Time pressure and shopping behaviour in retailing: a review and research agenda

Consumers often face a shortage of time in their daily lives. This phenomenon has been caused by a plethora of reasons, such as, a rising number of dual-income households and single-parent families, the proliferation of time-demanding social practices, frequent work and study deadlines and longer working hours (Kim and Kim, 2008). Time pressure (TP) is becoming increasingly pervasive and can have larger implications from leading to a sense of urgency and anxiety to affecting decision-making (Etkin et al., 2015). The notion of time is particularly important for retailers, as the time spent in the store in relation to the pleasure and satisfaction felt positively affects money spent in the store (Elmashhara and Soares, 2024). Researchers in the domain of retailing have long recognised the importance of studying how time pressure impacts consumer behaviour in retail environments (Sohn and Lee, 2017). While a variety of such studies have been carried out in this domain, no paper has yet comprehensively reviewed and compiled their findings. To address this gap, our paper aims to:

• review existing retailing literature and bring about conceptual clarity regarding TP;

• comprehensively document the empirical findings reported in these papers regarding how TP impacts consumer behaviour in retail environments; and

• provide directions for future research.

Keeping these objectives in mind, this paper has been organised as follows: Firstly, we offer a detailed conceptual clarification of TP including its evolution, theoretical foundations and methods of operationalisation. This is followed by a systematic review of empirical studies examining the antecedents and behavioural consequences of TP in retail contexts. We also contrast these effects across offline and online retail environments and evaluate the methodological approaches used in the literature. Finally, we outline a structured agenda for future research in this domain.

Time was first incorporated into economic choice models by Becker (1965) who proposed that time, like income and prices, acts as a constraint on choice. Marketing scholars extended this idea by exploring how time scarcity affects consumer decision-making. TP refers to a perceived limitation on available time to process information and make decisions (Yao and Oppewal, 2016; Van Steenburg and Naderi, 2020). In retailing, it reflects a consumer’s perception of insufficient time to complete shopping tasks, influencing decisions and behaviours (Sohn and Lee, 2017; Liu et al., 2017). While early work conceptualised TP as an external constraint, later research emphasised its subjective and psychological nature, often accompanied by stress and urgency (Etkin et al., 2015; Elmashhara and Soares, 2024). The perception of TP induces stress and a need to cope to avoid negative consequences (Etkin et al., 2015). Though initially viewed as situational and short term, TP is now also recognised as chronic and pervasive (Kim and Kim, 2008), reflecting ongoing lifestyle stress shaped by income, employment, age and family responsibilities (Collier et al., 2015). Table 1 provides a synthesis of how TP has been defined across the reviewed studies.

Various conceptual frameworks have been used to explain how consumers perceive and respond to TP in retail contexts. Resource Matching Theory posits that TP arises when available cognitive or temporal resources (RA) fall short of required resources (RRs), impairing decision quality (Collier et al., 2015). Stimulus–Organism–Response (S-O-R) models describe how environmental cues like flash sales or countdown timers trigger internal states such as urgency, stress or flow, leading to TP (Dong et al., 2023). Emotion-focused theories offer further insights: competitive arousal theory suggests that time scarcity heightens arousal, overriding rational processing (Broeder and Wentink, 2022), while anticipated regret frameworks argue that consumers accelerate decisions under TP to avoid future regret (Gupta and Gentry, 2019). Prospect theory explains that TP amplifies loss aversion, prompting faster decisions (Byun and Sternquist, 2012). Other perspectives address cognitive abstraction and autonomy. Construal Level Theory suggests TP increases reliance on surface-level cues over abstract reasoning (Kong et al., 2023), while psychological reactance theory holds that TP-centred promotional messaging may threaten perceived autonomy, leading to resistance (Khetarpal and Singh, 2023). Finally, qualitative studies in the retailing domain have also contributed to the conceptual understanding of TP: Geuens et al. (2001) found that TP shifts motivations from experiential to functional, increasing the need for efficiency. Silayoi and Speece (2004) observed greater reliance on visual heuristics and reduced cognitive processing under TP, fostering impulsivity.

The most widely adapted TP scale originates from Herrington and Capella (1995), who used five items rated on a five-point Likert scale. This scale has been adopted or adapted in several studies (Lin and Chen, 2013; Zhang et al., 2024). Beatty and Ferrell (1998) used a three-item scale focused on retail trade show contexts, later adopted by Tafesse and Korneliussen (2012). Suri and Monroe (2003) and Peng et al. (2019) used semantic differential items contrasting “No time pressure” versus “Too much time pressure”. Dhar and Nowlis (1999) and Dhar et al. (2000) used a two-item post-decision measure averaging TP while making the decision and decision speed, a format used by later studies (Yao and Oppewal, 2016; Li et al., 2023). Similarly, Godinho et al. (2016) used three nine-point items focusing on time adequacy and pressure during evaluation. Chronic TP has been assessed using the three-item scale proposed by Kim and Kim (2008) rated on a five-point scale. Elmashhara and Soares (2024) and Wu and Li (2023) have used similar versions of the scale.

Other scales used to measure TP include Lin et al.’s (2008) single-item seven-point scale, Chowdhury et al.’s (2009) direct self-report item and Liu et al.’s (2017) nine-point perceived time adequacy measure. Vermeir and Van Kenhove (2005) created a five-item grocery-specific TP scale, later adopted by Gelbrich and Sattler (2014), Gensler et al. (2017) and Sreen et al. (2023). Broader constructs, such as Etkin et al. (2015) temporal constriction measure, have also been used. Van Steenburg and Naderi (2020) used a three-item situational TP scale, while Lee and Yi (2022) assessed TP in product return contexts using three items related to rush and time scarcity. Li et al. (2023) measured perceived urgency with two items during shopping trips, and Dong et al. (2023) used four items referencing countdowns, stress and haste in livestreaming e-commerce. Some studies have also relied on objective or proxy indicators: Calvo et al. (2020) used campaign time completion percentage to infer TP; Kawaguchi et al. (2021) used real-world intervals before train arrivals to capture TP in vending contexts; and Liang and Lin (2023) measured physiological responses to TP such as galvanic skin response and brainwave activity.

A systematic review of empirical studies was conducted following the PRISMA protocol (Page et al., 2021) to examine the effects of TP on consumer behaviour in the domain of retailing. Articles were identified through keyword searches across two major databases: Scopus and Web of Science, yielding a total of 1,802 records. In addition, nine articles were identified from other sources, including conference papers (n = 1) mentioned in reviewed articles and articles known to the authors (n = 8). Search terms were structured using Boolean operators and truncation to capture variations of the concept. The search string used was: (“time press” OR “time availa” OR “time constrain*” OR “time scarc*”) AND (“retail*”). The search was limited to peer-reviewed articles written in English, published between 1983 and June 2025. To ensure comprehensiveness, broad search terms and multiple keywords related to the construct of time were intentionally used, which initially resulted in many articles from unrelated disciplines. Each record was carefully assessed for its relevance to consumer behaviour in retail settings, with screening decisions made collaboratively by the authors to maintain consistency. Ambiguities during full-text review were resolved through discussion to reach consensus. Duplicate entries were removed before screening. During the screening process, articles were first filtered based on journal scope to exclude those from unrelated disciplines. Next, titles and abstracts were reviewed to identify whether the focus was on perceived TP in consumer behaviour contexts. Finally, full texts were assessed to ensure alignment with our inclusion criteria. After the screening process, all papers included in this review met the following criteria:

• TP (or similar constructs like “time available” and “perceived time constraint”) was an integral part of the study;

• scope of the study included the examination of consumer behaviour in a retailing context;

• published in a peer-reviewed journal or conference proceedings; and

• involved an empirical study using quantitative research methods.

The flow chart (Figure 1) illustrates the step-by-step process for article selection.

In total, 58 empirical studies were included in the review. These span a wide range of peer-reviewed journals in marketing, retailing, consumer behaviour and related domains such as tourism, information systems and general management. The sample also includes one conference proceeding that met all other inclusion criteria.

Although not the focus of this review, the shortlisted articles were examined to identify the antecedents of TP in retail settings. In general, feelings of time scarcity and the presence of time limits can lead to perceptions of situational TP (Peng et al., 2019; Hao and Huang, 2025). However, there are many external variables specific to retail contexts that can also induce perceptions of TP. Sharma et al. (1983) found that shopping mode affects TP, with consumers using electronic shopping systems (ESSs) perceiving more TP than those using catalogues. Morschett et al. (2005) identified shopper motivation as a key antecedent, highlighting a segment of “time-pressed price shoppers” driven by speed and affordability. Promotional scarcity cues such as limited-time offers and availability signals heighten TP by increasing perceived scarcity and competitive arousal (Calvo et al., 2020). Etkin et al. (2015) showed that greater goal conflict during shopping amplifies TP perceptions. Chowdhury et al. (2009) reported that maximisers experience more TP than satisficers when under time constraints. In self-service contexts, both employee presence and order size significantly affect TP (Collier et al., 2015).

This section focuses exclusively on studies that examine the behavioural consequences of TP in retail settings, organised into three categories:

1. browsing and external search behaviours;

2. choice and decision-making; and

3. purchase behaviours.

Key findings and specific outcomes across these categories are summarised in Table 2.

Herrington and Capella (1995) report that the relative shopping time is inversely related to perceived TP. Beatty and Ferrell (1998) reveal that the extent of in-store browsing is a function of the time a shopper feels is available to them, which they point out as the opposite of TP. This has also been supported by Nilsson et al. (2017), who find that consumers under TP simplify their shopping strategies by minimising in-store browsing and focusing on primary goals. Kim and Kim (2008) found that chronic TP significantly weakens the impact of shopping enjoyment on in-store browsing. However, chronic TP has also been shown to shape consumers’ underlying motivations by increasing the salience of hedonic shopping goals, which subsequently enhances their desire to remain longer in retail environments (Elmashhara and Soares, 2024).

In online shopping contexts, Liu et al. (2017) found that time-pressured consumers spend more time browsing high (vs low) brand-awareness products. TP also reduces external search effort in offline contexts (Putrevu and Ratchford, 1997) and discourages cross-channel search as the added search effort is incompatible with time-constrained decision environments (Gensler et al., 2017). While most consumers reduce search under pressure, those high in need for closure maintain consistent effort (Vermeir and Van Kenhove, 2005). TP-driven nudges, such as scarcity cues, lower search duration and depth by inducing psychological urgency and impulsivity (Ghose et al., 2023). TP can also trigger in-store hoarding, particularly under heightened time urgency, and is further intensified by emotions like anticipated regret and traits such as competitiveness (Byun and Sternquist, 2012; Gupta and Gentry, 2019).

Under TP, consumers tend to choose higher-quality or easier-to-evaluate options, such as top-tier brands or less complex alternatives (Nowlis, 1995; Garbarino and Edell, 1997). Under TP, consumers are less likely to choose compromise or asymmetrically dominant options, indicating reduced compromise and attraction effects (Dhar et al., 2000; Pettibone, 2012). However, Lin et al. (2008) reported the opposite for the attraction effect, finding that it actually increases under TP. TP can also bring about choice deferral, though this effect is moderated by the degree of choice conflict and decreases when options are equally attractive (Dhar and Nowlis, 1999). Godinho et al. (2016) show that different TP sources (e.g. deadlines and stock-outs) affect decision strategies: task pressure leads to non-compensatory choices for goods and compensatory ones for services, while stock-out threats reduce choice deferral and lower choice utility under complexity. Overall, TP reduces information processing and narrows focus to easily comparable attributes.

TP also heightens arousal and reduces perceived control, prompting reliance on heuristics and affect-driven decisions (Liang and Lin, 2023). For example, unit pricing helps speed up decisions under TP (Yao and Oppewal, 2016). However, cognitive processing varies by motivation: under low motivation, moderate TP enhances systematic processing, but this declines at higher pressure; for highly motivated consumers, systematic processing decreases steadily with increasing TP, encouraging greater heuristic use (Suri and Monroe, 2003). TP further weakens the impact of various shopping motivations on purchase decisions (Lin and Chen, 2013) and fosters intuitive, affective responses in contexts like green purchases (Sreen et al., 2023). In vending machine settings, it reduces attention to product information but increases decision confidence and purchase utility (Kawaguchi et al., 2021). Also, consumers under chronic TP favour retail tools like augmented reality that reduce cognitive effort and simplify decisions (Romano et al., 2022).

The perception of TP impacts various purchase-related behaviours. Herrington and Capella (1995) found that a shopper’s relative purchase amount is positively related to perceived TP. It has been seen that purchase incidence decreases with an increase in perceived TP (Tafesse and Korneliussen, 2012). Consumers under TP are willing to pay a premium for their preferred brands (Jensen and Drozdenko, 2008). Also, research shows that bidders participating in online auctions with social competition are likely to place higher bids when TP is high, a phenomenon termed as “auction fever” (Adam et al., 2015). TP increases purchase failure and shortfall, particularly in unfamiliar store environments (Park et al., 1989) and reduces unplanned purchases, especially when store knowledge is low and time constraints are absent (Iyer, 1989). While impulsive consumers show higher unplanned purchase intentions under TP, non-impulsive consumers do not (Van Steenburg and Naderi, 2020). However, when financial pressure is high, non-impulsive consumers have higher purchase intentions under low TP.

Several studies have also linked TP to increased impulse buying. Sohn and Lee (2017) found that negative emotions combined with greater TP lead to consumers making more affective impulse purchases. Time-sensitive promotions intensify emotional arousal, urgency and flow experiences, driving impulsive purchases (Dong et al., 2023), especially when framed as supply-based scarcity appeals (Hao and Huang, 2025). In short video e-commerce, TP raises urgency and vulnerability, prompting more impulse buys during live sessions (Zhang et al., 2024). In tourism, TP boosts impulsivity by enhancing perceptions of rarity (Li et al., 2023), particularly among experienced travellers who exhibit stronger impulsive responses under time scarcity because of heightened overconfidence in their decision-making (Li et al., 2021). The effect of TP on impulsive buying is also shaped by contextual and consumer-specific moderators. For example, Kong et al. (2023) show that the impact of TP is stronger for search products than for experience products and more pronounced among promotion-focused individuals than among prevention-focused ones. Similarly, individual psychographic characteristics such as time orientation and impulsiveness moderate how TP influences attitudes and behaviours. Consumers who perceive time as scarce are more inclined toward online channels for convenience (Xu-Priour et al., 2012). Under time constraints, consumers also tend to rely more on hedonic evaluations and affect-driven processing, resulting in greater impulsive buying of pleasure-oriented products compared to utilitarian ones (Liu et al., 2022) and quick, value-driven decision-making in green product contexts (Sreen et al., 2023).

Emotional triggers like fear of missing out (FOMO) intensify impulse buying during time-limited promotions, especially among consumers with high impulsiveness and low persuasion knowledge (Khetarpal and Singh, 2023). While such tactics may boost short-term sales, they can also raise return rates and reduce profitability (Ghose et al., 2023). Time-related cues that highlight the cost of returning an item (e.g. by highlighting the value of consumers’ time) can lower return intentions by easing psychological discomfort (Lee and Yi, 2022).

Scarcity-driven promotional cues in online retailing have also been shown to shape purchase behaviour via heightened TP. Aggarwal and Vaidyanathan (2006) find that short-duration promotions, such as store coupons or flash deals, significantly accelerate purchases, while longer-duration offers, such as manufacturer coupons, do not. Peng et al. (2019) found that perceived TP negatively moderates the relationship between emotional and social value and purchase intention in online flash sales, particularly for high-involvement products. Jha et al. (2019) show that adding time restrictions to monetary discounts significantly enhances offer-related perceptions and purchase intentions, particularly when promotional cues emphasise both product and price attributes. Notices of limited-time availability increase perceived scarcity and competition, boosting purchase intentions, although effects may vary depending on the consumer’s cultural background (Broeder and Wentink, 2022). Similarly, low availability signals not only raise sales but also increase return rates, particularly toward the end of campaigns when TP peaks (Calvo et al., 2020). Both time- and quantity-based scarcity cues raise arousal and impulse buying by amplifying perceived urgency and rivalry (Wu et al., 2021). However, time scarcity promotions often underperform in digital settings unless justified by external factors (e.g. seasonal or personal events), in which case consumer interest improves, especially when the promotion is close to expiration (Hmurovic et al., 2022). Finally, TP has also been shown to shape technology adoption behaviours within retail settings, particularly self-service technologies (SSTs) like self-checkouts (Gelbrich and Sattler, 2014; Demoulin and Djelassi, 2016). These studies demonstrate that TP can either facilitate SST usage or hinder it when combined with contextual stressors such as crowding and technology anxiety. It also promotes the continued use of time-saving retail solutions such as self-service delivery boxes (Wu and Li, 2023).

Given the growing relevance of multi-channel retailing, it is important to synthesise how the effects of TP vary across offline and online retail environments. The behavioural consequences of TP diverge notably across offline and online retail environments, shaped by the affordances and limitations of each channel. Cross-channel differences primarily arise in the context of situational (vs chronic) TP. In offline retail contexts such as grocery stores, malls and physical retail venues, TP is often experienced as a function of situational factors such as store closing hours, upcoming personal appointments, peak-hour crowding or external time constraints (e.g. public transport departure timings). This, in turn, has been shown to compress browsing time, diminish external search effort and lead individuals to rely more on decision heuristics to simplify cognitive tasks (Herrington and Capella, 1995; Beatty and Ferrell, 1998; Vermeir and Van Kenhove, 2005; Yao and Oppewal, 2016). Offline shoppers under TP are generally observed to reduce their exploration of in-store promotions and search for fewer price comparisons (Putrevu and Ratchford, 1997). Moreover, time-constrained offline consumers tend to exhibit more focused, utilitarian behaviour, purchasing fewer items than originally planned (Park et al., 1989), while also deferring choices when confronted with high conflict (Dhar and Nowlis, 1999).

In online environments like e-commerce and livestreaming retail, TP is most commonly induced through countdown timers, flash sales, limited-quantity notices or real-time scarcity cues. These leave little opportunity for extended evaluation, encouraging rapid decision-making and, thereby, accelerating purchase behaviour (Broeder and Wentink, 2022; Hao and Huang, 2025). Time-limited online promotions induce psychological urgency, increase impulsivity and lead to more affective decision-making, often driven by emotional states like arousal and fear of missing out (Liang and Lin, 2023; Zhang et al., 2024).

TP online also has an impact on the nature of consumer cognition. The limited tactile feedback, absence of ambient cues (such as music or social presence) and lack of in-person interaction deprive consumers of compensatory decision aids common to offline environments (Hmurovic et al., 2022; Liang and Lin, 2023). In the absence of these contextual buffers, consumers in online settings often rely more heavily on visual cues like scarcity labels or unit pricing to guide decisions under time constraints. Simultaneously, the abundance of product alternatives, high information availability and low switching costs make it easier for shoppers to abandon a pressured offer in favour of better deals elsewhere (Wu et al., 2021; Hmurovic et al., 2022). Additionally, while product return behaviour is less prominently discussed in offline contexts, online retailing under TP has been consistently linked to elevated post-purchase regret and significantly higher return rates, especially when urgency tactics are deployed without transparency or consumer-relevant justification (Calvo et al., 2020; Hmurovic et al., 2022). Zhang et al. (2024) further report that consumers in live e-commerce environments frequently experience post-purchase regret.

Finally, a few studies offer direct empirical comparisons across channels. Hmurovic et al. (2022) found that time scarcity promotions consistently boost purchase intent and willingness to pay in offline settings but have weaker effects online – likely because of the absence of sensory cues and greater activation of persuasion knowledge unless justified externally. Xu-Priour et al. (2012) similarly suggest that consumers facing TP develop more favourable attitudes toward online shopping, especially if they are time-oriented and seek convenience. However, certain time-related effects, such as product return behaviour, operate similarly across both channels (Lee and Yi, 2022). Table 3 summarises these cross-channel findings.

Most of the studies in the behavioural consequences of TP have primarily made use of survey designs or controlled/field experiments. These studies vary in the extent of ecological validity that they manage to achieve. Certain studies (Morschett et al., 2005; Xu-Priour et al., 2012; Gupta and Gentry, 2019; Elmashhara and Soares, 2024) which have used mail-based as well as online surveys to test their hypotheses have lesser generalisability. On the other hand, field-based survey studies and experiments (Herrington and Capella, 1995; Byun and Sternquist, 2012; Jha et al., 2019; Ghose et al., 2023) have the advantage of greater external validity. However, field surveys face two key limitations – first, time-pressed shoppers are unlikely to have the time to participate in surveys (Herrington and Capella, 1995), and second, surveys lack control over extraneous variables, limiting causal inference.

To achieve better causal conclusions, it is important to manipulate TP and control for confounding variables. Field experiments often manipulate TP through imposed time limits in natural settings, as seen in Iyer (1989) and Park et al. (1989). They asked participants to give an estimate of the amount of time they would need to purchase a list of intended products. Subjects under the TP condition were taken to the grocery store and asked to carry out the intended purchases in half the amount of time while control group participants were allowed to carry out their shopping tasks within their initially estimated time. Another approach, demonstrated by Kawaguchi et al. (2021), relies on naturalistic proxies for TP. Their study analysed real-world beverage purchases from vending machines on Tokyo train platforms, using “time to the next train” (based on schedule data) to infer TP. Recent studies have also leveraged online retail environments for greater control and precision in the delivery of experimental treatments, improved tracking of consumer behaviour in real time and better control over some contextual variables. For example, Wu et al. (2021) experiment involved setting up a new online store on a well-known Chinese e-commerce platform, manipulating limited-time scarcity through fixed messages (e.g. “one hour”) while controlling for confounds by placing participants in individual and identical lab settings. Similarly, Ghose et al. (2023) partnered with a fashion retailer to randomly assign consumers to different time-scarcity nudges over a six-week period (e.g. “The deal ends in xx hours”) during limited-edition online sales.

Next, several studies use lab- or scenario-based experiments with carefully controlled manipulations of TP, often using countdown mechanisms or restricted decision windows. For example, participants were asked to make choices within varying time limits (Yao and Oppewal, 2016; Suri and Monroe, 2003) or under auction conditions with changing bid intervals (Adam et al., 2015). Others varied TP through between-subjects designs, contrasting short versus long decision windows, such as 1 h versus 1 day (Peng et al., 2019) or brief product availability windows (Van Steenburg and Naderi, 2020). Time-based scarcity was also induced via limited-time notices or promotions, sometimes alongside stock-out threats (Godinho et al., 2016; Broeder and Wentink, 2022). In simulated or online retail contexts, researchers embedded scarcity cues into flash sales or livestreaming platforms (Hao and Huang, 2025; Kong et al., 2023). Liu et al. (2017) used a high versus low urgency gift-shopping task (“party in 2 h” vs “2 weeks”) to manipulate TP, with browsing behaviour captured via eye-tracking.

While controlled lab settings offer internal validity, they often lack real-world realism (Mehta et al., 2013). To enhance both rigour and generalisability, several studies adopt mixed-method designs that triangulate across experiments, surveys and field data (Aggarwal and Vaidyanathan, 2006). For instance, Li et al. (2021, 2023) combine lab experiments with post-trip tourist surveys, while Liu et al. (2022) pair a correlational study with three online experiments. Lee and Yi (2022) integrate eye-tracking, MTurk-based experiments and retailer collaborations to study return behaviour. Liang and Lin (2023) blend a survey with physiological measures (galvanic skin response and EEG), extending beyond the eye-tracking approaches seen in Liu et al. (2017) and Lee and Yi (2022). Hmurovic et al. (2022) combine meta-analysis with experiments and field studies to contrast time scarcity effects across channels. Calvo et al. (2020) use a quasi-experimental difference-in-differences approach with transactional data to examine the impact of scarcity cues on purchase behaviour.

In terms of sample, the documented studies have used both student as well as non-student samples. While student samples are sometimes critiqued because of demographic limitations (e.g. age and employment status), they are considered to be appropriate when the goal is theory testing. Some recent studies continue to use student samples (Gupta and Gentry, 2019; Liu et al., 2022; Hmurovic et al., 2022), though most rely on non-student samples, including consumer panels (Park et al., 1989), mall-intercepts (Jha et al., 2019), online panels (Yao and Oppewal, 2016), users from e-commerce and livestreaming platforms (Dong et al., 2023; Kong et al., 2023) and retailer databases (Calvo et al., 2020). Finally, the studies have been carried out in various settings like malls (Jha et al., 2019), grocery stores (Herrington and Capella, 1995), retail trade shows (Tafesse and Korneliussen, 2012), online retail websites and livestreaming platforms (Wu et al., 2021; Hao and Huang, 2025), simulated e-commerce environments (Godinho et al., 2016; Liang and Lin, 2023) and large-scale retailer databases (Calvo et al., 2020; Ghose et al., 2023). A structured overview of the methodological approaches used across the reviewed studies is provided in Table 4.

We synthesised over four decades of empirical research on the behavioural consequences of TP in retailing. Across browsing, choice, decision-making and purchase stages, TP was found to affect how consumers search for information, evaluate alternatives and make buying decisions – often triggering heuristic processing, compressing deliberation and amplifying urgency. These effects are shaped by consumer traits, contextual factors and retail formats, with notable differences between online and offline environments. Our paper makes several important contributions to marketing and consumer behaviour research.

Our paper makes significant contributions to the domain of retailing. To the best of our knowledge, ours is the first paper that provides a comprehensive synthesis of the fragmented empirical literature in retailing on TP across a time period of more than four decades. Second, we enhance conceptual clarity regarding perceived TP by tracing its evolution in retailing, outlining its definitions and distinguishing between situational and chronic forms. Third, we consolidate the theoretical frameworks used to explain how TP is triggered in retail environments and how it shapes consumer behaviour, offering a holistic view of its psychological and contextual mechanisms. Finally, by structuring its behavioural consequences across browsing, decision-making and purchase stages, we provide an integrated understanding of TP’s impact across the retail journey. This is further enriched by highlighting its variation across online and offline contexts and its interaction with moderators such as consumer traits and product types. Collectively, this paper serves as both a comprehensive reference and a theoretical foundation for future research on perceived TP in marketing.

This review offers actionable insights for managers and retailers aiming to optimise shopping experiences in fast-paced, time-constrained retail environments. As consumers increasingly perceive time as a scarce resource, effectively managing TP – whether chronic or situational – can enhance satisfaction, improve sales efficiency and reduce post-purchase regret. Time-pressed consumers browse less, rely more on heuristics and prioritise task completion. Offline retailers can support these consumers by offering intuitive layouts, consistent shelf placements, clear signage, wide aisles, self-service tech, mobile checkouts and helpful staff. External factors like accessible locations and hassle-free parking can further ease time-related stress. Seamless experiences via well-organised merchandise, identifiable displays and time-saving cues can increase repeat patronage even when shoppers return without time constraints. Digitally, investments in virtual try-ons, personalised recommendations, one click check-outs can simplify decisions under pressure and meet expectations for convenience (Wu and Li, 2023).

Retailers can segment consumers based on time sensitivity using simple diagnostics such as chronic TP scales (Kim and Kim, 2008) or demographic proxies like household size, working status and age. These insights enable the customisation of retail strategies such as offering faster checkout lanes, curated product bundles, smart navigation prompts or enhanced employee assistance to improve efficiency for time-pressed shoppers.

Retailers must also recognise that TP affects shoppers differently across channels. Offline shoppers tend to be more utilitarian and task-focused, while online consumers are more susceptible to emotionally driven, impulsive responses triggered by scarcity tactics like flash sales, livestreaming or countdown timers. To avoid regret and product returns (Calvo et al., 2020), retailers must carefully calibrate the intensity of such cues. Notably, consumers are more likely to resist urgency appeals in online settings unless these constraints are clearly justified (Hmurovic et al., 2022). “Limited sales” strategies are more effective for high-brand-awareness products (Liu et al., 2017), underscoring the importance of transparent, well-framed scarcity messages, especially in digital contexts, to sustain effectiveness without eroding trust.

Finally, while urgency nudges can lift short-term conversions, they risk raising return rates because of post-purchase dissonance (Ghose et al., 2023; Zhang et al., 2024). Managers should pair such tactics with support tools like return-cost reminders or time-value framing (Lee and Yi, 2022). Table 5 provides a concise summary of the key findings from this review and their corresponding managerial implications.

First, the review restricts its scope to consumer behaviour in retail settings, excluding insights from adjacent domains such as psychology, service contexts, supply chains, health care, organisational behaviour or transportation where TP also has important behavioural consequences. A cross-disciplinary review might reveal broader psychological mechanisms or uncover novel conceptual frameworks that are currently underused in marketing. Second, the findings are organised based on the authors’ interpretive judgment rather than a validated taxonomy, which, while flexible, may reduce comparability with other structured reviews. Third, this review does not engage with potentially relevant conceptual constructs from adjacent literature domains such as psychological scarcity, urgency, temporal discounting or perceived control that could enrich the understanding of TP in consumer decision-making. Finally, the review does not conduct a meta-analysis or quantitative synthesis of effect sizes, which could offer stronger empirical generalisations.

Despite increasing scholarly interest, several gaps persist in the TP literature within retail contexts. Much of the foundational work predates the rise of digital and omnichannel formats, necessitating a redefinition of TP in light of digitally induced, interface-driven and algorithmic sources of TP. Future research should investigate how TP arises and evolves across different stages of the shopper journey and how its effects vary between situational and chronic forms. While most studies in this domain have traditionally relied on quantitative methods, qualitative approaches such as interviews, observational studies and netnography can offer valuable insights into how consumers experience and manage TP in today’s retail environments.

Second, while most studies in the domain of retailing have treated TP as a situational variable, it is being increasingly recognised among scholars that TP in a consumer’s daily life has become more pervasive and chronic in nature (Kim and Kim, 2008; Elmashhara and Soares, 2024). However, relatively little is known about how chronic TP, as opposed to situational constraints, influences retail behaviours over time. Future studies should, therefore, investigate how chronic TP shapes behavioural outcomes across different retail and shopping contexts. Third, there is a need to explore retail-specific antecedents of TP – both in-store (e.g. layout complexity, signage and salesperson interaction) and online (e.g. load times, promotional clutter and pop-ups). In promotional contexts, empirical work can compare how different stimuli and scarcity messages (e.g. limited-time vs limited-quantity) across channels differentially evoke TP.

At the consumer choice and decision-making level, future research can explore how TP influences risk aversion, such as a preference for familiar or well-known brands over less familiar ones and whether it nudges consumers toward indulgent choices as a coping mechanism. In online contexts, future studies might investigate how TP interacts with promotional cues (e.g. discount icons and reference prices) to influence browsing and decisions for low-awareness brands (Liu et al., 2017). Finally, the effect of TP on prosocial behaviours such as donation decisions is a promising area for future inquiry.

Fifth, more research is needed on how TP interacts with individual traits such as optimal stimulation levels, time orientation, impulsiveness or risk tolerance to shape retail outcomes, including how consumers process promotions and make trade-offs. Sixth, TP’s impact on post-purchase behaviour, including satisfaction, regret and returns, remains underexplored. Finally, few studies compare offline and online contexts; future experimental and longitudinal work could clarify how channel-specific factors shape time-constrained decision-making. Table 6 outlines a structured research agenda to guide these efforts.