Unpacking maximizing in consumer choices: a systematic literature review and research agenda

The field of consumer behavior presents a complex tapestry that significantly influences purchasing choices. Understanding the facets of this tapestry is crucial, for they dictate how individuals and collectives choose, acquire, use or relinquish goods, services, ideas or experiences (Foxall, 2001). Decision-making styles are central to understanding this complex web, which individuals use as consistent behavioral templates when navigating specific choice contexts (Dewberry et al., 2013). Among these styles, “maximizing” has attracted significant scholarly attention. Maximization (or optimization), first proposed by Simon (1955), refers to the tendency of individuals to seek the best possible outcome by exhaustively exploring all available alternatives. Simon differentiated between “maximizing” and “satisficing”, where maximizers strive to make the best choice by thoroughly analyzing all options, while satisficers opt for choices that meet acceptable standards rather than the absolute best. The concept of maximization evolved through research, particularly after Barry Schwartz and colleagues formalized it as a dispositional trait in 2002 (Schwartz et al., 2002). Schwartz highlighted that maximizers focus heavily on making the “best” choice across various life domains, leading them to conduct exhaustive searches and comparisons, which consume significant time and cognitive resources. However, research has shown that maximizing behavior is not always beneficial, as it is associated with negative emotional outcomes such as regret, lower happiness and increased stress – often referred to as the “maximization paradox” (Dar-Nimrod et al., 2009). Although maximizers may achieve higher objective outcomes, such as better salaries, they often feel less satisfied with these results than satisficers (Iyengar et al., 2006). This dissatisfaction can be explained by three key dimensions of maximization: high standards, alternative search and decision difficulty (Schwartz et al., 2002). Maximizers set very high standards, search extensively for all possible alternatives, and often face significant decision difficulty due to fear of making the wrong choice or missing out on better options. These dimensions contribute to the complexity of maximizing and explain why maximizers often experience frustration and dissatisfaction despite their thorough decision-making process.

Understanding the decision-making style of maximizing is vital in the field of consumer behavior for several reasons. Primarily, maximizing affects not only what consumers purchase but also their post-purchase satisfaction, which influences customer loyalty and overall experience (Mogilner et al., 2013; Xia and Bechwati, 2021). Second, maximizers’ exhaustive search and detailed comparison tendencies allow marketers to fine-tune their promotional strategies. For example, they can cater to this consumer segment by providing detailed, attribute-based information (Chowdhury et al., 2009). Finally, maximizing has psychological implications, as it can result in decision paralysis and post-purchase regret. Understanding these ramifications is essential for designing consumer well-being interventions (Luan et al., 2018; Kamiya et al., 2021). Yet, the literature on maximizing exhibits a significant degree of fragmentation. One critical point of discord lies in its psychological ramifications. While some researchers have proposed that maximizing invariably results in decreased life satisfaction and increased regret (Oishi et al., 2014; Luan and Li, 2017), others have challenged this narrative, asserting that these outcomes are context-specific (Belli et al., 2021). Moreover, scholarly discussions have also focused on the methodological issues surrounding the validity, reliability and cultural relevance of the scales developed to measure this construct (Diab et al., 2008; Nenkov et al., 2008).

Given these points of contention and scholarly discrepancies, the current research posits an urgent need for a systematic literature review (SLR) focused on the Maximizing Decision-Making Style in Consumer Behavior. This SLR aims to achieve two principal objectives:

1. first, to integrate and synthesize the disparate strands of existing literature to offer a unified, coherent understanding of maximizing, encompassing its antecedents, mechanisms, moderators and outcomes.

2. Second, a roadmap for future research should be set forth by identifying the existing gaps in the literature and suggesting possible avenues for new inquiries.

To serve these ends, we present three overarching research questions (RQ), focusing on the antecedents, consequences and ongoing debates within the maximizing literature:

This study distinguishes itself sharply from earlier reviews in the field (Chernev et al., 2012; Broniarczyk and Griffin, 2014; Misuraca and Fasolo, 2018), which have often sidelined the intricate dynamics of maximizing in their analysis of consumer behavior. In contrast, the present study adopts an integrative framework, rigorously examining the causes and the multifarious consequences of maximizing as a decision-making style. This nuanced approach also sets our study apart from the meta-analytical methodology adopted by Belli et al. (2021), which predominantly fixated on the well-being implications of maximizing. While these prior contributions have indubitably advanced academic and applied research, the current study endeavors to provide a more exhaustive and nuanced academic treatment of maximizing within consumer behavior, thereby filling a significant lacuna in existing scholarship. The scope of this review is limited to literature that specifically uses the term “maximizing”, as conceptualized by Schwartz et al. (2002). We have only included studies that operationalize the maximizing construct using the exact dimensions outlined by Schwartz, namely, high standards, alternative search and decision difficulty. The review does not cover broader aspects of consumer behavior beyond this specific framework.

Regarding structural organization, the remainder of this paper unfolds as follows: Section 1 lays the foundational premise by elaborating on the importance of understanding decision-making styles. Section 2 offers an exhaustive account of the SLR methodology, ensuring transparency and replicability. In Section 3, we present a comprehensive analysis of the data using descriptive statistics, shedding light on the key trends and patterns observed. Section 4 embarks on a historical exploration of maximizing, scrutinizing its conceptual evolution and the criticisms of various measurement scales. Section 5 introduces a synthesized framework that encapsulates the literature findings; discusses antecedents, consequences, moderators and mechanisms; and suggests gaps and future research agendas. Section 6 outlines their broader societal and academic implications, while Section 7 illuminates the future research agenda and Section 8 concludes the paper. Through this meticulous investigation, the study seeks to enrich the academic dialogue in consumer behavior and establish fertile ground for future scholarly endeavors.

The present study adopts a SLR, an approach well-regarded for its stringent guidelines and comprehensive investigation capabilities (Paul and Criado, 2020). Researchers have crafted various forms of SLRs, each tailored to dissect specific aspects of a research problem, ranging from topical domains and theoretical evolutions to methodologies employed. Recognizing the intricacies of the subject matter, this study uses a hybrid review approach. This meticulously crafted approach integrates a domain-based review with a specialized focus on the theories of maximizing within the purview of consumer behavior (MacInnis, 2011). This choice is not merely methodological but strategic; it aims to catalyze the conceptualization of a new, integrative framework that can serve as a cornerstone for future investigations.

We have used the SPAR-4-SLR protocol, developed by Paul et al. (2021), as it is a rigorous framework for conducting SLRs. This protocol ensures meticulous planning, consistent implementation and transparency, enabling replication and upholding research integrity (Khan et al., 2024; Negi and Jaiswal, 2024). It allows researchers to anticipate potential issues, reduce bias, promote accountability and maintain the highest standards of scholarly rigor (Paul et al., 2021). Compared to other protocols such as PRISMA, which is more suitable for medical research (Moher et al., 2015; Liberati et al., 2009), SPAR-4-SLR is tailored explicitly for social sciences, making it particularly relevant for marketing research. It has been widely adopted by prestigious journals like the International Journal of Consumer Studies, International Journal of Retail and Distribution Management and Psychology and Marketing, underscoring its effectiveness in providing state-of-the-art insights and advancing knowledge in the field (Negi and Jaiswal, 2024; Vasil et al., 2024). This structured methodology comprises three indispensable stages: assembling, arranging and assessing. Each stage contributes uniquely to the credibility and comprehensiveness of the review. Figure 1 illustrates the article selection process using SPAR-4-SLR, visually representing our SLR approach.

The assembling phase serves as the foundation for the entire review process. During this phase, the research domain was clearly defined and guided by meticulously formulated research questions (Paul et al., 2021). The thematic scope was intentionally narrowed to focus on maximizing within the disciplines of Business Management, Accounting, Decision Sciences, and Psychology. The search string “maximiz*” OR “satisfic*” AND “consumer” was used to identify the relevant papers. This string captures the two key decision-making styles – maximizing and satisficing – using wildcard operators to account for various forms of the terms, such as “maximize”, “maximization”, “satisfice” and “satisficing”. Including “consumer” ensures that the search is focused on literature relevant to consumer behavior, which is critical for narrowing the scope to studies within the field of consumer decision-making. Excluding synonymous terms such as “optimizing”, “perfectionism” or “decision fatigue” is justified in this context because the primary focus is on the established theoretical framework of maximizing and satisficing. While these related constructs may overlap with aspects of maximizing, they represent distinct phenomena with their own definitions and conceptual boundaries. Including these terms could dilute the search results, bringing in a wide array of literature that explores different decision-making behaviors not directly related to maximizing or satisficing in consumer contexts. By limiting the search to maximiz* OR satisfic*, we ensure the results are specifically relevant to the well-documented decision-making styles central to the research question, avoiding the inclusion of loosely related constructs that could detract from the focus of the review.

Data collection was conducted predominantly from the Scopus database. Scopus was chosen over the Web of Science (WoS) due to its broader coverage in social sciences and its extensive range of peer-reviewed literature (Kumar et al., 2021). Using a single database like Scopus helps avoid data homogenization issues when merging multiple sources, ensuring consistency and accuracy in our analysis (Khan et al., 2024; Perez-Vega et al., 2022).

Moreover, Scopus is recognized for its accuracy in author-based citation and co-citation analysis, as it includes comprehensive data for all authors in its references (Fazili and Sahaf, 2024). It is also the most commonly used database for quantitative analysis, which enhances the reliability of our SLR (Fazili and Sahaf, 2024). Thus, using Scopus exclusively allows us to maintain high standards of rigor and comprehensiveness in our review, adhering to best practices in research.

Articles published between 2002 and 2023 were considered in terms of time frame. This period was specifically chosen based on the seminal publication by Schwartz et al. (2002), which is widely acknowledged as a catalyst for rigorous consumer behavior research on maximizing.

In the arranging stage, 2,146 articles were initially identified and systematically organized in an Excel spreadsheet. Articles that were ABDC ranked (A* or A) and ABS ranked (≥2) were only chosen for further consideration. Each article was scrutinized based on its title, abstract and keywords to determine its relevance to the research question (Phulwani et al., 2021). Furthermore, a sub-stage named “purification” was executed to ascertain the literature’s relevance to the review’s primary focus. Here, articles not germane to maximizing were meticulously filtered out, leaving a refined selection of 76 articles. This selection was augmented by a targeted search in top-tier journals within the fields of marketing and psychology and by employing a snowball search method within the references of all included articles using Google Scholar. The exact terms used in this process are “paradox of choice”, “choice overload”, “decision paralysis”, “maximizing”, “satisficing”, “Maximization paradox” and “Comparative decision-making strategies”. This rigorous screening process yielded an additional 23 articles. The final sample comprised 99 articles published in 20 marketing and psychology journals.

Beyond mere enumeration, articles were coded on multiple attributes, including but not limited to, citation count, journal title, methodology, theoretical framework and empirical context. This rigorous categorization was instrumental in developing a conceptual framework geared explicitly towards understanding maximizing in consumer behavior.

The concluding stage, termed “assessing,” involved an exhaustive evaluation and synthesis of the selected articles. The collected literature was dissected using content analysis to shed light on the diverse facets of maximizing as a construct within consumer behavior, thereby cataloging critical publication outlets, methodologies and contextual applications (MacInnis, 2011). Concurrently, thematic analysis was leveraged to cluster articles based on their core themes, facilitating a clearer understanding of the existing literature and identifying glaring gaps and promising avenues for future research.

This section reveals the descriptive statistics of the literature on maximizing within the domain of consumer behavior. It includes publication trends, outlets, most cited articles, the sample’s country of origin and methodologies used in the reviewed articles.

Table 1 exhibits the distribution of articles across various journals. Of the 34 articles on maximizing published in marketing journals, the Journal of Consumer Research leads with seven articles. Other noteworthy contributions come from the European Journal of Marketing, Journal of Consumer Psychology and Psychology and Marketing. Non-marketing journals have also contributed substantially, with 65 articles published across fields such as psychology, decision-making and social sciences, thereby highlighting the multidisciplinary appeal of maximizing research.

Figure 2 illustrates the publication trend from 2002 to 2023. A gradual increase in the number of publications on maximizing is observed over this period, reaching a peak in 2018 with 11 articles. Significant publications in 2014, 2016, 2021 and 2022 indicate strong interest in this area. The consistent publication rate implies a maturing field, yet the single article of 2023 suggests a still-developing trend for the current year.

Table 2 lists the top 10 most cited articles. Schwartz et al. (2002) stand out with 1,043 citations, signifying its substantial impact. Another impactful paper by Scheibehenne et al. (2010) in the Journal of Consumer Research has garnered 561 citations. This literature across diverse sources underscores the interdisciplinary appeal and broad relevance of maximizing research.

As shown in Table 3, the USA and Canada lead in research contributions on maximizing, accounting for 51% of the studies. However, the global appeal of the research is evident from the contributions of various countries like Australia, Chile, China, India, Taiwan and countries across Europe. The utilization of online panels for 16% of the studies indicates an increasing reliance on online platforms for data collection. Cross-cultural studies underscore the global relevance of understanding these decision-making styles.

Table 4 reveals the dominance of experimental research methods in the study of maximizing, highlighting a focus on causal mechanisms. Data analysis methods vary, with ANOVA being the most prevalent. However, the current understanding of maximizing and satisficing is largely based on quantitative, self-reported measures like surveys (Cheek and Goebel, 2020; Moyano-Díaz and Mendoza-Llanos, 2021). This focus underscores a need for methodological diversification. Qualitative methods like in-depth interviews can offer nuanced insights into lived experiences and contextual factors. The integration of mixed-method approaches could provide a more holistic view, while innovative techniques like computational modeling could cater to the growing influence of digital technology in decision-making (Dellermann et al., 2019). Overall, diversifying research methods can enrich our understanding of maximizing and addressing existing gaps in the literature.

The historical significance of the concept of maximizing as a decision-making style can be traced through several interrelated disciplines such as psychology, economics and consumer behavior. The term itself gains prominence from the field of rational choice theory, which posits that individuals aim to maximize utility in their decisions (Schwartz et al., 2002). However, the application of maximizing in behavioral sciences and its differentiation from satisficing is more recent, emerging prominently in the early 21st century through the works of scholars like Barry Schwartz and Sheena Iyengar.

Schwartz’s seminal paper (Schwartz et al., 2002) popularized the dichotomy between maximizing and satisficing as distinctive decision-making styles. The paper argued that the abundance of choice in modern society, although ostensibly advantageous, can lead to anxiety, indecision and dissatisfaction, particularly among maximizers. This work has profoundly impacted how decision-making styles are understood, steering subsequent research toward exploring the psychological ramifications of maximizing.

In tandem with this, Sheena Iyengar’s work on the psychology of choice has elucidated how maximizing tendencies can lead to the phenomenon of “choice overload”, wherein an excess of options can result in reduced motivation to make a decision, as well as diminished satisfaction with the chosen option (Iyengar et al., 2006). Her research has particular significance for consumer behavior, showing how retailers and marketers must carefully consider the paradoxical effects of offering too many choices.

The concept of maximizing has thus evolved from a rational, utility-maximizing model from economics to a more nuanced psychological construct that takes into account emotional and cognitive variables. Its historical significance lies in its challenge to classical economic theories that had long dominated discussions on rational choice and decision-making. By revealing the complexities and often paradoxical outcomes associated with the maximizing style, researchers have catalyzed new inquiries into the role of emotions, social norms and cognitive limitations in decision-making (Luan and Li, 2017). Building on this foundational understanding, they have subsequently focused on devising and refining measurement scales to accurately capture the intricacies of maximizing tendencies. These scales have become pivotal in furthering our understanding of how maximizing interacts with various dimensions of decision-making and psychological well-being. To elaborate, four key scales have been developed to measure maximizing tendencies. First, Maximization Scale (MS): The initial 13-item Maximization Scale by Schwartz et al. (2002) was later streamlined by Nenkov et al. (2008) into a shorter six-item version, confirming its three fundamental factors – alternative search, decision difficulty and high standards. Second, Maximizing Tendency Scale (MTS): Diab et al. (2008) critiqued the MS for its limited precision and introduced the Maximizing Tendency Scale (MTS) with improved psychometric properties. Unlike MS, MTS did not find a correlation between maximizing tendencies and life dissatisfaction, challenging earlier assumptions. Third, Maximization Inventory (MI): Turner et al. (2012) further refined the field by distinguishing maximizing and satisficing as separate constructs and introduced the Maximization Inventory (MI) with enhanced psychometric reliability. Fourth, Decision-Making Tendency Inventory (DMTI): Misuraca et al. (2015) proposed the DMTI, which includes the categories of resolute maximizing, fearful maximizing, less ambitious satisficing and more ambitious satisficing. This inventory broadened the scope of our understanding by considering various tendencies in decision-making (Weinhardt et al., 2012; Cheek and Schwartz, 2016).

In conclusion, the realm of maximizing has evolved considerably, influenced by contributions from scholars like Simon, Nenkov, Turner and Misuraca. These advancements have enriched our understanding of the complexities involved in decision-making dynamics, reflected in the progression of measurement scales from the original Maximization Scale (MS) to the more nuanced Decision-Making Tendency Inventory (DMTI).

The field of maximizing has grown exponentially over the years, drawing significant contributions from diverse academic disciplines, most notably social psychology (Newman et al., 2018; Luan et al., 2022; Polman and Maglio, 2022). Furthermore, this socio-psychological construct has piqued the interest of marketing scholars (Olson and Ahluwalia, 2021; Xia and Bechwati, 2021), influencing an array of modern marketing strategies. Yet, despite this proliferation of insights and research, there has been a conspicuous absence of a unified framework that cohesively synthesizes this voluminous body of literature while also charting methodically reasoned pathways for future inquiry. Our work aims to fill this void with a meticulously researched and rigorously structured framework that serves as a seminal guide for understanding the role of maximizing in consumer decision-making.

Our framework is an outcome of an exhaustive literature review and critical evaluations, and it is organized into three cornerstone categories, each contributing uniquely to our understanding of maximizing. First, Mechanisms of Maximizing: This category dissects the processes or mechanisms that elucidate how maximizing impacts consumer decisions. We investigate theories that are less commonly used in the literature but are equally potent for explaining the psychological underpinnings of maximizing (e.g. Broaden-and-Build Theory, Self-Efficacy Theory). Second, Antecedents and Moderators of Maximizing: This category delves into the underlying factors that prompt individuals to engage in maximizing behavior. It covers psychological variables, cultural influences, and situational factors, scrutinizing how they predispose individuals toward maximizing. This section also focuses on the variables mediating or moderating the relationship between maximizing tendencies and outcomes. For instance, we examine how individual differences or contextual variables may amplify or diminish the effects of maximizing. Finally, we discuss the manifold effects of maximizing on consumer behavior cognitively, behaviorally and affectively. This involves a meticulous clustering of 19 variables studied in the literature, grouped into three broad categories: behavioral consequences, cognitive consequences and affective consequences.

Through this comprehensive framework (see Figure 3), we integrate and synthesize current findings to provide both a detailed analysis of what is known and a structured roadmap for future research (summarized in Tables 5 and 6).

Maximizing as a decision-making style is a richly layered phenomenon underpinned by various cognitive, emotional and behavioral factors. We have identified seven seminal theories in the literature illuminating the intricate maximizing mechanisms (see Appendix Table A1). This section explores key theories that elucidate maximizers’ behavior, decision-making processes and consequent emotional impacts while highlighting existing gaps in the literature.

The Social Comparison Theory serves as a cornerstone in understanding the behavior of maximizers, particularly their reliance on social comparisons (Festinger, 1954). Maximizers often evaluate their choices in comparison to those made by others, striving for both absolute outcomes (the best possible option) and relative outcomes (how their choice compares to others) (Harris et al., 2021). This dual motivation compels maximizers to constantly assess the quality of their decisions and their standing compared to others. For instance, a maximizer might select a product not only because it meets objective standards but because it bestows a higher social status than the choices made by peers (Olson and Ahluwalia, 2021). This focus on relative standing unveils a complex interplay where social comparisons can sometimes overshadow the pursuit of absolute quality.

Social Comparison Theory explains why maximizers are prone to feelings of regret and dissatisfaction. After making a decision, they compare their choice with others, leading to post-decision regret if they perceive that others have made superior choices (Weaver et al., 2015). This ongoing comparison amplifies the emotional toll of decision-making, as maximizers often ruminate on missed opportunities or better alternatives. Additionally, the competitive drive to outperform others further exacerbates their dissatisfaction, as even a high-quality choice may feel inadequate if someone else has made a seemingly better one (Olson and Ahluwalia, 2021; Weaver et al., 2015).

Despite these insights, several gaps remain in applying Social Comparison Theory to maximizing. One unresolved issue is the motivation behind social comparisons. While maximizers are thought to compare themselves to others to gather information about objective standards, evidence suggests that their primary goal may be to outperform others in relative terms (Olson and Ahluwalia, 2021; Weaver et al., 2015). This creates a tension between the pursuit of absolute quality and the desire for social superiority, which is not fully understood. Additionally, the role of context – such as whether a decision is made in public or private – may influence the extent to which maximizers rely on social comparisons. Research shows that maximizers are more likely to prioritize relative standing when their decisions are visible to others (Olson and Ahluwalia, 2021; Weaver et al., 2015). Future research should explore how different contexts influence the balance between absolute and relative motivations in maximizing behavior.

Reinforcement Sensitivity Theory (RST) provides a neurobiological framework for understanding the emotional and motivational conflicts that maximizers experience (rRST; McNaughton and Gray, 2000). According to RST, behavior is shaped by the sensitivity of three systems: the Behavioral Inhibition System (BIS), the Behavioral Activation System (BAS) and the Fight/Flight/Freeze System (FFFS) (Spunt et al., 2009). These systems mediate responses to rewards and punishments, making them critical in understanding how maximizers approach decisions.

Maximizers are characterized by heightened BIS sensitivity, which leads to anxiety and rumination during decision-making. The BIS is responsible for detecting potential threats and inhibiting behavior to avoid negative outcomes, making maximizers prone to overthinking and indecisiveness. For maximizers, the fear of making the wrong choice and the anticipation of regret activate the BIS, causing them to perceive decision-making as fraught with risks (Spunt et al., 2009). This anxiety often results in decision paralysis, as maximizers struggle to commit to a choice, fearing that it will not be the optimal one. Additionally, regret is closely linked to BIS activation, as maximizers constantly anticipate the negative emotional consequences of making a suboptimal decision.

Conversely, the BAS is responsible for approaching behaviors and pursuing rewards (Spunt et al., 2009). For maximizers, the BAS drives the desire to find the best possible option, motivating them to search for alternatives exhaustively. However, this reward-seeking behavior often conflicts with the inhibitory effects of the BIS. Maximizers experience a tension between their goal of achieving the best outcome (BAS activation) and their fear of failure or regret (BIS activation). This conflict between BAS and BIS results in prolonged decision-making processes, as maximizers oscillate between seeking rewards and avoiding risks (Spunt et al., 2009).

Despite the explanatory power of RST, there are significant gaps in understanding how maximizers navigate the simultaneous activation of BIS and BAS. How do maximizers reconcile their desire to achieve with their fear of failure in real-time decision-making? Additionally, the role of external factors, such as the decision-making context, in modulating these systems has not been thoroughly investigated. For instance, do maximizers experience different BIS and BAS activation levels in high-stakes versus low-stakes decisions? Furthermore, the FFFS, which mediates immediate responses to threats, has not been fully explored in the context of maximizing. Research is needed to determine whether maximizers activate the FFFS in high-pressure decision-making scenarios and how this influences their ability to make choices.

Cognitive Dissonance Theory (CDT) explains maximizers’ discomfort when their actions or decisions conflict with their beliefs or expectations (Festinger, 1957). This discomfort – cognitive dissonance – is particularly pronounced for maximizers because their high standards often make them doubt their decisions, even after extensive deliberation (Olson and Ahluwalia, 2021). Maximizers struggle to reduce dissonance because they are reluctant to commit to their choices, fearing that a better option may exist. As a result, they miss out on the psychological benefits of dissonance reduction, where individuals increase their preference for their chosen option while devaluing the alternatives.

CDT also highlights why maximizers are prone to post-decision regret. Even after deciding, maximizers often second-guess themselves, engaging in counterfactual thinking about what might have been had they chosen differently (Sparks et al., 2012). This ruminative process prevents them from reconciling their decision with their desire for the best, perpetuating dissonance and dissatisfaction. Additionally, maximizers frequently seek external validation to reduce dissonance, relying on reviews, opinions and comparisons with others to justify their choices. However, this strategy can backfire, as maximizers will likely encounter information that makes them question their decision even further, exacerbating their dissonance.

While CDT provides valuable insights into the psychological mechanisms of maximizing, several gaps remain. One major gap is the incomplete understanding of why maximizers are less likely than satisficers to commit to their decisions and reduce dissonance. What specific cognitive and emotional barriers prevent maximizers from experiencing the relief of dissonance reduction? Additionally, there may be variability in how maximizers experience dissonance, with some being more flexible in rationalizing their choices than others. Future research should explore the individual differences that influence dissonance reduction strategies, such as self-regulation capacity and emotional resilience. Furthermore, the long-term effects of chronic dissonance and indecision on maximizers’ well-being have not been sufficiently studied. Longitudinal research is needed to assess how repeated experiences of dissonance affect maximizers’ overall satisfaction and decision-making competence over time.

Self-determination theory (SDT) offers a framework for understanding how maximizing undermines key psychological needs, particularly autonomy, competence and relatedness (Ryan and Deci, 2000). Maximizers often experience choice overload, where the abundance of options leads to decision paralysis and diminishes their sense of autonomy. Instead of feeling empowered by the freedom to choose, maximizers may feel burdened by the responsibility of selecting the perfect option. This sense of being overwhelmed reduces their overall well-being and increases decision fatigue (Belli et al., 2021). Maximizers also struggle with feelings of incompetence because they continuously doubt whether they have made the right decision. The constant fear of missing out on a better option undermines their mastery over the decision-making process, leading to anxiety and dissatisfaction (Belli et al., 2021; Weaver et al., 2015).

While SDT provides a compelling explanation for the emotional consequences of maximizing, several research gaps remain. One key gap is how maximizers can recover their sense of autonomy and competence after making a decision. What strategies could help maximizers feel more in control of their decision-making, and how can they regain confidence in their choices? Additionally, while the negative effects of social comparison on relatedness have been acknowledged, research has not yet explored interventions that could reduce the reliance on external validation. Mindfulness techniques or self-compassion practices that encourage maximizers to focus on intrinsic satisfaction rather than social standing could be potential avenues for future research.

Cognitive Appraisal Theory (CAT) provides a framework for understanding how maximizers appraise decision-making situations as stressful or threatening. According to CAT, emotions arise from the cognitive evaluations individuals make about the significance of an event (Johnson and Stewart, 2005, p. 3). For maximizers, decision-making often involves a primary appraisal in which they perceive the abundance of choices as a threat to their goal of making the best possible decision (Tang et al., 2017). In the secondary appraisal, maximizers evaluate their ability to cope with the demands of the decision-making process, often concluding that they lack the necessary resources – such as time, knowledge or certainty – to make the optimal choice. This perceived lack of coping resources leads to stress, indecisiveness and decision paralysis.

Despite the insights provided by CAT, there are significant gaps in the literature on how maximizers manage decision-related stress. One key gap is the lack of research on coping strategies. While CAT suggests that secondary appraisal involves evaluating one’s ability to cope, it is unclear what coping mechanisms maximizers use to manage decision-making stress. Could decision aids or reframing techniques help maximizers reduce the emotional burden of choice overload? Additionally, there is limited understanding of the individual differences that influence the appraisal process in maximizers. Factors such as self-esteem, perfectionism and decision-making experience likely play a role in how maximizers appraise decision situations, but these have not been fully explored. Future research should investigate these individual differences and their impact on the emotional outcomes of maximizing.

Prospect Theory explains why maximizers are particularly susceptible to loss aversion, where the fear of missing out on a better option outweighs the potential benefits of making a decision (Kahneman, 1979, p. 278). Maximizers are more sensitive to potential losses than to equivalent gains, leading to decision paralysis and dissatisfaction (Traczyk et al., 2018). They often focus on what they stand to lose by not choosing the best option, rather than on its benefits (Lemmen et al., 2022). This framing bias causes them to continually doubt their decisions, even after making a reasonable choice (Chan and Wang, 2018).

While Prospect Theory provides a useful explanation for the decision-making behavior of maximizers, several gaps remain. One major gap is understanding how maximizers can shift their focus from potential losses to potential gains. Could interventions such as practicing satisficing or reframing decision contexts help reduce maximizers’ loss aversion? Additionally, research has not fully explored how different decision contexts – such as public versus private settings – influence the framing effects on maximizers. Do maximizers experience more loss aversion in public decisions, where their choices are visible to others than in private decisions? Further research is needed to explore how social, cultural and situational factors impact how maximizers frame decisions and how this affects their satisfaction.

Regret Theory emphasizes the role of counterfactual thinking in maximizing behavior (Loomes and Sugden, 1987). Maximizers frequently engage in counterfactual thinking, imagining better alternatives to their chosen option and regretting what might have been. This focus on “what could have been” intensifies their regret and dissatisfaction, even if their decision was objectively good (Hassan et al., 2020). Maximizers set unrealistically high standards for their decisions, and when these standards are not met, they experience heightened regret and decreased well-being (Peng et al., 2018).

While Regret Theory explains why maximizers are more prone to post-decision dissatisfaction, there is limited understanding of how individual differences impact the experience of regret. Some maximizers may be more resilient to regret, while others may struggle more intensely. What factors – such as personality traits, decision-making experience or emotional regulation skills – contribute to these differences in regret sensitivity? Additionally, the long-term effects of chronic regret on maximizers’ mental health have not been sufficiently explored. Repeated exposure to regret could lead to more severe outcomes, such as anxiety or depression, particularly in high-stakes decisions. Further research is needed to investigate the long-term psychological effects of chronic regret and develop interventions that could help maximizers reduce regret through decision-making strategies focusing on “good enough” outcomes.

In conclusion, these theories provide valuable insights into the cognitive and emotional mechanisms that underpin maximizing behavior. However, there are significant gaps in our understanding of how maximizers manage the tension between achieving the best possible outcome and avoiding regret or dissatisfaction. Future research should address these gaps, particularly by exploring the role of individual differences, decision contexts, and coping strategies. By filling these gaps, we can develop a more comprehensive understanding of maximizing and its emotional consequences and identify practical strategies for helping maximizers navigate decision-making processes more effectively.

Through a systematic review of extant literature on maximizing decision-making styles, we identified 21 distinct antecedents that serve as precursors to maximizing (see appendix Table A2). Uisng Excel for coding, we used cluster analysis as a methodological approach to categorize these antecedents for a more in-depth and structured understanding. The identified antecedents were systematically classified into two overarching clusters: Internal Factors and External Factors. These primary clusters were subdivided into six specific sub-clusters to delineate the nuances affecting maximizing behaviors. The ensuing discussion provides a comprehensive exposition of our analytical findings. Building on the theoretical frameworks discussed earlier, this section explores these antecedents and moderators within their proper context, highlighting how they align with existing theories such as SCT, CDT and SDT while pointing out research gaps and future directions.

Internal factors, including cognitive dispositions, emotions and personal characteristics, provide a foundation for understanding why some individuals are more prone to maximizing. Cognitive dispositions like frugality, subjective knowledge and time orientation are significant maximizing drivers. For instance, frugality has been found to push individuals toward maximizing as they aim to extract the best value from limited resources (Brannon, 2021), an outcome that aligns with Prospect Theory’s concept of loss aversion. Frugal individuals fear missing out on better opportunities, leading to a heightened sensitivity to potential losses rather than gains – a gap that future research could explore further, particularly by examining how individual perceptions of value shift under different economic conditions.

Similarly, individuals with lower levels of subjective knowledge tend to maximize as they seek additional information to compensate for their uncertainty (Hadar and Sood, 2014). This behavior resonates with CAT – maximizers are more likely to perceive decision-making as stressful due to their limited confidence in making informed choices (Johnson and Stewart, 2005). However, a gap remains in fully understanding how different types of knowledge (e.g. domain-specific knowledge versus general knowledge) affect the degree to which individuals maximize. Future research could investigate whether specific forms of knowledge mitigate the negative emotional effects of maximizing, such as anxiety and decision fatigue.

In addition, time orientation, particularly future orientation, plays a role in maximizing by encouraging individuals to thoroughly evaluate options in anticipation of long-term consequences (Besharat et al., 2014). This behavior is closely tied to RST, as maximizers may exhibit heightened BIS activation when faced with complex choices that could impact future outcomes (McNaughton and Gray, 2000). However, there is limited understanding of how time orientation interacts with BIS sensitivity in real-time decision-making. Investigating this dynamic in both short- and long-term decision contexts could offer insights into how maximizers navigate decisions that balance immediate and delayed rewards.

Emotional states also play a significant role in shaping maximizing. For example, sadness can drive individuals toward maximizing as they seek change or improvement in response to negative emotional experiences (Cryder et al., 2008). This finding aligns with SDT, which posits that maximizers may feel a lack of competence or autonomy, driving them to overcompensate through exhaustive searching (Ryan and Deci, 2000). Yet, emotional factors like sadness are often discussed in isolation, leaving a gap in understanding how emotions interact with cognitive factors like frugality or subjective knowledge. Future studies should explore how the interaction of cognitive and emotional antecedents intensifies maximizing behavior, especially under emotionally charged decision scenarios.

Similarly, personality traits such as neuroticism and obsessive-compulsive tendencies are key drivers of maximizing behavior (Oren et al., 2018; Traczyk et al., 2018). Neurotic individuals, who are prone to anxiety and self-doubt, often exhibit maximizing tendencies in their pursuit of perfection, which ties directly to CDT. These individuals are more likely to experience dissonance after decisions, perpetuating a cycle of indecision and regret. However, most research in this area remains cross-sectional, offering only a snapshot of how these traits affect decision-making (Oren et al., 2018; Traczyk et al., 2018). Longitudinal studies are needed to assess how these personality traits evolve over time and how they influence the development of maximizing tendencies.

External factors, such as market cues, task complexity and decision-making context, further shape maximizing behavior by creating an environment where individuals are prompted to evaluate more options. Market-related cues, such as product features and the desire to save money, often activate maximizing tendencies (Brannon and Soltwisch, 2017; Chang and Yang, 2022). However, there is a gap in understanding how maximizers balance absolute quality versus social comparison when making decisions in different contexts (Olson and Ahluwalia, 2021; Weaver et al., 2015). For example, public decision-making environments may heighten the influence of social comparison, while private contexts may encourage a focus on intrinsic satisfaction (Harris et al., 2021). Future research should examine how public versus private decision environments affect maximizing and whether social comparison cues exacerbate the negative emotional outcomes of maximizing, such as regret and dissatisfaction.

Task complexity, including the number of available options and the structure of decision tasks, is another important external factor. Complex decision environments can trigger choice overload, which is particularly problematic for maximizers (Levav et al., 2012; Mogilner et al., 2013; Chan and Wang, 2018). Maximizers perceive complex decisions as threatening due to their desire for the best outcome (Sims et al., 2013; Szrek, 2017). However, while existing research highlights the negative effects of task complexity on decision satisfaction, there is a limited exploration of how simplifying decision tasks could mitigate these effects for maximizers (Diehl and Poynor, 2010; Harman et al., 2018). Future research could explore the design of decision aids or interfaces that reduce task complexity, helping maximizers achieve satisfactory outcomes with less cognitive and emotional strain.

Our comprehensive review of the literature on decision-making styles has also identified 13 key moderators that affect maximizing (Table A3  in appendix). These moderators have been categorized into three primary clusters and further divided into four sub-clusters, providing a structured framework for understanding the nuanced influences on decision-making.

Demographic characteristics such as age and socioeconomic status, for instance, play a moderating role in decision-making styles. Older individuals tend to satisfice more frequently, whereas younger individuals, with greater risk tolerance, are more likely to maximize (Simpson et al., 2008; Duell et al., 2018). However, there is a need to further investigate how socioeconomic factors, such as access to resources, moderate the relationship between maximizing and life satisfaction. Future research could examine whether individuals with fewer resources experience greater emotional strain from maximizing due to the higher stakes involved in each decision.

Individual differences, including Need for Cognition (NFC) and decision-making competence, also moderate the likelihood of maximizing. Individuals with high NFC enjoy the cognitive labor of evaluating multiple options and are thus more prone to maximizing (Tang et al., 2017). However, there is limited research on how individual differences in NFC interact with emotional factors such as decision-related anxiety. Future studies should investigate whether high-NFC individuals are more resilient to the emotional costs of maximizing, such as regret and indecision, or are equally vulnerable.

Contextual moderators, such as time pressure, further influence maximizing behavior. Time constraints generally push individuals toward satisficing by limiting the cognitive resources available for thorough evaluation (Kumari et al., 2022). Yet, there remains a gap in understanding how time pressure interacts with task complexity and individual differences in maximizing tendencies. Future research could explore how decision aids to alleviate time pressure could help maximizers make more satisfying choices without experiencing decision fatigue.

In summary, the choice between maximizing and satisficing emerges from a complex interplay of demographic characteristics, individual differences and context-related factors. More integrated research focusing on the gaps highlighted above could provide a well-rounded perspective on the antecedents and moderators of maximizing. Such endeavors would not only fill existing gaps in the literature but also contribute substantively to the interdisciplinary discourse on decision-making processes.

Understanding the implications of maximizing is a pivotal aspect of consumer behavior research, having far-reaching implications for marketing strategies and policy-making. The study of maximizing and its consequences has burgeoned over the years, giving rise to an extensive body of literature that examines various facets of this decision-making style. In an attempt to structure this expansive field, this section classifies the consequences of maximizing into three principal clusters: Behavioral Consequences, Cognitive Consequences and Affective Consequences. These clusters collectively encompass 19 variables rigorously explored in the literature (see Table A4  in appendix).

While these areas provide a solid foundation for understanding maximizing, several gaps remain and future research directions are embedded within the following discussion.

Maximizers’ behavior is heavily shaped by their relentless quest to make the optimal choice, influencing several pre-purchase, purchase and post-purchase actions. One prominent behavioral outcome is the tendency to engage in extensive pre-purchase information searches. Maximizers meticulously gather and evaluate information to make the most informed decision possible. However, this exhaustive search can lead to choice overload, where the abundance of options becomes overwhelming, diminishing satisfaction – a consequence supported by CAT. The theory suggests that maximizers who perceive a high level of personal significance in decision-making are more prone to appraising choice overload as stressful (Dar-Nimrod et al., 2009). A gap exists in understanding how decision-making environments can be structured to mitigate choice overload for maximizers, potentially through the development of decision aids or filters that streamline option evaluation.

Maximizers’ behavioral tendencies extend beyond the pre-purchase phase into the decision-making process. Studies indicate that maximizers are more susceptible to decision paralysis due to their need to exhaustively evaluate all available options (Shortland et al., 2020). RST provides a lens through which this behavior can be understood. Maximizers’ heightened BIS sensitivity likely exacerbates their difficulty making final decisions, especially in high-stakes or time-sensitive scenarios. Yet, the extent to which task complexity interacts with decision paralysis remains underexplored. Future research could investigate how task complexity and time pressure variations influence decision paralysis in maximizers, particularly in complex digital purchasing environments.

In the post-purchase phase, maximizers often experience decision-related regret, leading to behaviors such as product returns or avoidance of repeat purchases (Ma and Roese, 2014). A gap persists in understanding how maximizers cope with post-purchase regret, particularly whether certain coping mechanisms (e.g. mindfulness) may reduce the likelihood of returns and enhance satisfaction. Additionally, maximizers’ fluctuating brand loyalty, influenced by their ongoing evaluations and susceptibility to cognitive dissonance, warrants further examination. Future studies could investigate how brands can foster stronger loyalty among maximizers by reducing the potential for post-purchase dissonance, perhaps through more transparent marketing or product guarantees.

The cognitive toll of maximizing is substantial, as it requires a higher level of mental engagement than satisficing. One key cognitive outcome is decision difficulty. Maximizers, driven to exhaustively evaluate all available options, often struggle with complex decisions, leading to increased cognitive load (Cheek and Schwartz, 2016). This burden is exacerbated when maximizers encounter many options, amplifying their mental effort and reducing cognitive efficiency. Despite these insights, there remains a gap in understanding the specific thresholds at which cognitive load becomes detrimental for maximizers. Future research could explore how cognitive overload affects their decision-making competence in different purchasing contexts, such as online versus in-store shopping.

Regret is another cognitive consequence that often plagues maximizers. The meticulous nature of their decision-making process and their tendency to engage in counterfactual thinking – reflecting on how alternative decisions might have led to better outcomes – frequently results in post-purchase dissatisfaction (Harris et al., 2021). Regret Theory further elucidates this phenomenon, highlighting that maximizers are more likely to experience regret due to their propensity to compare actual outcomes with unchosen alternatives. However, more research is needed to explore how different types of decisions (e.g. low-stakes versus high-stakes) influence the intensity of regret among maximizers. This gap presents an opportunity to investigate how regret differs across varying decision contexts and whether personalized decision support systems could help reduce regret in digital commerce settings.

Finally, maximizers are prone to experiencing cognitive dissonance when their chosen option fails to meet their expectations. This dissonance is particularly problematic when maximizers compare their decisions to other unchosen options (Lai, 2011). While much of the existing literature focuses on short-term dissonance, a gap exists in understanding the long-term cognitive impact of repeated dissonance experiences. Future studies should consider how chronic cognitive dissonance might affect a maximizer’s overall decision-making competence and well-being over time, especially in fast-paced consumer environments such as e-commerce, where multiple decisions are made quickly.

Maximizing also has profound emotional and psychological implications. One of the most pronounced affective outcomes is anxiety, particularly when maximizers confront the possibility of making suboptimal decisions. Pursuing the best option creates an emotional burden, as maximizers often fear that they will miss out on better opportunities (Worthy et al., 2014). However, there is still much to learn about the specific emotional triggers that exacerbate anxiety in maximizers. Future research could explore whether certain decision environments, such as high-pressure sales tactics or time-limited offers, intensify anxiety and reduce decision satisfaction among maximizers.

Stress is another common affective consequence of maximizing. The exhaustive search for the best option can lead to overwhelming stress levels, especially in complex or time-sensitive situations (Shortland et al., 2020). However, the role of coping mechanisms in mitigating stress remains underexplored. Future research should investigate whether stress-reduction techniques, such as mindfulness or decision delegation, could help maximizers alleviate stress and improve their decision-making satisfaction.

Maximizers are also susceptible to frustration when they cannot identify the optimal choice. Despite their exhaustive evaluations, maximizers are often more frustrated than satisficers when the ideal option remains elusive (Dar-Nimrod et al., 2009). This frustration is particularly prevalent in the post-purchase phase, where maximizers experience fluctuating emotional states, including diminished optimism and reduced well-being (Purvis et al., 2011). While some studies suggest that adaptive coping mechanisms, such as reframing decision outcomes, could help reduce frustration, a gap remains in understanding how maximizers can be guided toward more positive emotional states post-purchase. Future research could explore how interventions, such as post-purchase feedback or customer service initiatives, might improve emotional well-being among maximizers, particularly in high-involvement product categories like electronics or automobiles.

The findings of this SLR on maximizing decision-making style present significant contributions to both theoretical understanding and managerial practice. By analyzing the antecedents, moderators, and consequences of maximizing, this research deepens our understanding of the cognitive, emotional and behavioral dimensions of maximizing behavior (Schwartz et al., 2002). Furthermore, the insights gained offer valuable implications for businesses seeking to cater to consumers with maximizing tendencies. In this section, we discuss the theoretical advancements and provide detailed, actionable strategies for leveraging these insights in consumer-facing industries.

The theoretical contribution of this paper lies in its integration and extension of multiple established frameworks to provide a more comprehensive understanding of maximizing decision-making behavior. Prior research has often relied on singular theories, such as SCT (Weaver et al., 2015), RST (Kraines et al., 2017) or CDT (Harris et al., 2021), to explain specific aspects of maximizing. However, this paper bridges these key frameworks, offering a multidimensional view of how maximizers engage in decision-making. It explains how maximizers balance internal motivations, like seeking the best outcome, with external pressures, such as social comparisons. By synthesizing these different theoretical perspectives, the paper provides a richer understanding of the cognitive, emotional and behavioral processes underlying maximizing.

A significant theoretical contribution of this paper is the identification of gaps within these frameworks as they pertain to maximizing behavior. For example, while SCT explains how maximizers assess their decisions relative to others, this paper highlights the need for more research into how different decision contexts, such as public versus private settings, influence these behaviors (Weaver et al., 2015; Olson and Ahluwalia, 2021). Similarly, CDT effectively captures post-decision regret but lacks focus on why maximizers struggle to commit to decisions and experience the benefits of dissonance reduction (Kraines et al., 2017). Furthermore, this paper extends RST, which explores approach and avoidance motivations, by proposing future research into how these motivations interact in real-time decision contexts, particularly under conditions like time pressure (Harris et al., 2021).

Another key contribution of this paper is the exploration of how maximizing manifests in different decision-making environments, particularly in the digital age. Task complexity, time pressure and the unique nature of digital decision-making environments are considered influential in shaping maximizing behaviors. For example, in complex digital settings, where the number of options may be overwhelming, maximizers may be more prone to decision paralysis, an outcome that has not been fully explored in existing theoretical discussions (Olson and Ahluwalia, 2021). This expansion of the theoretical framework to consider the role of modern decision environments offers a fresh perspective on how maximizing behavior may be exacerbated by the technological landscape, providing a new angle on the interaction between decision context and consumer psychology.

Furthermore, the paper contributes to theory by outlining future research directions that will refine our understanding of maximizing. For instance, it highlights the need for further investigation into how cognitive dispositions like frugality interact with maximizing behaviors, as well as how emotions such as regret and anxiety play a role in perpetuating these decision-making patterns (Brannon, 2021; Cryder et al., 2008). Additionally, it emphasizes the importance of studying the long-term emotional and cognitive consequences of chronic maximizing, such as decision-related regret and dissonance, and how these might impact overall well-being (Harris et al., 2021).

In conclusion, the paper’s theoretical contributions are multifaceted. It synthesizes several key frameworks – SCT, CDT and RST – into a coherent explanation of maximizing behavior, while also identifying important research gaps. These gaps include the role of decision context, individual emotional responses and long-term behavioral patterns, all of which provide clear directions for future empirical work. By addressing these areas, the paper not only enriches the existing literature but also establishes a foundation for future research aimed at furthering our understanding of the complex dynamics that drive maximizing in consumer choices.

The managerial implications of maximizing are particularly significant for industries such as retail, e-commerce and services. Understanding the decision-making patterns of maximizers allows businesses to design experiences that reduce decision fatigue, improve satisfaction and enhance brand loyalty. Based on the findings of this review, we present specific strategies that can help managers address the unique needs of maximizing consumers (Diehl and Poynor, 2010).

One important strategy is simplifying the pre-purchase phase. Maximizers engage in extensive information searches before making decisions, which can lead to choice overload and decision paralysis (Schwartz et al., 2002; Dar-Nimrod et al., 2009). Businesses can mitigate this by offering tools that help streamline the decision process. For example, e-commerce platforms can implement advanced filtering systems or personalized recommendations based on the consumer’s past preferences. This would reduce the overwhelming number of options and guide maximizers toward informed decisions without the emotional exhaustion of evaluating countless alternatives (Botti and Iyengar, 2006). Retailers could also provide curated product lists or expert recommendations that help maximizers narrow their choices based on key attributes, minimizing their reliance on external validation (Schwartz et al., 2002).

Post-purchase behavior also presents challenges for businesses, as maximizers are more prone to experiencing regret, leading to potential returns or reluctance to make repeat purchases (Ma and Roese, 2014). To address this, businesses should introduce post-purchase support mechanisms that reinforce the consumer’s choice. Follow-up communications that highlight the benefits of their purchase, or product guarantees that assure them of its quality, can help reduce regret (Carmon et al., 2003). Flexible return policies also provide peace of mind, allowing maximizers to feel secure in their decision while reducing immediate post-purchase dissatisfaction (Iyengar et al., 2006).

To foster stronger emotional engagement, businesses should focus on creating low-pressure decision environments (Iyengar and Lepper, 2000). Online and offline retailers can reduce anxiety for maximizers by avoiding high-pressure sales tactics and instead providing a supportive space where consumers feel comfortable taking their time. For example, allowing customers to “save for later” in online carts or offering in-store assistance without urgency could alleviate decision stress (Iyengar et al., 2006). Marketing strategies should avoid scarcity tactics, which may exacerbate maximization anxiety, and instead emphasize long-term satisfaction and quality (Botti and Iyengar, 2006).

Building brand loyalty among maximizers, prone to re-evaluating their choices, requires a focus on transparency and trust (Ma and Roese, 2014). Offering clear, detailed product information and transparent return policies can reassure maximizers of their decision quality. Loyalty programs that reward feedback and engagement, rather than just repeat purchases, may also help foster a stronger emotional connection to the brand, reducing their tendency to reconsider other options (Carmon et al., 2003).

Finally, leveraging digital platforms to cater to maximizers is essential in today’s e-commerce landscape. AI-driven recommendations and decision aids can provide real-time support to consumers as they navigate complex purchasing environments (Diehl and Poynor, 2010). Retailers can offer virtual consultations, interactive product demos or decision support tools to help maximizers make confident choices, particularly in high-involvement product categories (Schwartz et al., 2002).

Following our detailed examination of the theoretical and managerial contributions, it becomes evident that while our study advances current understanding and offers practical insights for consumer-facing industries, several gaps remain that warrant further investigation. Building on these contributions, we now outline a Future Research Agenda that addresses these unresolved issues and proposes new directions to refine our understanding of maximizing behavior.

First, future studies should deepen our understanding of the cognitive and emotional mechanisms that underpin maximizing. Although current research integrates frameworks such as SCT (Weaver et al., 2015), RST (Spunt et al., 2009) and CDT (Sparks et al., 2012), further work is needed to elucidate how these processes interact in real-time decision contexts. Longitudinal studies could examine how the interplay between cognitive dispositions (e.g. frugality, subjective knowledge) and emotional responses (e.g. regret, anxiety) evolves over time, thereby affecting long-term satisfaction and well-being (Hadar and Sood, 2014; Oren et al., 2018).

Second, the influence of decision context remains underexplored. Researchers should investigate how external factors – such as task complexity, time pressure and the public versus private nature of decision environments – modulate maximizing tendencies. For instance, experimental studies comparing digital versus traditional purchasing environments could determine whether simplified interfaces or decision aids reduce choice overload and decision paralysis (Chan and Wang, 2018; Levav et al., 2012).

Third, the role of individual differences and cultural influences warrants further attention. Although personality traits like neuroticism and cognitive factors like Need for Cognition have been linked to maximizing (Tang et al., 2017), the variability in these effects across different socioeconomic and cultural contexts remains ambiguous. Future cross-cultural research is essential to determine whether maximizing and their outcomes differ globally, thus enhancing the generalizability of existing findings (Oishi et al., 2014; Kolańska-Stronka and Singh, 2024).

Finally, methodological diversification is needed. While much of the current evidence is derived from quantitative, survey-based approaches, qualitative methods and computational modeling could offer nuanced insights into the lived experiences of maximizers. Mixed-method studies could enrich our understanding by capturing both the measurable outcomes and the subjective perceptions that drive decision-making (Johnson et al., 2007; Dellermann et al., 2019).

By addressing these areas, future research can build a more comprehensive and dynamic model of maximizing in consumer behavior, thereby informing both theoretical advancements and practical interventions.

This systematic review makes important theoretical contributions by synthesizing diverse frameworks to build a more comprehensive understanding of maximizing behavior. It highlights maximizers’ cognitive and emotional conflicts and the underlying neurobiological mechanisms that drive their decision-making style. The managerial contributions offered here provide specific strategies for businesses to engage effectively with maximizing consumers, reducing decision fatigue, improving satisfaction and fostering brand loyalty. Addressing the needs of maximizers enhances the consumer experience and leads to better outcomes for firms, creating a win-win situation in competitive markets.

Table A1 

Table A2 

Table A3 

Table A4