Anders Bruun
email: bruun@cs.aau.dk

Simon Ahm

Mind the Gap! Comparing Retrospective and Concurrent Ratings of Emotion in User Experience Evaluation

Keywords: User Experience, Emotion, Memory-Experience Gap, Peak-End Rule

User experience (UX) is typically measured retrospectively through subjective questionnaire ratings, yet we know little of how well these retrospective ratings reflect concurrent experiences of an entire event. UX entails a broad range of dimensions of which human emotion is considered to be crucial. This paper presents an empirical study of the discrepancy between concurrent and retrospective ratings of emotions. We induced two experimental conditions of varying pleasantness. Findings show the existence of a significant discrepancy between retrospective and concurrent ratings of emotions. In the most unpleasant condition we found retrospective ratings to be significantly overestimated compared to concurrent ratings. In the most pleasant condition we found retrospective ratings to correlate with the highest and final peaks of emotional arousal. This indicates that we cannot always rely on typical retrospective UX assessments to reflect concurrent experiences. Consequently, we discuss alternative methods of assessing UX, which have considerable implications for practice.

INTRODUCTION

Emotion is considered a fundamental factor in determining user experience (UX) of interactive technologies [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF][START_REF] Thüring | Usability, aesthetics and emotions in human-technology interaction[END_REF]. Forlizzi and Battarbee state: "Emotion affects how we plan to interact with products, how we actually interact with products, and the perceptions and outcomes that surround those interactions" [START_REF] Forlizzi | Understanding Experience in Interactive Systems[END_REF].

The most frequent method to assess users' emotional states is subjective ratings where users fill in questionnaires such as the Self-Assessment Manikin (SAM) [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. However, studies outside a UX context have shown a critical caveat in subjective ratings of emotions. Recently, Scherer argued that emotions are fleeting, i.e. they are short term, intensive peaks of experiences which may be difficult to recall at a later point in time [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. This is supported by empirical observations of a discrepancy between overall retrospective ratings of an episode and the concurrent experiences during an episode [START_REF] Miron-Shatz | Memories of yesterday's emotions: does the valence of experience affect the memory-experience gap?[END_REF]. This discrepancy is denoted the memory-experience gap and is verified by several studies in e.g. pain research. Notably, Redelmeier and Kahneman found retrospective ratings of pain to correlate with the highest and final intensities of pain experienced [START_REF] Redelmeier | Patients' memories of painful medical treatments: real-time and retrospective evaluations of two minimally invasive procedures[END_REF]. Those observations led to what is now known as the peak-end rule. Thus, studies outside a UX context suggest that retrospective ratings of emotions will likely not reflect concurrent experiences of an entire event.

This caveat also seems to be recognized within UX where studies measure emotions concurrently. SAM is typically filled in after completing each task in an interaction sequence, see e.g. [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF][START_REF] Mahlke | Emotional Experiences and Quality Perceptions of Interactive Products[END_REF][START_REF] Mahlke | Studying Antecedents of Emotional Experiences in Interactive Contexts[END_REF]. However, very few studies within UX have been engaged with more detailed measurements of emotions [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. Kujala and Miron-Shatz present initial insights on the presence of the memory-experience gap in a Human-Computer Interaction (HCI) context. The emphasis in [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF] is on longitudinal assessments of emotions based on the Day Reconstruction Method (DRM). In DRM participants evaluate the set of experienced emotions by the ending of each day [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF]. Given the fleeting nature of emotions, DRM suffers from a recall bias and is therefore well suited for longitudinal studies with an interest in estimating overall averages of emotional reactions [START_REF] Diener | Review of the Day Reconstruction Method (DRM)[END_REF]. It is, however, ill-suited for measuring emotions at specific time points, e.g. at specific points in an interaction sequence [START_REF] Bylsma | Emotional reactivity to daily events in major and minor depression[END_REF].

Consequently, we still know very little of the existence and extent of the memoryexperience gap in an HCI context. Arguably, such a context is less extreme than Redelmeier and Kahnemans studies of pain.

This study contributes to the HCI community by showing that we cannot always rely on retrospective UX assessments to reflect concurrent experiences, even when they are assessed immediately after interacting with a system. This is critical as UX is primarily assessed retrospectively [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. We discuss alternative approaches to assess UX, which provide more accurate accounts of concurrent experiences than, e.g. the Day-Reconstruction Method.

In the remainder of this paper we provide a theoretical overview introducing potential caveats of retrospective ratings as well as a set of hypotheses. We then present related work, experimental method and results. Finally, we discuss and conclude on our findings.

THEORETICAL BACKGROUND

In this section we introduce theoretical stances dealing with the relationship between concurrent and retrospective ratings. We start by giving an account for the concept of emotions and how emotions can be measured. We then go through the theoretical background concerning the discrepancy between retrospective and concurrent ratings, i.e. the memory-experience gap and the peak-end rule. We conclude this section by presenting our hypotheses.

What are Emotions?

In the classical theory of James-Lange from 1884, emotions are defined as the result of physical changes in autonomic and motor functions. Input from our senses creates a range of responses in our body, and our awareness of these changes is what constitutes an emotion [START_REF] James | What Is An Emotion?[END_REF]. Individual emotions are distinguished based on their unique bodily expression. The theory states that when an event happens in our environment (e.g. being attacked by a predator) we instantaneously get physiological reactions like muscle tension, increased sweat production etc. We interpret these unique combinations of physiological reactions as being a specific emotion [START_REF] James | What Is An Emotion?[END_REF].

Defining emotions is a topic of much debate and we do not attempt to provide an exhaustive walkthrough of the literature. However, basic assumptions behind the classical James-Lange theory are still supported after more than a century. Recently, Scherer described emotions as a mobilization and synchronization of five organismic subsystems as a response to a cognitive evaluation of external or internal stimulus events that are "relevant to major concerns of the organism" [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. When an event happens that is of major concern, the event is evaluated through a comparison to innate prototypical responses, and a response (the emotion) is elicited through activation of the organismic subsystems [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. It is important to note that this "evaluation" is not a time consuming and conscious process as is often associated with the term in HCI. Instead it relies on fast subconscious processes [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF].

Measuring Emotions

Throughout research, two approaches have been used to elicit emotions: Subjective ratings and objective measurements.

Subjective Ratings. Subjective ratings of emotions are typically collected through questionnaires. These typically consist of standardized labels or pictograms representing emotions [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. Most studies of emotions in UX are based on such subjective rating methods where the Self-Assessment Manikin (SAM) is the most widely applied technique [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. SAM enables participants to assess their emotional states through graphical scales, cf. [START_REF] Lang | Behavioral treatment and bio-behavioral assessment: computer applications[END_REF]. It is based on a dimensional model of emotion denoted PAD (Pleasure, Arousal, Dominance) that uses three dimensions to represent emotions:

 Pleasure: Indicates how pleasant an emotion is, i.e. its valence. It spans from negative to positive.  Arousal: Indicates how intense an emotion. It spans from relaxed to excited.  Dominance: Indicates how dominating an emotion is. It spans from low to high dominance.

Participants are asked to rate emotions based on these three dimensions. Thus, they assess the level of Pleasure, Arousal and Dominance, each on a 1-9 point Likert scale.

Objective Measurements. Psychological research has recently seen an advancement in applying physiological sensors to objectively assess emotions [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. In doing this, participants do not rate emotions subjectively, but instead a sensor (or sometimes several) is attached on the body. A range of different sensors exist, each of which typically measures one dimension of the PAD model. As an example, Galvanic Skin Response (GSR) sensors have in particular been shown to correlate with arousal [START_REF] Lang | The emotion probe: Studies of motivation and attention[END_REF]. In essence, a GSR sensor reacts on changes in skin resistance (measured in mOhms) through varying levels of perspiration in sweat glands. A GSR sensor enables realtime measurements of arousal. Other sensors are Electromyography (EMG), Heart Rate (HR) and Electroencephalography (EEG). For a more comprehensive overview of measurement sensors and their relative performances, see [START_REF] Andreassi | Psychophysiology: Human Behavior and Physiological Response[END_REF]. As noted by Scherer, it is currently not feasible to collect all types of measurements [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF], and studies have also shown varying reliability of these in measuring emotions. However, GSR sensors are consistently correlated with arousal across different studies, this also includes the few studies of UX applying physiological sensors, see e.g. [START_REF] Ward | Physiological responses to different WEB page designs[END_REF].

Memory-Experience Gap

According to Scherer, the purpose of emotions is to deal successfully with an event that is of direct concern to the organism, and the activation of physiological subsystems require lots of resources to do so [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. Due to the level of intensity, which cannot be endured over a longer period of time, emotions are short-lived mental states. Furthermore, Scherer argues that emotions are always tied to a specific event [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF]. So, if emotions are short-lived and tied to a specific event, what is then measured in retrospective ratings based on recall? Several studies in psychology have demonstrated a discrepancy between the average of actual experienced emotions and the overall retrospective assessment of an experience [START_REF] Miron-Shatz | Memories of yesterday's emotions: does the valence of experience affect the memory-experience gap?[END_REF]. Thus, there is a gap between concurrent emotions and the recollection of these after a given episode. This discrepancy is denoted the memory-experience gap [START_REF] Miron-Shatz | Memories of yesterday's emotions: does the valence of experience affect the memory-experience gap?[END_REF]. Studies in psychology have shown that the memory-experience gap leads to overestimated retrospective ratings, i.e. that retrospective ratings reflect a higher emotional intensity compared to concurrent experiences [START_REF] Miron-Shatz | Memories of yesterday's emotions: does the valence of experience affect the memory-experience gap?[END_REF].

It is argued that the memory-experience gap is present for both positive and negative stimuli [START_REF] Miron-Shatz | Memories of yesterday's emotions: does the valence of experience affect the memory-experience gap?[END_REF]. However, a study conducted by Baumeister et al. indicated a larger memory-experience gap when experiencing negative stimuli compared to positive stimuli [START_REF] Baumeister | Bad is stronger than good[END_REF].

Transferring the above into an HCI context we could expect that 1) Retrospective ratings of UX are overestimated compared to concurrent ratings and 2) Retrospective ratings would especially be overestimated in episodes of negative experiences.

Peak-End Rule

The discrepancy between actual experienced emotions and retrospective ratings has also been studied in detail by Redelmeier and Kahneman. They made a striking discovery during their studies of how pain is experienced and recalled. They found that test subjects preferred a longer duration of pain over a shorter duration of pain [START_REF] Kahneman | When More Pain Is Preferred to Less: Adding a Better End[END_REF]. In the condition with the longest duration, pleasantness was increased towards the end, although still being painful. In the other condition, the level of pain was kept constant, but the duration was shorter. Test subjects primarily recalled the experience of pain towards the end of the experimental conditions, i.e. they preferred the longest duration of pain with increasing pleasantness. In a later study it was found that test subjects retrospectively rated the entire experience based on the highest intensity of pain (the peak) and the pain experienced towards the end [START_REF] Redelmeier | Patients' memories of painful medical treatments: real-time and retrospective evaluations of two minimally invasive procedures[END_REF]. This has since become known as the peak-end rule.

In relation to HCI, it is plausible that retrospective ratings of emotions correlate with the highest peak of emotional intensity and the intensity measured towards the end of an interaction sequence.

Hypotheses

We have formulated the following hypotheses based on the above considerations of the memory-experience gap and peak-end rule:

H1. Retrospective ratings of emotions are higher than concurrent ratings of emotions.

H2. The memory-experience gap is larger for episodes of negative emotions than for episodes of positive emotions.

H3. Concurrent and retrospective ratings of emotions correlate following the peakend rule.

RELATED WORK

A recent literature survey confirmed that most UX assessments are conducted retrospectively [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. Based on the theoretical background above, this led us to question the extent to which retrospective ratings reflect an entire experience. As mentioned in the introduction of this paper, emotion is a key dimension in determining UX of interactive technologies. Therefore we emphasize this particular dimension and now provide an overview of previous UX studies of emotions.

Most studies of emotions in UX research are based on questionnaires, typically SAM, through which users provide subjective ratings [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. From related work we primarily know that SAM ratings are affected by instrumental factors such as the level of usability, but we know very little of the discrepancy between concurrent and retrospective ratings. Hassenzahl and Ullrich studied the effect of giving users predefined tasks versus open ended interaction [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF]. SAM ratings were measured concurrently three times during interaction followed by a retrospective AttrakDiff rating. Primary findings showed that the type of tasks affected SAM and AttrakDiff ratings. As a byproduct of the entire experiment Hassenzahl and Ullrich found a correlation between concurrent SAM ratings and retrospective AttrakDiff ratings [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF]. Mahlke and Thüring conducted a similar study in which they examined the effect of high/low usability and high/low aesthetics on emotional responses [START_REF] Mahlke | Studying Antecedents of Emotional Experiences in Interactive Contexts[END_REF]. SAM ratings were taken three times during interaction followed by retrospective ratings of usability and visual aesthetics. Findings showed that concurrent SAM ratings differed significantly between high/low usability and to a minor extent between high/low aesthetics. In another study, Mahlke and Thüring studied the feasibility of measuring emotional states through a multiple components approach [START_REF] Mahlke | Measuring Multiple Components of Emotions in Interactive Contexts[END_REF]. This was done by combining objective psychophysiological measurements and subjective ratings. In that study SAM was filled in after each task while collecting real-time physiological data. Findings also showed that concurrent SAM ratings differed significantly between the high/low usability versions of the system. Other UX studies of emotions apply SAM to extract emotions during use and with similar findings, see e.g. [START_REF] Thüring | Usability, aesthetics and emotions in human-technology interaction[END_REF][START_REF] Mahlke | Emotional Experiences and Quality Perceptions of Interactive Products[END_REF].

Hassenzahl and Sandweg studied how concurrent experiences of mental effort related to retrospective assessments of perceived usability [START_REF] Hassenzahl | From Mental Effort to Perceived Usability: Transforming Experiences into Summary Assessments[END_REF]. Participants were given seven tasks and the SMEQ questionnaire (for measuring mental effort) was filled in after each task. Findings showed significant correlation between the last rating of mental effort and the perceived level of usability. In their longitudinal study, Kujala and Miron-Shatz [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF] applied the Day-Reconstruction Method (DRM) to elicit participant emotions of mobile phone usage. Participants were asked to report specific episodes at the end of each day and their related emotions over a five day period. On the sixth day, participants were asked to provide a summary rating of emotions. Findings showed that participants significantly overestimated the experienced positive emotion in their summary assessments.

From the above we know that ratings of emotions are affected by instrumental factors such as providing tasks or not, the level of usability and partly by the noninstrumental factor of aesthetics. It also seems that the potential memory-experience gap is recognized as participants are typically asked to provide SAM ratings concurrently rather than retrospectively. Yet, we know little of the potential discrepancy between retrospective and concurrent ratings. Notably, a single study showed that concurrent SAM ratings correlated with retrospective AttrakDiff ratings, cf. [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF]. Findings from that study could indicate that the memory-experience gap is insignificant in an HCI context. Yet, this finding is contradicted in [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF] and [START_REF] Hassenzahl | From Mental Effort to Perceived Usability: Transforming Experiences into Summary Assessments[END_REF]. Nevertheless, across all studies, we found that concurrent ratings were measured relatively few times, in some cases as few as three times per participant. Arguably, this may not reflect the whole set of short-lived emotions experienced during an entire interaction sequence. This critique has also been raised against DRM [START_REF] Bylsma | Emotional reactivity to daily events in major and minor depression[END_REF][START_REF] Dockray | A Comparison of Affect Ratings Obtained with Ecological Momentary Assessment and the Day Reconstruction Method[END_REF], on which the study by Kujala and Miron-Shatz [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF] is based. Thus, there is a critical need for more detailed studies of emotions in UX research, as is also stressed by Bargas-Avila and Hornbaek [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. In the following section we describe how we studied the memory-experience gap and peak-end rule in an HCI context.

METHOD

The objective of our study was to examine the memory-experience gap and peak-end rule in subjective ratings of emotions. Therefore we needed participants to elicit several emotional states reflecting concurrent experiences, i.e. during interaction, as well as retrospective ratings. However, collecting concurrent ratings during interaction is not as straightforward as collecting retrospective ratings. Following related work, concurrent ratings can be collected after each task, this, on the other hand provides relatively few measurement points. We collected concurrent ratings based on the Cued-Recall Debrief (CRD) method as outlined below. This enabled a higher density in measurements while avoiding interruptions during interaction.

4.1

Cued-Recall Debrief

CRD is a method based on situated recall. The method was developed by Omodei et al. [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF] to elicit emotional experiences while not interfering with participant behavior in naturalistic settings. The overall approach is to provide cues that enhance participants' ability to recall specific emotions after an event has occurred. This is done by re-immersing participants through replay of several snippets of video recordings, each showing a specific episode of an entire event [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF]. To foster re-immersion, it is crucial that video recordings resemble a first-person point of view. In Omodei et al.'s study, video recordings were collected by head-mounted cameras positioned on helmets of firefighters [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF]. CRD essentially builds on retrospection but several studies have validated the approach. In [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF] it was found that CRD leads to considerably more detailed responses compared to retrospective ratings based on free recall [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF].

Furthermore, Bentley at al. [START_REF] Bentley | Evaluation Using Cued-recall Debrief to Elicit Information About a User's Affective Experiences[END_REF] found correlation between CRD ratings and real-time physiological measurements in an HCI context. Also, a more recent study published in the renowned TOCHI outlet applied CRD to elicit participants' emotions, cf. [START_REF] Gao | What Does Touch Tell Us About Emotions in Touchscreen-Based Gameplay?[END_REF]. Thus, although CRD builds on retrospection, it has been shown to provide valid approximations of concurrent emotions. Furthermore, it does so without causing interference during interaction.

In practice CRD is conducted by selecting a set of video clips, each showing a specific episode of an entire event. Participants view one clip at a time for which they provide subjective ratings of emotions. This is then done for all video clips. In our case we selected video clips on the basis of real-time GSR data, i.e. we selected video clips surrounding peaks of arousal. Fig. 1 illustrates this principle.

Experimental Conditions

Related work show that instrumental goals such as tasks/no-tasks and usability problems affect user emotions when interacting with technology, see e.g. [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF][START_REF] Mahlke | Studying Antecedents of Emotional Experiences in Interactive Contexts[END_REF]. We were interested in studying the memory-experience gap and, based on previous studies, it was relevant to consider how this was affected by the level of usability.

We developed two versions of a software system (described in section 4.4 "System"). The two versions differed as we in one version seeded a set of five usability problems into the user interface. The other version was developed to the best of our abilities. Thus we had two experimental conditions: 1) Seeded and 2) Unseeded.

4.3

Participants

A total of 20 university students participated (between subjects) with 10 in each condition. Ages ranged from 19-28 years (mean 22.85). In each condition we had 2 females and 8 males. All subjects were kept unaware of the actual premise of the experiment and none of them had previous experience in using the system.

System

We designed a system to create and edit posters which was specifically developed for the experiment. The functionality included simple text editing and styling for creating bold, italic and underlined text as well as a color picker. It was also possible to include images etc. The unseeded and seeded versions were built on the exact same platform and source code. Versions differed in the user interface only. In the seeded version we introduced the following five usability problems: 1) Standard buttons for text styling (known from typical word processors) were replaced with checkboxes, 2) no font preview was available, 3) font required for the task was unavailable, 4) color picker was based on text rather than actual colors and 5) inserting an image led to an incomprehensible error message.

Task

The task was to use the system to create an exact copy of a poster pictured on paper. Participants were informed that there was a 10 minute time limit to solve the task. They could click a submit button at any time, if they felt the task had been solved before time the limit.

Setting and Data Collection

The experiment was conducted in a university classroom. The user was placed in front of a laptop with an external mouse, and given the paper describing their task as well as the poster to replicate. The student was alone in the room for the full duration. We collected concurrent and retrospective ratings of emotions via the SAM questionnaire. All sessions were recorded using screen capture software. This was a picture-in-picture setup primarily showing participants' interaction with the system and a small picture of the face, which was recorded with a high-resolution webcam. The desktop recording resembled a first-person point of view, which is in line with the Cued-Recall Debrief (CRD) method outlined previously. Finally, a Galvanic Skin Response (GSR) sensor was placed in the palm of participants' non-primary hand. This was done in order to determine specific points in the interaction sequence where participants experienced peaks of arousal. That information was used to select video clips on which to base concurrent SAM ratings.

4.7

Procedure

This section describes the CRD procedure applied in the study:

1. Introduction and setup: Participants were directed to the room where they received a piece of paper with the task and the poster to replicate using either the unseeded or seeded version of the software. The task was also described verbally by a researcher (one of the authors). The GSR sensor was then attached to their palm. Participants were not informed of the purpose of the study and the GSR sensor until they completed the experiment. After the user had confirmed that they understood the task at hand, the researcher started the software and left the room. 2. Creating a baseline: Since the GSR sensor reacts on arousal we needed to identify the relaxed state of each participant, i.e. peaks of arousal are observed relative to a baseline. This baseline was measured by showing a blank screen for the first 4 minutes, while playing a relaxing piece of music. The song "Weightless" by Marconi Union was chosen. Previous studies have shown that this particular piece of music has a relaxing effect on participants [START_REF] Belford | Music & Physical Performance: The effects of different music genres on physical performance as measured by the heart rate, electrodermal arousal, and maximum grip strength[END_REF]. 3. Completing the task: After the 4 minutes of relaxation, the user interface for the system appeared and the task could begin. Participants were allowed 10 minutes for this. All recordings were automatically stopped after 10 minutes and the researcher returned to the room. 4. Retrospective SAM rating: Participants were asked to subjectively rate the overall emotion immediately after the 10 minutes.

Concurrent SAM ratings (via CRD):

The video of the users face was superimposed over the lower right corner of the screencast. The GSR data was visualized as a graph and superimposed over the timeline of the video player. This allowed for the researcher to visually identify peaks in the GSR data, and fast-forward to about 5 seconds before these points. The user was then shown this part of the video (screencast as well as their own facial expressions), and asked to freely describe their own thoughts as to what they may have reacted to, and how. If the user was able to deduce a reaction, the peak was noted along with their description of the event. They were also asked to rate the emotional state at the time using SAM.

RESULTS

In this section we provide several measures, which will form the basis of our later discussion on whether to verify or falsify our three hypotheses.

Differences between Retrospective and Concurrent Ratings

According to theory, the memory-experience gap should cause retrospective ratings to be overestimated in comparison to concurrent ratings (hypothesis H1). Below we compare the mean of concurrent and retrospective ratings in both experimental conditions, see Table 1. In the seeded condition the mean retrospective rating of Pleasure is 6 (SD=1.6) while the mean concurrent rating is lower with 4.6 (SD=1.8). A repeated measures Wilks' Lambda (.05 level) shows a significant difference as indicated by the * in Table 1 (Wilks' Lambda = .41, F=11.3, p = .01). In the unseeded condition the retrospective rating of Pleasure is 5.6 (SD=1.8), which is similar to the concurrent rating of 5.7 (1.5). This difference is not significant (Wilks' Lambda = .99, F=.008, p = .93).

In terms of Arousal, the seeded condition reveals a retrospective rating of 4.6 (SD=2.2) and a similar concurrent rating of 4.4 (SD=1.8). A Wilks' Lambda test reveals no significant difference (Wilks' Lambda = .99, F=.1, p = .77). In case of the unseeded condition we also observe comparable retrospective and concurrent ratings of Arousal with 3.3 (SD=2.1) and 3.9 (SD=2.1) respectively. No significant difference is found (Wilks' Lambda = .95, F=.44, p = .52).

Considering Dominance, we see that the retrospective rating in the seeded condition is 5.3 (SD=2.1), which is higher than the concurrent rating of 4.4 (SD=1.7). This difference is significant (Wilks' Lambda = .41, F=11.3, p = .01). In case of the unseeded condition we see no significant difference between retrospective and concurrent ratings (Wilks' Lambda = .99, F=.008, p = .93).

In sum we found that all retrospective ratings in the seeded condition were higher than concurrent ratings. This was significant in terms of Pleasure and Dominance. In the unseeded condition we did not find significant differences between any of the retrospective and concurrent ratings. This indicates a larger memory-experience gap in the seeded condition.

Differences Between Conditions

Hypothesis H2 suggests that the memory-experience gap is larger for episodes of unpleasant emotions than for episodes of pleasant emotions. Below we seek to verify that the seeded condition leads to a worse experience compared to the unseeded condition. We do this by using two metrics: 1) Differences in ratings between conditions and 2) The level of emotional fluctuation experienced.

Differences in Ratings.

In Table 1, the overall mean refers to the mean of retrospective and concurrent ratings combined. In the seeded condition the overall mean rating of Pleasure is 4.7 (SD=1.8) while the overall mean is 5.6 (SD=1.5) in the unseeded condition. An independent samples t-test at the .05 level reveals a significant difference between conditions (t=-4.1, df=226, p=0.0).

Participants rated the experienced level of Arousal to be higher in the seeded condition (overall mean = 4.5, SD=1.8) than in the unseeded condition (overall mean = 3.8, SD=2.1). An independent samples t-test indicates that this difference is significant (t=-2.4, df=226, p=0.02).

Finally, participants expressed a lower level of Dominance in the seeded condition (overall mean = 4.5, SD=1.8) compared to the unseeded condition (overall mean = 5.4, SD=1.4). This difference is also significant (t=-4.1, df=226, p=0.0).

In sum, emotions experienced in the seeded condition were more negative with a higher level of arousal compared to the unseeded condition. Emotions in the seeded condition were rated as less dominant than emotions in the unseeded condition.

Differences in Emotional Fluctuation. As mentioned in section 2.2 "Measuring Emotions", GSR data shows fluctuations in arousal. Intuitively, the system version that was seeded with usability problems will lead to a higher level of fluctuation in arousal as the stress level increases when encountering a usability problem. Fig. 2 shows two typical examples of GSR data obtained, one example from the unseeded condition (top) and one from the seeded (bottom). By visual inspection, the above examples appear to fluctuate differently with the seeded example having more peaks and a higher level of variance in arousal. The number of peaks in the GSR graphs was counted on the basis of visual inspection by one of the researchers. Participants in the unseeded condition experienced a mean of 17.22 (SD=3.8) peaks while participants in the seeded condition experienced 22.2 (SD=5.6) peaks. An independent samples t-test revealed a significant difference between conditions (t=-2.21, df=15, p=0.042).

We also calculated the mean variance for both conditions. The mean variance for Pleasure is 1.58 (SD=1.48) in the unseeded condition and 2.93 (SD=0.77) in the seeded. An independent samples t-test reveals a significant difference in this respect (t=2.31, df=15, p=0.034). The mean variance for Arousal is 1.63 (SD=2.17) in the unseeded condition and 3.63 (SD=1.1) in the seeded, which is also significant (t=2.44, df=15, p=0.03). A similar pattern is observed in case of Dominance where the mean variance is 1.04 (SD=1.24) and 3.02 (SD=1.55) in the unseeded and seeded conditions respectively (t=2.92, df=15, p=0.01). Thus, we found significant differences in emotional fluctuations between the two conditions. In the seeded condition participants experienced significantly more peaks and the variance in ratings within this group was 2-3 times larger compared to the unseeded condition. Based on the overall mean ratings in Table 1 and differences in fluctuation of arousal, we find that the seeded condition leads to a worse experience compared to the unseeded condition.

Peak-End Rule Correlations

In the following we consider the extent to which retrospective and concurrent ratings correlate with the peak-end rule (hypothesis H3). Table 2 shows the correlation table between retrospective, concurrent, highest peak and last peak ratings for the unseeded condition. This is based on computation of Pearson r correlation coefficients.

In the unseeded condition there is significant correlation between retrospective ratings of Pleasure and ratings of Pleasure made towards the end of the interaction sequence, i.e. at the last peak (r=0.642, n=10, p=0.045). Additionally, we find significant correlation between retrospective ratings of Arousal and ratings of Dominance made at the highest peak (r=0.776, n=10, p=0.008) and the last peak (r=0.9, n=10, p=0.000). Finally, in the unseeded condition we see a significant correlation between retrospective and concurrent ratings of Arousal (r=0.774, n=10, p=0.009).

In the seeded condition (not shown in Table 2) we observe significant correlation between retrospective and concurrent ratings of Pleasure (r=0.677, n=10, p=0.031). Retrospective ratings of Pleasure also correlate with concurrent ratings of Dominance (r=.688, n=10, p=0.028).

Thus, in the unseeded condition we primarily see significant correlations between retrospective ratings and ratings given at the highest and last GSR peaks. This differs from the pattern in the seeded condition where we see correlations between retrospective and concurrent ratings.

GSR graph examples

DISCUSSION

In the following we discuss our findings in relation to the three hypotheses and related work. Based on our findings we discuss how to apply retrospective ratings of user experience, which has high relevance for practice.

Significant Memory-Experience Gap

Based on the theoretical background of emotions we formulated hypothesis H1: "Retrospective ratings of emotions are higher than concurrent ratings of emotions".

Looking across the two experimental conditions shows that we are able to verify hypothesis H1 in the case where the system had a relatively higher number of usability problems. Findings from our study revealed that retrospective ratings were higher than the mean of concurrent ratings. This finding applied to the system version that was seeded with five usability problems. This overestimation was especially apparent in terms of Pleasure and Dominance. In relation to this we also formulated hypothesis H2: "The memory-experience gap is larger for episodes of unpleasant emotions than for episodes of pleasant emotions". We were also able to verify hypothesis H2. The seeded system version led to a relatively higher number of usability problems, which in turn caused users to experience more negative emotions compared to the unseeded version. We also found that the seeded condition led to a significantly higher level of fluctuation in arousal. The more negative ratings and higher level of arousal indicates that the seeded condition was experienced as the most unpleasant episode. As mentioned previously, we found that retrospective ratings of Pleasure and Dominance were significantly higher than concurrent ratings in this condition. Thus, there was a larger memory-experience gap in the seeded condition compared to the unseeded condition.

Retrospective Ratings Follow the Peak-End Rule

In terms of the peak-end rule we formulated hypothesis H3: "Concurrent and retrospective ratings of emotion correlate following the peak-end rule". We validate H3 in the case where the system had a relatively lower number of usability problems. In the unseeded condition we identified significant correlations between retrospective ratings of Pleasure and ratings of Pleasure located at the last GSR peak measured. Furthermore, we found correlations between retrospective ratings of Arousal and ratings of Dominance at the highest and last peaks. This suggests that H3 could be verified. However, findings from the seeded condition show the opposite where e.g. retrospective ratings of Pleasure correlated with concurrent ratings. Thus, in the unseeded condition we primarily identified significant correlations following the peak-end rule while this was not the case in the seeded condition.

The difference between conditions can be explained by the level of emotional fluctuation experienced. In the seeded condition we observed significantly more peaks of arousal via the GSR sensor compared to the unseeded condition. The variance in SAM ratings in the seeded condition was also significantly higher on all Pleasure-Arousal-Dominance (PAD) dimensions. Consequently the highest and final peaks are interwoven with several other peaks of similar intensities. Arguably, such fluctuation obscures the peak and end experiences in the seeded condition. As a result, the most intensive and end experiences are evened out, hereby leading to correlations between retrospective and concurrent ratings. The opposite is the case for the unseeded condition where the lower level of fluctuation does not obscure peak and end experiences. Although the peak-end rule was not verified in the seeded condition we still stress that retrospective ratings were significantly overestimated in this case. This is in line with the findings of Hassenzahl and Sandweg who found that the last concurrent rating of mental workload correlated with the retrospective rating of perceived usability [START_REF] Hassenzahl | From Mental Effort to Perceived Usability: Transforming Experiences into Summary Assessments[END_REF]. Similarly, Kujala and Miron-Shatz found weekly ratings of emotions to be overestimated compared to the day-to-day ratings [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF]. Thus, it seems that the peak-end rule is present for multiple UX dimensions. In contrast, Hassenzahl and Ullrich found correlations between concurrent SAM ratings and retrospective measurements of AttrakDiff [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF]. This is in line with findings from our seeded condition, but contradictory of findings from the unseeded condition. As discussed above, peak-end correlation seems dependent on the level of fluctuation. Maybe the system applied in [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF] led to a comparable variance in fluctuations as experienced by participants in our seeded condition. This brings us to discuss the main caveat of retrospective UX assessments.

Bottom Line: Mind the Gap in Current Practices

We found that participants significantly overestimated the level of Pleasure and Dominance in retrospective ratings, at least in the system with a relatively higher number of usability problems. Conversely, retrospective ratings were not overestimated in the system version with fewer problems. These findings show that retrospective assessments of emotions do not always reflect the concurrent experience and that this memory-experience gap depends on the level of usability.

Although we in this study emphasized the UX dimension specifically related to emotions, we believe that our findings also apply to more general assessments of UX. This is also supported by the findings in [START_REF] Hassenzahl | From Mental Effort to Perceived Usability: Transforming Experiences into Summary Assessments[END_REF], which are based on measurements of mental workload. From related work we know that overall measurements of UX, e.g. AttrakDiff ratings, depend on emotions [START_REF] Hassenzahl | To Do or Not to Do: Differences in User Experience and Retrospective Judgments Depending on the Presence or Absence of Instrumental Goals[END_REF], and Forlizzi and Battarbee state: "Emotion affects how we plan to interact with products, how we actually interact with products, and the perceptions and outcomes that surround those interactions" [START_REF] Forlizzi | Understanding Experience in Interactive Systems[END_REF].

Given that such overall ratings are primarily collected in retrospect [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF], we now pose the following question: How do we know when we can rely on retrospective UX assessments to provide accurate accounts of concurrent experiences? In the unseeded condition we found no significant difference between retrospective and concurrent ratings. So an answer could be to apply current retrospective methods on a system with relatively few usability problems. This could e.g. be towards the end of a development process going into a phase of summative assessments. However, it is not trivial to decide when there are "few enough problems" such that retrospective ratings accurately reflect concurrent experiences.

Arguably, the current practice of applying questionnaires in retrospective UX assessment is defensible due to its simplicity. Others have justified this approach through the relative ease and low cost of use [START_REF] Mahlke | Measuring Multiple Components of Emotions in Interactive Contexts[END_REF]. From previous studies we also know that people do not remember all their experiences, which lead to the memoryexperience gap. Yet, retrospective evaluations are very important for people's later decisions, e.g. they buy products based on their memory of them [START_REF] Karapanos | Measuring the dynamics of remembered experience over time[END_REF]. Therefore, UX is not only about what happens concurrently but also how people remember their experience of products. However, the above discussion leads us to say that retrospective ratings (of emotions and overall UX alike) should be handled with care. Findings from this study have considerable implications for HCI practice and research, which we discuss in the following.

Moving Forward

As an alternative to retrospective questionnaire assessments we encourage practitioners to apply multiple methods in order to reflect concurrent experiences. In line with Scherer [START_REF] Scherer | What are emotions? And how can they be measured?[END_REF], we suggest to apply an approach relying on a combination of subjective ratings and objective psychophysiological measures. This is furthermore supported by Avila-Hornbaek who state that: "In emotional psychology there are many established and validated ways of measuring emotions that provide more detailed and richer data... Future research might benefit from these to do in depth studies of affective sates in UX" [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. Psychophysiological measurements are widely used within emotional psychology and we believe that these can contribute in providing the detailed measurements suggested in [START_REF] Bargas-Avila | Old wine in new bottles or novel challenges[END_REF]. As an example of an alternative method, our application of Cued-Recall Debrief (CRD) comprises a combination of subjective ratings (SAM) and objective psychophysiological measurements (GSR). We applied CRD by measuring real-time GSR data during interaction. We re-immersed users by showing video clips of their interaction at selected points, clips that were selected based on GSR peaks of arousal. Although ratings in CRD are retrospective in nature, CRD has been validated for measuring concurrent experiences [START_REF] Omodei | Studying complex decision making in natural settings: Using a head-mounted video camera to study competitive orienteering[END_REF][START_REF] Bentley | Evaluation Using Cued-recall Debrief to Elicit Information About a User's Affective Experiences[END_REF][START_REF] Gao | What Does Touch Tell Us About Emotions in Touchscreen-Based Gameplay?[END_REF]. Validity is also indicated through the verification of our initial hypotheses, i.e. we are able to explain our findings.

Our study also points to a more general implication for HCI research, which is in line with Karapanos et al. [START_REF] Karapanos | On the Retrospective Assessment of Users' Experiences over Time: Memory or Actuality? CHI EA[END_REF]. In [START_REF] Karapanos | On the Retrospective Assessment of Users' Experiences over Time: Memory or Actuality? CHI EA[END_REF] a highly relevant discussion of the reliability and veridicality of UX studies is brought up. Reliability in a temporal sense denotes the consistency with which people recollect emotional experiences while veridicality deals with the consistency between actual and recalled experiences. Within our study (and in using CRD in general) the aim is to increase veridicality. This is much more difficult to control in naturalistic and/or longitudinal studies such as the study by Kujala and Miron-Shatz [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF]. In those types of studies, the emphasis should be on reliability. We believe the HCI community needs further discussions and studies to understand veridicality and reliability in UX assessments and the challenges herein. Being aware of the different challenges inherent in different methods will assist HCI researchers in selecting appropriate methods in relation to study aims.

Limitations

A clear limitation in our study is the sample size where we had 10 participants in each condition. Also, our study is based on a system for creating posters. A larger sample and more diverse systems would increase generalizability. However, as stated in the introduction, the contribution of this study is to show that we cannot always rely on retrospective UX ratings to reflect concurrent experiences. We found e.g. that retrospective ratings of pleasure were significantly higher than concurrent ratings, even on this relatively small dataset. This is sufficient to prove the point of the paper, but further studies are needed to make claims about the extent of the memory-experience gap across a wider population and other systems. Another limitation is the artificial laboratory setting. Several UX studies are appearing based on "in the wild" methods as this reflects naturalistic system usage, e.g. the study by Kujala and Miron-Shatz [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF]. However, the strength of the lab is its ability to control for outside interferences such as being startled by a ringing phone, being interrupted by a colleague, emails etc. Factors like these can impact physiological measurements. It was crucial for us that all measurements reflected the usage of the system and not the presence of confounding factors from the context.

A third limitation is the retrospective nature of the Cued-Recall Debrief method. We applied this to enable approximations of concurrent experiences as participants were asked to rate their emotions immediately after the interaction took place. Although not truly concurrent, we argue that the recall bias is considerably reduced compared to the Day-Reconstruction Method applied in other studies, e.g. [START_REF] Kujala | Emotions, Experiences and Usability in Real-life Mobile Phone Use[END_REF].

CONCLUSIONS

The empirical study presented in this paper contributes to the HCI community by showing the existence of a memory-experience gap between concurrent and retrospective ratings of emotions. Findings showed that in the most unpleasant event, participants significantly overestimated retrospective ratings. In a more pleasant episode we found that retrospective ratings correlated with the highest and final emotional peaks of an entire experience. Thus, retrospective ratings do not always reflect concurrent experiences. This shows a potential caveat in current practices, which primarily are based on retrospection. Alternatively we encourage practitioners to apply multiple methods to get insights on concurrent experiences. In the future it is critical to conduct further studies with larger sample sizes and different systems. This should be done in order to determine the extent of the memory-experience gap across other populations and systems.

Fig. 1 .

 1 Fig. 1. Selecting video clips based on GSR peaks.

Fig. 2 .

 2 Fig. 2. Two examples of GSR graphs. Top: Unseeded condition, Bottom: Seeded condition

  Unseeded: No. of peaks=13. Mean variances: Pleasure=0.3 (SD=.5), Arousal=0.3 (SD=.5), Dominance=0.3 (SD=.5). Seeded: No. of peaks=24. Mean variances: Pleasure=2.4 (SD=1.6), Arousal=3.1 (SD=1.8), Dominance=1.9 (SD=1.4).

Table 1 .

 1 Mean SAM ratings distributed by condition and PAD dimensions. *=significant, n=No. of participants.

	Seeded (n=10)	Unseeded (n=10)

Table 2 .

 2 Correlation table of retrospective and concurrent SAM ratings. Unseeded condition. *= significant, n=No. of participants.

		SAM correlations	Pleasure	Arousal	Dominance
		(Unseeded condition, n=10)	(retrospective)	(retrospective)	(retrospective)
		Pleasure	Pearson	-.072	-.062	-.366
	Concurrent	Sig. (2-tailed) Pearson Sig. (2-tailed) Dominance Pearson Arousal	.844 -.249 .489 -.359	.866 .774* .009 .542	.299 .273 .445 -.054
			Sig. (2-tailed)	.309	.106	.882
	Highest Peak	Pleasure Arousal Dominance Pearson Pearson Sig. (2-tailed) Pearson Sig. (2-tailed)	-.184 .611 -.035 .924 -.103	.593 .071 .268 .454 .776*	-.187 .604 .447 .196 .367
			Sig. (2-tailed)	.777	.008	.296
		Pleasure	Pearson	.642*	.109	.555
	Last Peak	Arousal	Sig. (2-tailed) Pearson Sig. (2-tailed)	.045 -.597 .068	.765 .545 .103	.096 -.161 .657
		Dominance Pearson	-.395	.900*	.058
			Sig. (2-tailed)	.259	.000	.873


Samaneh Soleimani

Lai-Chong Law
email: elaw@mcs.le.ac.uk

The Influence of Motivation on Emotional Experience in E-commerce

Keywords:

To explore the notion of User Experience in regard to motivation and affect in the context of e-commerce, a preliminary research model was developed. According to this model, customers' motivations influence their experience of using e-commerce systems. A pilot study with 12 participants was designed to evaluate this hypothesis. The results suggested that customers' emotional experiences were associated with their motivation to visit an e-commerce website. Our future research will investigate the validity of this model with more thorough evaluation methods.

Introduction

According to the Forrester research, e-commerce generated 112 billion Euros in sales for European retailers in 2012, and is expected to yield more than $191 billion Euros in 2017 [START_REF]European Online Retail Forecast[END_REF]. These numbers imply the rapid growth of e-commerce technology. In addition, web-based retailing hinges crucially on the design and development of information and communication technology (ICT) [START_REF] Porat | It's a Pleasure Buying Here: The Effects of Web-Store Design on Consumers' Emotions and Attitudes[END_REF], thus information systems and human-computer interaction (HCI) should support a variety of users' goals and needs.

Until recently, research and practice in HCI has focused on improving usability qualities (e.g. effectiveness and efficiency) of interactive technologies (e.g. [START_REF] Venkatesh | Turning visitors into customers: a usability-centric perspective on purchase behavior in electronic channels[END_REF]). However, the limitations of the narrow focus of usability, which concerns more about workrelated and instrumental qualities [START_REF] Law | Understanding, scoping and defining user experience: a survey approach[END_REF] and less about aesthetic or affective qualities [START_REF] Zhang | The importance of affective quality[END_REF], have been identified. Consequently, the notion of User Experience (UX) has emerged to go beyond the usability of such interactions and include the desirability and experiential quality of ICT services. UX, as a phenomenon, is considered as subjective, dynamic and context-dependent [START_REF] Law | Understanding, scoping and defining user experience: a survey approach[END_REF]. Research [START_REF] Hassenzahl | User experience-a research agenda[END_REF] has described UX as a consequence of user's affect, emotions, needs, motivation (user's internal moods) and the characteristics of the context. In addition, [START_REF] Petrie | Measuring users' emotional reactions to websites[END_REF] have discussed that UX is about user's feelings while they are interacting with a product or service and these feelings and expectations are anticipatory. Thus, it can be concluded that experience, which is described as inseparable from emotions [START_REF] Mccarthy | Technology as experience[END_REF] can be probed and investigated by asking the question "How do you feel?" [START_REF] Hassenzahl | Needs, affect, and interactive products-Facets of user experience[END_REF].

Since precedent conditions can influence users' affective and cognitive states and thus their appraisal of the service of interest, this paper's objective is to address the relationship between users' motivations, emotions and experience. To evaluate this assumption, we developed a research model to explore how customers' motivations can be linked to different affective states, which lead to different experiences.

Emotions in E-commerce

Emotions influence customers' cognition and their shopping behaviors in online environments [START_REF] Tucker | Training tomorrow's leaders: Enhancing the emotional intelligence of business graduates[END_REF] and they apperceive service or product quality based on their feelings [START_REF] Ding | How does background music tempo work for online shopping?[END_REF]. For instance, it was suggested that affect mediates the perceived aesthetic quality of web-based stores [START_REF] Porat | It's a Pleasure Buying Here: The Effects of Web-Store Design on Consumers' Emotions and Attitudes[END_REF]. Scholars have also argued that affective states can lead to different cognitive responses such as the perception of a website's effectiveness and informativeness [START_REF] Mazaheri | The role of emotions in online consumer behavior: a comparison of search, experience, and credence services[END_REF]. As an example, [START_REF] Clore | Affective causes and consequences of social information processing[END_REF] reported that positive emotions can contribute to the simple and default way of information-processing and negative emotions to the scrutinized analysis of information, which demands high cognitive involvement. Emotions can also cause changes on the current flow of interactions undertaken by the user [START_REF] Stickel | Emotion detection: application of the valence arousal space for rapid biological usability testing to enhance universal access[END_REF]. For instance, if the user browses a website to identify certain information, and when the slow Internet connection impedes this goal, the user's frustration might lead him to search for information in another resource [START_REF] Stickel | Emotion detection: application of the valence arousal space for rapid biological usability testing to enhance universal access[END_REF]. Consequently, research has attempted to explore affective features of the e-commerce environments. For instance, some studies have investigated the impact of interface design (e.g. [START_REF] Porat | It's a Pleasure Buying Here: The Effects of Web-Store Design on Consumers' Emotions and Attitudes[END_REF], [START_REF] Ward | Physiological responses to different web page designs[END_REF]) and atmospheric cues such as background color [START_REF] Cheng | The effect of online store atmosphere on consumer's emotional responses--an experimental study of music and colour[END_REF], text color, and music [START_REF] Ding | How does background music tempo work for online shopping?[END_REF] to understand and evoke customers' emotions, which in turn have an influence on their cognitive states, decision making and their appraisal of the e-retailer [START_REF] Porat | It's a Pleasure Buying Here: The Effects of Web-Store Design on Consumers' Emotions and Attitudes[END_REF].

Motivation and Experience in E-commerce

Research findings support the assumption that motivation plays a critical role in user's experience [START_REF] O'brien | The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences[END_REF]. There are a number of studies that have reported two types of motivation for online shopping: recreational and task-oriented motivations (e.g. [START_REF] O'brien | The influence of hedonic and utilitarian motivations on user engagement: The case of online shopping experiences[END_REF][START_REF] Bui | E-tail emotion regulation: examining online hedonic product purchases[END_REF]). There are situations that consumers shop because of the hedonic values such as fun, pleasure, fantasy, escapism or, in other words, the experiential goal of shopping.

In this case, if the retailer could induce positive affect in customers by providing tailored content; this would lead to a positive perception of a company and its services [START_REF] Holzwarth | The influence of avatars on online consumer shopping behavior[END_REF]. On the other hand, task-oriented consumers shop based on their utilitarian goals; for example, to acquire items effectively and efficiently. Shopping is determined as a task or a rational and effective decision [START_REF] To | Shopping motivations on Internet: A study based on utilitarian and hedonic value[END_REF].

"Cognitive involvement", such as giving more attention to the product's features [START_REF] Ding | How does background music tempo work for online shopping?[END_REF].

Task-oriented consumers probably browse fewer products than the recreational shoppers [START_REF] Novak | The influence of goal-directed and experiential activities on online flow experiences[END_REF].

Recreational

Consumers visit stores to obtain gratification or pleasure from the visit per se [START_REF] Kaltcheva | A: When should a retailer create an exciting store environment?[END_REF].

Shopping is not a mission or boring task. It's based on experiential and emotional motives [START_REF] To | Shopping motivations on Internet: A study based on utilitarian and hedonic value[END_REF].

"Affective involvement", such as tendency to get pleasure of the shopping experience [START_REF] Ding | How does background music tempo work for online shopping?[END_REF].

Recreational consumers probably browse more products than the taskoriented shoppers [START_REF] Novak | The influence of goal-directed and experiential activities on online flow experiences[END_REF].

Modeling User Experience in E-commerce 4.1 S-O-R model

Many studies have applied the Stimulus-Organism-Response (S-O-R) model of [START_REF] Mehrabian | An approach to environmental psychology[END_REF] to assess the effect of the web-based stimuli on the online customers' behaviors and consequently on their emotional states of pleasure and arousal (e.g. [START_REF] Ding | How does background music tempo work for online shopping?[END_REF], [START_REF] Mazaheri | The role of emotions in online consumer behavior: a comparison of search, experience, and credence services[END_REF], [START_REF] Froh | The role of atmospheric cues in online impulse-buying behavior[END_REF]).

As stated by the S-O-R model, an environmental stimulus (S) incites user's internal affective appraisal (O), which in turn leads to the user's reaction or response (R). Within a website, signs, symbols, artefacts, ambient conditions, space and function are reported as stimuli [START_REF] Froh | The role of atmospheric cues in online impulse-buying behavior[END_REF]. Research has also observed that computer related events are not essentially different from other stimuli, they cause physiological changes and in turn these changes are expressed as level of arousal and manifest clues for valence [START_REF] Ward | Physiological responses to different web page designs[END_REF].

Proposed E-commerce UX Model

According to [START_REF] Yeh | A study of emotional and rational purchasing behavior for online shopping[END_REF], there are two types of characteristics in e-commerce: emotional thinking and rational thinking. For instance, looking for convenience and quickness in the process of searching, finding and shopping, accessibility of a variety of products and low prices [START_REF] Ariely | Buying, bidding, playing, or competing? Value assessment and decision dynamics in online auctions[END_REF] were regarded as the utilitarian attributes. Enjoyment and excitement resulting from online shopping, the searching process of new, unusual products and models, an experience of adventure, releasing stress and depression, and forgetting problems (e.g. [START_REF] Arnold | Hedonic shopping motivations[END_REF]) were considered as the emotional aspect of e-commerce.

Additionally, research has suggested that the motivation of shopping mediates the effect of arousal induced by the store environments on the pleasantness of the store visit [START_REF] Kaltcheva | A: When should a retailer create an exciting store environment?[END_REF]. Accordingly, we borrowed the theory of shopping motivation and its effects on shopping behaviors in stores and adopted the S-O-R model on web environments to propose that the motivations of e-commerce can influence user experience by inducing different emotional states. Our research model is presented in Fig. 1.

Method

Participants. To explore the relationship between customers' motivation and their affective state, we conducted 12 semi-structured interviews. Participants were recruited via personal email-invitation and they were asked to participate in this interview only if they have had a recent experience with an e-commerce website. All the participants volunteered to participate in this study and referred to their most recent experience within the past 10 days of the interview time. They were between 20-35 years old, 7 female and 5 male and all university educated (from bachelor to PhD degree).

Procedure. The interviews were carried out in English and performed through 8 faceto-face meetings and 4 Skype calls (one participant was from Australia and 3 of them were from the United States, therefore face-to-face meetings were financially not possible). In both cases (Skype and face-to-face) the same procedure was followed and the interview was audiotaped. The interviewees were asked to revisit the ecommerce website that they had used most recently, reflecting on their experience and repeating it retrospectively. Participants were asked to respond to the following questions:

• What type of product or service were you looking for?

• What was your motivation for this e-commerce visit and do you consider it as being recreational or task-oriented? (The interviewer gave examples of the two types of motivation to the interviewees.) • What was your emotional status before and after entering the website?

• What was your emotional status after finishing your visit?

• Can you tell me what emotions you felt while you were visiting the website and the reasons behind them? In addition, other information such as product price and participants' suggestions to improve the quality of their experience were collected during the interviews. The audio recordings were transcribed right after each interview.

Results and Discussion

To understand the relationship between emotion and motivation a list of emotion words that were verbalized by the interviewees during, before and after their ecommerce visit, was identified. Then according to each participant's motivation of their visit, each emotion word was linked to either hedonic or task-oriented. In addition, 5 HCI researchers rated each emotion word based on valence (being positive/negative) and on a 5-point-scale (ranging from calm to excited) to indicate the intensity of arousal. The average value of the arousal ratings was computed as the intensity of the emotion word. To analyze valence, each positive rating was assigned to 1 and negative rating to -1. If the average value was between -0.5 and 0.5, that emotion word was rated as Ambiguous. The only rated ambiguous emotion word was 'surprised'; a finding consistent with that of the previous research [START_REF] Petrie | Measuring user experience of websites: think aloud protocols and an emotion word prompt list[END_REF] (see Table 2 for details of the results). In addition, by looking at the emotion word list produced in this study, we observed that 9 emotion words were common in other similar research [START_REF] Petrie | Measuring user experience of websites: think aloud protocols and an emotion word prompt list[END_REF]. Moreover, a closer observation of the results suggested that those participants, who reported their motivation as hedonic, described their experience as emotional excitement (e.g. "impulsive shopping is exciting), curiosity (e.g. "I wanted to experience the website and see what is my next step") and boredom (e.g. "looking for fun", "to improve my mood"). On the other hand, participants who wanted to do the purchase as quickly as possible and with straightforward actions (as a task and having no recreational goal) did not assess their experience with pleasure and evaluated it as neutral, confused (e.g. "seeing so many options") and stressed (e.g. "money is stressful"). This result implies that the motivation of the customer influences emotional experience, which is consistent with the literature [START_REF] Kaltcheva | A: When should a retailer create an exciting store environment?[END_REF].

Limitations

There are several limitations related to the results and implications of this work. First of all, this study was exploratory with the aim to verify the research question and to gain insight for the future work. Secondly, the number of participants is too small to draw a solid conclusion. Thirdly, studying memorized emotions and experiences is very challenging due to the reason that they are prone to change, fade or get rebuilt according to the characteristics of the situated context [START_REF] Law | Attitudes towards user experience (UX) measurement[END_REF], which results in biased conclusions. Lastly, the reported emotions were the interviewees' subjective evaluation of their internal states. Thus the interpretation could be inaccurate because retrospective self-reported emotions differed from the emotions felt at the moment they were elicited. In addition, the data of this study were captured through semi-structured interviews whereas the validity and reliability of the implications should have been consolidated by applying other research methods such as qualitative and quantitative surveys.

Table 2. List of emotion words were verbalized by the interviewees when they repeated their last e-commerce experience.

Conclusion and Future Work

Empirical findings of our pilot study suggest that emotion as a factor of UX influences customers' online retail behaviors and the types of emotions experienced were mediated by customers' motivations. Therefore, it could be concluded that users' experience is mediated by users' motivation of their online commerce visit. In addi-tion, according to [START_REF] Hassenzahl | How motivational orientation influences the evaluation and choice of hedonic and pragmatic interactive products: The role of regulatory focus[END_REF] if we had a clearer understanding of how different motivational perspectives influence the use and appraisal of interactive products, we could create design rules for different circumstances without the need of knowing the details of the situation. Thus, in this study we endeavored to understand the effects of motivational orientation on the course of interaction by exploring the relationship between emotions and motivations. In addition, considering motivation as an antecedent of UX can help us obtain a more thorough understanding of this notion. Our future research will be carried out to substantiate our current findings by applying physiological measurements and subjective methodologies to evaluate users' affective states and motivations in live experiences.

Fig. 1 .

 1 Fig. 1. The proposed research model

Table 1

 1

summarizes a few studies to show to what extent task-oriented and recreational motivations can result in different shopping outcome, shopping experience, consumer involvement and product browsing.

Table 1 .

 1 Two shopping motivations and characteristics.

	Objective	Shopping	Shopping	Consumer	Product
		outcome	experience	involvement	browsing
	Task-oriented	Consumers visit
		stores to pur-
		chase required
		products or
		obtain
		information
		[20].


Jonna Häkkila
email: jonna.hakkila@ulapland.fi

Yun He

Ashley Colley

Experiencing the Elements -User Study with Natural Material Probes

Keywords: Material qualities, user experience, tangible user interfaces, design, user studies 1

In this paper, we present the first systematic user study exploring the user experience and perceptions towards different natural materials -water, ice, stone, sand, fire, wind and soup bubbles. By trying out different materials, participants (n=16) expressed their associations and perceptions, rated different qualities of the materials, and described their impressions through product reaction cards. Our findings reveal for example that light weight and ease of movement are perceived as central qualities when inspiring and fun elements are sought for. This exploratory study shines light on user experiences with natural elements, and provides an experimental grounding for naturalistic tangible user interface design. Material qualities in tangible user interface design create a subtle, but critical part of the user experience.

INTRODUCTION

In [START_REF] Hassenzahl | User Experience (UX): Towards an experiential perspective on product quality[END_REF], Hassenzahl defines user experience (UX) as "a momentary, primarily evaluative feeling (good-bad) while interacting with a product or service". User experience consists of both utilitarian and a hedonic aspect [START_REF] Hassenzahl | User experience -a research agenda[END_REF]. Whereas the utilitarian side typically dominates in the overall motivation for application design, in constructing the user interface, hedonic aspects are important to consider in order to create pleasurable and engaging user experiences. When designing tangible user interfaces, material qualities are an integral part of the holistic experience.

Material qualities of physical objects have been thoroughly considered in areas such as art, industrial design and mechanics, but research on the material's role in interactive systems has been quite sporadic, leaving much to explore. Especially, the use of natural materials has so far been little researched. As tangible user interfaces (TUIs) can make use of the human senses in a richer and more multidimensional way than conventional digital user interfaces (UIs) [START_REF] Ishii | Tangible bits: towards seamless interfaces between people, bits and atoms[END_REF], it is of interest to explore the different qualities that are perceived and associated with different physical materials.

In this paper, we take steps towards systematic exploration of natural materials. We are especially interested of these because of the new possibilities they provide for the design for tangible user interfaces, aesthetics and rich user experiences, and offer potential for user interfaces and application linked with, e.g., sustainable values or interactive environmental installations. [START_REF] Virolainen | Cool Interaction with Calm Technologies -Experimenting with Ice as a Multitouch Surface[END_REF] whilst water based input and output has been used e.g. in [START_REF] Geurts | Splash controllers: game controllers involving the uncareful manipulation of water[END_REF][START_REF] Pier | Using water as interface media in VR applications[END_REF]. Heavy stone based input [START_REF] Häkkilä | City Landmark as an Interactive Installation -Experiences with Stone, Water and Public Space[END_REF], and air based haptic feedback [START_REF] Sodhi | AIREAL: interactive tactile experiences in free air[END_REF] have also been demonstrated. Rydarowski et al. [START_REF] Rydarowski | Murmur: Kinetic Relief Sculpture, Multi-Sensory Display, Listening Machine[END_REF] present an installation, where individually controlled CPU fans are used to move paper clips with air pressure. The user experience findings reflect the curiosity [START_REF] Pier | Using water as interface media in VR applications[END_REF], aesthetics [START_REF] Virolainen | Cool Interaction with Calm Technologies -Experimenting with Ice as a Multitouch Surface[END_REF], and playful nature of the interaction [START_REF] Virolainen | Cool Interaction with Calm Technologies -Experimenting with Ice as a Multitouch Surface[END_REF][START_REF] Häkkilä | City Landmark as an Interactive Installation -Experiences with Stone, Water and Public Space[END_REF]. With water related interactive systems, the pleasant sensation the cool water creates against the skin has been highlighted by users [START_REF] Pier | Using water as interface media in VR applications[END_REF][START_REF] Häkkilä | City Landmark as an Interactive Installation -Experiences with Stone, Water and Public Space[END_REF], and spilling water has been reported to offer a more powerful UI feedback than a virtual one [START_REF] Geurts | Splash controllers: game controllers involving the uncareful manipulation of water[END_REF].

PRIOR ART AND POSITIONING OF OUR WORK

Whereas examples of the use of natural materials in TUIs exist, differing from us, the earlier research has reported the user perceptions focusing each time on a single system without a systematic comparison between different tangible elements. Moreover, the focus of the prior art has been more on the proof-of-concept level installations rather than on explicit investigation of the user experience aspects. A few explorations of different material qualities exist, as in [START_REF] Juhlin | Fashionable shape switching: explorations in outfit-centric design[END_REF] in the context of fashion, fabric and shape-change, but to the best of our knowledge, not in the domain of natural materials.

STUDY METHODOLOGY

Material Probes

In our user study, we applied the material probes method introduced in [START_REF] Jung | Material Probe: Exploring Materiality of Digital Artifacts[END_REF]. Material probes for seven [START_REF] Pier | Using water as interface media in VR applications[END_REF] natural materials were created. The materials were selected to have as wide a range of physical properties as possible, in order to elicit a full range of perceptions from our test participants when interacting with the probes. The material probes are shown in Fig. 1 and consisted of:  Water, contained in a small bowl.  Stone in the form of 3 smooth rocks.  Soap bubbles, created as required from a soap solution container and wand.  Ice, in the form of a tennis ball sized block.  Sand, as a small pile on a plate.  Wind, created as needed from a small desk fan.  Fire, in the form of a candle flame.

User Study Procedure

Our user study included an open think-aloud method, subjective rating of qualities and two tasks that required users to associate the materials with objects or activities.

Additionally we utilized the product reaction cards method adapted from [START_REF]Microsoft Product Reaction Cards[END_REF]. Each study session lasted for approximately 60 minutes and the study procedure was as follows:

 Completing a background questionnaire.  Interacting with each material probe in turn and giving a free form description of ideas, thoughts and associations the material created.  Selection of a favorite material (see Fig. 2)  Rating the materials a 7-point Likert scale against the criteria: Controllable, Calm, Inspiring and Overall preference.  Connecting each material with digital artifacts, e.g. such as an alarm clock.

Here, multiple artifacts could be connected with each material, and participants could leave some material unused (see Fig. 5).  Connecting each material with activities and thoughts, e.g. such as organizing files. Again, multiple activities could be connected with each material, and participants could leave some material unused (see Fig. 6).  For each material, selecting 3 adjectives from a list of 20 that best describe the material. Here following a Product Reaction Cards (PRC) based methodology [START_REF]Microsoft Product Reaction Cards[END_REF]. Table 1 contains the adjectives that were available.

The order in which participants interacted with the materials was counterbalanced to avoid any effects due to the presentation order. Altogether 16 participants (5 female, 11 male) took part in the study. The age range of the participants was 18-24:13%, 25-29:37%, 30-39:44% and 40-49:6%. The participants were recruited through advertising on university email lists, and through personal networks. Although participants predominantly represented university students, they came from various backgrounds with several different nationalities being represented. No UX professionals or UI designers were included.

RESULTS -PERCEPTIONS OF DIFFERENT MATERIALS

Favourite Material

The materials the participants selected as favorite are shown in Fig. 2. Water was clearly the favorite material with almost half of the participants (7/16) selecting it as their favorite. Here, many of the participants highlighted the relaxing nature of water, for example, "Water is comfortable and controllable to interact with. I can do it for a long time, just like swimming." (User #3).

Subjective ratings and Product Reaction Cards

When asked to subjectively rate their perceptions of the materials (Fig. 3), users considered fire to be the least controllable and least calm material. Several users commented on the danger of fire, "It is hot and untouchable. It hurts the hand. It is difficult to control and use." (User #9). Water and Ice were perceived as two of the calmest and most inspiring elements. For both materials issues related to cleanliness and transparency were voiced e.g. "Transparent and clean" (User #9, water), "Cold, beautiful and transparent" (User #10, ice). Examining the results from the Product Reaction Cards method (Fig. 4) provides congruent findings e.g. with fire and wind being considered uncontrollable by many users. Clearly soap bubbles are considered as fun, with all but 2 users (14/16) selecting this adjective.

Material Associations

The test participants associations between the materials and digital artifacts and activities are presented in Fig. 5 and Fig. 6 respectively. Whilst some of the strong as-sociations are obvious, e.g. the association between the pairs ice -fridge and watercoffee maker, other such as stone -mobile phone are less easily explained. Overall the high selection frequency of stone and low frequency of sand may be interesting to explore further. In general the association of the given activities with materials is rather even, with most activities being associated to some extent with all of the materials. One clear exception is that of sand, which was almost exclusively associated with organizing files

5

DISCUSSION AND CONCLUSIONS

Perceiving Materials

In this paper, we have reported the first user study seeking to systematically explore the user experiences perceived with different natural materials, investigated by using material probes. Based on the PRC results, it seems that lucid and light materialswater, wind and bubbles -are perceived as generally more playful, pleasant and fun. These materials also gained the highest ratings when the participants assessed in numerical scale how much they liked each material. Their light weight and ease of movement are perceived as central qualities when inspiring and fun elements are sought for. Although inspiring, fire was perceived uncontrollable and, based on the observations and comments, provoking reactions of respect and even fear. Solid materials -stone, sand and ice -were perceived as more controllable and mostly pleasant. These were also the only materials that were associated with the task of 'organizing' (see Fig. 4). Whereas these associations are somewhat unsurprising, they confirm how materials that are quantifiable and behave less erratically are more easily associated with tasks that require control. This aligns with the findings reported by [START_REF] Häkkilä | City Landmark as an Interactive Installation -Experiences with Stone, Water and Public Space[END_REF], where it is concluded that metaphors and interaction styles that match the physical characteristics of the interaction control should be utilized.

Limitations of the Study and Methodological Observations

In our study, we generally observed that people paid a lot of attention to each material -many spent a long time interacting with the materials in the tasks, and sometimes also came back to them. The test sessions generally took more time than we had first expected.

We acknowledge that our study is limited by the small sample size of participants. However, we believe that our research is valuable in its attempt to conduct a comparable study on different user experience qualities of natural materials. The laboratory type context was disconnected from any specific domains or use cases, and hence we believe provided a good environment for obtaining baseline results.

Fig. 1 .

 1 Fig. 1. User study set-up.

Fig. 2 .

 2 Fig. 2. Number of users preferring each material (n=16)

Fig. 3 .Fig. 4 .

 34 Fig. 3. Subjective ratings for each material

Fig. 5 .

 5 Fig. 5. Association between materials and digital artifacts. (The thickness of the base of each connection represents the number of users selecting that association).

Fig. 6 .

 6 Fig. 6. Association between materials and activities/thoughts

  Prior art has demonstrated the use of different natural materials in different types of interactive systems, often designed for playful purposes or as part of a larger, monumental installation. Döring et al. have demonstrated interaction with soap bubbles [4], Virolainen et al. have shown touch screen interaction with an ice wall

Table 1 .

 1 Adjectives used in product reaction card method. Participants selected three (3) that best described each material.

	Fast	Slow	Inconsistent	Consistent
	Responsive	Rigid	Uncontrollable	Controllable
	Fun	Serious	Unpleasant	Pleasant
	Restful	Stressful	Boring	Exciting
	Approachable	Unapproachable	Frustrating	Inspiring