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What is next? The effect
of reverse logistics adoption
on digitalization and
inter-organizational collaboration
Faisal Rasool
Department of Enterprise Engineering, University of Rome Tor Vergata, Roma, Italy
Marco Greco
Department of Civil and Mechanical Engineering,
University of Cassino and Southern Lazio, Cassino, Italy, and
Gustavo Morales-Alonso and Ruth Carrasco-Gallego
Departamento de Ingenier
ıa de Organizaci
on, ADE y Estad
ıstica,
Universidad Polit
ecnica de Madrid, Madrid, Spain
Abstract
Purpose –This study aims to examine and understand the impact of reverse logistics adoption on firms’
digitalization and collaboration activities. Specifically, leveraging the knowledge-based view, this study
examines how adopting sustainable logistic practices (reverse logistics) prepares firms to embrace
digitalization and encourages them to collaborate with other organizations.
Design/methodology/approach –The study used longitudinal survey data from two waves (2017 and 2019)
from the Mannheim Centre for European Economic Research. The authors used the negative binomial
regression analyses to test the impact of reverse logistics adoption on the digitalization and inter-
organizational collaboration dependent count variables.
Findings –The study’s findings highlight the usefulness of reverse logistics in enabling digitalization and
inter-organizational collaboration. The results show that the firms investing in sustainable supply chains will
be better positioned to nurture digitalization and inter-organizational collaboration.
Practical implications –For resource-bound managers, this study provides an important insight into
prioritizing activities by highlighting how reverse logistics can facilitate digitalization and collaboration. The
study demonstrates that the knowledge generated by reverse logistics adoption can be an essential pillar and
enabler toward achieving firms’digitalization and collaboration goals.
Originality/value –The study is among the first to examine the effect of reverse logistics adoption on firm
activities that are not strictly associated with the circular economy (digitalization and collaboration). Utilizing
the knowledge-based view, this study reports on the additional benefits of reverse logistics implementation
previously not discussed in the literature.
Keywords Circular economy, Firm partnership, Sustainable logistics, Green supply chain,
Closed-loop supply chain, Main machine interaction, Industry 4.0, Digital transformation,
German innovation survey, Internet of thing (IoT)
Paper type Research paper
Reverse
logistics
adoption and
digitization
© Faisal Rasool, Marco Greco, Gustavo Morales-Alonso and Ruth Carrasco-Gallego. 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 authors wish to thank the data provider, the Leibniz-Zentrum f€
ur Europ€aische
Wirtschaftsforschung, which granted access to the Mannheim Innovation Panel (MIP), ZEW,
Mannheim, Germany.
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/0960-0035.htm
Received 9 June 2022
Revised 5 October 2022
6 March 2023
Accepted 14 April 2023
International Journal of Physical
Distribution & Logistics
Management
Emerald Publishing Limited
0960-0035
DOI 10.1108/IJPDLM-06-2022-0173
1. Introduction
Logistics is the process of strategically managing the procurement, transport, and storage of
materials, components, and finished goods within and outside the boundaries of an
organization while maximizing current and future profitability through cost control
and process optimization. In other words, logistics entails delivering goods to suppliers and
customers in the best possible way (Min et al., 2019). However, managing only forward
logistics (traditional logistics) has become inadequate to compete and grow in an ever-
changing global business environment (Baah and Jin, 2019). In addition, government
regulations, stakeholder pressure and customer demands compel firms to take responsibility
for their products at the end of life stage (Andiça et al., 2012;Ni et al., 2021). As a result, it has
become essential for a firm to manage the forward and reverse flow of materials and goods.
This reverse flow of goods is generally referred to as “reverse logistics”. Reverse logistics
activities focus not only on recycling the products but also on properly disposing of harmful
components and repurposing the useful components in the returned products, for example,
using old computer chips in toy manufacturing (Meade et al., 2007). Studies have reported
many benefits of successful reverse logistics activities, such as higher profits, increased
customer satisfaction and loyalty, cost reduction and better environmental performance (Del
et al., 2019;Harold, 2011;Hazen et al., 2012;Mari
c and Opazo-Bas
aez, 2019;Ni et al., 2021;
Sangwan, 2011;Sehnem et al., 2019).
Though necessary, the process of implementing and managing reverse logistics is not
easy and requires a significantly larger amount of resources and expertise compared to
forward logistics to manage and implement it successfully (Gaur et al., 2017;Giri et al., 2017;
Jayaraman et al., 2008). Such a process includes building an appropriate infrastructure, IT
capabilities and culture to share information with internal and external partners (Hudnurkar
et al., 2014;Mari
c and Opazo-Bas
aez, 2019;Moktadir et al., 2020;Olorunniwo and Li, 2010;
Zhang and Cao, 2018). As a result, companies invest in business operations and personnel to
facilitate reverse logistics operations. Many studies have investigated how digitalization and
collaboration activities can facilitate the reverse logistics adoption process and improve
system efficiency (Aksin-Sivrikaya and Bhattacharya, 2017;Chen et al., 2017). But
surprisingly, no study has investigated the impact of reverse logistics adoption on firm
digitalization and collaboration levels. First of its nature, by utilizing Knowledge-Based View
(KBV), this study aims to empirically investigate the impact of reverse logistics adoption on
firm digitalization and collaboration levels. Such investigation will be useful in advancing
theory and managerial practices related to reverse logistics, digitalization and collaboration.
The study aims to empirically investigate the impact of reverse logistics adoption on firm
digitalization and inter-organizational collaboration by leveraging KBV and to demonstrate
that newly created knowledge can serve as a competitive advantage for the firm. This study
is the first of its nature to explore such relationships. Unlike the former flow of actions
(implementation of digitalization and then reverse logistics), the law often imposes the
adoption of reverse logistics on firms to achieve SDG 2030 goals. Therefore, in many cases,
firms will not have the choice to delay or ignore the implementation of reverse logistics
activities. Therefore, it is crucial to understand the impacts of reverse logistics adoption on
firm activities and study the unintended consequences of reverse logistics on firm operations.
The outcome of this study confirms that adopting reverse logistics positively influences
digitalization and collaboration in firms. As a result, the study presents several theoretical,
managerial and policy implications. The manuscript is organized into seven sections. The
following section, section two, presents the study’s theoretical background. Section three
focuses on developing and rationalizing the two hypotheses studied and tested in the study.
Section four presents the methodology and data sets used to test the hypotheses. Section five
is dedicated to reporting the results, while section six discusses the findings of the study.
Finally, section seven concludes the study and discusses the theoretical, managerial and
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policy implications. This section also reports on the limitation of the study and presents
possible future developments.
2. Theoretical background
2.1 Knowledge-based view
Barney (1991) argued that in the current information age, the Resource-Based View (RBV)
proposed by Wernerfelt (1984) has evolved into KBV. According to KBV, knowledge is the
most valuable resource created within the firm’s boundaries by utilizing and enhancing
employees’experience, skills and abilities (Curado and Bontis, 2006). Teece (2000) argued that
a firm’s competitive advantage is inextricably tied up with its ability to create, diffuse,
maintain and use difficult-to-imitate knowledge. In other words, knowledge management
enables firms to share information produced by different employees and sections to gain a
competitive advantage (Nisar et al., 2019). Ode and Ayavoo (2020) confirmed the positive
relationship between knowledge creation, management activities and firm innovativeness.
Knowledge is created when learning occurs at the individual level within an organization
(Ganesh et al., 2014). This individual learning is translated into organizational learning and
eventually into organizational knowledge that has a positive impact on a firm’s financial
performance (Silvestre, 2016), purchasing performance (Sch€
utz et al., 2020), flexibility (Blome
et al., 2014), innovation and operations capabilities (Javaid et al., 2021), and serves as a source
of major competitive advantage (Teece, 2000). These advantages are accumulated by
capturing and transferring implicit and explicit knowledge during the knowledge creation
and transfer process. Therefore, it is vital for a firm to create and manage information to
remain competitive.
2.2 Knowledge management
Knowledge management bridges the gap between information demand and supply by
encouraging learning processes that improve organizational performance (Curado and Bontis,
2011). The studies have argued that even though a firm needs external knowledge and
information, a lasting competitive advantage comes from creating and utilizing in-house
knowledge (Azyabi, 2018;Chong et al., 2014;Emden et al., 2005). However, the firm can only
enjoy these knowledge creation and sharing advantages when employees share information
freely with their co-workers (Caputo et al., 2021a). Bhatt (2019) defined knowledge as expertise
gained through accumulation of experiences or study to understand facts, procedures and rules.
For capitalization and value capture of knowledge, Loon (2019, p. 433) categorized knowledge
management mechanisms into “a. Learning and knowledge creation culture; b. organizational
knowledge architecture foradaptive and exaptive capacity; and ‘c.business model’”.Knowledge
is created in cycles andtransferred within the organization through different means and actions
(Nonaka, 1994), making it vital to continuously produce new knowledge to maintain a
competitive advantage by implementing new systems and processes. In other words, a valuable
transferable knowledge is created whenever a firm performs a new activity that will shorten the
learning curve in future projects (de Machado et al.,2022).
2.3 Sustainability
Knowledge transfer and management activities can be important mediators in using
sustainability tools in medium- and large-sized firms (H€
orisch et al., 2015), making them
essential for achieving sustainability goals. In the last two decades, the concept of
sustainability has gained significant interest from the popular press, policymakers and
scientific journals in various technical fields (Lintona et al., 2007). Sustainability can be
defined as the extent to which current business actions affect the natural environment,
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adoption and
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society and firms’economic viability in the future (Krysiak, 2009). In other words, it requires a
conscious decrease in the use of resources while still maintaining a steady flow of products to
allow commercial entities to gain revenues from deliverables to market. The scarcity of
resources and uninterrupted increase in pollution mainly caused by industrialized nations
(Tahvonen, 2000) combined with ever-increasing economic, social and environmental
uncertainties in recent years have encouraged researchers and practitioners to investigate
and develop solutions from many points of view. In addition, the stakeholders (customers,
shareholders, governments) are putting immense pressure on firms to change their habits of
indiscriminately using and discarding natural resources. As a result, sustainability has
become a major concern in decision making. Unlike in the past, where decisions were mainly
economic (Ohnishi et al., 2012), the decision-makers are being forced to consider the impact of
their decisions on the triple bottom line of sustainability (economic, environmental, social). If
implemented correctly, sustainable actions can offer firms several benefits and competitive
advantages. These include, among others, decreased cost for materials, waste reduction,
production/operations efficiency, increased firm reputation, brand value, better working
conditions, and profits (Sangwan, 2011), innovation (Nidumolu et al., 2009) and shared value
creation (Porter and Kramer, 2011a). These benefits are often the result of introducing
sustainable initiatives in the firms’value chain to make the supply chain green and lean. This
new supply chain is not just about cost and efficiency: it also considers the triple bottom line
of sustainability (Silvestre, 2016). Sustainable initiatives’success depends on using the right
strategies and frameworks at both the implementation and operational stages (Preuss and
Fearne, 2022) to increase the success of sustainable supply chain initiatives.
2.4 Supply chain and sustainability
In recent years, the pursuit of sustainability has been recognized as a viable strategy to
resolve many of the contemporary challenges global supply chains face (Giannakis and
Papadopoulos, 2016). Sustainable supply chains manage materials, information and capital
flows and result in cooperation between different actors along the supply chain
(Karaosman et al., 2020). Sustainability in supply chain practices improves firms’
financial performance and enhances competitiveness (Wang and Sarkis, 2013).
Furthermore, it creates a moral capital that firms can utilize to mitigate the
consequences of potential business risks (Jiang et al.,2020) and create new opportunities.
To this end, one powerful strategy used by the firms is to decrease the use of virgin
materials by bringing back the material for reuse or repurposing at the end of life and
creating a loop for the material. The process is generally termed as a “closed-loop supply
chain”.Min et al. (2006, p. 311) defined a closed-loop supply chain as the process associated
with the “acquisition, distribution, and marketing activities involved in product returns/
recoveries, source reduction/conservation, inspection, recycling, salvage, substitution,
reuse, disposal, disassembly, refurbishment, repair, and remanufacturing”. In other words,
an ideal closed-loop supply chain is a supply chain with zero-waste where all the products at
their end of life are recovered for reuse/repurpose or disposed of properly. However, the
studies have argued that the closed-loop supply chain systems are often complex and
unpredictable compared to the forward supply chain (Gaur et al., 2017;Giri et al., 2017)and,
therefore, often require greater resources and commitments to manage (Morgan et al., 2018).
But firms are still willing to invest in these risky endeavors to reap the tangible and
intangible benefits (Schenkel et al., 2015)theyoffer.
2.5 Reverse logistics
Drivers such as legislation, stakeholder pressure, social accountability, economic interests
and ethics compel a firm to adopt green activities in their supply chain (Andiça et al., 2012),
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including the reuse of materials and end-of-life products. Reusing materials/products is
generally referred to as “reverse logistics”. Reverse logistics is a key element of green supply
chain management since it helps decrease waste produced by processing and disposing
returned and used goods by implementing a variety of disposition alternatives (Pokharel and
Mutha, 2009). Many factors and points in the supply chain can lead to product returns,
including production, distribution and returns connected to customers (Rogers and Tibben-
Lembke, 2001), and reverse logistics aims to facilitate appropriate reuse, recycling and
disposal of these returns. In the past, reverse logistics was viewed as the process of recycling
used/malfunctioned products. However, in recent years, the definition and concept have
expanded to include processes connected with product return and collection for recovery,
repair, refurbishment, recycling, remanufacturing or disposal of used/end-of-life items
(Rogers and Tibben-Lembke, 2001). The expansion in concept has increased the benefits
obtained from reverse logistics, including increased profits (Mari
c and Opazo-Bas
aez, 2019;
Toffel, 2004), cost reduction (Sangwan, 2011), customer loyalty (Jayaraman et al., 2007),
customer satisfaction (Hazen et al., 2012) and environmental performance (Harold, 2011).
Reverse logistics is a complex process and requires a significantly higher level of
competencies and resources than forward logistics (Gaur et al., 2017;Giri et al., 2017).
Therefore a greater level of investment and organizational commitment is needed to
implement reverse logistics successfully (Morgan et al., 2018).
2.6 Digital technologies and supply chain
By directly connecting suppliers and consumers and vice versa, digital technology can tackle
some of the most pressing challenges in supply chain management by decreasing
information delays, cost, and increasing volume and flexibility, leading to greater service
levels (Agrawal and Narain, 2018). Although there have been academic contributions dealing
with the repair, reuse and refurbishment of items for decades, the advent of digitalization in
recent years has allowed a whole new set of tools to be implemented at the firm level (Hidalgo-
Carvajal et al., 2021). Unlike traditional supply chains, the Digital Supply Chain (DSC)
depends primarily on technologies (such as software, hardware and communication
networks) to enable operations such as buying, making, storing, moving and selling a product
by globally dispersed partners (Bhargava et al., 2013). Rasool et al. (2021, p. 1204) defined DSC
as “a seamlessly interconnected transparent supply chain, that independently performs
decision support activities to minimize human input needs”. Studies have reported several
benefits of DSC over the traditional supply chain, including improved transparency, speed,
flexibility, productivity and profitability (Haoud and Hasnaoui, 2019;Oorschot et al., 2022).
The list of these benefits expands when applied to a closed-loop supply chain. Some studies
have argued that without digital technologies, the activities associated with a closed-loop
supply chain are impossible to manage and control (Jayaraman et al., 2008;Pagoropoulos
et al., 2017;Wilson et al., 2022). The ability of digital technologies to facilitate a closed-loop
supply chain is well acknowledged and documented in the literature (Antikainena et al., 2018;
Awan et al., 2021;Pagoropoulos et al., 2017). Studies have reported that the technologies such
as A.I, M.L, data mining and IoT enable firms to not only trace, monitor and make decisions
on returned products/materials but also on the products still in circulation and their end-of-
life destination (Pagoropoulos et al., 2017;Rosa et al., 2020).
2.7 Collaboration and supply chain
In the fast-moving globalized economy, firms have realized that they cannot provide high-
quality products to their customers at competitive prices and with speed in silos. Therefore,
they are forced to collaborate and leverage the knowledge and skills of the entities outside of
their boundaries (Hudnurkar et al., 2014). Gulati (1998, p. 293) defined collaboration as
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“voluntary arrangements between firms involving exchange, sharing, or co-development of
products, technologies, or services”. It can involve just two firms that have created a bilateral
agreement or a complex system of multiple independent and interconnected entities working
together to achieve the same goals by creating a shared value ecosystem (Grant and Baden-
Fuller, 1995;Porter and Kramer, 2011b). The benefits of such collaborative activities often
include expedited product development and launch processes at a reduced cost with
significantly higher quality and technical specifications (Singh et al., 2018;Walter, 2003). In
addition, the collaborative activities help firms to share risk (Li and Nguyen, 2017) and reduce
transaction costs (Kalwani and Narayandas, 1995) by accessing complementary resources
(Park et al., 2004) and knowledge (Grant and Baden-Fuller, 1995). As a result, firms improve
their productivity (Kalwani and Narayandas, 1995), profits and competitive advantage
(Mentzer et al., 2000). Depending on the project’s objective and need, the firms may seek both
vertical (supplier and customers) and horizontal (competitors) collaboration together with
private-private (among firms) or cross-sectoral collaboration (public-private-third sector
collaboration). To identify which partners are best suited for collaboration in any given
project, Barratt (2004) recommended asking why, where, what and whom before undertaking
collaboration projects.
3. Hypotheses
3.1 Reverse logistics and digitalization
Adopting reverse logistics is a complex process and often requires firms to undergo
substantial changes and overcome multiple internal and external barriers (Gonz
alez-Torre
et al., 2010). Some of these barriers include a lack of firm infrastructure readiness, employee
skills, an appropriate support system to handle new activities, management commitment,
strategic planning as well as market uncertainties and high initial cost (de Campos et al.,
2017;Gonz
alez-Torre et al., 2010;Govindan and Bouzon, 2018;Prakash et al., 2015;Prakash
and Barua, 2017;Waqas et al., 2018). To overcome these barriers, firms must unlearn and
relearn several important competencies and develop new skills and knowledge. According
to KBV, knowledge is created by individual actors within a firm and is transferred to other
members and sections of organizations (Ganesh et al., 2014). As a result, firm performance
and maturity improve in different domains (Matos et al., 2020).Thecaseisnodifferentfor
reverse logistics, whose adoption increases a firm’s capabilities in different areas (Mihi
Ram
ırez, 2012;Ramirez and Girdauskiene, 2013), including IT competencies (Daugherty
et al., 2005) that will help in increasing the firm’s digitalization level. Azyabi (2018) reported
that the management of internally developed knowledge is the only factor influencing the
adoption of e-practices in Saudi SMEs. One of the most frequently reported barriers to
adopt digitalization and reverse logistics is management commitment. The main reasons
behind this attitude are the friction to change and the lack of understanding of the benefits
of these changes (Agrawal et al., 2020;Tham and Atan, 2021). Prior experience and
knowledge can help in reducing such friction and encourage management to invest in new
initiatives.
KBV dictates that valuable transferable knowledge is created whenever a firm performs
a new activity that will shorten the learning curveinfutureprojects.Followingthisview,
we infer that the managers have already developed the knowledge and experience needed
to manage and embrace change during their reverse logistics adoption activities. As a
result, participating managers will be more open to changes in the system for future
endeavors. Another big barrier to adopting digitalization is the initial cost of switching over
(Agrawal et al., 2020). This cost is mainly needed for employee training activities, building
systems and updating firm infrastructure to handle this new way of doing business. To a
firm’s advantage, these are all activities performed by a firm before successfully
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implementing reverse logistics. Generally, a newer IT system is adopted to increase
efficiency and enhance communication among stakeholders for reverse logistics, which
often require employee training and system upgrades. KBV dictates that this knowledge
and resources will be used in future firm activities (Marqu
es and Garrig
os-Sim
on, 2006).
Eller et al. (2020) confirmedthisrelationintheirstudyofAustralianSMEs,whereemployee
skills and IT positively influence the level of digitalization. Similarly, SMEs in Malaysia
were reported to have increased their digitalization level by managing internally and
externally created knowledge (Chong et al., 2014). Caputo et al. (2020) reported higher
returns on big data investments for firms having skilled employees. Therefore, this study
argues that the knowledge and competencies gained during the reverse logistics adoption
process will help firms overcome digital transformation barriers and serve as antecedents
for digital transformation. Hence, resulting in an increased level of digitalization. Therefore,
we hypothesize that.
H1. The adoption of reverse logistics will lead to a higher level of digitalization.
3.2 Reverse logistics and collaboration
Strong collaboration has been reported as one of the most critical activities that a firm can
perform to improve its performance (Mofokeng and Chinomona, 2019) and speed (Walter,
2003) to gain a competitive advantage (Mentzer et al., 2000). The activity is complex and
expensive, but the benefits outweigh the cost and efforts needed to implement it (Barratt,
2004). Studies have reported on both barriers hindering and antecedents enabling successful
collaboration. The commonly reported barriers include commitment, firm culture, trust and
market uncertainties (Enkel and Heil, 2014;Grant and Baden-Fuller, 1995;Hudnurkar et al.,
2014;Qazi and Appolloni, 2022;Zhang and Cao, 2018). Similarly, inter-organizational
systems, information sharing, and technology adoption, regulatory pressure (Enkel and Heil,
2014;Hsu et al., 2013;Hudnurkar et al., 2014;Zhang and Cao, 2018) are important antecedents
for successful collaboration. Several of these coincide with barriers and antecedents for
adopting reverse logistics, such as market uncertainties (Bressanelli et al., 2018), management
commitment (Agrawal et al., 2020;Gonz
alez-Torre et al., 2010;Tham and Atan, 2021),
complexity (Govindan and Bouzon, 2018;Waqas et al., 2018), coordination and information
sharing (Bressanelli et al., 2018;Zhu et al., 2018), and appropriate support systems (de Campos
et al., 2017;Govindan and Bouzon, 2018;Prakash et al., 2015).
Leaning on KBV, we argue that the competencies developed to adopt reverse logistics will
facilitate collaboration activities. A similar argument was also confirmed by Emden et al.
(2005) when they reported the experience gained in performing internal functions is a
valuable asset during collaboration activities. For example, an important competency
required to implement reverse logistics is to embrace the collaborative culture and
information sharing routines. These were also the prime antecedents that Hudnurkar et al.
(2014) identified in their literature review to increase collaboration activities. Similarly,
information-sharing practices, trust and appropriate infrastructure are needed for reverse
logistics success (Bressanelli et al., 2018;Govindan and Bouzon, 2018) and to kickstart and
increase collaboration levels with external entities (Barratt, 2004;Enkel and Heil, 2014;Zhang
and Cao, 2018). Additionally, Grant and Baden-Fuller (1995) argued that higher levels of
uncertainty encourage firms to collaborate more with external entities, and the introduction
of reverse logistics has been reported to introduce additional uncertainties in the firm value
chain (Gaur et al., 2017;Giri et al., 2017;Wilson et al., 2022). Therefore, the firms adopting
reverse logistics will be motivated to increase collaboration activities, resulting in an
increased level of collaboration.
H2. The adoption of reverse logistics will lead to a higher level of collaboration.
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4. Methodology
4.1 Data
The data for this analysis comes from two waves (2017 and 2019) of the German Community
Innovation Survey (CIS) performed by the Centre for European Economic Research (ZEW) in
Mannheim. The binomial regression analysis of the panel data was conducted using STATA
16.0. Using secondary data from Germany to test the hypothesis is useful for several reasons.
First, Germany is at the forefront of legislation on reverse logistics. In 1991, Germany
introduced the first-ever legislation on packaging materials and made manufacturers
responsible for collecting, sorting and recycling packaging material for their products
(
Alvarez-Gil et al., 2007). The 1994 European Union “Directive on Packaging and Packaging
Waste”and subsequent updates further intensified German efforts on reverse logistics. The
target is to recycle at least 65% of the product weight by 2025. Second, along with several
drives that can be used to test and control hypotheses, the 2017 wave of the CIS survey
specifically asked the participating firms about the adoption of reverse logistics in the past
three years (2014–2016). Similarly, the 2019 CIS wave asked the firms about adopting AI
techniques and collaboration activities in their day-to-day operations. Third, though the
responses are anonymized, the changes in a firm over time can be tracked using the firm ID,
and the impact of actions taken in the past can be estimated. Finally, by anonymizing the
responses, CIS has reduced the chances of self-serving bias (i.e. respondents have no incentive
to make their firm appear “better”than it is). Self-serving biases are a major concern for
researchers when analyzing system traits using self-reported responses (Ketokivi, 2019).
4.2 Variables used in study
The study needed one independent (reverse logistics adoption) and two dependent
(digitalization and collaboration) variables to test the proposed hypotheses. These three
variables will be sufficient to measure the changes in digitalization and collaboration level in
a given firm after adopting reverse logistics. Independent variables will be responsible for
information regarding reverse logistics adoption, and the dependent variable will be
responsible for information related to changes in digitalization and collaboration levels. The
collaboration variable’s development is consistent with similar studies (Cricelli et al., 2021). At
the same time, the available information from the survey instrument was used to develop the
digitalization variable. Along with these three main variables, multiple control variables were
also used in the study, discussed in detail in the following subsections.
4.2.1 Independent variable. We assess the impact of reverse logistics adoption on the
digitalization and collaboration increase in the firm through the “logi5”question in CIS 2017.
The independent variable “AdoptedRL17”(renamed logi5) is described with a Yes/No answer
to the question, “During the three years from 2014 to 2016 did your enterprise introduce any of
the following innovations in logistics? Reverse logistics (reuse and return of products and
materials, etc.)”.
4.2.2 Dependent variables. The two dependent variables, “DigitalizationCount”and
“CollaborationCount”, were generated by combining multiple variables from the CIS 2019
wave. The process and rationale for generating variables are given below.
4.2.2.1 DigitalizationCount. We defined the “DigitalizationCount”dependent variable by
considering all the items in the survey that could describe the use of digital technologies by
the focal firm. To this aim, we resorted to three sets of items.
The first describes the use of AI technologies in the firm activities. The CIS 2019 inquired
about using five different types of AI technologies for five different activities, producing 25
binary variables. In addition, two different binary variables focusing on AI use were also
available in the group. These 27 binary variables were merged into one binary variable digi_
AI, having a value of 0 and 1. Where one represents a firm that has participated in any
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activities related to AI (regardless of intensity), and zero represents the firms that have not
participated in any activity related to AI. The rationale for merging multiple variables from
the AI group into one binary variable was rooted in the idea that all groups should have
similar weightage and that one type of digitalization does not skew the results.
The second describes the use of software/big data for firm activities. Three binary
variables inquiring about using big data/software for firm activities were merged into one
count variable, digi_bd, having a value of 0–3.
The third describes the use of information processing techniques for firm activities. The
one variable focusing on the use of digital information processing techniques for firm
activities was renamed to digi_ip, having a value of 0–1.
The resulting three variables were merged into one “DigitalizationCount”dependent
count variable for further analysis. The count variable is useful in identifying firms that have
increased their level of digitalization and can help distinguish firms with a greater increase in
digitalization. The resulting count variable can have a value of 0–5 for any focal firm. For
example, a firm that has participated in both information processing and AI will have a value
of two, and the firm that has only participated in AI will have a value of one. In addition,
multiple other specifications have been attempted as a robustness check (see section 5.4). This
new “DigitalizationCount”variable contains information from all variables that deliver
information on the increase in digitalization levels between 2016 and 2018 in any given firm
(Figure 1).
4.2.2.2 CollaborationCount:. Similar to the previous dependent variable, we defined the
“CollaborationCount”by considering all the items in the survey that could describe the
variety of inter-organizational collaborations pursued by the focal firm. To this aim, we
resorted to four sets of items.
The first describes the location (domestic or foreign) of the collaborating partner. The CIS
2019 inquired about the location of the ten types of collaborating partners producing 20
binary variables. These 20 variables were merged into ten count variables, having one
variable for each type of partner. For example, collaboration with domestic and foreign
universities was merged into one count variable called collab_univer with a value of 0–2.
Merging these variables into ten count variables provides a greater depth of information on
inter-organization collaboration. It can distinguish firms with a higher level of collaboration
from firms with a lower level of collaboration. To keep the variables in a manageable form,
these ten variables were merged into one single count variable collab_location with a value of
0–20 (No information was lost).
Figure 1.
DigitalizationCount
generation
Reverse
logistics
adoption and
digitization
The second describes the collaboration for innovation. Five binary variables inquiring about
the collaboration activities specifically focusing on innovation activities were merged into
one count variable collab_innov, having a value of 0–5.
The third describes the collaboration for R&D. Three binary variables inquiring about the
collaboration for activities associated with R&D were merged into one count variable, collab_
rnd, having a value of 0–3.
The fourth describes the collaboration for other activities. Two binary variables inquiring
about the collaboration for other activities (AI and others) were merged into one count
variable collab_oth with a 0–2.
The resulting 13 count variables were merged into one “CollaborationCount”dependent
variable for further analysis. Similar to the previously developed variable, the
“CollaborationCount”variable can have a value of 0–30.Where0meansnoinvolvementin
collaboration activities and 30 means the involvement in all activities associated with collaboration
(Figure 2). Multiple other specifications have been attempted as robustness checks (see section 5.4).
4.2.3 Control variables. For testing hypotheses H1 and H2, we introduced multiple control
variables discussed in previous studies (Agrawal et al., 2020;Agrawal and Narain, 2021;
Becker et al., 2018;Wrede et al., 2020) that can facilitate the adoption of digitalization and
collaboration practices (Table 1). Becker et al. (2018) reported several cases where a hiring
Chief Technical Officer (CTO) expedited the digitalization process. Similarly, employee skills
and training (Agrawal et al., 2020) and the acquisition of newer technologies were major
contributors to increased digitalization and collaboration (Agrawal and Narain, 2021;Usman
Ahmad et al., 2019). Furthermore, we controlled the impact of public funding and
international competition by introducing variables “public_funding”and “export_perc”in
the model, as studies have reported that the firms engaging in these activities demonstrate
higher levels of engagement in both collaboration and digitalization activities (Caputo et al.,
2021b;Ito and Pucik, 1993;Matt et al., 2011). For example, a higher percentage of export
revenue is linked with higher foreign competition and higher R&D spending (Ito and Pucik,
1993), leading to greater digitalization investment. Similarly, public funding positively
influences collaboration (Matt et al., 2011) and digitalization activities (Nedilska and Oleniuk,
2020). Therefore, controlling these variables will eliminate any impact these two variables
have on the study’s dependent variables. The firm size, sector and sales volume were also
introduced as the additional control variables.
4.3 Econometric approach
This study uses the binomial regression analysis method to test the proposed hypotheses.
The binomial regression analysis of the panel data was conducted using STATA 16.0.
Regression analysis is a reliable method of identifying which variables impact a topic of
interest (independent variables). Several studies in the past have used regression analysis to
Figure 2.
CollaborationCount
generation
IJPDLM
empirically test the impact of one variable over another while utilizing similar data sets
(Czarnitzki et al., 2020;Horbach and Rammer, 2020;Kobarg et al., 2020). The process of
performing a regression allows you to confidently determine which factors matter most,
which factors can be ignored and how these factors influence each other (Myrtveit and
Stensrud, 1999). The dependent variables used in the study are count variables. The data
prediction based on count variables is supported perfectly by the Poisson-Gamma mixed
distribution (Hilbe, 2011), which the negative binomial regression is based on. We employed
the negative binomial regression method because the variance of dependent variables is
greater than the mean and contains only non-negative integer values (Table 2). According to
Joe and Zhu (2005), this dataset cannot support standard linear regression techniques
pertaining to the dependent variable’s abnormal, highly skewed and discontinuous nature.
The independent and control variables were then subjected to a Variance Inflation Factor
(VIF) test. VIF has an average value of 1.36 and a maximum value of 1.77. According to
Baltagi (2005) and Peter (1998), the multicollinearity problem among the variables is not
serious when the VIF value is below 10. Furthermore, to test the best fit for data AIC of the
independent variable was calculated. The lower value of AIC indicates a better fit for the data.
Hence, the model with the smallest AIC value was selected.
5. Results
5.1 Descriptive statistics
Table 2 presents descriptive statistics and a correlation matrix of the variables used. For
example, the variable “AdoptedRL17”is a binary variable that represents the firm responses
Variables Type Description
Reference
year
newemployee_
knowl
Ordinal factor
variable
Did the firm hire new employees who bring in know-
how from other firms
2017
new_tech_acq Binary
Variable
Did the firm purchase new machines that are based
on totally new technologies
2017
cost_employ_
train17
Ordinal factor
variable
How much did the firm spend on further education
and training of current employees in 2015 and 2016
2017
graduate_
employee17
Ordinal factor
variable
Share of employees in percentage holding a
university degree in 2015 and 2016
2017
Sector Ordinal factor
variable
Classification of participating firms in 21 economic
sectors
2019
export_perc Ordinal factor
variable
Percentage of revenue coming from exports in 2016 2016–2018
public_funding Binary
Variable
Did the firm receive public funding between 2014 and
2016
2016–2018
firm_size Ordinal factor
variable
Firm size
>50
50–249
≤250
2019
Sales Sales volume in millions 2018
AdoptedRL17 Binary
Variable
If firms adopted reverse logistics between 2014 and
2016
2014–2016
DigitalizationCount Count
variable
Generated by merging multiple variables to report on
all digitalization activities performed by the firm (see:
Section 4)
2016–2018
CollaborationCount Count
variable
Generated by merging multiple variables to report on
all collaboration activities performed by the firm (see:
Section 4)
2016–2018
Source(s): Table by authors
Table 1.
Description of
variables used
in the study
Reverse
logistics
adoption and
digitization
Variable Obs Mean
Std.
Dev Min Max
newemployee_
knowl
new_
tech_
acq
cost_
employ_
train17
graduate_
employee17 Sector
export_
perc
public_
funding
firm_
size Sales
newemployee_
knowl
1,961 0.2193 0.4139 0 1 1
new_tech_acq 1,830 0.1607 0.3673 0 1 0.19 1
cost_employ_
train17
1,642 0.0082 0.0148 0 0.12 0.093 0.023 1
graduate_
employee17
2,060 3.3966 2.6681 0 8 0.128 0.033 0.166 1
Sector 2,161 11.6622 5.9670 1 21 0.015 0.046 0.124 0.268 1
export_perc 1,916 0.1296 0.2337 0 0.85 0.162 0.131 0.0503 0.109 0.237 1
public_funding 2,161 0.2189 0.4136 0 1 0.147 0.210 0.0366 0.105 0.096 0.201 1
firm_size 1,889 1.4325 0.6451 1 3 0.249 0.174 0.0571 0.068 0.085 0.24 0.104 1
Sales 1,966 20.7927 73.8848 0 1373.45 0.177 0.061 0.0178 0.02 0.095 0.149 0.020 0.519 1
AdoptedRL17 4,491 0.0448 0.2068 0 1
DigitalizationCount 4,491 0.3295 0.7625 0 5
CollaborationCount 4,491 0.4438 0.9868 0 5
Source(s): Table by authors
Table 2.
Summary of
descriptive statistics
and correlation matrix
IJPDLM
(Yes/NO) to the question of adopting reverse logistics between 2014 and 2016. These
descriptive statistics indicate heterogeneity in the firms’reverse logistics adoption and offer
validity as control variables.
5.2 Regression results
We used STATA 16.0 to perform negative binomial regression on panel data from the CIS
2017 and 2019 waves to test the proposed hypothesis. The regression results for both
proposed hypotheses are reported in Tables 3 and 4. The first hypothesis investigating the
increase in digitalization level is accepted at the 99% confidence level (p-value <0.01).
Model 1: Base model Model 2: H1 digitalization
Coef Std. Err P>zCoef Std. Err P>z
AdoptedRL17 0.4217 0.1322 0.001
newemployee_knowl 0.5169 0.0769 0.000 0.5203 0.0761 0.000
new_tech_acq 0.4612 0.0816 0.000 0.4552 0.0810 0.000
cost_employ_train17 4.3537 2.0854 0.037 3.9750 2.0710 0.055
graduate_employee17
0≤3< 5 0.4871 0.1721 0.005 0.4894 0.1712 0.004
5≤3< 10 0.3767 0.1645 0.022 0.3617 0.1638 0.027
10 ≤3< 15 0.4359 0.1671 0.009 0.4262 0.1665 0.010
15 ≤3< 20 0.4810 0.1835 0.009 0.4446 0.1835 0.015
20 ≤3< 30 0.6474 0.1645 0.000 0.6615 0.1637 0.000
30 ≤3< 50 0.4889 0.1676 0.004 0.4639 0.1673 0.006
50 ≤3< 75 0.6556 0.1724 0.000 0.6490 0.1717 0.000
75 ≤3<5100 0.6898 0.1887 0.000 0.6764 0.1882 0.000
sector
Food/Tobacco 0.0050 0.2958 0.987 0.0404 0.2947 0.891
Textiles 0.4666 0.2701 0.084 0.4673 0.2681 0.081
Wood/Paper 0.0648 0.3312 0.845 0.0788 0.3293 0.811
Chemical 0.0585 0.2856 0.838 0.0600 0.2837 0.832
Plastics 0.4129 0.2814 0.142 0.3451 0.2806 0.219
Glass/Ceramics 0.2474 0.3208 0.441 0.1886 0.3206 0.556
Metals 0.2126 0.2462 0.388 0.2119 0.2446 0.386
Electrical equipment 0.4905 0.2318 0.034 0.4607 0.2303 0.045
Machinery 0.3584 0.2664 0.179 0.3588 0.2641 0.174
Retail/Automobile 0.4406 0.2886 0.127 0.4288 0.2868 0.135
Furniture/Toys/Medical tech. 0.0571 0.2685 0.831 0.0545 0.2666 0.838
Energy/Water 0.0293 0.2603 0.910 0.0538 0.2591 0.835
Wholesale 0.5950 0.2571 0.021 0.5397 0.2562 0.035
Transport equipment/Postal. 0.3649 0.2535 0.150 0.3823 0.2520 0.129
Media services 0.4939 0.2480 0.046 0.4933 0.2460 0.045
IT/Telecommunications 0.6066 0.2512 0.016 0.6111 0.2495 0.014
Banking/Insurance 0.9538 0.2497 0.000 0.9605 0.2480 0.000
Technical services/R&D serv. 0.5572 0.2397 0.020 0.5740 0.2385 0.016
Consulting/Advertisement 0.6978 0.2443 0.004 0.7175 0.2429 0.003
Firm-related services 0.6432 0.2595 0.013 0.6316 0.2577 0.014
firm_size
50–249 employees 0.3377 0.0887 0.000 0.3231 0.0882 0.0000
>5250 employees 0.2750 0.1307 0.035 0.2694 0.1296 0.0380
export_perc 0.4120 0.1504 0.006 0.4072 0.1489 0.0060
public_funding 0.1773 0.0802 0.027 0.1672 0.0796 0.0360
sales 0.0012 0.0004 0.005 0.0012 0.0004 0.0040
_cons 1.7453 0.2363 0.000 1.7439 0.2349 0.0000
Source(s): Table by authors
Table 3.
The results of the
regression for the
digitalization level
Reverse
logistics
adoption and
digitization
Similarly, the second hypothesis investigating the increase in firm collaboration level is also
accepted at a 95% confidence level (p-value <0.05). As a result, it is confirmed that the firms
adopting reverse logistics are more likely to have higher levels of digitalization and
collaboration than those that do not adopt reverse logistics. All the control variables used in
the study are also significant, hence confirming their impact and power in predicting
digitalization and collaboration adoption. The control variables “newemployee_knowl”,
“new_tech_acq”,“export_perc”,“public_funding”, and “sales”are strong predictors of both
increased digitalization and collaboration levels in a firm. When combined, the control
variable “sector”is significant. However, the analysis of individual sectors shows that the
Model1: Base model Model 2: H2: Collaboration
Coef Std. Err P>zCoef Std. Err P>z
AdoptedRL17 0.436 0.189 0.021
newemployee_knowl 0.4154 0.0989 0.000 0.410 0.099 0.000
new_tech_acq 0.5045 0.1084 0.000 0.514 0.108 0.000
cost_employ_train17 15.1095 2.6465 0.000 14.905 2.637 0.000
graduate_employee17
0<3< 5 0.7133 0.1995 0.000 0.705 0.199 0.000
5≤3< 10 0.5278 0.1913 0.006 0.516 0.191 0.007
10 ≤3< 15 0.6626 0.1908 0.001 0.627 0.191 0.001
15 ≤3< 20 0.8876 0.2124 0.000 0.863 0.212 0.000
20 ≤3< 30 0.7498 0.1981 0.000 0.753 0.198 0.000
30 ≤3< 50 0.6810 0.1947 0.000 0.649 0.195 0.001
50 ≤3< 75 0.8769 0.2092 0.000 0.856 0.209 0.000
75 ≤3<5100 1.1072 0.2239 0.000 1.091 0.224 0.000
sector 0.705 0.199 0.000
Food/Tobacco 0.1923 0.3431 0.575 0.2439 0.3434 0.478
Textiles 0.5158 0.3102 0.096 0.5300 0.3094 0.087
Wood/Paper 0.1236 0.3585 0.730 0.1485 0.3579 0.678
Chemical 0.7921 0.3096 0.011 0.7738 0.3088 0.012
Plastics 0.4342 0.3311 0.190 0.4072 0.3313 0.219
Glass/Ceramics 0.5411 0.3657 0.139 0.5063 0.3666 0.167
Metals 0.4172 0.2789 0.135 0.4194 0.2784 0.132
Electrical equipment 0.4129 0.2817 0.143 0.3909 1.3900 0.165
Machinery 0.6945 0.3138 0.027 0.7127 0.3131 0.023
Retail/Automobile 0.5486 0.3623 0.130 0.5148 0.3619 0.155
Furniture/Toys/Medical tech. 0.4623 0.2906 0.112 0.4404 0.2898 0.129
Energy/Water 0.1791 0.2953 0.544 0.1479 0.2948 0.616
Wholesale 0.0510 0.3249 0.875 0.0294 0.3249 0.928
Transport equipment/Postal. 0.2496 0.3108 0.422 0.2273 0.7300 0.464
Media services 0.5580 0.2962 0.060 0.5722 1.9400 0.053
IT/Telecommunications 0.4912 0.3140 0.118 0.5147 0.3135 0.101
Banking/Insurance 0.4703 0.3237 0.146 0.4994 0.3231 0.122
Technical services/R&D serv. 0.5916 0.2880 0.040 0.6196 2.1600 0.031
Consulting/Advertisement 0.6319 0.2897 0.029 0.6670 0.2894 0.021
Firm-related services 0.3549 1.130 0.257 0.3523 1.1300 0.260
firm_size
50–249 employees 0.4057 0.1085 0.000 0.3996 3.6900 0.000
>5250 employees 0.3921 0.1783 0.028 0.3814 2.1500 0.032
export_perc 0.7455 0.1958 0.000 0.7557 0.1953 0.000
public_funding 0.7378 0.0996 0.000 0.7326 0.0995 0.000
sales 0.0021 0.0007 0.006 0.0021 0.0008 0.005
_cons 1.6438 0.2658 0.000 1.6531 0.2655 0.000
Source(s): Table by authors
Table 4.
The results of the
regression analysis for
the collaboration level
IJPDLM
control is more significant for some industries than others. Furthermore, the percentage of
graduate employees impacts both digitalization and collaboration levels, but it does not
matter what percentage of employees possess a university degree. This finding contrasts
with the earlier studies, such as those performed by Agrawal et al. (2020), where it was
reported that employees with higher technical skills would be better able to guide firms in
adopting digitalization and collaboration practices.
5.3 Robustness check
The robustness of the model was tested by changing the definitions of dependent variables.
Multiple alternate “DigitalizationCount”and “CollaborationCount”variables were generated
using different combinations of variables to see if the test results would vary. The list of these
alternate variables and obtained regression results is presented in Table 5. The results in
Table 5 confirm the robustness of the dependent variables as the varied definitions of the
variables do not change the results significantly. Another measure adopted to check the
robustness of the model was using the Logit and Probit models instead of negative binomial
regression. The obtained results confirmed the robustness of the model as the results of these
new tests did not produce values notably different from the original model.
6. Discussion
The existing literature has discussed the impact of digitalization and collaboration on reverse
logistics and how these competencies facilitate a firm in moving toward sustainability
Impact of reverse logistics adoption on digitalization Impact of reverse logistics adoption on collaboration
Alternate definitions for
DigitalizationCount variable Coef p-value
Alternate definitions for
CollaborationCount variable Coef p-value
AI 0.7326 0.087 collab_dom þcollab_
inno þcollab_rnd þcollab_oth
0.4192 0.024
SDB 0.3563 0.049 collab_for þcollab_
inno þcollab_rnd þcollab_oth
0.3926 0.013
AI_lu þSDB þIP 0.4217 0.001 collab_inno þcollab_
rnd þcollab_oth
0.3660 0.017
AI_ir þSDB þIP 0.4249 0.001 collab_dom þcollab_
for þcollab_inno þcollab_oth
0.4200 0.024
AI_ml þSDB þIP 0.4217 0.001 collab_dom þcollab_
for þcollab_rnd þcollab_oth
0.3356 0.183
AI_kbs þSDB þIP 0.4252 0.001 collab_dom þcollab_
for þcollab_rnd þcollab_inno
0.4970 0.016
AI_oth þSDB þIP 0.4217 0.001 collab_dom þcollab_
rnd þcollab_oth
0.3133 0.203
AI þIP 0.4972 0.009 collab_dom þcollab_
for þcollab_inno
0.4843 0.017
SDB þIP 0.4001 0.004 collab_for þcollab_
rnd þcollab_inno
0.4390 0.009
Note(s): AI =Artificial Intelligence (Language understanding þImage recognition þMachine
Learning þKnowledge based systems þOthers)
AI_lu 5AI–Language understanding AI_ir =AI- Image recognition AI_ml =AI–Machine Learning
AI_kbs =AI–Knowledge based systems AI_oth =AI –others SDBs=Software data bases
IP =information processing collab_oth =Other type of collaboration
collab_dom 5Domestic collaboration collab_for =Domestic collaboration
collab_rnd =Collaboration for R&D collab_inno =Collaboration for innovation
Source(s): Table by authors
Table 5.
Robustness check
Reverse
logistics
adoption and
digitization
(Aksin-Sivrikaya and Bhattacharya, 2017;Chen et al., 2017) and eventually toward the
circular economy (Antikainen et al., 2018;Mishra et al., 2021;Pagoropoulos et al., 2017).
However, the literature has not investigated the impact of reverse logistics adoption on
digitalization and firm collaboration. This study aimed to empirically test this reverse
relation and highlighted the advantages firms can reap by implementing reverse logistics
earlier in their system, including greater levels of collaboration and digitalization.
Furthermore, KBV dictates that firms, while performing any activity, create new
knowledge, and this process of creating new knowledge is multiplied when firms perform
new activities or introduce new systems (H€
orisch et al., 2015;Yang, 2013). Therefore, the
management and utilization of this internally generated knowledge play a vital role in the
firm’s future endeavors. This impact also emerges after the adoption of reverse logistics. It
was argued earlier that while adopting reverse logistics, firms produce new implicit and
explicit knowledge that will provide a competitive advantage and learning to the practicing
firm. Proper knowledge management practices will help the firms effectively utilize these
advantages and knowledge. It is important to note that these advantages can only be derived
if the employees are willing to freely share this newly generated knowledge (Caputo et al.,
2021a). Therefore managers should focus on developing collaborative culture inside and
outside the firm boundaries.
Relying on institutional theory, Fauzi and Sheng (2022) argued that all firms would
eventually introduce digitalization into their system. But the introduction of digitalization
will pose several challenges for implementing firms during the implementation process and
before even starting the process. D’Este et al. (2012) divided barriers into two categories and
argued that firms should focus on overcoming deterring (hindering firms from initiating new
activities) barriers. The elimination of deterring barriers is an intricate process requiring top
management commitment. Leaning on the KBV, the study results confirm that firms that
implement reverse logistics are better positioned to overcome deterring barriers and engage
in digitalization activities, including the initial cost that was reported as one of the biggest
barriers toward digitalization (Agrawal et al., 2020;Tham and Atan, 2021). In addition,
additional gained employee skills and IT competencies will serve as enablers for the higher
digitalization levels (Chong et al., 2014;Eller et al., 2020). This argument aligns with the
previous studies that reported a positive impact on firm performance and capabilities after
adopting reverse logistics activities (Matos et al., 2020;Mihi Ram
ırez, 2012;Ramirez and
Girdauskiene, 2013). Similarly, introducing reverse logistics will lead to higher IT capabilities
for the practicing firm (Daugherty et al., 2005) as the initial training required to implement
digitalization will be already available to firm employees (Eller et al., 2020;Marqu
es and
Garrig
os-Sim
on, 2006). Furthermore, the results obtained in this study are in line with
Azyabi’s (2018) findings. The author reported that knowledge acquisition activities (creation
and transfer) are the most significant factors influencing the increase in the use of digital
technologies in Saudi SMEs. Therefore, the study results confirm that adopting reverse
logistics generates knowledge that not only encourages firms to participate in digitalization
activities but is also helpful in overcoming barriers associated with adopting digitalization.
This argument is in line with Chong et al. (2014), where authors reported that the knowledge
creation process facilitates the firms in introducing newer technologies.
Similarly, it is no longer feasible for a firm to thrive in silos. Instead, firms need to
collaborate with external entities to reduce risk and increase productivity and speed.
Appropriate knowledge creation and management activities (implementing new processes)
will enable the firm to share the newly created knowledge with both internal and external
partners (Nisar et al., 2019). The study’s findings also confirm that reverse logistics adoption
will help firms overcome collaboration barriers and make them open to utilizing external
knowledge and techniques. Inter-firm collaboration was also reported by Hudnurkar et al.
(2014) as an important activity to maximize success in reverse logistics activities.
IJPDLM
Similarly, an appropriate infrastructure and knowledge sharing capabilities are created
during reverse logistics adoption to serve as a base for inter-organization collaboration
(Enkel and Heil, 2014;Zhang and Cao, 2018). These findings are in line with Yang (2013),
where author confirmed that knowledge acquisition and dissemination activities
significantly improve firms’collaborative relations. Furthermore, the findings also confirm
the view of Olorunniwo and Li (2010), who argued that collaboration with partners and
competitors is needed for any successful reverse logistics operation, which increases over
time with the maturity of reverse logistics operations.
The adoption of reverse logistics offers many benefits and has become essential today.
However, the adoption of reverse logistics is often involuntary. It is adopted to respond to
policymakers and customers (
Alvarez-Gil et al., 2007;Hsu et al., 2013) and, in general, is
viewed as a money losing activity (Eltayeb and Zailani, 2011). The study’s findings assert
that though the activity is complex and expensive, it has lasting beneficial impacts on
operational activities and has the potential to help in future changes.
7. Conclusion
The study investigated the impact of sustainability practices (reverse logisticsadoption) on
firms’operational activities. In particular, it explored whether adopting reverse
logistics increased firms’digitalization and collaboration activities. To this end, an
econometric analysis was conducted using Germany’s Community Innovation Survey data
from the 2017 and 2019 waves. The analysis of the results confirms a strong relationship
between reverse logistics adoption and digitalization/collaboration increase. Furthermore,
the study confirms a positive relationship between sustainability practices and digitalization/
collaboration, confirming the proposed hypotheses. Therefore, the results highlight
additional benefits offered by investments in sustainability practices. Furthermore, the
study’s findings advance the KBV theory by confirming the view that the knowledge created
during the adoption of one activity is useful in performing different activities and can
significantly shorten the learning curve for future activities. The findings have several
implications for theory, practice and policymaking discussed below.
7.1 Theoretical implications
Previous studies have focused on the relationship between digitalization and reverse logistics
and digitalization and collaboration. However, the reverse relation among these two pairs has
never been explored. In particular, studies have not reported the knowledge-generating
capabilities of reverse logistics and its potential to facilitate the process of digitalization and
collaboration. This study is the first of its nature that explores this relationship. This is
important as, unlike the former flow of actions, the adoption of reverse logistics is often
involuntary and is adopted to comply with the law to achieve SDG 2030 goals. The outcome of
this study confirms that adopting reverse logistics has significant power in explaining the
increased digitalization and collaboration in German firms. This was confirmed by
developing two hypotheses that focused on the impact of reverse logistics adoption on firm
digitalization and collaboration levels. This experimental setting contributed to the growing
literature on digitalization, collaboration and reverse logistics (sustainability). As a result,
this study contributes to the literature to better understand these concepts and explain how
they are connected. Respectively, the findings contribute to management literature. The
study also contributes to the debate on the importance of KBV and provides empirical
evidence to support the theory. A recent literature survey by Pereira and Bamel (2021)
emphasized the need for further studies to demonstrate the usability and power of KBV.
The authors argued that KBV could explain the performance variance among firms.
Reverse
logistics
adoption and
digitization
Furthermore, they highlighted that knowledge generating firms are better off in the long run
and can easily transform their operations to respond to future technologies. The research
results confirm these views of Pereira and Bamel (2021).
7.2 Managerial implications
Higher levels of collaboration and digitalization have been reported to bring a significant
competitive advantage as they make the firm’s value chain fast, innovative, responsive and
cost effective. As a result, the firm becomes competitive and well-positioned to take
advantage of future opportunities. From the managerial perspective, identifying a key
enabler to increase firm collaboration and digitalization levels is important as firms are
constantly searching for ways to improve business in the current disruptive environment.
For example, when faced with the dilemma of investing in reverse logistics or digitalization/
collaboration, managers can choose reverse logistics to respond to stakeholder demand
(government, distributors, customers). This reverse logistics adoption will encourage firms to
increase their digitalization and collaboration levels while enjoying the benefits of reverse
logistics adoption. That includes customer loyalty, employee satisfaction, increased firm
reputation and higher profits. Furthermore, the knowledge generated by reverse logistics
adoption can be essential to achieving firms’digitalization and collaboration goals. This was
demonstrated by Schl€
uter et al. (2021) in their pilot study of a manufacturing firm where
machine learning triggered by reverse logistics (adopted to support reverse logistics)
significantly improved the reverse logistics performance. Therefore, adopting reverse
logistics can be an agent of change toward more digitalized operations. Wilson et al. (2022)
reported several examples where firms already performing reverse logistics adopted AI
technologies to improve their reverse logistics process. Similarly, Olorunniwo and Li (2010)
reported that it is vital for a firm to collaborate and share information with its partners to
manage reverse logistics operations successfully. Hence, reverse logistics strongly
incentivizes information sharing and collaboration with other organizations. As a result,
firms are more prepared to collaborate with their partners and competitors. SIGRAUTO in
Spain can be cited as an example of different bodies and organizations collaborating to
improve their reverse logistics operation. Where a single entity (SIGRAUTO) coordinates all
elements of end-of-life returned auto parts from different automotive manufacturers in Spain.
Hence, increasing collaboration and transparency among partners to enhance their reverse
logistics operations.
7.3 Policymakers implication
Reverse logistics adoption promotes resource conservation, recycling and material reuse at
the end-of-life stage. Europe in general and Germany, in particular, are working toward
achieving a maximum of 10% of municipal waste in the landfill by 2035, which means a
technical zero-waste. Numerous governmental regulations such as China’s“Circular
Economy”directive or European WEEE, the two EU Circular Economy Action Plans
(2015 and 2020) push firms toward adopting reverse logistics, together with the impulse
coming from the Next GenEU post-pandemic recovery program for a cleaner, circular and
digital Europe. Policy directives are among the most important drivers in the western world
for implementing reverse logistics. However, the adoption of reverse logistics is slow in
countries where there are fewer regulations to bind firms to reduce their waste. Hence, strong
regulations are essential for the speedy and early adoption of reverse logistics. This study
provides important information to policymakers by demonstrating the usefulness and
capability of reverse logistics in enabling firms to improve firm productivity and manage
future changes. This information is vital for policymakers to persuade firms to implement
reverse logistics earlier in their system. Though the study results come from the German
IJPDLM
industries, policy implications can be implemented in other European countries as they all
have similar work ethics and are moving towards achieving 2030 SDG goals, particularly
targets 12.1, 12.2, 12.3, 12.5 and 11.6.
7.4 Limitations
This study is not without limitations, which warrant future studies. Firstly, the article uses
data from only German firms. Germany is at the forefront of environmental and reverse
logistics regulations. Hence, some contextual results may not apply to other countries.
Secondly, this study did not differentiate between the types of digitalization and collaboration
activities. The study considered all types of activities related to digitalization and
collaboration to be equal. This is not often the case, and some activities are more intense
than others in practice. This may have undermined the importance and value of some
activities performed by the firm. Future studies should consider the relative importance of
these activities. Thirdly, the study only reported on a simple increase in digitalization and
collaboration levels and did not go into details about the breadth of this increase. Future
studies may consider the actual amount of increase in digitalization and collaboration levels.
Lastly, the data was collected in a pre-covid era, and industry and society have changed
drastically in the last three years. The adoption of digital technologies and collaboration has
increased initially, but its long-term impact is yet to be determined. Further empirical and
longitudinal studies are needed to answer these questions.
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About the authors
Faisal Rasool is currently a PhD student in the Department of Enterprise Engineering, University of
Rome “Tor Vergata”, Italy. He received his master’s degree in Industrial and Manufacturing
Engineering from the Asian Institute of Technology, Thailand. His research interests focus on
sustainable and digital supply chains and their impact on firm performance. Faisal Rasool is the
corresponding author and can be contacted at: faisal.rasool@uniroma2.it
Marco Greco is an Associate Professor of Industrial Marketing and Corporate Governance at the
University of Cassino and Southern Lazio. He published articles in reputed journals such as
Technological Forecasting and Social Change, European Management Journal, Journal of Business
Research, and International Journal of Project Management. His main research interests include open
innovation, intellectual capital, strategic management, negotiation and project management.
Gustavo Morales-Alonso is an Associate Professor in Economics, Entrepreneurship and Innovation
at Universidad Polit
ecnica de Madrid (UPM). With a sound interest in the drivers of economic
development, the sharing economy and sustainability, he has published in SCI-indexed journals such as
Technological Forecasting and Social Change, Journal of Business Research and Sustainability, among
others. Belongs to the Editorial Advisory Board of the European Journal of Innovation Management.
Ruth Carrasco-Gallego is an Associate Professor of Regenerative Value Ecosystems at Universidad
Polit
ecnica de Madrid (UPM), where she is the Dean for SDGs at the Industrial Engineering School. Dr
Carrasco-Gallego has published extensively in her areas of expertise and has participated in or led
several research projects. Currently, she leads the CircularizatE initiative, an on-campus living lab of real
circular economy, aiming to demonstrate that regenerative business models are technically viable,
economically profitable and socially inclusive.
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