86 Ekonomický časopis, 67, 2019, č. 1, s. 86 – 104
The Effectiveness of Reverse Logistics: The Empirical
Test of Its Factors for Product Returns Reduction1
Radoslav ŠKAPA – Alena KLAPALOVÁ*1
Abstract
Within the context of reverse logistics (RL), only a few studies have focused
on the management characteristics that lead to effectively implementing RL.
From the myriad of potential factors of effective RL, the paper focuses on the
role of knowledge management, 1st-tier integration and the extent of RL planning,
as these factors and their mutual relations have been neglected by
empirical research to date. The paper develops a theoretical model to fill this
gap and tests it using structural equation modelling on primary data. The findings
based on 146 cases support that knowledge management, the extent of RL
planning and the level of integration with the 1st-tier supply chain members are
the factors related to the effectiveness of RL. The study underpins the importance
of long-term RL planning and deeper integration and collaboration with customers
and suppliers for effective RL and so to reduce the negative impact of
product returns.
Keywords: reverse logistics, effectiveness, knowledge management, supply chain
integration, planning, product returns
JEL Classification: M10
Introduction
According to the most widely accepted definition, the reverse logistics (RL)
is “the process of planning, implementing, and controlling the efficient, cost-effective
flow of raw materials, in-process inventory, finished goods and related
information from the point of consumption to the point of origin for the purpose
* Radoslav ŠKAPA – Alena KLAPALOVÁ, Masaryk University, Faculty of Economics and
Administration, Department of Corporate Economy, Lipová 507/41a, 602 00 Brno, Czech Republic;
e-mail: Radoslav.Skapa@econ.muni.cz; Alena.Klapalova@econ.muni.cz
1
This research was supported by a grant Research Project No GA16- 16-16260S Managerial
Approach to Reduction of Reverse Flows in Connection to Customer Satisfaction and Continuous
Improvement, funded by the Grant Agency, Czech Republic.
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of recapturing value or proper disposal” (Rogers and Tibben-Lembke, 1999).
Since the 1990s, RL and its related disciplines such as reverse/closed-loop supply
chain management have received great interest from both the industrial and
academic worlds (Prahinski and Kocabasoglu, 2006; Badenhorst and Nel, 2012).
Companies have given a substantially higher priority to effective RL operations
due to the increasing volume of products returned due to the liberal return policies,
growing customer power and quality issues. These trends are reflected in
customer relationship management practices, the globalization of sourcing and
trade, shortening product life cycles, an increase of resource scarcity and the
growth of legislation regulating and providing for the proper disposal of reverse
flows as well as the still bigger imperative to search for the ways to reduce costs
and to find new business opportunities (Agrawal, Singh and Murtaza, 2015;
Govindan et al., 2015; Stock, Speh and Shear, 2002)
In harmony with the above processes, Vogt and de Wit (2005) and several
other authors coined a broader conceptualisation of RL encompassing the entire
management, financial flow, waste as one type of reverse flows, the potential of
sustainable profitability as an outcome and perspective of the whole business.
However, such a broadening of understanding also needs a relevant extension
and more demands on management, both inside and outside the boundaries of
a company, thus creating new challenges for research to discover which managerial
factors lead to successful RL management.
Existing empirical research offers strong evidence of the many positive outcomes
of effective RL including improved customer and supplier satisfaction
and reputation with other stakeholders (Álvarez-Gil et al., 2007; Mollenkopf,
Russo and Frankel, 2007), market protection (De Brito, 2004); value retrieval
(Mollenkopf and Closs, 2005); legislative compliance and the development
of a corporate image as a socially responsible enterprise (Piotrowicz, 2007;
Verstrepen et al., 2007); decreased resource investment levels, higher profitability,
gaining new business opportunities (Autry, Daugherty and Glenn, 2001);
possibility of differentiation (Jayaraman and Luo, 2007); waste and cost reduction
(Rogers et al., 2002; Stock, Speh and Shear, 2002); the speed and reliability
of flexibility/processes and quality/processes (Verstrepen et al., 2007) being the
most obvious effects stated by managers when asked.
The majority of companies recognise the importance of RL and the whole
product return process, but only rarely adopt specific practices to manage them
more efficiently and effectively (Russo and Cardinali, 2012).
Nevertheless, the research examining various characteristics of company
management that support the effectiveness of RL processes is scarce (Mishra
and Napier, 2014).
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Whether RL management is effective depends on numerous factors, such
as competencies of managers to cope with challenges and problems and their
capabilities, which are in logistics context defined as “those attributes, abilities,
organizational processes, knowledge, and skills that allow a firm to achieve
superior performance and sustained competitive advantage over competitors”
(Morash, Droge and Vickery, 1996, p. 1). However; studies devoted to RL capabilities
specifically are missing, except for a few works on
• IT reverse logistics capabilities (Hazen et al., 2012; Morgan, Richey and
Autry, 2016);
• information system capabilities (Hsiao, 2010; Jack, Powers and Sinner,
2010);
• return handling and reprocessing capabilities (Pfohl, Bode and Nguyen,
2012);
• capabilities of logistics information management, closed-loop, supply chain
integration and coordination, conformity and institutional incentives (Vlachos,
2016).
Knowledge management assists in improving capabilities and competencies
in general. This argument is very vaguely supported by research in logistics: As
Krčál (2015) argues based on an intensive literature review, only a few studies
exist that concentrate on knowledge management in the context of RL. Also,
Lambert et al. (2011) conclude that more studies investigating the relationship
between knowledge management processes and organisational effectiveness in
RL are necessary.
RL is very information- and knowledge-intensive mainly due to the higher
level of uncertainty and complexity (Bai and Sarkis, 2013; Wadhwa and Madaan,
2007). The level of uncertainty and complexity is deepened through different
aspects and sometimes even actors within the supply chain in comparison to the
forward flows. Thus, information and knowledge should be exchanged and
shared with the external partners through the integration and collaboration process.
RL can play an important role to integrate knowledge from customers
about their experience with products with the knowledge of suppliers to deliver
right inputs for the expected value creation for customers (Fugate, Stank and
Mentzer, 2009) and thereby to reduce the amount of product returns or to get
proper knowledge about the returned product status. Delivering right and expected
product to the customer is one of the main contributors of superior effectiveness
(Esper et al., 2010). Several experts call for the need to pay attention to the
supply chain (1st-tier) integration in reverse supply chain processes and its impact
on effectiveness, as this has been largely underexplored as well (Bernon
et al., 2013; Mellat-Parast and Spillan, 2014).
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Higher complexity and uncertainty as well as requirements for additional
investments and involvement of further processes and activities in the case of RL
demand not only operational or ad hoc decisions but also strategic planning
(Jayaraman and Luo, 2007). The lack of strategic planning and limited forecasting
is one of the greatest barriers to effective RL (Ravi and Shankar, 2005; Rogers,
Melamed and Lembke, 2012; Ye, Zhao and Prahinski, 2013). Planning and related
controlling are reckoned to be key success factors (De Brito, Dekker and
Flapper, 2005).
Although the analysis made by De Brito, Dekker and Flapper (2005) is more
than ten years old and carried out on the small sample of sources, after reviewing
currently available research we can agree with their findings and highlight
another shortage in the knowledge, which offers almost no insight into the
planning as one of managerial functions connected to RL having some outcome.
There is also just the very limited number of empirical findings demonstrating
the relationship between the involvement of RL planning and company performance,
so the relevance of this managerial factor is not clear enough and insufficiently
supported.
Summarizing the gaps mentioned above in current knowledge and for the
need for research, this paper aims to answer the following research questions and
react to these calls:
RQ 1. How do particular managerial factors, specifically the areas of knowledge
management, the extent of organisational planning and the level of integration
and collaboration with the 1st-tier partners affect the perceived effectiveness
of reverse logistics?
RQ 2. What is the nature of the mutual relationships between these factors?
1. A Literature Review of Selected Effectiveness Factors
1.1. Organizational Planning, Knowledge Management and Effectiveness
of Reverse Logistics
RL may be an important “opportunity to build competitive advantage” as
an integral part of organisational strategies and strategic planning (Stock, Speh
and Shear, 2002, p. 16). Such integration enables a more systemic grasp and
engagement of long-term goals that need the investment of specific resources
in facilities, human resource training and operating and managerial activities
(Ye, Zhao and Prahinski, 2013). Effectiveness is viewed in terms of goal attainment,
with the best explanation as “the capacity of an organization to use its
resources successfully toward specific ends” (Steers, 1975, p. 555) and as “the
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most useful in comparative organizational research” (Georgopoulos and Tannenbaum,
1957, p. 534), because it expresses the success of the organization and
thus its competitiveness.
RL planning should cover all planning horizons – strategic, tactical and
operational because every level represents specific tasks and demands specific
resources and results in specific expected and planned attainments (De Brito,
Dekker and Flapper, 2005). Planning, and strategic planning specifically, lead
to potentially higher effectiveness and competitiveness (Shaik and Abdul-Kadar,
2012), more than sustainable ones (Genchev, Richey and Gabler, 2011). Effective
strategic planning is based on knowledge with strategic value (McKeen,
Zack and Singh, 2006). Strategic knowledge management is interdependent
with strategic planning as it enables the nurturing and deployment of core capabilities
and resources across and from the outside of the company (Kruger and
Snyman, 2004; McKeen, Zack and Singh, 2009). However, experience has
shown that decisions in RL prevalently have an operational character, due to the
reactive and not proactive character of decision making related to RL (Rogers
et al., 2012).
1.2. Knowledge Management, 1st-tier Integration and Effectiveness
of Reverse Logistics
RL need adequate knowledge management to help companies to be efficient
and effective in their RL processes (Mihi Ramírez, 2012) due to its higher complexity
and uncertainty as well as the specificities of many aspects and activities
(Wadhwa and Madaan, 2007). Compared to forward logistics, reverse processes
are more information intensive (Stock, Speh and Shear, 2002), especially in
the case when organisations are dealing with several recovery options and of
the design of the reverse supply chain is more in the form of a network with different
actors.
Therefore, the information systems are a crucial actor in the success of RL
being the backbone of knowledge management for many (Gunasekaran and
Ngai, 2007) and being able to track and measure goal attainment (Hazen et al.,
2012) and to assess the effectiveness of RL decisions and activities.
Knowledge management is expected to have a positive effect on performance
as well as on relationship building and maintenance with customers and suppliers
and the integration and collaboration with these stakeholders in the supply
chain (Bernon et al., 2013; Tseng, 2014). One of the biggest barriers for its effective
implementation is a lack of support from top management (Robinson et al.,
2006) or low or lacking recognition of knowledge management as a strategic
asset (Wang, Ahmed and Rafiq, 2008).
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1.3. External Integration and Collaboration and Effectiveness of Reverse
Logistics
RL as the critical element in supply chain management requires planning and
effective execution and intensive interactions throughout the companies in the
whole supply chain (Rogers et al., 2002) and often beyond the boundaries of one
supply chain. Many successful stories testify to the fact that RL has a significant
strategic impact on corporate performance in term of economic, social and environmental
issues (Dowlatshahi, 2005; Lambert, Riopel and Abdul-Kader, 2011).
According to Gilmour (1999), customer and supplier integration are key logistics
process capabilities, combined with information technology capabilities
based on integration. Flynn, Wu and Melnyk (2010) explain the benefits of supplier
and customer integration that helps to reduce waste through information
sharing and joint planning and a better understanding of customer requirements
with the results of minimising the return of products and maximising customer
satisfaction. Better insight into supplier capabilities and resources, risk sharing,
costs and investments, mutual problem solving, systematic waste reduction and
elimination, reducing reverse operations of uncertainty and overcoming planning
complexity could be other benefits which can lead to higher RL effectiveness in
the supply chain (Liu et al., 2013; Mihi Ramírez, 2012).
2. Hypothesis Formulation and Proposed Model
To address the gaps in current knowledge introduced above, a model of RL
effectiveness, the extent of RL planning, knowledge management, and external
integration is proposed in Figure 1 for empirical testing. Four types of flexibility
exert mediating influences on the strategic planning and performance relationship.
The conceptual development of the model and theorised relationships were
discussed in the previous part of the paper. The model is composed of four variables
and the proposed, and expected relationships are expressed in the following
hypotheses:
H1: The extent of the formal planning of RL is positively linked to RL effec-
tiveness.
H2: The extent of integration with 1st-tier supply chain members is positively
linked to RL effectiveness.
H3: The extent of knowledge management principles applied is positively
linked to RL effectiveness.
H4: The extent of knowledge management principles applied is positively
linked to the extent of the formal planning of RL.
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H5: The extent of knowledge management principles applied is positively
linked to the extent of integration with 1st-tier supply chain members.
F i g u r e 1
Theoretical Model
Source: Authors based on literature review.
3. Research Methodology
The proposed model was tested statistically employing the cross-sectional
data from 146 companies; more specifically, nominal and ordinal data were
collected in personal interviews with company managers. In the interviews, the
facts about practices of particular companies were enquired, focusing on RL and
its (potential) factors. The paper utilises just a portion of the variables from all
interviews: The analysis tested the relationships between the RL effectiveness
and three potential factors: the extent of the formal planning of RL, the integration
with 1st-tier supply chain members, and knowledge management. Besides
the three dependent and one independent variable, the demographical data were
used for sample descriptions such as the respondent’s identification, company
size, and industrial affiliation.
As the proposed hypothesis are interlinked and create a complex model, the
covariance-based structural equation modelling (SEM) was chosen for reprocessing
statistical data. SEM is not mere statistical technique but rather “an analytical
process involving model conceptualization, parameter identification and
estimation, data-model fit assessment, and potential model respecification”
(Hancock, Mueller and Stapleton, 2010, p. 371), thus the analytical work followed
the recommended standards for conducting the SEM and presenting
its findings as suggested by Schreiber et al. (2006). The nature of SEM allowed
Formal planningof RL
Knowledge
management
1st tier integration
RL-Effectiveness
H2(+)
H4(+) H5(+)
H1(+)
H3
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us also to construct some variables as latent (unobserved), which improved the
reliability of measurement of the variables.
Formally, both parts of structural equation model (i.e. the measurement and
structural part) can be specified using the Bentler-Weeks (1980) format as follows
(for more details about this format and other standard formats developed
specifically for SEM see Hoyle, 2012, p. 131).
Measurement model (1)
rl_e1 = 1RL_Effect + e1
rl_e2 = *RL_Effect + e2
rl_int_c = 1RL_Ex_int + e3
rl_int_s = *RL_Ex_int + e4
km1 = *KM + e5
km2 = *KM + e6
km3 = 1KM + e7
Structural model (2)
RL_plan = *KM + e8
RL_Ex_int = *KM + d2
RL_Effect = *RL_Plan + *KM + *RL_Ex_int + d1
where
KM – unobserved, exogenous variable,
RL_Effect , RL_Ex_int – unobserved, endogenous variables,
RL_plan – observed, endogenous variables,
rl_e1, rl_e2, rl_int_c,
rl_int_s, km1, km2, km3 – observed, endogenous items,
d1, d2 – errors associated with unobserved, endogenous variables,
e1 – e8 – errors associated with items.
The content of particular variables and items is further described in Table 1,
and a graphical form of the model is depicted in Figure 2. The observed variables/items
were constructed as ordinal on a positively defined scale from 1 to 7;
higher scores denoted stronger agreement with a given statement, whereas “1”
stood for strong disagreement. The calculations were conducted in SPSS v.24
and SPSS AMOS v.23.
3.1. Measurement of Endogenous and Exogenous Variables
No exact financial measures from accounting documents could be utilized to
measure RL-effectiveness, as 75% of companies stated in a separate open-ended
question that they did not monitor the RL effectiveness by any exact mean. It is
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interesting that the remaining one fourth of companies in the sample utilized
accounting methods in 19 cases (e.g. KPI, maintaining the RL cost limit, process
costing, and stocktaking), quality management tools in 7 cases (e.g. quality
checklist and Cost of Quality Model) and marketing research in 4 cases (surveys/interviews
about customer satisfaction or an analysis of customer claims).
Also, the eight respondents stressed the importance of the support of information
systems for the above methods and tools.
As most of the companies disposed of no reliable data about the RL effectiveness,
the variable labelled as RL_Effect was designed as latent reflecting two
statements about perceived effectiveness. The wording of the items for all variables
and basic descriptive statistics are presented in Table 1.
T a b l e 1
Description of Variables and Items
Variables
and
their names
Items (scales) Median Mean Std. dev. N
RL_Effect
Effectiveness
of RL
(rl_e1) The executive management perceives reverse
logistics as useless (reverse coding)
5 5.02 1.59 146
(rl_e2) The executive management perceives reverse
logistics as a competitive advantage
4 4.37 1.57 146
KM
Knowledge
management
(km1) Knowledge is managed as a strategic asset. 5 4.74 1.63 143
(km2) Knowledge management is planned and
integrated into all corporate processes.
5 4.32 1.73 146
(km3) We have systems and venues for people to
share knowledge and learn from each other in the
company.
4 3.74 1.84 146
RL_Ex_int
1st-tier
integration
of RL
(rl_int_c) The external integration with your direct
customers regarding reverse flows is perfect.
3.5 3.55 1.60 146
(rl_int_s) The external integration with your direct
suppliers regarding reverse flows is perfect.
4 3.88 1.56 146
RL_Plan
Extent of
RL-planning
(RL_Plan) RL is part of: strategic planning +
functional strategic p. + tactical p. + operative p. .5 .541 0.36 146
Note: All items vary from 1 to 7, except for planning ranging from 0 to 1.
Source: Authors based on own empirical data.
The integration with 1st-tier supply chain members (RL_Ex_int) and Knowledge
Management (KM) was constructed as latent and reflective. The first one
merged two scale items: the integration with direct suppliers and direct customers;
as such, the RL_Ex_int expresses the level of SCM adoption regarding reverse
flows. The measurement of SCM adoption was restricted to the triad of
supplier-company in focus-customer, instead of evaluating the whole supply
chain/network as the broader SCM cooperation is hardly covered in the research
due to related difficulties (Autry and Griffis, 2008; Bellamy and Basole, 2013).
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The three items addressed the application of knowledge management in
a particular company. The items, which examined organisational memory, knowledge
receptivity, and sharing, were adapted from Wang, Ahmed and Rafiq
(2008). To keep the respondent load reasonably low, which was imposed by the
questionnaire, more complex maturity models for knowledge management were
omitted.
Planning RL, or more precisely the level of its planning, was expressed by an
index which counted the frequency of the four dichotomous variables reflecting
the presence or absence of RL on the following planning levels: on the companywide
strategic plan, the functional strategic plans, and on tactical and operational
plans. The companies were awarded a 1/4 point if RL planning was present at
any of these levels; the index varied from 0 to 1 in the sum, which indicated the
presence of RL plans on all four planning levels. RL planning was the most
common at the operative level (67%).
In the analysis, RL planning was modelled as an observed variable, i.e. the
variable measured precisely without any error. This decision was made in harmony
with a recommendation by Schumacker and Lomax (2010) for two reasons:
First, the meaning of the variable – RL planning – is a rather concrete and
specific piece of information, compared to the other variables in the model,
which are more abstract and complex and as such need to be modelled as latent.
We can expect that the planning is measured with sufficient precision. Second,
the alternative of constructing RL planning as a latent variable defined by a single
item (plus a measurement error) would add more complexity to the statistical
model (i.e. the number of estimated parameters would increase) without providing
any adequate benefit in return.
3.2. Research Sample and Its Description
For practical reasons, convenience sampling was chosen as an acceptable way
for research of an exploratory nature. The structured personal interviews were
conducted in 2015 and 2016 among representatives of companies operating on
the Czech market. The Czech Republic itself, as a member state of the EU,
adopted EU legislation and policy (including RL-related issues such as waste
management and consumer rights). Taken together with the fact that the Czech
Republic has been the member of OECD for more than two decades, the business
environment here can generally be regarded as similar to other European/
EU countries.
The sample comprised of 149 cases, out of which three cases were removed
due to missing data in the variables/items. The final sample consisted of 146
companies, of which 64% of them are the services according to their respective
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core business activity as declared by respondents. Small companies operating
in the hospitality industry forms the majority of this group. The remaining 36%
of the final sample is represented by manufacturing companies that operate
in mechanical engineering, and the chemical, food, and construction industries.
The structure of the sample regarding industry affiliation and size, as measured
by a number of employees is presented in Table 2.
T a b l e 2
Sample Structure (in %)
Affiliation
Size of the companies*
TotalSmall Medium-sized Large
Manufacturing 12.4 17.9 5.5 35.9
Services 57.2 6.9 0 64.1
Total 69.7 24.8 5.5 100.0
Note: * Small companies are defined by less than or equal to 50 employees and 250 for medium-sized comp.
Source: Authors based on own empirical data.
The companies in the sample perceive their RL as being effective, as the
mean for both respective items is through the cut-point of the scale (i.e. above 4;
see Table 1). The conclusion is supported by an estimated percentage of the RL
impact on corporate profitability, which was also investigated in the interviews,
but not utilised in the further analysis. The answers about the impact ranged
from 3% to 80%, with a mean value of 3.8%. The positive mean value suggests
that RL improves the economic performance of companies in general.
The respondents agreed more often that knowledge management was applied
in their companies than the external integration (SCM), which is apparent from
higher mean values for the knowledge management items. The latest study on
this topic revealed that knowledge management is not applied systematically in
85% of Czech companies (Maresova, 2010). Due to the lack of empirical studies
on SCM, the only available number comes from the Czech Statistical Office – in
the research on ICT adoption, one fifth of companies disposed of any information
technology for data interchange with 1st-tier supply chain members; however,
specific SCM software was implemented only 2% of the time (CZSO, 2016).
4. Results
The statistical analysis is presented in two steps - first, the measurement
model is evaluated using confirmatory factor analysis, followed by an analysis of
the structural model using structural equations modelling with an estimation
method of maximum likelihood.
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4.1. Test of the Measurement Model
The convergence validity of latent variables was tested by three different
measures as presented in Table 3: For each variable, Cronbach’s Alpha is above
the recommended cut point of 0.7. Similarly, two other indicators, Average Variance
Extracted and Jöreskog rho, are higher than their recommended cut points
of 0.7 and 0.5 respectively (Hair et al., 2009). Thus, the convergence validity of
the three latent variables was accepted to be sufficient for conducting further
analysis.
T a b l e 3
Measures of Convergence Validity
Indicators’ names KM RL_Ex_int RL_Effect
Cronbach alpha 0.769 0.715 0.742
Construct reliability (CR) Joreskog rho 0.807 0.731 0.775
Average Variance Extracted 0.598 0.581 0.642
Source: Authors based on own empirical data.
Confirmatory factor analysis was employed to test the measurement model,
which consisted of the three latent variables loaded by seven observed variables
(two for external integration, two for RL effectiveness, and three for knowledge
management). Confirmatory factor analysis is a part of SEM for testing the system
of variables; it checks the relationships between (observed) items and latent
variables or “factors”, which are reflected in the items (Hoyle, 2012, p. 361).
The validity of the measurement model was supported by chi-square test
χ²(11, 146) = 13.960, p = .235 – its non-significance indicated similarity between
the measurement model and the empirical data. As the Chi-square test is not
very reliable in some cases, additional model-fit tests were supplemented. The
test values of the Comparative Fit Index (CFI = 0.991), Incremental Fit Index
(IFI = .991) and Root Mean Square Error of Approximation (RMSEA = 0.043)
speak in favour of the model as their values exceeded the recommended cut
points of 0.95 for CFI and IFI, and were less than 0.06 for RMSEA (Hair et al.,
2009; Schreiber et al., 2006).
4.2. Test of the Structural Model
The structural model tested all the five hypotheses as presented in Figure 2.
The observed variables (in boxes) and unobserved variables (in circles; including
estimated errors) are connected with arrows representing the regression paths.
The associated numbers are the standardised regression weights (placed near
the arrows) and squared multiple correlations (in the upper right-hand corner of
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the boxes). The model-fit was evaluated according to the same criteria: The insignificance
of the Chi-square test χ² (16, 146) = 23.247, p = .107 supported the
idea that the model corresponds to the structure of empirical data. Additionally,
CFI = .981, IFI = .982 and RMSEA = .056 are in harmony with the recommended
values as specified above, which means that the whole model can be accepted,
and it is sensible to analyse its elements in detail.
F i g u r e 2
The Structural Model and the Standardised Regression Weights
Source: Authors based on own empirical data.
All structural relationships in the model were statistically significant (see
Table 4) and positive, as expected (see the p-value and standardised regression
weights that are positive). In other words, the hypothesis H1 – H5 were supported:
The planning of RL was the strongest factor of the RL effectiveness according to
its standardised regression weight (H1: β = 0.489, p < 0.000). The other two
factors had a lower impact (external integration of RL – H2: β = 0.297, p < 0.01;
knowledge management – H3: β = 0.235, p < 0.05), but were still statistically
significant. The low regression weight in knowledge management only reflected
its direct effect. As implicated by the theoretical model, the KM impact is
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mediated through both planning (H4: β = 0.344, p < 0.000) and external integration
(H5: β = 0.377, p < 0.000). When taking direct and indirect effects together,
the model documents the high significance of KM for RL. In statistical terms,
the model explains 55.2% of the variance in RL effectiveness and as such the
explanatory power of the model is satisfactory.
T a b l e 4
Regression Weights
Estimate S.E. C.R. P Stand. reg. weights Hypothesis
RL_Effect <--- RL_Plan 1.809 .294 6.149 *** .489 H1
RL_Effect <--- RL_Ex_int .289 .107 2.710 .007 .297 H2
RL_Effect <--- KM .363 .155 2.343 .019 .235 H3
RL_Plan <--- KM .144 .042 3.435 *** .344 H4
RL_Ex_int <--- KM .598 .178 3.358 *** .377 H5
km1 <--- KM 1.607 .283 5.687 *** .856
km2 <--- KM 1.821 .323 5.643 *** .915
km3 <--- KM 1.000 .472
rl_int_s <--- RL_Ex_int .736 .176 4.175 *** .633
rl_int_c <--- RL_Ex_int 1.000 .880
rl_e1 <--- RL_Effect 1.000 .852
rl_e2 <--- RL_Effect .795 .112 7.079 *** .682
Source: Authors based on own empirical data.
Conclusions
Our research reacts to the appeal of Hazen et al. (2012) and shows some
managerial factors that should be evaluated from a strategic perspective to be
pursued for the higher effectiveness of RL decision making.
The contribution of our research and consequent implications is manifold.
First, we present a theoretical model that simultaneously captures several managerial
factors and links them with the important outcome of RL management,
specifically concerning effectiveness (the perceived impact of RL management
on competitiveness). Second, the paper provides empirical support for the linkages
and roles of the factors tested from the model, so it can serve as a springboard
for considering existing practices in companies related to the investigated factors.
It also supports and expounds on the findings from several existing research
that call for verifying their findings, e.g. Bernon et al. (2013) and the linkage
between supply chain integration and performance; (Mihi Ramírez, 2012) and
the impact of knowledge management and performance in the context of RL; or
Liu et al. (2013, p. 2126), who claimed that there is little research devoted to
knowledge management tools “that can efficiently allow knowledge sharing and
re-use to support integrated supply chain waste elimination decisions”).
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Third, the research enriches current knowledge in the area of utilising knowledge
management, the role of planning and the role of the external integration
with supply chain partners for effective RL management and so responds to the
numerous calls for needed insight. Findings bring insights into several Supply
Chain Management processes (specifically Customer and Supplier Relationship
Management and Returns Management) within Supply Chain Management Framework
developed by Lambert and Cooper in 2000. Research reacts to Lambert
and Enz (2017) review of the progress related to this framework within 16 years
and their impetus for new research streams and demonstrates the importance of
knowledge, returns and RL management, as well as supply chain integration and
strategic management capabilities for organisational performance and sustainable
competitive advantage. Fourth, evidence from a European perspective is presented
since the sample is composed of Czech companies and the European Union has
been traditionally more active on RL, especially from the aspects of legislative,
environmental and consumer protection.
Results confirm the interdependencies between the planning and especially
strategic planning of RL processes, knowledge management, 1st-tier integration
and RL effectiveness. Companies that are aware of the potential of RL management
regarding the reduction of negative impact of product returns and of positive
impact on competitiveness, incorporate RL into planning on the higher hierarchical
level. These companies also consciously and strategically manage their
knowledge – again in terms of planning on the strategic level and in terms of
dealing with knowledge in all processes and taking care of sharing knowledge
and supporting the proper environment for continuous learning. Finally, companies,
which are aware of the positive effect of RL on performance utilise more
the benefits of the integration with customers and suppliers. Such integration
enables more effective information flow and knowledge sharing which can lead
to product returns decrease. It means that knowledge of the positive effect of RL
is connected with other processes of management in an organization, which are
also associated with profitability and competitiveness.
There are two main limitations of the findings presented: Due to funding
restrictions, a non-random sampling procedure, or convenience sampling, was
accepted, as the purpose of the study was largely explorative. Naturally, the limited
external validity of the results is the downside of this decision. The single
informant approach is the second limitation of the study: the reprocessed data are
biased by the subjectivity of the respondents, because several questions in the
interview asked for data that companies do not measure, collect and reprocess
thoroughly. Thus, the answers of the respondents reflect their perceptions and
estimations.
101
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