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This study aims to explore the gaps concerning the organizational operant resources (OORs) of logistics service providers (LSPs) expected in outsourcing relationships. The study considers the views of both manufacturing firms (M-firms) and LSPs in India and DACH region (Germany, Austria and Switzerland) seeking gaps within and across regions.

This research employed a survey targeting executives from large M-firms and LSPs in both India and DACH. The perceptions about the importance and improvement expectations of 17 OORs are analyzed. A modified version of importance-improvement analysis (A-B), a novel comparative A-B analysis (CABA) method, has been proposed to identify the importance and improvement gaps in OORs between M-firms and LSPs within and across India and the DACH region.

There are more gaps between M-firms and LSPs in India compared to DACH. Cross-country comparisons reveal that LSPs in India and DACH have similar perceptions concerning the OORs, but M-firms in India have significantly higher improvement expectations than those in DACH.

This study proposes an analytical approach that enables managers to identify improvement areas and better align with their outsourcing relationship partners. It also highlights aspects that need to be considered while entering emerging markets such as India.

The analysis approach using CABA is novel. Also, among the cross-country studies, this is the first to compare outsourcing relationships in India with the DACH region while involving both users' and service providers' perspectives.

Although scholars argue that artificial intelligence (AI) represents a tool to potentially smoothen the bullwhip effect in the supply chain, only little research has examined this phenomenon. In this article, the authors conceptualize a framework that allows for a more structured management approach to examine the bullwhip effect using AI. In addition, the authors conduct a systematic literature review of this current status of how management can use AI to reduce the bullwhip effect and locate opportunities for future research.

Guided by the systematic literature review approach from Durach et al. (2017), the authors review and analyze key attributes and characteristics of both AI and the bullwhip effect from a management perspective.

The authors' findings reveal that literature examining how management can use AI to smoothen the bullwhip effect is a rather under-researched area that provides an abundance of research avenues. Based on identified AI capabilities, the authors propose three key management pillars that form the basis of the authors' Bullwhip-Smoothing-Framework (BSF): (1) digital skills, (2) leadership and (3) collaboration. The authors also critically assess current research efforts and offer suggestions for future research.

By providing a structured management approach to examine the link between AI and the bullwhip phenomena, this study offers scholars and managers a foundation for the advancement of theorizing how to smoothen the bullwhip effect along the supply chain.

This study aims to compare the humanitarian supply chains and logistics of two countries in earthquake preparedness by modifying and using a previously established preparedness evaluation framework.

A European flood emergency management system (FEMS) is a seven-dimensional framework to assess a country’s preparedness for flood emergencies. The FEMS framework was modified to apply to earthquakes. Leveraging a multiple explanatory case study approach with data analysis, the authors reconstructed the events of the earthquakes in Pakistan (2005) and Japan (2011) with an applied grading (1–5). Findings were evaluated within the adopted FEMS framework. From a practitioner’s perspective, the framework is applicable and can accelerate support in the field.

Pakistan lacked emergency plans before the 2005 earthquake. In contrast, Japan possessed emergency plans before the disaster, helping minimise casualties. Overall, Japan demonstrated considerably better emergency management effectiveness. However, both countries significantly lacked the distribution of responsibilities among actors.

Practical factors in the humanitarian supply chain are well understood. However, synthesising individual factors into a comprehensive framework is difficult, which the study solves by applying and adopting the FEMS framework to earthquakes. The developed framework allows practitioners a structured baseline for prioritising measures in the field. Furthermore, this study exemplifies the usefulness of cross-hazard research within emergency management and preparedness in a real-world scenario.