The Dawn of Generative AI-Enabled Political Activism: How Kenyan Gen Z Used ChatGPT and Protest GPTs to Mobilize

Generative Artificial Intelligence (GenAI) tools hold significant promises for enhancing teaching and learning outcomes in higher education. However, continues usage behavior and satisfaction of educators with GenAI systems are still less explored. Therefore, this study aims to identify factors influencing academic staff satisfaction and continuous GenAI usage in higher education, employing a survey method and analyzing data using Partial Least Squares Structural Equation Modeling (PLS-SEM). This research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) and the Expectation Confirmation Model (ECM) as its theoretical foundations, while also integrating ethical concerns as a significant factor. Data was collected from a sample of 127 university academic staff through an online survey questionnaire. The study found a positive correlation between effort expectancy, ethical consideration, expectation confirmation, and academic staff satisfaction. However, performance expectancy did not show a positive correlation with satisfaction. Performance expectancy was positively related to the intention to use GenAI tools, while academic staff satisfaction positively influenced the intention to use GenAI. The social influence did not correlate positively with the use of GenAI. Security and privacy were positively associated with staff satisfaction. Facilitation conditions also positively influenced the intention to use GenAI. The findings of this study provide valuable insights for academia and policymakers, guiding the responsible integration of GenAI tools in education while emphasizing factors for policy considerations and developers of GenAI tools.

This study extends the Theory of Planned Behavior (TPB) to explore how students’ behavioral intentions toward using generative artificial intelligence (GenAI) are associated with their reflective engagement and self-directed learning (SDL) in higher education. As GenAI tools such as ChatGPT increasingly mediate learning, understanding how learners’ intentions are linked to autonomous and reflective learning behaviors becomes essential. Data were collected from 149 first-year university students (predominantly female) in Vietnam who had prior experience with GenAI for academic purposes. Using Partial Least Squares Structural Equation Modeling (PLS-SEM), the study examined relationships among attitudes, subjective norms, perceived behavioral control, behavioral intention, actual use, reflection, and two dimensions of SDL, including intentional learning and self-management. The results reveal that attitudes and perceived behavioral control significantly predict students’ intentions and actual use of GenAI, whereas subjective norms have no significant effect. Behavioral engagement is positively associated with reflection and both dimensions of SDL, while reflection is positively related to intentional learning and self-management, confirming its mediating role within the proposed model linking motivation-related constructs with autonomous learning outcomes. These findings highlight reflection as a metacognitive mechanism that links students’ behavioral engagement with GenAI and their SDL-related outcomes. Theoretically, the study advances TPB by positioning reflection and SDL as outcome constructs within the proposed model, rather than fixed learner traits. Practically, it suggests that educators and institutions working with first-year university students or similar learner populations should integrate reflective activities and AI literacy into curricula to promote critical, ethical, and autonomous engagement with GenAI. Designing learning environments that position AI as a reflective partner, rather than merely a content generator, supports learners’ self-regulation and reflective engagement. Overall, this research contributes to understanding how intentional and reflective interaction with GenAI is associated with deeper and more autonomous learning of students among first-year university students in a GenAI-supported learning context.

Partial Least Squares Structural Equation Modeling (PLS-SEM) in second language and education research: Guidelines using an applied example

Background Generative artificial intelligence (GenAI) is rapidly transforming higher education, yet empirical evidence remains limited on the factors associated with its acceptance and usage among medical students, especially in non-Western, high-stakes educational contexts such as China. A clear and contextualized understanding of these mechanism is essential to effectively integrate GenAI into medical curricula and prepare future healthcare professionals for AI-augmented clinical practice. Grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, this study systematically investigated the relationships between core UTAUT constructs, and Chinese medical students’ behavioral intention (BI) and actual usage (AU) of GenAI, testing direct, mediating, and exploratory moderated pathways. Methods A cross-sectional online survey was administered to students at a public medical university in China from October 2024 to January 2025, yielding 1781 valid responses. Validated scales were used to measure core UTAUT constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FCs), BI, and AU. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the hypothesized relationships. Results The model demonstrated strong explanatory power, accounting for 67.6% of the variance in BI and 66.3% in AU. PE ( β = 0.377, p < 0.001), FCs ( β = 0.333, p < 0.001) and SI ( β = 0.212, p < 0.001) were positively associated with BI. EE showed no significant direct association with BI ( β = 0.038, p = 0.209) but had a weak yet significant direct association with AU ( β = 0.057, p = 0.045). BI served as a significant mediator in the relationships between PE, SI, FCs, and AU (all p < 0.001) but failed to mediate the association between EE and AU ( p = 0.219). Age was the only significant moderator for the path from EE to BI ( β = 0.071, p = 0.043) and the path from BI to AU ( β = 0.024, p = 0.022); gender, major, and academic level showed no moderating effects. Conclusion This study empirically validates and extends the UTAUT framework within Chinese medical education. Key findings underscore the important roles of PE, FCs and SI, reveal the context-dependent role of EE, and identify the moderating effect of age. Strategic interventions including demonstrating GenAI’s tangible utility, improving technical infrastructure, leveraging peer / faculty advocacy, and tailing strategies to age-related differences are recommended. These insights provide evidence-based guidance for educators, policymakers, and AI developers to support responsible integration of GenAI into medical education, ultimately preparing future healthcare professionals for an AI-driven healthcare ecosystem.

Conversational generative artificial intelligence (GenAI) has emerged as a promising tool for second language (L2) speaking practice, but the mechanisms behind its effectiveness remain underexplored. This study aims to explore these mechanisms through the lens of the cognitive-motivational model of achievement emotions in the control-value theory. Specifically, we investigate how emotions (enjoyment, boredom, and curiosity), cognitive processing, and the ideal L2 self interact to influence speaking performance. The sample consisted of 158 Chinese L2 majors engaging in GenAI-assisted speaking practice in informal contexts. Using Partial Least Square-Structural Equation Modeling (PLS-SEM) with Smart PLS 4 software, key findings include that gender did not affect the constructs under study, but GenAI competence positively influenced speaking performance. Enjoyment had a direct effect on cognitive processing, which in turn enhanced speaking performance, though it did not influence the ideal L2 self. Curiosity positively influenced the ideal L2 self and speaking performance, but had no effect on cognitive processing. Boredom, however, did not affect either cognitive processing or the ideal L2 self. The study contributes theoretically by advancing understanding of how psychological factors shape L2 speaking performance in GenAI contexts. Pedagogically, it offers insights into optimizing GenAI for language practice.

Measurement errors wield the potential to introduce uncertainties and inaccuracies, casting shadows on data quality and jeopardizing the integrity of structural relationships. Notably robust against measurement errors, Partial Least Squares Structural Equation Modelling (PLS-SEM) has historically maintained a reputation for resilience. However, recent insights have unveiled its susceptibility to these errors, instigating a revaluation of its standing in the Structural Equation Modelling landscape. Overlooking measurement errors in PLS-SEM carry consequential repercussions, notably tainting the accuracy of structural relationships and introducing bias. This effect becomes particularly pronounced when dealing with an insufficient understanding of the intricate structural dynamics. Unfortunately, PLS-SEM currently lacks an all-encompassing remedy to address this concern. Consequently, the quantification of measurement errors impact in PLS-SEM gains paramount importance, fostering a demand for innovative strategies to propel its effectiveness forward. Notably, contemporary investigations have unmasked PLS-SEM's vulnerability to non-orthogonal errors. This revelation challenges the notion of its imperviousness to the detrimental influence of measurement errors, necessitating a comprehensive evaluation of its performance under such conditions. This study leveraged simulated data to extract empirical findings and employed parameters biasedness analysis. This analysis led to the determination that the stability of the PLS-SEM algorithm is compromised when exposed to diverse measurement error scenarios. Consequently, the outcomes generated exhibit both instability and bias. This bias becomes increasingly conspicuous as the magnitude of measurement errors intensifies. Thus, the study proposes avenues for elevating the robustness of PLS-SEM.

Aims: This study aims to examine the impact of lean management practices on organizational competitiveness, with operational performance positioned as a mediating mechanism. Specifically, the study seeks to analyse how lean management practices influence operational performance and how these operational improvements subsequently contribute to enhancing the competitive position of small and medium-sized enterprises (SMEs). Study Design: This study employs a quantitative explanatory research design using a cross-sectional survey approach. The relationships among lean management practices, operational performance, and organizational competitiveness are examined through Partial Least Squares Structural Equation Modeling (PLS-SEM). Place and Duration of Study: The study was conducted among small and medium-sized enterprises located in the Greater Bandung area, Indonesia. Data collection took place between 15 November to 5 December 2025. Methodology: Data were collected from 186 SME owner–managers in Bandung, Indonesia, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement items were adapted from established literature on lean management, operational performance, and competitiveness. The data were analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the reliability and validity of the measurement model and to test the proposed structural relationships, including the mediating role of operational performance. Results: The results indicate that lean management practices have a significant positive effect on operational performance. Operational performance, in turn, significantly enhances organizational competitiveness. In addition, lean management practices also exert a direct positive effect on organizational competitiveness. Mediation analysis reveals that operational performance partially mediates the relationship between lean management practices and organizational competitiveness, indicating that the competitive benefits of lean management are achieved primarily through improvements in operational performance. Conclusion: The study concludes that lean management serves as an effective strategic approach for enhancing the competitiveness of SMEs when it is implemented in a manner that strengthens operational performance. The findings underscore the importance of focusing on operational improvements as a key mechanism through which lean management translates into sustainable competitive advantages.

Background: Patient satisfaction is used as an important marker of the quality of medical services. This has an impact on patient loyalty and increases patient retention. Generally, perceptions of the quality of hospital services are based on patients' assessments of the services provided by the hospital, for example the relationship between patients and nurses, doctors and staff. Purpose: To determine factors related to general patient satisfaction. Method: Quantitative research with hypothesis testing without special treatment of subjects during the research period. Data was obtained through a survey of all patients who visited the emergency room at Budi Mulia Bitung Hospital in January 2024, totaling 170 respondents. The sample in this study was taken using a non-probability sampling method with the criteria, being more than 19 years old, cooperative, having a cell phone, and being able to be guided to fill out an online questionnaire. Results: All indicators in each dimension have an outer loading value of >0.7 and an AVE value for satisfaction with doctor services (0.703), emergency staff (0.860), emergency environment (0.656), general patient satisfaction (0.674). Cronbach's alpha and Composite Reliability values for all variables are above 0.7. The R-Square value for the general satisfaction variable is 0.948 and behavioral intention (0.944), so it can be said to be overfit. Based on the t-statistic value, p-value, and path coefficient, all variables in the research model tested are all significant. Conclusion: Satisfaction with physician services, emergency department staff, and emergency department environment are factors that are associated with general patient satisfaction and behavioral intentions. Keywords: Behavioral Intention; Patient Satisfaction; Partial Least Squares Structural Equation Modeling (PLS-SEM) Pendahuluan: Kepuasan pasien digunakan sebagai penanda penting untuk kualitas layanan medis. Hal ini berdampak pada loyalitas pasien dan meningkatkan retensi pasien. Umumnya persepsi kualitas pelayanan rumah sakit didasarkan pada penilaian pasien terhadap pelayanan yang diberikan rumah sakit, misalnya hubungan antara pasien dan perawat, dokter dan staf. Tujuan: Untuk mengetahui faktor-faktor yang berhubungan dengan general satisfaction patient. Metode: Penelitian kuantitatif dengan pengujian hipotesis tanpa perlakuan khusus terhadap subjek selama periode penelitian. Data yang diperoleh melalui survei kepada seluruh pasien yang berkunjung ke IGD RS. Budi Mulia Bitung pada bulan Januari 2024 sebanyak 170 responden. Sampel dalam penelitian ini diambil menggunakan metode non-probability sampling dengan kriteria, sudah berusia lebih dari 19 tahun, kooperatif, memiliki handphone, dan dapat dipandu untuk mengisi kuesioner online. Hasil: Semua indikator pada setiap dimensi memiliki nilai outer loading >0.7 dan nilai AVE physician care satisfaction (0.703), emergency department staff (0.860), emergency department environment (0.656), general satisfaction patient (0.674). Nilai cronbach’s alpha dan composite reliability pada semua variabel telah berada di atas 0.7. Nilai R-Square pada variabel kepuasan umum sebesar 0.948 dan niat berperilaku (0.944), sehingga dapat dikatakan overfit. Berdasarkan nilai t-statistik, nilai-p, dan koefisien jalur seluruh variabel dalam model penelitian yang diuji semuanya signifikan. Simpulan: Physician care satisfaction, emergency department staff, dan emergency department environment merupakan faktor-faktor yang berhubungan dengan general satisfaction patient dan behaviour intention. Kata Kunci: Behavioral Intention; Partial Least Squares Structural Equation Modeling (PLS-SEM); Patient Satisfaction.

Due to the fast adoption of digital technologies and artificial intelligence (AI), the operations of enterprises, especially in the context of supply chain management (SCM) and human resource (HR) practises, are being fundamentally reorganised by allowing data-driven decision-making, automating processes, and enhancing agility to organisational changes. Although in increased interest AI-driven digital transformation is also gaining momentum, empirical data describing the organisational processes in which AI-driven tools affect SCM and HR performance have scarce information, particularly the intermediating effects of transformational leadership and innovation. To fill this gap, the current research investigates the direct and indirect impacts of digital technologies and AI on changing the SCM and HR practises, and transformational leadership and innovation are modelled as the mediating constructs. The research design was a quantitative and survey research design, and its conceptual framework was empirically proved through Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings indicate that digital technologies and AI have a powerful positive impact on SCM transformation and HR transformation, whereas transformational leadership and innovation have important mediating roles in enhancing these correlations. The findings provide value to the theory because they expand the digital transformation and leadership views, including technological accounts of AI-driven enterprise systems, and offer practical advice to managers who need to harness the emergent technologies to achieve organisational transformation sustainability. The study was a survey design, consisting of 250 participants. Significant path coefficients were those of the relationship between AI and SCM transformation, where the path coefficient was 0.42 (p < 0.001). The SCM transformation explained variance (R2) was 0.72 and this represents a good fit. The approach was the Partial Least Squares Structural Equation Modelling (PLS-SEM) using the SmartPLS software.

Apathetic and disconnected from the political world, these are just two of the titles scientists, journalists and politicians have been attributing to youth. Hence, what factors weaken or encourage youth to participate in politics is forefront in academic and non-academic researches. Hence, in view of this context, the role of political socialization agents in engendering political activities among youth cannot be overstated. However, not much research is being carried out in this area. Thus, this study assessed the influence of family communicative environment and peer norms as political socialization agents on media and political participation. A cross-sectional survey was conducted on social sciences students of Pakistani universities. Questionnaire (228) was used to collect data which were analyzed by using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that among other political socialization agents, peer norms was found to be more significant for youth’s media consumption and political behavior. In particular, results revealed that information consumption through traditional newspaper; TV and online newspaper led to political participation, centering political expression via interpersonal communication. Moreover, interpersonal communication is found to have direct influence on Pakistani youths political participation. This study provides an empirical justification for the potential of peer group as agent of political socialization for enhancing political activities among youth in Pakistan. Keywords: Political socialization, family, peer norms, informational media use, interpersonal communication, political participation.

Introduction The rapid expansion of Generative Artificial Intelligence (GAI) is reshaping pedagogical practices and educational policies worldwide. One of its most notable contributions is its capacity to deliver personalized feedback, which has the potential to enhance student learning and academic performance. This study aims to propose and validate a conceptual model that examines the factors influencing student behavior in response to GAI-mediated feedback in online learning environments. Methods A Massive Open Online Course (MOOC) titled “Transforming Education with AI: ChatGPT” was designed within a university setting, in which students received feedback on their activities through the GAI tool ChatGPT. Data were collected through a survey completed by 161 participants. The proposed model was evaluated and validated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results Findings indicate that students hold a positive perception of GAI as a tool for receiving feedback within their learning process. Although concerns related to privacy and security remain, these factors do not exert a significant influence on students’ overall satisfaction with GAI-mediated feedback. Discussion The results suggest that GAI-mediated feedback is well-received by students and can be integrated effectively into online learning environments. While issues surrounding privacy and security should not be overlooked, they do not appear to hinder students’ acceptance or satisfaction. These insights contribute to the development of evidence-based strategies for the pedagogical incorporation of GAI in higher education.

In management accounting research, the capabilities of Partial Least Squares Structural Equation Modelling (PLS-SEM) have only partially been utilized. These yet unexploited capabilities of PLS-SEM are a useful tool in the often explorative state of research in management accounting. After reviewing eleven top-ranked management accounting journals through the end of 2013, 37 articles in which PLS-SEM is used are identified. These articles are analysed based on multiple relevant criteria to determine the progress in this research area, including the reasons for using PLS-SEM, the characteristics of the data and the models, and model evaluation and reporting. A special focus is placed on the degree of importance of these analysed criteria for the future development of management accounting research. To ensure continued theoretical development in management accounting, this article also offers recommendations to avoid common pitfalls and provides guidance for the advanced use of PLS-SEM in management accounting research.

Researchers across a wide range of disciplines exploited the capabilities of partial least squares structural equation modeling (PLS-SEM). The rise in popularity of PLS-SEM is particularly noticeable 2013 onwards. The banking and finance discipline, however, hardly exploits the advantages of the PLS-SEM approach. PLS-SEM can be used for prediction and exploration in complex models with relaxed expectations on data. PLS-SEM is useful in identifying relationships between constructs. If the primary objective is theory development, PLS-SEM is appropriate.

The rapid advancement of generative AI tools, such as ChatGPT, has sparked widespread debate over their impact on academic integrity and educational practices. As these tools become increasingly accessible to students, understanding the factors that influence their adoption in academic settings is essential. The current study explores the application of generative artificial intelligence (AI) tools by college students, such as ChatGPT and many others, for completing homework assignments. Drawing on the Task-Technology Fit (TTF) framework and the concept of moral obligation, this research aims to investigate the factors influencing students' behavioral intentions to use generative AI in academic contexts. Data were collected through an online survey of 136 Taiwanese college students. The results indicate that perceived technology characteristics and self-efficacy significantly enhance task-technology fit, positively affecting behavioral intention. Conversely, moral obligation shaped by perceived teacher attitudes negatively influences students' intention to use AI tools for coursework. The study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the hypotheses and explains a substantial proportion of the variance in behavioral intention. These findings provide theoretical insights into how technological and ethical considerations jointly influence AI adoption in education. The study also offers practical suggestions for educators and institutions aiming to guide the responsible use of generative AI in learning environments. This study contributes a novel framework for understanding responsible AI use in higher education.

The primary objective of this study survey is to close knowledge gaps by measuring the responses from construction experts and investigating the significant effects of using digital technologies in construction information management (CIM). This is attributed to the lack of thorough knowledge among construction professionals on the implications and efficacy of incorporating digital tools in construction information management. A thorough analysis of the literature on the use of digital technologies revealed outcomes related to digitized ways of managing construction information, which were then contextually tailored through a pilot study and presented in the form of a postulated model. A total of 257 stakeholders in the building industry were given questionnaire surveys to complete in order to gather primary data. The final model of the result of adopting digital technology was statistically validated using partial least squares structural equation modelling (PLS-SEM). By concentrating on the quantitative contribution of the most important result to the adoption of digital technologies throughout the process of CIM, this study closes this knowledge gap. The three primary benefits that digital technologies have the most influence on are communication, operational efficiency, and market intelligence, according to this paper’s conclusions. The research showed that encouraging relationships that enable the use of digital technologies should be promoted between technology providers and construction companies. In order to adopt and improve digital solutions, construction firms and technology providers will be able to collaborate in an ecosystem. By shedding light on the implementation and impact of digital technologies in the construction sector, the study helps to close this knowledge gap. The study offers valuable information for upcoming initiatives that support digital transformation through construction methods. The results serve as instructions for the government authorities to help them focus their efforts and distribute their resources more effectively.