Purpose The Federal Trade Commission (FTC)'s Judiciary Subcommittee on Antitrust, Competition Policy and Consumer Rights (FTC, 2022) has voiced increasing concern about growing concentration of advertising as threatening the US economy's shared prosperity and liberty. This article introduces a new geographic attribute: advertising localism, i.e. the proportion of advertising conducted within a county where the ads are targeted or displayed (irrespective of the brand's origin). The study then reports the effects of localism by platform (Search, Social, television) on customer's co-visits between national and local brands. Design/methodology/approach The article uses large anonymized location data sets of advertising expenditures and physical movements between stores derived from global positioning system (GPS) signals, weighted market-basket analysis and ordinary least squares (OLS) to develop competitor spillover metrics, and examine effects of advertising localism on such spillovers. Findings The article studies the effects of advertising localism on competitive spillovers (i.e. when customers of one brand co-visit a competitor brand) between national and local limited-service restaurant brands. Using geolocation data on advertising expenditures and physical customer movements between stores, the study finds that across 180 counties, advertising localism via user-intent-driven delivery, i.e. search engines, benefits national chains, driving more market-aggregation, whereas advertising localism via platform-driven delivery, i.e. social media, benefits local chains, driving more market-dispersion. Originality/value This article addresses needs and ways to utilize location data in advertising to assess changing market concentrations for oversight by brands as well as policy bodies.
Purpose- We explore the joint impacts of digital technologies (DT), intellectual capital (IC), IC components and corporate social responsibility (CSR) on marketing performance (MP), with a consideration of the moderating role of IC. Design/methodology/approach- 2SLS is employed for 14,579 firms operating in Vietnam to analyze the impacts. Findings- Our findings show that DT and IC enhance MP, while their interaction reveals diminishing returns. Excessive integration between DT and structural capital efficiency and capital employed efficiency decreases MP, while the interaction between DT and human capital efficiency enhances MP. CSR enhances sales per employee for all firms but reduces sales/marketing costs for large firms. Originality/value- Unlike previous scholars, we develop and test an integrated model that simultaneously integrates DT, IC, IC components and CSR through organizational and stakeholder mechanisms. We are among the first to explore the moderating role of IC in the digital technology-marketing performance (DT-MP) link, providing new insight into how IC can amplify MP. We offer an integrative, nonlinear perspective in which IC functions as a moderating role and CSR acts as a trade-off mechanism.
Purpose This study addresses critical challenges in inventory management, demand responsiveness and sustainability within modern supply chains. It investigates how lot-for-lot inventory policies, trade credit and cost-sharing can be integrated into a green closed-loop supply chain to enhance coordination, reduce costs and strengthen market responsiveness. The model explicitly links operational and financial strategies with marketing perspectives by incorporating consumer heterogeneity in green willingness-to-pay, retailer promotional tactics under trade credit and market segmentation effects, thereby connecting supply chain decisions to marketing intelligence, branding strategy and managerial practice. Design/methodology/approach A two-echelon green closed-loop supply chain, comprising a manufacturer and retailer, is modeled. The manufacturer follows a continuous production process, and the retailer faces deterministic demand based on price and greening level. Returned products are screened and either remanufactured or disposed of. Three models are developed: a basic model, one with trade credit and another combining trade credit with cost sharing. These models are analyzed under centralized, Stackelberg and Nash scenarios, utilizing numerical simulations and sensitivity analysis to evaluate the outcomes. Findings The results show that improving recycling efficiency enhances both environmental outcomes and profitability. Strategic trade credit terms improve cash flow coordination, while shipment frequency optimization reduces logistics and inventory costs. Cost-sharing mechanisms encourage greening efforts and demand, particularly in decentralized systems. Aligning sustainability initiatives with financial incentives fosters a balanced and competitive supply chain. Originality/value This research uniquely integrates environmental, financial and strategic dimensions in a lot-for-lot green closed-loop supply chain. It offers practical insights into how coordination mechanisms influence sustainability and profitability in competitive supply chain environments. By explicitly connecting green investment, trade credit and cost-sharing strategies to marketing outcomes such as segmentation, positioning and brand value, the study advances interdisciplinary understanding at the interface of operations management and marketing intelligence.
Purpose This study aims to improve the accuracy of customer equity (CE) estimation in complex retail environments by introducing a hybrid framework that integrates machine learning (ML) with Markov Chain modeling, enabling dynamic prediction of CE in settings characterized by frequent transactions and diverse product offerings. Design/methodology/approach This study develops a hybrid framework that integrates ML and Markov Chain modeling for dynamic CE prediction. ML models extract customer behavioral features, which are combined with a probabilistic model to enhance retention prediction. A newly introduced "stability" dimension extends the traditional Recency-Frequency-Monetary (RFM) framework, enabling dynamic clustering. These clusters form the basis for Markov states, allowing the modeling of customer transitions and more accurate CE estimation. Findings The findings demonstrate that integrating ML with Markov Chain modeling improves the accuracy of CE estimation. The approach enables managers to monitor both current and future customer states with precision. Dynamic clustering, enhanced by the stability dimension, identifies behavioral shifts early, supporting proactive churn detection and more targeted retention strategies for high-value customers. Originality/value This study contributes originality by introducing a hybrid ML-Markov Chain framework that extends the traditional RFM model with a stability dimension. This integration enables dynamic analysis of customer transitions and provides more accurate assessments of CE. Compared to traditional static models, the approach delivers deeper insights into customer behavior and supports more informed managerial decision-making.
Purpose-Despite growing interest in climate change engagement, its nature and dynamics remain poorly understood. Addressing this gap, we conceptualise consumers' climate change engagement (CCCE) and explore its role in enhancing sustainable consumption. Design/methodology/approach-Drawing on the knowledge-attitude-practice (KAP) model, we explore the role of enablers, including consumers' perceived knowledge and word-of-mouth (WOM) about climate change, in shaping CCCE. Using data collected from 285 respondents, we also examine the role of CCCE in shaping consumers' attitudes toward climate-friendly products and their purchase intentions. Findings-The findings reveal CCCE as a significant predictor of consumers' purchase intention for climate-friendly products. Consumers' perceived knowledge and WOM about climate change were also found to significantly drive CCCE, while perceived self-efficacy in solving environmental issues was found to moderate (i.e. augment) this effect. Originality/value-This research conceptualises consumers' climate change engagement (CCCE) and explores its role in enhancing climate-friendly, sustainable consumption from the KAP lens. The study advances the extant stock of knowledge from linear Knowledge -> Behaviour models to Knowledge -> Engagement -> Attitude -> Intention.
