Search Results (3,780)

This study investigates how regulatory pressure and organizational capabilities influence innovation-enabled compliance and supply chain performance in Thailand’s plastic recycling sector. Drawing on institutional theory, the resource-based view, and dynamic capability perspectives, the study develops and empirically tests a conceptual model using partial least squares structural equation modeling (PLS-SEM). Data were collected from 300 respondents across 20 plastic recycling facilities in the Bangkok Metropolitan Region. The results show that Digital Innovation Capability (DIC) is the strongest predictor of legal compliance behavior (LCB), followed by Organizational Regulatory Readiness (ORR), Regulatory Enforcement Intensity (REI), and Compliance Process Maturity (CPM). In turn, LCB significantly enhances supply chain resilience (SCR). The findings further indicate that REI exerts both direct and indirect effects on SCR through LCB. Although REI demonstrates a significant direct effect on SCR, the indirect effect through LCB is comparatively weaker than that of Digital Innovation Capability (DIC). Nevertheless, the mediation effect remains supported based on bootstrapped confidence interval analysis. These findings suggest that regulatory pressure alone may encourage compliance at a formal level, but sustainable operational performance ultimately depends on the development of internal organizational and technological capabilities. Mediation analysis further confirms that LCB serves as a key mechanism linking organizational and technological capabilities to supply chain performance. Overall, the findings position compliance as an innovation-enabled and capability-driven mechanism that supports digital transformation, operational resilience, and sustainability within the circular economy. Full article

In arid and semi-arid regions, severe salinity imposes strong abiotic constraints on farmland restoration. This study evaluated the one-season effects of organic amendments on soil quality, maize productivity, and short-term ecosystem multifunctionality (EMF) responses under severe salinity stress. We conducted a field experiment with biofertilizer (targeting plant–soil biological regulation) and composted manure (targeting direct soil amelioration) applied at different rates. The high-rate composted manure treatment (T6) showed the largest short-term improvements: compared with the chemical-fertilizer-only control under the same irrigation and plastic-mulch management (CK; soil quality index (SQI) = 0.716, yield = 6.657 t ha⁻¹, EMF_a = 0.456), SQI increased by 165%, maize yield increased by 41%, and EMF_a increased by 104% relative to CK within one growing season. Partial least squares path modeling (PLS-PM) suggested that under biofertilizer treatments, a plant-related association pathway was observed but relatively weak (β = 0.215), whereas under composted manure treatments, EMF variation was mainly associated with soil quality improvement (β = 0.992). Overall, these short-term results suggest that, under the tested application rates and severe salinity stress, high-rate composted manure can more effectively improve baseline soil conditions than biofertilizers during the initial season. These findings offer a preliminary conceptual perspective for a phased management strategy, serving strictly as a preliminary hypothesis where priority is given to soil amelioration in the initial phase and gradual integration of biologically oriented interventions as baseline conditions improve. However, future multi-year and multi-site studies are strictly required to validate the long-term viability of this proposed framework and to test whether these association patterns persist across longer time scales and broader regional contexts. Full article

Climate change is significantly impacting sustainable agriculture and poses a threat that is likely to motivate farmers to adapt by applying AI technology to reduce risks, costs, expenses, and the impact on greenhouse gas emissions. In other contexts related to climate change, it is important to assess whether perceived climate threats and perceived vulnerability to climate change influence farmers’ intention to use artificial intelligence and whether farmers believe AI is an effective method for addressing climate change, as well as their confidence in its effectiveness. This research examines whether the ability to learn about AI independently affects the intention to use AI, aligning with Protection Motivation Theory. It further evaluates whether perceived ease of use of AI influences perceived usefulness, considering the core factors of perceived ease of use and perceived usefulness based on the Technology Acceptance Model as influencing the intention to use AI. Furthermore, it investigates whether PEOU (Perceived ease of use) and PU (Perceived usefulness) affect attitude (a key factor in the Theory of Planned Behavior) and subjective norm (another core factor in TPB (Theory of Planned Behavior)) influencing farmers’ behavioral adaptation to AI use. Therefore, exploring farmers’ behavioral intention to use AI integrates three theories: PMT (Protection Mo-tivation Theory), TPB, and TAM (Technology Acceptance Model), presenting them as a conceptual model to examine the motivating factors influencing behavioral change. This research surveyed 471 farmers in Thailand using data analyzed from PLS-SEM (Partial Least Squares Structural Equation Mod-eling). The findings revealed that only eight hypotheses (AI self-efficacy, PEOU, PU, ATT (Attitude), and SN (Social Norm)) significantly influenced the intention to use AI, while three hypotheses (PS (Perceived severity), PV (Perceived vulnerability), and RE (Response efficacy)) did not. This will be useful for planning or strategizing AI adoption among farmers, focusing on reducing problems and obstacles from insignificant factors to achieve sustainable agriculture and minimize the impact that may lead to inequality from AI use, or the AI divide, in the future. Full article

Artificial intelligence is rapidly entering higher education, yet many students experience anxiety when learning to use it. This study examines how performance expectations, perceived explainability, and perceived ethical risks shape two algorithm attitudes, algorithm aversion and algorithm appreciation, and how these attitudes influence artificial intelligence learning anxiety. Using a hybrid partial least squares structural equation modeling–artificial neural network (PLS-SEM–ANN) approach, this study analyzed survey data from 409 university students. Results show that both algorithm aversion and algorithm appreciation significantly increase artificial intelligence learning anxiety, although the effect of algorithm aversion is much stronger, supporting an approach–avoidance account. Perceived ethical risk is the strongest predictor of algorithm aversion but has no significant effect on algorithm appreciation. By contrast, performance expectations and perceived explainability strengthen algorithm appreciation while also showing weaker positive effects on algorithm aversion. These findings suggest that, in educational settings, stronger performance value and greater explainability do not simply reassure students; they can also increase pressure by making errors, responsibility, and the need to use artificial intelligence effectively more salient. The artificial neural network results corroborate these patterns. This study extends research on algorithm attitudes and offers guidance for creating more supportive artificial intelligence learning environments. Full article

Large language models (LLMs) have been widely adopted in educational settings, particularly among university students. However, the behavioral mechanisms through which these systems influence academic outcomes remain insufficiently understood. This study develops and empirically tests a framework explaining how the technological attributes of LLMs—perceived usefulness, ease of use, system reliability, accessibility, and interface design—affect student motivation and personalization, which foster anthropomorphic perception and enhance self-efficacy and academic performance. Data were collected from university students in Saudi Arabia using a structured survey and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings indicate that technological attributes positively influence motivation and personalization, which strengthen anthropomorphism and subsequently improve self-efficacy and academic performance. The results provide practical insights into the effective application of LLMs in higher education and highlight the role of generative AI in supporting sustainable educational practices. Full article

Sustainability-oriented entrepreneurship is becoming more widely acknowledged as a mean of addressing social and environmental issues while promoting economic development, though little research has looked at the cognitive processes by which innovation-related thinking translates into sustainability entrepreneurial intention. The relationships between innovative mindset, entrepreneurial mindset, and sustainability entrepreneurial intention among university students are examined in this study. A mediation model is proposed in which innovative mindset positively influences entrepreneurial mindset (H1), entrepreneurial mindset positively influences sustainability entrepreneurial intention (H2), and entrepreneurial mindset mediates the relationship between innovative mindset and sustainability entrepreneurial intention (H3). In total, 163 university students in the United Arab Emirates provided the data, which was then analyzed using partial least squares structural equation modeling (PLS-SEM). All of the proposed hypotheses are supported by the results. These findings offer preliminary and partial support for a theoretically defined cognitive pathway connecting sustainability entrepreneurial intention, innovative mindset, and entrepreneurial mindset. In particular, the findings indicate a positive correlation between innovation-oriented cognitive abilities and entrepreneurial cognition, which is linked to sustainability-oriented intentions. The low explained variance in sustainability entrepreneurial intention, however, suggests that the model only partially explains the variables influencing SEI. As a result, this study advances a more complex, mechanism-based understanding of one potential cognitive pathway in sustainability entrepreneurship and emphasizes the need for more thorough models that include contextual, motivational, and sustainability-related predictors. Additionally, it provides cautious practical implications for entrepreneurship education, especially when it comes to combining learning that is focused on sustainability with the development of an innovative and entrepreneurial mindset. Full article

Environmental Management Accounting (EMA) is increasingly recognized as a vital internal tool for improving corporate environmental performance. This paper examines the hypothesis of the existence and degree of the impact of EMA on environmental performance (EP) in the Bangladesh ready-made garment (RMG) industry, the mediating factor is resource efficiency performance (REP), and the moderating boundary condition is good governance (GG). Based on the resource-based theory, dynamic capability theory, and institutional theory, the moderated mediation model is examined using partial least squares structural equation modeling (PLS-SEM) and survey data collected from 331 managers at medium- and large-scale RMG manufacturers. The findings confirm that EMA has a significant positive effect on EP, either directly or indirectly through REP, with REP accounting for about 47 percent of the overall effect. Good governance has a significant, albeit weakening, moderating effect on the EMA-REP pathway: in high-governance contexts, external regulatory pressures seem to partially replace internal EMA systems, thereby promoting resource efficiency. The results add to the literature on environmental accounting by explaining a process-based, governance-mechanism-contingent mechanism through which EMA affects environmental performance and by offering practical advice to managers and policymakers in the context of developing-economy manufacturing. Full article

Vibriosis caused by V. harveyi led to high mortality and enormous economic losses in Chinese tongue sole aquaculture. However, the intestinal metabolic alterations associated with V. harveyi infection remain unclear. In this study, ultra-performance liquid chromatography–mass spectrometry (LC-MS)-based metabolomics was used to investigate the variations in intestinal metabolic phenotypes among control, susceptible, and resistant Chinese tongue sole after 7 days of V. harveyi infection. Histopathological examination revealed severe intestinal damages in susceptible fish, whereas resistant fish displayed only mild changes. Principle components analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) revealed distinct separation of intestinal metabolites among three groups. A total of 2948 metabolites were identified, with 437 and 794 differential metabolites detected in the resistant and susceptible groups, respectively. The KEGG enrichment analysis revealed that resistant individuals primarily enriched amino acid metabolism and TCA cycle to support immunity and tissue repair, whereas susceptible individuals enriched sphingolipid and cGMP-PKG signaling pathways linked to inflammation and apoptosis, indicating divergent metabolic strategies during V. harveyi infection. Thirty-two potential metabolite biomarkers (area under the curve (AUC) = 1) were screened, which could effectively distinguish susceptible and resistant individuals. Correlation analysis further demonstrated strong interactions among these metabolite markers, host immune-related differentially expressed genes (DEGs), and intestinal microbes. Collectively, our findings reveal distinct intestinal histopathological changes and metabolic reprogramming in resistant and susceptible individuals following V. harveyi infection and identify a set of candidate biomarkers, providing a theoretical foundation for developing targeted prevention strategies and immune enhancement approaches against V. harveyi infection in Chinese tongue sole. 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

This research focuses on the effects of online destination brand experience on destination brand authenticity, destination brand engagement, and external search behavior and behavioral intention. It also investigates the mediating effect of destination brand authenticity on the relationship between online destination brand experience and destination brand engagement. The research population consisted of visitors who had experienced the Zeugma and Gaziantep cultural tourism destinations. The Smart PLS (Partial Least Squares) statistical program was used for data analysis. The analysis results showed that online destination brand experience positively affected destination brand authenticity and destination brand engagement. Destination brand engagement influenced external search behavior and behavioral intention positively. However, the findings revealed that the social engagement dimension of destination brand engagement did not have a significant effect on external search behavior. Furthermore, the effect of the cognitive engagement dimension on behavioral intention was also insignificant. Finally, it was found that destination brand authenticity partially mediated the relationship between online destination brand experience and destination brand engagement. Full article

The emergence of artificial intelligence (AI) tools, particularly generative models, in the last five years has fundamentally transformed the framework and methodologies of learning in higher education. Students are integrating AI for producing new ideas, assisted and personalized search, academic writing, advanced data analysis, and personalized learning. For this reason, an update of the theoretical and conceptual framework regarding the adoption of technologies in the educational environment is required. Based on traditional Technology Acceptance Model/Unified Theory of Acceptance and Use of Technology (TAM/UTAUT) models, we propose a new Partial Least Squares Structural Equation Modeling (PLS-SEM) model developed for the context of AI in higher education. The novelty of the model lies in the integration of the mediating relationship through trust (trust in AI outputs, TAIO) between perceived academic integrity risk (PAIR) and behavioral intention to use (BI), while anchoring perceived learning utility (PUL) and perceived effort expectancy (PEE) in AI literacy-specific self-efficacy (AILSE). The model is tested using a sample of 339 higher education students from economics and computer science specializations and validated using the R environment and the SEMinR package as specific software tools. Our proposed research hypotheses consider six reflective latent constructs and a mediating relationship, which we analyze using validated PLS-SEM techniques. All items included in the model constructs are formulated for use in university educational contexts and are adapted to specific AI tools for learning in the university environment. Full article

This study aimed to examine the association between digital life balance and flourishing in a sample of adolescents with a particular focus on the mediating roles of self-esteem and life satisfaction in the relationship between the two variables. A cross-sectional survey was conducted with a sample of 338 Romanian adolescents (mean age = 16.17 years; 66% girls) who completed measures of digital life balance (Digital Life Balance Scale), self-esteem (Rosenberg Self-Esteem Scale), life satisfaction (Satisfaction with Life Scale), and flourishing (Flourishing Scale). Data were analyzed using partial least squares structural equation modeling (PLS-SEM). The results showed that digital life balance was positively associated with flourishing both directly (β = 0.125) and indirectly through life satisfaction and self-esteem (β = 0.309). The total association was also significant (β = 0.434) (all p < 0.001). These findings suggest that digital life balance represents an important correlate of flourishing in adolescence. Full article

Indonesia, as the world’s major palm oil producer, has promoted the Indonesian Sustainable Palm Oil (ISPO) certification to sustain its global industrial competitiveness and address growing international environmental pressures. Despite being formally introduced in 2011, smallholder participation in ISPO certification remains critically low. In response, the Indonesian government enacted a mandatory ISPO compliance policy, with a transitional phase, for smallholders. This study examines the behavioral predictors of ISPO adoption intention and readiness among two categories of oil palm smallholders in Riau Province, Indonesia: scheme smallholders, who cooperate with firms under nucleus partnership, and independent smallholders, who rely on open market channels with minimal institutional support. Data were collected from 300 smallholders and analyzed using Partial Least Squares Multi-Group Analysis (PLS-MGA), drawing on an extended Theory of Planned Behavior (TPB) framework that incorporates environmental awareness (EA) and collective membership participation (COL) as additional constructs. The findings show that behavioral intention is the influential predictor associated with ISPO adoption readiness across both groups (β = 0.376 for independent; β = 0.229 for scheme smallholders), while perceived behavioral control (PBC) significantly influences readiness among scheme smallholders (β = 0.344), but not among independent smallholders (β = 0.097), reflecting the structural capacity constraints faced by the independent group, particularly land legality. Environmental awareness positively shapes adoption intention among scheme smallholders (β = 0.126) but shows no significant effect among independent smallholders. Collective farmer group membership consistently enhances both adoption intention and readiness across both groups, emerging as the most universally actionable driver of ISPO compliance. These findings underscore the need for differentiated policy interventions, particularly targeted structural support for independent smallholders in terms of land legalization, certification subsidies, and field-based capacity building, to ensure equitable and effective implementation of mandatory ISPO certification. Full article

Building Information Modeling (BIM) implementation is increasingly adopted in the architecture, engineering, and construction (AEC) industries. However, its integration into the academic curricula in developing countries remains limited. Therefore, this study aims to investigate the barriers to integrating BIM into the curricula of civil engineering in Jordanian higher education institutions (HEIs). A quantitative approach was used, including Exploratory Factor Analysis (EFA) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The data was collected from 102 respondents, including industry professionals and academics. Six key barrier constructs were identified: support, standards, delivery, resources, knowledge, and infrastructure and security. Altogether, they explain 66.896% of the BIM integration barriers. The results of the structural model indicate that institutional and governmental support is the most critical barrier (β = 0.486), followed by the lack of standards (β = 0.206) and curriculum-delivery constraints (β = 0.166). Other barriers, including infrastructure and security-related factors, knowledge gaps, and resource limitations, were found to have statistically significant effects on BIM integration. The findings revealed that the barriers to integrating BIM into civil engineering curricula in Jordanian HEIs are institutional and systemic rather than purely technical or resource-based. This study contributes to the BIM education literature by developing one of the first empirically validated PLS-SEM models to investigate barriers to integrating BIM curriculum in Jordan and in developing countries. This research is distinct from previous descriptive studies by prioritizing the institutional, technical, and curricular barriers to the integration of BIM into civil engineering education. Practically, the research provides a specific roadmap for Jordan to integrate BIM into curricula through improving the collaboration between HEIs and the Jordan Engineering Association, strengthening the accreditation standards, enhancing the support of the government for digital construction education, and endorsing the partnerships between HEIs and the industry to align the graduates with the needs for digital transformation of the construction sector in Jordan. Full article

This study investigated the use of Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR) as a cost-effective analytical approach for screening the bioactivity of green algal extracts. Samples of five Caulerpa species—C. ashmeadii, C. paspaloides, C. cupressoides, C. verticillata, and C. prolifera—were collected from Dzilam, Yucatán, Mexico, across seven seasonal campaigns. Sequential extraction was performed using solvents of increasing polarity: hexane, dichloromethane, acetone, and methanol. After solvent evaporation, extracts were analyzed via ATR-FTIR, and Total Phenolic Content (TPC) and Trolox Equivalent Antioxidant Capacity (TEAC) were quantified. Statistical analysis (PERMANOVA) revealed that the type of solvent accounted for most of the variance (61.6%), while species and collection date contributed minimally. Infrared (IR) band assignments highlighted functional groups associated with lipids, such as terpenes, and carbohydrates. K-means clustering enabled the subdivision of less polar extracts, notably grouping numerous samples from C. verticillata. Classification models comparing full-spectrum and IR band datasets showed that Partial Least Squares Discriminant Analysis (PLS-DA) with full-spectrum data achieved the best performance. TPC showed a positive correlation with absorption at 1730.8 cm⁻¹, which is associated with ester-containing metabolites. Although ATR-FTIR effectively distinguished extraction solvents, it was less sensitive to subtle biological variation among Caulerpa. However, the method remains a practical tool for rapid screening, with spectral data supporting solvent-based classification. Reduction of salt content prior to extraction may minimize interference in both FTIR measurements and biological assays. Full article