Role of IT Capabilities towards SC integration for improving
leanness and
Agility inthe supply chain of manufacturing firm
Presented by
Syeda Wajeeha
MBA in Supply Chain Management
Supervised by Sir Ahmed Wasif Uddin
Presentation Outline
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
Introduction and Background of the Study
Theoretical Background.
Problem Statement
Research Questions, Objectives and Hypothesis
Literature Review
Conceptual Model
Research Methodology
Data Analysis and Discussion
Conclusion
Recommendation
Introduction and Background of the Study

With the advancement in the technology, the role of IT has also advanced since it helps in playing a significant
role in SCM. The IT helps in providing the tools which can be used in order to pick p some of the relevant
information, which can be further break down in order to conduct proper analysis and can be used further on in
order to execute it for getting optimum performance.

However, since the global business environment has become more complicated, firms are now more vulnerable
to numerous SC risks (Garver, 2019). Multiple research have created interconnected systems that can endure,
respond to, bounce back from, and adapt to these serious dangers.

In today's global market, responsiveness is an increasingly crucial skill for organizations; hence, firms must be
agile. Obviously, an organization's agility relies on its SC agility. However, SC agility is the product of other
organizational competencies, including SC flexibility and information technology (IT) integration.

Using empirical data, we show that IT integration, SC agility, SC flexibility, and competitive organization
performance are interconnected. According to Patel and Sambasivan (2021), claim that IT integration allows a
company to use its SC flexibility, leading to increased SC agility and, therefore, greater business performance.
Problem Statement

Additionally, contemporary studies incorporate external and internal uncertainty when creating models and
studying the influence on SC performance, integration, and sustainable supplier sourcing. COVID
19/SARS/MARS outbreaks, also cause significant SC risk, accompanied by a high level of uncertainty, and
ultimately disrupt SC upstream and downstream elements, disrupting SC integration.

In today's highly unpredictable and fluid world, insufficient information flow having a lack of coordination
among disaster relief employees, whose work settings are sometimes very dynamic and unexpected, may
contribute to the long-term repercussions of catastrophic events (Tseng et al., 2021).

Firms are always under pressure from rapidly expanding technology, demanding consumers, shorter product life
cycles, more product diversity, and globalization, which treats the entire globe as a single market. Not only has
this increased supply and demand fluctuation and unpredictability, but it has also diminished market visibility
(Record, 2021).

These issues, combined with increasing rivalry among competing organizations, force the main organization to
develop quick-response skills to manage these changes via the organization of delivery, cost, quality, and
resource flexibility (Sharma et al., 2021).
Theoretical Background
Dynamic capabilities are a company's ability to integrate, expand, and restructure internal and external
competencies in response to continually shifting situations (Sun et al., 2021). Dynamic skills, according to Teece,
also include the ability to detect and shape opportunities, take chances, and preserve competitiveness by increasing,
merging, safeguarding, and reconfiguring a firm’s resources (Bhattacharyya et al., 2021). Dynamic capabilities are
basic, experienced, unstable procedures that rely on swiftly developed fresh insights to combine, change, or refresh
resources and competencies into capabilities required for uncertain markets in a highly unpredictable world
(McDougall et al., 2021).
Research Question(s)
RQ: What is the effect of IT capabilities on SC integration
leading towards lean and agile SC strategies among the
manufacturing firms of Pakistan?
Research Objectives
• The main objective of this study is to examine
the influence of IT skills. On SC integration,
hence increasing the lean and agile SC
strategies of Pakistani manufacturers.
Literature Review
Author(s)/Objective
Results
Technique
Conclusion/Implications
According to Ijaz et al.(2022)
in their article “Survival of the
fittest during pandemics: The
role of market orientation,
entrepreneurial
thinking,
strategic flexibility and supply
chain integration” states that
business endurance stays the
mark of conversation among
the policymakers, scientists,
and controllers all over the
planet since the development of
COVID pandemic.
The discoveries of current
review frame the
ramifications for the
proprietors/directors of
SMEs and administrative
experts in figuring out the
meaning of market
direction, enterprising
reasoning, vital
adaptability, and store
network coordination
towards the endurance of
SMEs.
SEM
The discoveries of present review show
that market direction, pioneering
thinking, vital adaptability altogether
contribute towards the endurance of
SMEs. Key adaptability fundamentally
and decidedly intercedes the connection
between market direction, innovative
reasoning and endurance of SMEs.
According toAslam et al. (2020),
analyzed the influence of SC
ambidexterity on SC resilience. SCAmbidexterity was defined as the
concurrent application of SC
adaptability and SC alignment
skills, and the role of SC agility in
the interaction between SCAmbidexterity and SC resilience
was investigated.
The relationship between SC
ambidexterity and SC
resilience is positively
mediated by SC agility.
Regardless of the degree of
environmental unpredictability,
this correlation persists
according to the data.
SEM
Based on the firm's DCV, they developed
a set of assumptions that were confirmed
by a survey of Pakistani manufacturing
firms using SEM. This research
demonstrated that SC ambidexterity
favorably influences SC resilience.
CONCEPTUAL MODEL
Hypothesis
H1: IT adoption has a significant effect on customer integration.
H2: IT adoption has a significant effect on supplier integration.
H3: IT adoption has a significant effect on internal integration.
H4: IT competency has a significant effect on supplier integration.
H5: IT competency has a significant effect on customer integration.
H6: IT competency has a significant effect on internal integration.
H7: IT appropriation has a significant effect on customer integration.
H8: IT appropriation has a significant effect on supplier integration.
H9: IT appropriation has a significant effect on internal integration.
H10: Customer integration has a significant effect on SC agility.
H11: Supplier integration has a significant effect on SC agility.
H12: Internal integration has a significant effect on SC agility.
H13: Customer integration has a significant effect on the lean SC.
H14: Supplier integration has a significant effect on the lean SC.
H15: Internal integration has a significant effect on the lean SC.
Research Methodology
Research Approach
Quantitative Approach
Research Purpose
Explanatory study
Research Design
Causal design
Data Source
Primary data
Data Collection & Instrument
5-point Likert Scale Questionnaire
Sampling Technique
Non- probability (Purposive Sampling)
Sample Size
286 responses (268 useful data)
Statistical Technique
SEM (Structural Equation Method)
Demographic Profile of Respondents
(n=268)
Firm Size
Education
Designation
Industry
Valid
Frequency
Percent
Less than 250
92
34.3
250 to 500
32
11.9
501 to 1000
12
4.5
More than 1000
132
49.3
Undergraduate
16
6.0
Graduate
132
49.3
Postgraduate
100
37.3
Others
20
7.5
Lower Management
56
20.9
Middle Management
148
55.2
Upper Management
64
23.9
Textile
36
13.4
Food Processing
16
6.0
Consumer Electronics
8
3.0
Pharmaceutical
200
74.6
Steel and Cement
8
3.0
Less than 3 years
52
19.4
3 to 5 years
56
20.9
5 to 10 years
92
34.3
More than 10 years
68
25.4
Reliability Testing & Convergent Validity
Loadings
CI1 <- Customer Integration
0.719
CI2 <- Customer Integration
0.798
CI3 <- Customer Integration
0.665
CI4 <- Customer Integration
0.809
CI5 <- Customer Integration
0.770
II1 <- Internal Integration
0.740
II2 <- Internal Integration
0.605
II3 <- Internal Integration
0.738
II4 <- Internal Integration
0.780
II5 <- Internal Integration
0.693
ITA1 <- IT Adoption
0.668
ITA3 <- IT Adoption
0.824
ITA4 <- IT Adoption
0.795
ITA5 <- IT Adoption
0.779
ITAp1 <- IT Appropriation
0.697
ITAp3 <- IT Appropriation
0.772
ITAp4 <- IT Appropriation
0.639
ITAp5 <- IT Appropriation
0.813
ITC1 <- IT Competency
0.787
ITC2 <- IT Competency
0.732
ITC3 <- IT Competency
0.829
CR
AVE
0.868
0.568
0.838
0.509
0.852
0.591
0.822
0.537
0.831
0.555
For determining the average
variance extracted (AVE), the
acceptable minimum cut-off
value is more than 0.5. If all of
the products of the factor
loadings are above 0.5, then the
convergence will be achieved
(Hair et al., 2016). Mallery and
George (2003) in their study
have delivered the boundaries
for the Cronback’s alpha which
is considered as excellent, good
and acceptable only on a
condition if α>0.90, α>0.80 and
α>0.70 and the lowest cut-off
value. Hair et al. (2016)
elaborated in his study that the
same rule for the CR while the
coefficient should be more than
or equals to 0.70.
Reliability Testing & Convergent Validity
LSC1 <- Lean SC
0.782
LSC2 <- Lean SC
0.779
LSC3 <- Lean SC
0.784
LSC4 <- Lean SC
0.830
LSC5 <- Lean SC
0.824
SCA1 <- SC Agility
0.791
SCA2 <- SC Agility
0.768
SCA3 <- SC Agility
0.858
SCA4 <- SC Agility
0.805
SCA5 <- SC Agility
0.710
SCA6 <- SC Agility
0.729
SI1 <- Supplier Integration
0.770
SI2 <- Supplier Integration
0.693
SI3 <- Supplier Integration
0.780
SI4 <- Supplier Integration
0.719
SI5 <- Supplier Integration
0.768
0.899
0.64
0.902
0.606
0.863
0.558
 AVE > 0.5(Hair Jr et
al., 2016)
 Outer loadings > 0.7
((Hair Jr et al., 2016)
Discriminant Validity
• Fornell and Larcker (Discriminant Validity)
Table 4: Fornell-Larcker Criterion
CI
ITAD
ITA
ITC
II
LSC
SCA
CI
0.754
ITAD
0.318
0.769
ITA
0.560
0.505
0.733
ITC
-0.374
-0.645
-0.646
0.745
II
0.482
0.593
0.652
-0.484
0.714
LSC
0.629
0.447
0.600
-0.546
0.585
0.800
SCA
0.718
0.438
0.643
-0.427
0.629
0.659
0.778
SI
0.680
0.359
0.571
-0.331
0.531
0.596
0.632
SI
0.747
CI = Customer Integration; ITAD = IT Adoption; ITA = IT Appropriation; ITC = IT Competency; II = Internal
Integration; LSC = Lean SC; SCA = SC Agility; SI = Supplier Integration
 Hair et al. (2014) have explained that the measured square root of AVE (as represented by
values in diagonal form) will always be more than that of the construct’s correlation (as
represented by values in off-diagonal form).
Heterotrat-Monotrait Ratio (HTMT) Results
Table 6:
Heterotrait-Monotrait Ratio (HTMT)
CI
ITAD
ITA
ITC
II
LSC
SCA
SI
CI
ITAD
0.389
ITAD
0.715
0.676
ITC
0.484
0.828
0.891
II
0.599
0.751
0.855
0.630
LSC
0.743
0.533
0.747
0.684
0.712
SCA
0.844
0.524
0.797
0.546
0.757
0.767
SI
0.839
0.440
0.735
0.418
0.676
0.707
0.752
CI = Customer Integration; ITAD = IT Adoption; ITA = IT Appropriation; ITC = IT Competency; II = Internal
Integration; LSC = Lean SC; SCA = SC Agility; SI = Supplier Integration
 HTMT criterion results shows that if HTMT value is less than 0.90 (Henseler et al.,
2015) altogether with the discriminant validity is to be identified within the two
constructs.
Factor Analysis
CI
ITAD
ITA
ITC
II
LSC
SCA
SI
CI1
0.719
0.264
0.478
-0.329
0.402
0.394
0.452
0.471
CI2
0.798
0.188
0.477
-0.289
0.292
0.504
0.495
0.547
CI3
0.665
0.200
0.304
-0.219
0.289
0.434
0.502
0.354
CI4
0.809
0.339
0.452
-0.418
0.492
0.645
0.601
0.613
CI5
0.770
0.192
0.392
-0.129
0.318
0.359
0.646
0.549
II1
0.452
0.553
0.529
-0.447
0.740
0.415
0.467
0.437
II2
0.269
0.334
0.565
-0.322
0.605
0.371
0.345
0.404
II3
0.229
0.414
0.334
-0.295
0.738
0.340
0.311
0.322
II4
0.303
0.505
0.509
-0.396
0.780
0.508
0.518
0.325
II5
0.437
0.272
0.354
-0.235
0.693
0.419
0.559
0.402
ITAD1
0.174
0.668
0.427
-0.425
0.346
0.216
0.190
0.168
ITAD3
0.261
0.824
0.477
-0.519
0.530
0.420
0.393
0.353
ITAD4
0.211
0.795
0.316
-0.422
0.455
0.351
0.286
0.228
ITAD5
0.311
0.779
0.343
-0.600
0.464
0.351
0.429
0.313
ITA1
0.327
0.353
0.697
-0.487
0.520
0.406
0.377
0.442
ITA3
0.268
0.400
0.772
-0.515
0.395
0.353
0.479
0.384
Factor Analysis
ITC1
-0.272
-0.503
-0.641
0.787
-0.360
-0.487
-0.328
-0.233
ITC2
-0.283
-0.431
-0.514
0.732
-0.405
-0.398
-0.314
-0.249
ITC3
-0.281
-0.662
-0.476
0.829
-0.412
-0.426
-0.334
-0.323
ITC5
-0.287
-0.270
-0.258
0.614
-0.236
-0.303
-0.300
-0.154
LSC1
0.453
0.360
0.591
-0.517
0.551
0.782
0.483
0.587
LSC2
0.459
0.343
0.506
-0.425
0.510
0.779
0.480
0.406
LSC3
0.482
0.330
0.462
-0.425
0.391
0.784
0.557
0.419
LSC4
0.583
0.350
0.440
-0.446
0.435
0.830
0.536
0.497
LSC5
0.537
0.403
0.393
-0.364
0.443
0.824
0.584
0.459
SCA1
0.545
0.397
0.518
-0.340
0.550
0.557
0.791
0.614
SCA2
0.671
0.294
0.554
-0.263
0.403
0.472
0.768
0.486
SCA3
0.675
0.362
0.498
-0.311
0.569
0.533
0.858
0.495
SCA4
0.501
0.416
0.598
-0.421
0.529
0.439
0.805
0.439
SCA5
0.460
0.236
0.429
-0.316
0.479
0.587
0.710
0.501
SCA6
0.462
0.336
0.391
-0.364
0.388
0.497
0.729
0.400
SI1
0.506
0.238
0.392
-0.250
0.448
0.524
0.439
0.770
SI2
0.632
0.211
0.374
-0.154
0.311
0.352
0.508
0.693
Predictive Power of Construct
Table 8:
Predictive Relevance
R Square
Q Square
Customer Integration
0.315
0.177
Internal Integration
0.524
0.254
Lean SC
0.517
0.326
SC Agility
0.630
0.374
Supplier Integration
0.343
0.185
 For the assessment on R2, Cohen (2013) have described a
thumb rule that the values of R2 of 0.26, 0.13 or 0.02 for the
endogenous latent constructs can be described as substantial,
moderate and weak respectively.
Hypothesis Testing
Table 7:
Path Analysis
Hypothesis
Estimate
S. D.
t-Stats
Prob.
Decision
CI-> LSC
0.346
0.081
4.274
0.000
Accepted
CI-> SCA
0.463
0.049
9.506
0.000
Accepted
ITAD -> CI
0.051
0.057
0.895
0.185
Rejected
ITAD -> I
0.402
0.065
6.186
0.000
Accepted
ITA -> SI
0.160
0.086
1.871
0.031
Accepted
ITA -> CI
0.539
0.064
8.488
0.000
Accepted
ITA -> II
0.522
0.065
8.018
0.000
Accepted
ITA -> SI
0.590
0.067
8.753
0.000
Accepted
ITC -> C I
0.008
0.073
0.107
0.457
Rejected
ITC -> II
0.113
0.065
1.725
0.042
Accepted
ITC-> SI
0.154
0.062
2.477
0.007
Accepted
II -> LSC
0.316
0.049
6.451
0.000
Accepted
II -> SCA
0.332
0.049
6.757
0.000
Accepted
S I -> LSC
0.193
0.081
2.381
0.009
Accepted
SI -> SCA
0.141
0.051
2.746
0.003
Accepted
 According to the Hair et al. (2014), to acquire the t-value in order to evaluate the
relationships that were having results of either significant or insignificant. The t-value >
1.96 with ρ<0.05 have considered as the cut-off value to support or not to support the
hypothesis.
Conclusion

The purpose was to examine the impact of IT capabilities on SC integration, leading toward improving the lean
and agile SC strategies of manufacturing firms. The study used a quantitative research approach. Additionally, the
study used a causal design to identify cause-and-effect relationships between variables. Explanatory research was
done as part of the study to give detailed explanations of the components.

The research determined that IT adoption had a little beneficial impact on CI, but a considerable positive impact on
SI and internal integration. Similarly, IT expertise has a good impact on SI and internal integration, but has a
negligible impact on CI. Similarly, IT allocation has a substantial favorable influence on CI, as well as on supplier
and internal integration. Furthermore, CI has a considerable beneficial impact on SC agility, while SI and internal
integration have a good impact on SCA.

A crucial resource and distinctive skill that enables firms to create value is technological competence (TC). By
acquiring unique resources and skills and engaging in high TC strategic activities, firms may gain competitive
advantages, increase profitability, and enhance organizational performance. TC is therefore essential to gaining a
competitive advantage. Additionally, it enhances the efficiency of firms, organizations, and even entire countries.
Recommendation
 Some future recommendations are included in the paper. Firstly, future studies
might collect data from other firms in more areas to increase the generalizability
of the findings.
 A future researcher suggested that qualitative analysis is used in future studies to
get robust results and represent the research population. Moreover, future
research should take into account a longer lead time and a larger range for
questionnaire participation.
 It should also provide the researchers adequate time and resources to manage
their study design and resources to gather samples from other industries, hence
minimizing industry bias in the findings and conclusions.