Light in Dark Places: The Hidden World of Supply Chain Fraud

DuHadway, Scott; Talluri, Sri; Ho, William; Buckhoff, Thomas A.

doi:10.1109/tem.2019.2957439

Cited by 23 publications

(19 citation statements)

References 88 publications

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“…These online platforms greatly expand potential participant pools, but also are subject to trade‐offs (Aguinis & Lawal, 2012, 2013). A key initial issue is whether the sample is appropriate for the research question; for example, online panels can be particularly useful when there are confidentiality concerns, such as when asking about abusive supervision (Porter et al, 2019) or supply chain fraud (DuHadway et al, 2020). A thorough exposition of the strengths and weaknesses of crowdsourcing platforms is offered by Goodman and Paolacci (2017).…”

Section: Considerations Of Different Research Design Choices That Maxmentioning

confidence: 99%

On making experimental design choices: Discussions on the use and challenges of demand effects, incentives, deception, samples, and vignettes

Eckerd

DuHadway

Bendoly

et al. 2020

J of Ops Management

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105

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In late 2018, the Journal of Operations Management published an invited methods article by Lonati et al. (2018) to provide guidance to authors on how to design behavioral experiments to achieve the rigor required for consideration in the journal. That article was written as a response to a number of behavioral research submissions to JOM, each dealing with interesting topics but viewed by the editors to possess poor design choices at inception. While the Lonati et al. (2018) piece provides experimental guidance fitting to certain research agendas, questions have arisen concerning whether and how exactly to implement some of the points that it makes, and how to best address trade‐offs in the design of behavioral experiments. Questions have also arisen concerning how to apply these concepts in operations management research. This technical note seeks to address these questions, by diving into the details of research risks and trade‐offs regarding demand effects, incentives, deception, sample selection, and context‐rich vignettes. The authors would like to recognize the input of a large number of senior scholars in the JOM community who have provided support and feedback as we have sought to help authors tease out what can reasonably be done in designing strong behavioral experiments that fit various research agendas.

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Section: Considerations Of Different Research Design Choices That Maxmentioning

confidence: 99%

On making experimental design choices: Discussions on the use and challenges of demand effects, incentives, deception, samples, and vignettes

Eckerd

DuHadway

Bendoly

et al. 2020

J of Ops Management

Self Cite

105

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show abstract

“…Page 12 of 50 Supply Chain Management: an International Journal apprehend buyers' manufacturing and supply risks (Cheng and Lu, 2017) like suppliers' opportunism to produce fraudulent products (Duhadway et al, 2020) or non-quality products (Tse et al, 2019).…”

Section: Buyer-led and Supplier-led Practices Interplay With Scrsmentioning

confidence: 99%

“…Supplier performance management can restrict environmental, supply and demand risks via improved risk detection, contingency intelligence and visibility (Brusset and Teller, 2017). It can also apprehend buyers’ manufacturing and supply risks (Cheng and Lu, 2017) such as suppliers’ opportunism to produce fraudulent products (Duhadway et al , 2020) or non-quality products (Tse et al , 2019).…”

Section: The Role Of Collaborative Interorganizational Relationships In Supply Chain Risksmentioning

confidence: 99%

“…Close relationships establish a mutual understanding between network-partners enabling better collaboration and visibility (Johnson et al , 2013). Healthy relationships make supply network structures more resilient (Day, 2014) to SCRs such as political and economic instability (Gölgeci and Kuivalainen, 2020) or supply risks (Chowdhury et al , 2019) such as suppliers’ opportunistic fraud (Duhadway et al , 2020), respect of sustainability obligations (Bird and Soundararajan, 2018), disruption (Dubey et al , 2019; Kalaitzi et al , 2019) and quality issues (Tse et al , 2019). Relationship closeness strengthens supplier-buyer communication and improves supply risk identification (Fan and Stevenson, 2018; Zhao et al , 2013), notably when suppliers are involved early in product development (Khan et al , 2012).…”

Section: The Role Of Collaborative Interorganizational Relationships In Supply Chain Risksmentioning

confidence: 99%

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The role of collaborative interorganizational relationships in supply chain risks: a systematic review using a social capital perspective

Daghar

Alinaghian

Turner

2020

SCM

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Purpose The purpose of this paper is systematically review, synthesize and critically evaluate the current research status on the role of collaborative interorganizational relationships (CIRs) in supply chain risks (SCRs) from a social capital perspective and provide an organizing lens for future scholarship in this area. Design/methodology/approach This study adopts a systematic literature review approach to investigate 126 articles from 27 peer-reviewed journals between 1995 and 2020. Findings This paper investigates supply chain CIRs using a social capital perspective to explain the role of structural, relational and cognitive capital that resides in these relationships in various SCRs (i.e. environmental, supply, manufacturing, demand, information, financial and transportation). The review reveals that the three social capital dimensions uniquely and both positively and negatively affect different SCRs. The findings further suggest that the perceived SCRs can influence the structural and relational capital. Practical implications This study calls for practitioners to consider the cognitive alignment with their supply network partners, their relational investments, as well as the interorganizational processes and systems in managing and alleviating SCRs. Originality/value This review offers a theoretical articulation of how various aspects of CIRs affect SCRs. Specifically, this study extends the existing understanding of the role of social capital in SCRs through offering a synthesis of dominant findings and discourses, and avenues for future research.

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“…The fraud triangle has been used extensively to identify motivations to engage in fraudulent behavior (Trompeter, Carpenter, Desai, Jones, & Riley, 2013) and has been so successful for predicting fraud that it has been formalized into recommendations for auditors investigating fraud in the Statement on Auditing Standards (SAS) No. 99/AU where §316.31 includes the dimensions of the fraud triangle as fraud risk factors (DuHadway, Talluri, Ho, & Buckhoff, 2020). One empirical investigation exploring the value of the fraud triangle on predicting fraud found a model using proxies for the fraud triangle was able to correctly predict a firm's fraud/no-fraud status 73% of the time, providing support of the use of the fraud triangle factors for detecting fraud (Skousen, Smith, & Wright, 2009).…”

Section: The Fraud Triangle and Opportunistic Behaviormentioning

confidence: 99%

Subverting Process‐Based Controls: Oscillation in Automotive Recalls and a Simulation on Opportunism within a Network

DuHadway

Narasimhan

2020

Decision Sciences

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The increase in the frequency and impact of automotive recalls has had broad reaching economic and social consequences. Recent examples of automotive recalls suggest that some are due to deliberate actions of firms to subvert processes designed to ensure quality. Such controls are dependent on partners acting in good faith in the relationship and abiding by relational norms and so do not recognize the risks of firms lying, falsifying data, or intentionally circumventing process-based controls. Data on automotive recalls shows that recalls exhibit a statistically significant oscillatory pattern with an estimated period of around 3.6 years. We argue that the cyclical pattern in automotive recalls is due, in part, to opportunistic behavior within a network, which is likely to spread and be reciprocated. To test whether opportunism can lead to similar network effects, we develop an agent-based simulation to model opportunism within a network of connected firms. The simulation is based on an extension of the prisoner's dilemma where the cooperate/defect decision is based on a dyadic relational model driven by trust, knowledge, and dependence levels within each relationship. The results from the simulation suggest that cyclical patterns, similar to those in automotive recalls, emerge as well as "behavioral clustering" within the network, where connected firms exhibit highly similar behaviors that tend to cycle within small clusters. Therefore, opportunistic behavior in supply networks might be an important, relational determinant of product recalls. Our dynamic modeling approach differs from current perspectives on understanding product recalls, contributing to the current literature. [

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Light in Dark Places: The Hidden World of Supply Chain Fraud

Cited by 23 publications

References 88 publications

On making experimental design choices: Discussions on the use and challenges of demand effects, incentives, deception, samples, and vignettes

On making experimental design choices: Discussions on the use and challenges of demand effects, incentives, deception, samples, and vignettes

The role of collaborative interorganizational relationships in supply chain risks: a systematic review using a social capital perspective

Subverting Process‐Based Controls: Oscillation in Automotive Recalls and a Simulation on Opportunism within a Network

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