Concept Big data and advanced analytics have radically transformed the retail industry's strategic landscape. In response, companies have adopted data-driven decision-making (DDDM) models as a competitive weapon to address these challenges. Objective This study examines the multidimensional relationship between the intensity of DDDM adoption and two outcomes: firm performance and consumer welfare. It also investigates how firm size and retail subsector moderates these relationships. Tools Using a mixed-methods design, we surveyed 386 retail firms across six subsectors and surveyed retail executives regarding application domain barriers in the United States. Quantitative methods included multiple regression, one-way ANOVA, and hierarchical moderated regression, supplemented by surveys from 1200 consumers measuring privacy percept (β = 0.61, p < 0.001, R2 = 0.492). Hierarchical modeling confirmed that firm size (ΔR2 = 0.026, p = 0.001) and specialized sectors like electronics and grocery retail act as significant positive moderators. Consumer satisfaction, product availability, and shopping experience quality improved significantly with increasing DDDM intensity. However, 61.3% of consumers express concern about excessive data collection, while 53.6% acknowledge that personalized offers enhance their shopping experience, revealing a 7.7 percentage point privacy paradox accompanied by a notable trust deficit (only 31.6% consumer trust). Application These findings provide retail managers with ROI estimates (ranging from 2.4× to 3.2×) for analytics investments across application domains while identifying key infrastructure bottlenecks (52% legacy system friction). They also offer policymakers evidence for data governance frameworks and guide practitioners on balancing personalization benefits with privacy protection.
Customer-facing AI agents are becoming frontline service interfaces, yet firms still face a consequential design question: should these agents look human-like or robot-like? This research examines how AI agent appearance shapes consumers' intentionality attributions in AI-mediated service encounters and how these inferences translate into satisfaction and future AI use intention. Drawing on attribution theory and anthropomorphism research, we propose that human-like and robot-like appearances do not have uniformly positive or negative effects; rather, their effects depend on consumers’ evaluations of service outcomes and their familiarity with AI. A 2 (AI agent appearance: human-like vs. robot-like) × 2 (service outcome: success vs. failure) between-subjects experiment was conducted with 350 U.S. participants in an airline customer-service chatbot context. Results reveal a reversal across outcome evaluations. When consumers experienced service success, robot-like AI elicited higher perceived intentionality than human-like AI, particularly among those with lower AI familiarity. When consumers experienced service failure, human-like AI elicited higher perceived intentionality than robot-like AI, although this effect weakened and reversed among consumers with higher AI familiarity. Perceived intentionality, in turn, increased satisfaction and future AI use intention, supporting a moderated mediation process. These findings reframe AI appearance as an attributional cue rather than a simple humanization strategy and demonstrate that consumers interpret the same AI appearance differently depending on how they evaluate the service encounter. Managerially, the results suggest that firms should not assume that increasing human likeness will universally improve customer responses. Instead, marketers should consider how different customer segments, particularly those varying in AI familiarity, are likely to interpret and respond to AI appearance cues across diverse service experiences.
Consumers frequently evaluate the healthfulness of packaged foods based on multiple on-pack cues presented simultaneously. While prior research has extensively examined individual signals such as nutrition labels or claims, less is known about how consumers integrate competing information and whether they rely on consistent or heterogeneous decision strategies. This study addresses this gap by investigating how different types of product-related cues jointly shape perceived healthfulness and by identifying distinct patterns of cue weighting across consumers. Using a mixed-methods design, we first conducted focus groups to explore how consumers conceptualize healthfulness in real-world evaluation contexts. Building on these insights, a full-profile conjoint experiment with a UK sample recruited by quota sampling on age and gender quantified trade-offs among four on-pack cues: ingredient composition, health-related claims, calorie information, and vitamin tables. At the aggregate level, ingredient-related information exerted the strongest influence, followed by claims and vitamin information, while calorie information played a more limited role. However, segmentation based on individual-level importance weights revealed three distinct health inference strategies: consumers primarily guided by ingredient simplicity, those anchored in energy-related information, and those relying on claim-based cues validated by nutrition tables. Accounting for these strategies substantially improved explanatory power, indicating that heterogeneity in healthfulness judgments is systematic rather than random. The findings contribute to research on consumer information processing by demonstrating that perceived healthfulness reflects distinct, strategy-based patterns of cue weighting rather than uniform cue effects and suggest the need for more nuanced approaches to food labeling and retail communication.
The increasing integration of artificial intelligence (AI) into service operations is reshaping employees' work roles by automating routine tasks and redirecting attention toward more complex and nonroutine service tasks. Drawing on adaptive structuration theory for individuals (ASTI), this study examines how AI-enabled job non-routinization relates to employees’ exploration and exploitation behaviours through exploratory and exploitative task adaptation, and whether polychronicity–monochronicity flexibility moderates these relationships. Based on two-wave survey data from 450 service employees in Pakistan, the findings show that AI-enabled job non-routinization relates to exploration behaviour through exploratory task adaptation and to exploitation behaviour through exploitative task adaptation. Exploitative task adaptation also mediates the relationship between AI-enabled job non-routinization and exploration behaviour. Polychronicity–monochronicity flexibility strengthens the relationship between AI-enabled job non-routinization and exploitative task adaptation, but not exploratory task adaptation. This study contributes to research on AI-enabled service work design, task adaptation, and service ambidextrous behaviour.
Hidden consumption spaces (HCS), such as non-street-facing ground-floor venues and non-ground-floor establishments with limited direct visibility, have become an increasingly recognizable part of contemporary urban consumption. Yet it remains unclear how consumers discover, evaluate, and patronize such spaces under digital conditions. This study examines HCS patronage through an exploratory mixed-methods survey of 532 urban consumers, focusing on three HCS types with different levels of spatial concealment. The analysis combines descriptive statistics, hiddenness-based subgroup comparison, type-specific predictive modeling, and qualitative coding of open-ended responses. The results show that HCS visitation is already widespread, but participation follows a clear hiddenness gradient: more accessible types are more widely visited, whereas more concealed types are associated with more selective engagement. As hiddenness increases, consumers rely more strongly on third-party review platforms, social media, reservation practices, and post-visit reviewing behaviors. Nevertheless, environmental and experiential concerns, including poor surroundings and unsatisfactory prior experiences, become more salient rather than disappearing. The type-specific models further show that the clearest interaction-rich patterns, combining venue attributes, information channels, and digitally mediated behaviors, appear in the most concealed HCS type, with weaker but similar evidence in the intermediate type. Overall, the findings indicate that digitalization does not eliminate the spatial disadvantages of hidden locations. Instead, it partially offsets them by making such spaces more discoverable and evaluable, while leaving their success conditional on navigability, environmental quality, and service reliability.
