Dataset Information

Firm-Level Analysis of Global Supply Chain Network: Role of Centrality on Firm’s Performance

ABSTRACT: Over the past few years, academics and practitioners have started paying more attention to analyze and understand value creation and business models from a business ecosystem perspective. Such business ecosystems have been constructed based on the connected networks of people, firms, industries, and countries. Developing strategies to help sustain the firms’ competitive advantage in business ecosystems is a key challenge for businesses and policymakers worldwide. Recent disruptions to global supply chains due to the COVID-19 pandemic have exposed the high risk and challenges of managing sustainable global and intertwined supply chain networks. Using the data mined from financial records, the present study constructs the global supply chain network of the auto manufacturing sector. The data from 32,396 notable first-tier forward and backward supply chain connections were mined to build the global supply chain network in this sector. The global supply chain network structure was analyzed using centrality measures and clustering analysis. We utilized path analysis to explore the effect of various supply chain centrality measures on firms' financial performance, investment risk, and market value volatility. The findings provide new insight into our understanding of the relationship between the firm's location characteristics in the global supply chain ecosystem and various aspects of the asset's performance. Furthermore, discussions are presented about strategies that support sustainable collaborative value creation and sustainable competitiveness of businesses across the global manufacturing ecosystems. The research method used in this study has the potential to be applied to several industries.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42943-021-00026-8.

SUBMITTER: Lavassani K

PROVIDER: S-EPMC8220882 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In this paper, we study the topological properties of the global supply chain network in terms of its degree distribution, clustering coefficient, degree-degree correlation, bow-tie structure, and community structure to test the efficient supply chain propositions proposed by E. J.S. Hearnshaw et al. The global supply chain data in the year 2017 are constructed by collecting various company data from the web site of Standard & Poor's Capital IQ platform. The in- and out-degree distributions are characterized by a power law of the form of γin = 2.42 and γout = 2.11. The clustering coefficient decays [Formula: see text] with an exponent βk = 0.46. The nodal degree-degree correlations 〈knn(k)〉 indicates the absence of assortativity. The bow-tie structure of giant weakly connected component (GWCC) reveals that the OUT component is the largest and consists 41.1% of all firms. The giant strong connected component (GSCC) is comprised of 16.4% of all firms. We observe that upstream or downstream firms are located a few steps away from the GSCC. Furthermore, we uncover the community structures of the network and characterize them according to their location and industry classification. We observe that the largest community consists of the consumer discretionary sector based mainly in the United States (US). These firms belong to the OUT component in the bow-tie structure of the global supply chain network. Finally, we confirm the validity of Hearnshaw et al.'s efficient supply chain propositions, namely Proposition S1 (short path length), Proposition S2 (power-law degree distribution), Proposition S3 (high clustering coefficient), Proposition S4 ("fit-gets-richer" growth mechanism), Proposition S5 (truncation of power-law degree distribution), and Proposition S7 (community structure with overlapping boundaries) regarding the global supply chain network. While the original propositions S1 just mentioned a short path length, we found the short path from the GSCC to IN and OUT by analyzing the bow-tie structure. Therefore, the short path length in the bow-tie structure is a conceptual addition to the original propositions of Hearnshaw.

Project description:The digital transformation of firms plays an increasingly important role in the economy and society. However, limited access to data on firm-level digital intensity is an impediment to advancement of multiple research projects concerned with firm digitalization. To alleviate this challenge, this paper proposes a method for estimating firm-level digital intensity based on other more readily available firm-level data and reference data on digitalization, which is available on sector-level. The proposed method utilizes firm-level revenue breakdown by sector to estimate sector revenue-weighted digital intensity scores, which lead to classification of firms into low, medium and high digital intensity groups. The output from the proposed method can be directly used in research concerned with firm digitalization and investigating this multifaceted phenomenon. Results from the application of the proposed method to an illustrative sample of large US and non-US firms (2000 observations in total) indicate that firm-level digital intensity can be efficiently estimated for large samples using data commonly available to researchers. The key differences between the proposed method and alternative methods are:•Recognition of the fact that firms might participate in more than one sector or industry, which partially explains within-sector heterogeneity in firm-level digital intensity. We found that 67.8% of large US firms and 78.6% of large non-US firms were engaged in more than one industry.•Use of reference sector-level digital intensity scores, which allows for rapid update, application across geographies and time, as well as parallel calculation of multiple digital intensity scores for each reference data. Furthermore, use of reference data enables supplementation of firm-level data on digitalization.•Replicability of the method and reproducibility of the results through inclusion of the source code and availability of data through research and commercial databases.

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Firm-Level Analysis of Global Supply Chain Network: Role of Centrality on Firm’s Performance

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