Purpose This research aims to evaluate the success of using business process management system (BPMS) as knowledge management system (KMS) by assessing user satisfaction and perceived usefulness. Design/methodology/approach The study is based on survey data collected from employees in 12 commercial companies, yielding 242 valid responses. Partial least squares structural equation modeling (PLS-SEM) is employed to test a conceptual model derived from the updated information system success model (ISSM). Our model incorporates constructs related to system quality, service quality, information and knowledge quality, system use, user satisfaction and perceived usefulness. Findings The results confirm that service quality and information and knowledge quality significantly influence the use of BPMS for KM. Furthermore, system use and user satisfaction are found to be strong predictors of perceived usefulness. Originality/value While previous research has highlighted the relevance of BPMS for process management and acknowledged the importance of KM in these contexts, few studies have examined the role of BPMS in directly supporting KM. This study contributes novel insights by clarifying the impact of service quality on system use and by revealing how this usage influences user satisfaction and the perceived usefulness of BPMS for KM. Beyond this, our work advances theory by reversing the conventional causal ordering. Specifically, we position user satisfaction as a driver rather than an outcome of perceived usefulness, highlighting that positive user experiences shape perceptions of utility. This reinterpretation builds on insights from organizational psychology, behavioral economics and knowledge-based view, offering a solid basis for rethinking BPMS adoption dynamics.

Background: Online registration at higher education institutions (HEIs) enhances the enrolment process by leveraging an Enterprise Resource Planning (ERP) system to digitise all required documents. The effectiveness of online registration remains largely underexplored within the HEI landscape. Objectives: This study addresses this gap by creating a framework to identify how system, information and service quality influence user satisfaction, behavioural intention and actual usage concerning students’ online registration. Method: A quantitative research approach was selected using a structured questionnaire. The study employed non-probability, convenience sampling. Descriptive and inferential statistics were used for the data analysis. The study utilised the DeLone and McLean Information Systems (D&M IS) Success Model to answer the study hypotheses. Results: Both system quality and information quality have a positive impact on user satisfaction. Information quality positively influences the intention to use the system, while service quality also contributes to user satisfaction. The intention to use the system has a positive effect on the actual usage of the online registration system. However, system quality, service quality and user satisfaction do not impact the intention to use the system, and user satisfaction does not influence the actual use of the online registration. Conclusion: Enterprise Resource Planning systems can enhance efficiency in HEIs by modifying operational workflows and simplifying online registration processes. Contribution: This study addresses the existing literature gap by presenting the need for accurate data to inform strategies that improve and enhance student registration experiences.

The following research analyzes the relation between the utilization of Artificial Intelligence (AI) tools among university students and their relation with critical thinking skills by combining the Technology Acceptance Model (TAM) model and critical thinking theory. This research introduced the Metacognitive TAM (Meta-TAM) integrated with the Information Systems (IS) Success model as a theoretical innovation. The current research was carried out with a quantitative method approach, with 200 respondents from the Office Administration Education Department, Universitas Sebelas Maret, Indonesia. The research data were analyzed using the SEM with SmartPLS 4.0 software. The key constructs of attitude toward use (ATU), motivation to use (MTU), perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI) were analyzed to assess the primary factors that influence the adoption of the AI-based tools. The findings emphasize that the TAM constructs significantly influenced behavioral intention, whereas critical thinking played a crucial mediating role. The strongest path was observed from attitude toward use to behavioral intention (β = 0.737), which emphasizes the importance of affective and evaluative assessment in students' decision-making. Then, it can be concluded that not only the usability or utility, but also students' strategic thinking, epistemic vigilance, and intellectual autonomy have a significant impact on the AI adoption among higher education students. This study offers practical implications for AI-integrated curriculum design and ethical technology implementation in learning environments. This research contributes a novel perspective to educational technology literature and encourages future cross-cultural, longitudinal studies that examine AI's cognitive impact while safeguarding critical thinking development in diverse academic contexts.

The aim of this paper is to find and analyse factors influencing consumers’ intention to use ChatGPT for travel planning. The research model is constructed using DeLone and McLean's Information Systems (IS) success model, along with literature related to factors such as private protection services, trust, visit duration, self-esteem, opportunity cost, user satisfaction, and intrinsic and extrinsic benefits. Positive relationships have been confirmed between Information Quality, System Quality, Privacy Protection, and Perceived Trust. The results also indicate that Self-esteem, Opportunity Cost and Intrinsic Benefits positively influence Satisfaction. The study also reveals the significant impact of Perceived Trust and Satisfaction on the intention to continue using ChatGPT for trip planning. However, the findings did not support the effects of Visit Duration and Extrinsic Benefits on Satisfaction.

Learning Management Systems (LMS) have become indispensable in today’s higher education landscape, offering flexible, technology-driven learning environments for students worldwide. Yet, one key question remains: what truly drives student academic success in these digital platforms? This study explores that question by examining the role of various quality dimensions in shaping academic performance. Grounded in the well-established DeLone and McLean Information System Success Model (2003), the research analyzes responses from 103 business students at two universities in the United Arab Emirates (UAE), using regression analysis to test the proposed model. The results reveal that four dimensions—learner quality, course content quality, educational system quality, and instructor quality—have a significant and positive impact on students’ academic achievement. Surprisingly, technical system quality, service quality, and support system quality did not show a meaningful influence. Collectively, the seven quality factors explain an impressive 75% of the variation in academic performance. These findings highlight where universities should focus their efforts: enhancing the quality aspects that directly shape students’ learning experiences. By doing so, institutions can not only improve student outcomes but also elevate the overall effectiveness of LMS platforms—ultimately supporting the advancement of higher education in the UAE and beyond.

In contemporary e-commerce, enterprises coordinate transactions, supply chains, and customer interactions within platform-based, data-intensive ecosystems. Integrated office application (IOA) serves as the operational backbone of these ecosystems by unifying communication, content management, workflow automation, and analysis across procurement, fulfillment, and after-sales service processes. As e-commerce processes become fully digitized, employees’ daily interactions with IOA directly impact service quality, operational efficiency, and sustainability outcomes. However, the micro-mechanisms by which IOA attributes translate into sustainable work practices are under-explored in the e-commerce literature. This study aims to explore how system quality, information quality, and collaboration quality influence user perceptions (perceived ease of use and usefulness), social influences (subjective norms), and satisfaction, thus jointly driving user intention and IOA-enabled sustainable behaviors. By integrating the Technology Acceptance Model, the Theory of Planned Behavior, and the IS Success Model, this research elaborates in a human-centered way on how an e-commerce enterprise’s system support can promote corporate and individual sustainability through employees’ adoption and continuous effective use.

Purpose This study proposes a novel consumer-centric conceptual framework for sustainable fashion consumerism in India that incorporates digital fashion and augmented reality/virtual reality. It combines the most significant constructs of the technology acceptance model (TAM), the information systems success model (ISSM) and the innovation diffusion theory (IDT) to present an overall view of the determinants of adoption intentions. Design/methodology/approach Using PRISMA 2020 guidelines, a systematic literature review and two-stage meta-analysis were applied. Due to the novelty of AR/VR in sustainable fashion, a construct-level approach was employed, involving the analysis of 29 empirical studies. Two methods of analysis were applied: path-specific random-effects meta-analysis to examine hypothesised relationships and global aggregate meta-analysis of 295 effect sizes, with Jamovi used for robustness checking. Findings Construct-level analysis revealed strong, significant central TAM path correlations. Information quality’s influence on perceived usefulness was confirmed to some extent (r = 0.265). The remaining IDT and ISSM constructs (for example, system quality, trialability and compatibility) were positive but weaker in influence, due to limited research. Aggregate analysis confirmed a significant overall positive correlation (r ≈ 0.30) among all adoption constructs, albeit with considerable heterogeneity, tracing contextual factors’ significance. Research limitations/implications The work is theoretical, necessitating additional empirical research to validate the suggested paradigm across diverse populations and places. Though cross-domain coverage is necessary for reliability, it must be at the expense of context-relevant precision. Future studies must empirically test the model in sustainable fashion contexts, and cultural and sustainability-oriented moderators should be examined. Practical implications Fashion firms may utilise AR/VR to mitigate environmental effects by promoting conscientious purchasing and enhancing the consumer experience. Digital fashion platforms and virtual try-ons can greatly reduce fashion's environmental impact by cutting down on returns and overproduction. Originality/value This study introduces a new, integrative conceptual framework combining TAM, ISSM and IDT – an untested combination in the sustainable fashion AR/VR context. It is one of the first to offer quantitative, meta-analytic evidence of adoption frameworks within this field as well as a theory-based model and empirical evidence upon which to ground future research and practice.

ABSTRACT E‐governance is widely recognized as a tool for enhancing transparency, efficiency, and accountability in public service. This study examines the mediation effect of Governance Structure (GS) on the relationship between e‐governance (EGOV) and Governance Performance (GP) in Malawi's local governments. DeLone and McLean IS Success Model (ISSM) using Information Quality (IQ), Service Quality (SQ), and System Quality (SYQ) is applied. Data from 266 respondents under the ministry of Local Government Assemblies (LGAs) and related agencies was collected and analyzed quantitatively using Structural Equation Modeling (SEM) in SmartPLS‐SEM (version 4.1.0.3). Measurement validity and reliability were established (e.g., AVE > 0.50; α > 0.70). The results of this study indicate that IQ, SQ, and SYQ each positively influence GS; GS, in turn, strongly predicts GP. Only IQ exhibits a significant direct effect on GP. Mediation tests confirm significant indirect effects from IQ, SQ, and SYQ to GP via GS ( t = 4.782; 3.744; 2.440, respectively). These findings advance ISSM by identifying GS as a novel mediator of EGOV within a socio‐technical perspective. It is also empirically proven that social factors are as important as technical factors for the success of any information system. Policy implications include budgetary and investment policies in technology, institutionalizing cross‐departmental coordination, capacity building and change management to translate digital systems into tangible governance gains. The study is unique in Least Developed Countries (LDCs) and new in the Malawi context offering empirical evidence of a transferable framework for aligning digital reforms with structural setup in resource‐constrained economies. This also provides the understanding that technology is not a total substitution for labor but a complement for effective, efficient and inclusive public service delivery.

The rapid development of immersive technologies has transformed the tourism experience, enabling tourists to explore destinations virtually before physical visits. This study investigates how virtual tourism experiences influence tourists’ intentions to visit real-world destinations, using the online museum of Porcelain Temple as a case study. Integrating the Stimulus–Organism–Response (SOR) framework with the Information System Success Model (ISSM) and the Technology Acceptance Model (TAM), the research examines the effects of system quality (SYQ), information quality (IQ), and service quality (SEQ) on perceived usefulness (PU), perceived ease of use (PEU), and perceived enjoyment (PE), which in turn affect tourists’ travel intentions (TI). Data collected from 602 online museum users were analyzed using structural equation modeling. Results indicate that IQ and SEQ significantly influence PU, PEU, and PE, while SYQ significantly impacts PEU and PE but not PU. PU, PEU, and PE all positively affect TI, highlighting the importance of both cognitive and affective responses in translating virtual experiences into real-world travel behavior. These findings provide theoretical insights into virtual-to-physical tourism conversion and practical guidance for optimizing online museum platforms to stimulate on-site visitation.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-24793-z.

The emergence of generative AI, particularly the widespread accessibility of ChatGPT, has led to challenges for higher education. The extent and manner of use are under debate. Local empirical investigations about the use and acceptance of ChatGPT contribute to effective policymaking. The study employs a specialized approach, utilizing an information system view based on the DeLone and McLean Information Systems Success Model as its theoretical framework. A survey was conducted to assess students’ opinions about ChatGPT regarding its usefulness in their studies. The model was tested using PLS-SEM with 466 Hungarian and Romanian higher education students. The model examined six constructs as information quality, system quality, service quality, use, user satisfaction, and net benefits. The results confirmed the effects of information quality and system quality on use and satisfaction, whereas service quality did not make a significant contribution. Satisfaction was found to be the key driver to use. The study contributes to a deeper understanding of AI acceptance in higher education and provides valuable considerations for policymaking. A data-oriented, task-focused policymaking is recommended over system-based regulation. Additionally, a comprehensive framework model is required for international comparisons, which combines information systems success and technology acceptance models.

Purpose This study aims to investigate the impact of artificial intelligence (AI) on risk management practices within Palestinian banks, specifically examining its application in credit, market and operational risk domains. The research assesses the extent to which AI enhances risk mitigation effectiveness within the unique economic and regulatory context of Palestine. Design/methodology/approach The study used an explanatory sequential mixed-methods design. The initial quantitative phase involved surveying 80 internal auditors, selected via simple random sampling from a population of 95. This was followed by a qualitative phase comprising in-depth interviews with 23 purposively selected participants to contextualize and elaborate on the quantitative findings. Data were analyzed using statistical methods and deductive thematic analysis, guided theoretically by the DeLone and McLean (D&M) IS Success Model (2003). Findings Findings demonstrate AI’s effectiveness in enhancing credit and operational risk management through improved decision-making accuracy, process automation and real-time anomaly detection. However, its potential contribution to market risk management is significantly constrained by infrastructural limitations, shortages in specialized expertise and competing strategic priorities, thereby underscoring the critical influence of contextual factors on successful AI adoption. Research limitations/implications The study acknowledges certain limitations. Primary reliance on internal auditors, while offering crucial oversight, may not capture the full experiential range; future work could benefit from including risk managers, IT specialists and senior management. The unique Palestinian politico-economic context necessarily limits direct generalizability, though identified themes regarding infrastructure, skills and strategy likely resonate with other emerging economies. Building on this study, future research should explore the longitudinal evolution of AI’s impact as infrastructure and skills develop. Comparative cross-country studies within diverse emerging markets would further elucidate national context influences. Integrating deeper analysis of organizational culture, change management and specific ethical considerations related to AI decision-making in risk management represents another fruitful avenue. Exploring the specific impact of different AI techniques (e.g. machine learning vs deep learning) across risk domains would also yield valuable insights. Such research will deepen the understanding of how AI can be effectively and responsibly leveraged to foster resilient global financial systems. Practical implications The findings yield significant practical implications for stakeholders within the Palestinian banking sector and, by extension, for other emerging economies confronting similar challenges. First, AI’s differential impact underscores the imperative for banks to adopt a nuanced, risk-specific integration strategy. For credit and operational risks, where AI is effective, institutions should optimize existing systems and ensure robust governance frameworks upholding transparency, accountability and regulatory compliance. Second, identified infrastructural and human capital deficiencies, pivotal impediments in market risk management, necessitate strategic investment in data infrastructure (especially real-time capabilities) and specialized expertise through training, recruitment and partnerships. Third, regulatory bodies should consider developing adaptive governance frameworks, balancing innovation with financial stability and ethics. Incorporating standards like ISO/IEC 42001:2023, with flexibility for local contexts, can guide responsible AI adoption. Finally, a phased, context-sensitive implementation, aligned with continuous evaluation of system performance and organizational readiness, is advocated over wholesale adoption to enhance long-term success and resilience, empowering leaders to maximize AI’s potential within resource-constrained and volatile environments. Originality/value This study advances understanding of AI in finance by providing empirical evidence on its differentiated impact across credit, market and operational risks within the Palestinian banking sector, a context marked by institutional and regulatory challenges. Theoretically, it extends the DeLone and McLean IS Success Model to AI-driven risk management. Practically, it offers actionable guidance on human capital, technological infrastructure and governance, fostering sustainable, context-sensitive AI-enabled risk management in emerging economies.

This study assesses De Lone and McLean’s Information System (D&M IS) Success Model concerning DAS throughout small and medium enterprises (SMEs) in Saudi Arabia (SA). The present work mainly sought to evaluate the impact of information quality (IQ), system quality (SysQ), service quality (SrvQ) serving, system utilization, and user satisfaction (Usat) on the usage of the Digital Accounting System (DAS), which is posited to ultimately improve the quality of sustainable decision-making. The research utilized a quantitative methodology, employing a self-administered questionnaire to collect data from 328 decision-makers who are knowledgeable about actual DAS usage by SMEs in SA. Subsequent to gathering data, validation was conducted via Structural Equation Modeling (SEM) by utilizing smart-PLS software. The findings indicate that SysQ and IQ significantly influenced system utilization, although SrvQ did not. DAS was determined to significantly influence user happiness. Moreover, system utilization and user satisfaction positively influenced DAS, thereby affecting the sustainability of decision-making and reflecting the overall benefits of DAS. This work enhances the current IS literature by identifying the characteristics that affect the net advantages of DAS, with the suggested model evaluated in SMEs in SA utilizing DAS. This study serves as a reference to elucidate the significance of DAS and offers consequences, limitations, and prospects for further research.

ABSTRACT AI-driven chatbots have become widely adopted as effective tools for engaging consumers in e-commerce. Grounded in an extended Stimulus-Organism-Response framework, integrated with the Social Response Theory and Information System Success Model, this study examines how chatbot attributes influence trusting belief and chatbot rapport to, in turn, spur impulse buying in e-commerce. Data were collected through a cross-sectional questionnaire survey that yielded 362 valid responses, which were subsequently analyzed using PLS-SEM. By considering both social, humanoid cues alongside functional, non-humanoid cues, this study presents a holistic outlook on the mechanism that underpins chatbot influences on impulse buying in e-commerce. The results show that human-like conversational tone, social presence, performance expectancy, and service quality positively influence users’ trusting belief and rapport with chatbots, which subsequently foster impulse buying. In contrast, anthropomorphic appearance and information quality showed no significant relationship with trusting belief or chatbot rapport. This study provides novel insights for both researchers and practitioners into how chatbot design elements can be strategically used to drive sales by inducing impulse buying in e-commerce settings.

This study develops TAM with system quality and service quality that individuals consider when using online financial services. The development of online financial transactions has increased every year, but the adoption of mobile payments has not been optimized. These conditions motivate this study to predict the factors that drive Gen Z's interest in adopting mobile banking for processing financial transactions or online payments. The objective of this study was to extend TAM with system quality and service quality that individuals consider when using online financial services. This study uses a survey method by distributing questionnaires to Gen Z regarding their intention to use mobile payments. 235 valid data points were processed using Structural Equation Modeling through Smart PLS. The findings show that Gen Z focuses on the ease and benefits of mobile payment services, while system quality and service quality are not the main considerations. This implies that for current financial service users, aspects such as convenience and immediate benefits are more dominant in shaping positive attitudes toward new technologies. Therefore, service providers should prioritize improving intuitive interface design and clear communication about the benefits of their products to encourage adoption.

The rapid advancement of information and communication technology has transformed learning practices globally, yet teaching activities at SMA Tri Dharma Palembang remain predominantly conventional and do not fully align with the requirements of the Merdeka Curriculum. This study was conducted to design and develop a web-based e-learning system that integrates learning material management, assignment submission, automated and manual assessments, discussion forums, and real-time student activity monitoring. The research applied a Research and Development (R&D) approach consisting of needs analysis, system design using Unified Modeling Language (UML), implementation with PHP, MySQL, and Bootstrap, and evaluation through black box testing and a Likert-scale user satisfaction survey. The system was tested by two teachers and ten students, and expert validation was conducted to assess the research instrument. Results show that all modules performed as expected during functional testing, and the user satisfaction survey yielded an overall score of 85.33%, categorized as very good. The findings were analyzed using the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the DeLone & McLean IS Success Model, confirming that system quality, ease of use, usefulness, and user satisfaction significantly influence e-learning adoption in secondary education. This study contributes theoretically by extending the application of established information systems success models in the Indonesian school context and practically by providing a digital platform that supports the implementation of the Merdeka Curriculum. Keywords: activity monitoring; e-learning; Merdeka Curriculum; TAM; UTAUT

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management.

ABSTRACT Food preparation and consumption are deeply tied to cultural heritage and serve as a medium for expressing personal values. The advent of online food delivery (OFD) services has therefore had a considerable impact on consumers’ food habits. In this study, we aimed to examine the factors shaping urban residents’ attitudes toward OFD services in Malaysia with a particular focus on the influence of cultural value orientation. Our research framework integrated Hofstede’s cultural value dimensions and key elements of the information system success model (ISSM) to offer a holistic perspective. We designed an online questionnaire to collect data from 269 urban OFD users in Malaysia with diverse backgrounds. The data were analyzed using partial least squares structural equation modeling. The study findings revealed that collectivism and indulgence were impactful cultural values that significantly influenced the respondents’ attitudes toward OFD services, while ISSM factors of service and system quality emerged as crucial technological factors. Overall, these findings underscore the importance of considering individual value orientations in conjunction with technological factors when predicting users’ attitudes toward food delivery innovations.

This study examines the essential reasons that determine the success and failure of Enterprise Resource Planning (ERP) implementation within international organizations. From a systematic review of thirteen peer-review articles between 2022 and 2025, the study identifies eight essential success factors, such as top management support, training of end-users, alignment of business processes, and technological readiness, and eight failure factors that are significant, among which, a lack of leadership involvement, resistance by end-users, and inefficient reengineering of processes. Another essential finding pertains to symmetry between the factors, such that exclusion of success components tends to lead to failure. The study incorporates current theoretical models such as TAM, TOE, and the IS Success Model to arrive at a systematic view of ERP dynamics. The study enriches research and practice by suggesting a holistic framework to guide strategy development, risk mitigation, and ERP implementation success within diverse business contexts.

This study explores how tourists’ knowledge acquisition, sharing, and application behaviours influence tourism satisfaction in smart tourism environments. Drawing on knowledge management theory and the information system success model, it introduces a dual mediation model involving perceived personalisation and tourist participation. Based on data from 315 valid questionnaires and structural equation modelling (SEM), results showed that knowledge behaviour positively impacted satisfaction both directly and indirectly through enhanced personalisation and participation. This research extends knowledge management theory to digital tourism, highlighting the importance of guiding user knowledge behaviours. It suggests that platforms should leverage intelligent recommendation and co-creation mechanisms to improve personalisation and engagement, offering theoretical insights and practical implications for designing knowledge-driven and value co-creation services in smart tourism.

Purpose The continuous professionalization of teachers is crucial for sustaining high-quality education. However, traditional professional development (PD) programs often neglect individual needs, specific subject-area demands and distinct career stages, leading to limited relevance and uptake. This study addresses that gap by deploying an AI-based chatbot to provide context-sensitive, personalized PD recommendations at scale. Grounded in technological pedagogical content knowledge (TPACK)and self-determination theory, the research aims to evaluate how tailored chatbot interactions can enhance teachers' motivation, autonomy and technological proficiency while meeting pedagogical and content-specific requirements. Design/methodology/approach Using a convergent parallel mixed-methods design, this study analyzed 2,030 valid chatbot interactions from 1,125 teachers in Austria's Burgenland region. Data collection incorporated the information systems success model (ISSM), the technology acceptance model (TAM) and TPACK as guiding frameworks. Quantitative metrics included fallback rates, implicit intent interpretation, sentiment analysis and confidence scores, whereas qualitative feedback examined perceived relevance. Descriptive and inferential statistics, alongside content analyses, were used to assess the chatbot's performance. This design enabled a comprehensive evaluation of both measurable indicators and user perspectives regarding chatbot-enabled PD recommendations. Findings Results demonstrated a moderate fallback rate of 14.4%, significantly below established benchmarks and an overall positive user sentiment (85%). Quantitative analyses indicated that teachers submitting highly specific queries reported greater satisfaction, while logistic regression revealed that targeted pedagogical keywords significantly increased the likelihood of positive feedback. Qualitative insights underscored the importance of both detailed query formulations and domain-specific terminology. Collectively, these findings highlight robust chatbot performance and emphasize the critical role of contextualized, technology-oriented PD solutions for meeting teachers' individualized professional needs. Research limitations/implications Due to the relatively brief observation period and the self-selecting nature of participating teachers, these findings may not be generalizable across broader educational settings. The sample, drawn from a single Austrian region, may limit external validity. Future research should incorporate larger, more diverse populations, extend the timeframe to measure long-term outcomes and collect additional demographic data to assess subgroup variations. Longitudinal investigations into the sustained impact of chatbot-based recommendations on teaching practice can further elucidate the role of AI-driven PD in different educational contexts. Practical implications Institutional stakeholders can optimize AI-based PD tools by encouraging teachers to submit more detailed queries and employ targeted pedagogical terminology. Additionally, systematic refinements, such as updating domain-specific vocabularies and improving natural language processing algorithms, can reduce fallback rates and enhance user satisfaction. Training programs aimed at familiarizing educators with chatbot functionalities and best practices can further increase engagement. By aligning professional development offerings with teachers' immediate needs and contexts, these strategies can strengthen the relevance, accessibility and overall impact of AI-driven PD initiatives. Social implications By providing accessible, context-sensitive PD resources, AI-driven chatbots may help democratize professional learning for teachers across diverse settings, including those with limited institutional support. This can contribute to narrowing digital skill gaps, especially in remote or underserved schools, thereby promoting educational equity. Enhanced teacher engagement in technology-enhanced PD aligns with broader objectives of fostering lifelong learning cultures and continuously improving educational quality. As teachers refine their digital competencies, they may experience greater autonomy and motivation, fostering a ripple effect on student outcomes and broader societal advancement. Originality/value This research uniquely synthesizes the ISSM, TAM and TPACK to evaluate chatbot-supported teacher PD, offering a multi-faceted assessment of both user experience and educational relevance. By emphasizing the significance of query specificity and targeted pedagogical language, the study advances understandings of how AI-driven tools can address individualized teacher needs in diverse contexts. The findings deliver practical guidance for refining chatbot technologies and theoretical insights into the interplay of technology acceptance, pedagogical content knowledge and AI-based support systems, thereby contributing to ongoing discourse on data-informed professional development.