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High-quality cold-chain logistics are key to effectively managing the quality of temperature-sensitive foods. Hence, this study investigates the service quality of such logistics, using a real-life case of temperature-sensitive milk delivery.

This study focuses on developing business analytics for quality control in cold-chain perishable-food logistics, grounded in normal accident theory and stakeholder theory, and tests them using real-world data.

The empirical business-analytics results indicate that cargo locations, logistics status and delivery times are the essential factors that influence the quality of temperature-sensitive milk.

This study confirms that a combination of normal accident theory and stakeholder theory can be usefully applied to the development of strategies for managing perishable-food logistics. As such, its proposed business analytics provide a fresh foundation for research on logistics quality management.

The balance between efficiency and service quality in a logistics system should be assessed carefully, and logistics companies should collaborate with their stakeholders and can help to mitigate potential cold-chain risks.

This pioneering analytical study explores the essential quality issues that confront cold chains and demonstrates how to extract vital insights from temperature-sensitive food logistics monitoring data. As such, it represents a noteworthy contribution to the field of logistics quality management.

Cargo loss has been a major issue in logistics management. However, few studies have tackled the issue of cargo loss severity via business analytics. Hence, the purpose of this paper is to provide guidance about how to retrieve valuable information from logistics data and to develop cargo loss mitigation strategies for logistics risk management.

This study proposes a research design of business analytics to scrutinize the causes of cargo loss severity.

The empirical results of the decision tree analytics reveal that transit types, product categories, and shipping destinations are key factors behind cargo loss severity. Furthermore, strategies for cargo loss prevention were developed.

The proposed framework of cargo loss analytics provides a research foundation for logistics risk management.

Companies with logistics data can utilize the proposed business analytics to identify cargo loss factors, while companies without logistics data can employ the proposed cargo loss mitigation strategies in their logistics systems.

This pioneer empirical study scrutinizes the critical cargo loss issues of cargo damage, cargo theft, and cargo liability insurance through exploiting real cargo loss data.

In the reduction of food waste and the provision of food to the hungry, food banks play critical roles. However, as they are generally run by charitable organisations that are chronically short of human and other resources, their inbound logistics efforts commonly experience difficulties in two key areas: 1) how to organise stocks of donated food, and 2) how to assess the donated items quality and fitness for purpose. To address both these problems, the authors aimed to develop a novel artificial intelligence (AI)-based approach to food quality and warehousing management in food banks.

For diagnosing the quality of donated food items, the authors designed a convolutional neural network (CNN); and to ascertain how best to arrange such items within food banks' available space, reinforcement learning was used.

Testing of the proposed innovative CNN demonstrated its ability to provide consistent, accurate assessments of the quality of five species of donated fruit. The reinforcement-learning approach, as well as being capable of devising effective storage schemes for donated food, required fewer computational resources that some other approaches that have been proposed.

Viewed through the lens of expectation-confirmation theory, which the authors found useful as a framework for research of this kind, the proposed AI-based inbound-logistics techniques exceeded normal expectations and achieved positive disconfirmation.

As well as enabling machines to learn how inbound logistics are handed by human operators, this pioneering study showed that such machines could achieve excellent performance: i.e., that the consistency provided by AI operations could in future dramatically enhance such logistics' quality, in the specific case of food banks.

This paper’s AI-based inbound-logistics approach differs considerably from others, and was found able to effectively manage both food-quality assessments and food-storage decisions more rapidly than its counterparts.

Delivery vulnerability is a critically important theme in logistics risk management. However, while logistics service providers often collect and retain massive amounts of logistics data, they seldom utilize such information to diagnose recurrent day-to-day logistics risks. Hence, the purpose of this paper is to investigate delivery vulnerabilities in a logistics system using its own accumulated data.

This study utilizes pragmatic business analytics to derive insights on logistics risk management from operations data in a logistics system. Additionally, normal accident theory informs the discussion of its management implications.

This study’s analytical results reveal that a tightly coupled logistics system can align with normal accident theory. Specifically, the vulnerabilities of such a system comprise not only multi-components but also interactive ones.

The tailored business analytics comprise a research foundation for logistics risk management. Additionally, the important research implications of this study’s analytical results arrived at via such results’ integration with normal accident theory demonstrate the value of that theory to logistics risk management.

The trade-offs between logistics risk and logistics-system efficiency should be carefully evaluated. Moreover, improvements to such systems’ internal resilience can help to alleviate potential logistics vulnerabilities.

This pioneering analytical study scrutinizes the critical vulnerability issues of a logistics service provider and therefore represents a valuable contribution to the field of logistics risk management. Moreover, it provides a guide to retrieving valuable insights from existing stockpiles of delivery-vulnerability data.

This paper aims to explore the dynamic context of the sharing economy in the transportation sector. This paper looks into the development of government regulations on the growing business of transportation network companies in Indonesia, the Philippines (represented as middle-income countries) and Taiwan (high-income country). How do government regulations and policies respond to the growing online-enabled transportation service (OETS) in Indonesia, the Philippines and Taiwan?

This study is qualitative-comparative research. Data on the transportation sector of each country have been gathered from reputable online sources.

Authors found evidence that the policy responses made by the Governments of Indonesia, Philippines and Taiwan to the sharing economy in the transportation sector are incremental and trial-error based policies.

This paper has not addressed the policy issues’ relationship between driver and platform companies.

The future of the relationship between sharing firms and local governments suggests that the focus should be on stronger consumer protections, deeper economic redistribution and achievement of other policy aims (Rauch and Schleicher, 2015).

This is a comparative study on different levels of economy, particularly between low- or middle-income and high-income country.