Exploring the processing of product returns from a complex adaptive system perspective

• DOI: https://doi.org/10.1108/IJLM-08-2018-0216
• Date: 12 August 2019
• Published date: 12 August 2019
• Pages: 699-722
• Author: Jennifer A. Espinosa,Donna Davis,James Stock,Lisa Monahan
• Subject Matter: Management science & operations

Exploring the processing of

product returns from a complex

adaptive system perspective

Jennifer A. Espinosa

Department of Marketing and Business Information Systems,

Rowan University, Glassboro, New Jersey, USA

Donna Davis and James Stock

Department of Marketing,

University of South Florida, Tampa, Florida, USA, and

Lisa Monahan

Department of Marketing, Meredith College, Raleigh, North Carolina, USA

Abstract

Purpose –The purpose of this paper is to explore the processing of product returns at five case companies

using a complex adaptive systems (CAS) logic to identify agent interactions, organization, schema, learning

and the emergence of adaptations in the reverse supply chain.

Design/methodology/approach –Using a multiple-case study design, this research applies abductive

reasoning to examine data from in-depth, semi-structured interviews and direct researcher observations

collected during site visits at case companies.

Findings –Costly or high-risk returns may require agents to specialize the depth of their mental schema.

Processing agents need freedom to interact, self-organize and learn from other agents to generate emergent

ideas and adapt.

Practical implications –Limiting the depth of individual agent schema allows managers to better allocate

labor to processing product returns during peak volume. To boost adaptability, managers need to craft a

dynamic environment that encourages agents with diverse schema to interact, anticipate, and self-organize to

brainstorm new ideas. Managers need to resist the urge to “control”the dynamic environment that ensues.

Originality/value –This paperbuilds on existing researchthat studies the key decisionpoints in the analysis

of product returnsby exploring how processing-agent behaviors can create adaptabilityin the reverse supply

chain. Additionally, this research follows in the tradition of Choi et al.(2001)andSuranaet al.(2005)and

proposes the application of CAS to a specificpart of the supply chain –the processing of productreturns.

Keywords North America, Case study, Reverse logistics, Supply chain processes

Paper type Research paper

1. Introduction

Customers today expect more than ever before when shopping (e.g. instantaneous access to

products, seamless shopping experiences), and forward supply chains have begun to

respond to these demands by developing fast moving r etail supply chains and

omnichannels ( Jones, 2018; Petro, 2018). Increasingly, customers are assimilating desires

for accessibility and convenience into their expectations for returning products. So far, few

supply chains have made modifications to their product return strategies to offset the costs

associated with these evolving expectations (Dennis, 2018; Lindsey, 2016). However, the

crucial role that product returns play in forging productive and sustainable consumer

relationships is undeniable (Hjort et al., 2013). While every consumer/firm transaction

The International Journal of

Logistics Management

Vol. 30 No. 3, 2019

pp. 699-722

© Emerald PublishingLimited

0957-4093

DOI 10.1108/IJLM-08-2018-0216

Received 28 August 2018

Revised 23 January 2019

Accepted 4 March 2019

The current issue and full text archive of this journal is available on Emerald Insight at:

www.emeraldinsight.com/0957-4093.htm

The authors gratefully acknowledge and thank the anonymous reviewers and editor Gammelgaard for

their helpful comments during the review process. The authors gratefully thank the University of

South Florida’s L. Rene Gaiennie Endowment and the University of South Florida’s Center for Supply

Chain Management and Sustainability for funding travel to collect data and the NVivo 10 software

necessary to complete this research.

699

Exploring the

processing

of product

returns

provides an opportunity to create consumer value (Kumar et al., 2010), returns are unique in

that they begin with a negative experience that precipitates the return. When a firm acts to

turn a negative experience into a positive one, consumers are often motivated to purchase

again from the same firm and spread the word to others about the turnaround experience

(Gesenhues, 2017).

Product returnsrequire companies to perform a balancingact between setting policiesthat

are simple enough to encourage repeat shopping, yet strict enough to prevent return abuse,

while also maintaining an effective process to dispose of returned products (Goldman, 2016;

Jack et al., 2010). Traditional product return strategies have focused on optimizing return

policies to minimize returns as much as possible and/or identifying characteristics of “serial”

returners (Daunt and Harris, 2012; Davis et al., 1998; Janakiraman and Ordonez, 2012).

However, this optimization approach significantly downplays the importance of human

behavior in product returns and largely ignores insights generated from the processing of

returns. Returning a product involvesthe interaction of at least two people (e.g. the customer

and the employee accepting the return) but typically more, all of whom have different

motivations, power, and information-processing capabilities. Companies have traditionally

assumed customersbehave rationally when returning products(i.e. return a product because

it is defective).However, shopping trendsindicate customers areincreasingly prioritizingtheir

needs at the expense of the seller’s (e.g. buying multiple sizes of the same product with

the intention of returning the sizes that do not fit; Welson-Rossman, 2018). Furthermore, the

employees who evaluate and process returned products are not immune to decision-making

errors. Thus, in comparison to return behaviors by customers, behaviors of employees

processing product returns have received little attention in the literature, despite their

substantial impact on a firm’s profitability.

Embracing and leveraging employee behaviors during the returns process represents

one relatively untapped, alternative approach to the optimization of customer behaviors,

with the potential to reveal product quality issues and suggest solutions to those issues in

real time (Storer et al., 2014). This research applies complex adaptive systems (CAS) theory

(Holland, 1995) to explore the different parts of the system that interact during the

processing of product returns. More specifically, we will closely examine how employee

behaviors can benefit or harm the system. Unlike an optimization strategy that assumes

customer and employee behaviors do no harm, CAS theory studies both the benefits

(e.g. creativity) and drawbacks (e.g. conflict) of human behavior on the system (Nilsson and

Gammelgaard, 2012). One important research question that arises is:

RQ1. How can employee behaviors during the processing of product returns increase the

reverse supply chain’s adaptability?

The objective of this paperis to explore the product returns process from a CAS perspective.

Specifically, thisresearch examines how interactions, learning, and adaptationsoccur during

the processing of product returns. To achieve this purpose, we examine the product returns

process at five different case companies to develop an in-depth understanding of the roles

employee behaviors play. In the next section, we review the relevant literature, and offer a

brief description of our abductive case study methodology. Next, detailed descriptions of the

product return processes observed at each of the five case companies are provided, followed

by a discussion of CAS-grounded patterns that emerged across the cases. The paper closes

with a discussionof the implications of the current researchand directions for future research.

2. Literature review

The conceptualresearch framework (seeFigure 1) for this researchis based on the intersection

of the product returnsprocess and CAS theory, and is grounded in researchinsights from the

reverse logistics, supply chain management, natural and social sciences literatures.

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