Eco-innovation has gained considerable attention in the academic and corporate world due to its potential in mitigating a firm’s sustainability issues. Most previous studies focused on the reactive approaches of eco-innovation using primary data. This study mitigates the existing research gap by highlighting proactive eco-innovation and using a secondary panel dataset. The prime objective of this study is to investigate the relationship between proactive eco-innovation and firms’ financial performance. Hence, the study will introduce the proactive eco-innovation index with the help of secondary panel data. In addition to that, the paper will also explore how proactive eco-innovation relates to circular economy. The theory of Resource-Based View (RBV) was used to explain the relationship among the variables. This study was conducted on 31 Malaysian public listed energy companies from 2015 to 2019. A proactive eco-innovation index was inferred by adapting three dimensions of eco-innovation (product, process, and technology) which is applicable for the energy sector. By applying random-effects GLS regression equation modeling, it was found that proactive eco-innovation (product eco-innovation, process eco-innovation, and technology eco-innovation) has a direct effect on firm financial performance. Furthermore, product and process eco-innovation is directly related to a circular economy through a sustainable product development process. The findings suggest that policymakers in the firm should proactively adopt eco-innovative practices. It will positively affect the circular economy as it will be cost-effective and help to reduce potential industrial pollution in the environment.

Eco-innovation has gained considerable attention in academia as well as in industry due to its potential in mitigating environmental challenges and its positive correlation with firm performance. However, there are limited studies which have investigated the moderating relation of International Organization for Standardization (ISO) 14001:2015 between eco-innovation and firm sustainability in their contribution to societal development. This research is supported by a resource-based theory which explores the core-competencies of firms and challenges the resources creating the competitive advantage of the firm without compromising on the social responsibility aspect of the firm. This study proposes a fishbone eco-innovation business model, which includes production (product, process, and technology) and non-production (organization and marketing) business activities mapped with the 17 Sustainable Development Goals (SDGs) for societal development. This fishbone eco-innovation business model signals to the stakeholders about the organization’s innovation in their green implementation, which goes beyond mere compliance. The contribution of the fishbone eco-innovation business model to societal development will create a unique competitive edge and green goodwill amongst the external stakeholders, which will attract sustainably responsible investors for investment. This article draws propositions and develops a conceptual model for future empirical research on eco-innovation and societal development.

Purpose: Artificial intelligence (AI) has emerged as a transformative force in advancing Sustainable Development Goals (SDGs) and redefining higher education. Recently, AI has garnered significant attention from academia and industry due to its potential to address SDGs and shape the future of higher education. The prime objective of this study is to critically investigate the intersection of AI, sustainability and higher education (SHE) on the existing literature. The study also aims to develop a conceptual framework toward AI driven SHE. Design/methodology/approach: This study is a systematic literature review employing Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, which is based on the Scopus and web of science databases based on the time frame of 2015–2024 in the context of higher education. Following the PRISMA guidelines, a systematic review was performed, yielding 39 pertinent articles for inclusion in the analysis. Descriptive analysis, thematic analysis, cluster analysis was conducted by VOS viewer software to reach reasonable conclusion on this intersection. Findings: After conducting the review analysis, the findings reveal that three key themes have emerged in AI driven higher education such as customized learning experiences (first theme), accessibility (second theme) and optimizing resources allocation (third theme). Additionally, the study has created two categories to show the challenge in implementing AI in higher education: pedagogical challenges (category one), and policy and governance challenges (category two). Originality/value: Finally, this study proposes and develops a SHE conceptual framework based on AI. To fully harness the potential benefits of AI technology towards achieving SHE, universities and policymakers should pay more attention to ethical aspects of AI implications and adoption in learning content.

Recently, eco-innovation has received a lot of attention in the academic and corporate world due to its potential to accelerate firm financial progression. To measure eco-innovation, mostly primary data and a reactive approach were employed. By emphasising the proactive approach and utilising a secondary panel dataset, this study fills the existing research gap. Data presented in this paper comprise 31 energy firms from Bursa Malaysia for the years between 2015 and 2019. Panel data associated with eco-innovation proactiveness and firm financial progression were collected from three different sources such as company websites, annual reports, and sustainability reports using content analysis. For data collection, an index was adapted comprising five dimensions of eco-innovation, named as product, process, technology, organizational, and marketing. In addition to that, Tobin’s Q was considered as a proxy dimension for firm financial progression because it considers both market value as well as book value. Following a unit root test, six specific data diagnostic tests were performed to ensure data reliability and validity for future potential usage. The results reveal that the panel dataset was organised and is eligible for further statistical model analysis.