Search Results (506)

In response to the increasing consumer interest in the health benefits of plant-based foods, in this study, fuzzy logic modeling (FLM) was used to optimize the lactic fermentation process of several buckwheat (Fagopyrum esculentum)-based substrates (B-bSs), which were bio-prospected for the development of pseudocereal-based fermented foodstuffs. The experimental methodology involved obtaining B-bSs, either green or roasted, under various milling conditions and subjecting them to two different types of thermal treatment. This experimental design allowed us to obtain a set of experimental data, based on which a fuzzy system was developed and calibrated. The main physicochemical characteristics (pH, total titratable acidity, dynamic viscosity, and color) and sensory attributes (appearance, color, aroma, taste, texture or mouthfeel, and overall acceptability) of B-bSs were evaluated. The fuzzy logic approach proved useful for monitoring the evolution of lactic fermentation and for the rapid and accurate identification of situations that require technological interventions, acting as a reliable tool for the ongoing optimization of fermentation processes. Our study’s results showed that the optimal technological variants identified using FLM corresponded to green buckwheat milled with a 0.12 mm gap disk and a hammer mill and subjected to ultrasonic water bath treatment. The hedonic descriptive sensory evaluation also validated this conclusion. Full article

This study empirically delves into whether residential heating methods significantly affect apartment prices in Uiwang City, a suburban city near the Seoul Metropolitan area, South Korea. Using data from 1256 apartment sales, where both district heating systems (DHSs) and individual heating systems (IHSs) coexist, a hedonic price equation was estimated to analyze the impact of the heating method choices on housing values. Various housing attributes, including physical, locational, and environmental factors, were controlled, and multiple regression models were compared to identify the best-performing specification. The results show that apartments equipped with a DHS are priced, on average, KRW 92 million (USD 72 thousand) higher than those with an IHS. The price difference corresponds to KRW 849 thousand (USD 665) per m² and possesses the statistical significance at the 5% level. Moreover, it is quite meaningful, representing roughly 11.2% of the price of an average apartment. These findings suggest that the use of DHS has a positive effect on apartment prices that reflect consumers’ preferences, beyond its advantages in stable heat supply and energy cost savings. This article provides empirical evidence that DHS can serve as an important urban infrastructure contributing to asset value enhancement. Although this study is based on a specific geographic area and caution must be exercised in generalizing its findings, it reports the interesting finding that residential heating method significantly affects housing prices. Full article

The rapid development of generative artificial intelligence (AI) systems such as ChatGPT (GPT-4) could transform teaching and learning. Yet, integrating these tools requires insight into what drives students to adopt them. Research on ChatGPT acceptance has so far focused on university settings, leaving school contexts underexplored. This study addresses the gap by surveying 506 upper secondary students in Baden-Württemberg, Germany, using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Performance expectancy, habit and hedonic motivation emerged as strong predictors of behavioral intention to use ChatGPT for school purposes. Adding personality traits and personal values such as conscientiousness or preference for challenge raised the model’s explanatory power only marginally. The findings suggest that students’ readiness to employ ChatGPT reflects the anticipated learning benefits and enjoyment rather than the avoidance of effort. The original UTAUT2 is therefore sufficient to explain students’ acceptance of ChatGPT in school contexts. The results could inform educators and policy makers aiming to foster the reflective and effective use of generative AI in instruction. Full article

Background/Objectives: The adoption of sustainable eating behaviors is not only crucial for environmental health but also has significant implications for individual health outcomes. A deeper understanding of the psychological determinants underlying such changes is needed. The Theory of Planned Behavior (TPB) has been widely used to understand the psychological factors influencing health behaviors, including dietary choices. Recent advances suggest integrating additional psychological constructs, such as personal values, to enhance TPB’s predictive power and the effectiveness of related behavioral interventions. Methods: A novel Food-Related Personal Values Questionnaire (FRPV-Q) was developed based on Schwartz’s circumplex model of basic values, and the role of these food-related personal values within an enhanced TPB framework was tested. Statistical analyses were conducted to explore the structure of the questionnaire. Results: Three components were identified within the FRPV-Q: Openness, Health and Security, and Autonomy (Kaiser–Meyer–Olkin test = 0.576). The regression analyses highlighted the potential role of personal values in predicting sustainable eating behaviors (adjusted R² = 0.318). Specifically, an orientation toward autonomy, hedonism, and self-directionality appeared to hinder the adoption of sustainable food choices, while an orientation toward health, security, and openness to novelty was found to promote more sustainable dietary choices. Conclusions: The results offer preliminary insights into the role of personal values in food-related behaviors. Future research aimed at understanding and promoting pro-environmental food-related behaviors should rigorously investigate the topic. Carefully tailored value-based psychological interventions may prove beneficial for the general population in the promotion of sustainable dietary lifestyles. Full article

Emotional difficulties are increasingly prevalent amongst young people, yet the use of AI technology for emotion regulation remains limited. This study aimed to identify young people’s attitudes toward AI technology for emotion regulation and to analyse the factors influencing their decision to use or not use AI technology. Forty participants from China, comprising twenty males and twenty females, with a mean age of twenty-five, took part in the study. Data were collected through semi-structured face-to-face interviews and were analysed using NVivo 11 software. Grounded theory techniques and a three-stage coding approach were used to categorise the data. The grounded theory model demonstrated that user behaviours are influenced by three contextual factors: personal, technological and environmental contexts. Key influencing factors for user behaviours include fulfilling utilitarian, hedonic and social value needs such as perceived usefulness, ease of use, trust, positive emotions, interest, social perception, high value, convenience and privacy protection. This study offered theoretical insights and practical recommendations for designing and developing AI technology aimed at emotion regulation in youth populations. Full article

Influencer marketing is estimated to reach USD 32.55 billion by the year 2025. The extant research on virtual vs. human influencers’ effectiveness has reported mixed results. Currently, research has yet to explore how consumption motivation and influencer gender (virtual vs. human) may differentially impact consumer behavior. Likewise, whether antecedent factors such as prior attitudes toward the brand may affect the perceived authenticity and attractiveness of influencers has rarely been investigated. To fill this research gap, the current study examined virtual vs. human influencers’ effectiveness utilizing a 2 (influencer type) × 2 (product type) × 2 (influencer gender) between-subject online experiment. Specifically, Airbnb (a recreational product) and NJM (an insurance product) were respectively designated as a hedonic and utilitarian brand. The findings (N = 468) demonstrated that while prior brand attitudes toward a hedonic product had no effect on perceived influencer authenticity, the opposite was true for a utilitarian product. No significant difference was shown in perceived authenticity and attractiveness between a male and female (virtual or human) influencer. Structural equation modeling suggested that perceived social similarity between a participant and an influencer positively impacted the perceived attractiveness and authenticity of influencers and purchase intention. Hedonic instead of utilitarian motivation was found to be a positive predictor of purchase intention. Full article

In this study, we extend the UTAUT2 model to explore the determinants of green purchase intention in Taiwan. By incorporating environmental concern, government support, and green trust, the model highlights how contextual and trust-based factors shape sustainable consumption. Based on 590 valid survey responses, analysis using covariance-based structural equation modeling reveals that performance expectancy, effort expectancy, social influence, price value, environmental concern, government support, and green trust all positively influence green purchase intention. Notably, green trust also mediates the relationship between the core UTAUT2 constructs and green purchase intention. In contrast, hedonic motivation and habit show no significant effects, suggesting that sustainable consumption has not become habitual or emotionally driven behavior in Taiwan. These findings emphasize the importance of rational evaluation, social context, and policy support in driving green behavior and offer practical implications for promoting sustainable consumption. Full article

Destination Management Organizations (DMO) increasingly harness social media to foster favorable online destination brand experiences (ODBEs) during travelers’ pre-trip planning. However, empirical knowledge about such experiences in ecotourism contexts remains limited. This study addresses the gap by proposing and validating an ODBE measurement scale adapted to ecotourism destinations. An online questionnaire was administered to Facebook users following seven certified Romanian ecotourism destinations, yielding 281 valid responses. Through exploratory factor analysis and confirmatory composite analysis, the scale was refined into three components—hedonic, utilitarian, and spatio-temporal—capturing emotional immersion, rational evaluation, and destination-specific spatial perceptions. Structural equation modeling further demonstrated that ODBEs exert a strong, positive effect on two key behavioral intentions: visiting the destination (β = 0.913) and sharing destination information online (β = 0.875). This study advances theories on tech-mediated pre-travel experiences by emphasizing nature and local culture. The findings provide DMOs with practical guidance for creating effective social media content to enhance destination branding and support sustainable tourism. Full article

This study investigated the intangible value transferred from urban megaprojects to surrounding residential property markets, focusing on Brisbane’s transformative urban regeneration projects currently in the development pipeline. The research objectives were twofold: first, to empirically investigate the dynamics of property markets influenced by urban megaprojects and second, to assess the impact of a specific case study on these markets through a longitudinal analysis of residential sales data. Drawing from environmental economics, the concept of willingness to pay (WTP) is used to quantify externalities associated with urban megaprojects. The research constructs a comprehensive dataset integrating geospatial and property-specific data. Through revealed preference methods, the intangible value transferred from mixed-use developments is identified and quantified via residential transaction prices. Utilising hedonic price modelling, this study systematically analysed residential transaction data to estimate implicit prices associated with spatial proximity to megaprojects. A comprehensive dataset integrating property-specific attributes, geospatial proximity measures, and temporal dynamics of project development phases underpins this analysis. This research and its findings advance the existing literature in several important dimensions. That is, this research represents the first microeconomic assessment of the property market’s impacts resulting from mixed-use megaprojects in Brisbane, offering novel empirical insights for both academic and practical applications, how urban megaprojects shape residential property values, and informing stakeholders involved in urban planning, policymaking, and real estate investment decisions. Practitioners and policymakers can leverage these insights to inform policy frameworks and strategic decisions. At the governmental level, the results offer applicable insights for urban revitalisation strategies, particularly relevant to central business districts undergoing similar developments. Private sector stakeholders can utilise these outcomes to anticipate market adjustments, managing supply and demand fluctuations more effectively. Full article

This study examines the psychological mechanisms underlying virtual reality (VR) tourism experiences through an integrated theoretical framework centered on flow experience and visit destination intention. Drawing upon flow theory, the research investigates how interactivity, perceived vividness, and telepresence influence flow experience and subsequently affect hedonic motivation and perceived visual appeal in VR tourism contexts. Using partial least squares structural equation modeling (PLS-SEM) analysis of data collected from 255 VR tourism users across major Chinese metropolitan centers, the study reveals that perceived vividness and telepresence significantly impact flow experience, while interactivity shows no significant effect. Flow experience demonstrates significant positive relationships with hedonic motivation and perceived visual appeal. Furthermore, hedonic motivation and perceived visual appeal significantly positively affect visit destination intention. The findings advance the theoretical understanding of VR tourism by illuminating the psychological pathways through which technological characteristics influence behavioral intentions. These results offer practical implications for destination marketers and VR tourism developers in designing more effective virtual experiences that enhance destination visit intentions. Full article

The objective of this study was to obtain and evaluate a coffee aroma extract/oil with sensorial attributes close to the original brew of Greek coffee for use in an instant Greek coffee powder. The oil was obtained directly from commercial Greek coffee by solid-liquid extraction using hexane as a solvent and treated with a series of hexane-ethanol mixtures (0:10, 1:4, 1:9) to remove the intense roasted flavor of the crude coffee oil obtained by hexane; the de-oiled coffee was used for the recovery of water-soluble compounds, and the produced water extract was freeze-dried. The aromatic volatiles of the coffee oil samples were analyzed by using a purge-and-trap device coupled to GC-MS, as well as sensory analysis. The instant Greek coffee powder was produced by mixing the freeze-dried base (74.4%) with the extract derived after treatment of the crude oil with hexane-ethanol mixture 1:4 (18.2%) and foaming agent (7.4%). Two different materials were studied as bases: instant coffee (F3_Gr-D) and ground Greek coffee (reference sample, C_Gr). The shelf-life stability of the produced powders was examined at three storage temperatures (25, 45, 60 °C). Instrumental analysis (purge-and-trap GC-MS) of aroma and sensory analysis (aroma, taste, staling, total sensory quality on a 1–9 hedonic scale) was conducted. Aroma loss (furfuryl alcohol, furfural, dimethyl pyrazines, ethyl methyl pyrazines) and scores for sensory attributes during storage were modeled using 1st and 0-order reaction kinetics, respectively. The storage temperature effect was expressed by the Arrhenius model (activation energy E_a). According to the results, the developed instant coffee powder presented satisfactorily the aroma characteristics of regular Greek coffee. The shelf life for the instant Greek coffee powder was estimated as 80 days (air packed) (based on 20% retention of furfuryl alcohol that was the most abundant aromatic volatile of Greek coffee aroma, ground as well as extract oil). Full article

Against the backdrop of the current rural economic transformation and the intensification of the ageing process, land transfer, as an important land policy tool, has gradually become a key factor influencing the consumption behaviour of farmers, especially older farmers. Based on the four-period panel data of the China Family Panel Studies (CFPS), this study uses a two-way fixed-effects model to examine the impact of land transfer (land transfer-out, land transfer-in, and two-way land transfer) on the consumption behaviour of older farmers. This study finds that land transfer-out significantly increases the total consumption of older farmers and promotes subsistence, healthy, and hedonic consumption. In contrast, land transfer-in does not show a significant effect on hedonic consumption. The mechanism test reveals that household income plays a key mediating role in the process of land transfer, affecting the consumption behaviour of older farmers. Two-way land transfer promotes the consumption level and the upgrading of the consumption structure of older farmers through income portfolio optimisation and risk diversification. Full article

With the rapid development of digital technology, the use of digital learning resources has become increasingly widespread and is now an integral part of higher education. However, there is a lack of research on engineering students’ behavioral intention to use digital resources and their digital literacy. This study, which is based on two waves of longitudinal survey data from 422 Chinese engineering students, employs the unified theory of acceptance and use of technology 2 (UTAUT2) model to systematically analyze the factors influencing engineering students’ behavioral intention to use digital learning resources and explore the longitudinal relationship between this intention and digital literacy. The results show that engineering students’ behavioral intention to use digital learning resources positively predicts their digital literacy. Effort expectancy, social influence, facilitating conditions, hedonic motivation, and habit are positively related to engineering students’ behavioral intention, whereas performance expectancy and price value do not have significant effects. The findings extend the application of the UTAUT2 model in the context of digital education and reveal a longitudinal link between the use of digital learning resources and digital literacy. This provides theoretical support and practical guidance for optimizing digital learning environments, enhancing digital literacy in engineering students, and improving the design of teaching resources in higher education, contributing to the development of engineering education in China in the digital age. Full article

The increasing digitalisation of housing markets has expanded the types and sources of data available for research. However, there is limited understanding of how these diverse data types are used across different themes in urban housing studies and which analytical approaches are applied. This study addresses these questions through a systematic review of 71 peer-reviewed studies published between 2010 and 2021, following PRISMA guidelines. The review identifies five dominant research themes: housing market analysis, rental market analysis, housing policy evaluation, housing affordability, and housing inequality. It also classifies five main data sources: official statistics, non-official statistics, surveys and qualitative data, big data, and social media. A cross-examination of themes and data types shows that official statistics remain the most frequently used across the themes, while emerging data sources such as big data and social media are underutilised—especially in research on informal housing and demand-side dynamics. Regression analysis and hedonic modelling are the most commonly applied analytical methods, with the choice of method largely shaped by research objectives and data types. By developing a cross-typology framework linking research themes, data sources, and methods, this study provides an evidence base for inclusive, responsive, and data-informed strategies that support socially and economically sustainable urban housing systems. Full article

Generative artificial intelligence tools, such as Microsoft Copilot, are transforming the teaching of programming by providing real-time feedback and personalized assistance; however, their impact on learning, motivation, and cognitive absorption remains underexplored, particularly in university settings. This study evaluates the effectiveness of Microsoft Copilot compared to instructional videos in teaching web programming in PHP, implementing a quasi-experimental design with 71 industrial engineering students in Chile, divided into two groups: one using Microsoft Copilot and the other following instructional videos, with pre- and post-tests applied to measure knowledge acquisition while surveys based on the Hedonic-Motivation System Adoption Model (HMSAM) assessed cognitive absorption (enjoyment, control, immersion, curiosity) and technology acceptance (perceived usefulness, ease of use, and intention to adopt). The results show that, while both methods improved learning, students who used instructional videos achieved greater knowledge gains, higher levels of curiosity, and a stronger intention to continue using the technique, suggesting that instructional videos, by providing structured explanations and reducing cognitive load, may be more effective in the early stages of programming learning. In contrast, AI tools could be more beneficial in advanced stages where students require adaptive feedback, providing empirical evidence on the comparative effectiveness of AI-based and video-based instruction in teaching programming and highlighting the importance of balancing structured learning with AI-driven interactivity, with the recommendation that educators integrate both approaches to optimize the learning experience, using videos for initial instruction and AI tools for personalized support. Full article