Intellectual capital, blockchain-driven supply chain and sustainable production: Role of supply chain mapping

PurposeThe main objective of this study is to test whether firms with a higher level of intellectual capital (IC) perform better in terms of their supply chain resilience compared to those with lower levels of IC. Likewise, the study also examines the impact of IC (characterized by human capital, relational capital and structural capital) on supply chain resilience directly and through supply chain learning.Design/methodology/approachData were collected from the 159 processed-food sector firms using a close-ended questionnaire during the corona virus 2019 (COVID-19) pandemic. Partial least squares structural equation modelling (PLS-SEM), partial least squares multigroup analysis (PLS-MGA) and one-way analysis of variance (ANOVA) were used to test a set of hypotheses emanating from a conceptual model of IC and supply chain resilience.FindingsEmpirical results revealed a significant influence of all dimension of IC on a firm's supply chain learning and supply chain resilience. Likewise, findings also exhibit a momentous role of supply chain learning in reinforcing the impact of IC on supply chain resilience. Cross-firm size comparison reveals that supply chain resilience of firms with a higher level of IC performed significantly better than those with lower levels of IC. Firms with a higher level of structural capital had a highly resilient supply chain.Practical implicationsFindings of the study imply that IC and supply chain learning should be considered as a strategic tool and should be strategically developed for uplifting a supply chain performance of a firm. The development of IC and supply chain learning (SCL) not only improves the supply chain resilience of a firm but also can help to integrate the internal and external knowledge for harnessing supply chain resilience.Originality/valueThis research study was conducted during the COVID-19 pandemic which provides a unique setting to examine resiliency and learning.

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Determinants of the Acceptance of Sustainable Production Strategies in Conventional and Organic Dairying in Europe: An Empirical Analysis

10.20944/preprints201708.0053.v1 ◽

2017 ◽

Author(s):

Simona Naspetti ◽

Serena Mandolesi ◽

Jeroen Buysse ◽

Terhi Latvala ◽

Philippa Nicholas ◽

...

Keyword(s):

Supply Chain ◽

Technology Acceptance ◽

Management Practices ◽

Sustainable Production ◽

Perceived Usefulness ◽

Dairy Farmers ◽

Alternative Protein ◽

Production Strategies ◽

The Impact

The goal of the study was to assess the farmers’ acceptance of three sustainable production strategies, namely ‘Agro-forestry’, ‘Alternative protein source’ and ‘Prolonged maternal feeding’. Data on the acceptance of these strategies were collected by a survey of dairy farmers in six EU countries (AT, BE, DK, FI, IT, UK). An extended version of the Technology Acceptance Model (TAM) was applied by means of Structural Equation Modelling to testing various hypotheses on attitudes and intentions of dairy farmers towards these novel production strategies, as well as the influence of organic practices and collaborative behaviours along the supply chain. We found that the most preferred strategy - across all countries - was soy substitution by alternative protein sources. We also found that the intention to adopt a sustainable production strategy may derive from the influence of opinions (and behaviours) of relevant others, showing the role of interactions among farmers and other stakeholders in the adoption of innovations. Finally, the perceived usefulness of all investigated strategies is higher for organic farmers, while collaborative patterns reduce the impact of subjective norm on usefulness and overall acceptance. Our findings should encourage policy makers to consider the important role of supply chain management practices, including collaboration, to enhance the sustainability of dairy farming systems.

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Exploring the role of intellectual capital in supply chain intelligence integration

Industrial Management & Data Systems ◽

10.1108/imds-06-2017-0285 ◽

2018 ◽

Vol 118 (5) ◽

pp. 1018-1032 ◽

Cited By ~ 5

Author(s):

Yongyi Shou ◽

Wenjin Hu ◽

Yongmei Xu

Keyword(s):

Supply Chain ◽

Intellectual Capital ◽

Structural Equation ◽

Design Methodology ◽

Primary Data ◽

Equation Modeling ◽

Relational Capital ◽

Content Type ◽

Structural Capital

Purpose The purpose of this paper is to explore the role of intellectual capital (IC) in supply chain intelligence integration (SCII) and the interrelationships of the three components of IC (i.e. human capital (HC), structural capital (SC) and relational capital (RC)) in the supply chain context. Design/methodology/approach This paper conducted an empirical study by using primary data from 389 sample firms. The authors applied structural equation modeling to test the proposed hypotheses. Findings The results indicate that both HC and RC have direct impact on SCII, whereas SC only influences SCII through RC. Originality/value This study evidences that IC is an enabler of SCII. Furthermore, this study reveals the interrelationships of human, structural and RC.

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Intellectual Capital and Technological Innovation: The Mediating Role of Supply Chain Learning

International Journal of Innovation Science ◽

10.1260/1757-2223.7.3.199 ◽

2015 ◽

Vol 7 (3) ◽

pp. 199-210 ◽

Cited By ~ 14

Author(s):

Hui-Ying Zhang ◽

Shuang Lv

Keyword(s):

Social Capital ◽

Supply Chain ◽

Technological Innovation ◽

Intellectual Capital ◽

Two Dimensions ◽

Four Dimensions ◽

Mediating Role ◽

Structural Capital ◽

The Relationship

This paper analyzes the influence of intellectual capital on firms’ technological innovation, and the intermediary effect of supply chain learning in the relationship between different dimensions of intellectual capital and technological innovation. Using a questionnaire to survey 167 Chinese high and medium-high technological manufacturing firms, our research provides a new insight with interesting results. (1) Among the four dimensions of intellectual capital, only two dimensions, internal social capital and external social capital, exert positive effect on technological innovation; (2) Among the two dimensions of supply chain learning, learning from both suppliers and customers exerts a significant effect on technological innovation, and learning from the customer has a more significant effect; (3) A complete intermediary effect occurs from supply chain learning in the relationship among human capital, structural capital and technological innovation, while an incomplete intermediary effect occurs from supply chain learning in the relationship among external social capital, internal social capital, and technological innovation.

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