Search Results (53)

The issue of modeling the personal innovativeness of project team members is determined in this study. Findings from prior research on social capital associated with innovations and innovative activities reveal that social capital factors such as trust, social networks and connections, and social values determine a person’s attitude to innovations. Different connections involved in bridging (external) and bonding (internal) social capital can create conflict between project team members in different ways. To stimulate innovation in a conflict environment, a specially configured conflict management system is required that is capable of regulating the strength and intensity of the relationship between project team members. This paper analyzes the relationship between three constructs—innovativeness, social capital, and conflict. The existence of these latent constructs, which are formed by observable indicators of employees, is proven using confirmatory factor analysis (CFA). The construct of innovativeness depends on indicators such as creativity, risk propensity, and strategicity. Social capital includes observable indicators such as trust, social networks and connections, and social norms and values. Conflict consists of observable indicators of conflict between tasks, processes, and relationships. Using structural equation modeling (SEM), the causal relationship between social capital and innovativeness is substantiated with the mediating role of conflict in project groups between its participants—innovators and adaptors. The developed sociodynamic model for measuring conflict between innovators and adapters examines the required values of the controlled parameters of intra-group and inter-group connections between innovators and adapters in order to achieve equilibrium conflict dynamics, resulting in cooperation between them. This study was conducted using data from a survey of employees of a research organization. All model constructs were tested on a sample of employees as a whole, as well as for groups of innovators and adaptors separately. Full article

In the context of China’s increasingly competitive agricultural product branding, authenticity has become a pivotal mechanism for shaping consumer trust and willingness to pay. This study takes Perceived Brand Authenticity (PBA) as its focal construct and builds a chained mediation framework incorporating experiential quality (EQ) and consumer trust. Employing a dual-evidence strategy that combines structural discovery and causal validation, the study integrates Jaccard similarity clustering and PLS-SEM to examine both behavioral patterns and psychological mechanisms. Drawing on 636 valid survey responses from across China, the results reveal clear segmentation in channel choice, certification concern, and premium acceptance by gender, age, income, and education. Younger and highly educated consumers rely more on e-commerce and digital traceability, while middle-aged, older, and higher-income groups emphasize geographical indications and organic certification. The empirical analysis confirms that PBA has a significant positive effect on EQ and consumer trust, and that the chained mediation pathway “PBA → EQ → Trust → Purchase Intention” robustly captures the transmission mechanism of authenticity. The findings demonstrate that verifiable and consistent authenticity signals not only shape cross-group consumption structures but also strengthen trust and repurchase intentions through enhanced experiential quality. The core contribution of this study lies in advancing an evidence-based framework for sustainable agricultural branding. Theoretically, it reconceptualizes authenticity as a measurable governance mechanism rather than a rhetorical construct. Methodologically, it introduces a dual-evidence approach integrating Jaccard clustering and PLS-SEM to bridge structural and causal analyses. Practically, it proposes two governance tools—“evidence density” and “experiential variance”—which translate authenticity into actionable levers for precision marketing, trust management, and policy regulation. Together, these insights offer a replicable model for authenticity governance and consumer trust building in sustainable agri-food systems. Full article

The successful implementation of digital transformation initiatives depends critically on public trust in experts guiding these processes. In today’s digital media environment, expert trust faces significant challenges, potentially hindering sustainable innovation adoption. This study investigates how expert credibility dimensions and information characteristics shape trust in digital transformation experts among Chinese social media users. We employed a mixed-methods approach combining a survey of 850 Chinese social media users, a quasi-experiment testing a digital expert verification feature, and secondary data analysis. The study measured multiple dimensions of expert trust while examining relationships with expert cognition factors and media usage variables through regression, mediation, and structural equation modeling. Expert trust in digital transformation exists at moderate levels (M = 6.82/10), with higher trust in digital innovation research (M = 7.12) than specific sustainability recommendations (M = 6.59). Expert authenticity emerged as the strongest predictor of trust (β = 0.27), followed by professional competence (β = 0.21). A “digital exposure paradox” emerged whereby higher volumes of expert information negatively predicted trust (β = −0.18), while information quality positively predicted trust (β = 0.25). The digital verification feature causally enhanced trust (DID = 0.57), with institutional sources strengthening trust while user-generated content diminished it. The findings reveal that digital transformation expert trust involves multi-dimensional evaluations beyond traditional credibility assessments. The “digital exposure paradox” suggests that prioritizing information quality over quantity, demonstrating expert authenticity, and implementing verification mechanisms can enhance trust and accelerate sustainable digital transformation adoption. Full article

This study examines the descriptive trajectories through which service innovation is associated with customer exit dynamics after service failures, drawing on a three-wave panel of 532 online travel agency users and employing partial least squares structural equation modeling with predictive assessment. We analyze how innovation is associated with switching intentions via brand hate and brand distrust over time. Results reveal distinct temporal patterns: service innovation is linked to consistent reductions in both hate and distrust, yet only hate emerges as a salient mediator whose marginal association with switching intensifies over time. In contrast, distrust, although mitigated by innovation, remains relatively stable and behaviorally inert. Rather than asserting a causal explanation, we document temporal associations—labelled here as a “dilution effect”—to indicate that innovation coincides with weakening negative emotions but only partial attenuation of their behavioral correlates. By distinguishing between the fading but influential role of hate and the persistent yet inert nature of distrust, this study clarifies differentiated pathways through which negative states coincide with customer exit. For managers, the results highlight the need for staged innovation strategies to dissipate hate, complemented by long-term trust-repair initiatives to address enduring distrust and reduce customer churn. Full article

Air pollution is a leading environmental risk that causes respiratory morbidity and mortality. The increasing availability of high-resolution environmental data and air pollution-related health cases have accelerated the use of machine learning models (ML) to estimate environmental exposure–response relationships, forecast health risks and call for the needed policy and practical interventions. Unfortunately, ML models are opaque, in a sense that, it is unclear how these models combine various data inputs to make a concise decision. Thus, limiting its trust and use in clinical matters. Explainable artificial intelligence (xAI) models offer the necessary techniques to ensure transparent and interpretable models. This systematic review explores online data repositories through the lens of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline to synthesize articles from 2020 to 2025. Various inclusion and exclusion criteria were established to narrow the search to a final selection of 92 articles, which were thoroughly reviewed by independent researchers to reduce bias in article assessment. Equally, the ROBINS-I (Risk Of Bias In Non-randomized Studies of Interventions) domain strategy was helpful in further reducing any possible risk in the article assessment and its reproducibility. The findings reveal a growing adoption of ML techniques such as random forests, XGBoost, parallel lightweight diagnosis models and deep neural networks for health risk prediction, with SHAP (SHapley Additive exPlanations) emerging as the dominant technique for these models’ interpretability. The extremely randomized tree (ERT) technique demonstrated optimal performance but lacks explainability. Moreover, the limitations of these models include generalizability, data limitations and policy translation. This review’s outcome suggests limited research on the integration of LIME (Local Interpretable Model-Agnostic Explanations) in the current ML model; it recommends that future research could focus on causal-xAI-ML models. Again, the use of such models in respiratory health issues may be complemented with a medical professional’s opinion. Full article

This paper proposes a hybrid predictive maintenance framework that combines the discriminative power of XGBoost with the interpretability of a Bayesian Network automatically learned from sensor data. Targeted at textile manufacturing equipment operating under Industry 4.0 conditions, the system addresses the trade-off between early fault detection and decision transparency. Sensor data, including vibration, temperature, and electric current, were collected from a multi-needle quilting machine using a custom IoT-based platform. A degradation-aware labeling scheme was implemented using historical maintenance logs to assign semantic labels to sensor readings. A Bayesian Network structure was learned from this data via a Hill Climbing algorithm optimized with the Bayesian Information Criterion, capturing interpretable causal dependencies. In parallel, an XGBoost model was trained to improve classification accuracy for incipient faults. Experimental results demonstrate that XGBoost achieved an F1-score of 0.967 on the high-degradation class, outperforming the Bayesian model in raw accuracy. However, the Bayesian Network provided transparent probabilistic reasoning and root cause explanation capabilities—essential for operator trust and human-in-the-loop diagnostics. The integration of both models yields a robust and interpretable solution for predictive maintenance, enabling early alerts, visual diagnostics, and scalable deployment. The proposed architecture is validated in a real production line and demonstrates the practical value of hybrid AI systems in bridging performance and interpretability for predictive maintenance in Industry 4.0 environments. Full article

Individual willingness to pay (WTP) for green energy plays a vital role in mitigating climate change. Based on social cognitive theory (SCT), which emphasizes the dynamic interaction among individual cognition, behavior and the environment, this study develops a theoretical model to identify factors influencing green energy WTP. The study is based on 585 valid questionnaire responses from urban areas in China and uses Structural Equation Modeling (SEM) to reveal the linear causal path. Meanwhile, fuzzy-set Qualitative Comparative Analysis (fsQCA) is utilized to identify the combined paths of multiple conditions leading to a high WTP, making up for the limitations of SEM in explaining complex mechanisms. The SEM analysis shows that social trust, social networks, and social norms have a significant positive impact on individual green energy WTP. And this influence is further transmitted through the mediating role of environmental self-efficacy and expectations of environmental outcomes. The FsQCA results identified three combined paths of social capital and environmental cognitive conditions, including the Network–Norm path, the Network–efficacy path and the Network–Outcome path, all of which can achieve a high level of green energy WTP. Among them, the social networks are a core condition in every path and a key element for enhancing the high green energy WTP. This study promotes the expansion of SCT, from emphasizing the linear role of individual cognition to focusing on the configuration interaction between social structure and psychological cognition, provides empirical evidence for formulating differentiated social intervention strategies and environmental education policies, and contributes to sustainable development and the green energy transition. Full article

Air pollution remains one of the environmental issues affecting some countries, which leads to health issues globally. Though several machine learning and deep learning models are used to analyze air pollutants, model interpretability is a challenge. Also, the dynamic and time-varying nature of air pollutants often creates noise in measurements, making air pollutant prediction (e.g., Sulfur Dioxide (SO₂) concentration) inaccurate, which influences a model’s performance. Recent advancements in artificial intelligence (AI), particularly explainable AI, offer transparency and trust in the deep learning models. In this regard, organizations using traditional machine and deep learning models are confronted with how to integrate explainable AI into air pollutant prediction systems. In this paper, we propose a novel approach that integrates explainable AI (xAI) into long short-term memory (LSTM) models and attempts to address the noise by Adaptive Kalman Filters (AKFs) and also includes causal inference analysis. By utilizing the LSTM, the long-term dependencies in daily air pollutant concentration and meteorological datasets (between 2008 and 2024) for the City of Kimberley, South Africa, are captured and analyzed in multi-time steps. The proposed model (AKF_LSTM_xAI) was compared with LSTM, the Gate Recurrent Unit (GRU), and LSTM-multilayer perceptron (LSTM-MLP) at different time steps. The performance evaluation results based on the root mean square error (RMSE) for the one-day time step suggest that AKF_LSTM_xAI guaranteed 0.382, LSTM (2.122), LSTM_MLP (3.602), and GRU (2.309). The SHapley Additive exPlanations (SHAP) value reveals “Relative_humidity_t0” as the most influential variable in predicting the SO₂ concentration, whereas LIME values suggest that high “wind_speed_t0” reduces the predicted SO₂ concentration. Full article

Organizational performance can be enhanced by adopting sustainable work policies. This study examined the relationship between psychological factors such as organizational trust, job satisfaction, well-being, and work engagement among workers in the Korean food industry. This study utilized the Korean Working Conditions Survey (KWCS) data, and a total of 472 workers were selected as subjects for the research, comprising 185 office workers and 287 production workers. Regression analysis was conducted by comparing office and production workers to test the relationship between psychological factors and to identify causal relationships through a mediation model. The results of hypothesis testing via regression analysis indicated that organizational trust is proportionally related to job satisfaction (p < 0.001), well-being (p < 0.001), and engagement (p < 0.001), while work engagement is proportionally related to job satisfaction (p < 0.001) and well-being (p < 0.001). In particular, in the regression equation analyzing organizational trust (T) and job satisfaction (y), as organizational trust increases, the rate of increase in job satisfaction of office workers (y = 1.131 + 0.610T) is greater than that of production workers (y = 1.131 + 0.557T). On the other hand, the initial level of work engagement (y) of office workers is higher than that of production workers in the regression equations concerning organizational trust (T) and work engagement (y = 1.753 + 0.516T vs. y = 1.634 + 0.516T), as well as well-being (W) and work engagement (y = 2.648 + 0.345W vs. y = 2.512 + 0.345W). According to mediation models, work engagement was directly affected by organizational trust and indirectly affected by job satisfaction or well-being, and office workers exhibited higher work engagement than production workers. The findings of this study emphasize the need for customized enhancements to working hours, work organization, and the work environment for production workers to ensure sustainable employment. Full article

Servant leadership has been identified as extremely important for organizational performance and success; therefore, much focus is placed on developing and maintaining leaders’ positive attitudes and behaviors toward their subordinates. Different servant models have been put out by earlier scholars. Nevertheless, only a small number of studies have focused on employee voice as a key precursor to servant leadership. The goal of this study is to look at the impacts of servant leadership style on employee voice by focusing on the mediating role of trust in a leader. Time-lagged data were gathered from 336 employees of small- and medium-sized enterprises in Pakistan. The perceived servant leadership style was positively and significantly associated with employees’voices mediated by trust in leaders. This study upgrades the comprehension of the components underlying the servant leadership and employee voice model by recognizing the intervening role of trust in the leader. Nonetheless, the survey design was not longitudinal, which restricts the study’s capacity to affirm causality. The results of this study acknowledge that servant leadership style and trust in leaders can promote constructive employee voice behavior. This study addresses the unproven mediating procedure of the link between servant leadership style and employee voice and offers new bearings for servant leadership and employee voice research, which, to the best of our knowledge, has not been explored before. Full article

Based on three theories—the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and Responsible Environmental Behavior (REB)—the present study proposes a model of AI product factors and pro-environmental behavior. This study aims to investigate AI product factors that promote pro-environmental behavior by examining behavioral intentions to use AI technology. Unlike previous research, which predominantly focused on external variables such as social norms, cost, and inconvenience, or individual variables like demographic and psychological factors, this study emphasizes the underexplored role of technological factors. It integrates the Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL), Structural Equation Modeling (SEM), and Artificial Neural Network (ANN) approaches to assess the relationships among constructs. For the F-DEMATEL, opinions were collected from 20 experts in the environmental field, while SEM and ANN data were gathered from 1726 participants in Taiwan. F-DEMATEL results demonstrated causal relationships between external factors (perceived trust, self-efficacy, and perceived awareness) and the main variables of the TAM. Likewise, SEM results revealed that perceived trust (PT), self-efficacy (SE), and perceived awareness (PA) influence the main variables of TAM. However, the direct relationships between PT and behavioral intention (BI) and PA and BI were not significant. PT and PA indirectly influence BI through perceived usefulness (PU) and perceived ease of use (PEOU). The results also established that BI positively influences pro-environmental behavior. The author has also outlined how stakeholders aiming to encourage sustainable environmental behaviors can utilize the study’s findings to protect the environment. Full article

Most research exploring the effect of trust on tax compliance focuses on institutional trust or diluted trust. In contrast, the role of dense social trust has been scarcely investigated, even less through rigorous empirical contrasts that determine the potential causal relationship between this type of trust and tax compliance. This paper contributes to this line of research, providing empirical evidence in this regard. Based on a sample of 2059 young university students, and using a structural equation model, we conclude that the behaviors and attitudes towards tax fraud and the economy that occur in the family potentially influence young people’s fiscal awareness. Full article

Entrepreneurship has been critical in fostering economic growth. The technological innovations and quality of institutions are crucial in promoting entrepreneurship and promoting an environment conducive to entrepreneurial activities. This study investigated the effect of technological innovations and institutional quality on entrepreneurial development with annual data from 2014 to 2021 across Southeastern European countries. The cross-sectional auto-regressive regressive distributed lag model (C-S ARDL), quantile regression and Granger causality were employed to achieve the objectives of this study. A dynamic panel generalized method of moments (GMM) estimator was also applied to perform a robust analysis. The findings revealed a significant long-term relationship between technological innovations and entrepreneurial development, with a coefficient of 0.088. There also exists a significant and positive impact on institutional quality and entrepreneurial development in the long run, with a coefficient of 5.912. Furthermore, the outcome revealed that the exchange rate negatively influences entrepreneurial development in Southeast Europe. The Granger causality reports a bi-directional relationship between technological innovations and entrepreneurial development in Southeastern Europe. The study concluded that a significant relationship exists between technological innovations, institutional quality, and entrepreneurial development in Southeastern Europe. The study recommends that governments of Southeastern European countries strengthen their regulatory structures and institutions to improve the welfare of society through a reduction in political, social, and economic unpredictability while boosting trust and investment from entrepreneurs. Full article

Blockchain technology holds the potential to significantly enhance efficiency and safety in freeport operations. However, fully realising its benefits necessitates a thorough assessment of the obstacles hindering its applications, which often depends on expert opinions characterised by uncertainty and inconsistency. This issue remains inadequately addressed in the existing literature due to the limitations of currently employed methods. To address this gap, this study aims to develop a novel methodology for assessing blockchain adoption barriers in freeports. It makes methodological contributions by combining the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Bayesian Network (BN) methods to identify and quantify complex interrelationships between barriers and facilitate probabilistic predictions of barrier strength. The model is parameterised using the ranked nodes method to reduce the reliance on expert-assigned probabilities. Primary data on barriers’ causal relationships are collected from experts with interdisciplinary experience in blockchain and freeport operations, grounding the analysis in real-world insights. This study makes practical contributions by analysing the blockchain application within a new context (i.e., freeports) and presenting novel findings. Key managerial insights include identifying high investment costs as the most interactive barrier and lack of trust among stakeholders as the most essential barrier. Additionally, evaluating the overall impact of barriers enables targeted strategies for freeport policymakers. Full article

This paper provides a critical analysis of the molecular mechanisms presently used to explain transcriptional strand asymmetries of single base substitution (SBS) signatures observed in cancer genomes curated at the Catalogue of Somatic Mutations in Cancer (COSMIC) database (Wellcome Trust Sanger Institute). The analysis is based on a deaminase-driven reverse transcriptase (DRT) mutagenesis model of cancer oncogenesis involving both the cytosine (AID/APOBEC) and adenosine (ADAR) mutagenic deaminases. In this analysis we apply what is known, or can reasonably be inferred, of the immunoglobulin somatic hypermutation (Ig SHM) mechanism to the analysis of the transcriptional stand asymmetries of the COSMIC SBS signatures that are observed in cancer genomes. The underlying assumption is that somatic mutations arising in cancer genomes are driven by dysregulated off-target Ig SHM-like mutagenic processes at non-Ig loci. It is reasoned that most SBS signatures whether of “unknown etiology” or assigned-molecular causation, can be readily understood in terms of the DRT-paradigm. These include the major age-related “clock-like” SBS5 signature observed in all cancer genomes sequenced and many other common subset signatures including SBS1, SBS3, SBS2/13, SBS6, SBS12, SBS16, SBS17a/17b, SBS19, SBS21, as well as signatures clearly arising from exogenous causation. We conclude that the DRT-model provides a plausible molecular framework that augments our current understanding of immunogenetic mechanisms driving oncogenesis. It accommodates both what is known about AID/APOBEC and ADAR somatic mutation strand asymmetries and provides a fully integrated understanding into the molecular origins of common COSMIC SBS signatures. The DRT-paradigm thus provides scientists and clinicians with additional molecular insights into the causal links between deaminase-associated genomic signatures and oncogenic processes. Full article