Search Results (1,341)

This study explores the perceptions of U.S. college-level language teachers regarding students’ readiness for online language learning. Drawing on a sequential explanatory mixed-methods approach, quantitative data were collected from 309 language teachers via an online survey, and qualitative data were collected from follow-up interviews with 21 respondents. The study identifies three key components of perceived student readiness: access and technological competence, self-responsibility, and use of support for online learning. In response to concerns regarding students’ responsibility, teachers reported that they employed strategies aimed at building trust and promoting student ownership of the language learning process. The findings also highlight the interplay between perceived student readiness and teachers’ self-confidence and their perceived value of online language teaching. Further, perceived student readiness shaped teachers’ increased willingness to adopt online and hybrid language teaching in the future. Full article

Artificial intelligence (AI) technology has brought unprecedented impact and opportunities for the sustainable development of family firms. This paper examines the impact of AI on innovation investment in family firms using a sample of Chinese A-share listed family firms from 2007 to 2024. The results show that AI significantly promotes innovation investment in family firms to achieve sustainable development. Mechanism analysis shows that AI enhances both the willingness and capability of family firms to invest in innovation by improving their risk-taking levels and resource allocation efficiency, thereby promoting innovation investment. Heterogeneity analysis shows that the promotion effect of AI on innovation investment of family firms is more significant in smaller family firms, those directly founded by families, those with more family involvement in management, and those prior to intergenerational succession. Furthermore, the study finds that AI significantly improves the innovation performance of family firms. Our findings provide important theoretical and practical guidance for enterprises seeking to leverage AI to catalyze innovation investment and thereby achieve long-term value growth and sustainable development. Full article

Background/Objectives: Patient interest in pharmacogenomics (PGx) is growing, yet literacy remains low. This study aims to evaluate patient perspectives on pharmacist-led PGx services, assessing community perceptions of PGx pharmacists, their perceived role in care, literacy levels, and willingness to pay for services. Methods: A brief survey was distributed via social media to participants in southern Ohio, northern Kentucky, and southeastern Indiana. This survey included the Minnesota Assessment of Pharmacogenomic Literacy (MAPL), Likert-style questions to assess preferences, and willingness to pay questions with open fields. Upon completion, 152 responses were received. After data cleaning, 82 responses were analyzed. Results: While 66% of participants preferred their primary care provider to order testing, 45% preferred a PGx pharmacist over their primary care provider to explain their results and medication implications. Conclusions: After being educated on the role of a PGx pharmacist, respondents preferred a PGx pharmacist to explain their PGx testing results and any medication implications. Full article

Drawing on social comparison and attribution theories, this study examines how employees’ attitudinal dissimilarity within work groups shapes their willingness to speak up or remain silent. We conceptualize dissimilarity in psychological ownership and job stress as individual-level differences that trigger internal attributions, leading employees to direct attention inward and reduce their likelihood of speaking up. In contrast, dissimilarity in perceived status conflict, an individual-level perception of a structural feature of the group, induces external attributions toward the social system, motivating employees to express voice aimed at preserving or challenging the status quo. Using multi-source data from 202 employees nested in 39 work groups in South Korea, hierarchical regression analyses support all proposed hypotheses: individual-level dissimilarities are negatively related and structural-level dissimilarity is positively related to voice. These findings reveal that the behavioral consequences of difference depend on where attribution is directed—toward the self or the system. The study contributes to the voice literature by integrating attributional reasoning into social comparison processes and by identifying two forms of attitudinal minorities: invisible minorities who remain silent, and boisterous minorities who speak up for change. Full article

Data, as a key production factor in modern logistics systems, plays a crucial role in enhancing industry efficiency and promoting supply chain coordination. To address challenges in data sharing among logistics enterprises—such as conflicts of interest, unequal risk allocation, and insufficient security governance—this study develops a tripartite evolutionary game model involving logistics enterprises, data partners, and supervisory institutions. The payoff matrix incorporates prospect theory to account for risk attitudes, loss–gain perceptions, and subjective judgments. Stable equilibrium points are derived using the Jacobian matrix, and numerical simulations examine strategic evolution under varying parameters. Results indicate that increased returns for data partners reduce their motivation to provide truthful data, while higher enterprise profits suppress logistics enterprises’ willingness to share. Compensation levels have limited impact, whereas excessively high supervision subsidies weaken participation and oversight across all parties. Stronger penalties and higher-level enforcement significantly promote compliance and positive system evolution. Enterprise investment positively correlates with data-sharing behavior, and risk preferences of all parties accelerate convergence to stable equilibria. Conversely, excessively low risk preference in supervisory institutions may lead to an unstable “sharing–false data–non-regulation” pattern. These findings provide theoretical support and policy guidance for designing a dynamic governance mechanism that balances incentives, constraints, and collaboration, thereby facilitating secure and effective logistics data sharing and informing the development of the data factor market. Full article

The rapid integration of artificial intelligence (AI) into organizational workflows is re-shaping traditional patterns of interaction between leaders and employees. Grounded in power dependence theory, this study investigates how employees’ voluntary dependence on AI influences leader–subordinate power relations and, consequently, influences employees’ voice behavior. We propose that employees’ dependence on AI can increase their perceived power when interacting with leaders, which subsequently enhances their willingness to offer constructive suggestions or question established practices. Furthermore, we propose that the extent to which leadership tasks can be substituted by AI plays a moderating role in this process. Coaching leadership, characterized by its emphasis on guiding task performance and developing employee skills, may be particularly sensitive to such substitution. Using two experimental studies and two survey investigations, we provide evidence that employees’ AI dependence is positively associated with voice behavior through heightened perceptions of personal power, and that this relationship is strengthened under high levels of coaching leadership. These findings advance leadership theory by explicating how AI adoption alters foundational power structures in the workplace and by identifying a novel, power-based pathway linking AI use to proactive employee behaviors. The study contributes to emerging discussions on effective leadership in technologically augmented organizations and offers empirical insights into how leaders can adapt their roles and behaviors in the new era of AI-driven work. Full article

Farmers’ low-carbon transition has become a critical issue for achieving sustainable agricultural development. Fundamentally, this transition is driven by multi-actor collaboration and is subject to stochastic disturbances. However, the collaborative governance mechanisms that facilitate farmers’ low-carbon transformation remain insufficiently understood, particularly under the influence of random factors. To address this gap, we construct a four-party game model involving farmers, government, enterprises, and financial institutions by employing a stochastic evolutionary game approach that incorporates random disturbance factors to capture real-world uncertainty. Numerical simulations are conducted to examine how different policy tools and external environments shape the system’s evolutionary path. The results show the following: (1) In the early transition stage, external uncertainties cause notable fluctuations in strategy evolution, during which the government, farmers, and enterprises gradually form a collaborative mechanism, while financial institutions remain reluctant to participate due to risk and policy uncertainty. (2) Government subsidies, profit returns, and risk-sharing mechanisms exhibit a substitutive relationship, and an appropriate mix of these tools can effectively enhance the willingness of farmers and enterprises to adopt low-carbon practices. (3) Excessive government incentives may crowd out the role of green credit from financial institutions. (4) The profit-sharing ratio among farmers exerts the strongest motivational effect in the early stage, while higher levels of risk-sharing and reputation benefits are more effective in stabilizing the system structure and enhancing transition resilience. This study reveals the dynamic mechanisms of multi-actor interaction in agricultural low-carbon transition and provides theoretical and policy insights for differentiated government strategies and collaborative emission reduction. Full article

Green agricultural technologies play a vital role in enhancing ecological quality, and biopesticides, as a key component, can reduce chemical pollution while improving soil health. Drawing on micro-level survey data from farmers in Sichuan Province, China, this study employs an ordered probit model to assess the effect of farmers’ time preferences on their willingness to adopt biopesticides. It examines the underlying mechanisms through the lens of perceived value theory. The results indicate that farmers with a stronger orientation toward future returns are significantly more likely to adopt. Time preference influences adoption decisions by shaping the perceived value of biopesticides in terms of ecological improvement, intergenerational health protection, and food safety assurance, with mediating effects accounting for 22.90%, 57.18%, and 26.14% of the total effect, respectively. Heterogeneity analysis shows that the influence of time preference is weaker among farmers with higher educational attainment, and that larger cultivated areas further attenuate its impact on large-scale farmers’ adoption willingness. These findings provide micro-level evidence and targeted policy insights to foster the adoption of green agricultural technologies. Full article

The rapid digitalization of higher education has transformed how students learn, collaborate, and engage with emerging technologies such as artificial intelligence (AI). While earlier research emphasized generational or academic-level differences in digital behavior, recent evidence suggests convergence in technology use. This study explores whether undergraduate and postgraduate students at the Faculty of Economics and Business, University of Maribor, display distinct technology engagement patterns across five constructs: excessive technology use, online collaboration, the use of digital learning tools (E-boards), the use of AI in education, and perceived academic success. A survey among 285 students was analyzed using the non-parametric Mann–Whitney U test due to a non-normal data distribution. The findings showed that postgraduate students did not report higher levels of E-Board use, online collaboration, or perceived academic success. Undergraduate students scored higher on one item related to excessive technology use, but not across the full construct. However, significant differences emerged in AI use, where postgraduate students showed greater confidence and willingness to integrate AI tools. The findings suggest that digital competence and the quality of technology integration, rather than study level, shape students’ learning experiences. Higher education institutions should promote balanced and ethical technology use, strengthen AI literacy, and foster self-regulated learning skills. Full article

Digital platforms face a fundamental strategic decision between subscription-only, advertising-only, and freemium (hybrid) monetization models. We develop a game-theoretic framework that unifies these strategies, explicitly modeling consumer heterogeneity in both willingness-to-pay and advertising disutility, while incorporating network effects through the platform’s valuation of user-base size. Our analysis yields closed-form solutions identifying optimal strategy thresholds based on advertising market conditions. We show that subscription-only dominates when advertising prices are low, advertising-only prevails when prices are high, and freemium emerges as strictly optimal in the intermediate region. Under freemium, we demonstrate strategic complementarity: both subscription fees and advertising intensity exceed their levels in pure strategies because each instrument’s effectiveness is amplified by the other through user reallocation across tiers. Network effects universally reduce monetization intensity but alter instruments’ relative sensitivities differently across regimes—when advertising prices are moderate, freemium adjusts ad length more aggressively, while the opposite holds at high prices. Critically, freemium’s profitability requires sufficient consumer heterogeneity in ad tolerance. As consumer preferences converge, the screening mechanism fails and freemium collapses to the superior pure strategy. These results provide operational guidance for platform monetization decisions and clarify when hybrid models create value beyond traditional approaches. Full article

As AI chatbots are increasingly used in digital fundraising, it remains unclear which communication strategies are more effective in enhancing consumer trust and donation behavior. Drawing on construal level theory and adopting a human-AI interaction perspective, this research examines how message framing in AI-mediated persuasive communication shapes trust and donation willingness. Across four studies, we find that when AI chatbots employ high-level construal (abstract) message framing, consumers perceive the information as less credible compared to when the same message is delivered by a human agent. This reduced message credibility weakens trust in the charitable organization through a trust transfer mechanism, ultimately lowering donation intention. Conversely, low-level construal (concrete) framing enhances both trust and donation willingness. Moreover, the negative impact of abstract message framing by AI chatbots is significantly attenuated when the chatbot features anthropomorphic visual cues, which increase perceived credibility and restore trust and donation willingness. These findings reveal potential risks in deploying AI chatbots for interactive fundraising marketing and offer practical insights for nonprofit organizations seeking to leverage AI in donor engagement. Full article

Background: Pertussis is a respiratory infection that represents a significant threat worldwide, especially for infants. The global incidence of pertussis is on the rise, with 20–40 million cases occurring every year. Maternal vaccination offers protection to newborns and, therefore, is recommended by numerous healthcare organizations. The aim of this study was to systematically assess the level of knowledge regarding pertussis and the pertussis vaccine, as well as the willingness to receive the vaccine among pregnant women, and identify the most significant reasons for vaccine hesitancy among the obstetric population. Methods: A systematic literature search was conducted in the Web of Science, Embase, and Scopus databases between 1 April 2024, and 31 July 2024. Our search strategy aimed to identify studies published from 1 January 2014 to July 2024 in order to capture a decade’s worth of the most recent evidence and updates in maternal pertussis vaccination. Results: We screened 955 articles altogether, with 11 studies included in the analysis. The general awareness of pertussis infection prior to participation in the study varied from 5% in a study performed in Turkey to 95.9% in the Norwegian population. Moreover, the willingness to receive the vaccine ranged from 11.2% in the Turkish population to 94.8% in the Netherlands. Several statistically important factors affecting the decision have been identified, such as belief in safety and effectiveness, fear of adverse reactions, or healthcare professional recommendation. Conclusions: The general awareness regarding pertussis vaccine in pregnant women differs significantly depending on the population studied. However, it remains unsatisfactory even in populations with a high declared level of knowledge if asked specific questions. Presented results may indicate the need for studies on the efficacy of educational interventions for raising awareness about the meaning of pertussis immunization during pregnancy and preventing infection among neonates. Full article

The rapid advancement of Advanced Driver Assistance Systems (ADAS) is reshaping the future of mobility by offering potential improvements in safety, efficiency, and driving experience, yet consumer acceptance remains uneven across regions. This study addresses the gap in knowledge and trust by examining how Hungarian drivers, as part of the Central and Eastern European context, perceive and adopt ADAS technologies. To achieve this, we conducted two expert in-depth interviews to refine the research instrument, followed by an online survey of 179 drivers. Using k-means cluster analysis, we identified three distinct consumer segments: Conservative Controllers, who demonstrate low levels of trust and willingness to adopt ADAS; Cautious Adopters, who weigh costs and benefits carefully; and Pragmatic Innovators, who are open to experimentation and display the highest acceptance and willingness to pay. The results reveal that awareness and familiarity strongly influence acceptance, highlighting the role of consumer education and transparent communication in shaping adoption. The findings suggest that manufacturers, driving schools, and policymakers can accelerate the diffusion of ADAS by developing targeted strategies tailored to different consumer groups. Strengthening knowledge and trust in these systems will not only support their market success but also contribute to safer, more sustainable transportation. Full article

Consumer digital hoarding is becoming increasingly common in agricultural live social e-commerce, where the abundance of product information, seasonal promotions, and origin-based narratives make consumers more inclined to accumulate digital content such as product links, coupons, and live-stream screenshots. This phenomenon not only affects consumers’ digital mental health, consumption behavior, and decision-making ability, but also poses challenges to agricultural merchants and platforms in terms of customer conversion, precision marketing, and supply chain management. Drawing on the SOR model and integrating construal level theory, this paper constructs a research framework to analyze the key factors influencing consumers’ willingness to digitally hoard in the context of agricultural live social e-commerce. Based on a questionnaire survey of 322 consumers, and using the Ordered Probit (O-Probit) model, the empirical results show that emotional interaction significantly influences digital hoarding intention through the chain mediating effects of emotional attachment and fear of missing out (FOMO). Furthermore, social distance and immersion serve as boundary conditions in this mechanism. Our findings not only deepen the understanding of consumer digital hoarding behavior in agricultural live e-commerce, but also provide new insights for agricultural merchants and platforms to better design interaction strategies, balance consumers’ digital accumulation with actual purchasing conversion, and enhance the efficiency of agricultural product marketing. Full article

Geothermal energy provides a stable baseload renewable source that is less affected by weather variability compared with solar and wind power, and is therefore increasingly considered in national energy transition and net-zero strategies. Yet its environmental externalities and associated social benefits are not fully priced in existing electricity markets, raising the question of how much the public is willing to pay for geothermal-based generation. This study applies non-market valuation theory to estimate citizens’ additional annual electricity payment required to replace coal-fired generation with geothermal energy. A contingent valuation method (CVM) survey was conducted through face-to-face interviews, employing a closed-ended single-bounded dichotomous choice format with incentive compatibility. Stratified random sampling yielded 678 valid observations. The estimated mean willingness to pay (WTP) per person per year is USD 56.18 (NTD 1792) under the Probit model and USD 52.16 (NTD 1663) under the Logit model, representing approximately 0.2–0.3% of average annual income and 16–20% of the average annual electricity bill. Aggregated to the population level, total annual WTP amounts to USD 688 million (NTD 21,934 billion; Probit) and USD 638 million (NTD 20,355 billion; Logit). These estimates correspond to support for developing approximately 108–335 MW of geothermal capacity, sufficient to supply around 202,000–624,000 four-person households. The findings indicate substantial public support for geothermal power as part of Taiwan’s renewable energy transition, and provide empirical evidence relevant to regions with comparable geothermal potential. Full article