Purpose: The purpose of this paper is to investigate how citizens’ perception of empowerment can influence the intention to use and intention to recommend e-participation.Design/methodology/approach: A research model is evaluated using structural equation modelling. An online survey questionnaire was used to collect data from 210 users of e-participation.Findings: The results show that psychological empowerment influences the intention to use and recommend e-participation. Performance expectancy and facilitating conditions were the strongest predictors of intention to use; effort expectancy and social influence had no significant effect on the prediction of intention to use e-participation.Research limitations/implications: The use of psychological empowerment as a higher-order multidimensional construct is still insufficiently researched. Future research may explore the effect of each dimension of psychological empowerment in different scenarios of e-participation adoption. Caution is needed when generalising our findings towards the adoption of e-participation in different locations or with different participants.Practical implications: The findings can help the local governments to design strategies for the promotion and diffusion of e-participation amongst the citizenry. Those strategies should focus on citizens’ perception of empowerment, thereby creating a positive attitude towards intention to use and recommend e-participation.Originality/value: An innovative research model integrates the unified theory of acceptance, use of technology and psychological empowerment; the last as a higher-order construct.

This article explores the main factors that drive the adoption of e-participation. A weight and meta-analysis was carried out from previous quantitative research studies related to individual e-participation adoption published in journals and conferences over the last 17 years. A total of 60 studies were used for the weight and meta-analysis. We identify the ‘best’ and ‘promising’ predictors used in research models to study e-participation. The best predictors are: trust, effort expectancy, perceived usefulness , attitude, trust in government and social influence on intention to use, perceived ease of use on perceived usefulness , perceived usefulness on attitude , and intention to use on use . General public in urban areas account for the 69.78% of the respondents across all articles. Two thirds of all respondents belong to Asia and the Middle East. The countries with highest number of articles found are United States and Jordan. The article provides a wide view of the performance of the 483 relationships used in research models to study e-participation, which may allow researchers to identify trends, and highlights issues in the future use of some constructs. Implications for theory and practice, limitations and directions for future research are discussed.

As artificial intelligence systems gain traction, their trustworthiness becomes paramount to harness their benefits and mitigate risks. This study underscores the pressing need for an expectation management framework to align stakeholder anticipations before any system-related activities, such as data collection, modeling, or implementation. To this end, we introduce a comprehensive framework tailored to capture end-user expectations specifically for trustworthy artificial intelligence systems. To ensure its relevance and robustness, we validated the framework via semi-structured interviews, encompassing questions rooted in the framework's constructs and principles. These interviews engaged fourteen diverse end users across the healthcare and education sectors, including physicians, teachers, and students. Through a meticulous qualitative analysis of the interview transcripts, we unearthed pivotal themes and discerned varying perspectives among the interviewee groups. Ultimately, our framework stands as a pivotal tool, paving the way for in-depth discussions about user expectations, illuminating the significance of various system attributes, and spotlighting potential challenges that might jeopardize the system's efficacy.