Search Results (1,399)

Background: Evaluating centralized purchasing performance is a complex multi-criteria decision-making problem involving uncertainty, linguistic assessments, and subjective judgments from internal clients. Existing approaches provide limited support for handling these characteristics simultaneously. Methods: This study proposes a Mamdani fuzzy inference model integrating four criteria: Service Quality, Responsiveness, Compliance, and Collaboration. The fuzzy rule base was developed using expert knowledge and organizational evaluation practices. The model was applied to a real industrial case study based on an annual evaluation conducted collaboratively by four internal evaluators. Results: The model transformed qualitative assessments into an interpretable performance score while capturing interactions among evaluation criteria and handling uncertainty in the evaluation process. Conclusions: The proposed approach provides a structured decision-support framework for evaluating centralized purchasing performance. It enables the integration of linguistic assessments and expert knowledge, offering a flexible and coherent evaluation tool for industrial environments. Full article

Live streaming commerce is increasingly relying on high-intensity persuasive tactics, yet such tactics may activate consumers’ persuasion knowledge and trigger defensive processing. This research examines whether AI streamers mitigate this defense more effectively than human streamers. Drawing on the Persuasion Knowledge Model, two experiments reveal that, under conditions of high persuasive intensity, consumers perceive lower persuasive intent from AI streamers than from human streamers, which, in turn, reduces consumer suspicion and increases purchase intention. Moreover, this serial mediating effect is stronger for independent accounts than for brand official accounts. These findings provide evidence for a PKM-based mechanism of AI-mediated persuasion and suggest that platforms should consider using AI streamers in high-pressure promotional contexts. Full article

The growing use of artificial intelligence (AI) in social media marketing has accelerated the emergence of AI-generated virtual influencers. While these influencers offer brands advantages such as scalability and message control, they also raise concerns regarding manipulation and ethical persuasion. Grounded in the Persuasion Knowledge Model (PKM), this study examines how different AI disclosure conditions influence perceived manipulation, perceived ethics, and purchase intention in AI influencer marketing. A three-condition between-subjects experimental design was employed to compare a human influencer, a disclosed AI influencer, and an undisclosed AI influencer using identical Instagram stimuli. Data were collected from 762 Generation Z female consumers in Türkiye. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that both disclosed and undisclosed AI influencer conditions significantly increased perceived manipulation. Perceived manipulation negatively affected perceived ethics, whereas perceived ethics positively influenced purchase intention. In addition, AI literacy positively affected perceived manipulation and perceived ethics while negatively affecting purchase intention. The findings further demonstrated that disclosure conditions indirectly influenced purchase intention through sequential cognitive and ethical evaluation processes. The study contributes to the AI influencer and digital persuasion literature by demonstrating that disclosure cues shape consumer responses through interconnected psychological mechanisms. 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

Acquisition costs account for a significant share of total construction project costs, underscoring the importance of purchasing and supply management for organizational success. Supplier selection and evaluation are particularly critical because they involve multiple criteria, qualitative and quantitative attributes, and several decision-makers. In the construction industry, these activities become even more complex due to sector-specific characteristics such as convergent material flows, temporary facilities, buyer–supplier conflicts, price-oriented decisions, and the volatility of project-based markets. This paper investigates the supplier evaluation process in a construction company and identifies the company’s requirements and decision-makers’ expectations. Based on the collected data, this research proposes a model aligned with the company’s characteristics and the decision-makers’ expectations. The model combines two methods: the Intuitionistic Fuzzy approach to aggregate decision-makers’ opinions and ELECTRE TRI to classify suppliers based on predefined criteria and thresholds. The proposed model handles different weights assigned to each decision-maker for each criterion without allowing compensation among criteria. This model also explores the role of symmetry in multicriteria decision-making by combining Intuitionistic Fuzzy Sets with the ELECTRE TRI method. Decision-makers validated the proposal and emphasized its simplicity and flexibility, which allow future adjustments to both the criteria weights and the decision-makers’ assigned weights. Full article

Although existing studies have shown that AI recommendation systems have potential in enhancing consumers’ purchase intention, there remains a lack of systematic research. This study aims to explore how the interaction between information presentation formats and AI role types influences consumers’ purchase intention. Based on metacognitive experience theory, two experiments are designed to analyze processing fluency as a mediator and consumers’ AI knowledge as a moderator. The results reveal that the interaction between information presentation format and AI role type significantly affects consumers’ purchase intention, while their separate effects are insignificant. Different from existing studies on separate factors, this study demonstrates that AI interactive marketing performance hinges on the matching of design and role positioning. This study extends the application of the theory of metacognitive experiences in the field of human–AI interaction and provides targeted recommendations for the interface design of AI recommendation systems. Full article

Traditional food systems are increasingly threatened by industrialised agri-food production based on standardised processes, economies of scale, and lower production costs. This transformation risks undermining not only the economic viability of artisanal producers but also the cultural heritage, pastoral knowledge, and territorial identities embedded in traditional foods. This study contributes to rural studies and food heritage research by examining whether consumers’ willingness to pay a premium for traditionally produced foods can sustain endangered rural production systems within competitive PDO markets. Focusing on Fiore Sardo PDO cheese, the study combines a Bertrand duopoly framework with the Theory of Planned Behaviour (TPB) to connect market competition, consumer beliefs, and support for traditional agri-food systems. Data from 1640 Italian consumers were analysed using structural equation modelling. The findings show that attitudes towards cultural preservation, social recognition of traditional production, and perceived support for shepherd communities significantly influence consumers’ willingness to purchase and pay premium prices for traditionally produced cheese. Consumers associate artisanal production not only with superior sensory quality and authenticity but also with the protection of cultural identity, traditional pastoral practices, and rural landscapes. By integrating behavioural and economic perspectives, the study demonstrates that willingness to pay operates as a market mechanism through which consumers actively contribute to the resilience of traditional food systems facing industrial competition. The study advances existing literature by showing how cultural values, behavioural intentions, and market dynamics jointly shape the economic sustainability of traditional foods. 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

Background: Live-streaming e-commerce has emerged as a significant retail channel, especially in the apparel industry, characterized by high impulse-driven purchase rates and elevated product returns. Reverse logistics processes associated with these returns generate considerable environmental impacts that require systematic evaluation. Methods: This study performs a gate-to-gate Life Cycle Assessment (LCA) using SimaPro software, with a functional unit of 1 kg for one pair of returned jeans. Secondary inventory data were obtained primarily from the Ecoinvent database and supplemented with literature-based estimates for transport distances and packaging masses. Results: Key hotspots analyzed include transportation modes, packaging materials, and waste disposal pathways. Transportation mode selection was the dominant environmental hotspot, with air freight exhibiting the highest impacts across most midpoint and endpoint categories. Low-density polyethylene (LDPE) packaging and landfill disposal of textile waste were also major contributors to global warming, ozone formation, and resource depletion. Conclusions: The findings underscore the necessity of integrating Circular Supply Chain (CSC) principles into reverse logistics network design for live-streaming platforms. Optimizing transportation modes and packaging choices can effectively balance operational responsiveness with environmental sustainability. This study offers empirical evidence and practical decision-supporting insights for more sustainable return management in high-return digital retail environments. Full article

The rapid proliferation of artificial intelligence (AI) in digital advertising has fundamentally transformed how brands communicate with consumers, shifting from generic mass messaging toward highly personalised, emotionally targeted experiences. Despite growing interest in AI-driven marketing, limited empirical research has examined how consumers’ socio-emotional processing mechanisms, particularly perceived empathy and social cognition, mediate the relationship between AI-powered ad personalisation and downstream consumer decision-making outcomes. This study addresses this gap by investigating the emotional and cognitive responses triggered by AI-personalised advertising among Romanian consumers. Using a quantitative survey design, data were collected from a sample of 234 adult respondents (18–65 years) in Romania, broadly aligned with key Romanian demographic distributions across age, gender, and residential area. Structural equation modelling using the Partial Least Squares (PLS-SEM) approach was employed to test the proposed conceptual model, which integrates constructs of AI-powered ad personalisation, trust in AI, perceived AI empathy, emotional arousal, cognitive elaboration, social cognition, consumer engagement, and purchase intention. The results reveal that perceived empathy toward AI-generated advertising positively influences emotional arousal and cognitive elaboration, which in turn significantly predict consumer engagement and purchase intention. Trust in AI emerged as a critical sequential mediator, while social cognition moderated the personalisation-to-trust pathway. The study yields a validated marketing model that captures the socio-emotional dynamics underlying consumer responses to AI advertising. These findings contribute to the theoretical understanding of human–AI interaction through a social cognition and emotions lens, while offering practical implications for the design of emotionally intelligent, AI-driven advertising strategies. Limitations and future research directions are discussed. Full article

The lack of publicly available demographic and prevalence data on service dog (SDog) teams in Canada challenges our understanding of how and to what degree limited industry regulations, unharmonized standards, differing pathways to acquiring an SDog, and other variables can affect individuals with disabilities’ (i.e., handlers/consumers) ability to acquire, train with, or live with an SDog in Canada. The present study aims to develop empirical knowledge on SDog handler/consumer experiences with navigating the Canadian SDog industry. Current, former, and prospective Canadian SDog handlers/consumers (N = 263) were surveyed on personal demographics, SDog acquisition experiences, and experiences training/working with an SDog. Descriptive statistics were calculated for all quantitative data and open-ended responses were content analyzed. Participants reported diverse experiences and processes in acquiring an SDog. The typical respondent was a novice SDog handler, inexperienced in formally training with dogs, grew up with dogs and cats, had no negative experiences with dogs, needed an SDog to support a mental health disability/ies, trained their SDog on their own or with some professional support, did not join a wait list, completed basic obedience, public access, and/or task-specific training with their SDog 0 to 5 h daily using positive reinforcement or fear-free training approaches, spent on average $2567 to purchase their dog and $6695 for ongoing training costs, and had minimal but satisfactory experiences with Canadian SDog organizations. There are numerous gaps in our understanding of SDog team experiences in Canada, and future research is warranted. Full article

Corporate social responsibility (CSR) has grown in importance as a means for companies to engage with customers who are increasingly environmentally and socially conscious. This study examines how CSR affects sustainable consumer buying tendencies, emphasizing the mediating role of consumer happiness and corporate image. Scientists contend that customers are more inclined to support businesses whose values align with CSR programs that foster positive feelings and trust. Therefore, a conceptual model was developed by following cognitive consistency theory. Data from 504 customers in Pakistan, an expanding market where awareness of sustainability issues is continually rising, were gathered to test this. The results demonstrate that CSR has a significant and favorable influence on consumer purchasing preferences, as assessed using partial least squares structural equation modeling (PLS-SEM). Crucially, the proposed relationship is not only direct: CSR improves consumer happiness and corporate image, leading to better purchase decisions. By emphasizing the emotional and perceptual processes involved, these findings provide a better understanding of how CSR influences consumer behavior. The study demonstrates how CSR can encourage more conscientious consumption habits from a sustainability standpoint, supporting Sustainable Development Goal 12 (Responsible Consumption and Production). Findings suggest that well-thought-out CSR programs may truly affect how and why customers make purchase decisions, especially in emerging countries, going beyond reputation-building. Full article

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) systems remain a widely deployed defense against automated abuse, but advances in machine learning have reduced the effectiveness of traditional challenge-based designs and exposed limitations in proprietary risk-scoring systems. This paper presents an adaptive, reinforcement learning-based CAPTCHA defense framework for high-security web applications. The proposed system formulates bot detection as a partially observable Markov decision process and uses a Proximal Policy Optimization (PPO) agent with Long Short-Term Memory to analyze streamed behavioral telemetry, including mouse movements, clicks, keystrokes, and scrolling, over sequential interaction windows. During the observation phase, the agent can continue observing or deploy a honeypot as an early-intervention and evidence-gathering action; after sufficient session evidence is accumulated, it can issue graded CAPTCHA challenges, allow a session, or block it. To complement the sequential agent, the framework also includes an XGBoost classifier that produces a session-level human-likelihood score as a supervised benchmark. The accompanying reinforcement learning environment and code base are publicly available, allowing future researchers to train, evaluate, and extend adaptive CAPTCHA policies as bot capabilities evolve. Experiments conducted on a sandbox ticket-purchasing web application demonstrate that the proposed methodology achieves strong preliminary performance on human-generated sessions and real bot sessions produced by scripted, replay-based, and Large Language Model (LLM)-powered agents. Among the evaluated reinforcement learning algorithm variants, Soft PPO achieved the best performance with 97.7% accuracy, 100% precision, and a 97.6% F1 score. Correspondingly, the XGBoost classifier achieved 99.48% accuracy, a 1.000 ROC-AUC (receiver operating characteristic area under the curve), and a 0.9919 F1 score. Our results indicate that sequential reinforcement learning can support accurate and low-friction bot detection, while the accompanying classifier provides a complementary binary benchmark. Compared to proprietary systems, the proposed framework emphasizes transparency, auditability, and explicit sequential decision-making rather than black-box risk scoring. Overall, this work introduces a publicly available, open, and adaptive CAPTCHA defense framework that supports transparent experimentation with behavior-based bot mitigation while also identifying the remaining limits that must be addressed before commercial deployment. Full article

Agri-food systems account for a substantial share of global greenhouse gas (GHG) emissions, with a significant proportion arising from upstream supply-chain activities beyond direct operational control. In this context, effective decarbonization requires systematic assessment of emissions across all life-cycle stages. This study applies an ISO 14040/44-compliant cradle-to-grave Life Cycle Assessment (LCA) to CERELAC^® infant cereal, a processed dairy-based product, to quantify Scope 1, Scope 2, and Scope 3 emissions and identify mitigation pathways across the full product life cycle. Results indicate that Scope 3 emissions account for 94.3% of total product emissions, with product use (44.7%) and purchased goods and services (36.9%) as the primary contributors. Upstream agricultural inputs—particularly milk powder—emerge as the dominant hotspot due to methane emissions and energy-intensive processing. Scenario-based evaluation suggests that regenerative sourcing, ingredient optimization, packaging redesign, logistics improvements, and consumer-phase engagement could significantly reduce life cycle emissions. The findings demonstrate how product-level LCA can operationalize Scope 3 decarbonization strategies in processed food systems, bridging corporate net-zero ambitions with actionable supply chain interventions. These results provide transferable insights for cleaner production transitions within the agri-food sector. Full article

In the decision-making process related to the purchase of land properties intended for construction investments, the Technical Due Diligence (TDD) process plays a key role. In accordance with current market practice, this process precedes both land acquisition and the commencement of a construction investment. Within this process, the feasibility of the planned investment is evaluated. This article analyzes the impact of selected factors affecting the implementation of a future construction investment on the decision-making process regarding the purchase of land properties. To support the decision-making process, the most widely used machine learning algorithms were applied and compared, including Decision Trees, Random Forests, the k-Nearest Neighbors’ method, Support Vector Machines, and Artificial Neural Networks (ANNs). The analysis demonstrated that the highest accuracy, precision, and recall (ACC, PPV, and REC indicators) in making correct purchase decisions were achieved using the ANNs algorithm. Additionally, it should be noted that decision trees are characterized by high interpretability of results, which distinguishes them from other methods. Machine learning methods may be used to develop a system supporting investment decisions related to the purchase of land properties for future construction projects; however, it should be remembered that the final decision will always be made by the investor based on their subjective assessment. Full article