Conference Agenda

Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
MB1 - SO2: Auditing for sustainability
Time:
Monday, 27/June/2022:
MB 10:30-12:00

Session Chair: Bengisu Urlu
Location: Forum 1-3


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Presentations

Examining the Impact of Leniency Bias on Supplier Audits

Tim Kraft1, Xiaojin Liu2, Robert Handfield1, Sebastian Heese1, Balaji Soundararajan1

1North Carolina State University, United States of America; 2Virginia Commonwealth University, United States of America

We study the impact of monitor leniency on supplier CSR risk. Using audit data from a global apparel brand, we find that leniency helps to reduce CSR risk. Testing interaction effects with our moderators, we find that greater leniency helps to reduce CSR risk when a facility’s compliance ability is low; when a facility has been audited a small number of times; and when a facility is located in a developing country. Our work provides insight into the relational factors that can influence supplier audits.



Multi-tier sustainability incentives: audits and supplier development in a two-stage principal agent problem

Alexander Bloemer, Stefan Minner

Technical University of Munich, Germany

Manufacturers are increasingly being held responsible for sustainability violations across their whole supply chain. We examine a three-tier supply chain where a manufacturer and its direct supplier incentivize sub-supplier sustainability through auditing and supplier development. We show that the mechanisms are substitutive for the supplier but can be complementary for the manufacturer. Moreover, the manufacturer delegates the effort in case of very low or very high external pressure.



Stop auditing and start to care: paradigm shift in assessing and improving supplier sustainability

Bengisu Urlu1, Tarkan Tan2, Hakan Akyuz3, Santiago Ruiz-Zapata4

1INSEAD, France; 2Eindhoven University of Technology, The Netherlands; 3Erasmus University Rotterdam, The Netherlands; 4Schiphol Airport, The Netherlands

We propose a conceptual framework for supplier sustainability improvement that we refer to as CARE, based on self-assessments and consisting of Collect, Assess, React, and Enhance phases. CARE is highly scalable, making use of machine learning techniques to understand the link between the general supplier characteristics and their verified sustainability profile, predict the future sustainability levels of even unassessed suppliers, and determine the best plan for improvement.



 
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