buying research article

Impulse buying: a meta-analytic review

• Review Paper
• Open access
• Published: 09 July 2019
• Volume 48 , pages 384–404, ( 2020 )

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• Gopalkrishnan R. Iyer 1 ,
• Markus Blut 2 ,
• Sarah Hong Xiao 3 &
• Dhruv Grewal 4  

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Impulse buying by consumers has received considerable attention in consumer research. The phenomenon is interesting because it is not only prompted by a variety of internal psychological factors but also influenced by external, market-related stimuli. The meta-analysis reported in this article integrates findings from 231 samples and more than 75,000 consumers to extend understanding of the relationship between impulse buying and its determinants, associated with several internal and external factors. Traits (e.g., sensation-seeking, impulse buying tendency), motives (e.g., utilitarian, hedonic), consumer resources (e.g., time, money), and marketing stimuli emerge as key triggers of impulse buying. Consumers’ self-control and mood states mediate and explain the affective and cognitive psychological processes associated with impulse buying. By establishing these pathways and processes, this study helps clarify factors contributing to impulse buying and the role of factors in resisting such impulses. It also explains the inconsistent findings in prior research by highlighting the context-dependency of various determinants. Specifically, the results of a moderator analysis indicate that the impacts of many determinants depend on the consumption context (e.g., product’s identity expression, price level in the industry).

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Consumers spend $5,400 per year on average on impulse purchases of food, clothing, household items, and shoes (O’Brien 2018 ). Thus, there is considerable need to investigate consumer impulse buying, defined as episodes in which “a consumer experiences a sudden, often powerful and persistent urge to buy something immediately” (Rook 1987 , p. 191). Products purchased impulsively often get assigned to a distinct category in marketing texts, yet decades of research reveal that impulsive purchases actually are not restricted to any specific product category. As Rook and Hoch ( 1985 , p. 23) assert, “it is the individuals, not the products, who experience the impulse to consume.”

Academic research that explores the various triggers of impulse buying consists of three main schools of thought. First, some scholars argue that individual traits lead consumers to engage in impulse buying (e.g., Verplanken and Herabadi 2001 ). For example, people who are impulsive are more likely to engage in impulse buying (Rook and Hoch 1985 ), whereas those who do not display this trait may be less likely to engage in spontaneous behaviors while shopping. Among the psychological factors that might evoke impulse buying, researchers have explored the traits of sensation seeking, impulsivity, and representations of self-identity. Second, both motives and resources might drive impulse buying. Researchers have identified the effects of two types of motives (hedonic and utilitarian), as well as subjective norms, and argued that mere impulsiveness is often not strong enough to trigger impulse buying. Instead, the availability of resources coupled with a failure of self-control also is required to enact impulse buying (Baumeister 2002 ; Hoch and Loewenstein 1991 ). Considerable research has investigated the specific influences of different types of resources, including psychic, time, and money resources (Vohs and Faber 2007 ), with the assumption that resource-based motives, availability, and constraints impact consumer impulse buying. Third, some studies focus on the role of marketing drivers, highlighting how impulse buying can result from store or shelf placements, attractive displays, and in-store promotions. This view holds that impulse buying can be influenced, so retailers invest in marketing instruments designed to trigger it (Mattila and Wirtz 2001 ).

Although these diverse research streams approach impulse buying from different angles and have established considerable insights into its triggers, a unified and comprehensive view of the drivers of impulse buying would further enhance our understanding. We perform a meta-analysis on an accumulation of prior empirical research, focusing on disparate drivers and the most impactful antecedents, and the substantive insights obtained from the estimation of effect sizes. Our study can guide further research and the results also could aid managers in crafting strategies to stimulate impulse purchases by targeting the most receptive customers and investing in effective marketing campaigns. In addition to the direct effects of various antecedents on impulse buying, our proposed framework identifies several mediating mechanisms, including self-control (Vohs and Faber 2007 ) and positive and negative emotions (Rook and Gardner 1993 ). We test the joint effects of emotions and self-control, which enables us to specify their concurrent mediating roles, as well as the potential for serial moderation (i.e., self-control influences emotions). Apart from the typical study moderators, we examine industry moderators—namely, the average price level, advertising, and distribution intensity in the industry, as well as the identity expression capacity of the product category—in line with Rook and Fisher’s ( 1995 , p. 312) call for “a better understanding of various contextual factors that are also likely to contribute to this relationship [between determinants and impulse buying].” The precise roles of these moderating variables have not been explored in prior impulse buying studies, and a better understanding of their influence can provide new insights and spur further in-depth research.

Our use of a meta-analysis is in line with calls in recent research (Grewal et al. 2018b ; Palmatier et al. 2018 ) highlighting the importance of such integrative reviews. An earlier meta-analysis by Amos et al. ( 2014 ) summarized the impacts of various factors on consumer impulse buying; our review extends on their work in several ways. First, we recognize the diverse perspectives on impulse buying and the need to obtain a more comprehensive understanding by combining insights from different research streams. To this end, we have sourced extensively and include 186 papers in our meta-analysis, compared with 63 in Amos et al. ( 2014 ). Second, Amos et al. focus primarily on main effects, whereas we examine moderators and mediators, in addition to the main effects. This scrutiny of the moderating effects also allows us to consider individual relationships rather than pool the effect sizes of all antecedents (Amos et al. 2014 ) and thus identify stronger and weaker effects. Third, by examining mediating effects, we can test alternate theory-based relationships of the various antecedents on impulse buying. The resulting insights help provide a more inclusive understanding of impulse buying as compared with the use of only one theoretical perspective.

Conceptual framework

Several determinants of impulse buying appear in prior research. In line with Dholakia ( 2000 ), we explore the effects of trait determinants, motives, resources, and marketing stimuli on impulse buying. Beyond these categories of main effects, our integrated model explores their impacts through the mediation of self-control and individual emotional states as well (Mehrabian and Russell 1974 ). We also account for contextual differences in effects by examining the moderating influences of industry-related characteristics. Furthermore, we consider the possible influence of study characteristics (i.e., impulse buying measure, sample composition, and publication year) on the effects obtained. Our conceptual model is in Fig.  1 , and we offer a summary of the predicted relationships in Table  1 .

Meta-analytic framework

Determinants of impulse buying

Trait and related determinants.

Several individual traits and self-identity may serve as internal sources of impulse buying. Psychological impulses strongly influence impulse buying (Rook 1987 ; Rook and Hoch 1985 ), and prior research shows that people who score high on impulsivity trait measures are more likely to engage in impulse buying (Beatty and Ferrell 1998 ; Rook and Fisher 1995 ; Rook and Gardner 1993 ). Moreover, other traits are also associated with impulse buying and studies in the past have attempted to study their impacts as well (e.g., Mowen and Spears 1999 ; Sharma et al. 2010 ).

First, we examine the role of sensation-seeking as having a direct impact on impulse buying. Sensation-seeking, variety-seeking, novelty-seeking, and similar dispositions are arguably distinct from other traits such as impulsivity and reported as contributing to impulse buying (Punj 2011 ; Sharma et al. 2014 ; Van Trijp and Steenkamp 1992 ). Second, an impulse buying tendency , which includes the trait of impulsivity, reflects an enduring disposition to act spontaneously in a specific consumption context. This well-recognized concept captures a relatively enduring consumer trait that produces an urge or motivation for actual impulse buying (Rook and Fisher 1995 ). Impulse buying tendencies, are easier to observe than other traits and are also highly predictive of impulse buying (Beatty and Ferrell 1998 ; Rook and Gardner 1993 ). Third, buyer-specific beliefs about self-identity and its deficits influence impulse buying decisions (Dittmar et al. 1995 ). Impulse purchases are more likely to involve items that are symbolic of a preferred or ideal self as well as products that offer high identity-expressive potential, to compensate for the buyer’s own identity deficits (Dittmar et al. 1995 ; Dittmar and Bond 2010 ). However, contextual factors may play a role on the impacts of such perceptions of identity deficits (e.g., Dittmar et al. 2009 ).

Motives and norms

Consumers’ motives, such as hedonic or utilitarian motives , are important internal sources of impulse buying that reflect goal-directed arousal, leading to specific beliefs about consumption. For example, consumers may believe that buying objects will provide emotional gratification, compensation, rewards, or else minimize their negative feelings. Such beliefs may be especially relevant if the objects are unique and feature a marked opportunity cost, such that they need to be purchased immediately (Rook and Fisher 1995 ; Vohs and Faber 2007 ).

Norms invoked by consumers about their own impulsiveness also might affect impulse buying decisions. As Rook and Fisher ( 1995 , p. 307) explain, “consumers’ own prior impulse buying experiences may serve as a basis for independent, internalized evaluations of impulse buying as either bad or good.” From a self-regulation perspective, when prior impulse buying evokes positive experiences, consumers likely engage in it again, as a promotion-focused strategy (Verplanken and Sato 2011 ).

Customers with greater psychic resources or interest in a product category are more likely to engage in impulse buying, whereas those who lack the necessary resources (time, money) engage less in impulse buying (Hoch and Loewenstein 1991 ; Jones et al. 2003 ; Kacen and Lee 2002 ). Age and gender might capture shopping-related resources, such that impulse buying tendencies often are more prevalent among specific social or demographic cohorts (Kacen and Lee 2002 ; Tifferet and Herstein 2012 ; Wood 1998 ). Drawing from prior research, Kacen and Lee ( 2002 ) offer that younger shoppers may be more likely to buying impulsively while older adults may be better able to regulate their emotions and engage in self-control.

Several research and practical observations have highlighted gender differences in shopping (e.g., Underhill 2000 ). Dittmar et al. ( 1995 ) find that men and women are likely to buy different products to buy impulsively and also use different buying considerations when buying on impulse. Also, it has been found that women are more likely as compared to men to experience regret or a mixture of pleasure and guilt (Coley and Burgess 2003 ).

• Marketing stimuli

Marketers deliberately design external stimuli to appeal to shoppers’ senses (Eroglu et al. 2003 ). Managers expend substantial time and effort in designing retail environments and the resulting retail interactions to increase shoppers’ psychological motivation to purchase (Berry et al. 2002 ; Foxall and Greenley 1999 ). It has been estimated that about 62% of in-store purchases are made impulsively and online buyers are more likely to be impulsive (Chamorro-Premuzic 2015 ). Thus, impulse buying can be triggered by various marketing stimuli such as merchandise, communications, store atmospherics, and price discounts (Mohan et al. 2013 ).

Mediators of impulse buying

Baumeister ( 2002 ) has established the importance of motives and resource depletion for driving impulse buying; therefore, we also consider whether self-control and emotions might be triggered. By including these mediating mechanisms in our meta-analysis, we avoid over- or underestimating the importance of various impulse buying triggers. In particular, we assess the joint effects of emotions and self-control, which enables us to specify their concurrent mediating roles, as well as the potential for serial mediation (i.e., self-control influences emotions).

Self-control as a mediator

Countering prior arguments that impulse purchases stem from irresistible urges, Baumeister ( 2002 ) has argued that individuals’ self-control can and do resist such urges. Muraven and Baumeister ( 2000 ; p. 247) submit that self-control, or the “control over the self by the self,” involves attempts by individuals to curb their desires, conform to rules and change how think, feel or act. Also, individuals differ in self-control leading to the view that self-control is an inherent strength or trait (Baumeister 2002 ). It has also been argued that a failure of self-control could occur due to conflicting goals, reduction in self-monitoring or depletion of mental resources (Baumeister 2002 ; Verplanken and Sato 2011 ). The depletion of mental resources, or “ego depletion,” may also be temporal, i.e., more likely to occur at the end of the day (Baumeister 2002 ; p. 673). The “ever-shifting conflict between desire and willpower” (Vohs and Faber 2007 , p. 538) demonstrates the importance of self-control as a key mediator in the impacts of various antecedents noted in our model and impulse buying.

Emotions as mediators

Environmental psychology research, and particularly the stimulus–organism–response model proposed by Mehrabian and Russell ( 1974 ), highlights experienced emotions as potential mediating constructs. Input variables such as environmental stimuli or individual traits jointly influence individual affective responses, which then induce response behaviors (Baker et al. 1992 ). Verplanken and Herabadi ( 2001 ) explain that customers engaging in impulse buying tend to display emotions at any point of time during the purchase (i.e., before, during, or after). Extant findings are somewhat inconsistent though. It has been argued that impulse buying behavior relates strongly to positive emotions and feelings such that impulse buyers experience more positive emotions such as delight and consequently spend more (Beatty and Ferrell 1998 ). Impulse buyers have a strong need for arousal and experience an emotional lift from persistent repetitive purchasing behaviors (O'Guinn and Faber 1989 ; Verplanken and Sato 2011 ). Such arousal even might be a stronger motive for impulse buying than product ownership (Dawson et al. 1990 ).

Rook and Gardner ( 1993 ) acknowledge that while pleasure is an important precursor, negative mood states such as sadness, can also be associated with impulse buying. For example, various studies suggest self-gifting to be a form of retail therapy that helps customers in managing their moods (Mick and Demoss 1990 ; Rook and Gardner 1993 ; Vohs and Faber 2007 ). Other researchers concur that impulse buying can serve to manage or elevate negative mood states but also suggest that this influence occurs through a self-regulatory function (Rook and Gardner 1993 ; Verplanken et al. 2005 ). Thus, emotional states—whether positive or negative—likely affect impulse buying, but we find no consensus about whether or how negative moods, positive moods, or both determine impulse buying uniquely.

Finally, research rooted in environmental psychology asserts that exposure to environmental stimuli, consumers’ personalities, and personal motives can cause specific (positive or negative) emotional reactions (e.g., Babin et al. 1994 ; Donovan and Rossiter 1982 ; Mehrabian and Russell 1974 ). These in turn mediate the impacts of personal, situational, and external factors on impulse buying (Parboteeah et al. 2009 ; Verhagen and van Dolen 2011 ). The limited empirical evidence on the mediating role of emotions refers to specific contexts; for example, Adelaar et al. ( 2003 ) show that pleasure, dominance, and arousal triggered at the moment of purchase mediate the effect of a media format on impulse buying intentions online. Verhagen and van Dolen ( 2011 ) found that positive emotions mediate the effects of consumer beliefs about online stores and their likelihood of buying impulsively. Store environments and circumstances such as time and money resources also might prompt negative emotional reactions (Lucas and Koff 2014 ; Vohs and Faber 2007 ), suggesting the need for more empirical evidence to determine which emotions are more prominent.

The serial mediation of self-control and emotions also deserves examination. The motivational role of self-control also suggests that a successful exercise of self-control may also contribute to positive affect; in other words, individuals with higher self-control not only resist temptations successfully but may experience other consequent states such as fewer emotional problems and greater life satisfaction (Baumeister 2002 ; Baumeister et al. 2008 ; Hofmann et al. 2012 ; Tice et al. 2001 ). The conceptualization of self-control as a strength and self-control failure as ego-depletion (c.f., Baumeister 2002 ) also paves the way for understanding how the exercise of self-control and the unpleasant consequence of self-regulation of a pleasant task may contribute to seeking other pleasurable pursuits (Finley and Schemichel 2018 ). Thus, individuals may counter the distasteful after-effects of a self-control act by pursuing opportunities that would contribute to positive emotions (Finley and Schemichel 2018 ). This view of self-control views ego-depletion as a process, whereby the exercise of self-control in one time period leads to the individual seeking subsequent positive experiences (Finley and Schemichel 2018 ). Another view of self-control offers that self-control may not be all about inhibitions and restrictions; the trait of self-control may also engage in a promotion focus and thereby engage in initiatory behaviors towards achieving the same goal (Cheung et al. 2014 ). While the above discussion sheds light on the relationship between self-control and positive emotions, there is a lack of clarity in current literature on the precise direction of the relationship between self-control and emotional states relative to impulse buying as well as the impact of self-control on negative emotions.

Contextual moderators

We seek novel insights by examining industry characteristics as potential contextual moderators. Based on extant studies, we identify the price levels, advertising, and distribution intensity within the industry context as moderators that may influence the effects of other factors on impulse buying. The identity expression capability of the products themselves could moderate the impacts of the various determinants too. Prior impulse buying studies do not test the effects of these moderators; to derive our predictions, we thus turn to relationship marketing research that reveals how industry-level variables determine effectiveness (Fang et al. 2008 ). Product price levels matter, because financial constraints suppress impulse purchases (Rook and Fisher 1995 ), and impulse buying triggers are less effective in more expensive product categories. In their meta-analysis, Samaha et al. ( 2014 ) find that advertising intensity in a specific industry reduces the effectiveness of a firm’s communication activities. We posit that similarly, impulse buying triggers may be less effective in industries in which all firms invest heavily in advertising, because consumers are less likely to recognize and consider these various triggers. In addition, distribution intensity in an industry might influence impulse buying, because the urge to purchase likely increases when products are rare or exclusive (Troisi et al. 2006 ). Finally, some products are more prone to impulse purchases, especially if they symbolize a preferred or ideal self (Dittmar et al. 1995 ; Dittmar and Bond 2010 ). Thus, we anticipate differing effectiveness of impulse buying triggers according to the product.

Method moderators

Meta-analyses frequently consider the influence of the methods adopted by the included studies, such as how they measure key constructs, on the strength of the focal relationships. Impulse buying studies frequently use different measures for similar constructs; we use the scale for buying impulse developed by Rook ( 1987 ) as a baseline to assess whether other measures perform differently. Meta-analyses also can reveal whether the use of specific samples influences the findings (Orsingher et al. 2009 ). In particular, student samples tend to be more homogeneous than non-student samples and thus produce stronger effect sizes. Finally, we assess the influence of the study period. The emergence of the Internet and advanced communication technologies have left customers more knowledgeable, with altered expectations of retailers (Blut et al. 2018 ). Accordingly, we consider whether customers’ impulse buying behaviors might have changed over time.

Data collection and coding

We collected the data for this study by searching electronic databases, including EBSCO, Proquest, Ingenta Journals, Elsevier Science Direct, Google Scholar, the web, and several pertinent leading journals (e.g., Journal of the Academy of Marketing Science, Journal of Consumer Research, Journal of Marketing, Journal of Marketing Research ). We also identified relevant articles by examining the reference lists of the collected articles. Our search used various terms, including “impulse buying” and “impulsive buying,” “impulsivity,” “compulsive buying,” and “unplanned buying,” and encompassed titles, abstracts, and keywords. The document types included articles and reviews (c.f., book review); the language was English; and the subject areas spanned marketing and advertising, management, business, economics, sociology, and psychology. We also obtained some unpublished studies from their authors. We sent 159 emails to authors of published papers seeking at least minimally relevant statistics for conducting the analysis. After excluding theoretical papers, qualitative studies, book reviews, studies that mention but do not measure impulse buying, and studies that do not report the necessary effect sizes, we pared down the list of 386 articles to a final data set of 186 articles reporting empirical results. Footnote 1

We coded each effect size according to the relationship of the independent variables (traits, motives, resources, and marketing stimuli), the mediators (self-control, positive emotions, and negative emotions) and impulse buying. We also coded the industry and method moderator variables, such that we assessed industry characteristics (i.e., product-identity relation, price level, advertising intensity, and distribution intensity) using the industry description reported by the studies. We similarly coded the method moderators (i.e., study year, measurement of impulse buying, and student sample) using information provided in each study. Two coders achieved agreement greater than 90% and discussed any inconsistencies, using the construct definitions in Table  2 to classify all the variables.

We included studies that reported (1) correlations (r) between the variables of interest, (2) the standardized regression coefficients (beta coefficients), (3) F- or t-values, or (4) frequencies, to calculate as as many effect sizes, so as to enhance the generalizability (Peterson and Brown 2005 ).

Integration of effect sizes

Correlation coefficients were used as effect sizes in our meta-analysis. If such coefficients were not reported in the collected studies, we transformed alternative statistics, such as regression coefficients, into correlations (Peterson and Brown 2005 ). Following Peterson and Brown ( 2005 ), we imputed correlations from the beta coefficients using the formula: r = .98β + .05λ with λ = 1 when β > 0 and λ = 0 when β < 0. Some studies also report more than one correlation for the same relationship between two constructs, in which case, we averaged the two correlations and treated them as if they were from a single study (Hunter and Schmidt 2004 ). We did not have enough effect sizes to include some determinants in all analyses, such as the four marketing stimuli of communication, price stimuli, store ambience, and merchandise. We therefore examined these determinants separately when possible and merged them as necessary to include them in other analyses. If a study had measured more than one of the four instruments, we calculated an average effect size for the aggregate marketing stimuli variable. This approach ensures the use of only one aggregate marketing stimuli effect size for each study. After transforming and averaging the effect sizes, the total data set in the meta-analysis consists of 968 effect sizes, extracted from 231 samples obtained from 186 articles. The total combined sample includes 75,434 respondents.

We used a random-effects approach (Hunter and Schmidt 2004 ) to calculate the average correlations. Effect sizes were corrected for measurement error in the dependent and independent variables using the coded reliability coefficients. We followed the Hunter and Schmidt ( 2004 ) recommendation of dividing the correlations by the product of the square root of the respective reliabilities of the two constructs involved. Further, reliability-adjusted correlations were weighted by sample size to adjust for sampling error. It has been recommended that reliability-adjusted effect sizes should be transformed into Fisher’s z coefficients before weighting them by sample size (Kirca et al. 2005 ). This transformation is not without controversies, and some studies suggest that Fisher’s z overestimates true effect sizes by 15%–45% (Field 2001 ). However, when we compare the results of both approaches, we find no significant differences.

Next, for each sample size–weighted and reliability-adjusted correlation, we calculated standard errors and 95% confidence intervals. We used a chi-square test and applied a 75% rule-of-thumb to assess the homogeneity of the effect size distribution (Hunter and Schmidt 2004 ). To assess the robustness of our results and potential publication bias, we estimated Rosenthal’s ( 1979 ) fail-safe N; in other words, the estimation of the number of studies that had null results and therefore not published before the Type I error probability can be brought to a barely significant level ( p  = .05). We also tested the influence of sample size and effect size outliers on integrated effect sizes, but the results remained largely the same (Geyskens et al. 2009 ). To assess the practical relevance of the different determinants, we calculated the shared variance with impulse buying for each predictor, as well as the binomial effect size display (BESD) (Grewal et al. 2018b ), which indicates the likelihood that a customer (e.g., female) would purchase impulsively compared with a reference group (e.g., male customers). A value greater than 1 indicates a greater relative likelihood, whereas a value lower than 1 signals a lower likelihood.

Descriptive statistics

Direct effects.

As Table  3 indicates, the averaged effect sizes for most motives, resources, and trait predictors are significant; however, socio-demographic predictors seem to matter less for impulse buying. We find strong support for the impacts of the three trait-related predictors on impulse buying. As expected, an individual's tendency to act impulsively has a stronger effect than other traits, reflecting its stronger link to the behavior of interest.

Utilitarian and hedonic motives show about equal impacts on impulse buying; further research should pay more attention to these determinants. We find support for gender effects but observe no differences for age. The former results are in line with prior research that suggests women generally are more likely to purchase impulsively than men (Dittmar et al. 1995 ). However, the insignificant results for age suggests there are not many differences between older and younger customers with regard to spending money impulsively. Moreover, we find that marketing stimuli exert a direct influence on customers’ impulse buying behavior. When examining the specific marketing instruments, we find the strongest effects for communication and price stimuli and weaker effects for store ambience and merchandise.

We uncover significant effects for emotions and self-control (Table  4 ). Descriptive statistics were also examined to gauge the impact of the predictors on the mediators (Table 4 ); 30 of the 39 predictor–mediator relationships (77%) are significant. Thus, we obtain a preliminary indication of the mediating roles of emotions and self-control, and we can proceed to test the proposed mediating effects in the SEM.

The shared variances and BESD give some indication of the practical relevance of different determinants. Using these criteria, we observe strong effects of impulse buying tendencies, utilitarian motives, and communication. All the significant relationships are robust to publication bias because the file-drawer N is many times greater than the tolerance levels proposed by Rosenthal ( 1979 ). We also examined funnel plots and do not find any indication of publication bias. In all cases, the significant chi-square tests of homogeneity suggest moderation.

Evaluation of structural equation model

We tested the mediating effects using structural equation modeling (SEM) and included variables for which correlations with all other variables could be identified. The complete correlation matrix includes correlations between the most often studied variables in prior research (Table  5 ). It served as the input to LISREL 8.80 and the harmonic mean of all sample sizes ( N  = 1726) was used as input. Since the harmonic mean is lower than the arithmetic mean, SEM estimations are more conservative (Viswesvaran and Ones 1995 ). Note that since each construct had only a single indicator and since measurement errors were taken into account when estimating the mean effect sizes, the error variances in the SEM could be set to 0. The different marketing instruments could not be individually included in the SEM, due to the small number of effect sizes, so we aggregated all marketing instruments into one determinant variable and examined its influence in the SEM; if a study included two or more marketing stimuli effects, we averaged them. The proposed model with both mediators and the effect of self-control on emotions performs well and displays a good fit (Fig.  2 ).

Results of the structural equation model. Notes : A dotted line indicates that the path is not significant. Model fit: χ 2 /1 = 67.74; confirmatory fit index = .99; goodness-of-fit index = .99; root mean residual = .02; standardized root mean residual = .02

Positive moods

The SEM results suggest that positive moods are important mediators (Fig. 2 ). Customers with stronger hedonic motives are more likely to experience positive feelings; customers with utilitarian motives are less likely to experience such feelings. Those with favorable subjective norms and high self-control also experience positive moods. These effects are new to extant impulse buying literature. Similarly, customers who are generally high in impulsivity experience positive feelings. Finally, marketing stimuli relate significantly to positive feelings, though the effect is relatively weak.

Negative moods

Negative mood states relate significantly to impulse buying, and each of the determinants link to this mediator, with the exception of marketing stimuli and self-control. Customers high in hedonic and utilitarian motives are less likely to experience negative moods. Favorable subjective norms increase the likelihood of negative feelings. Impulse buying tendency is positively related to the experience of negative moods. The insignificance of marketing stimuli suggests that the stimuli do not trigger negative moods in customers. Self-control also does not reduce the experience of negative emotions.

• Self-control

Unlike mood states, self-control reduces the likelihood of impulse purchases. This cognition intervenes when customers experience an urge to buy impulsively. According to the SEM results, several predictors either trigger individual awareness of the long-term consequences of spending or reassure consumers that spending is acceptable. For example, customers high in impulsivity are less likely to exhibit self-control. Subjective norms that encourage impulse buying lower self-control perceptions, but marketing stimuli serve to increase self-control. Finally, hedonic and utilitarian motives increase self-control perceptions. The positive effect of marketing stimuli on self-control suggests that customers are aware of how firms try to influence them to make them impulsive purchases.

Similar to Pick and Eisend ( 2014 ), we tested the importance of mediation effects using two approaches. First, we examined the ratio of indirect effects to total effects as displayed in Table  6 . We find significant indirect effects and high ratios for most determinants, including self-control (20%), impulse buying tendency (46%), utilitarian motives (34%), norms (49%), and marketing stimuli (39%). Only the indirect effect of hedonic motives is insignificant, leading to a low ratio of indirect effects to total effects (8%). The direct, indirect, and total effects differ for some determinants; self-control has a negative direct effect on impulse buying, yet the indirect effect through mediators is positive, which mitigates the total negative effect. Impulse buying tendency has positive direct and indirect effects on impulse buying, such that the total effect is nearly twice as strong as the direct effect. Utilitarian motives have a positive direct effect on impulse buying and a negative indirect effect that lowers the total effect. Norms display a negative direct effect and a positive indirect effect; we observe the opposite effects for marketing stimuli. The mediation model thus provides a clearer view of how these determinants influence impulse buying.

Second, we compare the proposed model, which assumes partial mediation effects, with two models with only indirect effects of the determinants through moods and self-control (full mediation). As suggested by Pick and Eisend ( 2014 ), we compare the models using a chi-square difference test (Δχ 2 /df). Both full mediation models exhibit significantly worse model fit than the proposed model (mood: Δχ 2 /df = 630.51/6, p  < .01; self-control: Δχ 2 /df = 755.28/8, p  < .01). Thus, the mediating effects of moods and self-control are partial rather than full.

Moderator analysis results

The need for a moderator analysis was assessed through the chi-square test of homogeneity and a 75% rule (Hunter and Schmidt 2004 ). The 75% rule indicates that if the proportion of variance in the distribution of effect sizes attributed to sampling error and other artifacts is less than 75%, a moderator analysis is warranted. In our results, the chi-square value is significant in all cases, and the 75% rule suggests values lower than 75%, in support of a moderator analysis. We coded several moderators in our random effects regression model as dummy variables, including the four industry moderators: product identity relation (1 = high expressive, 0 = low expressive), price level (1 = high, 0 = low), advertising intensity (1 = high, 0 = low), and distribution intensity (1 = high, 0 = low). Footnote 2 For the two method moderators, impulse buying measure (1 = Rook, 0 = non-Rook) and sample (1 = student, 0 = non-student), we used dummy codes. The year of the study came directly from the articles.

Using meta-regression procedures suggested by Lipsey and Wilson ( 2001 ) and the provided macros, we assess the influence of the moderators in our model with random-effects regression (Hunter and Schmidt 2004 ). Using reliability-corrected correlations as the dependent variable, we conducted tests of the moderators for 18 predictor variables and regressed correlations on four industry variables and three method variables. To test moderation effects, we ensured that at least 10 effect sizes were available (Samaha et al. 2014 ).

Product identification

We confirm a moderating influence of product identification (Table  7 ). If a product’s expressiveness is high (i.e., product identity is coded as 1 for high expressiveness), some predictors lose their relevance, including self-identity and subjective norms. Products that facilitate consumer self-expression are more likely to be bought impulsively, because they represent a preferred or ideal self (Dittmar et al. 1995 ; Dittmar and Bond 2010 ). Products with high expressiveness also suppress the effects of norms. In these conditions, other determinants become less effective. However, some determinants related to communication and negative feelings gain importance, because consumers are very sensitive with regard to their self-perceptions.

Price level

As expected, the average price level of products in an industry buffers the impacts of several predictors. Most predictors lose some relevance when prices are high (i.e., price level is coded as 1), including sensation-seeking, impulse buying tendency, hedonic motives, utilitarian motives, psychic resources, and positive moods. Only self-control gains importance, in line with our reasoning. Higher prices alert consumers to the financial consequences of their urge to buy impulsively, making these determinants less effective (but self-control more effective).

Advertising intensity

The influence of advertising is quite interesting. On the one hand, it appears to increase desire for certain products, so some predictors gain relevance. On the other hand, the predictors may lose relevance, because products seem less unique when they are advertised everywhere. Negative moods and merchandise gain importance with greater advertising intensity, but norms, psychic resources, and store ambience matter less.

Distribution intensity

Product availability in an industry depends on its distribution intensity. For example, Dholakia ( 2000 ) explains that physical proximity is essential for the experience of an impulsive urge, but a product that is unusually difficult to purchase may be more appealing to customers than products that are available everywhere. We anticipated and find that at least some impulse buying predictors, such as utilitarian motives, psychic resources, merchandise, and negative mood states, become less effective when a product is more widely available. Moreover, communication gains relevance with greater distribution intensity.

When examining the moderating influence of the method adopted in the different studies, we find that several predictors, such as impulse buying tendency and utilitarian motives, gain importance over time. We do not observe a specific pattern for the measures employed. The results with regard to the measures used in the studies suggest that the widely employed Rook scale performs as well as alternative impulse buying measures. We also find generally weaker effects in studies using student samples. In further meta-regression models, we assessed the influence of country culture and emerging markets but do not find notable differences.

Implications and directions for further research

This meta-analysis aims to provide a comprehensive and coherent understanding of impulse buying behavior, by synthesizing previous research. Our meta-analytic review seeks deeper insights into impulse buying, and our comprehensive model of impulse buying integrates constructs and relationships from studies over the past four decades of empirical research on impulse buying. The results from our meta-analysis provide new insights into the impacts of various antecedent factors and call particular attention to the tensions between the inherent urge to buy impulsively and the constraints and control on such buying impulses. Also, the results clarify the impacts of marketing stimuli on consumer impulse buying and highlight the context-dependency of impulse buying research. These meta-analysis results in turn suggest several implications for practice and directions for further research.

Managerial implications

Consumer buying on impulse has long been an area of interest for managers; even a small proportion of impulse purchases on each shopping trip or a small base of impulse shoppers can contribute significant annual incremental sales (Rostoks 2003 ). It is therefore important to identify not just which consumers may be more inclined to purchase on impulse but also specific environmental factors that may prompt and encourage impulse buying. Impulse purchases can increase retail sales (top-line) and profits (bottom-line), especially for high-margin products. As summarized in Table  8 , our results suggest employing a variety of marketing strategies.

In their attempt to devise strategies to encourage impulse shopping and/or promote impulse buying behaviors, retailers have not been averse to making large investments in marketing stimuli, such as merchandising, displays, lighting, music, and other environmental factors that might trigger impulse purchases (Mattila and Wirtz 2001 ). Our review acknowledges that impulse buying can be triggered by external factors, so retailers should devise new, unique marketing stimuli to convey the value of their offerings and encourage impulse buying. Yet not all marketing stimuli are equally effective. Communication and price stimuli are more effective in prompting impulse buying than are store ambience and merchandise. Although retailers often devote considerable expenses to store design, store atmosphere, store layout, and merchandise placement, they may be better off investing more in price promotions and advertising, which likely have stronger impulse buying effects.

An important practical insight from this meta-analysis is that though marketing mix stimuli have positive impacts on impulse buying, they also heighten awareness of such tendencies and thus may curb impulse buying overall. This finding suggests consumers are becoming increasingly familiar with firms’ tactics to persuade them to buy impulsively and skeptical of various marketing practices. For practitioners, these findings may be somewhat discouraging; impulse buying is not simply a response to marketing stimuli, and psychological, social, and situational variables also have impacts. Additional research is warranted to understand how shopper skepticism evoked by marketing tactics might inhibit impulse buying. Retailers may need to try harder to devise unique or new marketing stimuli that can get past consumers’ defenses and convey the value of their offers.

The identification of an impulse buying segment of customers would be of great importance to retailers that currently rely solely on marketing stimuli. But if impulse buying were only trait driven, marketing strategy would have no effect on impulse purchases. The good news from our meta-analysis is that impulse buying is triggered by both factors internal to consumers and external marketing stimuli. Thus, it may be possible to identify consumers prone to impulse buying but also specify situations that enable it. That is, marketers could identify a distinct impulse buying segment and then design the shopping environment to make their impulse buying more likely. In some challenging findings though, we show that demographics such as age and gender matter less for predicting impulse buying, so retailers likely need to undertake deeper research into consumer psychographics to identify an impulse buying segment.

Shopping motives, whether hedonic or utilitarian, also matter when it comes to impulse buying. These motives are inherent to the consumer, so marketers should design stores and offers to evoke and facilitate appropriate motives. Yet consumers’ resource constraints (e.g., time, money) curb their buying impulses, so marketers also could focus on devising tactics to reduce the impacts of resource constraints. For example, access to speedy financing and faster checkouts likely help mitigate credit and time constraints.

Consumers with high self-control and those influenced by social norms also may be less prone to impulse buying, because the uninhibited urge to buy impulsively is curbed by self-control and social norms. Understanding these restrictions can help ethical marketers develop stimuli that both facilitate unplanned purchases but discourage purely uninhibited, impulsive purchases that may lead to later regret and consumer dissatisfaction. Ultimately, marketers must choose between making an immediate sale that might produce consumer dissatisfaction and exhibiting concern for the consumer to encourage future patronage. Similarly, both positive and negative emotions enhance impulse buying, and ethical marketers should leverage affective strategies to encourage impulsive purchases that align with available consumer resources. Public policy makers also might take heed of self-control, norms, and emotions to devise policies to reduce unhealthy impulse buying.

Because industry characteristics also matter in impulse buying, managers need to understand how the industry context moderates the impacts of various consumer traits, motives, and resources on impulse buying. Even if impulse buying is common in industries with low price levels, our findings caution that it is not the only relevant industry context; rather, impulse buying also occurs when product–identity relationships are strong. In such contexts, marketers should place due emphasis on communications that encourage impulse buying.

Directions for research

Our meta-analysis, while revealing, was restricted given the lack of sufficient studies testing and/or reporting all possible effects in all possible contexts using multiple methods. In exploring the main effects of various factors on impulse buying (Fig. 1 ), we had to use aggregations in several cases, due to the insufficient number of effects available in prior research. Future studies should undertake explicit examinations of each effect, especially specific marketing stimuli, self-identity, positive and negative moods, specific types of social norms, and consumer resources. The most glaring deficiencies in prior research provide the bases for our recommendations for further research, which we detail in Table  9 and summarize briefly here.

We indicate the effects of various individual drivers, including marketing stimuli, on impulse buying in Table 3 , which suggests an important facilitating role for impulse buying. We test the individual impacts of traits, motives, resources, and stimuli on impulse buying, but interactions among these antecedents also could be influential. For example, experimental research might determine how the effects of traits, motives, and resources on impulse buying are moderated by marketing stimuli (e.g., communication, price, store ambience, merchandise elements). The size of the motive effects (r = .34 for hedonic, r = .36 for utilitarian) implies their potential significance; they could be activated by communications delivered to customers in stores, using digital displays (Roggeveen et al. 2016 ) or mobile devices (Grewal et al. 2018a ). Furthermore, the synergistic effects of various communication and promotional elements on impulse buying warrant further exploration.

Most studies make assumptions about the context, rather than actively manipulating or exploring its effects. In most cases, the context refers solely to the product category (e.g., food, beauty products), shopping environment (e.g., retail store, online), or industry (grocery, apparel). But various other contextual cues could be relevant, such as consumer decision stage, whether consumption is private or public, demographic variables, and whether the shopper is alone or accompanied by someone (Table 9 ). Such contextual cues should function as moderators in future studies to help reveal how various antecedent factors affect impulse buying.

Studies exploring impulse buying also tend to use surveys and examine correlational data. Such descriptive analyses provide generalizable insights, though manipulations of various marketing stimuli, motives, and resources in experiments also could enable causal inferences. Longitudinal research that relies on panel data could also reveal how consumer motives and resources interact with the context to prompt impulse buying. New technologies, such as eye-tracking methods, could demonstrate the specific impacts of marketing stimuli (e.g., product placements) and how consumers’ attention paid to various details in the shopping environment contributes to their impulse buying. Finally, we find some evidence that is contradictory with theoretical predictions, so qualitative research would be helpful to explain why.

Our meta-analytic review aims to provide empirically generalizable, robust findings pertaining to the impacts of various antecedents of impulse buying, its potential mediators, and the moderators of these relationships. As a unique feature, our meta-analysis includes a test of alternate theoretical perspectives that previously have sought to explain impulse buying. As Palmatier et al. ( 2007 ) attest, on the basis of their comparative consideration of multiple theoretical perspectives on interorganizational relationships, various perspectives could receive empirical support individually, but their relative impacts cannot be determined unless all explanatory perspectives are subjected to a comparative test. With the greater number of effects sizes available for each model, achieved by compiling data for the meta-analysis, our comparative test of various perspectives on impulse buying brings the relative impacts of various dominant explanatory factors in each perspective into sharper relief.

In summary, our meta-analysis explores the direct effects of consumer traits, motives, and resources and marketing stimuli on impulse buying, along with the mediating impacts of self-control and positive and negative emotions. Our joint examination of these mediators reveals the inner affective and cognitive psychological processes of impulse buying and their relations. Industry and method moderators also influence impulse buying. This meta-analysis provides a comprehensive summary of extant research, underlying various implications. We hope it also sheds some new lights on directions for research that can continue to enhance our understanding of impulse buying.

The complete list of studies used in this meta-analysis is available from the authors.

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Iyer, G.R., Blut, M., Xiao, S.H. et al. Impulse buying: a meta-analytic review. J. of the Acad. Mark. Sci. 48 , 384–404 (2020). https://doi.org/10.1007/s11747-019-00670-w

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Consumers' Impulsive Buying Behavior in Online Shopping Based on the Influence of Social Presence

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Associated data.

The data used to support the findings of this study are available on request from the corresponding author.

The rapid development of online shopping has contributed to marketing strategy. Social presence plays an important role in the field of marketing. Therefore, this paper studies the influence of social presence on online impulse buying. The key marketing strategy is that consumers make impulsive buying behavior in online shopping. This paper proposes a research scheme for an impulsive buying sharing model based on user features and an article studied before which has some factors presented. The model is evaluated on a data analysis based on SPSS24.0 and AMOS23.0. The results show that the main factors such as interactivity, vividness, and media richness, all have positive effects on social presence. Therefore, in the variable relationship, social presence has a direct impact on impulsive buying behavior. This result has a theory contribution on the marketing theory model which also has an important practice significance in marketing strategy for enterprises.

1. Introduction

With the development of online shopping, consumers' shopping methods can be divided into rational purchases and impulsive purchases. Therefore, it is necessary to study impulsive shopping behavior for which social presence is the key factor in online shopping. Gunawarden [ 1 ] defined social presence as the perception formed by participants during their online participation, which emphasizes satisfaction or the real perception of others in video conferencing interactions. Research studies show that social presence is a strong predictor of satisfaction and that participants with strong social presence use emoticons to express nonverbal cues to enhance their social emotional experience. It has also been found that the interactive nature of websites can promote the comparison of personal presence and that the social presence conveyed by websites can influence behavior intention by influencing one's enjoyment and perceived usefulness. It has also been found that social presence can improve consumers' safety perception and purchase attitudes in regard to virtual shopping. However, there is no empirical study on the direct impact of social presence on impulsive buying. Based on the current research on impulsive buying in online shopping, this paper mainly discusses the main antecedents of social presence in online shopping, discusses how these factors affect social presence, and then discusses how social presence in online shopping affects impulsive buying behavior. Finally, the current paper discusses how customers' impulsive buying tendency impacts their impulsive buying behavior in online shopping. This paper is organized as follows: Section 1 discusses what are the main variables of social presence in online shopping. Section 2 is about how about these factors affect the social presence. Section 3 discusses how social presence affects impulse buying in online shopping. Section 4 explores how did customers' impulsive buying tendency modulate consumer on impulsive buying behavior.

2. Related Work

2.1. impulse buying behavior.

Rook and Fisher [ 2 ] defined impulsive buying behavior as the description of the thoughts and emotions experienced by consumers in the case of impulsive buying. This is a kind of purchase behavior that is not controlled by emotion. Many scholars have studied and defined impulsive purchase behavior.

Piron [ 3 ] defined impulsive buying according to three aspects, namely, an unplanned purchase, emotional stimulation, and the timeliness of the behavior, which is seen as the purchase behavior of making decisions immediately; the author also divided impulsive buying behavior into experiential impulsive buying and nonexperiential impulsive buying. However, later studies further pointed out that impulsive purchases were accompanied by emotional reactions. Wood [ 4 ] believed that impulsive purchase was an unplanned purchase without careful consideration and accompanied by high emotional conflicts. Parboteeah et al. [ 5 ] showed that external environmental stimulation may significantly affect consumers' perception of product usefulness and hedonism, thus affecting their impulsive purchase intention. It can be seen that the stronger the consumers' knowledge of the usefulness, the more positive mood in buying intention, thus further promoting impulsive buying.

2.2. Social Presence in Online Shopping

The idea of social presence originated from the two concepts of directness and intimacy, which were involved in the study of interpersonal interaction in the field of social psychology and mainly focus on remote exchange media by Cui et al. [ 6 ]. Gefen and Straub [ 7 ] introduced the concept of social presence into the field of consumption research and found that the level of customers' trust in electronic products and services was strongly affected by social presence. However, in the context of online live shopping, the “human-to-machine contact experience” was completely different from the social presence brought about by the “human-to-human contact experience” on the live platform.

In the online broadcasting room, consumers can see the physical display of sellers on the other side of the communication port in real time with the help of the live broadcasting platform, and they can communicate with the sellers through text. Generally, the live broadcast interface is attached to a payment link through which one can place an order directly and these transactions can be concluded directly under the guidance of the seller. This means that given the real-time physical display and the seller's hard sales guidance, consumers can make consumption decisions quickly.

2.3. Interactivity

Interactivity has been defined in many ways. For example, Blattberg and Deighton [ 8 ] defined interactivity as the facility for persons and organizations to communicate with another regardless of distance or time directly. Steuer [ 9 ] suggested that interactivity was “the extent to which users can participate in modifying the format and content of a mediated environment in real time.” Nowak et al. [ 10 ] defined interactivity as the degree of interaction through media and machines. From the perspective of consumers, the communication between consumers and sellers through the live broadcasting platform was bidirectional and synchronous [ 11 , 12 ]. Because of its unique feature as a medium that was not only received but also transmitted, when people have positive interactivity, the social presence was shown in the live broadcasting room.

2.4. Vividness

Vividness was the degree of connected objects that combine the sensory experience of actual objects with the nonsensory experience of hallucinations [ 13 ]. In the context of e-commerce, vividness has been widely used in product quality demonstrations [ 14 ]. Similar to interactivity, vividness also help consumers imagine and experience products in a future consumption environment [ 15 ]. Online shopping platforms use colors, charts, music, and video to stimulate consumer's visual, auditory, and other senses [ 16 ]; however, these were relatively static in traditional online platforms. The live shopping platform can provide an all-around display and comparison in real time.

Both interactivity and vividness significantly affected social presence and indirectly affect participation. Engagement has the most significant effect on all advertising effectiveness indicators, i.e., greater than the initial hypothesis of this study. The key role of engagement found the process of buying consumers may interact with new media; it is a more conservative emotional process that is more consistent with the information processing view than initially thought, thereby supporting the usage and satisfaction paradigm [ 17 ]. If, from a cognitive perspective, the media itself is more participatory than other media, then the increase in engagement of a particular advertisement within that media may depend more on emotion than on the cognitive dimension.

2.5. Media Richness

Media richness, as proposed by Daft and Lengel [ 18 ], refers to the media's capacity to facilitate shared meaning and understanding. The communication environment created by enterprises and the diversification of communication means are the key premises of creating media diversity. The live broadcast platform not only provides interaction between the anchor and consumers but also arranges special online customer services in the sales link to assist customers in placing orders and engaging in after-sales communication. They argued that organizations process information to reduce levels of uncertainty (lack of information) and ambiguity (multiplicity). They also argued that this task improves the ability to convey information when the task needs to be matched with the media, a better use of the media to convey “rich” information improves the task. It is also believed that the richness of the website and its more social, nonverbal, and complex cues, all contribute to consumers having a positive attitude towards the website. Thus, a rich online shopping environment has a positive impact on consumers' online purchase intention. This result was not surprising, as previous studies have found that consumers prefer rich looks, regardless of the nature of the product. Consumer attitude is the strongest factor influencing online shopping intention by Mattila [ 19 ].

Therefore, we summarize the existing literature on social presence and e-commerce, combined with the interactive characteristics of live broadcasting platforms; this study argued that interactivity, vividness, and media richness mainly affect social presence. Interactivity was considered to be a kind of environment which users can participate in and make adjustments to from time to time [ 9 ].

2.6. Moderator Variable

Moderating variables can be qualitative (such as gender, race, school type, etc.) or quantitative (such as age, years of education, number of stimuli, etc.), and they affect the direction (positive or negative) and strength of the relationship between the variable and the number of arguments. The research history and application of impulsive buying traits as a moderating variable will be reviewed in the following. The moderating effect of impulsive buying tendency.

Wu and Guo [ 20 ] took individual impulse buying traits as a moderating variable, and their research results showed that individual impulse buying traits played a moderating role in the relationship between flow experience and impulse buying, as well as in the relationship between website interaction and vividness through flow experience and impulse buying. Jones et al. [ 21 ] believed that impulsive buying tendency was defined as the buying process of individuals that have no thinking and unplanned which is impulsive buying behavior. This trait can predict the likelihood of impulsive purchase. Therefore, in the field of marketing and consumer behavior research, many scholars have done a lot of research on impulsive purchase tendency. Different online consumers have different personality traits, which affects consumers' willingness to shop in online stores and their final behavioral decisions. Studies have proven that consumers' personality traits have a moderating effect on situational factors and purchasing.

3. Hypothesis Development

3.1. interactivity and social presence.

The existing literature points out that interactivity is a key factor affecting social presence by Hassanein and Head [ 22 ]. Research studies on the impact of website presence show that vividness and interactivity are effective means by which to manipulate the level of presence [ 17 ]. Therefore, this paper believes that interactivity will have a positive impact on social presence. This study proposes the following research hypothesis:

H1: Interactivity has a positive impact on social presence in online shopping.

3.2. Vividness and Social Presence

Vividness can improve depth through richness, which refers to the width and breadth of the quality of information perceived by media users and to the sensory dimension that a communication medium can provide (Daugherty et al. [ 16 ]). The more vivid the performance of the interactive process of online shopping is, the stronger the telepresence effect is, the higher the trust level of consumers is, and the stronger the purchase intention is. Liu et al. [ 23 ] and others have confirmed that visual attraction can significantly improve immediate satisfaction and thus affect the display of impulse purchase goods. It is more comprehensive and multiangle way to present information to consumers through a live screen than in the pictures that are used in traditional online shopping. Video displays are also more comprehensive. According to the theory of multisensory interaction and integration, a good visibility effect can increase the level of virtual touch and enhance the presence of online shopping. Therefore, this study puts forward the following research hypothesis:

H2: Vividness has a positive impact on social presence in online shopping.

3.3. Media Richness and Social Presence

Parker et al. [ 24 ] put forward the concept of social telepresence. They called the degree of people's perception of the significance of others and the presentation of this interpersonal relationship through the media via the process of using the media for communication “social telepresence”, which has the ability to convey nonverbal clues and prompt the social context when there is a higher level of social presence compared to when the degree of social presence is low. The media enrichment theory came into being under this academic background. Although media enrichment theory and social telepresence theory have different definitions of media attributes, they both follow a similar position; i.e., media selection depends on the characteristics of the media, and each communication media has a unique ability to convey some informational content, which has an important impact on the appropriateness of media selection. As an open social system, an organization needs to continuously reduce the “difference between the amount of information required to perform tasks and the amount of information already owned by the organization” by Galbraith and Underwood [ 25 ]. Thus, the amount of information is a key factor in an organization's information-processing activities. Daft and Lengel [ 18 ] interpreted the concept of information richness as “the ability of information to change understanding and cognition over a period of time”. Some scholars have found that media richness affects social telepresence. The social telepresence theory believes that media can affect social telepresence, which is also related to the media richness theory. A high degree of social telepresence is typical in face-to-face communication. Therefore, this study puts forward the following research hypothesis:

H3: Media richness has a positive impact on social presence in online shopping.

3.4. Social Presence and Online Impulsive Buying Behaviour

Waller and Bachmann [ 26 ] point out that because trust arises in the social environment, social presence is a necessary condition for generating trust. An environment with a rich social presence can stimulate a sense of trust among participants. Song et al. [ 27 ] found that the stronger the presence of consumers on the clothing sales website is, the higher the trust level of online consumers is. Hausman and Siekpe [ 28 ] found that intoxication has a significant positive impact on consumers' online revisit intention and purchase intention. Korzaan and Boswell [ 29 ] also found that consumers' intoxication during browsing websites helps to improve their consumption attitude and purchase intention. Animesh et al. [ 30 ] found a study on 3D virtual space that social presence can promote users' purchase intention. Therefore, this study puts forward the following research hypothesis:

H4: Social presence has a positive impact on online impulsive buying behavior.

4. Research Methodology

4.1. study location and sample.

The data collection for the current study was greatly supported by an electronic questionnaire, which was divided into two stages. One stage was based on the principle of judgment sampling surveys and was presented in the form of a questionnaire that was sent through WeChat, electronic mail, and other social media platforms, as well as forwarded for use through the network broadcast platform, and was mainly distributed in Guangdong, Macau, Beijing, Shandong, Northeast China, and other places for volunteers to fill out and submit. The questionnaire used in this study was mainly distributed through Internet research, and the survey subjects were people who had impulsively consumed goods in an online live broadcast room. The surveyed was completed mainly by social people and was supplemented by graduate and undergraduate students.

Initially, 340 self-questionnaires were distributed during a year of field visits and survey collection. After deleting incomplete and mismatched questionnaires, 319 valid questionnaires (93.8%) were retained and ultimately constituted the research sample. Among the respondents, 63.23% were female and 36.76% were male. Regarding the educational level, 3.82% of the respondents had finished middle school or below, 8.24% had finished junior college, 44.41% had finished a bachelor's degree, 31.47% held a master's degree, and 12.06% held a doctoral degree. The ages of the online shopping consumers were as follows: 3.82% were under the age of 20; 37.06% were between 21 and 30; 37.35% were between 31 and 40; 13.53% were between 41 and 50; and 8.23% were above 50. The annual incomes of the respondents were as follows: 21.76% had an annual income below RMB 150,000 (RMB 1,000 = approximately USD 140); 24.12% had an annual income between RMB 160,000 and 300,000; 20.59% had an annual income between RMB 310,000 and 450,000; 10.59% had an annual income between RMB 460,000 and 600,000; and 22.94% had an annual income above RMB 610,000.

4.2. Measures

All the scales used in this study are mature scales that have been used in international journals which have good reliability and validity. Descriptive statistics is conducted for the items located in the questionnaire survey of this study, mainly including average values and the standard differences of the variables, and the data distribution states of the measurement items were judged according to each value.

4.3. Reliability Analysis

First, the reliability of each variable dimension measurement index was determined. Reliability mainly tests the consistency and reliability of variables in a study for each measurement item to ensure the validity of the model fitting degree. Nunnally (1998) believed that an alpha coefficient greater than 0.9 indicates the best reliability, a value between 0.7 and 0.9 indicates good reliability, a value between 0.35 and 0.7 indicates medium reliability, and a value less than 0.35 indicates low reliability. As Devellis (1991) suggested, a Cronbach's alpha value greater than 0.7 indicates that the reliability is satisfactory. Therefore, reliability is a method by which to measure internal consistency. The higher the coefficient is, the higher the consistency and reliability of the data are.

As seen from Table 1 , the alpha coefficients of the prevariables of interactivity, vividness, and media richness in this study are 0.925, 0.855, and 0.890, respectively. The alpha coefficient of the core variable of social presence is 0.935. The alpha coefficient of impulsive buying behavior is 0.878. The alpha coefficient of the moderating variable impulse buying tendency is 0.896. Cronbach's alpha coefficient reference indices are all larger than 0.7, which indicates that each variable in the study has good reliability.

The Cronbach's reliability analysis.

As seen from Table 2 , the main purpose of confirmatory factor analysis (CFA) is to validate validity and to analyze common method bias (CMB). There are many kinds of validity, such as content validity, structure validity, convergence validity, and discriminant validity. The differences in each type are described as follows. As seen from the above table, a total of 7 factors and 48 analysis items were analyzed by CFA. The effective sample size of this analysis was 340, which was 5 times more than the number of analysis items but less than 10 times the number of analysis items.

CFA analysis.

As seen from Table 3 , the AVE values corresponding to the total of 7 factors are all greater than 0.5, and the CR values are all higher than 0.7, which means that the analyzed data have good convergence (convergence) validity. At the same time, the corresponding factor loading value of each measurement item is generally required to be greater than 0.7. Sometimes, model fitting indices and model MI values may be combined to achieve better conclusions.

The results of convergence validity analysis.

4.4. Correlation Analysis between Variables

As seen from Table 4 , the correlation value between PP and IBB was 0.222 and showed a significant level of 0.01, indicating that there was a significant positive correlation between PP and IBB. The correlation value between PP and IBT was 0.116, and it was significant at the level of 0.05, indicating that there was a significant positive correlation between PP and IBT. Correlation analysis was used to study the correlations between PP, IBB, and IBT, and the Pearson correlation coefficient was used to indicate the strength of these correlations. The correlation value between PP and IBB was 0.222 at a significance level of 0.01, which indicates a significant positive correlation between PP and IBB. The correlation value between PP and IBT was 0.116 at a significance level of 0.05, which indicates a significant positive correlation between PP and IBT.

Correlation analysis.

∗ p < 0.05, ∗∗ p < 0.01.

4.5. Structural Equation Model

Structural equation modeling (SEM) is a confirmatory analysis method that is used for multivariate statistical analysis. Through the reliability and validity testing of the questionnaire, it was concluded that all the scales used in this study have good reliability and validity. Furthermore, AMOS 23.0 was used for hypothesis testing to verify whether the model fitting degree and path were significant and to test whether the independent variable had a significant impact on the dependent variable. AMOS uses the chi-square value as the fitting test result, and the fitting degree usually uses CMIN/DF, GFI, RMR RMSEA, AGFI, NFI, CFI and IFI as indicators.

4.5.1. Structural Equation Model Testing of Interactivity, Vividness, MR, and SP

As seen from Table 5 , the SEM regression relation table of a structural equation model includes two kinds of relations, namely, the influence structure relation and the measurement relation. The normalized path coefficient value is usually used to represent the relationship, i.e., whether it affects the structure relationship or the measurement relationship. If this value is significant, this indicates that there is a significant influence/measurement relationship; otherwise, there is no influence/measurement relationship between the description items. If more path coefficients are not significant compared to those that are significant, this indicates that the model is poor. In this case, it is recommended to reset the model relationship, that is, to adjust the model. In this study, p values between the interactivity, vividness, and MR and SP variables were all less than 0.05. Interactivity had a significant positive effect on social presence ( β  = 0.205, p < 0.001), and vividness had a significant positive effect on social presence ( β  = 0.43, p < 0.001). Finally, media richness has a significant positive impact on social presence ( β  = 0.208, p < 0.001), and the hypothesis is valid, which indicated that these three variables all had positive and significant effects on SP; thus, our hypothesis was valid. Social presence has a significant positive impact on impulsive buying behavior ( β  = 0.037, p < 0.001) and the hypothesis is true. The hypothesis holds that price promotion has a significant positive effect on impulse buying behavior ( β  = 0.162, p < 0.001).

Structural equation model coefficient parameter table.

4.5.2. Test of Model Fitting Degree

As seen from Table 6 , the results of the software output were used to verify the fit of the model. The model suitability test indicate involved in the SEM included 10 categories and up to 38 index test quantities. There are three commonly used fitness test indicates, namely, absolute fitting indicate, relative fitting indicate, and simplified fitting indicate. The meanings and discriminant criteria of each index are as follows.

Model fitting index.

Default Model: χ 2 (561) = 7168.007, p  = 1.000.

4.5.3. Goodness-of-Fit Index (GFI)

Since the goodness of a chi-square test depends on the sample size, the goodness-of-fit index (GFI) does not depend on sample size for easy measurement; rather, the goodness-of-fit index can measure the degree to which the covariance matrix predicts the S matrix. When the GFI is 1, it is an ideal model; however, during modeling and analysis, it is generally believed that if the GFI value is greater than 0.9, the model results are ideal. In this study, the GFI was 0.893, which is close to 0.9. Thus, it can be judged that the model fits well.

4.5.4. Modified Goodness-of-Fit Index (AGFI)

The AGFI is similar to the GFI in that the closer it is to 1, the better the fitting effect is. Generally, the value of the AGFI is required to be at least greater than 0.85; however, for the same model, the AGFI must not be greater than the GFI. In this study, the AGFI was 0.877, which is greater than 0.85, and it was less than the GFI; thus, the model fitting effect was good.

4.5.5. Root Mean Square of Approximate Error (RMSEA)

Among the numerous fitting indicates, the probability of making type I and type II errors is small, which presents a reasonable fitting index. The closer the fitting index is to 0, the better the fitting effect of the model. In this study, the root mean square error value was 0.034, which is lower than 0.1; this indicates that the model had a good fitting effect.

4.5.6. Akaike's Information Criterion, AIC

AIC is often used to evaluate general statistical models. The better the model fits, the smaller the AIC value will be; however, the value is affected by the number of samples and parameters in the model. A large number of samples and parameters will easily cause a bad model to be mistaken for a good one.

4.5.7. Standardized Root Mean Square Residual (SRMR)

For SRMR, some models are not greatly affected by N, while others are greatly affected by N. Hu and Bentler believed that when SRMR < 0.8, the model fitting effect is better. In this paper, the SRMR value was 0.036, which is less than 0.8. Therefore, it can be considered that the overall fitting effect of the model was good.

5. Discussion

In this paper, the structural equation model tested by data collection and investigation, and the correlations between them were discussed. Through research methods such as theoretical deduction, model, and statistical tests, which were used to determine the main characteristics of compulsive purchase behavior in regard to online shopping variables, the main variables affecting social presence, impulsive purchase, and consumer personality tendencies, with core variables focusing on the relation between social presence and action, formed the following four conclusions.

First, the literature review shows that with the rapid development of the Internet, online shopping is more convenient than offline shopping for many platform enterprises. At present, platform enterprises are still developing operation plans for offline brands; therefore, it is very important for enterprises to study the impulsive buying behavior of consumers in relation to online shopping.

Second, the influence of interactivity, vividness, and media richness on social presence and the corresponding influence path have been clarified. The empirical results show that interactivity, vividness, and media richness, all have positive effects on social presence. Therefore, in online shopping, the atmosphere felt by consumers in the live broadcast room and the characteristics presented in the live broadcast room must meet the premises of interaction, vividness, and media richness in order to have a positive impact on consumers' impulse buying behavior.

Third, the current study proves that social presence has a direct influence on impulsive buying behavior through the variable relationship. This outcome shows that in the context of Internet shopping, the sense of social presence is a core variable, and consumers are prone to impulsive buying behavior when they perceive that they are physically present, i.e., if they feel that they are really in the live broadcast room. Social presence plays an important role, and the user's experience of social presence directly affects his or her degree of impulse consumption. Therefore, the challenge of how to make consumers have a sense of social presence is a core issue when constructing online live shopping experiences.

Fourth, the current study verifies the positive moderating effect of consumers' personal trait of having an impulse buying tendency on social presence and impulse buying behavior. Among these factors, the higher the impulse buying tendency is, the stronger the positive impact of social presence on impulse buying behavior is. The research study of foreign scholars has also confirmed that impulse buying tendencies have a positive response on impulse buying behavior.

6. Conclusion

An effective marketing module is proposed in this paper, which is based on the SOR theory module and predecessors research, aiming at the problem of online impulsive buying. In order to verify, the module is added to the feature extraction network. Experiments were carried out on the data analysis, the module gets the best performance in the data analysis index GFI gets 0.903, and the results show that the module proposed in this paper can improve the hypothesis.

The innovation of this paper are two theoretical contributions. First, the concept of social presence comes from the field of psychology. The concept has attracted attention from brand management, marketing, and communication studies. Second, the academic contributions of this study mainly include the following two aspects: the variables affect the social presence of platforms and establishing the relevant of impulsive buying behaviors. Previous research on social presence has mainly focused on artificial intelligence, online education, and VR, which has filled the gap in brand affection research on live broadcast platforms and provided new ideas for follow-up research on brand affection in the Internet content.

6.1. Limitations and Further Research

For the factors related to impulsive buying behavior in online shopping, this paper only focuses on the influence of the three leading variables of interactivity, vividness, and media richness; it does not consider the mediating or moderating effects of other variables in the influencing path. Undeniably, the relevant factors affecting impulsive buying behavior in online shopping are complex and diverse; there is even a model of other unknown factors that influence impulsive buying behavior in online shopping in the Internet context. It is hoped that further research can further explore more potential influencing factors, intermediary variables, and moderating variables in order to supplement and improve the results of this study and make further contributions to the study of online shopping platforms. There is a lack of discussion on social presence in the field of live broadcasting. Social presence is a multidimensional concept, and the degree of its strength differs in different product categories or industries. Therefore, the influence of various dimensions of social currency on brand affection can be studied on this basis in the future. Therefore, research on consumers' impulsive buying behavior in relation to online shopping requires more in-depth and comprehensive discussion.

Data Availability

This research study was based on data security and privacy protection under the background of big data which is from the consumers who have the experience of online impulsive buying.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

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Research: How Price Changes Influence Consumers’ Buying Decisions

• Ioannis Evangelidis
• Manissa Gunadi

Online shopping platforms allow people to see changes in a product’s price over time — offering opportunities for buyers and sellers alike.

Whether on retailers’ own platforms or through third-party price tracking services, today’s consumers often have access to detailed information regarding changes in a product’s price over time. But how does this visibility influence their purchasing decisions? Through a series of studies, the authors found that buyers are more likely to buy now if they see a single large price decrease or a series of smaller price increases, because they’ll assume that the price will go up if they wait. Conversely, they’re more likely to hold off on buying if they see a single large price increase or a series of smaller decreases, because they’ll assume the price will fall. As such, they argue that sellers should consider this effect when pricing their products, while buyers should recognize and question this natural tendency — to expect price streaks to continue and single large changes to reverse — before acting on it.

Whether you’re looking to buy a plane ticket or a pair of socks, more and more online shopping platforms now offer consumers a detailed look into products’ historical prices. But how does this information influence buying decisions?

• IE Ioannis Evangelidis is an associate professor of marketing at ESADE Business School, Ramon Llull University, in Barcelona, Spain. His research focuses on how consumers make decisions, particularly how their purchase behavior can be influenced by changes in the decision environment.
• MG Manissa Gunadi is an assistant professor of marketing at EADA Business School in Barcelona, Spain. In her research, Manissa primarily investigates how different forms of numerical information influence consumers’ judgments, decision-making, and behavior.

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Impulse buying behaviour: an online-offline comparative and the impact of social media

Spanish Journal of Marketing - ESIC

ISSN : 2444-9695

Article publication date: 24 April 2018

Issue publication date: 5 June 2018

This paper aims to explore the phenomenon of impulse buying in the fashion industry. The online and offline channels are compared to determine which is perceived as leading to more impulsive buying.

Design/methodology/approach

As the result of the literature review, three research questions are proposed and examined through an online self-administered survey with 212 valid responses.

Results show that the offline channel is slightly more encouraging of impulse buying than the online channel; factors that encourage online impulse buying explain this behaviour to a greater extent than do discouraging factors; social networks can have a big impact on impulse buying.

Research limitations/implications

Findings are limited by the sampling plan, the sample size and the measurement of some of the variables; only one product type is analysed. Further research is needed to confirm that shipping-refund costs and delayed gratification (traditionally, discouraging factors of online buying) encourage online impulse buying; clarify contradictory results regarding the role of online privacy and convenience. This research contributes to the validation of a scale to measure the influence of social media on impulse buying behaviour.

Practical implications

Offline companies can trigger the buying impulse to a greater extent than online retailers. Managers must carefully select social networks to encourage impulse buying, Facebook and Instagram being the most influential; Twitter has the least impact.

Originality/value

This study compares the impulse buying phenomenon in both the physical store and the internet. Moreover, the influence of social networks on impulse buying is also explored.

Este trabajo explora la compra por impulso en el sector de la moda, comparando los canales físico y online para determinar cuál se percibe como más impulsivo.

Diseño/metodología/enfoque

De la revisión de la literatura se extraen tres preguntas de investigación, examinadas a través de una encuesta auto-administrada online con 212 respuestas válidas.

Los resultados muestran que: el canal offline es ligeramente percibido como más impulsivo que el online; los factores motivadores de la compra impulsiva online explican mejor este comportamiento que los desmotivadores; las redes sociales pueden tener un gran impacto en la compra impulsiva.

Limitaciones/implicaciones de la investigación

Las limitaciones radican en el plan de muestreo, el tamaño muestral, y la medición de algunas variables; sólo una industria es analizada. Futuras investigaciones deberán: confirmar que los gastos de envío-devolución, así como la gratificación retrasada (tradicionalmente considerados como motivadores de la compra online) pueden motivar la compra impulsiva online; clarificar resultados contradictorios sobre la privacidad y la conveniencia de Internet. Esta investigación contribuye a la validación de un instrumento para medir la influencia de las redes sociales en la compra impulsiva.

Implicaciones para la gestión

Las tiendas físicas pueden estimular la compra por impulso más que los vendedores online. Los gestores deben seleccionar cuidadosamente las redes sociales para favorece la compra por impulso, siendo Facebook e Instagram las más influyentes; Twitter tiene el menor impacto.

Originalidad/valor

Este estudio compara el fenómeno de la compra impulsiva tanto en el canal físico como online, y explora la influencia de las redes sociales en la compra impulsiva.

• Social networks
• Impulse buying
• Physical store
• Compra impulsiva
• Tienda física
• Motivadores
• Redes sociales

Aragoncillo, L. and Orus, C. (2018), "Impulse buying behaviour: an online-offline comparative and the impact of social media", Spanish Journal of Marketing - ESIC , Vol. 22 No. 1, pp. 42-62. https://doi.org/10.1108/SJME-03-2018-007

Emerald Publishing Limited

Copyright © 2018, Laura Aragoncillo and Carlos Orus.

Published in Spanish Journal of Marketing - ESIC . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original 43 publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

The importance of impulse buying in consumer behaviour has been clear for some years. Previous research both in the academic and the professional fields has shown that impulse buying represents between 40 and 80 per cent of all purchases, depending on the type of product ( Amos et al. , 2014 ; Marketingdirecto, 2012 ). Impulse buying has aroused the interest of researchers and organizations which have tried to understand the psychological underpinnings of this behaviour, as well as “impulse temptations”, to boost sales ( Beatty and Ferrell, 1998 , Kacen and Lee, 2002 ; Kacen et al. , 2012 ; Amos et al. , 2014 ).

However, because of the serious impact of the economic crisis and the growing use of the internet as an information search and purchase channel, consumer behaviour seems to have changed towards a more planned and informed process ( Experian Marketing Services, 2013 ; Banjo and Germano, 2014 ). At the same time, several authors claim that the internet indeed favours impulse buying ( Gupta, 2011 ; Rodríguez, 2013 ). Thus, a certain degree of uncertainty now exists about the role of impulse buying, both in the conventional, physical store and the online channel, as well as about which channel encourages this behaviour to a greater extent. Although previous research has addressed the impulse buying phenomenon, focusing either on the physical store or on the internet in isolation, there is a lack of studies analysing both the channels simultaneously.

the consumer’s perceptions about how the internet and the traditional, physical store affects his or her impulse buying behaviour;

which characteristics of the internet, compared to the physical channel, encourage or discourage online impulse buying; and

because of the growing impact of social media on consumer behaviour ( Xiang et al. , 2016 ), the influence of social networks on impulse buying is also explored.

This research is focused on the fashion industry for several reasons. First, a significant proportion of consumers’ purchases are of clothing and shoes ( INE -Instituto Nacional de Estadística, 2015 ). Second, online shopping in this industry has been steadily growing during the past years ( CNMC -Comisión Nacional de los Mercados y la Competencia, 2016 ; Eurostat, 2017 ) and this growth is particularly observed in social media ( IAB Spain, 2016 ). Finally, it is one of the industries that IS most prone to impulse buying ( Luna and Bech-Larsen, 2004 ).

2. Literature review

2.1 impulse buying.

The phenomenon of impulse buying was first acknowledged as an irrational behaviour in the decade of the 1940s ( Luna and Quintanilla, 2000 ). This phenomenon aroused the interest of numerous researchers, who thereafter faced the challenge of measuring it: participants in experiments were reluctant or unwilling to overtly declare all the products that they intended to purchase (these were subsequently compared with actual purchases; Kollat and Willett, 1969 ). Even though there is still no consensus in the literature about the definition of the concept ( Amos et al. , 2014 ), this review aims at offering a clear overview of its evolution.

The first studies on impulse buying can be found in the consumer buying habits studies carried out by the Du Pont de Nemours and Co. (1945 / 1949 / 1954 / 1959 / 1965 ; cited in Rook, 1987 ), which focused mainly on understanding how the phenomenon occurred and its extent. Some years after the first studies, the importance of impulse buying was underlined by another study which showed that a considerable percentage of sales in retail stores came from unplanned purchases ( Clover, 1950 ). In this research, an impulse buy was first conceptualized as an unplanned purchase, that is, “the difference between a consumer’s total purchases at the completion of a shopping trip, and those that were listed as intended purchases prior to entering a store” ( Rook 1987 , p. 190).

However, several authors have argued that defining impulse buying only on the basis of unplanned purchases is rather simplistic ( Stern, 1962 ; Kollat and Willett, 1969 ; Rook, 1987 ) and went a step further by arguing that while all impulse purchases can be considered as unplanned, not all unplanned purchases can be considered as impulsive ( Koski, 2004 ). An unplanned purchase may occur simply because the consumer needs to buy a product but it has not been placed on the shopping list in advance. Unplanned purchases are not necessarily accompanied by an urgent desire or strong positive feelings, which are usually associated with an impulse buy ( Amos et al. , 2014 ).

In this way, authors such as Applebaum (1951) , Stern (1962) and Kollat and Willett (1969) , extended the concept by establishing that impulse buying emerged after the exposure to a stimulus. Applebaum (1951 , p. 176) defined it as “buying which presumably was not planned by the customer before entering a store, but which resulted from a stimulus created by a sales promotional device in the store”. However, this definition was also considered limited, given that the stimulus that provoked the impulse was exclusively a sales promotion device. On the other hand, Stern (1962) distinguished four types of impulsive buying: pure impulse buying totally breaks the normal buying pattern. It occurs when the consumer has no purchase intention but the product elicits emotions that eventually lead to the act of buying; reminder impulse buying occurs when the consumer sees an item and remembers that the stock at home is low, or recalls an advertisement or other information about the product and a previous wish to purchase it; suggestion impulse buying takes place when the consumer sees an item for the first time and detects a need that it can satisfy; and planned impulse buying occurs when the consumer enters the store with the intention to purchase some specific products, but also expects to make other purchases depending on the special offers and promotions that he or she finds at the store.

The contribution of Rook to the literature had a significant impact on the conceptualization of the term ( Rook and Hoch, 1985 ; Rook, 1987 ; Rook and Fisher, 1995 ). This author affirmed that:

[…] impulse buying occurs when a consumer experiences a sudden, often powerful and persistent urge to buy something immediately. The impulse to buy is hedonically complex and may stimulate emotional conflict. Also, impulse buying is prone to occur with diminished regard for its consequences ( Rook, 1987 , p. 191).

Further investigations focused on the study of consumer behaviour in the buying decision process with the goal of identifying factors, both internal (related to personal characteristics) and external (related to situational – store and product – characteristics) that affect impulse buying ( Amos et al. , 2014 ; Muruganantham and Bhakat, 2013 ; Badgaiyan and Verma, 2014 ). Previous studies emphasized that impulse buying was primarily affective in nature, wherein the hedonic and emotional aspects of these purchases determine consumer behaviour to a greater extent than the utilitarian and rational aspects ( Luna and Quintanilla, 2000 ). Recently, impulse buying has been defined as “a sudden, compelling, hedonically complex purchase behavior in which the rapidity of the impulse purchase decision precludes any thoughtful, deliberate consideration of alternatives or future implications” ( Sharma et al. , 2010 , p. 277).

2.2 Online impulse buying

There is a need to study impulse buying on the internet, because of the increasing importance of this medium as a sales channel. According to Google Consumer Barometer (2015) and Eurostat (2017) , around two-thirds of the European population makes online purchases. If we focus on the fashion industry, clothing and sport garments were the bestselling categories in Europe in 2016 ( Eurostat, 2017 ).

One may argue that online buying behaviour is rather rational, as the consumer tends to search for information and make comparisons before making the final decision. However, rational choices are not always made, and impulsive buying also has room in this medium ( Jeffrey and Hodge, 2007 ; Verhagen and van Dolen, 2011 ). Taking into account the importance of impulse buying for companies’ revenues, it would appear worthwhile to investigate this phenomenon in the online channel.

In the late 1980s, it was acknowledged that impulse buying had become easier because of innovations such as credit cards, direct marketing and in-home shopping ( Rook, 1987 ). The ease of choosing a product and “clicking” on it may create temptation and thus increase the likelihood of impulse buying ( Greenfield, 1999 ). Other authors argue that the internet may lessen consumers’ capacity to control their buying impulses. LaRose (2001) found that the characteristics of the internet that empowered consumers to control their buying impulses were few (13), compared to those that weakened such control (50). On the other hand, other authors state that consumers carry out less impulse purchases online than offline ( Kacen, 2003 ). In fact, most research on e-commerce has considered online purchase decisions as rational processes, based on problem solving and information processing ( Verhagen and van Dolen, 2011 ). In the specific context of this research, McCabe and Nowlis (2003) indicate that products for which touch is important, such as clothing, are more impulsively acquired at physical stores than online, given that the internet prevents consumers from touching and trying on the garments.

The evolution of the internet to the 2.0 Web has dramatically changed the way in which consumers and companies interact and carry out transactions. Specifically, it has been noted that social commerce is as branch of e-commerce which incorporates the use of social media in all kinds of commercial activities ( Xiang et al. , 2016 ). In this sense, 65 per cent of social media users affirm that social networks influence their shopping processes, and almost half of them say that social media inspire their online purchases ( IAB Spain, 2016 ; PWC, 2016 ). Previous research has shown that consumers are influenced by others at the time of buying a product, and this influence may be higher online than offline ( Riegner, 2007 ). Therefore, social media can represent a powerful tool to boost impulse buying.

3. Research questions

Which channel – online or offline – is considered by the consumer as leading to more impulse buying?

Which factors encourage and discourage online impulse buying?

What is the influence of social networks on impulsive buying?

Table I summarizes the conceptual framework that attempts to address the research questions.

3.1 Impulsiveness of the online versus offline channel (RQ1)

On the one hand, authors such as Greenfield (1999) and LaRose (2001) argue that the online channel can lead to more impulse buying than the offline channel: the greater product assortment, the possibility of making purchases 24/7 from any location and the use of advanced marketing techniques based on personalization, have the capacity to encourage online shopping to a greater extent than other factors, such as delayed possession or shipping costs, that might discourage it. Furthermore, despite the fact that the internet prevents consumers from touching and trying on garments ( McCabe and Nowlis, 2003 ), this limitation can be overcome by good quality product presentation, with realistic pictures and detailed information about sizes and measures. Offering the possibility of free shipping or in-store refunds can also be used to overcome the limitations of online shopping.

On the other hand, the capacity of physical stores to create sensory experiences, as well as the store’s atmosphere, can lead the physical channel to be more impulsive than the online channel ( Gupta, 2011 ). The previous literature review has pointed out that impulse buying is hedonically complex and has a strong emotional character ( Luna and Quintanilla, 2000 ; Sharma et al. , 2010 ). Emotions and hedonic experiences are strongly related to sensory stimulation ( Krishna, 2012 ). To the extent that physical stores are able to stimulate the senses better than the internet, we might expect that consumers will perceive the physical channel as more impulsive than the online channel. A recent report by Kearney (2013) revealed that 40 per cent of the participants in a survey (3,000 consumers from the USA and the UK) spent more money than planned in physical stores, while the percentage doing so in the online channel was 25 per cent.

Finally, several authors argue that, beyond channel characteristics, personal and situational characteristics also determine impulse buying ( Badgaiyan and Verma, 2014 ; Lim and Yazdanifard, 2015 ). Sociodemographic variables, such as gender or age, can strongly affect behaviour ( Youn and Faber, 2000 ). As we noted in our introduction, the economic crisis of the past years may have changed consumer behaviour and the way they use new technologies, pivoting in general towards more planned purchases.

3.2 Encouraging and discouraging factors for online impulse buying (RQ2)

The literature review also reveals differentiating characteristics of the online and the offline channels that can encourage or discourage impulse buying ( Table I ). Among the encouraging factors with regard to online impulse buying, we find the following defining characteristics of the internet: greater product assortment and variety, sophisticated marketing techniques, credit cards, anonymity, lack of human contact and easy access and convenience. First, greater assortment and product variety is one of the most influential factors for online consumers in carrying out impulse purchases ( Brohan, 2000 ; Chen-Yu and Seock, 2002 ). Online stores have the capacity to offer greater assortment and variety than physical stores, which are more limited by physical constraints.

Regarding the second factor, the use of advanced marketing techniques, such as personalized emails based on purchasing history or with information about new products and a direct link to the electronic store, can be highly effective in encouraging online impulse buying ( Koufaris, 2002 ; LaRose, 2001 ). Sales promotions devices, though they are also available at physical stores, seem to be more effective in online shopping. In the virtual environment, the possibilities for multisensory stimulation are limited and sales promotions and offers more easily grab consumers’ attention ( Kacen, 2003 ). Furthermore, online promotions can be more customized than offline promotions, so consumers will be more likely to be offered products of specific, personal interest ( Koski, 2004 ).

Third, credit cards can encourage impulse buying ( Karbasivar and Yarahmadi, 2011 ; Koski, 2004 ). This payment method is commonly used in offline purchases, but it is more widespread in the online channel. Consequently, use of the online channel could encourage more impulse buying than the offline channel. When using virtual payment methods, money appears less real and consumers have the feeling that they are not really spending it ( Dittmar and Drury, 2000 ; Tuttle, 2014 ). Thus, the monetary consequences of making (impulse) purchases are not perceived immediately ( LaRose, 2001 ).

The anonymity and lack of human contact that the internet provides can also encourage online impulse buying. According to Rook and Fisher (1995) , impulse buying is more likely to occur when the situation assures anonymity, so this characteristic may be an important advantage of the internet over the physical store. Consumers may feel more comfortable buying online those products which would make them feel embarrassed if purchased offline ( Koufaris, 2002 ). Similarly, we may state that, by and large, online consumers carry out their purchases alone and in private; if the purchase is made offline, it is common to have physical contact and interaction with other people (salespeople, companions). Taking into account that human contact leads to a better control of the impulse to buy ( Greenfield, 1999 ), its absence may encourage impulse buying on the internet.

Finally, buying at physical stores is limited to a geographic location and to opening hours; on the internet, these limitations disappear ( Koufaris, 2002 ). Furthermore, access to an online store does not entail any cost or effort on the part of the consumer (transportation, parking, etc.), so the probability of a spontaneous visit, with no initial purchase plan but ending up in an impulse buy, is higher online than offline ( Moe and Fader, 2004 ). Also, consumers browsing online are constantly exposed to products that they might like, even though they are not intentionally searching for them, or plan to purchase them; and buying these items is only one click away. This ease of completing transactions can lead to more impulse buying than in the physical channel ( Dawson and Kim, 2009 ; Koski, 2004 ; Koufaris, 2002 ).

Regarding the discouraging factors for online impulse buying, the specialized literature identifies the following: delayed satisfaction or gratification, the impossibility of using the five senses, easy comparisons, shipping and refund costs and easy access and convenience ( Table I ). One of the defining elements of impulse buying is the urgent need to possess the product; immediate possession provides satisfaction and encourages impulse buying ( Rook, 1987 ; LaRose, 2001 ). Consumers have to wait for product delivery when buying online (in the context of physical goods), and this time lapse can deter them from carrying out impulse buying ( Kacen, 2003 ; Koski, 2004 ).

Impulse buying is the result of seeing, touching, hearing, smelling and/or tasting ( Underhill, 2009 ). However, the internet does not have the same capacity to stimulate the five senses as does the physical store, and therefore, the online channel can be less encouraging of impulsive buying than the offline channel ( Kacen, 2003 ; Koski, 2004 ). Online stores can only stimulate sound and sight, but they cannot do anything (at the moment) to appeal to the other senses. This can be especially important in the context of clothing, where touch is a fundamental sense that can trigger impulse buying ( Peck and Childers, 2006 ).

The ease with which consumers can make comparisons online, and the existence of shipping and/or refund costs, can also discourage online impulse buying. The internet allows consumers to easily compare products and prices before making the purchase decision ( Brohan, 2000 ; LaRose, 2001 ; Koski, 2004 ). In addition, one of the most important deterrent factors for online shopping is the cost of shipping and refunding merchandise ( Kukar-Kinney and Close, 2010 ). Consumers try to avoid these costs as much as possible. Therefore, high shipping and refund costs can restrain their buying impulse.

Finally, easy access and convenience, while previously described as an encouraging factor, may also be considered a discouraging factor. When the consumer carries out his or her shopping in a physical store, he or she may more readily follow the impulse to make the purchase to avoid the costs involved in returning to the store to make the purchase later. In the online environment, coming back to the store does not entail much effort, and consumers may better control their impulses and thus delay their purchase decision ( Moe and Fader, 2004 ).

3.3 The role of social networks in impulse buying behaviour (RQ3)

RQ3 explores the influence of social networks on impulse buying behaviour in the fashion industry (clothing, shoes and accessories). Social media strongly affect individuals’ behaviours, and particularly consumer behaviour ( IAB Spain, 2016 ). Social media users share a wide spectrum of experiences, ranging from what they are in the mood to do that day, to vigorously evaluating the products and services they consume ( Anderson et al. , 2011 ). This behaviour is leading consumers to influence others, through sharing pictures of their purchases and offering recommendations. These actions can stimulate unplanned and impulse buying ( Xiang et al. , 2016 ). Furthermore, recommendations and opinions not only affect buying behaviours but also help to build favourable brand images, which also stimulate impulse buying ( Kim and Johnson, 2016 ).

Thus, we may expect that consumers will use information from social media to gain ideas that can subsequently turn into purchase actions; after seeing a garment on social media, the consumer may also search for it and buy it either online or at a physical store. Moreover, previous research reveals that because of recommendations and photographs showing purchases in social media, information coming from other consumers is the most influential factor on consumer behaviour ( Anderson et al. , 2011 ; Xiang et al. , 2016 ). Therefore, this research explores whether users use social media as a tool to inspire their purchases. At this point, it is important to note that the photograph or recommendation shared by a consumer must represent an external stimulus that motivates the impulse buying. That is, the recommendation is not a piece of information that the consumer has been considering as part of his or her product research (within a planned purchase decision process), but it is a stimulus that triggers the desire to acquire the product without further deliberation.

Also, we aim at identifying which social networks affect impulse buying to a greater extent. This knowledge would help fashion brand companies in their commercial strategies. Specifically, we focus on the four social networks with the highest penetration rates and which could therefore have the greatest impact on the fashion industry ( AIMC -Asociación para la Investigación de Medios de Comunicación, 2016 ; IAB Spain, 2016 ): Facebook, Twitter, Instagram and Pinterest.

4.1 Data collection procedure

We conducted an online self-administered survey to address the research questions. The sampling procedure consisted of a non-probabilistic, convenience sampling method ( Malhotra and Birks, 2007 ), obtaining a total of 243 questionnaires. The survey was structured in five sections. In the first section, introductory questions were asked regarding the participants’ fashion product preferences and how frequently they bought clothing. The second section gathered information about their impulse buying behaviour, both in the offline channel and in the online channel (participants only answered the online-channel questions if they had ever made any online purchase in the product category). If the participants declared that they had made online purchases of clothing, shoes and/or accessories, they answered the third block of questions regarding their perceptions about the encouraging and discouraging factors associated with online impulse buying. The participants who were users of social networks (regardless of the previous section) were asked about their influence on their shopping behaviour. The fifth and last section gathered the participants’ sociodemographic information (age, gender, occupation and their experiences with the internet, social networks and online shopping).

4.2 Measurement instruments

The majority of the variables were measured using scales validated in prior studies, with minor modifications to ensure contextual consistency. The Appendix shows the full list of items used in the survey, together with the references used to measure impulse buying (both online and offline) as well as the encouraging and discouraging factors for online impulse buying. However, the items related to the influence of social networks were developed for this present research, as we found no appropriate scale in the literature. All the items used seven-point Likert scales. In addition, the section about the use of social networks asked participants whether or not they were users of the four networks (Facebook, Twitter, Instagram and Pinterest). If they were users, the participant indicated whether he or she had ever seen a garment in that social network and felt the need to buy it (1 = yes, 0 = no), as well as the probability of using it to carry out purchases (1 = not at all likely; 7 = very likely).

4.3 Sample characteristics

Once the sample was refined by screening out questionnaires with mistakes and inconsistencies, the final valid sample consisted of 212 participants. We used IBM SPSS software (v22) to analyse the data. The characteristics of the sample appear in Table II . It should be noted that, through the convenience nature of the sample, we were satisfied with the sample profile because it showed similarities to recent studies about the use of the internet and e-commerce ( AIMC -Asociación para la Investigación de Medios de Comunicación, 2016 ; ONTSI -Observatorio Nacional de las Telecomunicaciones y de la Sociedad de la Información, 2016 ), with the exception of gender. The majority of participants in the survey were female ( Table II ). Although this consumer segment has been widely used in research about the fashion industry ( Luna and Bech-Larsen, 2004 ; Lee and Kim, 2008 ), this imbalance represents a limitation of the current study.

Table II presents information for the three groups of participants and is used to analyse all three research questions. Specifically, out of the 212 participants, 62.3 per cent ( n = 132) affirmed that they had carried out online purchases of clothing, shoes and/or accessories. We used this subsample to compare the indices of impulsiveness for the online and the offline channels ( RQ1 ), as well as to examine the impact of the encouraging and discouraging factors for online impulse buying ( RQ2 ). In addition, 81.1 per cent of participants were social media users ( n = 172), who were used for the analysis of the RQ3 .

5. Data analysis and results

5.1 impulse buying scales’ validation.

Prior to the analysis of which channel is perceived as encouraging more impulse buying, we checked the validity of the scales in two steps. First, we carried out an analysis of reliability and dimensionality ( Churchill, 1979 ; Anderson and Gerbing, 1988 ). Regarding the scales’ reliability, we based this on Cronbach’s alpha ( Cronbach, 1970 ), considering a cut-off value of 0.7 ( Nunnally, 1978 ), and on the item-total correlations ( Bagozzi, 1981 ), taking 0.3 as the threshold value ( Norusis, 1993 ). The dimensionality of the scales was examined through an exploratory factorial analysis based on principal components ( Hair et al. , 1998 ). After this exploratory analysis, two offline impulse buying items (IMPUL2 and IMPLUL8), and one online impulse buying item (IMPUL8), were removed from their corresponding scales.

The second step of the validation process consisted of a Confirmatory Factor Analysis with the partial least squares (PLS) method and the SmartPLS 2.0 software ( Ringle et al. , 2005 ). The initial factor structure revealed that all the item loadings scored above the recommended benchmark of 0.7 ( Henseler et al. , 2009 ), with the exception of the item IMPUL1 of the offline impulse buying scale (λ = 0,600). This item was removed from the scale. The composite reliabilities were above 0.65 ( Jöreskog and Sörbom, 1993 ), being ρ c = 0.886 for the offline impulsiveness scales and ρ c = 0.936 for the online impulsiveness scale. These results supported the internal consistency of the scales. In addition, the average variance extracted (AVE) was higher than 0.5 ( Fornell and Larcker, 1981 ) for both scales (AVE impul_off = 0.565; AVE impul_on = 0.647), assuring convergent validity. Finally, discriminant validity was supported, as the square root of the AVE was higher than the shared variance among the constructs (correlations) ( Fornell and Larcker, 1981 ), and the heterotrait-monotrait ratio (HTMT) was 0.534, below 0.85 ( Henseler et al. , 2015 ).

5.2 Impulse buying offline and online (RQ1)

Once the scales were validated, the items were summed to create indices of impulse buying, following the procedure developed by Rook and Fisher (1995) . Those participants who scored above 60 per cent of the index (25.2 for the physical channel, 33.6 for the online channel) were considered as impulsive. Table III shows the descriptive statistics and the results of the analysis carried out to test RQ1 . It is observed that the average value of perceived impulsiveness demonstrated in the offline channel was around the middle point of the scale, and the percentage of impulsive participants was nearly 30 per cent. In the online channel, the average value of impulsiveness was significantly lower than the middle point of the scale, and less than 25 per cent of participants perceived this channel as leading to impulse buying.

Next, we calculated the mean values of the indices to make them comparable. In line with the previous results, the participants say they are more likely to plan their purchases (less impulsive) in the online channel than in the offline channel. The results of a non-parametric Wilcoxon test ( Leech et al. , 2008 ) revealed that this difference was significant ( Table III ). Finally, we directly asked participants which channel they considered to be associated with more impulsiveness: 35.4 per cent ( n = 75) chose the offline, whereas 10.8 per cent ( n = 23) chose the online (one sample chi 2 test: p = 0,000)[ 1 ]. Therefore, in response to RQ1 we may conclude that, although the participants perceived that neither channel led them to carry out impulse buying, the online channel was perceived as less impulsive than the offline channel.

5.3 Encouraging and discouraging factors of online impulse buying (RQ2)

Multiple regression analyses were carried out to analyse RQ2 ( Hair et al. , 1998 ). Taking into account the limited sample size ( n = 132), and that the diverse nature of the items prevented us from grouping or reducing them to more reliable constructs, two separate regressions were conducted, corresponding to the encouraging and the discouraging factors, respectively. The dependent variable was the mean value of the impulsiveness perceived in the online channel. All the variables were standardized prior to the analysis. The results of the regressions showed that the encouraging factors had more explanatory power of online impulse buying than the discouraging factors (adjusted R 2 0,581 vs. 0,175) ( Table IV ).

The use of credit cards (MOT1), the greater product assortment and variety (MOT4) and the possibility of receiving personalized recommendations (MOT8), had a significant positive impact on online impulse buying. The easy access and convenience (MOT2) and the lack of human contact (MOT6) also had a positive influence, although these effects were only marginally significant ( Table IV ). However, the anonymity that the internet offers (MOT5) had a marginally significant negative effect. This result is somewhat unexpected, given that the specialized literature states that impulsive buying is likely to occur in contexts that provide anonymity ( Rook and Fisher, 1995 ).

Regarding the discouraging factors, they did not have the proposed influence, with the exception of the ease by which the internet allows the making of comparisons (DMOT5) ( Table IV ). However, we found several unexpected results. First, the existence of shipping and refund costs (DMOT4) had a significant, positive influence on online impulse buying ( Table IV ). This result is in line with previous studies ( Huang and Oppewal, 2006 ) and could be explained by the fact that some online stores offer free shipping in exchange for a minimum purchase volume; this circumstance may lead to higher spending on spontaneous purchases. Second, the factors related to delayed gratification and satisfaction (DMOT6 and DMOT7) had a positive impact on online impulse buying ( Table IV ). The literature review showed that immediate possession provides satisfaction and thus encourages impulse buying ( LaRose, 2001 ), and the lack of it on the online environment could prevent consumers from impulsively buying online ( Kacen, 2003 ; Koski, 2004 ). However, our results are in line with those of Dittmar and Drury (2000) who argue that consumers derive satisfaction from the buying process itself, and not just from having the product. Thus, feeling the thrill while waiting for a product after buying it online may encourage impulse buying.

5.4 Influence of social media on impulse buying (RQ3)

For the analysis of RQ3 , we examined those participants who used social networks ( n = 172). Table V shows the descriptive usage data for each social network considered. The data are consistent with recent studies, Facebook being the most used social network, followed by Instagram, which has overtaken Twitter and confirms the growth of this social network ( AIMC -Asociación para la Investigación de Medios de Comunicación, 2016 ; IAB Spain, 2016 ). It should be noted that, although Pinterest is the least used social network, 80.7 per cent of its users are online buyers of clothing and accessories. In addition, users of each social network indicated whether they had ever seen a garment on these platforms and had felt the need to buy that item, as well as their purchase intention through the social network. Instagram stood out as the social network than most affects impulse buying, followed by Facebook and Pinterest; Twitter received the lowest scores ( Table V ).

buyers and non-buyers of clothing and accessories;

impulsive and planned buyers in the offline channel; and

impulsive and planned buyers in the online channel[ 2 ].

Descriptive data and results of the analyses are in Table VI . Online buyers gave significantly higher scores than non-buyers to all the items; however, participants’ answers were below the midpoint of the scale (except for the IMP_SN1). In addition, online buyers on average used more social networks than non-buyers. Similar results were obtained for participants with high and low levels of impulsiveness. In both channels, impulsive buyers perceived social networks to encourage their impulse buying behaviour to a great extent. Only the item IMP_SN3 was below the midpoint of the scale ( Table VI ). In sum, the results indicate that social networks are not generally perceived as tools that stimulate impulse buying to a great extent, even though they are acknowledged as a source of ideas and inspire purchases of clothing and accessories.

As previously indicated, the items to capture the influence of social networks on impulse buying were built ad hoc for the current study, as it was not possible to find a validated scale in the literature. In our view, it is interesting to analyse the validity of this scale. Although the development of a scale is outside the scope of this research, it may have utility for future research. The scale showed adequate indices of reliability (Cronbach’s α = 0.867; item-total correlations > 0,314) and dimensionality (only one Eigen value greater than the unit explained 65.60 per cent of the variance). The confirmatory factor analysis yielded one item with a loading below 0.7 (IMP_SN3). After removing this item, all the remaining analyses were satisfactory ( λ s > 0.822; ρ c = 0.913; AVE = 0.725 the square root of which was above the correlations with the rest of variables; HTMT = 0.581). Thus, the four-item scale can represent a valid measure of the influence of social networks on impulse buying behaviour.

6. Conclusions

This research tries to offer a better understanding of the current role of impulse buying. Traditionally, impulse buying has had an important influence on consumer behaviour. However, the growth of the internet and social networks may provoke changes in behavioural patterns towards more planned and rational purchase processes ( Experian Marketing Services, 2013 ). Taking this question as a starting point, this research reviews the specialized literature about the concept of impulse buying, paying special attention to the phenomenon in the online channel and tries to uncover the factors or characteristics of this medium that can encourage and discourage this behaviour. In addition, considering the emerging influence of social media on consumer behaviour ( Xiang et al. , 2016 ), the influence of social networks on impulse buying has been explored.

The results of the analysis offer several conclusions and implications. First, we must reject the notion defended by authors such as Banjo and Germano (2014) who advocate that rigorous planning will end impulse buying. According to our findings, almost 30 per cent of offline consumers, and 25 per cent of online consumers, consider themselves impulsive buyers. When comparing both channels, we must note that impulse buying is determined by the senses’ capacity to generate a sudden response, and it has a strong hedonic component, which leads to a decision without further deliberation ( Sharma et al. , 2010 ). Thus, the physical store is still superior in terms of sensory stimulation, which can trigger the emotional and unconscious response that leads to the buying impulse to a greater extent than the online channel ( Peck and Childers, 2006 ; Krishna, 2012 ). Nevertheless, our results point to the possibility that the degree of impulsivity may depend more on personal factors than on channel factors; for our sample, we observed that the participants who perceived themselves as impulsive in the offline channel also perceived they were impulsive in the online channel, and vice versa (Pearson correlation between the two indices: r = 0 .649; p = 0.000).

Second, the regression analyses showed that encouraging factors are more influential for online impulse buying than the discouraging factors. The ease of payment, the greater variety and the existence of personalized recommendations can be powerful tools to encourage impulsive buying through this channel. However, the results regarding the privacy that the internet provides are somewhat confusing: lack of human contact can boost impulse buying, whereas anonymity can restrain it. In a similar vein, the analysis regarding the convenience of the internet (easy access and comfort), which was proposed as both an encouraging and a discouraging factor of online impulse buying, did not offer conclusive results. Finally, factors that were alleged to undermine online impulse buying were revealed as just the opposite: shipping and refund costs, and delayed gratification, can indeed encourage this behaviour. Despite the exploratory nature of this research, and the caution with which our results must be treated, these findings may offer important implications for retail managers operating in both the online and the offline channels.

Third, social networks can play a relevant role in motivating impulse buying behaviour. The results of this research reveal that Facebook and Instagram have a great degree of penetration; moreover, the participants acknowledged that these social networks had triggered some impulse buying and showed a notable intention to use them to make purchases. On the contrary, Twitter is the social network with the lowest potential to inspire impulse buying. This result may be explained by the fact that Twitter offers less visual support than the other social networks; although Twitter incorporates photograph functionality, it is fundamentally a text-based platform. If a buying impulse is provoked by a sensory stimulation, the lack of an image that usually accompanies a tweet can represent a limitation. Again, these results offer opportunities for effective management of social media by fashion brand companies.

Finally, online buyers of clothing and accessories consider social networks as a source of inspiration that can trigger their buying behaviour. As expected, the influence of social networks on impulse buying was evidenced for those individuals who consider themselves as impulsive, both in the offline and the online channels. This result confirms the potential of social media to affect shopping behaviour ( Xiang et al. , 2016 ). Furthermore, this research offers the first step for the validation of a scale that effectively measures the influence of social media on impulse buying behaviour.

6.1 Limitations and future research lines

This research has several limitations that should be addressed in future research lines. First, the validity of the empirical study is limited by the sampling plan (non-probabilistic, convenience sampling) and the low sample size. In addition, the sample was very largely made up of women, which biases the analysis and interpretation of results. As a consequence, this investigation can be considered as merely exploratory and the results cannot be generalizable. Further research should use large, representative samples, using probabilistic sampling methods, to confirm or refute our findings.

The second limitation is related to the measurement of the study variables. The items used in the questionnaire were based on the specialized literature ( Appendix ). Regarding the impulse buying indices, we were able to use previously validated scales. However, for the measurement of the encouraging and discouraging factors of the online impulse buying, a parsimony criterion was used and we considered only one or two items to measure each factor. This prevents us from obtaining conclusive results from the analysis. Along the same lines, we were not able to find scales to measure the influence of social networks on impulse buying. Future studies are needed to analyse this behaviour in depth and to identify and develop scales for their correct measurement.

Third, this research explores impulse buying behaviour for only one type of product. Previous research has demonstrated differences on impulse buying depending on product characteristics, such as price, materials, or quality perceptions ( Amos et al. , 2014 ). Therefore, future research should take into account the impact of product or other situational characteristics (e.g. degree of involvement) when analysing impulse buying behaviour in the offline and online channels.

Conceptual framework for the research questions

Sample characteristics

Indices and average values of impulsiveness of the offline and online channels