absorb the effects of the risk disturbance caused by ... 2019 proceedings papers/paper_57.pdf2.1...

DOI: http://dx.doi.org/10.17501........................................

Absorb The Effects of the Risk Disturbance Caused by Vulnerability Factors, to

Maintain the Levels of Resilience and Sustainability in the Complex Supply Chains

: A Study on Aeronautical Sector in Morocco

EL ABDELLAOUI Mohamed

University Hassan II, Faculty of Law, Economic and

Social Sciences Laboratory LARNED, Morocco.

Beausite, BP: 2634 Ain-Sebaâ, Casablanca.

+212 670 904 379

[email protected]

MOFLIH Youssef

University Hassan II, Faculty of Law, Economic and Social

Sciences Laboratory LARNED, Morocco.

Beausite, BP: 2634 Ain-Sebaâ, Casablanca.

+212 638 385 551

[email protected]

Abstract- In today's complex business contexts, disruptions are

exacerbating their complexity and the interdependence of

interfaces systems and supply chains that are becoming more

and more extensive. Faced with this observation, practitioners

and supply chain risk management academics are engaging in

integrated reflections on resilience and sustainability practices

for structural designs of more sustainable, regulated but surely

balanced supply chains. With inherent capabilities to

continuously, support performance levels, sustainability and

resilience even in the presence of potential disruptions in

vulnerable business contexts. The ultimate objective of this

paper is to propose some premises in terms of thinking about

the possible causal links between supply chain risk management

and sustainable supply chains. Otherwise, the viable resilience

of supply chains, in which resilience, sustainability and

performance remain unchanged at acceptable levels in the

presence of potential disruptions by supporting performance

while minimizing the spread and severity of risks through

resilient and sustainable practices regulated but balanced.

Based on these findings, we developed a conceptual research

model that examines the causal links between supply chain risk

management concepts and the two concepts of sustainability

and performance in the supply chain. Through our seventeen

hypotheses, built on the basis of the relevant theoretical

frameworks (theory of normal accidents, theory of dependence

of resources and theory of high reliability of organizations), we

seek to justify that it has a sort of complementarity between the

concepts of risk, vulnerability, resilience and sustainability.

Using a questionnaire survey administered during the period of

September 2017 and January 2018 to companies in the

Aeronautics sector, thus making it possible to build a database

of 102 companies. For the evaluation we use a modeling by

structural equations and a partial least squares analysis to

explore all the possible links between the five variables (64

factors) constituting our study. Our results empirically justify

the advantageous relationship between SCRM and SSCM.

Wishing to enrich the existing body of literature on supply

chain risk management, a reflection on the possible causal links

between risk management and sustainability in the supply chain

was explored through an empirical analysis, to evaluate some

links that remain unexplored.

Keywords-Supply chain risk management; Sustainability supply

chains management; Supply chain performance; Moroccan

Hospital sector.

I. INTRODUCTION

Given the ambivalence of international agreements and

conflicts, competitive contexts become more turbulent and

more complex, making them more vulnerable to disruptive

events characterized by spreading, uncertainties and

dynamic diffusion effects (Sokolov and Dolgui, 2014;

Mason and Hartl, 2016). Alternatively, the supply chains

competitive factors are required to change managerial

practices by integrating new approaches focused on risk

management and sustainability in the management of their

logistics operations (Blackhurst et al, 2017, Ivanov et al,

2017). This change or reconfiguration of global value

chains will have a certain impact on public policies,

especially those of social voices (eg Health Policy), with

more pressure also due to the restriction of public spending

which does not seem to allow for increase resources. All

these conditions combined erode efforts to make hospital

systems more reactive in terms of the level of services and

care services, which can lead to a deterioration of the

organization, flow control modes and impact the continuity

of operations in hospitals, particularly in developing

countries. As a result, these organizations are faced with

challenges that do not correspond to their cultures, that of

developing models of reflection that enable them to be

maintained in difficult contexts and to improve their

governance systems. In this respect, these premises allow us

to direct our research thinking by recognizing that logistics

chains are benchmarks for revealing the ability of resilient

and sustainable practices to support performance levels, to

control both the degree of exposure to risks and the

vulnerability factors of the logistic chains. So the objective

of this study is to propose a confirmatory analysis of the

possible causal links between the five concepts that form

our conceptual model on risk management and

sustainability in the supply chain (risk, vulnerability,

resilience, sustainability and performance). Whose rest of

this article is organized as follows section one revises to a

literature review quite relevant on the main concepts

mobilized under this conceptual reflection, while the second

section addresses the support of three theoretical

frameworks sometimes conflicting but complementary


(theory of normal accidents, theories of high reliability of

organizations and the theory of dependence of resources)

on our main proposals relating to research thinking. The

third section sets out the purpose also the methodology

adopted, the method of data collection and the structure of

the questionnaire. The fourth and fifth sections discuss the

rationale and usefulness of the statistical method for

choosing PLS-SEM, so the last section is devoted to the

analyzes and results of the two models of external and

internal structural measurement. Towards the end of this

article a discussion, managerial implication and a

conclusion that trace the strong and weak points thus the

limits and new reflections of future researches.

II. THEORETICAL AND CONCEPTUAL

FRAMEWORK

2.1 Supply Chain Risk Management

The supply chain risk management literature SCRM

continues to develop and expand, by integrating new fields

of thinking of supply chain management such as

sustainability or SSCM (Tummala and Schoenherr, 2011;

Ivanov, 2017), as well as new ways, design and validation

methodologies covering all the areas of the supply chain

(Scheibe and Blackhurst, 2017; El Abdellaoui and Moflih,

2017b). In this sense, the first basic frameworks for risk

management have been more influenced by traditional

approaches to managing systemic financial risks (Kaufman

and Scott 2003; Shi 2004), and this is clearly seen in the

nature of the reflective frameworks and the research

positions that do not reflect the contextual challenges

characterizing the supply chains. Other authors have

pointed out that this branch is not yet able to reach a level

of maturity to meet the major challenges related to the

dynamic, systemic and uncertain nature of the risks, as well

as to the complexity of the interconnected interfaces

constituting the chains and supply systems (Thun and

Hoeing 2011; Surya Prakash et al 2017; El Abdellaoui et al

2017a).

The evolution of the supply chain risk management SCRM

has enriched and expanded the fields and methodological

reflections for each process, perimeters of the supply chain.

As a result, the research dimensions have well-founded the

process of supply chain risk management, which (Jüttner,

Peck and Christopher 2003; Jüttner, 2005) have indicated

that the objective of the SCRM is limited to risk

identification and management, thus enabling the supply

chain to reduce its vulnerability through a collaborative

approach by its members. In addition to these two steps, the

analysis and evaluation of the potential losses caused by the

occurrence of disruptive incidents (Lavastre, Gunasekaran

and Spalanzani, 2014). What is needed to secure,

perpetuate both the efficiency and continuity of supply

chain flows. To further this goal, other authors recommend

combining inter-organizational collaborative efforts of

various methodological frameworks to mitigate, control,

monitor, and provide feedback or knowledge transfer on

conditions, attributes of disruptive events, and complex

contextual characteristics of supply chains (Pfohl, Köhler

and al, 2010; Scheibe and Blackhurst, 2017).

On the other hand, these efforts are limited by certain

difficulties in deploying risk management approaches in the

failing of high costs that are part of a complete cost logic

and the absence of a consensus on the conceptual

dimensions allowing effective communication and

exchange with and from the fields of investigation in

practice (Tang and Nurmaya Musa, 2011; Diehl and

Spinler, 2013; Evrard Samuel, 2013). This deficit is due to

the uncertain and unpredictable nature of the systemic

disruptive risks, although the extent of their cumulative

spread has resulted in inefficient risk management practices

that managers did not believe to be efficient for sustainable

management of operations in their supply chains (Saenz and

Revilla, 2014; Revilla and Saenz, 2017). This sustainability

translates into a better match of responsiveness and

flexibility to sources of disruptive risks generating medium-

term commercial and long-term financial losses, whose

resilience capabilities reinforce traditional supply chain risk

management processes (Faisal, 2009; Aqlan and Lam

2015).

2.1.1 Micro Supply Chain Risk

Industry 4.0 looks like supply chains are becoming more

complex, interdependent and vulnerable, but at the same

time a competitive factor that creates value (Christopher

and al, 2011; Gurnani and Gupta, 2014). It is true that this

structural evolution has increased the efficiency of the

supply chain through dependent and narrow

interconnections allowing a secure continuity in the supply

chain management, which results in a balanced level of

performance and customer satisfaction (Craighead and al,

2007; Tuncel and Alpan, 2010; Zhao and al, 2013). Both of

which support the definition of the two authors (Hou, Zeng

and Zhao, 2010; Yu and Goh, 2014) “or the supply risk is

considered as the sudden unavailability of supplies due to

the occurrence of an unforeseen event rendering one or

several sources of supply completely unavailable”. In this

context, supply risks are characterized by cumulative,

retroactive and reciprocal disturbances of speed, extent and

magnitude of loss that vary with the time of disruption and

the complex design of the supply chain structures (Pettit

and al, 2013; Blackhurst and al, 2017; Chowdhury and

Quaddus, 2017), hence the importance of studying the

possible fertile links between the concepts of supply chain

risk management and / or sustainable supply chains (Chopra

and Sodhi, 2014; Dmitry Ivanov, 2017).

In these circumstances, disruptive supply risk events are

likely to influence the flexibility and responsiveness of

operations, considered as a source of vulnerability

generating potential losses throughout the supply chain

(Faisal, 2009; Waters, 2011), and that the following issues

are being addressed: deficiencies in the structure of the

provider networks, intrinsic supplier failures, and purchased

inbound products as well as capacity constraints and

resource dependence (Wagner and Neshat, 2010; Manuj,

Esper and Stank, 2014). Bearing in mind that supply risks


potentially influence the performance of the supply chains

whose first hypothesis of research will be:

(H1) More the risk of supply risks are greater, the more the

performance of the supply chain is influenced.

2.1.2 Micro Demand Risk

In order to remain competitive, companies continue to

pursue the efficiency and speed of developing global supply

chains, making their designs and structures more complex,

and therefore more vulnerable to disruption, which in the

event of their occurrence effects throughout the supply

chain (Chen and Paulraj 2004; Blackhurst et al 2017). In

this sense we recognize that supply chain risk factors and

events are interrelated, and that an incident by a leading

supplier or transport provider potentially affects the end

customer, more importantly, their negative impacts increase

in severity even if it results from a small initial disturbance

of significant probability (Pettit and al, 2013; Blackhurst

and al, 2017). This interconnectedness of structures and

risks affects the inherent capabilities of resilient and

sustainable supply chains needed to secure sustainable

competitive advantages (Tang and Tomlin, 2008; Das,

2017; Hong, Zhang and Ding, 2018a,b). Otherwise, these

risks can affect medium and long-term operational and

logistic performance levels including the loss of profits,

costs, inventory levels, reputation, and also levels and

satisfaction requirements of the supply chain members.

Worse in extreme cases can threaten the safety and even the

survivability of the downstream supply chains as customers

(Chen and Paulraj, 2004; Ghadge, Dani and Kalawsky,

2011; Nikookar and al, 2014).

This contextual evolution of supply chains in an

unpredictable and turbulent environment, is due to the

adaptive capacity to challenges resulting from downstream

supply chain operations such as the unpredictability of

demand, the short life cycle of products and the variability

of customer trends that increase the vulnerability of supply

chains by increasing instability and unpredictability even

with resilient and sustainable practices (Roberta and al,

2014; Gligor and Holcomb 2015; Duong and Paché, 2015).

For example, the interdependence of real demand and the

anticipation of the forecast demand of all the interfaces of

the supply chain and their concordance with the supply

process also generate pressure on manufacturers' limited

productive capacities, to continuously respond to market

changes as a result of shorter product life cycles, a loss of

commercial capacity that results in strangulation or

amplification of inventory volume and transportation flows

throughout the supply chain (Boyle and al, 2008; Zhao and

al, 2013; Duong and Paché, 2015).

Therefore, the ability of the downstream supply chain to

manage risk is to better position itself than competitors to

adjust levels of disruption and vulnerabilities through

sustainable practices remains the core of the resilience of

demand-side operations. Following this theoretical

anchoring on the risks of requests and the performance, our

second hypothesis will be:

(H2) The higher the demand risk events are, more the

performance of supply chain is lower.

2.1.3 Micro Transport Risk

The recent literature is giving increasing importance to

these types of disruptive events that currently their

occurrence frequencies are growing alongside the highly

vulnerable, uncertain and complex context of the supply

chains, this translates into potential losses capacity and

resources with a cyclical effect on the various supply chain

partners (Blackhurst, Dunn and Craighead, 2011; Chopra

and Sodhi, 2014). As global competition intensifies, supply

chains are required to gain a competitive advantage that

enables them to continually meet their downstream

challenges in terms of increased demand, on-time delivery

and technological change (Olson and Wu, 2010; Tang,

Gurnani and Gupta, 2014; Roberta and al, 2014).

Consequently, this reduces the decision-making time

required to ensure the continuity and the efficient

circulation of physical flows with a level of quality, cost

and time in line with objectives, at the same time a

prerequisite for the success of supply chains, but cases of

exposure to unexpected incidents may erode or even

completely paralyze them (Wagner and Neshat, 2010;

Tang, Matsukawa and Nakashima, 2012).

The literature on systemic risk confirms that the

characteristics and locations of disruptive events change

unexpectedly and that a small event can turn into more

potential failures with serious consequences on the

performance of the supply chain (Zhao and al, 2013;

Deloitte and Touche LLC, 2013). This largely depends on

the variability of the disturbance time and the appropriate

mitigation measures put in place to control the recovery

time through more reactivity necessary for the sustainable

management of the flows of the supply chain (Ponomarov

and Holcomb, 2009; Gligor, Esmark and Holcomb, 2015;

Blackhurst and al, 2017). Therefore, transport risks can not

be treated in isolation because of their impacts on the

sustainability of upstream and downstream logistics

operations, this is supported by the example of both authors

Blackhurst and al, 2017 and Singhal and al, 2012 or a risk

macro natural disaster, regulatory change or micro

modification of transportation standards and processes can

change the upstream and downstream order shipment

execution time thus creating an unexpected bottleneck, with

changes in demand with a direct effect on transport modes,

inventory levels and sales.

In this example, several interdependent and interconnected

supply chain risks exacerbate each other even if it is an

isolated perturbation of a branch or part of the supply chain

(Heal and Kunreuther 2010; Zhao and al, 2013; Blackhurst

and al, 2017). The following disruptive events are

supported for this study: these risks are mainly related to

the distribution of the efficiency of logistics services, the

lack of integration of transport providers, and the

interruption due to a disruption of the physical distribution

of products to the customer (McKinnon, 2006) and in


transport operations (Thun and Hoenig, 2011), changing

modes of transport, time records and late deliveries,

uncontrolled transport costs (Tummala and Schoenherr,

2011; Hahn and Kuhn, 2012) and finally delays due to the

choice of the delivery mode on budget (eg planning

problem, delivery on time and on budget) (Schoenherr

Tummala and Harrison, 2008; Tadeusz Sawik, 2013). As a

result, it can be assumed that transportation failures can

indirectly affect the performance of the supply chain, so our

third hypothesis will be:

(H3) The greater the risks related to transport are, the

more the performance of the supply chain will be

questioned.

2.1.4 Micro Infrastructurel Risk

To collaborate and coordinate in a cross-cutting way with

their complex systems that supply chains invest more in

ensuring technological change, making them more tributary

and dependent on technologies, this exposes them to

computer threats and failures, in the event of the occurrence

of a disruptive event thus affecting its infrastructural and

technical support capacity necessary for a secure circulation

of information flows. In this sense studies such as Global

Risks 2017, 12th Edition of the World Economic Forum

have pointed out that more than there is technological

progress more than the frequency and the degree of impact

of cyberattacks extend to reach all interfaces and supply

chains systems. Therefore, we support the remarks of the

EY study on information security 2016, 19th edition that

this wave of computer threats become more capable of

spreading and paralyze all operations of the supply chain

hence the need to secure data platforms (Kachi and

Takahashi, 2011; El Abdellaoui and Moflih, 2017b). Two

recent examples are healthcare institutions in the United

Kingdom and American and French multinational

automobile companies installed in the Kingdom of

Morocco in 2017 were victims of data theft and block

access to IT infrastructure. This potential failure has had

repercussions on production capacities, which lead to a

complete halt in production and delivery, translated by

levels of structural deficit and vulnerability in the supply

chains in the presence of infrastructural risk factors. Given

these clarifications, the fourth hypothesis will be:

(H4) The greater the infrastructure risks, the more the

performance of the supply chain is influenced.

2.2 Supply Chain Performance

The concept of supply chain performance is

multidimensional in nature because of the different

objectives, structural design, contexts and interests of one

partner to another (Chow and al, 1994; Neely and al, 1995).

In this sense, logistics performance tolerates two

complementary perspectives, one based on efficiency and

effectiveness in the execution of logistics activities and

control of resources so the second on productivity or return

on assets of firms in order to achieve the level of customer

satisfaction according to objectives (Gunasekaran and Ngai,

2005, Duong and al, 2015). As much as a dominant

performance dimension resulting from resilience the value

of customers (Wieland and Wallenburg, 2012), relies on the

speed in the execution of their requirements via a certain

visibility, flexibility and controllability of the logistic

processes (Lee and al, 1997; Narasimhan and al, 2005)

through indicators to quantify and measure the performance

of logistics operations (Jüttner and Maklan 2011; Forslund

2012). The latter represents a significant part of the overall

performance with a significant impact on the economic and

operational (Green and al, 2008; C.B Muller, 2008; Ivanov,

Sokolov and Dolgui, 2014). Therefore, our reflection will

be on the assessment of the direct and indirect causal links

of the types of risks (micro: supply, demand, infrastructure,

transport and macro: socio-political and ecological),

vulnerable, resilient capacity, sustainability and logistical-

operational performance in the supply chain.

Logistics performance in the supply chain is measured by a

variety of evaluation models of criteria and scales of

measures that vary according to the contexts and problems

studied. For example Bowersox and al, 2000 used customer

satisfaction criteria, speed, flexibility and reliability of

delivery. Rodrigues and al, 2004 used six constructs

including logistics costs, delivery time, delivery reliability,

order execution capability, inventory turnover and customer

satisfaction. While Stank and al, 2001 adopted eleven items

to evaluate it that are prior shipping notice, satisfaction and

responsiveness towards the customer, the respect of

delivery times, the speed and flexibility of delivery, the

rotation of stocks, support of information systems,

reduction of logistics costs, execution capacity and

flexibility of orders. Panayides and Venus Lun, 2009 used

seven items such as responsiveness, cost reduction, delivery

reliability, leadtime, specification compliance, process

improvement and time to market. More recently some

authors have combined several scales of measures for the

assessment of logistic performance such as (Green and al,

2008; Hsiao and al, 2010). In this study, we will consider a

sixteen items combined scale with a particular interest in

the social aspect (Bowersox and al, 2000; Stank and al,

2001; Rodrigues et al. and al, 2004; Kim 2010; Gallmann

and Belvedere, 2011; J. Roy and M. Beaulieu and al, 2013;

Kim 2013). We can admit that the supply chain

performance is influenced in a reciprocal way with the

concepts of vulnerability, resilience and the sustainability of

the supply chain so we hypotheses of research will be:

(H5) The lower the performance of the supply chain, the

more vulnerability of the supply chain is greater.

(H6) The lower the performance of the supply chain, the

more resilience of the supply chain is lower.

(H7) The lower the supply chain performance, the more

sustainability of the supply chain is lower.

2.3 Resilience in Supply Chain

We believe that supply chains must further introduce

resilience and agility mechanisms with balanced or

regulated levels to improve their adaptive capacity in


complex and vulnerable contexts (Wagner and Neshat,

2012; Vlajic et al 2012). As a result, the structural

complexity and unpredictability of upstream and

downstream supply chain forecasts have made them less

responsive to change and therefore less resilient and less

sustainable (Tang and Tomlin, 2008; Dmitry Ivanov, 2017),

this is in line with the observation of the two authors

Kleindorfer and Saad, 2005, where supply chains optimized

to be cheaper and faster have the opposite effect by

becoming more vulnerable to disruptive incidents of

increasing probability and severity (Craighead and al, 2007;

Hun and Hoenig, 2011). Faced with this propagation of

nature, progressive attribute and effect as cumulative-

dynamic remains the importance of protecting the

continuity and security of operations through the necessary

safeguards to mitigate before, during and after the

occurrence of a potential disturbance (Pettit and al, 2015).

In addition, the authors argue that often logistical chains

that are likely to survive are not necessarily the most robust

but resilient ones that can adapt, grow and recover quickly

in the face of turbulence (Ponomarov and Holcomb, 2009;

Croxton 2010, Barroso and al, 2015).

In the 46 years since Holing’s 1973 work on ecosystem

resilience, based on the study of evolutionary behaviors of

ecological systems, the concept of resilience has expanded

and adapted to a wide range of disciplines, such as physics,

engineering, psychology, sociology, disaster management

and supply chain management despite lack of consensus on

its unified definition (Dani and al, 2011). Indeed, resilience

refers to both an inherent capacity or property of the

interfaces of the emerging supply chain, this double level or

competence that can be manifested implicitly reflects their

attributes and their intrinsic mechanisms which remains

exclusive for this moving concept. (Holling 1987; Holling

2004; Carl Folk and al, 2004). In this sense the extent, the

location of the propagation and the correlation of structures,

systemic cumulative adverse events that reciprocal effect

makes the choice of the properties of resilient strategies

necessary to mitigate them inefficient (Surya Prakash, and

al 2017; Chowdhury and al, 2017). In the absence of the

location of events that change unexpectedly, the agility

achieved through a flexibility strategy that serves to reduce

the risks of demand and transport, as well as the robustness

achieved through a strategy of redundancy adjusting supply

risks (Chopra and Sodhi 2004; Wallenburg and al, 2012),

losses their resilient or attenuating capacity (Pettit and al,

2013).

To illustrate it in an unstable context a small disruption

related to correlative supply can turn into more potential

failure related to demand or downstream. Otherwise,

choosing a mitigation strategy for interrelated risks may

result in a new risk instead of mitigating it. Hence, the

importance of considering these factors for balanced or

regulated resilience management and the vulnerability

needed for sustainable supply chains (Perrow 1999;

Ackermann and al, 2007; Gligor and Holcomb, 2012;

Dmitry Ivanov, 2017b). So resilience can be presented as a

preventive and reactive capacity acting before and after the

disturbance on the causes by decreasing their probability of

occurrence through active detection that early. And during

the disruption a proactive capacity acting on the

consequences thus reducing their gravities by maintaining

the micro and macro conditions of protection, backup,

control and adjustment necessary for the continuity of

operations for progressive and sustainable levels of stability

(Christopher and Peck, 2004; Ta and al, 2009; Spiegler and

al, 2012; Gong and al, 2014; Pant and al, 2014). Resilience

is thus a proactive, reactive and proactive capacity inherent

in the supply chain and emerging network interfaces acting

before, during and after on the location, magnitude of

macro and micro disturbances by decreasing the probability

of occurrence. The appearance of their causes also the

gravity of their consequences through the conditions of

recovery, protection, safeguarding, control and adjustment

necessary for the continuity of their operation for

sustainable levels of stability (Christopher and Peck, 2004;

Ta and al, 2009; Spiegler and al, 2012; Gong and al, 2014;

Pant and al, 2014).

In the absence of the location of events that change

unexpectedly, the agility achieved through a flexibility

strategy that serves to reduce the risks of demand and

transport, as well as the robustness achieved through a

strategy of redundancy adjusting supply risks (Wallenburg

and al, 2012; Kim and al, 2013), losses their resilient or

attenuating capacity (Pettit and al, 2013). To illustrate it in

an unstable context a small disruption related to correlative

supply can turn into more potential failure related to

demand or downstream. Otherwise, choosing a mitigation

strategy for interrelated risks may result in a new risk

instead of mitigating it. Hence, the importance of

considering these factors for balanced or regulated

resilience management and the vulnerability needed for

sustainable supply chains (Perrow 1999; Gligor and

Holcomb, 2012; Dmitry Ivanov, 2017). So resilience can be

presented as a preventive and reactive capacity acting

before and after the disturbance on the causes by decreasing

their probability of occurrence through active detection that

early. And during the disruption a proactive capacity acting

on the consequences thus reducing their gravities by

maintaining the micro and macro conditions of protection,

backup, control and adjustment necessary for the continuity

of operations for progressive and sustainable levels of

stability (Ponomarov and Holcomb, 2009; Ta and al, 2009;

Spiegler and al, 2012; Gong and al, 2014; Dmitry Ivanov,

2017). Resilience is thus a proactive, reactive and proactive

capacity inherent in the supply chain and emerging network

interfaces acting before, during and after on the location,

magnitude of macro and micro disturbances by decreasing

the probability of occurrence. The appearance of their

causes also the gravity of their consequences through the

conditions of recovery, protection, safeguarding, control

and adjustment necessary for the continuity of their

operation for sustainable levels of stability (Ponomarov and


Holcomb 2009; Ta et al 2009; Smith et al 2011; Gong et al

2014; Dmitry Ivanov, 2017).

As a result, resilience is influenced by the factors of

vulnerability, namely the characteristics of the supply chain

or its environment and more particularly by the spread of

micro and macro risks related to the supply chain (Henry

and Ramirez-Marquez, 2012; Gong and al, 2014; Pant and

al, 2014). Being more specific Pettit and al, 2013 argue that

resilient capabilities designed in a stable business context

can increase risk levels during disruptions, including the

collaboration capabilities, visibilities, and integrations

needed for upstream and cross-functional management of

operations. Downstream fails in a context that is

characterized by a reduction in the supply-provider supply

base, unpredictable demand and fluctuating forecasting

could have a negative impact on the supply chain's

performance (Kim, 2013; Pettit and al, 2015). Wishing to

complete the work of Pettit et al 2013 and Wagner et al

2010, we suppose that he has a vicious circle of influence

between the concepts of resilience, risk, vulnerability and

performance, considering their possible links of causality.

From the presentation of these theoretical elements several

hypotheses of research appear:

(H11): The lower of supply chain resilience, the more

sustainability of the supply chain lower.

2.4 Vulnerability of the Supply Chain

Several recent global examples show the magnitude and

effects of dynamic profit disruptions on the capacity of the

resilience tools and practices needed for viable as well as

sustainable supply chains (Marcus brandenburg and al,

2014; Fahimnia and al, 2014). In the same sequence of

ideas, this viability results in levels of resilience, durability

and sustained performance even in the presence of potential

disturbances that are due to increased sensitivity to risk

sources (Ambulkar and al, 2015; Prakash and al, 2017).

This sensitivity or degree of risk exposure characterizes the

supply chain vulnerability whose researchers consider that

the structures and characteristics of their supply chains,

their internal and external environments, and their processes

and infrastructures are the antecedents or the very factors of

its vulnerability (Wagner and Bode 2012; Cutter 2013).

Moreover, from which we assume that the understanding of

vulnerability passes by that of the sources of risk this

reveals to us that it has a close relation between the two

concepts risk and vulnerability (Berle, Norstad and

Asbjørnslett, 2013).

In parallel, we join the conclusions of authors Pettit,

Croxton and Fiksel 2010 and 2013 on the fact that the

supply chain resilience increases as vulnerability decreases

with a positive effect on the performance of the supply

chain, contributing through resilience balanced with the

sustainability of their logistics operations at all levels

(Jüttner and Maklan, 2011). This can be complemented by

the ability to properly assess and measure the vulnerability

profile in order to compensate for it by a just resilient

capacity needed to improve sustainability, with a cost-

benefit ratio, balanced without eroding logistical

performance (Pettit and al., 2015; Ivanov and al, 2017).

Assuming that sustainability and resilience are mutually

correlated with vulnerability, thus supporting the supply

chain vulnerability increases as disturbances increase and

resilience-durability tools lose their mechanisms which

reduces both performance and competitiveness of supply

chains. In sum of our theoretical concepts we can deduce

the following research hypotheses:

(H12) The more supply chain vulnerability are, the more

supply chain resilience is lower.

(H13) The greater supply chain vulnerability, the more

sustainability of the supply chain is impacted.

2.5 Sustainable Supply Chain

Sustainability in the supply chains as a field of reflection

has undergone remarkable development over the past two

decades (Seuring 2013; Fahimnia and al, 2015) with a

particular focus on the sustainable design of operations and

supply chains. Unlike the traditional ones that focus on

economic, financial and logistical performance (Duong and

Paché 2015), sustainable supply chains SSCM extend

beyond the economic dimension to integrate social and

environmental or so-called short-term the Triple Bottom

line TBL (Elkington, 1998) in design-planning and whose

holistic approaches, that reflect these three dimensions

remain relatively limited in the literature (Seuring and

Müller, 2008). Currently the question of adaptability and

complementarity, in the design of sustainable supply chains

and resilient capacities remains an interesting new research

avenue in the measurement of deploying sustainable and

resilient supply chains in which performance, sustainability

and resilience remains stable in the face of disruptions, as

well as balanced by the ability of resilience to keep

vulnerability at acceptable thresholds (Golicic and Smith

2013; Ivanov and al, 2017). One of the problems that is

justified in our study is the interconnected nature of

performance, risk management and sustainability in our

research area (Aqlan and Lam 2015; Ivanov, Tsipoulanidis

and Schönberger 2017), which we wish to evaluate in this

connection, possible causalities between the concepts of

risk, vulnerability, resilience and sustainability, arguing that

sustainability ensures business continuity and reduces long-

term risks (Fahimnia and Jabbarzadeh 2016; Giannakis and

Papadopoulos 2016).

So to measure sustainability in supply chains we have the

box through the capabilities of companies in the aerospace

sector to ensure sustainable collaboration with their

suppliers and their customers, see also the selection of

sustainable suppliers based on sustainable practices, adapt

their systems to greener procurement strategies. Formalized

risk management processes and experiential learning

processes are also in place as part of the contingency plans

needed to capitalize business continuity guidelines in the

event of potential disruptions. In accordance with the above

and the purpose of our study, we can identify the following

research hypotheses:


(H14) The higher the micro-supply risks, the higher is the

vulnerability, and the resilience and sustainability is lower.

(H15) The higher the micro risks of demand, the higher is

the vulnerability, and the resilience and sustainability is

lower.

(H16) The higher the micro risks associated with transport,

the higher is the vulnerability, and the resilience and

sustainability is lower.

(H17) The higher the micro-infrastructural risks, the higher

is the vulnerability, and the resilience and sustainability is

lower.

III. THEORY MOBILIZED

In order to formalize the analysis of the possible causal

links between the concepts of risk management and

sustainability in the supply chain (that is, to reach our

ultimate goal of research), we adapt three theoretical areas

that inspire still the researchers (Perrow 1984; Speier and

al, 2011) based on the theory of normal accidents (NAT)

(Perrow 1999, 2004; Weick Skilton and Robinson 2009),

theory of high reliability (THR) (Weick and Sutcliffe,

2001) and the Resource Dependence (DVR) (Barney, 1991;

Penrose, 1959), which is useful for explaining why the

active combination of the two interfaces of resilience and

sustainability to absorb the effects of potential disturbances

caused by vulnerability factors, as well as to stabilize the

levels of sustainability, resilience and performance on

systems characterized by interactive complexity and a or

narrow.

Due to the nature and structure of the systems, failures

represent unexpected but ordinary defects in out-of-control

operation (Perrow, 1994; Marley, Ward, and Hill 2014)

resulting from the interaction of the dependence of different

interfaces and the interactive complexity of supply chains

(Wolf 2001). In this sense, efforts to design sustainable

supply chains taking into account certain sustainability

measures to increase the reliability of system security are of

great importance because of the inherent capacity of the

structures and the complexity of the supply chains, to

prevent the detection and control of failures (Skilton and

Robinson 2009, Weick and Sutcliffe 2001).

This idea is of crucial importance as sustainable and

resilient efficiency-oriented practices must provide some

balance between these resilient capabilities that ensure

greater reliability and the vulnerability factors due to the

complexities and dynamic nature of the disruptions that

influence the validity of risk management tools. This

viability-balance (Pettit and al, 2013; Ivanov and al, 2017)

synonymous with the reliability of the systems will improve

the detectability of the failures, and the controllability-

monitor the potential new sources of risk due to the

interactive complexity or dependence towards other

interfaces of the system, which helps to reduce uncertainty

in the decision-making process as well as to sustain the

sustainability of the competitive advantage that drives

strategic action across the system (Barney et Arikan, 2001;

Christopher 2010; Camman and Guieu 2013; R. Calvi et al

2014).

IV. RESEARCH MODEL ANDHYPOTHESIS

Through our conceptual model, we try to validate the direct

and indirect causal links between the conceptual boundaries

of two disciplinary fields of supply chain management,

namely the supply chain risk management and the

sustainability supply chain management. In this sense, more

than 82 factors affecting the concepts of resilience,

vulnerability, risks, sustainability and performance in the

supply chain are mobilized to examine the possible

relationships between the concepts studied.

Figure 01: Integrative Conceptual Research Model SCRM

4.1. Objective and methodology of Research

4.1.1 Research Objective

Several studies in academic and professional circles

emphasize the dynamic scale of sustainable and resilient

practices in complex business contexts (Wright, 2013; EY's

2016 Information Security Study, 19th Edition), thus

enabling managers in supply chains to defend and recover

quickly in the face of this complexity and dynamism that

characterize both the nature of risks and the structure of

supply chains. (Ivanov and Sokolov 2013; R. R. Levalle

and S.Y. I 2017; Global risks 2017, 12th Edition World

Economic). So our ultimate goal is to offer some premises

or roadmap in terms of thinking about the fertile links

between risk management and the sustainability of the

supply chain. Otherwise the viable resilience of supply

chains, in which resilience, sustainability and performance

remain unchanged at acceptable levels in the presence of

potential disruptions by supporting performance (logistic

and operational) while minimizing the spread and severity

of risks through resilient, regulated and resilient capacities

and resilient practices (Marcus Brandenburg and al, 2014;

Fahimnia and al, 2014; Prakash and al, 2017).

Based on these findings, we have developed a conceptual

research model that examines the causal links between the

concepts of supply chain risk management (Risk,

Vulnerability and Resilience), the concept of sustainability

supply chain and the concept of supply chains performance

(logistics and operational). Throughout, the twenty

hypotheses, constructed based on the relevant theoretical

frameworks (Theory of normal accidents, theory of

dependence of resources and theory of high reliability) that


we seek to validate the senses of influences deemed

important. (See Figures 01, 02; 03 and 04). Table 01 : Coding of the Variables of the Integrating model SCRM2

N° Variable Items Code Types

1

Macro and

Micro Risks

in the Supply

Chain.

Micro Supply Risks 12 Supply Risks Exogenous 2 Micro demand Risks 6 Demand Risks Exogenous 3 Micro Infrastructural

Risks

4 Infrastructural

Risks

Exogenous

4 Micro Transport Risks 4 Transport

Risks

Exogenous

7 Resilience in

the Supply

Chain

Résilience de la chaîne

logistique

21 Resilience Exogenous

8 Vulnerability

of the Supply

Chain

Vulnérabilité de la

chaine logistique

13 Vulnerability Exogenous

9 Supply Chain

Performance

Performance dans la

chaine logistique

13 Performance Exogenous

10 Sustainability

of the Supply

Chain

Durabilité de la chaine

logistique

7 Sustainability Endogenous

Table 02 : Summary of SCRM2 Model Assumptions

H01(-) The more micro risks of supply are important, the more supply chain

performance is influenced.

H02(-) The more micro risks of demand are higher, the more supply chain performance

is lower.

H03(-) The more micro risks of transport are greater, the more supply chain performance

will be questioned.

H04(-) The more micro risks of infrastructural are important, the more supply chain

performance is influenced.

H05(-) The more supply chain performance is lower, the more vulnerability of the

supply chain is higher.

H06(+) The more supply chain performance is lower, the more resilience of the supply

chain is lower.

H07(+) The more supply chain performance is lower, the more sustainability supply

chain is lower.

H08(+) The more disruptive events in supply chain are higher, the more vulnerability of

the supply chain is higher.

H09(-) The more disruptive events in supply chain are higher, the more resilience of the

supply chain is impacted.

H10(-) The more disruptive events in supply chain are higher, the more sustainability of

the supply chain is impacted.

H11(-) The more resilience of the supply chain is lower, the more sustainability of the

supply chain is challenged.

H12(-) The more vulnerability of the supply chain is important, the more resilience of

the supply chain is lower.

H13(-) The more vulnerability of the supply chain is important, the more sustainability

of the supply chain is influenced.

H14(-) The more micro supply risks are higher, the more vulnerability is higher and

resilience-sustainability are influenced.

H15(-) The more micro demand risks are higher, the more vulnerability is higher and

resilience-sustainability are impacted.

H16(-) The more micro transport risks are higher, the more vulnerability is higher and

resilience-sustainability are influenced.

H17(-) The more micro infrastructural risks are higher, the more vulnerability is higher

and resilience-sustainability are influenced.

4.1.2 Research Methodology

Our research methodology will initially focus on the

characteristics of the field of investigation namely the

Aeronautical sector in Morocco, then on the survey method

and sample structure and on the definition of the statistical

analysis method adopted under this work.

4.2. Survey Method and Questionnaire Design

4.2.1. Survey Method

Through a quantitative positivist approach of the

hypothetico-deductive, we seek to validate the seventeen

hypotheses constituting the present conceptual model of

research. For this fact a questionnaire survey composed of

six parts on concepts related to risk management and

sustainability in the supply chain namely: Risk,

vulnerability, resilience, sustainability and performance in

the supply chain. The participants of which were asked

according to their expertise, and the extent of their

agreements deal with situations concerning their logistic

operations over the last four years, using two types of Likert

scales at five and seven points: the first from (1) "Strongly

disagree" to (5) "Agree" for the performance and

sustainability concepts of the supply chain, and the second

from (1) "Strongly disagree" to (7) "Absolutely agree" for

the concepts of risk, vulnerability and resilience in the

supply chain, which can be seen in more detail in the next

section

4.2.2. Questionnaire Design

In order to measure the possible causal links between the

explanatory and dependent variables, we carried out a

questionnaire survey during the period from October 2017

to January 2018 with the heads of the aeronautical

companies, of which it consists of six parts which is as

follows:

The first part concerns micro and macro disruptive risks

related to supply, demand, infrastructure, transport as well

as sociopolitical-ecological.

With respect to supply risks eleven items were used that

capture disruptive events related to the complex structure of

supply markets; inherent vendor failures that affect flow

continuity; failures specific to supply networks and the risks

associated with capacity constraints on incoming products

in the event of dependence on external sources. For demand

risks six items were chosen associated with unpredictability

and downstream market uncertainty; change in trends and

availability of substitute products. In the same sense the

infrastructural risks are evaluated using five items, thus

encompassing the failures of the resident infrastructure of a

material, technical and human error; security failures

following a massive incident of fraud or theft of data; the

risk of loss of own production capacity due to local or

technical disruptions and increased failure failures and

cancellations of operational capabilities. Now for transport

risks five items have been fixed on the deficiencies relating

to the choice of mode of transport and distribution engaged;

failures due to authority conflicts within logistics teams or

complex pilot error chain interactions; Transport budget

planning failures and lack of integration of transport and

distribution service providers which results in an

interruption of the delivery chain. While macro ecological,

socio-political risks were measured using five items relating

to climate change mitigation and adaptation failures; failure

of economic slowdown and liberalization of the national

currency; and the price fluctuation of energy products at the

national level.

The second part concerns the notion of resilience in the

supply chain to evaluate by twenty-one relative items:

The flexibility of the supply chain in terms of changes to

the supplier order and alternative sources of supply;

flexibility of contracts with partners in terms of partial

delivery; consolidation and centralization of requests and

purchasing requirements; flexibility in distribution in terms

of calendar change; flexibility or pooling of transport to

ensure continuity of flows; ability to exchange and


communicate internally and externally; capacity of stock-

level systems to support flexibility strategies; ability to take

into account disruptions when forecasting information

sharing and when reconfiguring the operations of the supply

chains; Visibility on upstream and downstream physical

inventory through a vendor-managed strategy ability of

technical and human resources to approve a high level of

productivity but manage to make cost-benefit trade-offs

when deploying corrective risk management actions finally

ability of partners to take immediate action to mitigate the

effects of disruptions and assess the cost-benefit of their

mitigation plan using unconventional methods such as time

driven activity based costing

The third part concerns the concept of vulnerability in the

supply chain evaluated by ten items associated with:

The complexity of supply chains (single source supply

strategy in markets with strong price fluctuations); the

unpredictability of demand and price pressure; the

establishment and accessibility of distribution centers;

Failure to adapt the communication tools according to the

size of the organization to ensure a secure transfer of

information at the intersection points without loss of data

and without forgetting the psychological influence or the

Mobbing effect absenteeism

The fourth part relates to the sustainability of the supply

chains materialized by five items including:

Collaborative capacity for sustainability upstream and

downstream of the supply chain; formalized risk

management and business continuity processes and

procedures for supply chains operations in the event of an

emergency; the company's ability to adapt green

procurement practices and the transfer of experience in risk

management.

For the fifth is the last part, it deals with the concept of the

performance in the supply chain to evaluate by eight

relative items:

The time and reliability of the delivery of sales orders;

flexibility, speed and responsiveness of urgent deliveries;

quality of deliverables; satisfaction of customer demand on

a regular basis with available resources; inventory rotation

and support for information systems; the size and

consolidation of the warehouses; reductions in logistics

costs and rapid adaptation to customer needs. Therefore,

the table below shows the main constructs with their

theoretical origins. Table 03 : Natures and Origins of Constructs

Variables

Natures

Code/Variables Origines des variables Construits

Types

Micro Supply

Risks

Supply-Risk Zsidisin 2003; Giunipero et Eltantawy

2004; Christopher et Peck 2004;

Gaudenzi et Borghesi 2006; Manuj et

Mentzer 2008; Wagner et Bode 2008;

Zsidisin 2010; Thun et Hoenig 2011;

Hahn et Kuhn 2012a; J. Samvedi et Chan

2013; Jacques Roy and al, 2015; Hsu et al

2016.

Exogenous

Micro

Demand Risks

Demand -Risk Chopra et Sodhi 2004; Rao et Goldsby

2009; Tang et Nurmaya Musa 2011;

Martin Beaulieu and al, 2012; Jacques;

Wagner et Bode 2012; Roy and al, 2015.

Exogenous

Micro

Infrastructural

Risks

Infrastructural-

Risk

El Abdellaoui et Moflih, 2017b; Walter

Merkle 2016; Wagner et Bode 2008; The

Global Risks Report 2017, 12th Edition

du WEF; Etude sur la sécurité de

l’information 2016 : Cap sur la

Exogenous

cyberrésilience, 19éme édition du EY

Micro

Transport

Risks

Transport-Risk Walter Merkle 2016; Jacques Roy and al,

2015; M. Beaulieu et al 2013; M.

Beaulieu and al, 2012; Thun et Hoenig,

2011; Gallmann et Belvedere 2011; Olson

et Wu, 2010; Sawik and al, 2008; Juttner,

2005; Cooper et al. 1980.

Exogenous

Resilience in

Supply Chain

Resilience Cranfield, 2003; Fiksel 2003; Christopher

et Peck 2004; Martin 2004; Sheffi 2005;

M. Duclos et al. 2005; Blackhurst et al.

2005; Weinstein, 2006; Swafford et al.

2006; Gunasekaran et al. 2008; Tang et

Tomlin, 2008; Francis, 2008; Ponomarov

et Holcomb 2009; Blos et al. 2009;

Braunscheidel et Suresh 2009; Pfhol et al

2010; Wei et Wang, 2010; Jüttner et

Maklan 2011; J. Blackhurst et al. 2011;

Waters 2011; Beaulieu and al, 2012;

Carvalho et al 2012; K.E. Samuel 2013;

Wieland et Wallenburg 2013; Jacques

Roy and al, 2015; Pettit et al 2015;

Ambulkar et al. 2015; Barroso et al.

2015; Brusset et Teller 2016; Chowdhury

et al 2017.

Exogenous

Vulnerability

of the Supply

chain

Vulnerability Andersen 1990 ; Landry and al, 1998;

Juttner 2005; Fabbe cost et al 2007;

Wagner & Neshat 2010; J. Blackhurst et

al. 2011; Chowdhury et al. 2012; Arthur

& Martin Beaulieu and al, 2012; Pettit et

al 2015; Walter Merkle 2016.

Exogenous

Supply Chain

Performance

Performance Ballou1999; Bowersox et al. 2000; Stank

et al. 2001; Gunasekaran et al. 2004;

Rodrigues et al. 2004; Green Jr. et al.

2008; Panayides et Vénus Lun 2009;

Darling et Wise 2010; M. Beaulieu et al

2013; Richards 2011; Gallmann et

Belvedere 2011; Forslund 2012;

Exogenous

Sustainability

Supply Chain

Sustainability B. Ageron and al, 2012; Morali et

Searcy 2013; Beske et al 2014; A.

Alexander and al, 2014; Paulraj et al.

2015; P.B Janssen and al, 2015; Jacques

Roy and al, 2015; Hendrik et David 2016;

Esfahbodi et al. 2016; C. Julia and al,

2016; A. Rajeev and al, 2017; J. Hong

and al, 2017; Debadyuti Das 2017

Endogenous

4.3. Justification for the Choice of PLS-SEM

4.3.1. Definition of the Method and Objective of

Research

In recent years, SCM supply chain management researchers

have increasingly engaged in understanding complex

interdisciplinary conceptual relationships that are a

prerequisite for framing evolutionary issues under different

disruptive contexts (Xiaosong Peng and Lai 2012;

Chowdhury and al, 2017; Hair and al, 2017). The

emergence of complex conceptual models in the SCRM and

SSCM challenges us on the importance of analytical

methods of analysis to set up to anchor new voices and

avenues of research necessary for the development of

theories related to the different disciplines of management

of the supply chain.

The method of modeling SEM structural equations (Rigdon,

1998) is often used across a variety of management

disciplines (Pavlou & Chai, 2002; Henseler and al, 2009;

Hair and al, 2011; Richter and al, 2016) strategic

management (Hulland, 1999; Sosik, Kahai, & Piovoso,

2009), marketing and trade (Fornell & Robinson, 1983;

Hair, Sarstedt, Pieper, & Ringle, 2012), accounting (Lee et

al 2011; Christian Nitzl 2016) including human resources

(Christian M. Ringle, et al 2018), tourism management

(Valle and Assaker, 2016) hospital management (Faizan

Ali, et al 2017) and supply chain management (Xiaosong

Peng et al 2012; Lutz Kaufmann et al 2015). This is a

method capable of defining complex systems in mutual

interactions explaining and predicting the causal links


between several latent variables (Chin 1998; Fernandes

2012; Hair et al 2012). Originally developed by the author

Wold 1975 under the name NIPALS (Nonlinear iterative

partial least squares), and extended by Lohmöller 1989

under the name SEM-PLS as an alternative to CB-SEM

with a main difference or the first one maximizes the

Explained variance of the endogenous latent variables,

while the second estimates the model parameters that the

gap between the estimate and the covariance matrices of the

sample is reduced to the minimum otherwise the CB SEM

focuses on the variances of the common factors while SEM-

PLS considers that the differences are common and unique

(Shmueli and al, 2016) it is somewhat similar to the

difference between factor analysis and principal component

analysis (Chin 1998).

Thus becoming a widely used method for exploratory

conceptual models, for example those that determine the

impact of resilient proactive and reactive practices on

operational vulnerability as well as on the performance of

the supply chain (Chowdhury and al, 2017), as well as the

trust and integration of suppliers in a dyadic relationship on

performance and customer satisfaction (Peng and al, 2012).

In this sense, the SEM-PLS or the modeling of partial least-

squares structural equations remains potentially interesting

when one seeks to estimate the direct and indirect effects

through the moderation and the mediation of multiple

constructions of formative or reflective nature, taking into

account the measurement error that makes it possible to

examine a series of interrelated relationships between

dependent and independent constructions that would

otherwise be difficult if using traditional methods in the

face of small samples.

4.3.2. Reason for using the SEM-PLS

Because of the exploratory nature of our study, which

focuses on cause-and-effect relationships that can express

and predict the relationship between risk management

concepts (Risk, Vulnerability and Resilience) and

sustainability in the supply chain, with a special emphasis

on performance in the supply chain not sufficiently

explored empirically. This makes the conceptual model

mobilized consists of nine latent variables of reflective and

formative natures renders it more complex, with moderation

and mediation analyzes for the indirect relations necessary

for the development of the theory that combines risk

management and sustainability in the anchored and tested

supply chain in the aerospace sector of an underlying

population of 115 companies operating in the Kingdom's

territory, of which only 102 companies were targeted,

which represents a small but fairly homogeneous sample.

These results are consistent with related studies in supply

chain management as compared to other management

disciplines (See Rigdon 2016; Cheveux and al., 2017; Hair

and al, 2017). Then we chose for our exploratory study, the

SEM-PLS approach because it is adapted to both the

characteristics of the complex conceptual model, and the

small sample size. Indeed, a guide to the use of SEM-PLS

for evaluating measurement and structural models is as

follows:

Figure 02 : Guide and Research methods flow chart

4.4 Measurement and Evaluation of the exterior and

interior model

The evaluation of the results via the SEM-PLS method is

carried out according to a complementary approach in two

stages: the evaluation of the measurement model (1) and the

evaluation of the structural model (2) (Chin, 2010).

4.4.1 Evaluation of the External Measurement Model

The measurement model includes the evaluation of the

constructs, the reliability and the validity of the

measurements. The goal of this step is to ensure that

constructs and measures have the ability to measure and

reflect the problematic in different contexts otherwise the

measures are well represented on their constructs before

evaluating the causality between the variables. So this

evaluation relies on different measures, which changes

depending on the nature of the constructs is such formative

or reflective organized as:


4.4.1.1 Converging Validity

A. Reflective Measurement Model

The evaluation of the reflective measurement model

consists of evaluating the reliability and validity of the

measures (i.e. the reliability indicator, the reliability internal

consistency and the convergent and discriminant validity).

Thus the indicators of reliability and validity of the

instruments measures according to the error theory

(Roehrich, 1993) the quality of the measurements initiated

via Cronbach's alpha, composite reliability (CR), rho-A

which must be greater than the threshold of 0.7 also

acceptable at the threshold of 0.6 for new measurements

and the mean variance extracted (AVE) large than 0.5 to

evaluate the convergent validity (Bagozzi and Yi, 1988;

Hair and al, 2017).

B. Formative Measurement Model

The evaluation of the formative measurement model

consists in evaluating the convergent validity using the

redundancy analysis (Q2), the multicollinearity or the

method of inflation of the variance between the constructs

(VIF), the importance of the weights of measurements

(level of significance t and p values, T-stat), the correlation

of the constructs and the coefficient of determination (R2)

for the endogenous latent constructs of the external model

(Cenfetelli and Bassellier, 2009; Hair and al 2017). Whose

researchers (Diamantopoulos, 2008) claim that this type of

measurement plays an important role in future research.

4.4.1.2 Discriminant Validity

For the discriminant validity, we try to validate that the

instruments measure only the constructs to which belong

but at the same time this different with the rest of the

constructs and measurements of the model in question. In

this sense the evaluation indicators are the Cross loading,

the Fornell-Larcker criterion and the Heterotrait-Monotrait

(HTMT) criterion. For the HTMT must be less than 0.85

for conceptually distinct constructs and less than 0.90 for

conceptually similar constructs, while Fornell-Larcker must

be greater than the 0.70 threshold and the Cross loading of

each measure must be higher on its line and column

otherwise the intersection of the same measure should show

a higher value on the same measure (line) and on the other

constructs (columns) (Fornell and Larcker 1981; Chin,

1998).

4.4.2 Evaluation of the Internal Measurement Model

Once the external measurement model has been evaluated,

the second step is to estimate the estimate of the internal

model structure. A useful first step in this evaluation is to

analyze the path between exogenous and endogenous

constructs using the Standard Beta (Standard and T and P

value) to evaluate its importance (Tenenhaus, Vinzi,

Chatelin & Lauro, 2005) as well as total effect and indirect

specific effect (Standard Beta, Standard Deviation, T and P

value) as a summative evaluation of direct and indirect

relationships on endogenous latent constructs (Albers,

2010). Subsequently, a second step is to evaluate the

coefficient of determination R2 to characterize the ability of

the model to explain and predict endogenous latent

constructs whose relevance depends on the research context

(Hair and al., 2011), and where the literature suggests that

the R2 values (0.19-0.33-0.67) are substantially (low-

medium-high) (Chin, 1998). In the same sense, the size of

the effect f2 necessary to evaluate the proportion of the

variance that remains unexplained in the latent endogenous

construct ie. evaluate each exogenous construct individually

on the endogenous construct (Cohen 1988).

According to the same author the values of f2 (0.02-0.15-

0.35) are considered as (low-medium-strong). A third step

is to evaluate the predictive power of the research model by

the value of the index stone Q2 which must be greater than

zero for the accepted (Geisser 1975, Rigdon 2012). Now to

calculate the quality criterion of the fit of the global search

model (GoF) that (Tenenhaus and al, 2005) concretized it

by the square root of the geometric mean of the coefficient

of determination R2 multiplied by the geometric mean the

average variance extracted, otherwise this criterion

measures the quality of the overall exogenous measurement

model in terms of average intercommunality and the quality

of the structural model in terms of average coefficient of

determination.

Towards the end and to ensure that results can not be

compromised on the level of aggregated data (Becker, Rai,

Ringle, & Völckner, 2013) due to the critical levels of

unobserved heterogeneity, as complementary analyzes

intervention-based segmentation or finite mixture

regression model (FIMIX-PLS) which simultaneously

estimates the parameters of the inner model and controls the

heterogeneity data structure by calculating the probability

of observations as well as mediation and moderation

(Sarstedt and Ringle, 2010; Rigdon, Ringle and al, 2011).

V. ANALYSIS AND RESULT

The research model mentioned above (Fig. 6), in which

latent risk variables Micro and Marco related to the supply

chain (Micro Procurement Risk, Micro Demand Risk,

Micro Transport Risk, Micro Infrastructurel Risk and

Macro Risk Ecological), Resilience, Vulnerability and

Sustainability in the supply chain are modeled as reflective

and formative variables with reflective measures. While

performance in the supply chain is modeled by both

variables and formative measures. So to operationalize our

scales and measure our conceptual research model, we use

SmartPLS version 3 software, based on data collected from

92 companies operating in the Aerospace sector at

Kingdom level out of a population of 115 companies whose

characteristics present: Table 04 : Characteristics of the sample

Titre de l’Offreur Reponses Pourcentage Cumul

Responsables Logistique 34 29% 29%

Responsables des Approvisionnement et

des Stocks

25 23% 52%

Responsable des Achats 23 25% 77%

Ingénieurs Analystes et Planifications 18 16% 93%

Responsables Risk Manager 2 7% 100%

Total 102 100%


Chiffre d’Affaire en (Millions de MAD) Reponses % (CA) % (Eff) Cumul

Plus de 1000 Millions de MAD, jusqu'à

5000 Millions de MAD

20 60,03% 19,61% 19,61%

Plus de 300 Millions de MAD, jusqu'à 800

Millions de MAD

29 23,83% 28,43% 48,04%


Millions de MAD

27 9,89% 26,47% 74,51%


Millions de MAD

26 6,26% 25,49% 100%

Total 102 100% 100% 100%

5.1 Validity of the Measurement Model

5.1.1 Convergent Validity of Reflective Measures

We begin our analysis by evaluating the internal

consistency and the convergent validity of the measurement

model, through the composite measurement loads, the

composite reliability CR and the average extracted variance

AVE and the Goldstein Rho-A index of the items

Reflections whose estimates are grouped in Table 4 or

composite reliability values and the Rhô-A index for each

variable are greater than 0.7 (Nunnally and Bernstein 1994;

Hair and al, 2010). While to verify the correlation between

the items measuring a construct, on the one hand, and the

items constructed to measure on the other hand that the

average variance extracted AVE is used remaining above

the threshold of 0.5 (Fornell and Larcker, 1981; Evrard and

Pras, 2009; Hair and al, 2010), the results of which are

acceptable, suggesting a convergent validity of high

reflective measurements (Hair and al, 2010). Table 05 : Results of Measurement Model - Convergent Validity

Construct Variables

Mesures Factor loading

Composite Reliability

CR

rho-A Average variance extracted AVE

Micro Supply Risks

Risk Approv-2 0,701 0,874 0,854 0,511

Risk Approv-5 0,701

Risk Approv-11 0,779





Micro Demand Risks

Risk Demand-1 0,739 0,799 0,786 0,537

Risk Demand-4 0,741

Risk Demand-5 0,758

Risk Demand-6 0,776

Risk Demand-9 0,720

Micro Infrastructural

Risks

Risk Infras-2 0,724 0,842 0,826 0,587

Risk Infras-3 0,909

Risk Infras-4 0,687

Risk Infras-5 0,821

Micro Transport Risks

Risk Transp-2 0,793 0,819 0,704 0,555

Risk Transp-3 0,731

Supply Chain Performance

Perform-1 0,905 0,931 0,918 0,590

Perform-2 0,782

Perform-3 0,828

Perform-4 0,787

Perform-5 0,719

Perform-6 0,878

Perform-7 0,796

Perform-8 0,713

Resilience in Supply Chain

Resilience-1 0,719 0,937 0,912 0,601

Resilience-3 0,788

Resilience-4 0,834

Resilience-5 0,772

Resilience-6 0,757

Resilience-7 0,751

Resilience-8 0,722

Resilience-9 0,791

Resilience-11 0,721

Resilience-13 0,768

Vulnerability of the Supply

Chain

Vulnerability-1 0,703 0,913 0,869 0,568

Vulnerability-2 0,809






Vulnerability- 0,722

10

Sustainability Supply Chain

Sustn-1 0,882 0,912 0,908 0,731

Sustn-2 0,816

Sustn-3 0,891

Sustn-4 0,847

Sustn-5 0,838

5.1.2 Discriminant Validity

Following the acceptable level of convergent validity, we

can continue the evaluation of the measurement model by

checking the absence of correlation between the items of

the constructs otherwise, we try to verify that the items are

well represented on their constructs of which they belong.

As a result, indicators such as Fornell-Larcker's criterion,

the ratio of Heterotrait Monotrait HTMT and of course

Cross loading are mobilized. Whose authors (Bagozzi and

Yi, 1988; Chin 1998; Klein and Rai, 2009) indicate that

each latent variable must display a higher value on its line

with respect to the remains of the constructs but also on its

column for the items of the same construct. Thus Tables 5,

6 and 7 show successively higher values for each construct

between 0.80-0.70 for the Fornell-Larcker criterion

measuring the correlation between the constructs and 0.80-

0.67 for the HTMT ratio, thus higher cross loading values

for each measure of the same construct with satisfactory

results. So all these conditions together prove the

discriminating validity of our external reflective

measurement model. Table 06 : Discriminant Validity - Cross Loading

Construct Durabilité Perofrmance Risk Approv

Risk Dmd

Risk Infra

Risk Trsp

Résilience Vulnérabilité

Sustn-1 0,880 0,685 0,045 0,162 0,134 0,053 0,407 -0,323

Sustn-2 0,822 0,636 0,084 0,092 0,152 0,012 0,329 -0,322

Sustn-3 0,892 0,616 0,154 0,120 0,133 0,036 0,353 -0,386

Sustn-4 0,842 0,663 0,136 0,255 0,177 0,116 0,312 -0,273

Sustn-5 0,838 0,573 0,050 -0,059 0,028 -0,023 0,298 -0,409

Perform-1 0,623 0,723 0,112 0,123 0,101 0,178 0,213 -0,258

Perform-3 0,547 0,781 0,118 0,143 0,179 0,339 0,328 -0,240

Perform-5 0,502 0,739 0,073 0,137 0,128 0,211 0,331 -0,321

Perform-6 0,692 0,929 0,218 0,075 0,249 0,155 0,329 -0,454

Perform-7 0,677 0,747 0,178 0,153 0,028 0,003 0,154 -0,280

Perform-8 0,566 0,707 0,123 0,039 0,100 0,006 0,006 0,287

Risk Approv-2 0,173 0,027 0,678 0,620 0,610 0,406 -0,020 0,058

Risk Approv-5 0,051 0,064 0,712 0,320 0,401 0,257 -0,070 0,204

Risk Approv-11 0,066 -0,010 0,616 0,171 0,284 0,255 -0,058 0,045

Risk Approv-12 0,058 0,056 0,759 0,569 0,618 0,456 -0,132 0,001

Risk Approv-13 0,066 0,002 0,709 0,499 0,512 0,272 -0,114 0,940

Risk Approv-14 0,241 -0,276 0,816 0,535 0,728 0,545 -0,019 0,035

Risk Approv-16 -0,179 -0,077 0,700 0,390 0,500 0,262 -0,246 0,051

Risk demand-1 0,150 0,137 0,411 0,715 0,418 0,282 -0,130 0,177

Risk demand-4 0,091 0,050 0,391 0,786 0,465 0,481 -0,264 0,141

Risk demand-5 -0,060 -0,097 0,601 0,755 0,650 0,505 -0,075 0,080

Risk demand-6 0,286 0,179 0,310 0,755 0,474 0,444 -0,113 0,104

Risk demand-9 -0,250 -0,219 0,160 0,686 0,369 0,234 -0,122 -0,288

Risk infras-2 0,171 0,292 0,699 0,326 0,732 0,339 -0,148 0,057

Risk infras-3 0,080 0,135 0,636 0,678 0,907 0,569 -0,076 0,082

Risk infras-4 0,142 0,051 0,486 0,609 0,730 0,350 -0,147 0,054

Risk infras-5 0,093 0,187 0,595 0,522 0,820 0,585 -0,074 0,055

Risk transp-2 0,059 0,190 0,420 0,591 0,474 0,848 0,198 0,053

Risk transp-3 0,001 0,124 0,337 0,211 0,396 0,626 0,058 0,066

Résilience-1 0,203 0,196 -0,078 0,123 0,145 0,187 0,714 -0,413

Résilience-3 0,493 0,433 -0,014 -0,037 -0,049 0,104 0,788 -0,448

Résilience-4 0,315 0,236 -0,066 -0,072 -0,175 0,130 0,834 -0,421

Résilience-5 0,316 0,331 -0,022 -0,044 -0,056 0,020 0,775 -0,521

Résilience-6 0,367 0,383 -0,077 -0,201 0,129 0,294 0,783 -0,448

Résilience-7 0,159 0,218 -0,029 -0,144 -0,032 0,099 0,773 -0,430

Résilience-8 0,227 0,125 -0,420 -0,086 -0,107 -0,055 0,714 -0,527

Résilience-9 0,344 0,359 -0,054 -0,030 -0,041 0,166 0,811 -0,393

Résilience-11 0,198 0,193 -0,065 -0,053 -0,064 0,068 0,715 -0,397

Résilience-13 0,264 0,146 -0,092 -0,093 -0,142 0,024 0,778 -0,503

Vulnérability-2 -0,215 -0,269 -0,049 -0,039 -0,042 -0,030 -0,370 0,801

Vulnérability-3 -0,245 -0,032 -0,033 -0,099 0,011 0,060 -0,342 0,704

Vulnérability-4 -0,339 -0,336 0,028 0,011 0,049 0,136 -0,628 0,797

Vulnérability-5 -0,283 -0,346 0,021 0,097 0,025 -0,036 -0,405 0,735

Vulnérability-8 -0,392 -0,380 0,169 -0,001 0,160 0,046 -0,334 0,735

Vulnérability-9 -0,432 -0,385 0,023 -0,110 -0,025 -0,201 -0,598 0,832

Vulnérability-10 -0,116 -0,248 0,077 -0,018 0,055 0,074 -0,407 0,725

Table 07 : Latent variables correlation (fornell-larcker criterion) Construct Sust Perfm Pertrb R.Aprv R.Dmd R.Infr R.Trnp Resi Vulnb

Sustainable 0,855 0 0 0 0 0 0 0 0

Performance 0,742 0.769 0 0 0 0 0 0 0

Perturbation 0,145 0,163 0,645 0 0 0 0 0 0

R.approvision 0,109 0,110 0,639 0,715 0 0 0 0 0

R.Demande 0,140 0,153 0,513 0,584 0,753 0 0 0 0

R.Infrastructure 0,148 0,180 0,515 0,557 0,679 0.766 0 0 0

R.Transport 0,048 0,218 0,384 0,510 0,576 0,584 0,745 0 0

Resilience 0,400 0,321 0,024 -0,018 -0,019 -0,056 0,124 0,776 0


Vulnerability -0,399 -0,312 0,036 0,032 0,018 0,042 -0,007 -0,591 0,753

Table 08 : Discriminant Validity - Ratio Heterotrait Monotrait HTMT Construct Sust Perfrm Perturb R.Apprv R.Dmd R.Infrs R.Trnsp Resl Vulnrb

Sustainable in SCM 1 0 0 0 0 0 0 0 0

Performance in SCM 0,864 1 0 0 0 0 0 0 0

Perturbation in SCM 0,288 0,753 1 0 0 0 0 0 0

R.approvisionnement 0,301 0,323 0,756 1 0 0 0 0 0

R.Demande 0,313 0,293 0,693 0,731 1 0 0 0 0

R.Infrastructure 0,198 0,255 0,689 0,685 0,856 1 0 0 0

R.Transport 0,219 0,373 0,543 0,547 0,673 0,839 1 0 0

Resilience in SCM 0,395 0,335 0,241 0,187 0,276 0,198 0,797 1 0

Vulnerability in SCM 0,379 0,326 0,193 0,165 0,209 0,158 0,343 0,767 1

5.1.3 Validity of the Formative Measures

Alors une nouvelle voix d’évaluation est suggérée

commençant par la vérification de la multicolinéarité entre

les items via le facteur d’inflation de la variance (VIF); le

poids, le signe des mesures formatifs au lieu de leurs

chargements (Andreev and al, 2009; Götz and al, 2010).

Donc le tableau 8 ci-dessous affichent pour chaque mesures

formatives un poids significatif supérieur au niveau de 0,10

avec des signes compatibles à la théorie sous-jacente

(Andreev and al, 2009) à l’exception du (performance 5)

qui n’est pas significatif au niveau 0,01 et qu’on le garde

sur le modèle de mesure du fait de son poids conceptuel

comme aspect cruciale du concept performance dans la

chaine logistique (Petter and al, 2007; Ivanov, Sokolov et

Dolgui 2014). Concernant la multicolinéarité (VIF) n’est

pas sévère pour l’ensemble des mesures avec des valeurs

inférieures au 3,3 (Diamantopoulos et Siguaw, 2006; Petter

and al, 2007). Evaluation of the validity of formative

measures can not be achieved by traditional test theory

(Bollen 1989, p.222), due to the weak correlation of

training indicators or (Hair and al, 2006; Bollen and

Diamantopoulos, 2017) proves that internal consistency is

not a consistent validation criterion for evaluating formative

measures.

Then a new evaluation voice is suggested starting with the

verification of multicollinearity between items via the

variance inflation factor (VIF); weight, the sign of

formative measures instead of their loadings (Andreev and

al, 2009; Götz and al, 2010). Thus Table 8 below shows for

each formative measure a significant weight greater than the

0.10 level with signs compatible with the underlying theory

(Andreev and al, 2009) with the exception of (Performance

5) which is not significant at the 0.01 level and is kept on

the measurement model because of its conceptual weight as

a crucial aspect of the performance concept in the supply

chain (Petter and al, 2007; Ivanov, Sokolov and Dolgui

2014). Regarding multicollinearity (VIF) is not severe for

all measurements with values below 3.3 (Diamantopoulos

and Siguaw, 2006; Petter and al, 2007).

5.2 Evaluation of the inner structural model

Once the external measurement model is satisfied, the next

step is to evaluate the internal structural model. Then, to

test the hypothetical relations formulated on our research

model, we begin by evaluating the importance of the path

coefficients by using Bootstrap techniques specifying for

each hypothesis the Standard Beta; Standard Deviation; the

T and P value (Tenenhaus, Vinzi; Chatelin, & Lauro,

2005). Subsequently evaluate the direct and indirect

relationships on endogenous variables as a summative

evaluation through the total effect and the indirect specific

effect (Albers, 2010).

As for the rest of this step, the power and predictive power

is measured by the coefficient of determination R2, the size

effect f2 and the coefficient of cross redundancy Q2 (Hair

and al, 2011). For the rest of our evaluation we use the

Bootstrap method recommended by the authors Preacher

and Hayes 2008 to test the mediation, this non-parametric

statistical procedure repeatedly samples all the data to

calculate the indirect effects, in the same direction a

moderation test between latent variables is used through the

procedures of (Aiken and West 1991; Dawson 2013;

Dawson and Richter 2006) to interpret and plot the effects

of bidirectional interactions (Henseler and al, 2010).

Towards the end of our structure model, a non-observed

heterogeneity analysis of the data structure remains

essential to reinforce the cause-prediction of our results and

to avoid the risk that they are not significant on the

aggregated data (Becker, Rai, Ringle, & Völckner, 2013,

Gudergan Ratzmann, & Bouncken, 2016).

Tables 9, 10, 11 and 12 show the values for the path

coefficient, total effect, indirect special effect of the

assumptions of our model; the coefficient of determination

R2; cross redundancy Q2; effect size f2 and of course the

GoF fit quality criterion. For the coefficients the literature

suggests that they must be significant with t and p value less

than (p˂0.05-0.1), to evaluate the ability of the model to

explain and predict the latent variables as the coefficient of

determination, the effect size f2 and the cross-redundancy

Q2 are calculated by successively displaying values

between 0.19 and 0.67 for the R2 thus of 0.02 and 0.35 for

the f2 and greater than zero for the Q2 (Chin, 2010; Cohen

1988; Rigdon, 2012). Table 10 : Path Coefficient of Research Hypotheses.

Hypo Relationship Standar

d Beta

Standar

d Error

T-value P-value Decision

H01 Risque Approvionnemnet-

Performance

-1,592 1,925 0,827 0,040* Accepted

H02 Risk Demande-Performance -1,082 1,122 0,965 0,034* Accepted

H03 Risque Infrastructure-

Performance

-0,766 0,994 0,771 0,043* Accepted

H04 Risque Transport-Performance 0,074 0,043 0,899 0,049* Accepted

Hb01 Risque Approvisionnement-

Perturbation

0,482 0,058 8,365 0,000** Accepted

Hb02 Risque Demande-Perturbation 0,361 0,062 5,823 0,013* Accepted

Hb03 Risque Infrastructure-

Perturbation

0,252 0,042 5,944 0,000** Accepted

Hb04 Risque Transport-Perturbation 0,075 0,047 1,600 0,108 Rejected

Hb05 Perturbation-Performance -0,591 0,504 1,179 0,000** Accepted

H05 Performance-Vulnérabilité -0,628 0,572 1,098 0,049* Accepted

H06 Performance-Resilience 0,288 0,289 0,996 0,045* Accepted

H07 Performance-Durabilité 0,835 0,456 1,831 0,032* Accepted

H08 Perturbation-Vulnérabilité -0,046 0,192 0,233 0,460 Rejected

H09 Perturbation-Résilience -0,349 0,134 2,604 0,050* Accepted

H10 Perturbation-Durabilité -0,071 0,168 0,154 0,677 Rejected

H11 Resilience-Durabilité 0,318 0,150 2,120 0,028* Accepted

H12 Vulnérabilité-Résilience -0,531 0,116 4,593 0,045* Accepted

H13 Vulnérabilité-Durabilité 0,242 0,178 0,429 0,368 Rejected

Hb07a Prfm. Résilience-Durabilité 0,131 0,135 0,978 0,038* Accepted

Hb07b Prfm. Vulnérabilité-Durabilité -0,213 0,108 1,971 0,049* Accepted

Hb10a Prtb. Résilience-Durabilité 0,080 0,159 0,505 0,614 Rejected

Table 09 : Measurement Properties of Formative Constructs.

Construct Indicator Item weight T-Stat VIF

Supply Chain Performance

Perform-1 0,251 1,702 3,180

Perform-3 0,165 1,176 2,567

Perform-5 0,148 1,054 1,878

Perform-6 0,667 2,490 3,255

Perform-7 0,195 1,423 3,061

Perform-8 0,164 1,102 2,468


Hb10b Prtb. Vulnérabilité-Durabilité 0,120 0,160 0,752 0,453 Rejected

*Significate at p˂0,05 (5%). **Significate at p˂0,01 (1%).

For the criterion of the quality of adjustment of the overall

model (see table below) display values according to the

literature, between 0.10 and 0.36 ie. that if the values of the

GoF are less than 0.1 the model is rejected; between 0.1

and 0.25 the degree of quality is low; between 0.25 and

0.36 the quality of the model is average; when the criterion

exceeds 0.36 (Wetgels, Odekerker-Schroder and Van

Oppen 2009, Latan and Ghozali, 2012). (See table.12)

Our last step is to evaluate the mediation and moderation of

latent variables to validate the possible causal links between

latent variables (Performance, Vulnerability, and

Resilience) and sustainability (Sarstedt and Ringle 2010;

Henseler et al 2010; Rigdon, Ringle 2012). Then we

proceed to the calculation of the Bootstrapped confidence

interval (Lower and Upper level) to evaluate the indirect

links of the endogenous variables through mediation, in this

sense the values of BCI (95% LL and 95% UL) are

accepted if the zero their separates. According to Table 14,

all our mediations fail to fulfill the preconditions for

exploiting in advance the indirect causal links between our

endogenous variables in the presence of the explanatory

variables. Table 14 : Mediation - Bootstrap the indirect effect (Total effect).

N° Mediation

Indirect Relationship IV-Mediation

Mediation-DV

Indirect Effect

Standard Deviation

T-value

Bootstrapped confidence interval (Lower and Upper

level)

M01 Performance-Durabilité 0,288 0,318 0,092 0,157 0,586 0,399304 -0,216136

Performance-Durabilité -0,628 0,242 -0,152 0,157 -0,968 0,155744 -0,459696

M02 Performance-Résilience -0,628 -0,531 0,333 0,517 0,644 1,346788 -0,679852

M03 Perturbation-Durabilité -0,071 -0,349 0,025 0,766 0,033 1,526139 -1,476581

M04 Perturbation-Résilience -0,591 0,288 -0,170 0,533 -0,320 0,874472 -1,214888

M05 Perturbation-Vulnérabilité -0,591 -0,628 0,371 0,591 0,623 1,529508 -0,787212

M06 Vulnérabilité-Durabilité -0,528 0,318 -0,168 0,903 -0,168 1,601976 -1,937784

To complete our complementary analysis that began with

mediation, an analysis by moderation is recommended to

reinforce or weaken the explanatory power of the

independent variables over the dependent ones. Through

this analysis we seek to explain the cause of the

relationships between latent variables risk, vulnerability,

resilience and sustainability to validate the purpose of our

research namely the causal relationship between the

variables of risk management (Risk, vulnerability;

resilience) on one side and sustainability in the supply

chain.

As shown in Table 15 or the indirect performance-

durability relationship (MdR2) displays a negative beta

standard with an acceptable level of significance p ˂ 0.05

explained by the effect of resilient practices on improving

performance, translated by a sensitive sustainability in the

supply chain even in the presence of a vulnerable context,

however the relationship disturbance-sustainability displays

a negative beta standard with a level of significance p ˂

0.01 which allows to lower the effect of disturbances this is

translated by the ability of sustainable practices to absorb

the effect of disruptive risks in a turbulent environment. So

to interpret and plot their interactions effects of our two

relationship performance and disruption on sustainability by

moderating the resilience and vulnerability that is used in

the test (Aiken and West, 1991; Dawson, 2013; Dawson

and Richter, 2006).

Then on the basis of the test of moderation we can adopt the

following reflections which come to enrich our study through the

effects of interaction between our endogenous variables from two

different explanatory angles. As a result, we sought to explain

both the effect of performance and disruption on the sustainability

of the supply chain in the presence of a vulnerable environment

while adapting resilient practices. So on the basis of the four

figures above we try to further explore the ability of resilient

practices to strengthen the sustainability of logistics operations

which is likely to make logistic chains able to adjust their

resilience levels, durability and performance in the presence of a

more vulnerable and turbulent context at acceptable and stable

thresholds conform to their fixed objectives.

Table 11 : Path Coefficient of Research Hypotheses.

Dec Relationship Standard Beta

Standard Error

T-value P-value Decision

H14 Risque Approv-Prfm-Vul-Rsl-Sus -0.158 0.344 0,459 0.081*** Accepted

H15 Risque Demand-Prfm-Vul-Rsl-Sus -0.145 0,262 0,553 0.088*** Accepted

H16 Risque Infstruc-Prfm-Vul-Rsl-Sus -0.140 0,295 0,474 0,096*** Accepted

H17 Risque Transpt-Prfm-Vul-Rsl-Sus +0,005 0,156 0,032 0,460 Rejected

H14a Risque Approv-Prfm-Sus -0,330 0,500 0,660 0,050* Accepted

H15a Risque Demand-Prfm-Sus -0,199 0,288 0,691 0,056*** Accepted

H16a Risque Infrastructure-Prfm-Sus -0,173 0,226 0,764 0,061*** Accepted

H17a RisqueTranspt-Prfm-Sus -0,113 0,178 0,635 0,067*** Accepted

H14b Risque Approv-Prfm-Rsl-Sus -0,054 0,138 0,631 0,062*** Accepted

H15b Risque Demand-Prfm-Rsl-Sus -0,045 0,122 0,591 0,071*** Accepted

H16b Risque Infrastructure-Prfm-Rsl-Sus

-0,036 0,116 0,437 0,083*** Accepted

H17b RisqueTranspt-Prfm-Rsl-Sus +0,002 0,006 0,333 0,645 Rejected

*Significate at p˂0,05 (5%). ***Significate at p˂0,1 (10%).

Tableau 15 : Moderation N°

Mediat Indirect Relationship Std

Beta Stad

Deviation T-

statist P - values

MdR1 Prfm. Résilience-Durabilité 0,150 0,738 0,203 0,631

MdR2 Prfm. Vulnérabilité-Durabilité

-0,246 0,454 0,542 0,025*

MdR3 Prtb. Résilience-Durabilité -0,021 0,957 0,022 0,980

MdR4 Prtb. Vulnérabilité-Durabilité

-0,127 0,547 0,232 0,087**

*Significate at p˂0,05 (5%). ***Significate at p˂0,1 (10%).

Table 11: R-square, Communality, and

Redundancy.

Construct R2 AVE Q2 f2

Sustainable 0,667 0,731 0,425 **

Performance 0,284 0,590 0,154 0,546

Perturbation ** 0,501 0,365 0,030

Resilience 0,632 0,601 0,370 0,231

Vulnérability 0,372 0,568 0,245 0,120

Table 12: Goodness of fit (GoF).

Construct R-square ajusté AVE GoF

Durabilité 0,593 0,731 0,658

Performance 0,192 0,590 0,337

Perturbation ** 0,501 **

Résilience 0,605 0,601 0.603

Vulnérabilité 0,344 0,568 0.442

Table 13 : Predictive relevance Q-square.

Construct SSO SSE 1-SSE/SSO

Durabilité 225 129,383 0,425

Performance 360 304,389 0,314

Perturbation 765 492,862 0,365

Resilience 630 535,484 0,370

Vulnérabilité 450 395,817 0,245


In the first two figures we can deduce that the sustainability

of the supply chain and the levels of performance in the

supply chain become more stable and mutually increase in

the presence of resilient capacities. This stability, which is

due to sustainability and resilience, allows logistic chain

partners to safeguard the continuity of logistical operations

even in vulnerable contexts. This conclusion is

complemented by the last two figures, where resilient and

sustainable practices make it possible to remain more

reactive to disruptions, which is necessary to control the

vulnerability of supply chains. Indeed, this long empirical

analysis validated the relationship between the concepts of

risk management and sustainability in the supply chain, but

remains incomplete or even misleading in the presence of

unobserved heterogeneities in our analysis sample. For this

fact an unobserved heterogeneity analysis or PLS-FIMIX

remains necessary in order to confirm or reject the results.

5.3 Analysis of Unobserved Heterogeneity PLS-FIMIX

Then, to illustrate the validity of the results obtained

through the analysis of this conceptual model, which aims

to explain both the effects of the concepts of risk

management related to the supply chain (Resilience,

vulnerability and risk) and the concept of performance over

the sustainability of the supply chain. This is concretized by

the 82 measurement items reflecting our main latent

variables of formative and reflective nature. In this sense,

PLS-SEM analyzes in our case a complete set of data

derived from a single population that may not be

homogeneous (Jedidi and al., 1997). Otherwise, the

hypothesis relating to the homogenization of data

characteristics is often unrealistic because of the difference

that exists, for example, in the chosen control variables,

namely: the behavior (the idiology, the culture and the

perception of the individuals to the risks); the organization

(size, experience and corporate structure), and the pooling

of observations may produce misleading results (Sarstedt

and al, 2010). Given this observation, an unobserved

heterogeneity analysis using the PLS-FIMIX test remains

crucial to reinforce the validity of our SEM-PLS results

(Becker and al, 2013; Sarstedt and al, 2016).

Calling also the latent class approach or PLS-FIMIX is

based on the concept of finite mixing models, whose

population is considered to be a set of group specific

density functions (Hahn and al, 2002; Ringle and al, 2015)

by estimating in a regression framework for each group the

parameters (eg the path coefficient, the indirect effect, the

adequacy index and the coefficient of determination)

relating to their endogenous latent variables for this

purpose, this test follows four successive steps:

First Step: Execute the Procedure of PLS-FIMIX

We followed the recommendations of Sarstedt authors;

Matthews and Ringle 2015 starting by setting the execution

parameters namely the stopping criterion (1-10 1.0E-10-

10!), The maximum number of iterations (5000) and the

number of repetitions (10) at this level the problem of

missing values is not posed as inadequate or incomplete

observations were discarded upstream of our statistical

analysis (Kessel and al, 2010; Sarstedt and Mooi, 2014).

After the convergence but just before analyzing the results

we have to re-execute the PLS-FIMIX up to ten times for

the segment solutions (I-II and III) in order to determine

both the upper limit of the range of segment solutions and

the minimum sample size required which is necessary to

investigate the occurrence of a local optimum (Steinley

2003; Cheveux and al, 2016). In general, the results of

several FIMIX-PLS calculations must be similar. On the

other hand, in the opposite case, we have a local optimum,

which is not the case in our analysis (see Table 16).

Second Step: Determine the Number of Segments:

Once we have made sure that it is not a local optimum, we

have to determine the number of segments from our data, so

we will need to examine the indices of adequacy

summarized in Table 16 below gives an overview of the

information criteria, the entropy statistics and the log

likelihood of values (Sèche and al, 2016). Then the optimal

solution is concretized by the number of segments which

display values as high as close to the information criterion

AIC substituted by the two factors AIC 3 and AIC 4 as well

as that of the information criterion of Akaike CAIC and the

BIC Bayesian information criterion with an entropy

indicator greater than 0.5 (see bold numbers in Table 16)

indicating that there is better separation of the I segment

and therefore the correct number can not be greater than

that (Hair and al, 2016).

To complete our reasoning table.17 examines the size of

segments that validates that the selection of more than one

segment can not be valid. For example for a three segment

solution the distribution is broken down as follows: segment

I with (38.4% -39,163 observations); segment II (34.8% -

35,496 observations); segment III with only (27% -27.54

observations), even for a two-segment solution the

distribution remains inadequate with segment I (71.4% -

72,828 observations); segment II (28.6% -29,172

observations). As can be seen from our analysis, segments

two and three with small observations can not justify the

validity of our analysis which justifies the weight of

segment I which is large enough to justify the strategic

intent (Sarstedt and Mooi, 2014).

To conclude our results indicate that there is not a level of

heterogeneity not observed on the data of our study this

allows us to limit our analysis to the overall data set which

justifies the validity of our results without past by the third

and fourth stages of explanation of latent segment structure

and estimation of segment models (Hair and al, 2014; Hair

and al, 2016).

Table 16 : FIMIX-PLS Résultats pour le segment des critères de conservation

(K =Nombre de segments pré-spécifiés 3).

Critère Segment I Segment II Segment III

LnL -195,314 -120,375 -135,530

AIC 242,627 213,001 178,716

AIC 3 244,270 266,001 258,716

AIC 4 247,627 319,001 338,716

BIC 249,601 308,754 323,249

CAIC 249,697 361,754 403,249

HQ 260,139 248,697 232,596

MDL 5 253,493 557,883 770,691

EN 1,000 0,978 0,992


VI. DISCUSSION AND IMPLICATION

Based on the implications of this work, we have attempted

to evaluate the possible causal links between risk

management and sustainability concepts in the supply

chain, with a view to promoting the inherent attributes of

sustainable and resilient practices. Competitive

Competitiveness of supply chains (Golicic and Smith,

2013), translated by their ability to continuously manage

their logistics operations under different conditions and

turbulent business contexts. However, for companies in the

Aeronautical sector, the nature of the micro and macro

disruptive events does not in itself dictate the nature, level

and severity of the propagations in their supply chains, this

observation is explained by the limit of the classical

approaches based on the static risk assessment by

probability and severity parameters, while the dynamic and

interconnected nature of disruptive events requires in

addition to other factors that Blackhurst and al, 2017 have

grouped into the structure, design of supply chains and

human reflection as three cornerstones for any effective and

efficient decision-making process. And any model and risk

management plan that ignores the interactions of these three

factors can be devastating upstream as well as downstream

(Ivanov, 2017; Blackhurst and al, 2017).

A recent example of the central dairy company in Morocco

following a boycott company on the rise in prices has

suffered significant losses, so the intervention of a

disruptive event downstream has spread throughout its

supply chain with a complete shutdown of a production unit

following the drastic drop in turnover and indirectly on

upstream direct suppliers. This proves once again that the

causes with propagating effects are difficult to anticipate

their transmissions and their destinations, because of the

bad answer which is due to the lack of trust between the

partners of the same supply chain (Christopher and Lee,

2004). A second finding is the ability of supply chains to

continuously adjust these resources through resilient

practices to guide and control the nature of disturbances or

what is called by the author (Ambulkar and al, 2015), as the

synergistic effect of risk management on the resilience of

the business. These two aspects of risk disruption in a

vulnerable environment and resilient practices can be

complemented by the issue of sustainability in the supply

chain through the ability of resilient and sustainable

practices to ensure a more sustainable and balanced

resilient supply chain design (Pettit and al, 2015). This

viability and balance are due to the will of the actors to

master the design of their supply chains to include two

complementary compromises which are most often the

effectiveness-resistance and the vulnerability-resilience for

practices of risk management more common than

integrators necessary for a fair distribution of the risk-return

ratio between the different partners of a supply chain.

Indeed this quantitative research is not without limit as the

results depend strongly on the reflection, reasoning,

interpretation of the researchers (Clark and al, 2010) but

also on the nature of the non-exhaustive information and the

specific context of the field investigation, which can be

brought closer by factors beyond our reach. Thus the

exploratory nature of our scope of study and the emphasis

on the aeronautics sector do not allow us to generalize our

results, which still require confirmation efforts tested in

other sectors (eg Hospital Sector). However and from a

point of view of these limitations, this research provides a

level of interest in theoretical underpinnings and empirical

results.

VII. CONCLUSION

This paper aims to assess the causal relationship between

the concepts of risk management, performance and

sustainability in the supply chain with particular support for

the complementarity of resilience, sustainability and

performance in the supply chain. In this research, structural

equation modeling and partial least squares analysis were

designed to assess the causal links between the concepts of

risk, vulnerability, resilience, sustainability, and

performance. Seventeen hypotheses were tested as part of

our conceptual model and the results justify that the supply,

demand and infrastructure risks, resilience and performance

have a significant impact on the sustainability of the supply

chain. With an interesting aspect observe relating to the

mutual relationship between disruptive events, vulnerability

and resilience on the one hand and the relationship between

performance, resilience and sustainability in the supply

chain at the same time the indirect relationship between

micro risks and the sustainability of the supply chain. We

join the author Ivanov on the research potential of this

future subject that requires a special intention in continuing

to evaluate the interface of resilience and sustainability on

other more potential areas. Which will take us to a new line

of thinking about management practices with a compromise

between sustainability, resiliency and efficiency such as the

Shopfloor and the ability to evaluate it using conventional

approaches the time-driven activity costing using real data

on the costs of hospitals in Morocco.

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