Abstract
A common problem faced by contemporary retailers is consumers’ tendency to avoid purchases that postpone or prevent cash flow for retailers, thus negatively affecting retailers’ sales and profits. Little is known about the factors that drive consumers to avoid purchases or about marketing tactics that may reduce the tendency, especially from a cultural perspective. The authors attempt to fill this gap by exploring the role of an important cultural variable, namely, power distance belief (PDB), on consumers’ tendency to avoid purchases. PDB is the extent to which people accept and endorse inequalities in society. A series of 14 studies (including 6 studies in the Web Appendix) using a variety of operationalizations of the key variables suggest that consumers high (vs. low) in PDB are less likely to avoid purchases (Studies 1a, 1b, and 1c) because they generally perceive greater constraints on their behavior (Study 2). These constraints are aversive, triggering the desire to overcome them and to have more as a compensatory mechanism, thereby reducing the tendency to avoid purchases. Accordingly, low (but not high) PDB consumers’ tendency to forgo purchases is significantly decreased when they perceive greater constraints on their choices and decisions (Study 3) and when they experience a high social density (Study 4). However, high (but not low) PDB consumers’ tendency to avoid purchases significantly increases when individuals perceive that constraints facilitate hierarchy (Study 5) or that constraints lead to positive outcomes (Study 6). Theoretical and managerial implications are discussed.
Keywords
We don’t want to become a showroom for the online operators such that people come and look around, and then order from someplace else. —Warren Buffett speaking about Berkshire Hathaway's brick-and-mortar furniture operations at the 2019 Annual Shareholders Meeting
A growing problem faced by contemporary retailers is consumers’ tendency to avoid purchases (Liao and Keng 2014). Indeed, many retailers are struggling to convert shoppers to buyers (Holmes 2016). For example, the home improvement store chain Lowe's recently reported that traffic to its stores is booming, but sales are falling (Ruff 2018). Consumers’ tendency to avoid purchases reduces cash flow for retailers, negatively affecting sales and profits (Anderson and Wilson 2003). Thus, it is important to identify consumer segments that are more or less likely to avoid purchases, as well as factors that influence consumers’ tendency to avoid purchases; this knowledge enables marketers to influence purchase avoidance tendencies.
An important, but surprisingly underexplored, factor that may influence the tendency to avoid purchases—defined as the act of not purchasing any available options, including not purchasing at all (i.e., forgoing) or purchasing later (i.e., deferring) (Anderson 2003; White et al. 2011)—is the consumer's cultural background or values. Cultural factors are some of the most important determinants of consumer behavior (Shavitt et al. 2006), but there is little understanding of how cultural segments may differ on purchase avoidance tendency. Moreover, some research points to national differences in the mobile conversion rate, that is, the ratio of the number of sales to the number of users using mobile (Criteo 2014). For example, mobile conversion rates are much higher in Japan (202) and South Korea (161) than in France (68) and Italy (59); the U.S. score was benchmarked to 100 (see also Briley, Morris, and Simonson 2005). However, these findings lack sufficient explanation.
Although the studies noted previously may indirectly point to cultural differences in purchase avoidance tendency, they have several limitations. First, because these studies use nationality as a proxy for culture, they do not provide information on which dimension of culture, if any, is responsible for the effects. Second, they do not provide a theoretical framework linking cultural constructs to purchase avoidance tendency. Third, they do not shed light on underlying mechanisms and boundary conditions. Fourth, they provide no guidance to managers on how to reduce consumers’ tendency to avoid purchases.
In this article, we address these gaps by proposing an important determinant of purchase avoidance, namely, power distance belief (PDB)—the extent to which people accept and endorse hierarchy and inequality in society (Zhang, Winterich, and Mittal 2010). High (vs. low) PDB cultures tend to be rigid, be inflexible, discourage movement across social classes, restrict emotional expression, limit freedom of articulation, and have strict rules for conduct and behavior (Carl, Javidan, and Gupta 2004; Hofstede 2001). These characteristics enhance the restrictions perceived by individuals on their judgments, behaviors, and choices, and induce a “constraints mindset,” which is an aversive state that consumers are motivated to alter or attenuate (Clee and Wicklund 1980) by triggering a desire for abundance; one way to satisfy this desire is by acquiring products (Park, Lalwani, and Silvera 2020).
We focus on PDB among other cultural dimensions because of its increasing relevance in modern society. The size of the U.S. middle class has been decreasing for the last several decades (Pressman 2019) and inequalities are rising (Azhar 2022), especially after the COVID-19 pandemic, leading researchers to issue urgent calls to understand the ramifications (Perry, Aronson, and Pescosolido 2021). Moreover, the issues we address have several theoretical and managerial implications. Theoretically, our research is the first to systematically examine how consumers’ cultural backgrounds and values (e.g., PDB) influence their tendency to avoid purchases. 1 In addition, we advance theory by showing that the relationship between PDB and purchase avoidance tendency runs through participants’ perceived constraints. We also identify boundary conditions. In so doing, we go significantly beyond previous research (Mandel et al. 2017) that suggests a main effect relationship between perceived constraints and purchasing behavior.
Managerially, our findings enhance retailers’ ability to understand when their target consumers are more or less likely to avoid purchases and suggest several tactics they can use to increase sales. For example, managers who wish to reduce purchase avoidance among their consumers should target high PDB countries, states, and neighborhoods; activate a high PDB via ads, slogans, or point-of-purchase material; increase perceived constraints; or increase social density. We elaborate on these tactics and discuss implications for policy makers in the “General Discussion” section.
Conceptual Background
PDB refers to the extent to which people accept and endorse hierarchy in society (Han, Lalwani, and Duhachek 2017). PDB significantly influences varied consumer behaviors (summarized in the Web Appendix), such as charitable giving (Han, Lalwani, and Duhachek 2017; Winterich and Zhang 2014), preference for national- versus private-label brands (Wang, Torelli, and Lalwani 2020), and the persuasiveness of celebrity endorsements (Winterich, Gangwar, and Grewal 2018). However, no previous research has examined how PDB influences either perceived constraints or the tendency to avoid purchases. We propose that PDB increases the constraints perceived by consumers, which, in turn, reduces the tendency to avoid purchases.
PDB and Perceived Constraints
High (vs. low) PDB cultures are characterized by the existence of a defined place for everyone within the social order. People in such cultures tend to be inflexible, hold firm beliefs, and act in a scripted fashion, and they do not deviate from norms (Hofstede 2001). Such cultures also discourage social mobility (i.e., movement across social classes) and tend to be rigid and closed-minded (Carl, Javidan, and Gupta 2004). For example, high PDB cultures endorse clear-cut boundaries between social classes and fixed (instead of malleable) societal structures. These cultures are characterized by a large number of people at the bottom of the pyramid with scarce resources, which increases perceived constraints (Carl, Javidan, and Gupta 2004). In contrast, low PDB cultures foster active movement across social classes (Hofstede 2001), enabling malleable and ambiguous societal structures. They are characterized by a vibrant and booming middle class, whose members have considerable freedom and limited constraints on their activities (Shukla 2009).
We propose that the rigidity and inflexibility of high (vs. low) PDB cultures, their strict adherence to rules and norms, and the limited opportunities for individuals in such cultures to move around—in both thought and deed—increase the constraints perceived by individuals on their behavior in general and induce a constraints mindset. For example, in ancient and medieval India (a high PDB culture), people belonging to the different castes had fixed and set roles, such as carpenter, plumber, or cleaner, and children were expected to follow in the same profession as their parents. Importantly, individuals were not expected to deviate from those roles; this imposed constraints on the role of each person (Blunt 2010). Similarly, in the workplace, subordinates in high PDB societies are expected to conform to existing rules and procedures, to follow their superiors, and not to deviate from existing norms (Hofstede 2001). The employees in high PDB organizations have limited discretion and a submissive attitude owing to power discrepancies between managers and employees (Khatri 2009). These restrictions apply not only to job settings but also to social interactions. For instance, in China (a high PDB culture) (Hofstede 2001), seating arrangements at a banquet dinner are based on each person's social standing, and deviations are frowned upon (Winterich and Zhang 2014). When talking to someone superior in status, people in Korea, Japan, India, and China extensively use honorifics and utilize special vocabulary and grammar forms to mark respect. The use of honorifics also suggests that people are expected to follow rules and norms, indicating greater constraints.
Further, in high (but not low) PDB cultures, people are expected to suppress and restrict the expression of emotions (Matsumoto, Yoo, and Nakagawa 2008), students are expected to refrain from speaking out to teachers, and adults are expected to control and resist individual idiosyncrasies in deference to socially normative behaviors (Hofstede 1980, 2001). PDB is also known to cause role overload (Carl, Javidan, and Gupta 2004), causing greater stress and constraints.
PDB, Perceived Constraints, and Tendency to Avoid Purchases
We propose that the restrictions and constraints perceived by high (vs. low) PDB individuals trigger a desire to compensate for the lack and to have more as a compensatory mechanism. One way to overcome this lack is to purchase products. Consequently, a constraints mindset increases the tendency to make (rather than defer or forgo) a purchase. Research suggests that a feeling of constraints can limit one's freedom and can be aversive and threatening, leading to psychological reactance and an attempt to “make up” for the constraints (Clee and Wicklund 1980; see Cannon, Goldsmith, and Roux [2019] for a detailed review). That is, people are aversive to constraints and attempt to attenuate or alter that state (Rucker and Galinsky 2008). For example, constrained budgets to play a game lead participants to excessively borrow resources in terms of both money and time (Shah, Mullainathan, and Shafir 2012), hunger promotes the acquisition of both food (Sevilla and Redden 2014) and nonfood objects (Xu, Schwarz, and Wyer 2015), and the perception that resources in the world are constrained leads individuals to seek and consume high-calorie foods as a means of providing security (Laran and Salerno 2013). Constraints also lead people to attempt to restore control via stereotyping (Krosch and Amodio 2014) and creative thinking (Mehta and Zhu 2016), as well as by making varied choices (Levav and Zhu 2009). Constraints arising out of scarcity also trigger the desire for abundance (Park, Lalwani, and Silvera 2020).
One way to compensate for these constraints is to acquire products. Indeed, people who face constraints (vs. those who do not) tend to be more materialistic and are more likely to engage in compulsive buying as a coping mechanism (Baker et al. 2013). Similarly, children who perceive constraints on family resources tend to be more materialistic (Rindfleisch, Burroughs, and Denton 1997). Further, the presence of “sold out” labels on products reduces consumers’ tendency to avoid purchases because it signals that product availability is constrained (Ge, Messinger, and Li 2009). Resource constraints have also been shown to trigger a desire for abundance and a need to procure products (Park, Lalwani, and Silvera 2020).
Research in the field of economics (e.g., Carvalho, Meier, and Wang 2016) consistently suggests that constraints caused by scarce resources are associated with a higher time discount rate (i.e., they increase impatience and, consequently, the desire to obtain products immediately rather than later). In addition, a state of powerlessness (vs. powerfulness), which is characterized by constraints, increases the willingness to pay for some products (Rucker and Galinsky 2008). The foregoing discussion suggests that PDB increases perceived constraints, which may increase the desire to possess a product immediately, thereby increasing the urgency to make purchases and reducing the tendency to avoid purchases. Thus:
Boundary Conditions
We were also interested in examining theoretically relevant moderators that shed light on our underlying mechanism. First, we examined the role of social density, that is, the number of people in a given space. When social density is high, people's sense of control is threatened (Hui and Bateson 1991). In high social density situations, people have less control over their movements and an increased likelihood of being pushed and jostled by others (Hui and Bateson 1991), thereby increasing constraints on their movement and behavior. Thus, a high (vs. low) social density should increase the perceived constraints (and, consequently, lower the purchase avoidance tendency) of low PDB individuals, whose baseline level of perceived constraints is low and has greater potential for an increase. However, a high social density should not influence the perceived constraints (and purchase avoidance tendency) among high PDB individuals, whose baseline level of perceived constraints is already high and has a lower potential for increase (akin to a ceiling effect; Lalwani and Forcum 2016). See Figure 1 for the conceptual framework. Thus:
Conceptual Framework.
Our theoretical framework suggests that high (vs. low) PDB individuals are less likely to avoid purchases (i.e., more likely to make purchases) because they perceive greater constraints. Because constraints are aversive, such individuals are motivated to overcome the constraints and compensate by seeking abundance by making (vs. avoiding) purchases. However, what happens when constraints are perceived as desirable instead of aversive? Certainly, constraints can be beneficial in some situations to certain types of people. For example, when participants believe that constraints facilitate hierarchy, high (but not low) PDB individuals should perceive the constraints as more desirable, because these constraints aid in their societal belief and structure. In this condition, high PDB individuals should no longer engage in compensatory behaviors or seek abundance, because constraints are no longer a source of threat. However, low PDB individuals, who do not desire a hierarchical society, should not be influenced by this belief. In other words, they should not be influenced by factors that facilitate hierarchy (i.e., the belief that constraints facilitate hierarchies). Thus, people high (but not low) in PDB should be more likely to avoid purchases (i.e., less likely to make purchases) when they believe that constraints facilitate hierarchy in society. Thus:
Constraints can sometimes lead to positive outcomes in other ways as well. For example, research suggests that scarcity increases creativity (Mehta and Zhu 2016) and encourages thinking out of the box (consistent with the proverbial expression “necessity is the mother of invention”). In these situations, people should have a more favorable attitude toward constraints, and the compensatory behavior outlined previously should not be triggered. Therefore, we propose that when people believe that constraints lead to positive outcomes, high PDB individuals—whose baseline level of perceived constraints is higher—should wholeheartedly embrace them and not strive to overcome them, leading to lower motivation to engage in compensatory behavior or to seek abundance. That is, when constraints lead to positive outcomes, high PDB individuals’ tendency to avoid (vs. make) purchases should significantly increase, compared with a control condition in which perception of constraints is unaltered. However, low PDB individuals, whose baseline level of perceived constraints is low, should not be influenced by the belief toward constraints. Thus:
Alternative Explanations
First, because high PDB cultures desire clear and unambiguous roles and responsibilities (Carl, Javidan, and Gupta 2004; Lalwani and Forcum 2016), they may reduce tolerance for ambiguity. People with lower tolerance for ambiguity are known to be motivated to arrive at firm answers quickly and to dislike uncertainty (Nowlis, Kahn, and Dhar 2002; Webster and Kruglanski 1994), which may reduce the tendency to defer or forgo purchases. Second, high (vs. low) PDB individuals tend to have higher self-regulation (Zhang, Winterich, and Mittal 2010), and thus may be able to expend more time and effort on comparing alternatives, thereby enhancing their ability and motivation to make (vs. defer or forgo) purchases (Nielsen and Escalas 2010). Third, owing to their rigid and inflexible structures, high PDB individuals may have lower self-efficacy because they are unable to change things. In turn, the lower self-efficacy may lead to a belief that they will be unable to find a better option later if they avoid their purchases, and it should prompt them to make a purchase (rather than defer or forgo it). Fourth, because high (vs. low) PDB contexts tend to be fixed, structured, and immutable (Lalwani and Forcum 2016), they offer limited scope of raising one's status in society and may discourage individuals from attempting to do so. This may lead individuals to be satisfied with what they have (i.e., a “satisficing mindset”) instead of pursuing the best (“maximizing mindset”), and make them accept the currently available option, leading to a lower purchase avoidance tendency. Fifth, high (vs. low) PDB individuals tend to have a greater tendency to compromise, leading them to pick one of the available options (Carl, Javidan, and Gupta 2004).
Sixth, consumers avoid purchases less when the difference in attractiveness among alternatives is large (vs. small; Dhar 1997). Because high PDB individuals are motivated to rank people and objects in a hierarchical order (Lalwani and Forcum 2016), they may have a better ability to view some options as superior to others (i.e., they may be more likely to perceive a greater difference in attractiveness of the given options; Wang et al. 2018). Seventh, high PDB individuals have a greater need for closure (Lee, Lalwani, and Wang 2020). However, people high (vs. low) in need for closure may attain closure either by making a purchase or by forgoing a purchase. Not surprisingly, research points to inconsistent effects of the need for closure on purchase deferral (Huang 2020; Kardes et al. 2002). Nevertheless, we tested it for the sake of completeness. Eighth, high PDB individuals may feel a greater obligation to make a choice because of greater conformity (Hofstede 2001). Ninth, high (vs. low) PDB individuals are less price sensitive (Lee, Lalwani, and Wang 2020), which may reduce their tendency to avoid purchases. Finally, high PDB individuals may derive greater value from material goods (vs. monetary rewards) (Carl, Javidan, and Gupta 2004), which may reduce their tendency to avoid purchases.
Overview of Studies
A multimethod approach was used to ascertain the generalizability and robustness of the results. All the key variables were either measured or manipulated. Following Anderson (2003), we conceptualized purchase avoidance as the tendency to not buy at a specific time, which includes both forgoing and deferring. Previous research suggests that consumers avoid purchases for different reasons. For example, consumers who avoid purchases may do so because they are unsure about the currently available options, because they wish to look for other alternatives, or simply because of choice difficulty or lack of information (Dhar 1997; Gunasti and Ross 2009; Thomas and Tsai 2011). Accordingly, to enhance generalizability, in some studies we explicitly gave participants the option to defer purchase and look for other alternatives (following Dhar 1997), whereas in other studies we assessed the tendency to purchase (vs. not purchase) (following Gunasti and Ross 2009). We also disentangle consumers’ tendency to forgo purchase from their tendency to defer purchases. In studies in which participants were provided with two options, we ensured via pilot testing that the two options were perceived as equivalent and that PDB did not influence the relative preference between these two options (see the Web Appendix). Thus, perceived differences in the attractiveness of options cannot account for our results. For the same reason, we assessed the relationships with different operationalizations of PDB and purchase avoidance across several different product categories.
Study 1a provided evidence of the negative relationship between PDB and purchase avoidance tendency using country-level data that tend to have high external validity. Study 1b demonstrated the causal role of PDB on purchase avoidance tendency by manipulating PDB, and Study 1c replicated the effect with a real behavioral measure of purchase avoidance (movie choice). Study 2 demonstrated the mediating role of perceived constraints and replicated the effect using another real behavioral measure of purchase avoidance (hand cream choice). Study 3 provided evidence of the mediating role of perceived constraints using an experimental approach. Study 4 showed that low (but not high) PDB individuals’ purchase avoidance tendency significantly reduces when social density is high. Further, the tendency to avoid purchases among high (but not low) PDB consumers increases when individuals perceive that constraints facilitate hierarchy (Study 5) or that constraints lead to positive outcomes (Study 6). We also demonstrate that low (but not high) PDB consumers’ purchase avoidance significantly reduces when product availability is limited (Study 12 in the Web Appendix).
Study 1a: Cross-National Differences in Consumers’ Tendency to Avoid Purchases (Secondary Data)
Study 1a was designed to test the relationship between PDB and consumers’ tendency to avoid purchases using country-level secondary data, which tend to have high external validity.
Method
Purchase avoidance
We used the conversion rate of shoppers at an online store (percentage of visitors to the website who make a purchase) to derive our key dependent variable. We procured a data set containing the customer conversion rate to an anonymous merchant that sold a wide range of women's and men's clothing and accessories in 20 countries in 2017 from Roach (2017). The conversion rate ranged from 1.51% to 4.67%. We subtracted this number from the total (100%) to represent nonconversion rates to be consistent with purchase avoidance. Since nonconversion rates capture the percentage of visitors to the website who do not make a purchase for the time being for any reason, we regard this as consumers’ purchase avoidance. Higher numbers indicate greater purchase avoidance.
Control variables
We included important economic control variables that can impact consumers’ online shopping behavior, such as constant dollar gross domestic product (GDP) (i.e., real GDP), household final consumption expenditure (percentage of GDP; household final consumption expenditure is the market value of all goods and services, including durable products purchased by households), clothing and footwear prices, inflation, e-commerce index, 2 and the globalization index from 2017 (Yang et al. 2019). See the Web Appendix for the data sources.
Cultural variables
Individualism, masculinity, uncertainty avoidance, and long-term orientation scores were obtained from Hofstede (2001). Power distance belief (i.e., power distance values) and power distance perception (PDP) at the country level were obtained from Carl, Javidan, and Gupta (2004). 3
Results and Discussion
Combining all the aforementioned variables resulted in 15 countries 4 in our final data set, because we could not import country-level cultural variables for all 20 countries from Hofstede (1980) and Carl, Javidan, and Gupta (2004). Although the sample size is small, it enables a preliminary and crude investigation of the association between country-level PDB and purchase avoidance, while controlling for several economic and cultural variables. To our knowledge, no other cross-national data are available to shed light on consumers’ tendency to avoid purchases.
A regression equation with e-commerce nonconversion rate (i.e., purchase avoidance) as the dependent variable and all economic and cultural variables, including PDB, as independent variables (adjusted R2 = .94, p = .053) revealed a significant effect of PDB (standardized β = −.82, SE = .64, t(2) = −5.37, p = .033). Thus, PDB significantly and negatively influenced the tendency to avoid purchases independent of all other variables (see the Web Appendix for detailed results), suggesting that an increase of one standard deviation in PDB leads to a .82 standard deviation decrease in the nonconversion rate. The effect of PDB (standardized β = −.72, SE = .88, t(3) = −3.45, p = .041) remained significant when we excluded PDP from the model. The correlation between PDB and PDP was nonsignificant (r = −.24, p > .35).
Study 1a provided initial support for H1 by showing that consumers in high (vs. low) PDB countries are less likely to avoid purchases at an e-commerce website and that this effect is independent of multiple control and other cultural variables. The country-level data provide for high external validity. However, it is important to understand the study's limitations. First, the sample size was small. Second, the incoming traffic (and associated conversion rates) of the different websites in different countries can also vary significantly for various unknown reasons (e.g., country-specific promotional campaigns, the popularity of the brand within the country, differences in target markets in different countries, accessibility of the website, online shopping norms), which are not accounted for by the current data. Third, the correlational nature of the findings cannot ascertain causal direction. Fourth, because high (vs. low) PDB individuals have a greater tendency to repeatedly engage in the same task (Carl, Javidan, and Gupta 2004), it is possible that high (vs. low) PDB individuals made more purchases out of habit. Study 1b attempted to address these issues by temporarily heightening the salience of PDB with participants from the same country. If the results of Study 1a were driven by high PDB individuals’ tendency to purchase out of habit, the effects should disappear when PDB is primed.
Study 1b: The Role of Salient PDB
Study 1b was conducted to test H1 by manipulating PDB to reveal the causal relationship between PDB and purchase avoidance.
Method
Participants and design
Participants were 167 Amazon Mechanical Turk (MTurk) workers (62.3% female, 37.7% male; Mage = 40 years) who participated for a small monetary remuneration (see Table 1 for details on sample size determination and exclusions for all studies). Participants were assigned to either the high (n = 76) or the low (n = 91) PDB condition.
Details About Sample Determination and Exclusions in Each Study.
For all studies except Study 1a, the sample size was determined by G*Power software (Faul et al. 2007) and the available budget. In all studies, we targeted a higher sample size than that indicated by the software because of the expected dropouts and budget expiration date. Because large sample sizes increase statistical power (Sawyer and Ball 1981), we aimed for more (rather than fewer) subjects than that indicated by power analysis.
α = .05, power = .80. This applies to all studies.
We observed sample size differences despite the randomization feature used in Qualtrics because some participants dropped out of the survey in the middle (e.g., some participants dropped out after being assigned to an experimental condition). This aspect applied to all studies.
Study 1a used country-level data.
In Study 1a we had no role in data collection, so we did not calculate the required sample size using G*Power beforehand.
This number includes 13 participants who were excluded from the main analysis.
PDB manipulation
First, we manipulated PDB using the procedure outlined by Zhang, Winterich, and Mittal (2010). Participants were asked to write three reasons either to support (high PDB) or to oppose (low PDB) the following sentence: “There should be an order of inequality in this world in which everyone has a rightful place; high and low are protected by this order.” A pilot study (N = 100) revealed that participants in the high (vs. low) PDB condition endorsed inequality and hierarchy more (Mhigh PDB = 3.03, Mlow PDB = 2.18; t(98) = 2.43, p = .017). See Table 2 for the manipulation check items.
Summary of Key Measures Used Across Studies.
Purchase avoidance
Following the procedure outlined by Dhar (1996, 1997), participants were asked to assume that they were shopping for three products (briefcase, shoes, and sunglasses) for themselves (see the Web Appendix for stimuli) and that they had narrowed their choice to two options for each product (a pilot study revealed that the two options were perceived as equivalent; see the Web Appendix). They were further told that, in this task, they would choose between the two options for each product, although they could also skip both options and continue to look for others. Their responses were coded as 0 (if they chose either option) or 1 (if they did not choose either option and preferred to continue looking). Higher scores indicate a greater tendency to avoid purchases. We also measured participants’ gender, native language (English, non-English), and age at the end of the survey.
Results and Discussion
Because the purchase avoidance tendency for the three products was correlated (rbriefcase−shoes = .26, rbriefcase−sunglasses = .30, rshoes−sunglasses = .30, all p < .005), we summed them (Min = 0, Max = 3). We predicted that participants in the high (vs. low) PDB condition would be less likely to avoid purchases (H1). The data supported this hypothesis. A t-test revealed that high (vs. low) PDB individuals scored lower on purchase avoidance (Mlow PDB = 1.20, Mhigh PDB = .82; t(165) = 2.45, p = .015). An analysis of covariance with purchase avoidance index as the dependent variable revealed a significant main effect of PDB (F(1, 158) = 4.72, p = .031) after controlling for gender (F(1, 158) = 9.87, p = .002), native language (F(1, 158) = 4.15, p = .043), and age (F(1, 158) = 4.29, p = .04). Four participants who entered an invalid age were excluded from this analysis.
Study 1b provided further support for the relationship between PDB and the tendency to avoid purchases (H1) by randomly assigning participants to high and low PDB conditions. Thus, it is unlikely that the relationship is driven by high (vs. low) PDB individuals’ habit to buy (vs. not buy). However, participants were given only two options to choose from for each product. Therefore, in the next study, we asked participants to write their own choices (without choice constraints) by measuring individuals’ real purchasing behavior.
Study 1c: Implications for Real Purchasing Behavior
Study 1c was designed to assess consumers’ real purchasing behavior to enhance the external validity of the findings. We gave participants the opportunity to rent their preferred movie using real money. We predicted that individuals high (vs. low) in PDB would be more likely to rent a movie (i.e., less likely to avoid purchases). We also tested possible alternative explanations based on self-efficacy, obligation to rent a movie, maximizing mindset, self-regulation, price sensitivity, need for closure, tendency to compromise, and the value of material goods versus monetary rewards (i.e., cash).
Method
Participants
The respondents were 301 TurkPrime (now CloudResearch; Prime Research Solutions LLC) workers who participated for monetary remuneration of $1.00 (67.1% female, 32.9% male; Mage = 42 years). Participants were assigned to either the high (n = 145) or the low (n = 156) PDB condition.
PDB manipulation
First, we manipulated PDB. Following Tu, Kwon, and Gao (2022), participants assigned to the high (low) PDB condition were asked to assume that they live in a hierarchical (equal) society and to identify with the associated values (see the Web Appendix). Participants in the high (vs. low) PDB condition scored higher on a three-item PDB scale developed by Zhang, Winterich, and Mittal (2010) (α = .98; Mhigh PDB = 2.88, Mlow PDB = 2.44; t(299) = −2.26, p = .024).
Purchase avoidance tendency
To make the purchase task more realistic and relevant, we asked participants to write down a movie that they were interested in renting at the moment (i.e., we did not constrain participants’ choice options). Thereafter, they were provided with the opportunity to rent the movie in lieu of the remuneration for the study ($1.00). They were told that if they decided to rent the movie, we would apply the remuneration toward it. However, if they were to decide otherwise, they would receive the monetary amount promised. Importantly, they were provided three options to choose from: (1) I will rent the movie today, (2) I prefer to defer my choice but I will rent the movie in the future, and (3) I will not rent the movie either today or in the future. The responses were coded as 0 (Rented; option 1) or 1 (Avoided; options 2 or 3). A higher score indicates a greater tendency to avoid purchases.
Alternative explanations
Thereafter, participants completed the following measures tailored to the context of movie purchases: self-efficacy via a three-item, seven-point Likert scale (α = .79); obligation to rent a movie via a four-item, seven-point Likert scale (α = .90); maximizing mindset via a three-item, seven-point Likert scale (α = .82); self-regulation via a three-item, seven-point Likert scale (α = .89); price sensitivity via a three-item, seven-point Likert scale (α = .81); and need for closure via a ten-item, seven-point Likert scale (α = .76). We also measured the general tendency to compromise via a three-item, seven-point Likert scale (α = .72), as well as the value of the money (“How valuable was the cash amount of $1.00 to you?”; 1 = “not valuable at all,” and 7 = “very valuable”) and the movie (“How valuable was the option to rent a movie to you?”; 1 = “not valuable at all,” and 7 = “very valuable”). See Table 2 for all items. Finally, we measured participants’ age, gender, and income.
Results and Discussion
Test of hypotheses
We predicted that high (vs. low) PDB individuals would be more likely to rent a movie (i.e., they would be less likely to avoid movie purchases; H1). The data supported this hypothesis. A logistic regression with movie rented (vs. avoided) as the dependent measure (dummy-coded as 0 = Rented, 1 = Avoided) and PDB as an independent variable revealed a significant negative effect of PDB (β(1) = −.72, Exp(β) = .49, Wald = 4.29, p = .038), as predicted. 5 Specifically, 90.4% of participants in the low PDB condition and 82.1% of participants in the high PDB condition decided to avoid the purchase (options 2 and 3).
Alternative explanations
Next, we conducted the same logistic regression as noted previously, except that we added all the variables related to the alternative explanations and PDB as independent variables. The results suggested that the effect of PDB (β(1) = −.82, Exp(β) = .44, Wald = 4.02, p = .045) remained significant after controlling for those variables. Additional analyses suggested that none of those variables mediated the link between PDB and purchase avoidance tendency (see the Web Appendix for details). Study 1c provided further support for the hypothesis that individuals high (vs. low) in PDB are less likely to avoid purchases (i.e., more likely to make purchases) in a real purchasing context, and ruled out several alternative explanations. To ascertain the generalizability of these findings, we conducted another study using a different operationalization of PDB and with a different price point for the movie, and found similar results (see Study 7 in the Web Appendix). In the next study, we examined the mechanism underlying the relationship. We also tested an alternative explanation based on the difference in the attractiveness of options.
Study 2: The Mediating Role of Perceived Constraints
The objectives of Study 2 were threefold. First, we examined the mediating role of perceived constraints in the relationship between PDB and the tendency to avoid purchases. Second, although we examined real purchasing behavior as in Study 1c, in Study 2 we used another real consumption setting using a different product category (hand cream) to rule out the possibility that the results of Study 1c were driven by the idiosyncratic nature of the product category used (movies). Third, we tested another alternative explanation based on the perceived difference in the attractiveness of options.
Method
Participants and PDB measure
The respondents were 101 MTurk workers 6 who participated for a small monetary remuneration (31.7% female, 68.3% male; Mage = 35 years). PDB was measured via a three-item, seven-point Likert scale (α = .94) developed and validated by Zhang, Winterich, and Mittal (2010). See Table 2 for items. PDB was measured immediately after the purchase avoidance tendency and the overall attractiveness of the two hand creams measures.
Purchase avoidance tendency
We used the product category hand cream 7 to assess participants’ tendency to avoid purchases. The respondents were informed that as a token of our appreciation for their participation, they would receive either $.50 or one of two hand creams (the two options were perceived as equivalent; see the Web Appendix). They were also informed that if they were to choose one of the hand creams, they would be provided with a link to a voucher, which could be downloaded and redeemed for the chosen hand cream at any online or retail store, such as Walmart, Target, or Best Buy. Their responses were coded as 0 (if they chose either hand cream) or 1 (if they did not choose either hand cream). A higher score indicates a greater tendency to avoid purchases. See the Web Appendix for stimuli.
Perceived constraints
We measured participants’ perceived constraints via a three-item, seven-point scale (α = .87). A sample item is “During this time, I believe that other people determine most of what I can and cannot do” (1 = “strongly disagree,” and 7 = “strongly agree”). An exploratory factor analysis with principal component analysis and varimax rotation showed that the items loaded on a single factor. See Table 2 for all items.
Alternative explanation
We also measured the perceived attractiveness of the two hand creams via a three-item, nine-point Likert scale (for option 1, α = .84; for option 2, α = .86; see Table 2 for the items). Then, we calculated the absolute difference between the perceived difference in the attractiveness of the two options. We also measured participants’ gender, native language (English, non-English), and age at the end of the survey.
Results and Discussion
Purchase avoidance tendency
We predicted that high (vs. low) PDB individuals would be less likely to avoid purchases. A logistic regression with hand cream choice as the dependent measure (dummy-coded as 0 = Chose, 1 = Did not choose) and PDB as an independent variable revealed a significant negative effect of PDB (β(1) = −.37, Exp(β) = .69, Wald = 7.60, p = .006), 8 as predicted.
Mediating role of perceived constraints
A bootstrapping procedure with 10,000 iterations (Model 4; Hayes 2022) revealed that the indirect effect of perceived constraints on the link between PDB and hand cream choice was significant (β = −.24, SE = .13, CI95 = [−.5346, −.0091]), which indicated that the effect of PDB on purchase avoidance is mediated by perceived constraints (see the Web Appendix).
Alternative explanation
Next, we conducted the same bootstrapping procedure with the perceived difference in the attractiveness of options as the mediator (PROCESS Model 4). The results suggested that the indirect effect of perceived difference in the attractiveness of options was not significant (β = .03, SE = .12, CI95 = [−.0464, .2636]), which suggests that this variable is not responsible for our effects.
In Study 2, we examined the effect of PDB on real purchasing behavior of hand creams, demonstrated the mediating role of perceived constraints, and ruled out an alternative explanation based on perceived difference in the attractiveness of options. The results supported the hypothesis that high (vs. low) PDB individuals have a constraints mindset, which reduces their tendency to avoid purchases. Because participants were made to believe that they would actually receive a hand cream (if they chose one of the hand cream options), we believe we measured their real purchasing behavior. To strengthen confidence in the underlying process, we conducted another study using a different set of products (i.e., gloves, wallet, and watch) and found similar results (see Study 8 in the Web Appendix). Additional support for the mediation is provided in Study 9 in the Web Appendix. A follow-up study (Study 10 in the Web Appendix) provided more direct evidence of the compensatory consumption mechanism by shedding light on the role of psychological reactance in the relationship between perceived constraints and purchase avoidance tendency.
Study 3: Experimental Test of Mediation
Study 3 was designed to test the mediating role of perceived constraints in the relationship between PDB and purchase avoidance tendency (H2) using an experimental approach (Spencer, Zanna, and Fong 2005). We manipulated perceived constraints to be high or not (in a control condition), and we assessed the relationship between PDB and purchase avoidance tendency in the two conditions separately. In the control condition, we predicted that high (vs. low) PDB individuals are more likely to perceive greater constraints (and are therefore less likely to avoid [vs. make] purchases) (H1, H2). We further predicted that a manipulation enhancing the perception of constraints would affect low PDB individuals more because these individuals’ baseline level of perceived constraints is low and, thus, has a greater scope for increase. In contrast, such a manipulation is expected to influence high PDB individuals less (or not at all), because these individuals perceive greater constraints and there is a lower scope of increasing it further (ceiling effect; Lalwani and Shavitt 2013; Lalwani and Wang 2019). In addition, we assessed participants’ actual purchase avoidance tendencies using real money, and then distinguished participants’ tendency to defer purchases from their tendency to forgo purchases.
Method
Participants, design, and PDB measure
The respondents were 546 TurkPrime workers who participated for monetary remuneration of $1.00 (65.4% female, 34.6% male; Mage = 41 years). The study employed a perceived constraints (high vs. control; between subjects) × PDB (continuous) mixed design. PDB was measured via a three-item, seven-point scale (α = .98) as in Study 2. We measured PDB after the purchase avoidance tendency and perceived value of the book and perceived value of money measures.
Manipulation of perceived constraints
Participants in the high perceived constraints condition (n = 278) were asked to plan a three-day vacation for ten individuals with certain constraints (e.g., you can only stay at places where all ten of you can be accommodated and must be close to some form of public transport). Participants in the control condition (n = 268) were asked to summarize a passage on the Coca-Cola company (see the Web Appendix for stimuli). A pilot study (N = 160) revealed that participants in the high (vs. control) constraints condition reported feeling greater restrictions and constraints on a three-item scale (α = .60; sample item, “During this time, I believe that other people determine most of what I can and cannot do”; Mhigh constraints = 3.35, Mcontrol = 3.02; t(158) = −2.03, p = .044).
Purchase avoidance tendency
We informed the participants that a purportedly new and real website called “AllFunStuffHere.com” had commissioned us to conduct a survey to gauge customer responses prior to its launch. Participants were informed that the website rents books. To make the purchase task more realistic and relevant, we provided participants a list of books that they could consider renting; however, they could also write down the name of a book that was not listed (i.e., we did not constrain participants’ choice options). Participants were told that the book whose title they wrote down was available for rent and that if they decided to rent it, we would apply the remuneration they would have received for completing the survey ($1.00) toward the book rental. However, if they were to decide not to rent the book, they would receive the monetary amount promised. Importantly, they were provided three options to choose from: (1) I will rent the book today, (2) I prefer to defer my choice but I will rent the book in the future, and (3) I will not rent the book either today or in the future. Their responses were coded as 0 (Rented; option 1) or 1 (Avoided; option 2 or option 3). A higher score indicates a greater tendency to avoid purchases.
Alternative explanations
We used the same measures for alternative explanations (self-efficacy, obligation to rent a book, maximizing mindset, self-regulation, price sensitivity, need for closure, tendency to compromise, and the value of material goods [i.e., book] vs. monetary rewards [i.e., cash]) as in Study 1c. However, we modified the items to fit the context of book (instead of movie) rentals. See Table 2 for all items. Finally, we measured participants’ age, gender, and income.
Results and Discussion
Test of hypotheses
We predicted that low (but not high) PDB individuals would be less likely to avoid purchase in the high constraints (vs. control) condition (H2). The data supported this hypothesis. A logistic regression with purchase avoidance tendency as the dependent variable and with PDB, constraints condition (0 = control condition, 1 = high constraints condition), and their interaction as the independent variables revealed significant main effects of PDB (β(1) = −.33, Exp(β) = .72, Wald = 8.37, p = .004) and constraints condition (β(1) = −1.54, Exp(β) = .22, Wald = 7.73, p = .005), and, importantly, also revealed a significant interaction between the two variables (β(1) = .34, Exp(β) = 1.41, Wald = 5.16, p = .023). 9 Floodlight analysis (Spiller et al. 2013) indicated a significant negative effect of the high constraints (vs. control) condition on purchase avoidance tendency for participants whose PDB score was less than 2.90 (bJN = −.54, SE = .28, p = .05), indicating that low PDB individuals’ tendency to avoid purchases significantly decreased when they perceive constraints when compared with the control condition. However, for individuals whose PDB score was higher than 2.90, the effect of constraints condition on purchase avoidance tendency was not significant, as predicted (see Figure 2). In addition, we examined whether the tendency to defer purchases (i.e., will purchase later) and to forgo purchases (i.e., not purchasing at all) differs by PDB in the control condition. Thus, we focused on those who chose either option 2 or option 3, and we conducted a logistic regression with the purchase option as the dependent variable and PDB as the independent variable in the control condition. The result revealed a nonsignificant main effect of PDB (β(1) = −.09, Exp(β) = .91, Wald = 1.36, p > .20), which suggests that these two options did not differ by PDB. 10
The Moderating Role of Situational Constraints on the Relationship Between PDB and Purchase Avoidance Tendency (Experimental Test of Mediation; Study 3).
Ancillary analyses
A logistic regression with purchase avoidance tendency as the dependent measure and PDB as an independent variable revealed a significant negative effect of PDB (β(1) = −.33, Exp(β) = .72, Wald = 8.37, p = .004) in the control condition (but not in the high constraints condition; β(1) = .01, Exp(β) = 1.01, Wald = .01, p > .90); this replicated the previous studies.
Alternative explanations
To test other alternative explanations (self-efficacy, obligation to rent a book, maximizing mindset, self-regulation, price sensitivity, need for closure, tendency to compromise, and the value of material goods [i.e., book] vs. monetary rewards [i.e., cash]), we focused on the control condition and conducted a logistic regression with purchase avoidance tendency as the dependent variable and all these variables as well as PDB as independent variables. The results suggested that the effect of PDB (β(1) = −.31, Exp(β) = .74, Wald = 5.42, p = .02) was significant after controlling for other variables. Additional analyses suggested that none of the other variables mediated the link between PDB and purchase avoidance tendency (see the Web Appendix for details). The results of Study 3 bolster our process account by demonstrating that a manipulation that enhances consumers’ perceived constraints reduces low (but not high) PDB individuals’ purchase avoidance tendency. However, some may argue that the purchase task was framed as a choice between a product (book) versus a monetary reward (i.e., cash) and that this may drive the effect. Therefore, we conducted another study using course credit as a reward (instead of money) and replicated the previous findings (see Study 11 in the Web Appendix). In the next study, we demonstrate an important boundary condition that identifies when low PDB individuals are less likely to avoid purchases, while also shedding light on the underlying mechanism based on perceived constraints.
Study 4: The Moderating Role of Social Density
The purpose of Study 4 was twofold. First, we tested the moderating role of social density (H3). Second, we used another realistic (i.e., behavioral) measure of purchase avoidance. Specifically, we recruited participants who were in the market to buy a book and assessed their actual purchase avoidance tendency using real money.
Method
Participants, design, and PDB manipulation
The respondents were 162 MTurk workers 11 who participated for a monetary remuneration of $.50 (53.1% female, 46.9% male; Mage = 40 years). The study employed a 2 (social density: high vs. control condition) × 2 (PDB: high vs. low) between-subjects design. PDB was manipulated as in Study 1b.
Manipulation of social density
Participants were randomly assigned to the high social density (n = 66) or the control (n = 83) condition. In the high social density condition, participants read a passage in which they were asked to imagine that they were attending a free concert at which the space was limited and crowded, leaving little area in which to move around. Each participant was asked to summarize the given passage. In the control condition, each participant was asked to describe what they did during the past week (see the Web Appendix for stimuli). A pilot study (N = 239) revealed that participants in the high (vs. control) social density condition reported feeling more restricted and crowded (α = .94; sample item, “During this time, how emotionally crowded do you feel?”; Mhigh social density = 6.69, Mcontrol = 3.35; t(237) = −13.91, p = .000).
Purchase avoidance tendency
Using a few filter questions, we allowed only those respondents who were in the market to purchase a book online to participate in the study (see the Web Appendix for details). Purchase avoidance tendency was measured as in Study 3. However, instead of asking participants to list a book title they might be interested in purchasing, we provided participants a list of book genres they might be considering purchasing at that point in time: (1) Biographies, (2) Business, (3) Fiction, (4) Diet, Health, and Fitness, and (5) Self-help and Relationships. Next, we provided participants with two different books in the genre they picked, and we also gave them the option to avoid purchase. They were told that if they were to decide to rent one of the books provided, we would apply the remuneration they would have received for completing the survey ($.50) toward it. However, if they decided not to rent either book, they would receive the monetary amount promised (see the Web Appendix for stimuli). Their responses were coded as 0 (if they chose to purchase either option) or 1 (if they did not purchase either option). A higher score indicates a greater tendency to avoid purchases. Finally, we measured participants’ gender, native language (English, non-English), and their age.
Results and Discussion
Test of hypotheses
We predicted that low (but not high) PDB individuals would be less likely to avoid purchase when social density is high (vs. in the control social density condition) (H3). The data supported the hypothesis. A logistic regression with purchase avoidance tendency as the dependent variable and PDB (0 = low PDB, 1 = high PDB), social density (0 = control social density, 1 = high social density), and their interaction as the independent variables revealed significant main effects of PDB (β(1) = −1.40, Exp(β) = .25, Wald = 4.62, p = .032) and social density (β(1) = −1.28, Exp(β) = .28, Wald = 3.75, p = .053); importantly, it also revealed a significant interaction between the two variables (β(1) = 1.98, Exp(β) = 7.26, Wald = 4.77, p = .029). 12
To examine the two-way interaction further, we split the data into two groups based on the manipulated PDB condition: high and low. In the high PDB condition, a logistic regression with purchase avoided (or made) as the dependent measure (dummy-coded as 0 = Chose, 1 = Avoided) and social density (0 = control social density, 1 = high social density) as an independent variable revealed a nonsignificant effect of social density (β(1) = .70, Exp(β) = 2.02, Wald = 1.27, p > .25). However, in the low PDB condition, a similar logistic regression revealed a marginally significant negative effect of social density (β(1) = −1.28, Exp(β) = .28, Wald = 3.75, p = .053), which suggests that low PDB individuals are less likely to avoid purchases when social density is high (vs. control), as predicted. These results supported H3. See Figure 3.
The Moderating Role of Social Density on the Relationship Between PDB and Purchase Avoidance Tendency (Study 4).
Ancillary analyses
A logistic regression with book purchase avoided (or made) as the dependent measure (dummy-coded as 0 = Chose, 1 = Avoided) and PDB as an independent variable revealed a significant negative effect of PDB (β(1) = −1.40, Exp(β) = .25, Wald = 4.62, p = .032) in the control condition (but not in the high social density condition; β(1) = .58, Exp(β) = 1.79, Wald = .85, p > .35), which replicates the previous studies.
The results of Study 4 provide further support for our underlying mechanism by demonstrating the moderating role of social density, which has been shown to influence perceived constraints (Hui and Bateson 1991). We used a behavioral measure of purchase avoidance in the book purchase context, which increases the generalizability and external validity of our findings. We acknowledge that the social density manipulation in this study was weak; however, we deliberately designed it to be incidental to the shopping task for two reasons. First, previous research suggests that when participants are able to link a prime with a dependent measure (which often happens with “strong” primes or primes that are closely associated with the dependent measure), the prime backfires, leading to a contrast instead of an assimilation effect (Strack et al. 1993). Second, the incidental manipulation actually provides a strong test of the theory. Indeed, if both the manipulation and the dependent measure are in the same context, something else about the context could be driving the effect. In a follow-up study (Study 12 in the Web Appendix), we examined another boundary condition (product availability) that sheds light on the underlying role of perceived constraints.
Study 5: When Constraints Facilitate Hierarchy
Study 5 was designed to test the relationship between PDB and purchase avoidance tendency when constraints are desired by high (but not low) PDB individuals (H4). We predicted that when participants believe that constraints facilitate hierarchy in society, high (but not low) PDB individuals no longer find constraints aversive, and their motivation to compensate for the lack reduces. Consequently, their tendency to avoid purchases significantly increases when compared with a control condition in which such beliefs are not altered.
Method
Participants, design, and PDB measure
The respondents were 345 TurkPrime workers who participated for monetary remuneration of $.70 (63.8% female, 36.2% male; Mage = 42 years). The study employed a perception of constraints (constraints facilitate hierarchy vs. control condition; between-subjects) × PDB (continuous) mixed design. PDB was measured via a three-item, seven-point scale (α = .98) as in Studies 2 and 3. PDB was measured first.
Manipulation of perception of constraints
Participants in the constraints facilitate hierarchy condition (n = 166) were provided a short article that stated that constraints facilitate order, stability, and hierarchy, and then they were asked to summarize the given article. In contrast, participants in the control condition (n = 179) were asked to summarize a passage on the Coca-Cola company (see the Web Appendix for stimuli). A manipulation check revealed that participants in the constraints facilitate hierarchy (vs. control) condition scored higher on a three-item scale measuring the extent to which constraints foster hierarchy and order (α = .63; Mconstraints facilitate hierarchy condition = 4.19, Mcontrol = 3.59; t(343) = −4.51, p = .000).
Purchase avoidance tendency
Purchase avoidance tendency was assessed as in Study 1b; however, to ascertain the generalizability of our findings we used a different set of products (gloves, wallet, and watch; see the Web Appendix for stimuli and for evidence that the two options were perceived as equivalent).
Results and Discussion
Test of hypotheses
Because the purchase avoidance tendency for the three products was correlated (rgloves−wallet = .52, rgloves−watch = .38, rwallet−watch = .56, all p < .001), we summed them (Min = 0, Max = 3). We predicted that high (but not low) PDB individuals would be significantly more likely to avoid purchasing in the constraints facilitate hierarchy (vs. control) condition (H4). The data supported this hypothesis. An analysis of variance with purchase avoidance tendency as the dependent variable and with PDB, belief condition (0 = control condition, 1 = constraints facilitate hierarchy condition), and their interaction as the independent variables revealed nonsignificant main effects of PDB (F(1, 341) = 1.80, p > .15) and belief condition (F(1, 341) = 2.41, p > .12), and, importantly, a significant interaction between the two variables (F(1, 341) = 4.25, p = .04). Floodlight analysis indicated a significant positive effect of the constraints facilitate hierarchy (vs. control) condition on purchase avoidance tendency for participants whose PDB score was greater than 6.27 (bJN = .54, SE = .27, p = .05), which suggests that high PDB individuals’ tendency to avoid purchases significantly increased when they believed that constraints facilitate hierarchy and order in society. However, for individuals whose PDB score was less than 6.27, the effect of belief condition on purchase avoidance tendency was not significant, as predicted (see Figure 4).
The Moderating Role of Perception of Constraints on the Relationship Between PDB and Purchase Avoidance Tendency (Study 5).
Ancillary analyses
A regression with purchase avoidance tendency as the dependent measure and PDB as an independent variable revealed a significant negative effect of PDB (β = −.17, SE = .05, t(177) = −2.35, p = .02) in the control condition (but not in the constraints facilitate hierarchy condition; β = .04, SE = .05, t(164) = .52, p > .60), which replicates the previous studies.
The results of Study 5 further support our process account based on compensatory consumption by demonstrating that a manipulation that induces the belief that constraints facilitate hierarchy is not aversive to high (but not low) PDB participants. As a result, high PDB individuals are no longer motivated to compensate for the lack or to seek abundance, and their tendency to avoid purchases significantly increases. In the next study, we explore another important boundary condition in which both low and high PDB individuals perceive that constraints lead to positive outcomes.
Study 6: When Constraints Lead to Positive Outcomes
Study 6 tested the hypothesis that high (but not low) PDB individuals are more likely to avoid purchases (i.e., less likely to make purchases) when they believe that constraints lead to positive outcomes (H5). We reasoned that when constraints are viewed favorably, high PDB individuals would not strive to overcome their current state of greater constraints or to compensate for their lack, leading to a lower motivation to make purchases (compared with a control condition wherein the perception of constraints is unaltered). However, low PDB individuals already perceive that constraints on their behavior are low. Thus, they should not be influenced by such a manipulation. Therefore, this study will bolster our compensatory consumption account by demonstrating that a manipulation encouraging the perception that constraints lead to positive outcomes attenuates the proposed effect among high PDB individuals. Moreover, we used a real purchasing context.
Method
Participants, design, and PDB measure
Two hundred six respondents from TurkPrime participated for a small monetary remuneration (59.2% female, 40.8% male; Mage = 43 years). The study employed a perception of constraints (constraints lead to positive outcomes vs. control condition; between-subjects) × PDB (continuous) mixed design. PDB was measured via a three-item, seven-point scale (α = .97) as in Studies 2 and 3 (see Table 2 for all items). We measured PDB after the manipulation of the perception of constraints and purchase avoidance tendency measures.
Manipulation of perception of constraints
All participants read and summarized a passage. The passage in the constraints lead to positive outcomes condition (n = 97) described several positive consequences of constraints (e.g., “Children who face monetary constraints have been found to perform better in school”). In the control condition (n = 109), the passage was an essay on babies (see the Web Appendix for stimuli). Further, we measured the perception of constraints on a five-item, seven-point scale (α = .95; sample item, “Constraints and restrictions tend to produce positive outcomes in life”). The results revealed that participants in the constraints lead to positive outcomes (vs. control) condition viewed constraints as more favorable (Mconstraints lead to positive outcomes = 4.15, Mcontrol = 3.49; t(204) = −3.04, p = .003), which suggests that the manipulation was effective.
Purchase avoidance tendency
As in Studies 1c, 2, 3, and 4, we assessed participants’ tendency to actually purchase a product in lieu of their monetary remuneration ($.50). To enhance the generalizability of the findings, we used five different products (air freshener, ball pen, dental floss, disposable mask, and portable sanitizer). For each product, participants were given three options: (1) I will buy this product today in lieu of the remuneration for the study, (2) I prefer to defer my choice but I will buy this product in the future, and (3) I will not buy this product either today or in the future. Participants were told that they would be entered in a lottery and if they won, they would be provided with a gift certificate at the end of the survey for any one of the products they indicated an interest in purchasing. At the end of the survey, participants were debriefed and informed that they would get the monetary remuneration ($.50) regardless of their choice. For the dependent measure, we examined whether participants avoided making product choices. Specifically, their responses were coded as 0 (Make a purchase; option 1) or as 1 (Avoid a purchase; option 2 or 3) for each product and summed to create a dependent variable (range, 0–5).
Results and Discussion
Test of hypotheses
We predicted that high (but not low) PDB individuals would be more likely to avoid making purchases when they believed that constraints lead to positive outcomes, compared with the control condition (H5). The data supported this hypothesis. A general linear model with purchase avoidance tendency as the dependent variable and with PDB, the perception of constraints (0 = control, 1 = constraints lead to positive outcomes), and their interaction as independent variables revealed a significant main effect of PDB (F(1, 202) = 9.91, p = .002) and a marginal main effect of perception of constraints (F(1, 202) = 2.90, p = .09). Importantly, the interaction between perception of constraints and PDB was significant (F(1, 202) = 4.62, p = .033). Further, floodlight analysis indicated a significant positive effect of the perception of constraints on purchase avoidance tendency for participants whose PDB score was greater than 6.27 (bJN = .71, SE = .36, p = .05), which indicates that high PDB individuals were no longer motivated to compensate for their lack, and thus were more likely to avoid purchases. This difference was not observed for low PDB individuals, as predicted (see Figure 5).
The Moderating Role of Perception of Constraints on the Relationship Between PDB and Purchase Avoidance Tendency (Study 6).
Ancillary analyses
A regression with purchase avoidance tendency as the dependent measure and PDB as an independent variable revealed a significant negative effect of PDB (β = −.34, SE = .07, t(107) = −3.75, p = .000) in the control condition (but not in the constraints lead to positive outcome condition; β = −.07, SE = .07, t(95) = −.71, p > .45), which replicates the results of Studies 1a through 1c.
General Discussion
Collectively, the 14 studies (including 6 studies in the Web Appendix) provide converging and robust evidence for the role of PDB on purchase avoidance, the underlying mechanisms, and boundary conditions. Study 1a provided an initial demonstration of the relationship using country-level secondary data and showed that this effect is independent of several economic and cultural variables. Study 1b validated the causal role of PDB by manipulating it. Study 1c provided additional evidence for the effect with real purchasing behavior (movie choice). Study 2 supported the mediating role of perceived constraints. Study 3 revealed that a manipulation that enhances individuals’ perceived constraints reduces low (but not high) PDB individuals’ purchase avoidance tendency. Study 4 demonstrated that low (but not high) PDB individuals are less likely to avoid purchases when social density is high (vs. a control condition) in a real purchase context (book choice). Importantly, we demonstrated that when there is no need to compensate for the constraints (Studies 5 and 6), high (but not low) PDB individuals are more likely to avoid purchases, which provides further evidence for our compensatory consumption mechanism. Across the studies, we also ruled out several alternative explanations, such as tolerance for ambiguity, self-regulation, and self-efficacy.
Theoretical and Managerial Implications
Briley, Morris, and Simonson (2005) found that participants primed with Cantonese (vs. English) language are more likely to avoid purchases. However, these authors did not study the effect of PDB. Countries differ on numerous cultural (e.g., individualism–collectivism, masculinity–femininity) and economic (GDP, income) factors, and it is possible that the findings of Briley, Morris, and Simonson are driven by factors other than PDB. Further, Zhang, Winterich, and Mittal (2010) suggest that PDB reduces consumers’ tendency to make impulsive purchases, which may seem to conflict with our findings. However, purchase avoidance tendency is conceptually quite different from impulsive purchase behavior. Indeed, impulsive purchases manifest in greater temptation to purchase vice (but not virtue) products (Zhang, Winterich, and Mittal 2010), whereas purchase avoidance can occur for both virtue and vice products (Dhar 1997). Further, impulsive purchases usually provide immediate gratification and pleasure (Rook 1987), whereas purchase avoidance tendency is independent of immediate or delayed gratification. Furthermore, impulsive buying is driven by reduced self-control (Zhang, Winterich, and Mittal 2010), whereas purchase avoidance is driven by diverse factors (e.g., time pressure [Dhar and Nowlis 1999]; preference fluency [Novemsky et al. 2007]; response mode [Dhar and Nowlis 2004]). Moreover, all impulsive buying is unplanned (Iyer 1989), whereas the tendency to avoid purchases can occur for planned (e.g., our Study 4) or unplanned purchases (our Studies 1b, 1c, 2, 3, 5, and 6). Thus, it is not surprising that our results are independent of impulsive buying tendencies. It is noteworthy that, in study 1a, Zhang, Winterich, and Mittal showed that PDB reduces unplanned purchases. However, in that study Zhang, Winterich, and Mittal did not measure participants’ tendency to make unplanned purchases after being exposed to the product. Instead, they measured participants’ generic tendency to plan ahead, without being exposed to the product. In contrast, purchase avoidance occurs after the participant is exposed to the product.
While some research (Mandel et al. 2017; Rucker and Galinsky 2008) suggests that self-discrepancies drive compensatory behavior, we focus on a specific and special type of self-discrepancy that is triggered by perceived constraints. Previous research does not provide insight into how PDB might influence perceived constraints. Thus, our findings contribute to both the constraints and the compensatory consumption literature by showing that the restrictions perceived by high PDB individuals lead to a constraints mindset, which triggers a desire to compensate for the lack. In so doing, we are among the first to bring any cultural variable (not only PDB) to the constraints literature. In addition, expanding previous research (Mandel et al. 2017), which suggests a main effect relationship between perceived constraints and purchasing behavior, we identified several moderators for the phenomenon. Importantly, contrary to previous research, we also demonstrated some conditions in which constraints are perceived as desirable (in Studies 5 and 6), which increases consumers’ tendency to avoid purchases. These findings further support our compensatory consumption mechanism by demonstrating that consumers do not engage in compensatory behavior when they perceive that constraints are not aversive.
Ultimately, managers can reduce consumers’ purchase avoidance tendency by targeting high PDB countries (e.g., Malaysia, the Philippines, and Venezuela; see Hofstede [2001] for PDB scores of countries), U.S. states (e.g., Louisiana, Alabama, and Mississippi; see Wang and Lalwani [2020] for PDB scores of the 50 U.S. states), and neighborhoods (e.g., those characterized by a dominant Asian and/or Latin American, as opposed to a white, population; Lee, Lalwani, and Wang 2020). In low PDB settings, however, managers concerned about purchase avoidance tendency may need to rely on other means (e.g., hiring trained and persuasive salespeople) to reduce consumers’ purchase avoidance tendency. Managers can also reduce consumers’ purchase avoidance tendency by priming high PDB via ads, billboards, or point-of-purchase materials (e.g., via the slogan “Hierarchy protects our lives”). Moreover, stores or firms with loyalty programs can evoke high PDB among loyalty-status customers by emphasizing the special treatment being provided to them (e.g., “Premium class customers like you have earned the right to the best treatment and service”). Marketers also may be able to reduce purchase avoidance tendency among low PDB consumers by increasing perceived constraints (e.g., via slogans such as “Think about the times you did not have many options”), by increasing social density (e.g., by increasing either human or spatial density; Machleit, Eroglu, and Mantel 2000), or by emphasizing limited product availability. Policy makers who wish to restrict consumption (e.g., during crisis or natural disasters) are more likely to succeed with low PDB consumer segments by activating a low PDB or by reducing a constraints mindset (e.g., via open spaces). Among high PDB consumers, policy makers should prime the notion that constraints are favorable (e.g., the slogan “Necessities generate greater inventions”).
Limitations and Directions for Future Research
Most of our studies were conducted in controlled environments using participants from MTurk. In some studies, we provided participants either two or three options to choose from. Future research should ascertain if our relationships hold after addressing these issues. Although our research suggests that several alternative explanations do not account for the relationship between PDB and purchase avoidance tendency in the contexts we examined, it is possible that they drive the relationship in other situations. Research also suggests that the perception of constraints can influence diverse outcomes, such as categorization tendency (Park, Lalwani, and Silvera 2020), self- versus other-focused behavior (Cannon, Goldsmith, and Roux 2019), creativity (Mehta and Zhu 2016), and variety seeking (Levav and Zhu 2009), and future research should examine whether PDB influences these outcomes as well. Finally, Paharia and Swaminathan (2019) have used political identity as a proxy for PDB, and future research should explore whether political identity influences purchase avoidance.
Supplemental Material
sj-pdf-1-mrj-10.1177_00222437231182600 - Supplemental material for Power Distance Belief and Consumer Purchase Avoidance: Exploring the Role of Cultural Factors in Retail Dynamics
Supplemental material, sj-pdf-1-mrj-10.1177_00222437231182600 for Power Distance Belief and Consumer Purchase Avoidance: Exploring the Role of Cultural Factors in Retail Dynamics by Hyejin Lee and Ashok K. Lalwani in Journal of Marketing Research
Footnotes
Acknowledgments
The authors are grateful for the helpful comments of Jessie Wang and Hanyong Park on previous versions of this manuscript. In addition, financial support from the Carolan Research Institute, San Antonio as well as from the Center for Brand Leadership and the Dean's office at the Kelley School of Business, Indiana University, Bloomington, is gratefully acknowledged.
Coeditor
Maureen Morrin
Associate Editor
Dhruv Grewal
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Dean’s office at the Kelley School of Business, Indiana University, Bloomington, and the Carolan Research Institute, San Antonio.
Notes
References
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