# Will I regret not buying? Unpacking the dual pathways of anticipated regret that influence online impulsive buying

Xiang He, Mengxiang Li, Yi Liu

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Xiang He, Mengxiang Li, Yi Liu. Will I regret not buying? Unpacking the dual pathways of anticipated regret that influence online impulsive buying. Information and Management, 2026, 63, 10.1016/j.im.2026.104323. hal-05526450

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# Will I regret not buying? Unpacking the dual pathways of anticipated regret that influence online impulsive buying

Xiang He<sup>a</sup>, Mengxiang Li<sup>a, *</sup> <sup>‡</sup>, Yi Liu<sup>b, ‡</sup>

## ARTICLE INFO

Keywords:

Online review valence

Scarcity message

Regret theory

Anticipated regret

Impulsive buying

## ABSTRACT

Impulsive buying accounts for a significant portion of online transactions. However, it is challenging for retailers to stimulate consumers' impulsive buying in the quick decision-making environment of online shopping, which is often driven by promotional events. As consumers often regret when shopping online, a key factor influencing their impulsive buying is the anticipation of that regret. Yet few studies have examined the role of anticipated regret in impulsive buying. Drawing on regret theory, we explore how online review valence shapes two forms of anticipated regret: anticipated action regret (for buying) and anticipated inaction regret (for not buying). Furthermore, we examine how quantity- and time-based scarcity messages moderate the relationship between review valence and anticipated regret. Findings from two laboratory experiments show that positive review valence reduces anticipated action regret while increasing anticipated inaction regret, with scarcity messages amplifying these effects. We further find that anticipated inaction regret, rather than anticipated action regret, plays a pivotal role in driving impulsive buying. Our qualitative survey provides rich evidence to validate our theoretical arguments and findings. This study enriches the literature by highlighting the affective mechanism of anticipated regret in impulsive buying and offering a nuanced understanding of its dual nature. It provides actionable suggestions to online retailers for optimizing the use of review valence and scarcity messages to enhance sales in fast-paced online shopping contexts.

## 1. Introduction

> "Better safe than sorry."

> – Ancient Proverb

> "Twenty years from now, you will be more disappointed by the things you didn't do than by the ones you did."

> – Mark Twain

Impulsive buying, a spontaneous purchase driven by immediate emotions and situational triggers [1], is a critical phenomenon in quick online shopping decision-making [2]. This behavior accounts for a substantial share of online transactions, with estimates accounting for nearly 40% of all online spending in 2025.<fnref n="1" /> Moreover, about 84% of global consumers report having made impulsive purchases, under-scoring its prevalence in modern online retail environments. Given its economic and behavioral significance, understanding the mechanisms

driving impulsive buying in fast-paced digital contexts is imperative.

Impulsive buying can lead to a strong feeling of regret if wrong purchase decisions are made [3]. Consumers tend to avoid such regret, especially given the high level of uncertainty inherent in online shopping [4]. Evidence shows that consumers often anticipate regret when shopping online.<fnref n="2" /> *Anticipated regret* refers to the concerns individuals experience that a decision may turn out to be wrong [5]. According to regret theory, avoiding negative emotions such as regret significantly influences individuals' decision-making [5]. Consequently, anticipated regret could play a significant role in affecting impulsive buying, especially in the context of quick online shopping decision-making. Despite its significance, prior research has largely concentrated on examining two main factors that affect impulsive buying: the design of IT artifacts for impulsive buying (e.g., value-added search mechanisms, media formats, website characteristics) [6–8] and marketing strategies (e.g., bonuses, discounts, product prices, etc.) [9–11]. The crucial role of

<footnote><sup>a</sup> Department of Management, Marketing, and Information Systems, School of Business, Hong Kong Baptist University, Hong Kong, PR China</footnote>

<footnote><sup>b</sup> Department of Operations and Analytics, Rennes School of Business, Rennes, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: xianghe@life.hkbu.edu.hk (X. He), mengxiangli@hkbu.edu.hk (M. Li), yi.liu@rennes-sb.com (Y. Liu).</footnote>

<footnote n="1">¹ https://www.invespcro.com/blog/impulse-buying/?form=MG0AV3</footnote>

<footnote n="2">² https://www.lesleighfrank.com/2015/05/26/how-to-shop-online-without-regret/</footnote>
anticipated regret has largely been overlooked.

Furthermore, anticipated regret has a dual nature: anticipated action regret and anticipated inaction regret [12–14]. Anticipated *action* regret refers to the negative feelings that consumers expect to experience if their active decision (e.g., making a purchase) results in an unfavorable outcome. In contrast, anticipated *inaction* regret captures the negative feelings anticipated when an individual foresees that forgoing a purchase might lead to a missed opportunity or suboptimal outcome [4]. In the context of quick online shopping decision-making, consumers may anticipate regret for buying or not buying a product. Although previous research has considered the role of anticipated regret in online shopping settings [15], little attention has been paid to how its two distinct forms might differentially drive impulsive buying. Consequently, our first research question (RQ1) is *How do anticipated action regret and anticipated inaction regret influence consumers’ impulsive buying?*

An in-depth examination of consumers' impulsive buying behavior reveals that factors eliciting immediate emotional responses and situational triggers play a pivotal role in driving such behavior [16]. In online shopping environments, platforms typically employ two complementary approaches to influence purchase decisions. The pull-oriented approach emphasizes the dissemination of informational cues, such as user-generated reviews, to evoke rapid emotional reactions and facilitate decision-making [2]. In contrast, the push-oriented approach leverages situational triggers, such as flash sales, to propel consumers toward making a purchase [17].

Within a pull-oriented approach, consumers frequently rely on consumer reviews to gauge product quality and evaluate potential gains and losses [18], since consumer reviews are the most influential consumer-generated feedback, reflecting the overall assessment of products or services [19,20]. Regret theory suggests that expected feedback concerning potential gains and losses is a central determinant of anticipated regret [5]. When making quick decisions, as consumers do not have much time to scrutinize the details of online reviews, they often resort to simple heuristics [17]. Consequently, review valence, the aggregate positive or negative rating, emerges as a powerful, high-level cue that directly signals a purchase's potential and is therefore likely to be a significant antecedent of anticipated regret. The extant research predominantly focused on the influence of online reviews on retailers' performance (e.g., profits, sales, etc. [20,21]) and consumers' responses (e.g., purchase decisions, perceived helpfulness [22–26]). However, the mechanisms by which review valence influences consumers' anticipated regret and consequently, impulsive buying behavior, remain insufficiently elucidated. Thus, our second research question (RQ2) is *How does review valence influence anticipated regret, ultimately affecting impulsive buying?*

Complementing the pull-oriented approach, retailers also employ a push-oriented approach to facilitate purchases. This strategy leverages seller-generated cues such as scarcity messages (whether quantity-based or time-based) to create a sense of urgency and nudge consumers toward making a purchase [27]. While consumers may perceive user-generated information as more trustworthy, seller-generated scarcity cues can significantly influence how that information is interpreted, especially when consumers rely on heuristic processing in the face of complex choices [28,29]. Scarcity messages may interact with review valence, amplifying or altering consumers' perceptions of the risks and rewards toward a purchase. However, the moderating role of scarcity messages in the relationship between review valence and anticipated regret remains underexplored.

Existing research highlights the need for a more nuanced investigation of how different informational cues, including scarcity, collectively shape consumers' preferences [30]. A comprehensive understanding of this interplay is critical for online retailers, as it would allow them to combine scarcity messages strategically with review valence to optimize sales outcomes. For example, retailers might implement time-based scarcity messages more aggressively when positive reviews were prevalent or adopt a more cautious approach in the face of negative reviews.

Without such insights, there is a risk that retailers could misuse these scarcity cues, potentially leading to unintended adverse consequences. This prompts our third research question (RQ3): *How do online scarcity messages moderate the effect of review valence on anticipated regret?*

To address these research questions, this study draws on regret theory to propose that anticipated regret provides a novel theoretical lens for understanding how online review valence influences impulsive buying behavior, especially when seller-generated scarcity cues (e.g., quantity and time-based messages) are present. Our empirical investigation employs a robust experimental design to test the hypotheses, followed by a qualitative survey, which further reveals consumers' decision-making processes. The experiments confirm that review valence influences anticipated regret significantly. Positive review valence reduces anticipated action regret while increasing anticipated inaction regret, with scarcity messages amplifying these effects. We further find that anticipated inaction regret, rather than anticipated action regret, plays a pivotal role in driving impulsive buying.

Our research makes three theoretical contributions. First, although prior studies on online reviews have largely focused on cognitive dimensions (e.g., perceived helpfulness), they have predominantly overlooked how consumers' emotional reactions shape decision-making. By examining anticipated regret as a mediating mechanism linking review valence with impulsive buying, our study redirects attention toward emotional reactions, a perspective further supported by prior research emphasizing the role of regret in consumer behavior [15].

Second, this study deepens our understanding by distinguishing the dual nature of anticipated regret. Whereas some studies have treated anticipated regret as a unidimensional construct, our work differentiates between anticipated action regret and anticipated inaction regret. In doing so, it reveals that anticipated inaction regret exerts a particularly pivotal influence in the online shopping context, a nuance that enriches the general findings on regret effects reported in impulsive buying studies [16].

Third, our research advances the literature on scarcity by demonstrating that seller-generated scarcity messages serve as critical boundary conditions that moderate the relationship between review valence and consumers' anticipated regret. Specifically, our findings indicate that both quantity-based and time-based scarcity cues amplify the emotional reactions, thereby enhancing impulsive buying. This finding not only extends the scarcity literature [17,27], but also provides practical insights for online retailers and platforms to dynamically combine review valence with scarcity messages to promote quick purchase decisions.

## 2. Theoretical background

### 2.1. *Regret theory and anticipated regret*

Regret theory suggests that individuals evaluate their decisions by comparing the outcomes of chosen and unchosen options [31]. The central premise is that people are generally regret-averse [4]; they engage in forward-looking thinking, imagining the potential regret they might experience if they decide to take action or make a particular decision, and ultimately minimize the potential for future regret [32]. This anticipation of future regret, known as anticipated regret, causes decision-makers to be more contemplative before committing to a choice. Hence, information such as expected feedback concerning potential gains and losses is a central determinant of anticipated regret [5]. Table 1 summarizes the research on anticipated regret.

Anticipated regret is especially salient in quick online shopping, a context characterized by high uncertainty [33,34]. Although previous work has investigated anticipated regret in online shopping, the results have been inconclusive. Some studies suggest that anticipated regret could motivate consumers to choose a product impulsively without forethought [35,36]. However, this finding is inconsistent with the work of McConnell et al. [37], who conclude that anticipated regret can lead
<table><caption>**Table 1**
Summary of prior information systems research on anticipated regret.</caption><thead><tr><th>No</th><th>Source</th><th>Focus</th><th>IV (Mediator/ Moderator)</th><th>DV</th><th>Key findings/insights</th></tr></thead><tbody><tr><td>1</td><td>Jiang, B., Narasimhan, C., & Turut, Ö. (2017). Anticipated regret and product innovation. Management Science, 63 (12), 4308–4323.</td><td>Explore whether and how anticipated regret influences competing firms' profits and product innovation.</td><td>Presence of anticipated regret</td><td>Firm profits<br>Product innovation</td><td>The presence of anticipated regret can increase or decrease firms' profits. Anticipated regret may foster or hinder product innovation depending on whether it makes the consumer segments more or less polarized.</td></tr><tr><td>2</td><td>Zou, T., Zhou, B., & Jiang, B. (2020). Product-line design in the presence of consumers' anticipated regret. Management Science, 66(12), 5665–5682.</td><td>Investigate how anticipated regret influences firms' product-line design and profitability.</td><td>Anticipated overpurchased regret<br>Anticipated underpurchased regret<br>Product quality<br>Restocking fee</td><td>Firms' profits</td><td>Anticipated overpurchased regret lowers the firm's profit. Anticipated underpurchased regret can benefit the firm if consumers' anticipated overpurchased regret is not strong.</td></tr><tr><td>3</td><td>Mattke, J., Maier, C., Reis, L., & Weitzel, T. (2021). Bitcoin investment: a mixed methods study of investment motivations. European Journal of Information Systems, 30(3), 261–285.</td><td>Identify bitcoin-specific motivations and how their configurations influence bitcoin investment.</td><td>Anticipative inaction regret<br>Experienced inaction regret<br>Profit expectancy<br>Ease of bitcoin acquisition<br>Support of bitcoin ideology<br>Investment skills<br>Risk affinity</td><td>Intention to invest in bitcoin</td><td>Investment skills are a necessary condition of motivation for bitcoin investment. Depending on the other motivations in a configuration, some motivations, in terms of ease of bitcoin acquisition, support of bitcoin ideology, risk affinity, experienced inaction regret, and anticipated and inaction regret, have no influence on the motivation of bitcoin investment.</td></tr><tr><td>4</td><td>Polites, G. L., Karahanna, E., & Seligman, L. (2018). Intention-behaviour misalignment at B2C websites: When the horse brings itself to water, will it drink? European Journal of Information Systems, 27(1), 22–45.</td><td>Investigate the misalignment between users' purchase intentions and their actual behavior in the context of online shopping.</td><td>Purchase intention<br>Anticipated regret<br>Uncertainty<br>Behavioral beliefs<br>Actual behavioral control<br>Channel preference</td><td>Actual purchasing behavior of users during the website visit</td><td>This paper proposes that anticipated regret, uncertainty, behavioral beliefs, actual behavioral control, and channel preference will influence the intention-behavior relationship.</td></tr><tr><td>5</td><td>Park, E. H., Ramesh, B., & Cao, L. (2016). Emotion in IT investment decision making with a real options perspective: The intertwining of cognition and regret. Journal of Management Information Systems, 33(3), 652–683.</td><td>Describe the situation that trigger the feelings of regret, the strategies used to manage and control regret, and how these management strategies affect the valuation, creation, exercise, and revaluation of IT real options.</td><td>/</td><td>/</td><td>The effects of anticipated regret and experienced regret on IT real options decisions vary in the approaches taken to manage the regret and the objectives of the decision maker.</td></tr><tr><td>6</td><td>Keil, M., Park, E. H., & Ramesh, B. (2018). Violations of health information privacy: The role of attributions and anticipated regret in shaping whistleblowing intentions. Information Systems Journal, 28(5), 818–848.</td><td>Investigate the role of attributions, the nature of wrongdoing, and anticipated regret in shaping individuals' whistleblowing intentions in context of health information privacy.</td><td>Anticipated regret about remaining silent<br>Intentionality of wrongdoing<br>Stability of wrongdoing<br>Seriousness of wrongdoing</td><td>Whistleblowing intention</td><td>Stability of wrongdoing influences anticipated regret about remaining silent; stability of wrongdoing has a direct impact on whistleblowing intention.</td></tr><tr><td>7</td><td>Lee, J. S., Keil, M., & Wong, K. F. E. (2021). When a growth mindset can backfire and cause escalation of commitment to a troubled information technology project. Information Systems Journal, 31(1), 7–32.</td><td>Investigate how growth mindset promotes IT project escalation.</td><td>Growth mindset<br>Anticipated regret about IT project failure<br>Anticipated likelihood of IT project success<br>Technology unfamiliarity</td><td>IT project escalation</td><td>The growth mindset encourages IT project escalation, and this influence is partially mediated by anticipated regret about project failure and anticipated likelihood of project success. When dealing with unfamiliar technology, the indirect impact of the growth mindset on the anticipated likelihood of project success is stronger compared to projects that utilize familiar technology.</td></tr><tr><td>8</td><td>Ding, Y. (2018). Modelling continued use of information systems from a forward-looking perspective: Antecedents and consequences of hope and anticipated regret. Information & Management, 55 (4), 461–471.</td><td>Investigate the effects of hope and anticipated regret on continued IS use.</td><td>Anticipated regret<br>Disconfirmation<br>Involvement<br>Hope</td><td>Continued IS use</td><td>Hope and anticipated regret have separate and cumulative influence on Continued IS use. The impact of hope on continued IS use only becomes significant in the later stage.</td></tr><tr><td>9</td><td>Tseng, C. H., & Huang, T. L. (2016). Internet advertising video facilitating health communication: Narrative and emotional perspectives. Internet Research, 26(1), 236–264.</td><td>Understand how different types of advertising video and online audience characteristics influence the health communication behavior.</td><td>Internet advertising video<br>Anticipated regret<br>Positive anticipated emotions<br>Negative</td><td>Intention to help others<br>Willingness to seek help<br>Intention to pass</td><td>Narrative advertising videos are more effective in stimulating positive anticipated emotions and flow, which in turn encourage the audience to adopt health risk-reducing behaviors. High-flow narrators in advertising</td></tr></tbody></table>

(continued on next page)
<table><caption>**Table 1 (continued)**</caption><thead><tr><th>No</th><th>Source</th><th>Focus</th><th>IV (Mediator/ Moderator)</th><th>DV</th><th>Key findings/insights</th></tr></thead><tbody><tr><td>10</td><td>Verkijika, S. F. (2019). "If you know what to do, will you take action to avoid mobile phishing attacks": Self-efficacy, anticipated regret, and gender.<br>Computers in Human Behavior, 101, 286-296.</td><td>Examine how anti-phishing self-efficacy, anticipated regret and gender influence mobile users phishing avoidance behavior</td><td>anticipated emotions<br>Audience flow</td><td>along online video advertising</td><td>videos can directly and positively influence the audience's intention to adopt risk-reducing behaviors through emotional contagion.</td></tr><tr><td>11</td><td>Pokharel, M., Lillie, H. M., Nagatsuka, K., Barbour, J. B., Ratcliff, C. L., & Jensen, J. D. (2023). Social media narratives can influence vaccine intentions: the impact of depicting regret and character death.<br>Computers in Human Behavior, 141, 107,612.</td><td>Understand the effectiveness of narrative messages in promoting vaccination by leveraging anticipated regret and emotional responses.</td><td>Anti-phishing self-efficacy<br>Anticipated regret<br>Gender<br>Phishing avoidance motivation<br>Political party affiliation<br>Depicted regret messages<br>Character death messages<br>Replotting<br>Anticipated regret</td><td>Phishing avoidance behavior<br>COVID-19 vaccine intention</td><td>Both anti-phishing self-efficacy and anticipated regret positively influence mobile phishing avoidance motivation and behavior.<br>Depicted regret messages positively influence COVID-19 vaccine intention via the replotting and anticipated regret.</td></tr><tr><td>12</td><td>AlGhanboosi, B., Ali, S., & Tarhini, A. (2023). Examining the effect of regulatory factors on avoiding online blackmail threats on social media: A structural equation modeling approach.<br>Computers in Human Behavior, 144, 107,702.</td><td>Examine how information security regulation factors influence online avoidance motivation and behavior.</td><td>Anticipated regret<br>Information security awareness<br>Information security culture<br>Perceived mandatories of information security<br>Specification of policies<br>Attitude<br>Subjective norms<br>Self-efficacy<br>Apathy</td><td>Avoidance motivation<br>Avoidance behavior</td><td>Personal factors like anticipated regret and apathy significantly influence users' avoidance motivation. A positive attitude toward avoiding blackmail threats, information security culture, and awareness significantly enhance users' motivation to avoid such threats. In addition, specifying clear information security policies strongly impacts the perception of these policies as mandatory, which in turn influences avoidance behavior.</td></tr><tr><td>13</td><td>Gauld, C. S., Lewis, I., White, K. M., Fleiter, J. J., & Watson, B. (2017). Smartphone use while driving: What factors predict young drivers' intentions to initiate, read, and respond to social interactive technology? Computers in Human Behavior, 76, 174-183.</td><td>Investigate the factors that influence young drivers' intentions to engage in three distinct behaviors to social interactive technology on mobile phones while driving.</td><td>Anticipated inaction regret<br>Anticipated action regret<br>Attitude<br>Subjective norm<br>Perceived behavioral control<br>Moral norm<br>Mobile phone involvement<br>Cognitive capture</td><td>Intention to initiate<br>Intention to monitor/ read<br>Intention to respond</td><td>Anticipated action regret negatively impacted the intention to respond.<br>Attitude, subjective norm, cognitive capture and perceived behavioral control significantly predicted intentions for all behaviors.</td></tr></tbody></table>

to a more prudent decision process and vigilant decision-making, such as searching for more information before buying. One plausible reason for the inconclusive findings could be that previous studies overlook the dual nature of anticipated regret: anticipated action regret and anticipated inaction regret [12-14]. Anticipated action regret occurs when individuals compare their current choice with an alternative and realize that taking action will diminish the benefits they experience. Anticipated inaction regret occurs when individuals foresee that failing to take action will lead to a worse outcome [38]. Prior studies have either treated them as a single construct [39] or focused exclusively on anticipated action regret [33,37,40], thereby overlooking that consumers often weigh both possibilities simultaneously. This oversight may lead to an incomplete understanding of online consumers' psychological processes, particularly when they resolve uncertainty by considering both the consequences of purchasing and the missed opportunities of not purchasing [41,42].

## 2.2. Review valence

Online review valence is an aggregated rating that reflects the overall positive or negative sentiment of consumer feedback for a product or service [43]. In online markets, there is often an information asymmetry between consumers and retailers, with consumers having less information about product quality than sellers [44]. To mitigate this online

shopping uncertainty, consumers routinely turn to online review valence to infer the quality of a product or service [45], recognizing that online review valence is more credible and meaningful than seller-generated content [46]. Indeed, existing research has indicated that online review valence serves as a powerful heuristic cue for product quality [47], but its influence is polarized. Positive review valence (e.g., high star ratings) acts as a signal for high product quality and user satisfaction, reducing a consumer's perceived risk. Conversely, negative review valence (e.g., low star ratings) functions as a clear warning signal for potential product quality issues, increasing perceived risk. Given consumers' preference for simplified presentations (e.g., numerical scales) over complex information, the polarity of review valence emerges as a highly effective and easily interpretable factor in consumer decision-making [44].

As a composite of numerous consumer opinions, aggregated review valence is also seen as a more accurate reflection of a product's subjective value than individual reviews, since individual reviews can be manipulated by retailers aiming to artificially boost ratings [48]. Consequently, research on online review valence frequently utilizes aggregated ratings to explore its effects on consumers' responses. Previous work shows that review valence significantly and directly helps consumers make purchase decisions [19,49]. Typically, positive review valence motivates consumers to buy the product, whereas negative review valence, which directly signals potential quality issues, discourages
consumers from purchasing [18]. Moreover, understanding the psychological mechanisms of decision-making has led to much research on the cognitive processing of online reviews. For instance, Kuan et al. [50], analyzing the reviews on Amazon.com, find that negative reviews are perceived as more helpful than positive ones. Taking this finding a step further, Yin et al. [25] demonstrate that review valence shapes consumers' initial beliefs, influencing perceived helpfulness. Whereas these studies highlight the cognitive aspects, research on the affective impact of review valence remains limited. There is a need for further investigation into how emotions evoked by review valence influence subsequent buy-or-not-buy decisions in turn.

## 2.3. Online scarcity message

Scarcity refers to the limited availability of a specific product or service [51]. Online retailers frequently leverage scarcity messages to persuade consumers and boost sales, typically in two forms: limited-quantity scarcity (LQS) and limited-time scarcity (LTS) [52,53]. LQS messages convey the information that a certain product is available only in a predefined quantity. LTS messages, on the other hand, convey the information that a certain product is available only for a limited and predefined period. While both are effective, some research suggests that the LQS message is more effective than the LTS message in driving purchases [54,55].

The effectiveness of scarcity messages stems from their ability to influence consumer psychology. Scarcity enhances a product's perceived value and desirability while simultaneously instilling a sense of urgency. This psychological pressure is often described as a fear of missing out, where the perceived risk of losing a scarce product elevates a consumer's action urgency, compelling them to make faster purchase decisions [55]. Luo et al. [56] exhibit that the scarcity nudge amplifies desirability and induces consumers to perceive a product or service as more urgent across shopping goal stages. However, despite a rich literature highlighting the powerful main effects of scarcity messages, research tends to focus on only a single type of message (e.g., LQS or LTS) in isolation [17]. This limits our understanding of how different scarcity messages (LQS and LTS) influence consumers' processing of online reviews during their online shopping journeys.

# 3. Hypothesis development

Our research model is presented in Fig. 1, and we discuss our hypotheses below.

Online shopping often involves a high level of uncertainty about product quality due to information asymmetry between consumers and retailers [56]. To reduce this uncertainty, especially when making quick online shopping decisions, consumers typically rely on heuristic cues to

evaluate potential risks in their purchases [18,57]. Consumers depend heavily on average product ratings to form their initial impressions of products [25,58,59]. In this situation, review valence, an easily accessible and encapsulated cue in online shopping contexts [58], becomes critically important in enabling consumers to infer the overall quality of a product or service [45].

Typically, positive reviews from previous buyers signal high product value and reflect satisfying experiences [59]. Regret theory suggests that consumers weigh potential gains from making a purchase against potential losses from not making it, leading to strong anticipatory emotions of various worries about missing out [5]. Given that an average rating, acting as a reference point, is often more persuasive than individual reviews [60], positive reviews prompt consumers to consider a potential trade-off: Purchasing may secure a widely appreciated product, while not purchasing risks missing out on a high-quality product that may be difficult to find elsewhere. Sensitivity to potential loss reduces the anticipated regret for buying but increases the anticipated regret for not buying high-quality products.

In contrast, negative reviews clearly signal that a product has flaws and that previous buyers were dissatisfied with it [61]. According to regret theory, consumers strive to avoid making decisions that could lead to regret in the near future [32]. Thus, negative reviews prompt consumers to evaluate a situation where purchasing risks acquiring a potentially low-quality product, whereas not purchasing avoids any immediate loss. Consumers naturally envision the potential consequences of buying or not buying a poor-quality product before incurring any losses [62]. Thus, the anticipated discomfort associated with purchasing increases the anticipated action regret while decreasing the anticipated inaction regret. Accordingly, we propose:

**Hypothesis 1.** (a) *A more positive online review valence negatively influences anticipated action regret.*

**Hypothesis 1.** (b) *A more positive online review valence positively influences anticipated inaction regret.*

Scarcity messages function as urgency signals. For instance, LQS messages emphasize the limited availability of a product, creating a sense of competition and uncertainty among consumers. LTS messages, on the other hand, offer promotions for only a short period, imposing time pressure to purchase [27]. Both types of scarcity messages convey that the product is rarely available and highlight the feasibility constraints on obtaining it. In other words, they act as an attention grabber that induces fear of missing out [63]. Consequently, a high-scarcity message (compared with a low-scarcity message) is more likely to evoke a heightened urgency.

In this situation, psychological stress arises when consumers experience urgency due to a first-come, first-served basis or the risk of a

<figure><img src="image_2.png" /><figcaption>**Fig. 1.** Research model.</figcaption></figure>
product being unavailable soon [17]. This sense of urgency interferes
with in-depth information processing, leading consumers to rely more
heavily on readily available information when evaluating products [17,
64]. Review valence is therefore expected to be intensively used for
quickly assessing product quality. When review valence is positive,
consumers weigh the potential gains of purchasing a high-quality
product against the potential losses of not purchasing it. In this situa-
tion, the urgency created by high-scarcity messages amplifies the weight
placed on the review valence, making its influence more pronounced.
That is, when exposed to a high-scarcity message, consumers rely more
heavily on review valence to evaluate a product, thereby strengthening
its impact on anticipated regret. Therefore, we expect that the positive
effect of review valence on anticipated inaction regret and the negative
effect of review valence on anticipated action regret will both be
stronger in the presence of high-scarcity messages. We thus posit:

**Hypothesis 2.** (a) *The presence of a high-scarcity message strengthens the negative relationship between online review valence and anticipated action regret.*

**Hypothesis 2.** (b) *The presence of a high-scarcity message strengthens the positive relationship between online review valence and anticipated inaction regret.*

Previous research suggests that people attempt to avoid regret when facing uncertainty in decision-making [65] and that anticipated regret influences risk-averse behavior [5]. When experiencing anticipated regret, people tend to weigh various alternatives and choose actions that minimize the degree of regret caused by potential losses [40]. Thus, when consumers experience anticipated action regret (i.e., anticipated regret for buying), their risk aversion drives them to expect future regret in advance and regulate their purchase decisions more cautiously [66]. This concern over potential decision errors fosters rational thinking [67], which diminishes the likelihood of impulsive purchases, as anticipated action regret prompts consumers to reconsider their immediate urge to buy [17,68]. Under such conditions, instead of purchasing impulsively, consumers are more likely to pause and reflect, weighing the potential benefits against anticipated regret. This heightened awareness of potential future disappointment effectively reduces the likelihood of impulsive buying. The desire to minimize future regret, therefore, promotes more deliberate and considered purchase decisions, replacing impulsive reactions with more rational choices. Therefore, we hypothesize:

**Hypothesis 3.** (a) *Anticipated action regret negatively influences impulsive buying.*

Anticipated inaction regret focuses on the potential negative feelings associated with not making a purchase, such as regret over missing out on a desirable product or service. When consumers experience anticipated inaction regret (i.e., anticipated regret for not buying), they fear missing a high-quality product or service, particularly in situations that require quick decision-making [17,62]. This fear of missing out creates a sense of urgency and potential loss aversion, prompting consumers to act quickly, even impulsively, to avoid anticipated negative emotions. Under these circumstances, urgency prioritizes immediate action over rational consideration, as consumers fear that any short delay from overthinking their decision may result in missing out on the purchase opportunity [69]. Therefore, the sense of urgency associated with anticipated regret for not buying prompts consumers to buy impulsively. Hence, we hypothesize:

**Hypothesis 3.** (b) *Anticipated inaction regret positively influences impulsive buying.*

# 4. Research methodology and results

## 4.1. Research design

We employed a multi-study research design to test our hypotheses. The investigation began with a laboratory experiment (Study 1) that manipulated review valence (high vs. low) and quantity-based scarcity to examine their effects on our full conceptual model. To provide a thorough illustration of different types of scarcity messages used by online retailers and validate our findings, a second laboratory experiment (Study 2) replicated the procedure of Study 1 but replaced the quantity-based scarcity message with a time-based one. Finally, to provide evidence for our theoretical arguments, we conducted a follow-up qualitative survey to explore the reasoning underlying consumers' decision-making processes.

### 4.1.1. *Experimental website design*

To enhance experimental realism and generalizability, we designed a simulated e-commerce website closely mirroring the interface and functionality of JD.com, a well-known online marketplace. The mock-up website featured a central homepage with additional product detail pages. The homepage displayed target products along with their respective scarcity messages and review valence, represented by aggregated star ratings on a five-star scale. Each product detail page showed an objective description, including the product's name, price, picture, specifications, scarcity message, and aggregated star rating. A new domain name was created for this mock-up website. Fig. 2 shows a screenshot of this mock-up website.

### 4.1.2. Manipulation

**Target product selection.** To select the target product shown in the experiment, we ran a pretest (n = 34) in which we prepared a list of five products and asked participants to rank their likelihood of purchasing each product from an online shop. Picture attractiveness, information display, willingness to buy, online shopping experience, subjective knowledge, and target product price were all controlled. After excluding the most and least preferred products, the smartwatch was selected as the target product in the experiment.

**Review valence and scarcity message.** To manipulate review valence effectively, we identified the specific star ratings for positive and negative reviews in the pretest. For a positive review, five solid stars were displayed, whereas for a negative review, one solid star was displayed.

To determine the specific numerical values for quantity scarcity and time scarcity, we recruited participants online three months before the experiment. Twenty participants were asked to evaluate their perception of availability when the target product's quantity fell below a certain level and to gauge their sense of ample supply when the number of target products exceeded a specific quantity. Another 20 participants were asked to assess whether the limited time made them feel pressured and whether they felt relaxed when ample time was provided. The scarcity message was manipulated based on the remaining quantity or limited time, with "only 2 items left in stock" or "only 3 min left until the purchase ends" representing a high-scarcity message, and "600 items left in stock" or "7 days, 23 h, 59 min, and 59 s remaining" indicating a low-scarcity message. In addition, we implemented a feature where the scarcity message and review valence would flash and change color when the mouse hovered over them; clicking on these elements also enabled users to navigate to the product detail page.

### 4.1.3. Measurements

We adapted the measurement scale of anticipated regret from Zhao et al. [15] and the measurement scale of impulsive buying from Wells et al. [70]. Eleven control variables were included in the data analysis: online shopping self-efficacy, attitude toward online purchase, online involvement, price sensitivity, prior product knowledge, gender,
<figure><img src="image_3.png" /><figcaption>Fig. 2. Screenshot of mock-up website.</figcaption></figure>

education, age, average online monthly expense, online shopping experience, and online shopping frequency. Tables A1, A2, and A3 in Appendix A list the measurement items.

### 4.1.4. Participants and procedures

Participants were recruited from a subject pool at a large university in China for two primary reasons. First, university students represent a significant and highly active population of online shoppers. They are frequent users of online retail platforms and are regularly exposed to the types of informational and marketing cues (i.e., online reviews and scarcity messages) examined in this research, making them a theoretically relevant population for studying impulsive buying behavior. Second, recruiting students from a university allowed the experiment to be conducted in a controlled laboratory setting, which enhances the internal validity of our findings. Recruitment was managed through an online sign-up system accessible to students from various schools across the university. Pre-screening questions were included in the sign-up process to ensure that all participants had experience with online shopping. We applied a sufficient statistical power calculation of around 80% to determine the sample size [71]. The power analysis assumed a medium effect ($f = 0.25$) and a significance level at 0.05. A total of 160 students were recruited and randomly assigned to one of the four conditions in each experiment. Table 2 shows the participant demographic information.

Upon arrival at the laboratory, participants were randomly assigned

<table><caption>**Table 2**
Demographic characteristics of participants (study 1 and study 2).</caption><thead><tr><th>Characteristic</th><th>Study 1 (N=151)</th><th>Study 2 (N=156)</th></tr></thead><tbody><tr><td>Gender</td><td></td><td></td></tr><tr><td>Male</td><td>48%</td><td>19%</td></tr><tr><td>Female</td><td>52%</td><td>81%</td></tr><tr><td>Age</td><td></td><td></td></tr><tr><td>Under 18</td><td>1%</td><td>1%</td></tr><tr><td>18-24</td><td>99%</td><td>98%</td></tr><tr><td>25-30</td><td>0</td><td>1%</td></tr><tr><td>Education</td><td></td><td></td></tr><tr><td>Undergraduate</td><td>100%</td><td>100%</td></tr><tr><td>Postgraduate</td><td>0</td><td>0</td></tr><tr><td>Avg. monthly online spending (RMB)</td><td>535</td><td>561</td></tr><tr><td>Avg. online shopping experience (years)</td><td></td><td></td></tr><tr><td>Less than 1 year</td><td>3%</td><td>2%</td></tr><tr><td>1-2 years</td><td>10%</td><td>7%</td></tr><tr><td>2-3 years</td><td>0</td><td>0</td></tr><tr><td>3-4 years</td><td>30%</td><td>36%</td></tr><tr><td>4-5 years</td><td>26%</td><td>22%</td></tr><tr><td>Over 5 years</td><td>31%</td><td>33%</td></tr><tr><td>Avg. online shopping frequency (per month)</td><td></td><td></td></tr><tr><td>Less than once a month</td><td>1%</td><td>3%</td></tr><tr><td>A few times a month</td><td>65%</td><td>65%</td></tr><tr><td>A few times a week</td><td>30%</td><td>29%</td></tr><tr><td>About once a day</td><td>4%</td><td>3%</td></tr></tbody></table>
to computers and given a brief introduction to the study's purpose. To ensure that all participants understood how to accomplish the experiment task, they watched a short instructional video showing the key features of the website. They were then asked to log into the simulated shopping website using the account number assigned to them and begin the product purchase task. Participants were presented with a cover story asking them to imagine that they needed to make a quick purchase decision for a good friend's birthday gift. The target product was a smartwatch. They were instructed to browse the product page and make a purchase decision as they usually would in a time-sensitive online shopping scenario. After exiting the shopping page, participants were automatically directed to a post-task questionnaire. This questionnaire included manipulation check questions, followed by measurement items for the key variables (anticipated action/inaction regret, impulsive buying), control variables, and finally, demographic questions. After completing the questionnaire, participants were debriefed on the true purpose of the study, thanked for their time, and provided with the promised 0.5 course credit and monetary compensation (RMB 20 per person). The entire session lasted approximately 20–25 min. Fig. 3 shows the experimental flow.

### 4.1.5. Validity checks

We performed several analyses to establish the reliability and validity of the measured variables. The convergent validity was evaluated using the following two criteria: (1) The measurement items load significantly on their theoretically expected constructs. This means that the item loadings should be higher than the threshold value of 0.700. (2) The average variance extracted (AVE) value should range above the cited minimum value of 0.50. As shown in Table 3, all items' factor loadings are higher than the suggested benchmark of 0.700. The values of composite reliabilities are also higher than the benchmark of 0.700. Furthermore, the values of AVE are higher than the threshold of 0.500. Therefore, the constructs have acceptable convergent validity. Table 3 also shows that the square root of AVE for each construct was larger than the construct's correlations with other constructs, thus supporting the discriminant validity of these measured variables.

## 4.2. Study 1

In study 1, a 2 (review valence: positive vs. negative) × 2 (quantity-based scarcity message: low vs. high) factorial between-subjects design was employed. A total of 160 questionnaires were collected, with nine failing the attention check questions, yielding a final sample of 151 subjects. Participants were randomly assigned to four groups. The sample size for each group is shown in Table 4.

### 4.2.1. Manipulation checks

Regarding the manipulation check, participants were asked to evaluate the perceived scarcity and review valence manipulation using a seven-point Likert scale (1 = strongly disagree, 7 = strongly agree). As expected, participants perceived greater scarcity under the high quantity-based scarcity condition (M = 5.38, SD = 1.68) than under the low quantity-based scarcity condition (M = 2.80, SD = 1.39). The difference was significant (F(1,149) = 105.06, p < 0.001). Furthermore, participants under the positive review condition (i.e., five stars) reported a mean score of 5.72 for their perceived level of positive review (SD = 1.22), whereas those under the negative review condition (i.e., one star) reported a mean score of 2.52 (SD = 1.60). This difference was also significant (F(1,149) = 189.95, p < 0.001). Therefore, both manipulations were deemed successful.

### 4.2.2. Experimental results

Following previous research [72], we first conducted ANOVAs (analysis of variance) to examine the between-subject effect of the manipulated variables on anticipated action regret and anticipated inaction regret. Statistically significant differences in both anticipated regret variables were observed between the negative review and the positive review conditions. Specifically, anticipated action regret was significantly lower under the positive review condition, which means that a negative review (M = 4.751, SD = 1.510) can lead to greater anticipated action regret than a positive review (M = 3.315, SD = 1.568, F(1,147) = 33.044, p < 0.001). By contrast, anticipated inaction regret was lower under the negative review condition (M = 2.893, SD = 1.464) than under the positive review condition (M = 4.350, SD = 1.297, F(1, 147) = 44.010, p < 0.001), and this difference was statistically significant. Therefore, H1(a), and H1(b) were supported.

<figure><img src="image_4.png" /><figcaption>Fig. 3. Experimental flow.</figcaption></figure>
<table><caption>**Table 3**
Construct reliabilities and correlations.</caption><thead><tr><th></th><th>1</th><th>2</th><th>3</th><th>4</th><th>5</th><th>6</th><th>7</th></tr></thead><tbody><tr><td>1. Anticipated action regret</td><td><strong>0.964</strong></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>2. Anticipated inaction regret</td><td>-0.746***</td><td><strong>0.966</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>3. Attitude toward online purchase</td><td>-0.087</td><td>0.132**</td><td><strong>0.760</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>4. Impulsive buying</td><td>-0.529***</td><td>0.648***</td><td>0.071</td><td><strong>0.928</strong></td><td></td><td></td><td></td></tr><tr><td>5. Involvement</td><td>-0.080</td><td>0.136**</td><td>0.711***</td><td>0.113**</td><td><strong>0.865</strong></td><td></td><td></td></tr><tr><td>6. Prior product knowledge</td><td>-0.107</td><td>0.171**</td><td>0.127**</td><td>0.086</td><td>0.206***</td><td><strong>0.901</strong></td><td></td></tr><tr><td>7. Price sensitivity</td><td>0.048</td><td>-0.030</td><td>0.195***</td><td>0.029</td><td>0.212***</td><td>-0.043</td><td><strong>0.773</strong></td></tr><tr><td>Cronbach's alpha</td><td>0.962</td><td>0.964</td><td>0.700</td><td>0.920</td><td>0.888</td><td>0.840</td><td>0.772</td></tr><tr><td>Composite reliability</td><td>0.975</td><td>0.977</td><td>0.796</td><td>0.949</td><td>0.922</td><td>0.895</td><td>0.806</td></tr><tr><td>AVE</td><td>0.930</td><td>0.934</td><td>0.577</td><td>0.862</td><td>0.748</td><td>0.811</td><td>0.597</td></tr><tr><td>Loadings</td><td>0.954~0.975</td><td>0.963~0.972</td><td>0.851~0.862</td><td>0.919~0.937</td><td>0.829~0.928</td><td>0.797~0.994</td><td>0.851~0.917</td></tr></tbody></table>

Note: The squared roots of the AVEs are reported in the diagonal.

<table><caption>**Table 4**
Sample size in each group (study 1).</caption><thead><tr><th rowspan="2">Treatment group</th><th rowspan="2"></th><th colspan="2">Valence of consumers' review</th></tr><tr><th>Positive review</th><th>Negative review</th></tr></thead><tbody><tr><td rowspan="2">Scarcity message situations</td><td>High quantity-based scarcity</td><td>39</td><td>38</td></tr><tr><td>Low quantity-based scarcity</td><td>37</td><td>37</td></tr></tbody></table>

We then examined the moderating effect of quantity-based scarcity messages using two-way ANOVA. In this model, review valence (coded as positive vs. negative) and quantity-based scarcity message (coded as low vs. high) were entered as independent variables. Anticipated action regret and anticipated inaction regret were entered as the dependent variables in separate analyses. The moderation effect is represented by the interaction term (Review Valence × Quantity-based Scarcity), which is automatically estimated within the ANOVA framework to test whether the effect of review valence is contingent upon the level of the scarcity message. The interaction was statistically significant for both anticipated action regret, $F(1, 147)= 5.25$, $p= 0.023$, and anticipated inaction regret, $F(1, 147)= 14.06$, $p< 0.001$ (see moderating graph in Figs. 4 and 5, respectively). Pairwise comparisons showed that when presented with high quantity-based scarcity messages, participants in the positive review conditions had higher anticipated inaction regret, $t(147)=-7.520$, $p< 0.001$, and lower anticipated action regret, $t(147)= 5.742$, $p< 0.001$. When presented with low quantity-based scarcity

messages, participants in the positive review conditions had higher anticipated inaction regret, $t(147)=-1.992$, $p< 0.05$, and lower anticipated action regret, $t(147)= 2.421$, $p< 0.05$. These results confirmed H2 (a) and H2(b).

Next, to test our full model, we conducted moderated mediation analyses using a parameter-based bootstrapping approach with 5000 resamples and a 95% confidence interval (see the results in Table 1 in Appendix B) [73]. As shown in Fig. 6, review valence was related negatively to anticipated action regret ($\beta=-0.813$, $t=-2.229$, $p= 0.027$) and positively to anticipated inaction regret ($\beta=0.613$, $t= 1.992$, $p= 0.048$). Quantity-based scarcity messages also moderated these relationships significantly (for anticipated action regret, $\beta=-1.132$, $t=-2.242$, $p= 0.027$; for anticipated inaction regret, $\beta=1.765$, $t= 3.842$, $p< 0.001$). After controlling for the direct effect of review valence and the interactive effect of review valence and quantity-based scarcity messages, anticipated inaction regret was positively related to impulsive buying ($\beta=0.589$, $t= 5.004$, $p< 0.001$), but anticipated action regret did not significantly influence impulsive buying ($\beta=-0.020$, $t=-0.213$, $p= 0.831 > 0.05$). Moreover, the mediating effect of anticipated inaction regret was positive and had a 95% confidence interval without zero for the high-scarcity message ($\beta=1.323$, BootSE= 0.383, 95% CI [0.637, 2.150]) but not for the low-scarcity message ($\beta=0.282$, BootSE= 0.197, 95% CI [-0.083, 0.707]), as indicated by pairwise contrasts of the indirect effect (see Table 5). Accordingly, H3(b) was supported, whereas H3(a) was not.

### 4.2.3. Discussion

Study 1 offers initial empirical evidence that quantity-based scarcity

<figure><img src="image_5.png" /><figcaption>**Fig. 4.** Effects of review valence and quantity-based scarcity message on anticipated action regret.</figcaption></figure>

<footnote>**$p<0.05$, ***$p<0.01$.</footnote>
<figure><img src="image_6.png" /><figcaption>Fig. 5. Effects of review valence and quantity-based scarcity message on anticipated inaction regret.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>Fig. 6. Moderated mediation analysis results (study 1).</figcaption></figure>

<table><caption>**Table 5**
Indirect effects and pairwise contrasts (study 1).</caption><thead><tr><th>Paths</th><th>Indirect effect</th><th>95% confidence interval</th></tr></thead><tbody><tr><td rowspan="2">Moderator: Low quantity-based scarcity message</td><td>0.016</td><td>[-0.178, 0.199]</td></tr><tr><td>0.038</td><td>[-0.366, 0.456]</td></tr><tr><td rowspan="2">Moderator: High quantity-based scarcity message</td><td>0.282</td><td>[-0.083, 0.707]</td></tr><tr><td>1.323</td><td>[0.637, 2.150]</td></tr><tr><td rowspan="2">Index of moderated mediation: 0.022, 95% CI= [-0.233, 0.297]</td><td></td><td></td></tr><tr><td></td><td></td></tr><tr><td rowspan="2">Moderator: Low quantity-based scarcity message</td><td>0.282</td><td>[-0.083, 0.707]</td></tr><tr><td>1.323</td><td>[0.637, 2.150]</td></tr><tr><td rowspan="2">Index of moderated mediation: 1.041, 95% CI= [0.374, 1.899]</td><td></td><td></td></tr><tr><td></td><td></td></tr></tbody></table>

messages moderate the relationship between review valence and anticipated regret significantly. However, the joint effects of LQS messages and review valence on impulsive buying occur only through anticipated inaction regret. In addition to quantity-based scarcity, we

are highly motivated to further investigate the time-based scarcity message to assess the robustness of our results.

### 4.3. Study 2

In Study 2, we conducted a between-subjects experiment using a 2 (review valence: positive vs. negative) × 2 (time-based scarcity message: low vs. high) design. This study followed a procedure similar to that of Study 1 and used the same scenario. A total of 160 questionnaires were

<table><caption>**Table 6**
Sample size in each group (study 2).</caption><thead><tr><th rowspan="2">Treatment group</th><th rowspan="2"></th><th colspan="2">Valence of consumers' review</th></tr><tr><th>Positive review</th><th>Negative review</th></tr></thead><tbody><tr><td rowspan="2">Scarcity message situations</td><td>High time-based scarcity</td><td>40</td><td>39</td></tr><tr><td>Low time-based scarcity</td><td>39</td><td>38</td></tr></tbody></table>
collected. We used the responses from the 156 subjects who passed attention checks, who were randomly assigned to four groups. Table 6 shows the sample size for each group.

### 4.3.1. Manipulation checks

Regarding the manipulation check, participants were asked to evaluate the perceived scarcity and review valence manipulation using a seven-point Likert-type scale (1= strongly disagree, 7= strongly agree). As expected, participants perceived greater scarcity in the high time-based scarcity condition (M= 5.44, SD= 1.63) than in the low time-based scarcity condition (M= 3.62, SD= 1.53). The difference was significant (F(1154)= 51.30, p< 0.001). Furthermore, participants in the positive review condition (i.e., five stars) reported a mean score of 5.42 for their perceived level of positive review (SD= 1.26), while those in the negative review condition (i.e., one star) reported a mean score of 2.30 (SD= 1.42). This difference was also significant (F(1154)= 209.12, p< 0.001). Therefore, our manipulations were successful.

### 4.3.2. Experimental results

We first examined the effects of our manipulation on the mediators (anticipated action regret and anticipated inaction regret) using ANOVAs. Statistically significant differences in both mediators were observed between the positive review and negative review conditions. Specifically, a negative review (M = 4.169, SD = 1.397) was related to higher anticipated action regret than a positive review (M = 4.004, SD = 1.891, F(1152)= 46.008, p< 0.001). In contrast, anticipated inaction regret was lower under the negative review condition (M = 3.623, SD = 1.285) than under the positive review condition (M = 3.801, SD = 1.817, F(1152)= 47.060, p < 0.001), and this difference was significant. These results, once again, confirmed H1(a) and (b).

Second, we examined whether the effects of review valence were moderated by time-based scarcity messages. As in Study 1, we used two-way between-subjects ANOVAs to examine the effects of our manipulations. The independent variables were review valence (positive vs. negative) and time-based scarcity message (low vs. high). The dependent variables were anticipated action regret and anticipated inaction regret. The interaction term (review valence × time-based scarcity) in the ANOVA model was used to test our moderation hypothesis. We discovered a significant moderation by time-based scarcity message for the effects of review valence on anticipated action regret (F(1, 152)= 5.08, p= 0.026) and anticipated inaction regret (F(1, 152)= 9.36, p=

0.002) (see Figs. 7 and 8, respectively). Pairwise comparisons showed that when presented with high time-based scarcity messages, participants under the positive review conditions had higher anticipated inaction regret, t(152)= -7.090, p< 0.001, and lower anticipated action regret, t(152)= 6.455, p< 0.001. When presented with low time-based scarcity messages, participants under the positive review conditions had higher anticipated inaction regret, t(152)= -2.701, p= 0.008, and lower anticipated action regret, t(152)= 3.207, p= 0.002. Taken together, these results indicate that the effects of review valence on anticipated regret depend on a product's scarcity message, thus confirming H2(a) and (b).

Finally, we applied moderated mediation analyses using a parameter-based bootstrapping approach with 5000 resamples and a 95% confidence interval to test our full model (see the results in Table 1 in Appendix B) [73]. As shown in Fig. 9, review valence was significantly related to both anticipated action regret (β= -1.024, t= -2.976, p= 0.003) and anticipated inaction regret (β= 0.738, t= 2.326, p= 0.021). Time-based scarcity messages also moderated these relationships significantly (for anticipated action regret, β= -1.148, t= -2.373, p= 0.019; for anticipated inaction regret, β= 1.553, t= 3.482, p< 0.001). After controlling for the direct effect of review valence and the interactive effect of review valence and scarcity messages, anticipated inaction regret was significantly and positively related to impulsive buying (β= 0.503, t= 4.793, p< 0.001), but anticipated action regret did not significantly influence impulsive buying (β= -0.163, t= -1.549, p= 0.121 > 0.05). Moreover, the mediating effect of anticipated inaction regret was positive and had a 95% confidence interval without zero for both high (β= 1.153, BootSE= 0.346, 95% CI [0.543, 1.879]) and low time-based scarcity messages (β= 0.371, BootSE= 0.181, 95% CI [0.062, 0.766]) as indicated by pairwise contrasts of the indirect effect (see Table 7). Accordingly, H3(b) was supported, whereas H3(a) was not.

### 4.3.3. Discussion

In sum, Study 2 confirmed the findings of Study 1 that review valence can arouse either anticipated action regret or anticipated inaction regret. These relationships depend on time-based scarcity messages. More importantly, only anticipated inaction regret can affect consumers' impulsive buying.

<figure><img src="image_8.png" /><figcaption>Fig. 7. Effects of review valence and time-based scarcity message on anticipated action regret.</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 8. Effects of review valence and time-based scarcity message on anticipated inaction regret.</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 9. Moderated mediation analysis results (study 2).</figcaption></figure>

<table><caption>**Table 7**
Indirect effects and pairwise contrasts (study 2).</caption><thead><tr><th>Paths</th><th>Indirect effect</th><th>95% confidence interval</th></tr></thead><tbody><tr><td>Review valence → Anticipated action regret → Impulsive buying</td><td></td><td></td></tr><tr><td>Moderator: Low time-based scarcity message</td><td>0.167</td><td>[-0.082, 0.467]</td></tr><tr><td>Moderator: High time-based scarcity message</td><td>0.355</td><td>[-0.165, 0.940]</td></tr><tr><td>Index of moderated mediation: 0.187, 95% CI= [-0.080, 0.602]</td><td></td><td></td></tr><tr><td>Review valence → Anticipated inaction regret → Impulsive buying</td><td></td><td></td></tr><tr><td>Moderator: Low time-based scarcity message</td><td>0.371</td><td>[0.062, 0.766]</td></tr><tr><td>Moderator: High time-based scarcity message</td><td>1.152</td><td>[0.543, 1.879]</td></tr><tr><td>Index of moderated mediation: 0.781, 95% CI= [0.267, 1.462]</td><td></td><td></td></tr></tbody></table>

### 4.4. Additional investigation: a qualitative exploration of the decision-making process

We conducted a qualitative survey to explore consumers' decision-making processes. The objective was to explore the reasoning consumers employ when faced with our experimental scenarios, thereby providing evidence to validate theoretical arguments underlying our hypotheses and potentially uncovering new insights.

We recruited participants from the same university student pool used in Studies 1 and 2 and provided them with the same scenarios used in those studies. Participants were randomly assigned to one of four experiment scenarios. In the survey, participants were asked to respond to an open-ended question: "Please describe in detail the reasons and feelings that went through your mind as you considered whether or not to purchase this watch." A total of 83 complete responses were collected for analysis. Table 8 shows the characteristics of the participants.

We analyzed the qualitative data using a thematic analysis approach
<table><caption>**Table 8**
Demographic characteristics of participants.</caption><thead><tr><th>Characteristic</th><th colspan="2">Qualitative survey (N= 83)</th></tr></thead><tbody><tr><td>Gender</td><td colspan="2">Avg. Online Shopping</td></tr><tr><td></td><td>Experience</td><td></td></tr><tr><td>Male</td><td>11%</td><td>Less than 1 year</td><td>1%</td></tr><tr><td>Female</td><td>89%</td><td>1-2 years</td><td>9%</td></tr><tr><td>Age</td><td></td><td>3-4 years</td><td>41%</td></tr><tr><td>Under 18</td><td>0</td><td>4-5 years</td><td>11%</td></tr><tr><td>18-24</td><td>100%</td><td>Over 5 years</td><td>38%</td></tr><tr><td>25-30</td><td>0</td><td>Avg. Online Shopping Frequency (per month)</td><td></td></tr><tr><td>Education</td><td colspan="2">Less than once a month</td><td>0</td></tr><tr><td>Undergraduate</td><td>99%</td><td>A few times a month</td><td>70%</td></tr><tr><td>Postgraduate</td><td>1%</td><td>A few times a week</td><td>29%</td></tr><tr><td>Avg. monthly online spending (RMB)</td><td>538</td><td>About once a day</td><td>1%</td></tr></tbody></table>

[74,75]. Two of the authors independently read all responses to identify recurring keywords and phrases (e.g., “worry about quality,” “fear of missing out”). These initial codes were then categorized and grouped into broader, more interpretive themes that captured the essence of the participants’ decision-making process. Any discrepancies were resolved through discussion until a consensus was reached. Our analysis revealed several key themes that illuminate how consumers make purchase decisions.

**Theme 1:** Review valence served as a powerful judgment about product quality, which directly influenced anticipated regret. Low ratings consistently triggered concerns about making a poor purchase. As one participant noted, “The low rating may indicate potential quality issues with the watch, making it feel less worthwhile [to purchase].” Another stated simply, “The rating is too low, and I feel it’s not worth the price.” Notably, one participant considered both review valence and scarcity messages, demonstrating the power of the low-quality signal: “The rating is too low and the inventory is too large, which easily makes people think the quality is poor and they’re just trying to clear out stock.”

**Theme 2:** Scarcity message as a trigger for fear of missing out, interacting with review valence to significantly influence anticipated regret for buying or not buying. This theme validates the interaction effect central to our hypotheses. When faced with quantity-based scarcity messages, one participant weighed potential losses from not making a purchase decision, leading to strong anticipatory emotions of worries about missing out: “Although I’m not satisfied with the watch’s rating, the stock is very limited, so I’m a bit worried that if I hesitate, I might miss out. I decided to buy it first and see how it turns out.” Similarly, time-based scarcity messages were shown to either amplify the anticipated regret of not buying a high-rated product or increase the anticipated regret of buying a low-rated one. One participant explained, “Since the purchase period was about to end, I just quickly glanced at the prices and features of the watches. This one has high ratings, seems to be of good quality, and with time running out, I feel like I’d regret not buying it.” Conversely, another participant facing a low-rated product under time pressure felt a clear anticipated regret for buying: “The rating is very low. Overall quality isn’t great, and with only a few minutes left, I really don’t have enough time to decide whether to buy a watch with such poor reviews. There’s a high chance it’ll be a bad purchase, and I’m sure I’ll regret it if I buy it.”

**Theme 3:** Skepticism toward marketing cues, which respondents often perceived as deceptive persuasion attempts. Respondents frequently labeled scarcity cues, such as limited time and low inventory levels, as “tricks” or “marketing tactics” designed to create false urgency rather than reflect genuine product demand. One participant articulated, “Using countdowns and low stock to rush consumers psychologically.” This was particularly pronounced when marketing cues were incongruent. For instance, a low-rated product presented with high time or quantity scarcity was often dismissed as a deceitful attempt to sell off undesirable goods, with one participant saying, “High prices, low ratings, and limited product availability may be tactics of scarcity marketing.”

Consequently, instead of increasing purchase intention, these perceived manipulative tactics often trigger psychological reactance. A participant explicitly stated this: “The stock is marked in low quantity, I suspect it’s false advertising, [so] I don’t want to buy it.”

In summary, the findings from this qualitative study provide contextual evidence supporting our theoretical arguments. Themes 1 and 2 directly validate the mechanisms proposed in our hypotheses. Furthermore, Theme 3 offers valuable additional insight. It reveals that the persuasive power of these cues is not absolute but is contingent upon their perceived authenticity.

## 5. Discussion and implications

Drawing on regret theory, we developed a moderated mediation model in which anticipated inaction regret is an indispensable mediator linking review valence and consumers’ impulsive buying with scarcity messages moderating this mediation model. The consistent results of our two laboratory experiments offer compelling evidence to support most of our hypotheses. Our findings confirm that review valence is a crucial factor that triggers anticipated regret in the context of quick online shopping decision-making. Specifically, review valence negatively affects anticipated action regret (H1a) and positively affects anticipated inaction regret (H1b). Our qualitative analysis provides a rich explanation for these hypotheses. Participants consistently use review valence as a direct heuristic for product quality, which in turn influences their anticipated regret.

In addition, our analysis also reveals that these effects are contingent upon the presence of scarcity messages (i.e., quantity-based scarcity messages or time-based scarcity messages). Specifically, the negative effect of online review valence on anticipated action regret is strengthened when a high scarcity message is presented (H2a). Similarly, the positive effect of review valence on anticipated inaction regret is also strengthened when a high scarcity message is present (H2b). Indeed, our qualitative survey confirms that the scarcity message triggers the fear of missing out and interacts with review valence to influence anticipated regret for buying or not buying significantly. Furthermore, our results confirm our prediction that anticipated inaction regret is positively associated with consumers’ impulsive buying. We demonstrate that, typically, only anticipated inaction regret plays a mediating role. That is, the indirect effect of review valence on impulsive buying via anticipated inaction regret is significant when a scarcity message is presented, whether it is a high quantity-based scarcity message or both high and low time-based scarcity messages.

Unexpectedly, anticipated action regret has a nonsignificant relationship with impulsive buying. We observe no mediating role of anticipated action regret. A plausible explanation is that quick online shopping contexts, amplified by online scarcity messages, are designed to exploit consumers’ fear of missing out, shifting their attention away from the risks of buying and toward the risks of not buying. However, our qualitative survey revealed that consumers often perceive scarcity messages as “tricks” or “marketing tactics.” When these messages are deemed inauthentic, especially when incongruent (e.g., a low-rated product being marketed as scarce), they trigger psychological reactance and lead to a conscious resistance to the marketing messages, effectively neutralizing any impulse to buy.

### 5.1. Theoretical contribution

With a growing attention to product reviews in the online shopping context [72], a considerable body of research has investigated the gatekeeping role of review valence. Our research extends this research stream in several respects.

First, this paper contributes to the online review literature by examining the affective mechanism underlying the effect of review valence—an important step before making buy-or-not-buy decisions—on impulsive buying. Most previous investigations have focused
on how review valence impacts either firm performance (e.g., sales growth and profit) [18,76-78] or consumer purchase decisions (e.g., purchase intention) [19,22,79]. Among these studies, some scholars have further explored various underlying theoretical mechanisms, primarily from cognitive perspectives such as perceived credibility [80] and perceived helpfulness [25,50]. However, focusing solely on consumers' cognitive efforts may limit our understanding of how their emotional reactions influence subsequent product judgments. Psychology research has noted that an individual's emotional reactions influence their judgment in uncertain environments [81]. Our research is among the first to explore how review valence influences anticipated regret, a crucial emotional reaction, in the online shopping environment. It thereby supplements the extant literature on review valence by introducing a novel perspective on regret and revealing its function in adjusting pre-purchase behavior.

Second, we facilitate an in-depth understanding of the role of anticipated regret in the online shopping context by examining both anticipated action regret and anticipated inaction regret. Although IS literature has applied anticipated regret to a significant number of contexts, including product innovation [4,82], financial investment [83], project management [84], IT adoption [85], online advertising [86], and cybersecurity [87-89], few studies have examined it within the e-commerce context [36,90]. Even within e-commerce research, no study has yet explored the dual nature of anticipated regret, which encompasses both action regret and inaction regret, as highlighted by Sandberg et al. [14]. Consumers facing pre-purchase uncertainty often anticipate such regret to mitigate potential losses (e.g., wasted time or missed opportunities), considering the consequences of both buying and not buying a product. The results of our two experimental studies show that both anticipated action regret and anticipated inaction regret are more likely to be elicited by review valence. More importantly, only consumers' anticipated inaction regret was found to mediate the interaction between review valence and scarcity message effects on impulsive buying. In this way, rather than providing a general understanding of the role of anticipated regret, our findings contribute to this research stream by instead offering the narrower, more nuanced insight that a specific type of anticipated regret (i.e., anticipated inaction regret) serves as the underlying theoretical mechanism.

Finally, we test a critical contingency for the influence of online review valence: the scarcity message. Numerous studies have documented the effectiveness of scarcity messages as a marketing strategy. Scarcity messages directly affect consumers' consumption behavior because they serve as attention-grabbers, prompting them to take action in response to the fear of missing out [55,91-93]. Recently, there have been loud calls to consider how scarcity messages interact with other online information cues to influence consumer behavior [30]. In response to that call, the present study transcends the notion of a simple direct effect in favor of a moderating effect, explicating that scarcity messages may play different roles in the decision-making process depending on whether consumers are exposed to positive or negative reviews. Furthermore, whereas past literature has widely examined the isolated effects of either quantity-based or time-based scarcity messages [56,94], our two robust experiments extend current knowledge by noting that both LQS and LTS messages have equally boosting effects. Thus, our work complements and extends recent scarcity studies by revealing the moderating role of retailer-provided LQS and LTS messages, identifying specific boundary conditions.

## 5.2. Practical implications

This research also offers several implications for practice. First, online retailers should strategically align seller-generated scarcity cues with consumer-generated review valence to maximize sales. Specifically, retailers are advised to consider combining quantity- or time-based scarcity messages with products that have positive review valence, as this combination synergistically amplifies consumers'

anticipated inaction regret, the primary driver of impulsive buying. Conversely, retailers must be cautious with low-rated products; applying scarcity messages in such contexts can trigger consumer skepticism regarding the authenticity of the offer, as indicated by our qualitative findings. Thus, marketing efforts for lower-rated products should prioritize quality improvement over urgency tactics to avoid eliciting psychological reactance.

Second, for consumers, this study highlights the importance of emotion regulation in the quick online shopping decision-making contexts. Consumers should be aware that their impulsive buying behavior is often driven by the interaction of positive product review valence and scarcity messages, which heightens anticipated inaction regret. To mitigate potential post-purchase regret, consumers are encouraged to utilize a heuristic check. That is, viewing the high-scarcity messages and low product review valence not as a signal of popularity, but as a potential indicator of manipulative marketing. By recognizing that these cues are designed to elicit fear of missing out, consumers can decouple the immediate anxiety induced by the scarcity message from the product's intrinsic value, thereby fostering more deliberate decision-making.

Finally, e-commerce platforms must prioritize platform governance and interface optimization to sustain the credibility of informational cues. Given that the persuasive power of scarcity messages relies on their perceived authenticity, platforms should implement rigorous verification mechanisms to ensure that limited-time or limited-quantity messages reflect actual inventory states, thereby preventing the erosion of consumer trust. Furthermore, platforms can enhance decision-making efficiency by designing interfaces that present review valence and scarcity messages in close proximity, facilitating the rapid heuristic processing required in quick online shopping decision-making contexts.

## 5.3. Limitations and future research

This research has several limitations that offer exciting opportunities for future research directions. First, the target product used in our experiments was a smartwatch selected in the pretest. Further research is needed to determine whether our results can be generalized to different product types. For instance, Ren and Nickerson [18] pointed out that review valence may exert a greater influence on utilitarian products than on hedonic products. Hence, future research is merited to explore whether the interplay of review valence and scarcity messages might differ for other product categories. Additionally, the effects of impulsive buying are manifested through self-reported measures. Future studies can test and replicate the effects of field settings to consolidate our findings by collaborating with e-commerce companies and can capture consumers' actual consumption records using the superior behavioral metric of clickstream data.

Second, this work includes bilateral interaction between word-of-mouth-related variables from the buyer's side and promotion-related variables from the seller's side. We focus on review valence rather than other aspects of online reviews because, for quick decisions, consumers often seek information encapsulated in summary statistics of ratings, which directly conveys the potential gains and losses of the purchase. When consumers have ample time, they tend to scrutinize the details of online reviews before making final purchase decisions. Future studies could expand beyond review valence to investigate review content, using text-mining methods in broader online shopping contexts.

Finally, regarding scarcity messages, our study merely considers the isolated moderating effects of either quantity-based or time-based scarcity. Although the findings yield valuable insights, consumers often encounter both types of scarcity messages simultaneously during online shopping. Hence, future studies could extend this research by exploring the potential synergistic effects of combined quantity- and time-based scarcity messages on consumer behavior.
CRediT authorship contribution statement

**Xiang He:** Writing – original draft, Visualization, Validation, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Mengxiang Li:** Writing – review & editing, Validation, Supervision, Resources, Methodology, Investigation, Conceptualization. **Yi Liu:** Writing – review & editing, Validation, Supervision.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.im.2026.104323.

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Xiang He is a Ph.D. graduate at the Department of Management, Marketing and Information Systems, School of Business, Hong Kong Baptist University. Her research interests include digital commerce, user-centered technology design, and artificial intelligence. Her papers have been published in proceedings of leading international conferences in Information Systems discipline such as International Conference on Information Systems.

Mengxiang Li is an Associate Professor of Information Systems at Department of Management, Marketing and Information Systems, School of Business, Hong Kong Baptist University. His papers have appeared in top-tier journals such as MIS Quarterly, Information Systems Research, Journal of MIS, Information & Management. Dr. Li's current research examines innovative technology use, strategic decision about technology, user-centered technology design, digital resilience, and remote everything. He has served as Associate Editor at Information & Management, Internet Research, Journal of Global Information Management, and Journal of Electronic Commerce Research. Dr. Li received a Ph.D. in Information Systems from College of Business, City University of Hong Kong in October 2013. Prior to joining HKBU, he worked as a faculty member at School of Computing and Information Technology, University of Wollongong, Australia.

Yi Liu is an Associate Professor of Information Systems at Rennes School of Business, France. He obtained his PhD from ETH Zurich, Switzerland. His research focuses on social media, digital platforms, and online privacy.