This research addresses the trade-offs between the benefits and ‘costs’ of new product customisation in the context of gifting. Specifically, our study investigates the relationship between perceived complexity, anxiety, and self-perceived design skill, and measures their impact on value attributed to online gift customisation and ultimately, on purchase intentions. A field study, involving a real brand, employed a scenario-based experiment with two manipulations: the complexity of online customisation task (high vs. low complexity) and the nature of the recipient (self vs. gift giving). The data were collected via an online self-completion questionnaire. Partial Least Squares-based Structural Equation Modeling using bootstrap, and ANOVA were employed in data analyses. Results show that consumers with strong perceptions regarding their design skill feel less anxious towards the customisation task and have greater purchase intentions of the customised product. Conversely, perceived task complexity causes anxiety and decreases willingness to purchase. These results are unaffected by whether one is creating a gift for oneself, or for someone else. Our study contributes to knowledge in the domains of consumer value, innovations in online gifting, and customisation. The results provide guidelines for market researchers to capture novel, interactive and engaging ways for developing customisation toolkits.
Brand love is a widely researched marketing concept, yet its prevalence across different high involvement product categories is relatively unexplored. The absence of clear empirical guidelines concerning the prevalence of brand love undermines the ability, for both researchers and marketers, to turn evaluations of brand love into meaningful conclusions for brand-building efforts. To address this issue, this study replicates and extends prior research by investigating how common brand love is across high- and low-involvement product categories, and its relationship with purchase intentions and behavioural loyalty, measured via Share of Category Requirements (SCR). The analysis of survey data from 1,554 US consumers for four product categories (cars, running shoes, retail banking, and beer) suggests that four in ten category users loved at least one brand within each category. Yet, brand-level love is more uncommon, ranging between 3.5% to 15%, on average, across the product categories tested. Brand love was higher in transformational high-involvement categories (cars, running shoes) than in informational ones, but high-involvement categories alone did not exhibit more love compared to low-involvement ones. Additionally, although brand love correlates with higher purchase intentions, the link with behavioural loyalty appeared rather weak across all categories. These results challenge assumptions about brand love's scalability and strategic value of brand love, offering empirically grounded benchmarks for marketing researchers and practitioners.
In an era of heightened ideological polarization, characterized by intense divisions in political and social beliefs, both political and business environments face unprecedented challenges. Socio-political risks, historically explored in strategic management, have gained renewed significance as polarization amplifies their impact on business operations and consumer behavior. This paper examines Cathay Pacific as a case study to investigate how ideological polarization influences consumer reactions to service failures and controversies. Using the Belief-Action-Outcomes (BAO) framework, we analyze the interplay between polarization-driven beliefs and anti-brand actions during periods of heightened political tension in Hong Kong. Our findings offer critical insights into the role of ideological polarization in shaping consumer responses to service failures, contributing to the broader understanding of socio-political risks in polarized contexts. This research provides actionable implications for businesses navigating ideological divisions, enabling them to anticipate and mitigate polarization-driven challenges in their strategic and operational decisions.
In online surveys, response time data are often used to make inferences about respondents' cognitive processing of survey questions and to assess survey data quality. Adequate data preparation is crucial prior to analysis of response time data, in particular the detection and handling of outliers, which are extremely short or long response times. While several outlier detection methods exist, there is little empirical guidance on which method to use and how the choice affects response time data. We compared nine outlier detection methods commonly used in survey research across nine survey questions with varying characteristics, using data from a probability and a nonprobability online panel. Results show substantial differences between outlier detection methods in the proportion of outliers identified and in the effects of outlier exclusion on the response time data, with the effects being more pronounced in the nonprobability panel. Moreover, outlier detection methods differ systematically in the types of cases they classify as outliers, particularly with respect to respondent age and education. Based on these findings, recommendations for outlier detections methods in survey research are discussed.
Although the arousal of ambient scents has been widely studied, little is known about how it affects consumer decision-making and how these effects occur. This study provides an information-processing mechanism for how scent arousal influences decision-making through a heuristic-systematic model. Using a field experiment and two eye-tracking studies, this study demonstrated that a high-arousal scent promotes a heuristic processing style, leading to faster decision-making and greater reliance on external cues, such as in-store recommendations. Eye-tracking data provided biometric evidence that high-arousal scents induce fast fixation behavior, whereas low-arousal scents foster broader and deeper evaluations. Additionally, external cues reduce visual attention to other information in high-arousal environments. Examining time pressure as a boundary condition revealed that when a scent promotes a systematic processing style and time pressure demands a heuristic one, information-processing fluency and efficiency decrease, and the effect of visual attention on decision-making weakens. These findings advance the sensory marketing theory by integrating olfactory cues with the heuristic-systematic model and eye-tracking technology. Managerially, the findings offer valuable insights for optimizing scent strategies in the retail and service contexts.
Personalized digital ads cut both ways. Relevance can pull consumers in; intrusiveness and perceived manipulation can push them away. This study proposes and tests an integrated model linking perceived ad relevance, advertising skepticism, and intrusiveness to ad avoidance and purchase intention, and examines whether brand engagement in self-concept (BESC) softens defensive reactions. We surveyed 550 Vietnamese consumers and analyzed the data with PLS-SEM (SmartPLS 4). Results show that relevance lifts purchase intention and reduces avoidance, while intrusiveness and skepticism undermine persuasion. BESC buffers the skepticism forward avoidance link, indicating identity-based protection for favored brands. Mediation tests reveal that skepticism, relevance, and avoidance channel the harm from intrusiveness to purchase outcomes. The validated model exhibits strong explanatory and predictive performance and clarifies how constructive (relevance) and defensive (skepticism, avoidance) mechanisms operate simultaneously in digital advertising.
This article reviews investigations of online survey chatbots across disciplines and offers insights into their potential to encourage innovative and practical approaches to marketing research surveys. Online surveys deployed in service practice can effectively capture customer feedback. Using chatbots to administer such surveys can have desirable outcomes, although specific advantages and disadvantages are not clear at this point. Based on a meta-analysis of prior research on the use of chatbots in online surveys, this article presents a typology of relevant themes and avenues for further research. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method initially yielded 1,827 unique papers from Scopus, of which 30 qualify for in-depth analysis. The analysis identifies five key survey administration capabilities of chatbots: data collection, conversational interfaces, interactivity, humanization, and effective response generation. It also highlights the challenges and research gaps associated with each capability.
When brands back sustainability claims with real investment, measurable targets, and third-party verification-what we term proactive sustainability strategy (PSS)-do consumers reward them with loyalty? We address this question through a serial mediation framework. Signaling theory grounds our core prediction: PSS should first enhance perceived authenticity, then foster trust, then create value-a chain that ends in loyalty (H1-H2). Skeptical consumers, we argue, should respond more strongly-not less-to credible PSS signals (H3). Three experimental studies provide converging support. Studies 1 (N = 60) and 2 (N = 251) validate the experimental materials and test the complete theoretical model using a fictional skincare brand, confirming the serial mediation pathway (indirect effect = 0.073, 95% CI [0.042, 0.113]) and significant skepticism moderation (b = 0.23, p = .027). Study 3 (N = 280) extends these findings with a four-condition signal decomposition design incorporating incentive-compatible behavioral measures: the bundled PSS configuration outperforms third-party certification alone (d = 1.20, p < .001) and generates a 34.8% price premium over a no-signal baseline via BDM auction, with 88.6% brand choice rates versus 28.6% at baseline. These findings offer theoretical contributions to sustainability marketing literature and practical guidance for brands seeking to build authentic connections with increasingly discerning consumers.
Companies are increasingly using predictive modeling to manage customer churn proactively. While extant customer retention literature centers mainly on propensity models, recent research indicates merits of uplift models for targeting retention efforts toward customers. However, prior research on uplift modeling relies largely on experimental data and tailored uplift algorithms, making it difficult and costly for practitioners and researchers to apply. Thus, we investigate the applicability and competitiveness of an uplift modeling procedure for customer retention management combining propensity score matching with meta-learner approaches (standard machine learning algorithms). Using a semi-synthetic churn dataset with 1980 customers, we affirm the effectiveness of propensity score matching for reducing covariate imbalance in observational data. The empirical experiments show that meta-learner uplift models outperform tailored uplift random forest approaches regarding Qini scores and computation efficiency. Moreover, the results imply that targeting retention efforts based on a meta-learner uplift model reduces churn more effectively than using propensity models.
What would happen to consumer trust, brand strategies, and digital ownership in the event of a systemic disruption to the cryptocurrency ecosystem? This paper explores the marketing implications of disruptions in crypto markets, focusing on how such shocks affect token-based brand engagement, loyalty architectures, and consumer psychology. Drawing on trust repair literature and recent empirical findings on NFTs, and using illustrative cases such as the FTX exchange collapse, the contraction of the NFT market, and the Frosties rug pull, we highlight how marketers can prepare for post-speculation realities. This perspective invites marketing scholars to reconsider the role of decentralized assets in consumer-brand relationships and calls for resilient digital marketing models under conditions of technological fragility.
Extant research suggests experiential purchases are more identity informative than material purchases. Building on this discourse, this research expands on the intricacies of how consumers integrate purchases into their life's activities. Specifically, activity facilitating purchases - a purchase type distinct from the material-experiential continuum - are explored. Activity facilitating purchases are made with the intent of integrating them into one's consumption practices or hobbies. Through three studies, activity facilitating purchases are shown to be more identity informative than material purchases. However, activity facilitating purchases are equally informative as experiential purchases. Researchers are encouraged to continue to explore the role activity facilitating purchases have on consumer identity construction. Practitioners may want to conduct interviews or surveys to identify the identity relevant meanings customers find in their activities and then align their brand values with these meanings.
We investigate the role of consumer-oriented media influence on their purchase intentions, how this influence changes from a stable economic situation to a recessionary period and what are its repercussions on marketers' media selection. We use survey data from the US in the clothing category for the 2008 economic recession to investigate the individual and synergy effects of Promotional and Informational Media and how this evolves with the economic cycle. In line with Construal Level Theory, we find that consumers are in a low-level mindset during recessions and hence value the feasibility of a purchase important, whereas they are in a high-level mindset during stable economic times and focus on the desirability of a purchase. This results in positive media synergy (across multiple media) during stable times, however during recessions, influence of individual media is stronger. We provide robustness checks with electronics and groceries categories. Insights from this research provide marketers with an approach for strategic media selection during economic fluctuations using survey data, that is robust across categories and hence can be implemented across other recession scenarios.
This manuscript investigates how curvilinear (organically curved) retail designs influence consumer behavior, extending traditional views of retail performance beyond brand strategy, pricing, and operations to include environmental psychology. Grounded in the Stimulus-Organism-Response (SOR) model, biophilic design theory, and store atmospherics, this research demonstrates that curved store layouts, fixtures, and point-of-purchase containers enhance store and product attractiveness, elevate expected product cost, and increase purchase intent. Overall, curvilinear elements act as subconscious biophilic cues, eliciting positive affect and approach behaviors that drive consumer engagement. Experimental findings across multiple retail settings indicate that curved designs can increase perceived value, enhance perceived cost, and improve the likelihood of purchasing a given product, thereby creating opportunities for premium positioning. However, implementation must consider product compatibility and operational challenges. By spanning atmospheric dimensions from architectural layout to display design, this research offers actionable insights and theoretical contributions, highlighting the strategic potential of curvilinear aesthetics in retail environments.
Hyperlocal sharing platforms are gaining traction as a sustainable alternative for distributing resources, particularly in the case of food sharing, where success relies heavily on community engagement and local interactions. Despite their growing popularity, there is little understanding of what contributes to the growth of sharing platforms and their evolution over time. In collaboration with the UK's largest food sharing platform, this study explores over five million anonymised sharing instances across 312 districts in England over a 51-month period. Building on prior literature, we empirically examine platform growth using temporal network structures, user behavioural variation, and local demographic and environmental characteristics, to model user acquisition and retention. Using a machine learning approach, we utilise SHAP variable importance to determine the most impactful attributes for platform growth and identified key predictors of the proliferation of food sharing platforms. This includes the distribution of super-users, the presence of active volunteers, and the formation of structured communities. Our findings demonstrate how simultaneously combining data from users, networks and geographical dimensions provides a more useful explanation of growth than any isolated disciplinary theory. The results contribute to the theorisation of growth in sharing platforms, offering managerial insights that support the development and sustainability of food sharing networks within the sharing economy.
Self-brand connection (SBC) has been manipulated in different ways, often tautologically. This paper provides an effective SBC manipulation that can be used across symbolic and functional brands in online and offline settings, demonstrating internal and external validity. We tested our manipulation in a 2 (SBC: High vs. Low) & times; 4 (Brands: Audi, Benz, BMW, Porsche) in the USA and in a 2 (SBC: High vs. Low) & times; 2 (Brands: Samsung, Apple) in the UK on manipulation check and brand attitude (BA) measures, controlling for gender, brand familiarity and brand ownership. Results show that our SBC manipulation has a strong main effect (omega(2) = .47 experiment 1; .39 experiment 2) and the effect does not vary with the brands. Market research firms can use different elements from our manipulation, such as enhancing congruency of personalities, brand aspiration, desirability, impression management, and word of mouth, to develop advertisements and promotions for the target consumers.
Addressing contemporary debates on the integration of artificial intelligence (AI) in qualitative research, this article proposes a hybrid methodological approach positioned within the "human-in-the-loop" paradigm. This approach preserves interpretive authority while leveraging computational capabilities. When applied to the Album On Line (AOL) method, the use of generative AI (eight different chatbots) and semantic AI, via natural language processing (NLP), improves the interpretation of outcome mapping. Ultimately, the outcomes generated by AI are compared with those derived from researchers' efforts. This study aims to provide a broader vision of the world of AI and its potential contribution to research. It offers a critical complementarity framework that enhances efficiency, saves time, and improves the quality of results. It enriches the researcher's toolbox by demonstrating how to leverage AI for research purposes.
Recently, there has been growing interest among the global marketing research community to create space for Indigenous knowledges and perspectives. This article introduces EveryWhen, positioning it as both an Australian Indigenous knowledge system that offers a nonlinear perception of time and a methodology with wide-ranging applications in marketing research. Time is culturally constructed, and for Aboriginal and Torres Strait Islander peoples', EveryWhen represents time as the infinite present-unmeasured, indivisible, and elastic, where the past, present, and future co-exist. Through a case study with Australian Indigenous doctoral student consumers, this article demonstrates how the multisensory EveryWhen methodology utilises Australian Indigenous storytelling/yarning, three-dimensional earth-drawn artefact creation, and an adaptation of Bishop and Tynan's (2025) Kin and Country five-perspective analytical lens to capture nonlinear consumer temporalities. Conducted by and with Indigenous Australians and adhering to Indigenous research design principles, the case study also models best practice in Indigenous research. The EveryWhen findings revealed layered, dynamic consumer journeys, showing how new marketing research insights emerge when temporality is foregrounded. EveryWhen is portable across a range of contexts and applicable to all consumers, representing a pathway to decolonisation and Indigenising marketing research. For marketing researchers, the value of EveryWhen lies not only in introducing nonlinear temporalities but also in extending multisensory methodologies to broaden the ways consumer journeys are accessed, captured, and represented in marketing research.
Artificial intelligence is reshaping market research from a set of analytical tools into an active participant in the research process. As AI begins to generate questions, analyse data, and produce insights, it challenges the field's reliance on human judgement, methodological rigour, and interpretive expertise. This research note introduces the special issue on "AI in Market Research" and argues for a socio-technical perspective in which AI augments rather than replaces researchers. It highlights the epistemic, methodological, and ethical questions that will shape the future of market research.
This study investigates how Black consumers perceive their representation in fashion advertising, exploring whether current portrayals reflect authentic inclusion or diversity-washing. Based on semi-structured interviews with 19 Black participants, thematic analysis revealed widespread perceptions of tokenism, conditional visibility, and aesthetic stereotyping. While increased representation was acknowledged, participants stressed that only authentic, culturally resonant portrayals fostered identity affirmation and brand trust. The study advances theory by extending racial congruence theory through the concept of conditional congruence, showing that representation's impact depends on authenticity and respect. It also refines Social Dominance Theory, illustrating how symbolic inclusion can perpetuate rather than challenge racial hierarchies. Introducing diversity-washing as a distinct construct, the research reframes representation as a moral and relational act, asserting that genuine inclusion requires narrative agency, structural diversity, and cultural respect.
Marketing analytics rises or falls on one quiet moment: the data definition phase, when teams decide what to measure and how. Despite its centrality, the field lacks a standardized methodology for doing this well. This study asks how practitioners actually run data definition and what distinguishes mature from ad-hoc practice across agencies, in-house teams, and freelancers. Evidence comes from 40 semi-structured interviews analyzed inductively with reliability checks. Four dimensions consistently shape outcomes: process formality, stakeholder collaboration, documentation and tooling, and recurring failure points such as definitional drift, onboarding friction, and governance gaps. Comparative patterns show agencies emphasize standardization, in-house results hinge on leadership and culture, and freelancer-driven gains fade without internal ownership. The study introduces a five-level Data Definition Maturity Model that specifies practical capabilities, aligned KPI glossaries, facilitated definition workshops, versioned metric repositories, and privacy checkpoints. Higher maturity reduces "whose numbers are right" disputes, speeds consensus, and improves analytic reliability. The contribution is a shared language and actionable roadmap for a phase too often improvised; we argue that rigorous data definition is a necessary precondition for reliable analytics and AI-ready marketing data ecosystems.
