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The purpose of this paper is to empirically demonstrate that drivers of venture capital (VC) investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist across industries within each of these sectors.

The basic hypothesis is that “not only different stages of VC investments have different drivers, but VC investments in different sectors of the economy are also driven by different drivers.” The paper tests this hypothesis using a Poterba (1989) type supply and demand framework in the multivariate time-series regression analysis.

This paper empirically demonstrates that drivers of VC investments are different across three broadly defined sectors: high-technology manufacturing, medium-technology manufacturing and services, and low-technology services. Moreover, such differences also exist by stages of investment and across industries within each of these sectors. In particular, the paper finds that the importance of the number of VC-led initial public offering (IPO) transactions as the main driver of VC investment decreases with the level of technology involved in the sector. IPO transactions are particularly important in software, networking and equipment, and business products and services industries. In contrast to earlier literature, however, the paper do not find a more pronounced effect of IPOs for seed and late stages of VC investments. Similarly, the positive impact Sarbanes-Oxley Act of 2002 – which mainly impacts public companies – also intensifies with a decrease in the level of technology involved in the sector, and the paper do not find a negative impact. The Act is important particularly for VC investments in medium- and low-tech sectors and in early or expansion stages.

In analyzing the determinants of VC in a supply and demand framework as in Poterba (1989), the paper differentiates between different sectors (17 industries) and stages of VC (four stages: seed, early, expansion, late). Such level of differentiation is novel and allows more refined and better targeted public policy measures.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.