimulus, such
as an ICT’s specific design features or attributes. This category includes affective quality and
affective cues. Affective cues manifest affective quality and can be directly perceived by a
person. For example, in our scenario, affective cues are “a set of sharp, colorful and dynamic
screen displays” that attracted Alex.
3. Residing between a person and an ICT related stimulus
These affective concepts can be about a person’s responses to a stimulus and thus
considered to be between a person and a stimulus. For the same stimulus, different people may
have different responses; while for the same person, different stimuli may generate different
responses. To build on the distinction between self-reference in induced feelings (directed
towards subject) and evaluative scales on measuring the hedonic value of a stimulus (directed
towards object) (Batra and Ray 1986), we consider two types of affective responses: emotions
(induced by stimuli) and evaluations of affective quality of stimuli. For example, “I feel excited
(by playing the new computer game),” “I am anxious (about computers),” and “I am bored (by
this application),” are emotions within the ICT context because these are directed towards the
person’s feelings. “This interface is pleasing” and “playing this game can be exciting” are
evaluations of affective qualities of the game because these are directed toward the stimulus’s
affective properties.
Affective evaluation is a general term for a set of concepts whose focus is on evaluating or
appraising the affective quality of a stimulus. Its meaning resides between a person and a
stimulus with the meanings towards the stimulus (“I think the object is pleasing”), not toward the
MISQ Forthcoming Zhang, ARM
18
person (“I am pleased by the object”). The person will determine the specific value of an
affective evaluation based on his or her appraisal of the stimulus’ affective quality. Affective
evaluations can be primitive (such as perceived affective quality), cognitively latent (such as
perceived enjoyment of using an application), or experientially heavy (such as satisfaction with a
decision support system). Affective evaluations happen all the time (with or without
accompanying emotions) because stimuli enter our consciousness affectively interpreted
(Russell, 2003).
Affective response, also known as affective reaction (AR), is a broader term to include both a
person’s emotions induced by a stimulus and affective evaluations of the stimulus. In other
words, affective response/reaction can be both emotion and affective evaluations.
Affective responses are of particular interest to ICT researchers due to their focus on the
interaction and intersection between humans and ICTs. Among affective responses, affective
evaluations need even more attention. There has been much confusion in the literature regarding
affective concepts in this category, prompting the need to clarify. As such, affective evaluation
concepts will be heavily attended to by the taxonomy.
The Temporal Dimension
The affective concepts that reside within a person or between a person and a stimulus can be
examined by whether their values are constrained by time.
There are several studies in the ICT literature examining the trait vs. state nature of certain
affective concepts. For example, a recurring question in the study of “computer anxiety” is
whether it is a relatively stable personality trait or a mutable temporary state (Beckers et al.
2007). Interestingly, this question regarding temporal nature can be applied to a number of
affective concepts. Specifically, an affective concept might refer to a stable value that can be
MISQ Forthcoming Zhang, ARM
19
applied in similar situations consistently, or it might refer to a temporary value that is gone once
conditions or the environment change. For example, “cognitive absorption” is studied as a state
of deep involvement with IT (Agarwal and Karahanna 2000). It emphasizes how participants feel
at a given moment when they encounter an IT. Thus, it is a state variable. In one study,
“perceived playfulness” is regarded as an individual’s state, because an individual can feel more
or less playful at various points during his/her visit to a web portal (Lin et al. 2005). In another
study, “playfulness” is the pleasure and inherent satisfaction derived from a specific activity and
is regarded as a personality trait (Venkatesh 1999). Yet in another study, “computer playfulness”
refers to an individual’s tendency to interact spontaneously with a computer and is considered a
system specific trait from using the specific system over time (Hackbarth et al. 2003); therefore it
is not a state in particular, not a personality either. Such inconsistencies in the literature lead to
the need to examine the temporal aspect of these and all affective concepts.
Categorizing Affective Concepts along the Temporal Dimension
Attributions for affect can be constrained by the duration of affective conditions. The
temporal/duration constraint (Clore and Schnall 2005; Clore et al. 2001) has been used to help
understand the connections and differences among several important affective concepts such as
mood, emotion and attitude. Clore and Schnall (2005) explain that: “moods and emotions are
ephemeral and cannot be stored [in memory]. Whatever evaluative information they carry is
temporally constrained, existing only as long as the supporting cognitions, perceptions, or other
elicitors are active, and vanishing as soon as one is no longer in that state. The same is not true
for attitudes, because attitudes are not evaluative states, but evaluative tendencies, that do not
necessarily vanish when one stops thinking about the attitude object” (Clore and Schnall 2005,
pp. 438). Regarding moods and temperaments, “the evaluative inclinations of moods are
MISQ Forthcoming Zhang, ARM
20
momentary or constrained by time. In contrast, evaluation inclinations based in temperament are
neither object-specific nor temporally-specific. Thus one can be said to have a cheerful
temperament, even if one is momentarily cheerless” (Clore and Schnall 2005, pp. 438).
Affective evaluations are about something specific and the values of the evaluations do not
disappear when one stops thinking about the specific object. In this regard, affective evaluations
are very similar to attitudes in Clore et al.’s discussion. As we have discussed, attitude can be
considered a type of affective evaluation if the focus is on its affective component.
Table 3 applies the temporal dimension to categorize the concepts that reside within a
person or between a person and a stimulus (affective cue and affective quality reside within a
stimulus thus are not included). In our scenario, “Alex does not like playing computer games in
general” and “Alex tends to like colorful things” are not temporally constrained, they are
dispositions or tendencies, and both have to do with some stimulus (computer games, or colorful
things). “Alex felt engaged, stimulated, playful, and overall was having a lot of fun” is
temporally constrained; the state would be true for that moment but would disappear once Alex
stopped playing and walked away from the store. Yet, “Playing this game was really engaging
and enjoyable” as an affective evaluation does not disappear when Alex stopped playing the
game. He would remember this and be able to recall it later without experiencing the specific
emotions he had during play. Thus this affective evaluation is not temporally constrained.
**** Insert Table 3 about here ****
Two Dimensions for Stimulus Specificity: Object vs. Behavior, Particular vs. General
The stimulus, especially if ICT-related, can be further examined from two aspects: object vs.
behavior, and particular vs. general.
MISQ Forthcoming Zhang, ARM
21
Computer anxiety is defined as the degree of apprehension, or even fear, when an individual
is faced with the possibility of using computers (Venkatesh and Bala 2008). Thus, the stimulus
should be “using computers,” which is a behavior on objects. Computer anxiety has been
measured by: (1) Computers do not scare me at all; (2) Working with a computer makes me
nervous; (3) Computers make me feel uncomfortable; and (4) Computers make me feel uneasy
(Venkatesh and Bala 2008, p313). A careful examination of these items shows that only the
second item explicitly has to do with using computers, while the other three all have to do with
computers themselves. One could argue that “computers make me feel uncomfortable” might
have a high correlation with “using computers makes me feel comfortable.” It should be noted,
however, that “computers” and “using computers” are two different kinds of stimuli. It is
possible for one to state that “I am not scared of computers but I am scared of using computers.”
The literature shows that some affective concepts tend to be associated with an object, while
others tend to be associated with behaviors. For example, “first impression of a website”
(Schenkman and Jonsson 2000), “identification quality of images” (Hassenzahl 2004), and
“perceived affective quality of a website” (Zhang and Li 2004), are about ICTs (objects); while
“(perceived) enjoyment of using a computer” (Chin and Gopal 1995; Yi and Hwang 2003) and
“(perceived) playfulness of using a computer” (Igbaria et al. 1996; Venkatesh 1999) are about
behaviors with ICTs. The confusion between object-based and behavior-based evaluations, and
the importance of differentiating such evaluations, are articulated in both social psychology
(Ajzen and Fishbein 2005) and the ICT (Zhang et al. 2008; Zhang and Sun 2009) literature.
Intuitively, it seems that studying ICT use as a stimulus is more relevant than studying ICTs
themselves as a stimulus. Yet, given that ICTs compete for users’ attentions, many of them will
not even get a chance to be acted upon if they do not pass the object-based level of pure exposure.
MISQ Forthcoming Zhang, ARM
22
Hence we argue that object-based evaluations are an important part of ICT research.
A person’s affective responses to a particular stimulus may be different from the person’s
affective responses to a general stimulus. For example, “I enjoy using this decision support tool”
(using a particular ICT) is different from “I enjoy using decision support tools” (using a general
ICT, or a type of ICT). “Using mobile internet services is enjoyable” (Thong et al. 2006) is about
a general stimulus, whereas “using (this) website is enjoyable” (Heijden 2004) is about a
particular stimulus. Therefore, confusion and/or less consistent results may occur when
researchers treat these two affective responses equally or make inference about one based on the
other.
Categorizing Affective Concepts along the Two Stimulus Specificity Dimensions
Recent developments in attitude theories assert that Attitude Toward Object (ATO) is
defined as a psychological tendency expressed by evaluating a particular entity with some degree
of favor or disfavor (Eagly and Chaiken 1998), or as a combination of evaluative judgments
about an object (Crites et al. 1994). Attitude Toward Behavior (ATB) is defined as an
individual’s positive or negative feelings (evaluative affect) about performing the target behavior
(Fishbein and Ajzen 1975). The same distinction can be applied to affective evaluations. For
example, one can say “this website looks attractive” (toward object) or “using this application is
enjoyable” (toward behavior).
The argument for distinguishing the general vs. particular stimuli is in line with social
judgment research. Attitude studies, for instance, have established that an attitude toward a
particular object may not always be the same as the attitude toward a class of similar objects
(Ajzen and Fishbein 2005; Reeve 2005). For example, one may have a negative attitude toward
certain groups of people (either classified by geographical locations, ethnicities, or political
MISQ Forthcoming Zhang, ARM
23
views), while this same person may have a positive attitude toward a particular individual from
that group. “I like hard working people” does not translate into “I like John who is hard
working.” Similarly, “I like John who is hard working” does not necessarily imply, “I like hard
working people.” In fact, stereotypes (a general attitude that may be formed with a bias) may be
corrected or adjusted by one’s encounters with particular individuals who challenge the accuracy
of those stereotypes. Thus, it is necessary to explicitly state whether an affective concept is about
a general stimulus or a particular one. In the ICT context, a statement such as “Apple products
look attractive” refers to a person’s affective responses to a group of objects (such as iPods,
iPads, iPhones, or other Apple products). This is about the same as saying, “Apple products in
general look attractive.” It should be noted that this person may have a different and specific
affective evaluation of a particular new Apple product, such as an Apple laptop. When a study is
concerned with a particular ICT as an object, the affective responses should be related to that
particular ICT. Likewise, if the focus of the study is on a type of ICTs, then the affective
responses should be about that type of ICTs (Zhang et al. 2008; Zhang and Sun 2009).
The differences between object and behavior, and between general and specific can be
illustrated with the scenario. “Alex does not like playing computer games in general” is an
affective evaluation about behaviors (playing) toward a general type of objects (computer
games); while “Alex likes colorful things” is an affective evaluation of a general type of objects
(colorful things). “This is a really cool game that is well designed” is an affective evaluation of a
specific object (cool game).
The Process vs. Outcome Dimension
Several studies in the ICT context reveal the need to distinguish process-based affective
evaluations from outcome-based ones. For example, the “first impression,” “visual appeal” or
MISQ Forthcoming Zhang, ARM
24
“immediate aesthetic perception” a person gets from an initial encounter with an ICT can be as
short as a fraction of a second (Lindgaard et al. 2006; Tractinsky et al. 2006). “Perceived
enjoyment,” the extent to which the activity of using the computer is perceived to be enjoyable in
its own right, apart from any performance consequences that may be anticipated (Davis et al.
1992, p. 1113), seems to focus on the duration of the interaction. “Affective award,” the sense of
emotional gratification often expressed by participants after a successful meeting (Reinig et al.
1996), indicates that it is about a conclusion or summary of the session. Hassenzahl (2004)
separates perceptions of a product’s attributes from their evaluations, positing that “perceptions
of hedonic or pragmatic attributes can potentially lead to a positive evaluation but they must not
necessarily do so (pp. 322-323).” In this case, “perceptions of hedonic or pragmatic attributes”
would be process-based, and “positive evaluation” would be outcome-based. In addition,
Hassenzahl makes a distinction between “substantive” (relating to the essence or substance) and
“verdictive” (relating to a final judgment or decision that is a high-level evaluation). Such
distinctions imply that there may be different levels of processing involving different information
sources and different information processing intensities. This leads to the need to examine the
process vs. outcome dimension.
Categorizing Affective Concepts along the Process vs. Outcome Dimension
Action identification theory posits that any action or behavior can be identified in many
ways, ranging from low-level identities that specify how the action is performed to high-level
identities that signify why or with what effect the action is performed (Vallacher and Webner
1987). The theory has been used in the ICT literature with illustrative examples: low-level
identity could be striking keys; higher-level identity could be writing a report, and highest-level
could be achieving one’s annual sales goal (Davis and Venkatesh 2004). Along with the different
MISQ Forthcoming Zhang, ARM
25
levels of identities of actions, there are cognitive perceptions and evaluations such as perceived
ease of use (low-level or process-based focusing on means and tactics that relate to concrete
behavioral actions such as hands-on system usage) and perceived usefulness (high-level or
outcome-based relating more closely to goals, plans and consequences on jobs) (Davis et al.
1989; Davis and Venkatesh 2004). It is thus reasonable to say that just as with cognitive
evaluations, affective evaluations could occur at both the low and high levels.
Specifically, we posit that affective evaluations can occur at two levels of identity: at the
level of interaction process (which itself can have different levels such as exposure or the in-
depth exploration/action levels) and at the level of interaction outcome (with a focus on goals,
relevance, consequences or overall take-away messages).
When one is merely exposed to a particular, relatively new ICT (that is, the individual
receives information presented to him/her but has not acted upon the ICT), the amount of
obtained information is limited. Therefore, the information processing is shallow, and one does
not know much beyond what is presented except some references from the memory (further
discussion on this appears in the next section). The formation of affective evaluations at this
stage can be immediate, preliminary, and toward the ICT as an object. In our scenario, “wow,
that is cool!” represents Alex’s first affective evaluation of the computer game upon immediate
exposure. This shallow information-processing stage is part of the interaction process where
affective evaluations are formed. In other words, these are process-based affective evaluations.
Theoretical reasoning and empirical evidence in psychology and social sciences also support this
notion of quick formation of affective evaluations. One example is the concept of perceived
affective quality in that growing empirical evidence shows that an initial perception of affective
quality takes place automatically within 25 ms of encountering the stimulus (Russell, 2003).
MISQ Forthcoming Zhang, ARM
26
When the individual is actively involved in experiential interaction with the ICT, such as
exploring, playing, and using it, information about the ICT is accumulated. At this stage, the
individual can evaluate the ICT based on his or her direct interaction with it. That is, one forms
his/her affective evaluations upon the behavioral actions s/he has with the ICT. In our scenario,
“playing this game was really engaging and enjoyable” is an example of this type of affective
evaluation. It is based on the specific interaction with the object during the process, and thus is
another kind of process-based affective evaluations.
More in-depth information processing occurs when one forms overall affective evaluations
as a conclusion of one’s direct ICT interaction experience. These are higher level information
processing results that include reflections, deliberations, conclusions and summaries about the
object itself or behaviors with the object. For example, “this is a really cool game that is well
designed” and “I liked playing it” are both summation-based affective evaluations. Hassenzahl’s
“verdictive” notion (2004), an expression of authoritative judgments that represent a “take-away”
message, is a good representation of the outcome-based affective evaluations. Attitude, with the
adopted definition in this paper, is thus an outcome-based affective evaluation.
It is worth noting that not all affective evaluations formed at the concluding moment are
outcome-based. If the content and/or depth of such evaluations are about the process per se, then
they are process-based evaluations, even if they are formed after the process is over.
Summary of the Taxonomy
The five dimensions discussed so far can be put together into the outline in Table 4 to form
super-categories and categories of all affective concepts.
**** Insert Tables 4 about here ****
The first level of organization is the residing dimension. As discussed earlier, affective
MISQ Forthcoming Zhang, ARM
27
responses include those affective concepts whose meanings reside between a person and a
stimulus. Then, those concepts whose meanings reside either within a person or between a
person and a stimulus are divided by the temporal dimension. In Table 4, the first four categories
contain some of the specific affective concepts we have introduced so far: mood, temperament,
affective quality and affective cue, and emotion. In fact, they are considered specific examples of
four categories of affective concepts respectively: mood belongs to Free-floating Affective State
(Category 1), temperament belongs to Affectivity (Category 2), affective quality and affective cue
belong to Affective Characteristics (Category 3), and emotion belongs to Induced Affective State
(Category 4).
Induced affective state is between a person and a stimulus, thus it is one type of affective
responses, and is different from residing within a person and different from residing within a
stimulus. Induced affective state is temporally constrained, thus it is different from affective
evaluation/disposition.
Affective evaluations are those affective concepts that reside between a person and a
stimulus and are temporally unconstrained. There can be many types of affective evaluations.
Here we provide names for the super-categories and categories for easy reference during the rest
of the paper. Particular Affective Evaluations are evaluations of a particular ICT object and
behaviors. There are four categories of them: Process-based Affective Evaluations toward a
Particular Object (Category 5.1), Outcome-based Affective Evaluations toward a Particular
Object (Category 5.2), Process-based Affective Evaluations toward Behaviors on a Particular
Object (Category 6.1), and Outcome-based Affective Evaluations toward Behaviors on a
Particular Object (Category 6.2). Both induced affective state and particular affective evaluation
are about a particular interaction episode and concern both the person and the stimulus in the
MISQ Forthcoming Zhang, ARM
28
episode. The main difference is that induced affective state focuses on the person feelings, while
particular affective evaluation focuses on the person’s appraisal of the stimulus’ affective
quality.
Learned Affective Evaluations are evaluations about a type of ICT stimuli, are the results of
higher-level reflections, and are normally stored in memory for future use. There are two types:
Learned Affective Evaluations toward a Type of Objects (Category 7), and Learned Affective
Evaluations toward Behaviors on a Type of Objects (Category 8). It is worth noting that the
notion of learned affective evaluations is broader than just being evaluative. The essence of this
super-category is disposition, tendency, being conditioned, or “learned.” There can be three
facets of the learnedness: learned thinking, learned being, and learned action. Learned Thinking
focuses on affective appraisal and can be illustrated by the example of attitude toward general
stimuli, which influences future formation of attitude toward a new but similar stimulus. Learned
Being focuses on affective states and indicates that if the same or similar condition that caused
certain emotion is given again, the same or similar emotion may occur. This can be through auto-
association without cognitive appraisal or evaluation, or through repeated experiences, or
through other knowledge. For example, while “CMC anxiety” (Brown et al. 2004) is an emotion
(Category 4) induced by using a CMC technology, “computer anxiety” is the tendency of
individuals to be uneasy, apprehensive, or fearful about current or future use of computers
(Brown et al. 2004), thus it is a Learned Being toward a behavior (Category 8). Learned Action
focuses on emotion-driven behaviors and functions in a similar way as learned being: the person
can be action ready if the same condition that caused certain emotion-driven action in the past is
present again. For the purposes of parsimony, we put these three types of learnedness into one
super category and only differentiate them by object and behavior, to be consistent with the
MISQ Forthcoming Zhang, ARM
29
Particular Affective Evaluations. Thus to reflect the true nature of the learnedness, Categories 7
and 8 should be named as Learned Affective Evaluations/Dispositions. It is worth noting that for
generic affectivity or tendencies that have little to do with ICTs, they belong to Category 2. For
ICT related tendencies, dispositions, or predispositions, they belong to Categories 7 and 8 as
being learned. For example, playfulness as a generic, non-ICT related human trait (Venkatesh
1999) is Category 2, while computer playfulness (Agarwal and Karahanna 2000; Venkatesh
2000; Webster and Martocchio 1992) is a learned disposition related to ICT, thus Category 8.
Table 5 recaptures the names, definitions and characteristics of various super categories and
categories of affective concepts newly introduced in ARM.
**** Insert Tables 5 about here ****
To illustrate the ARM taxonomy, the following is a complete analysis of the affective
concepts in the scenario, with the ARM taxonomy categories denoted. All ARM categories
appear in the scenario.
Imagine a person named Alex. Alex usually does not get easily over excited about novel
things in his surroundings (Category 2: Affectivity - Temperament). He does not like
playing computer games in general (8: Learned Affective Evaluation/Disposition toward
Behaviors on a Type of Objects – Attitude toward Behavior), yet tends to like colorful
things (7: Learned Affective Evaluation/Disposition toward a Type of Objects – Attitude
toward Object). One day, while passing by an electronics store in a calm mood (1: Free-
floating State - Mood), Alex was attracted to a set of colorful and dynamic screen
displays of a game (3: Affective Characteristics - Affective Quality and Affective Cues).
He said to himself, “Wow, that is cool!” (5.1: Process-based Affective Evaluation toward
a Particular Object) He stepped in and started exploring the game. Soon, he felt engaged,
stimulated, playful, and overall was having a lot of fun (4: Induced Affective States -
Emotions). Alex were really enjoying himself (4: Emotions) without realizing the passing
of time. Once he finished exploration, he was thinking: “Playing this game was really
engaging and enjoyable.” (6.1: Process-based Affective Evaluation toward Behaviors on
MISQ Forthcoming Zhang, ARM
30
a Particular Object) As a result of this experience, Alex concluded that “This is a really
cool game that is well designed (5.2: Outcome-based Affective Evaluation toward a
Particular Object), and I liked playing it (6.2: Outcome-based Affective Evaluation
toward Behaviors on a Particular Object).” Then, he wondered that maybe “playing
computer games is not such a bad idea (8: Learned Affective Evaluation/Disposition
toward Behaviors on a Type of Objects).”
Appendix A lists the ICT-related affective concepts/constructs in their original terms/labels,
their formal definitions or measurements if definitions are unclear or missing, and their
classifications with ARM taxonomy. Within the same category, concepts are listed by the
chronological order of the publications. The appendix shows that all ARM categories are studied
to some extent in the ICT literature, although they are not equally attended to. Appendix A also
illustrates the gaps in the literature as mentioned earlier: identical terms or labels are
conceptualized or operationalized differently, and identical concepts are labeled with different
names.
It is worth noting that the characteristics of a stimulus can be applicable to induced states
just as they are to affective evaluations. For example, one’s outcome-based emotions can occur
after careful reflections and deeper processing; emotions can be caused by objects or behaviors,
and objects can be general or particular. Few studies in the ICT literature cover more than
affective feelings, which is just one of the four facets of emotions. Regardless what may cause
emotions (an object, an event, a person, a behavior, etc.), the concept of emotion is still the same.
It emphasizes the person’s feelings (along with biological reactions, etc.) induced by the
stimulus. For this reason, we do not plan to examine emotions further.
MISQ Forthcoming Zhang, ARM
31
A Nomological Network and Relationship Propositions Besides synthesizing various affective concepts in the ICT context, we are also interested in
the relationships among them. As mentioned earlier and demonstrated in Appendix B, there is
only a very small number of studies that provided very limited examinations of the relationships
among the affective concepts in the ICT context. Such limited coverage does not provide us with
an understanding of the nature or the underlying processes of the relationships among affective
concepts. In theorizing the possible relationships with ARM, we depict the psychological
processes that underline the formation and influence of various affective concepts during an ICT
interaction episode. Such processes are both event-driven and appraisal-driven, as indicated by
the scenario. Consistent with the scope of this paper, we focus only on affective concepts,
although we acknowledge the involvement other factors in affective reaction formations and
changes.
Our analysis of the relationships among various affective concepts in the ICT context is
influenced by two perspectives in psychology and the social sciences on affect, emotion, and
attitude: the evolutionary (e.g. the notion of hard-wired set-up for human survival) and the
cognitive (e.g. affect appraisal theories, affect infusion theory, dual-process theories on attitude
formation and change). It is also heavily influenced by several specific models of emotional
episodes. It is worth noting that these perspectives and theories are not considered to be in
conflict but more complementary with each other. They all have supporting empirical evidence
and are various ways of understanding the complex affect phenomenon. Since a unified theory of
affect does not exist, it is worth trying to integrate various theories to understand various facets
of the affect phenomenon in the ICT context. Next, we overview emotional episode models that
provide the basis for the ARM nomological net.
MISQ Forthcoming Zhang, ARM
32
Emotional Episode and an Overview of the ARM Nomological Net
According to the Component Process Model (CPM) (Scherer 2005, p700-701), emotion as
an episode has several characteristics: (1) it is event driven in that something happens to the
person that stimulates or triggers a response after having been evaluated for its significance; (2)
it is appraisal driven in that the eliciting event and its consequences must be relevant to major
concerns of the person because we do not generally get emotional about things or people we do
not care about; (3) the relevance of an event is determined by a rather complex yet very rapidly
occurring evaluation process that can occur on several levels of processing ranging from
automatic to implicit to conscious conceptual or propositional evaluations; and (4) different
emotions are produced by a sequence of cumulative stimulus evaluation or appraisal checks with
emotion-specific outcome profiles. Scherer also distinguishes intrinsic and extrinsic appraisals:
intrinsic appraisal evaluates the feature of a stimulus independently of the current needs and
goals of the person based on generic (e.g. sweet taste) or learned (e.g. bittersweet food)
preferences. Extrinsic or transactional appraisal evaluates events and their consequences with
respect to their conduciveness for salient needs, desires, or goals of the person.
Russell (2003) depicts a prototypical emotional episode model (PEEM) that consists the
following elements that are ongoing and often temporally overlap: (1) an obvious external
antecedent event (2) is perceived in terms of its affective quality, (3) and dramatically alters core
affect which continues to change as the episode unfolds; (4) the current core affect is attributed
to the antecedent, which becomes the Object thus the person has this salient experience: the
Object is making me feel the way I feel now; (5) the perceptual-cognitive processing of the
Object continues, assessing such qualities as its future prospects, its relevance to one’s goals, its
causal antecedents, and so on (appraisal); (6) (instrumental) action is directed at the Object; (7)
MISQ Forthcoming Zhang, ARM
33
facial, vocal, and autonomic changes (physiological and expressive changes) occur and are
accounted for (a) by core affect and (b) as part of, preparation for, or recovery from instrumental
action; (8) in addition, there is a flood of metacognitive judgments being accompanied by core
affect (subjective conscious experiences); (9) there is an additional and separate subjective
conscious experience (emotional meta-experience): the person experiences a specific emotion;
(10) finally, one helps place one’s current state and situation within a broader body of
knowledge, including social norms and roles (emotion regulation).
Addressing affective experiences at work place, the Affect Event Theory (AET) (Weiss and
Cropanzano 1996) considers work environment features, work events, dispositions, affective
responses, work attitudes, judgment driven behaviors and affect driven behaviors. Event driven
and appraisal driven are key ideas of AET, along with the roles of dispositions that can (1)
influence affective reactions directly and (2) moderate the effects of the event on affective
reactions (Weiss and Cropanzano 1996).
The ARM nomological net is a direct application of the emotional episode models that
focuses primarily on the affective aspect, omitting the cognitive and behavioral aspects.
Specifically, an ICT stimulus is encountered by a person (an event) who appraises the ICT for its
relevance and responds affectively during the episode. In Figure 2, we depict the main
relationships among the categories of affective concepts that come into play when one is in an
episode of interacting with a particular ICT. Affective antecedents that trigger and influence the
episode are a person’s free-floating affective state such as mood, affectivity such as
temperament, and the ICT stimulus’ affective characteristics such as affective cues and quality.
In the episode, there are three types of affective responses: induced affective states, particular
affective evaluations, and learned affective evaluations/dispositions. These can influence each
MISQ Forthcoming Zhang, ARM
34
other and be influenced by the antecedents. Figure 3 provides a more detailed view of the model
to indicate how affect is manifested and eventually turned into learned affective
evaluations/dispositions. Directional lines indicate causal relationships; double arrowed lines
indicate correlations or mutual influence. Particular affective evaluations are positioned along the
stages of process and outcome. Induced states can happen during the process. Induced states may
influence affective evaluations, both learned and particular. Learned affective
evaluations/dispositions have an impact on induced states and contribute to the formation of
particular affective evaluations. Upon higher level reflections, learned affective
evaluations/dispositions might be updated by particular affective evaluations to reflect new
information gained during the episode.
**** Insert Figures 2 and 3 about here ****
To make the relationships parsimonious and less graphically overwhelming, we focus on
super categories as dashed boxes in Figure 3, and at times may provide discussions at the super
category level. In other words, some propositions (such as P1-P7) reflect such groupings and can
be detailed into sub-propositions. For example, induced states can influence all four categories of
particular affective evaluations, thus P3 can have four sub-propositions.
There are other factors that can influence affective responses in an ICT interaction episode.
These can include particular goals or needs of ICT interaction (Briggs et al. 2008), and social,
situational and cognitive factors that influence the formation of the affective reactions, according
to well-established theories and empirical evidence (see Sun and Zhang 2006b for a meta-
analysis of moderating factors). To be parsimonious, we acknowledge the effects these factors
have but omit discussing them in this paper. Thus, ARM is not intended to depict the full scope
of person-ICT interaction but rather to emphasize only the affective aspect.
MISQ Forthcoming Zhang, ARM
35
It is worth noting that some induced states and immediate affective evaluations may not be
influenced by learned affective evaluations, but rather are solely caused by stimuli. This is
known as the affective primacy hypothesis (Zajonc 1980; Zajonc 1984) that is favored by the
evolutionary perspective of affect. There is plenty of empirical evidence, including some from
recent neurophysicological studies, to show that affective responses can occur without any
cognitive processing or deliberation. Emotions can be triggered far more quickly than rational
responses (Ekman 1992), and some believe that such affective responses are “hard-wired”
(Barnard and Teasdale 1991), requiring no learning on the part of the individual as they are
purely human survival instincts. On the other hand, the affect appraisal theories posit that
cognitive processes should be involved when emotions or particular affective evaluations are
influenced by learned affective evaluations/dispositions.
Next in this section, we develop propositions in ARM based on emotional episode models
and other related theoretical work. Along the way, we highlight the ICT literature on
relationships among affective concepts. Appendix B provides a detailed summary of the
empirical evidence of the relationships among affective concepts in the ICT literature, their
theoretical justifications or implications, and the corresponding ARM propositions. A
relationship is considered in Appendix B if the involved affective concepts are about the same
ICT related stimulus. For example, McCoy et al. (McCoy et al. 2008) find that irritation by
advertisement (on a website) leads to negative attitude toward the website. The two concepts
have different stimuli thus such relationship is outside our scope for discussion.
ARM Proposition Development
Propositions are grouped by the categories of concepts they connect.
MISQ Forthcoming Zhang, ARM
36
Between Affective Antecedents and Affective Reactions (P0)
Moods are transient states that wax and wane, fluctuating and lasting longer than emotions,
not as intensive as emotions, yet often providing a background to influence consciousness,
including a threshold-like influence on emotion elicitation (Rosenberg 1998). For example, the
affect infusing model (AIM) suggests that being in a good mood (regardless of its sources or
causes) should lead to a host of processing on the stimulus in terms of attending to its
information, selectively retrieving information from the memory, selectively coding its
significance, and making associations and interpretations (Forgas 1995). This means that to some
extent, the particular mood a person has at the time of an ICT interaction episode should
influence other affective components and processes.
P0a. The person’s mood influences his/her affective reactions during an interaction
episode with a particular ICT.
By definition, temperaments and affective traits influence affective reactions in the ICT
context, just as in any other contexts being studied in psychology and the social sciences. This is
not to dismiss or underplay the event or contextual influences, nor the influences from some
more stimulus specific dispositions (such as learned affective evaluations/dispositions related to
the ICT stimulus). Nevertheless, scholars have noticed the dispositional influences on affect
across a diverse contexts and disciplines (Rosenberg 1998). For example, in AET, dispositions
have a direct impact on affective responses (Weiss and Cropanzano 1996).
P0b. The person’s temperament influences his/her affective reactions during an
interaction episode with a particular ICT.
The stimulus-organism-response (S-O-R) paradigm posits that environmental cues act as
stimuli that influence an individual’s cognitive and affective reactions, which in turn influence
MISQ Forthcoming Zhang, ARM
37
behavior (Mehrabian and Russell 1974). Affect appraisal theories directly posit that people
appraise a stimulus during an emotional episode where the affective cues trigger people to react
affectively in the form of induced states and affective evaluations. It has been well established
that affect (attitude) may be activated automatically from memory on the mere exposure of an
affect-loaded stimulus (Fazio et al. 1986) and such automatic activation effect is a pervasive and
relatively unconditional phenomenon (Bargh et al. 1992; Hermans et al. 1994). All these
theoretical bases point out that in an interaction episode with an ICT, the ICT’s affective cues
and quality would trigger the corresponding affective states and affective evaluations.
P0c. The ICT’s affective cues and quality influence the person’s affective reactions
during an interaction episode with a particular ICT.
As indicated in Appendix B, the ICT literature supports both P0b and P0c. For P0b,
Negative Affectivity and Trait Anxiety (both Category 2) are theorized to influence Computer
Anxiety (8), although only Trait Anxiety’s effect is found in the empirical data (Thatcher and
Perrewe 2002). The latter relationship is also found by Igbaria and Parasuraman (1989). The
theoretical justification is that dynamic, IT specific individual differences (i.e., computer
anxiety) are a function of stable situation-specific (i.e., personal innovativeness in IT) and broad
(i.e., negative affectivity and trait anxiety) traits. Studies are also found to support P0c. Visual
characteristics of web pages (3) have been found to be highly correlated with immediate
impressions of the web pages (5.1) (Lindgaard et al. 2006). The authors justify the findings
according to the mere exposure effect (Zajonc 1980), in that one can quickly form immediate
visual appeal impressions even given an extremely short period of exposure time that does not
permit cognitive processing. They conclude that feelings happen to us whether we like it or not,
and they can happen in a matter of a few milliseconds. Using the stimulus-organism-response (S-
MISQ Forthcoming Zhang, ARM
38
O-R) paradigm (Mehrabian and Russell 1974), Mood Relevant Cues (3) are found to influence
Perceived Enjoyment (6.1) (Parboteeah et al. 2009). Although there is no empirical evidence
found to support P0a (mood influences affective reactions), the scenario can provide some
examples: while passing by an electronics store in a calm mood, Alex was attracted by the
displays and eventually had fun exploring and concluded the experience as positive. A different
mood at the time may yield different affective reactions.
To avoid repeating information, from now on for the rest of this section, we will only briefly
summarize the findings on relationships in the ICT context, and leave the specific theoretical
justifications and implications in Appendix B.
Between Induced States and Learned Affective Evaluations/Dispositions (P1 and P2)
Stimulus based affective tendencies or dispositions (i.e., learned affective evaluations/
dispositions) are more enduring than states (i.e., emotions), but may remain latent and
unexpressed and can be activated by an attitude-relevant stimulus (Clore et al. 2001), which then
influences congruently the induced states during the episode. Thus learned evaluations/
dispositions influence induced states.
P1. Learned affective evaluations influence induced affective states during a person’s
interaction with a particular ICT.
The affect-as-information theory posits a direct link between an affective state and
evaluative judgments in that a person may ask oneself: “How do I feel about this?” and in doing
so the person considers the feeling as an evaluation or judgment of the target (Schwarz 2001;
Schwarz and Clore 1983). This means that the feelings one has toward a stimulus can carry over
to the evaluation of the stimulus. Such evaluations can be both particular affective evaluations
(thus supporting P3) and learned affective evaluations/dispositions, thus supporting P2.
MISQ Forthcoming Zhang, ARM
39
P2. Induced affective states influence learned affective evaluations/dispositions.
There is empirical evidence in the ICT literature to support P1. Flow as a state (4) is an
outcome of the individual characteristics of cognitive playfulness of microcomputers (8)
(Webster and Martocchio 1995). Individual dispositions such as computer playfulness (8) are
likely to have an effect on experiential states such as cognitive absorption (4) (Agarwal and
Karahanna 2000). More general anxieties such as computer anxiety (8) are found to influence
more specific ones such as CMC anxiety (4) (Brown et al. 2004). No studies are found to focus
on P2. Using the scenario as an example, Alex being engaged, stimulated, playful, and overall
having a lot of fun (induced affective state) may directly enforce his liking colorful things (7:
learned evaluation/disposition toward things), and directly influence or challenge his not liking
to play computer games (8: learned evaluations/dispositions toward behaviors on games). It is
worth noting that such influences may also be indirect through particular affective evaluations.
Between Induced States and Particular Affective Evaluations (P3 and P4)
When affective evaluations are formed, emotions as induced states may or may not co-occur
(Russell 2003). When emotions do occur, they should be congruent with the corresponding
affective evaluations.
The flow theory (Csikszentmihalyi 1975; Csikszentmihalyi 1990) depicts a direct
relationship between an affective state and affective evaluations of the activity. Flow represents a
state of consciousness where one is so absorbed in an activity that s/he excels in performance
without consciously being aware of his or her every movement or time spent on it. A strong
indication of flow is that one experiences heightened enjoyment (a positive state). Flow
represents an optimal experience during interaction with an ICT (Finneran and Zhang 2005). Due
to the desirability of reaching flow again, one forms process-based affective evaluations toward
MISQ Forthcoming Zhang, ARM
40
the behavior that causes flow. The impact of such evaluations, along with the state felt during the
action, should be significant for outcome-based affective evaluations when one concludes the
interaction, leading to future decisions and behaviors in respect to the action. This is to say that
the affective states felt during the interaction can influence particular affective evaluations, both
process-based and outcome-based. Therefore, we have P3.
P3. Induced states influence particular affective evaluations during a person’s
interaction with a particular ICT.
Russell’s Prototypical Emotional Episode Model (PEEM) (Russell 2003) depicts a directed
relationship between immediate affective evaluation and a specific emotion/state. PEEM
illustrates the causal connection and temporal process of a stimulus first being received by an
individual from an immediate affective evaluation, to cognitive deliberation, to specific feelings,
to final behavior response and emotion regulation. In other words, the immediate affective
evaluation (one type of particular affective evaluations) leads to certain feelings (affective states)
that are congruent with the affective evaluations.
Similarly, the component process model of emotional episodes states that different emotions
are produced by a sequence of cumulative stimulus evaluations or appraisal checks with
emotion-specific outcome profiles (Scherer 2005). Therefore both episode models lead to P4.
P4. Particular affective evaluations influence induced states during a person’s
interaction with a particular ICT.
The ICT literature supports P3. Flow as an induced state (4) is found to influence attitudes
toward CMC technologies (5.2) (Trevino and Webster 1992). Mood as an induced state (4) is
found to influence intrinsic motivation (or perceived enjoyment) (6.1) over both the short and
long term (Venkatesh 1999). Individuals experiencing high CMC anxiety (4) are found to have
MISQ Forthcoming Zhang, ARM
41
less favorable attitudes toward using the CMC (6.2) (Brown et al. 2004). Perceived playfulness
that is treated as an induced state (4) is found to influence satisfaction with a web portal (6.2)
(Lin et al. 2005). Feelings (4), including both pleasure and arousal, are found to influence
attitude toward mobile Internet services (6.2) (Kim et al. 2003). No studies are found to focus on
P4.
Between Learned and Particular Affective Evaluations (P5 and P6)
The affect priming principle posits that affective states can indirectly inform judgments by
facilitating access to the memory and associated cognitive processes (Bower 1981). This means
that when constructing particular affective evaluations (either as process-based or outcome-
based), one is primed by the currently experienced affective state to access memory on
previously established situations when such a state is experienced (thus the learned affective
evaluations/dispositions). This supports P5.
Attitude theories posit that one may have a particular attitude toward a certain type of object
but a different attitude toward a particular object (Ajzen and Fishbein 2005). This occurs when
other factors may be involved, such as more information being available regarding the particular
individual object, or when situational factors come into play. Due to their nature, general
attitudes have the function of “guiding” the formation of a new attitude toward a particular
stimulus. That is, when all other things being equal, attitude toward a particular stimulus is about
the same as the attitude toward a class of similar stimuli. This further supports P5.
P5. Learned affective evaluations/dispositions influence particular affective
evaluations during a person’s interaction with a particular ICT.
Attitude change theories such as cognitive dissonance theory (Festinger 1957) assert that
existing attitudes have to be changed if a newly formed attitude differs so that the person reduces
MISQ Forthcoming Zhang, ARM
42
dissonance. Applying this position to affective evaluations, we anticipate that learned affective
evaluations/dispositions will change if particular affective evaluations differ. This means the two
types should influence each other, although at different stages of human-ICT interaction.
Learned affective evaluations/dispositions can influence particular affective evaluations during
the interaction. On the other hand, learned affective evaluations/dispositions can be influenced
by particular affective evaluations at the conclusion of the interaction.
P6. Particular affective evaluations influence learned affective evaluations/dispositions
after a person’s interaction with a particular ICT.
In the ICT literature, no studies are found to focus on P5, and one is on P6. Satisfaction with
a website (6.2) is found to influence attitude toward commercial websites (7) (Teo et al. 2003).
Among Particular Affective Evaluations (P7, P8 and P9)
It has been established that most stimuli, after being automatically appraised for being
positive or negative (that is, the formation of process-based affective evaluations), are further
appraised (Reeve 2005, p338). Russell’s PEEM suggests in this further processing that early
immediate evaluations have an influence on the later more reflective evaluations. This means
that process-based affective evaluations influence outcome-based affective evaluations.
According to dual-process theories of attitudes, via the central route or deliberative fashion,
one can carefully consider available information in forming evaluations. Such information can be
the emotions the person feels at the time, the cognitive reasoning on the importance related to an
ICT or ICT use, and/or other affective evaluations that have occurred so far. Effortful evaluations
should occur during the process and during the conclusion stages of the interaction when such
types of information are available. Thus, dual-process theories provide additional support to the
following: the formation of some process-based affective evaluations is based on information
MISQ Forthcoming Zhang, ARM
43
from other process-based affective evaluations, and the formation of outcome-based affective
evaluations is based on information from process-based affective evaluations. Positive emotions
and evaluations in early stages induce more positive evaluations in later stages than neutral
emotions and neutral evaluations (Cacioppo et al. 1999; Wegener 2001).
According to action identification theory (Vallacher and Webner 1987) and its application in
the ICT context (such as TAM studies), process-based affective evaluations should have a
positive effect on outcome-based affective evaluations.
P7. Process-based affective evaluations influence outcome-based affective evaluations.
Research in attitude theories makes a distinction between attitudes toward an object and
attitudes toward a behavior (Ajzen and Fishbein 1980; Ajzen and Fishbein 2005; Eagly and
Chaiken 1998; Forgas 2000; Wixom and Todd 2005; Zhang et al. 2008; Zhang and Sun 2009),
and theoretically justifies and empirically validates that object-based attitudes have an impact on
behavioral-based attitudes regarding a particular object (Ajzen and Fishbein 1980; Ajzen and
Fishbein 2005). We extend this relationship to particular affective evaluations toward objects and
toward behaviors to yield the following propositions.
P8. Process-based affective evaluations toward a particular ICT influence process-
based affective evaluations toward behaviors on the ICT.
P9. Outcome-based affective evaluations toward a particular ICT influence outcome-
based affective evaluations toward behaviors on the ICT.
P7 is supported by a good number of studies in the ICT literature. First impressions of a set
of web pages (measured by beauty, illustration/text, overview) (5.1) relate with webpage
preferences (5.2) (Schenkman and Jonsson 2000) while hedonic attributes (5.1) relate to the
beauty (5.2) of a MP-3 player with various skins (Hassenzahl 2004). Both perceived
MISQ Forthcoming Zhang, ARM
44
entertainment and perceived irritation are Category 5.1 and are found to influence attitudes
toward a website (5.2) (Gao and Koufaris 2006). Perceived playfulness as a playful experience
(6.1) influences attitudinal outcomes such as attitude toward use (6.2) (Moon and Kim 2001).
Similarly, shopping enjoyment (6.1) is found to influence attitudes toward shopping at the
website (6.2) (Jiang and Benbasat 2007).
No study is found to focus on P8. One study supports P9 in that satisfaction as an object-
based attitude (5.2) influences behavior-based attitude such as attitude toward behavior (6.2)
(Wixom and Todd 2005).
Among Learned Evaluations/Dispositions toward Objects and Behaviors (P10)
Although learned affective evaluations can also be about objects and behaviors, we posit
that they have a slightly different relationship with each other than with, for example, 5.1 and
6.1, and 5.2 and 6.2. Since learned affective evaluations are generalized and stored in memory,
they do not have causal relationships but should be congruent with each other.
P10. Outcome-based affective evaluations/dispositions toward a type of ICTs correlate
with outcome-based affective evaluations/dispositions toward behaviors on this
type of ICTs.
In the ICT literature, computer anxiety (8) is found to influence attitude toward computers
(7) (Igbaria and Parasuraman 1989).
Among Affective Responses of the Same Category (P11)
One proposition that is not shown graphically in Figures 2 and 3 concerns the relationships
among affective responses that belong to the same category. We posit that affective responses of
the same category should correlate with each other if these responses are about the same
stimulus. This is because these responses have the same specificity by sharing the same stimulus,
MISQ Forthcoming Zhang, ARM
45
the same information process level (and thus the similar amount and intensity), and the same
person/evaluator.
P11. Affective responses toward the same stimulus correlate with each other if these
affective responses belong to the same category.
In the ICT literature, flow as a state (4) with two dimensions (concentration and enjoyment)
is found to influence positive affect, another state variable (Zaman et al. 2010).
Summary of the Relationships in ARM
It has been realized that an emotional episode can be very complex with many processes
occurring and overlapping, leading to constantly changing/evolving feelings and evaluations
(Russell 2009; Scherer 2005). Thus it should be noted that the relationships in Figures 2 and 3
should be true in general, yet any particular relationship may not hold true at any given moment
in an episode. In addition, it might be easier to focus on an association between two affective
concepts rather than a causal relationship, which is what some of the empirical studies did.
Nevertheless, we attempt to crystallize the psychological underpinning that depicts which
specific types of affective concepts are influenced by other affective concepts. Such
underpinning further illustrates the connections and differences among the various types of
affective concepts.
In the ICT literature, the coverage on relationships is spotty, although the findings are
largely consistent with the ARM propositions. Some relationships are relatively heavily attended
to, such as five times on P3 (induced affective states influence particular affective evaluations)
and P7 (process-based affective evaluations influence outcome-based affective evaluations)
respectively, four times on P0 (affective cues/qualities or moods/temperament influence induced
states and evaluations), and three times on P1 (induced states influence learned
MISQ Forthcoming Zhang, ARM
46
evaluations/dispositions). Some are barely addressed: once for P6, P9, and P10 respectively, and
some are never attended (P2, 4, 5, 8). The lack of empirical studies on the propositions indicates
the gap in the literature, which can be a natural result of a lack of comprehensive and systematic
examination of the relationships. This is where ARM can contribute, and lead to future research
efforts on empirical studies.
Discussion Affect has become an increasingly important research area in several social sciences
disciplines, including ICT related disciplines. Affect represents a complex set of affective
concepts, which are not all the same, nor all unrelated. In this paper, we proposed the Affective
Response Model (ARM) as a means to address the questions of “What are pertinent affective
concepts in the ICT context?” “In what ways are these affective concepts similar to or different
from each other?” And “how do these affective concepts related to each other?”
ARM considers several dimensions that are theoretically validated to provide conceptual
clarity. Therefore, ARM is a theoretically-bound framework that can provide a systematic and
holistic reference map for any ICT study that considers affect. The proposed taxonomy and the
resulting nomological network offer a detailed and coherent understanding of the complex
phenomenon related to affect in the ICT context. Using ARM, the relatively discrete findings
from the ICT literature can be better connected, compared, analyzed, and synthesized, as we
have demonstrated in Appendices A and B. As a result, research gaps, opportunities, and
directions can be identified and pursued.
Boundaries and Limitations
Although investigating the causes of affective responses that lead to onset, magnitude, and
MISQ Forthcoming Zhang, ARM
47
direction can enhance the understanding of affective concepts themselves, we decided to be more
focused and exclude some salient factors, especially non-affective factors, as causes or
moderators. For example, we did not include goals and needs, although it is quite likely that they
are causal factors for affective responses (Briggs et al. 2008). We excluded cognitive
deliberations, which can be important in reaching affective responses. In addition, the context of
interaction is likely to play an important role in affective responses (van Schaik and Ling 2009)
and their relationships with each other. ARM does not prescribe the consequences of the
affective constructs on other factors either. For example, affect has been found to influence
cognitions, intentions, and behaviors in the ICT context, which are excluded from ARM.
Understanding the causes and consequences of affective responses, as well as the interplay
between affect and cognition, is clearly important, and hence deserves scholarly attention. While
some early efforts have started to show promise (see Sun and Zhang 2006a), much more
attention is needed in this area for affective concepts to be better understood.
Explanations of Inconsistencies
One use of ARM is to understand the relatively discrete ICT literature on affect. Here we
demonstrate ARM’s explanatory power with one example. Attitude is studied extensively in the
ICT context, especially in user technology acceptance, adoption and use, and IS success research
(Davis et al. 1989; Goodhue 1988; Zhang et al. 2008; Zhang and Sun 2009). Yet, attitude has
been defined in multiple ways, such as an individual’s positive or negative feelings (evaluative
affect) about performing a target behavior (Davis et al. 1989), a person’s affective evaluation of
a specific object (Brown et al. 2004), an overall evaluation of a shopping experience at a
particular website (Jiang and Benbasat 2007), a positive affective variable (Gao and Koufaris
2006), a learned implicit response that refers to an individual’s evaluation of a concept (Agarwal
MISQ Forthcoming Zhang, ARM
48
and Prasad 1999), personal affect toward IT usage (Bhattacherjee and Premkumar 2004),
predispositions to respond in a particular way towards a specified class of objects (Teo 2003),
and satisfaction with process and outcomes (Taylor and Todd 1995). Based on a survey of the
literature showing its inconclusive roles, attitude toward using technology is claimed not to be a
direct determinant of intention, and thus is excluded from the Unified Theory of Acceptance and
Use of Technology (Venkatesh et al. 2003). There are four specific treatments of attitude in the
ICT literature (summarized in Appendix A): a learned affective evaluation/disposition toward (i)
a type of ICTs (thus Category 7), or (ii) behaviors on a type of ICTs (8), or an outcome-based
affective evaluation toward (iii) a particular ICT (5.2) or (vi) behaviors on the ICT (6.2). Case
(ii) can be demonstrated by “affect” (Compeau and Higgins 1995; Compeau et al. 1999), which
is often considered as attitude as well). ARM is able to unearth the sources of inconsistencies in
attitude studies, thus providing directions to correct them in future research efforts.
Theoretical Implications
ARM is a theoretical framework to conceptualize and abstract various affective concepts
along a set of dimensions that specify the target stimulus a person reacts to in the human-ICT
interaction context. ARM has the power for explaining and predicting (Gregor 2006). ARM
describes the affective phenomenon in the ICT interaction context. It analyzes what types of
affective concepts exist and what kinds of relationships these concepts have. Its power is
demonstrated by its ability to classify all the affective constructs and relationships reported in the
ICT literature (as shown in Appendices A & B), and its ability to identify and explain the
discrepancies in the literature, and synthesize consistent findings even if different names are used
in different studies. With the ability to explain and predict, ARM can identify gaps and
opportunities, and new affective concepts or relationships to inspire future research investigation.
MISQ Forthcoming Zhang, ARM
49
For example, there might be more Category 2 concepts that have not been studied in the ICT
context. There should be many more affective cues to be studied, especially from guiding ICT
design perspective. And there are several propositions that are never covered in the ICT literature
and yet they can be tested and validated.
ARM goes beyond being just affective. There is abundant empirical evidence, in IS and
other disciplines, to show that affective concepts can have significant interplay with other
fundamental aspects of human beings, such as cognition, motivation, and behavior (for a review,
see Sun and Zhang 2006a). In fact, as we developed ARM in this paper, we pointed out the
interplay between affect, cognition and behavior when necessary. Some affective concepts (such
as affective evaluations) can be cognitively latent, meaning that they are the result of cognitive
processing (but the content being processed is affective). This is to say that one should not over-
simplify the relationships that affect may have with other aspects. As a first step in better
understanding various affective concepts, and the extent to which they are cognitively latent,
ARM provides insight that helps one to make sense of empirical findings about the interplay
between affect, cognition, and potentially other factors.
ARM can be used to study in depth a specific affective concept, emotion, in the ICT context,
which is timely due to the heightened interests in studying emotions in the ICT context. One way
of studying emotions is to examine the characteristics of the target stimulus. The multi-
dimensional structure of stimuli can be a great starting point for such an investigation.
ARM (and logic behind it) may be used to study affect related issues in other aspects of ICT.
For example, research on affective responses to ICT services can be guided by ARM in at least
the following ways: whether the involved affective concepts should be moods, emotions, or
affective evaluations; what/who would be the stimuli (the service websites, the people
MISQ Forthcoming Zhang, ARM
50
responsible for the websites, or the customers who deal with either the websites or the
personnel); and which stages of interaction the researchers should focus on.
Finally, ARM may be extended to non-ICT contexts and disciplines. The key is to identify
the stimuli of interest.
Practical Implications
This paper has strong practical implications for ICT design, evaluation, use, training,
management, and improvement in a variety of settings. ARM distinguishes affective concepts
whose meanings reside in an ICT (affective cues and affective quality) or between a person and
an ICT (emotions and affective evaluations). From the design and improvement perspective,
distinguishing and understanding the roles of affective concepts allows designers to focus on
areas that can be most effective in receiving desired user affective reactions. For example, one
challenge that designers face is to identify and construct effective affective cues that can induce
strong desired affective responses. Understanding the importance of process-based affective
evaluations, especially those based on mere exposure, is helpful as it can guide designers toward
interfaces that draw users into the interaction, rather than turning them away to competitors.
Trainers, managers, and evaluators of ICTs can use ARM to better understand the reasons
for, and sources of certain affective responses. As such, they can apply corresponding strategies
and tactics to reach their desired goals. For companies that serve customers with ICTs, there are
many types of affective responses worth paying attention to. The stimuli that may cause various
affective reactions can also be varied and represent different things. For instance, a customer
may have strong affective evaluations or emotions toward the sold products, the provided
service, the staff who handle the service or transactions, the communication channel, the website
of the company, or the company itself. A consumer’s attitude toward the company may be a
MISQ Forthcoming Zhang, ARM
51
summary of all affective responses toward these different stimuli, and that attitude can become a
key factor to build customer loyalty. How could a company build positive customer attitude?
ARM points out the potential affective contributors (such as affective antecedents, particular
affective evaluations, induced states, and learned affective evaluations/dispositions) that may
influence the attitudes.
Future Research Directions
There are a number of future directions that we wish to prescribe. The first is toward
development of validated instruments for various affective concepts. This can be both interesting
and challenging in light of the many different types of affective concepts.
The second is about empirically validating the propositions in ARM. The current literature
shows a scattered coverage of the propositions, demonstrating a lack of systematic effort to
investigate the relationships, and leaving an open landscape for future research. All propositions
are important in providing a holistic understanding of the affective concepts in the ICT context.
Yet, specific studies may focus on any subset of the entire net due to its complexity. Testing the
model can be conducted with a variety of ICT artifacts, at various stages of adoption/use, and
within various use contexts to reveal whether the relationships are supported in general, and
whether they are restricted to specific contextual factors.
Third, researchers should be able to reexamine empirical findings regarding the causes and
consequences of affect in the ICT context in order to have a clearer and more holistic
understanding of the various affective concepts as explained by ARM.
Fourth, for scholars interested in a systematic examination of emotions, it is worth
considering the nature of the stimuli that cause the emotions. Many of the dimensions in ARM
for affective evaluations may apply to studying emotions.
MISQ Forthcoming Zhang, ARM
52
Fifth, along with efforts similar to ARM to better define affective concepts and clarify
inconsistencies of affect’s causes and effects, scholars can conduct more research to examine
both affective and cognitive factors, as well as other factors such as behavioral, motivational,
social, contextual, cultural, and economical, in human interaction with ICTs. Such investigations
should strive to provide insight and guidelines for designing new ICTs that are effective,
efficient, pleasant, encouraging, and interesting to use for various purposes.
Finally, continued ICT-use research is considered one of the most welcome developments in
recent IS scholarship (Ortiz de Guinea and Markus 2009). In particular, affective or emotional
responses are speculated to contribute to continued ICT-use intention, and may influence
continued ICT-use behaviors directly (Ortiz de Guinea and Markus 2009). This presents
intriguing and potentially ground-breaking opportunities for ICT scholars and practitioners. It
should be interesting to find out how various affective responses occur and interact in continued
ICT-use situations, and in what ways these affective responses can be managed and taken into
account when considering ICT management issues such as training and support.
Acknowledgments I thank the SE Lynne Markus for her encouragement and insightful suggestions. I appreciate
the constructive reviews by the AE and three anonymous reviewers. The early ideas were
presented at the 2006 JAIS theory development workshop and research seminars in City
University of Hong Kong, Renmin University, and Australia National University. I am grateful
for the following individuals who shared their insights during the development of the article:
Kevin Crowston, Robert Heckman, Dov Te’eni, Shirley Gregor, and my former and current
students Na Li, Heshan Sun, Nathan Prestopnik and Michael Scialdone.
MISQ Forthcoming Zhang, ARM
53
References Agarwal, R., and Karahanna, E. 2000. "Time Flies When You're Having Fun: Cognitive Absorption and
Beliefs About Information Technology Usage," MIS Quarterly (24:4), pp. 665-694. Agarwal, R., and Prasad, J. 1999. "Are Individual Differences Germane to the Acceptance of New
Information Technologies?," Decision Sciences (30:2), Spring, pp. 361-391. Ajzen, I., and Fishbein, M. 1980. Understanding Attitudes and Predicting Social Behavior. Englewood
Cliffs, NJ: Prentice-Hall. Ajzen, I., and Fishbein, M. 2005. "The Influence of Attitudes on Behavior," in Handbook of Attitudes and
Attitude Change, D. Albarracin, B.T. Johnson and M.P. Zanna (eds.). Mahwah, NJ: Erlbaum. Ang, S., Cummings, L.L., Straub, D.W., and Earley, P.C. 1993. "The Effects of Information Technology
and the Perceived Mood of the Feedback Giver on Feedback Seeking," Information Systems Research (4:3), pp. 240-261.
Bagozzi, R.P. 2007. "The Legacy of the Technology Acceptance Model and a Proposal for a Paradigm Shift," Journal of the Association for Information Systems (8:4).
Bagozzi, R.P., Gopinath, M., and Nyer, P.U. 1999. "The Role of Emotions in Marketing," Journal of the Academy of Marketing Science (27:2), pp. 184-206.
Bargh, J.A., Chaiken, S., Govender, R., and Pratto, F. 1992. "The Generality of the Attitude Activation Effect," Journal of Personality and Social Psychology (62:893-912).
Barnard, P.J., and Teasdale, J.D. 1991. "Interacting Cogntiive Subsystems: A Systematic Approch to Cogntive-Affective Interaction and Change," Cognition and Emotion (5), pp. 1-39.
Barrett, L.F., Mesquita, B., Ochsner, K.N., and Gross, J.J. 2007. "The Experience of Emotion," Annual Review of Psychology (58), pp. 373-403.
Barrett, L.F., and Russell, J.A. 1999. "The Structure of Current Affect: Controversies and Emerging Consensus," Current Directions in Psychological Science (8:1), pp. 10-14.
Batra, R., and Ray, M.L. 1986. "Affective Responses Mediating Acceptance of Advertising," Journal of Consumer Research (13), pp. 234-249.
Batson, C.D., Shaw, L.L., and Oleson, K.C. 1992. "Differentiating Affect, Mood, and Emotion: Toward Functionally Based Conceptual Distinctions " in Emotion, Review of Personality and Social Psychology, M.S. Clark (ed.). Thousand Oaks, CA: Sage Publications, Inc, pp. 294-326.
Beaudry, A., and Pinsonneault, A. 2010. "The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use," MIS Quarterly (34:4), pp. 689-710.
Beckers, J.J., Wicherts, J.M., and Schmidt, H.G. 2007. "Computer Anxiety: "Trait" Or "State"?," Computers in Human Behavior (23), pp. 2851-2862.
Bhattacherjee, A., and Premkumar, G. 2004. "Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test," MIS Quarterly (28:2), pp. 229-254.
Blythe, M.A., Overbeeke, K., Monk, A.F., and Wright, P.C. (eds.). 2005. Funology: From Usability to Enjoyment. Norwell, MA: Kluwer Academic.
Bower, G.H. 1981. "Mood and Memory," American Psychologist (36), pp. 139-148. Brave, S., and Nass, C. 2003. "Emotion in Human-Computer Interaction," in The Human-Computer
Interaction Handbook, J. Jacko and A. Sears (eds.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc., pp. 81-96.
Brief, A.P. 2001. "Organizational Behavior and the Study of Affect: Keep Your Eyes on the Organization," Organizational Behavior and Human Decision Processes (86:1), September, pp. 131-139.
Briggs, R.O., Reinig, B.A., and de Vreede, G.-J. 2008. "The Yield Shift Theory of Satisfaction and Its Application to the IS/IT Domain," Journal of the Association for Information Systems (9:5).
Brown, S.A., Fuller, R.M., and Vician, C. 2004. "Who's Afraid of the Virtual World? Anxiety and Computer-Mediated Communication," Journal of the Association for Information Systems (5:2), pp. 79-107.
Cacioppo, J.T., and Berntson, G.G. 1994. "Relationship between Attitudes and Evaluative Space: A Critical Review, with Emphasis on the Separability of Positive and Negative Substrates," Psychological Bulletin (115), pp. 401-423.
Cacioppo, J.T., Gardner, W.L., and Berntson, G.G. 1999. "The Affect System Has Parallel and Integrative Processing Components: Form Follows Function," Journal of Personality and Social Psychology (76), pp. 839-855.
MISQ Forthcoming Zhang, ARM
54
Carroll, J.M., and Thomas, J.C. 1988. "Fun," SIGCHI Bulletin (19:21-24). Childers, T.L., Carr, C.L., Peck, J., and Carson, S. 2001. "Hedonic and Utilitarian Motivations for Online
Retail Shopping Behavior," Journal of Retailing (77:4), pp. 511-535. Chin, W.W., and Gopal, A. 1995. "Adoption Intention in Gss: Relative Importance of Beliefs," The Data
Base for Advances in Information Systems (26:2-3), Spring-Winter, pp. 42-64. Chowdhury, R.M.M.I., Olsen, G.D., and Pracejus, J.W. 2008. "Affective Responses to Images in Print
Advertising: Affect Integration in a Simultaneous Presentation Context," Journal of Advertising (37:3), pp. 7-29.
Clore, G.L., and Schnall, S. 2005. "The Influence of Affect on Attitude," in Handbook of Attitudes and Attitude Change, D. Albarracin, B.T. Johnson and M.P. Zanna (eds.). Mahwah, NJ: Erlbaum, pp. 437-480.
Clore, G.L., Wyer, R.S., Diened, B., Gasper, K., Gohm, C., and Isbell, L. 2001. "Affective Feelings as Feedback: Some Cognitive Consequences," in Theories of Mood and Cognition: A User's Guide, L.L. Martin and G.L. Clore (eds.). Mahwah, NJ: Lawrence Erlbaum Associates, pp. 27-62.
Compeau, D.R., and Higgins, C.A. 1995. "Computer Self Efficacy: Development of a Measure and Initial Test," MIS Quarterly (19:2), pp. 189-211.
Compeau, D.R., Higgins, C.A., and Huff, S. 1999. "Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study," MIS Quarterly (23:2), pp. 145-158.
Crites, S.L., Jr., Fabrigar, L.R., and Petty, R.E. 1994. "Measuring the Affective and Cognitive Properties of Attitudes: Conceptual and Methodological Issues," Personality and Social Psychology Bulletin (20), pp. 619-634.
Csikszentmihalyi, M. 1975. Beyond Boredom and Anxiety. San Francisco, CA: Jossey-Bass. Csikszentmihalyi, M. 1990. Flow: The Psychology of Optimal Experience. New York: Harpers Perennial. Damasio, A. 2001. "The Fundamental Feelings," Nature (413:6858), October 25, 2001, p. 781. Davis, F. 1993. "User Acceptance of Information Technology: System Characteristics, User Perceptions
and Behavioral Impacts," International Journal of Man-Machine Studies (38:3), 3, pp. 475-487. Davis, F., Bagozzi, R.P., and Warshaw, P.R. 1989. "User Acceptance of Computer Technology: A
Comparison of Two Theoretical Models," Management Science (35:8), August, pp. 982-1003. Davis, F., and Venkatesh, V. 2004. "Toward Preprototyping User Acceptance Testing of New Information
Systems: Implications for Software Project Management," IEEE Transactions on Engineering Management (51:1), February, pp. 31-46.
Eagly, A.H., and Chaiken, S. 1998. "Attitude Structure and Function," in The Handbook of Social Psychology, D.T. Gilbert, S.T. Fiske and G. Lindzey (eds.). New York: Oxford University Press, pp. 269-322.
Ekman, P. 1992. "An Argument for Basic Emotions," Cognition and Emotion (6), pp. 169-200. Fazio, R.H. 1986. "How Do Attitudes Guide Behavior?," in The Handbook of Motivation and Cognition:
Foundations of Social Behavior, R.M. Sorrentino and E.T. Higgins (eds.). New York: Guilford Press, pp. 204-243.
Fazio, R.H., Sanbonmatsu, D.M., Powell, M.C., and Kardes, F.R. 1986. "On the Automatic Activation of Attitude," Journal of Personality and Social Psychology (50), pp. 229-238.
Festinger, L. 1957. A Theory of Cognitive Dissonance. Oxford, England: Row, Peterso. Finneran, C.M., and Zhang, P. 2003. "A Person-Artefact-Task (Pat) Model of Flow Antecedents in
Computer-Mediated Environments," International Journal of Human-Computer Studies (59:4), 10, pp. 475-496.
Finneran, C.M., and Zhang, P. 2005. "Flow in Computer-Mediated Environments: Promises and Challenges," Communications of the Association for Information Systems (15), pp. 82-101.
Fishbein, M., and Ajzen, I. 1975. Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Reading, MA.: Addison-Wesley.
Forgas, J.P. 1995. "Mood and Judgment: The Affect Infusion Model (Aim)," Psychological Bulletin (117:1), 1, pp. 39-66.
Forgas, J.P. 2000. "Feeling Is Believing? The Role of Processing Strategies in Mediating Affective Influences on Beliefs," in Emotions and Beliefs: How Feelings Influence Thoughts, N.H. Frijda, A.S.R. Manstead and S. Bem (eds.). Cambridge, United Kingdom: Cambridge University Press, pp. 108-143.
Forgas, J.P., and George, J.M. 2001. "Affective Influences on Judgments and Behavior in Organizations: An Information Processing Perspective," Organizational Behavior and Human Decision
MISQ Forthcoming Zhang, ARM
55
Processes (86:1), 9, pp. 3-34. Gao, Y., and Koufaris, M. 2006. "Perceptual Antecedents of User Attitude in Electronic Commerce,"
Database for Advances in Information Systems (37:2/3), pp. 42-50. Ghani, J.A. 1995. "Flow in Human Computer Interactions: Test of a Model," in Human Factors in
Information Systems: Emerging Theoretical Bases, J. Carey (ed.). New Jersey: Ablex Publishing Corp., pp. 291-311.
Goodhue, D. 1988. "IS Attitude: Toward Theoretical and Definition Clarity," Data Base (19:3/4), Fall/Winter, pp. 6-15.
Gregor, S. 2006. "The Nature of Theory in Information Systems," MIS Quarterly (30:3), pp. 611-642. Hackbarth, G., Grover, V., and Yi, M.Y. 2003. "Computer Playfulness and Anxiety: Positive and Negative
Mediators of the System Experience Effect on Perceived Ease of Use," Information & Management (40:3), 1, pp. 221-232.
Hassenzahl, M. 2001. "The Effect of Perceived Hedonic Quality on Product Appealingness," International Journal of Human-Computer Interaction (13:4), pp. 481-499.
Hassenzahl, M. 2004. "The Interplay of Beauty, Goodness, and Usability in Interactive Products," Human Computer Interaction (19), pp. 319-349.
Heijden, H.v.d. 2004. "User Acceptance of Hedonic Information Systems," MIS Quarterly (28:4), pp. 695-704.
Hermans, D., De Houwer, J., and Eelen, P. 1994. "The Affective Priming Effect: Automatic Activation of Evaluative Information in Memory," Cognition & Emotion (8:6), pp. 515-533.
Hess, T., Fuller, M., and Mathew, J. 2006. "Involvement and Decision-Making Performance with a Decision Aid: The Influence of Social Multimedia, Gender, and Playfulness," Journal of Management Information Systems (22:3), pp. 15-54.
Igbaria, M., and Parasuraman, S. 1989. "A Path Analytic Study of Individual Characteristics, Computer Anxiety, and Attitudes toward Microcomputers," Journal of Management (15:3), pp. 373-388.
Igbaria, M., Parasuraman, S., and Baroudi, J.J. 1996. "A Motivational Model of Microcomputer Usage," Journal of Management Information Systems (13:1), pp. 127-158.
Isen, A.M., Daubman, K.A., and Nowicki, G.P. 1987. "Positive Affect Facilitates Creative Problem Solving," Journal of Personality and Social Psychology (52), pp. 1122-1131.
Izard, C.E. 1993. "Four Systems for Emotion Activation: Cognitive and Noncognitive Processes," Psychological Review (100:1), 1, pp. 68-90.
Jiang, Z., and Benbasat, I. 2007. "Investigating the Influence of the Functional Mechanisms of Online Product Presentations " Information Systems Research (18:6), pp. 454-470.
Johnson, D., and Wiles, J. 2003. "Effective Affective User Interface Design in Games," Ergonomics (46:13/14), pp. 1332-1345.
Kim, J., Lee, J., and Choi, D. 2003. "Designing Emotionally Evocative Homepages: An Empirical Study of the Quantitative Relations between Design Factors and Emotional Dimensions," International Journal of Human-Computer Studies (59:6), 12, pp. 899-940.
Koufaris, M. 2002. "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information Systems Research (13:2), June, pp. 205-223.
Lazarus, R.S. 1991. Emotion and Adaptation. New York: Oxford University Press. Liljander, V., and Mattsson, J. 2002. "Impact of Customer Preconsumption Mood on the Evaluation of
Employee Behavior in Sevice Encounters," Psychology & Marketing (19:10), pp. 837-860. Lin, C.S., Wu, S., and Tsai, R.J. 2005. "Integrating Perceived Playfulness into Expectation-Confirmation
Model for Web Portal Context " Information & Management (42:5), pp. 682-693. Lindgaard, G., Fernandes, G.J., Dudek, C., and Brownet, J. 2006. "Attention Web Designers: You Have
50 Milliseconds to Make a Good First Impression!," Behaviour & Information Technology (25:2), pp. 115-126.
Lisetti, C.L., and Nasoz, F. 2002. "Maui: A Multimodal Affective User Interface," Proceedings of the Tenth ACM International Conference On Multimedia, France, pp. 161-170.
Loiacono, E., and Djamasbi, S. 2010. "Moods and Their Relevance to Systems Usage Models within Organizations: An Extended Framework," AIS Transactions on Human-Computer Interaction (2:2), pp. 55-72.
McCoy, S., Everard, A., Polak, P., and Galletta, D.F. 2008. "An Experimental Study of Antecedents and Consequences of Online Ad Intrusiveness," International Journal of Human-Computer Interaction (24:7).
MISQ Forthcoming Zhang, ARM
56
Mehrabian, A., and Russell, J.A. 1974. An Approach to Environmental Psychology. Cambridge, MA: The MIT Press.
Mittal, V., and Ross, J., William T. 1998. "The Impact of Positive and Negative Affect and Issue Framing on Issue Interpretation and Risk Taking,," Organizational Behavior and Human Decision Processes (76:3), 12, pp. 298-324.
Moon, J.-W., and Kim, Y.-G. 2001. "Extending the Tam for a World-Wide-Web Context," Information & Management (38:4), 2, pp. 217-230.
Moore, B.S., and Isen, A.M. 1990. "Affect and Social Behavior," in Affect and Social Behavior, B.S. Moore and A.M. Isen (eds.). New York: Cambridge University Press, pp. 1-21.
Morris, W.N. 1989. Mood: The Frame of Mind. New York: Springer-Verlag. Norman, D. 1992. Turn Signals Are the Facial Expressions of Automobiles. New York: Addison-Wesley. Norman, D. 1993. Things That Make Us Smart. New York: Addison-Wesley. Norman, D.A. 1983. "Design Rules Based on Analyses of Human Error," Communications of the ACM
(26:4), pp. 254-258. Norman, D.A. 1988. The Design of Everyday Things. New York: Doubleday. Norman, D.A. 2002. "Emotion and Design: Attractive Things Work Better," Interactions: New Visions of
Human-Computer Interaction (IX:4), July + August, pp. 36-42. Norman, D.A. 2004. Emotional Design: Why We Love (or Hate) Everyday Things. Cambridge, MA: Basic
Books. Norman, D.A., Ortony, A., and Russell, D.M. 2003. "Affect and Machine Design: Lessons for the
Development of Autonomous Machines," IBM Systems Journal (42:1), pp. 38-44. Ortiz de Guinea, A., and Markus, M.L. 2009. "Why Break the Habit of a Lifetime? Rethinking the Roles of
Intention, Habit, and Emotion in Continuing Information Technology Use," MIS Quarterly (33:3). Parboteeah, D.V., Valacich, J.S., and Wells, J.D. 2009. "The Influence of Website Characteristics on a
Consumer's Urge to Buy Impulsively," Information Systems Research (20:1), March, pp. 60-81. Park, J., Stoel, L., and Lennon, S.J. 2008. "Cognitive, Affective and Conative Responses to Visual
Simulation: The Effects of Rotation in Online Product Presentation," Journal of Consumer Behaviour (7:1), Feb, pp. 72-87.
Petty, R.E., DeSteno, d., and Rucker, D.D. 2001. "The Role of Affect in Attitude Change," in Handbook of Affect and Social Cognition, J.P. Forgas (ed.). Mahwah, New Jersey: Lawrence Erlbaum Associates, pp. 212-233.
Petty, R.E., Wegener, D.T., and Fabrigar, L.R. 1997. "Attitudes and Attitude Change," Annual Review of Psychology (48), pp. 609-647.
Pham, M.T., Cohen, J.B., Pracejus, J.W., and Hughes, G.D. 2001. "Affect Monitoring and the Primacy of Feelings in Judgment," Journal of Consumer Research (28:2), pp. 167-188.
Reeve, J. 2005. Understanding Motivation and Emotion, (4th ed.). New York: John Wiley & Sons, Inc. Reinig, B.A., Briggs, R.O., Shepherd, M.M., Yen, J., and Nunamaker, J.F. 1996. "Affective Reward and
the Adoption of Group Support Systems: Productivity Is Not Always Enough," Journal of Management Information Systems (12:3), pp. 171-185.
Remington, N.A., Fabrigar, L.R., and Visser, P.S. 2000. "Reexamining the Circumplex Model of Affect," Journal of Personality and Social Psychology (79:2), 8, pp. 286-300.
Rosenberg, E.L. 1998. "Levels of Analysis and the Organization of Affect," Review of General Psychology (2:3), pp. 247-270.
Russell, J.A. 1980. "A Circumplex Model of Affect," Journal of Personality and Social Psychology (39), pp. 1161-1178.
Russell, J.A. 2003. "Core Affect and the Psychological Construction of Emotion," Psychological Review (110:1), 1, pp. 145-172.
Russell, J.A. 2009. "Emotion, Core Affect, and Psychological Construction," Cognition and Emotion (23:7), pp. 1259-1283.
Schenkman, B.N., and Jonsson, F.U. 2000. "Aesthetics and Preferences of Web Pages," Behaviour & Information Technology (19:5), September, pp. 367-377.
Scherer, K.R. 2004. "Which Emotions Can Be Induced by Music? What Are the Underlying Mechanisms? And How Can We Measure Them?," Journal of New Music Research (33:3).
Scherer, K.R. 2005. "What Are Emotions? And How Can They Be Measured?," Social Science Information (44:4), pp. 695-729.
Schlosberg, H. 1941. "A Scale for the Judgment of Facial Expressions," Journal of Experimental
MISQ Forthcoming Zhang, ARM
57
Psychology (29), pp. 497-510. Schlosberg, H. 1952. "The Description of Facial Expression in Terms of Two Dimensions," Journal of
Experimental Psychology (44), pp. 229-237. Schwarz, N. 2001. "Feelings as Information: Implications for Affective Influences on Information
Processing," in Theories of Mood and Cognition: A User's Guide, L.L. Martin and G.L. Clore (eds.). Mahwah, NJ: Lawrence Erlbaum Associates, pp. 159-176.
Schwarz, N., and Clore, G.L. 1983. "Mood, Misattribution, and Judgments of Well-Being: Informative and Directive Functions of Affective States," Journal of Personality and Social Psychology (45:3), pp. 513-523.
Soldat, A.S., Sinclair, R.C., and Mark, M.M. 1997. "Color as an Environmental Processign Cue: External Affective Cues Can Directly Affect Processing Strategy without Affecting Mood," Social Cognition (15:1), pp. 55-71.
Sun, H., and Zhang, P. 2006a. "The Role of Affect in IS Research: A Critical Survey and a Research Model," in Human-Computer Interaction and Management Information Systems: Foundations, P. Zhang and D. Galletta (eds.). Armonk, NY: M.E. Sharpe, pp. 295-329.
Sun, H., and Zhang, P. 2006b. "The Role of Moderating Factors in User Technology Acceptance," International Journal of Human-Computer Studies (64:2), February, pp. 53-78.
Taylor, S., and Todd, P. 1995. "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research (6:2), pp. 144-176.
Te'eni, D. 2001. "Review: A Cognitive-Affective Model of Organizational Communication for Designing IT," MIS Quarterly (25:2), pp. 251-312.
Teo, H.-H., Oh, L.-B., Liu, C., and Wei, K.-K. 2003. "An Empirical Study of the Effects of Interactivity on Web User Attitude," International Journal of Human-Computer Studies (58:3), 3, pp. 281-305.
Thatcher, J.B., and Perrewe, P.L. 2002. "An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy," MIS Quarterly (26:4), December, pp. 381-396.
Thong, J.Y.L., Hong, S.-J., and Tam, K.Y. 2006. "The Effects of Post-Adoption Beliefs on the Expectation-Confirmation Model for Information Technology Continuance," International Journal of Human-Computer Studies (64:9), pp. 799-810.
Tractinsky, N., Cokhavi, A., Kirschenbaum, M., and Sharfi, T. 2006. "Evaluating the Consistency of Immediate Aesthetic Perceptions of Web Pages," International Journal of Human Computer Studies (64:11), pp. 1071-1083.
Trevino, L.K., and Webster, J. 1992. "Flow in Computer-Mediated Communication," Communication Research (19:5), pp. 539-573.
Valdez, P., and Mehrabian, A. 1994. "Effects of Color on Emotions," Journal of Experimental Psychology: General (123:4), 12, pp. 394-409.
Vallacher, R.R., and Webner, D.M. 1987. "What Do People Think They're Doing? Action Identification and Human Behavior," Psychological Review (94:1), pp. 3-15.
van Schaik, P., and Ling, J. 2009. "The Role of Context in Perceptions of the Aesthetics of Web Pages over Time," International Journal of Human-Computer Studies (67), pp. 79-89.
Venkatesh, V. 1999. "Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation," MIS Quarterly (23:2), June, pp. 239-260.
Venkatesh, V. 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research (11:4), pp. 342-365.
Venkatesh, V., and Bala, H. 2008. "Technology Acceptance Model 3 and a Research Agenda on Interventions," Decision Sciences (39:2), pp. 273-.
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. 2003. "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly (27:3), September, pp. 425-478.
Venkatesh, V., and Speier, C. 1999. "Computer Technology Training in the Workplace: A Longitudinal Investigation of the Effect of Mood," Organizational Behavior and Human Decision Processes (79:1), pp. 1-28.
Watson, D., and Clark, L.A. 1984. "Negative Affectivity: The Disposition to Experience Aversive Psychological States," Psychological Bulletin (96), pp. 465-490.
Watson, D., and Clark, L.A. 1994. "Emotions, Moods, Traits, and Temperaments: Conceptual Distinctions and Empirical Findings," P. Ekman and R.J. Davidson (eds.). New York: Oxford University Press, pp. 89-93.
MISQ Forthcoming Zhang, ARM
58
Watson, D., Clark, L.A., and Tellegen, A. 1988. "Development and Validation of Brief Measures of Positive and Negative Affect: The Panas Scales," Journal of Personality and Social Psychology (54:4), pp. 1063-1070.
Watson, D., Wiese, D., Vaidya, J., and Tellegen, A. 1999. "The Two General Activation Systems of Affect: Structural Findings, Evolutionary Considerations, and Psychobiological Evidence," Journal of Personality and Social Psychology (76:5), pp. 820-838.
Webster, J., and Martocchio, J.J. 1992. "Microcomputer Playfulness: Development of a Measure with Workplace Implications," MIS Quarterly (16:1), pp. 201-226.
Webster, J., and Martocchio, J.J. 1995. "The Differential Effects of Software Training Previews on Training Outcomes," Journal of Management (21:4), pp. 757-787.
Wegener, D.T. 2001. "Understanding Effects of Mood through the Elaboration Likelihood and Flexible Correction Models," in Theories of Mood and Cognition: A User's Guide, L.L. Martin and G.L. Clore (eds.). Mahwah, NJ: Lawrence Erlbaum Associates, pp. 177-210.
Weiss, H.M., and Cropanzano, R. 1996. "Affective Events Theory: A Theoretical Discussion of the Causes and Consequences of Affective Experiences at Work," in Research in Organizational Behavior, B.M. Staw and L.L. Cummings (eds.). Greenwich, CT: JAI Press, pp. 1-75.
Weiss, H.M., Nicholas, J.P., and Daus, C.S. 1999. "An Examination of the Joint Effects of Affective Experiences and Job Beliefs on Job Satisfaction and Variations in Affective Experiences over Time," Organizational Behavior and Human Decision Processes (78:1), 4, pp. 1-24.
Wixom, B.H., and Todd, P.A. 2005. "A Theoretical Integration of User Satisfaction and Technology Acceptance " Information Systems Research (16:1), pp. 85-102.
Yi, M.Y., and Hwang, Y. 2003. "Predicting the Use of Web-Based Information Systems: Self-Efficacy, Enjoyment, Learning Goal Orientation, and the Technology Acceptance Model," International Journal of Human-Computer Studies (59:4), 10, pp. 431-449.
Yik, M.S.M., Russell, J.A., and Barrett, L.F. 1999. "Structure of Self-Reported Current Affect: Integration and Beyond,," Journal of Personality and Social Psychology (77:3), 9, pp. 600-619.
Zajonc, R.B. 1980. "Feeling and Thinking: Preferences Need No Inferences," American Psychologist (35:2), February, pp. 151-175.
Zajonc, R.B. 1984. "On the Primacy of Affect," American Psychologist (36:2), pp. 117-123. Zajonc, R.B. 2000. "Feeling and Thinking," in Feeling and Thinking, J.P. Forgas (ed.). New York:
Cambridge University Press, pp. 31-58. Zaman, M., Anandarajan, M., and Dai, Q. 2010. "Experiencing Flow with Instant Messaging and Its
Facilitating Role on Creative Behaviors," Computers in Human Behavior (26:5), pp. 1009-1018. Zanna, M.P., and Rempel, J.K. 1988. "Attitudes: A New Look at an Old Concept," in Social Psychology of
Knowledge, D. Bar-Tal and A.W. Kruglanski (eds.). New York: Cambridge University Press, pp. 315-334.
Zhang, P., Aikman, S.N., and Sun, H. 2008. "Two Types of Attitudes in ICT Acceptance and Use," International Journal of Human-Computer Interaction (24:7), pp. 628-648.
Zhang, P., and Li, N. 2004. "Love at First Sight or Sustained Effect? The Role of Perceived Affective Quality on Users' Cognitive Reactions to Information Technology," International Conference on Information Systems (ICIS'04), Washington, D.C., pp. 283-296.
Zhang, P., and Li, N. 2005. "The Importance of Affective Quality," Communications of the ACM (48 9), September, pp. 105-108.
Zhang, P., Li, N., and Sun, H. 2006. "Affective Quality and Cognitive Absorption: Extending Technology Acceptance Research," Hawaii International Conference on System Sciences (HICSS), Kauai, Hawaii.
Zhang, P., and Sun, H. 2009. "The Complexity of Different Types of Attitude in Initial and Continued ICT Use," Journal of American Society for Information Science and Technology (60:10), October, pp. 2048-2060.
MISQ Forthcoming Zhang, ARM
59
About the Author Ping Zhang is a professor in the School of Information Studies at Syracuse University, and
holds multi-year guest professor positions with the Inner Mongolia University, Renmin
University, and Xi’An Jiao Tong University in China. Her research interests include the
intellectual development of information related fields; human-centeredness in ICT development,
evaluation and use; affective, cognitive, motivational and behavioral aspects of individual
reactions towards ICT; and the impact of ICT design and use on individuals, organizations,
societies and cultures. She has published in information systems, human-computer interaction
and information science journals and conference proceedings. She is co-editor (with Dennis
Galletta) of two edited books on HCI and MIS of the Advances in MIS series (by M.E. Sharpe,
2006), and is co-author (with Dov Te’eni and Jane Carey) of the first HCI textbook for non-CS
students (by John Wiley, 2007). She and Dennis Galletta are founding Editors-in-Chief for AIS
Transactions on Human-Computer Interaction since 2008. In addition, she is a former Senior
Editor for the Journal of Associations for Information Systems, guest Senior Editor for MIS
Quarterly, former Associate Editor for the International Journal of Human-Computer Studies
and Communications of Association for Information Systems, on the editorial board of Journal of
Management Information Systems, and a guest Senior Editor of eight special issues.
MISQ Forthcoming Zhang, ARM
60
Table 1. Fundamental and Basic Concepts
Concept Definition and Characteristics
Core affect
An intrinsic aspect of consciousness (Barrett et al. 2007) that is mental but not cognitive or reflective (Zajonc 2000). A neurophysiologic state consciously accessible as a simple, non-reflective feeling inside oneself. The specific feeling itself may change from time to time, but a person will always have some feeling (core affect) at any moment. Core affect may have no known causes (mood) or be linked to stimuli (such as perceptions of affective quality and emotions). It is a primitive concept and fundamental for all affective events (Barrett and Russell 1999; Russell 1980; Russell 2003; Russell 2009).
Stimulus That which a person responds to. It is a psychological representation, thus can be real, imagined, fictions, remembered, anticipated, and in forms of virtual reality. Also called “the Object” (Russell 2003).
Perceived Affective Quality
One’s estimation of a stimulus’ ability to change his or her core affect. It begins with a specific stimulus and stays with the stimulus to emphasize on the stimulus’ affective properties rather than on a person’s feelings. Different people may perceive the same stimulus to have different affective qualities (Russell 2003).
Affective Quality A stimulus’ capacity to cause a change in a person’s core affect. It is independent of the perceivers because it is the stimulus’ attributes or properties (Mehrabian and Russell 1974; Russell 2003).
Affective Cue Specific features or characteristics of a stimulus that can manifest the affective quality of the stimulus. Also regarded as environmental cues or signals with affective information (Soldat et al. 1997).
Mood Prolonged core affect with no stimulus (simple mood) or with quasi-stimulus. It is often regarded as an affective state without a specific stimulus (Lazarus 1991; Moore and Isen 1990; Watson and Clark 1994).
Temperament A characteristic, habitual inclination, or mode of emotional response (Watson and Clark 1994).
Emotion
An affective state induced by or attributed to a specific stimulus. Emotions typically arise as reactions to situational events and objects in one’s environment that are relevant to the needs, goals, or concerns of an individual. Emotion emphasizes a person’s subjective feeling. The feeling is short-lived, existing only as long as the supporting cognitions, perceptions, or other elicitors are active, and vanishing as soon as one is no longer in that state. An emotional episode depicts the complex process of the emotion in responding to a stimulus (Russell 2003; Scherer 2005).
Attitude
A summary evaluation of a stimulus that may help guide behavior regarding that stimulus; can be considered as either a multidimensional construct comprised of cognitive, affective, and behavioral components, or a two dimensional construct with instrumental (mostly cognitive) and experiential aspects (mostly affective) (Ajzen and Fishbein 2005).
Table 2. Affective Concepts in Relation to the Person and/or Stimulus
Reside within a person Reside within a stimulus Reside between a person and a stimulus Mood Temperament
Affective Cue Affective Quality
Emotion Perceived Affective Quality Attitude Affective Evaluation Affective Reaction/Response
Note: Core affect is a fundamental concept that is the basis to all other affective concepts making them affective in nature. Therefore, core affect is not treated in parallel with other affective concepts, thus not listed here.
MISQ Forthcoming Zhang, ARM
61
Table 3. Affective Concepts by Temporal and Stimulus Conditions (Adapted from Clore and Schnall 2005; Clore et al. 2001)
Temporally Constrained (State)
Temporally Unconstrained (Disposition)
No Stimulus (Reside within a person)
Mood Temperament
Stimulus (reside between a person and a stimulus)
Emotion Perceived affective quality Attitude Affective evaluation
Affective Reaction/Response
Table 4. Taxonomy of Affective Concepts: Super Categories and Categories
Residing within a person Residing within a
stimulus
Residing between a person and a stimulus (Affective Responses)
Temporally Constrained
(State)
Temporally Unconstrained (Disposition)
Temporally Constrained
(State)
Temporally Unconstrained
(Evaluation/Disposition)
(1) Free-floating Affective State (e.g. Mood)
(2) Affectivity (e.g. Temperament)
(3) Affective Characteristics (e.g. Affective Quality, Affective Cue)
(4) Induced Affective State (e.g. Emotion)
Particular Stimulus
General Stimulus
Process-Based Outcome-Based O
bjec
t St
imul
us (5.1)
Process-based Affective Evaluation Toward a Particular Object
(5.2) Outcome-based Affective Evaluation Toward Behaviors on a Particular Object
(7) Learned Affective Evaluation/ Disposition Toward a Type of Objects
Beh
avio
r St
imul
us (6.1)
Process-based Affective Evaluation Toward Behaviors on a Particular Object
(6.2) Outcome-based Affective Evaluation Toward Behaviors on a Particular Object
(8) Learned Affective Evaluation/ Disposition Toward Behaviors on a Type of Objects
MISQ Forthcoming Zhang, ARM
62
Table 5. Definitions and Characteristics of Super Categories and Categories in ARM
Super Category/Category Name ID Definitions and Characteristics
Free-floating Affective State 1 Affective state with unclear contributing stimulus. Affectivity 2 Habitual inclinations, dispositions, tendencies, personality traits.
Affective Characteristics 3 A stimulus’ features, properties, or natures that contain affective information independent of the perceivers.
Affective Response, Affective Reaction
4,5,6,7,8
A general term to represent affective concepts whose meanings reside between a person and a stimulus. Two broad types are induced affective states and affective evaluations.
Induced Affective State 4 Affective state with a clear contributing stimulus. Emphasize on the feelings the perceiver has, rather than the ability or affective characteristics of the stimulus.
Affective Evaluation 5,6,7,8
A general term to represent a set of affective concepts that represent the person’s appraisals of the stimulus’ affective quality. The meanings of affective evaluations are between a person and a stimulus, and an affective evaluation’ values are temporally unconstrained.
Particular Affective Evaluation 5,6 A general term for affective evaluations of a particular ICT object and
behaviors.
Process-based Affective Evaluation toward a particular object
5.1
Appraisals during an episode from exposure to (either immediate or long duration) in-depth interaction with a particular ICT. It is a particular ICT object. Can be automatic, spontaneous, quick, and immediate. Can be long-lasting or short-lived, and is easily modified.
Outcome-based Affective Evaluation toward a particular object
5.2 Summative, concluding the direct experience. Toward a particular object. A higher or global level evaluation beyond the interaction process.
Process-based Affective Evaluation toward behaviors on a particular object
6.1 Appraisals during direct experience/interaction with a particular ICT. Toward behaviors on the particular ICT. Experiential in nature. Focus on the process. Can be long lasting, or short lived, and is easily modified.
Outcome-based Affective Evaluation toward behaviors on a particular object
6.2 Summative, concluding the direct experience. Toward behaviors on the particular object. Focus on the outcome. A higher or global-level evaluation beyond the interaction process.
Learned Affective Evaluation/Disposition 7,8
A general term for affective evaluations of general types of ICT. Dispositions, inclinations, or predispositions toward stimuli. These can be results of higher-level reflections stored in memory. These can be toward future thinking, being, or action readiness.
Learned Affective Evaluation/Disposition toward a type of objects
7 A tendency to form certain affective evaluations based on prior experience or knowledge. Toward a general type of ICT objects. Long-lasting, learned over time, stored in memory, and not so easy to change.
Learned Affective Evaluation/Disposition toward behaviors with a type of objects
8
A tendency to form certain affective evaluations based on prior experience or knowledge. Toward behaviors with a general type of objects. Long-lasting, learned over time, stored in memory, and not so easy to change.
MISQ Forthcoming Zhang, ARM
63
Figure 1. The Structure of Affect: the Circumplex Model (Russell 2003)
Figure 2. A Generic Nomological Net for Causal Relationships
P4 P6
4: Induced Affective States(e.g. Emotions)
5, 6: ParticularAffective Evaluations
Affective Responses in an ICT Interaction Episode
7, 8: Learned Affective Evaluations/Dispositions
P1
P0
P2
P3 P5ICT Stimulus
3: Affective Characteristics(Affective Cue
Affective Quality)
Human1: Free-floating State
(Mood)2: Affectivity
(Temperament)
AffectiveAntecedences
MISQ Forthcoming Zhang, ARM
64
Figure 3. A Detailed Nomological Net for Causal Relationships
P4
P6
Affective Responses in an ICT Interaction Episode
P1
P0
P2
P3
P5
Outcome-basedAffective Evaluations
5.2: Toward Object
6.2: Toward Behavior
P9
Process-basedAffective Evaluations
5.1: Toward Object
6.1: Toward Behavior
P8
LearnedAffective Evaluations/Dispositions
7: Toward Object
8: Toward Behavior
P10
Particular Affective Evaluations
P7
4: Induced Affective States(e.g. Emotions)
ICT Stimulus3: Affective Characteristics
(Affective CueAffective Quality)
Human1: Free-floating State
(Mood)2: Affectivity
(Temperament)
AffectiveAntecedences
MISQ Forthcoming Zhang, ARM
65
Appendix A. Summary of Affective Concepts/Constructs in the ICT Literature ARM ID Original Construct Article Operational Definition
1 Perceived mood (Ang et al. 1993) (no definition) Measured by: The feedback giver Looked as if he had had a bad day; Seemed to be in a good mood today; Looked as if he did not want to be disturbed
1 Mood (Loiacono and Djamasbi 2010)
An individual’s mild, enduring, and objectless affective state (Fredrickson, 2003; Isen et al., 2003; Lazarus, 1991). One’s global feeling state at a given time. Moods are not necessarily a product of reflection or cognitive analysis, but simply describe how people feel at a moment.
2 Trait anxiety (Igbaria and Parasuraman 1989)
Reflects a chronic and generalized predisposition to be anxious and nervous.
2 Playfulness as Trait (Venkatesh 1999) The pleasure and inherent satisfaction derived from a specific activity. 2 Negative Affectivity (Thatcher and Perrewe 2002) The general experience of negative emotions such as guilt or shame regardless of the situation 2 Trait anxiety (Thatcher and Perrewe 2002) The general feeling of anxiety when confronted with problems or challenges. 3 Design factors (Kim et al. 2003) Of background: shape, texture, color. Of relation: match 9title, menu, images) 3 Visual Characters of
WebPages (Lindgaard et al. 2006) (no definition) Measured by interesting-boring, good design-bad design, good colour-bad colour, good
layout-bad layout, imaginative-unimaginative, simple-complex, clear-confusing 3 Webpage Aesthetics (Robins and Holmes 2008) A low aesthetic treatment (LAT) is one in which content is simply placed on a web site without
professional graphic design. A high aesthetic treatment (HAT) presents a professional look and feel appropriate to the organization it represents.
3 Mood relevant cues (Parboteeah et al. 2009) The characteristics, such as visual appeal, that affect the degree to which a user enjoys browsing a website but that do not directly support a particular shopping goal.
4 Flow (Ghani et al. 1991) Holistic sensations that people feel when they act with total involvement 4 Flow (Trevino and Webster 1992) Characterizes the perceived interaction with CMC technologies as more or less playful and
exploratory. Has 4 dimensions: control, focused attention, aroused curiosity, intrinsically interested. 4 Positive Mood (Webster and Martocchio
1992) No specific definition provided. Just implied it is a “demonstrated subjective experiences resulting from higher playfulness”.
4 Flow (Webster and Martocchio 1995)
Is characterized by arousal of curiosity and by the extent to which the user finds the interaction intrinsically interesting
4 Mood (Venkatesh and Speier 1999) A state variable, refers to how people feel when they are engaged in any number of activities (George & Jones, 1996)
4 Cognitive Absorption (Agarwal and Karahanna 2000)
Cognitive absorption is a state of deep involvement with IT. It has five components including curiosity, control, temporal dissociation focused immersion, heightened enjoyment.
4 Satisfaction (Bhattacherjee 2001; Bhattacherjee and Premkumar 2004)
A psychological or affective state related to and resulting from a cognitive appraisal of the expectation-performance discrepancy.
4 Concentration (Koufaris 2002) The holistic sensation that people feel when they act with total involvement. 4 Secondary Emotion (Kim et al. 2003) A non-basic, individual-dependent, and domain specific emotions derived from the primary emotions
(Averill, 1994). A concept that is closely related to the secondary emotion is aesthetic responses or affects. The secondary emotion is usually elicited by external stimuli.
4 Anxiety (Venkatesh et al. 2003) Evoking anxious or emotional reactions when it comes to performing a behavior (e.g., using a computer). Measured by: I feel apprehensive about using the system; It scares me to think that I could lose a lot of information using the system by hitting the wrong key; I hesitate to use the system for fear of making mistakes I
MISQ Forthcoming Zhang, ARM
66
ARM ID Original Construct Article Operational Definition cannot correct; The system is somewhat intimating to me.
4 Computer/system anxiety (Hackbarth et al. 2003; Hwang and Kim 2007)
The apprehension or fear that results when an individual is faced with the possibility of using an IS.
4 CMC anxiety as application specific anxiety
(Brown et al. 2004) An individual’s level of fear or apprehension associated with actual or anticipated use of IT to communicate with others. Measured by: Using email makes me nervous; using email makes me uneasy; I feel comfortable using email (R); I would be comfortable sending email messages that I know a lot of people will read (R); while composing an email message to someone I don't know, I feel tense; I would be fearful of sending email to someone I don't know.
4 Flow (Hsu and Lu 2004) An extremely enjoyable experience, where an individual engages in an online game activity with total involvement, enjoyment, control, concentration, and intrinsic interest
4 Positive emotion, negative emotion
(Cenfetelli 2004) (no definition) Measured by Diener et al. 95 (negative = shame, embarrassment, loneliness, fear, depression, sadness, rage, nervousness, disgust, regret, worry, anger, unhappiness, anxiety, irritation. Positive = happiness, contentment, love, affection, caring, pride, fondness, joy)
4 Customer Satisfaction (Kim et al. 2004) Is an affective state that is the emotional reaction to a transaction experience (Spreng et al., 1996). Measured by: I am satisfied with the transaction with this store; I am pleased ...; I am contented ...; I am delighted …
4 Perceived Playfulness (Lin et al. 2005) Is regarded as an individual state because an individual can feel more or less playful at various points during his/her visit to a web portal.
4 Feeling (pleasure, arousal) (Kim et al. 2007) Feelings and emotions are treated synonymously. Has pleasure and arousal as main components of feelings: pleasure – the degree to which a user feels good or happy with the target object; arousal – refers to the degree to which a user feels excited, stimulated, or active.
4 Concentration Transcendence of self Transformation of time Perceived control Mergence of action and awareness Autotelic experience
(Guo and Poole 2009) All based on Csikszentmihalyi’s flow theory.
4 Joy & Fear (Li et al. 2008) Emotional responses to interacting with a vendor’s website: joy is positive affective state, fear is negative affective state.
4 Irritation (McCoy et al. 2008) Irritation by ads is a feeling of annoyance as the advertisement has interrupted the user so much that she is unable to continue her task.
4 Emotion (excitement, happiness, anger, anxiety)
(Beaudry and Pinsonneault 2010)
Emotions are defined as a mental state of readiness for action that arises from the appraisal of an IT event (in this study, the IT event is the announcement of the imminent deployment of a new system)
4 Positive Affect (Zaman et al. 2010) Co-occurrence of positive emotional states, such as joy, interest, excitement and confidence 4 Flow (Zaman et al. 2010) Two dimensions: concentration and enjoyment. (no definition for neither) 4 Affective involvement (Jiang et al. 2010) Refers to the heightened emotional feelings associated with a website and is made up of feeling states. 4 Negative reaction to
scanning (Suh et al. 2011) (no definition) Measured by:
Describe the extent to which the following wards describe your typical feelings when being scanned (Diener et al., 1995): Shame, sadness, anger
5.1 First Impression (Schenkman and Jonsson 2000)
The first visual impression that a person gets of a webpage.
MISQ Forthcoming Zhang, ARM
67
ARM ID Original Construct Article Operational Definition 5.1 Aesthetics (of the
WebPages)
(Hall and Hanna 2004) To the extent a webpage is pleasing and stimulating to the eye.
5.1 Hedonic Attributes (Hassenzahl 2004) One of the two groups of a product's perceived characters: the one that are primarily related to the users' self. Contains two aspects: stimulation (being challenging and novel; a prerequisite of personal development, i.e. the proliferation of knowledge and development of skills) and identification (the human need to express one’s self through objects).
5.1 Perceived affective quality (Benlian et al. 2010; Zhang and Li 2004; Zhang and Li 2005; Zhang et al. 2006)
An individual’s perception of the ability of a stimulus such as IT to change his or her core affect
5.1 Immediate Impression of Visual Appeal
(Lindgaard et al. 2006) A physiological, hard-wired reaction to objects in one’s environment that requires no learning.
5.1 Immediate Aesthetic Perception
(Tractinsky et al. 2006) Visually pleasing
5.1 Classic and Expressive Aesthetic Perceptions
(Tractinsky et al. 2006) Classic: orderliness or clarity of the design. Expressive: the creativity and the richness of the design
5.1 Beauty (Tractinsky et al. 2006) Perception of aesthetics of ATM layouts 5.1 Perceived Entertainment (Gao and Koufaris 2006) Reflects a website’s ability to enhance the experience of visitors to the site. 5.1 Perceived Irritation (Gao and Koufaris 2006) Can manifest itself in the form of visitor feelings of confusion, distraction, messiness, the structure,
navigation, function or design elements of a site. 5.1 Perception of an IT’s
capability to induce positive affect and negative affect
(Zhang and Li 2007) Perception of an IT’s Capability to induce Positive Affect (PC-PA) is an individual’s perception or evaluation that an IT has the capability to induce positive affect in him or her; and Perception of an IT’s Capability to induce Negative Affect (PC-NA) is the person’s perception that an IT has the capability to induce negative affect in him or her.
5.1 Aesthetic Perception (van Schaik and Ling 2009) Perceived attractiveness 5.2 Attitude (Trevino and Webster 1992) Attitude toward particular CMC technology email and voice mail. Measured by:
Dreary/fun, unpleasant/enjoyable, cold/warm, mundane/challenging, humanizing/dehumanizing 5.2 WebPage preference (Schenkman and Jonsson
2000) (no definition) Single item measure: preferred completely
5.2 Beauty (Hassenzahl 2004) A high level evaluative construct that is an expression of authoritative judgment of being ugly or beautiful (vs. substantive or low level determinants such as elegance)
5.2 Attitude (Brown et al. 2004) Is a person’s affective evaluation of a specific object 5.2 Attitude (Galletta et al. 2004) Is defined as the satisfaction with the site. 5.2 Satisfaction (attitude
toward an object) (Wixom and Todd 2005) A person's feelings or attitudes toward a variety of factors affecting that situation.
5.2 Satisfaction with Decision Aid
(Hess et al. 2006) Attitudes toward this task or object
5.2 Attitude toward a site (Gao and Koufaris 2006) A positive affective variable. 5.2 Affective Commitment (Li et al. 2006) A situation in which an end user demonstrates an affective and emotional attachment to the
relationship with an e-vendor 5.2 Attitude (McCoy et al. 2008) Overall reactions to the website 5.2 Emotional attachment (Suh et al. 2011) An emotion-laden, target-specific bond between a person and a 67pecific object (Thomson et al. 2005,
p77). A personal’s affection for a specific object connected with him or her such as pets, gifts, or a
MISQ Forthcoming Zhang, ARM
68
ARM ID Original Construct Article Operational Definition brand.
6.1 Perceived Enjoyment (Chin and Gopal 1995; Chin et al. 2003; Davis et al. 1992; Hong and Tam 2006; Hwang and Kim 2007; Igbaria et al. 1996; Lin et al. 2005; Parboteeah et al. 2009; Thong et al. 2006; van der Heijden 2004; Venkatesh 2000; Venkatesh and Bala 2008; Yi and Hwang 2003)
The extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated (Davis et al. 1992, p. 1113)
6.1 Intrinsic Motivation (operationalized as perceived enjoyment)
(Venkatesh 1999; Venkatesh et al. 2003; Venkatesh and Speier 1999; Venkatesh et al. 2002)
The extent to which the activity of using the computer is perceived to be enjoyable in its own right, apart from any performance consequences that may be anticipated (Davis et al. 1992, p. 1113)
6.1 Emotion (Agarwal and Venkatesh 2002; Venkatesh and Agarwal 2006; Venkatesh and Ramesh 2006)
The extent to which a website evokes emotional reactions from you (a user).
6.1 Perceived Playfulness (Chung and Tan 2003; Moon and Kim 2001)
The strength of one’s belief that interacting with the WWW will fulfill the user’s intrinsic motives. Has three aspects: attention is focused, curious during interaction, interaction enjoyable/interesting.
6.1 Shopping Enjoyment (Jiang and Benbasat 2007; Koufaris 2002)
An affective or emotional response as part of experience in shopping online. Measured by: I found my visit interesting, enjoyable, exciting, fun.
6.1 Perceived Playfulness (Fang et al. 2006) The extent to which the activity of using a specific system is perceived to be enjoyable in its own right, aside from any performance consequences resulting from system us.
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
6.2 Affect (Thompson et al. 1991) Affect toward PC use: the feelings of joy, elation, or pleasure, or depression, disgust, displeasure, or hate associated by an individual with a particular act. Affect is the affective component of attitude. Measured by: PCs made work more interesting; working with PCs was fun, PCs were all right for some jobs but not the kind of job wanted.
6.2 Affect (Thompson and Higgins 1994)
Feeling toward using a personal computer
6.2 Attitude (Karahanna et al. 1999) The individual’s positive and negative evaluations of performing the behavior 6.2 Affect (= attitude) (Limayem and Hirt 2003) Emotional response to the thought of the behavior 6.2 Attitude (Mathieson 1991) The user’s evaluation of the desirability of his or her using the system 6.2 Attitude (Bhattacherjee and Sanford
2006; Moon and Kim 2001; Taylor and Todd 1995)
Satisfaction with process and outcomes; The strength of one’s willingness to use an ICT. Measurement adopted from Taylor & Todd 95: Using (XYZ) in my job is a (bad … good) idea; (foolish … wise) idea; (unpleasant … pleasant) idea; overall, I (dislike … like) the idea of using (XYZ) in my job.
6.2 Affective Reward (Reinig et al. 1996) Sense of emotional gratification often expressed by participants after a successful meeting, or the positive emotional response sometimes associated with goal attainment. Measured by:
MISQ Forthcoming Zhang, ARM
69
ARM ID Original Construct Article Operational Definition This process was stimulating, fulfilling, arousing; today’s meeting was satisfying, dissatisfying; this session was dull, boring, interesting; I felt motivated to generate a large number of ideas; solving the problem was gratifying; I did not enjoy myself; it felt like we won; we really accomplished something here today; I’d like to participate in another scenario.
6.2 Attitude (Jackson et al. 1997) An individual’s positive or negative feelings (evaluative effect) about performing the target behavior (from Fishbein and Ajzen, 1975)
6.2 Satisfaction (Devaraj et al. 2002) An ex post evaluation of consumers' experience with the service and is captured as a positive feeling, indifference, or a negative feeling.
6.2 Satisfaction (Teo et al. 2003) Satisfaction towards commercial website measures the affective appeal of commercial Web sites through a sense of involvement, control and affective feelings.
6.2 Attitude (Bhattacherjee and Premkumar 2004)
Personal affect toward IT usage. Measured by: All things considered, using ~ will be a: 1. Bad idea/good idea; 2. Foolish move/wise move; 3. Negative step/positive step. 4. Ineffective idea/effective idea.
6.2 Attitude (Hong et al. 2004; Hong et al. 2005; Malhotra and Galletta 2005; Wixom and Todd 2005)
An individual’s positive or negative feelings about performing a behavior (Ajzen & Fishbein 80)
6.2 Satisfaction (Lin et al. 2005) Satisfaction in using web portal. Measurement adapted from Spreng & and Olshavsky 93: Using the web portal makes me feel very satisfied; very pleased; very contented; very delighted
6.2 Attitude (Lim et al. 2006) The belief that purchasing from iBook should, with good probability, result in either an overall positive or an overall negative outcome.
6.2 Attitude (Kim et al. 2007) A summary evaluation of a psychological object (by Ajzen 2001) captured in both the functional and hedonic dimensions. Attitude highlights summary evaluation.
6.2 Attitude (Jiang and Benbasat 2007) Attitude toward shopping at a website refers to their overall evaluations of a shopping experience at a particular website. Measured by: I like shopping on this website; shopping on this website is a good idea; shopping on this website is appealing.
6.2 Enjoyment (Nah et al. 2011) The hedonic outcome that can result from the experience. Measured by: I found my virtual tour of <xxx> enjoyable, boring, interesting, fun.
7 Attitude (Igbaria and Parasuraman 1989)
Attitudes toward microcomputers are conceptualized as having three components: cognitive (knowledge or perception of the object), affective (feelings or emotional reactions) and behavioral (predisposition to act in a certain way toward the object).
7 Attitude (Agarwal and Prasad 1999) The mediating affective response between beliefs and usage intentions. A learned implicit response that refers to an individual’s evaluation of a concept.
7 Attitude (Teo et al. 2003) Predispositions to respond in a particular way towards a specified class of objects (Rosenberg, 1960). Affective component refers to the feelings formed without conscious thoughts and they can be expressed in verbal statements of affect. Cognitive component consists of the ideas and beliefs formed through conscious thoughts and they can be expressed in verbal statements of beliefs and values.
8 Computer anxiety (Igbaria and Parasuraman 1989)
The tendency of a person to be uneasy, apprehensive, or fearful about the current or future use of computers in general.
8 Microcomputer Playfulness/computer playfulness
(Agarwal and Karahanna 2000; Agarwal and Prasad 1998; Hess et al. 2006;
The degree of cognitive spontaneity in microcomputer interactions (Webster & Martocchio 92), an individual difference variable
MISQ Forthcoming Zhang, ARM
70
ARM ID Original Construct Article Operational Definition Venkatesh 2000; Venkatesh and Bala 2008; Webster and Martocchio 1992)
8 Cognitive playfulness of microcomputers
(Webster and Martocchio 1995)
A situation-specific individual characteristic that represents a type of intellectual playfulness. The degree of cognitive spontaneity in microcomputer interactions.
8 Affect (Compeau and Higgins 1995; Compeau et al. 1999)
Liking of particular behavior. Measurement drawn from Computer Attitudes Scale (Loyd and Gressard, 84): I like working with computers; I look forward to those aspects of my job that require me to use a computer; Once I start working on the Computer, I find it hard to stop; Using a computer is frustrating for me; I get bored quickly when working on a computer
8 Computer Anxiety (Brown et al. 2004; Compeau and Higgins 1995; Compeau et al. 1999; Harrison and Rainer Jr 1992; Venkatesh 2000; Venkatesh and Bala 2008; Webster and Martocchio 1992)
The tendency of individuals to be uneasy, apprehensive, or fearful about current or future use of computers.
8 Computer Anxiety (Thatcher and Perrewe 2002) About the implications of computer use such as the loss of important data or fear of other possible mistakes.
8 Computer Playfulness (Hackbarth et al. 2003) Refers to an individual’s tendency to interact spontaneously with a computer (Webster 89). It is defined as being a system specific trait that can change because the experience in using a specific technology increases over time.
MISQ Forthcoming Zhang, ARM
71
Appendix B. Summary of Relationships Among Affective Concepts in the ICT Literature (See Appendix A for definitions of the involved affective concepts)
Article Empirical Relationship Theoretical Justification/Implication Corresponding ARM Proposition
(Igbaria and Parasuraman 1989)
Trait anxiety -> Computer anxiety More general anxieties are determinants of more specific ones. 2 -> 8 P0b Computer anxiety -> attitudes toward computers
Computer anxiety operates an intervening variable between individual differences and attitudes toward computers. A reduction in computer anxiety will improve attitudes toward computers.
8 -> 7 P10
(Trevino and Webster 1992)
Flow -> attitudes toward CMC technologies
Previous studies show that positive affect, pleasure, and satisfaction result from the flow experience; IS that provides more perceived control (a flow dimension) results in more positive user attitudes and satisfaction.
4 -> 5.2 P3
(Webster and Martocchio 1995)
Cognitive playfulness of microcomputer -> flow Flow is an outcome of the individual characteristics of playfulness. 8 -> 4 P1
(Venkatesh and Speier 1999)
Mood -> Intrinsic motivation (perceived enjoyment)
Based on both the associative network model and the resource allocation model. Positive moods result in more favorable assessments of one’s abilities thus potentially increasing perceptions of enjoyment and thereby intrinsic motivation. Additionally, individuals in positive moods tend to use heuristic (as opposed to analytical) processing, resulting in increased creativity and playfulness, leading to greater task enjoyment and thus greater intrinsic motivation. Negative moods result in more pessimistic assessments regarding oneself and the adequacy of existing knowledge, which in turn generates uncertainty and/or lack of confidence in one’s ability and can result in a negative judgment towards a given situation.
4 -> 6.1 P3
(Agarwal and Karahanna 2000)
Computer playfulness -> cognitive absorption Individual traits are likely to have an effect on experiential states. 8 -> 4 P1
(Schenkman and Jonsson 2000)
First impression <-> Webpage preference
In the framework of evolutionary psychology, the appreciation of beauty is seen as hard-wired into our genetic set-up and the aesthetic feeling fulfills an adaptive, biological function.
5.1 -> 5.2 P7
(Moon and Kim 2001) Perceived playfulness -> attitude toward use
Attitudinal outcomes, such as positive affect, pleasure, and satisfaction, result from the playful experience. 6.1 -> 6.2 P7
(Thatcher and Perrewe 2002)
Trait affectivity -> Computer anxiety
Dynamic, IT specific individual differences (i.e., computer anxiety) are a function of stable situation-specific (i.e., personal innovativeness in IT) and broad (i.e., negative affectivity and trait anxiety) traits.
2 -> 8 P0b
(Teo et al. 2003) Satisfaction with a website -> attitude towards websites
Attitude is shaped through the internalization of value formed through affective and cognitive evaluations. 6.2 -> 7 P6
(Brown et al. 2004) Computer Anxiety -> CMC anxiety as application specific anxiety More general anxieties are determinants of more specific ones. 8 -> 4 P1
CMC anxiety as application specific anxiety -> Attitude toward use
Individuals high in computer anxiety will have negative attitudes toward using a computer. Due to its application-specific focus, CMC anxiety is a more proximal predictor of attitude toward a CMC application than either computer anxiety or communication apprehension. Thus, it should exhibit a significant effect on attitudes regarding the CMC application, such that individuals with high CMC anxiety would have less favorable attitudes toward using the CMC.
4 -> 6.2 P3
MISQ Forthcoming Zhang, ARM
72
Article Empirical Relationship Theoretical Justification/Implication Corresponding ARM Proposition
(Hassenzahl 2004) Hedonic attributes <-> beauty Beauty is rather related to self-oriented, hedonic attributes of a product than to its goal-oriented, pragmatic attributes. 5.1 <-> 5.2 P7
(Lin et al. 2005) Perceived playfulness -> Satisfaction in using web portal
Previous research has shown that higher degrees of pleasure and involvement during computer interactions lead to concurrent subjective perceptions of positive affects and satisfaction. Attitudinal outcomes, such as positive affect, pleasure, and satisfaction, resulted from playful experiences.
4 -> 6.2 P3
(Wixom and Todd 2005)
Satisfaction (attitude toward an object) -> cognitive perceptions -> attitude toward behavior
Object-based attitudes influence behavior-based attitudes via cognitive perceptions. 5.2 -> 6.2 P9
(Lindgaard et al. 2006) Visual characters of webpages <-> immediate impression of visual appeal
According to the mere exposure effect (Zajonc 1980), one can quickly form immediate visual appeal impression even given an extremely short period of exposure time that does not permit cognitive processing. Feelings happen to us whether we like it or not, and they can happen in a matter of a few milliseconds.
3 -> 5.1 P0
(Gao and Koufaris 2006)
Perceived entertainment -> attitude toward the website Perceived irritation -> attitude toward the website
Uses and gratifications research indicates that the entertainment value of a commercial exchange lies in its ability to fulfill the audience's needs for escapism, diversion, aesthetic enjoyment, or emotional release. Research in traditional advertising identified irritation as a significant factor that influences consumer attitude.
5.1 -> 5.2 P7
(Jiang and Benbasat 2007)
Shopping enjoyment -> Attitude toward shopping at a website
The degree to which a website is visually attractive, fun, and interesting is perceived as part of the website’s system quality, which directly affects consumer satisfaction. Similarly, entertainment features that enhance shopping enjoyment improve consumers’ attitudes toward shopping at a website. Therefore, the more enjoyment consumers derive from a shopping experience, the more likely that customers would prefer their online shopping experience.
6.1 -> 6.2 P7
(Kim et al. 2007) Feeling (pleasure, arousal) -> attitude toward using mobile internet services
Users may feel pleasure as well as arousal from the services. These feelings could influence attitude according to the Elaboration Likelihood Model, where feelings operate through peripheral route processing by means of classical conditioning. Previous research has also shown that affect can influence the formation of attitude in the absence of product beliefs. Therefore, attitude formation can be done via direct affect transfer.
4 -> 6.2 P3
(Parboteeah et al. 2009) Mood relevant cues -> perceived enjoyment
The stimulus-organism-response (S-O-R) paradigm posits that environmental cues act as stimuli that influence an individual’s cognitive and affective reactions, which in turn affect behaviors (Mehrabian and Russell 1974)
3 -> 6.1 P0c
(Zaman et al. 2010) Flow -> positive affect Even though flow is a positive emotional state, Csikszentmihalyi (1996) argued that positive affect and flow are two distinct constructs. He explained that, while experiencing flow, a person does not realize the joy. A person feels ‘‘only what is relevant to the activity” (p.123), and anything else would be a distraction. It is shortly after experiencing flow that they have a positive affect towards the activity.
4 ->4 P11
MISQ Forthcoming Zhang, ARM
73
References for the Appendices Agarwal, R., and Karahanna, E. 2000. "Time Flies When You're Having Fun: Cognitive Absorption and
Beliefs About Information Technology Usage," MIS Quarterly (24:4), pp. 665-694. Agarwal, R., and Prasad, J. 1998. "A Conceptual and Operational Definition of Personal Innovativeness
in the Domain of Information Technology," Information Systems Research (9:2), pp. 204-215. Agarwal, R., and Prasad, J. 1999. "Are Individual Differences Germane to the Acceptance of New
Information Technologies?," Decision Sciences (30:2), Spring, pp. 361-391. Agarwal, R., and Venkatesh, V. 2002. "Assessing a Firm's Web Presence: A Heuristic Evaluation
Procedure for the Measurement of Usability," Information Systems Research (13:2), pp. 168-186. Ang, S., Cummings, L.L., Straub, D.W., and Earley, P.C. 1993. "The Effects of Information Technology
and the Perceived Mood of the Feedback Giver on Feedback Seeking," Information Systems Research (4:3), pp. 240-261.
Beaudry, A., and Pinsonneault, A. 2010. "The Other Side of Acceptance: Studying the Direct and Indirect Effects of Emotions on Information Technology Use," MIS Quarterly (34:4), pp. 689-710.
Benlian, A., Titach, R., and Hess, T. 2010. "Provider- Vs. User-Generated Recommendations on E-Commerce Websites - Comparing Cognitive, Affective and Relational Effects," International Conference on Information Systems (ICIS), St. Louis.
Bhattacherjee, A. 2001. "Understanding Information Systems Continuance: An Expectation-Confirmation Model," MIS Quarterly (25:3), September, pp. 351-370.
Bhattacherjee, A., and Premkumar, G. 2004. "Understanding Changes in Belief and Attitude toward Information Technology Usage: A Theoretical Model and Longitudinal Test," MIS Quarterly (28:2), pp. 229-254.
Bhattacherjee, A., and Sanford, C. 2006. "Influence Processes for Information Technology Acceptance: An Elaboration Likelihood Model," MIS Quarterly (30:4), pp. 805-826.
Brown, S.A., Fuller, R.M., and Vician, C. 2004. "Who's Afraid of the Virtual World? Anxiety and Computer-Mediated Communication," Journal of the Association for Information Systems (5:2), pp. 79-107.
Cenfetelli, R. 2004. "Getting in Touch with Our Feelings Towards Technology," Proceedigns of Academy of Management Annual Meeting.
Chau, P.Y.K., and Hu, P.J. 2001. "Information Technology Acceptance by Individual Professionals: A Model Comparison Approach," Decision Sciences (32:4), Fall 2001, p. 699.
Chin, W.W., and Gopal, A. 1995. "Adoption Intention in Gss: Relative Importance of Beliefs," The Data Base for Advances in Information Systems (26:2-3), Spring-Winter, pp. 42-64.
Chin, W.W., Marcolin, B.L., and Newsted, P.R. 2003. "A Partial Least Squares Latent Variable Modeling Approach for Measuring Interaction Effects:Results from a Monte Carlo Simulation Study and an Electronic-Mail Emotion/Adoption Study," in: Information Systems Research.
Chung, J., and Tan, F.B. 2003. "Antecedents of Perceived Playfulness: An Exploratory Study on User Acceptance of General Information-Searching Websites " Information & Management (41:7), pp. 869-881.
Compeau, D.R., and Higgins, C.A. 1995. "Computer Self Efficacy: Development of a Measure and Initial Test," MIS Quarterly (19:2), pp. 189-211.
Compeau, D.R., Higgins, C.A., and Huff, S. 1999. "Social Cognitive Theory and Individual Reactions to Computing Technology: A Longitudinal Study," MIS Quarterly (23:2), pp. 145-158.
Davis, F. 1989. "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology," MIS Quarterly (13:3), September, pp. 319-340.
Davis, F., Bagozzi, R.P., and Warshaw, P.R. 1989. "User Acceptance of Computer Technology: A Comparison of Two Theoretical Models," Management Science (35:8), August, pp. 982-1003.
Davis, F., Bagozzi, R.P., and Warshaw, P.R. 1992. "Extrinsic and Intrinsic Motivation to Use Computers in the Workplace," Journal of Applied Social Psychology (22:1111-1132).
MISQ Forthcoming Zhang, ARM
74
Devaraj, S., Fan, M., and Kohli, R. 2002. "Antecedents of B2c Channel Satisfaction and Preference: Validating E-Commerce Metrics," Information Systems Research (13:3), pp. 316-333.
Fang, X., Chan, S., Brzezinski, J., and Xu, S. 2006. "Moderating Effects of Task Type on Wireless Technology Acceptance," Journal of Management Information Systems (22:3), pp. 123-158.
Galletta, D.F., Henry, R., McCoy, S., and Polak, P. 2004. "Web Site Delays: How Tolerant Are Users?," Journal of the Association for Information Systems (5:1), pp. 1-28.
Gao, Y., and Koufaris, M. 2006. "Perceptual Antecedents of User Attitude in Electronic Commerce," Database for Advances in Information Systems (37:2/3), pp. 42-50.
Ghani, J.A., Supnick, R., and Rooney, P. 1991. "The Experience of Flow in Computer-Mediated and in Face-to-Face Groups," Proceedings of the Twelfth International Conference on Information Systems, J.I. DeGross, I. Benbasat, G. DeSanctis and C.M. Beath (eds.), New York, NY.
Guo, Y.M., and Poole, M.S. 2009. "Antecedents of Flow in Online Shopping: A Test of Alternative Models," Information Systems Journal (19:4), pp. 369-390.
Hackbarth, G., Grover, V., and Yi, M.Y. 2003. "Computer Playfulness and Anxiety: Positive and Negative Mediators of the System Experience Effect on Perceived Ease of Use," Information & Management (40:3), 1, pp. 221-232.
Hall, R.H., and Hanna, P. 2004. "The Impact of Web Page Text-Background Colour Combinations on Readability, Retention, Aesthetics and Behavioural Intention," Behaviour & Information Technology (23:3), pp. 183 - 195.
Harrison, A.W., and Rainer Jr, R.K. 1992. "The Influence of Individual Differences on Skill in End-User Computing," Journal of Management Information Systems (9:1), pp. 93-112.
Hassenzahl, M. 2004. "The Interplay of Beauty, Goodness, and Usability in Interactive Products," Human Computer Interaction (19), pp. 319-349.
Hess, T., Fuller, M., and Mathew, J. 2006. "Involvement and Decision-Making Performance with a Decision Aid: The Influence of Social Multimedia, Gender, and Playfulness," Journal of Management Information Systems (22:3), pp. 15-54.
Hong, S.-J., and Tam, K.Y. 2006. "Understanding the Adoption of Multipurpose Information Appliances: The Case of Mobile Data Services " Information Systems Research (17:2).
Hong, W., Thong, J.Y.L., and Tam, K.Y. 2004. "Does Animation Attract Online Users’ Attention? The Effects of Flash on Information Search Performance and Perceptions," Information Systems Research (15:1), March, pp. 60-86.
Hong, W., Thong, J.Y.L., and Tam, K.Y. 2005. "The Effects of Information Format and Shopping Task on Consumers’ Online Shopping Behavior: A Cognitive Fit Perspective," Journal of Management Information Systems (21:3), pp. 149-184.
Hsu, C.-L., and Lu, H.-P. 2004. "Why Do People Play on-Line Games? An Extended Tam with Social Influences and Flow Experience," Information & Management (41:7), September, pp. 853-868.
Hwang, Y., and Kim, D.J. 2007. "Customer Self-Service Systems: The Effects of Perceived Web Quality with Service Contents on Enjoyment, Anxiety, and E-Trust," Decision Support Systems (43), pp. 746-760.
Igbaria, M., and Parasuraman, S. 1989. "A Path Analytic Study of Individual Characteristics, Computer Anxiety, and Attitudes toward Microcomputers," Journal of Management (15:3), pp. 373-388.
Igbaria, M., Parasuraman, S., and Baroudi, J.J. 1996. "A Motivational Model of Microcomputer Usage," Journal of Management Information Systems (13:1), pp. 127-158.
Jackson, C.M., Chow, S., and Leitch, R.A. 1997. "Toward an Understanding of the Behavioral Intention to Use an Information System," Decision Sciences (28:2), Spring, pp. 357-389.
Jiang, Z., and Benbasat, I. 2007. "Investigating the Influence of the Functional Mechanisms of Online Product Presentations " Information Systems Research (18:6), pp. 454-470.
Jiang, Z., Chan, J., Tan, B.C.Y., and Chua, W.S. 2010. "Effects of Interactivity on Website Involvement and Purchase Intention," Journal of the Association for Information Systems (11:1), pp. 34-59.
MISQ Forthcoming Zhang, ARM
75
Karahanna, E., Straub, D.W., and Chervany, N. 1999. "Information Technology Adoption across Time: A Cross-Sectional Comparison of Pre-Adoption and Post-Adoption Beliefs," MIS Quarterly (23:2), June, pp. 183-213.
Kim, H.-W., Chan, H.C., and Chan, Y.P. 2007. "A Balanced Thinking–Feelings Model of Information Systems Continuance," International Journal of Human-Computer Studies (65), pp. 511–525.
Kim, H.-W., Xu, Y., and Koh, J. 2004. "A Comparison of Online Trust Building Factors between Potential Customers and Repeat Customers," Journal of the Association for Information Systems (5:10), pp. 392-420.
Kim, J., Lee, J., and Choi, D. 2003. "Designing Emotionally Evocative Homepages: An Empirical Study of the Quantitative Relations between Design Factors and Emotional Dimensions," International Journal of Human-Computer Studies (59:6), 12, pp. 899-940.
Koufaris, M. 2002. "Applying the Technology Acceptance Model and Flow Theory to Online Consumer Behavior," Information Systems Research (13:2), June, pp. 205-223.
Li, D., Browne, G.J., and Chau, P.Y.K. 2006. "An Empirical Investigation of Web Site Use Using a Commitment-Based Model," Decision Sciences (37:3), pp. 427-444.
Li, H., Sarathy, R., and Zhang, J. 2008. "The Role of Emotions in Shaping Consumers' Privacy Beliefs About Unfamiliar Online Vendors," Journal of Information Privacy & Security (4:3), pp. 36-62.
Lim, K.H., Sia, C.L., Lee, M.K.O., and Benbasat, I. 2006. "Do I Trust You Online, and If So, Will I Buy? An Empirical Study of Two Trust-Building Strategies," Journal of Management Information Systems (23:2), pp. 233-266.
Limayem, M., and Hirt, S.G. 2003. "Force of Habit and Information Systems Usage: Theory and Initial Validation," Journal of Association for Information Systems (4:3), August, pp. 65-97.
Lin, C.S., Wu, S., and Tsai, R.J. 2005. "Integrating Perceived Playfulness into Expectation-Confirmation Model for Web Portal Context " Information & Management (42:5), pp. 682-693.
Lindgaard, G., Fernandes, G.J., Dudek, C., and Brownet, J. 2006. "Attention Web Designers: You Have 50 Milliseconds to Make a Good First Impression!," Behaviour & Information Technology (25:2), pp. 115-126.
Loiacono, E., and Djamasbi, S. 2010. "Moods and Their Relevance to Systems Usage Models within Organizations: An Extended Framework," AIS Transactions on Human-Computer Interaction (2:2), pp. 55-72.
Malhotra, Y., and Galletta, D.F. 2005. "A Multidimensional Commitment Model of Volitional Systems Adoption and Usage Behavior," Journal of Management Information Systems (22:1), pp. 117-151.
Mathieson, K. 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research (2:3), pp. 173-191.
McCoy, S., Everard, A., Polak, P., and Galletta, D.F. 2008. "An Experimental Study of Antecedents and Consequences of Online Ad Intrusiveness," International Journal of Human-Computer Interaction (24:7).
Mehrabian, A., and Russell, J.A. 1974. An Approach to Environmental Psychology. Cambridge, MA: The MIT Press.
Moon, J.-W., and Kim, Y.-G. 2001. "Extending the Tam for a World-Wide-Web Context," Information & Management (38:4), 2, pp. 217-230.
Nah, F.F.-H., Eschenbrenner, B., and DeWester, D. 2011. "Enhancing Brand Equity through Flow and Telepresence: A Comparison of 2d and 3d Virtual Worlds," MIS Quarterly (35:3), pp. 731-747.
Parboteeah, D.V., Valacich, J.S., and Wells, J.D. 2009. "The Influence of Website Characteristics on a Consumer's Urge to Buy Impulsively," Information Systems Research (20:1), March, pp. 60-81.
Reinig, B.A., Briggs, R.O., Shepherd, M.M., Yen, J., and Nunamaker, J.F. 1996. "Affective Reward and the Adoption of Group Support Systems: Productivity Is Not Always Enough," Journal of Management Information Systems (12:3), pp. 171-185.
Robins, D., and Holmes, J. 2008. "Aesthetics and Credibility in Web Site Design," Information Processing and Management (44), pp. 386-399.
MISQ Forthcoming Zhang, ARM
76
Schenkman, B.N., and Jonsson, F.U. 2000. "Aesthetics and Preferences of Web Pages," Behaviour & Information Technology (19:5), September, pp. 367-377.
Suh, K.-S., Kim, H., and Suh, E.K. 2011. "What If Your Avatar Looks Like You? Dual-Congruity Perspectives for Avatar Use," MIS Quarterly (35:3), pp. 711-729.
Taylor, S., and Todd, P.A. 1995. "Understanding Information Technology Usage: A Test of Competing Models," Information Systems Research (6:2), pp. 144-176.
Teo, H.-H., Oh, L.-B., Liu, C., and Wei, K.-K. 2003. "An Empirical Study of the Effects of Interactivity on Web User Attitude," International Journal of Human-Computer Studies (58:3), 3, pp. 281-305.
Thatcher, J.B., and Perrewe, P.L. 2002. "An Empirical Examination of Individual Traits as Antecedents to Computer Anxiety and Computer Self-Efficacy," MIS Quarterly (26:4), December, pp. 381-396.
Thompson, R.L., and Higgins, C.A. 1994. "Influence of Experience on Personal Computer Utilization: Testing a Conceptual Model," Journal of Management Information Systems (11:1), pp. 167-187.
Thompson, R.L., Higgins, C.A., and Howell, J.M. 1991. "Personal Computing toward a Conceptual Model of Utilization," MIS Quarterly (15:1), pp. 125-136.
Thong, J.Y.L., Hong, S.-J., and Tam, K.Y. 2006. "The Effects of Post-Adoption Beliefs on the Expectation-Confirmation Model for Information Technology Continuance," International Journal of Human-Computer Studies (64:9), pp. 799-810.
Tractinsky, N., Cokhavi, A., Kirschenbaum, M., and Sharfi, T. 2006. "Evaluating the Consistency of Immediate Aesthetic Perceptions of Web Pages," International Journal of Human Computer Studies (64:11), pp. 1071-1083.
Trevino, L.K., and Webster, J. 1992. "Flow in Computer-Mediated Communication," Communication Research (19:5), pp. 539-573.
van der Heijden, H. 2004. "User Acceptance of Hedonic Information Systems," MIS Quarterly (28:4), December, pp. 695-704.
van Schaik, P., and Ling, J. 2009. "The Role of Context in Perceptions of the Aesthetics of Web Pages over Time," International Journal of Human-Computer Studies (67), pp. 79-89.
Venkatesh, V. 1999. "Creation of Favorable User Perceptions: Exploring the Role of Intrinsic Motivation," MIS Quarterly (23:2), June, pp. 239-260.
Venkatesh, V. 2000. "Determinants of Perceived Ease of Use: Integrating Control, Intrinsic Motivation, and Emotion into the Technology Acceptance Model," Information Systems Research (11:4), pp. 342-365.
Venkatesh, V., and Agarwal, R. 2006. "Turning Visitors into Customers: A Usability-Centric Perspective on Purchase Behavior in Electronic Channels," Management Science (52:3), 03/01/, pp. 367-382.
Venkatesh, V., and Bala, H. 2008. "Technology Acceptance Model 3 and a Research Agenda on Interventions," Decision Sciences (39:2), pp. 273-.
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. 2003. "User Acceptance of Information Technology: Toward a Unified View," MIS Quarterly (27:3), September, pp. 425-478.
Venkatesh, V., and Ramesh, V. 2006. "Web and Wireless Site Usability: Understanding Differences and Modeling Use," MIS Quarterly (30:1), pp. 181-206.
Venkatesh, V., and Speier, C. 1999. "Computer Technology Training in the Workplace: A Longitudinal Investigation of the Effect of Mood," Organizational Behavior and Human Decision Processes (79:1), pp. 1-28.
Venkatesh, V., Speier, C., and Morris, M.G. 2002. "User Acceptance Enablers in Individual Decision Making About Technology: Toward an Integrated Model," Decision Sciences (33:2), Spring, p. 297.
Webster, J., and Martocchio, J.J. 1992. "Microcomputer Playfulness: Development of a Measure with Workplace Implications," MIS Quarterly (16:1), pp. 201-226.
Webster, J., and Martocchio, J.J. 1995. "The Differential Effects of Software Training Previews on Training Outcomes," Journal of Management (21:4), pp. 757-787.
Wixom, B.H., and Todd, P.A. 2005. "A Theoretical Integration of User Satisfaction and Technology Acceptance " Information Systems Research (16:1), pp. 85-102.
MISQ Forthcoming Zhang, ARM
77
Yi, M.Y., and Hwang, Y. 2003. "Predicting the Use of Web-Based Information Systems: Self-Efficacy, Enjoyment, Learning Goal Orientation, and the Technology Acceptance Model," International Journal of Human-Computer Studies (59:4), 10, pp. 431-449.
Zaman, M., Anandarajan, M., and Dai, Q. 2010. "Experiencing Flow with Instant Messaging and Its Facilitating Role on Creative Behaviors," Computers in Human Behavior (26:5), pp. 1009-1018.
Zhang, P., and Li, N. 2004. "Love at First Sight or Sustained Effect? The Role of Perceived Affective Quality on Users' Cognitive Reactions to Information Technology," International Conference on Information Systems (ICIS'04), Washington, D.C., pp. 283-296.
Zhang, P., and Li, N. 2005. "The Importance of Affective Quality," Communications of the ACM (48 9), September, pp. 105-108.
Zhang, P., Li, N., and Sun, H. 2006. "Affective Quality and Cognitive Absorption: Extending Technology Acceptance Research," Hawaii International Conference on System Sciences (HICSS), Kauai, Hawaii.
Zhang, P., and Li, N.L. 2007. "Positive and Negative Affect in IT Evaluation: A Longitudinal Study," Annual Workshop on Human-Computer Interaction Research in Management Information Systems, Montreal, Canada.