Researchers Seyed Mohammad Javad Sobhani, Maryam Karimi Malek-Abadi and Morteza Taki surveyed 231 forest-dwelling households across 31 villages in Khuzestan Province, using multi-stage stratified random sampling drawn from a population of roughly 3,200 households. Their instrument measured eight latent constructs on a five-point Likert scale, from perceived usefulness and ease of use to cost burden, institutional trust, environmental awareness, subjective norms, behavioral intention and usage continuity. Data were collected face-to-face by trained local enumerators between June 2025 and February 2026, achieving a 93.1 percent effective response rate after quality screening.

The study’s central theoretical move is the introduction of an extended Socio-Ecological Technology Acceptance Model, or SETAM, which fuses the classic Technology Acceptance Model with the tripartite Social Acceptance Framework distinguishing socio-political, market and community acceptance. The authors argue that established frameworks such as TAM2, TAM3, UTAUT and UTAUT2 were built for stable institutional environments with formal credit systems and accessible after-sales markets, and therefore translate poorly to informal economies where upfront solar investment is psychologically weighed against immediate subsistence needs rather than long-term amortization.

SETAM departs from its predecessors in four ways. It folds maintenance literacy and post-installation support networks into perceived ease of use, recognizing that rural servicing logistics matter as much as technical complexity. It situates cost perception within irregular income streams. It treats institutional trust as a gatekeeper capable of amplifying or suppressing every other adoption driver. And it elevates subjective norms in tightly knit communities from a transient social influence to a structural determinant of whether a technology is seen as legitimate at all.

Empirically, the team combined Partial Least Squares Structural Equation Modeling with an artificial neural network to capture both linear causal pathways and non-linear thresholds. The model explained 58.3 percent of the variance in behavioral intention, with predictive relevance confirmed through blindfolding and PLSpredict procedures. The single-hidden-layer neural network, trained with Levenberg-Marquardt backpropagation and validated through Monte Carlo cross-validation, achieved a mean test RMSE of 0.178 against a training RMSE of 0.117, indicating robust generalization without overfitting.

The results are unambiguous about what drives adoption. Subjective norms emerged as the strongest predictor, with a standardized path coefficient of 0.33 and the highest normalized importance in the neural network at 100 percent. In collectivist settings, solar panels become socially legitimate only when neighbors endorse them and local leaders approve. Perceived cost and maintenance burden ranked second as the most formidable barrier, with a negative coefficient of 0.31. Institutional trust followed at 0.26, while perceived usefulness registered 0.28. Environmental awareness influenced intention primarily indirectly, mediated through perceived usefulness, accounting for 56 percent of its total effect—a finding that challenges awareness-centric campaigning.

Perhaps the most policy-relevant discovery comes from the neural network’s partial dependence analysis: adoption intention rises sharply only once perceived cost falls below a Likert-scale tipping point of 2.8. Cost perception, the authors conclude, does not behave linearly. Households operating under tight budget constraints experience a psychological threshold, below which marginal reductions in perceived financial burden yield disproportionate gains in willingness to adopt. Uniform subsidies, they argue, are inefficient because they ignore this tipping point; tiered, income-sensitive financing aimed at the households with the highest perceived burden would deliver far greater leverage per unit of public spending.

Multi-group analysis added a further nuance: first-time adopters are significantly more sensitive to cost and maintenance concerns than experienced solar users, with path coefficients of negative 0.423 versus negative 0.194 and a significant group difference at p equals 0.018. Institutional trust, by contrast, operated as a universal enabler with no significant difference between users and non-users, suggesting that credible subsidies, transparent warranty frameworks and consistent government communication reduce perceived risk for everyone. Robustness checks, including Gaussian copula tests for endogeneity and measurement invariance testing, confirmed that these estimates withstand omitted-variable bias and hold across education levels.

The authors translate their findings into four evidence-informed policy pathways for fossil-rich developing economies. First, replace uniform subsidies with tiered, income-sensitive co-payment structures that build financial literacy alongside ownership. Second, institutionalize community-based maintenance cooperatives—training local technicians, establishing spare-part supply chains and creating peer-support networks—to convert post-installation anxiety into sustained usage. Third, shift from top-down hardware deployment to participatory governance in which local leaders, forest authorities and energy agencies co-design subsidy timelines and guarantees. Fourth, exploit the power of social diffusion through visible demonstration installations and testimonial-based outreach, which the data suggest may be more cost-effective than traditional awareness campaigns, especially when reinforced by institutional credibility.

The stakes extend well beyond household convenience. The study frames solar adoption as a dual-purpose intervention advancing both SDG 7 on affordable clean energy and SDG 13 on climate action: every household that swaps firewood for photovoltaics eases deforestation pressure, reduces indoor air pollution and cuts carbon emissions from biomass combustion. By positioning social acceptance as a structural driver rather than an implementation afterthought, the Zagros research offers a transferable diagnostic framework for biomass-dependent communities across Sub-Saharan Africa, South Asia and Latin America. Its core message is deceptively simple: the energy transition will be won not on rooftops alone, but in the trust between governments and communities, and in the quiet consensus of neighbors watching a neighbor’s panels work.

Subject of Research: Socio-behavioral drivers and institutional enablers of household solar energy adoption among forest-dwelling communities in the Zagros Mountains of Iran.

Article Title: From fuelwood to photovoltaics: Socio-behavioral drivers and institutional enablers of household solar energy adoption in fossil-rich developing contexts

Article References: From fuelwood to photovoltaics: Socio-behavioral drivers and institutional enablers of household solar energy adoption in fossil-rich developing contexts. (n.d.). https://doi.org/10.1016/j.indic.2026.101515

Image Credits: AI Generated

DOI: 10.1016/j.indic.2026.101515

Keywords: solar energy adoption, Zagros Mountains, technology acceptance, institutional trust, subjective norms, energy poverty, PLS-SEM, artificial neural network, renewable energy policy, fuelwood, SDG 7, just energy transition

The study, conducted by Firas Asmar of the Department of Education at Al-Ummah University College in Jerusalem, surveyed 236 students drawn from a college population of 550, a sample size calculated with a 95 percent confidence level and a 5 percent margin of error. Participants were recruited through an online questionnaire distributed via Facebook and WhatsApp between March 25 and May 25, 2025, and represented five academic departments: Education, Hebrew Language, Business Administration, Engineering and Technology, and Graphic Design. Students ranged from first-year to fourth-year levels and came from cities, villages, and refugee camps, giving the survey a breadth of demographic coverage unusual for a single-institution study in the region.

Methodologically, the research followed a descriptive, correlational, quantitative, non-experimental, cross-sectional design. The instrument was a structured questionnaire organized into four sections: sociodemographic information, followed by three ten-item domains measuring knowledge, practice, and attitudes toward AI in education, each scored on a five-point Likert scale from strongly disagree to strongly agree. Content validity was established by a panel of three expert judges, including two professors specializing in information technology and artificial intelligence and a specialist in statistics and research methodology. Each item was rated for relevance on a four-point scale, and items achieving an Item-level Content Validity Index of at least 0.78 were retained, yielding an average Scale-level CVI of 0.94, a figure the author characterizes as indicating excellent content validity. A pilot study with 36 students, who were excluded from the final sample, produced a Cronbach’s alpha of 0.862, confirming good internal consistency.

The statistical analysis, performed in SPSS version 26, was rigorous for a survey of this scale. Kolmogorov-Smirnov tests with Lilliefors correction and visual inspection of Q-Q plots confirmed that knowledge, attitude, and practice scores did not significantly deviate from normality, justifying the use of parametric tests. Independent samples t-tests and one-way ANOVA compared mean scores across demographic groups, while Pearson correlation coefficients examined the relationships among the three core variables. Domain means were transformed to a 0-10 scale using the formula Transformed score = [(Mean − 1) / 4] × 10, with a cutoff of 6.00 used to categorize scores descriptively as good or appropriate.

The headline results are encouraging on the surface. Transformed mean scores reached 7.20 for knowledge, 7.48 for practice, and 7.76 for attitude, all comfortably above the 6.00 threshold. Most participants agreed or strongly agreed with statements about understanding AI concepts and expert systems, and most reported that AI facilitates access to information, improves communication, supports cognitive and research skills, and helps with big data analysis. Students recognized AI’s potential to enrich learning experiences and foster more effective, interactive, and personalized educational environments, findings the author links to the widespread accessibility of tools such as ChatGPT across academic disciplines.

Beneath the averages, however, the demographic breakdown tells a more nuanced story. Male students scored significantly higher than female students on knowledge (mean 7.58 versus 7.12, p = 0.028), though the author cautions that the small male subsample of 36 students limits interpretation, and no significant gender differences emerged for attitude or practice. Academic level was a significant factor across all three domains: first- and second-year students outperformed third- and fourth-year students in knowledge (p < 0.001), attitude (p = 0.002), and practice (p = 0.044), a pattern suggesting that younger cohorts arrive with greater digital fluency and reliance on technology.

Discipline and place of residence also shaped the results in unexpected ways. Differences across specialties were significant only for practice (p = 0.008), with Engineering and Technology students achieving the highest mean practice score of 7.85 and Graphic Design students the lowest at 6.57. Perhaps most striking, students living in refugee camps reported the highest mean practice score of all residential groups at 7.98, compared with 7.55 for rural students and 7.31 for urban students. The author speculates that this may reflect increased reliance on digital tools when physical educational resources are scarce, though he notes the relationship warrants further investigation.

The correlational analysis provided some of the study’s most theoretically meaningful findings. Knowledge correlated moderately with attitude (r = 0.396, p < 0.001) and with practice (r = 0.344, p < 0.001), while attitude showed the strongest association with practice (r = 0.553, p < 0.001). This pattern aligns closely with the Technology Acceptance Model, which holds that perceived usefulness and attitude strongly predict technology adoption behavior. In practical terms, the results suggest that building positive attitudes, through success experiences, role modeling, and exposure to well-designed AI applications, may be the most powerful lever for increasing actual use, more so than simply transmitting factual knowledge about AI.

The attitude domain itself revealed the barriers students perceive. Participants generally agreed that inadequate infrastructure, insufficient training, poor institutional planning, and limited AI awareness within educational organizations significantly hinder AI integration. The author situates these findings within a broader regional literature: Arab studies have repeatedly identified weak technical infrastructure, shortages of qualified personnel, high application costs, and limited institutional readiness as fundamental obstacles to AI adoption in higher education. In Palestine, these challenges are compounded by political and economic constraints, and the pace of AI development has simply outstripped the capacity of local educational systems to adapt. The author is explicit that the gap between knowledge and practice is not merely a matter of individual motivation but reflects systemic failures, including the absence of hands-on training, lack of institutional support, and deficient infrastructure.

The study concludes with a clear set of recommendations for Palestinian higher education: invest in technological infrastructure, integrate AI-related competencies into curricula, provide specialized and continuous training for students and academic staff, and foster a culture of responsible and ethical AI use. The author acknowledges limitations, including the online administration of the survey, the use of convenience sampling from a single institution, which means the sample should not be considered statistically representative of all Palestinian university students, and the exclusion of many colleges in the West Bank and Gaza Strip due to the current political situation. Even so, as one of the first field analyses of AI knowledge, attitudes, and practices among Palestinian university students, the study fills a significant regional gap and offers a data-driven roadmap for institutions seeking to ensure that students can use AI effectively, ethically, and productively as the technology reshapes global education.

Subject of Research: University students' knowledge, attitudes, and practices regarding artificial intelligence in education in Palestine

Article Title: Assessing university students’ knowledge, attitudes, and practices towards artificial intelligence in education in Palestine

Article References: Asmar, F. (2026). Assessing university students’ knowledge, attitudes, and practices towards artificial intelligence in education in Palestine. Discover Education, 5(1), Article 956. https://doi.org/10.1007/s44217-026-02195-1

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02195-1

Keywords: artificial intelligence, higher education, Palestine, KAP study, technology acceptance, students, ChatGPT, educational technology, cross-sectional study, AI literacy, institutional barriers, Discover Education

This investigation brought forth crucial insights into the dual dimensions of trust that employees harbor towards AI—cognitive trust and emotional trust. Cognitive trust relates to the rational assessment of AI capabilities, encompassing beliefs about its efficiency and accuracy. In contrast, emotional trust involves the subjective feelings employees possess towards AI, such as anxiety, confidence, or discomfort. The interplay between these forms of trust profoundly influences both AI performance and its subsequent acceptance in the workforce. The findings highlight that even the most sophisticated AI systems can falter if they fail to inspire both cognitive and emotional confidence among their users.

The research was grounded in interviews conducted with employees of a medium-sized software development firm. These interviews revealed four distinct trust configurations, each demonstrating how employees engage differently with AI based on their trust levels. The configurations identified were full trust, characterized by a high level of both cognitive and emotional trust; full distrust, which showed low levels on both fronts; uncomfortable trust, where cognitive assessments were high but emotional responses were low; and blind trust, marked by high emotional trust juxtaposed with low cognitive trust.

The consequences of these configurations are particularly striking. Employees categorically displayed varied behaviors under the influence of these trust levels. Those who possessed high cognitive trust engaged in detailed documentation and analysis of their digital footprints, reflecting an active engagement with the AI systems. Conversely, employees exhibiting high emotional distrust tended to manipulate or confine their interactions with AI, or even withdraw entirely from engaging with these systems. This variety of responses ultimately sets off a problematic cycle, termed a “vicious cycle” by the researchers, wherein biased and incomplete data inputs erode AI performance, subsequently diminishing trust and hampering further adoption of these technologies.

This dynamic is particularly critical in today’s fast-paced business environment, where AI technologies are positioned as key facilitators of organizational success. The cyclical deterioration of trust and reliance on AI paints a concerning picture for leadership within companies. The research posits that the successful integration of AI systems is no longer merely a concern of technological implementation; it demands a nuanced understanding of trust and the emotional landscape of employees. Failing to prioritize these human-centric elements could lead to the wastage of significant investments and the unrealized potential of smart technologies.

Natalia Vuori, DSc, from Aalto University, emphasized the need for a shift in perspective among organizational leaders—one that recognizes the central role trust plays in facilitating AI adoption. She underscores the importance of addressing emotional concerns and aligning AI implementation strategies with employee sentiments and perceptions. The study ultimately suggests that the most advanced AI systems will not fulfill their intended roles if they are not embraced by the very personnel who rely on them.

Moreover, the findings challenge pre-existing narratives that frame AI as merely a technological challenge to be overcome. Instead, the research highlights that the social and psychological dimensions significantly shape the relationship between humans and machines. Employees’ comfort levels with AI hinge not solely on technological sophistication, but rather on the perceived reliability and emotional resonance of these systems.

As organizations forge ahead with their AI agendas, the study serves as a valuable guide for managers on how to foster a workplace environment that encourages trust. Engaging employees in dialogues about their perceptions of AI and working collaboratively to bolster both cognitive and emotional trust can significantly mitigate the pitfalls identified in the research. Managers can promote transparency around AI functionalities, solicit employee input on AI developments, and cultivate a culture that values continuous learning and adaptation.

In a world where AI capabilities are advancing at an unprecedented pace, understanding the psychological frameworks through which employees evaluate and engage with these technologies becomes paramount. The path to successful AI adoption is paved by a foundation of trust—one that requires organizations to be attuned not only to the technological intricacies but also to the human element that drives organizational dynamics.

This multifaceted insight repositions the discourse on AI in business, emphasizing the interplay between technology and human emotion. By fostering a culture of trust, organizations can unlock the transformative potential of AI, enabling it to fulfill its promise and drive significant competitive advantage. The findings articulated in the study underscore the importance of viewing AI not simply as a technological tool, but as an aspect of the workplace ecosystem that requires genuine investment in human relationships and trust-building initiatives, thus reshaping the future of work in the age of intelligent technology.

As this conversation continues to evolve, organizations must recognize that establishing cognitive and emotional trust is essential in shaping a shared vision for AI and its role in the workplace. The successful navigation of this complex relationship will ultimately determine the future trajectory of AI within corporate frameworks, setting the stage for innovations that are not only smart but also aligned with the human experience.

In a nutshell, the research illuminates the pivotal role trust plays in the acceptance and performance of AI technologies in organizations, urging leaders to adopt a more comprehensive framework that harmonizes technological progression with the emotional landscape of their workforce.

Subject of Research: The impact of cognitive and emotional trust on AI performance and adoption in organizations.

Article Title: It’s Amazing – But Terrifying!: Unveiling the Combined Effect of Emotional and Cognitive Trust on Organizational Member’ Behaviours, AI Performance, and Adoption.

News Publication Date: 22-Jan-2025.

Web References: Journal of Management Studies

References: DOI link to article 10.1111/JOMS.13177

Image Credits: N/A.

Keywords

• Artificial intelligence
• Trust dynamics
• Cognitive trust
• Emotional trust
• Organizational behavior
• AI adoption
• Human-computer interaction
• Leadership strategies
• Workplace innovation
• Decision-making frameworks
• Employee engagement
• Trust-building practices