Great partners: how deep learning and blockchain help improve business operations together

Luo, Suyuan; Choi, Tsan‐Ming

doi:10.1007/s10479-021-04101-4

Cited by 27 publications

(13 citation statements)

References 75 publications

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“…Cai et al (2021) analytically explore how blockchain can help to address the cheating problems in the use of markdown contract in a supply chain with a rental platform. Luo and Choi (2021) conduct a review related to AI and blockchain. They highlight that the integration of these two critical disruptive technologies can help improve cyber-security.…”

Section: Related Literaturementioning

confidence: 99%

E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?

Luo

Choi

2022

Production and Operations Management

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E‐commerce supply chains and their members face risks from cyber‐attacks. Consumers who purchase goods online also risk having their private information stolen. Thus, businesses are investing to improve cyber‐security at a nontrivial cost. In this paper, we conduct a Stackelberg game‐theoretical analysis. In the basic model, we first derive the equilibrium pricing and cyber‐security level decisions in the e‐commerce supply chain. Based on real‐world practices, we then explore whether governments should impose cyber‐security penalty schemes. Our findings show that when the government is characterized by having sufficiently high emphasis on consumer surplus, implementing the penalty scheme is beneficial to social welfare. Then, we extend the analysis to examine how adopting systems security enhancing technologies (such as blockchain) will affect the government's choice of imposing penalty. We uncover that when it is beneficial to have government's penalty scheme, the technology benefit‐to‐cost ratio is a critical factor that governs whether the optimal penalty will be lower or higher with the adoption of systems security enhancing technologies. To generate more insights, we conduct further analyses for various extended modeling cases (e.g., with alliance, competition, and the defense‐level dependent penalty scheme) and find that our main results remain robust. One important insight we have uncovered in this study is that imposing government penalty schemes on cyber‐security issues may do more harm than good; while once it is beneficial to implement, the government should charge the heaviest possible fine. This finding may explain why in the real world, governments basically always adopt a polarized strategy, that is, either do not impose penalty or impose a super heavy penalty, on cyber‐security issues.

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Section: Related Literaturementioning

confidence: 99%

E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?

Luo

Choi

2022

Production and Operations Management

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“…However, Qu et al (2019) investigated the intense reconstruction of urban core blocks using the DEA-DL approach. The present study contributes to the body of knowledge by filling a void by utilizing the integrated DEA and DL methods (Luo & Choi, 2021).…”

Section: Data Envelopment Analysis and Deep Learningmentioning

confidence: 94%

“…As a result, robust techniques to assess and forecast the pulp and paper industry's efficiency are required. This research presents performance management and efficiency prediction for managing sustainable performance in the paper industry (Cao et al, 2011;D'Ecclesia and Clementi, 2019;Luo & Choi, 2021). We used the DEA-DL methodology to assess and forecast the pulp and paper industry's environmental efficiency.…”

Section: Introductionmentioning

confidence: 99%

A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

et al. 2022

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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers.

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“…With this exceptional characteristic, blockchain equipment is projected to bring the superiority of system security and information visibility to supply chain operations (Carson et al, 2018 ). During the past three years, numerous comprehensive reviews of blockchain technology and its application to supply chains have been conducted (e.g., Chang & Chen, 2020 ; Luo & Choi, 2021 ; Müßigmann et al, 2020 ; Pournader et al, 2020b ; Saberi et al, 2019 ; Wang et al, 2019a ). While we refer the readers to these reviews for an in-depth discussion of blockchain application to supply chains, in this section we primarily focus on the literature on trust and trust dimensions, to be adopted as a core framework in the following sections.…”

Section: Conceptual Foundationmentioning

confidence: 99%

Blockchain as the “trust-building machine” for supply chain management

2022

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This paper aims to investigate the impact of blockchain application on trust levels in supply chains. Through the systematic review of the relevant literature, three dimensions of trust, i.e., the trustor–trustee perspective, forms of trust, and time orientation, are investigated. Our findings show that, first, there are three pairs of trustors and trustees involved in blockchain implementation: (a) the user and the blockchain, (b) two supply chain partners, and (c) the consumer/public and a supply chain unit. Second, the two forms of trust, namely cognition-based and institution-based trust, are likely to be enhanced by blockchain execution, while affect-based trust may not be directly impacted by the technology. Third, the presence of blockchain technology would facilitate swift trust-building between unknown supply chain partners under specific circumstances. Moreover, we also find contradicting assertions among scholars on the implications of blockchain for trust in supply chains. While some studies pointed out that blockchain will enable a trustless trusted scheme, others expected the reinforcement of interorganizational trust. To test these assertions, we develop the blockchain-entrusted supply chain models to present the three-step process of how trust is developed through the blockchain and diffused to supply chain partners and external stakeholders.

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Great partners: how deep learning and blockchain help improve business operations together

Cited by 27 publications

References 75 publications

E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?

E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?

A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

Blockchain as the “trust-building machine” for supply chain management

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