Investigating the Roles of Affective Processes,Trait Impulsivity,and Working Memory in Impulsive Buying Behaviors

Abstract

The aim of the present study was to test the reliability and validity of a scale for impulsive purchases. We also explored the interaction between two distinct facets of affective processes (hedonic pleasures and negative affect reduction), trait impulsivity, and working memory in impulsive buying behavior. We recruited 155 undergraduate students (late adolescence—early 20s) as they represent a cohort in which financial independence is relatively recent and they are required to assume financial responsibility for their purchasing decisions. The findings indicated that affective processes (hedonic pleasures and negative affect reduction) and some aspects of trait impulsivity (motor impulsivity and cognitive complexity) were significant and unique predictors of impulsive buying behavior. However, working memory did not predict impulsive shopping. The model that provided the best fit using the data was one where affective processes and trait impulsivity were independent predictors of impulsive behaviors. A novel contribution of the present study is the development and investigation of the reliability and validity of a scale that taps the role of negative mood reduction as a driving force behind impulsive buying.

Keywords

impulsive buying behaviors hedonic consumption avoidant working memory trait impulsivity

Introduction

Shopping has long been associated with the desire to consume and acquire luxury items and nonessentials. In the United States, impulsive purchases generated roughly $4 billion in annual revenue (Kacen & Lee, 2002), with approximately 40% of consumers referring to themselves as impulsive shoppers (Park, Kim, & Forney, 2006; Target Group Index, 1997). Impulsive buying behavior is defined as a “sudden, compelling, hedonically complex buying behavior in which the rapidity of an impulse decision precludes thoughtful and deliberate consideration of alternative information and choices” (Bayley & Nancarrow, 1998, p. 99). This behavior is characterized as a choice made under the fixation of immediate, gratifying benefits, rather than considering the potential negative consequences following an unplanned purchase (Rook & Fisher, 1995). Impulsive buyers also do not have a prespecified item to purchase and, thus, are more susceptible to buying unnecessary items (Beatty & Ferrell, 1998; Engel & Blackwell, 1982). Such impulsivity is often referred to as myopic and suboptimal, where short-term consequences are favored over potential long-term hindrances (Green, Myerson, & McFadden, 1997; Hinson, Jameson, & Whitney, 2003). In the context of buying behavior, these impulsive decisions may result in buyer’s remorse and harm an individual’s financial situation. There are multiple factors that contribute to impulsive purchases (see Muruganantham & Bhakat, 2013, for a review), which can broadly be characterized as external factors (e.g., situational and product-related factors, demographic and sociocultural factors) and internal factors (e.g., affective processes, personality traits). In the present study, we focused primarily on internal factors, specifically, affective processes and personality traits.

Affective Processes

There are two affective processes that drive unplanned purchases: One is to satisfy hedonic desires or emotional needs for new and exciting experiences (Rook & Fisher, 1995), while another is to escape or avoid negative feelings or moods (Silvera, Lavack, & Kropp, 2008; Verplanken, Herabadi, Perry, & Silver, 2005). When consumers purchase a new product, or even fantasize about the potential of the new product, they experience positive emotions such as happiness and pleasure (Hausman, 2000; Hirschman & Holbrook, 1982). Such positive emotions have the immediate benefit of amplifying enthusiasm, satisfaction, and energy during shopping trips (Cobb & Hoyer, 1986).

An alternative motivation for engaging in impulsive buying is to alleviate an unpleasant psychological state such as stress or anxiety (Verplanken et al., 2005). By avoiding negative emotions, the buyer experiences gratifying benefits, thus strengthening the impulsive buying behavior (Mick & DeMoss, 1990; Silvera et al., 2008). To date, however, affective scales related to buying behavior have been restricted to positive or hedonic aspects. Given that reducing a negative mood is also a reason for impulsive buying, it is important to explore the effects of negative mood reduction in the context of impulsive buying. One possibility is that negative rather than positive affect is the driving force behind impulsive buying, as it is in chronic buying (see Verplanken et al., 2005).

Personality Traits: Impulsivity

The role of affective processes in impulse buying can be exacerbated by trait impulsivity (Verplanken & Herabadi, 2001). Trait impulsiveness includes both cognitive and motor impulsivity such as attentional impulsiveness, nonplanning impulsiveness, and motor impulsiveness (Patton, Stanford, & Barratt, 1995). Impulsive consumers also tend to score low on the need to evaluate decisions. This pattern suggests that impulsive consumers may not have the cognitive ability to perform effective evaluative strategies to make decisions, ultimately resulting in impulsive actions. Indeed, Hinson et al. (2003) reported a strong predictive relationship between all three aspects of trait impulsivity and working memory.

Working Memory

One cognitive skill that is associated with impulse control is working memory, a cognitive processing system that activates, manages, and integrates information in the environment with items retained in memory, in order make optimal decisions and regulate behavior (Nagel, Herting, & Cservenka, 2012). Working memory is a vital element in decision making by allowing the individual to discriminate between relevant and irrelevant incoming stimuli, while simultaneously coordinating this information with current and future goals. Individuals with limited working memory capacity can become overwhelmed when processing an increasing amount of information and trying to match it with their intended goals. In an effort to minimize cognitive effort and overload, they often rely on the simplest available information to make a decision. This response can result in myopic or impulsive decisions because other possible outcomes were not considered (Hinson et al., 2003; Nagel et al., 2012). In the context of shopping, individuals with poor working memory may not be able to process the necessary information to determine if a purchase is necessary and financially responsible. Indeed, impulsive consumers tend to adopt an effort saving device, where they do very little in terms of information processing while in a store (Cobb & Hoyer, 1986).

Present Study

With the prevalence of impulsive consumers on the rise (Park et al., 2006), coupled with the negative consequences of impulsive purchases, an exploration of the underlying factors that drives impulsive buying is both timely and important. Previous research indicates that both affective processes and trait impulsivity influence impulsive buying behavior. We wanted to extend this research by creating a scale to measure impulsive buying as a function of avoiding negative affect. The majority of research on affective processes has focused on the pursuit of hedonic pleasures (e.g., Hausman, 2000) but not its corollary: the avoidance of negative or aversive states. To address this issue, we developed the Negative Affect Reduction Consumption Scale, to measure the extant to which individuals will seek out impulsive purchases to avoid negative responses such as stress. One aim of the present study was to investigate the reliability and validity of this scale.

We also wanted to explore the role of working memory as an indicator of impulse control, which has not been considered in the context of impulsive buying. One possibility is that for an individual with poor working memory, the combination of information overload, along with an inability to inhibit distractors and weigh all possible outcomes, may compromise their decision making, resulting in unplanned purchases. However, it is unclear whether affective processes or trait impulsivity mediates the relationship between working memory and impulsive buying behavior.

The aim of the present study was to explore the nature of the interaction between affective processes, trait impulsivity, and working memory, with respect to impulsive buying. There are three possible hypotheses: (1) affective processes and trait impulsivity mediate the relationship between working memory and impulsive buying behavior as individuals with impulsive tendencies may naturally gravitate toward impulsive behaviors such as impulsive spending (Vohs & Faber, 2007); (2) affective processes and working memory mediate the link between trait impulsivity an impulsive buying; or (3) each of these factors uniquely predict buying behavior, as they each capture different motivations for impulsive spending (Muruganantham & Bhakat, 2013).

Method

Participants

There were 155 undergraduate students, ranging between 18 and 30 years (29% aged between 18 and 20 years, 55% aged between 21 and 26 years; 79% women). Academic class standing was represented as follows: 20% freshman, 8% sophomores, 34% juniors, and 38% seniors. Regarding ethnicity, 67% described themselves as Caucasian or White, 13% as African American, 10% as Hispanic, and 7% as Asian.

Participants also responded to questions relating to their living and financial situations. Regarding independence, approximately half the participants (46.5%) lived independently from their parents or guardians. Participants also indicated their annual income, as low-income households tend to engage in impulsive purchasing rather than delaying gratification (Dittmar, Beattie, & Friese, 1996): 42% made at least $10,000 a year. Only 26% of participants had more than one credit card, and 28% of participants had an outstanding balance on their credit card(s).

Measures

Affective processes

There were two measures of affective processes. The Hedonic Consumption Scale measures the hedonic motivations behind shopping behavior (Hausman, 2000). Sample items include, “I like to shop for the novelty of it,” and “I feel like I’m exploring new worlds when I shop” on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Cronbach’s α was reported to be .86. All seven items are framed positively, and ratings were averaged; high scores indicate that shopping represents a positive experience (maximum score = 35).

We also measured an individual’s tendency to engage in impulsive buying as an avoidant behavior by creating the Negative Affect Reduction Consumption Scale (see Appendix). Using a 5-point Likert scale, participants responded to five questions such as “After a new purchase, I feel relief,” and “When I have many things to complete, shopping is a good distractor.” Cronbach’s α was reported to be .86. Ratings were averaged, and high scores represent the presence of shopping to avoid negative emotions.

Personality trait: Impulsivity

The Barratt Impulsivity Scale (BIS; Patton et al., 1995) is a widely used measure that captures the personality construct of impulsiveness not associated exclusively to buying behaviors (see Stanford, Mathias, Dougherty, Lake, Anderson, & Patton, 2009, for a review). The BIS is a 30-item questionnaire (Cronbach’s α = .82, Patton et al., 1995), using a 4-point Likert scale ranging from 1 (never/rarely) to 4 (almost always/always). The BIS consists of six subscales: attention, cognitive instability, motor, perseverance, self-control, and cognitive complexity. Sample questions include “I get easily bored when solving thought problems” (cognitive complexity subscale) and “I have ‘racing’ thoughts” (cognitive instability subscale). High scores are indicative of trait impulsivity.

Working memory

This skill was measured using a standardized working memory assessment, the Alloway Working Memory Assessment 2 (Alloway, 2007). All test trials began with two items and increased by one item in each block, until the participant was unable to recall three correct trials at a particular block. There were four trials in each block, and the number of correct trials was scored for each participant. The move forward and discontinue rules, as well as the scoring, were automated by the program.

The screener version was administered and this comprised one verbal and one visuospatial working memory test. In Processing Letter Recall (verbal working memory), the participant views a letter in red that stays on the computer screen for 1 second. Another letter in black immediately follows this on the screen. Participants verify whether the black letter was the same as the red letter by clicking on a box marked either “Yes” or “No” on the screen. They then click on the red letters they saw in the correct sequence. Visual working memory was tested using the Mr. X test. Participants are presented with a picture of two Mr. X figures. They identify whether the Mr. X with the blue hat is holding the ball in the same hand as the Mr. X with the yellow hat. The Mr. X with the blue hat may also be rotated. At the end of each trial, participants have to recall the location of each ball in Mr. X’s hand in sequence, by pointing to a picture with eight compass points. Both the Mr. X figures and the compass points stayed on the computer screen until a response is provided. Test reliability of the Automated Working Memory Assessment was established in a random selection of the normative sample tested on two separate occasions, 4 weeks apart. The reliability coefficient for the verbal working memory tests was .86 and for the visuospatial working memory test was .84 (Alloway, 2007).

Buying behavior

The Buying Impulsiveness Scale measures generalized urges to spend impulsively and is a reliable test for impulsive buying behavior (Cronbach’s α = .88; Rook & Fisher, 1995). Participants rate their response on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) to questions such as “Buy now, think about it later,” and “I often buy things spontaneously.” Scores were averaged, and a high score represents impulsive buying behavior.

Procedure

Volunteers were recruited over a 3-month period. The study was advertised on the university research participation system. The criteria for participation was English was their first language and aged between 18 and 30 years. The working memory tests and the surveys were presented via a third-party website, Qualtrics. Participants completed the study on a computer in a lab setting.

Results

None of the situational factors led to a difference in buying behaviors: living independently, t(152) = 1.65, p = .10, income level, t(151) < 1, or the number of credit cards, t(152) < 1. Thus, these factors were not included in subsequent analyses. Performance on the affective processes, trait impulsivity scale, working memory tests, and buying behavior is shown in Table 1.

Table 1.

Descriptive Statistics for the Measures of Working Memory, Affective Process, Trait Impulsivity, and Impulsive Buying Behavior.

	M	SD
Affective processes
Hedonic Consumption Scale	3.06	0.70
Negative Affect Reduction Consumption Scale	2.90	0.92
Trait impulsivity
BIS attention	2.20	0.64
BIS motor impulsiveness	2.02	0.49
BIS self-control	2.07	0.57
BIS cognitive complexity	2.30	0.53
BIS perseverance	1.70	0.50
BIS cognitive instability	2.15	0.61
Working memory
Verbal working memory^a	103.85	14.61
Visuospatial working memory^a	95.65	19.41
Buying impulsiveness	3.16	0.61

Note. BIS = Barrett Impulsivity Scale.

Standard scores (M = 100, SD = 15).

A correlational analysis indicated the following significant patterns (Table 2). The two working memory tests were significantly related to each other (r = .31) but not to any other scores. The two affective processes were positively related: hedonic desires and negative affect reduction, r = .59. Both these affective process were also positively correlated with buying behaviors: hedonic desires (r = .46) and negative affect reduction (r = .49). Buying behaviors were also negatively related to five subscales of the trait impulsivity scale for all subscales except for the Perseverance subscale.

Table 2.

The Relationship Between Affective Processes, Trait Impulsivity, and Impulsive Buying Behaviors.

	1	2	3	4	5	6	7	8	9	10
1. Verbal WM
2. Visuo-spatial WM	.31**
3. Hedonic consumption	−.01	.01
4. NARCS	−.02	.01	.59**
5. Buying Impulsiveness scale	−.06	.12	.46**	.49**
6. BIS attention	−.04	.07	−.01	−.08	−.22**
7. BIS motor impulsiveness	.15	.05	−.33**	−.33**	−.65**	.23**
8. BIS self-control	−.05	−.03	−.06	−.09	−.36**	.52**	.44**
9. BIS cognitive complexity	−.10	−.11	−.12	−.17*	−.40**	.29**	.33**	.40**
10. BIS perseverance	.01	.13	.03	.05	−.16	.26**	.3**	.40**	.20*
11. BIS cognitive instability	.05	.05	−.22**	−.14	−.23**	.45**	.33**	.32**	.18*	.31**

WM = Working memory; NARCS = Negative Affect Reduction Consumption Scale; BIS = Barrett Impulsivity Scale.

p < .05. **p < .01.

Predictive Factors of Impulsive Buying Behaviors

A stepwise regression analysis was conducted, with impulsive buying behavior as the outcome variable. The predictors were entered simultaneously in a stepwise fashion and comprised the six trait impulsivity subscales and the two affective processes (hedonic and negative affect reduction). Model statistics, as well as standardized beta values, and t statistics are provided in Table 3. The following four scores were significant predictors: trait impulsivity (motor subscale accounting for 44% of unique variance and cognitive complexity for 2.9% of unique variance) and affective processes (negative affect reduction for 6.8% of unique variance and hedonic motivations for 2.2% of unique variance).

Table 3.

Stepwise Regression Analyses Predicting Impulsive Buying Behaviors.

Dependent		Independent	R ²	ΔR²	df	ΔF	Β	t
Impulsive Buying Behaviors	1	Working memory tests	.03	–	2, 134	2.12	–	–
	2	Motor (BIS)	.45	.42	1, 133	101.20*	−0.47	−7.25*
	3	Negative Affect Reduction Consumption Scale	.53	.08	1, 132	21.51*	0.20	2.78*
	4	Cognitive complexity (BIS)	.56	.03	1, 131	9.63*	−0.20	−3.22*
	5	Hedonic average	.58	.02	1, 130	5.93*	0.17	2.44*

BIS = Barrett Impulsivity scale; Β = standardized beta values.

p < .05.

Interaction Between Affective Processes and Trait Impulsivity

To investigate the interaction between affective processes and trait impulsivity on buying behaviors, we tested two different models. Based on the regression analyses described earlier, the following scores were included: trait impulsivity (motor and cognitive complexity subscales) and affective processes (hedonic and negative affect reduction). We did not include the working memory scores in order to test Hypothesis 2, as the previous analyses indicated that neither the verbal nor visuospatial scores were significantly associated with trait impulsivity.

Model 1 is based on the view that individuals with impulsive tendencies may naturally gravitate toward impulsive spending (Vohs & Faber, 2007), and affective processes may mediate this relationship (Figure 1; Hypothesis 1). Model 2 is based on the view that both trait impulsivity and affective processes may uniquely predict buying behavior, as they each capture different motivations for impulsive spending (Hypothesis 3; Muruganantham & Bhakat, 2013).

Figure 1.

Theoretical model where trait impulsivity predicts the affective process, which in turn predicts impulsive buying behaviors (Model 1).

We used the lavaan package from R program (Rosseel, 2012) to test these two theoretical models. A commonly used index of goodness of fit is the χ² statistic, and a good fit is determined by nonsignificant χ² values, though this statistic is sensitive to variances in sample sizes. Model adequacy was also evaluated using additional global fit indices that are more sensitive to model specification than sample size (Kline, 1998). Fit indices such as the Comparative Fit Index (CFI; Bentler, 1990) provide a further measure of fit computed by comparing the hypothesized model against a null model in which the relations between the latent variables are not specified and consequently are set at zero. Fit indices with values equal to or higher than 0.90 demonstrate a good fit. Further assessment of the extent to which the specified model approximates to the true model is the root mean square error of approximation (RMSEA). Values of 0.08 or lower are indicative of a good fit (McDonald & Ho, 2002).

The inputs for the model were the raw scores from the individual test items (observed variables), and the standardized parameter estimates, which are similar to regression weights, are reported (alpha level set to .05). Model 1 did not provide a good fit with the data χ² (4, N = 145) = 31.64, p < .00l; CFI = 0.86, RMSEA = 0.22. Model 2 yielded an acceptable fit index: χ² (4, N = 145) = 21.79, p < .00l, CFI = 0.91, RMSEA = 0.18. A χ² difference test confirmed that Model 2 was a significantly better fit (p < .005), and the path weights between constructs are displayed in Figure 2. It is worth noting that the RMSEA values for both models were higher than 0.08. This is typical of samples with low df, as they can have artificially large RMSEA values and some researchers suggest not computing RMSEA in such instances (e.g., Kenny, Kaniskan, & McCoach, 2014).

Figure 2.

Path model where both trait impulsivity and affective processes predict impulsive buying behaviors (Model 2). All factor loadings are significant at the p < .05 level.

Discussion

The present findings extend previous research and suggest that both affective processes and impulsive traits are independent predictors of buying behaviors. Looking first at affective processes, the link between hedonic motivations and impulsive buying is in line with previous research that consumers buy products for reasons that give them pleasure and improve mood, such as items that are fun or entertaining (Hausman, 2000). Less attention has been devoted to the role of negative affective processes, with studies focusing primarily on food purchasing, rather than products (Verplanken et al., 2005). The findings from the present study extend this research and suggest that while hedonic and avoidant processes are related to each other, avoiding negative feelings, like stress, accounted for a larger predictive value of buying behaviors than hedonic desire (6.8% compared with 2.2%), in a college-age population.

The present study also established the validity of the Negative Affect Reduction Consumption Scale. First, there was good internal reliability (Cronbach’s α = .86). Second, there was moderate convergent validity with the hedonic scale (r = .59), suggesting that scores on these two scales were related, yet not equivalent as they only demonstrated a 35% overlap. Finally, concurrent validity was established with impulsive buying behaviors as the outcome variable. The Negative Affect Reduction Consumption Scale was a unique predictor of impulsive buying behavior and predicted a larger amount of variance than the hedonic scale. This pattern suggests that a scale that captures negative mood reduction in buying behaviors is valuable and can be used for a range of purposes, including for consumer marketing and treatments.

Looking next at personality traits, two subscales from the trait impulsivity scale predicted impulsive buying: motor impulsiveness and cognitive complexity. One explanation for why motor impulsivity predicted buying behaviors could be due to the overlap in questions between the two surveys. For example, the buying behavior scale includes items such as “I often buy things spontaneously,” while the motor impulsivity subscale includes questions such as “I act on spur of the moment” and “I buy things on impulse.” The motor impulsiveness subscale explicitly captures key features of impulsive shopping.

Cognitive complexity, a subscale of trait impulsivity, also predicted impulsive purchases. This finding is also in line with previous research on decision making, indicating that when a situation requires increased cognitive effort, the individual is forced to use resources necessary for employing other cognitive mechanisms such as analyzing consequences and applying salient information (Hinson et al., 2003; Nagel et al., 2012). To avoid such an increase with maintained effort in complex situations, an individual will resort to an impulsive decision such as an impulsive purchase.

In contrast, working memory did not predict impulsive buying. One reason why working memory did not predict buying behaviors could be the type of test used to measure working memory. In the present study, working memory was measured using tests that capture working memory capacity (see Alloway, Gathercole, & Pickering, 2006). In contrast, cognitive complexity predicted impulsive buying behavior, possibly because it focuses on problem-solving skills. Thus, it may be that the cognitive skills more closely associated with impulsive buying relates to problem solving rather than cognitive capacity. Applied to consumer behavior, the implication is that how well an individual solves problems, rather than the memory capacity to solve problems, is more closely linked to buying decisions. Additional situational factors, such as mood and stress, could also have influenced cognitive load in buying decisions (see Vohs & Faber, 2007), however, these were not explored in the present study.

Another explanation is that while impulse buying has been considered wasteful and shortsighted (Rook & Fisher, 1995), the participants in the present study may not have viewed it as a negative activity. Rather, they may have viewed it as normal behavior, or even entertaining, so they would not need to moderate their actions or utilize working memory in their decision making process (see Hausman, 2000). In support of that, Rook (1987) reported that only 20% of people surveyed reported feeling bad about their purchase. Similarly, many consumer researchers believe that shoppers view purchases as an expression of their identity (Dittmar et al., 1996; Holbrook & Hirschman, 1982). Thus, an impulsive purchase would not be viewed as a negative decision that involves cognitive deliberation, but rather as a leisure activity, driven by a need for self-expression.

Finally, the models revealed an interesting pattern regarding predictive factors of impulsive buying behavior. Model 2 was the better fitting model that represented affective processes and trait impulsivity, not as mediating factors, but separate factors that independently predicted impulsive spending. This finding suggests that the driving force behind impulsive behaviors could be either aspects of trait impulsivity or affective processes.

An important caveat is that while the fit index in Model 2 met the required standard (CFI < 0.90), another index did not (RMSEA). While the latter finding is typical in samples with small degrees of freedom, this finding must be replicated in future studies. Additional research is needed to extend these findings beyond a college population. This group may experience some stress with respect to spending behaviors, as financial independence is a relatively new situation for the majority of young adults, as they have left home and are required to assume some financial responsibility. Furthermore, brain imaging research suggests that the prefrontal cortex, the home of working memory, continues to develop in the early 20s (Fuster, 1980). Thus, their working memory system may not be fully developed to monitor funds and refrain from making impulsive decisions in emerging adulthood. A different explanatory model of impulsive buying may emerge in an older adult population that has a more fully developing working memory system and less financial insecurity.

In summary, the present study adds to the existing research by exploring the interaction between different factors that contribute to impulsive buying behaviors. There were several key findings. First, we found that avoiding negative feeling is a unique predictor of impulse spending. This finding can be beneficial to help young adults, students especially, to be more cognitively aware of their coping habits while experiencing negative feelings or avoiding the pervasiveness of stress. Another key finding was that impulsive spenders can be driven by either impulsive traits or affective processes. This information could be useful to retailers as they can target their marketing strategy accordingly. Clinicians may also find these findings useful to support those seeking help in changing their buying behaviors.

Footnotes

Appendix

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Author Biographies

Packiam Alloway, PhD, is an associate professor and graduate program director in the Department of Psychology at the University of North Florida.

Ashlee Gerzina was an undergraduate student at the University of North Florida when she conducted this study.

Robert Moulder was an undergraduate student at the University of North Florida when he conducted this study.

References

Alloway

T. P.

(2007) Automated Working Memory Assessment, London, England: Pearson Assessment.

Alloway

T. P.

Gathercole

S. E.

Pickering

S. J.

(2006) Verbal and visuo-spatial short-term and working memory in children: Are they separable? Child Development 77: 1698–1716.

Bayley

Nancarrow

(1998) Impulse purchasing: A qualitative exploration of the phenomenon. Qualitative Market Research: An International Journal 1: 99–114.

Beatty

Ferrell

(1998) Impulse buying: Modeling its precursors. Journal of Retailing 74: 169–191.

Bentler

P. M.

(1990) Comparative fit indexes in structural models. Psychological Bulletin 107: 238–246. doi:10.1007/BF02294170.

Cobb

J. C.

Hoyer

W. D.

(1986) Planned versus impulse purchase behaviour. Journal of Retailing 62: 384–409.

Dittmar

Beattie

Friese

(1996) Objects and decision considerations in impulse purchases. Journal of Economic Psychology 16: 491–511.

Engel

Blackwell

(1982) Consumer behavior, Chicago, IL: Dryden Press.

Fuster

J. M.

(2008) The prefrontal cortex, 4th edition. London, England: Academic Press.

10.

Green

Myerson

McFadden

(1997) Rate of temporal discounting decreases with amount of reward. Memory & Cognition 25: 715–723.

11.

Hausman

(2000) A multi-method investigation of consumer motivations in impulse buying behavior. Journal of Consumer Marketing 17: 403–419.

12.

Hinson

J. M.

Jameson

T. L.

Whitney

(2003) Impulsive decision making and working memory. Journal of Experimental Psychology: Learning, Memory and Cognition 29: 298–306.

13.

Hirschman

E. C.

Holbrook

M. B.

(1982) Hedonic consumption: Emerging concepts, methods and propositions. Journal of Marketing 46: 92–101.

14.

Holbrook

M. B.

Hirschman

E. C.

(1982) The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of Consumer Research 9: 132–140.

15.

Kacen

J. J.

Lee

J. A.

(2002) The influence of culture on consumer impulsive buying behavior. Journal of Consumer Psychology 12: 163–176.

16.

Kenny

D. A.

Kaniskan

McCoach

D. B.

(2014) The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research 44: 486–507.

17.

Kline

R. B.

(1998) Principles and practice of structural equation modelling, New York, NY: The Guilford Press.

18.

McDonald

R. P.

M. R.

(2002) Principles and practice in reporting structural equation analyses. Psychological Methods 7: 64–82.

19.

Mick

D. G.

DeMoss

(1990) Self-gifts: Phenomenological insights from four contexts. Journal of Consumer Research 17: 322–332.

20.

Muruganantham

Bhakat

R. S.

(2013) A review of impulse buying behavior. International Journal of Marketing Studies 5: 149–160.

21.

Nagel

B. J.

Herting

M. M.

Cservenka

(2012) Working memory and addictive behavior. In: Alloway

T. P.

Alloway

R. G.

(eds) Working memory: The connected intelligence, New York, NY: Psychology Press.

22.

Park

E. J.

Kim

E. Y.

Forney

J. C.

(2006) A structural model of fashion-oriented impulse buying behavior. Journal of Fashion Marketing and Management 10: 433–446.

23.

Patton

J. I. I.

Stanford

M. S.

Barratt

E. S.

(1995) Factor structure of the Barratt Impulsiveness Scale. Journal of Clinical Psychology 51: 768–774.

24.

Rook

D. W.

(1987) The buying impulse. Journal of Consumer Research 14(2): 189–197. Retrieved from https://dx-doi-org.web.bisu.edu.cn/10.1086/209105.

25.

Rook

D. W.

Fisher

R. J.

(1995) Normative influences on impulsive buying behavior. Journal of Consumer Research 22: 305–313.

26.

Rosseel

(2012) lavaan: An R package for structural equation modeling. Journal of Statistical Software 48(2): 1–36.

27.

Silvera

D. H.

Lavack

A. M.

Kropp

(2008) Impulse buying: The role of affect, social influence, and subjective wellbeing. Journal of Consumer Marketing 25: 23–33.

28.

Stanford

M. S.

Mathias

C. W.

Dougherty

D. M.

Lake

S. L.

Anderson

N. E.

Patton

J. H.

(2009) Fifty years of the Barratt Impulsiveness Scale: An update and review. Personality and Individual Differences 47: 385–395.

29.

Target Group Index (1997) Annual lifestyle survey, TGI, London, , April 16.

30.

Verplanken

Herabadi

(2001) Individual differences in impulse buying tendency: Feeling and no thinking. European Journal of Personality 15: S71–S83.

31.

Verplanken

Herabadi

A. G.

Perry

J. A.

Silver

D. H.

(2005) Consumer style and health: The role of impulsive buying in unhealthy eating. Psychology and Health 20: 429–441.

32.

Vohs

K. D.

Faber

R. J.

(2007) Spent resources: Self-regulatory resource availability affects impulse buying. Journal of Consumer Research 33(4): 537–547.