Learning-based dynamic capabilities in closed-loop supply chains: an expert study

• DOI: https://doi.org/10.1108/IJLM-01-2021-0044
• Published date: 05 January 2022
• Date: 05 January 2022
• Pages: 69-84
• Subject Matter: Management science & operations,Logistics
• Author: Ilkka Ritola,Harold Krikke,Marjolein C.J. Caniëls

Learning-based dynamic

capabilities in closed-loop supply

chains: an expert study

Ilkka Ritola, Harold Krikke and Marjolein C.J. Cani€

els

Open Universiteit, Heerlen, The Netherlands

Abstract

Purpose –Product returns information gives firms an opportunity for continuous strategic adaptation by

allowing them to understand the reasons for productreturns, learning from them and improving their products

and processes accordingly. By applying the Dynamic Capabilities (DCs) view in the context of closed-loop

supply chains (CLSC), this study explores how firms can continuously learn from product returns information.

Design/methodology/approach–This study adopts a qualitative Delphi study-inspired approach. Experts

from industry and academia are interviewed in two interview rounds. First round of interviews are based on

extant research, while the second round allows the experts to elaborate and correct the results.

Findings –This study culminates into a conceptual model for incremental learning from product returns

information. The results indicate incremental learning from product returns can potentially lead to a

competitive advantage. Additionally, the authors identify the sources of information, capabilities along with

their microfoundations and the manifestations of product return information. Three propositions are

formulated embedding the findings in DC theory.

Research limitations/implications –This study supports extant literature in confirming the value of

product returns information and opens concrete avenues for research by providing several propositions.

Practical implications –This research elucidates the practices, processes and resources required for firms

to utilize product returns information for continuous strategic adaptation. Practitioners can use these results

while implementing continuous learning practices in their organizations.

Originality/value –This study presents the first systematic framework for incremental learning from

product returns information. The authors apply the DC framework to a new functional domain, namely CLSC

management and product returns management. Furthermore, the authors offer a concrete example of how

organizational learning and DC intersect, thus advancing DC theoretical knowledge.

Keywords Closed-loop supply chain, Circular economy, Reverse logistics, Product returns, Organizational

learning, Knowledge management, Information systems, Innovation, Dynamic capabilities

Paper type Research paper

1. Introduction

Closed-loop supply chain (CLSC) management has been receiving increasing attention by

both researchers and practitioners during recent years. The increased attention can be partly

explained by the increased demand for sustainable business practices by consumers and

regulations but also, to a large extent, by the value creation opportunities that CLSC presents

for enterprising firms (Schenkel et al., 2015,2019;Krikke et al., 2013;Koppius et al., 2014;

Jayaraman and Luo, 2007). Importantly, CLSC value creation is not geared for short-term

profit optimization. Instead, researchers have identified four types of values that firms can

generate long term from CLSC activities (Krikke et al., 2013;Koppius et al., 2014;Schenkel

et al., 2015): economic value, customer value, environmental value and informational value.

This study focuses on the least understood (Krikke et al., 2013;Schenkel et al., 2015), and yet

Learning-

based dynamic

capabilities

69

© Ilkka Ritola, Harold Krikke and Marjolein C.J. Cani€

els. Published by Emerald Publishing Limited. This

article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may

reproduce, distribute, translate and create derivative works of this article (for both commercial and non-

commercial purposes), subject to full attribution to the original publication and authors. The full terms of

this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode.

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

https://www.emerald.com/insight/0957-4093.htm

Received 25 January 2021

Revised 30 May 2021

17 August 2021

Accepted 22 August 2021

The International Journal of

Logistics Management

Vol. 33 No. 5, 2022

pp. 69-84

Emerald Publishing Limited

0957-4093

DOI 10.1108/IJLM-01-2021-0044

highly significant (Jayaraman and Luo, 2007;R€

ollecke et al., 2018), type of CLSC value

creation: the informational value.

Informational CLSC value is a broad concept encompassing information from product

inspections, customer feedback and data from the product return process (Ritola et al., 2020).

It can be used to improve processes, products, strategic change and innovation. In short, by

systematically learning from product returns information, firms generate the other three

types of CLSC value. Scholars have examined CLSC value creation from many vantage

points, including resource-based view (Daugherty et al., 2002,2005;Jayaraman and Luo,

2007), organizational knowledge creation theory (Mihi-Ramirez, 2012;Mihi-Ram



ırez and

Morales, 2011), game theory (Heese et al., 2005), transaction cost economics (Martin et al.,

2010) and stakeholder theory (Schenkel et al., 2015). Informational value stems from customer

contact that is always context-specific and based upon the reason for the return that bears

importance on specific processes, products, policies and other organizational resources

during a specific time.

It has long been argued that an essential source of learning comes from customers who use

the product (Rosenberg, 1982;Fundin and Bergman, 2003;Fundin and Elg, 2010). As product

returns information opens a window into customer behavior, we postulate that CLSC

informational value is a source for continuous organizational learning. Further, we argue that

systematic learning and application from this information is an instance of a dynamic

capability (DC). The DC view aims at understanding competitive advantage in dynamic

market environments (Teece, 2007) by maintaining and restoring fit with changing customer

requirements (Mahringer and Renzl, 2018). Scholars have identified DCs in the context of

supply chain management (Blome et al., 2013) and sustainable supply chain management

(Beske, 2012;Beske et al., 2014). Extending this line of research into the domain of CLSC

management, we argue that by continuously learning from product returns information,

firms can better understand the needs of their customers thus better meeting their needs,

ultimate leading to competitive advantage.

This exploratory research aims to develop a framework for incremental organizational

learning from product returns by integrating organizational learning practices and CLSC

practices under the DC framework. More specifically, this study conceptualizes product

returns information as a source for continuous incremental learning and explores its viability

from the DC perspective. Considering the novelty of this topic, both in practice and in

research, we undertake an exploratory Delphi study using interviews.

This study is the first one that applies the DC framework to CLSC management and the

informational value of product returns. It contributes to practice by providing a conceptual

framework, agreed upon by practitioners and academic experts, that assists business

practitioners in building the necessary capabilities needed for their specific business needs.

The theoretical contribution lies in combining organizational learning theory with the DC

framework. This study clarifies the different types of information available from product

returns. Furthermore, it answers to the call to apply the DC framework to new functional

domains (Schilke et al.,2018),by exp loringt he role of DC in the domain of CLSC management and

product returns information. By analyzing product returns information and providinginsights

intohow other kinds of valuescan be generatedfrom it, this study also contributes to knowledge

value chain research (Gaimon and Ramachandran, 2020) and in increased knowledge regarding

the enablers and barriers to learning processes, feedback loops and innovation (Koteshwar,

2017). We capture our contribution in three propositions, which can be used in further research.

2. Literature

2.1 Closed-loop supply chain management

A CLSC is a supply chain that integrates traditional forward supply chains with a reverse

supply chain (Govindan et al., 2015). Formally, CLSC management is defined as “the design,

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