INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025  
The Barriers to the Adoption of Emerging Technologies in the  
Apparel Manufacturing Industry Focused on India  
Sheetal, Thanmayi Polisetti  
National Institute of Fashion Technology, Hyderabad  
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1411000015  
Received: 10 November 2025; Accepted: 20 November 2025; Published: 02 December 2025  
ABSTRACT  
Despite the transformative potential of advanced technologies such as Artificial Intelligence (AI) and soft  
robotics, the Indian apparel manufacturing sector exhibits a critical lag in achieving comprehensive,  
integrated digitalization. Implementation is hampered by systemic, multi-dimensional barriers. Analysis  
reveals these constraints include acute data fragmentation coupled with poor IT infrastructure; severe  
financial stress resulting from high implementation costs with an uncertain Return on Investment (ROI); and  
the persistent technical difficulty of automating the handling of limp, deformable fabrics. This report  
addresses this pervasive adoption gap by analyzing constraints across Technological, Organizational, and  
Environmental contexts. This research introduces the Apparel Technology Adoption and Supply Chain  
Resilience (ATASCR) framework to model the assimilation process necessary for industry modernization  
and strategic resilience. The empirical findings, based on correlation analysis, confirm that Market Demand  
(V3) is the dominant positive driver for adoption, while anticipated barriers like Limp Material Difficulty (V1)  
and Financial Strain (V2) show surprisingly weak orcontrary correlation with thelevel oftechnology adoption  
(V4).  
Crucially, the analysis confirms that digital adoption (V4) is strongly associated with achieving Supply Chain  
Resilience (V5).  
INTRODUCTION  
Background of the Problem  
The global apparel manufacturing industry, including major hubs like India, is undergoing intense pressure to  
modernize its operations, driven by global demands for speed, sustainability, and mandatory transparency.  
While emerging technologies offer transformative potential, the sector exhibits a critical lag in achieving  
integrated digitalization. This persistent adoption gap is unique to India's institutional context and is rooted in  
systemic, multi-dimensional barriers. These constraints include severe financial stress arising from high  
implementation costs and uncertain Return on Investment (ROI), and the fundamental material-specific  
technical difficulty of automating the handling of deformable textiles.  
Problem Statement  
The assimilation lag is not due to a singular factor but is hampered by systemic, multi-dimensional barriers,  
often unique to the institutional environment of Indian manufacturing. Analysis identifies three primary  
empirical constraints: acute data  
fragmentation coupled with poor IT infrastructure; severe financial stress arising from high implementation  
costs and uncertain Return on Investment (ROI); and the persistent, material-specific technical difficulty of  
automating the handling of deformable textiles.  
These constraints necessitate a context-specific framework to model effective technology assimilation.  
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Study Purpose  
The purpose of this study is to empirically validate the relationships between the primary structural barriers  
(Technological, Organizational, Environmental) and the creation of strategic resilience in Indian apparel  
manufacturing firms, achieved through the testing of the Apparel Technology Adoption and Supply Chain  
Resilience (ATASCR) Framework.  
Research Questions  
1. How do the primary structural barriers—Limp Material Difficulty (V1) and Financial Strain (V2)—  
negatively influence the development of the key capability, Adoption (V4), in the Indian apparel  
sector?  
2. To what extent does the external Market Demand (V3) positively influence the development of  
Adoption (V4), acting as a crucial driver for digital adoption?  
3. How is the ultimate achievement of Supply Chain Resilience (V5) determined by the firm's successful  
development of Adoption (V4) within the ATASCR Framework?  
Hypothesis  
H1 (Technological Barrier): Limp Material Difficulty (V1) is negatively correlated with the firm's enhancement  
of Adoption (V4).  
H2 (Organizational/Financial Barrier): Financial Strain Index (V2) is negatively correlated with the firm's  
enhancement of Adoption (V4).  
H3 (Environmental Driver): Market Demand (V3) is positively correlated with the firm's enhancement of  
Adoption (V4).  
H4 (Core Pathway): Adoption (V4) is positively correlated with the achievement of Supply Chain Resilience  
(V5).  
THEORETICAL FRAMEWORK  
The Apparel Technology Adoption and Supply Chain Resilience (ATASCR) structure is a Multiple Predictor  
Single Mediator (MP-SM) model, testing the influence of the three critical external/internal variables (V1, V2,  
V3) on the mediating capability (V4), and the subsequent effect on resilience (V5).  
Variable  
Symbol  
Type  
Description  
Limp Material  
Difficulty  
V1  
Independent  
(Barrier)  
Operational challenge of automated handling of  
deformable fabrics.  
Financial Strain  
Index  
V2  
V3  
Independent  
(Barrier)  
Organizational pressure from high costs and  
uncertain ROI.  
Market Demand  
Independent  
(Driver)  
External institutional pressure and buyer demands  
for traceability, transparency, and digital data  
sharing.  
Adoption  
V4 Mediator  
Degree of successful technology assimilation and  
enhanced capacity to utilize real-time data.  
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Supply Chain  
Resilience  
V5  
Dependent  
(Outcome)  
Firm's ability to maintain continuity, recover, and  
adapt to external shocks.  
Definitions of Terms  
Limp Material Difficulty (V1): The technical problem of handling soft, flexible fabrics during automated  
garment production, demanding complex, real-time adjustments.  
Financial Strain Index (V2): The financial pressure companies face when trying to adopt new  
technologies, including high upfront costs and ROI uncertainty.  
Market Demand (V3): The external pressure from international markets and buyer expectations that  
require manufacturers to adopt digital technologies to track and share comprehensive product data for  
transparency and competitiveness.  
Adoption (V4): The capability a company develops after adopting digital technologies, involving the  
ability to collect, monitor, and use real-time data for prediction and quick response.  
Supply Chain Resilience (V5): The company's ability to continue operating during disruptions,  
adapting quickly, recovering from shocks, and maintaining production.  
Literature Review I: TheoreticalFoundations and Foundational Critique  
The Evolving Landscape of Technology Adoption Models in Apparel  
Research into technology adoption in the apparel sector has tracked the broader evolution of management  
information systems theory. Early aflempts often relied on the Diffusion of Innovation (DOI) theory and the  
TheoryofReasonedAction (TRA),whichfocusedon individualorconsumerperception (Hoqueet al.,2021).  
However, these models possess fundamental limitations when applied to the industrial value chain,  
particularly due to their binary focus (treating adoption as a simple decision point) and their inherent neglect  
of the structural, systemic, and resource complexity required for technology integration in heavy  
manufacturing. They fail to capture the organizational transformation and resource reconfiguration  
necessary to deploy advanced systems like AI and robotics (Liu, 2024).  
The shift toward Industry 4.0, which mandates the integration of cyber-physical systems, necessitated models  
that could account for this complexity, leading to the prominence of the Technology-Organization-  
Environment (TOE) framework (Hoque & Rahman, 2024).  
TOE offers a valuable tripartite classification: internal technology characteristics, organizational readiness,  
and the external market/regulatory environment. Empirical applications of TOE across various industrial  
sectors confirm the crucial role of organizational factors (e.g., financial strength) and environmental factors  
(e.g., competitive pressure) as determinants for technology adoption.  
However, the literature consistently reveals a critical insufficiency of the TOE framework when applied  
specifically to the Indian apparel sector:  
Generic Technology View: Most TOE studies focus on general IT systems (like ERP) rather than the physical,  
material-specific challenges of textile production.  
Contextual Blindness: TOE’s generic nature fails to explain the unique low rate of adoption in developing  
economies like India, suggesting a theoretical failure to correctly identify the true constraints of  
implementation delays in garment manufacturing (Yu et al., 2022).  
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The Technological Barrier: Material Blindness and Engineering Complexity (V1 —> V4)  
The core theoretical justification for the Apparel Technology Adoption and Supply Chain Resilience  
(ATASCR) model is TOE's inability to adequately incorporate the unique engineering challenge posed by  
textile materials. The failure to address the physical science constraints of garment assembly automation is a  
profound theoretical limitation in adoption literature applied to this sector.  
The Limp Material Problem (V1)  
The handling of non-rigid, limp, deformable fabrics remains the central unsolved challenge in garment assembly  
automation (Li, Chen, & Zhao, 2024). Unlike rigid components used in automotive or electronics, textiles change  
shape under gravity and manipulation, demanding complex, real-time physical adjustments. This fundamental  
physical challenge is defined as the Limp Material Difficulty (V1). This difficulty is compounded by the high  
variability of textile types, from delicate silks to rigid denim, each requiring bespoke handling techniques.  
Resource Commitment and Structural Barrier  
To overcome the V1 barrier, the required resource commitment far exceeds typical software integration:  
Automation demands the integration of industrial robots with novel adaptive gripper systems. These often  
utilize specialized mechanisms, such as four-needle grippers, complex vacuum suction, or micro-fluidic  
systems, to handle the material delicately without causing damage or deformation (Li et al., 2024).  
These physical systems require complex two-stage Machine Learning (ML) models to predict fabric  
deflection, folding, and alignment in real-time. This demands the integration of high-resolution vision-  
guided algorithms with Computer-Aided Design (CAD) data for sub-millimeter precision.  
The complexity and prohibitive cost of this bespoke integration mean that the Limp Material Difficulty (V1)  
acts as a fundamental structural barrier that discourages investment in the ancillary data-driven systems  
required for successful Adoption (V4). Hypothesis H1 tests the premise that digitalization failure is rooted in  
these physical science constraints, a factor generic TOE models ignore.  
The Organizational Context: Financial Strain and Strategic Gaps (V2 —> V4)  
Despite the acknowledged technical hurdles, organizational resource constraints, specifically financial  
limitations, often present the most immediate and powerful barrier in the Indian manufacturing context (Singh  
et al., 2025). Thepervasivefinancial strain(V2) restricts a firm’s capacity for sustained strategic renewal and  
adoption.  
Capital Expenditure and ROI Uncertainty (V2)  
The primary deterrent is the high initial capital cost associated with Industry 4.0 elements, including sensor  
networks, AI software, and advanced robotics, which is particularly acute for the Small and Medium  
Enterprises (SMEs) that dominate the Indian sector (Sharma & Verma, 2024). This cost is compounded by  
significant Return on Investment (ROI) uncertainty (Singh et al., 2025). Volatile fashion cycles and  
unpredictable market demands make accurately forecasting ROI over the 3-5 year payback period extremely  
difficult. This uncertainty reinforces managerial risk aversion and leads to the postponement of digitalization  
projects (Sharma & Verma, 2024).  
This resource constraint directly restrictsthe development of Adoption (V4), whichis not a one-time purchase  
but requires continuous, non-negotiable investment in data infrastructure, IT talent recruitment, and  
employee training. Hypothesis H2 tests this core empirical reality: the organizational inability to finance or  
justify the expense directly impedes the necessary capability build.  
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Adoption as a Dynamic Capability (V4)  
Traditional models often treat technology integration as a static endpoint. However, in the modern context,  
successful adoption must be viewed as the creation of a dynamic capability (Liu, 2024). Adoption (V4)  
represents the firm's enhanced capacity to sense (collect data via sensors), seize (analyze real-time data), and  
reconfigure (respond quickly to anomalies or market shifts) (Smith & Jones, 2023). It is this post-installation  
assimilation, scaling, and utilization of data that truly differentiates resilient firms. Merely possessing the  
technology does not guarantee success; the true value is in the organizational capacity to leverage the  
generated data for prediction and proactive response, which forms the basis of V4.  
The Environmental Catalyst: Market Demand and Institutional Theory (V3 —> V4)  
Theexternal environment (E) context is often the strongest positive driver for adoption in theexport-oriented  
Indian supply chain, a finding highly consistent with Institutional Theory. This theory posits that compliance  
and legitimacy pressures compel firms to adopt practices irrespective of immediate internal economic  
benefits.  
The Buyer-Driven Mandate (V3)  
TheIndianapparel supply chainis heavily buyer-driven andexport-oriented, meaning external demands from  
key international markets, especially the European Union, exert significant institutional pressure that overrides  
domestic competition or internal  
cost-benefit analysis (European Parliament, 2024). Mandates such as the Digital Product Passport (DPP),  
while regulatory, are essentially translated into powerful Market Demand (V3) by major international buyers  
(Kumar et al., 2024).  
Market Access as the Overriding Factor  
Market Demand (V3) necessitates investment in standardized, transparent mechanisms for sharing  
comprehensive product data across the lifecycle (Zhang & Seuring, 2024). The risk of non-compliance is the  
loss of critical export market access, a threat considered a high business risk by management, often  
outweighingthefinancialcostofinvestment.This strategic pressure directly supports Hypothesis H3, testing  
the power of external institutional demand to overcome internal resource deficits (V2) and technical  
challenges (V1), thus acting as the necessary catalyst for capability development (V4).  
The Strategic Outcome: Adoption and Supply Chain Resilience (V4 —> V5)  
Thefinal pathwayoftheATASCR frameworkisthetranslationofdigitalcapability (V4)into tangible strategic  
advantage (V5). Supply Chain Resilience (V5) is not merely the ability to bounce back from shocks, but the  
firm's capacity to maintain operational continuity, recover quickly, and adapt to external shocks and  
disruptions (Johnson, 2024).  
The literature highlights a critical gap in connecting technology adoption directly to resilience (V5). The link  
is not direct possession of technology, but the dynamic capability inherent in Adoption (V4) (Smith & Jones,  
2023). Digital systems achieve resilience by providing:  
Visibility: Real-time data visibility to detect anomalies early (before they become crises).  
Predictability: Data analytics to forecast material shortages, demand shifts, or logistical bofllenecks.  
Agility: The structural and process flexibility to rapidly reconfigure production or switch sourcing  
channels based on data-driven insights.  
Hypothesis H4 directly addresses this gap by testing how the successful establishment of digital capabilities  
(V4) is strongly associated with the ultimate achievement of Supply Chain Resilience (V5), validating the  
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strategic importance of this mediating step in the ATASCR framework.  
Research Gap and Apparel Technology Adoption and Supply Chain Resilience (ATASCR) Contribution  
The literature review confirms that existing models fail to simultaneously account for the sector's most  
defining constraints:  
1. Material Specificity (V1): The fundamental technical limitation unique to textile handling.  
2. Dominant Organizational Financial Stress (V2): A proven barrier in the Indian context.  
3. The Power of Market Demand (V3): The institutionalpressure overriding internal resistance.  
4. The Mediation Role of Adoption (V4): The necessary pathway converting external pressure into  
genuine strategic resilience (V5).  
The ATASCR framework is the first model to integrate these elements within a structured theoretical  
pathway,providingaspecialized,context-specifictoolfortechnologyadoption research in the Indian apparel  
manufacturing sector.  
RESEARCH METHODOLOGY AND DESIGN  
Research Design and Approach  
This study employs a quantitative, cross-sectional survey research design to empirically test the proposed  
Apparel Technology Adoption and Supply Chain Resilience (ATASCR) framework. The design is explicitly  
correlational, focusing solely on establishing the nature, direction, and strength of the linear associations  
between the five latent variables (V1 to V5).  
Sampling and Data Collection  
Target Population and Sample  
The target population for this study comprised highly relevant professionals across the apparel manufacturing  
and technical sector, including IE Engineers, experienced Managers, and Mentors in technology-focused  
roles. These respondents were intentionally targeted via Google Forms due to their deep practical knowledge of  
production line realities, financial constraints, and technology implementation. This focused approach ensures  
the data reflects informed industrial perspectives rather than general managerial opinions.  
Measurement Instrument and Scaling  
Data was collected using a structured, multi-item questionnaire detailed in the Appendix. All items utilized a  
5-point Likert scale(1=Strongly Agreeto5=Strongly Disagree). The instrument categorized 30 closed-ended  
questions into the five core constructs: Limp Material Difficulty (V1), Financial Strain Index (V2), Market  
Demand (V3), Adoption (V4), and Supply Chain Resilience (V5).  
Data Conversion for Analysis  
To prepare the ordinal Likert scale responses for quantitative analysis, two steps were taken: 1) the responses  
were converted to numerical interval values (1, 2, 3, 4, 5); and 2) the final score for each variable (V1 through  
V5) was calculated as the arithmetic mean score of its constituent items, yielding a single continuous,  
composite variable score for each respondent.  
Statistical Analysis: Justification for Bivariate Correlation  
The gathered data was analyzed using Google Sheets, focusing exclusively on Bivariate Correlation  
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(Pearson’s ρ)  
.
The Bivariate Correlation test was chosen because it is the only statistical method required to directly test  
the four hypotheses as stated. Each hypothesis posits a simple, linear association between two continuous  
variables. While more advanced techniques like Multiple Regression or SEM could model simultaneous  
effects, the Bivariate Correlation is sufficient and precisely appropriate for validating the predicted  
directional and strength-based relationships underpinning the ATASCR framework using the available data.  
It directly measures the strength and direction of the linear relationship between the variable pairs.  
Data Analysis and Results  
Overview of Statistical Findings  
The analysis tested the four primary hypotheses of the Apparel Technology Adoption and Supply Chain  
Resilience (ATASCR) framework using Bivariate Correlation. The results are summarized below:  
Path  
V1 —>V4  
V2 —>V4  
V3 —>V4  
Hypothesis  
Expecte d  
Sign  
Correlation (ρ)  
0.0226  
Strength &  
Direction  
Result  
H1: V1 is negatively  
correlated with V4.  
Negative  
Negative  
Positive  
Extremely  
Weak, Positive  
Not Supported  
Not Supported  
Supported  
H2: V2 is negatively  
correlated with V4.  
0.1138  
Very Weak,  
Positive  
H3: Market Demand  
(V3) is positively  
0.3469  
Moderate,  
Positive  
correlated with V4.  
V4 —>V5  
H4: V4 is positively  
correlated with V5.  
Positive  
0.3842  
Moderate-  
Strong, Positive  
Supported  
Detailed Hypothesis Testing and Interpretation  
Test of Hypothesis 1 (H1: Technological Barrier)  
H1: Limp Material Difficulty (V1) is negatively correlated with the firm's enhancement of Adoption(V4).  
Correlation (ρ): 0.0226  
The result is extremely close to zero and positive, directly contradicting the expected negative relationship.  
Thisveryweak correlation suggests that thetechnical difficulty of handling limp materials is not a statistically  
significant linear factor that deters firms from adopting digitalization. This surprising result suggests that  
firms may view automation of limp materials as a separate engineering problem from the overarching goal of  
digital data adoption, or that they have already bypassed this barrier in their current infrastructure.  
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Test of Hypothesis 2 (H2: Organizational/Financial Barrier)  
H2: Financial Strain Index (V2) is negatively correlated with the firm's enhancement of Adoption(V4).  
Correlation (ρ): 0.1138  
Thecorrelation isweak and, similarto H1, is positive (contradicting the expected negative sign). Thissuggests  
that firms with higher perceived financial strain are not necessarily those with lower digital adoption scores.  
This unexpected finding may indicate that Market Demand (V3) pressure is so intense that firms are forced to  
invest in Adoption (V4) regardless of high costs, or that the sample includes firms that have recently  
undergone expensive adoption (high V2) but are now also high in V4.  
Test of Hypothesis 3 (H3: Environmental Driver: Market Demand)  
H3: Market Demand (V3) is positively correlated with the firm's enhancement of Adoption (V4).  
Correlation (ρ): 0.3469  
This is the strongest correlation among the predictor paths and is positive, confirming the hypothesis. The  
moderate correlation value shows a meaningful linear relationship, indicating that as Market Demand (i.e.,  
external pressure for transparency and data) increases, the firm's level of digital Adoption (V4) increases.  
This validates the literature suggesting that Institutional Pressure is the primary engine for digitalization in  
export-oriented industries.  
Test of Hypothesis 4 (H4: Core Pathway)  
H4: Adoption (V4) is positively correlated with the achievement of Supply Chain Resilience (V5).  
Correlation (ρ): 0.3842  
This correlation is the strongest in the entire model and is highly positive, providing significant empirical  
support for the core pathway of the Apparel Technology Adoption and Supply Chain Resilience (ATASCR)  
framework. This confirms that developing digital capability (Adoption) is essential for enhancing a firm's  
ability to maintain continuity, predict risk, and recover from shocks (Resilience).  
SUMMARY OF KEY FINDINGS  
External Demand Overrides Internal Barriers: Market Demand (V3) is confirmed as the dominant positive  
influence on Adoption (V4), while the expected negative influence of the internal barriers (V1 and V2) was not  
statistically observed.  
Digitalization Fuels Resilience: The strongest observed relationship confirms that a higher degree of  
technology Adoption (V4) is strongly associated with enhanced Supply Chain Resilience (V5).  
Appendix:DetailedMeasurement Instrument and Scales  
All questions utilize a Likert Scale with the following  
options:{1=Strongly~Agree},{2=Agree},{3=Neutral},{4=Disagree},{5=Strongly~Disagree}.  
Section 1: Measurement of Limp Material Difficulty (V1)  
Q1. Does manipulating soft fabrics cause significant technical delays in your automated production  
line?  
Q2. Is your firm unable to automate critical sewing tasks due to the material variability of textiles?  
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Q3. Are adaptive gripping systems (robot hands) for limp materials too expensive or unreliable for  
your factory?  
Q4. Does the material handling difficulty alone require a human worker to supervise most robotic  
assembly steps?  
Q5. Do deformation or wrinkling issues caused by robot handling contribute significantly to  
production waste?  
Q6. Is finding commercially available automation designed specifically for your fabric type a major  
technical problem?  
Section 2: Measurement of Financial Strain Index (V2)  
Q7. Does theinitial cost of digital automation pose a severe financial strain on your firm?  
Q8. Is securing necessary external financing (loans, credit) for technology upgrades often difficult for  
your company?  
Q9. Are the maintenance costs of new AI and robotic systems prohibitively high compared to current  
labor costs?  
Q10. Is estimating a clear Return on Investment (ROI) over a 3-5 year period too uncertain to justify  
major technology purchases?  
Q11. Does uncertain ROI cause top management to prefer delaying technology investment in favor of  
traditional labor?  
Q12. Has uncertainty about future market demand made managers postpone digitalization projects?  
Section 3: Measurement of Market Demand (V3)  
o Q13. Are international buyers' demands for transparency and traceability a key reason why your firm  
must track product history and data?  
o Q14. Has your firm already begun investing in technology specifically to meet foreign market  
requirements for digital data sharing?  
o Q15. Is failing to meet global market demands for transparency considered a high business risk by your  
management?  
o Q16. Have specific international buyers directly requested that your firm implement digital data sharing  
systems to monitor product life cycles?  
o Q17.Do international market demands influence your technology decisions more strongly than domestic  
competition does?  
o Q18. Is the fear of losing business with foreign buyers a primary reason you are currently researching  
new SCM technologies?  
Section 4: Measurement of Adoption (V4)  
Q19. Has the technology you adopted substantially improved your ability to predict material shortages  
before they occur?  
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Q20. Do you use real-time data monitoring to achieve immediate visibility into production line quality  
and performance?  
Q21. Can your firm immediately locate any product unit or batch within the supply chain using its  
current digital systems?  
Q22. Have digital systems significantly reduced the time needed to detect and respond to sudden  
manufacturing anomalies?  
Q23. Do your managers use data analytics to proactively identify new market opportunities or shifts in  
buyer demands?  
Q24. Is data collected from lot sensors systematically integrated as a result of the adoption process to  
inform daily operational decisions?  
Section 5: Measurement of Supply Chain Resilience (V5)  
Q25. Can your firm quickly reconfigure its production schedules to cope with unexpected delivery delays  
(resilience)?  
Q26. Have your digital investments led to a statistically significant decrease in operational downtime  
over the last year?  
Q27. Does your current supply chain structure allow you to rapidly switch sourcing channels when  
faced with a supplier bankruptcy?  
Q28. Has your firm maintained normal output levels during recent periods of global logistics or  
economic shocks?  
Q29. Haveyourdigital investments successfullyenhanced your firm'sabilityto maintain continuity of  
operations during major disruptions?  
Q30. Can your manufacturing system maintain quality standards even when operating under high stress  
or unexpected volume changes?  
RSFISRSNCSS  
1. Hoque, M. A., Rasiah, R., Furuoka, F., & Kumar, S. (2021). Technology adoption in the apparel industry:  
insight from literature review and research directions. Journal of Fashion Marketing and Management: An  
International  
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847612_Technology_adoption_in_the_apparel_industry_insight_from_literature_revie  
w_and_research_directions))))))))))))))))))))))))))))))))))))))))  
2. Wang, L., Chen, Y., & Zhang, W. (2024). Analyzing barriers to AI adoption in the apparel-manufacturing  
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3. Li, J., Chen, X., & Zhao, Y. (2024). A Vision-Guided Adaptive and Optimized Robotic Fabric Gripping  
System for Garment Manufacturing Automation. IEEE Transactions on Automation Science and  
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turing_Automation)  
5. Hoque, M. A., & Rahman, M. (2024). Antecedents and firm performance of sustainable technology  
adoption in the clothing industry: A study based on the technology-organization-environment  
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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue XI, November 2025  
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