Past, present, and future of sustainable finance: insights from big data analytics through machine learning of scholarly research

Abstract

Sustainable finance is a rich field of research. Yet, existing reviews remain limited due to the piecemeal insights offered through a sub-set rather than the entire corpus of sustainable finance. To address this gap, this study aims to conduct a large-scale review that would provide a state-of-the-art overview of the performance and intellectual structure of sustainable finance. To do so, this study engages in a review of sustainable finance research using big data analytics through machine learning of scholarly research. In doing so, this study unpacks the most influential articles and top contributing journals, authors, institutions, and countries, as well as the methodological choices and research contexts for sustainable finance research. In addition, this study reveals insights into seven major themes of sustainable finance research, namely socially responsible investing, climate financing, green financing, impact investing, carbon financing, energy financing, and governance of sustainable financing and investing. To drive the field forward, this study proposes several suggestions for future sustainable finance research, which include developing and diffusing innovative sustainable financing instruments, magnifying and managing the profitability and returns of sustainable financing, making sustainable finance more sustainable, devising and unifying policies and frameworks for sustainable finance, tackling greenwashing of corporate sustainability reporting in sustainable finance, shining behavioral finance on sustainable finance, and leveraging the power of new-age technologies such as artificial intelligence, blockchain, internet of things, and machine learning for sustainable finance.

Alexa, what do we know about conversational commerce? Insights from a systematic literature review

“…Delineating the performance of the field is a common endeavor of bibliometric reviews-it is akin to the reporting of the profile of participants in empirical studies, though with greater analytical rigor (Donthu et al, 2021c). Similarly, the mapping of the intellectual structure or scientific knowledge in the field through a bibliometric analysis is highly valuable as it provides an objective means for identifying the main contributions (themes) of prior research, which subsequently guides future research agenda to advance the field (Kumar et al, 2022a(Kumar et al, , 2022b. Unlike integrative and narrative reviews that often rely on a single qualitative analysis to arrive at conclusions about a field, bibliometric reviews can leverage on a myriad of quantitative analytical techniques for the same purpose (e.g., cooccurrence analysis and bibliographic coupling), thereby enhancing rigor by making the triangulation of insights possible through review studies (Goodell et al, 2021;Kumar et al, 2022a).…”

“…Noteworthily, the use of bibliometric analysis is well suited for large-scale reviews because it is a quantitative analysis that is exploratory yet data driven, thereby enabling the development of an objective evaluation of the performance of the field and the mapping of the scientific knowledge in that field in its entirety (Donthu et al, 2021c;Kumar et al, 2022aKumar et al, , 2022bMariani et al, 2022). Alternatives such as integrative and narrative reviews typically rely on qualitative analysis and thus tend to be subjective and prone to researcher bias, whereas meta-analysis, which is the other quantitative alternative to bibliometric analysis, is usually focused on a series of highly researched statistical relationships and thus discounting insights emerging from nonquantitative studies (e.g., conceptual and qualitative) in the field (Donthu et al, 2021c;Paul et al, 2021).…”

“…Similarly, the use of Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) as a review protocol is highly suitable because (1) it provides a step-by-step procedure that can be systematically documented and transparently disclosed for replication, (2) it is derived by marketing researchers for reviews in the social sciences (as opposed to its alternative such as PRISMA, which was developed by medical researchers for reviews in the sciences), (3) it's use has been called for by past scholars for future reviews (Paul et al, 2021), and ( 4) it has been widely adopted and used for comprehensive and systematic literature reviews, including those relying on a bibliometric methodology and published in premier journals such as Annals of Operations Research (Kumar et al, 2022b), Journal of Business Research (Lim et al, 2022), Public Management Review (Palumbo & Manesh, 2022), and Technological Forecasting and Social Change (Kumar et al, 2022a).…”

Artificial intelligence and blockchain implementation in supply chains: a pathway to sustainability and data monetisation?

“…In addition, leveraging the synergistic technical capabilities of multiple digital systems and applications such as AI and BCT helps pursue Sustainable Development Goals (Del Río Castro et al, 2021 ). Collaborative digital ecosystems promote sustainable supply chains performance via enabling the improved management of resources, supporting waste monitoring and management, reducing energy consumption, and informing the development and diffusion of sustainable financing instruments (Belhadi et al, 2021 ; Kumar et al, 2022 ).…”

Global Trends and Hot-Spots in Research on Virtual Simulation in Nursing: A Bibliometric Analysis From 1999 to 2021

“…Bibliometric analysis is widely accepted for reviewing big data of articles in a field or publications in a journal using quantitative techniques ( 9 , 10 ). It usually applies bibliometric tools (e.g., Bibliometrix R, Gephi, Pajek, CiteSpace, Leximancer, and VOSviewer) to analyze the publication trend, the top articles, primary contributors, and major theme and frontier topics in a given field ( 11 – 13 ). There are several existing bibliometric analysis about nursing topics ( 14 – 16 ).…”