Search Results (411)

This study investigates the effectiveness of designing a gamified Virtual Reality (VR) intervention, grounded in Kolb’s Experiential Learning Theory, in enhancing social reciprocity, affective expression, and daily living skills among Thai children with Autism Spectrum Disorder (ASD). Utilizing the Meta Quest 2 headset, the intervention simulated five real-world contexts—home routines, classroom behavior, street crossing, supermarket shopping, and fast-food interactions. A quasi-experimental pretest-posttest control group design was employed with 33 children aged 6–12, complemented by thematic analysis of caregiver and therapist interviews. Results from the Social Responsiveness Scale, Second Edition (SRS-2), indicated significant improvements in social communication (p = 0.001) and social motivation (p = 0.045), while changes in social awareness (p = 0.233) and repetitive behaviors (p = 0.169) were not statistically significant. However, an ANCOVA analysis controlling for pre-test scores found that the difference in post-test outcomes between the intervention and control groups did not reach statistical significance (p = 0.073), suggesting that observed improvements may be influenced by baseline variability. Qualitative feedback highlighted the program’s engagement value, cultural relevance, and usability, alongside suggestions for increased adaptivity and contextual nuance. These results underscore the potential of VR-based learning while highlighting the need for further research with larger samples and more adaptive systems. These findings offer practical insights for educators, researchers, teachers, and program developers by demonstrating how culturally grounded, gamified VR interventions when guided by experiential learning principles can effectively enhance engagement and support targeted social skill development in children with ASD. Full article

Waste animal fat (WAF) can be converted to distillate fractions similar to petroleum solvents and used as solvents via pyrolysis and fractional distillation. Pyrolysis oil from triglyceride materials presents adequate viscosity and volatility, compared to petroleum fuels, but shows acid values between 60–140 mg KOH/g, impeding its direct use as biofuels without considerable purification of its distillates. Fractional distillation can be applied for the purification of bio-oil, but only a few studies accurately describe the process. The purpose of this study was to evaluate the effect of temperature in the conversion of waste animal fat into fuel-like fractions by pyrolysis and fractional distillation in a semi-batch stirred bed reactor (2 L) according to reaction time. Waste animal fat was extracted (rendering) from disposed meat cuts obtained from butcher shops and pyrolyzed in a stainless-steel stirred bed reactor operating in semi-batch mode at 400–500 °C. The obtained liquid fraction was separated according to reaction time. The pyrolysis bio-oil at 400 °C was separated into four distinct fractions (gasoline, kerosene, diesel, and heavy phase) by fractional distillation with reflux. The bio-oil and distillate fractions were analyzed by density, kinematic viscosity, acid value, and chemical composition by gas chromatography coupled to mass spectra (GC-MS). The results show that, for semi-batch reactors with no inert gas flow, higher temperature is associated with low residence time, reducing the conversion of fatty acids to hydrocarbons. The distillate fractions were tested in a common application not sensible to the fatty acid concentration as a diluent in the preparation of diluted asphalt cutback for the priming of base pavements in road construction. Kerosene and diesel fractions can be successfully applied in the preparation of asphalt cutbacks, even with a high acid value. Full article

Global ambitions for net-zero emissions and resource circularity are propelling industry from linear “make-use-dispose”models toward closed-loop value creation. Remanufacturing, which aims to restore end-of-life products to a “like-new” condition, plays a central role in this transition. However, its stochastic inputs and complex, multi-stage processes pose significant challenges to traditional production planning methods. This study delivers an integrated overview of remanufacturing scheduling by combining a systematic bibliometric review of 190 publications (2005–2025) with a critical synthesis of modelling approaches and enabling technologies. The bibliometric results reveal five thematic clusters and a 14% annual growth rate, highlighting a shift from deterministic, shop-floor-focused models to uncertainty-aware, sustainability-oriented frameworks. The scheduling problems are formalised to capture features arising from variable core quality, multi-phase precedence, and carbon reduction goals, in both centralised and cloud-based systems. Advances in human–robot disassembly, vision-based inspection, hybrid repair, and digital testing demonstrate feedback-rich environments that increasingly integrate planning and execution. A comparative analysis shows that, while mixed-integer programming and metaheuristics perform well in small static settings, dynamic and large-scale contexts benefit from reinforcement learning and hybrid decomposition models. Finally, future directions for dynamic, collaborative, carbon-conscious, and digital-twin-driven scheduling are outlined and investigated. Full article

Fast fashion is a rapidly expanding sector characterized by high production volumes, low costs, and short product lifecycles. While recent efforts have focused on improving sustainability within supply chains, consumer behavior remains a critical yet underexplored driver of environmental impacts. This study presents a web-based calculator tool designed to estimate both the carbon and plastic footprints associated with individual fast fashion consumption, with a particular focus on shopping behaviors, garment disposal, and laundry habits. Adopting a geographical perspective, the analysis explicitly considers the spatial dynamics of consumption and logistics within the urban context of Milan (Italy), a dense metropolitan area representative of high fashion activity and mobility. By incorporating user-reported travel patterns, logistics routes, and localized emission factors, the tool links consumer habits to place-specific environmental impacts. By involving over 360 users, the tool not only quantifies emissions and plastic waste (including microfibers) but also serves an educational function, raising awareness about the hidden consequences of fashion-related choices. Results reveal high variability in environmental impacts depending on user profiles and behaviors, with online shopping, frequent use of private vehicles, and improper garment disposal contributing significantly to emissions and plastic pollution. Our findings highlight the importance of integrating consumer-focused educational tools into broader sustainability strategies. The tool’s dual function as both calculator and awareness-raising platform suggests its potential value for educational and policy initiatives aimed at promoting more sustainable fashion consumption patterns. Full article

This study developed a simple genetic algorithm (SGA) enhanced by a random forest (RF) surrogate model, namely ${SGA}_{RF}$, to solve the permutation flow-shop scheduling problem with order acceptance under the conditions of limited capacity, weighted-tardiness, and past-sequence-dependent (PSD) setup times (PFSS-OAWT with PSD). To the best of our knowledge, this is the first study to investigate this problem. Our proposed algorithm increases the setup time for each successive job by a constant proportion of the cumulative processing time of preceding jobs to capture the progressive slowdown that often occurs on real production lines. In the developed algorithm with maximum ${10}^5$ fitness evaluations, the RF surrogate model predicts objective function values and guides crossover and mutation. On the PFSS-OAWT with PSD benchmark (up to 500 orders and 20 machines, 160 instances), ${SGA}_{RF}$ represents improvements of 0.9% over SGA, 0.8% over ${SGA}_{LS}$, and 5.6% over SABPO. Although the surrogate incurs additional runtime, the gains in both profit and order-acceptance rates justify its use for high-margin, offline planning. Overall, the results of this study suggest that integrating evolutionary search into data-driven prediction is an effective strategy for solving complex capacity-constrained scheduling problems. 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

This study investigates how university students adopt large language models (LLMs) for online consumption of sustainable products, integrating perceived value theory with the technology acceptance model (TAM). Cross-sectional survey data were analyzed using structural equation modeling (SEM) and artificial neural networks (ANNs). SEM results reveal partial mediation. Performance expectancy value (PEV) and information quality value (IQV) directly shape continue using intention (CUI). They also influence CUI indirectly through perceived ease of use (PEU) and perceived usefulness (PU). Green self-identity value (GSV) influences CUI both directly and via PEU, while trust transfer value (TTV) and green perceived value (GPV) affect CUI only via PEU. ANN findings confirm this hierarchy, as PU (86.7%) and PEU (85.7%) are the strongest predictors of CUI, followed by GSV (73.7%). Convergent evidence from both methods indicates that instrumental utility, effortless interaction, and sustainability identity congruence drive sustained LLM use in the context of online consumption of green products, whereas credibility cues and sustainability incentives play secondary roles. This study extends TAM by incorporating multidimensional value constructs and offers design recommendations for engaging and high-utility AI shopping platforms. Full article

The purpose of this study is to reveal the characteristics of wine tourists by determining their demographic and psychographic profiles, the effects of their travel motivations and lifestyles on their attitudes towards wine tourism, and the relationships between their demographic characteristics and attitudes. Based on a quantitative approach, study data were collected using a questionnaire. The sample consists of tourists who had visited a winery or participated in the vintage in a wine destination. Wine tourists’ principal motivations were pull factors and wine-related motivations. Their attitudes towards wine consumption had higher mean values than attitudes towards info-seeking, meaning that wine tourists preferred to taste wine and shop from wineries more than learn about wine. Regarding lifestyles, they were mostly innovators and experiencers. For destinations like Türkiye, wine tourism is a significant economic and sociocultural development tool. It is the first study to identify the wine tourists’ profile based on VALS-2, thereby providing a different perspective for the literature. Full article

The aim of this study is to investigate how consumers’ cognitive and affective responses to web-based augmented reality affect their intention to continue to use augmented reality. The novelty of this study is the integration of the Stimulus–Organism–Response model with Technology Continuance Theory, allowing for an investigation of the relationships among the following critical variables: augmented reality (AR), utilitarian value, perceived risk, user satisfaction, attitude toward AR, and continuance intention. The study sample consisted of 452 participants. Data were analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach. The results indicate significant direct relationships between all variables. Furthermore, this study demonstrated an indirect relationship between AR and continuance intention, mediated sequentially by cognitive responses, namely, utilitarian value and perceived risk, and affective responses, including user satisfaction and attitude toward AR. Consequently, it was revealed that all indirect relationships were significant, except for the pathways from AR to continuance intention involving perceived risk. This study presents key insights for online retailers, demonstrating how the integration of AR technology into conventional online shopping platforms can optimize user experiences by enhancing the cognitive and affective responses of customers. This, in turn, strengthens their intention to continue using AR technology, fostering sustained engagement and the long-term adoption of AR technology. Full article

This paper presents an approach for integrating data between a production system and its digital twin, focusing on achieving proactivity in production control. Recognizing the unique nature of each production system, this research highlights that a universal, plug-and-play solution is only partially feasible, primarily through general guidelines. The study successfully applied and automated proposed data acquisition methods, resulting in a functional, simulation-based digital twin that adheres to the latest ISO standards. The developed solution incorporates multiple data acquisition strategies, including files containing comma-separated values, a permanent connection to the production control system database, open platform communications unified architecture, and external command files for scenario alteration. The main motivation behind the presented implementation is its application on the shop-floors of small and medium enterprises, where it could provide useful tools for keeping up with the ever-rising competition in the manufacturing sector. This integrated approach allows for affordable and accurate system representation within the proactive simulation concept. The methodology was empirically validated across two distinct production systems: a lab-scale food and beverage line focusing on product tracking, and a sub-assembly line with automated guided vehicle optimization. Despite system variability, the core data acquisition methods demonstrated remarkable adaptability. Full article

This study investigates the enhancement of condition and the extension of service life in architectural structures of shopping malls through the application of a hybrid methodological framework that integrates Life Cycle Assessment (LCA) and Service Life Planning (SLP). Thisresearch identifies key parameters related to physical performance, sustainability indicators, and functional characteristics of architectural systems that are subject to deterioration and shifting market conditions during the operational phase. The methodology encompasses a theoretical synthesis of LCA/SLP principles and advances in modeling for both the integrated design of new facilities and the monitoring and renewal of existing ones—from data collection and early-stage planning, through construction, use, and maintenance, to end-of-life phases. A second component of the model focuses on quantitative assessment and condition forecasting, based on Markov chain modeling, applied to the case study of the “Deva 1” shopping mall in Serbia. The results demonstrate the model’s ability to correlate physical condition indices with predictive service life scenarios.This study further contributes by integrating time-dependent impact categories, usage profiles, and planning parameters into a unified evaluation matrix, which can be applied to the development and improvement of systems aimed at enhancing the structural, functional, esthetic, and indirectly economic value of shopping mall buildings throughout their entire life cycle—from an architectural perspective. Full article

Purpose—This research paper examines the critical factors in customer satisfaction while purchasing fruits and vegetables at farmers’ markets. Design/methodology/approach—This study was conducted using a prepared questionnaire to collect data on a random sample of 235 customers of farmers’ markets in the state of Maharashtra, India. The research was carried out in the year 2023. Seven hypotheses were tested concerning the relationships between the variables of interest. The variables of convenience, variety, quality, price, health and hygiene, and service conditions were used as independent constructs and were proxied by reflective indicators. Customer satisfaction and customer loyalty were treated as an exogenous variable and an endogenous variable, respectively. Structural equation modeling was used to investigate the model relationships and confirm the theoretical model. Findings—The findings validate all the reflective indicators used in the study. The latent variables of convenience, variety, quality, price, health and hygiene, and service conditions positively and significantly affect customer satisfaction, and customer satisfaction positively and significantly affects customer loyalty toward farmers’ markets. The structural equation explains approximately 55% of the variation in customer satisfaction related to convenience, variety, price, quality, health and hygiene, and service conditions. Significance—The study results provide insights into the factors that influence consumer behavior and attitudes toward farmers’ markets. By identifying these predictors, this study can help farmers’ markets and other stakeholders develop effective marketing strategies to attract and retain customers, ultimately promoting sustainable food production and consumption. Additionally, the results can inform policymakers on how to support and promote farmers’ markets as healthy and sustainable food sources. Practical implication—By implementing the practical suggestions derived from the implications of this research, farmers’ markets can optimize customer satisfaction, boost customer loyalty, and reinforce their position as valuable contributors to local communities’ well-being and sustainability. Originality/value—The acceptance of farmers’ markets in India as an alternative shopping destination for fruits and vegetables is gradually increasing. This exploratory study conducted on farmers’ markets examined several factors, including price, in assessing customer satisfaction and farmers’ markets’ effectiveness at positioning themselves as shopping destinations for consumers in India. Full article

In an increasingly digitalized marketplace, understanding Generation Z’s (Gen Z) online consumer behavior has become a critical priority, particularly in relation to newly launched technological products. Although online consumer behavior has been widely studied, a gap remains in understanding how the location of the e-shop (domestic vs. international) moderates this behavior. Addressing this gap, the present study adopts a quantitative, cross-sectional design with data from 302 Gen Z participants, using a hybrid sampling method that combines convenience and systematic techniques. A structured questionnaire, grounded in 19 well-established behavioral theories, was employed to examine the influence of six key factors, behavioral and attitudinal traits, social and peer influences, marketing impact, online experience, brand perceptions, and Gen Z characteristics, across various stages of the consumer journey. Moderation analysis revealed that e-shop location significantly affects the strength of relationships between these factors and both purchase intention and post-purchase behavior. Notably, Gen Z’s values and marketing responsiveness were found to be more predictive in the context of international e-shops. These findings highlight the importance of marketing strategies that are both locally relevant and globally informed. For businesses, this research offers actionable insights into how digital engagement and brand messaging can be tailored to meet the unique expectations of Gen Z consumers across diverse e-commerce contexts, thereby enhancing consumer satisfaction, loyalty, and brand advocacy. Full article

The acceleration of industrialization and urbanization worldwide has dramatically improved living standards but has also introduced serious environmental and public health challenges. One of the most critical challenges is air pollution, particularly indoors, where individuals typically spend over 90% of their time. Ensuring good Indoor Air Quality (IAQ) is essential, especially in heavily frequented public spaces such as shopping malls. This study focuses on assessing IAQ in a large shopping mall located in Tabuk, Saudi Arabia, covering retail zones as well as an attached underground parking area. Monitoring is conducted over a continuous two-month period using calibrated instruments placed at representative locations to capture variations in pollutant levels. The investigation targets key contaminants, including carbon monoxide (CO), carbon dioxide (CO₂), fine particulate matter (PM_2.5), total volatile organic compounds (TVOCs), and formaldehyde (HCHO). The data are analyzed and compared against international and national guidelines, including World Health Organization (WHO) standards and Saudi environmental regulations. The results show that concentrations of CO, CO₂, and PM_2.5 in the shopping mall are generally within acceptable limits, with values ranging from approximately 7 to 15 ppm, suggesting that ventilation systems are effective in most areas. However, the study identifies high levels of TVOCs and HCHO, particularly in zones characterized by poor ventilation and high human occupancy. Peak concentrations reach 1.48 mg/m³ for TVOCs and 1.43 mg/m³ for HCHO, exceeding recommended exposure thresholds. These findings emphasize the urgent need for enhancing ventilation designs, prioritizing the use of low-emission materials, and establishing continuous air quality monitoring protocols within commercial buildings. Improving IAQ is not only crucial for protecting public health but also for enhancing occupant comfort, satisfaction, and overall building sustainability. This study offers practical recommendations to policymakers, building managers, and designers striving to create healthier indoor environments in rapidly expanding urban centers. Full article

This study examines culture-led urban regeneration as a strategy for revitalizing Meezan Chowk, a historically significant yet deteriorating public space in Quetta, Pakistan. Once a central site of social and commercial exchange, the area suffered from infrastructural decline, overcrowding, and the erosion of its architectural identity. The research proposes a design intervention to restore the site’s heritage value while enhancing its functional and social relevance. A qualitative approach is adopted, incorporating surveys, focus group discussions, and site observations to assess user needs and spatial dynamics. A SWOT analysis serves as the analytical framework to identify the site’s internal strengths and weaknesses, as well as external opportunities and threats. By utilizing the Geographic Information Systems (GIS) and OpenStreetMap data, further information can enhance understanding of the site’s urban morphology. The proposed design integrates vernacular elements, such as arched facades, shaded corridors, and communal courtyards, with contemporary features, including cafes, local artisan shops, and accessible public amenities. Full article