Impact of a country’s logistical capabilities on
supply chain risk
R. Sreedevi
S P Jain Institute of Management and Research, Mumbai, India
Haritha Saranga
Indian Institute of Management Bangalore, Bangalore, India, and
Sirish Kumar Gouda
Indian Institute of Management Tiruchirappalli, Tiruchirappalli, India
Abstract
Purpose – This paper aims to examine the relationship between environmental factors, risk perception and decision-making in risk management.
Specifically, using attribution theory, the authors study the influence of macro-level logistical capabilities of a host country on a firm’s actual and
perceived supply chain risk, and examine if this country-level factor and the firm level perception of risk affect a firm’s decision-making in risk
management.
Design/methodology/approach – This study uses a combination of primary data from 932 manufacturing firms from 22 countries and secondary
data from the logistics performance index (LPI), and empirically tests the conceptual framework using partial least squares structural equation
modeling.
Findings – Key results reveal that a country’s logistical capabilities, measured using LPI, have a significant impact on managers’ risk perception.
Firms located in countries with high LPI perceive lower risk in their supply chain both in the upstream and downstream, and therefore do not invest
much in external integration, compared to firms in low LPI countries, and hence are exposed to high risk.
Originality/value – This is one of the first empirical studies linking a country’s logistical capabilities with supply chain risk perceptions,
objective supply chain risk and supply chain risk management efforts of a firm using the International Manufacturing Strategy Survey
database.
Keywords Supply-chain management, Risk management, Global supply chain, Logistics capabilities, Supply risk
Paper type Research paper
behavior and perceptions in supply chain decision-making
(Ambulkar et al., 2016; Cantor et al., 2014). On the industry
front too, senior supply chain executives have been
emphasizing the importance of understanding behavioral
factors that affect decision-making, to effectively manage risk.
For example, according to John J. Brown, the director of supply
chain risk management at Coca-Cola [2]:
1. Introduction
Perception of risk influences a manager’s decision-making in
supply chains as much as, if not more than, objective measures
of risk (Ellis et al., 2010; March and Shapira, 1987; Tazelaar
and Snijders, 2013; Yates and Stone, 1992). For instance,
perception of a high supply disruption risk is shown to influence
supply chain managers to look for alternative sources of supply
(Ellis et al., 2010). The recent COVID-19 pandemic has
affected supply chains across the world which have been heavily
dependent on Chinese supplies. This black swan event has
influenced several managers to think about developing
alternative sources of supply in the future to de-risk their supply
chains [1]. While this event will directly impact the objective
risk, future decisions of supply chain managers will also be
impacted by their individual perceptions of the extent to which
they were exposed to supply disruptions due to this event.
There has been growing interest among academic
researchers regarding the need to understand the role of
“We end up managing the risks that we perceive to be of a higher likelihood
or higher consequence, but those may not be the most important risks to our
company. That is why it is important to understand the role of psychological
factors on risk identification and risk assessment [. . .] Risk perception is
memory based and experience based [. . .] If we are in control of a situation,
we feel much better about it.”
So, what happens if supply chain managers perceive the risk to
be lower than it actually is? Studies on behavioral risk theory
posit that risk perception is a complex product of innate biases and,
as risk assessment is usually based on “intuitive heuristics,” it
results in biased decision-making (Hardcopf et al., 2017;
Pidgeon, 1998; Renn and Rohrmann, 2000). Decision-making
This work was supported by Research Seed [grant number 22072] from
Indian Institute of Management Bangalore, India.
The current issue and full text archive of this journal is available on Emerald
Insight at: https://www.emerald.com/insight/1359-8546.htm
Received 9 May 2020
Revised 9 February 2021
20 May 2021
2 August 2021
13 August 2021
Accepted 13 August 2021
Supply Chain Management: An International Journal
28/1 (2023) 107–121
© Emerald Publishing Limited [ISSN 1359-8546]
[DOI 10.1108/SCM-09-2020-0504]
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R. Sreedevi, Haritha Saranga and Sirish Kumar Gouda
Volume 28 · Number 1 · 2023 · 107–121
in supply chains can also be biased if assessments are based on
heuristics (Tazelaar and Snijders, 2013). Several researchers
have shown the importance of considering objective measures
to effectively evaluate the risk in the supply chain (Blackhurst
et al., 2008; Kull and Talluri, 2008; Talluri et al., 2006).
Attribution theory explains that individuals, especially
managers, possess an innate need to comprehend and identify
the root cause of an outcome, especially negative and
unanticipated events such as disruptions (Folkes, 1984; Oflaç
et al., 2012). Supply chain managers develop perceptions of a
risky event by attributing potential causes to outcomes based on
controllability, stability and locus (Selviaridis, 2016; Weiner,
1985). While the locus of causality could be either internal or
external to a firm, we focus on external factors influencing the
perception of supply chain risk in this study.
Some of the external factors identified in the extant literature
that impact supply chain risk perception (SCRP) are supply
uncertainty, information insufficiency (Mantel et al., 2006),
technological uncertainty, market thinness in terms of the
availability of alternative sources of supply (Ellis et al., 2010);
the uncertainty of supply chain disruption (Cantor et al., 2014)
and the extent of control the firm has over its new ventures in a
foreign country (Forlani et al., 2008). Among the external
factors that influence managers’ perception of supply chain
risk, environmental factors have been found to play a crucial
role (Ellis et al., 2010). However, studies on the influence of
external environmental factors on risk perception have been
sparse.
Also, several studies have attributed the success of a firm’s
supply chain management to the logistical capabilities of that
firm (Fugate et al., 2010; Schoenherr, 2009). However, apart
from the internal logistical capabilities of a firm, supply chains
are also exposed to macro-level aspects such as logistics
infrastructure in the country, ease of transportation and the
ability to receive shipments on time, etc. A report on global
competitiveness by the World Economic Forum shows that the
supporting environment for firms varies depending on their
host countries’ infrastructure [3], and infrastructural
dissimilarities between countries have a major impact on the
supply chain performance of firms operating in those countries
(Al-Shboul, 2017; Kinra and Kotzab, 2008; Wiengarten et al.,
2014). Further, globalization of markets has also increased the
complexity of supply chains (Christopher et al., 2011; Wagner
and Bode, 2008) and is one of the key drivers of supply chain
risk (Hu et al., 2019; Manuj and Mentzer, 2008). Among the
various infrastructural factors, the logistical capability of a
firm’s host country is one of the key external factors impacting
supply chain performance, and hence, could impact the risk
perception of supply chain managers. Therefore, it has become
highly imperative for supply chain managers and academics to
understand the influence of macro-level factors, such as a
country’s logistical capabilities, on SCRP of managers, their
consequent reactions and the subsequent impact on actual risk
in a globalized supply chain network. While supply chain risk
could be caused due to a natural disaster (referred to as
disruption risk) or man-made errors (called as operational risk)
(Ho et al., 2015), in the context of this study, we look at supply
chain operational risk only.
In the current study, by drawing from attribution theory, we
examine the relationships between environmental factors, risk
perception and decision-making in risk management within the
supply chain context. In particular, we look at the influence of
macro-level logistical capabilities on a firm’s perceived supply
chain risk and examine if this country-level factor and the firm
level perception of risk affect a firm’s decision-making in
external integration (EI). Specifically, we strive to address the
following research questions:
RQ1.
Do firms perceive their susceptibility to supply chain
risk depending on the logistical capabilities of their host
country?
RQ2.
Does the macro-level logistical capability of a firm’s
country influence the degree to which it invests in EI?
We use a combination of primary and secondary data to
empirically test these research questions in our study. The
primary data pertains to manufacturing firms across the globe
collected through the International Manufacturing Strategy
Survey (IMSS). The secondary data, which measures a
country’s logistical capabilities using an index called the
“Logistics Performance Index” (LPI), comes from the
International Trade Department of the World Bank (Arvis
et al., 2014). We study the conceptual linkages between the
various constructs using the partial least squares structural
equation modeling (PLS-SEM) technique. In line with our
conjectures, we find evidence that firms located in high LPI
countries do perceive lower risk in their supply chains.
Interestingly, we find that despite the actual risk (objective
supply chain risk [OSCR]) in high LPI countries being lower, a
country’s logistical capabilities do not help much in mitigating
operational risk in the presence of high global sourcing and
sales (GSS). In other words, our results show that firms that
operate globally must invest in EI efforts, irrespective of their
home country’s logistical capabilities, to reduce their supply
chain risk.
2. Literature review and theoretical background
2.1 Macro-level logistical capabilities
A review of extant literature reveals that among the many
macro-level factors in the context of global supply chains, a
country’s logistical capabilities are critical determinants of a
firm’s supply chain performance. The logistical capability of a
country is defined as “the quality and breadth of logistics
services and infrastructure available to plants located in that
country” (Wiengarten et al., 2014, p. 53). This macro-level
capability is captured using the international LPI, developed by
the International Trade Department of the World Bank. The
LPI is a benchmarking tool which determines countries’ trade
logistics challenges and helps identify ways to improve their
logistics performance. The international LPI is based on a
global survey of “global freight forwarders and express
carriers,” who rate the logistics capabilities of the countries in
which they operate and the countries with which they trade
(Arvis et al., 2018). It captures two dimensions of logistics
capability, namely, service and infrastructure. Logistical service
elements comprise warehousing, cross docking, distribution
and customized logistics solutions. Infrastructure components
consist of superiority and availability of physical infrastructure
(roads, rails, ports and airports) and the promptness of customs
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clearance procedures. While, in general, the study of such
macro-level factors has not been given much attention in the
supply chain literature, some studies have reiterated the
importance of understanding the impact of macro-institutional
factors on the functioning of organizations. Kinra and Kotzab
(2008), for example, apply a strategic contingency view and
argue that the macro level logistics environment influences an
organization’s decision-making for operating in a global supply
chain. Wiengarten et al. (2014) discuss the role logistical
capabilities of a country play in supply chain management and
the extent to which firms in that country engage in supply chain
integration. They show that firms located in countries with
good logistics infrastructure do not find the need for
coordination with their supply chain partners, and hence, adopt
lower levels of integration with their suppliers and customers
than those located in countries with poor logistics
infrastructure. However, they only examine the impact of a
country’s logistical capabilities on the operational performance
of the firm, in terms of quality, cost, delivery and flexibility
parameters. We believe logistical capabilities have an impact on
supply chain risk measures too, and therefore, in our study, we
examine the influence of macro-level logistical capabilities on a
firm’s perceived and OSCR measures.
or impedes the information, material or product flows from
original suppliers to the delivery of the final product to the
ultimate end-user” (Peck, 2006, p. 132). While it is difficult to
measure financial losses associated with operational risk in an
objective way unlike financial risks, measuring supply chain risk
in terms of deviations in performance is more useful and
pragmatic (Heckmann et al., 2015; Peck, 2006). The potential
deviations in supply chain performance include the inability to
satisfy customers’ demand, decreased service level, delayed
shipping, inefficient utilization of resources, etc. (Heckmann
et al., 2015). Based on the literature, we operationalize OSCR
as any potential deviation from the expected value of a supply
chain performance measure.
Risk mitigation is one of the most important aspects of supply
chain risk management. To effectively cope with different forms
of supply chain risks, firms invest in a variety of risk management
techniques (Kleindorfer and Saad, 2005). For example, to
manage risk in the upstream (supply disruption), firms allocate
resources to assess their suppliers a priori, choose a more reliable
supplier, have backup suppliers and monitor all suppliers
regularly (Ho et al., 2015). Similarly, to mitigate risk in their
downstream such as shipment disruptions, firms invest resources
in alternative modes of transportation and continuous tracking of
their shipments (Manuj et al., 2014). These risk mitigation efforts
are an outcome of risk identification and risk assessment
processes that precede this activity. Various risk management
efforts (RME) used by firms can be classified as preventive RME
and reactive RME (Thun et al., 2011; Thun and Hoenig, 2011).
Studies have suggested the importance of understanding the
interdependencies between the source of risk, type of risks and
the mitigation strategies that include both preventive and reactive
efforts (Manuj et al., 2014). Evaluating and training suppliers and
delivery partners on improving their processes, continuous
improvement programs, inspection and tracking of internal
processes are examples of preventive RME. Buffering practices
such as maintaining a backup supplier, a logistics partner and
having extra capacity qualify as reactive RME. While preventive
RME focus on reducing the probability of occurrence of a risk
event, reactive RME aim to reduce the impact of the occurrence
of a risk event. Such flexibility practices increase supply chain
agility, which, in turn, improves firm performance. Though
fundamentally both these efforts by firms reduce the overall risk
experienced, the benefits of the preventive RME accrue to the
firm well before a risk event occurs (Thun and Hoenig, 2011).
Extant literature has looked at the impact of RME on supply
chain risk using different constructs. Using primary data from
German firms, Kern et al. (2012) study the impact of supply
chain RME on the supply chain risk performance of the firm and
demonstrate that the choice of risk mitigation strategy should
depend on the firm’s operating environment. Therefore, these
risk mitigation efforts, when applied based on a scientific risk
assessment process, will help reduce the supply chain risk.
2.2 Supply chain risk management
Extant research in the area of supply chain risk management
has examined several topics including linkages of the construct
with supply chain integration (Talluri et al., 2010; Zhao et al.,
2013), global supply chains (Christopher et al., 2011; Manuj
and Mentzer, 2008) and supply chain uncertainty (SCU)
(Sreedevi and Saranga, 2017). However, to our knowledge,
very few studies (Sato et al., 2020) empirically evaluate the
linkages of external environmental factors on supply chain risk
management or risk perception.
There are the following three phases in the supply chain risk
management process: Phase 1 includes risk identification, risk
measurement and risk assessment; Phase 2 includes risk evaluation
and risk mitigation; Phase 3 includes risk control and monitoring
(Tummala and Schoenherr, 2011). These processes are similar
for both operational and disruption-based risks. While risk
identification involves identifying various sources of risk and
categorizing them, risk measurement deals with the evaluation
of the potential damage or consequence of the risk event’s
occurrence. In the risk assessment step, the likelihood of each
risk factor is determined (Kern et al., 2012). While an objective
assessment of risk is recommended, many firms use subjective
information based on prior knowledge, beliefs and judgments
(Tummala and Schoenherr, 2011). In the context of our study,
risk measurement and risk assessment are combined and
considered as a single entity, as proposed by Zsidisin et al.
(2004) and referred to as risk perception. SCRP is
operationalized as the probability of occurrence of a risk event
and the corresponding impact of that event on performance
(Zsidisin et al., 2004; Ellis et al., 2010).
OSCR is defined as “the potential deviations from the initial
overall objective that, consequently, trigger the decrease of
value-added activities at different levels” (Kumar et al., 2010,
p. 3717). These activities are characterized by the availability
and quality of products at the right time and the right location.
Another relevant definition of OSCR is “anything that disrupts
2.3 External integration
The extant literature defines External (supply chain)
integration as the degree to which a firm tactically
communicates and aligns with its partners in the upstream
and downstream of the supply chain (Wiengarten et al.,
2014). EI focuses on integration between a firm and its
suppliers and customers. Given the context of our study, we
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focus only on the collaborative aspect of integration and not
on strategic integration. Collaborative integration deals with
actions pertaining to coordinating supply chain flows
through information sharing and developing collaborative
approaches through knowledge sharing, planning and
forecasting (Wiengarten et al., 2014). EI with key suppliers
and customers has also been considered an effective tool to
mitigate supply chain risk (Chen et al., 2013; Chopra and
Sodhi, 2004). The importance of collaboration with supply
chain partners in upstream and downstream is reflected in
the definitions of supply chain risk management (SCRM).
For instance, Tang (2006) defines SCRM as the
management of supply chain risks “through coordination or
collaboration” among supply chain members. Jüttner et al.
(2003) also define SCRM as the management of risks
“through a coordinated approach” among supply chain
partners. Talluri et al. (2010) demonstrate how a
manufacturer’s collaboration with suppliers in supplier
development can effectively reduce risk.
Table 1 summarizes the representative research on logistical
capabilities and supply chain risk management, and their
contributions using a concept-based structure (Webster and
Watson, 2002).
3. Conceptual framework and hypotheses
development
In this section, we develop the conceptual linkages between the
various constructs based on the extant literature and anecdotal
evidences. The conceptual framework illustrating the
relationships between LPI, supply chain risk and EI are shown
in Figure 1.
3.1 Logistics performance index, supply chain risk
perception and external integration
Attribution theory helps understand an individual’s perception
of how a phenomenon happened and how an individual
attributes causes for the occurrence of the same (Kelley and
Michela, 1980). Attribution to a specific cause among the most
probable is identified based on the controllability and stability of
the cause. When managers experience risks that are more
controllable, they fear them less (Rickard, 2014; Slovic, 1987),
Table 1 Overview of the extant literature on logistical capabilities and supply chain risk management
S. no.
SCRP
Study
Objectives/results
1
Forlani et al.
(2008)
H
2
Ellis et al. (2010)
3
Christopher et al.
(2011)
4
Thun et al. (2011)
5
Chen et al. (2013)
Empirically shows that the extent of control the firm
has over its new ventures in a foreign country and
firm capabilities influence manager’s perceptions of
risk
Empirically examines the factors impacting buyer’s
perception of supply chain disruption risk
Emphasizes the need for a structured approach to
global supply chain risk management and classifies
risks into different types: supply, demand, process
and environmental risks
Empirically analyzes the use of preventive and
reactive risk mitigation approaches by small and
medium sized firms and large enterprises and shows
that SMEs primarily focus on reactive risk mitigation
Empirically shows the effect of supply chain
collaboration as a risk mitigation strategy
7
Cantor et al.
(2014)
Manuj et al. (2014)
Examines how decision-makers’ regulatory focus and
SCU impact their decision-making in RME
Examines the appropriateness of different risk
mitigation strategies in different risk conditions and
highlights the need for a match between the risk
type and risk mitigation approach
Shows that supplier integration is a useful risk
mitigation tool in environments with a high risk of
conducting business
Investigates the moderating effect of international
asset dispersion on supply chain risk management
Investigates the linkages between logistical
capabilities, SCRP, OSCR and EI
H
8
9
Wiengarten and
Longoni (2015)
10
Hu et al. (2019)
11
This study
H
Variables
OSCR LC
RME
H
H
H
H
Context
Method
US firms
ANOVA
US manufacturing
firms
UK-based
companies
CB-SEM
Qualitative
analysis
H
H
German
manufacturing
firms
Qualitative and
factor analysis
H
H
Australian
manufacturing
firms
US respondents
CB-SEM
H
Regression
H
H
US manufacturing
firms
ANOVA
H
H
Cross-Country
Regression
H
H
Cross-Country
Regression
H
Cross-Country
PLS-SEM
H
H
Notes: SCRP – supply chain risk perception; OSCR – objective supply chain risk; LC – logistical capabilities; RME – risk management efforts (including EI);
CBSEM – Covariance based Structural Equation Modeling; SMEs – Small and Medium Enterprises; ANOVA – Analysis of Variance
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Figure 1 Conceptual framework
country-level logistical capabilities significantly influences the
way firms to engage in their supply chains and the extent to
which they collaborate with their supply chain partners
(Wiengarten et al., 2014). Firms with access to superior
logistical capabilities tend to outsource the coordination role
with its upstream and downstream players to third party
logistics providers (Zacharia et al., 2011). Further, increased
certainty in terms of delivery schedules due to the presence of
logistical capabilities makes the firms underestimate the need
for integration (Gimenez et al., 2012). However, supply chain
visibility is crucial for increasing supply resiliency (Blackhurst
et al., 2011), and in a global supply chain, supply chain visibility
is improved significantly by sharing of information among
supply chain partners (Christopher and Lee, 2004). Hence, EI
with partners is widely considered an effective risk mitigation
strategy. However, firms which perceive lower risk in their
supply chains do not appreciate the need for sharing of
information and collaboration with their supply chain partners
(Zhao et al., 2013). Therefore, we posit the following:
and hence, the perception of risk is usually very close to
the absolute risk experienced by firms. Similarly, the higher the
stability of the cause, the higher will be the attribution to the
cause. As the logistic capabilities of a country cannot be
changed overnight, the perception of risk in countries with
lower LPI will continue to persist in the minds of the managers.
While large multinational firms can and do choose the
location of operations of a plant based on criteria such as the
infrastructure of the host country, many local firms have little
control over the infrastructural capabilities of the country in
which they are operating. Firms in countries with weak
logistical capabilities, thus perceive a higher probability of a
disruption in their supply chain due to logistical failures.
Given that the physical transport of goods is an integral part
of any manufacturing firm, its logistical capabilities, which is a
function of the host country’s logistical capabilities, impact the
performance of its supply chain (Fugate et al., 2010; Gimenez
et al., 2012). Further, firms located in countries with good
logistical capabilities generally experience predictability in
customs clearance and certainty in their delivery schedules.
Also, an organization’s risk perception reduces with increasing
credibility and trust in the risk-managing institutions and
agencies (Oflaç et al., 2012; Renn, 1991). Risk management
agencies for the firm could either be internal (for instance,
supply chain management or risk management departments) or
external to the firm. External agencies could majorly be the host
country’s infrastructure and services management agencies and
their ability to manage, inter alia, logistics-related risks. Thus,
we hypothesize that as follows:
H1b.
H1a.
The impact of a country’s logistical capabilities on a
firm’s EI efforts is mediated by SCRP.
3.3 Logistics performance index and objective supply
chain risk
The physical infrastructure component of logistical capabilities
plays a very important role in facilitating the seamless production
and distribution of goods manufactured by firms. Firms
positioned in physically well-networked locations will be able to
fulfill customer order with the least glitches. These infrastructural
elements also help firms cut down on idle production time, which
arises due to supplier delivery delay. Ikenson (2009) points out
that the success of a firm in offering an undisrupted flow of goods
and services depends to a great extent on the excellence and
reliability of the trade infrastructure established in that firm’s
country. Further, Al-Shboul (2017) shows that the logistical
infrastructure framework plays a significant role in increasing
supply chain agility.
Firms located in countries which provide industry friendly
and standardized customs processes benefit from a reduction in
the number of instances of delayed shipping and the probability
of losing out on orders due to the same. Firms located in
countries with poor logistics infrastructure and inefficient
customs procedures find it difficult to receive goods on time
and to fulfill their delivery requirements (Arvis et al., 2014).
Logistics performance is, therefore, strongly linked to the
reliability and predictability of delivery schedules pertaining to
different players in the supply chain. Therefore, we hypothesize
that as follows:
The higher the logistical capability of the host country,
the lower the level of supply chain risk perceived by
firms situated in that country.
H2.
3.2 Mediating role of supply chain risk perception
between logistics performance index and external
integration
Firms can achieve superior operational performance by
coordinating with their partners in the supply chain (Sanders,
2008; Scholten and Schilder, 2015). The presence of superior
Firms situated in countries with high LPI have lower
OSCR than those in countries with low LPI.
4. Research methods
4.1 Sampling and data collection
Data used in this study is a combination of primary and
secondary data. Secondary data on logistical capabilities of
various countries is collected from the World Bank while that
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Volume 28 · Number 1 · 2023 · 107–121
on individual risk management elements and other constructs
in the study comes from the primary data collected from
various manufacturing firms across the world. IMSS is a survey
conducted every four years by a global network of research
scholars. The survey seeks responses from various
manufacturing firms on their efforts, capabilities and
performance aspects. We use data from the sixth edition of the
IMSS, which was conducted in 2013. In all, 22 countries
participated in this edition and a total of 932 survey responses
were obtained. The survey was filled by senior managers and
executives, of a level equivalent to or higher than the
production manager of the plant. Participating plants had to
have a minimum of 50 employees to be eligible to participate in
the survey. Several noteworthy studies in the field of operations
and supply chain management have used data collected for the
IMSS project (Cheng et al., 2016; Frohlich and Westbrook,
2001; Gouda and Saranga, 2018; Wiengarten et al., 2014,
2019).
We use LPI data from the “International Bank for
Reconstruction and Development/The World Bank” as a
measure of the logistical capabilities of countries. LPI data from
different editions has been used for analysis extensively in
supply chain and logistics literature (Kabak et al., 2020; Kinra
et al., 2020; Lin and Cheng, 2019; Wiengarten et al., 2014;
Wong et al., 2017). We used the 2014 LPI scores for which the
data was collected in 2013, and hence, this is the information
companies would have had when they filled the survey in early
2013, i.e. their perception of risk would be based on the then
logistical capability of their country. The 2014 LPI survey was
answered by 1,000 respondents from 143 countries. The
respondents represented all economic regions equitably with
around 23% from high income countries, 53% from middle
income countries and the rest from lower income countries. A
majority of the respondents were senior executives, area or
country managers and department managers directly involved
in the day-to-day logistics of the companies. These respondents
rated eight random countries which were chosen based on the
importance of the export and import markets to the country in
which the respondent’s firm was located. Despite the
perceptions about bilateral ties between countries, it was found
that perception does not bias the scores, and thus established
the reliability and validity of the scores (Arvis et al., 2014).
Further, the reason for combining country-level logistical
capabilities with firm level data on risk is due to the pressing
need for understanding the role of macro-level factors on firm
level decision-making in supply chain risk management. With
the globalized nature of supply chains and international trade,
the quality of logistic infrastructure and services in a firm’s
operating environment has a significant impact on how firms
operate their supply chains and how they interact with other
players in their supply chain (Wiengarten et al., 2014; Zacharia
et al., 2011). Further, we are motivated by several relevant
studies in this field which have combined country-level data of
indices similar to LPI (such as Hofstede’s dimensions of
national culture, GLOBE indices, Global Competitiveness
Index and UN World Risk Index) with firm level data (Kauppi
et al., 2016; Sancha et al., 2015; Wiengarten et al., 2014). A
combination of primary and secondary data brings together the
benefits of capturing the constructs of interest and reduced the
chances of common method bias (Calantone and Vickery,
2010). Extant literature has used secondary data from different
sources such as archival studies, textual data, social media data,
large scale surveys conducted by organizations such as the
United Nations (UN) and World Trade Organization and
other independent research bodies such as Hofstede’s
dimensions and GLOBE concept of national culture (data for
which was collected using a large scale survey across multiple
countries) (Calantone and Vickery, 2010; Wong et al., 2017).
Other similar indicators which are based on large scale surveys
are the Worldwide Governance Index (for instance,
Wiengarten et al., 2016) and the Corruption perception index
(for instance, Larson, 2020).
Our sample overview with respect to the LPI scores and
industry wise distribution of primary data is shown in Tables 2
and 3, respectively.
4.2 Measures
The constructs were measured by multiple items from the
survey, designed based on the existing literature. Table 4 shows
Table 2 Sample summary of participant firms and LPI score by country
Country
Malaysia
Germany
Slovenia
Norway
Taiwan
Belgium
Spain
Canada
Switzerland
Brazil
Sweden
Finland
Portugal
Denmark
Romania
Italy
USA
The Netherlands
Hungary
Japan
India
China
Frequency
LPI score
14
15
17
26
28
29
29
30
30
31
32
34
34
39
40
48
48
49
57
82
91
128
3.59
4.12
3.38
3.96
3.72
4.04
3.72
3.86
3.84
2.94
3.96
3.62
3.56
3.78
3.26
3.69
3.92
4.05
3.46
3.91
3.08
3.53
Table 3 Profile of firms that participated in the IMSS survey
Industry grouping
Fabricated metal products (ISIC 25)
Computer, electronics and optical products (ISIC 26)
Electrical equipment (ISIC 27)
Machinery and equipment not elsewhere classified
(ISIC 28)
Motor vehicles, trailers and semi-trailers (ISIC 29)
Other transport equipment (ISIC 30)
Total
112
Frequency (%)
282
123
153
231
30
13
16
25
93
50
932
10
5
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R. Sreedevi, Haritha Saranga and Sirish Kumar Gouda
Volume 28 · Number 1 · 2023 · 107–121
Table 4 Measures used in proposed constructs
Constructs and items
Measurement model
SCRP
Inbound Risk (IR) (1- Low, 5-High)
1. A key supplier fails to supply affecting your operations – Probability
2. A key supplier fails to supply affecting your operations – Impact
Shipment risk (SR) (1-Low, 5-High)
1. Your shipment operations are interrupted affecting your deliveries – Probability
2. Your shipment operations are interrupted affecting your deliveries – Impact
EI
Supplier Integration (SI) (1-Low, 5-High)
1. Sharing information with key suppliers
2. Developing collaborative approaches with key suppliers
3. System coupling with key suppliers
Customer integration (CI) (1-Low, 5-High)
1. Sharing information with key customers
2. Developing collaborative approaches with key customers
3. System coupling with key customers
OSCR
1. Number of days of lost production past year due to supply failures or operations disruption
2. Percentage of customer deliveries affected by operational failures
3. Late shipments (as a percentage of orders delivered)
SCU (1-Low, 5-High)
1. Your demand fluctuates drastically from week to week
2. Your total manufacturing volume fluctuates drastically from week to week
3. The mix of products you produce changes considerably from week to week
4. Your supply requirements (volume and mix) vary drastically from week to week
5. Your products are characterized by a lot of technical modifications
6. Your suppliers frequently need to carry out modifications to the parts/components they deliver to your plant
Position in supply chain (PSC)
Percentage of sales to end users [6]
Global sourcing
Percentage of sourcing outside the firm’s country
Global sales
Percentage of sales outside the firm’s country
Second-order formative
First-order reflective
First-order reflective
Second-order formative
First-order reflective
First-order reflective
First-order reflective
First-order reflective
First-order reflective
First-order reflective
First-order reflective
a scale from one (worst) to five (best)” (Arvis et al., 2018,
p. 43). Measures include the performance of a country in terms
of six elements of the existing logistics environment:
the summary of the items used to measure each construct. In
this study, risk perception is measured in terms of probability
and impact of failure in upstream and downstream of the
supply chain. Items for this construct are adopted from the
existing literature (Ellis et al., 2010; Tsai et al., 2008) and the
construct is modeled as a second-order formative construct.
The OSCR is a measure of the firm’s operational risk in
terms of supply (inbound) risk and shipment (delivery) risk.
This construct is measured using as follows:
Number of days of lost production due to supply and
operational disruptions;
Number of customer deliveries affected by operational
failures; and
Late shipments (as a percentage of orders delivered)
(Kumar et al., 2010).
“Efficiency of customs clearance, quality of trade and transport-related
infrastructure, ease of arranging competitively priced shipments,
competence and quality of logistics services, ability to track and trace
consignments and frequency with which shipments reach the consignee
within the scheduled or expected time.” (Arvis et al., 2018, p.8).
These six measures loaded on LPI, and therefore, LPI is a
unidimensional construct derived by standardizing the
weighted average of the individual scores of the six items (Arvis
et al., 2018).
4.3 Control variables
Uncertainty in the supply chain is a major factor influencing
managers’ risk perception (Zsidisin, 2003), and hence, we control
for it. We operationalize SCU in terms of supply, demand, product
and manufacturing uncertainty (Ho et al., 2005). Further, risk
perception is also impacted by the volume of GSS a firm engages
in (Ellis et al., 2010), and hence, we use the percentage of global
sourcing and percentage of global sales as control variables. For the
OSCR as a dependent variable, we use the firm’s position in the
The multidimensional scale of EI is drawn from Frohlich and
Westbrook (2001) and the items constitute the extent to which
the focal firm concentrates on coordinating planning and
inventory decisions with key suppliers and customers.
To capture the essence of the logistical capabilities of a
country, World Bank has developed the LPI. “The LPI is a
multidimensional assessment of logistics performance, rated on
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Volume 28 · Number 1 · 2023 · 107–121
supply chain as a control variable since the need for logistics likely
varies from manufacturers of subsystems to manufacturers of
finished products as they cater to different types of players in the
supply chain (Wiengarten et al., 2014). Position in the supply
chain is captured as the quantum of sales to customers who are
end-users. We also control for GSS as these would have an impact
on the OSCR. GSS were captured as the percentage of suppliers
and customers, respectively, situated outside the firm’s country
(Wiengarten et al., 2014).
Table 6 Discriminant validity assessment
4.4 Reliability and validity
To evaluate the reliability and validity of the constructs, we
performed confirmatory factor analysis and used four tests to
determine the convergent validity and the internal consistency
of the reflective constructs as follows: Item loading, Cronbach’s
alpha, composite reliability (CR) and average variance
extracted (AVE) of the constructs (Fornell and Larcker, 1981).
Table 5 shows the survey items and descriptive statistics for
each of the constructs. The values for Cronbach’s alpha (a) and
CR exceed the 0.7 cut-off accepted in the literature. Further, all
constructs surpassed the established 0.50 cut-off value for
AVE. The results endorse that the convergent validity of all the
reflective constructs in our model is adequate (Fornell and
Larcker, 1981).
To assess discriminant validity, the AVE and CR values were
compared with the squared latent variable correlations. The
Construct CR
LPI
IR
SR
SI
CI
OSCR SCU PSC GSS
LPI
IR
SR
SI
CI
OSCR
SCU
PSC
GSS
1.00
0.01
0.01
0.02
0.03
0.00
0.00
0.00
0.01
0.68
0.29
0.01
0.00
0.02
0.04
0.00
0.00
0.68
0.04
0.04
0.00
0.05
0.00
0.00
0.64
0.50
0.01
0.01
0.00
0.01
0.64
0.00
0.04
0.01
0.01
0.49
0.00 0.54
0.00 0.00 1.00
0.00 0.00 0.00 1.00
1.00
0.81
0.78
0.84
0.80
0.76
0.87
1.00
1.00
results as shown in Table 6 indicate that the AVE (shown in the
matrix diagonal) and CR values are higher than the squared
inter-construct correlation values shown in the off-diagonal of
the correlation matrix, thereby validating a satisfactory level of
discriminant validity (Fornell and Larcker, 1981).
4.5 Common method variance assessment
One of the major issues with survey-based studies is the
presence of common method variance (CMV). To reduce
CMV, the survey was filled by multiple respondents. Different
sections of the survey were required to be filled by
corresponding section heads. The survey questionnaire was,
Table 5 Measurement model results (descriptive statistics, factor loadings, item reliability and convergent validity)
Factor
Inbound risk
(a = 0.77, CR = 0.84, AVE = 0.68)
Item
IR1
IR2
Mean
2.718
3.628
SD
1.174
1.205
Loading
0.748
0.728
Shipment risk
(a = 0.91, CR = 0.78, AVE = 0.65)
SR1
SR2
2.272
3.321
1.190
1.331
0.697
0.725
Supplier integration
(a = 0.84, CR = 0.89, AVE = 0.68)
SI1
SI2
SI3
3.186
3.160
3.016
1.000
1.038
1.027
0.722
0.761
0.749
Customer integration
(a = 0.75, CR = 0.84, AVE = 0.56)
CI1
CI2
CI3
2.955
2.755
3.035
1.130
1.215
1.095
0.847
0.829
0.648
OSCR
(a = 0.75, CR = 0.84, AVE = 0.56)
OSCR1
8.363
14.462
0.783
OSCR2
OSCR3
4.674
9.840
9.840
16.855
0.748
0.537
SCU1
2.485
1.073
0.718
SCU2
SCU3
SCU4
SCU5
SCU6
2.542
2.442
2.685
3.142
3.081
1.125
1.281
1.136
1.039
1.212
0.766
0.687
0.808
0.698
0.773
(a = 0.75, CR = 0.84, AVE = 0.56)
SCU
(a = 0.75, CR = 0.84, AVE = 0.56)
(a = 0.75, CR = 0.84, AVE = 0.56)
Notes:
p-value < 0.01; p-value < 0.05;; p-value < 0.1; SD = Standard Deviation
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Volume 28 · Number 1 · 2023 · 107–121
therefore, answered by senior executives ranging from general
managers to managers of production, purchasing, marketing
and supply chain functions. We also tested for CMV using
Harman’s one-factor assessment (Podsakoff and Organ, 1986).
As part of this test method, we performed exploratory factor
analysis with principal components extraction and no rotation
for all the constructs. The existence of common method bias
would have resulted in a single factor accounting for a key
fraction of covariance in the variables. Our results reveal that no
single factor stands out in the factor structure and 10 factors
had Eigen values more than the cut-off value of one, with the
first factor explaining only 15% of the variance. Thus, we can
conclude that common method bias is not an issue in this
study.
With respect to mediation analysis for H1b, we reported our
results in Table 7. We used the bootstrapping method
proposed by Shrout and Bolger (2002) to test the mediation
effect. As pointed out in a study by Malhotra et al. (2014), this
method does not call for the mediation effects to be normally
distributed and has a more robust statistical power than the
methods proposed by Baron and Kenny (1986) and Sobel
(1982). The following three paths were assessed: the path from
the independent variable (LPI) to the mediating variable
(SCRP) (path a); the path from the mediating variable (SCRP)
to the dependent variable EI (path b); and the direct path from
LPI to the dependent variable (EI) (path c, when tested
concurrently with the indirect paths linking a and b). In total,
5,000 bootstrap samples were generated in SmartPLS 2.0M3.
The results as shown in Table 7 indicate that the interval did
not contain zero for both indirect and direct effects, thereby
confirming partial mediation effects. The effect of LPI on EI
was partially mediated by SCRP, thereby supporting our
hypotheses H1b. Our results indicate that if a country’s LPI is
very high, companies perceive the risk to be low, and hence,
make lesser investments in EI and vice-versa.
The structural model results indicate that the LPI score of
the host country in which the firm is located has a negative
impact on the OSCR. Thus, we find support for Hypothesis H2
( b = 0.091, p < 0.01).
The results from our control variables are in line with the
extant literature. OSCR and Perception of risk are both
positively associated with SCU (Sreedevi and Saranga, 2017).
We also find that the higher the GSS percentage, the higher is
the OSCR experienced by the firm (Manuj and Mentzer,
2008).
Our post hoc analysis showed that the OSCR of firms located
in high LPI countries is indeed significantly lower than that of
firms in low LPI countries. To understand the impact of LPI on
supply chain risk, we explored the impact of the COVID-19
pandemic on supply chain risk in different countries.
Manufacturing Purchasing Managers Index (PMI) is a good
proxy for the manufacturing activity and business continuity in
the sector (and hence, of supply chain risk). While COVID-19
affected all countries severely, we tried to understand the dip in
Manufacturing activity in high LPI and low LPI countries. We
found that between January and August 2020, in high LPI
countries such as Germany, Sweden, Singapore and Taiwan,
the dip was minimal (by a margin of around 10 points) while
low LPI countries such as India, Russia and Nigeria had a
larger dip in the manufacturing PMI (by a margin in excess of
20 points [4]). This indicates the resilience shown by firms
located in high LPI countries despite the best support from
regulators across the world to revive their economies.
To explore this finding further, we evaluated the impact of
global supply chain configuration on supply chain risk. To test
the moderating role of GSS on the relationship between LPI
and OSCR, we median split the sample into high/low levels of
GSS. We then did a multigroup moderator regression analysis
to examine the main effects of high and low levels of LPI for
high and low levels of GSS. The results are shown below.
The interaction plot as shown in Figure 3 indicates that the
effect of LPI on mitigating OSCR has been reduced
significantly when GSS is higher than average. This result
5. Results
We use the partial least square (PLS) structural equation
modeling (SEM) to test our conceptual model using SmartPLS
2.0M3 software (Ringle et al., 2005). PLS is one of the most
extensively used methods for examining the direct and indirect
effects of several variables simultaneously (Hair et al., 2011).
We use PLS in this study for the following three reasons:
1 It is suitable for studying complex models with mediation
effects and second-order constructs (Peng and Lai, 2012);
2 PLS can model both formative and reflective constructs,
which is crucial for our study (Wetzels et al., 2009); and
3 PLS is based on a series of ordinary least squares
regressions, and hence, has fewer statistical specifications
and doesn’t have restrictions on sample size unlike the
covariance based SEM and also offers higher statistical
power (Reinartz et al., 2009).
H1a posits that LPI is negatively associated with SCRP.
Figure 2 shows the results of the structural model along with
standardized path coefficients and the corresponding levels of
significance. As seen from Figure 2, the direct effect is negative
and significant ( b = 0.066, p < 0.1), providing support to
H1a.
Figure 2 Results of the structural model
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Impact of a country’s logistical capabilities
Supply Chain Management: An International Journal
R. Sreedevi, Haritha Saranga and Sirish Kumar Gouda
Volume 28 · Number 1 · 2023 · 107–121
Table 7 Summary of mediation test results
Mediation path
Indirect effect (a b)
Direct effect (c’)
5% Lower bound 95% Upper bound Zero included 5% Lower bound 95% Upper bound Zero included Mediation type
LPI fi SCRP fi EI
0.02
0.01
No
0.254
0.124
No
Partial
studies (Rickard, 2014; Slovic, 1987) to establish the role of
external factors such as logistical capabilities on risk perception.
We not only find that firms in high LPI countries perceive
lower risk in their supply chains but also that, as a result, these
firms do not invest much in supply chain risk management such
as EI efforts with their suppliers and customers. This result is in
line with extant literature which has discussed the role of the
external environment on supply chain decision-making (Kinra
and Kotzab, 2008; Wiengarten et al., 2014). Also, while
Wiengarten et al. (2014) find that firms that are present in
countries with high LPI invest in lower levels of EI, we show
that this is because of low risk perception.
Research on usage of appropriate methodologies (Hair et al.,
2011; Peng and Lai, 2012) has suggested that inappropriate
measurement models (for instance, reflective models instead of
formative and first order models instead of higher order
models) in PLS-SEM can severely bias the results. Our study
uses the appropriate formative measurement model for SCRP
and EI which are estimated based on both the constructs part of
these second-order constructs. Also, the use of second-order
constructs makes our model more parsimonious and increases
the degrees of freedom.
Figure 3 Interaction plot
shows that the effect of LPI in mitigating supply chain risk
reduces when firms operate in a global supply chain.
We find that in high LPI countries, firms that do high levels
of GSS benefit less from their home country’s logistical
capabilities when it comes to supply chain operational risk. Our
results also show that there is no significant difference in OSCR
between high LPI and low LPI countries when firms operate in
a global supply chain network. The reason for this could be that
as levels of GSS increase, risks that were earlier confined to
local geographical regions get diffused globally involving
numerous supplier and customer networks across the globe
(Donner and Kruk, 2009; Manuj and Mentzer, 2008).
Therefore, the host country’s logistic capabilities play an
insignificant role in such cases.
6.2 Managerial and policy implications
Our empirical study has several important managerial insights.
First, our research reinforces anecdotal evidence from extant
literature and empirically establishes that firms’ decisionmaking pertaining to the adoption of risk mitigation strategies is
based on their perceptions of risk. In particular, we find that
managers’ risk perception is influenced by the firm’s home
country logistics infrastructure. In addition, our findings also
demonstrate that in today’s globalized supply chain
environment, a home country’s logistics infrastructure is not
sufficient to reduce the supply chain risk. Therefore, our
findings point toward a potential risk that supply chain
managers from high LPI countries may set themselves up for if
they go simply by their home country’s LPI infrastructure and
turn a blind eye to the fact that their supply chains are global in
nature. Hence, firms need to foster better-decision making by
looking at objective risk estimation, which is more reliable than
subjective risk assessment.
Second, our study demonstrates the importance of EI in
mitigating supply chain risk. In a global supply chain, firms
operating in one country will have to depend on a number of
firms operating in several other countries to source raw
materials or market their goods, and hence, supply chain
managers must be aware of the differences between
infrastructural support in countries they are operating in so as
to effectively manage their supply chains.
We find that while firms with low levels of GSS benefit from
their host country’s logistical capabilities, such macro-level
capabilities alone do not help firms which source and sell
globally. While both LPI and GSS seem to affect the supply
6. Discussion and implications
The objective of this study was twofold as follows: to determine
the relationship between a firm’s supply chain operational risk
and its host country’s logistical capabilities and to evaluate the
pivotal role of risk perception in explaining how macro-level
logistical capabilities impact a firm’s decisions on EI with its
supply chain partners.
6.1 Theoretical implications
Our findings show that a manager’s risk perception is strongly
influenced by the firm’s supporting environment, in particular
the logistical capabilities of a firm’s host country. Extant
literature in Supply Chain Risk Management has studied the
factors affecting SCRP and identified the importance of
external environmental factors (Moktadir et al., 2018;
Schoenherr et al., 2008; Tummala and Schoenherr, 2011; Sato
et al., 2020). However, most of the research has used metaheuristic methods to study this phenomenon.
Using Attribution theory, similar empirical studies were
conducted in Strategy (Mitchell, 1995) and Organizational
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Supply Chain Management: An International Journal
R. Sreedevi, Haritha Saranga and Sirish Kumar Gouda
Volume 28 · Number 1 · 2023 · 107–121
chain risk experienced by a firm, ideally firms need to invest in
those activities that will maximize their returns in terms of
reduction in risk. With demand becoming stagnant in the
developed economies and growth coming from developing
countries, it is becoming imperative for firms across the globe to
target and cater to the growing demand in these developing
markets (Mudambi et al., 2017). The recent coronavirus
pandemic has also disrupted several global supply chains,
pushing firms to think of reshoring a majority of their supplies.
Further, as local governments in economies emphasize on local
production and local sourcing in return for market access, the
need for good logistics infrastructure is becoming critical for
success in these markets. Firms in Low LPI countries such as
India, therefore, need to work closely with industry associations
and regulators to develop local logistics infrastructure. After the
COVID-19 pandemic disrupted the global supply chains, many
auto original equipment manufacturers and Auto component
manufacturers in India started focusing on local suppliers [5].
Even though this was a call to all businesses by the nation’s
leader, these firms have been working with policymakers to
achieve this in an effective manner. This would not only benefit
the global firms operating locally but also would help develop
the local industry. Market and associated network effects would
contribute to overall economic activity. However, given that
these infrastructure development projects take a long time, the
chances of them reducing the risk in the short-term are low.
Despite that, firms should work toward these for long-term
sustainable risk reduction activities.
On the other hand, firms in the developed countries (high
LPI) with the high level of global sourcing should work toward
developing a strong local supplier base, given the increasing
number of supply disruptions internationally. This local
supplier base will not only help firms reduce the supply chain
operational risk of sourcing products globally but also it will
reduce overall lead times, giving them a competitive advantage.
In more recent times, firms are also investing in local sourcing
as a part of their overall sustainability strategy.
who are the main respondents of the LPI survey might not give
a complete picture of the logistics infrastructure in poorer
countries which are primarily dominated by traditional
operators.
The COVID-19 pandemic has put several supply chains in
countries across the world and their logistical capabilities to
test. This black swan event can bring out the real differences in
the logistical capabilities and it will be interesting to study these
relationships with data from the post COVID-19 world.
Notes
1 www.bloomberg.com/news/articles/2020-03-27/iphonemakers-look-beyond-china-in-supply-chain-rethink
2 www.enterrasolutions.com/2013/03/perceptions-can-affectsupply-chain-risk-management.html
3 www3.weforum.org/docs/WEF_TheGlobalCompetitiveness
Report2019.pdf
4 Based on data from Statista and Trading Economics - www.
statista.com/statistics/1033981/pmi-developed-emergingcountries/andhttps://tradingeconomics.com/singapore/
manufacturing-pmi
5 www.livemint.com/companies/news/auto-vendors-go-vocalfor-local-11595384625904.html
6 Percentage of sales to end users was chosen to represent
firms position in a supply chain. A higher percentage of
sales to end users represent downstream players such as
retailers and resellers while a lower value would represent
upstream players such as suppliers and OEMs.
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While this study is based on data on manufacturing firms from
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Volume 28 · Number 1 · 2023 · 107–121
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About the authors
R. Sreedevi is a faculty in the area of Operations & Supply
Chain Management at SPJIMR, Mumbai. She has completed
her doctorate from IIM-Bangalore. Her research has been
recognized as one of the winners of the Emerging Economies
Doctoral Student Finalist Award, presented at the POMS 27th
Annual Conference in Orlando, USA. In addition to her
dissertation research, other areas of research interests include
responsible supply chains, humanitarian logistics, behavioral
operations, marketing-operations interface and affordable
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Impact of a country’s logistical capabilities
Supply Chain Management: An International Journal
R. Sreedevi, Haritha Saranga and Sirish Kumar Gouda
Volume 28 · Number 1 · 2023 · 107–121
health-care operations. Prior to joining IIM-Bangalore, she
worked for two years in the industry and two years in
Industrial Research and Development. She holds a BTech
degree from Madras University and MTech from IIT Delhi.
Sloan Management Review and Journal of Operational Research
Society. She also co-authored a book, titled Reliability and Six
Sigma, published by Springer and has written many cases and
book chapters.
Dr Haritha Saranga is a Professor in the Production and
Operations Management Area at the Indian Institute of
Management Bangalore. Haritha’s research interests include
studying quality, productivity and sustainability aspects of
firms operating in agricultural, automotive, pharmaceutical
and health-care supply chains in India and abroad. She uses
both empirical and analytical methods in her research,
including data envelopment analysis, multiple regression,
Time series, panel data and optimization. Haritha Saranga is a
Fellow of MIRCE Akademy, Exeter, UK. She has published
several articles in refereed international journals, such as
Production and Operations Management, Journal of International
Business Studies, European Journal of Operational Research, MIT
Prof. Sirish Kumar Gouda is a faculty in the Operations
Management and Decision Sciences Area at the Indian
Institute of Management Tiruchirappalli. His research
interests include Sustainable Operations, Supply Chain Risk
and Behavioral Operations. He has several publications in
leading management journals such as European Journal of
Operational Research, Information & Management, International
Journal of Production Economics, International Journal of
Production Research, Supply Chain Management Review and The
TQM Journal. He has also presented his research work at
several national and international Conferences. Sirish Kumar
Gouda is the corresponding author and can be contacted at:
sirish@iimtrichy.ac.in
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