How impulsivity affects consumer decision-making in e-commerce

Electronic Commerce Research and Applications 11 (2012) 582–590

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Electronic Commerce Research and Applications journal homepage: www.elsevier.com/locate/ecra

How impulsivity affects consumer decision-making in e-commerce Yu-Feng Huang, Feng-Yang Kuo ⇑ National Sun Yat-Sen University, Kaohsiung, Taiwan

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Article history: Available online 26 October 2012 Keywords: Decision-making Empirical research Eye-tracking Impulsivity Mood Online shopping Purchase behavior

a b s t r a c t This research investigates whether a person’s mood can influence impulsivity in online shopping decisions, and how involvement can regulate it. We adopt a process view of impulsivity, and recorded the detailed information search patterns of consumers using an eye-tracker methodology. The results show that incidental moods tend to increase process impulsivity, and this effect may not be restrained by involvement. We also demonstrate that the decision-making process can be separated into two stages – orientation and evaluation. We further find that differences in impulsivity are most evident in the evaluation stage. These results suggest the importance of mood-elicited impulsivity of purchases in e-commerce. Ó 2012 Elsevier B.V. All rights reserved.

1. Introduction Impulsivity is a critical factor in consumer decision-making in online and traditional shopping. Many marketing techniques, such as limited-time sales and store atmospherics, can be used to entice consumers to buy on impulse. In addition, advances in information technology (IT) seem to exacerbate impulsive buying; individual online shoppers are likely to fall prey to brick-and-mortar techniques (Donthu and Garcia 1999). Indeed, in the United States, for example, more than 48% of consumers are estimated to have impulsively bought things online (GSI Commerce 2008). Moreover, severe online impulse buying has been reported, such as addiction to eBay (Cantelmi and Talli 2009). Online retail market sales in the US were forecasted to reach US$226 billion in 2012 (Forrester Research 2011). So the study of impulse purchases is a critical area of research in e-commerce. This will give us insights into how to improve the welfare of online shoppers. Previous studies have conceptualized online impulsivity as an unplanned purchase behavior (Koufaris 2002), and an uncontrollable urge to buy (Parboteeah et al. 2009). These authors have shown that emotional factors are likely to promote both types of impulses. Online impulsivity also can be conceptualized as an unreflective decision process (Rook and Fisher 1995). With advances in such process-tracing technology as eye-tracking, researchers now can examine the details of the decision-making process to understand the cognitive and emotional activities underlying decision-making behavior. Eye-tracking approaches have been widely used to examine decision-making processes in consumer choice, including ⇑ Corresponding author. Tel.: +886 75254731; fax: +886 75254799. E-mail addresses: [email protected] (Y.-F. Huang), [email protected] (F.-Y. Kuo). 1567-4223/$ - see front matter Ó 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.elerap.2012.09.004

risk and advertisement perceptions (Russo and Leclerc 1994, Pieters and Warlop 1999, Glockner and Herbold 2010). Surprisingly few studies have adopted eye-tracking methods to examine online impulsivity from a process view, or tried to reveal how emotional and motivational factors interact to influence impulsivity over time. An investigation of the temporal evolution of the decision-making process may augment our knowledge regarding ‘‘how’’ it works, in comparison to ‘‘what’’ its outcomes are in online impulse shopping (Johnson et al. 2008). This will also allow us to understand better how emotional factors can increase impulsivity in online shopping, and how motivational factors, such as involvement, may restrain it and promote more deliberate processing throughout the decision-making process. Thus, our first research question concerns whether a shopper’s mood that arises incidentally may affect impulsivity in online shopping. Process impulsivity refers to the pattern of impulsivity exhibited by e-commerce consumers during their decision process (Day et al. 2009, Kuo et al. 2009). From the information processing perspective, consumers are problem-solvers who search for information before making their buying decisions (Payne et al. 1993, Wedel and Pieters 2008). Furthermore, a consumer’s information search pattern reflects the person’s attention distribution throughout the decision-making process (Payne 1976, Russo and Leclerc 1994, Pieters and Warlop 1999, Simola et al. 2008, Day et al. 2009, Patalano et al. 2010, Eivazi and Bednarik 2011). These patterns may differ between those who are affected by impulsivity and those who are not. Although few studies have examined incidental moods and impulsivity in e-commerce, several studies have shown that emotional factors are critical in promoting online impulsivity (Koufaris 2002, Madhavaram and Laverie 2004). For example, website design can influence perceived enjoyment, which in turn promotes the

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feeling of an urge to buy (Parboteeah et al. 2009). These studies point to the need for understanding the effect of the incidental mood on impulsivity. Incidental mood is an affective state elicited by cues that are irrelevant to the decision target (e.g., the product or service). Previous studies have found that incidental moods may result in impulsive decision-making (Beatty and Ferrell 1998, Hsee and Rottenstreich 2004). We seek to empirically test whether incidental moods can induce process impulsivity in online shopping. Our next research question is to investigate whether the level of involvement in online information search may restrain moodelicited impulsivity. Past studies have suggested that consumer involvement in such an activity might decrease their level of impulsivity (Petty and Cacioppo 1979, Celsi and Olson 1988, Pieters et al. 1996). We seek evidence for the effect of this involvement on impulsivity restraint. Finally, previous research has shown that decision-making may follow the orientation–evaluation decision stage model, which suggests that people first orient themselves to relevant task characteristics to choose an information search pattern, and then execute the chosen pattern in the evaluation stage (Beach and Mitchell 1978, Kleinmuntz and Schkade 1993, Payne et al. 1993). Our last research question therefore concerns how impulsivity, if elicited, may affect the information search pattern in these two stages. In other words, if involvement regulates impulsivity, can we identify in what decision stage this regulation occurs and how this understanding can contribute to the theory and practice? Our research is intended to contribute to understanding impulsivity in e-commerce. As more transactions are conducted in e-commerce, we need to understand how consumer cognition may be affected by the elicitation of emotions.

2. Literature 2.1. Online impulsivity Researchers of consumer behavior have approached the concept of impulsivity from three different views. The first is behavior impulsivity (Stern 1962), which holds that impulsivity is manifested when consumers make unplanned purchases or make inferior choices (Koufaris 2002). This view assumes that certain types of behaviors are rational, and behaviors that deviate from the rational ones should be considered as irrational or impulsive (Kahneman 2003, Pham 2007, Slovic et al. 2007). The second view is psychological impulsivity, meaning that impulsivity is manifested when consumers feel the urge to buy (Rook 1987). This view is proposed because not all unplanned purchases are impulsively decided. For example, consumers may use store products as cues to recall other intended purchases (Rook 1987). The third view is process impulsivity, which is based on the assumption that consumers are problem-solvers who search for information before making their buying decisions (Payne et al. 1993, Pieters and Wedel 2007). This view focuses on the analysis and interpretation of detailed information search patterns, and holds that patterns can be identified as deliberate or impulsive with a quantitative index (Bettman and Jacoby 1976, Payne 1976). For example, Creyer et al. (1990) have shown that people who restrain their effort during decision-making may fail to conduct a comprehensive evaluation of attributes across different alternatives. In contrast, Patalano et al. (2010) have shown that indecisive people, who engage in more deliberate decision-making, were less likely to perform exhaustive assessments of attributes relevant to decision-making problems. Similarly, consumers who are unmotivated or under high time pressure reveal effort-saving and incomplete information processing patterns (Pieters and Warlop 1999).

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With the advent of e-commerce, researchers have been interested in whether impulsivity is evident in this new shopping environment. The early evidence has shown that online shoppers tend to be impulsive buyers (Donthu and Garcia 1999). In subsequent studies, researchers started to conceptualize online impulsivity and empirically tested it in the business-to-consumer (B2C) shopping environment (LaRose 2001, Koski 2004, Madhavaram and Laverie 2004). These studies indicate that, similar to offline consumers, online consumers often deviate from rational buying behavior (LaRose 2001, Madhavaram and Laverie 2004, Wu and Cheng 2011). Online impulsivity also has been examined in terms of behavior, psychology, and process impulsivity. For example, Koufaris (2002) has investigated the effects of trait and website design on unplanned purchases. In addition, website design characteristics may influence psychological impulsivity, defined as the urge to buy (Parboteeah et al. 2009). Using a process tracing method such as eye-tracking, researchers found that flash banners and music could make website users more likely to adopt impulsive information search patterns (Day et al. 2006, 2009; Ding and Lin 2012). In general, these studies have shown that certain website design elements can promote impulsivity in the e-commerce domain. 2.2. Mood elicitation and impulsivity in consumer research It is postulated that positive mood encourages people to buy, whereas negative mood does otherwise (Rook and Gardner 1993). This assertion is evidenced in both offline and online scenarios. In the offline setting, Beatty and Ferrell (1998) have found that a positive mood induced by in-store browsing was positively related to the urge to buy, whereas a negative mood did not show this effect. In the online setting, an exploratory study has shown that people might be more willing to shop online when they were feeling good (Madhavaram and Laverie 2004). Koufaris (2002) has proposed that website design elements could increase the flow experience of browsing, which in turn increases the likelihood of an unplanned purchase. In another study, Parboteeah et al. (2009) have further shown that website design elements could increase the perceived enjoyment, which then leads to psychological impulsivity. Other studies have investigated the relationship of mood elicitation and process impulsivity in e-commerce. For example, when the tempo of background music in a website increased, people increased their decision speed and exhibited an impulsive information search pattern (Day et al. 2009). However, these studies have not directly addressed the role of mood. Mood elicitation can occur integrally or incidentally (Peters et al. 2007, Pham 2007, Slovic et al. 2007). Integral elicitation means that emotion is caused by the task-relevant cues, such as the product per se. For example, product-elicited positive moods can carry over to subsequent evaluations and promote positive evaluations toward the product (Pham et al. 2001). In contrast, the sources of incidental moods are task-irrelevant, and usually are cues existing in the environment or context. For example, the mood elicited by viewing a task-irrelevant movie could carry over to and influence financial decision tasks (Lerner et al. 2004). Some studies have directly distinguished decision-making behavior between people whose mood was suppressed and people whose mood was elicited by task-irrelevant cues. They found that the task-irrelevant mood still influenced the decision results (Dickert et al. 2011). In one study, Hsee and Rottenstreich (2004) asked one group of people to report their subjective feelings toward task-irrelevant concepts (such as ‘‘baby’’) and asked another group to calculate math problems. They found that the group with elicited moods demonstrated less rational decision making because they were insensitive to the quantity of items under buying consideration (Hsee and Rottenstreich 2004).

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The reason that mood affects decision impulsivity is explained by mood-managing theory. According to this theory, decisionmakers not only try to pursue accuracy but also attempt to stay in a positive mood and avoid any negative mood (Wegener and Petty 1994, Andrade 2005). Decision-makers who emphasize accuracy tend to maximize the decision benefits by selecting a deliberate information search pattern that may produce better decision benefits than an impulsive one (Bettman and Jacoby 1976). In contrast, decision-makers who emphasize the mood-managing aspect of decision-making are inclined to select an impulsive search pattern because effortful processing of decision information is likely to induce negative mood (Garbarino and Edell 1997, Carmon et al. 2003). In other words, the more difficult the information that is to be processed, the more likely it will induce a negative mood (Garbarino and Edell 1997). It is plausible that when mood is elicited, people might feel the need to protect their mood and therefore avoid effortful processing of the decision information at hand. Therefore, we hypothesize that people whose mood is elicited tend to adopt an impulsive information search pattern compared with those whose mood is suppressed, even though the mood is task irrelevant. The first hypothesis is: Hypothesis 1 (The Incidental Emotion Elicitation Hypothesis). Incidental emotion elicitation increases online process impulsivity. 2.3. Restraint of impulsivity Consumers who are more involved in web information searches might be more motivated to search information deliberately. The essential characteristic of involvement is the subjective personal relevance to an issue, activity, situation, or object (Celsi and Olson 1988). Involvement has been shown to motivate people to search information deliberately in an ad or a product (Celsi and Olson 1988). For example, low-involvement consumers, compared with high-involvement consumers, spent less attention to processing printed ads (Pieters et al. 1996). In the e-commerce domain, studies have shown that highly involved people are less susceptible to message framing (Lee et al. 2008, Cheng and Wu 2010), a type of impulsive decision induced by reference point manipulation (De Martino et al. 2006, Kahneman and Frederick 2007). Thus, we hypothesize that involvement in a web information search can restrain the effect of mood on process impulsivity. In other words, compared with low-involvement consumers, highinvolvement consumers should be more motivated to deliberately process online information despite the effect of mood. The second hypothesis is: Hypothesis 2 (The Web Information Search Involvement Hypothesis). Involvement in a web information search restrains process impulsivity elicited by incidental mood 2.4. The temporal dynamics of emotion regulation The eye-mind hypothesis holds that overt search patterns, such as eye-movement, are tightly linked to human cognitive processing (Just and Carpenter 1980, Rayner 1998). This hypothesis has received strong support, suggesting that a search pattern is associated with a distinct decision strategy or stage. Early evidence has shown that eye-movement search patterns are linked to the problem-solving activities that are currently being executed (Carpenter and Just 1976). In the decision-making domain, evidence suggests that search patterns are consistent with patterns predicted by the manipulated decision strategies (Payne 1976, Creyer et al. 1990, Day et al. 2006, Glockner and Herbold 2010). Based on the eye-

mind hypothesis, researchers have been using overt search patterns to infer decision-makers’ strategies or stages (Russo and Leclerc 1994, Luce et al. 1997, Velichkovsky et al. 2002, Simola et al. 2008, Patalano et al. 2010, Eivazi and Bednarik 2011). For example, Russo and Leclerc (1994) have shown that decision stages exist when significant changes in the search pattern between segments can be detected. Other examples come from design science studies in which eye-tracking data are used to infer the users’ current information processing strategy in using a computer system (Velichkovsky et al. 2002, Simola et al. 2008, Eivazi and Bednarik 2011). In short, the eye-mind hypothesis is widely accepted as a means to infer decision stage changes based on overt search pattern changes. Theoretically, decision-making may follow the orientation– evaluation model, which stipulates that people first orient themselves to task characteristics to choose an information search pattern, and they then execute the chosen pattern in the evaluation stage (Beach and Mitchell 1978, Kleinmuntz and Schkade 1993). Russo and Leclerc (1994) have analyzed eye-movement data of consumer information processing and found that, with indices of information search patterns, the decision course may be divided into the orientation and evaluation stages, each of which reveals a different information processing pattern. This orientation–evaluation model also has received support from a recent neuroscience study (Venkatraman et al. 2009). Specifically, the selection and execution of decision strategy involves different areas in the prefrontal cortex, further evidence supporting the distinction of choice-execution stages. To investigate processing data across decision stages, some researchers explicitly manipulate the stages by asking decision makers to follow specific decision procedures. For example, researchers can explicitly ask decision-makers to form a consideration set first (the screening stage) and then ask them to choose the final alternative from this set (the choice stage; e.g., Bettman and Park 1980, Levin et al. 2000). Some other researchers have focused on naturally occurring decision processes, and collect process-tracing data with techniques such as eye-tracking, an information display board (IDB), and verbal protocol (Payne 1976, Russo and Leclerc 1994). Several methods have been used to identify stages in the natural data, including pattern recognition algorithms (Simola et al. 2008), coding of information processing patterns (Payne 1976), and interpretation of eye re-fixation (Russo and Leclerc 1994). Other studies adopt a normalization procedure, which maps each decision process onto a 0–100% scale, where 0% means the beginning of a decision and 100% means the end of a decision. Normalized decision processes are then divided into equal-length segments so that comparison among segments and conditions is possible (Biehal and Chakravarti 1986, Jacoby et al. 1994). Several studies have investigated how motivational factors influence the decision-making process across decision stages. Dividing the decision process into three stages, Lee et al. (1999) found that high-involvement subjects reveal fewer impulsive search patterns than those not involved; this difference is evident in the middle and late stages but not in the early stage. Another study divides the decision process into two stages and finds that people who are motivated to process information tend to use an effortful search pattern and then turn to an effort-saving one (Patalano et al. 2010). We hypothesize that the differences in involvement-related information search patterns are most evident in the evaluation stage and less evident in the orientation stage. This is because in the orientation stage, decision makers have to scan the task characteristics to select an information-processing pattern (Kleinmuntz and Schkade 1993, Russo and Leclerc 1994). However, the scanning of the task characteristics may be limited. Only easily perceivable

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characteristics, such as format and complexity, can be scanned because further acquisition of information will demand detailed investigation of the task information (Fennema and Kleinmuntz 1995). Because decision-makers need to scan task characteristics, and few characteristics are possible to be scanned, decisionmakers may exhibit similar scanning patterns in the orientation stage. Next, the chosen strategies are executed in the evaluation stage. Major information-processing differences among selected patterns may be manifested most significantly here. Specifically, because mood-managing decision makers tend to adopt impulsive strategies, it is expected that these decision-makers exhibit a pattern of impulsive information processing in the evaluation stage. The third hypothesis is: Hypothesis 3 (The Information Search Pattern Hypothesis). The information search pattern exhibited by mood-elicited subjects differs from that of the mood-suppressed subjects in the evaluation stage

3. Methods 3.1. Eye-tracking We adopted eye-tracking methodology because it may reflect the mental process of decision-makers (Wang and Day 2007, Day et al. 2009). Eye-tacking has been identified as an important tool to explore the cognitive activities that occur during decisionmaking (Todd and Benbasat 1987). For example, prior research has found that, by using the eye-tracking technique, the importance of a piece of information is positively related to the attention paid to it (Day et al. 2009, Kuo et al. 2009, Glockner and Herbold 2010). Eye-movement data can be classified into fixations and saccades (similar to acquisitions and transitions in IDB data). Fixations (acquisitions) are the locations where pieces of information to which people devote their attention are presented; saccades (transitions) signal people’s switches of attention among locations (Rayner 1998). 3.2. Experiment overview The experiment was a 2 (mood: elicited vs. suppressed)  2 (involvement: high vs. low) between-group factorial design. Dependent variables were decision time and information search pattern. Mood was a manipulated variable; the participants’ levels of involvement were not manipulated and were measured after the experiment. In this study, participants were randomly assigned into either the mood-elicited group or the mood-suppressed group, and involvement was measured after the eye-tracking experiment. The participants with involvement scores higher than the mean were classified as the high-involvement group, and those with scores lower than the mean were classified as the low-involvement group. Involvement in a web information search was measured with a 6-item, 5-point semantic difference scale, modified from the version developed by Zaichkowsky (1985). Researchers have used mean or median split on involvement to investigate its effects on dependent variables such as satisfaction (Oliver and Bearden 1983) and purchase intention (Berger et al. 1999, Prendergast et al. 2010). The participants’ information search patterns were recorded with an eye-tracker. Furthermore, to examine how the information search pattern evolved through the decision stages, the decision course was normalized and divided into four equal segments (Russo and Leclerc 1994). Specifically, we divided the total number of eye fixations into equal segments. Past studies have used various numbers of time segments (Biehal and Chakravarti 1986, Russo and Leclerc 1994, Luce et al. 1997, Lee et al. 1999; Patalano et al.

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2010). We chose four segments because four-segment analysis had been adopted in previous studies (Luce et al. 1997) and can reveal the finest temporal resolution without losing significant eye-movement data. That is, with more than four segments, a substantial proportion of the participants would have been excluded from the statistical analysis due to an insufficient number of eye fixations available within each segment to calculate a search pattern. 3.2.1. Participants A total of 42 college students (19 males and 23 females) were recruited from southern Taiwan. The age range was from 18 to 25 years, with an average age of 21.2 years. Participants knew in advance they would each receive NT$50 as a participation reward. 3.2.2. Materials and tasks Participants were instructed to finish five preferential choice tasks (one practice trial and four experiment trials) presented in a matrix with four alternatives and two attributes (e.g., price and distance). Four tasks are necessary to rotate the location of the price and distance values to prevent the possibility that people pay more attention to the alternatives presented in the upper columns (Lohse 1997, Glaholt et al. 2009). The tasks chosen were ones frequently performed by local students, who are used to accessing these services online and from whom we recruited our participants. In addition, price and distance are two important attributes across these services. In each task, participants saw three screens: first, the priming material, second, the task question, and third, a table containing the multi-attribute decision material (see Fig. 1). This three-screen design allowed clear separation of eye-movement data related to the decision process from unwanted eye-movement data related to reading the primes and tasks. In each task, participants were asked to imagine that they were ordering services or products from four Internet stores and would go to receive the services or products shortly. For example, the dining task required participants to make an online reservation from four restaurants with similar qualities. A table showing prices and restaurant distances was provided in the next screen. The other three types of services are movies (the movie task), karaoke (the karaoke task), and book handling services (the book task). The practice task was to reserve a hotel room. Participants performed the tasks at their own pace and chose one alternative they preferred by clicking the left mouse button. In each task, the price and distance were negatively correlated and no obvious superior alternative could be detected (Johnson et al. 1989). The order of the tasks was randomized. 3.2.3. Mood manipulation This study adopts the priming paradigm used to elicit or suppress incidental mood by Hsee and Rottenstreich (2004), who elicited mood by asking people to report their subjective feelings toward concepts such as ‘‘baby,’’ and encouraged accuracy-based processing by asking people to calculate math problems. We designed four mood-eliciting primes and four mood-suppressing primes. The mood-eliciting primes required participants to reflect on the concepts of ‘‘love,’’ ‘‘baby,’’ ‘‘sunset,’’ and ‘‘chocolate,’’ and the mood-suppressing primes were simple math questions that did not consume much effort, such as ‘‘How much does clothing cost that was originally priced at $NT 6000 and is now at 35% off?’’ An independent group of 28 students were recruited to test the validity of the primes. They reported their feelings on a five-point Positive Affect Negative Affect Schedule scale (Watson et al. 1988) after reflecting on the concepts and finishing the math questions. The results of a paired t-test shows that, after receiving the mood-suppressing primes, these participants reported both weak-

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Fig. 1. Example of a decision task. Participants are required to read three screens to make a decision. The first screen is the priming material. In this example, the first screen asks participants to report their feelings toward ‘‘love’’ by choosing one of four positive and four negative words (Plutchik 1980). The second screen describes a decision task, which asks participants to order a movie ticket from four online alternatives. The third screen is the choice task, where values of two attributes (price and distance) and four alternatives are displayed. Participants click on one of the alternatives to make a choice.

er positive and weaker negative feeling states than the mood-eliciting primes (both p < 0.01). This test shows that the eliciting primes cause a stronger awareness of moods than do the suppressing primes. 3.2.4. Dependent variables This study considers effort and the information search pattern to be indices of impulsivity. According to the literature, the shorter the decision time and the more the pattern is attribute-based, the more the decision process may be impulsive. For example, Dickman and Meyer (1988) have directly tested that high-impulsive people were more willing than low-impulsive people to sacrifice accuracy for speed. Also, attribute-based processing has been strongly associated with unreflective decision-making. Specifically, Russo and Dosher (1983) and Payne et al. (1993) have shown that attribute-based processing is cognitively easier than alternative-based processing. Empirically, people who are under-motivated or under time pressure made decisions not only faster but with more attribute-based information processing than their counterparts (Creyer et al. 1990, Pieters and Warlop 1999). Decision time captures the amount of processing resource devoted to a decision task. This measure was calculated as the time starting from the onset of the decision material screen to the time participants clicked the mouse button to make a choice. The information search pattern is a measure of a decisionmaker’s attention distribution throughout the decision-making process (Bettman and Jacoby 1976). The search pattern is a

continuum in which attribute-based processing is at one end, and alternative-based processing is at the other end (Payne 1976). On the one hand, consumers who adopt the attribute-based pattern consider values in one attribute across several alternatives before processing the next attribute; on the other hand, consumers who adopt the alternative-based pattern consider attributes in one alternative before processing the next alternative (Payne 1976, Payne et al. 1993). Studies on decision-making processes have found that non-deliberate, effort-saving decision makers tend to adopt attribute-based processing that might lead to suboptimal choices (Payne 1976, Creyer et al. 1990, Pieters and Warlop 1999, Patalano et al. 2010). For example, decision-makers emphasizing the accuracy goal tend to adopt alternative-based processing, whereas those emphasizing the effort-saving goal tend to use attribute-based processing (Creyer et al. 1990). Another example is that indecisive decision makers, who tend to deliberate on decision problems, are less likely to adopt attribute-based processing (Patalano et al. 2010). The measure of information search patterns is calculated in the following way: search pattern = (SB SA)/(SB + SA), where SA is the total number of within-attribute fixation pairs, and SB is the total number of within-alternative pairs. The range of processing direction is from 1 to 1, where 1 means an individual is using complete alternative-based processing, and 1 means an individual is using complete attribute-based processing (Payne 1976, Jarvenpaa 1989). Because the attribute-based pattern indicates an impulsive search, this index suggests that the smaller the score the more the impulsive a search is.

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Y.-F. Huang, F.-Y. Kuo / Electronic Commerce Research and Applications 11 (2012) 582–590 Table 1 Summary for the hypotheses and results. Dependent variable

Hypothesis

Results

RQ 1: Does incidental mood elicit process impulsivity? Search pattern Mood-suppressed > elicited Decision time Mood-suppressed > elicited

Supported Supported

RQ 2: Does involvement in browsing webs decrease impulsivity? Search pattern High involvement > low involvement Decision time High involvement > low involvements

Partially supported (effect of involvement only occurs in calculation mood manipulation) Not supported

RQ 3: If incidental mood elicits difference in the search pattern, when does the difference occur? Search pattern Difference occurs in the evaluation stage. Supported

3.2.5. Apparatus Eye-movement data were collected with an EyeLink II system (SR Research, Canada) at a 250-Hz sampling rate. A nine-point calibration was executed before the experiment. Decision material was presented on a 19-in. LCD display with a resolution of 800  600 pixels. The display was located at the eye level of the participants and at a distance of approximately 60 cm.

Table 2 ANOVA table for the effects of emotion and involvement. Source

Mood Involvement Involvement  Mood Error

3.3. Procedure *

Participants were led into a room with the experiment computer. After the experimenter introduced the procedure and finished the eye-tracking calibration, participants were left alone in the room to finish the tasks. Instructions were presented on the screen. After the participants finished the practice trial, experimenters came in to make sure participants understood the instructions. Then participants were again left alone to finish four experimental trials. When the experiment ended, participants were asked to finish a questionnaire about their demographics and personal data. Finally, the participants received their rewards. 4. Results Participants generally chose the same alternatives. There were no priming or involvement differences in the choices in each task (all p > 0.124). The effect of mood manipulation was successful. Participants in the mood-elicited group reported that they felt a positive emotion when receiving the eliciting priming (95.4% of the trials), and those in the mood-suppressed group chose the correct answer to the mathematical questions (85.0% of the trials). To rule out variance due to task type, it is included as a within-subject variable in the following analysis. The mean-split categorized 22 participants into the high-involvement group (52.4%, 12 moodelicited and 10 mood-suppressed) and 20 into the lowinvolvement group (47.6%, 10 mood-elicited and 10 moodsuppressed). Similar to the studies that used mean- or median split method investigating involvement (Oliver and Bearden 1983, Berger et al. 1999), the involvement level of the high-involvement group (mean score = 3.99) was significantly higher than that of the low-involvement group (mean score = 2.88, t = 7.71, p < 0.01). Because the distribution of decision time data was skewed, it was log-transformed before the following analysis (Ratcliff 1993). A summary of the results is presented in Table 1. 4.1. Mood and involvement The Incidental Emotion Elicitation Hypothesis (H1) postulates that the mood-elicited group might show more process impulsivity than the mood-suppressed group. A repeated measures ANOVA on the two dependent measures with two between-group factors (mood manipulation and involvement level) and one within-group factor (task type) shows that mood manipulation had a significant main effect on decision time, p < 0.01, and was marginally signifi-

**

Search pattern F

Decision time

SS

df

Sig.

0.492 0.274 0.064 2.893

1 6.467 0.065 1 3.597 0.015* 1 0.845 0.364 38

SS

df

1.958 0.125 0.219 6.896

1 10.791 0.002** 1 0.691 0.411 1 1.205 0.279 38

F

Sig.

p < 0.05 p < 0.01

cant on search pattern, p = 0.065 (see Table 2). This result supports the hypothesis. The Web Information Search Involvement Hypothesis (H2), which postulates that involvement might restrain the impulsivity elicited by mood, was not supported. As shown in Table 2, there was a lack of a significant interaction effect between mood and involvement in terms of either decision time or search pattern. Table 2 also shows a significant main effect of involvement on search pattern. However, the further contrast analysis in Fig. 2 revealed that this effect was evident only in the mood-suppressed group (F(1, 18) = 6.842, p = 0.018), and not in the mood-elicited group (F(1, 20) = 1.204, p = 0.286). Therefore, this hypothesis is only partially supported. 4.2. Validation of decision stages The Information Search Pattern Hypothesis (H3) holds that the decision process can be divided into orientation and evaluation stages, and that the difference in the search pattern should be most evident in the evaluation stage. One participant was dropped because he had only two fixations in his movie task. To examine this hypothesis, validation of the orientation–evaluation decision model should be conducted (see Table 3 and Fig. 3). Two repeated measures ANOVAs were conducted on the search pattern with two within-group factors (task type and time), one with mood manipulation as the between-group factor and the other with involvement as the between-group factor. Fig. 3 shows the means of the search patterns in each time segment. We examined the difference in search patterns between each pair of time segments to investigate the stage main effect. As Table 3 shows, the search pattern was more evident at T2 than at T1, and this effect was evident when data were aggregated across mood manipulation and involvement. We then examined whether differences existed between T2 and T3 and between T3 and T4. The result shows no time segment difference on search pattern, except a marginal significance detected in T2 and T3. Considering both results, this study considers T2, T3, and T4 to be the evaluation stage because no evident strategy shift was found. These results reveal a clear strategy shift only from T1 to T2, supporting the two-stage model.

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Fig. 3. Analysis for the effect of involvement, collapsed by mood manipulation.

5. Discussion and conclusion

1. Emotion: the participants who received emotion primes 2. Calculation: the participants who received calculation primes 3. Inv: involvement in web information search Fig. 2. Analysis for the effect of involvement, collapsed by mood manipulation. (1) Emotion: the participants who received emotion primes. (2) Calculation: the participants who received calculation primes. (3) Inv: involvement in web information search.

Table 3 ANOVA table for the stage analysis. Stage Mood manipulation Emotion T1 T2 T3 Calculation T1 T2 T3 Involvement Low

High

T1 T2 T3 T1 T2 T3

SS

F

Sig.

vs. vs. vs. vs. vs. vs.

T2 T3 T4 T2 T3 T4

5.728 0.008 0.064 2.104 0.074 0.003

30.304 0.034 0.300 17.492 0.826 0.031

0.000** 0.856 0.590 0.001** 0.375 0.861

vs. vs. vs. vs. vs. vs.

T2 T3 T4 T2 T3 T4

6.833 0.054 0.039 1.499 0.364 0.000

39.214 0.260 0.279 13.688 3.172 0.000

0.000** 0.615 0.603 0.002** 0.091 0.991

(1) F(1, 20) in emotion and low involvement condition, F(1, 19) in calculation and high involvement condition. ** p < 0.01

4.3. Involvement effect across decision stages The Information Search Pattern Hypothesis (H3) holds that the difference in the search pattern should be most evident in the evaluation stage. Table 4 shows the difference in the search pattern of the two types of decision makers in each time segment, and reveals that the difference was most evident in the evaluation stage. The data show that when participants were mood-suppressed, at T2, T3, and T4, the high-involvement group revealed a significantly less (or marginally significant) attribute-based information processing pattern. In contrast, the effect of involvement was evident only at T2 in the mood-induced group.

In this study, we have found that incidental mood elicitation increases process impulsivity. In the e-commerce domain, moodelicited impulsivity has been conceptualized as behavioral impulsivity or psychological impulsivity (Koufaris 2002). We contribute to this line of research by providing evidence that incidental moods may also result in process impulsivity. In addition, we demonstrated that the mood can be elicited from cues irrelevant to the current online shopping environment. Past studies have shown that task-irrelevant website elements, such as background music, flash banners, and navigation ability, can influence decision impulsivity. Our study extends this line of study by showing that mood elicited by elements outside the current website can still influence decision impulsivity. The result implies that B2C service providers have ample opportunities to manipulate consumer mood. They can either elicit mood within their websites or they can take advantage of platforms such as YouTube or Facebook. For example, a service provider may put ads in the YouTube movie clips. Consumers who come upon an ad embedded in a clip that elicits mood (e.g., a comedy) might then have a high level of buying impulsivity. From the consumer perspective, this may be problematic, and the situation seems to get worse since our finding also shows that involvement in an information search may not restrain the effect of incidental mood. We did not find clear evidence to show that involvement can regulate the effect of incidental mood. Specifically, involvement does not prolong decision time. Furthermore, involvement promotes a deliberate information search pattern when mood is suppressed, but this effect is not clear when mood is elicited. Consistent with the arguments of previous studies (Koufaris 2002, Parboteeah et al. 2009), this finding suggests the importance of mood on impulsivity in e-commerce. From the consumer’s viewpoint, more research is needed to understand whether and how other factors can restrain mood-elicited impulsivity. Our temporal dynamic analysis further confirmed that the orientation–evaluation decision model can well describe the information search pattern in the online environment. When eye movements are normalized and divided into four equal-length segments, a pattern shift occurred between the first and second segments of the decision process, implying the existence of the orientation and evaluation stages. No clear strategy shift was detected in the following stages, suggesting that all the other stages might belong to the evaluation stage. In addition, neither mood nor involvement changes the structure. The temporal dynamics analysis further suggests that the reason for the ineffectiveness of involvement on impulsivity regulation is that its effect does not last throughout the evaluation stage, possibly overridden by the moods. Our analysis shows that for people whose

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Y.-F. Huang, F.-Y. Kuo / Electronic Commerce Research and Applications 11 (2012) 582–590 Table 4 The effect of involvement on search pattern across stages, with results aggregated by mood manipulation. Mood manipulation

Emotion Calculation

T1

T2

Difference

Sig

0.013 0.093

0.817 0.222

Difference 0.203 0.166

T3

T4

Sig

Difference

Sig

0.042⁄ 0.061

0.011 0.201

0.889 0.000**

Difference 0.017 0.129

Sig 0.847 0.079

(1) Difference is calculated by low-involvement minus high involvement. Negative value means that high involvement group is more analytical (less impulsive) than the low involvement group. * p < 0.05 ** p < 0.01

mood is suppressed, the involvement indeed promotes a deliberate search pattern throughout the evaluation stage (from Stage 2 to Stage 4, and evident at the aggregate level). In contrast, for people whose mood is elicited, involvement promotes a deliberate search in only a relatively short time (only in Stage 2, and not evident in the aggregate level). This finding raises a critical issue: conceptually, the definition of process impulsivity could be re-examined. If a factor influences the search pattern only at the stage level but not at the aggregate level, should we still claim that the factor is ineffective? One possible way to address this issue is to concurrently measure behavior, psychology, and process impulsivity. If the factor occurs only at the stage level, we might be able to claim its effectiveness when it can result in a difference in terms of behavior or psychology impulsivity. However, the concurrent measures of behavior, psychology, and process data might bring up another issue: which one is the best description of decision impulsivity if they do not agree with each other? For example, evidence in decision-making literature has demonstrated the disagreement between process and behavior data (Patalano et al 2010). Similarly, using multi-attribute decision tasks and process tracing techniques, Johnson et al. (2008) have shown that a decision theory might describe the behavioral results but fail to describe the information-processing patterns underlying the choice behaviors. In our experiment, although mood manipulation changes the search pattern, it does not change choice behavior. Although we did not define which choice is more impulsive than another because we are interested only in process impulsivity, this result still provides preliminary evidence of process-behavior dissociation. The agreement and disagreement between process data and behavior data is still an open issue and requires more future research. In this study, we used only positive mood primes to induce moods. However, in future studies it will be interesting to examine the effects of other types of moods and emotions in the online shopping domain. For example, whether a negative mood can impede or enhance deliberateness during online shopping has not been investigated. Also, it is suggested that the study of emotion can go beyond the dichotomy of positivity and negativity to examine the effects of specific emotions on decision-making behaviors. For example, evidence has shown that the negative emotions of disgust and sorrow exert distinct effects on buying and selling behaviors (Lerner et al. 2004). More studies are needed to explore the effect of emotion and its interaction with online shopping factors on decision-making. Sometimes pictures and videos can induce even stronger positive moods, however (Bradley and Lang 1999). In addition, if stronger mood primes had been used, the decision stage structure might be inconsistent with the orientation–evaluation model. Although it has been shown that the strength of positive moods (mild vs. extreme) can influence the information search pattern (Roehm and Roehm 2005), it is still not clear whether the strength of positive moods and priming techniques (words vs. videos) can change the temporal dynamics of decision making. One possibility is that extreme positive moods can reduce the decision process into one stage. Specifically, extreme positive moods impede the two critical cognitive abilities of planning and execution (Cyders and Smith

2008), assumed in the orientation–evaluation model. Therefore, people with extreme positive moods might process little information and randomly pick one alternative. In this case, there may be only one decision stage (random picking). Conversely, it is also likely that if motivation is stronger than involvement (e.g., significant financial stake), consumers repeatedly review the decision information, resulting in re-examination or validation stages. We believe that with temporal dynamics analysis, these hypotheses can be tested and contribute more insight to the study of online impulsivity and decision-making. In conclusion, research of impulsivity in the e-commerce environment may yield fruitful insight into the consumer decision-making process. As increasingly more transactions are being conducted online, vendors are required to provide processes that facilitate sound decision-making, and consumers need to understand how their own cognition may be affected by emotion elicitation. Future research may therefore further our theories of impulsivity and e-commerce consumer decision-making. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.elerap.2012.09. 004. References Andrade, E. B. Behavioral consequences of affect: combining evaluative and regulatory mechanisms. Journal of Consumer Research, 32, 3, 2005, 355–362. Beach, L. R., and Mitchell, T. R. A contingency model for the selection of decision strategies. Academy of Management Review, 3, 3, 1978, 439–449. Beatty, S. E., and Ferrell, M. E. Impulse buying: modeling its precursors. Journal of Retailing, 74, 2, 1998, 169–191. Berger, I. E., Cunningham, P. H., and Kozinets, R. V. Consumer persuasion through cause-related advertising. Advances in Consumer Research, 26, 1999, 491–497. Bettman, J. R., and Jacoby, J. Patterns of processing in consumer information acquisition. Advances in Consumer Research, 3, 1976, 315–320. Bettman, J. R., and Park, C. W. Effects of prior knowledge and experience and phase of the choice process on consumer decision processes: a protocol analysis. Journal of Consumer Research, 7, 3, 1980, 234–248. Biehal, G., and Chakravarti, D. Consumers’ use of memory and external information in choice: macro and micro perspectives. Journal of Consumer Research, 12, 4, 1986, 382–405. Bradley, M. M. and P. J. Lang. Affective norms for english words (anew): Instruction manual and affective ratings. Working paper, Center for Research in Psychophysiology, University of Florida, Gainesville FL, 1999. Cantelmi, T., and Talli, M. Cyberspace psychopathology. Annual Review of Cybertherapy and Telemedicine, 7, 2009, 27–32. Carmon, Z., Wertenbroch, K., and Zeelenberg, M. Option attachment: when deliberating makes choosing feel like losing. Journal of Consumer Research, 30, 1, 2003, 15–29. Carpenter, P. A., and Just, M. A. Eye fixations and cognitive processes. Cognitive Psychology, 8, 1976, 441–480. Celsi, R., and Olson, J. The role of involvement in attention and comprehension processes. Journal of Consumer Research, 15, 2, 1988, 210–224. Cheng, F. F., and Wu, C. S. Debiasing the framing effect: the effect of warning and involvement. Decision Support Systems, 49, 3, 2010, 328–334. Creyer, E. H., Bettman, J. R., and Payne, J. W. The impact of accuracy and effort feedback and goals on adaptive decision behavior. Journal of Behavioral Decision Making, 3, 1, 1990, 1–16. Cyders, M. A., and Smith, G. T. Emotion-based dispositions to rash action: positive and negative urgency. Psychological Bulletin, 134, 6, 2008, 807–828.

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