Understanding the impact of perceived visual aesthetics on user evaluations: An emotional perspective

Information & Management 56 (2019) 85–93

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Information & Management journal homepage: www.elsevier.com/locate/im

Understanding the impact of perceived visual aesthetics on user evaluations: An emotional perspective

T

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Upasna Bhandaria, , Klarissa Changa, Tillmann Nebenb a b

National University of Singapore, Singapore University of Mannheim, Germany

A R T I C LE I N FO

A B S T R A C T

Keywords: Aesthetics Valence Arousal Quality perceptions mobile apps

Studies aimed at predicting user judgments have been dominated by the usability and efficiency perspective. An important assumption of this perspective is that higher order judgments such as quality perception and download intention are mainly cognitive processes. Increasingly, research has shown that this perspective is incapable of fully explaining user judgments. Emerging research posits that emotions and emotional subcomponents that arise from aesthetic-based design factors are at least equally important for understanding how users form higher order judgments such as quality perception and attractiveness. In this article, light is shed on the role of emotions in affecting these judgments. This is performed for the particular case of mobile apps. Specifically, the relationship between various aesthetic subdimensions (classical and expressive) and emotional subcomponents (valence and arousal) is explored. First, an explanatory model from theories of aesthetics, emotions, and visual perception is derived. Second, a laboratory experiment is conducted, and it provides empirical evidence for the relationships between visual aesthetics, emotions, and higher order evaluations such as users' quality perceptions and the intentions to download. Specifically, significant relationships were found between aesthetic subdimensions and valence, whereas arousal was partially significant. Selective emotional subdimensions also significantly impacted quality perceptions, attractiveness, and intention to download. Finally, implications for theory and practice are discussed.

1. Introduction Mobile phones are used for a varied numberof tasks in our daily lives. In this list of tasks, there are some tasks that are often achieved by small independent pieces of software installed on the mobile phone called mobile applications or popularly known as mobile apps. With its launch in 2008, Apple’s App Store revolutionized the app market and has grown from strength to strength with users spending half a billion dollars on the app and in-app purchases (Apple 2015). Despite their huge popularity, less is known about why users prefer certain apps. While standard metrics such as downloads, ratings, and user reviews remain obvious indicators to answer that question, there are hundreds of apps doing the same task with similar efficiency, but users prefer certain apps compared to others. Users may simply like the style of the app, as it resonates with them better or they feel emotionally connected to the app [61,8]. In case of mobile apps, users take decisions in two phases. The first is to decide whether to download an app, and for this reason, interface design factors become more important than having an efficiency-based

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perspective that focuses on the functional aspect of an app [2,33,66,29]. Studies have also looked at the experiential value of the interactions that users have [6,7]. How do people feel when they interact with the technology? This view is critical to understand to design systems that offer more on the overall experience front rather than just catering to usability requirements. With every step, progress is being made toward systems that not just functionally work better but leave end users with pleasure rather than the absence of pain [19]. With regard to this, two important factors, emotions and perceived visual aesthetics of design, and their impact on various user decisions in the context of mobile applications (mobile apps) need to be explored further. Perceived visual aesthetics that comprise classical and expressive aesthetics can precut user judgments such as quality, which are popularly associated with higher order judgments. Quality judgments can be influenced by how clean and symmetric the design is or how creative the design is. Studies need to explore how visual aesthetics-based design components relate to quality perceptions. Emotions are closely related to aesthetics. Emotions are extremely critical to the designing of

Corresponding author. E-mail address: [email protected] (U. Bhandari).

https://doi.org/10.1016/j.im.2018.07.003 Received 20 August 2016; Received in revised form 20 June 2018; Accepted 15 July 2018 Available online 29 July 2018 0378-7206/ © 2018 Elsevier B.V. All rights reserved.

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commonly called as feeling [50]. User choice can be predicted by the subcomponents valence and arousal. Core affect represents a mental representation rather than a cognitive or a reflective one [67]. This is an important aspect to note because it defines our design strategy to capture the immediate processing/emotional response of users when exposed to aesthetic stimuli. Designs can be more desirable if they induce a positive valence [14,21,25]. Similarly, higher arousal can lead to a more favorable decision choice [55].

systems [43]. This is because positive aesthetic responses often lead to positive interface interactions [10]. Users are exposed first to the design of the system, and this design is often linked to the first “affective” response users have, which is also called as first impression. This impression often is devoid of well thought out rational thinking processes and thus simply guided by users’ initial emotions. Thus, the linkages between these so-called first impression emotions and aesthetic subdimensions can be critical to understanding users’ initial design processing.

2.3. Perceived visual aesthetics 2. Theoretical background Visual aesthetics has been explored from various perspectives including beauty, response to the product, and appeal [26,30,64]. Lindgaard suggests that the underlying factor for these definitions could be dealing pleasure and harmony that is experienced by users. Lavie and Tractinsky suggested two broad dimensions of aesthetic parameters, namely, classical and expressive aesthetics. Classical aesthetics are close to visual clarity dimensions. It contains a design that has an orderly appeal to it and is clear. Expressive aesthetics deal with subjective design factors such as creativity, originality, and sophistication. Both these factors together deal with two different dimensions of design, one is more pragmatic in nature and the other representing hedonic dimension of design. The classical dimension of aesthetics provides a design “order” and “harmony,” and it portrays a kind of mathematical view of aesthetics which Hassenzahl mentioned as “normative values” [19]. Expressive aesthetics deal with the subjective processing by users. User’s creativity can determine the interpretation of these factors. Hassenzahl explains it as an “experiential value” that a user gains from this interaction. Schenkman and Jonsson, while considering the aesthetic evaluation, explained that meaning–function relationship cannot be undermined [52]. Therefore, the context of making aesthetic judgments has immense importance.

2.1. Aesthetic information processing The environmental psychology model is also called the M–R model [37]. The framework posits that users respond to stimuli through the underlying mediating role of emotions. These emotions are the first affective response to the stimuli. This model is highly popular in marketing studies with special focus on studying consumer responses [12,38]. The theory of perception [3] suggests that users do not perceive design in a piecemeal fashion rather it is consumed as a whole [4]. From a psychology point of view, which focuses on why people appreciate aesthetical art, Leder proposed “information processing stage model,” which focuses on aesthetic processing [27]. The first two stages of information analysis in modern art deal with perceptual analysis and implicit information integration. These two stages deal with the visceral-level processing of a design and are critical for understanding initial impressions. This model does not discount the impact of cognitive evaluation of a design, but, as explained in the model, this happens at a later stage in time and is accompanied by constant “affective” evaluation. This importance of emotions is highlighted in the psychology domain of aesthetic judgments. Recently, studies have explored the impact of various studies in the domain of website design characteristics. More popular characteristics such as color, navigation, and graphics have been focused on [9,1]. Features such as vividness and interactivity have also been looked at [53].

2.4. Perceived visual aesthetics and emotions Multiple information processing models have been cited in the literature. Interesting models herein are those that involve different levels of processing, specifically those that are making distinctions between surface levels and deeper processing. With regard to this, Norman [41,42] brings forward the three levels that have both cognitive and affective dimensions in his model. A study showed that users were able to make a “decision about the credibility of a website in 3.42 s” [49]. It occurs within the first few seconds of exposure. Although the ultimate user decision is a combination of visceral, behavioral, and reflective processing, for this study, only visceral processing is looked into, as it is most relevant to interfaces with short exposure time. Lynch found that users form an impression of beautiful things to be useful (also resonated by Norman), and this effect lingers for a long time after the conscious decision-making is complete [34]. Thus, this study simply tries to tap into the subconscious decision that users make after being exposed to an app for a short amount of time. The reasons are extremely varied. It ranges from how, owing to evolution certain design, aesthetics are preferred to subjective preferences for certain alignments and space designs. A number of studies are trying to further explore this link for its importance in explaining design preferences in users. Quality perceptions are evaluations that can be done at a glance. If the immediate affective reaction to a stimulus is positively skewed, it is likely that the user will rate that stimulus higher. This is simply because decision consistency is desired as emotions tend to be in sync with evaluations. This might not be the case always, but in cases of first impressions when there is a lack of functionality-based information, first “affective” response becomes the key in guiding further user judgments. A study by Mahlke and Thuring has attempted to link perceived quality with emotions [35]. They distinguish between three subdimensions of quality, namely, instrumentality, aesthetics, and

2.2. Emotions There are two kinds of emotions relevant to this study: core affect, which is the neurophysiological state that is accessed to know our feelings, and emotions that are embedded in the stimuli/object, i.e., affective quality. For core affect, classifications are made from two perspectives: dimensional and discrete. The discrete perspective builds on work by Ekman, who presented the nine characteristics that distinguish one emotion from others [13]. They also contributed to understanding the differences between affective states such as mood, emotional trait, and emotional attitude. Ortony et al. offered a slightly different interpretation in which they suggest that basic emotions are associated with single facial muscle and thus can be called “basic”; however, a combination of emotions cannot hold true for this explanation [44]. Another classification was provided by Plutchik, which gave the eight basic emotions and proposed that other emotions can be explained as a combination of these basic emotions; for example, disappointment is the sum of sadness and surprise [46]. Dimension-based perspective suggests that core affect can be measured as a combination of arousal and valence. The framework used frequently for studies related to emotions is the Russell's circumplex model of emotions. As per the definition, “core affect can be defined as a neurophysiological state which is a nonreflective feeling and is a blend of valence (pleasure-displeasure) and arousal (sleepy-activated) values” [51], where valence represents positive or negative emotions experienced and arousal is the amount of activation associated. Earlier works have referred to this as activation [58], affect [57], mood [39], and what is 86

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memory” seems to be assessed for getting information on the fly. Admittedly, first impressions are difficult to judge, but they are of utmost importance, as they subsequently impact further interactions. Experiments studying the response time to establish “first impressions” found that users were able to judge the web pages within 50 ms of viewing [32,45]. It has also been seen that users also form a quick judgment on quality during our first impression evaluation [10]. Norman explains that “attractive things work better,” and it offers explanation to the fact that users can extend first impression–based judgments on quality evaluation despite it being a rational decision. Because we form our initial impression within the first few seconds of exposure to the interface design, we believe it is crucial to look at how emotions can offer an explanation to the underlying mechanism of how visual aesthetics impact user judgments such as perceived quality perception.

symbolism and found positive support. It should be noted that most studies linking aesthetic design and emotion focus on classifying existing products on the basis of an aesthetics scale; however, for this study, we manipulated independently generated prototypes based on the guidelines for aesthetic parameters that offer us the confidence of effectively manipulating aesthetics. The aesthetically pleasing design is more appealing, for example, users are drawn toward balanced designs as compared to unbalanced designs. That is because they help in maintaining the amount of information that enters the autonomous processing system (ANS) balanced; thus, the processing resources needed are minimum. Similarly, for elements that are grouped together, it gives an illusion that they are related to each other, which makes it more appealing. It is known that by focusing on a pattern that is repetitive, the individual elements can be processed quickly and more efficiently. Gestalt psychology explains that while the eyes can process design in a piecemeal fashion processing each element separately, these elements are brought together as a single unit made out of recognizable and simple patterns for preference formation [23]. Leder’s information processing model for aesthetic judgment confirms this point of view [27]. This is also evident in Norman’s theory that explains that human information processing happens at three levels: visceral, behavioral, and cognitive. The initial emotional response occurs at the visceral level without any cognition involved. This is the focus of this study as well. The aim is to capture the initial emotional response to design stimuli and try to find a mapping between various aesthetic subcomponents and emotional arousal/valence and how they lead to higher user judgments.

2.6. Emotions and user judgments (Quality perceptions, attractiveness, and intention to download) While more focus has been laid on valence and arousal dimensions of emotion, many other affect-based constructs have been explored as a response to IT artifacts, for example, pleasure, excitement, and perceived affect quality [14,69,21,25]. For example, well-designed systems elicit higher valence than ill-designed systems [59]. Specific to human–computer interaction research, low arousal has been connected with higher usability [18]. Dominance has been under debate for being more of a cognitive state rather than an affective state. Thus far, no study has found a significant relationship between dominance and preferences or attitude, thus it is not considered in this study [47]. Arousal dynamics theory (Berlyne) describes the role of emotions on aesthetics evaluation [5]. It explains the phenomenon of aesthetic response in terms of reward and reversion. Arousal stands in relationship with aesthetic pleasure. However, if the complexity of the system designs reaches a certain threshold, it can cause a reverse effect. This explains a “U”-shaped relationship. This is helpful in this study particularly because it is related to the psychobiological response of users to physiological response of stimulus along with other ecological variables. Thus, arousal does play an important role in evaluating aesthetic preferences. The components of user experience (CUE) model brought forward by Thuring and Mahlke [59] helps in establishing a relationship between emotions; aesthetics; and evaluation of perceived quality, attractiveness, and intention to download. The model is suitable for this study, as it brings together instrumental and noninstrumental factors to explain the impact of different components on usage and evaluation of the system.

2.5. Perceived visual aesthetics and user judgments (Quality perceptions, attractiveness, and intention to download) The usability theory [40] has proposed several measures for technology acceptance. Extant work has shown the impact of visual appeal on outcome ranging from usability [63,54], to satisfaction [24], to pleasure [60], and to trustworthiness [15,17]. Effectiveness and efficiency have also been looked at extensively, and their relationship with design has been explored. This view has been criticized lately with constant debate on whether much focus has been given to efficiency or design [31,22]. Other user decisions such as attractiveness and perceived quality have been relatively less explored. The explanation can be found in “mere exposure effect” which refers to certain user preferences being shadowed by the first exposure to the stimulus. Although it is heavily debated, this theory supports why users after using websites with a specific aesthetic bent (classical aesthetics/utilitarian oriented) prefer certain design parameters to others. This can be due to a positive reaction induced by these visual aesthetic features, and thus, developers stick to these guidelines to get favorable responses from users [65]. It has been previously suggested that aesthetics might even be more important than usability [42] because users become aware of it first, and hence, intention to use can be affected by visual appeal by a great deal. Additionally, short-term memory popularly known as “working

3. Proposed study 3.1. Model development Based on existing literature, the theoretical underpinning and a

Fig. 1. Research framework for the Role of Aesthetics and Emotions in User Decision-Making. 87

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determined by the “affective” response to it. A positive emotion will lead to high-level evaluation of quality perception, as it is consistent with the initial emotional response from the user after he/she was exposed to the stimuli. The reverse is true for negative emotions. Thus, it is hypothesized as follows: H3 (a): Positive valence compared to negative valence will lead to greater perceived pragmatic quality. In a similar vein, decisions that are consistent with the internal state of feeling tend to be made. Studies have shown that choices driven by emotional components lead to more decision consistency [29]. Thus, positive valence also leads to a positive evaluation of design in terms of attractiveness and intention to download. Thus, it is hypothesized as follows: H3 (b): Positive valence compared to negative valence will lead to greater attractiveness. H3(c): Positive valence compared to negative valence will lead to greater intention to download. While valence deals with the pleasure/unpleasure dimension of emotions, arousal has been rather overlooked and its impact on usability judgments has not been tested extensively. Arousal along with valence has been shown to cover most of the variance in emotions [55] and is an important predictor of perceived hedonic quality perceptions. One explanation of why arousal can lead to better interface quality perceptions can be found in affect being part of the more Type 1 information processing system. Thus, it is hypothesized as follows: H4 (a): High arousal compared with low arousal will lead to greater perceived hedonic quality. Subjects when associating objects with emotions store the object as a “unit of meaning” and recruit them for supportive components of “associative structures” [51]. This needs extensive information processing and activation of emotions, which adds the arousal component. In the absence of arousal, such structures are not activated and the recall is unorganized. Extending this principle, it is hypothesized that when experiencing high arousal–based emotions, users will perceive interfaces to be of higher quality perceptions as well as perceive higher attractiveness and higher intention to download. Thus, it is hypothesized as follows: H4 (b): High arousal compared to low arousal will lead to greater attractiveness. H4 (c): High arousal compared to low arousal will lead to greater intention to download.

novel context of mobile apps where the impact of aesthetics on affective responses is under focus, and the research framework is presented in Fig. 1. This also results from the fact that there is a shift in understanding the human–artifact interaction from a more functional (efficiency, performance, etc.) perspective to a holistic one that involved creating positive interactions [16]. The model reflects the information processing flow from interface to the first affective response from a user, which leads to user evaluations (see Fig. 1). The details of the research framework are presented in the next section. While classical aesthetics–based design factors such as symmetry have been largely explored owing to their stronger effects on user judgments, expressive aesthetics–based design factors remain underexplored. Studies have shown a significant effect for classical aesthetics but have not shown for expressive aesthetics. Based on the theoretical foundation discussed, it can be posited that clean, clear, and symmetrical designs following a classical aesthetic paradigm will lead to a more positive (higher valence) experience for the user. This is due to the relatively easier processing of a design with these factors as compared to a design that does not have these design factors. Similarly, a higher amount of creativity and originality of a design along with design being additionally [1] sophisticated will also contribute to the increased valence dimension due to overall increase in ease of processing. Leder also supported that design evaluation has an underlying emotional component, which is always accompanied at various stages of design processing [27]. Thus, it is hypothesized as follows: H1 (a): Higher classical aesthetics compared to lower classical aesthetics will lead to higher valence. H1 (b): Higher expressive aesthetics compared to lower expressive aesthetics will lead to higher valence. Arousal is the activation of particular emotions, and design factors such as creativity and special effects help leverage emotions to their desired activation state. Extant research found that classical aesthetics lead to valence dimension of emotion and expressive aesthetics lead to arousal dimension [36,56]. However, there is no concrete evidence to these connections. While it can be estimated that expressive aesthetics dealing with creativity and sophistication in design can lead to increased amounts of arousal, arousal from classical aesthetic perspective is less understood. A closely related factor is complexity. Studies have shown that increase in complexity is positively related to increase in arousal [5,72]. With decrease in classical aesthetics, it becomes more difficult for a user to process the design in a single unit, hence causing arousal to increase. Given strong empirical research-related design factors such as complexity (achieved by lowering classical aesthetics), we predict that classical aesthetics design factors will have an important influence on the both valence and arousal that a user experiences. Based on the above, it is hypothesized as follows: H2 (a): Higher classical aesthetics compared to lower classical aesthetics will lead to higher arousal. H2 (b): Higher expressive aesthetics in comparison with lower expressive aesthetics will lead to higher arousal. Extant work suggests that affect influences the user’s evaluations and perceptions. For example, affect influences the user’s quality perceptions. Furthermore, research has observed that this process does not require cognitive elaboration [68,67]. Experiments have used explanations such as perceptual fluency, ease of processing, and recognition along with other psychological mechanisms for explaining this phenomenon [20,48]. Evidence from neuroscience asserts that affect along with cognition are potentially independent of each other [28]. Thus, in controlled laboratory experiments where stimuli appear for a very short period, the exposure effect can be delineated, which shows a high correlation with usability parameters, thus confirming the presence of subconscious processing. Attractiveness and intention to download are two such user judgments that can be predicted by affective responses to stimuli. As mentioned earlier, because users were able to judge web pages within a short frame of 50 ms, it is possible that judgments such as the attractiveness of the design of a mobile app are

4. Methodology To test the research framework, a controlled laboratory experiment was conducted with 2 × 2 (classical: high or low, expressive: high or low) within-subject factorial design, where classical aesthetics (symmetry, clarity, and cleanliness) and expressive aesthetics (originality, creativity, and special effects) were the two independent variables. Valence and arousal are captured using subjective measures. Quality perceptions, attractiveness, and intention to download are measured as dependent variables using existing and prevalidated scales (see Appendix C). We focus on three dimensions of classical aesthetics (symmetry, cleanliness, and clarity) and three dimensions of expressive aesthetics (originality, creativity, and special effects) in the aesthetics scale. For the experimental study, interfaces are prepared by manipulating all six dimensions of classical and expressive aesthetics scale guided by design guidelines that are explained in Tables 2 and 3. 4.1. Dependent variables For subjective measurement of dependent variables, existing scales are used and adapted, as they have been proved for adequate reliability and validity. For classical and expressive aesthetics, the User Engagement Scale (UES) is used by Lavie and Tractinsky [26]. Because 88

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Table 1 Classical and Interfaces.

Expressive

Aesthetics

and

were used. For example, high creativity manipulation interface has a significantly large rectangle in bold color displaying “New Arrivals” on the home page. This gives a distinct look to the app and is different from a regular “list-based” menu style because here we give users an immediate option to access the “New Arrivals” section. Further, underneath every product listing, a unique “share this product” functionality is provided. These unique ways of displaying functionalities add to the creativity of the manipulation.

Corresponding

No.

Interfaces

1 2 3 4

High Classical + High Expressive High Classical + low Expressive Low Classical + High Expressive Low Classical + low Expressive

4.3. Experiment design

this work is manipulating three dimensions from each construct, we use Likert (1–7) scale measurement for each subcomponent, i.e., symmetry, clarity, cleanliness, creativity, originality, and special effects. For perceived quality perceptions, attractiveness, and intention to download, AttracDiff 2 scale by Hassenzahl is used [18]. Please refer to Appendix for details.

A real-life scenario where users browse mobile apps in an app store was recreated in a laboratory setting. Participants were then shown the manipulated interfaces of the system. One of the four combinations of classical and expressive aesthetics was deployed (refer to Table 1). Each interface appeared on the screen for 1500 ms. Existing work suggests that this time duration is enough to record the “affective” response; however, cognitive processing is limited. The purpose of this mechanism was to grasp users glance at the app design, thus including initial visual processing. Prolonged exposure was not given; hence, users are restricted to a quick glance of all manipulations. Such constraint was introduced to recreate a realistic scenario. Post interface presentation, participants rated the interface on all six dimensions of classical and expressive aesthetics according to the UES scale by Lavie and Tractinsky [26]. These were later used for manipulation checks. Twenty-second intermittent pauses were provided between the previous and the next interface that shows up. Participants later filled a postexperimental survey that contains measurements for interface quality evaluations, attractiveness, and intention to download.

4.2. Independent variables Two independent variables are considered in this study: classical aesthetics and expressive aesthetics, each comprising three subdimensions. Three of them are manipulated, each to obtain high–low manipulations of both classical and expressive aesthetics to understand how manipulation of subcomponents of classical and expressive aesthetics was performed. To test the research framework, mobile app interfaces with four combinations were created. Each app went through four stages of a regular task on an e-commerce app: starting with home page, moving on to a product page, to checkout page, and finally landing on a payment page. The users did not perform any task on the app. They see these manipulated interfaces on the screen in front of them. This was done to make the interface appear as a regular mobile app process. Further presentation order was randomized across participants to avoid learning experience and exposure effect. For example, creativity (expressive aesthetics) and symmetry (classical aesthetics) on the homepage were manipulated, whereas special effects (expressive aesthetics) were manipulated on the checkout page and payment page. Some adjustments were made to fit the mobile app context (refer to Appendix B). For example, for interfaces with low clarity, we did not have color gradient and drop shadows for separating the text area from the image area on the home page, thus leading to lesser clarity. This inhibits the visual processing of the design. Second, the images on the home page were not at a large scale and detailed, thus reducing further the clarity of the manipulation. In previous literature, this limitation has been referred to as lack of contrast and makes things difficult for users to process visually. Guided by our principles in Table 3, to lend a creative element to the design, a unique navigation style and bold color (red)

5. Results 5.1. Sample descriptive Of the total 46 usable responses (five responses were excluded owing to incomplete data), the majority of participants were Germans (84%), followed by Asian participants (17%). They had a mean age of 22 years, and the highest age was 29 years. Participants were majorly seniors (in the final year of their education program), followed by juniors (in year 1 and year 2 of their education program), and other graduate students. Approximately 62% of the participants browsed apps online frequently. 5.2. Results of perceived visual aesthetics on emotions (Valence and arousal) Multivariate analysis of variance (MANOVA) was conducted on

Table 2 Manipulation of Classical Aesthetic Parameters for Interface Design. Visual Design Attribute

High

Low

Clarity

Classical Aesthetics Contrast: Contrasting differences between different design elements. High contrast achieved with large-scale detailed images that can grab users’ attention first and engage. Layout: Layouts are in half arc segments that help the inconsistent organization of elements. Mirroring: Mirror reflection of interface elements appears along either of the focal axes. Grouping: Grouping similar elements together (text and graphics equally spaced throughout the interface).

Contrast: No clear distinction between different elements of the interface. Low contrast between images and text, lack of detailed images lead to lack of interest from users. Layout: Regular layout styles with images and text occupying consistent space. Mirroring: This is absent for low manipulation of symmetry. Elements are in a haphazard manner. Grouping: Random arrangement of elements (text and graphics in one quadrant and rest of the interface space was empty).

• • •

Symmetry

Clean

• •

89

Source

[70]

• • • •

[71]

•

[70,71]

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Table 3 Manipulation of Expressive Aesthetic Parameters for Interface Design. Visual Design Attribute

High

Low

Creativity

Expressive Aesthetics Navigation Style: Navigation style is unusual and unexpected. Creative ways to display different functionalities on the interface.

• •

Originality

Custom vs. Default Shapes: Custom shapes instead of default shapes are used to show various functionalities that add to the originality in design. Information is presented using custom shapes.

Special Effects

Animation Effects: Use of animation to display information to enhance user experience. Using cascading style sheets (CSS) and gradients to create more depth in the interface design.

• • • •

Mean Square

F-value

Sig.

Classical Expressive

11.836 38.653

3.388 21.056

.067 .000

both classical and expressive aesthetics for manipulation checks. Results showed that various parameters of classical and expressive subdimensions (high expressive aesthetics was higher than low expressive aesthetics) (p < 0.05) were effectively manipulated. For classical aesthetics, the value was marginally significant (p = 0.065). Refer to Table 4. Repeated measures analysis of variance (ANOVA) was performed on the valence dimension, and results showed significant main effects of classical and expressive aesthetics (see Table 5). Thus, H1a and H1b were supported. Repeated measures ANOVA on arousal did not lead to significant results. Thus, H2a and H2b were not supported. The results show that although classical and expressive aesthetics have significant effects only on the valence dimension, the interaction effect between them is not significant. A repeated measure considers the data to vary within-subjects only, whereas regression considers each participant as a speared; therefore, a regression was also performed to see whether it yielded different results.

Table 5 Repeated Measures ANOVA Summary for Subjective Valence. Mean Square

F-value

Sig.

Classical Expressive

1 1

99.049 5.682

28.760 5.839

.000 .020

[26] & Self-developed

• •

[26] and Self-developed

As was predicted, the findings in general showed that both aesthetics, i.e., classical and expressive, impact emotional responses. Literature has previously shown that valence has been associated with classical and expressive aesthetics; arousal, on the other hand, remained unexplained. It was found that valence is significantly affected by the classical dimensions of aesthetics, whereas arousal is not significantly impacted by the classical and expressive aesthetic dimensions (Hypotheses H1 and H2). Theoretically, classical aesthetics appeal to the more positive valence–based emotions because it is associated with a symmetrical and clean interface design, whereas expressive aesthetics involves more the creativity and special effects aspect of a design and is expected to affect more of the excitement part of emotion spectrum (arousal). However, lack of significant results for arousal highlights alternative nonemotional processes that might influence impact of visual aesthetics on emotional responses. One such explanation could be that valence is the stronger and more salient of the two emotional subcomponents, with relatively easier access for users to reflect on. Existing studies have found similar results by measuring arousal. However, it cannot be completely ruled out that there are cognitive mechanisms contributing to these effects as well. Strong effects of various aesthetic conditions in the emotional responses could not be found. Certain cognitive evaluations could also be contributing to the effect observed. Prior research on aesthetics has often shown that factors such as simplicity, complexity, and order prototypically contributed to higher usability measures and user judgments such as approach-avoidance behavior [11,62]. Thus, design factors can have a direct effect on user judgments, which is not mediated by emotions. An interesting finding of the present study was to identify aesthetic parameters that lead to an emotional response and thus impacting user judgments. The results from all fours sets of the hypotheses (H1-H4) highlight how salient the impact of design factors such as symmetry, cleanliness, and creativity is for app interface design. While classical aesthetic parameters can elevate users’ valence dimension of emotions making them evaluate pragmatic quality perceptions and attractiveness

Exploratory factor analysis was first performed on valence, arousal, pragmatic quality perception, hedonic quality perception, attractiveness, and intention to download. Construct reliability was measured using Cronbach’s alpha and composite reliability. Cronbach’s alpha values ranged from 0.721 to 0.889 (see Tables 6 and 7). The composite reliability was also above 0.7 (with the exception of arousal; however, average variance extracted [AVE] for arousal is approximately 0.5). In addition to this, all the AVE were higher than 0.5. The square roots of AVEs were greater than the diagonal elements and thus demonstrate the discriminant validity of all the constructs. This ensured that the psychometric properties of the scale were reliable for measurement of

df

• •

6. Discussion and implications

5.3. Understanding quality perceptions, intention to download, and attractiveness

Source: Within-subjects

• •

[26] & Self-developed

these sets of variables. The items were averaged into a single score for further analysis. Ordinary least square (OLS) regression was used to see the impact of valence and arousal on quality perception (pragmatic and hedonic), intention to download, and attractiveness. Linear regressions showed that valence had a significant and positive effect on pragmatic quality perception, hedonic quality perception, intention to download, and attractiveness. Thus, H3a, H3b, and H3c are supported. Arousal had a significant and positive effect on hedonic quality perception and intention to download but not on attractiveness. Thus, H4a and H4b are supported, but H4c is rejected.

Table 4 Manipulation Check for Classical and Expressive Aesthetics. Aesthetic Dimension

Navigation Style: Navigation style inspired from regular menu-based styles. Display of functionalities on the interface lacks creativity and is not unexpected. Custom vs. Default Shapes: Default shapes are used to show various functionalities, thus making the design lack originality. Information is presented using standard shapes, e.g., regular boxes and rectangle shapes. Animation Effects: Regular methods of information presentation, e.g., static display of images/information. Lack of features like CSS shadows and gradients.

Source

90

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Table 6 Item Loadings (Standardized), Alpha Values, CR, and AVE.

Item ATT1 ATT2 ATT3 ATT4 ATT5 Ar1 Ar2 HQS1 HQS2 IR1 IR2 PQ1 PQ2 Val1 Val2 Val3

ATT 0.854 0.848 0.8685 0.827 0.8082 0.3814 0.2283 0.405 0.4847 0.5649 0.5322 −0.0904 0.0246 0.2379 0.095 −0.1419

Arousal 0.2553 0.2682 0.3656 0.4152 0.1677 0.9148 0.7758 0.15 0.1726 0.3456 0.3185 0.1965 0.209 0.4665 0.2736 0.28

HQ 0.5529 0.461 0.4897 0.3196 0.2133 0.1881 0.1052 0.9144 0.8384 0.6246 0.4201 0.1258 −0.011 0.0691 −0.1939 −0.2106

IR 0.5267 0.5508 0.5368 0.4591 0.3242 0.3451 0.2369 0.5298 0.4378 0.9479 0.9411 0.0012 0.0764 0.3929 −0.0206 −0.162

PQ −0.0609 0.005 −0.0503 −0.0047 −0.1055 0.2077 0.2171 0.0942 0.0298 0.1029 −0.0238 0.8498 0.7933 0.106 0.1441 0.1213

Valence −0.222 0.0792 0.0801 0.0253 −0.0328 0.2608 0.2514 −0.2375 −0.1202 −0.0797 −0.089 0.1818 0.0421 0.6139 0.9408 0.9355

Alpha value

CR

AVE

0.889

0.900

0.643

0.721

0.649

0.489

0.761

0.708

0.549

0.875

0.887

0.799

0.724

0.688

0.577

0.804

0.849

0.657

Note: ATT (Attractiveness), Ar (Arousal), CA (Classical Aesthetics), EA (Expressive Aesthetics), HQS (Hedonic Quality Stimulating), IR (Intention to download), PQ (Pragmatic Quality), Val (Valence). Factor loadings given as bold values load on their intended construct. Table 7 Square Root of AVE and Factor Correlation Coefficients.

ATT Ar CA EA HQS IR PQP Val

ATT

Ar

CA

EA

HQP

IR

PQP

Val

0.802 0.178 0.126 −0.034 0.606 0.621 0.445 −0.097

0.842 0.137 −0.266 0.087 0.364 −0.190 0.135

0.757 −0.438 0.265 0.327 0.062 −0.265

0.810 −0.061 −0.238 0.400 0.195

0.894 0.745 0.425 0.026

0.741 0.261 0.146

0.699 0.230

0.760

Note: ATT (Attractiveness), Ar (Arousal), CA (Classical Aesthetics), EA (Expressive Aesthetics), HQS (Hedonic Quality Stimulating), IR (Intention to download), PQ (Pragmatic Quality), Val (Valence).

at a higher level, expressive aesthetic parameters reflected in the arousal dimension and thus higher evaluations of hedonic quality partitions and intention to download. In addition to the impact of classical and expressive aesthetics on valence and arousal, their impact on dependent variables was also studied. ANOVA was used for all dependent variables, and the results support that aesthetics have a significant impact on mobile application judgments such as pragmatic and hedonic quality perceptions, intention to download, and attractiveness (Hypotheses 3 and 4). The participants indeed could evaluate the interfaces differently in the dependent variables because of the aesthetic manipulations (see Table 6). This confirms that aesthetics will lead to different mobile application judgments depending on the aesthetic evaluation of the app interface. A higher classical aesthetics leads to increased positive valence, which leads to higher pragmatic and hedonic quality perception. By contrast, a more expressive interface impacts arousal, which leads to higher intention to download. This can be understood by saying that valence is more pragmatic in nature; therefore, it impacts the decision where the user decides whether a design is attractive and rates it higher in pragmatic quality perception. However, an expressive design gives more excitement to a user and impacts the decision to download the app. In addition, users were expected to have significantly different reactions to high classical and low expressive as well as low classical and high expressive aesthetics. However, results for these two combinations were nonsignificant on separate occasions. This could be because users find it difficult to differentiate between these two combinations. An interesting point to note is that both these combinations along with high classical and high expressive as well as low classical and low

Fig. 2. Linear regression Results for Effects of Emotions on Quality Perceptions, Attractiveness, and Intention to Download (- - not significant).

expressive aesthetics fared significantly. These could indicate that when there is a combination of two aesthetic dimensions, they need to be much stronger than high–high and low–low combinations (Fig. 2). This study had three major theoretical and practical implications. First, a research model was developed; this explains how classical and expressive aesthetics can influence emotional responses from users toward mobile app interfaces and also users’ follow-up judgments of the app interface quality. By doing so, delineation of first impressions from so-called “long-term” judgments was done, and it was shown that affective responses and cognitive responses are equally important (Tables 8 and 9).

Table 8 Effects of Affective Responses on Dependent Variables. Emotional Subdimension

Dependent Variable

Significance

P value

Valence

Pragmatic Quality Hedonic Quality Intention to Download Attractiveness Pragmatic Quality Hedonic Quality Intention to Download Attractiveness

significant significant significant significant not significant significant significant not significant

P = 0.000 P = 0.000 P = 0.04 P = 0.000 – P = 0.03 P = 0.002 –

Arousal

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Table 9 Descriptive Statistics for Classical and Expressive Aesthetic Subdimensions. [8] Aesthetic Subdimension

Cleanliness Clarity Symmetry Creativity Originality Special Effects

HCHE

HCLE

LCHE

LCLE

Mean

SD

Mean

SD

Mean

SD

Mean

SD

5.72 5.83 6.41 4.8 4.52 4.5

1.47 1.34 0.75 1.51 1.77 1.92

4.85 5.15 5.93 3.85 3.74 3.41

1.3 1.37 1.14 1.38 1.57 1.6

2.98 3.2 2.54 3.57 3.57 3.87

1.42 1.53 1.41 1.66 1.44 1.75

2.57 2.78 2.35 2.83 2.83 2.67

1.47 1.53 1.35 1.23 1.43 1.48

[9]

[10] [11] [12]

[13]

Second, explicit linkages were established between aesthetics and emotions. While classical aesthetics are known to have a stronger impact on emotions, it was shown that in the case of products such as mobile apps, arousal is a critical emotional dimension that needs to be tapped into. This offers a new understanding in terms of why certain apps “stick better” with the user. It is because of their innovative and creative approach to design, which calls to the user’s arousal. Finally, a new perspective to app design was provided, which is different from the traditional focus on usability based on website design. To date, most design guidelines are based on website domain and have not been tested on the domain of mobile apps. This study showed that user judgments such as quality perceptions and first impressions such as attractiveness can both be influenced by the affective response to design. As compared to core cognitive-based models on user judgments, this study showed that emotions are equal, if not more important, in determining various aspects of user decision-making.

[14] [15] [16] [17]

[18]

[19] [20] [21] [22] [23] [24]

7. Conclusions and limitations

[25] [26]

The importance of mobile platform is undeniable. Moving forward, user experience is going to become key to differentiating brands/platform owner and for users to associate with. In this light, this study was able to show a process-oriented approach to understand what could be the potential reasoning behind users choosing certain apps compared to other apps based on aesthetic evaluation and “affective” responses. However, these findings come with their limitations. First, the interfaces were self-developed, and they were prototypical e-commerce apps. However, different categories bring with them different aesthetical prototypes with regard to design. Second, valence was measured using facial electromyography. While physiological measurements are the closest to what happens at a biological level, it cannot be said with absolute certainty that which particular emotion is at play. For this, supplementation of various physiological measurements is needed. Thus, surprise might be at play in this experiment. To be sure, more studies need to combine various NeuroIS measurements to further find out which core emotions are driving these results.

[27] [28] [29] [30] [31]

[32]

[33] [34]

[35]

[36]

Appendix A. Supplementary data

[37]

Supplementary material related to this article can be found, in the online version, at doi:https://doi.org/10.1016/j.im.2018.07.003.

[38]

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Upasna Bhandari is a PhD candidate at National University of Singapore. She received her Masters from Delhi technological University (formerly known as Delhi College of Engineering, India). Her research interests include human computer interaction, emotions using NeuroIS, adoption and continued use of digital media. Her research articles have been published in top ranked conferences and workshops like International Conference Information Systems (ICIS), International Conference on Human Computer Interaction (HCII) and Special Interest Group on Human Computer Interaction (SIGHCI).

Klarissa T.T. Chang is Assistant Professor at the Department of Information Systems, National University of Singapore. She received the PhD. Degree in Organizational Behaviour and Theory from the Carnegie Mellon University. Her research interests include knowledge management, social computing, and virtual communities. Her work has appeared in IEEE Transaction on Engineering Management, Journal of Applied Psychology, other journals and international conferences.

Tillmann Neben is a PhD candidate at the University of Mannheim, Germany. He received his Master’s degree from the University of Mannheim. His research interests are human computer interaction, information behaviour, and trust. His research articles have been published at the Conference Information Systems (ICIS), European Conference on Information Systems (ECIS), International Conference on Human Computer Interaction (HCII), and Special Interest Group on Human Computer Interaction (SIGHCI).

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