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Variety-Seeking Behavior in Consumption: A Literature Review and Future Research Directions

Variety-seeking is a popular choice strategy in consumers’ daily lives, and many factors influence it. This study conducted a narrative and structured literature review based on three popular online academic databases to understand how researchers used influencing factors, adopted theoretical perspectives and underlying mechanisms, and developed measure methods in their studies. This paper consolidated and analyzed 61 articles on variety-seeking behaviors in consumer research, including empirical studies spanning from 2000 to 2021. This paper primarily focused on articles published at top tiers in the marketing literature. From these articles, a collection of internal and external factors, theoretical perspectives, underlying mechanisms, and measure methods adopted was summarized and tabulated for easy reference and comprehension. A research framework was developed to illustrate the relationships between influence factors and variety-seeking proposed by previous researchers. The literature review may not be exhaustive because variety-seeking behaviors could involve various research topics; however, the proposed research framework and suggested directions may be representative references for future research. This study is a more comprehensive literature review of variety-seeking behaviors in consumption research after 2000, and it contributes to a better understanding of the causes and effects of variety-seeking behaviors in consumption.

Introduction

In daily life, when consumers face various selectable products, although they can repeatedly select their favorite products, they often choose ones in different categories, regarded as variety-seeking behavior ( Kahn and Louie, 1990 ). To meet consumers’ needs and maximize their satisfaction ( Sevilla et al., 2019 ), enterprises need to pursue the most accurate marketing segments. Consumption-related variety-seeking behavior provides an effective market segmentation standard for enterprises ( Trivedi, 1999 ). In addition, such behavior helps increase sales volume and market share ( Simonson and Winer, 1992 ), classify products, and effectively combine marketing strategies ( Sela et al., 2019 ).

Variety-seeking behavior in consumption refers to individuals switching among products, categories, or brands to avoid the decreasing utility due to repeat purchases or consumption of the same products ( Ratner et al., 1999 ). Over time, people tend to switch between options or select different options within a choice set ( Shaddy et al., 2021 ). In the marketing domain, variety-seeking behavior also covers switching between marketing activities and services. Previous research found that consumers buy a certain number of diversified products even if they can repeatedly buy their favorite products from a given selection set ( Ratner and Kahn, 2002 ). Repeating purchase or consumption reduces products’ marginal utility, thus reducing product attractiveness and causing boredom among consumers ( McAlister, 1982 ; McAlister and Pessemier, 1982 ); existing products no longer meet consumers’ needs for stimulation ( Choi, 1991 ). Therefore, consumers pursue freshness, change, and diversity by experiencing goods with different attributes to form satiety ( Seetharaman and Che, 2009 ; Sevilla et al., 2016 ). This tendency shows that variety-seeking is common among consumers making product purchase decisions ( McAlister, 1982 ) and a common choice strategy ( Drolet and He, 2010 ).

Research on variety-seeking behaviors has a long history. Previous researchers have conducted valuable reviews on variety-seeking ( McAlister and Pessemier, 1982 ; Kahn, 1995 ; Herrmann and Heitmann, 2006 ). However, the first two were published two or three decades ago. McAlister and Pessemier (1982) focused on the taxonomy of varied behavior and divided variety-seeking behaviors into two classes (decried and direct). Kahn (1995) similarly discussed three primary motivations for variety-seeking in the marketing literature: satiation/stimulation, external situation, and future preference uncertainty. The last one, Herrmann and Heitmann (2006) , highlights the relevant literature on the domains of cultural psychology as well as marketing psychology with a review of consumers’ perception of variety-seeking. This study differs from the extant literature on the timeframe, method, and analysis. This study’s value lies in its narrative literature review on marketing and consumption articles published from 2000 to 2021 and their proposed conceptual models and frameworks. In contrast to previous reviews, this paper overviews the methodology approach, influencing factors, theoretical perspective, and underlying mechanism of variety-seeking behaviors in consumption. Based on these findings, a research framework of variety-seeking behaviors in consumption was developed to illustrate the inter-relationships among the adopted research constructs. This framework can provide a reference for researchers, serve as a research road map, and stimulate new ideas in future research in this subject area.

This review article is organized as follows. This paper first briefly describes the method of conducting the search process. Next, this paper summarizes and discusses the internal and external factors of variety-seeking behaviors in consumption, followed by a generalization of the theoretical perspectives and underlying mechanisms of variety-seeking behaviors. Then, this paper reviews various measurement methods used by researchers and recommends the directions for future research based on the summarization of the current findings ( Figure 1 ).

Flow diagram of sections.

Research Methodology

To investigate the work of previous researchers on variety-seeking behaviors in the consumption domain, this paper searched for empirical studies in the extant literature after 2000. The literature search was conducted from the Scopus database, which is the largest abstract and citation database of the peer-reviewed literature. The keyword “variety-seeking” was applied in the search process. The scope of this study is limited to the timeframe of 2000–2021 because there was only one literature review paper during this period. This search generated 293 records in total. Two hundred and thirty-five literature were omitted due to non-article type (8 records), neither SCI nor SSCI journal (70 records), non-English (2 records), specific subjects (e.g., children, older people, nonhumanity; 7 records), non-empirical paper (e.g., conceptual, review, and interview papers; 7 records), using modeling method (40 records), focusing on personality traits (52 records) and personal motivation (9 records) of variety-seeking, and no relation to consumption (40 records). Another 3 relevant papers were added. Finally, 61 papers were selected for in-depth analysis.

The search for relevant research in this process was by no means exhaustive; however, the findings nevertheless serve as a representative summary of the research conducted thus far. Only refereed journal articles were included in the study; conference papers, doctorate and master theses, textbooks, and documentaries were excluded because I believe refereed journal articles represent state-of-the-art research outputs ( Chan and Ngai, 2011 ; Ngai et al., 2015 ). Moreover, because the current paper focus on “variety-seeking behavior in consumption,” most journals involving marketing and consumer psychology in top tiers were selected, such as Journal of Marketing, Journal of Marketing Research, Journal of Consumer Research, Journal of Consumer Psychology, European Journal of Marketing, Marketing Letters, International Journal in Marketing, Journal of Consumer Psychology, and Journal of Personal and Social Psychology as well as some journals in psychology and tourism and hospitality management. Finally, this study focused on papers presenting empirical studies, and the adopted variables and proposed models were reviewed and included in the framework.

Analysis and Results

This section begins with a narrative review of the influencing factors adopted in the 61 identified empirical studies. The section then continues with the development of the research framework embedded in an analysis of theoretical perspectives and underlying mechanism, and measuring methods investigated by previous researchers in the formation of their conceptual models or frameworks. It should be noted that there are seven classical articles in the area published before 2000 described in this section, which are severed as background information.

Internal Factors

The extant literature on variety-seeking behaviors in consumption considers five aspects of internal influencing: individual demographics, personality characteristics, emotional and physical states, sensory clues, and mindset.

Individual Demographics

A factor that could affect consumers’ variety-seeking behaviors is individual demographics, such as gender and age. For the effect of gender on variety-seeking behavior, researchers focused on the feminine menstrual cycle and gender differences. For example, across the reward domains of mating and hedonic food, Faraji-Rad et al. (2013) showed that women seek more variety in rewards when they are closer to ovulation because of their increased reward sensitivity caused by hormonal shifts during the fertile phase of the menstrual cycle. Similarly, Durante and Arsena (2015) revealed that women select a greater number of unique options from consumer product sets at high fertility, which is particularly strong for those in committed relationships. Chen et al. (2016) focused on two genders and demonstrated that men’s variety-seeking behavior in the product consumption domain increases in the presence of short-term, not long-term mating cues; by contrast, women’s variety-seeking behavior decreases in the presence of long-term but not short-term mating cues. For the effect of age on variety-seeking behavior, Novak and Mather (2007) found that younger adults selected similar levels of variety when choosing between what to consume immediately and later. By contrast, older adults consistently selected less variety when choosing something to be consumed later than immediately.

Personality Characteristics

Individual characteristics could influence consumers’ variety-seeking behaviors. Consumers who feel powerful ( Jiang et al., 2014 ) are chronically indecisive ( Jeong et al., 2016 ; Jeong and Drolet, 2016 ) and are novices ( Sela et al., 2019 ) present more variety-seeking behaviors. First, building on an action-orientation perspective of power, Jiang et al. (2014) demonstrated that because high power is associated with a readiness to act and switching behavior generally requires taking actions in some form, consumers who feel powerful are more likely to switch in choice tasks. Second, Jeong et al. (2016) and Jeong and Drolet (2016) highlighted that chronic indecisiveness is associated with increased variety-seeking behavior. Chronically indecisive consumers (vs. not) feel less anxious and more positive after selecting a mix of products. Finally, consumers can acquire knowledge and signal their status in the marketplace during variety-seeking. Sela et al. (2019) argued that novices (vs. experts) perceive greater (vs. less) variety-seeking to indicate expertise because of perceived category breadth knowledge (vs. within-category discernment). Thus, novices (vs. experts) seek more (vs. less) variety to signal expertise. However, privately self-aware consumers are less inclined to opt for a varied choice set ( Goukens et al., 2009 ).

In recent years, researchers explored luck beliefs, mindset traits, and self-oriented perfectionism in consumers’ variety-seeking behaviors. For instance, Zhao et al. (2021b) analyzed data from 593 respondents and showed that personal luckiness and belief in luck positively affect variety seeking. Li and Sun (2021) investigated 364 participants in the United States and found that consumers with a growth (vs. fixed) mindset are more likely to engage in variety seeking. As a purchasing strategy, variety-seeking also could be positively influenced by self-oriented perfectionism ( Fu et al., 2021 ; N  = 312).

Other personality traits, such as goal orientation and trait anger, influence variety-seeking behaviors depending on the situations. Considering decision tasks, Wu and Kao (2011) found that in the sequential choices for sequential consumption conditions, promotion-focused consumers tend to select a greater variety of items than prevention-focused consumers. The effect reversed in the simultaneous choices for sequential consumption conditions for prevention-focused consumers. Considering state anger, Zhao et al. (2021a) showed that people from relatively resource-abundant environments generally tend to seek variety when they are temporarily in an angry mood, independent of trait anger; although those with low trait anger tend to choose more variety compared to those with high trait anger. For people growing up in relatively resource-scarce environments, those with a low trait of anger tend to choose less variety when they feel angry than those with a high trait of anger.

Emotion and Physical State

Early researchers mainly explored the relationship between broad emotions (positive and negative feelings) and variety-seeking. For example, Kahn and Isen (1993) explored the influence of the positive effect on variety-seeking among safe and enjoyable products. The findings revealed that the positive affect induced by a gift bag of candy or sugarless gum enhanced consumers’ variety-seeking in choice behavior in three food categories (i.e., crackers, soup, and snack food) when circumstances did not make negative features of the items. However, the different degrees of positive feelings could produce distinct effects. Roehm and Roehm (2005) believed that more extreme positive moods might reduce variety-seeking—unlike mild positive moods—because the moderate stimulation obtained from variety-seeking is insufficient to meet people’s demands of extreme positive moods. The results of two pilot studies and two experiments showed that participants who viewed an ad cultivating an extremely positive mood switched less between candy bar snack brands on successive choices and selected fewer brands.

Then, researchers discussed how specific emotions and physical conditions, including positive and negative emotions ( Chuang et al., 2008 ), sadness and happiness ( Lin and Lin, 2009 ; Chien-Huang and Hung-Chou, 2010 , 2012 ; Lin et al., 2011 ; Lin, 2014 ), local optimism and pessimism ( Yang and Urminsky, 2015 ), and winning-losing perception ( Chang et al., 2021 ), affect consumers’ decision-making behaviors when faced with multiple choices. In these moods and states, seeking variety helps people change their current status. For example, a study with 124 subjects demonstrated that people are likely to include more variety in their consumption decisions when they are induced to a negative emotion than a positive emotion ( Chuang et al., 2008 ). Moreover, a series of research discussed the effect of two specific emotional states (sadness and happiness) on variety-seeking behaviors and found similar conclusions ( Lin and Lin, 2009 ; Chien-Huang and Hung-Chou, 2010 , 2012 ; Lin et al., 2011 ; Lin, 2014 ). These studies used choice task scenarios and revealed that participants with a sad mood selected more variety than those with a happy mood. Furthermore, Yang and Urminsky (2015) demonstrated that local optimism increases sequential choice consistency, whereas local pessimism increases sequential variety-seeking. Finally, Chang et al. (2021) found that consumers who have failed in a competition or not achieved a goal tend to seek less variety in their later consumption than consumers who have succeeded because losing feedback weakens consumers’ perception of their control of personal mastery.

Interestingly, some special physiological states have effects on variety-seeking, such as hunger ( Goukens et al., 2007 ) and sleepiness ( Huang et al., 2019 ). When people felt hunger or thirst, visual food or drink cues encouraged them to seek variety in relevant domains because these cues were more attractive to consumers who were in hunger or had just finished a fitness ( Goukens et al., 2007 ). Another physiological state influencing variety-seeking is sleepiness. Huang et al. (2019) used multiple methods and revealed that sleepier consumers tended to seek more variety because of the need for arousal to maintain wakefulness. Particularly in Study1, a natural experiment based on the change of DST policy provided practical evidence for the positive effect of DST (decreasing short-term sleeping time and increasing sleepiness) on variety-seeking in products purchased by using Nielsen panel data (approximately 60,000 U.S. households data).

Sensory Clues

Individuals’ perception of the external circumstances depends on their keen sensory system, which receives various stimuli from the outside and then influences individuals’ mindset and decision making. People seek various choices when consuming to satisfy the sensory demand of vision ( Maimaran and Wheeler, 2008 ; Deng et al., 2016 ; Huang and Kwong, 2016 ) and taste ( Inman, 2001 ; Mukherjee et al., 2017 ), which have been discussed more in the current research.

Initially, the structural and superficial features of vision affect consumers’ variety-seeking behaviors subconsciously. First, individuals’ choices could be causally influenced by novel visual stimuli. For example, Maimaran and Wheeler (2008) demonstrated that exposure to variety arrays (arrays of differing shapes) increases variety-seeking, whereas exposure to uniqueness arrays (e.g., one circle among six squares) increases the choice of unique over common objects. Second, the display of products further influences variety-seeking in consumption because of the direction match between displays and eye movements. For example, Deng et al. (2016) used multiple methods (e.g., field study, laboratory study, and eye-tracking study) and demonstrated that consumers chose more variety (i.e., distinct fragrances, different candies, unique chocolates, and different types of lollipops) when alternatives were horizontally assorted or displayed. Third, a superficial feature can affect various perceptions even when the actual content or structure of an assortment remains unchanged. Huang and Kwong (2016) revealed that when the menu or catalog of an assortment is more difficult to read, the individuals perceived a higher variety. This readability effect stems from the subjective interpretation of the feeling of difficulty, that is, consumers generally endorse a lay belief that it is more difficult to make choices when they face a greater variety of options.

Subsequently, people might seek variety of taste stimuli to satisfy their needs. Inman (2001) believed that people switch more on sensory attributes (e.g., flavor) than nonsensory attributes (e.g., brand) to seek more pleasure. Inman (2001) used ACNielsen wand panel data for purchases of tortilla chips and cake mixes from almost 2000 consumers over 3 years (Study 1) and examined actual consumption behavior using a six-week consumption diary panel from over 850 consumers in two cities (Study 2) and employed a survey methodology (Study 3; 1056 responses) to verify his hypotheses: the difference of variety-seeking based on sensory and brand could be explained by “sensory-specific satiety,” that is, because of the high correlation between sensory-specific satiety and variety-seeking on sensory attributes, consumers switched more on flavors than brands. The research of Inman (2001) on sensory is broad, and subsequently, Mukherjee et al. (2017) discussed the relationship between a more specific taste—spicy and variety-seeking consumption. Based on embodied cognition and the metaphor “variety is the spice of life,” the authors found that spicy gustatory sensations (e.g., spicy vs. mild potato chips) activate a desire to be interesting that leads to greater variety in the subsequent unrelated choices (e.g., candy bars).

Ultimately, Lee and Sergueeva (2017) demonstrated an interesting “chewing effect” and argued that chewing more increases the viewing time and consumers’ thought-engagement while shopping and then increases variety-seeking behavior among consumers.

Variety-seeking could also be the behavioral result of spontaneous thinking. The priming mindset influences variety-seeking in follow-up consumption, including past experiencing priming ( Shen and Wyer, 2010 ) and semantic concept priming ( Fishbach et al., 2011 ; Huang and Wyer, 2015 ; Zhang and Guo, 2019 ).

First, people’s past experiences can affect variety-seeking in the future. When individuals’ past behaviors associated with “same” were primed, they would get the feeling of boredom and then switch to a “different” decision rule (e.g., various types of herbal tea for four consecutive days) when performing a later task to eliminate this negative feeling ( Shen and Wyer, 2010 ).

Second, the influence of semantic concepts on variety-seeking is nonconscious. For example, Fishbach et al. (2011) showed that when the negative concept related to “repetition” (e.g., boredom) was primed, it triggered an individual’s consumption structure based on satisfaction, that is, encouraging them to seek variety in order selection (e.g., buying smaller bottles of different shampoo, preferring CDs from different artists, staying in different hotels in the same city, visiting different cities in Europe, shopping at different stores, and choosing different snacks). Moreover, the influence of semantics is not only manifested in words related to choice behaviors but also has the same effect in words unrelated to choice behavior. For instance, Huang and Wyer (2015) found two opposite effects of mortality on variety-seeking: anxiety-inducing and concept-activation effects. The former was driven by the desire for stability and decreased the variety of individuals’ choices in an unrelated multiple-choice decision situation, whereas the latter induced a global processing style and increased variety-seeking. In addition, individuals’ temporal perspectives also trigger different seeking mindsets and affect variety-seeking behavior. Zhang and Guo (2019) demonstrated that past thinking brings familiar seeking and decreases variety-seeking, whereas future thinking induces novelty seeking and increases variety-seeking.

External Factors

Whether or not people seek variety in the choice and decision-making process of consumption is not only affected by internal factors but also external environmental factors. These external environmental factors include social environment, physical environment, and marketing strategies.

Social Environment

The social environment’s influence on people’s daily behavior is subtle and has potential that is not easy to detect. Social factors that influence consumption variety are mainly from the two aspects of social relationships and social culture.

People would like to make various decisions to maintain well social relationships. The first social relationship comes from social pressure. Ratner and Kahn (2002) demonstrated that people choose more variety when they make decisions in public than in private because they expect to receive positive evaluations from others (perceived as “social pressure”; Ratner and Kahn, 2002 ). The second social relationship comes from interpersonal motivation. According to Ratner and Kahn (2002) , Choi et al. (2006) showed that people have a stronger tendency to seek variety when they make choices for others. The explanations are as follows: (a) people should be responsible for their choices (the interpersonal mechanism; b) people expect to be satisfied more quickly when they choose for others (the intrapersonal mechanism). In addition, to maintain the self and interpersonal relationships, individuals’ perceived relational threat affects variety seeking in snack choices. Across three studies, Finkelstein et al. (2019) experimentally manipulated relational self-threat and found that those who experience high (vs. low) threat seek less variety, even when the same choice set is construed as having more (vs. less) variety. The third social relationship comes from the acquisition of interpersonal resources, that is, social influence. Ariely and Levav (2000) showed that the original groups choose more varied dishes than created groups, which is attributable to the interaction among group members and help individual satisfy goals of information gathering and self-presentation in the form of uniqueness in the group context. Chuang et al. (2013) maintained that to derive more enjoyment from a shared product, people show less variety and make choices consistent with the opinions of others in online information.

Furthermore, people in love form a special social relationship. For example, Etkin (2016) argued that consumers prefer more variety for joint consumption with their partners (e.g., going out to dinner, a movie, and a concert on a weekend), when they perceive more (vs. less) time ahead in a committed relationship. Huang and Dong (2019) found that a salient relationship state—romantic crush—can increase consumers’ variety-seeking tendency in unrelated consumption situations.

Variety-seeking behaviors in consumption could be influenced by the root of social culture. Kim and Drolet (2003) highlighted that as a choice rule, people in a unique culture display greater variety. Similarly, Yoon et al. (2011) reported that because members of a collectivist culture tend to follow group members’ choices, their choices in snacks are associated with a higher uniformity-seeking tendency than those of individualistic cultural backgrounds. Moreover, building on the compensation consumption literature, Yoon and Kim (2018) demonstrated that consumers with low socioeconomic status and perceive low economic mobility (e.g., economically stuck consumers) seek more variety than others to compensate for their lack of personal control. Finally, political ideology has a counterintuitive effect on variety-seeking. Fernandes and Mandel (2014) showed that conservatism is positively related to variety-seeking because of social normative concerns.

Physical Environment

The physical environment factors that affect variety-seeking in consumption mainly include the space environment and time point.

First, the constraints of a physical space enhance variety-seeking in consumption. Based on resistance theory, Levav and Zhu (2009) found that consumers confined by space make more various and unique choices to resist the invasion of their private space and seek freedom. The authors revealed that people in narrower aisles sought more varied candies than people in wider aisles (Study 1), and this effect of confinement in narrow aisles is extended to more unique choices in charities (Study 2), particularly in those with high chronic reactance tendency (Study 3). Moreover, the field study (94,110,967 usable transactions) used crowding as a proxy for confinement and found a positive relationship between crowding and variety-seeking in real grocery purchases.

As another type of space environment, the restaurant atmosphere, store environment, and web feature also could influence consumers’ variety-seeking. For example, Ha and Jang (2013a) collected 309 useable responses and pointed out that consumers’ desired hedonic and utilitarian values of the restaurant positively influence their variety-seeking intentions. Similarly, according to 617 usable responses to the restaurant experience, Ha and Jang (2013b) showed that atmospheric quality, overall boredom, and boredom with atmospheric attributes significantly influence dinners’ variety-seeking intentions positively. For the off-line store environment, Mohan et al. (2012) investigated 350 shoppers in Dubai and established that the store environment (including lighting, scent, and music) affects variety-seeking positively. For the online web feature, with 698 usable responses, Hung et al. (2011) demonstrated that quality web features affect interpersonal trust and platform credibility positively, and both constructs drive a user’s online community usage and brand variety-seeking behavior.

Second, the objective time of day could further influence variety-seeking in consumption. Given the influence of physical laws, people exhibit different levels of variety-seeking in consumption at different time points. For example, Roehm and Roehm (2004) found that people are more likely to seek variety in candy choices at low arousal (e.g., 9 AM; 10:00 AM–11:20 AM) than peak arousal (e.g., 4 PM; 3:10 PM–4:20 PM) moments of the day. However, the latest research provided an inconsistent result of diurnal variation in variety-seeking. Based on circadian rhythms in chronobiology, Gullo et al. (2019) applied four studies, including an empirical analysis of millions of purchases, and stated that individuals pick less varied flavors of yogurt when choosing in the morning. Furthermore, different external environments and changes in life events can change people’s variety-seeking. Koschate-Fischer et al. (2018) showed that consumers reduce their variety-seeking tendency after experiencing a life event (1,475 panelists).

Marketing Strategy

The marketing strategy influences consumers’ variety-seeking behaviors primarily in the purchase stage. Kahn and Louie (1990) first studied the relationship between retail stores’ promotion strategy and variety-seeking. They found that if only one shampoo brand is promoted and people are generally loyal to the last brand purchased, they tend to switch among shampoo brands when the promotion is withdrawn.

In the later stages, the research on the impact of marketing strategy has become in-depth, such as product packaging, product bundle strategy, product category and information, and product assortment. For example, product packaging uniformity is associated with arousal potential and influences consumers’ variety-seeking. Roehm and Roehm (2012) showed that consumers’ variety-seeking is greater in product categories where packaging is similar among competitors.

Furthermore, the product bundle strategy affects consumers’ variety-seeking when they experience multiple products. Mittelman et al. (2014) found that consumers seek more variety when choosing from single offerings (e.g., a choice of two individual candy bars) than from bundled offerings (e.g., a choice of a bundle of two candy bars), which is termed “offer framing effect.” Kim et al. (2018) based on the decision-framing effect and found that travelers show higher variety-seeking in travel package decisions when the bundle package is selected from a combined decision rather than from two single decisions.

Moreover, product category and product information affect variety-seeking behavior. For the product category, several researches were conducted from various perspectives. Based on a specific-abstract categorization strategy, Kim and Yoon (2016) showed the “category specificity effect” and revealed that individuals are likely to order a greater variety of dishes when the menu contains no category labels or abstract category labels due to the enhanced perception of variety offered in the menu. Baltas et al. (2017) indicated that in hedonic product categories, consumers seek more variety in sensory attributes, whereas, in utilitarian product categories, they seek more variety in functional attributes. What is the difference between digital and consumable goods? Adomavicius et al. (2015) showed a reduction in behavioral effects of bundle cohesion and timing on variety of preferences for digital goods. For the product information, Lin et al. (2017) indicated that when people purchase products for themselves, the presence of risky information and health claims, and high product involvement promote more variety-seeking.

Finally, as detailed in Section “Theoretical Perspective and Underlying Mechanism”, the displays and assortments of products affect consumers’ variety-seeking behaviors. For example, the display of novel geometric figure arrangement combinations (various shapes) increases consumers’ variety-seeking ( Maimaran and Wheeler, 2008 ). The horizontal assortment is easier to process and can increase individuals’ perceived variety, thereby ultimately leading to greater variety-seeking ( Deng et al., 2016 ; Table 1 ).

Factors investigated in variety-seeking bahavior in consumption.

Based on the previous research of variety-seeking behavior in consumption. Two are duplicated due to research desgin: Maimaran and Wheeler (2008) , Deng et al. (2016) .

Theoretical Perspective and Underlying Mechanism

Many theories and effects are used in the extent of variety-seeking behaviors in consumption research to explain the underlying mechanism that consumers seek variety during decision making and purchasing. The theoretical perspectives and underlying mechanism can be summarized in six groups: optimal stimulus level, personality characteristics perspective, emotional coping perspective, compensatory consumption perspective, environmental psychology perspective, and evolutionary psychology perspective. Several significant theories and effects were selected in each group and briefly discussed.

Optimal Stimulus Level

Optimal stimulation level theory is an early and fundamental theory to explore variety-seeking behavior in consumption, which is widely applied in the existing literature. One reason consumers seek variety in product selection is to meet their demand for stimulation ( Menon and Kahn, 1995 ). According to optimal stimulus level theory , the relationship among internal individual factors, external environmental factors, and consumer preference response can be represented by an inverted U-shaped curve function. In this curve function, the peak vertex of the curve is the optimal stimulus level, the attribute set under this level can cause the consumer’s satisfaction to reach the highest level, and the stimulus level on both sides of the vertex is too low or too high to satisfy the consumer ( McAlister, 1982 ). If consumers often buy the same product or category, their effective stimulus level in decision-making decreases. Therefore, to obtain greater stimulation, consumers attempt to buy different products or products to achieve their goals ( Roehm and Roehm, 2005 ). In addition, because of physiological stimulation and arousal (e.g., body temperature), consumers receive the least stimulation in the morning and produce a lower variety-seeking ( Gullo et al., 2019 ). Arouse theory was also applied by Roehm and Roehm (2004) and Huang et al. (2019) to explain consumers’ need for stimulation.

As the internal influencing factors, much research focus on the effect of personality characteristics on variety-seeking behavior in consumption from the individual perspective. As a result, theories and underlying mechanism of these effects are in varied forms, which are mostly based on the consumers’ personality traits. For example, Self-awareness theory and Goal orientation theory were adopted to explore how consumers’ self-awareness and promotion–prevention orientation affect their variety-seeking behaviors ( Goukens et al., 2009 ; Wu and Kao, 2011 ). According to Implicit Theory , consumers with a growth (vs. fixed) mindset are more likely to engage in variety seeking due to their changing preferences ( Li and Sun, 2021 ). Based on Signal theory , Sela et al. (2019) found that variety-seeking behavior can serve as a signal to indicate expertise. Personality characteristics also can shape consumers’ variety-seeking mindset and then promote variety-seeking behaviors ( Kim and Yoon, 2016 ; Zhang and Guo, 2019 ).

Emotional Coping

Emotions are the psychological states that people need to face every day. Different emotional states bring different stimulation levels to consumers. Based on the Mood evaluation framework , compared with positive emotions (such as happiness), negative emotions (such as sadness) bring low satisfaction to consumers; therefore, consumers experiencing negative emotions increase their satisfaction through variety-seeking behaviors ( Roehm and Roehm, 2004 ; Lin and Lin, 2009 ; Chien-Huang and Hung-Chou, 2010 , 2012 ; Lin et al., 2011 ; Lin, 2014 ). Building on Processing style theory , mortality salience increases variety-seeking behaviors in consumption by influencing an individual’s global processing style ( Huang and Wyer, 2015 ). Variety-seeking behavior in consumption is observed to help cope with and alleviate the negative effects of negative emotions. Optimal stimulus level theory also can help explain this. In an extremely positive mood state, consumers reduce their variety-seeking behaviors because the stimulus provided by variety-seeking behaviors in consumption belongs to the middle level, which is not enough to meet the demand for extreme positive emotions for stimulation ( Roehm and Roehm, 2005 ). However, consumers’ variety-seeking behavior when in a mildly positive mood (moderate degree) is influenced by product characteristics, such as security and pleasantness ( Roehm and Roehm, 2005 ).

Compensatory Consumption

The theory of sense of control is the core element in the compensatory consumption perspective. Compensatory consumption means that consumers engage in certain consumption behaviors to make up for the lack of psychological needs because of the lack of overall self-esteem or self-realization ( Gronmo, 1988 ). The essential feature of compensatory consumption is to make up for psychological defects or threats through consumption behavior, emphasizing consumption behavior as an alternative means and tool—rather than functional value—to meet demand. Compensatory consumption is a kind of pure psychological consumption and self-presentation of psychological imbalance. Therefore, in a variety of scenarios in which psychological defects and threats might occur, variety-seeking in consumption can be used as an alternative means to meet psychological needs and cope with threats. For example, because consumers with low social status and perceived low social mobility tend to have a low sense of personal control, they show more variety-seeking behaviors in consumption to compensate for their psychological defects ( Yoon and Kim, 2018 ). If people in love are “left out,” their sense of control in a romantic relationship is reduced—to restore a sense of control, they seek a variety of choices in consumption ( Huang and Dong, 2019 ).

Environmental Psychology

As mentioned earlier, environmental psychology focuses on the relationship between the environment and individuals’ psychology and behavior. The environment includes the physical and social environments, both of which have an important impact on people’s behavior.

First, spatial perception is a physical environment. According to the Resistance theory , if consumers feel constrained (such as in narrow aisles and among crowded people), they resist the invasion of private space through more various and unique choices, which is equivalent to resisting the constraint ( Levav and Zhu, 2009 ). In addition, according to the spontaneous effect , a diversified display of commodities stimulates consumers’ variety mindset, leading to the emergence of variety-seeking in consumption ( Maimaran and Wheeler, 2008 ). Finally, because of a match between the human binocular vision field and the dominant direction of eye movements (which are both horizontal in direction), it is easier for horizontal (vs. vertical) displays to be processed. This processing fluency allows people to browse information more efficiently, which increases perceived assortment variety and ultimately leads to more variety being chosen ( Deng et al., 2016 ).

Second, social groups and the cultural and political factors in the social environment affect variety-seeking behaviors in consumption from different aspects. The influence of society on consumer behavior is mainly constrained by social norms, which could be generalized by interpersonal and intrapersonal motivation . To maintain consistency with the group (normative constraints) and given the influence of group norms or opinion leaders, people might change their original consumption habits that are inconsistent with the reference group (to promote variety-seeking in consumption) or insist that the original consumption habits are consistent with the group (to prevent variety-seeking in consumption; Ariely and Levav, 2000 ; Ratner and Kahn, 2002 ; Choi et al., 2006 ; Fernandes and Mandel, 2014 ; Etkin, 2016 ; Finkelstein et al., 2019 ). Cross-culture theory explains the individual difference in variety-seeking from the root cultural perspective, and collectivism vs. individualism is the main cultural difference. Members of a collectivist culture tend to consist of group members, and their choices are associated with a less variety-seeking tendency than those of individualistic cultural backgrounds ( Kim and Drolet, 2003 ; Yoon et al., 2011 ).

Evolutionary Psychology

Evolutionary psychology research focuses on the influence of women’s ovulation period and gender differences, and scholars use the carry-over effect to investigate variety-seeking behavior in consumption between men and women. Given the influence of hormonal changes during the physiological cycle and to meet reproduction needs, women may be more sensitive to rewards and seek variety when seeking a spouse; therefore, they seek various and novel choices extend to irrelevant consumption choice tasks ( Faraji-Rad et al., 2013 ; Durante and Arsena, 2015 ; Chen et al., 2016 ). From an evolutionary perspective, Life-history theory demonstrates that people from relatively resource-abundant or relatively resource-scarce childhoods (i.e., childhood SES) often respond differently when faced with an environmental threat ( Griskevicius et al., 2013 ). Variety-seeking may be a risk reduction strategy against uncertainty about future taste preferences in simultaneous choices for future sequential consumptions among people from different degrees of resource childhoods ( Zhao et al., 2021a ; Table 2 ).

Theories and underlying mechanism used in variery-seeking behavior in consumption.

Based on the previous research of variety-seeking behavior in consumption .

Measurement Method

Presently, variety-seeking behavior in consumption could be measured by the survey and experimental methods. Although the diversified consumption scenarios and variety-seeking measurement methods used by scholars are different in research using the experimental method as the paradigm, they also can be roughly divided into three types: scenario simulation, real choices in experiments, and real shopping behavior data.

Measurement Scale

In the survey method, five-point and seven-point Likert scales are applied to measure participants’ variety-seeking. The items in the scales were adopted from the previous studies. Participants assess how much they would like to purchase or consider new and unfamiliar brands and products. To test the hypothesized relationships, structural equation modeling (SEM) is performed in research. For example, Hung et al. (2011) measured variety-seeking from Kahn et al. (1986) , which used five-point Likert scales (1 = strongly disagree, 5 = strongly agree). In the research of Fu et al. (2021) , variety seeking was measured by three items from Grünhagen et al. (2012) , with a five-point probability scale ranging from 1 (not probable) to 5 (very probable). A sample item is “I am willing to see different food products and brands.” In the research of Zhao et al. (2021b) , variety seeking was measured with the five-item Variety Seeking Scale ( Helm and Landschulze, 2009 ). A sample item is “Buying the same product or brand is boring, even if the product or brand is good.” Furthermore, Van Trijp et al.’s (1996) seven-point Likert type scale is also used by Ha and Jang (2013a , b) and Liu et al. (2021) . A sample item is “I am very cautious in trying new or different products.”

Scenario Simulation

In the experimental method, researchers usually describe a consumption scenario and ask participants to imagine a choice in this scenario. The two common choices are simultaneous selection (multiple products or services choices at one time) and sequential selection (one product or service at a time, multiple choices in a row). These choice scenarios include food consumption, purchasing behavior, tourism consumption, and so on. Typically, researchers use the number of products or services selected by participants as the variety-seeking index.

Purchasing and selecting products tasks are frequently used as selection scenarios in the research, the majority of which are used for the food selection task. For example, Simonson (1990) asked participants to imagine that they are going to the supermarket, and their shopping list contained eight products, each a different type of good. The author asked the participants to choose one good every day or choose for three days at a time ( Simonson, 1990 ). Many studies followed this research design ( Mukherjee et al., 2017 ; Gullo et al., 2019 ), such as the purchasing socks task (five out of nine; Yoon and Kim, 2018 ), the outing task (potato chips choose three out of four; Chen et al., 2016 ), the teatime reservation task (25 snacks, 20 options; Roehm and Roehm, 2005 ), and the sandwich pre-arranged task (seven out of nine; Goukens et al., 2007 ). The number of brand categories that participants selected is recorded as variety-seeking. In addition, the drinks choosing task is also applied in the research. For example, Goukens et al. (2007) designed a drink-selection scenario, in which participants imagined that they received a gift basket and could choose six drinks among eight flavors. Similarly, volunteer tasks (five out of six; Chen et al., 2016 ) and the tea beverage task (four options) exist ( Shen and Wyer, 2010 ).

Some studies also adopted other forms of selection scenarios. For example, Levav and Zhu (2009) designed the charitable donation task, in which participants can donate their reward for participating in the experiment to one, several, or all six charities. Goukens et al. (2007) designed a holiday scenario, in which participants imagined that they had won a free trip to Sri Lanka, including air tickets, accommodations, and four experience activities. They could choose four out of 16 activities: four beach activities, four outdoor adventures, four sports activities, and four cultural experiences ( Goukens et al., 2007 , 2009 ; Huang and Wyer, 2015 ). Furthermore, other studies considered cross-product categories’ choice tasks, such as food and stationery categories (tea drinks, potato chips, and books; Shen and Wyer, 2010 ; Huang and Wyer, 2015 ), daily necessities categories (lipstick, high heels, yogurt, candy, nail polish, and restaurant; Durante and Arsena, 2015 ), and entertainment activities (drinks, movies, weekend activities; Etkin, 2016 ; Gullo et al., 2019 ). In addition, some studies also used behavior switching to measure the variety-seeking in consumption ( Jiang et al., 2014 ). For example, Yang and Urminsky (2015) used magazines, music, and movies as experimental materials and measured their preference conversion through participants’ choices before and after.

Real Choice in the Experiment

This measurement method requires participants to make real choices during the experiment, but participants were not aware that their choices were influenced and recorded. This measurement method makes the variety-seeking behavior appear in a more realistic scenario, reflecting people’s relatively real, and potential choices and increasing the validity of the research results. Researchers usually let participants choose by selecting experimental rewards or compensation.

In the real selection task, many studies use candies or chocolate as the selection stimuli that are finally selected as rewards or compensation for participation considering the convenience of the experiment and the sample. For example, Simonson (1990) rewarded participants with snacks and asked them to choose between sweet and salty snacks (three total groups). Similarly, five rewards were available for choosing among nine snacks ( Choi et al., 2006 ; Durante and Arsena, 2015 ), up to five desserts, candies, or yogurts ( Yoon and Kim, 2018 ; Huang et al., 2019 ), the candy list selection task ( Ratner and Kahn, 2002 ; Roehm and Roehm, 2004 ), choosing three out of six types of candies ( Levav and Zhu, 2009 ), rewarding three of four lollipop flavors ( Chen et al., 2016 ), and the chocolate selection design for three out of four choices and six choices ( Maimaran and Wheeler, 2008 ; Yoon and Kim, 2018 ). In addition, Deng et al. (2016) fabricated a research purpose as investigating the influence of virtual store lighting on shopping patterns and gave each participant two dollars to buy the displayed candies.

In addition to snack choice tasks, researchers also asked participants to choose stationery frequently used by college student samples. For example, Levav and Zhu (2009) asked participants to choose three out of six color highlighters as rewards in Experiment 4, which was also applied in Gullo et al. (2019) . Another distinct and interesting selection task was the flower arrangement task designed by Mittelman et al. (2014) , who provided participants with differently colored roses that needed to be put in vases, and used the number of selected colors as a variety-seeking measure.

Real Purchase Behavior Data

In recent years, researchers began to call for the study of consumer behavior in the real environment. Scholars used purchasing data generated by consumers to measure the variety-seeking in consumption and analyzed variety-seeking using data obtained from various methods. Among them, consumer panel data from Nielsen and retail stores are often used by researchers ( Inman, 2001 ; Levav and Zhu, 2009 ; Yoon et al., 2011 ; Gullo et al., 2019 ); in such research, researchers typically used the ratio of the number of categories purchased to the total number of categories as a variety-seeking measure. In addition, some researchers conducted field studies among cities ( Koschate-Fischer et al., 2018 ), field experiments ( Yoon et al., 2011 ; Deng et al., 2016 ), or natural experiments ( Huang et al., 2019 ) to obtain real behavior data. Kahn et al. (1986) provided an analytical framework for how to use panel data to define and measure variety-seeking and offered seven simple and verifiable models commonly used in the marketing domain.

Universal Product Codes (UPCs) are useful and helpful when adopted to calculate consumers’ variety-seeking behaviors. Inman (2001) used UPCs to construct two indexes to measure consumers’ observed switching (the observable flavor or brand switching percentage) and expected switching (which is calculated based on Zero Order; Grover and Srinivasan, 1987 ). The switching index is then calculated as: Relative Switching Intensity = (Observed – Expected) Flavor –(Observed – Expected) Brand .

Levav and Zhu (2009) used purchasing data in Study 5 to compute a variety-seeking index that captured the extent of variation in a transaction. This was computed for each customer by dividing the number of unique UPCs purchased in a category by the category’s total purchases. The authors used its log odds to conduct an OLS regression using this variety index, with log (variety/(1- variety)), as the dependent variable. Gullo et al. (2019) followed Levav and Zhu (2009) , using scanner panel data from a major grocery chain’s single California location. They defined variety as the number of unique UPCs purchased in a category relative to the number of total items purchased. Similarly, Huang et al. (2019) used the Chicago Nielsen consumer panel data set and the number of UPCs per trip to measure variety-seeking.

In addition, Koschate-Fischer et al. (2018) combined two datasets, an individual-level consumer panel and a survey, collected over 3 years. They used the change in SOW and SOU to compute variety-seeking. SOW is the share of wallet, defined as the percentage of money a customer allocates to the preferred brand in a category (our unit of analysis). SOU is the share of units, defined as the percentage of units purchased for the preferred brand in a particular category, controlling for price level effects ( Table 3 ).

Summary of measurements of variety-seeking behavior in consumption in literature.

Based on the previous research of variety-seeking behavior in consumption.

Implications and Future Research Directions

Based on the proposed research framework of variety-seeking behaviors in consumption, this section discusses the implications of the aforementioned findings and identifies opportunities for future research in variety-seeking.

Implications of the Findings

This literature review shows that numerous researchers have studied the relationships between various internal and external factors and variety-seeking behaviors from distinct theoretical perspectives by using various measurement methods. All these attributes are delineated in the proposed framework of variety-seeking behaviors in consumption (see Figure 2 ).

Research framework of variety-seeking behavior in consumption. Based on the previous research on variety-seeking behavior in consumption.

Concerning internal factors, gender and age in the category of individual demographics are the two most adopted aspects. Researchers have attempted to discover the different effects of variety-seeking between males and females and between younger and older people. Power, indecisiveness, and novice have attracted considerable research attention for personality characteristics. Some researchers also study the effects of various emotions, such as positive moods, sadness and happiness, local optimism and pessimism, and winning-losing perception. In addition, some notable and interesting physiological states, such as hunger and sleepiness, are discussed in variety-seeking behaviors in consumption. In the category of sensory clues, researchers have focused on investigating the effect of vision (e.g., novel visual stimulus, the display way of products, a superficial feature) and taste (e.g., flavor and spicy). A few papers examine how consumers’ mindset affects variety-seeking behaviors.

Regarding external factors, social relationships and social culture are widely used to investigate the effect of the macro social environment on variety-seeking, as variety-seeking behaviors could meet some social motivations. Space and temporal factors from the external environment can also influence variety-seeking in consumption. Moreover, some researchers are concerned with marketing strategies in variety-seeking, including product packaging, product category, attribute type, and the displays and assortments of products.

Among the theoretical perspectives and underlying mechanisms, optimal stimulus level theory is the most fundamental and widely applied theory to explain consumers’ variety-seeking behaviors when facing external stimuli. Personality characteristics perspective is applied in much research. How these traits affect variety-seeking depends on core characteristics of individual difference, which is mostly related to “changing” or “uniqueness.” Emotional coping is another common perspective used by researchers, and it has been explored from the board mood (e.g., positive mood) to the specific mood (e.g., happiness, sadness, mortality salience). Some researchers found that variety-seeking can meet the lack of psychological needs: the compensatory consumption perspective. In recent years, researchers have drawn on environmental psychology and evolutionary psychology theories to examine how environmental factors and gender differences affect consumers’ variety-seeking behaviors, which provides novel insights into the literature.

The four main measurement methods used by researchers include measurement scale, scenario simulation, real choices in experiments, and real shopping behavior data. The measurement scale is adopted from previous studies. Scenario simulation is applied primarily to the experiment method, and the number of consumers’ various choices is used as the variety-seeking index. Researchers also adopt real choices in experiments and real shopping behavior data from real retailers to investigate consumers’ variety-seeking behaviors, reflecting their actual choices and behaviors.

Future Research Directions

This paper reviews and combs through the related research on variety-seeking behavior in consumption. The current framework summarizes internal and external influencing factors, theoretical perspective, and underlying mechanisms and measurement methods of variety-seeking behavior in consumption, which has theoretical value for further insights into the literature. Despite the ongoing progress, future research can focus on the following aspects.

First, additional research is needed to widely and deeply explore the external factors influencing consumption variety-seeking behavior. The proposed research framework shows that most past research concentrated on internal factors; thus, future research should extend to external environmental factors. Regarding the social environment, other factors, such as economic inequality ( Goya-Tocchetto and Payne, 2022 ) and perceived social mobility ( Wang et al., 2022 ), are also rooted in people’s lives and determine their thinking styles and behaviors; therefore, it should be determined how these societal factors drive the variety-seeking behavior in consumption. Regarding the physical environment, the space environment has many presentation modes. Excepting narrow space, individuals may also experience a chaotic physical environment ( Vohs et al., 2013 ), encouraging them to break the tradition and change consumers’ preferences, choices, and behaviors. Future research could explore whether physical order in the external consumption environment influences variety-seeking behaviors. In terms of marketing strategy, the influence of salespersons has been little concerned. Many characteristics of salespersons affect consumers’ emotional or irrational decision-making and purchase intentions, such as appearance attractiveness ( Li et al., 2021 ) and tone and voice ( Liu et al., 2021 ). Future research could investigate variety-seeking behavior in consumption from the aspect of salespersons.

Second, future research could investigate variety-seeking behavior in consumption with specific situations, such as catastrophes and significant public health affairs. In these specific situations, variety-seeking behavior in consumption also shows particular functions. For example, consumers’ psychology and behavior have changed during the COVID-19 pandemic. Given that this period differs from previous times, the factors affecting consumers’ variety-seeking behavior should be determined, along with the psychological process and underlying mechanisms. From the perspective of compensatory consumption theory, it is also worth considering whether the health, economic, social, informational, and environmental threats caused by the epidemic can influence variety-seeking behavior in consumption ( Campbell et al., 2020 ). These threats may decrease consumers’ perceived personal control ( Burger et al., 2011 ) and ontological security ( Banham, 2020 ). As an “adaptive” response, the variety-seeking behavior may help consumers largely cope with sudden threats ( Min and Schwarz, 2021 ). Future research should further explore this question.

Third, future research could explore variety-seeking behaviors in diversified consumption contexts. Current studies primarily examined purchasing or shopping for daily essentials ( Choi et al., 2006 ; Shen and Wyer, 2010 ; Durante and Arsena, 2015 ; Gullo et al., 2019 ). Some scholars also tried to extend research scenarios to other consumption contexts, such as dining in restaurants ( Huang and Kwong, 2016 ) and charitable donations ( Levav and Zhu, 2009 ). Future researchers could investigate more variety-seeking behaviors in other common consumption behaviors in daily life, which lack attention. In addition, people could also have consumption behaviors in other situations, such as online shopping, purchasing service in massage shops, traveling across cities or countries, sporting goods purchases, or medical inquiries in the online community. The factors influencing consumers’ variety-seeking behaviors in such different situations have not been discussed in detail or sufficiently. This research gap provides an opportunity for scholars to introduce variety-seeking into the domains of e-marketing, service marketing, cause-related marketing, the online health community, and others. It is an essential step to enrich the current findings and provide novel research perspectives for other research fields.

Fourth, future research could explore variety-seeking behavior in the digital consumption world, which the current field has not fully discussed. As a digital platform to promote information sharing and user-created content, social media has innovated the way people connect, communicate, and develop relationships. The unique characteristics of social media may challenge the existing theories and frameworks explaining cognition, emotion, and behaviors ( McFarland and Ployhart, 2015 ), meaning that future research on variety-seeking behavior should also consider the impact of the new media environment ( Woolley and Sharif, 2022 ). For example, because people have anonymous perceptions, their communication on social media could avoid the negative influence of face-to-face connections. Future research can determine if social pressure from traditional communication still has the same effect on variety-seeking behaviors. Since social media provides more opportunities to share information across an extensive range of people, future studies can examine whether this broad mindset triggers variety-seeking behaviors. Furthermore, social media is an essential platform for companies to deliver brand information to target consumers, and future research could investigate the impact of brand display style in social media on variety-seeking behaviors in consumption.

Fifth, with the development and application of emerging technology in marketing (such as artificial intelligence, virtual reality, and augmented reality), future research could focus more on the relationship between these high-end technologies and variety-seeking behaviors. For instance, service robots may bring novelty experiences to consumers. Service robots in the consumption context may influence variety-seeking behaviors because the satisfaction of novelty and curiosity is a significant internal motivation for individuals seeking variety ( McAlister, 1982 ). Service robots could bring novelty and curiosity or result in fear and rejection if anthropomorphic forms are overused ( Mende et al., 2019 ). Consumers could adopt self-defense and protection mechanisms out of vigilance against fear and threats. Affected by a sense of identity threat, consumers may seek additional choices among similar commodities to avoid risks and make compensatory consumption ( White et al., 2013 ). Meißner et al. (2020) explored how virtual reality affects consumer choice and found that consumers show more variety-seeking in high-immersive than low-immersive virtual reality. Future research could investigate the underlying mechanism of the effect of virtual reality on variety-seeking behaviors and how augmented reality could affect such behaviors ( Rauschnabel et al., 2019 ).

Sixth, future research could consider solving inconsistencies in the existing literature, such as the effect of personal arousal level. Roehm and Roehm (2004) showed that people seek more variety at low arousal than high arousal moments. In contrast, Gullo et al. (2019) pointed out that individuals’ variety-seeking is lower in the early morning due to the lower arousal and stimulations. Another inconsistency is the effect of lack of personal control. Chang et al. (2021) found that failure weakens consumers’ perception of control, and consumers who have failed in a competition or not achieved a goal tend to seek less variety in subsequent consumption; however, according to compensatory consumption, prior research illustrated that variety-seeking as a compensatory strategy could restore the lack of personal control ( Yoon and Kim, 2018 ; Huang et al., 2019 ). Thus, researchers could investigate the deeper mechanism and boundary conditions of these incongruent findings.

Last, future research requires more diversified research designs and data collections. Most studies measured variety-seeking behavior in consumption in the laboratory environment or adopted simulated or physical selections to explore consumers’ more real choice behavior. Furthermore, some scholars used actual shopping panel data to explore variety-seeking behavior in consumption at different times ( Levav and Zhu, 2009 ; Yoon et al., 2011 ; Gullo et al., 2019 ); however, the current research on measuring variety-seeking behavior in consumption in the real environment is still insufficient. Researchers can increase their use of field experiments in future studies and explore more diverse and abundant physiological and behavioral data in real sales scenes to measure variety-seeking behavior in consumption. Additionally, more eye-tracking and neuromarketing EEG technologies also could be applied to obtain more accurate physiological data.

Variety-seeking, as a common choice strategy for consumers, benefits market segmentation, promotion performance, and consumers’ welfare, which has led directly to the increase in academic research and studies in recent years ( Koschate-Fischer et al., 2018 ; Gullo et al., 2019 ; Huang et al., 2019 ; Huang and Dong, 2019 ; Sela et al., 2019 ; Chang et al., 2021 ). The current article provides an intensive review of 61 identified papers in the marketing literature to understand how prior scholars explore the influencing factors of variety-seeking, investigate the underlying mechanism from distinct perspectives, and measure variety-seeking behaviors by various methods. These three parts are incorporated into a proposed research framework.

The influencing factors that researchers have adopted are classified into two categories: internal and external factors. Notably, internal factors have been widely discussed from five aspects: individual demographic, personality characteristics, emotion and physical state, sensory clues, and mindset. External factors involve three aspects at the present stage: social environment, physical environment, and marketing strategy, which are needed to extend. Thus, previous research is bound to various theoretical perspectives due to different influencing factors. Optimal stimulus level theory is a fundamental theory that has been widely applied in many studies to explain variety-seeking behavior. Other theoretical perspectives are also adopted to interpret variety-seeking behaviors in consumption, including personality traits, emotional coping, compensatory consumption, environmental psychology, and evolutionary psychology. These perspectives extend research fields of variety-seeking. Given measurement methods, survey scales are used to measure people’s intentions of variety-seeking, and scenario simulation is the most used approach to measure consumers’ variety-seeking in the experiment. Meanwhile, to observe variety-seeking behavior more objectively, researchers record participants’ real behaviors in experiments and analysis individuals’ real purchase behavior data from retailers.

Conversely, other important areas, such as digital consumption, emerging technology, and physiological measurement technology, have not received sufficient research attention, as well as other influencing factors and consumption contexts. Accordingly, this study identified several research gaps and proposed seven potential research directions for these areas. In addition, there are inconsistent findings in the existing literature. Future research could address these inconsistencies and provide explanations.

Overall, the contribution of this study is significant. Qualitatively, this paper conducted an intensive review of identified articles to reveal the influencing factors, theoretical perspectives, and measure methods of variety-seeking behavior in consumption and key findings, which can be used as an immediate reference for other researchers in this area. Quantitatively, this paper devised one research framework to incorporate the influencing factors, theoretical perspectives and underlying mechanisms, and measurement methods used in the 61 empirical studies, which provides a pictorial summary and enables readers to understand the body of research conducted on variety-seeking behavior in consumption. Further, this paper suggested seven future research directions, which may help researchers identify related topics in this subject area. The results of this study also have practical implications for the real world. Marketing managers could make segmentation based on internal factors, such as individual demographic and personality characteristics. Other internal factors, including emotion and physical state, sensory clues, and mindset, as well as external factors, could be manipulated in marketing activities, help to shape consumers’ variety-seeking behaviors and benefit promotion performance.

While this research has its merits, certain limitations remain. First, the review of the extant literature may not be exhaustive. More works are required to include relevant papers from different sources. Second, variety-seeking behavior in consumption is still in its concerning stage. Thus, additional journal papers with empirical results will continue to surface. More recently published variety-seeking research should be considered in future studies. Finally, in terms of article types, this paper focused on empirical studies, other conceptual or qualitative research is required.

Author Contributions

The author confirms being the sole contributor of this work and has approved it for publication.

Funding was provided by Huaqiao University’s Academic Project Supported by the Fundamental Research Funds for the Central Universities (21SKGC-QG05).

Conflict of Interest

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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In this matrix, based on Figure 3, Assael uses the difference between brands and the level of involvement of the consumer to give four genarlised buying behaviours. ‘Habitual’ is highlighted.

• Most frequently demonstrated type of buying behaviour.
• bought very frequently
• does not cost much money
• perceived to have few significant differences between brands.
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• toothpaste.

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• Published: 30 August 2020

Cognition and affect in consumer decision making: conceptualization and validation of added constructs in modified instrument

• Shakeel Ahmad Sofi 1 ,
• Faizan Ashraf Mir 2 &
• Mubashir Majid Baba 3  

Future Business Journal volume  6 , Article number:  31 ( 2020 ) Cite this article

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Metrics details

Cognition and affect have had stretched history of influencing the buying behaviour of an individual. The change in one of the dimensions leads to some proportionate change in corresponding factor, and a number of research studies have been carried out to ascertain the role of cognition and affect in consumer decision making. But most of the studies lack the evidence of scientific reliability and validity and nature of itemization in previous scales/papers has not been comprehensive as well. In the current endeavour, application of exploratory factor analysis and structure equation modelling has significantly tested the reliability and validity measures needed for impulsive buying scale that would largely facilitate different stake holders. This paper explores the process for how highly reliable and valid indicators of cognition and affect have been developed. The research design employed was a mixture of both exploratory and descriptive approaches that assisted author in classifying factors along with underlying items. Structured questionnaire was employed for the collection of data from the respondents. For validation and development of the modified scale, a set of reliable and scientific tools were employed included. Overall findings revealed that the instrument is vastly consistent and possesses both discriminant and convergent validity. Additionally, other reliability forms are on higher side which sustains the reliability of the scale. The current study will have larger credibility for researchers in the area of organizational behaviour, consumer behaviour and in other interdisciplinary areas.

Introduction

More often than not, purchasers settle on ample choices identifying with each component of their day to day life. In any case, a large portion of these goals are made without a lot of examination and just less accentuation is given to end results related with a specific choice. For the most part, in greater part of customer dynamic conditions, purchasers scarcely engage the sufficient level of data investigation. Or maybe, it would become tedious practice if all the purchasing choices involve the requirement for broad exertion. In any case, in opposition to it, in the event that all the buys are made generally, at that point they would frequently have the affinity to be exhausting, dreary and would barely carry happiness or newness to a purchaser. The level of an effort that a customer practices for getting to the base of issue to a great extent relies upon the degree of his/her accuracy for choice measures, the extent of data he/she is as of now having about the item previously and the openness to the quantity of substitute choices [ 1 ].

Essentially buyers only from time to time have all the fundamental data or acceptably exact data or even a sufficient degree of intrigue or motivation to make the purported perfect judgment. It is hence that purchasers are constantly confined by their current abilities, unyielding conventions of life, by all accounts and aspiration forever, and by their constrained degree for understanding [ 1 ].

Consumers are always seen reluctant to engage themselves in expansive decision making for they have no time in the world and are thus always prepared to patch up just for good enough. For the most part, purchasers do not have the opportunity to look for options which limits their extension for settling on sound choice [ 2 ]. Past examinations in the field of consumer behaviour have made every effort to make a differentiation between the individuals who are rash purchasers and the individuals who are not [ 3 , 4 ]. Despite the fact that such undertaking is costly and important in its methodology, it is not liberated from being dark and the way that pretty much everyone takes part in inconsistent suddenness and that even well unsurprising rash purchasers can and do have the control over their inclination now and again to control their lack of caution [ 5 ].

Hitherto, there have been number of attempts to develop scales for determining impulsiveness and reasons thereof. But it still requires immense workout for developing comprehensive framework that could facilitate different stakeholders in the estimation of impulsiveness across different consumer groups which the present study on scale development has fittingly taken into consideration [ 5 ].

Cognition and affect have had stretched history of influencing the buying behaviour of an individual. The change in one of the dimensions leads to proportionate change in corresponding factor. A plethora of research has been conducted to ascertain the cognition and affect of a consumer. But most of the studies lack the evidence of scientific reliability and validity. The nature of itemization in previous scales/papers has not been comprehensive as well. In the current endeavour, the application of SEM and EFA has brought some significant reliability and validity to the impulsive buying scale that would largely facilitate the academicians and corporate as well.

The present work on cognition and affect associated with an end user focuses on improved, validated and inclusive framework of scale development from consumer behaviour perspective. An attempt has been made to re-validate earlier developed scales in the subject of two psychological paradigms. In this study, a sample of 405 was selected and multi-stage sampling method was espoused to reach the ultimate sample unit.

This paper explores the process for how highly reliable and valid indicators of cognition and affect have been developed. The research design employed was a mixture of both exploratory and descriptive approaches that helped the investigator in classifying various factors of the study. Exploratory factor analysis was employed for classification of factors and items, confirmatory factor analysis was utilized to establish the reliability and validity of the proposed scale as was applied in study conducted by Sofi and Nika [ 6 ].

The current study was largely designed to modify and develop a scale for measuring impulsive buying behaviour among young consumers. The study is based on core objectives where primary focus has been to reframe the impulsive buying behaviour into two major psychological dimensions of cognition and affect. This also included methodical procedure of item exploration for various sub-constructs of cognition and affect and also to develop the construct for ascertaining buying tendency of a consumer. In this direction, an effort was also prerequisite and of absolute magnitude to validate different constructs of the modified scale and to test different constructs of the modified scale for its reliability.

The entire work is grounded on six progressive sections where “ Introduction ” section is based on introduction about the study, which proposes rationale and back ground of the study. Furthermore, “ Literature review and itemization ” section is focused on the literature review on the two psychological frameworks of cognition and affect and also includes discussion on research gap. “ Research methodology ” section discusses research methods employed for the development of scale and comprises of the results associated with EFA and CFA, and results of the pilot study are also part the section. Moreover, analysis along with results and discussion and conclusion is elucidated in “ Analysis ”, “ Results and discussion ” and “ Conclusion ” sections, respectively.

Literature review and itemization

( How to diagnose impulsive buying )

The process that has been adopted in this study for exploring different constructs of impulsive buying and buying tendencies has been comprehensively discussed as a part of literature review. Furthermore, itemization of impulsiveness has been classified into two major psychological components of affect and cognition.

Cognitive determinants

Various traits of impulsive buying are required to be set apart so as to perceive the hasty purchasing conduct of youth. In past, incalculable exploration has led to investigate significant qualities of impulsive buying behaviour.

In prior research studies, a few traits have been recognized to gauge hasty purchasing inclinations of a purchaser. These develop to a great extent fit into two significant mental segments of cognition and affect. Few constructs including scant planning, prudence and cognitive deliberation and no prominence to potential results emerging from a specific purchase to a great extent decide the discernment of a shopper [ 5 ].

Scant planning

The level of chase with impulsive buyers is consistently on lower side, and they could scarcely stand to look for elective alternatives. The impulsive buyers do not have time on the planet to come out from their day by day calendar of meandering guilty pleasure. Youthful buyers all in all do not search for anything and do not incline towards arranging about explicit items during a shopping trip. Spontaneous buying behaviour crops up when buyers have un-conscientious crave to unexpectedly acquire a product [ 3 , 7 , 8 ]. More often than not, it is the desire for style that convinces spontaneous purchasers to superfluously buy the things prompting impulsive buying. A large portion of the prior investigations have discovered meager arranging as an essential segment related with impulsive buying. So inadequacy in arranging is without a doubt one of the noteworthy components that uncovers impulsive buying, yet the idea of arranging is as yet obscured and is subject to the situation as well [ 5 ].

Prudence and cognitive deliberation

Impulsive shoppers have the tendency to relate their unreasonableness to fragile and personal factor of indulgence and gratification. In a study related to the current topic and in particular to the mood-impulse buying relationship, impulse buying has been defined as an umbrella idiom that involves unpredictable, spontaneous and deliberate performances [ 9 ].

Weinberg and Gottwald [ 10 ] originally recognized that spontaneous shoppers display unrelenting push for emotions preoccupied with enjoyment, joy and eagerness. It was also confirmed that spontaneous buying behaviour also rests on the personality of an individual and that cognition cannot be the sole factor to discriminate the range of preferences. As per their views, even though processing of information plays pivotal role in the affirmation of buying decisions, but its heaviness is only miniscule than from that of emotions.

Insufficiency in cognitive forethought may result in superfluous decisions such as displeasure, lament, remorse feelings, financial tribulations and low self-esteem. These fallouts are the indicators of decisions being made out of hassle [ 6 ] and without any prudence and cognitive deliberation. Further, this rationalization supports the conviction that the propensity to purchase something on craze is conveyed by negligible cognitive efforts.

No prominence to potential consequences

Impulsive buyers are not really worried about the final products related with spontaneous purchasing choices, and spontaneous purchasers do not consider the expenses related with such choices. Impulsive buyers are overall unreflective in nature. It is prompt delight that manages all the contending variables of levelheadedness and fulfilment of the quick joy is the bone of dispute inserted in impulsive buyers. As for the reason, impulse buying behaviour is a means of satisfying the short-lived desires [ 7 , 8 ].

The idea of inclination to offer significance to adjacent prizes above distal prizes has been concentrated in the psychological systems of self-discipline [ 11 ]. In social sciences, impulsivity is conceptualized as the decision of quick, however, littler prizes over bigger postponed ones [ 12 , 13 ]. The inclination to deform the evaluation of results gives in poise to allure feelings, which can be recognized as acting naturally focused, narcissistic, intolerant and narrow-minded, happy-go-lucky and missing a thoughtfulness for the upcoming events in life [ 5 ].

Belief about impulsive buying

Conviction shapes the focal piece of insight and of significant purchasing choices made by a typical purchaser. The conviction about impulsiveness is significant segment of impulsive buying since conviction about lack of caution would to a great extent decide the future purchasing expectation. The more grounded the conviction about incautious purchasing being unreasonable, the more slow the purchasing recurrence and the other way around. An impulsive purchaser hardly cares about the buying frequency, and his/her belief would negatively correlate with impulsive buying. There is each opportunity that a normal purchaser and impulsive buyer would give some distinction of supposition regarding conviction about impulsive buying [ 5 ].

Affective determinants

Affective determinants are hard to be estranged from its cognitive facets for the reason that the two psychological dimensions of affective and cognitive responses are reflected to be experienced concurrently and are strongly interrelated. The following sub-sections discussed here underneath have been adopted for item generation for affective dimensions of an impulsive buyer.

Undesirable advocacy to buy

A drive is a genuinely goal-oriented stage where an individual experiences feelings and physiological incitement and when need is induced, it incites the customer and channels him/her into the following phase of drive. As drive further increases, the energy for dynamic misrepresents, following predominant degree of association and data regulation. Shopper impetuses and need distinguishing proof go together and here motivating forces are promptings related with the items, administrations and data that customers perceive the specific purchase will delight. Inducements also known as enticements can be reflected as enforcements that persuade the shopper’s behaviour in the direction of heartwarming needs [ 14 ]. At the end, inducement is related to the need distinguishing proof stage, where promptings go about as impetus to thin the space between the genuine and foreseen stage. Purchasing motivations are outlined as an overpowering inclination to purchase just as mighty and emotionally animated and to be related with unrivaled potential for passionate incitement. So definitive, possible, purchasing urges take need over all the demonstrative or sound investigation relating to the buying choice. Hirschman [ 15 ] suggested that more often than not and in lion’s share purchasing circumstances the customer’s placid sentiments potentially impact the covered up hungers for that invigorate a sudden purchasing choice. The second the chase for want is set off, the desire gets so legitimate and tenacious that it orders immediate achievement. Customers are biased by an event of inside difference between both normal and stirring drives when a rushed purchasing motivation strikes [ 5 ].

Cognitive dissonance

It is as yet mysterious whether impulsive buyers go through post-purchase cognitive disequilibrium. Disequilibrium after buying would mean countering the rash purchasing conduct as post-purchase conflict, if any surfaces within the buyer will constrain the purchaser to examine about future purchasing choices. Yet, research in past has discovered that impulsive buying is restricted in centre and does not take part in any reflection about upcoming results emerging out of the reckless purchase. Be that as it may, after such purchase, negative feelings surface inside a customer, which change to a more elevated level of pressure and this post-purchase negativeness along with stress is known post-purchase cognitive disequilibrium or cognitive dissonance [ 8 , 16 , 17 ] and is significant viewpoint related with estimation of impulsive buying conduct. These examinations in the field of impulsive buying indicate that, at the pre-purchasing stage, spontaneous purchasers might be progressively open to their sensations or mindset states. At the post-obtainment stage, spontaneous customers exhibit more incitement joined by sensations than do non-spontaneous end clients. The psychological cacophony manifests when the end clients take part in extreme interior trade of thoughts caught between purchasing driving forces and the soul of ability to restrict them. Rook additionally stated, giving up to purchasing tendencies may bring about inciting defenseless feelings contiguous the purchasing want. Subsequently, bargaining to passionate clashes and cacophony might be connected with unconstructive and skeptical musings (for example, regret feeling or regretting self) that buyer may have in the wake of settling on a careless purchasing choice [ 5 ].

Affirmative buying sensations

It was initially emphasized that spontaneous buyers exhibit enlarged feelings of enjoyment, amusement, eagerness and joy [ 10 ]. Chang et al. [ 18 ] argued that consumers who had more positive emotional responses to the retail environment were more likely to make higher impulsive purchases [ 19 ]. Piron [ 20 ] came up with his recommendations that in-house stimuli refer to cravings, irresistible desires and domestic feelings that stimulate consumer’s deep longing and force an unexpected purchase.

In a study conducted by Weinberg and Gottwald [ 10 ], that was designed to examine the role of emotions in non-spontaneous shoppers and spontaneous shopper, it was observed that impulsive buyers tend to be extremely engrossed, more thrilled and highly passionate than non-spontaneous purchasers which was also corroborated through other studies [ 6 ]. Studies conducted in past and findings associated with them have been documented in tabular form given below (Table  1 ).

Research gap

Most of the preceding efforts have been deficient in view of approaches adopted in general and statistical measures in particular which plays dominant role in the studies focused on scale development, and this inadequacy requires to be done away with the application of number of arithmetical procedures perquisite for ascertaining psychometric properties of any scale. In addition and more importantly, the youth with varied level of cognition and affect are still at wide expanse and at opposite ends of the continuum. There is large deficiency in terms of factors of cognition and affect with reference to youthful consumers. Besides this, there is deficiency of reliable findings with regard to assessment of the degree of cognitive deliberation, scant planning and prominence on potential consequences arising from impulsive purchase in a comprehensive manner. In the same way, there is huge discrepancy with regard to affect determinants as of now, there have been only modest attempts to explore the scale of irresistible urge to buy, mood management, positive buying emotions and post-purchase disequilibrium or emotional conflict that occur as a result of spontaneous purchase among dissimilar youth having diverse personalities. Though there have been potential attempts by number of researchers to outline and frame out the components of cognition and affect, itemization in each of the constructs lacked comprehensibility.

In the yester years, a number of studies have been conducted on smaller scale to diagnose the association between interior aspects of an individual and impulsive buying behaviour, but such consumer impulsive studies in past could only help categorize people as “spontaneous” or “cautious” consumers and forecast whether an individual might act impulsively, but these endeavours did not recognize the grounds for such impulsivity, nor did they account for how impulsively a person may act. Therefore, the current work has bestowed academicians with a framework that would elucidate how intrinsic dimensions of a consumer can trigger needs for pleasure and manipulate urges to act impulsively and determine the buying tendencies towards a particular item.

Moreover, previous studies distinguished impulsive buying into two chief components of cognition and affect and reduced the overall study into constructs which lacked the universal reliability and validity. The constructs merely consisted of few items that lacked the premise of generalizing and universalizing the results, and this has been fittingly accounted in the current study. A study on affect and cognition was conducted by Coley [ 21 ], which although distinguished cognition and affect into various sub-dimensions but was deficient in the utilization of higher statistical tools necessary for attaining reliable results and determining the validity of an instrument, and this limitation has been done away in the current endeavour through the application of higher order statistical measures. Lately, research study conducted by Sharma [ 22 ], adopted the conceptual framework of cognition and affect for exploring impulsive buying behaviour. Even though the two psychological components of cognition and affect were further divided into sub-constructs, but item adoption was not comprehensive and it was again taken care in the current study and the results of the present work in this regard can help researchers interested in the subject of impulsiveness, cognition and affect in future endeavours.

Therefore, the present work is an attempt to fill all these deficiencies and modified ABC Scale (Affect, Buying Tendency and Cognition) focuses on to frame out a model that would highlight in particular, the association between cognition, affect and the tendency to buy a particular product and on the whole would helpful to explicate the psychological paradigms of a consumer.

Research methodology

It is an established fact that the appropriateness of the methodology chosen for a research determines the quality of research in management science especially in consumer behaviour because it involves investigating people from the psychological perspective. In this section, an effort has been made to present various parameters of research methodology employed in the current study.

Research design

The research design included both explorative and descriptive approaches where former approach was employed for preliminary identification of the problem and then redressing the problem through the application of descriptive research design. Exploratory design was primarily adopted to explore different constructs, and data were collected through longitudinal design as data during preliminary instances did not achieve various reliability and validity measures. Several additions and deletions were made during various phases until the scale achieved desired model fit indices, reliability and validity measures.

Sampling design

Area of study and sample frame.

The area of study was Srinagar City, and data were collected from the institutes of higher learning that included Government Degree Colleges.

Population for the Study

The population above 18 years of age was considered as sample for the study. Majority of the population as said earlier included students studying in various colleges of the Srinagar city. The population was further dived into three groups of Early Adulthood (18–23), Middle Adulthood (24–29) and Late Adulthood (30–35).

Sample size

Selection of an optimum sample size is always the core issue that researchers face to make their study more reliable. Based on scientific research table and after ascertaining various measures prerequisite for sample size determination, a sample size of 405 was chosen for this study.

Sampling method (technique)

Multi-stage sampling was adopted for this study. Sample size being 405 was divided among 7 colleges in view of total strength of government recognized colleges in Srinagar city being 7 and furthermore, a sample of 58 from each of the college and a sample of 59 from one of the colleges were taken for appropriate sample distribution. Further, 20, 20 and 18 students were taken from three broader specializations including Basic Science, Non-medical and Humanities, respectively. In addition, systematic sampling was used for the selection of final respondents and was selected based on specific college identification mark.

Structured instrument (ABCS-Questionnaire based on-Affect-Cognition-Buying Tendency Scale) consisted of three sections, Section A included demographic characteristics of the respondents, while as B comprised of impulsive buying behaviour variables and finally section ‘C’ included eight statements about buying tendencies for specified products. Apart from demographics which consisted of nominal scales, 5-point scale was adopted for rest of the items following the methodology of previous studies [ 6 ].

Preliminary testing

A sample of 100 respondents was chosen from the University of Kashmir. The participants included students including both male as well as female from various postgraduate streams. Furthermore, for equal representation, the sample was evenly distributed across various groups including Gender, Age, Income, Marital Status and Nativity.

Assessment of scale properties/preliminary phase

Assessment of scale properties is imperative both at the preliminary stage and during the course of main study. Before a research is conducted at larger scale, it becomes important to test both the reliability and validity of an instrument at lower scale.

• Reliability

Reliability of the questionnaire during pilot study was assessed mainly through overall Cronbach alpha, split-half reliability and inter-rater reliability. The findings associated with the results of three measures are discussed in the followings sub-sections.

Overall Cronbach alpha

The “Cronbach alpha” associated with the pilot study calculated through SPSS is .76 which is much higher than the acceptable level in social science research [ 23 ] (Table  2 ).

Inter-rater reliability

As with overall Cronbach alpha, inter-rater reliability results also supported the reliability of the instrument and are depicted in Table  3 .

Split-half reliability

Through the application of SPSS, 60 items were split into two portions with 30 items in each. The results show satisfactory correlation coefficients alpha yielded from the split-half reliability test and are shown in Table  4 .

Composite reliability

Composite reliability was also assessed during pilot study to support the reliability of the scale at lower level. It is clear from Table  5 , composite reliability for all the factors is much higher than minimum acceptance level of .60 supporting the reliability of the instrument.

Before performing factor analysis on the data set collected during pilot study, normality of the data was assessed through skewness and kurtosis and for all the measures; skewness and kurtosis were within the range of ± 1.96. Factor analysis was carried on data collected for factor extraction necessary for determining validity of the scale. Based on the results of factor analysis, 10 factors were extracted in total. Initially, a total number of items being 70 were reduced to only 60 items and rest of the items had to be deleted. Besides it, initial EFA on impulsiveness extracted 10 factors, but after examining rotated matrix only 8 could be retained.

Face and content validity

During the course of preliminary investigations, the questionnaire was given to some research experts for their critical observation and suggestions. The validity of the instrument was examined through the application of Face and Content Validity, Construct Validity, Convergent Validity and Discriminant Validity . During preliminary investigation, the instrument was discussed with different experts for their critical analysis with reference to overall shape of the questionnaire and their suggestions and changes were also incorporated in final framework.

Construct validity

Both construct and discriminant validities have enormous importance in scale validation and scientific studies, and the results associated with both the validity concepts are discussed as under:

Convergent validity

Convergent validity shows whether items in a factor are converging in the particular factor. The higher the association of items in the particular factor, higher would be convergence. Convergent validity was assessed using AVE concept, and it could be seen (Table  5 ) that AVE is more than acceptable level of .50 for all the constructs [ 24 , 25 ].

Discriminant validity

This form of validity indicates dissimilarity and constructs being different from each other. For assessing discriminant validity of the instrument at preliminary stage, Fornell and Larcker [ 24 ] procedure/formula was employed and it could be observed from the matrix represented below that for all the factors, square root of AVE is more than their correlation coefficient (Table  6 ).

The data (at full scale) were initially analysed through SPSS for exploratory factor analysis, and finally AMOS was used for confirmatory factor analysis.

Factor analysis

To explore different factors that could be obtained by reducing overall 52 items into several meaning and compressive dimensions, exploratory factor analysis was employed. In all the scale development problems, factor analysis is fundamental and has to be carried on the data to extract number of correlated dimensions representing the whole set of observed items.

To start with, initially there were 60 items but for reliable results prerequisite for factor analysis and measurement model and for having lower loadings, eight items were omitted which reduced total number to 52 and 9 factors were produced by principal component analysis. After reviewing the 9 underlying factors, the items of impulsiveness produced eight factors. One factor had loadings lower than items in other factors and one stronger loading as well, but it was not taken for further analysis and hence impulsiveness produced only seven factors. In addition, the observed items of buying tendencies were factor reduced to 1 underlying construct.

Comprehensive process of factor analysis

At the very outset, the EFA analysis produced desirable results with KMO figures being .85 being much elevated than tolerable range of .50 and thus accordingly the assumption of null proposition that the Correlation Matrix is an identity matrix that was discarded by “Bartlett’s Test of Sphericity”. Following the EFA manoeuvre, the fairly accurate Chi-Square 18,601.835 with 4005 degrees of freedom, which is significant at .05 level ( p  < .05). Thus, the EFA has been deemed to be suitable for analysing the correlation matrix as has been done in previous study [ 6 ].

Number of factors

Tables  7 ,  8 and  9 illustrate the application of principal component analysis to impulsive buying behaviour and impulsive buying tendencies problem. Under communalities initial column, it can be seen that the communality for each variable from 1 to 52 is 1 as unities were inserted in the diagonal of the correlation matrix.

It can be seen from Tables  7 and  8 that the eigenvalues for every construct/factor expectedly are in the diminishing order ranging from construct/factor 1 to 52 with constructs/factors having eigenvalue greater than 1 only being retained (9 in this case) and which account to 70.006 per cent of the total variance.

The scree plot evidently extracted 9 factors having eigenvalue above one (see Fig.  1 ), and rest of the constructs/factors in twist-shaped were disqualified from further investigation.

Determining items falling in the respective constructs/factors

For this purpose, instead of component matrix, rotated matrix as shown in Table  10 was employed.

It is obvious that 8 factors could only be retained and 9th factor has only two items with loadings of Item A13 = .541 but the same item loaded in factor 8 which is highly supported by the past literature and hence was retained for factor 8 only as is indicated in rotated matrix (see Table  11 ).

Further, item (A23) has higher loadings in factor 8 than in factor 9 and is therefore retained for factor 8 only. The eight (8) underlying factors together explain 67.98 per cent of the data which are highly acceptable.

Testing statistical assumptions

For generalization of the findings, certain assumptions comprising of skewness, kurtosis, Q–Q plots and homoscedasticity were tested whose results are discussed in following section.

Skewness and kurtosis

Normality was explicitly demonstrated by the data as the skewness for the variables including cognition and affect was well within the range of + 1.96. Skewness was assessed across all indicators of impulsive buying behaviour (cognition and affect) and one variable of buying tendencies.

Kurtosis was also worked out in the present study, and kurtosis for majority of the variables was found within the range of ± 1.96. Furthermore, it was observed that for all variables, kurtosis approached to zero but not to absolute zero. Therefore, based on the findings of skewness and kurtosis, it could be assumed that the data are approximately normal.

Q–Q plots were also employed for examining the normality of the data. Following the analysis of the data, all Q–Q plots supported the normality assumption as the dots were found very close to central line with only little departure from the centre.

Homoscedasticity

Homoscedasticity is indicated if the variance of errors is same across all the levels of independent variables. It indicates that variance in the dependent variables does not come from the limited range of independent variables. The data that do not meet the assumption of same variance of errors suffer from heteroscedasticity. Heteroscedasticity can weaken the multivariate analysis raising the probability of type I error. The scatter plot of standardized predicted dependent variable against the independent variable (Residual) broadly shows a pattern-less cloud of dots, with no wider coning, and it stays consistent when we move towards top confirming the assumption that data are Homoscedastic (Fig.  2 ).

Results and discussion

In this section, an effort has been made to test the data for exploring factors through exploratory factor analysis which were then confirmed through confirmatory factor analysis.

Impulsiveness and its dimensions

In rotated factor matrix (see Table  11 ), Factor 1 has higher loadings for the variables B1, B2, B3, B4, B5, B6, B7 and B8 and was labelled as buying tendencies for specified products (Buying Tendencies-BT).

In rotated factor matrix (Table  11 ), Factor 2 has higher loadings for the variables A7, A10, A31, A32, A34, A35 and A42 and having coherence for determining the degree of planning within the observed items, and the resultant factor was labelled as scant planning.

Further, Factor 3 has higher loadings for the items A1, A17, A25, A26, A30, A33, A36, A37 and A41 and was labelled as undesirable advocacy to buy.

Factor 4 has higher loadings for the variables A2, A11, A12, A15 and A16 and was labelled as affirmative buying sensations.

Factor 5 has higher loadings for the variables A4, A8, A9, A14 and A20 and was given the theme belief about impulsive buying.

Factor 6 has higher loadings for the variables A6, A18, A19, A38, A43, and A44 and was labelled as cognitive dissonance.

Factor 7 has higher loadings for the items A27, A28, A29, A39 and A40 and based on the review of the previous literature and researchers pragmatism, it was labelled as no prominence to potential consequences.

Factor 8 has higher loadings for the variables A3, A5, A13, A21, A22, A23 and A24 and was labelled as prudence and cognitive deliberation.

To conclude the results of exploratory factor analysis, EFA has produced eight (8) underlying factors. During the process of EFA, some of the items were deleted and consequently added. It took three EFA repetitions to attain final factors for the study. Because of the space limitations, it was not possible to highlight all the repetitions in final set; hence, only final EFA results have been documented here. Now, factors generated through EFA are required to be tested through CFA as well which has been comprehensively discussed in “ Confirmatory factor analysis ” section.

Confirmatory factor analysis

To examine composite reliability measures, path analysis, item loadings, error terms and to inspect various fit indices of the measurement model, confirmatory factor analysis was employed. It was particularly employed to establish various validity measures and for this purpose standardized regression weights and correlations were supplemented by CFA.

Confirmatory factor analysis was carried on the data using EFA results of observed items of impulsive buying and impulsive buying tendencies for specified products. The results of the measurement model being part of structural equation modelling have been expansively discussed below. Overall, the scale was found to be reliable and both the construct validity and discriminant validity were achieved for all the constructs.

Measurement model of the study

In the current study, the Measurement Model was based on the premise of EFA results and all the constructs were permitted to correlate with each other in a single measurement model to assess validity and reliability measures (please see Fig.  3 ) (Item loadings not visible can also confirmed from Table  16 of Appendix).

Measurement model

Measurement model fit indices

The Fit Indices of the current framework were well within the satisfactory level, and indicators investigated robustly were [Chi-square = 2454.304, Probability level = .000 ( p  < .05)], and CFI was found to be .933, GFI was .91, AGFI = .81, NFI = .91, TLI = .92, PNFI = .92 RMR = .09, and RMSEA = .049.

Reliability and convergent validity

Apart from model data fit requirements, various psychometric characteristics having relevance to scale development were also tested. As is demonstrated in Table  12 , with reference to composite reliability (CR), it exceeded the desired cut-off value of .60; thus, it is logical to state that the ABC scale is robustly reliable in view of higher value of CR [ 6 ].

For Convergent Validity, Average Variance Extracted was tested. It is clear from Table  16 , the constructs have accomplished Convergent Validity for Average Variance Extracted being higher than minimum satisfactory level of .50. For Table  16 , refer to “ Appendix ” for scale constructs along with their itemization.

Discriminant Validity was also tested through the intra-evaluation of square root of average variance extracted and correlation of the constructs [ 24 ]. It has been observed that for all the constructs, Square Root of Average Variances extracted was much larger than their Correlation Coefficient which confirmed Discriminant Validity of the Instrument ( see Table  12 ).

Common variance method

In the current study for exploring common method variance, ‘Harman’s One - Factor ’ diagnostic assessment was employed to classify the likely occurrence of undue errors. Furthermore, after employing exploratory factor analysis through the application principal component analysis with rotation being limited to Varimax method, all the underlying items were forced to one factor extraction. The outcome of single-factor exploration was found reliable as solitary factor resulted in 22.460 of the total variance thereby pointing probable nonexistence of common method variance. Furthermore, using AMOS 20, all items were exposed to one factor examination and to explore fit of the Confirmatory Factor Analysis Model . Results in this case showed that single-factor model had fit issues, with approximate fit scores of χ 2  = 760, p  = 000; GF = .641; CFI = .591; LI = .583; and RMSEA = .131. Therefore, based on the results associated with one Factor Extraction and one Factor Model Fit , it was resolved that larger portion of the variance in this data is explained by the specific constructs with study being unaffected by common method variance [ 26 ].

Nomological validity

Eight constructs/factors were split up into two factors of impulsiveness vis-a-vis positive and negative indicators. Prudence and cognitive deliberation, belief, scant planning (reversed) and no to potential consequences (reversed) were pooled together for their strength in determining rational decision making within an end shopper. Negative indicators of a consumer viz. affirmative buying sensations, undesirable advocacy to buy, emotional conflict (cognitive dissonance) and buying tendencies were pooled for having tendency to classify irrational and emotional buying behaviour of an end user [ 6 ].

In the current endeavour, the nomological validity was tested across positive indicators and negative indicators and model based on positive and negative indicators produced robust indices: χ 2 / df , root mean square error of approximation (RMSEA) and incremental fit indices like normed fit index (NFI), relative fit index (RFI), incremental fit index (IFI), Tucker–Lewis index (TLI) and comparative fit index (CFI) which were recorded as .083, .872, .863, .894, .886, .912 and .893, respectively. The negative regression weights (beta value = − .816) and significant critical ratio (CR) values (− 2.909) of the relationship between positive and negative indicators confirmed the nomological validity of the scale.

Other forms of reliability

It would be unscientific and irrelevant not to test other reliabilities which share significance in studies such as the current endeavour on scale development. For this purpose, following reliabilities were examined to support composite reliability (CR).

Overall reliability: this was tested through Cronbach alpha coefficient whose value associated with ABCS which is .877 (Please See Table  13 ) (greater than .60; [ 27 ].

Split-half reliability was also examined for the current scale. Through the application of SPSS, investigators evaluated and divided scale in two portions for ascertaining their degree of correlation and it resulted in satisfactory results as is demonstrated in Table  14 .

Inter - rater reliability: For this purpose ‘Average measures Intra-Class Correlation’ was explored as is shown in Table  15 . It may be noted that ‘Average measures Intra-Class Correlation’ value in Table  15 is higher than .70 and is well supported.

In the current endeavour, investigators have made rigorous efforts to identify items for the current scale (ABCS) which were further content validated and thoroughly tested for reliability. This study based on scale development is corroborated and has improved on past studies on the subject as factors/constructs have been modified on the whole and following the operation of various arithmetical procedures like SEM, and EFA, it has accomplished reliability of the multi-item scale that was wanting in past scales and their application has largely validated the current instrument as well. It will not be exaggeration to reaffirm that the current study on scale development would act as estimable source for corporate to envisage, how impulsivity shapes across varied youth having different buying tendencies? How level of cognition and affect and their determinants change across different consumer groups? Moreover, it would also facilitate corporate to realize, how degree of cognition and affect together determine the propensity to purchase impulsively the particular brand or an item?

To the researcher’s awareness, this endeavour is the first of its kind to conceptualize and operationalize the association between impulsive buying behaviour and buying tendencies for particular shopping items in complex framework. In view of highly acceptable results associated with reliability and demonstration of convergent and discriminant validity necessitate that the process espoused for establishing the underlying relationship between affect, cognition and buying tendency scale (ABCS) is a unambiguously valid, within a solid theoretical base. Therefore, the current study bequeaths future researchers and marketing intelligence personnel with a legitimate measurement instrument to construct healthier theories on impulsiveness that consumers have for different products. Though the researcher has included only eight statements for determining impulsiveness for specific products, different products on each of the eight statements may be added in future and further analysed to explore how people will report their impulsiveness on each of the specified item.

It is appropriate to call that theoretically current attempt contributes in innumerable ways and in wider perception, this effort augments scientific community associated with an end consumer research with uncovered insights in consumer spontaneousness and predominantly related to impulsiveness while controlling the effects of psychological mediators.

It would be apposite to reaffirm that a plethora of research efforts have been made in yester years concerning psychological paradigms of a consumer vis-a-vis cognition and affect but most of them had restricted factors having only few number of items that reduced their property of generalizability across globe. The current research has modified constructs of impulsive buying vis-a-vis cognition and affect and buying tendencies for specific products among youth at large. In the current endeavour, validated and reliability tested measures in the subject of impulsive buying have been put forward. This work has greater bearing and acceptance in the areas of consumer domains and to make this study a unique one, a number of dimensions were extracted through the comprehensive mechanism of literature review and exploratory factor analysis and were item modified to evolve factors/constructs persistent with consumer behaviour.

The current work has bestowed academicians with a framework that would elucidate how intrinsic dimensions of a consumer can trigger needs for pleasure and manipulate urges to act impulsively and determine the buying tendencies towards a particular item. Also previous studies distinguished impulsive buying into two chief components of cognition and affect and reduced the overall study into constructs which lacked universal reliability and validity. The constructs merely consisted of fewer items that lack the premise of generalizing and universalizing the results, and this has been fittingly accounted in the current study. A study on affect and cognition was conducted by Coley [ 21 ], which although distinguished cognition and affect into various sub-dimensions but was deficient in the utilization of higher statistical tools necessary for attaining reliable results and determining the validity of an instrument, and this limitation has been done away in the current endeavour through the application of various higher order statistical measures. Lately, research conducted by Sharma [ 22 ] adopted the conceptual framework of cognition and affect for exploring impulsive buying behaviour. Even though the two psychological components of cognition and affect were further divided into sub-constructs, but item adoption was not comprehensive again and it was again taken care in the current study and the results of the present work in this regard can help researchers interested in the subject of impulsiveness, cognition and affect in future endeavour.

Preceding studies on impulsiveness and buying tendencies have had explored relationship between the two aspects without any modification in items which have been mostly organizational behaviour oriented. In the present work, a number of impulsive buying behaviour dimensions were extracted through the comprehensive mechanism of literature review and exploratory factor analysis and were item modified to evolve factors/constructs persistent with consumer behaviour. Studies in past on impulsive buying have had exploited independent approach in the ascertainment of association between the variables which was again done away through the application of combined framework of measurement model. Furthermore, the items for the scale in the current study have been identified, content validated and reliability tested through entirely different process. This work improves on previous studies as constructs have been given new shape altogether and findings of the study could be a good source for different stakeholders to picture out, how impulsivity shapes across youth? How level of cognition and affect and their determinants change across different consumer groups? Furthermore, it would also facilitate them to realize, how the impulsiveness determines the propensity to purchase impulsively the particular brand or an item? To the researcher’s awareness, this endeavour is the first of its kind to conceptualize and operationalize the association between items of impulsiveness and buying tendencies for specific products in complex framework. Higher reliability measures and significant results on convergent and discriminant validity demonstrated that the current model/scale utilized for measuring association between items of affect, cognition and buying tendencies is a reliable and valid. Therefore, the study offers researchers and marketing managers a legitimate framework that could be employed in future for better understanding the nature of impulsiveness and that consumers have for different products.

Research in past on impulsive buying itself and in association with buying tendency has been a subject of phenomenological shortcomings and largely failed to classify what essentially decides impulsiveness. In this research endeavour, following precise attempts by researchers, novel items and constructs have been appended to cognition and affect which is an achievement that will assist majority of stakeholders. The results of the current study are reliable and corroborate with earlier findings on impulsive buying and are distinguishable as well. These findings on the other hand are impermanent and provisional by the composition of respondents in view of their sample and by methodological limitations [ 6 ].

Limitations and directions for future research

Efforts have been made to make the study representative of the population and result oriented by choosing an optimal sample size but still following limitations are felt:

The unnoticed inhibition on the part of some consumers to divulge the true feelings which is a normal feature in such kind of surveys can be a limiting factor.

Researchers in future could employ intrinsic (personality) and extrinsic (advertisement) variables in combined framework to examine their impact on impulsive buying behaviour.

The conceptual frame work adopted in the present study could be employed on other consumer groups different from young people to explore their response on impulsive buying behaviour.

Cohort analysis on impulsive buying across several consumer groups in relation to different products could also be taken to study the change in impulsive buying behaviour over a period of time.

Though the researcher has included only eight statements for determining impulsiveness for specific products, different products on each of the eight statements may be added in future and further analysed to explore how people will report their impulsiveness on each of the specified items.

Availability of data and materials

The data sets used and analysed during the current study would be available from the corresponding author on reasonable request, and furthermore, all the data analysed during this study is included in the published article.

Abbreviations

Exploratory factor analysis

Structure equation modelling

Affect cognition behaviour

Average variance extracted

Analytical moment of structures

Statistical package for social sciences

Kaiser–Meyer–Olkin

Comparative fit index

Goodness of Fit Index

Tucker–Lewis index

Normed fit index

Adjusted Goodness of Fit Index

Root mean square error of approximation

Root mean square residual

Square root of average variance extracted

No to potential consequences

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Sofi, S.A., Mir, F.A. & Baba, M.M. Cognition and affect in consumer decision making: conceptualization and validation of added constructs in modified instrument. Futur Bus J 6 , 31 (2020). https://doi.org/10.1186/s43093-020-00036-7

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Habitual Buying - Definition, Importance & Example

What is habitual buying.

Habitual buying is the buying behavior of customers where they are making repeat purchases number of times of an already known brand without the process of high involvement and decisioning. The product is perceived as commodity and doesn’t provide much difference from its rivals. Customer under habitual buying behavior goes for the products which they are buying regularly and where they don’t give much thought before buying it.

Importance of Habitual Buying

Habitual buying consumer buying behaviour may also be due to fact that customer finds the product best fit for his use and keeps on buying it without looking for alternative. And it doesn’t mean that there is less number of choices in front of customer and he has to choose it anyhow. The customer repeatedly chooses the product without giving much thought as the product does not have much difference with others. Repeated purchase is known as habit buying. And the products they purchase are also cheap and there is no hazard in buying it.

Companies put in a lot of effort in ensuring that habitual buying behaviour of a customer is created. This habit formation is done using continuous efforts in delivering customer needs, improving relations and thereby enhancing customer loyalty.

• Buying Motives
• Dissonance Reducing Buying
• Consumer Buying Behaviour
• Buying Pattern
• Complex Buying

Examples of Habitual Buying

1. One of the simplest example of habitual buying is purchase of goods of daily needs. The purchase of milk or bread in the nearby store is the example of habitual buying behavior.

2. Despite several brands of beverages or cola items, people develop preferences of a few brands or flavours. This is another example of habitual buying.

Hence, this concludes the definition of Habitual Buying along with its overview.

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10 Aspects of Habitual Buying: Understanding the Super Power of Psychology and Implications

Introduction.

Habitual buying, a fascinating aspect of consumer behavior, revolves around the repetitive purchasing habits individuals develop over time. It is a phenomenon deeply ingrained in our daily lives, where certain products or brands become an automatic choice due to familiarity, convenience, and emotional attachment. Understanding the psychology behind habitual buying is crucial for marketers to design effective strategies that tap into this behavior and create lasting customer loyalty.

At its core, habitual buying is when individuals engage in repeated purchasing behavior without much conscious thought or consideration. It is driven by the power of routine and automation, where certain products become an integral part of our everyday lives. For instance, imagine the morning routine of a coffee enthusiast who cannot start their day without a hot cup of their preferred brand. This habitual buying behavior is deeply rooted in their daily routine, and they automatically reach for that particular coffee brand without actively considering alternatives.

A classic example of habitual buying can be seen with the success of tech giant Apple. Once individuals become accustomed to the user-friendly interface, sleek designs, and seamless integration of Apple products into their lives, they often develop a strong attachment and loyalty to the brand. This emotional connection, combined with the ease and convenience of using Apple products, keeps customers continuously coming back for more, even when there may be other options available in the market. Habitual buying becomes a part of their identity, reflecting their lifestyle and values.

Factors Influencing Habitual Buying

Routine and Automation: The Influential Factors of Habitual Buying

In today’s fast-paced world, routine and automation play a significant role in shaping consumer behavior, particularly when it comes to habitual buying. Let’s delve deeper into the factors that contribute to habitual buying within the context of routine and automation:

Convenience and Familiarity

One of the primary reasons for habitual buying is the convenience and familiarity associated with certain products or brands. When individuals establish a routine of purchasing a specific item, it becomes ingrained in their daily lives. For example, buying a certain brand of toothpaste or a preferred brand of coffee becomes an automatic decision due to its familiarity and ease of acquisition.

Time-saving and Effortless

Habitual buying provides consumers with a sense of time-saving and effortlessness. When a particular product has consistently met their needs in the past, individuals find it more efficient to continue purchasing it without considering alternatives. This aspect is especially prevalent for frequently consumed products or everyday necessities, such as groceries or personal care items.

Reduced Cognitive Load

Making decisions requires cognitive effort, and habitual buying helps reduce this load. Once a routine is established, consumers no longer need to actively evaluate different options, compare prices, or research alternatives. They rely on their ingrained habits, allowing them to make quick and effortless purchasing decisions.

Psychological Comfort

Habitual buying can also provide individuals with a psychological sense of comfort and security. When people stick to what they know and trust, they feel reassured that their needs will be consistently met. This emotional attachment to familiar products or brands can act as a driving force for habitual buying behavior.

Inertia and Resistance to Change

Changing one’s habitual buying behavior can be challenging due to the concept of inertia. Once a habit is formed, individuals may experience resistance or discomfort when attempting to switch to a different product or brand. This resistance often stems from the fear of the unknown, potential dissatisfaction, or the inconvenience associated with trying something new.

The Psychological Aspect of Habitual Buying

1. Habit formation : Habits are deeply ingrained behaviors that become automatic responses to specific cues or triggers. They are formed through a process called habituation, where repeated actions or routines create neural pathways in the brain that make the behavior more automatic and less conscious. Marketers can leverage this natural tendency towards habit formation to encourage repeated usage of their products.

2. Cognitive biases : Habitual buying is also influenced by various cognitive biases. One such bias is the mere exposure effect, which suggests that people tend to develop a preference for something simply because they are familiar with it. This bias is often exploited by marketers through consistent brand exposure, thereby increasing the likelihood of habitual buying.

3. Loss aversion : Once a habit is formed, individuals may experience discomfort or resistance when trying to change their purchasing behavior. This is due to loss aversion, a psychological phenomenon where people are more motivated to avoid losses than to acquire equivalent gains. As a result, individuals are more likely to stick to their habitual buying patterns rather than explore new options.

4. Inertia : Habitual buying is often characterized by inertia, where individuals continue to buy products out of habit rather than actively considering alternatives. This is because habitual buying requires minimal cognitive effort as individuals rely on their automatic responses. Marketers can capitalize on this inertia by making their products easily accessible and convenient to purchase.

5. Emotional attachment : Emotional attachment plays a significant role in habitual buying. Consumers may develop a strong emotional bond with a particular brand or product, which reinforces their tendency to make repeated purchases. This emotional attachment can be cultivated through effective branding, storytelling, and creating positive experiences that resonate with consumers on a deeper level.

6. Decision-making shortcuts : Habitual buying is also influenced by heuristics, which are cognitive shortcuts or rules of thumb that individuals use to make decisions quickly and efficiently. For example, individuals may rely on the availability heuristic, where they base their decision on how easily they can recall instances of purchasing a particular product in the past. Marketers can leverage these decision-making shortcuts by ensuring their brand is easily recognizable and associated with positive experiences.

Effects of Habitual Buying

1. Customer Retention

Habitual buying plays a vital role in customer retention. When consumers develop a habit of purchasing a particular product or brand, they are more likely to continue purchasing it over time. The familiarity and convenience associated with habitual buying reduce the effort required for decision-making, leading to increased customer loyalty.

Effective customer retention through habitual buying provides several benefits to businesses:

– Increased customer lifetime value: By consistently meeting the needs of habitual buyers, businesses can generate a steady stream of revenue from loyal customers. This extends the customer relationship, increases the average purchase frequency, and enhances the overall value of the customer over time.

– Cost savings: Acquiring new customers can be more expensive than retaining existing ones. Habitual buying reduces the need for continuous marketing efforts aimed at acquiring new customers, thus saving costs associated with customer acquisition.

– Word-of-mouth marketing: Habitual buyers who are satisfied with a product or brand are more likely to recommend it to others, contributing to positive word-of-mouth marketing. This can lead to an influx of new customers and further reinforce customer retention.

2. Competitive Advantage

Habitual buying can confer a significant competitive advantage on businesses. Once customers develop a habit of buying a specific brand or product, they become less likely to switch to alternatives, even when presented with competitive offers. This advantage can manifest in various ways:

– Market share dominance: Brands that successfully establish themselves as habitual choices in the minds of consumers can capture a substantial share of the market, making it difficult for competitors to gain traction.

– Pricing power: The loyal customer base built through habitual buying allows businesses to have more flexibility in pricing. Customers who have developed a habit of purchasing a particular brand are often willing to pay a premium price, reducing the price sensitivity of these buyers.

– Protection against price wars: Habitual buying mitigates the impact of price wars in the marketplace. When consumers are loyal to a particular brand, they are less affected by price fluctuations and less likely to switch to cheaper alternatives, thus shielding businesses from intense price competition.

3. Barriers to Entry

Habitual buying creates barriers to entry for new entrants in the market. When consumers have strong habits associated with a specific brand or product, they are less likely to experiment with unfamiliar alternatives. This poses challenges for new entrants looking to establish their presence and gain market share.

– Consumer resistance to change: Habitual buying triggers a sense of attachment and loyalty among consumers. Breaking these habits and convincing consumers to switch to a new brand requires significant effort and resources. The resistance to change acts as a barrier for new entrants, making it harder for them to win over customers from established players.

– Brand recognition and trust: Habitual buying is often a result of a long-standing relationship between consumers and a particular brand. Established brands enjoy higher levels of recognition, credibility, and consumer trust, which further reinforce habitual buying behavior and create obstacles for new players.

4. Brand Equity

Habitual buying has a profound impact on a brand’s equity. Brand equity encompasses the value and perception associated with a brand in the minds of consumers. Habitual buying contributes to the development and strengthening of brand equity in several ways:

– Emotional connection: Habitual buying can foster emotional connections between consumers and a brand. Emotional attachment leads to increased brand loyalty, positive brand associations, and a willingness to advocate for the brand.

– Perceived quality and reliability: Habitual buying reflects consumer confidence in the consistent quality and reliability of the product or brand. The repetition of purchases builds the perception that the brand consistently delivers on its promises, further enhancing brand equity.

– Positive brand image: The consistent purchasing behavior associated with habitual buying creates positive brand image and visibility. As more consumers engage in habitual buying, the brand gains prominence and recognition in the marketplace.

– Premium pricing: Habitual buying enables brands to command premium prices based on the trust and loyalty established with consumers. Consumers who habitually buy a brand are often willing to pay a higher price, perceiving the brand to be of higher value than its competitors.

Strategies for Marketers to boost Habitual Buying

Reinforce positive experiences

    – Providing exceptional customer service: Prompt and helpful assistance creates a positive experience, increasing customer satisfaction and the likelihood of habitual buying.

    – Consistent quality: Ensuring the product or service consistently meets or exceeds customer expectations builds trust and reinforces habitual buying behavior.

   – Creating memorable experiences: Going above and beyond to create memorable experiences through personalized interactions or surprise elements can leave a lasting impression, strengthening the emotional attachment to the brand.

Create brand rituals

   – Developing rituals or routines associated with the product: Marketers can encourage habitual buying by integrating the brand into consumers’ daily routines. For example, a coffee brand can associate itself with the morning ritual of brewing coffee, creating a strong habitual connection.

   – Utilizing sensory cues: Associating the brand with specific sounds, scents, visuals, or taste can trigger an automatic response from consumers, reinforcing habitual buying behavior.

Loyalty programs

    – Rewarding loyal customers: Implementing loyalty programs that offer exclusive benefits, discounts, or personalized offers to repeat customers can enhance their sense of appreciation and strengthen their attachment to the brand.

   – Personalized recommendations: Utilizing customer data to provide personalized recommendations or suggestions tailored to individual preferences can increase the chances of habitual buying.

Stimulate word-of-mouth

    – Encourage satisfied customers to share their experiences: Leveraging the power of word-of-mouth marketing, marketers can encourage satisfied customers to share their positive experiences and recommendations with their social networks. This can significantly amplify the effects of habitual buying, as recommendations from trusted sources carry more weight.

   – Utilize social media influencers: Collaborating with social media influencers or brand ambassadors who align with the brand values and target audience can help generate buzz, increase brand visibility, and drive habitual buying behavior.

Seamless and convenient purchasing experience

    – Streamline the purchasing process: Simplifying the buying process, offering multiple payment options, and ensuring a user-friendly interface on both online and offline platforms can remove barriers and encourage habitual buying.

   – Subscription models : Offering subscription-based services or products can create a sense of convenience and automates repeat purchases, making it easier for customers to maintain their habitual buying behavior.

   – Predictive analytics: Utilizing customer data and predictive analytics to anticipate customer needs and proactively offer relevant products or services enhances convenience and reinforces habitual buying.

Continuous innovation and personalization

   – Regularly updating products or services: By introducing new features, flavors, or variations, marketers can keep customers engaged and prevent habit boredom, ensuring long-term habitual buying.

   – Personalization based on customer preferences: Leveraging customer data, marketers can personalize product offerings, recommendations, and messaging, creating a sense of exclusivity and fostering habitual buying.

Building trust and credibility

   – Transparent communication: Openly sharing information about the brand, its values, ethics, and sourcing can build trust and credibility, enhancing habitual buying behavior.

   – Social responsibility: Engaging in socially responsible initiatives or sustainability efforts can resonate with conscious consumers and reinforce their commitment to the brand, leading to habitual buying.

Implications for Marketers

Understanding the influence of routine and automation on habitual buying behavior can be invaluable for marketers. Here are a few strategies to consider:

Establishing Convenience : Make sure your product or brand is readily available and easily accessible to consumers. This could involve optimizing distribution channels, offering online purchasing options, or partnering with retailers who align with your target audience’s routines.

Reinforcing Familiarity : Build brand loyalty by consistently delivering a positive experience and maintaining product quality. Through effective marketing and communication strategies, emphasize the benefits and reliability of your product, creating a sense of familiarity and trust in your target audience.

Exploiting Automaticity : Identify opportunities to position your product as a “go-to” or default choice within consumers’ routines. By aligning your product with existing habits, you increase the likelihood of habitual buying. For example, positioning your energy drink as the perfect afternoon pick-me-up for office workers can create a habitual purchasing pattern.

Leveraging Personalization : Use automation and technology to customize your offerings based on individual preferences and previous purchasing behavior. By tailoring product recommendations or sending personalized offers, you can strengthen the connection between your brand and habitual buying.

Challenges and Ethical Considerations

Consumer manipulation : Marketers must strike a balance between influencing buying behavior and respecting consumer autonomy, avoiding manipulative tactics.

Over-reliance on habits : Excessive reliance on habitual buying can hinder innovation and prevent consumers from exploring new and potentially better alternatives.

Environmental impact : Habitual buying can contribute to overconsumption and waste, making it essential for marketers to promote sustainable purchasing habits.

Habitual buying is a complex psychological phenomenon that plays a significant role in consumer behavior. Understanding the factors influencing habitual buying and employing effective marketing strategies can lead to increased customer loyalty, brand equity, and competitive advantage. However, marketers must also be mindful of the ethical implications and long-term sustainability of encouraging habitual buying behavior

Samrat is a Delhi-based MBA from the Indian Institute of Management. He is a Strategy, AI, and Marketing Enthusiast and passionately writes about core and emerging topics in Management studies. Reach out to his LinkedIn for a discussion or follow his Quora Page