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This paper seeks to establish design quality indicators (DQIs) that can be utilized for assessing the design of community centers, with an emphasis on the technical, functional, and behavioral performance domains.

A comprehensive literature review resulted in identifying 79 DQIs for community centers. A three round Delphi evaluation approach was utilized to rate the importance of the DQIs through their relative importance index (RII) values. The assessment of the DQIs involved a diverse group of stakeholders including facilities managers, architects/engineers (A/Es), community centers’ staff, and regular visitors of community centers.

The majority of the established DQIs were considered to be either “Very Important” or “Important”.

The established DQIs can be utilized to identify best practices in the design of community centers and benchmark the performance of different community centers.

The design quality of community centers could significantly impact the community's quality of life and user experience. The development of DQIs provides for enhanced accountability and improved service delivery for the communities they serve. This enables community centers to be more effective, efficient, and responsive to the needs of the users they support.

Artificial Intelligence (AI) technology, having powerful capabilities and rapid development, has been able to move the structures of businesses and organizational processes towards intelligent automation. The role of digital transformation in universities and educational institutions has an increasing trend. New business structures and the digitization of processes, other than the advantages they bring about, might have different effects on the environment and sustainability. This study aims to identify the effective factors on AI adoption and the effect of using this technology in educational institutions and universities on their sustainable performance.

This research is applied using a quantitative approach. Universities selected for the study were ranked by Quacquarelli Symonds (QS). Of the 111 QS listed universities in the Middle East in 2023, 30 universities were randomly selected, and the research questionnaire was emailed to 50 people (administrative, educational and research staff) from each university. Information related to the level of AI technology acceptance and use was collected using a questionnaire among the university staff and faculty members; moreover, their relationship with universities’ sustainable performance scores was assessed. Path analysis and Smart PLS software have been used for data analysis.

The research findings showed that factors of technology performance, enjoyment, trust, social influence and organizational capabilities all have positive effect on AI adoption at universities. Also, the adoption of AI is considered as an effective factor in improving university sustainable performance. Therefore, based on exact data analysis using AI, universities can manage their activities and better control their environmental performance. Also, the use of AI can be effective in the availability to sustainable education in universities and the establishment of social justice in society. Accordingly, to facilitate executive processes and decision-making, policymakers in the field of science and university principals can improve administrative, educational and research processes via investing on AI, in addition to improving environmental activities and sustainable development.

The theoretical contribution of this research, other than designing an AI acceptance model for universities includes evaluating the relationship between using AI and university sustainable performance.

The purpose of this study is to evaluate the impact of green human resource management (GHRM) practices on Indian companies of different sectors and to identify the most critical GHRM practices that can lead to a more sustainable and environmentally friendly workplace.

This study uses an integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach to determine the importance of 32 GHRM practices classified into eight categories, as identified through literature review and expert consultation. This study also identifies the best sector for GHRM practices in India.

This study reveals that employee engagement is the most critical practice among all the GHRM practices identified. India’s Information Technology-Enabled Services sector benefited the most from GHRM practices, followed by the Insurance sector.

This study contributes to the literature on GHRM practices and their impact on organisations and sectors. The integrated Analytic Hierarchy Process-Evaluation based on Distance from Average Solution approach used in this study is innovative and can be helpful for Indian companies to prioritise and implement effective GHRM practices.