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Drivers of Supply Chain Transparency and its effects on Performance
Measures in the Automotive Industry: Case of a Developing Country
Waqar Ahmed1
Department of Management Sciences,
IQRA University, Karachi-75300, Pakistan
Email: waqar120@gmail.com
Tel: +92-300-241-3913
(Corresponding Author)
Mohammad Omar2
Department of Management Sciences,
IQRA University, Karachi-75300, Pakistan
Email: umr13489@gmail.com
1Mr. Waqar Ahmed is a Lecturer in Department of Management Sciences at Iqra University. He holds B.E
degree in Industrial & Manufacturing Engineering from NED UET and MBA/MS (Management Sciences)
from University of Karachi. He is pursuing his PhD from Iqra University. He teaches supply chain
management, strategic sourcing, distribution and channel management, business process reengineering,
research thesis etc. His interest in research lies in the areas of supply chain & logistics, industrial
engineering and management, operations management and optimization.
2Mr. Omar is a research candidate completed his MBA/M.Phil in the field of Management Sciences from Iqra
University. His interest is to work on Logistics Management. His interest in research lies in the areas of
distribution management, ERP & e-commerce.
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Drivers of supply chain transparency and its effects on performance measures
in the automotive industry: Case of a developing country
Abstract
In this competitive era, smooth flow of Information with clarity and consistency has gain lot of
importance to improve effective and timely decision making for managing logistics and
distribution. This research investigates the factors which are important to augment supply chain
transparency. The study further examined the impact of supply chain transparency on the various
supply chain performance indicators.
Partial least squares structural equation modeling (PLS-SEM) is applied through SmartPLS3.4.2
for an empirical validation of the proposed research model and to identify the factors contributing
supply chain transparency (SCT) and outcomes of SCT on four performance indicators (i.e.
operations, supplier, relational and technical performance). The questionnaire survey was utilized
in this study. The survey was distributed to around 440 supply chain professionals associated with
automotive companies in Pakistan, with 218 valid responses collected with response rate of 49.5
per cent.
The result reveals that supply chain transparency has a significant positive effect on various
dimensions of supply chain performance (SCP). SCT influences high on Operational performance
and relational performance. It is further confirmed that strategies and factors like disintermediation
(DI), formulization (FRM), supply chain coordination (SCC), supplier integration (SI) and trust
(TR) contributes significantly on supply chain transparency (SCT). While FRM and SCC are the
highest contributor in maintaining SCT.
This paper is the enhanced framework to measure the drivers and practices that impacts supply
chain transparency and also explains the relationship of SCT with the various performance
dimensions in the automotive sector of developing economy. The study will be beneficial for
supply chain specialists in formulating strategies to improve distribution performance through
improving transparency in their multitier supply chains.
Keywords Supply Chain Transparency, Supply chain visibility, Disintermediation, System
Integration, Trust, Formalization, Relationship, Operational, Technical and Performance, PLS
SEM, Automotive sector
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Drivers of supply chain transparency and its effects on performance measures
in the automotive industry: Case of a developing country
1. Introduction
Increasing requirements for transportation have led to the rapid worldwide expansion of
the automotive sector (Anderson et al., 2009, Gan. 2003). Managing cost and quality in the phase
of mass customization, inventory minimization and global competition have posed major
challenges in maintaining effective supply chain information flow (Michalos et al., 2010). These
complexities are further exacerbated by the huge network of automotive vendors and the large
amount of parts involved in the manufacturing of automobiles (Imran et al., 2015). The
manufacturing sector should, therefore, be holistically responsive and flexible enough to deal with
dynamic customer demands by prioritizing the right strategic actions (Wiengarten et al. 2010).
Collaboration is a key enabler of supply chain operations among OEMs and vendors (Fadly
Habidin and Yusof. 2013), while incorporating Just-in-Time (JIT) and total Quality Management
(TQM) as supply chain practices for enhancing production (Dinsdale and Bennett. 2015,
Vanichchinchai. 2014). The success of logistics applications such as JIT rely on effective
collaboration among supply chain partners (Saad and Patel. 2006) as well as process
standardization for maintaining operational visibility, performance benchmarking and comparison
(Dingwerth and Eichinger. 2010). Collaboration among all supply chain tiers should be effective
enough to enable the identification of discrepancies so that timely corrective action can be taken
to remove or minimize operational and distributional inefficiencies.
Some Pakistani automotive OEMs and vendors have been able to keep pace with current standards
while others are struggling to adopt and/or follow current standards leaving some companies
uncertified or with expired certifications (Ahmed et al., 2013).
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In the current state of dynamics in which the automotive industry is operating, it is vital that
information flow stays timely and reliable throughout the distributional network. Automobile
intermediaries are hesitant to implement new technologies for operational advancement
considering it a monetary burden and is time consuming effort (Perween et al., 2013). Meeting
standards at the international level and improving competitive edge are requirements for industries
in emerging economies in order to make their products internationally marketable. The vendors
and distributors integrated with various Japanese automobile manufacturers operating in Pakistan
fall subject to continuous auditing and improvement programs (Khan and Nicholson. 2014) as
well as international joint ventures in order to aid in their technological development (Khan and
Nicholson 2015).
The appropriate speed of information distribution helps maintain an innovative standing in
the industry (Irland 2007). Some intermediaries lack the skill set necessary to contribute
productively in the supply chain (Niziolek et al., 2012). International standards have significantly
multiplied in the past decade, to cater the requirements of mass customization (Llach. 2011), which
are not fully or are variably adhered to by the local automobile vendors (Ahmed et al., 2013).
Clear and reliable information sharing is vital for performance improvement (Nyaga et al.,
2010) which is questionable considering the collaborative asymmetries which are proving to be
problematic for multinationals when operating with the developing economy like Pakistan (Khan
and Nicholson. 2014). Most of the organizations both working in developed and under developing
countries are striving to increase supply chain transparency to improve their business operations,
legal, finance or purchasing (Schooner, 2014). The trend of outsourcing and contracting out of
supply chain activities will also require a complete understanding of performance measurement
which implies that improving logistics efficiency and effectiveness requires transparency of the
current processes (Dörnhöfer, M., Schröder, F., & Günthner, W. A. 2016).
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The complete supply chain of an automobile contains hundreds and thousands of diverse
connected organizations, and further each of them can engage with many thousands of
miscellaneous products. Supply chain really includes a very complex configuration of flow of good
and information between the connected partners which makes coordination of information
increasingly complicated (Waters, D., 2011). The objective of the study is to envisage the factors
which contribute towards supply chain transparency and the effect of supply chain transparency
various performance measures. This would help in identifying insightful operational aspects
which indicate the presence of asymmetric information effecting performance.
2. Review of Related Literatures
There are very few past research that focus on information transparency and reliability
among supply chain partners. Various aspects such as cost (Aggarwal and Jorion. 2012) have been
identified and analyzed in past researches which have favorably affected the application of supply
chain transparency.
2.1 Theoretical background and hypotheses
Supply chain transparency, being a partially explained component of supply chain
management (Bastian and Zentes. 2013), has been given foundational support through system
theory developed in the 19th century by Hegel, a theory to explain the principles common to all
complex entities in a dynamic process like any channel distribution. In distribution where multiple
parties are involved in fulfilling the customers need, information flow plays the key role like blood
circulation in any organism.
Supply
chain
performance,
in a demand driven
economy
like Pakistan,
depends a lot upon reasonably smooth and transparent information flow among all logistics stake
holders if not absolutely fluid and crystal clear. This information flow is somewhat similar to an
oxygen flow through blood in human system.
5
All tiers operating within the supply chain manage operational synchronization with each
other through information sharing which enables them to coordinate task requirements in order to
carry out organizational functions in the most efficient and effective methods (Sarkis et al., 2011).
The information shared should be communicated in formats which are comprehendible to the
supply chain members who are meant to be directed and the medium should be free of distorting
hurdles, noise generating nuances, delaying operators etc. so that no information is lost during
transition (Beulens et al., 2005). Slight information imbalances, such as forecasted operational
sequences not falling in line with actual operational requirements, create functional disorders in
terms of distrust among supply chain members (Han and Dong. 2015). This indicates that trust
falls in line as a construct supporting SCT.
Two aspects significantly facilitate effective application of strategies involving
information sharing which is ‘coordination’ accompanied with the ‘sharing of information’ which
can apply for information sharing done at an inter-organizational level (Hung et al., 2011). Better
quality information shared is preferable over a massive amount of information shared (Wang et
al., 2014). The flow of information is facilitated through the integration and proper application of
resources which are specifically designed to aid information flow. These resources are
incorporated into the collaborative structures of industrial entities in order to achieve and sustain
competitive advantage as suggested by ‘Resource based view’ (Barney.
2012)
. This multi-
functional IT integration can help foster capabilities such as responsiveness in terms of supply
chain information exchange, coordination and integration of activities (Wu et al., 2006). Summing
up the assumptions underlying resource based view; firms in similar and
complementary
industrial
settings should be resourceful enough to have access to varying levels of technologies as compared
to the industry as a whole in which information varies due to the varying sources from which the
information originates (Hunt and Davis. 2012). This implies that resourceful companies may have
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the capabilities of dealing with information complexities which for them is advantageous in
contrast with the industry as a whole.
2.1.1 Disintermediation
Involvement of supply chain members, in order to be supportive for supply chain
performance, should be with integrity in terms of functional relevance to the overall supply chain
objective. Therefore, it is critical that supply chain members be chosen with diligence. (Sarkis et
al., 2011) found that, as suggested by theory, greater number of intermediaries’ results in greater
complexities for all involved supply chain members. Cost advantages can be directly linked with
disintermediation even though transportation costs may increase in the absence of intermediaries;
the cost reductions of the former are greater than the additional costs of the latter (Niziolek et al.,
2012). Greater degree of transparency demands that information disclosure in part of the supplier
to the buyer remains significant (Greer and Purvis. 2016). Therefore, it can be stated that effective
reduction of nonproductive intermediaries through disintermediation can lead to better supply
chain transparency.
H1: Disintermediation has a significant impact on supply chain transparency.
2.1.2 Formalization
Formalization of processes among the intermediaries involved in value chain with the
objective of improving control, in order to fulfill ethical and quality requirements through standard
practices.
Worldwide standardization through certification bodies such as ISO has lead towards
positive performance outcomes and improved supplier-customer comprehension of standardized
management procedures and practices (Llach et al., 2011, Heras-Saizarbitoria and Boiral. 2013)
along with other accepted procedures like Walmart and Yums. Even with the presence of such
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standards, the outcomes have varied with respect to the depth of standard application (Prajogo et
al., 2012). It is evident through past research that preceding standard implementation, standard
development processes and dissemination are undertaken to make implementation effective
(Heras-Saizarbitoria and Boiral. 2012). Transparency can be improved in the supply chain with the
proper implementation of standards in terms of ordering, freight management, storage etc.
(Trienekens et al., 2012) which increases the common understanding of how business is expected
to manage. Various acts, legislation and standards have been developed in the past to improve the
transparency in the supply chain in order to keep the supply chain members from deviating from
their actual operational objectives (Greer and Purvis. 2016) but formulization with a win-win
situation for all the intermediaries is critical to achieve.
H2: Formalization has a significant impact on supply chain transparency.
2.1.3 Third-party integration
Monitoring standards and the enforcement of any irregularities in standard application
through various modes of control are decisions significant for ensuring the supply chain
governance leads towards sustainability. Three common modes used at a basic level are; first party
mode, i.e. monitoring done internally to investigate the processes being carried out by members of
the supply chain, second party mode, i.e. monitoring done by members who part of sectors
associated with the focal supply chain function and lastly the third party mode, i.e. monitoring
done by organizations which are independent of the focal supply chain function (Alvarez. 2010).
Some third party certifications, implemented voluntarily, have higher complexities associated with
their fulfillment as compared to other general certifications usually adhered to and accepted by
organizations as norms and are generally rejected by prevailing certification bodies. Superior
standards, as compared to general standards, are reassured through such third party certifications
making the certification holders more acceptable to their buyers by improving their operational
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visibility (Raynolds et al., 2007). Third party involvement has been used as a measure to disclose
the activities of involved supply chain members and disclosures of irregularities have resulted in
the development of legislation to be imposed on organizations involved in such irregularities and
standardize the transparency of their operations (Greer and Purvis. 2016). Some criteria used for
judging the level of legitimacy, imposed by third parties, to be implemented on organizations
includes disclosure, transparency, inclusivity etc. i.e. a measure used to tackle low levels of
transparency is to increase third party involvement (Mueller et al., 2009). Transparency is viewed
as a favorable aspect by independent organizations for establishing relationships between suppliers
and public organizations (Lamming et al., 2004).
H3: Third Party integration has a significant impact on supply chain transparency.
2.1.4 Supply chain communication
Supply chain complexities, shared among all supply chain members, comprise of
collaborative factors forming the basis for information sharing such as sophisticated
communication. It was argued that supply chain networks are required to adapt quasi meta-
organizational structures due to the increasing awareness in terms of the ethical and environmental
context worldwide. This necessitated organizations to establish coordination with respect to all
stages of production, i.e. from extraction of raw materials to final customer delivery, in terms of
material, functional and financial information (Gold et al., 2010). Companies make investments
when trying to achieve a more sustainable state of their supply chains by shifting away from their
purchasing portfolios. The other motive of this effort is to achieve supplier continuity with the
prevailing market dynamics. The companies involved showed continuing efforts with respect to
this aspect and transitional periods were experienced by these companies as the efforts for
continuity gained complexity (Pagell et al., 2010). Supply chain planning and coordination are
both significant drivers for mass customization capacity; coordination provides corporate entities
9
with greater opportunity to establish functional networks within the industry and drive operational
performance through these networks and using the information provided by supply chain members
for production planning improves value chain transparency (Yinanet al., 2014). Integrated
information flow has proven to be a significant functional aspect leading towards improvements
in overall manufacturing capacities for large scale projects by allowing transparent flow of
information among technical divisions and creating information compatibility
(Cˇusˇ
-Babic et al.,
2014). Transparency is viewed as a means of unraveling new opportunities and enables change
management with effectiveness in collaborative relationships due to its dynamic operational
capacity which
encompasses
effective techniques for information sharing and allows creative input
(Lamming et al., 2004).
H4: Supply Chain Communication has a significant impact on supply chain transparency.
2.1.5 System Integration
System integration has a key role in structuring the flow of information and managing
complexity of information exchanged among supply chain partners. Systems integration in
multitier supply chain helps in information flow asymmetries and its influence in enabling mass
customization, production requirements, business relationships in the long run and ultimately
performance levels (Sarkis et al., 2011). Integration of logistics information in the supply chain
improves performance of all the supply chain member (Prajogoet al., 2016) by improving
mechanism and infrastructure of data dissemination. With the proper application of extended
ERP systems, large scale projects have benefited significantly through transparent flow of
information and thus scheduling accuracy and effective change management (Cˇusˇ-Babic et al.,
2014). Information systems, as a prerequisite for enabling transparent flow of information, have
shown great implications in areas of governance execution, compliance and coordination with
respect to quality standards, collaborative functionality and support the fulfillment of market
10
requirements and visibility among the partners (Trienekens et al., 2012). Systems like distribution
resources planning (DRP), vendor managed inventory (VMI), Collaborative planning forecasting for
replenishment (CPFR) with proper implementation, has resulted in an improved cost effectiveness
accompanied with information sharing and transparency (Wadhwa et al., 2010).
H5: System Integration has a significant impact on supply chain transparency
2.1.6 Trust
Supplier-buyer trust is a key component in a supply chain’s operational domain which
serves as a foundation for the establishment of long-term business commitments and relationships.
Among other important components responsible for assisting firms with managing a supply chain
at corporative level, trust has been identified as a significant antecedent in maintaining effective
operational collaboration (Sharfman et al., 2009). Trust enhances the collaborative practices in
logistics networks (Gold et al., 2010). Enhanced commitment and trust have improved supplier-
customer relationship satisfaction and resulted in favorable supply chain performances in the past
(Nyaga et al., 2010). Collaborative environments subject to fall in complexities regarding sharing
of information which can be tackled with the creation of relationships formed on the basis of trust
which is a valuable strategic tool for effectiveness of operational decisions (Caiet al., 2010).
Various acts, supported by legislation, were formed to enable the identification of supply chain
operators trustworthy enough to let them operate with continuity i.e. they had transparent
information flow present throughout their operations visible to all tiers of the supply chain (Greer
and Purvis. 2016). Trust in reality as information integrity cannot always be guaranteed with
contractual agreement clauses. Prolonged business relationships lead towards the development of
trust among members involved which acts as an indicator of behavior driving opportunistic
intentions supporting governance and ensuring transparency, in terms of information shared
(Trienekens et al., 2012).
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H6: Trust has a significant impact on supply chain transparency.
2.1.7 Relationship Performance
Asymmetric information has shown as a negative driver of long term business relationships
and results in contracts lasting for short time intervals, therefore, enabling transparency in
information flow can result in successful long term relationships (Costello. 2013). A trustworthy
supplier-buyer relationship composed of attributes depicting exchange of information in social
manners such as power alongside reciprocity and commitment is a prerequisite of long-term
strategy formulation for enhancing collaboration (Wu et al., 2014). Alliances comprising of
transparency among buyers and suppliers in the long run are successful due to greater visibility for
responsiveness (Ellram and Krause. 2014). Collaborative efforts resulting in information flow
transparency lead towards successful collaborative execution due to which future collaborations
are positively affected (Ramanathan and Gunasekaran. 2014). Thus, quality relationships are the
product of transparency enabling characteristics inherent in organizations and efforts employed by
organizations to enhance transparency.
H7: Supply Chain Transparency has a significant impact on Relationship Performance.
2.1.8 Operational Performance
Reliable information systems have positive effects on operational performance due to their
information transparency enabling capacities (Green Jr et al., 2012). During transfer of operational
knowledge, from a theoretical point of view, the prevailing knowledge structures present in the
organizations are taken into account which facilitates in the creation of value through the process
of knowledge transfer (Modi and Mabert. 2007). Operational performance, to a certain degree, is
favorably affected by positive transparency in the supply chain (Bastian and Zentes. 2013).
Industries with significant information flow related operations rely on timely information diffusion
and collection in order to stay in line with industrial competitive pressures. Operational
12
benchmarks, such as supply chain effectiveness, information flow efficiency accompanied with
reasonable cost reductions, have been met by large corporations through refinement of supplier
integration and operations carried out internally (Najmi & Khan, 2017). This implies that
operational firm performance, in industries composed of corporations which heavily rely on
information flow, is favorably affected by proper supplier integration as part of supply chain
management (Ou et al., 2010). Information system reliability, transfer of operational knowledge
and timeliness in information flow through supplier integration, being attributes of a transparent
supply chain network, therefore, favorably affect operational performance.
H8: Supply Chain Transparency has a significant impact on Operational Performance.
2.3.9 Supplier Performance
Supplier performance is improved through value created by activities involving transfer of
reliable information. Currently, companies operating in uncertain business environment with
higher supply disruption risks makes supply process management a very tough job (Rampini, Sufi
& Viswanathan, 2014). This can be overcome by the appropriate availability of precise information
encompassing the performance plan of the supply chain partners. These efforts are, therefore,
focused on creating greater transparency for enhancing supplier performance.
H9: Supply Chain Transparency has a significant impact on Supplier Performance.
2.3.10 Technical Performance
Technical skills, alongside knowledge which is specific to the firm, induced in
organizational personnel are a requisite for effective information system functionality. Systems
designed for carrying out information flow are utilized in intensive knowledge transfer activities
and require technical capacities. With the appropriate levels of information technology
responsiveness in terms of flexibility is enhanced, therefore, enabling rapid provision of solutions
13
for technical issues (Ravichandran et al., 2005) and ensures a better leveraged information system
through technical knowledge. Supply chain members involved in the development stages of new
products benefit from back and forth supply chain technical assessments resulting in favorable
performance of new product designs (Petersen et al., 2005). Transfer of technical information, as
part of logistics integration, is crucial for ensuring that the information transferred contains no loop
holes and the information managed by the supply chain is transparent enough to not compromise
integrity and facilitate performance (Prajogo and Olhager. 2012).
H10: Supply Chain Transparency has a significant impact on Technical Performance.
< Insert Fig 1 Here
>
3.0 Methodology
3.1 Instrument and Measures
The survey instrument used in this research has been adopted and adapted from various past
studies supporting the measurement requirements for this study. There were two sections in the
measuring instrument; first section measures the latent variables and second measures the
demographics of the respondents. The variables disintermediation, Formalization, Third party
integration, supply chain communication, Supply chain transparency, Relationship performance
and Operational performance have been adapted from Bastian and Zentes (2013); System integration
from Prajogo and JanOlhager (2012); Trust and supplier performance from Humphreys et al., (2011)
and Technical performance from Inemek (2009). The instrument comprises of 51 items measuring
managerial perceptions on a five point likert scale for each item having statement to measure
against (1 = Strongly Disagree, 2= Disagree, 3= Neutral, 4= Agree, 5= Strongly Agree).
Survey instruments, in order to be effective for research have to be evaluated by research
scholars and experts with relevant experience to the subject of research (Rai et al., 2006).
14
3.2 Sample and Data Collection
Supply chain professionals are selected related to automotive manufacturing and
distribution as valid respondents to generate the desired results, without misrepresentation, while
staying in line with the research objective (Kannan and Tan. 2005, Sahay and Mohan. 2003). The
target population was through self-administered survey questionnaire. Around 440 survey
questionnaire were distributed out of them 231 were returned leading to the response rate of 52.5%.
From the collected sample, 13 questionnaires were rejected because of incomplete responses thus
leading to a final sample of 218 which includes supply chain officials associated with automotive
industry. Sample sizes greater than 200 are considered a comfort zone in the context of past
research (Iacobucci. 2010). The sampling technique used for this research is convenience sampling
as new dimensions are being explored and the focus was on a particular industry where
respondents are easily accessible through online as well as face to face survey (Zhu et al., 2012).
Demographic profiling of the respondents was done under 3 categories which were broad
in terms of the characteristics analyzed through them (Table 2).
< Insert Table 1 Here
>
3.3 Common Method Variance (CMV)
There is a possibility of common variance bias (CVB) in analysis when all the constructs whether
dependent or independent were gauged through the common survey instrument (Podsakoff,
MacKenzie, Lee, and Podsakoff, 2003; Najmi et al., 2017). CVB is a major issue when a single
factor appears from the factor analysis which accounts for most covariance among the measures
(Podsakoff et al., 2003). Couple of statistical methods were deployed to check the level of CVB.
Primarily, CMV is examined by using Harman’s (1967) single factor approach using SPSS. Factor
15
analysis without rotation and keeping Eigen’s value greater than 1 was executed and the result
shows that 9 factor emerged which explains 65.285% of the variance. The first factor explained
only 28.673% of variance showing CMV is not a problem. Secondly, if inter-construct correlations
is higher than 0.9 also indicates the presence of method bias (Ali, Kim and Ryu, 2016). In this
study the highest value of inter-construct correlation is found to be 0.568 as shown in table 5. This
concludes that method bias is a major concern in this study.
4. STATISTICAL ANALYSIS AND RESULTS
The key objective of this research was to examine the hypotheses of proposed model that
is to check the impact of disintermediation (DI), formulization (FRM), supply chain coordination
(SCC), supplier integration (SI) and trust (TR) on supply chain transparency (SCT). Moreover, to
find the impact of SCT on operational performance (OP), relational performance (RP), supplier
performance (SP) and technical performance (TP).
Statistical analysis is a core part of quantitative research and is carried out to validate the
collected data in terms of data composition, reliability, instrument authenticity, underlying variable
relationships, model fit etc. (Hair, jr et al., 2010, Leech et al., 2005). The raw form of the data was
screened and pilot tested using SPSS followed by Partial Least Square regression and
bootstrapping using Smart PLS 3.2.4 to assess model fit, validity and variable relationships.
4.1 The Measurement of Outer Model
Using the software SmartPLS 3.2.4 the validity and reliability of outer model was
established, before examination of the proposed hypotheses (Ringle et al., 2015), the inner model.
Details of analysis are described in the following sections. Three criteria were put forward to test
the validity and reliability of outer model that includes content validity followed by convergent
validity and discriminant validity
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4.1.1 Content Validity
Content validity is examined through cross loadings and is a depiction of Confirmatory
Factor Analysis (CFA). For content validity, strong factor loadings of items within a constructs is
required over all the other constructs in the model (Chin, 1998, Hair et al., 2013). Therefore, those
items which are violating this criterion were removed from the constructs to improve outer model
validity. In addition to that, most of the factor loadings within a representative construct are more
than 0.7 which reflects the property of measuring the related concept. Table 2 and 3 confirms the
content validity of the measurement model where all items were significantly and strongly loaded
on their respective constructs.
< Insert Table 2 Here
>
< Insert Table 3 Here
>
4.1.2 Convergent validity
The validity levels achieved due to the collective convergence of a group of items towards
a conceptual measurement is called convergent validity (Hair et al., 2013). Three measures
collectively contribute towards identifying convergent validity levels; First, statistically significant
and strong factor loadings greater than 0.7. Second, average variance extracted (AVE) greater than
0.5 for every construct in the analysis is considered acceptable (Fornell and Larcker. 1981). Third,
composite reliability measured for each construct should be at 0.7 or above. All of the assumptions
have been satisfactorily met (Table 4).
< Insert Table 4 Here
>
4.1.3 Discriminant Validity
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The collective capacity of a set of items forming a construct to distinguish them from all
other constructs in a model is called discriminant validity (Mehmood & Najmi, 2017). In this study
discriminant validity was evaluated through three methods; First, difference between loadings of
all items in their construct and cross loading on all other non-relevant constructs should be greater
than 0.1 (Gefan and straub. 2005)
Second, the criterion was tested against the approach suggested by Fornell and Lacker (1981)
which says that the correlation matrix as shown in Table 5 having elements in a diagonal line
representing the square roots of AVE should be greater than the absolute value of their correlation
with the constructs in rows and columns. This confirms the discriminant validity of the
measurement model. Third measure used is heterotrait-monotrait ratio of correlations (HTMT).
Any value in the matrix is greater than 0.85 is considered unacceptable and represents non-
conformity with discriminant validity (Henseler et al., 2015). In this study Table 5 and 6 validates
all the above mention criteria.
< Insert Table 5 Here
>
< Insert Table 6 Here
>
On a side note, third party integration was removed from the overall analysis because it was
interfering with model validation and was not meeting two key criterions. Due to the unreliable
nature of Third party integration hypothesis 3 was completely removed from the analysis (Fridin
and Belekopytov. 2014) and is not mentioned in the test result table.
4.2 The structural model and test of hypotheses
The test of hypotheses was followed by validation procedures and was carried out through
the use of PLS structural equation modeling (Ringle et al., 2015). PLS-SEM was chosen due it
18
relatively great potential for evaluating reflective structural models as compared to other statistical
techniques used for SEM (Hair et al., 2011, Henseler et al., 2015) and supersedes other estimates
based on approaches involving covariance (Hair et al., 2011, Hair et al., 2012). Analysis of this
research after executing a bootstrap of up to 500 samples using SmartPLS is reported in Figures 2
and 3.
< Insert Fig 2 Here
>
< Insert Fig 3 Here
>
4.2.1 Predictive relevance of the model
R-Square is used to measure the collective capacity in terms of variance explanation of the
desired model (Hair et al., 2011). Values near 0.26 are considered strong while values near 0.13 and
0.02 are considered mild and weak respectively (Cohen. 1988). Predictive relevance of a statistical
model is measured by a
quantity
known as Q-Squared, predictive power can be identified by values
greater than 0 and are high if the difference between R-Squared and Q-Squared is low (Hair et al.,
2011,F.HairJret al., 2014). From the values of the results generated it can be stated that predictive
power of the exogenous constructs (i.e. Predictors of Supply chain transparency) is well above
strong. The predictive power of supply chain transparency for the performance measures is
strong with respect to operational and relationship performances (i.e. greater than the threshold of
0.26) and with respect to supplier and technical performances lies in the middle of the strong and
mild thresholds (Table 7). The predictive relevance for all the constructs being predicted is
reasonable as the Q-Squared values are greater than 0 and more than half the values of R- Squared.
< Insert Table 7 Here
>
Another criterion used to measure the validity of modelis goodness of fit (GoF) in PLS-SEM
(Tenenhaus et al., 2005). Although as per latest guide lines the goodness of fit measure is not
19
suitable for every research using PLS (Hair Jr et al., 2016). The test involves the use of average
communality (AVE) and predictive indicators (R-Squared) to calculated model fit. It is measured
by the formula
𝐺𝑜𝐹 = √𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑅2∗ 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐴𝑉𝐸
The criterion values are 0.1, 0.25 and 0.36 as small, medium and large respectively. The GOF
value for this model is 0.46 which is well above the maximum threshold.
< Insert Table 8 Here
>
The Beta coefficient is a depiction of how much and in what direction, positive or negative,
a dependent construct will shift with a unit’s change in an independent variable with every other
quantitative aspect remaining constant (Hair, jr et al., 2010, Leech et al., 2005). ‘Disintermediation,
Formalization, Supply Chain Communication, System Integration and Trust’ have beta
coefficients of 0.244, 0.307, 0.286, 0.179 and 0.257 respectively with p values less than 0.001.
The effect of Supply chain transparency is 0.568, 0.550, 0.434 and 0.477 on Relationship,
Operational, Supplier and technical performances respectively with p values less than 0.001.
Therefore all the hypotheses have been supported by the analysis (Table 9).
5. Conclusion and Implications
This research examines the indicators which contributes to enhance supply chain
transparency (SCT) and at the same time it has tested the impact of SCT on the various supply
chain performance (SCP) factors narrowed on automotive sector. Transparency is the key supply
chain aspect as it comprises of the levels of information flow among all supply chain partners
involved in an industrial operation (Bastian and Zentes. 2013).
20
In this study, various determinants like Disintermediation, Formalization, Supply Chain
Communication,
System Integration and Trust were extracted as the relevant factors from different
past researches which have evident positive impact in making transactions and information flow
more reliable and clear. These factors were then built in the model in order to understand the
phenomenon better. Results show that all the determinants are significantly impacting the supply
chain transparency with p<0.001. The greatest contributor for transparency is found to be
formalization having a beta coefficient of 0.307 followed by Supply chain communication as the
second best contributor with a beta coefficient of 0.286. Trust and Disintermediation have shown
similar statistical estimates with coefficients of 0.257 and 0.244 respectively indicating that trust
has slightly greater importance than disintermediation as a transparency estimator. System
integration being a significant (p < 0.001) estimator for transparency is the weakest among all five
with a beta coefficient of 0.179.
Results of this research explain that supply chain transparency on performance measures
are all significant and Operational and relationship performances are being affected the most with
significant (p < 0.001) and estimates of 0.568 and 0.550 respectively. Supplier and technical
performances (additional constructs in this study) have been significantly estimated (p < 0.001) by
supply chain transparency levels with beta coefficients of 0.434 and 0.477 respectively.
5.1. Discussion
Statistical output shows; all of the constructs analyzed in the study in order to identify
their contribution towards supply chain transparency have shown positive effects on
transparency with formalization having the greatest. This is suggested to supply chain policy
makers to emphasis more on Formalization which involves standardization or harmonization
or consistency in terms of buyer-supplier operational information, material and monetary
21
flows to achieve better synchronization and transparency. This will also help in examining
supplier operations performance through buyer-supplier continuous monitoring and periodic
audits which results in suggesting distributive improvement programs or partner evaluation.
Thus the finding suggests supply chain leaders in a specific supply chains to formulate effective
strategies adaptable and acceptable to all supply chain partners and make standardized policies and
KPIs. This will help to prevail visibility and transparency among the members of supply chain.
Internationally expanding multi nationals setting up joint ventures in foreign territory operating in
Pakistan have extensively implemented their standardized practices (Khan and Nicholson. 2015),
therefore it can be stated that formalization is the greatest contributor towards supply chain
transparency.
Communication practices is another critical aspect in improving transparency (Gold et al., 2010)
which is confirmed from the results of this study as a second most important parameter to improve
supply chain transparency. More fluent, timely and effective communication at different levels
among distributive partners will improve the visibility on complete supply chain which in turn
helps in better planning and coordination.
Trust and Integration among supply chain members are another strong indicator for
supply chain transparency. Trust is a factor prevalent in all forms of joint business activity and
cannot be denied (Sharfman et al., 2009). System integration has facilitated supply chain
collaboration (Prajogo and Olhager. 2012) implying better communication among supply chain
partners and indirectly enhancing transparency according to this research. This can also be proven
by past research as the automotive vendors of Pakistan lack the appropriate technology to be
comparable to foreign industries (Khan and Nicholson. 2015).
Third party integration was removed from the analysis due to statistical limitations.
Measurement lower reliability implies that internal consistency with respect to the collective
22
response patterns of items in a construct is low (Hair et al., 2010). In advance economies the role
of third parties may extend to as significant as orchestrators but due to the weak supply chain
practices of the automotive industrial setting in Pakistan (Perween et al., 2013, Khan and Nicholson.
2014) the control over intermediaries are somehow uncertain.
All the performance measures are affected by transparency included in the analysis, with
positive effects represented by the respective coefficients. Supply chain transparency has the
greatest effect on Operational performance and relationship performance. The study reveals that
operational performance will improve with improved SCT which means positive affect on cost
and quality effectiveness. Supplier performance and technical performance, have also been
positively
affected by
transparency
. This signifies that performance can be improved through
higher trustworthy, visible, timely and well organized and strategized information flow. Supplier
performance gauged through items measuring design specification and quality requirement
adherence as well as effective delivery scheduling and fulfillment is positively affected by
improved transparency therefore, the mentioned criterions have been positively affected.
Relationship performance in terms of quality and benefit fueled by innovative project initiation
and commencement, stability and satisfaction is positively affected by transparency. Technical
performance, composed of items measuring capacities such as research and development,
engineering, new product development etc. is also showing a positive shift with increased
transparency.
5.2. Implication
This study provides great deal of insights for the supply chain policy makers and mangers
to enhance their information flow capabilities. Just in time, alongside TQM, is an effective
23
operational application in automotive industries and with the effective efforts towards
disintermediation wasteful communication can be avoided while minimizing lead time for
inventory flow. Information distortion can still exist if the productive intermediaries are not
operating with integrity. In its current state the channel of intermediaries may still be not as
effective as per the requirements of multi-national stakeholders and has given rise to efforts made
by multi-nationals to make the supply chain as productive as possible through standardization.
The positive effects of disintermediation identified can be further enhanced by adapting the formal
standards inducted through supplier development programs being carried out by foreign
automotive multi nationals in Pakistan thereby improving transparency.
Moreover, Enhancing the effectiveness of communication practices in the supply chain is
another major objective of automotive multi nationals therefore they have employed the local
automotive vendors to enhance their communication practices. It is vital that local vendors also
make independent efforts in identifying communication barriers prevalent in the current industrial
setting. Such barriers may negatively affect the efforts employed for improving communication
and may result in wasteful outcomes of such endeavors. Local vendors should proactively involve
themselves in facilitating their suppliers by identifying loop holes in their communication channels
and help standardize sub-vendor communication practices as applied by foreign multi-national on
significant local vendors. Advancing the prevalent communication practices to a more
sophisticated level may also call for radical change as sub-vendors are still communicating back
and forth throughout the supply chain using primitive communication practices therefore business
process re-engineering can also be a transparency enabling course of action.
In addition to that, application of proper information technology based communication
systems is a general requirement for the local automotive vendors. The prevalent state of
24
technology may be positively affecting supply chain transparency but not to the extent it should as
compared to industrial IT applications in advance economies. This is another indicator of the
procrastinating
nature of local automotive vendors in accepting new technologies. With the current
advancement in software and internet applications a vast and diverse range of tailor made
communication IT applications are available. Better buyer-supplier integrating communication
systems should be adopted by the local vendors so that better forms of transparency can be
achieved as well as making available systematic supply chain structural outlooks throughout the
supply chain. Primitive forms of communication technology currently used for tracking supplies
and expediting them should be replaced with internet based tracking systems to guide supplies
through better routes considering the adversely dynamic traffic conditions of Pakistan.
Furthermore, Trustworthy supplier retention has proven to be a positive influence on
transparency and can be further enhanced with the removal of operational discrepancies at each
tier level of the supply chain. Supply chain members may have trust among one another but may
be conditional considering the varying resource availability for each supplier; therefore, it is
advisable that suppliers evaluate themselves by analyzing superior supply chain members and
setting up benchmarks followed by efforts to improve operational attributes which would enable
them to serve better as supply chain members. This enhanced contribution in the supply chain may
further develop trust among supply chain members as requirements fulfilled at each tier level will
be greater than before. Greater trust implies greater sharing of knowledge and therefore greater
transparency.
Another important aspect is standardization which is considered as the key driver towards
improving transparency and, as already indicated by past research, plays a vital role in positively
influencing the drivers of transparency. The formal standards introduced by foreign automotive
multi-nationals, specifically Japanese multi-nationals, have reasonably, if not fully, contributed
25
towards improving transparency by improving communication practices, adjusting intermediation,
enabling technological adaptation etc. It is advisable that the vendor industry practices screening
more standards which would enable greater transparency and collaborate with other supply chain
members to identify the most successful collaboration practices circulating in the vendor industry.
This would enable best practice utilization by all members of the supply chain and may also
positively influence trust leading towards better transparency.
This research in Pakistani automotive industrial market suggests supply chain specialist to
continuously emphasis on improving supply chain transparency along multiple tiers of chain to
strengthen their performance indicators. Effective information sharing among the supply chain
partners results in better decision making and performance outcomes (Hung et al., 2011).
5.3. Future research
However, this research was adversely influenced by the limited availability of respondents
partially due to confined nature of the research and partially due to the professional qualification
level of respondents. The results of this research can be contextualized even further by broadening
the scope of industrial sectors analyzed or targeting a larger geographic setting. Transparency
levels analyzed in this research is on the basis of the theoretical implications of information theory,
which defines the role of information asymmetries in supply chain and resource based view, which
suggests the role of resources employed in a business setup in facilitating sustainable competitive
advantage. Further research can be conducted on new dimensions based on other supply chain
theories which may have varying effects on supply chain transparency and other performance
measures. Other dimensions which can be explored may also include for future research such as
supplier evaluation, partnership strategy, Influencers, supply chain status, External pressures,
26
collaborative processes etc. and performance measures such as financial performance, supply chain
performance, customer responsiveness and also risk and uncertainty measures.
27
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35
Figure 1: The Research Framework
Source: Authors’ Construction
36
Figure 2: SEM Output (Factor Loadings and Path Coefficients)
Source: Authors’ Estimation
37
Figure 3: SEM Output (T-Statistics)
Source: Authors’ Estimation
Table 1 Demographic Profiles
Description (Sample Size = 218)
Frequency
Percentage
38
Designation
Top Management (CEO, CFO, CIO, etc)
38
17.4
Middle Management (GM,RM, DM,etc)
111
50.9
Lower Management (Managers, etc)
69
31.7
Company Size
Less than 100
6
2.8
100 to 300
50
22.9
More than 300
162
74.3
Company Type
OEM
45
20.6
Vendor
173
79.4
Source: Authors' estimation
Table 2 - Factor Analysis Results
Constructs
Items
DI
FRM
OP
RP
SCC
SCT
SI
SP
TP
TR
DI
DI1
0.858
0.220
0.364
0.414
0.302
0.434
0.306
0.295
0.326
0.133
DI2
0.880
0.197
0.479
0.455
0.439
0.470
0.359
0.346
0.387
-0.047
FRM
FRM1
0.131
0.826
0.237
0.176
0.219
0.393
0.148
0.136
0.040
0.090
FRM2
0.242
0.843
0.222
0.249
0.138
0.389
0.192
0.170
0.111
0.144
FRM3
0.145
0.725
0.171
0.175
0.104
0.327
0.140
0.116
0.064
0.092
FRM5
0.234
0.803
0.287
0.203
0.161
0.467
0.253
0.213
0.200
0.111
OP
OP3
0.420
0.301
0.861
0.404
0.455
0.484
0.369
0.367
0.253
-0.088
OP4
0.430
0.236
0.862
0.400
0.403
0.510
0.325
0.348
0.316
-0.039
OP5
0.466
0.270
0.876
0.473
0.465
0.531
0.346
0.368
0.423
-0.006
OP6
0.334
0.180
0.816
0.347
0.406
0.398
0.352
0.302
0.321
-0.136
RP
RP1
0.419
0.181
0.441
0.686
0.378
0.440
0.310
0.272
0.243
0.023
RP2
0.399
0.228
0.332
0.755
0.302
0.426
0.255
0.338
0.317
0.077
39
Table 3 - Factor Loadings Significant
RP3
0.461
0.219
0.387
0.738
0.392
0.430
0.251
0.306
0.345
0.132
RP4
0.267
0.148
0.288
0.758
0.415
0.378
0.423
0.269
0.357
0.091
RP5
0.290
0.149
0.317
0.793
0.377
0.360
0.370
0.295
0.343
0.035
SCC
SCC1
0.273
0.191
0.421
0.400
0.775
0.469
0.375
0.257
0.315
0.052
SCC3
0.418
0.171
0.401
0.438
0.836
0.468
0.323
0.345
0.363
0.088
SCC4
0.380
0.073
0.372
0.440
0.827
0.406
0.322
0.390
0.297
0.028
SCC5
0.299
0.189
0.424
0.307
0.753
0.387
0.333
0.340
0.404
0.042
SCT
SCT1
0.496
0.479
0.516
0.473
0.487
0.893
0.422
0.355
0.407
0.298
SCT2
0.413
0.419
0.508
0.467
0.472
0.890
0.445
0.364
0.409
0.308
SCT3
0.523
0.394
0.443
0.527
0.476
0.866
0.462
0.384
0.412
0.326
SCT4
0.242
0.343
0.396
0.318
0.344
0.606
0.238
0.326
0.340
0.178
SI
SI4
0.373
0.203
0.385
0.329
0.345
0.420
0.810
0.281
0.400
0.040
SI5
0.259
0.194
0.261
0.304
0.374
0.332
0.746
0.314
0.385
0.034
SI6
0.265
0.161
0.303
0.375
0.292
0.389
0.807
0.333
0.375
0.107
SP
SP1
0.306
0.111
0.336
0.259
0.314
0.315
0.238
0.689
0.267
0.022
SP2
0.260
0.184
0.287
0.247
0.309
0.253
0.337
0.739
0.278
0.076
SP3
0.283
0.174
0.311
0.381
0.327
0.400
0.324
0.850
0.333
0.143
TP
TP2
0.246
0.101
0.243
0.299
0.237
0.308
0.279
0.225
0.708
0.107
TP3
0.308
-0.005
0.250
0.328
0.323
0.254
0.286
0.357
0.649
0.013
TP4
0.356
0.166
0.349
0.339
0.385
0.456
0.479
0.301
0.847
0.109
TR
TR2
0.042
0.113
-0.066
0.013
0.016
0.270
0.100
0.103
0.053
0.817
TR3
0.066
0.129
-0.045
0.137
0.131
0.338
0.114
0.109
0.169
0.903
TR4
-0.021
0.080
-0.074
0.069
-0.022
0.196
-0.077
0.047
0.009
0.663
Source: Authors' estimation
40
Constructs
Items
Loadings
Standard Error
T Value
P Value
DI
DI1
0.858
0.029
29.619
***
DI2
0.880
0.028
31.952
***
FRM
FRM1
0.826
0.029
28.758
***
FRM2
0.843
0.029
28.887
***
FRM3
0.725
0.054
13.473
***
FRM5
0.803
0.030
26.708
***
OP
OP3
0.861
0.025
33.886
***
OP4
0.862
0.023
36.735
***
OP5
0.876
0.016
54.469
***
OP6
0.816
0.034
24.125
***
RP
RP1
0.686
0.041
16.669
***
RP2
0.755
0.035
21.593
***
RP3
0.738
0.049
15.043
***
RP4
0.758
0.042
18.074
***
RP5
0.793
0.034
23.346
***
SCC
SCC1
0.775
0.031
25.161
***
SCC3
0.836
0.025
32.977
***
SCC4
0.827
0.028
29.146
***
SCC5
0.753
0.039
19.080
***
SCT
SCT1
0.893
0.014
65.282
***
SCT2
0.890
0.017
53.325
***
SCT3
0.866
0.018
47.060
***
41
SCT4
0.606
0.051
11.836
***
SI
SI4
0.810
0.030
26.588
***
SI5
0.746
0.049
15.172
***
SI6
0.807
0.040
20.412
***
SP
SP1
0.689
0.061
11.322
***
SP2
0.739
0.065
11.345
***
SP3
0.850
0.033
25.484
***
TP
TP2
0.708
0.055
12.799
***
TP3
0.649
0.061
10.579
***
TP4
0.847
0.032
26.333
***
TR
TR2
0.817
0.075
10.947
***
TR3
0.903
0.034
26.362
***
TR4
0.663
0.101
6.557
***
Note: *** p<0.001
Source: Authors' Estimation
Table 4 –Convergent Validity
Constructs
Items
Loadings
CR
AVE
DI
DI1
0.858
0.860
0.755
DI2
0.880
FRM
FRM1
0.826
0.877
0.641
FRM2
0.843
42
FRM3
0.725
FRM5
0.803
OP
OP3
0.861
0.915
0.729
OP4
0.862
OP5
0.876
OP6
0.816
RP
RP1
0.686
0.863
0.558
RP2
0.755
RP3
0.738
RP4
0.758
RP5
0.793
SCC
SCC1
0.775
0.875
0.638
SCC3
0.836
SCC4
0.827
SCC5
0.753
SCT
SCT1
0.893
0.891
0.677
SCT2
0.890
SCT3
0.866
SCT4
0.606
SI
SI4
0.810
0.831
0.621
SI5
0.746
SI6
0.807
SP
SP1
0.689
0.805
0.581
43
SP2
0.739
SP3
0.850
TP
TP2
0.708
0.781
0.547
TP3
0.649
TP4
0.847
TR
TR2
0.817
0.841
0.641
TR3
0.903
TR4
0.663
Source: Authors' Estimation
1.
Table 5 - Correlations of Discriminant Validity
Constructs
DI
FRM
OP
RP
SCC
SCT
SI
SP
TP
TR
DI
0.869
FRM
0.239
0.800
OP
0.488
0.292
0.854
RP
0.501
0.252
0.479
0.747
SCC
0.429
0.197
0.507
0.499
0.799
SCT
0.521
0.499
0.568
0.550
0.545
0.823
SI
0.383
0.235
0.406
0.427
0.424
0.486
0.788
SP
0.370
0.203
0.407
0.399
0.414
0.434
0.390
0.762
TP
0.412
0.137
0.386
0.429
0.430
0.477
0.490
0.387
0.739
TR
0.045
0.137
-0.073
0.097
0.068
0.344
0.077
0.112
0.112
0.801
44
Source: Author's Estimation
Table 6 - Heterotrait-Monotrait Ratio (HTMT) Results
Constructs
DI
FRM
OP
RP
SCC
SCT
SI
SP
TP
TR
DI
FRM
0.318
OP
0.624
0.336
RP
0.667
0.307
0.560
SCC
0.575
0.237
0.600
0.615
SCT
0.684
0.602
0.663
0.664
0.659
SI
0.551
0.306
0.515
0.576
0.568
0.626
SP
0.562
0.277
0.543
0.538
0.581
0.585
0.592
TP
0.641
0.182
0.519
0.628
0.613
0.653
0.727
0.635
TR
0.153
0.182
0.109
0.138
0.104
0.429
0.172
0.156
0.204
Source: Authors' estimation
Table 7 - Predictive Power of the construct
R Square
Q²
OP
0.322
0.228
45
RP
0.302
0.160
SCT
0.612
0.403
SP
0.188
0.100
TP
0.228
0.116
Source: Authors' Estimate
Table 8 - Hypothesis Test Results
No
Hypothesis
Estimate
S.E.
T-Values
Decision
1
DI -> SCT
0.244***
0.053
4.655
Supported
2
FRM -> SCT
0.307***
0.046
6.637
Supported
4
SCC -> SCT
0.286***
0.055
5.226
Supported
5
SI -> SCT
0.179***
0.047
3.816
Supported
6
TR -> SCT
0.257***
0.060
4.323
Supported
7
SCT -> OP
0.568***
0.046
12.233
Supported
8
SCT -> RP
0.550***
0.049
11.228
Supported
9
SCT -> SP
0.434***
0.053
8.264
Supported
10
SCT -> TP
0.477***
0.052
9.158
Supported
Note: ***p<0.001
Source: Author's Estimation
