Search Results (269)

The rapid growth of e-commerce has increased the strategic importance of last-mile delivery as a critical determinant of customer satisfaction and retention. This study examines consumer perceptions of last-mile delivery performance in the Greece e-commerce market, focusing on Millennials and Generation Z. Data were collected from 1252 consumers through a structured questionnaire covering demographics, online purchasing behavior and satisfaction with third-party logistics (3PL) services. Reliability analysis confirmed strong internal consistency of the measurement scales. The findings show that Greek logistics service providers generally meet consumer expectations at a moderate-to-high level, particularly in tracking information, pre-order information and service quality. However, delivery time, communication during delays and compensation procedures remain weaker service dimensions. Comparative analysis revealed no significant differences in satisfaction between Millennials and Gen Z. Regression analysis demonstrated that communication quality, delivery execution and problem resolution positively affect satisfaction, while longer lead times reduce it. Furthermore, positive customer experience significantly increases future intention to continue online shopping, highlighting the strategic importance of efficient last-mile delivery. Full article

Artificial intelligence (AI) is increasingly used to help consumers detect greenwashing in online food markets, yet how AI-use motivations relate to rational green food consumption intention (RCI) remains unclear. Integrating information processing theory, the consumer decision-making process model, and the theory of planned behavior, this study examines how risk avoidance (RA), performance expectations (PE), and health benefits (HB) are associated with RCI through subjective norm (SN) and perceived behavioral control (PBC). Based on 619 valid responses from a cross-sectional online survey, the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN). The PLS-SEM results show that PE, RA, SN, and PBC are significantly associated with RCI, whereas HB has no significant direct association. SN is strongly associated with PBC, and the SN–PBC sequential mediation path is supported for RA, PE, and HB. The RA–PBC–RCI path is not supported, indicating that risk awareness does not automatically translate into perceived control. The ANN results identify PE as the strongest nonlinear predictor, followed by RA, while HB shows the weakest predictive importance. The findings advance AI-mediated sustainable consumption research and provide intention-level evidence for responsible online green food purchasing. Full article

This study examines how the 4Es framework—Experience, Exchange, Everyplace, and Evangelism—influences online cosmetics purchase behavior in Thailand, addressing the growing importance of digital consumer engagement in emerging markets. A mixed-methods approach was employed, combining quantitative data from a structured survey with qualitative insights from semi-structured interviews. Binary logistic regression analysis was used to assess the effects and predictive power of the 4Es and selected demographic and behavioral variables. The results indicate that all four dimensions significantly influence purchase behavior, with Evangelism emerging as the strongest predictor, followed by Experience, Everyplace, and Exchange. The model demonstrates strong predictive capability, highlighting the importance of behavioral factors such as platform usage, purchase frequency, and social media engagement, while several demographic variables show limited influence. Qualitative findings further support these results, revealing that consumers place strong emphasis on social influence, emotional engagement, and convenience in their online shopping experiences. The study concludes that online cosmetics purchase behavior is shaped by a combination of experiential, relational, and socially driven factors, with social influence playing a dominant role. These findings demonstrate that the 4Es framework remains highly relevant in digitally mediated consumer environments, where purchase decisions are increasingly influenced by interactive experiences, omnichannel accessibility, value co-creation, and consumer advocacy. By integrating quantitative and qualitative evidence, the study extends the application of the 4Es framework beyond traditional marketing contexts and demonstrates its value as a comprehensive model for understanding consumer engagement and online purchasing behavior in contemporary digital marketplaces. The mixed-methods approach provides both generalizable and contextually grounded insights, offering theoretical contributions to digital marketing literature and practical guidance for marketers seeking to strengthen consumer engagement and brand advocacy in increasingly competitive online markets. Full article

This study introduces a comprehensive framework for understanding sustainable online shopping preferences, validated using survey data collected in Serbia and Slovenia in 2025 (n = 572), thereby enhancing its generalizability. The primary aim of this research is to examine the extent to which specific environmental, social, and economic indicators influence decision-making processes for online purchasing and delivery. A detailed quantitative analysis was conducted using a structured questionnaire that included a wide range of variables related to online shopping behaviors and delivery preferences. The findings indicate that preferences for sustainability are inherently complex and multifaceted, shaped by critical factors such as environmental concerns, social responsibility, trust, skepticism towards sustainability claims, willingness to pay (WTP), and price sensitivity. Demographic variables, particularly gender and age, show consistent links to preferences for environmental considerations and corporate social responsibility (CSR), while income impacts trust-related behaviors and WTP. Furthermore, the analysis distinguishes between two distinct decision-making approaches: a value-driven sustainability cluster represented by EcoIndex, SocialIndex, and WTPIndex, and a cost-minimization strategy focused on price sensitivity (PriceIndex), with trust acting as a related yet separate factor (CredibilityIndex). Overall, this study emphasizes that a range of interconnected dimensions significantly shape sustainable online shopping preferences. The study was conducted in two developing European countries. Additionally, the findings highlight the need to address universal market barriers, such as price sensitivity, information asymmetry, and consumer skepticism. In a business context, they underscore the importance of adopting advanced analytical methods to enhance decision-making and optimize sustainable business strategies. Full article

Despite the growing popularity of digital payment methods and online shopping environments, little is known about the psychological mechanisms through which they affect consumer buying patterns. Drawing on the Stimulus–Organism–Response (SOR) framework, this study introduces the concept of spendception and examines its dual dimensions: perceived spendception ease (PSE) and perceived spendception control (PSC). These dimensions serve as mechanisms linking digital twin environments (DTEs) to impulse buying. Data were collected from 571 Generation Z consumers engaged in social commerce in Shanghai, China. Structural equation modeling (SEM) and machine learning techniques were employed to test the proposed relationships and evaluate predictive validity. The results reveal that DTE significantly increases impulse buying behavior both directly and indirectly through PSE. Specifically, PSE serves as a significant mediator by reducing the psychological friction associated with spending, thereby encouraging impulse buying decisions. In contrast, PSC acts as a significant moderator that weakens the positive relationship between DTE and impulse buying by enhancing consumers’ perceived ability to regulate their spending behavior. These findings demonstrate that spendception operates through two opposing psychological mechanisms: spending facilitation and spending control. This study contributes to the literature by conceptualizing spendception as a distinct transaction-centered construct and by extending the SOR framework through a dual-mechanism explanation of how digital commerce environments simultaneously encourage and restrain impulsive consumption. Full article

This study aims to provide insight into consumer purchasing decisions by integrating eye-tracking data with forecasting techniques. First, the study investigates how consumption motives (hedonic vs. utilitarian) and purchasing purposes (for oneself vs. for others) influence visual attention and decision-making processes. An experimental design was conducted with 128 participants in a simulated online shopping environment, where eye-tracking data were collected based on fixation counts and durations across defined Areas of Interest (AOIs). Second, a total of 20 input features were collected, comprising fixation counts and fixation durations for 10 review-related Areas of Interest (AOIs), and these features were evaluated across the experimental scenarios, while the binary output variable represented the participant’s purchase decision. These biometric features, together with scenario information, were used to forecast purchasing decisions using six machine-learning methods, including Artificial Neural Networks, Random Forest, Support Vector Machine, K-Nearest Neighbors, Naive Bayes, and Logistic Regression. The results indicate that consumers’ visual attention aligns with their consumption motives and purchasing purposes, revealing distinct gaze patterns across different scenarios. In the forecasting phase, the accuracy of different methods for predicting purchasing decisions using review-related eye-tracking data is evaluated. Support Vector Machines achieved the highest overall accuracy, approximately 59–60% across the evaluated datasets, compared with a validation-specific majority-class baseline of 53.85%. This corresponds to a modest improvement of approximately 5.15–6.15 percentage points over the naive benchmark. Overall, the findings suggest that objectively recorded review-related eye-tracking data can be operationalized as behavioral input features in a machine-learning-based purchase-decision classification framework, highlighting the methodological value of integrating eye-tracking insights with consumer behavior forecasting. Full article

Online grocery retailing remains effort-intensive because consumers must coordinate many item-level decisions, making basket construction and correction a key barrier to more frequent use. This study examines consumer acceptance of an AI-pre-filled basket in Austrian online grocery retailing and links Technology Acceptance Model (TAM) mechanisms to objective interaction performance. Using a field-proximate one-group pre–post design, participants edited an AI-pre-filled basket in a standardized online shop and completed pre- and post-task surveys using 0–100 slider scales; log data captured processing time and edit actions. Analyses are based on n = 297 valid cases. Results show a substantial and statistically significant within-person increase in online grocery usage intention after the AI-basket interaction, rising from 39.17 to 59.78. The TAM results support the core mechanism: perceived ease of use is positively associated with perceived usefulness, and perceived usefulness strongly predicts behavioral intention, whereas a direct ease-of-use effect on intention is not supported. Linking perceptions to log data shows that acceptance is more strongly associated with correction demand than with processing time. AI-basket acceptance depends less on speed than on reducing rework while preserving user control; retailers should therefore design AI baskets as controllable, editable, low-rework systems rather than speed-oriented automation tools. Full article

Background/Objectives: Multiple Traffic Light (MTL) front-of-pack (FOP) labels are being considered in Bahrain. We tested whether an adapted MTL label improves the nutritional quality of grocery purchases. Methods: In a two-arm randomized controlled intercept trial (January–May 2025), adults (≥21 years) responsible for household grocery shopping were recruited in high-footfall public venues and asked to complete a one-time shop on a tablet-based, purpose-built online grocery platform. The MTL label was adapted for Arabic reading direction and displayed per-serving nutrients and % recommended daily intake. Treatment effects were estimated using ordinary least squares regressions with robust standard errors and covariate adjustment. Results: Of 395 randomized participants, 360 were included in primary analyses (control n = 183; MTL n = 177). MTL exposure was not associated with a significant change in the primary outcome (basket weighted average MTL score per serving; β = 0.037; p = 0.64) or in per-serving calories and nutrients of concern (all p > 0.17). In the post-shop assessment, only 47.2% of participants correctly interpreted MTL labels, indicating modest objective label comprehension under the study conditions. Conclusions: These findings suggest that the impact of front-of-pack labels likely depends on both implementation features and consumer understanding, and that pairing labels with public communication and nutrition literacy initiatives may be necessary to maximize the effectiveness of labels in Bahrain and the wider Gulf region. Full article

Background: An individual’s health status is determined by the interaction of genetic, environmental, social, and lifestyle variables. Nutrition plays a fundamental role in disease prevention, a notion widespread among the informed public, which is keen on participating in initiatives for personalized recommendations informed by advanced biological data. Objectives: To evaluate whether a personalized nutrition service can produce measurable changes in nutritional behavior and biological outcomes, we established the GENIE digital platform. This platform delivers personalized online food shopping and recipe recommendation informed by integrated data from (i) biochemical blood markers, (ii) nutrigenetic profiles, (iii) gut microbiota composition, and (iv) consumer preferences. Methods: We conducted a single-arm study in a Spanish cohort that used a specific online retailer for food shopping, totaling 1177 participants. Group 1 (n = 620) had recommendations based only on the biochemical blood test; Group 2 (n = 357) included the nutrigenetic test; and Group 3 had the gut microbiome test (first batch, n = 200; second batch, n = 97). After one month of informed, tailored dietary advice, a quantitative evaluation of the experience was conducted. Results: The GENIE platform led to strong engagement (mean session time 7.07 min; +154% e-commerce use), with 71% of participants following at least part of the recommendations. This was associated with an increase in microbiome diversity in about 70% of participants, after just one month of guided recommendations. Conclusions: The GENIE platform represents a pragmatic model for translating nutrigenetic and microbiome data into actionable dietary recommendations, bridging the gap between scientific evidence and consumer behavior. Full article

The extensive implementation of AI-driven chatbots in e-commerce has generated both acceptance and objection; however, previous studies have predominantly analyzed these responses separately. This study examines the mechanisms that drive dual customer responses to AI chatbots in the pre-purchase phase of the online shopping customer journey. The study integrates the Artificially Intelligent Device Use Acceptance (AIDUA) model with the Stimulus–Organism–Response (SOR) framework to explain how consumers develop both positive and negative behavioral responses to AI chatbot usage and to derive design-relevant implications for AI chatbot systems in online shopping. Data were collected from active or recent chatbot users in online shopping and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results demonstrate that social influence and chatbot anthropomorphism significantly affect consumers’ attitudes toward AI chatbot usage, whereas novelty value does not show a significant effect. Attitude toward using serves as a significant psychological mechanism that increases willingness to accept AI chatbots and purchase intention, while also being positively associated with objection to use among active or recent chatbot users. The findings extend understanding of AI-enabled interactive marketing and provide applied implications for AI chatbot system design, particularly with respect to interface anthropomorphism, transparent pre-purchase support, objection-sensitive escalation, and adaptive interaction strategies. Full article

Multi-objective flexible job shop scheduling requires balancing conflicting objectives while supporting real-time decision-making in industrial environments. However, although traditional metaheuristics are effective for global search, their high computational cost limits their applicability in time-sensitive scenarios. To address this issue, this paper proposes dual-pool guided preference-conditioned graph reinforcement learning (DPG-GRL), an encoder–decoder framework for the multi-objective flexible job shop scheduling problem. In DPG-GRL, a graph attention network encoder extracts operation and machine-level representations from a heterogeneous graph, while the decoder is conditioned on a preference vector to generate scheduling solutions with different trade-offs using a single trained policy. To improve sample efficiency and training stability, a dual-pool guidance mechanism is introduced, in which an offline expert pool provides a stable behavioral prior for policy initialization and an online elite pool continuously replays high-quality trajectories to refine the policy. Experimental results show that DPG-GRL outperforms representative multi-objective evolutionary algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II) and the multi-objective evolutionary algorithm based on decomposition (MOEA/D), on synthetic instances, with more pronounced advantages in solution quality and inference efficiency as the problem scale grows. In addition, evaluations on public benchmark instances using a model trained only on the small synthetic setting demonstrate rapid Pareto-front approximation, high-quality solution sets, and promising generalization to unseen instances. These results indicate the potential of DPG-GRL for real-time production scheduling and energy-aware manufacturing. Full article

Present-day artificial intelligence systems (AI), virtual assistants, and devices connected to the Internet of Things (IoT) are playing an increasingly important role in decision-making processes in the everyday lives of individuals and daily operations of organizations. In this respect, the users’ trust is a key factor determining their acceptance and effective use. In contemporary digital ecosystems, this trust increasingly becomes a component of sustainable digital marketing, in which transparent data practices and responsible communication shape long-term consumer–technology relationships. This paper analyzes the halo effect as a psychological mechanism affecting the perception of competences, reliability, and ethics in the case of technologies based on AI. Based on the literature on behavioral economics, it was shown how positive associations with the interface, brand, or previous experience of the user may lead to excessive trust in technology. Such mechanisms also play a significant role in shaping sustainable consumption patterns, as users—guided by cognitive shortcuts—can adopt technologies in ways that either strengthen or weaken responsible digital behaviors. Moreover, the potential risks associated with this phenomenon were also indicated. The aim of this paper was to present how the utilization of the halo effect influences the generation of trust in smart systems and the formulation of implication for management practices and technology design. These implications are increasingly important in the context of sustainable digital marketing policy, where organizations must align persuasive communication with ethical standards and with rising expectations regarding sustainable digital transformation. Relationships between variables were analyzed using structural equation modeling (SEM), making it possible to verify complex dependencies between the perceived image of technology, the halo effect, and the users’ trust. This study tested three core hypotheses regarding the halo effect’s role, the foundational importance of security, and the mediating function of trust in technology adoption. The results of these analyses indicate that the halo effect significantly affects the level of trust in each of the investigated areas, with the strongest effect observed in the case of virtual assistants, where perception of the human-like characteristics of the interface considerably strengthened trust in the competences and reliability of the system. This finding has particular relevance for AI-driven personalization mechanisms, which increasingly guide consumer decision-making and shape their long-term behavioral patterns in online environments, with direct implications for sustainable consumption. This paper provides contribution to innovation management and technical marketing, stressing the importance of cognitive and emotional factors in the acceptance of new technologies. At the same time, it highlights the theoretical need to integrate responsible AI design with sustainable digital marketing strategies The findings suggest that ensuring trust, once established, has the potential to support not only technological innovation but broader societal goals related to responsible consumption, environmental stewardship, and long-term digital well-being aligned with sustainable development principles. However, this study stops short of empirically measuring sustainable consumption behaviors, offering instead a conceptual link that requires further empirical validation. Full article

In the online shopping context, the proliferation of digital platforms has contributed to an increase in impulsive buying behavior (IBB), which can sometimes lead to regret. This study aims to explore the intrinsic and extrinsic stimuli that influence consumers’ online impulsive buying behavior, which subsequently affects their post-purchase cognitive dissonance, with the moderating role of price consideration (PC). The conceptual framework was formulated using the Stimulus–Organism–Response (S-O-R) model. A total of 813 responses were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived utilitarian value (PUV), perceived enjoyment (PE), fear of missing out (FOM), and green trust (GT) positively impact online impulsive buying behavior (IBB), which, in turn, positively impacts post-purchase cognitive dissonance (PCD). Moreover, a significant moderating role of PC is found in the relationship between IBB and PCD, suggesting that consumers with low price consideration tend to regret their impulsive buying more. The findings provide insights that guide online retail sellers and digital marketers to develop or implement customized strategies based on the intrinsic and extrinsic stimuli that influence customers’ impulsive buying and subsequent post-purchase cognitive dissonance. Full article

The objective of this article is to identify and analyze the factors shaping the behavior of Chinese consumers in the organic product market, with a particular focus on young members of Generations Y and Z. These factors are examined in the context of organic consumption and sustainable development, taking into account global and local trends in the organic food market as well as the role of consumers in stimulating clean production and a circular economy. The article applies a research approach that combines a review of the literature with an analysis of quantitative data. In 2022, an online survey was conducted among 1012 Chinese users of the most popular social media platforms, primarily WeChat and Sina Weibo. The respondents were young consumers from Generations Y and Z. The sample was drawn from the IMAS International online panel. The study identified the characteristics attributed to organic food, the frequency and structure of purchases (product categories and share of organic products in the shopping basket), key motives and choice criteria, barriers to purchase, sources of information on organic products, and the role of promotional tools in shaping attitudes and behaviors. The results show that pro-environmental consumption fosters sustainable development and cleaner production, with younger generations emerging as the driving force behind sustainable consumption. The analysis revealed both stimulating and limiting factors influencing the development of sustainable consumption, and highlighted the critical role of digital channels in shaping consumer attitudes and decisions. The study also discusses implications for market stakeholders (producers, distributors, educational institutions, and policymakers) in leveraging the potential of young Chinese consumers as a catalyst for cleaner production and the circular economy. Full article

Materialism is recognized as an important determinant of consumer behavior. However, studies exploring how materialism influences impulsive buying through the Diderot effect in digital contexts remain scarce. This study analyzes data from 416 adult consumers in Türkiye using Structural Equation Modeling (SEM). Four hypotheses were tested: (i) materialism increases the Diderot effect, (ii) the Diderot effect strengthens impulsive buying, (iii) materialism directly influences impulsive buying, and (iv) materialism indirectly affects impulsive buying through the Diderot effect. The findings support all four hypotheses. Materialism significantly increases both the Diderot effect and impulsive buying in online environments. The Diderot effect partially mediates this relationship and acts as an independent predictor of impulsive tendencies. The study also points out that digital stimuli such as recommendation systems, fast payment options, and social proof may reinforce this mechanism, highlighting the importance of understanding consumer behavior in digital settings. Overall, the results underline that materialistic desires, when combined with the completion motive of the Diderot effect, strongly shape impulsive tendencies. Beyond advancing theory, the study emphasizes that marketing strategies should go beyond promoting higher consumption and instead foster conscious and sustainable shopping practices. This approach not only supports consumer well-being, but also contributes to building ethical and sustainable markets. Full article