Search Results (2,800)

Adoption of the use of robots in the foodservice industry has increased, and research in the quality of service provided by robots is required. Our research objective is to determine interrelationships among service quality, perceived value, restaurant image, satisfaction, and revisit intentions among customers at robotic restaurants. Data collection was conducted, with 342 South Korean restaurant consumers considered suitable to offer accurate responses to the survey questions. An online survey was employed to examine hypothesized relationships. Data analysis used descriptive statistics, confirmatory factor analysis, and structural equation modeling. Three dimensions of service quality (atmosphere quality, food quality, and interaction quality) at robotic restaurants were critical for higher perceived value by diners at robotic restaurants. Perceived value increases robotic restaurant image, customer satisfaction, and customer revisit intention. Additionally, findings reveal that robotic restaurant image is a positive predictor of satisfaction, and that satisfaction is a positive predictor of revisit intention. Robotic restaurants have become pervasive in hospitality service environments. Accordingly, theoretical and empirical findings about multiple dimensions of service quality in this context likely will be of interest to marketing researchers and practitioners for sustainable restaurant business. Full article

This study aims to analyze the effects of responsible consumption behavior on impulsive buying, and the mediating role of brand literacy in this relationship. Data collected from 524 Turkish participants using an online survey were analyzed using structural equation modeling (SEM). Responsible consumerism exhibited a negative direct effect on impulsive buying and a positive effect on brand literacy. Brand literacy, in turn, was negatively associated with impulsive buying and partially mediated the responsible consumerism–impulsive buying link. Over-consumption and impulsive buying have received considerable scholarly attention. Yet, only a small number of studies have tested whether value-driven orientations, such as responsible consumerism, can curb these impulses. Even fewer still probe the mediating cognitive role of consumer knowledge (e.g., brand literacy). Furthermore, most existing evidence comes from Western high-income settings. This study addresses that gap by empirically testing the responsible consumerism, brand literacy, and impulsive buying pathway in Türkiye, an emerging and rapidly digitalizing economy. Considering its cross-sectional nature and cultural limitations, this study recommends conducting future longitudinal studies and research in various cultural contexts. Full article

This study utilizes a discrete choice experiment (DCE) to estimate consumer valuation of sustainable aquaculture certification for Atlantic salmon (Salmo salar), one of the most important imported seafood products in South Korea. This experiment investigates consumer preferences across five product attributes: country of origin, product type, preparation method, sustainability certification, and price. Data were collected through an online survey administered by a professional research firm that yielded 24,000 valid choice observations from 1000 respondents. Conditional logit estimates revealed that all specified attributes significantly influenced consumer choices among seafood alternatives. Among the key product attributes, sustainability certification has emerged as the most influential factor affecting consumer decisions. The marginal willingness to pay for sustainably certified aquaculture is estimated to be USD 1.33 per 100 g. These findings provide valuable insights for seafood marketers and policymakers who seek to promote sustainable aquaculture in South Korea. Full article

The accelerated development of e-commerce has given rise to sophisticated systems defined by significant user interaction, a variety of product offerings, and considerable quantities of structured and unstructured data. Upholding trust and operational security is becoming ever more essential. E-commerce platforms are susceptible to deceptive practices, including counterfeit reviews, dubious transactions, and anomalous usage behaviors. This research introduces a framework for anomaly detection powered by artificial intelligence, integrating deep learning and natural language processing (NLP) with the isolation forest algorithm tree to enhance the identification of unusual activities on e-commerce platforms. We leveraged customer feedback, transaction logs, and user interaction data obtained from Kaggle. Textual reviews were interpreted using natural language processing (NLP), while deep learning was utilized to discern behavioral patterns. The isolation forest algorithm tree was employed to detect statistical anomalies in multidimensional data. The hybrid model surpassed conventional techniques in terms of detection accuracy, recall, and interpretability. It successfully detects suspicious actions and clarifies anomalies in their relevant context. The application of AI techniques, particularly natural language processing, deep learning, and isolation forest algorithm trees, establishes a solid foundation for anomaly detection in the realm of e-commerce. This approach fosters a more secure and trustworthy experience for online consumers. Full article

Food product labels serve as a critical source of information, providing details about nutritional content, ingredients, and health implications. These labels enable Food and Drug Authorities (FDA) to ensure compliance and take necessary health-related and logistics actions. Additionally, product labels are essential for online grocery stores to offer reliable nutrition facts and empower customers to make informed dietary decisions. Unfortunately, product labels are typically available in image formats, requiring organizations and online stores to manually transcribe them—a process that is not only time-consuming but also highly prone to human error, especially with multilingual labels that add complexity to the task. Our study investigates the challenges and effectiveness of leveraging large language models (LLMs) to extract nutritional elements and values from multilingual food product labels, with a specific focus on Arabic and English. A comprehensive empirical analysis was conducted using a manually curated dataset of 294 food product labels, comprising 588 transcribed nutritional elements and values in both languages, which served as the ground truth for evaluation. The findings reveal that while LLMs performed better in extracting English elements and values compared to Arabic, our post-processing techniques significantly enhanced their accuracy, with GPT-4o outperforming GPT-4V and Gemini. Full article

Fashion products are typically sold through both online and offline channels during two distinct phases: the launch and markdown period. Pricing strategies present significant challenges for manufacturers, particularly as consumers increasingly adopt strategic purchasing behaviors. Key factors, including product fashion utility, purchase timing, and consumer characteristics, complicate manufacturers’ channel selection, pricing decisions, and service strategy formulation—necessitating deeper investigation. This paper establishes a two-echelon supply chain model featuring a fashion manufacturer and a retailer to determine optimal channel, pricing, and service strategies across both selling periods amid strategic consumer behavior. We examine four channel strategies: (1) the MM strategy: the manufacturer operates both channels (online and offline channels) during both periods (launch and markdown period); (2) the MR strategy: the manufacturer operates both channels during the launch stage, and the retailer sells online during the markdown period; (3) the RR strategy: the manufacturer sells offline, and the retailer operates the online channel during both stages; (4) the RM strategy: the manufacturer sells online during both stages, and the retailer sells through the offline channel. Our analysis yields critical insights: When off-season discounts are limited, the manufacturer should maintain direct control of both channels. However, when the off-season discount is significant, the manufacturer needs to set the channel strategy according to the fashion utility. If the fashion utility is small, direct sales through offline channels during the launch period, while entrusting the retailer to distribute in online channels during both periods, should be adopted. If the fashion utility is large, a dual-channel, two-stage, entirely direct sales strategy should be adopted. This study elucidates the optimal manufacturer channel and pricing strategy options and provides some theoretical contributions and practical implications. Full article

This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation. Full article

This study examines how bourbon label design complexity and local product information influence consumers’ willingness-to-pay (WTP), while considering the moderating effects of objective bourbon knowledge and age. Using artificially intelligent (AI) generated bourbon labels varying in complexity, data were collected from American bourbon consumers (n = 276) through an online survey. The results indicate that consumers’ preference for label simplicity positively influences WTP for bourbons with concise labels, while preference for locality shows positive associations with WTP across all label types. Objective bourbon knowledge negatively moderates the relationship between simplicity preference and WTP for concise labels, as well as between locality preference and WTP for complex labels. Age positively moderates the relationship between locality preference and WTP, particularly for moderately concise labels. These findings provide practical implications for bourbon producers in designing labels that effectively communicate product information while meeting diverse consumer preferences across different market segments. The study contributes to the limited research on bourbon label design and extends the application of General Evaluability Theory in the spirits industry. Full article

As Generation Z gradually becomes the dominant consumer group, the catering industry, as a critical sector affecting people’s livelihood, warrants an in-depth investigation into its consumption decision mechanisms. This study, grounded in the online–merge–offline (OMO) context, empirically examines the impact mechanism of frontline employee service competence on the repurchase decisions of Generation Z consumers in the foodservice sector, while testing the mediating roles of customer satisfaction and brand trust, as well as the moderating effect of the OMO scenario. Data were collected via a survey of 326 Generation Z customers who consumed in integrated OMO dining environments. Partial least squares structural equation modeling (PLS-SEM) was employed for the data analysis. The findings reveal that frontline employee service competence significantly and positively influences consumer repurchase intention and customer satisfaction, but does not have a significant effect on brand trust. Customer satisfaction fully mediates the relationship between employee service competence and repurchase decisions, whereas brand trust, despite having the strongest direct effect on repurchase intention, is predominantly shaped by systemic factors such as food safety and supply chain transparency, rendering its mediating pathway non-significant. Furthermore, the OMO context does not exhibit a significant moderating effect between employee service competence and customer satisfaction, nor between employee service competence and brand trust, reflecting that the current digital integration has yet to effectively address Generation Z’s core needs for privacy protection and emotional resonance. This study elucidates the “contact–cognition–behavior” pathway by which service competence influences consumer decision-making through satisfaction, while clarifying the systemic formation mechanism of brand trust. Based on these results, it is recommended that enterprises prioritize emotional service training for frontline employees to enhance satisfaction, build brand trust through ingredient traceability systems, and optimize OMO scenario design to better align with Generation Z’s expectations for emotional interaction. Full article

Digital platforms in fashion e-commerce are progressively shaping sustainable consumption practices. This research explores the interplay between consumer behaviors toward recycled and second-hand fashion, and the adoption of digital platform-driven innovations. The analysis is based on a structured questionnaire and an online survey of 1000 consumers conducted in 2025, employing a combination of descriptive and inferential statistical techniques, including both cluster and factor analysis. The findings suggests that demographic factors—particularly age, education, and gender—significantly shape consumer attitudes toward digital innovations in fashion e-commerce apps. The analysis also confirms that the perceived effectiveness of AI and AR tools is significantly correlated with an increased interest in circular fashion options, including second-hand marketplaces and recycled clothing. The study emphasizes the strategic importance of platform features in fostering conscious fashion choices, thereby offering practical insights for retailers aiming to harmonize technological innovation with sustainability goals. Full article

With the rapid development of e-commerce, ensuring product quality on online platforms has become increasingly important, especially in developing countries where market regulations are still underdeveloped. By treating different sellers offering the same brand’s products as an industry, this study examines the impact of flagship store entry on online product quality by constructing a multiple period difference-in-difference model and conducts detailed empirical tests using full-category and large-span data from Taobao. The empirical results demonstrate that flagship store entry not only prompts the exit of incumbent sellers and deters potential new entrants due to the competition effect, but also facilitates the exit of low-quality sellers while attracting high-quality sellers as a result of a consumer-learning effect. Consequently, the overall quality of the industry is improved, and this effect is more pronounced in high-priced and durable goods industries. The findings of this study have important implications for market structure design and online quality governance in online marketplaces. Full article

The integration of AI into online platforms is reshaping consumer experience and behavior. While existing research has largely focused on the role of AI in search services and experience services, few studies have examined the role of AI in the context of credence services. This study fills this gap by investigating an AI-powered preview tool in the context of online medical aesthetic platforms. Specifically, this study investigates how the AI Magic Mirror influences consumer purchase intentions in medical aesthetic services. Using secondary data analysis and two experimental studies, we examine the main effects, as well as mediation and moderation effects. The findings consistently demonstrate that the AI Magic Mirror significantly increases consumer purchase intentions. This relationship is positively mediated by perceived value and negatively mediated by perceived risk. In addition, the main effect is stronger for procedures with higher fit uncertainty and is more pronounced for those with lower popularity. These results provide theoretical insights into AI application in credence service contexts and offer practical implications for the design of AI-enhanced online service platforms. Full article

The rapid growth of live e-commerce, a sector valued at over USD 100 billion worldwide, demonstrates its transformative impact on the retail industry, especially in markets like China, where platforms such as Taobao Live and TikTok Shop have markedly altered consumer interaction. This transition is further expedited by Netflix-like entertainment marketing methods, which have demonstrated the capacity to enhance consumer retention by as much as 40%. As organizations adjust to this evolving landscape, it is essential to optimize supply chain strategies to align with these dynamic, consumer-centric environments. This paper examines the complexity of decision-making in live e-commerce supply chains, specifically regarding Netflix-inspired marketing strategies. The primary aim of this study is to design a game-theoretic framework that examines the interactions between producers and online celebrity retailers (OCRs) across different power dynamics. As live commerce integrates digital retail with immersive experiences, businesses must optimize pricing, quality, and marketing strategies in real-time. We present engagement-driven marketing as a strategic variable and incorporate consumer regret and switching costs into the demand function. To illustrate practical trade-offs in strategy, we incorporate a multi-criteria decision-making (MCDM) layer with AHP-TOPSIS, assessing profit, consumer surplus, engagement score, and channel efficiency. The experiment results indicate that Netflix-style marketing markedly increases demand and profit in retailer-led frameworks, whereas centralized tactics enhance overall channel performance. TOPSIS analysis prioritizes high-effort, high-engagement methods, whereas the Stackelberg experiment underscores the influence of power dynamics on profit distribution. This study presents an innovative integrative decision-making methodology for enhancing live-streaming commerce tactics in data-driven and consumer-focused markets. Full article

The growing concern over environmental sustainability has intensified the focus on consumers’ perceptions of eco-friendly food packaging, especially among younger generations. This study aims to investigate the attitudes, preferences, and barriers faced by Greek university students regarding sustainable food packaging, a demographic considered pivotal for driving future consumption trends. An online questionnaire assessing perceptions, preferences, and behaviours related to sustainable packaging was administered to students, with responses measured on a five-point Likert scale. Three hundred and sixty-four students took part in this survey, with the majority (60%) of them being female. Principal component analysis was employed to identify underlying factors influencing perceptions, and k-means cluster analysis revealed two consumer segments: “Innovatives”, including one hundred and ninety-eight participants (54%), who demonstrate strong environmental awareness and willingness to adopt sustainable behaviours, and “Sceptics”, including one hundred sixty-six participants (46%), who show moderate engagement and remain cautious in their choices. Convenience, affordability, and clear product communication emerged as significant factors shaping student preferences. The findings suggest that targeted educational campaigns and transparent information are essential to converting positive attitudes into consistent purchasing behaviours. This research provides valuable insights for policymakers and marketers looking to design effective sustainability strategies tailored to the student population. Full article

In recent years, online streaming has encountered the challenge of retaining its user base. This study considers the role of transaction cost economics theory in consumer choices to continue subscribing. Participants respond to their top three streaming services, resulting in 797 responses, accounting for multiple selections by each respondent. Respondents could choose their top three services from a list of Netflix, Disney, Hulu, Amazon Prime Video, HBO Max, and Apple TV+. The study’s conclusions highlight the impact of uncertainty, a negative measure of streaming quality, on online subscription-based video streaming. Additionally, asset specificity, reflecting uniqueness and exclusive content, is found to be positively related to continuing a subscription. This research distinguishes itself by examining individuals who are already subscribers to provide insights and guidance through the lens of Transaction Cost Economics, to help marketing professionals seeking a deeper understanding of consumer behavior in the online streaming landscape. Full article