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Limits of user acceptance model proposed in the literature
Whether the TAM has brought interesting insights to the study of user acceptance, many researchers have criticized user acceptance models such as the TAM. The most common criticism of user acceptance models is that they focus principally on a person‘s cognitions regarding an innovation and do not capture all of antecedents of behaviours such as emotional processes (Bagozzi, 2007). Indeed, emotions are widely recognized as a critical predictor of human behaviours and technology can trigger both positive and negative feelings (Mick & Fournier, 1998). As highlighted in Bagozzi (2007), consumers can, on the positive side, be pleasantly surprised, excited, and confident as they consider the adoption of technology, whereas on the negative side people can be annoyed, worried, or scared. Indeed, individuals are looking for ―fun, amusement, fantasy, arousal, sensory stimulation and enjoyment‖ (Hirschman & Holbrook, 1982, p. 4). It is nowadays commonly admitted that individuals decide to use or not an innovation not only to obtain useful benefits but also to enjoy the experience of using them.
Thus, emotion-based models are needed to complement rational-based approaches. In the next section, we review recent research addressing the influence of emotion on the decision to use innovation. We will see that emotion has been conceptualized in different ways within user acceptance research. Thus in the next section, we use for the sake of clarity the terms referring to emotion as they are originally used in the studies described.
Toward an emotional view of user innovation acceptance
Users do not only perform behaviour in order to achieve utilitarian goals (i.e. improve job performance) but also for hedonic reasons because it triggers positive emotions. That is, from now on, researchers are motivated to examine and incorporate emotional experiences in our understanding of user behaviour. By integrating emotion, researchers attempted to explain more variance in users‘ intention and behaviour (Davis, Bagozzi & Warshaw, 1992). Here, we describe how emotion is conceptualized in innovation research.
Intrinsic motivation: Perceived enjoyment and flow experience
The extension of user acceptance models to motivational models has probably been a first step towards taking into account the emotional factors. The motivational models introduce two constructs: extrinsic motivation and intrinsic motivation. The former refers to an individual‘s personal benefits associated with using an innovation. The latter relates to the desire to perform the behaviour because it is enjoyable (Vallerand, 1997). In this section, we describe two types of intrinsic motivation: perceived enjoyment and flow experience.
Nowadays, it is widely recognized that intrinsic motivation such as enjoyment/fun, playfulness and flow experience are key drivers of individual‘s intention to perform behaviour (Davis, Bagozzi & Warshaw, 1992; Igbaria, Parasuraman, & Baroudi, 1996).
First of all, the construct of enjoyment refers to the extent to which using an innovation is perceived to be enjoyable distinct from any performance results that might be obtained (Venkatesh & Speier, 1999). Empirical findings have shown that enjoyment is sometimes a determinant of behavioural intention (Davis et al., 1992; Ha, Yoon, & Choi, 2007) and other times a determinant of perceived ease of use (Sun & Zhang, 2008; Venkatesh, 2000) or both perceived usefulness and perceived ease of use (Yi & Hwang, 2003). For instance, Koufaris (2002) has shown that enjoyment is positively related to one‘s intention to return to an online shopping website. Therefore, researchers have extended the TAM by including ―perceived enjoyment‖ or ―fun‖ (Bruner & Kumar, 2005; Davis et al., 1989; Davis et al., 1992; Ha et al., 2007).
Secondly, the concept of enjoyment is considered by Moon and Kim (2001) as one of the dimensions of the concept of playfulness. So, the concepts of enjoyment and fun are directly associated to the concept of playfulness. Playfulness refers to three dimensions (i) concentration, (ii) curiosity and (iii) enjoyment (Moon & Kim, 2001). People experiencing playfulness are considered to be more absorbed and interested in their interaction.
At last, most of research on playfulness is based on the concept of flow experience (Csikszentmihalyi & Csikszentmihalyi, 1975) which refers to ―the holistic experience that people feel when they act with total involvement‖ (p. 36). In the literature, flow has been treated as multi-dimensional construct with several characteristics such as enjoyment and concernment (Ghani & Deshpande, 1994), control, attention, curiosity and intrinsic interest (Trevino & Webster, 1992). Agarwal and Karahanna (2000) considered cognitive absorption (CA) as a state of flow and they have described five of its dimensions in the context of software – temporal dissociation, focused immersion, enjoyment, control and curiosity. Moreover, some researchers have examined how ―flow experience‖ influence motivation adoption. For example, Hsu and Lu (2004) have revealed that the acceptance of on-line games can be predicted by extended TAM and that flow experience significantly and directly affected intentions to play on-line games.
In general, concepts of enjoyment, playfulness or flow experience seem to be good predictors of intention to use an innovative product or service. However, these concepts seem more appropriate to study the ―acceptation‖ of an innovation rather than the ―acceptability‖ since it involves the use or the manipulation of the innovation before deciding. Indeed, as mentioned by Terrade and collaborators, the intention to use an innovation can be studied before the user have had the opportunity to manipulate the innovation (i.e. acceptability) or when the user have had the possibility to manipulate at least once the innovation (i.e. acceptation) (Terrade, Pasquier, Reerinck-Boulanger, Guingouain, & Somat, 2009). We will see that a large part of research on intention to use an innovation focuses on ―acceptation‖ rather than on ―acceptability‖.
The Influence of Specific Emotions on Judgment and Decision Making.
Alarge number of papers has focused on the relationship between affect and cognitive processes (e.g. attention, judgment) and especially on the influence of mood (instead of emotion) in cognitive processes (see Bagozzi, Gopinath & Nyer, 1999; Cohen, Pham, Andrade, 2008 for reviews). Much of early evidence has shown that affective states color our perception of the world and influence our behaviours. For instance, it has been found that objects or individuals are judged more favourably when one is in a good moon than in a bad mood (Forgas & Bower, 1987; Forgas & Moylan, 1991; Isen, Shalker, Clark, & Karp, 1978a; Mayer, Gaschke, Braverman, & Evans, 1992). This is known in the literature as the congruency effects of mood – the tendency for people to judge objects or situations in accordance with their affective mood state. Furthermore, numerous studies have suggested that affective states influence the depth of information processing. For example, research in the field of persuasion has found that people in a good mood are more influenced by peripheral elements of messages than people in a sad or neutral mood (Mackie & Worth, 1989; Schwarz, Bless, & Bohner, 1991). That is, ―heuristic cues‖ allow one to make decision without going in a careful information processing. In a broad sense, negative mood have been associated with careful processing and positive mood with superficial processing.
How to explain these results? The classical literature suggests the existence of various mechanisms to explain the effects of mood states on what people think and also how they think (see Lerner & Tiedens, 2006). First, associative network mechanism refer to the fact that emotion-congruent-judgments arise because of associations to mind (Bower, 1981). Thus, when an affective state arises, the semantic categories related to this state become available and guide the encoding, retrieval and interpretation of information. Second, informational mechanism refers to the fact that people attend to their feeling as a source of information in forming judgments towards objects and people (Schwarz & Clore, 1988; Schwarz & Clore, 1983). That is, when faced with an evaluative judgment, one would ask the implicit question ―how-do-I feel-about-it‖. So, a positive state would lead to a positive evaluation of the stimulus at hand whereas a negative state would lead to a negative evaluation. Lastly, motivational mechanism allows explaining effects of mood states on information processes. It refers to the fact that individuals are, in general, motivated to avoid negatives states and maintain positives states (Bless, Bohner, Schwarz, & Strack, 1990; Isen & Levin, 1972). Thus, people in a good mood would avoid investing cognitive resources in tasks which might change their emotional state. In literature, positive moods have been linked to a ―mood maintenance‖ motive whereas negative moods have been linked to a ―mood repair‖ motive (Isen & Geva, 1987; Isen, Nygren, & Ashby, 1988).
Originally, these mechanisms have been identified under a valence-based approach to emotions, i.e. on whether an affect is positive or negative. In fact, prior research has shown that consumer behave differently in positive versus negative mood. Yet, recent studies have revealed more nuanced influences of specific emotions. For example, Lerner and Keltner (2001) have shown that emotions of the same valence (e.g., anger and fear) can have distinct effects in choice and judgments, whereas emotions with different valence (e.g., anger and happiness) can have similar effects.
In the next section, we review contemporary insights and findings of psychology of emotions in the context of judgment and decision making. We first consider the Appraisal-Tendency framework (ATF) (Lerner & Keltner, 2000), which allow understanding and predicting effects of emotions. Then, we discuss the link between emotion and motivation, an issue at the center of our research. Finally, we review contemporary empirical research to emotion.
Beyond Valence: The Appraisal-Tendency Framework
In a large and pioneering body of research, Lerner and Keltner (2000) have suggested that a specific-based approach is more effective to understand and predict the influence of emotions on judgment and decision making. Their model, the Appraisal-Tendency framework (ATF) is widely used today to make predictions concerning the influences of specific emotions on judgement and choice (DeSteno, Dasgupta, Bartlett, & Cajdric, 2004; Lowenstein & Lerner, 2003; Tiedens & Linton, 2001).
The ATF is based on two conceptual frameworks: first appraisal approaches to emotion, based on diverse cognitive dimensions (Ellsworth & Smith, 1988; Lazarus, 1991; Roseman, 1984, 1991; Scherer, 2001; Smith & Ellsworth, 1985) and second functional approaches to emotion, resting on the assumption that emotions serve a co-ordination role and that they trigger a set of responses (physiology, behaviour, experience, and communication) (Frijda, 1986).
Therefore, while early studies of emotion tended to focus only on the dimension of valence, the ATF assumes that emotions activate appraisal tendencies (automatic processes) that guide subsequent judgment and decisions. The term valence refers to the extent that an experience is pleasant or unpleasant, positive or negative, good or bad. That is, positive and negative moods have been experimentally induced or observed naturalistically, and these general feeling states have been expected to produce more positive and negative judgments respectively (Isen, Shalker, Clark, & Karp, 1978b; Johnson & Tversky, 1983; Kavanagh & Bower, 1985; Mayer et al., 1992; Wright & Bower, 1992).
The ATF has led to major advances in emotion research. Indeed, going beyond the dominant valence approaches, a growing literature has demonstrated that two emotions of the same valence affect judgments in a very different way. For instance, individuals feeling anger tend to make optimistic judgments about future events, whereas individuals feeling fear tend to be more pessimistic (Lerner & Keltner, 2000). Besides, when faced with a gambling and job selection task, anxious people tend to prefer uncertainty reducing options (low-risk/low-reward option), whereas sad people tend to prefer reward seeking options (high-risk/high-reward options) (Raghunathan & Pham, 1999). So, together these findings support posit of the AFT that specific emotions influence decision making in a manner consistent with the emotion‘s underlying appraisal tendency. That is, emotions can be characterized not only by the primary appraisal of valence but also by a number of secondary appraisals including perceptions of certainty (how certain am I about the situation?), required attention and effort (how much attention do I need to devote to the situation?) and so on. Additional research has further investigated the influence of specific emotions on information processing. For example, Tiedens and Linton (2001) have shown that a state of anger lead to heuristic strategies of information processing whereas a state of sadness lead to systematic information processing. These results are consistent with findings from Weary and Jacobson (1997) who found that people who feel ―uncertain‖ process information more systematically that people who feel ―certain‖.
Dynamic affect regulation model versus static affective evaluation model
How might incidental emotions influence behavior and cognitive processes? Two theoretical perspectives are generally used to answer this question: static affective evaluation models and dynamic affect regulation model. First, static affective evaluation theories include theories such as affect as information (Schwarz & Clore, 1983) and mood-congruency (Bower, 1981; Isen et al., 1978b). These models focus on the impact of affective states on cognition during an evaluative judgment (so, at a single point in time). They suggest that affect influences cognition and action tendencies either directly, by providing people with unique information (Schwarz & Clore, 1983), or more indirectly, by making mood congruent information more accessible in people minds (Bower, 1981; Isen et al., 1978). Second, dynamic affect regulation theories mostly rely on theories such as mood-maintenance (Clark & Isen, 1982), coping (Lazarus & Folkman, 1984), mood management (Zillmann, 1988) and emotion regulation (Gross, 1998). These theories incorporate dynamic aspects such as individuals‘ hedonic goals (i.e., preferences for feeling good). They propose that users may consider possible affect discrepancy between two points in time (i.e., what I feel now and what I could feel in the future as a result of the behavioral activity), and this anticipated affective change is likely to influence behavior.
However, Andrade (2005) showed in two experiments that it is possible to integrate these two groups of model to explain and predict the impact of affect on behavior and behavioral intentions. In their first experience, participants were induced to feel either positive affect or negative affect or neutral affect. Then, a picture of chocolate bars was presented and participants were asked to indicate in which extent they would be willing to try the product. Also, they were told that they would have to answer a 6 min. survey if they decided to taste it. This experiment was based on the premise that women consider that chocolate has some mood-lifting properties more often than men. Results showed that when the mood-lifting cue was present (female participants), sad and happy participants were more willing to try the chocolate than the control participants. Conversely, when the mood-lifting cue was absent (male participants), only positive participants were more willing to try the chocolate than the control participants. Sad participants were less willing to try chocolate than the control participants. Accordingly, when no mood changes are expected (absent mood-lifting cue), affective evaluation mechanism guides behavioral intention and when participants expect the behavioral activity to make them feel better, affect regulation mechanism guides behavioral intention.
Besides, Garg, Wansink, and Inman (2007) have extended findings from Andrade (2005) by studying the impact of specific incidental affect on actual consumption. For example, in their study 2, participants were asked, after the affect manipulation, to read an event narrative in order to give time to consume either M & M‘s (hedonic unhealthful product) or Raisins (less hedonic healthful product) according to the experimental condition. Consistent with the integrative mood management and mood evaluation framework (Andrade, 2005), authors found that sad participants consumed more M & M‘s than control and happy participants. In contrast, in the case of raisins, happy participants consumed the most and sad participants consumed the least.
Table of contents :
CHAPTER 1 – THE ROLE OF EMOTIONS IN USER ACCEPTANCE
I. INNOVATION
II. PRESENTATION AND CRITICS OF USER ACCEPTANCE THEORY
II.1 An overview of user acceptance models
II.2 Limits of user acceptance model proposed in the literature
III. TOWARD AN EMOTIONAL VIEW OF USER INNOVATION ACCEPTANCE
III.1 Intrinsic motivation: Perceived enjoyment and flow experience
III.2 Pleasure and arousal
III.3 Positive and negative emotions
III.4 Satisfaction
III.5 User experience
III.6 Anxiety
IV. CONCLUDING COMMENTS
CHAPTER 2 – AN OVERVIEW OF CONTEMPORARY APPROCHES TO EMOTIONS
I. WHAT IS AN EMOTION?
I.1 Conceptual Definitions and Distinctions
I.2 Appraisal Theory Approaches to Emotion.
II. THE INFLUENCE OF SPECIFIC EMOTIONS ON JUDGMENT AND DECISION MAKING.
II.1 Beyond Valence: The Appraisal-Tendency Framework
II.2 Emotion and Motivation
II.3 Empirical Contemporary Research to Emotion
a. Differentiating specific emotions to understand their behavioural implications
b. What about positive emotions?
CHAPTER 3 – PRESENTATION OF THE RESEARCH QUESTIONS
PART 2 – EMPIRICAL STUDIES
CHAPTER 1 – AFFECT EXPECTATIONS AND SPECIFIC EMOTIONS AS GUIDES TO BEHAVIORAL INTENTION WITH RESPECT TO INNOVATIONS INTRODUCTION
I THEORETICAL BACKGROUND
I.1 Expectation disconfirmation theory versus Affective expectation theory
I.2 A model of expectations and post-use emotions: satisfaction or delight?
I.3 The present study and hypothesis
II METHOD
II.1 Participants and Design
II.2 Stimuli description
II.3 Procedure
III RESULTS
DISCUSSION
CHAPTER 2 – WHY ARE WE PURCHASING PRODUCTS? TOWARDS A DYNAMIC VIEW OF THE IMPACT OF SPECIFIC EMOTIONS ON PRODUCT DESIRABILITY INTRODUCTION
I THEORETICAL BACKGROUND
I.1 Dynamic affect regulation model versus static affective evaluation model
I.2 The present study and hypothesis
II METHOD
II.1 Participants and Design
II.2 Procedure
III RESULTS
DISCUSSION
CHAPTER 3 – LET’S PLAY OR SAVOR: THE INFLUENCE OF SPECIFIC EMOTIONS ON INNOVATIVE PRODUCT DESIRABILITY INTRODUCTION
I RESEARCH IMPLICATIONS: THE MEDIATING ROLE OF EMOTIONSPECIFIC SHORT-TERM GOALS
I.1 Satisfaction
I.2 Amusement
I.3 Overview
II STUDY 3
II.1 Method
II.2 Results
II.3 Discussion
III STUDY 4
III.1 Method
III.2 Results
III.3 Discussion
GENERAL DISCUSSION
PART 3 – GENERAL DISCUSSION AND CONCLUSIONS
GENERAL DISCUSSION
