Published at : 10 Jul 2024
Volume : IJtech
Vol 15, No 4 (2024)
DOI : https://doi.org/10.14716/ijtech.v15i4.5395
Jamal Ahmed Al-Douri | Business Administration, Al-Ahliyya Amman University, Hakem bin Hizam street, Amman, Jordan, 19328 |
Zeyad Alkhazali | Business Administration, Al-Ahliyya Amman University, Hakem bin Hizam street, Amman, Jordan, 19328 |
Rowaida Al Aqrabawi | Business Administration, Al-Ahliyya Amman University, Hakem bin Hizam street, Amman, Jordan, 19328 |
Khaleel Al-Daoud | Business Administration, Al-Ahliyya Amman University, Hakem bin Hizam street, Amman, Jordan, 19328 |
The
purpose of this research is to understand the link between trust, technology
and supply chain collaboration and their impact on firms’ operational
performances. Design/methodology/approach based on extant literature, a
hypothesized model was developed and tested using structural equation modelling
(PLS). A survey was conducted to collect data from the supply chain managers of
fast-moving consumer goods (FMCG) companies in Iraq. The study findings suggest
that trust evolves and is shaped over time through an on-going relationship and
can form a competitive capability that may not be easy for competitors to
replicate. Both trust and technology are found to have a significant impact on
supply chain collaboration and on firms’ operational performances. One of the
major limitations of the study is that the data was obtained from one single
economy, which restricts its generalizability across other economies. The study
was a cross-sectional and descriptive sample of the FMCG industry at a given
point in time. A more stringent test of the relationships between trust,
technology, supply chain collaboration, and operational performance requires an
in-depth case study or longitudinal study.
FMCG; Operation performance; Technology; Trust
Collaboration
is the focused support required to sustain an exchange relationship. Till now,
researchers have explored the subtleties of supply chain collaboration (SCC) (Fawcett et al., 2012), its planes, results,
and performance (Zacharia, Nix, and Lusch, 2009),
buyer-supplier correspondence in terms of creation expansion, the role of
technology (Fawcett et al., 2011) and
application of the model (Fawcett et al.,
2008). Moreover, Al-Doori et al.,
(2021) believe that effective collaboration can help firms to achieve
drastic results by maximizing SCA (Supply Chain Activities). Fawcett et al. (2012) considers trust to be
an important factor in the supply chain collaboration though its efficacy is
still vague. Fawcett et al., (2015) deliberate
devices of (SCC) Supply Chain Collaboration and believes technology and trust
to be the sociological and structural resistor of collaboration. Both resistors
are considered to be interrelated and work together to eradicate difficulties.
Similarly, Ramanathan and Gunasekaran (2014) assume
effective collaboration opens better opportunities for the future. Mutual trust
is an essential prerequisite for collaboration between the two firms. Though
merely trusting the partner to give desired performance can be a risk. Business
collaborations may also be interrupted by external forces, miscommunication, or
internal interests.
Generally, trust is believed to be vital as it ensures supportive behaviour and encourages
adaptive organizational structures Abdulameer, Yaacob,
and Ibrahim (2020), decline destructive conflict, reduce operation cost,
enable instant formulation for specific purpose teams at work (Mohammadi and Mukhtar, 2018), and encourage an
effective solution to catastrophes. Therefore, this study tends to develop and
propose a theoretical agenda that describes the association among trust,
technology, collaboration, and their character in spreading organizational
performance, specifically operations. Numerous empirical studies have been
found to explain the relationship between enablers for collaboration and
positive collaboration and its influence on businesses operative presentation
and this study discovered an association between SCC (Supply Chain
Collaboration) and firm operational presentation (Luo
et al., 2022). Likewise, (Autry, 2013)
came up with the conclusion that there is a positive relation between
collaboration and operative presentation.
Literature Review
2.1. Trust and
Supply Chain Collaboration
After reviewing the literature on the
impact of trust technology on operational performance through mediation of
Supply chain Collaboration Overall, four variables were used in this research.
two of which are independent, one mediator and one dependent i.e. faith,
knowledge, Source cable teamwork, and working presentation individually. That
research was observed in Thailand, and the main objective of this article was
to have a deep understanding of relationship among technology, trust &
operational performances through mediating role of supply chain collaboration (Babkin et al., 2021). The model was
developed and analyzed through survey of 200 people belonging to the supply
chain at decision making position in debauched poignant shopper properties
(FMCG) in the particular origin of Thailand. The investigation discoveries
propose that through an ongoing association, faith advances and is formed after
some period and can shape a serious ability that may not be simple for
contenders to reproduce. Both faith and novelty remain originate to consume
critical effect on SCC coordinated effort and on businesses' working recital (Shahbaz et al., 2019).
2.2. Technology
and Supply Chain Collaboration
Studied the importance of technology in
the supply chain Coloration technology was considered as an independent
variable, whereas supply chain collaboration was a dependent variable. This
study mainly focused on firms’ organizational endeavors to all things
considered contend that supply chains is a key worry of store network the
executive researchers, and specialists. One road, improving shared social
capacities that help store network mix, offers a guarantee. In any case, the
viability of coordinated effort as an inventory network asset has been
addressed because of concerns related to communitarian innovations, and along
these lines, earlier research has required a more profound assessment of the
job that advances play in encouraging mix (Shahbaz et
al., 2022).Using a Service-Dominant Logic perspective that
emphasizes the importance of service in business, and grounded in Resource
Advantage Theory, this study tests a model that examines the relationship
between levels of resources, cooperation, integration, and interfirm
coordination advancements, and their related performance outcomes (Autry et al., 2014). The study explores
how these factors are connected and how they impact business performance. An
example of 282 store network directors from various businesses was studied,
with proposed connections inspected utilizing basic condition displaying. Test
results show that joint effort and incorporation associate to shape higher
request assets that impact firm execution results through interfirm
coordination advances.
2.3. Supply
Chain Collaboration and Operation Performance
Studied the impact of trust technology
on operational performance through mediation of Supply chain Collaboration.
Overall, four variables were used in this research, of which two are
independent, one mediator, and one dependent i.e., faith, knowledge, Source
cable teamwork, and working presentation individually. That research was
observed in Thailand. The main objective of this article was to have a deep
understanding of relationship among technology, trust & operational
performances through the mediating role of supply chain collaboration. The
model was developed and analyzed over SEM (AMOS) survey of 200 people belonging
to the supply chain at decision making position in debauched poignant shopper
properties (FMCG) in the particular origin in Thailand. The investigation
discoveries propose that through an on-going association, faith advances and is
formed after some period and can shape a serious ability that may not be simple
for contenders to reproduce. Both faith and novelty remain originate to consume
critical effect on SCC coordinated effort and on businesses' working recital.
find out the operational performance practices and their adoption of them and
the implementation of those practices on the performance of the supply chain
and also on the performance of the firm. This research is performed in India in
their retail industry. The sample size is 125, which were collected by the
heads only using the Quantitative approach. The purpose of this study was to
determine the relationship between supply chain performance, firm performance,
and supply chain management. They used regression analysis. Results show that
all three have a positive relationship between them, and firm performance has
the maximum weightage in terms of impact in India (Shahbaz
et al., 2019).
Barber et
al. (2017) finds out that SCC (Supply Chain Collaboration) and
operation presentation of manufacturing firms. This schoolwork was conducted in
Jordan firms of Manufacturing. A quantitative approach was used to collect the
data using a questionnaire. We got 249 respondents as a sample of our research.
For the analysis, we used Structural Equation Modelling to check the data
results. Furthermore, the relationship of MFP and the SCP both are positive in
relation to each other. Lastly, SSCM has a positive impact on the performance
of the firm. Researches to find the SSCM on the food firms and to find their
performance regarding ISO 9001 assurance of food safety. Kazmi et al. (2021) conducted a study in
which they collected data from 162 Chinese food firms using a questionnaire and
quantitative techniques. The researchers utilized the Structural Equation
Modeling (SEM) approach on Partial Least Squares (PLS) software to interpret the
collected data. The study results indicate that a friendly environment and
sustainable practices can have a positive impact on the performance of food
firms, supporting the study hypothesis. Moreover, the researchers found a
positive correlation between social and environmental performance of the firms.
Lee (2015) Investigated operational
performances in terms of a Green Supply Chain in the the Environmental
perspective of the Supplier. The research is conducted in South Korea, and the
author investigates the GSCM effect on performances regarding environmental and
operational activities. Moreover, using Quantitative approach and exploratory
factor analysis through using SPSS.
Targeted the supplying industries in Korea, having a sample size of 207
using SEM modeling for the results. The results indicate the win situation in
both operation and the environment of the Korea supply chain industries.
Lastly, GSCM contributes more to the environment.
2.4. Hypothesis
Ha1: There is a
significant relationship between trust and supply chain collaboration.
Ha2: There is a
significant relationship between trust and operational performance.
Ha3: There is a
significant relationship between technology and supply chain collaboration.
Ha4: There is a
significant relationship between technology and operational performance.
Ha5: There is a
significant relationship between supply chain collaboration and operational
performance.
Ha6: The relationship
between trust and operational performance is mediated by supply chain
collaboration.
Ha7: The relationship
between technology and operational performance is mediated by supply chain
collaboration.
2.5. Theoretical Framework
Methodology
The research philosophy of this study is positivism, as there
are hypothesis testing and data generalizability. The research strategy is
deductive, as the aim of this study is theory testing and empirical
verification of the existing framework. The quantitative method has been used
in this research to the Relationship between trust, technology, and information
sharing on operational performance with Mediating Role of Supply Chain
Collaboration. Quantitative study always used questionnaires as the data collection
method. This research approach is principally used for explanatory research.
Furthermore, quantitative research approach is mostly done to develop theories
and mathematical models associated with a particular observable fact.
Quantitative approach is an unbiased technique because it contains three main
characteristics that are structured questions, interviews, and statistical
data.
This chapter will explain
the whole results in data analysis section. All the analysis is constructed
using the Smart PLS 3 Software which includes validity, reliability, path
coefficient and discriminate validity. Table 1 shows that the research
respondents are 89% Male, while 11% are females from FMCG sector 178 and 22 as
follow total 200 Gender Respondents. The highest age bracket among the 70
respondents was 51 years and above, comprising 35% of the total, followed by
the age bracket of 41 to 50, which accounted for 29.5% of the respondents. Only
a few respondents (1.5%) fell into other age brackets. Furthermore, 40% of the
respondents held master's degrees, while 31% held bachelor's degrees. Among the
respondents, 33.5% had 6 to 10 years of work experience, and 29.5% had more
than 11 years of experience. In terms of income, 61% of respondents aged 51 and
above had a high-income level, while only 6.5% of total respondents earned
between 20k to 30k.
Table 1 Demographics statistics
Variable |
Category |
Frequency |
Percentage
(%) |
Gender |
Male |
178 |
89% |
Female |
22 |
11% | |
200 |
100% | ||
Age |
25 to 30 |
32 |
16% |
31 to 40 |
39 |
19.5% | |
41 to 50 |
59 |
29.5% | |
51 and above |
70 |
35% | |
200 |
100% |
To review the model
measurement, partial least square software has been used in table 2. PLS is the
inactive variable meaning the procedure that merges various denied and poor
builds explicitly see an estimation bungle. Partial Least Squares (PLS) path
down. In particular, the luminous PLS is utilized as it takes into thought
assessing both the estimation display and support model simultaneously.
The idea of the estimation show was
tried by assessing the individual thing and scale enduring quality taken after
by convergent and discriminant validity of constructs' measures. At begins the
correlations were displayed between the variables, Trust, Technology,
Collaboration, and Operational performance.
4.2. Convergent Validity
Convergent validity refers to the amount of accord between
two or more two measures of a similar construct (Colicchia
et al. 2019).
Evidence of convergent validity was assessed by study of difference
extract for each factor (Fornell and Larcker,
1981). According to (Fornell and Larcker,
1981) if the extracted variance value is exceeded from 0.50it shows that
convergent validity is established. Furthermore, results indicate that the
variance extracted in four scales 0.55 to 0.927.
Table 2 Factor loadings, Cronbach’s
alpha, composite reliability, and AVE
Construct
Reliability and Validity | |||||
Constructs |
Items |
Loading |
AVE |
Composite
Reliability |
Cronbach
Alpha |
Trust |
Tru1 |
0.900 | |||
Tru2 |
0.862 | ||||
Tru3 |
0.811 |
0.758 |
0.94 |
0.921 | |
Tru4 |
0.874 | ||||
Tru5 |
0.877 | ||||
Technology |
Tec1 |
0.749 | |||
Tec2 |
0.761 | ||||
Tec3 |
0.808 |
0.648 |
0.901 |
0.864 | |
Tec4 |
0.838 | ||||
Tec5 |
0.552 | ||||
Collaboration |
Col1 |
0.733 | |||
Col2 |
0.831 | ||||
Col3 |
0.751 |
0.561 |
0.883 |
0.836 | |
Col4 |
0.863 | ||||
Col5 |
0.692 | ||||
Operational
Performance
|
OP-P1 |
0.741 |
|
|
|
OP-P2 |
0.676 |
|
|
| |
OP-P3 |
0.862 |
0.603 |
0.864 |
0.803 | |
OP-P4 |
0.752 |
|
|
| |
OP-P5 |
0.625 |
|
|
|
4.3. Discriminant Validity
Table 3 concludes the result of Discriminant validity as it
shows that no single factor is the same as every other factor in the model. The
discriminant validity was evaluated by two criteria (Fornell
and Larcker, 1981) and cross-loading criterion. Discriminant validity
can be measured by comparing an indicator outer loading on the other related
construct and it should be greater than all of its loading than other
constructs (Gandhi et al. 2017). All
the items measuring a particular construct loaded higher on that construct and
loaded lower on the other constructs that confirms the discriminant validity of
the constructs. The Discriminant validity is adequate when variables have an
AVE stacking greater than 0.5, and it should not be less than half of the
estimation fluctuation wedged by the development (Riazi
and Nawi, 2018).
Table 3 Forrnell -Larcker
Variables |
Coll |
Opp |
Tec |
Tru |
Coll |
0.777 | |||
Opp |
0.481 |
0.749 | ||
Tec |
0.456 |
0.633 |
0.805 | |
Tru |
0.474 |
0.614 |
0.453 |
0.871 |
4.4. Cross Loading
Table 4 shows the cross-loading of each item of their
particular variable. Each value in a row should have a greater value
deferentially within its variable. The above table 4.3 of HTMT tells the
discriminant validity through the results generated. The values obtained of AT,
CT, EI, PU, PT, and US are all less than 0.90. Structural Model Analysis. A
structural model analyzes the statistics concerning some endogenous and latent
variables. The most convenient feature in Partial Least Squares (PLS) method is
that it can examine structural model and hypothesis by calculating path
coefficients. Since PLS does not necessitate a normally distributed data, it is
evaluated with R-squared calculation for latent dependent variables (Qu and Yang 2015). The hypotheses were tested by
running a bootstrapping procedure as suggested by (Sahin,
and Topal, 2019).
Table 4 Cross Loading
|
Coll |
OPP |
Tec |
Tru |
OP-P4 |
0.415 |
0.752 |
0.606 |
0.560 |
OP-P5 |
0.349 |
0.699 |
0.360 |
0.445 |
Tec1 |
0.339 |
0.625 |
0.864 |
0.512 |
Tec2 |
0.135 |
0.549 |
0.749 |
0.219 |
Tec3 |
0.422 |
0.477 |
0.761 |
0.271 |
Tec4 |
0.519 |
0.477 |
0.808 |
0.457 |
Tec5 |
0.376 |
0.406 |
0.838 |
0.310 |
Tru1 |
0.459 |
0.651 |
0.552 |
0.903 |
Tru2 |
0.501 |
0.623 |
0.439 |
0.900 |
Tru3 |
0.331 |
0.473 |
0.333 |
0.862 |
Tru4 |
0.447 |
0.373 |
0.264 |
0.811 |
Tru5 |
0.277 |
0.481 |
0.311 |
0.874 |
Coll1 |
0.733 |
0.463 |
0.310 |
0.315 |
Coll2 |
0.831 |
0.321 |
0.293 |
0.310 |
Coll3 |
0.751 |
0.255 |
0.276 |
0.260 |
Coll4 |
0.863 |
0.360 |
0.384 |
0.391 |
Coll5 |
0.692 |
0.402 |
0.446 |
0.488 |
OP-P1 |
0.446 |
0.741 |
0.427 |
0.448 |
OP-P2 |
0.254 |
0.676 |
0.470 |
0.347 |
OP-P3 |
0.307 |
0.862 |
0.458 |
0.456 |
The existing research analysed the relationship among technology,
trust, collaboration, and firm performance while focusing on the FMCG sector in
Iraq. The analysis deeply explains that trust and technology are the factors
associated with increasing collaboration with great effect. If it is said that
effective collaboration in supply chain partner adds value that led better
operational performance. In other words, collaboration can be characterized as
either tangible or intangible. The intangible outcomes can be categorized as
communication and relationship, higher level of trust, on-time information
sharing, and quickly respond to problem solving the innovation. Other factors
include intangible factors, which mean continuous improvement in all processes.
Though, Supply Chain collaboration is a tangible strategy.
Abdulameer, S.S., Yaacob,
N.A., Ibrahim, Y.M., 2020. Measuring Leagile Supply Chain, Information Sharing, And Supply
Chain Performance: Pre-Test and Pilot Test. International Journal of
Technology, Volume 11(4), pp. 677–687
Al-Doori, J.A., Khdour, N., Shaban, E. A., Qaruty, T.M., 2021. How
COVID-19 Influences the Food Supply Chain: An Empirical Investigation of
Developing Countries. International Journal of Technology, Volume 12(2),
pp. 371–377
Autry, C., 2013. Adversarial to Collaborative Relationships, Game
Changing Trends in Supply Chain. University of Tennessee, Knoxville: Global
Supply Chain Institute and Ernst & Young.
Autry, C.W., Rose, W.J., Bell, J.E., 2014. Reconsidering The Supply
Chain Integration-Performance Relationship: in Search of Theoretical
Consistency And Clarity. Journal of Business Logistics, Volume 35(3), pp.
275–276
Babkin, A., Glukhov, V., Shkarupeta, E., Kharitonova, N.,
Barabaner, H., 2021. Methodology For Assessing Industrial Ecosystem Maturity in
The Framework of Digital Technology Implementation. International Journal of
Technology, Volume 12(7), pp. 1397–1406
Barber, K.D., Garza-Reyes, J.A., Kumar, V., Abdi, M.R., 2017. The Effect
of Supply Chain Management Practices on Supply Chain and Manufacturing Firms’
Performance. Journal of Manufacturing Technology Management, Volume 28(5),
pp. 577–609
Colicchia, C., Creazza, A., Noè, C., Strozzi, F., 2019. Information
Sharing in Supply Chains: A Review Of Risks And Opportunities Using The
Systematic Literature Network Analysis (SLNA). Supply chain management:
an international journal, Volume 24(1), pp. 5–21
Fawcett, S.E., Magnan, G.M., McCarter, M.W., 2008. A Three-Stage
Implementation Model for Supply Chain Collaboration. Journal of Business
Logistics, Volume 29(1), pp. 93–112
Fawcett, S.E., Wallin, C., Allred, C., Fawcett, A.M., Magnan, G.M.,
2011. Information Technology as An Enabler of Supply Chain Collaboration: A Dynamic-Capabilities
Perspective. Journal of Supply Chain Management, Volume 47(1), pp. 38–59
Fornell, C., Larcker, D.F., 1981. Structural Equation Models with
Unobservable Variables and Measurement Error: Algebra and Statistics. American
Marketing Association, Volume 18(3), pp. 382–388
Fawcett, S.E., Fawcett, A.M., Watson, B.J., Magnan, G.M., 2012.
Peeking Inside the Black Box: Toward An Understanding of Supply Chain
Collaboration Dynamics. Journal of Supply Chain Management, Volume 48(1),
pp. 44–72
Fawcett, S.E., McCarter, M.W., Fawcett, A.M., Webb, G.S., Magnan,
G.M., 2015. Why Supply Chain Collaboration Fails: The Socio-Structural View of
Resistance to Relational Strategies. Supply Chain Management: An
International Journal, Volume 20(6), pp. 648–663
Gandhi, A.V., Shaikh, A., Sheorey, P.A., 2017. Impact of Supply
Chain Management Practices on Firm Performance: Empirical Evidence from A
Developing Country. International Journal of Retail & Distribution
Management, Volume 45(4), pp. 366–384
Kazmi, S.H.A., Shahbaz, M.S., Mubarik, M.S., Ahmed, J., 2021.
Switching Behaviors Toward Green Brands: Evidence from Emerging Economy. Environment,
Development and Sustainability, Volume 23(8), pp. 11357–11381
Luo, D., Shahbaz, M., Qureshi, M., Anis, M., Mahboob, F., Kazouz,
H., Mao, J., 2022. How Maritime Logistic Smes Lead and Gain Competitive
Advantage By Applying Information Technology? Frontiers in Psychology, Volume
13, p. 955145
Lee, S.Y., 2015. The Effects of Green Supply Chain Management On
The Supplier’s Performance Through Social Capital Accumulation. Supply Chain
Management: An International Journal, Volume 20(1), pp. 42–55
Mohammadi, M., Mukhtar, M., 2018. Comparison Of Supply Chain
Process Models Based on Service-Oriented Architecture. International
Journal of Technology. Volume 9(1), pp. 35–45
Qu, W.G., Yang, Z., 2015. The Effect Of Uncertainty Avoidance And
Social Trust On Supply Chain Collaboration. Journal of Business
Research, Volume 68(5), pp.911–918
Ramanathan, U., Gunasekaran, A., 2014. Supply Chain Collaboration: Impact
of Success in Long-Term Partnerships. International Journal of Production
Economics, Volume 147, pp. 252–259
Riazi, S.R.M., Nawi, M.N.M., 2018. Project Delays in The Malaysian Public
Sector: Causes, Pathogens And The Supply Chain Management Approach. International
Journal of Technology, Volume 9(8), pp. 1668–1680
Sahin, H., Topal, B., 2019. Examination of Effect of Information
Sharing on Businesses Performance in The Supply Chain Process. International
Journal of Production Research, Volume 57(3), pp .815–828
Shahbaz, M.S., Javaid, M., Kazmi, S.H.A., Abbas, Q., 2022.
Marketing Advantages and Sustainable Competitiveness Through Branding for The
Supply Chain of Islamic Country. Journal of Islamic Marketing, Volume 13(7),
pp. 1479–1491
Shahbaz, M.S., Mubarik, M.S., Mubarak, M.F., Irshad, M.B., 2019.
The Impact of Lean Practices on Educational Performance: An Empirical
Investigation for Public Sector Universities of Malaysia. Journal Of
Independent Studies and Research: Management, Social Sciences and Economics,
Volume 17(2), pp. 85–96
Shahbaz, M.S., Kazi, S.,
Bhatti, N.U.K., Abbasi, S.A., Rasi, R.Z.R., 2019. The Impact of Supply Chain Risks on Supply
Chain Performance: Empirical Evidence from The Manufacturing of Malaysia. International
Journal of Advanced and Applied Sciences, Volume 6(9), pp. 1–12
Zacharia, Z.G., Nix, N.W., Lusch, R.F.,
2009. An Analysis of Supply Chain Collaborations and Their Effect on
Performance Outcomes. Journal of Business Logistics, Volume 30(2), pp. 101–123