Configurations of financing instruments for supply chain cost reduction: evidence from Chinese manufacturing companies

Li, Lixu; Wang, Zhiqiang; Zhao, Xiande

doi:10.1108/ijopm-12-2021-0755

Cited by 8 publications

(8 citation statements)

References 48 publications

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“…Srivastava and Chandra (2018) interviewed 29 virtual world users to corroborate the inferences obtained from the quantitative research. Li et al. (2022b) interviewed 24 companies to support and explain the inferences obtained from the quantitative research.…”

Section: Qualitative Studymentioning

confidence: 96%

“…Srivastava and Chandra (2018) interviewed 29 virtual world users to corroborate the inferences obtained from the quantitative research. Li et al (2022b) interviewed 24 companies to support and explain the inferences obtained from the quantitative research. Similar to previous studies, we used qualitative data to gain complementary insights from an alternative divergent perspective.…”

Section: Big Data and Big Disastermentioning

confidence: 96%

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Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Gong

Wang

et al. 2022

IJOPM

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PurposeAlthough big data may enhance the visibility, transparency, and responsiveness of supply chains, whether it is effective for improving supply chain performance in a turbulent environment, especially in mitigating the impact of COVID-19, is unclear. The research question the authors addressed is: How do logistics firms improve the supply chain performance in COVID-19 through big data and supply chain integration (SCI)?Design/methodology/approachThe authors used a mixed-method approach with four rounds of data collection. A three-round survey of 323 logistics firms in 26 countries in Europe, America, and Asia was first conducted. The authors then conducted in-depth interviews with 55 logistics firms.FindingsIn the first quantitative study, the authors find mediational mechanisms through which big data analytics technology capability (BDATC) and SCI influence supply chain performance. In particular, BDATC and SCI are two second-order capabilities that help firms develop three first-order capabilities (i.e. proactive capabilities, reactive capabilities, and resource reconfiguration) and eventually lead to innovation capability and disaster immunity that allow firms to survive in COVID-19 and improve supply chain performance. The results of the follow-up qualitative analysis not only confirm the inferences from the quantitative analysis but also provide complementary insights into organizational culture and the institutional environment.Originality/valueThe authors contribute to supply chain risk management by developing a three-level hierarchy of capabilities framework and finding a mechanism with the links between big data and big disaster. The authors also provide managerial implications for logistics firms to address the new management challenges posed by COVID-19.

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Section: Qualitative Studymentioning

confidence: 96%

Section: Big Data and Big Disastermentioning

confidence: 96%

Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Gong

Wang

et al. 2022

IJOPM

Self Cite

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“…In contrast, when the focal company is highly dependent on its major suppliers, most purchases of the focal company are related to the few major suppliers. To maintain the stability of working capital, the focal company trend to give priority to their capital situations when selecting these major suppliers (Li et al , 2022). In this case, given that major suppliers typically have enough financial capital on hand, the focal company may no longer rely on SCC to enhance financial collaboration, thus mitigating the link between SCC and SCF adoption.…”

Section: Hypothesis Developmentmentioning

confidence: 99%

“…Supply chain finance (SCF) is defined as a type of management behavior that integrates logistics operations, commercial operations and financial management (Lam and Zhan, 2021), aiming to unite various members in the supply chain and external service companies, quickly respond to the capital needs of the companies in the chain, and ultimately achieve common value creation (Chen et al , 2021; Li et al , 2022). Because SCF provides great potential in optimizing the capital flow of the companies in the chain, many world-renowned companies, such as Walmart, Siemens, JD.com and Boeing, have actively promoted their suppliers and customers to adopt SCF (Wuttke et al , 2019).…”

Section: Introductionmentioning

confidence: 99%

Supply chain collaboration and supply chain finance adoption: the moderating role of information transparency and transaction dependence

Wang

Chen

et al. 2023

SCM

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Purpose Although supply chain collaboration (SCC) theoretically boosts the adoption of supply chain finance (SCF) through information sharing and cost savings, many companies with good supply chain partnerships still hesitate to engage in SCF. To disentangle this puzzle, this study aims to explore how two dimensions of information transparency (i.e. information quantity and information quality) and two types of transaction dependence (i.e. dependence on suppliers and dependence on customers) influence the relationship between SCC and SCF adoption. Design/methodology/approach This study uses secondary survey data from a Chinese bank, including 464 Chinese companies that have adopted SCF to varying degrees. This study then performs the logistic regression analysis to test the hypotheses. Findings This study empirically confirms that SCC shows a positive relationship with SCF adoption. More interestingly, information quantity negatively moderates this positive relationship, whereas information quality positively moderates this positive relationship. Most surprisingly, dependence on customers rather than dependence on suppliers strengthens this positive relationship. Originality/value This study makes theoretical contributions to the SCF literature by demonstrating the distinct moderating mechanisms regarding the relationship between SCC and SCF adoption. The findings also help companies reexamine their interactions with supply chain members.

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“…Through the accurate mining and feature extraction of the statistical big data of human resource innovation performance influence under the background of high-yield human capital of enterprises, the evaluation of human resource innovation performance influence under the background of high-yield human capital of curriculum enterprises can be realized [2]. Among them, a statistical evaluation method of human resource innovation performance influence under the background of high-yield human capital of local area network enterprises based on coding fusion is proposed in [3], which expands the statistical sequence space of human resource innovation performance influence under the background of high-yield human capital of enterprises in high dimensions, resulting in high calculation cost; In the [4], a particle swarm optimization (PSO)-based method for evaluating the impact of human resource innovation performance under the background of high-yield human capital of enterprises is proposed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Impact of Human Resource Innovation Performance Based on Regression Analysis

Bai¹

2023

Proceedings of the 2022 2nd International Conference on Business Administration and Data Science (BADS 2022)

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In order to improve the ability of dynamic quantitative evaluation of the impact of human resource innovation performance under the background of high-yield human capital, this paper puts forward an evaluation model of the impact of human resource innovation performance based on regression analysis. Under the traditional modes of human capital investment, such as talent introduction, education and training, the index parameter set which can effectively reflect the influence of enterprise human capital and human resource innovation performance under the background of high returns is constructed. Considering the investment cost and future returns, as well as knowledge updating, experience accumulation, skills and other factors, the statistical sample sequence model of index parameter regression analysis is established. Combined with the statistical big data analysis method, The performance evaluation and characteristic analysis of human resource innovation in the context of high-yield human capital of enterprises are carried out. Through the fuzzy index parameter fusion, the horizontal parameter distribution and equilibrium control model of knowledge and comprehensive ability are adopted, and the quantitative regression analysis model and Markov model are established in the process of impact evaluation. The impact evaluation of human resource innovation performance in the context of highyield human capital of enterprises is realized by using regression analysis learning method, big data fusion and adaptive optimization method. The empirical analysis results show that the dynamic balance of the impact assessment of human resource innovation performance under the background of high returns of enterprise human capital is good, the convergence of the impact assessment of human resource innovation performance is high, and the confidence level of the assessment results is improved.

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Configurations of financing instruments for supply chain cost reduction: evidence from Chinese manufacturing companies

Cited by 8 publications

References 48 publications

Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Big data and big disaster: a mechanism of supply chain risk management in global logistics industry

Supply chain collaboration and supply chain finance adoption: the moderating role of information transparency and transaction dependence

Evaluation of the Impact of Human Resource Innovation Performance Based on Regression Analysis

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