How do social-based cues influence consumers' online purchase ...

Electron Commer Res (2016) 16:1–26 DOI 10.1007/s10660-015-9209-0

How do social-based cues influence consumers’ online purchase decisions? An event-related potential study Qiuzhen Wang1 • Liang Meng1 • Manlu Liu2 Qi Wang3 • Qingguo Ma1

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Published online: 14 December 2015 Springer Science+Business Media New York 2015

Abstract Product rating and sales are two important social-based cues in online shopping. This study applies the event-related potential (ERP) approach to explore the underlying neural mechanism of the joint influence of these two cues on consumers’ decision-making. Behavioral data show that product rating has a greater impact on the purchasing rate than sales, which positively moderates the latter’s effect and supports cue-diagnosticity theory. Electrophysiological data provide further explanations for the observed behavioral pattern. Analyses of main ERP components suggest that consumers go through a series of cognitive processes from processing of perceived risk (N2) and informational conflict (N400) to evaluative categorization (LPP) before making the final purchasing decision. Specifically, product rating significantly influences the risk perception while the combination of high rating and low sales elicits significant cognitive conflict. Both cues are adopted by consumers to make an overall evaluation based on their similarity to the criterion. Keywords Cue-diagnosticity theory Event-related potentials Online shopping Product rating Sales

& Manlu Liu [email protected] 1

School of Management, Zhejiang University, Hangzhou, People’s Republic of China

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Saunders College of Business, Rochester Institute of Technology, Rochester, NY, USA

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NetEase, Inc, Hangzhou, People’s Republic of China

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1 Introduction Cue utilization theory claims that products deliver a series of cues, which can be applied by consumers to judge their quality [13, 45]. Product cues can be divided into intrinsic cues and extrinsic cues [45]. While intrinsic cues are related to direct physical attributes of products, extrinsic cues are generally related to indirect signals. Nowadays, with the burgeoning and prevalence of e-commerce, online shopping has become an important part of our daily life [58, 60]. However, in online markets where products cannot be directly observed and physically examined, consumers generally experience a high degree of uncertainty, and it is more difficult for them to make inferences about product quality [47]. Since consumers cannot directly observe the intrinsic cues of the products, they are more likely to apply extrinsic cues to assess product quality [62]. Thus, extrinsic cues play a significant role in consumers’ decision-making in online environments. Among various extrinsic cues, product rating and sales are commonly adopted by shopping websites to exert informational social influences, with the aim of inducing hesitant buyers to place an order [28, 60]. Informational social influence refers to an individual’s tendency to conform to others’ opinions, based on the obtained information as evidence in one’s judgment [26]. Product rating refers to the quantitative evaluation received by a product, and sales describe the number of products sold. Product rating and sales as two important cues have drawn many researchers’ attention and have been extensively studied. For example, product rating (or review) was suggested to have a significant influence on perceived trust and can reflect whether vendors are reliable [34], and high product rating was found to facilitate the building of consumers’ initial trust [51]. In addition, sales were shown to bring about herding behavior and then significantly influence consumers’ product choice [22]. In the real online purchasing situation, consumers are simultaneously exposed to multiple extrinsic cues, and they usually process one product cue in relation to another. Thus, it is important to understand how consumers utilize varied cues to develop overall product evaluations and make purchasing decisions when faced with either consistent or inconsistent extrinsic cues. Past research categorized product cues as either high- or low-scope according to their diagnosticity [18], and high-scope cues were perceived to be more credible and consequently more diagnostic relative to low-scope cues [50]. The cue diagnosticity theory suggests that the positive (negative) inferences evoked by the high-scope cues transfer over to the low-scope cues, making the low-scope cues more (less) diagnostic [50]. Some studies have provided strong support for cue diagnosticity theory [2, 42, 50]. However, others reveal that, when faced with multiple cues, individuals rely more on the high-scope cue to make decisions, and the influence of other cues is attenuated [21, 60]. Thus, result patterns indicating the joint effect of multiple cues with varied diagnosticity are divergent and remain to be further clarified. In the present study, although product rating and sales are both social-based information cues, the two cues hold different credibility and vary in their diagnosticity. Product rating is based on buyers’ own purchasing or using

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experience and is inherently more associated with product quality. Thus, product rating is more difficult to manipulate and is regarded as more reliable and credible than other cues [60]. According to previous research [18, 55], product rating and sales are typically conceptualized as high versus low-scope cues. However, in the real online purchase, these two cues do not always provide consistent information. The situations such as high product ratings but low sales or low product ratings but high sales are not uncommon. Therefore, investigating consumers’ cognitive processing during decision-making in a dilemma would be interesting and valuable. Since there has been little research effort directed at exploring the joint influence of product rating and sales in the online shopping context, the present study aims to fill in this gap and investigate the combined influence of rating and sales. The findings of the present study may clarify the seemingly inconsistent results concerned with cue diagnosticity. Unlike most previous studies that adopted the traditional self-report approach, the current study adopts the ERP approach to examine the underlying neural mechanism of how these two online social-based cues (product rating and sales) influence consumers’ purchasing decisions. One major drawback of self-report is that it does not open the black box of the brain and explore the corresponding informationprocessing activities [13, 28]. In addition, the use of psychometric self-reported data is often blamed for bringing about subjective biases, which hinders objective measurements [28]. As a result, in recent years, researchers in the interdisciplinary fields of consumer neuroscience and NeuroIS began to apply neuroscientific methods to gain better insights into human information-processing and decisionmaking [13, 17]. Given the advantage of better temporal resolution and unobtrusiveness relative to other neuroscientific methods such as functional magnetic resonance imaging (fMRI) and positron emission topography (PET), the current study adopts the ERP method to probe the real-time processing of varied combinations of product rating and sales cues in the online shopping scenario. The findings of this study greatly contribute to the current literature in the following ways. First, as an initial attempt, it empirically explores the joint effects of social cues of varied diagnosticity (e.g., product rating and sales) on consumers’ decision-making, extending the current understanding of their independent effect. Second, since there are mixed findings regarding the effect of high- and low-scope cues in influencing people’s product evaluations, the finding of our study that product rating positively moderated the effect of sales provides additional evidence for cue-diagnosticity theory. Third, this study innovatively introduces a cognitive neuroscience approach (ERP) to thoroughly explore the corresponding neural bases. To sum up, this research intends to offer new insights into the joint effects of these two social-based cues with the aim of providing recommendations for e-retailers on how to take advantage of these two extrinsic cues to exert positive social influence. The structure of this paper is as follows. In Sect. 2, we review relevant literature on online product cues and relevant ERP components, and propose several hypotheses subsequently. In Sect. 3, we describe our research methodology, in other words, the design of our ERP experiment. Behavior and ERP data analyses and result patterns are presented in Sect. 4. Finally, we conclude with a summary of results, contributions and limitations, and directions for future research.

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2 Theory development 2.1 Online cues As has been pointed out above, cue utilization theory suggests that a product provides a series of cues, and consumers judge the quality of the product based on these cues [12, 45]. Previous literature consistently suggested these cues as the basis for consumers’ purchasing decision-making in the online shopping environment [21, 46]. For example, visual attractiveness of a website [52], product price [27], third-party assurance seals [21], online store reviews [60], as well as reputation [53] are all demonstrated to play important roles. The present study focuses on two important and unique social-based cues in the online shopping environment: product rating and sales. Product rating is the quantitative evaluation that a product has received and usually appears in the form of a favorable rating on a 5-point Likert scale. Because a review is the text description and evaluation that consumers write for a product, rating can be regarded as a simplified version of that review. Most e-commerce websites present average product rating in a prominent position, as this information is comprehensive and comparable, which well facilitates consumers’ purchasing decision-making. Existing literature indicated that product rating (or review) influences consumers’ perception of a product to a great extent. In particular, product rating (or review) influences consumers’ perceived usefulness and social presence [29], as well as perceived trustworthiness of e-commerce websites [1], and these factors are widely known to influence purchasing decisions. Sales are the number of products sold during a certain period of time (e.g. the last 3 months), which demonstrates decisions made by past consumers. People tend to make decisions concordant with others [22], a phenomenon widely known as ‘‘herding’’. While sales have been extensively studied in the marketing literature, it was not until the past few years that online consumer researchers began to take this factor into consideration [9, 10, 22]. It was found that online consumers exhibit herding behavior, and sales significantly influences consumers’ product choice [22]. For instance, through three sequential experiments, Chen and colleagues illustrated that sales, product rating, and recommendations have significant influences on consumers’ online book choice [9]. Another study of online auctions indicated that online bidding behavior is influenced by the number of bids [10]. In addition, results of Kuan and colleagues showed that are sales that are more than expected (positive information) do not seem to enhance consumers’ attitude and purchasing intention, whereas sales that are less than expected (negative information) have a negative influence [28]. While shopping online, consumers are frequently faced with multiple cues. According to cue-diagnosticity theory, consumers are inclined to prioritize cues based on their diagnosticity in discerning between product alternatives [54, 55]. Previous research categorized product cues as either high- or low-scope according to their diagnosticity [18]. High-scope cues are generally established over time, and are more credible and reliable. On the contrary, low-scope cues can be easily

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manipulated, and are perceived to be ambiguous in giving predictions. Previous research suggests that a high-scope cue can either facilitate or inhibit a low-scope cue by changing the latter’s diagnosticity. To be specific, positive or negative highscope cues transmit their corresponding implications to low-scope ones, making the low-scope cues more or less diagnostic [2, 42, 50]. For example, Purohit and Srivastava [50] found that reputation (a high-scope cue) has not only a direct influence, but also an indirect effect on quality perception, and the latter effect is achieved by changes in the diagnosticity of low-scope cues. Similarly, Miyazaki et al. [42] demonstrated that higher price (a low-scope cue) leads to higher perceived quality when brand name (a high-scope cue) is strong. A recent study examining the effect of brand reputation and price on consumers’ product quality perception also showed that a positive high-scope cue (i.e., brand reputation) would reinforce the diagnosticity of a less diagnostic quality cue (i.e., price) [2]. However, some previous research suggested that when faced with multiple cues, individuals rely more on the high-scope cue to make decisions, and the influence of other cues is attenuated [21, 60]. For example, Hu et al. [21] found attenuating rather than synergistic interaction effects between privacy and security assurance functions as well as between privacy and transaction-integrity assurance functions. In other words, either function’s effect on enhancing consumers’ initial trust is weakened by the presence of privacy assurance functions. Another study also suggested that various information cues are not combined in a simple additive manner, and the effects of other relatively less diagnostic cues (e.g. assurance seals, store reputation) will not be significant when presented with consumer review (a more diagnostic cue) [60]. In the present study, compared with sales, product rating cannot be easily manipulated by e-retailers. Thus, it is generally viewed as a high-scope cue with high diagnosticity. Due to divergent findings on the joint effect of multiple cues applying traditional methods, this research investigates the joint effect of product rating and sales, which are of different information diagnosticity. In addition, the ERP method is applied to probe the underlying neural mechanism of the cognitive processing course, which may help us better understand online consumers’ behavior when faced with product rating and sales cues. 2.2 ERP method and ERP components Event-related potential (ERP) is the electrophysiological brain signal associated with cognitive or neural responses to an event such as the presentation of a stimulus [20]. Previously, the ERP method has been widely used in cognitive research, which mainly focused on basic cognitive processes, such as auditory mismatch [43] and target detecting [49]. Recently, researchers began to apply the ERP approach to measure more complex cognitive processes, such as emotional response [6] and behavioral choice [61]. In the current study, ERP is adopted mainly due to its temporal accuracy in tracking the overall decision-making process. During the whole process, the initial stage is fundamental to the final decision-making, which generally starts upon the onset of stimuli and lasts for several hundred milliseconds. Consequently, brain activities during this stage are recorded and analyzed.

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During recent years, individuals’ purchasing decisions gained increasing attention from researchers in the interdisciplinary fields of cognitive neuroscience and management. For instance, in a pioneering study, Chen and colleagues investigated the neural mechanism underlying consumers’ conformity behavior [7] and the neural representation of experienced cognitive and emotional conflict upon inconsistent reviews when purchasing books online [8]. Kuan et al. examined the effect of online group-buying information on consumers’ experienced emotions using electroencephalogram (EEG) data [28]. Studies mentioned above suggested that applying cognitive neuroscience theories and methodologies to online consumer behavior studies is both feasible and advantageous. Up to now, this research stream is still exploratory, and rarely did we find studies that examine the neural mechanism underlying the influence of online cues on consumers’ behaviors. Therefore, this paper attempts to identify how the two online cues, product rating and sales, take effect from the cognitive neuroscience perspective. Since consumers are expected to experience a series of cognitive processes when shopping online, the current study focuses on three ERP components that have been frequently investigated in previous Decision Neuroscience research, which are closely related to processing of perceived risk (N2) and informational conflict (N400), as well as evaluative categorization (LPP). 2.2.1 N2 N2 is a negatively-going component that typically peaks around 250–350 ms after the onset of a stimulus. It generally arrives at the largest amplitude in the frontal region of the scalp [14, 15]. Previous studies consistently suggested that the amplitude of N2 serves as an indicator of conflicts calling for online control [14, 33, 56]. Recent studies began to report that N2 could robustly reflect decision risk, since the two concepts of risk and conflict are interrelated [38, 64]. After all, higher perceived risk during decision-making leads to increased decision difficulty, which then causes greater decisional conflict. As changes in N2 amplitude reflect this relationship, it can be viewed as an indirect indicator of perceived risk. According to existing literature on product rating and sales [1, 9, 22, 29], higher product rating and better sales can reduce consumers’ perceived risk through social influence. Therefore, we speculate that an initial cognitive processing of risk perception will be involved in online purchasing decision-making. Specifically, we hypothesize: H1 Lower product rating and lower sales will lead to larger perceived risk and greater decisional conflict, as reflected in the enhanced amplitude of N2. 2.2.2 N400 The N400 is a negative-going component that reflects semantic or non-semantic conflictions [8, 31, 57]. It generally peaks around 400 ms after the onset of a stimulus and is mainly found in the central and frontal area of the scalp [16]. Although the N400 is traditionally associated with semantic or lexical violations

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[31], more recent research has demonstrated that it may reflect other non-semantic conflicts [8, 23, 40]. In fact, informational conflict is a more general concept, and semantic conflict is just a special case of it. It was found that when a stimulus provides varied information, and the provided information is in conflict, an evident N400 component would be elicited. For example, Ma et al. [40] showed that sweet food presented along with risk information on possible diseases elicited a higher informational conflict compared with salty food, which was reflected in the enlarged N400. In the present study, subjects are faced with various combinations of product rating and sales. Therefore, we hypothesize: H2 A more negative N400 will be elicited for the informational conflict condition (inconsistent cues) than for the informational non-conflict condition (consistent cues). 2.2.3 LPP LPP is a late positive-going component that typically peaks around 600 ms after the presentation of a stimulus, and is mainly distributed over the posterior scalp. The peak latency of LPP may vary due to stimulus discriminability, and stimuli that are difficult to discriminate produce an LPP with longer latency [20]. Previous studies consistently showed that the amplitude of LPP reflects the allocation of attentional resources. The more attentional resources are captured by participants, the larger the LPP amplitude [32]. Since participants tend to pay varied attention to different stimuli, recent studies began to report that LPP might reflect the cognitive process of evaluative categorization [7, 24]. In this experiment, participants are exposed to various combinations of product rating and sales, which can be categorized into four situations (high product rating & high sales, high product rating & low sales, low product rating & high sales, and low product rating & low sales). Based on the expected/desirable extrinsic cues, participants go through an evaluative categorization process of product rating and sales cues before making the buying decisions. Similarity is a central classification factor, and most psychological categorization models are based on similarity [4]. Since individuals generally pay more attention to desirable information, previous studies have demonstrated that the higher the similarity between expected and displayed information, the larger the P300 amplitude [4, 39, 41]. As a long latency P300 or sustained P300-like component [5, 19], LPP is suggested to involve a cognitive and neutral process similar to that of P300. Thus, the amplitude of LPP was also demonstrated to be sensitive to category similarity [7]. In this study, the combination of high product rating and high sales is positively associated with decision confidence and thus can be treated as an evaluation standard. Therefore, we hypothesize: H3 The higher the product rating and the higher the sales, the greater the amplitude of LPP.

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3 Research methods 3.1 General experimental design This research adopted the ERP approach and a 2 (high product rating vs. low product rating) 9 2 (high sales vs. low sales) repeated measure design. In this experiment, subjects were asked to decide whether they would like to buy a pair of headphones according to specific product rating and sales information. Headphones were selected as experimental products in this research because Chinese college students frequently purchase them online. Categorization of high and low product rating and sales was based on results from a pretest. During the experiment, the two cues appeared simultaneously at the center of a computer monitor. In order to better simulate the online shopping scenario, a simulated e-commerce website was built, which resembled taobao.com, the dominant e-commerce website in China. Subjects browsed this website before they participated in the ERP experiment, and were asked to imagine that they were shopping online throughout the experiment. 3.2 Subjects Nineteen Chinese college students (8 females, 11 males) were recruited from an online discussion forum affiliated with a major university in southern China.1 Their average age was 23.58 years (ranging from 22 to 25). Subjects were screened to ensure right-handedness, normal or corrected-to-normal vision, and no history of mental disease. Two subjects were excluded because they stayed up late the night before the experiment and couldn’t stay focused. All of the subjects were experienced online consumers, who participated in the experiment voluntarily and signed an informed consent form. Subjects were paid RMB 30 (around $5) for their participation. 3.3 Materials In order to determine the standard for high versus low product rating and sales, a group interview of Chinese college students was held. Based on results of the interview and the distribution of rating scores on taobao.com, rating scores ranging from 2.00 to 2.25 were classified into the low product rating (LR) condition, and scores ranging from 4.75 to 5.00 were identified as high product rating (HR) in the current research, with 1.00 referring to the lowest rating score and 5.00 corresponding to the highest rating score. High versus low sales were determined by real sales information on taobao.com. Specifically, we ranked all the headphones displayed on taobao.com based on their sales within the most recent month, and 5000 headphones were picked out. Among the 5000 headphones, average sales of the 100 best-selling headphones on taobao.com is 1357, and 27 is the average sales 1

Due to equipment characteristics and time considerations, ERP and other neuroscience approaches typically involve multiple trials (e.g., 320 trials in the present study) with a relatively small number of subjects [13, 28].

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of the 100 lowest-selling headphones. Thus, high sales (HS) was manipulated to be around 1357 (±5 %), and low sales (LS) was manipulated to be around 27 (±5 %). Forty headphones, which were similarly attractive for subjects, were selected on taobao.com, and 320 pictures were produced for these headphones by transforming their shooting angles. 3.4 Procedure During the experiment, subjects sat in a comfortable chair in a sound-proof room. Before the ERP experiment formally started, they were instructed to read the introduction to the experiment and browse a simulated e-commerce website, imagining that they were searching for headphones online. Stimulus presentation and data collection were controlled by E-Prime software (PST, Psychology Software Tools, Inc.). The visual stimuli (black stimuli on a white background) were presented on a 19-inch computer monitor (1280 9 1024 pixels, 60 Hz) connected to a 2 GHz Pentium computer, and those stimuli were viewed from a distance of 100 cm at the center of the computer screen and with a visual angle of 2.58 9 2.4. Subjects were told to look straight at the center of the monitor and avoid moving throughout the experiment. Thus, subjects’ eye movement during the experiment is relatively negligible and the electrooculogram (EOG) can be filtered subsequently. The experiment comprised 320 trials shown in a random order (80 trials per condition). The current number of trials is appropriate for ERP studies since ERP experiments typically require a minimum of 30 valid trials per condition [36]. Figure 1 shows how sequential stimuli were presented within each trial. Each trial began with a cross (‘‘?’’) as a fixation, which is followed by a headphone picture. In the present study as well as other electrophysiological studies [7, 23, 33, 37], a stimulus irrelevant to the research goal is generally presented for around 1000 ms. Considering that we are actually interested in the cognitive processing of

Fig. 1 Sequence of stimuli in each trial (R for rating, S for sales)

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different product cues rather than the fixation and headphones pictures, the latter two irrelevant stimuli were presented for only 1000 ms in order to reduce the duration of the whole experiment [36]. In addition, for typical electrophysiological analyses, the whole epoch (-200 to 800 ms) is baseline-corrected using the 200 ms interval prior to stimulus onset. Thus, in order to guarantee that the electrophysiological data to be analyzed are clean, a blank screen has to be displayed before the stimulus if a target stimulus is to be examined. Accordingly, in the present study, a random interval ranging from 500 to 800 ms was displayed before the presentation of product cues [36]. After that, a stimulus containing both product rating and sales information was presented for a maximum of 3000 ms, which disappeared upon subjects’ purchasing decisions. In order to eliminate the potential influence of reading order, the relative position of product rating and sales cues is counter-balanced. Specifically, subjects were asked to decide whether to buy a product within a limited time range based exclusively on the product’s rating and sales. In addition, subjects were explicitly told that (1) they were involved in a lottery, that is, 10 % of the participants would actually receive one pair of headphones they decided to buy during the ERP experiment2; (2) the price of each pair of headphones was similar (about $4); and (3) the purchasing decision for each pair of headphones should be independently made. Subjects were instructed to press the left key for no purchase and the right key for a purchase decision. The 320 trials were divided into five blocks, and subjects could take a break during the intervals.3 The whole experiment lasted for 20 to 30 min. Researchers believed that participants would not be overloaded given the duration of the present experiment [36]. 3.5 Electroencephalogram recording and analysis Stimulus-locked ERP was analyzed for the four conditions. The electroencephalograms of subjects were recorded with Neuroscan Synamp2 Amplifier (Scan 4.5, Neurosoft labs, Inc., Sterling, USA) with a 0.05–100 Hz band pass and 500 Hz sampling rate. An electrode cap with 64 Ag/AgCI electrodes was used, and the electrode impedance was maintained below 10 KX during the experiment. The left mastoid served as an on-line reference, and EEGs were off-line re-referenced to the average of the left and the right mastoids. In addition, the electrode on the cephalic region was applied as ground. The vertical electroencephalogram was recorded from the right eye by supra-orbital and infra-orbital electrodes. The horizontal electroencephalogram was recorded from electrodes on the outer canthi of both eyes. E-prime also collected all behavioral responses including participants’ buying decisions and response times. Trials that contain eye movement artifacts were rejected. EEG recordings were cut into epochs of 1000 ms. As is demonstrated in the grand-averaged ERP waveforms of main ERP components being analyzed (e.g., Fig. 4 for N2, Fig. 5 for 2

We bought the corresponding headphones after the whole experiment was completed and then sent the headphones to the ‘‘lucky’’ subjects.

3

The interval lasted for 2 min on average.

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N400, and Fig. 6 for LPP), for typical electrophysiological analysis, time windows of 200 ms before and 800 ms after the onset of a stimulus (e.g., a specific combination of product rating and sales cues in the present study) are segmented. ERP tracks temporal dynamics of brain activities in response to different stimuli, and the rapid cognitive processing of a stimulus generally takes place within 1000 ms. Thus, 1000 ms has been proved to be sufficient for people to process a stimulus [36]. In addition, brain activities from -200 to 0 ms served as the prestimulus baseline,4 and all the valid trials were corrected according to this baseline. Epochs whose deflection exceeded ±80 lV were also excluded, after which they were filtered with a low pass at 12 Hz (24 dB/Octave), and the final standard calibration was implemented according to the baseline. The EEG epochs were then averaged for the four conditions respectively, and data were further analyzed using within-subjects repeated-measures analysis of variance (ANOVA).

4 Results 4.1 Manipulation check of the pretest We used the data collected from a questionnaire that 150 Chinese college students completed to check the manipulated variables of product rating and sales. Perceived rating levels were measured with a 7-point Likert scale, which worked as a manipulation check for the high and low rating treatments. Results showed a significant difference (t = 24.454, p \ 0.001) in perceived rating levels between high product rating (Mean = 5.54, SD = 1.278) and low product rating (Mean = 2.29, SD = 1.279). Similarly, perceived sales levels were used as a manipulation check for high versus low sales treatments. There was also a significant difference (t = 17.429, p \ 0.001) in perceived sales levels between high sales (Mean = 5.91, SD = 1.255) and low sales (Mean = 2.90, SD = 1.841). Thus, results from the manipulation check of product rating and sales were satisfactory, and the designed stimuli were then used in the formal experiment. 4.2 Behavioral data Purchasing rate and the mean response time across the four conditions are shown in Table 1. A 2 9 2 within-subjects repeated measure ANOVA showed that the main effects of product rating (F (1, 16) = 159.57, p \ 0.001) and sales (F (1, 16) = 32.86, p \ 0.001) on the purchasing rate were significant. The interaction effect between product rating and sales was also significant (F (1, 16) = 33.30, p \ 0.001). As revealed by Fig. 2 and results of the LSD test, better sales led to significantly higher purchasing rate in the high rating condition (M = 0.946 vs. M = 0.414; F (1, 16) = 5.758, p \ 0.001). However, for the low rating condition, an increase in sales did not imply higher purchasing rate (M = 0.088 vs. M = 0.015; F (1, 16) = 1.573, 4

-200 ms means 200 ms before the onset of the stimulus.

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Table 1 Mean purchasing rate and mean response time across four conditions Condition

Purchasing ratea

Response time (RTs) (ms)

SD of response time

C1: HR & HS

0.946

946.82

C2: HR & LS

0.414

1077.84

31.78 48.92

C3: LR & HS

0.009

880.36

32.83

C4: LR & LS

0.001

786.41

31.56

HR high product rating, HS high sales, LS low sales, LR low product rating a

Purchasing rate is the percentage of purchasing choices among all valid choices

Fig. 2 Purchasing rate varies with rating and sales

p = 0.135). A pairwise comparison showed that the purchasing rate in C1 was significantly higher than that in C2 (p \ 0.001), C3 (p \ 0.001) and C4 (p \ 0.001). Pairwise comparison also showed that the purchasing rate in C2 was significantly higher than that in C3 (p \ 0.01) and C4 (p \ 0.01). Meanwhile, results of a 2 9 2 repeated measure ANOVA showed that product rating had a significant main effect on RTs (F (1, 16) = 39.68, p \ 0.001) while sales did not demonstrate such an effect (F (1, 16) = 0.76, p = 0.397). Results from pairwise comparison showed that RTs in C2 were significantly longer compared with those in C1 (p \ 0.05), C3 (p \ 0.05) and C4 (p \ 0.001). Besides, RTs were significantly shorter in C4 than in C1 (p \ 0.001), C2 (p \ 0.001), and C3 (p \ 0.01).

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4.3 Stimulus-locked analysis Figure 3 shows the stimulus-locked grand-averaged ERP waveforms across the four conditions at 9 electrode sites. These electrodes can be classified into three regions of interest (ROIs): the frontal (F3, FZ, F4), central (C3, CZ, C4) and posterior (P3, PZ, P4). 4.3.1 N2 Because N2 usually appears in frontal electrode sites, we performed a 2 (HR vs. LR) 9 2 (HS vs. LS) 9 3 (frontal electrode sites: F3, FZ, F4) within-subjects repeated measure ANOVA on ERP amplitudes between 300 and 315 ms (Fig. 4). The ANOVA results indicated that the main effect of product rating was significant (F (1, 16) = 4.911, p \ 0.05), while this was not the case for sales (F (1, 16) = 0.099, p [ 0.05). The interaction between product rating and sales was not significant either (F (1, 16) = 0.489, p [ 0.05). As assumed, N2, which is associated with risk perception, was elicited in the purchase decision-making process. Specifically, the amplitude of N2 was larger for low rating products (C3 & C4) than highly rated ones (C1 & C2), which implies higher perceived risk and greater decisional conflict in the low product rating condition (see Table 2). However, we did not find a significant sales effect on the N2. Thus, H1 is partially supported.

Fig. 3 Grand-averaged ERP waveforms for four conditions: C1, C2, C3, and C4 at nine electrode sites: F3, FZ, F4, C3, CZ, C4, P3, PZ, P4

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Fig. 4 Stimulus locked (-200 to 800 ms) N2 (300–315 ms)

Table 2 Mean N2 and planned contrasts

Sales

Product rating High

High

Low

M

SD

-0.435

0.937

[C1] Marks in brackets denote the treatment cells M mean, SD standard deviation * p \ 0.05 a

n.s

Low

-0.780 [C2]

M

SD

-1.367

0.736

[C3] 0.770

-1.227

0.709

[C4]

| [C3] ? [C4] | [ | [C1] ? [C2] |* F (1, 16) = 4.911, p = 0.044 | [C2] ? [C4] | [ | [C1] ? [C3] |a F (1, 16) = 0.099, p = 0.758

4.3.2 N400 Since N400 is an ERP component that reflects informational conflict, a 2 (consistency: consistent vs. conflictive) 9 3 (central electrode sites: C3, CZ, C4) within-subjects repeated measure ANOVA was performed on ERP amplitudes between 400 and 430 ms (Fig. 5). When product rating and sales give consistent predictions, the case is defined as consistent (HRHS vs. LRLS). Otherwise, it is defined as conflictive (HRLS vs. LRHS). The main effect of consistency was significant (F (1, 16) = 4.977, p \ 0.05). Post-hoc analysis (Table 3) showed a significantly larger N400 for the conflictive condition (C2 & C3) compared with the consistent condition (C1 & C4). As expected, there is an obvious informational conflict detection process before subjects made a final purchasing decision.

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Fig. 5 Stimulus locked (-200 to 800 ms) N400 (400–430 ms)

Table 3 Mean N400 and planned contrasts

Sales

Product rating High

High

Low

M

SD

M

SD

1.295

0.528

-0.145

0.555

[C1] Marks in brackets denote the treatment cells M mean, SD standard deviation * p \ 0.05

Low

0.484 [C2]

[C3] 0.637

0.503

0.526

[C4]

| [C3] ? [C2] | [ | [C1] ? [C4] |* F (1, 16) = 4.977, p = 0.043

Moreover, a more negative N400 was elicited for informational conflict (inconsistent cues) than for the non-conflict condition (consistent cues). Thus, H2 is supported. In order to further understand the informational conflict effects of rating and sales, we also conducted a 2 (HR vs. LR) 9 2 (HS vs. LS) 9 3 (central electrode sites: C3, CZ, C4) within-subjects repeated measure ANOVA. The results showed insignificant main effects of rating (F = 3.100, p = 0.100) and sales (F = 0.035, p = 0.853), but a significant interaction effect between them (F = 4.977, p = 0.043). To further understand the interaction effect, we conducted simple effect analyses, and the results showed that when sales were high, moving from consistent condition (high rating) to conflictive condition (low rating) significantly increased the amplitude of N400 (mean difference = 1.44; p = 0.002); while when sales were low, moving from the consistent condition (low rating) to the conflictive condition (high rating) had little effect on the amplitude of N400 (mean difference = 0.019, p = 0976).

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4.3.3 LPP In this experiment, the latency of LPP was approximately 550 to 700 ms. Thus, a 2 (HR vs. LR) 9 2 (HS vs. LS) 9 3 (frontal electrode sites: P3, PZ, P4) withinsubjects repeated measure ANOVA was performed on ERP amplitudes between 550 and 700 ms (Fig. 6). ANOVA results indicated significant main effects of product rating (F (1, 16) = 37.345, p \ 0.001) and sales (F (1, 16) = 6.507, P = 0.023). The interaction effect between product rating and sales was not significant (F (2, 32) = 0.589, p = 0.456). Post-hoc tests (Table 4) showed a significantly larger LPP for high product rating (C1 & C2) compared with the low product rating condition (C3 & C4). The amplitudes of LPP were also more enhanced for high sales (C1 & C3) compared with low sales (C2 & C4). As shown in Table 4, LPP amplitudes were greatest in C1, while minimal in C4. Thus, H3 is supported. As LPP was suggested to reflect the cognitive process of evaluative categorization, differences among LPP amplitudes across the four conditions indicated that there was an overall categorization process before subjects made a purchasing decision.

Fig. 6 Stimulus locked (-200 to 800 ms) LPP (550–700 ms) Table 4 Mean LPP and planned contrasts

Sales

Product rating High

Low

M

SD

M

SD

High

5.420

0.644

3.253

0.700

Low

3.975

0.733

2.438

[C1] Marks in brackets denote the treatment cells M mean, SD standard deviation *** p \ 0.001; * p \ 0.05

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[C2]

[C3] 0.645

[C4]

| [C1] ? [C2] | [ | [C3] ? [C4] |*** F (1, 16) = 37.345, p \ 0.001 | [C1] ? [C3] | [ | [C2] ? [C4] |* F (1, 16) = 6.507, p = 0.023


17

5 Discussion 5.1 Research findings By applying the ERP method, this research aims to investigate brain activities associated with the influence of product rating and sales on online consumers’ purchasing decisions. Special attention is paid to brain activities from cue presentation to final decision-making. Analysis of behavioral data indicated significant main effects of product rating and sales as well as an interaction effect between the two factors on purchasing rate. The results showed that the purchasing rate was influenced by sales (a low-scope cue) to a greater extent when product rating (a high-scope cue) was high, whereas participants did not manifest a higher purchasing rate for products of higher sales when product rating was low; these results provide support for cue diagnosticity theory [2, 50]. From behavioral results, we can also deduce the task difficulty and cognitive load under each condition. Previous studies suggested that task completion time (i.e. RTs) was positively related to cognitive load and task difficulty, and the higher a participant’s cognitive load, the longer he/she would take to complete the task [11, 59]. Compared with a simple task, a difficult task requires more in-depth cognitive processing, such as psychological comparison [35]. Figure 7 shows how the average response time varies under each condition as the experiment moves forward. It suggests that participants gradually arrive at a certain decision pattern and then invest less cognitive effort as time goes on. The reason for this is that the learning effect takes place in the repeated measure experiment. Figure 7 also shows that the interval between two blocks leads to an increase in the average response time at the beginning of the next block. However, this interval effect does not impair the overall trend. Despite the existence of a similar trend across all conditions, we can see clearly from Fig. 7 that the mean RTs in C2 are longer than those in C1, C3 and C4, while the mean RTs in C4 are shorter than those in C1, C2 and C3 (statistical analyses showed that these discrepancies were significant). The discrepancy in decision time indicates that subjects might need to exert extra cognitive effort when making decisions in C2 due to its highest decision difficulty, while making decisions in C4 is the easiest among the four conditions. The purchasing rate also supports this argument. The purchasing rate under C2 is about 40 % (0.414), indicating that subjects did not have a clear preference under C2 and had to cautiously consider whether to buy the corresponding headphones or not. In contrast, the purchasing rate under C4 is near 0 (0.001). Combined with the RTs data, this might show that subjects seldom consider buying headphones under C4, thus they expend the least cognitive effort under C4 across the four conditions. Electrophysiological results reflected the cognitive and neural processes of cue evaluation and purchasing decision-making. Remarkable N2, N400 and LPP were evoked during decision-making. N2 is an early ERP component, which reflects preliminary and semi-automatic information processing. In the risk and uncertainty decision-making domains, the amplitude of N2 is established to be sensitive to risk

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Fig. 7 Scatter plot of reaction time for each condition

information. In this experiment, we only found a significant main effect of product rating on its amplitude, and the amplitude of N2 (300-315 ms after the onset of the stimuli) was significantly smaller in the high rating condition (C1, C2) than in the low product rating condition (C3, C4). The N2 amplitude has also been demonstrated to be positively related to response conflict, which depends on features of external stimuli and one’s internal control state [15, 56]. In our study, the low product rating condition generally implies higher risk, which leads to higher response conflict and requires the exertion of more cognitive control, all of which contribute to enhanced N2 amplitude. Product rating and sales are both important extrinsic cues, which can exert social influence in online shopping. However, compared with sales, product rating provides a more robust prediction of the performance of a product and is viewed as highly diagnostic in perceiving risks. Thus, results of this study provide preliminary evidence that product rating plays a major role during the early decision stage. When considering the potential risk associated with making a decision, subjects may place themselves in a loss domain. Previous studies consistently showed that people are risk- and loss-averse [25, 44]. Thus, the current result pattern suggested

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that, in order to avoid losses, individuals are apt to depend exclusively on the highscope cue during semi-automatic risk detection. N400 is an ERP component that reflects cognitive mismatch, which can be induced by both semantic and non-semantic conflictions [8, 57]. Previous research mainly focused on the semantic aspect of N400 and consistently showed that inconformity in sentences or word pairs elicited evident N400 [30]. Inconsistent online-based cues bring about informational conflict. Thus, our study aimed to replicate and extend previous research by showing that informational conflict could also be encoded in the N400. In the present study, the N400 amplitude was significantly larger in a conflictive case (high product rating vs. low sales, low product rating vs. high sales) than in a consistent case (high product rating vs. high sales, low product rating vs. low sales). This finding indicated that there would be a stage of informational conflict detection during online product evaluation when consumers were facing more than two extrinsic cues. Importantly, the greater the cognitive mismatch, the larger the amplitude of N400. This finding supports our hypothesis and is in line with recent studies, which demonstrated that N400 is closely related to informational conflict, and a high level of informational conflict induces larger N400 amplitude [23, 40]. Furthermore, the salience of this informational conflict as reflected in the magnitude of N400 was contingent on different sales situations. When sales were high, the N400 amplitude was significantly larger in the conflictive condition (low product rating) than in the consistent condition (high product rating). However, when sales were low, there was no significant difference on the N400 amplitude between the consistent condition (low product rating) and the conflictive condition (high product rating). We speculate that this is due to the fact that the condition of high product rating and low sales is not uncommon in the online shopping scenario. For example, when a new product is launched in an online store, it may enjoy a high product rating, while sales are relatively low. Thus, from online consumers’ point of view, the condition of high product rating & low sales might not seem to be conflictive. LPP is a late ERP component that is sensitive to explicit categorization [24]. During this late cognitive processing stage, main effects of both product rating and sales were significant. Amplitudes of LPP were in the order of C1 (high product rating vs. high sales) [ C2 (high product rating vs. low sales) [ C3 (low product rating vs. high sales) [ C4 (low product rating vs. low sales), and the LPP amplitudes were consistent with the purchasing rate across the four experimental conditions. Differences in LPP amplitudes demonstrated that there was an obvious categorization and evaluation stage before subjects made a final purchasing decision. Previous research suggested that the amplitude of LPP is sensitive to category similarity. Specifically, the more similar the criterion category and other categories are, the more attentional resources will be allocated and the higher the LPP amplitude will be [7]. In the current experiment, participants would gradually form expectations of desirable attributes of a product. For example, a desirable headphone might have a good product rating as well as high sales, and these specific attributes together became the categorizing criterion over time. When actual information of a product was presented, it would be automatically compared with

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the criterion (or the expectation). The more similar actual cues and expected cues are, the larger the LPP amplitudes elicited. For example, the high product rating & high sales condition is strictly in line with the criterion while the low product rating & low sales condition has the least similarity with the criterion. As people are inclined to allocate more attentional resources to stimuli that provide more favorable information, the two conditions would produce the largest and the smallest LPPs, respectively. Therefore, LPP patterns suggest that, during this late cognitive processing stage, consumers make an overall evaluation for all the conditions based on their similarity to the criterion. It is worth noticing that N2 and LPP patterns were not completely consistent with the behavior pattern, which manifested an interaction effect of two cues. The seemingly inconsistent results may be explained by characteristics of the electrophysiological measurement. To begin with, N2 is an early ERP component with a semi-automatic nature, which is widely indicated to reflect perceived risk and decisional conflict. However, online purchasing decision-making is a relatively complex process, and perceived risk may influence but will not determine the final decision. Thus, it is no surprise that the N2 pattern is not consistent with the actual purchasing behavior. Besides, while the electrophysiological measurement was time-locked to the presentation of extrinsic product cues, which are evaluated promptly, behavioral responses may involve more elaborate processes. Although LPP is regarded as a late positive component within the 800 ms time window, the actual purchasing/non-purchasing decision may happen much later. In fact, many previous studies explicitly reported the existence of inconsistencies between the observed ERP pattern and the behavior data [37, 63]. However, these inconsistencies did not compromise the significance of their findings. 5.2 Theoretical contributions and practical implications One major deficiency of previous studies is that the role of product rating and sales was always examined in isolation. In order to overcome this disadvantage, this study seeks to explore the joint effect of these two social-based cues. Behavioral results of this research showed that there was a significant interaction effect of product rating and sales on the purchasing rate, and product rating positively moderated the effect of sales. This finding provided additional support for cue-diagnosticity theory and extended previous research by showing that, when consumers make use of cues of varied diagnosticity to make a purchasing decision with the aim of maximizing their utilities, a positive (negative) social-based cue with high diagnosticity (i.e., rating) would reinforce (attenuate) the diagnosticity of a less diagnostic social-based cue (i.e., sales). In addition, with the aim of opening the black box of the brain, this study innovatively introduces a cognitive neuroscience method (ERP) to thoroughly explore the neural mechanism underlying how varied extrinsic cues online jointly affect consumers’ decision-making. Importantly, we found that when faced with two social-based cues (product rating and sales), the time course of the whole decision-making process could be divided into three main stages, and N2 (risk perception-related component), N400 (informational conflict-related component)

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and LPP (evaluative categorization-related component) were successively elicited during the process. In the first stage, consumers take decision risk into consideration. Within this early time window, consumers pay more attention to the perceived risk of purchasing a product as suggested by its extrinsic cues. Compared to sales, product rating seems to play a more significant role in forming the risk perception at the early stage, and low product rating induces higher N2 amplitude. During the next stage, informational conflict is processed, and the sales cue comes into play as well. When product rating and sales provide inconsistent information (especially for high sales and low product rating), higher N400 amplitude will be induced, indicating the processing of informational conflict. Finally, at the evaluative categorization stage, one specific combination of product rating and sales is processed as a whole, and consumers make an overall evaluation based on its similarity to the criterion. To sum up, adopting the ERP method, the present research provides further explanations of the observed behavioral pattern according to neural evidence. Findings of this research will be of great interest to online retailers. Product rating and sales are two important social-based cues in the online shopping environment. For online sellers, results illustrated the importance of obtaining both high product rating and sales. E-retailers should take advantage of these two extrinsic cues to exert positive social influence. However, it is worth pointing out that, in the real online purchase, these two cues do not always provide consistent information. We conducted a small content analysis by collecting product rating and sales information of the 100 best-selling and lowest-selling products (extracted from the first 100 pages on taobao.com). Three different product categories were investigated, including cellphones, headphones and women’s apparel. According to data from CNNIC (http://www.cnnic.cn/hlwfzyj/hlwxzbg/), apparel and digital products are currently the two types of products that have the maximum domestic online transactions. Results show that among the 100 best-selling cellphones, 56 % of them receive low product ratings. When it comes to the 100 lowest-selling cellphones, the percentage of highly rated ones is 51 %. Similarly, 45 % of high sales headphones are lowly rated, while 49 % of low sales headphones have above average ratings. This case becomes even more extreme for apparel products. To be specific, 67 % of women’s apparel does not possess ratings compatible with its high sales, while only 39 % of the lowest-selling apparel receives low product ratings. Intuitively, one could expect that high sales should be a result of high product rating. However, products with a high rating and low sales are still understandable, especially if they are recently released. This evidence is also in line with the results of N400, which showed that the condition of high rating and low sales did not elicit pronounced conflict perception. With regard to the unexpected pattern of high sales and low product rating, we suspect that the false sales information sent by e-retailers might be a primary reason to bring about this counter-intuitive situation. Thus, this combination of social cues is considered as evidently manipulated and irregular to online consumers. Accordingly, ERP results of N400 showed that low rating along with high sales gave rise to pronounced conflict perception in online consumers. The present results also show that product rating has a greater impact on consumers’ purchasing decision compared with the sales cue, and the effect of sales

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cue depends on product rating. To be specific, behavioral results suggest that consumers will perceive the high sales information as more credible only when product rating is high. However, when product rating is low, the influence of sales on the purchasing rate remains negligible no matter how it changes. This suggests that high sales alone may not have much impact on consumers’ purchasing intentions. Therefore, as marketing resources are usually limited for e-retailers, they should pay more attention to improving the quality and service of their products in the first place, with the aim of improving the product rating. After all, increasing sales without investing in the promotion of product rating is not very useful in increasing customers’ purchasing rate. 5.3 Limitations and future research This study has several limitations that may provide avenues for future research. First, since brain activities are intricate and sensitive, ERP experiments have to follow strict environmental and equipment requirements. Thus, the purchasing scenario during the experiment is generally highly abstract, which is quite different from the actual online purchasing experience. Second, all the recruited subjects were college students. Subjects with more varied backgrounds would be more helpful to form a comprehensive view of the general consumers’ brain activities during online purchasing decision-making. Third, in this study we focused on only one digital product (headphones), which is relatively cheap and belongs to the category of utilitarian products. A wider range of products could be investigated in future research in order to generalize the present findings. For example, more expensive products may bring about different result patterns with respect to the utilization of these two extrinsic cues. In addition, previous studies showed that the level of involvement determines the depth, complexity and extensiveness of cognitive and behavioral processing during consumers’ choice [3, 48]. Thus, future research may adopt products that are discrepantly involved with consumers and attempt to investigate their corresponding decision process and information processing mode upon these two social-based extrinsic cues. Acknowledgments This research was supported by the grant from National Natural Science Foundation of China (No: 71272167, No: 71202157) and Ministry of Education of the People’s Republic of China (No: 11YJA630130). The authors would like to thank Qian Yao and Jun Bian for their valuable inputs on earlier versions of this article.

References 1. Aiken, K. D., & Boush, D. M. (2006). Objective-source ratings, and implied investments in advertising: Investigating online trust and the context-specific nature of internet signals. Journal of the Academy of Marketing Science, 34(3), 308–323. 2. Akdeniz, B., Calantone, R. J., & Voorhees, C. M. (2013). Effectiveness of marketing cues on consumer perceptions of quality: The moderating roles of brand reputation and third-party information. Psychology & Marketing, 30(1), 76–89. 3. Andrews, J. C., Durvasula, S., & Akhter, S. H. (1990). A framework for conceptualizing and measuring the involvement construct in advertising research. Journal of Advertising, 19(4), 27–40.

123


23

4. Azizian, A., Freitas, A. L., Watson, T. D., & Squires, N. K. (2006). Electrophysiological correlates of categorization: P300 amplitude as index of target similarity. Biological Psychology, 71, 278–288. 5. Cacioppo, J. T., Crites, S. L., Gardner, W. L., & Bernston, G. G. (1994). Bioelectrical echoes from evaluative categorizations: a late positive brain potential that varies as a function of trait negativity and extremity. Journal of Personality and Social Psychology, 67, 115–125. 6. Carretie´, L., Mercado, F., Tapia, M., & Hinojosa, J. A. (2001). Emotion, attention and the ‘‘negativity bias’’, studied through event-related potentials. International Journal of Psychophysiology, 41(1), 75–85. 7. Chen, M., Ma, Q., Li, M., Dai, S., Wang, X., & Shu, L. (2010). The neural and psychological basis of herding in purchasing books online: An event-related potential study. CyberPsychology, Behavior & Social Networking, 13(3), 321–328. 8. Chen, M., Ma, Q., Li, M., Lai, H., Wang, X., & Shu, L. (2010). Cognitive and emotional conflicts of counter-conformity choice in purchasing books online: An event-related potentials study. Biological Psychology, 85(3), 437–445. 9. Chen, Y. F. (2008). Herd behavior in purchasing books online. Computers in Human Behavior, 24(5), 1977–1992. 10. Chen, Y. F. (2011). Auction fever: Exploring informational social influences on bidder choices. CyberPsychology, Behavior & Social Networking, 14(7), 411–416. 11. Cowen, L., Ball, L., & Delin, J. (2002). An eye movement analysis of webpage usability. Interface, 28(1), 317–335. 12. Cox, D. F. (1967). The sorting rule model of the consumer product evaluation process. In D. F. Cox (Ed.), Risk taking and information handling in consumer behavior (pp. 324–369). Boston, MA: Graduate school of Business Administration, Harvard University. 13. Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., et al. (2012). On the use of neurophysiological tools in IS Research: Developing a research agenda for neuroIS. MIS Quarterly, 36(3), 679–702. 14. Folstein, J. R., & Van Petten, C. (2008). Influence of cognitive control and mismatch on the N2 component of the ERP: A review. Psychophysiology, 45, 152–170. 15. Forster, S. E., Carter, C. S., Cohen, J. D., & Cho, R. Y. (2010). Parametric manipulation of the conflict signaland control-state adaptation. Journal of Cognitive Neuroscience, 23(4), 923–935. 16. Frishkoff, G. A., TuckeL, D. M., & Davey, C. (2004). Frontal and posterior sources of event-related potentials in semantic comprehension. Cognitive Brain Research, 20(3), 329–354. 17. Fugate, D. L. (2007). Neuromarketing: A layman’s look at neuroscience and its potential application to marketing practice. Journal of Consumer Marketing, 24(7), 385–394. 18. Gidron, D., Koehler, D. J., & Tversky, A. (1993). Implicit quantification of personality traits. Personality and Social Psychology Bulletin, 19, 594–604. 19. Hajcak, G., & Olvet, D. M. (2008). The persistence of attention to emotion: Brain potentials during and after picture presentation. Emotion, 8, 250–255. 20. Herring, D. R., Taylor, J. H., White, K. R., & Crites, S. L. (2011). Electrophysiological responses to evaluative priming: The LPP is sensitive to incongruity. Emotion, 11(4), 794–806. 21. Hu, X., Wu, G., Wu, Y., & Zhang, H. (2010). The effects of Web assurance seals on consumers’ initial trust in an online vendor: A functional perspective. Decision Support Systems, 48, 407–418. 22. Huang, J. H., & Chen, Y. F. (2006). Herding in online product choice. Psychology and Marketing, 23(5), 413–428. 23. Huang, Y., Kendrick, K. M., & Yu, R. J. (2014). Social conflicts elicit an N400-like component. Neuropsychologia, 65, 211–220. 24. Ito, T. A., & Cacioppo, J. T. (2000). Electrophysiological evidence of implicit and explicit categorization processes. Journal of Experimental Social Psychology, 36, 660–676. 25. Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). The endowment effect, loss aversion, and status quo bias. The Journal of Economic Perspectives, 5(1), 193–206. 26. Kassarjian, H. H. (1971). Personality and consumer behavior: A review. Journal of Marketing Research, 8(4), 409–418. 27. Kim, D., & Benbasat, I. (2009). Trust-assuring arguments in B2C e-commerce: Impact of content, source, and price on trust. Journal of Management Information Systems, 26(3), 175–206. 28. Kuan, Kevin K. Y., Zhong, Y. Q., & Chau, Patrick Y. K. (2014). Informational and normative social influence in group-buying: Evidence from self-reported and EEG data. Journal of Management Information Systems, 30(4), 151–178.

123

24

Q. Wang et al.

29. Kumar, N., & Benbasat, I. (2006). The influence of recommendations and consumer reviews on evaluations of websites. Information Systems Research, 17(4), 425–439. 30. Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: Finding meaning in the N400 component of the event-related brain potential (ERP). Annual Review of Psychology, 62, 621–647. 31. Kutas, M., & Hillyard, S. A. (1980). Reading senseless sentences: Brain potentials reflect semantic incongruity. Science, 207(4427), 203–205. 32. Langeslag, S. J. E., Jansma, B. M., Franken, I. H. A., & Strien, J. W. V. (2007). Event related potential responses to love-related facial stimuli. Biological Psychology, 76, 109–115. 33. Larson, M. J., Clayson, P. E., & Baldwin, S. A. (2012). Performance monitoring following conflict: Internal adjustments in cognitive control? Neuropsychologia, 50, 426–433. 34. Lee, J., Park, D. H., & Han, I. (2008). The effect of negative online consumer reviews on product attitude: An information processing view. Electronic Commerce Research and Applications, 7(3), 341–352. 35. Leuthold, S., Schmutz, P., Bargas-Avila, J. A., Tuch, A. N., & Klaus, O. (2011). Vertical versus dynamic menus on the world wide web: eye tracking study measuring the influence of menu design and task complexity on user performance and subjective preference. Computers in Human Behavior, 27(1), 459–472. 36. Luck, S. J. (2005). An introduction to the event-related potential technique (cognitive neuroscience). Cambridge, MA: MIT Press. 37. Ma, Q. G., Meng, L., Zhang, Z. X., Xu, Q., Wang, Y., & Shen, Q. (2015). You did not mean it: Perceived good intentions alleviate sense of unfairness. International Journal of Psychophysiology, 96(3), 183–190. 38. Ma, Q. G., Pei, G. X., & Wang, K. (2015). Influence of negative emotion on the framing effect: Evidence from event-related potentials. NeuroReport, 26(6), 325–332. 39. Ma, Q. G., Wang, X. Y., Shu, L. C., & Dai, S. Y. (2008). P300 and categorization in brand extension. Neuroscience Letter, 431, 57–61. 40. Ma, Q. G., Wang, C. C., Wu, Y. F., & Wang, X. Y. (2014). Event-related potentials show taste and risk effects on food evaluation. NeuroReport, 25, 760–765. 41. Marchand, Y., Inglis-Assaff, P. C., & Lefebvre, C. D. (2013). Impact of stimulus similarity between the probe and the irrelevant items during a card-playing deception detection task: The ‘‘irrelevants’’ are not irrelevant. Journal of Clinical and Experimental Neuropsychology, 35(7), 686–701. 42. Miyazaki, A. D., Grewal, D., & Goodstein, R. C. (2005). The effect of multiple extrinsic cues on quality perceptions: A matter of consistency. Journal of Consumer Research, 32(1), 146–153. 43. Naä¨ta¨nen, R. (1990). The role of attention in auditory information processing as revealed by eventrelated potentials and other brain measures of cognitive function. Behavioral and Brain Sciences, 13, 201–288. 44. Novemsky, N., & Kahneman, D. (2005). The boundaries of loss aversion. Journal of Marketing Research, 42(2), 119–128. 45. Olson, J. C. (1972). Cue utilization in the quality perception process: a cognitive model and an empirical test. Dissertation for the degree of Doctor, Purdue University. 46. Parboteeah, D. V., Valacich, J. S., & Wells, J. D. (2009). The influence of website characteristics on a consumer’s urge to buy impulsively. Information Systems Research, 20(1), 60–78. 47. Pavlou, P. A., Liang, H. G., & Xue, Y. J. (2007). Understanding and mitigating uncertainty in online exchange relationships: A principal-agent perspective. MIS Quarterly, 31(1), 105–136. 48. Petty, R., Cacioppo, J. T., & Schumann, D. (1983). Central and peripheral routes to advertising effectiveness: The moderating role of involvement. Journal of Consumer Research, 10, 135–146. 49. Picton, T. W. (1992). The P300 wave of the human event-related potential. Journal Of clinical of Neurophysiology, 9, 456–479. 50. Purohit, D., & Srivastava, J. (2001). Effect of manufacturer reputation, retailer reputation, and product warranty on consumer judgments of product quality: A cue diagnosticity framework. Journal of Consumer Psychology, 10, 123–134. 51. Ruyter, K. D., Wetzels, M., & Kleijnen, M. (2001). Customer adoption of e-service: An experimental study. International Journal of Service Industry Management, 12(2), 184–207. 52. SanJose´-Cabezudo, R., Gutie´rrez-Arranz, A. M., & Gutie´rrez-Cillań, J. (2009). The combined influence of central and peripheral routes in the online persuasion process. CyberPsychology & Behavior, 12(3), 299–308. 53. San-Martıń, S., & Camarero, C. (2012). A cross-national study on online consumer perceptions, trust, and loyalty. Journal of Organizational Computing & Electronic Commerce, 22(1), 64–86.

123


25

54. Skowronski, J. J., & Carlston, D. E. (1987). Social judgment and social memory: The role of cue diagnosticity in negativity, positivity, and extremity biases. Journal of Personality and Social Psychology, 52, 689–699. 55. Slovic, P., & Lichtenstein, S. (1971). Comparison of Bayesian and regression approaches to the study of information processing in judgment. Organizational Behavior and Human Performance, 6, 649–774. 56. Spape´, M. M., Band, G. P. H., & Hommel, B. (2011). Compatibility-sequence effects in the Simon task reflect episodic retrieval but not conflict adaptation: Evidence from LRP and N2. Biological Psychology, 88, 116–123. 57. Steffensen, S. C., Ohran, A. J., Shipp, D. N., Hales, K., Stobbs, S. H., & Fleming, D. E. (2008). Gender-selective effects of the P300 and N400 components of the visual evoked potential. Vision Research, 48(7), 917–925. 58. Suki, N. M. (2013). Consumer shopping behaviour on the Internet: Insights from Malaysia. Electronic Commerce Research, 13, 477–491. 59. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. 60. Utz, S., Kerkhof, P., & van den Bos, J. (2011). Consumers rule: How consumer reviews influence perceived trustworthiness of online stores. Electronic Commerce Research and Applications, 11(1), 49–58. 61. Walsh, M. M., & Anderson, J. R. (2012). Learning from experience: Event-related potential correlates of reward processing, neural adaptation, and behavioral choice. Neuroscience and Biobehavioral Reviews, 36, 1844–1870. 62. Wells, J. D., Valacich, J. S., & Hess, T. J. (2011). What signal are you sending? How website quality influences perceptions of product quality and purchase intentions. MIS Quarterly, 35(2), 373–396. 63. Wu, Y., Zhou, Y. Q., van Dijk, E., Leliveld, M. C., & Zhou, X. L. (2011). Social comparison affects brain responses to fairness in asset division: An ERP study with the ultimatum game. Frontiers in Human Neuroscience, 5, 131. 64. Yang, J., Li, H., Zhang, Y., Qiu, J., & Zhang, Q. L. (2007). The neural basis of risk decision-making in a blackjack task. NeuroReport, 18(14), 1507–1510.

Qiuzhen Wang is an Associate Professor of Information Systems at Zhejiang University. She received her Ph.D. in management science and engineering from Zhejiang University. Her present research interests are online consumer behavior and Human–Computer Interface (HCI) design in E-Commerce. Her research has been published in Decision Support systems, International Journal of Project Management, Enterprise Information Systems and other academic journals. Liang Meng is a Ph.D. candidate of Decision Neuroscience at School of Management, Zhejiang University. Currently, adopting the cognitive neuroscience perspective, he endeavors to probe the influencing factors of individuals’ intrinsic motivation, especially in the workplace. His research has been published in Behavioural Brain Research, International Journal of Psychophysiology, PLOS ONE, Frontiers in Neuroscience, NeuroReport and other academic journals. Manlu Liu is an Associate Professor of Management Information Systems and Accounting at Saunders College of Business at Rochester Institute of Technology (RIT). She received her Ph.D. in Management Information Systems from Eller College of Management at the University of Arizona. She obtained an MBA degree from the Hong Kong University of Science & Technology. Prior to join RIT, she was an Associate Professor at the School of Management at Zhejiang University in China. Her research interests include community-based open source, open innovation, electronic commerce, big data in accounting and finance, and sustainable development. Her research has been published in leading academic journals, including Communications of the ACM, Journal of Global Information Management, Decision Support Systems and Journal of Systems and Software etc. She serves as the member of the Editorial Review Board for the following journals: Journal of Electronic Commerce Research, Electronic Government: An International Journal, and International Journal of Electronic Finance.

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Qi Wang is working at NetEase, Inc., which is a well-known Internet company in China. She focuses on the improvement of the user experience. She received her master degree in management science and engineering from Zhejiang University. Qingguo Ma is a professor at the School of Management, Zhejiang University, China. His research interests include information management, neuromanagement and industrial engineering. His work has been published in Information & Management, Decision Support Systems, Neuroscience letters, Cyberpsychology, Behavior, and Social Networking, Enterprise Information Systems and other academic journals.

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