Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions

Niu, Baozhuang; Zhi-peng, Dai; Zhuo, Xiaopo

doi:10.1016/j.tre.2019.01.011

Cited by 41 publications

(3 citation statements)

References 43 publications

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“…In recent years, Big Data analysis has been applied to research on pricing decisions in various fields, such as container liner shipping companies (Han et al , 2018), air cargo companies (Niu et al , 2019) and manufacturing-remanufacturing management (Xu et al , 2019b). Moreover, considering the overconfidence behavior of decision-maker and the influence of the reference price effect, Xiang and Xu (2020) and Ma and Hu (2020) explored the best decision-making of a closed-loop supply chain under different conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services

Liu

Cui

Zhang

et al. 2021

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PurposeThe purpose of this paper is to solve new pricing issues faced by low-carbon companies in the Yellow River Basin, which is caused by the change of key pricing factors in the mixed appliance background of Big Data and blockchain, such as product quality and carbon-emission reduction CER level (hereafter, CER level).Design/methodology/approachWe choose a low-carbon supply chain with a low-carbon manufacturer and a retailer as our research object. Then, we propose that using the ineffective effect of the CER level and the quality and safety level to reflect the relationships among the CER level, the quality and safety level and the market demand is more suitable in the new environment. Based on these, we revise the demand equation. Afterwards, by using Stackelberg game, four cost-sharing situations and their pricing rules are analyzed.FindingsResults indicated that in the four cost-sharing situations, the change trends and the magnitudes of the best retail prices were not affected by the changes of the inputs of the demand information and the traceability services costs (hereafter, DITS costs), the proportion about retailer's DITS costs undertaken by the manufacturer, the ineffective effect coefficient of the CER level and the quality and safety level and the cost optimization coefficient. However, the cost-sharing situations could affect the change magnitudes of the best revenues.Originality/valueThis paper has two main contributions. First, this paper proposes a demand function that is more suitable for the mixed appliance background of Big Data and blockchain. Secondly, this paper improves the cost-sharing model and finds that demand information sharing and traceability service sharing have different impacts on key pricing factors of low-carbon product. In addition, this research provides a theoretical reference for low-carbon supply chain members to formulate pricing strategies in the new background.

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Section: Literature Reviewmentioning

confidence: 99%

Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services

Liu

Cui

Zhang

et al. 2021

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“…As a result of the increased adaptability of logistics information, logistics has been transformed into a dynamic data process (Xu, 2016). Niu et al (2019) studied two competing air cargo carriers and showed how using big data analytics benefits a carrier by allowing them to receive updated demand signals. There are many quality papers in this field; some of these journal articles are summarized in Table 3.…”

Section: Big Data In Logisticsmentioning

confidence: 99%

An overview of big data analytics application in supply chain management published in 2010-2019

Ghalehkhondabi

Ahmadi

Maihami

2020

Prod.

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“…For example, the distribution of fresh products requires a high efficiency. In the future, we can study the impact of the promised delivery time (Niu et al, 2019) of fresh products on the selection of logistics service. Second, we assume that the capital of the enterprise is infinitely sufficient.…”

Section: Future Research Directionsmentioning

confidence: 99%

Impact of the introduction of marketplace channel on e‐tailer's logistics service strategy

et al. 2023

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We recently observe that some giant e-tailers (e.g., Amazon and JD.com) introduce marketplace channels by allowing third-party sellers to sell products through their platforms. Logistics service acts as a vital role in the operation of online platforms. In practice, the platform can outsource logistics service to third-party logistics providers (named as a third-party logistics strategy) or establish a self-run logistics network to distribute products (named as a self-run logistics strategy). This paper investigates the e-tailer's logistics service strategy in the presence of the marketplace channel's introduction. We reveal that the cost of logistics service plays a major role in determining the optimal logistics strategy. The self-run logistics strategy provides a low logistics service cost to the platform, which results in a higher consumer surplus in comparison with the third-party logistics strategy. Further, we find that when the logistics service cost is low, the platform prefers to establish self-run logistics in the presence of the marketplace channel. Finally, our results show that under the thirdparty logistics strategy, the platform benefits from the marketplace channel in the presence of an intermediate commission fee and a low discount factor. Based on game-theoretic models, some managerial insights are suggested for online retailers to choose the optimal logistics service strategy in the presence of introducing the marketplace channel.

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Co-opetition effect of promised-delivery-time sensitive demand on air cargo carriers’ big data investment and demand signal sharing decisions

Cited by 41 publications

References 43 publications

Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services

Pricing strategies of low-carbon enterprises in the Yellow River Basin considering demand information and traceability services

An overview of big data analytics application in supply chain management published in 2010-2019

Impact of the introduction of marketplace channel on e‐tailer's logistics service strategy

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