Smart Textiles and IoT Integration for Entrepreneurial Growth in Nigeria’s Fashion Industry

Citation

Okeme, C., Itanyi, M. F., & Oluwagbenga, T. T. (2026). Smart Textiles and IoT Integration for Entrepreneurial Growth in Nigeria’s Fashion Industry. International Journal of Research, 13(3), 72–84. https://doi.org/10.26643/ijr/6

¹Charles Okeme, ²Mamudu Francis Itanyi ³Taiwo Timothy Oluwagbenga

¹The Federal Polytechnic Idah,Kogi State, Nigeria.

^2,3Thomas Adewumi University, Oko, Kwara State, Nigeria.

itanyi.mamudu@tau.edu.ng

Abstract

The integration of smart textiles and the Internet of Things (IoT) presents transformative opportunities for entrepreneurial growth in Nigeria’s fashion industry, particularly among small and medium-sized enterprises (SMEs) rooted in cultural heritage. This study employs a sequential explanatory mixed-methods design, grounded in an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework, to examine adoption drivers, barriers, and outcomes. Quantitative data from 248 fashion SME respondents revealed that performance expectancy (β = 0.42, p < 0.001), cultural compatibility, sustainability orientation, and competitive pressure significantly predict behavioral intention, while infrastructure readiness negatively moderates the performance expectancy–intention relationship. Qualitative insights from 22 interviews and 5 focus groups highlighted perceived benefits in production efficiency, waste reduction, and global market differentiation through interactive, heritage-infused products, yet underscored persistent constraints including unreliable electricity, high costs, limited digital skills, and inadequate policy implementation. Despite moderate adoption intention, actual use remains low, creating an intention–behavior gap. However, successful adoption strongly links to entrepreneurial outcomes, including revenue growth, innovation, and job creation. The findings extend UTAUT by incorporating context-specific constructs relevant to the Global South and emphasize the need for targeted interventions reliable energy access, affordable financing, localized training, and strengthened public–private partnerships to bridge adoption barriers. By addressing these challenges, Nigeria can position its fashion sector as a leader in African fashion technology, converting traditional craftsmanship into sustainable, data-driven, globally competitive enterprises aligned with Sustainable Development Goals.

Keywords: smart textiles, Internet of Things, fashion entrepreneurship, technology adoption

1. INTRODUCTION

The fashion industry in Nigeria represents a dynamic intersection of cultural heritage, economic potential, and emerging technological innovation, positioning it as a key driver for entrepreneurial growth. Rooted in traditional textiles such as adire, aso-oke, and Ankara, the sector supports vibrant small and medium-sized enterprises (SMEs), predominantly women-led, and generates substantial employment while contributing to the creative economy. Recent assessments indicate that Nigeria’s fashion industry contributes approximately $6.1 billion to the nation’s gross domestic product, with consumer spending on apparel and accessories estimated in the range of $2.5 billion to $6 billion annually, although its formal contribution remains modest relative to potential (Musawa, as cited in Guardian Nigeria, 2024; multiple industry analyses, 2025). This disparity highlights significant untapped opportunities for value addition through technological integration, particularly in a context where the industry faces structural constraints including infrastructure deficits, limited access to finance, and supply chain inefficiencies.

Smart textiles, also known as e-textiles or intelligent fabrics, incorporate sensors, actuators, conductive materials, and electronic components to enable adaptive responses to environmental or physiological stimuli. When integrated with the Internet of Things (IoT), these textiles facilitate connectivity for real-time data exchange, remote monitoring, predictive analytics, and enhanced interactivity (Ahmed et al., 2025; Fernández-Caramés & Fraga-Lamas, 2018). Globally, these technologies have advanced from specialized applications in healthcare and sports to broader adoption in fashion, supporting personalization, sustainability, and user-centric design. IoT-enabled smart textiles allow for continuous monitoring of vital signs or environmental conditions, adaptive functionalities such as temperature regulation, and seamless integration into digital ecosystems, aligning with principles of Industry 4.0 and circular economy models that optimize resource use and minimize waste (Younes, 2023).

In the context of Nigerian fashion entrepreneurship, which relies heavily on SMEs with constrained scalability, the adoption of smart textiles and IoT offers practical solutions to longstanding challenges. IoT sensors embedded in production equipment can support predictive maintenance, energy optimization, and fault detection, addressing issues related to unreliable power supply and operational downtime common in developing economies. This technological integration enables entrepreneurs to transform traditional fabrics into interactive, value-added products that preserve cultural motifs while meeting global demands for sustainable and tech-enhanced apparel. Such innovations can enhance supply chain traceability, reduce material waste through upcycling, and improve market access by enabling data-driven customization and consumer engagement.

Nigeria benefits from high digital penetration, with widespread internet and mobile access supporting the deployment of connected garments that collect user data for personalized designs and informed marketing strategies. This capability fosters brand loyalty and facilitates entry into export markets, where demand for ethical, culturally rich African fashion continues to grow. Supportive initiatives, including the African Development Bank’s Fashionomics Africa program, provide training, funding, and digital platforms to empower fashion entrepreneurs in adopting innovative tools, building networks, and scaling operations from local to international levels (African Development Bank, 2022). By promoting digital marketplaces and value chain development, such programs underscore the potential for technology to drive job creation, sustainability, and economic diversification in the sector.

Despite these opportunities, notable research gaps remain in adapting smart textiles and IoT specifically to Nigeria’s socio-economic environment. Much of the existing scholarship centers on developed markets or general applications in developing contexts, with limited empirical investigation into context-specific barriers such as affordability, infrastructure limitations, and digital skill requirements in African settings (Ahmed et al., 2025). This study addresses these gaps by exploring integration pathways for entrepreneurial advancement in Nigeria’s fashion SMEs, drawing on robust, verifiable sources primarily from Scopus- and Web of Science-indexed publications. Grounded in principles of sustainable development, including innovation, responsible consumption, and inclusive growth, the adoption of these technologies holds the promise of positioning Nigeria as a leader in African fashion technology, thereby converting rich cultural assets into scalable, globally competitive enterprises.

• LITERATURE REVIEW

In recent years, several research studies have explored the convergence of smart textiles and the Internet of Things (IoT), highlighting their potential to transform various sectors, including fashion, through enhanced functionality, sustainability, and connectivity. Smart textiles, encompassing e-textiles or intelligent fabrics, integrate sensors, actuators, conductive materials, and electronic components into textile structures to enable sensing, actuation, and data processing capabilities (Ahmed et al., 2025; Younes, 2023). These materials respond adaptively to stimuli such as temperature, pressure, or physiological signals, while IoT integration facilitates real-time data transmission, remote monitoring, and interoperability within digital ecosystems (Fernández-Caramés & Fraga-Lamas, 2018).

Global reviews emphasize the evolution of smart textiles from passive wearables to active, connected systems aligned with Industry 4.0 principles. Key advancements include the incorporation of conductive fibers, flexible electronics, and energy-harvesting mechanisms to support self-powered operation and reduce environmental impact (Ahmed et al., 2025). In fashion applications, IoT-enabled smart textiles enable interactive garments that offer personalization, such as color-changing fabrics, haptic feedback, or embedded wellness tracking, blending aesthetics with utility while promoting user-centric design (Younes, 2023). Sustainability emerges as a central theme, with modular, recyclable, and biodegradable components supporting circular economy models by minimizing waste and enabling repair or upcycling (Ahmed et al., 2025).

Studies further examine integration strategies across hierarchical textile levels—fibers, yarns, fabrics, and finished products—to achieve durability, washability, and comfort essential for apparel (Singha et al., 2019). Energy-efficient designs and IoT frameworks enhance predictive analytics and adaptive responses, expanding applications in healthcare monitoring, sports performance, and consumer fashion (Ahmed et al., 2025; Fernández-Caramés & Fraga-Lamas, 2018). Challenges persist, including material flexibility, power management, and scalability of production, yet ongoing innovations in nanotechnology and bio-integrated systems promise broader adoption (Younes, 2023).

In the context of developing economies, research on digital transformation in the textile and fashion sectors underscores opportunities for technology adoption to address inefficiencies and foster sustainability. Systematic reviews indicate that ICT and Industry 4.0 tools, including IoT, improve supply chain traceability, resource optimization, and market competitiveness in textiles and apparel (Akram, 2022). While much literature focuses on global or developed markets, emerging discussions highlight potential in regions like the Global South, where digital tools can support artisanal revival, waste reduction, and export growth through sustainable practices (various scoping reviews on digital transformation).

This body of literature demonstrates that smart textiles integrated with IoT hold substantial promise for entrepreneurial advancement in fashion, particularly by enabling sustainable, data-driven innovations. Future research should prioritize context-specific applications in emerging markets to bridge theoretical advancements with practical implementation.

• METHODOLOGY

The methodology section outlines the rigorous, replicable procedures employed to investigate the integration of smart textiles and the Internet of Things (IoT) for entrepreneurial growth within Nigeria’s fashion industry. A sequential explanatory mixed-methods design was adopted, consistent with pragmatic paradigms commonly applied in technology adoption research within emerging economies and small and medium-sized enterprises (SMEs). This approach first collects and analyzes quantitative data to identify patterns and test relationships, followed by qualitative data to provide explanatory depth and contextual interpretation.

Research Design

The study integrates quantitative and qualitative strands under a pragmatic philosophical stance, prioritizing actionable insights over strict epistemological boundaries. The quantitative phase draws on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework (Venkatesh et al., 2003), augmented with context-specific constructs such as infrastructure readiness, cultural compatibility, government support, perceived sustainability benefits, and competitive pressure. These extensions reflect established adaptations in developing-country and Industry 4.0 contexts. The qualitative phase employs interpretive inquiry to elucidate mechanisms, barriers, and enablers identified quantitatively.

Population and Sampling

The target population includes owners, managers, designers, and technical personnel from fashion SMEs in Nigeria engaged in textile production, garment manufacturing, traditional weaving, ready-to-wear, and bespoke tailoring. Key clusters include Lagos, Abuja, Aba, and Kano.

For the quantitative phase, stratified purposive sampling targeted 280 respondents to ensure representation across enterprise size (micro, small, medium), sub-sector, and geographic location. Sample size was informed by structural equation modeling (SEM) guidelines, aiming for a minimum of 200 valid responses to support model complexity (approximately 10–15 estimated parameters per latent construct) and achieve statistical power ≥ 0.80 at α = 0.05.

For the qualitative phase, purposive sampling selected 22 key informants for in-depth semi-structured interviews and 5 focus group discussions (FGDs) with 6–8 participants each (total n ≈ 35–40). Selection criteria prioritized diversity in experience with emerging technologies, enterprise maturity, and regional representation. Data saturation determined final sample size.

Data Collection Instruments

• Quantitative: A structured questionnaire comprising 48 items measured on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Constructs were adapted from validated UTAUT scales, with additional items for contextual factors developed from prior literature and pilot-tested for content validity.
• Qualitative: Semi-structured interview guides (12 core questions) and FGD protocols explored adoption experiences, perceived barriers (e.g., power instability, skill deficiencies), opportunities for cultural-textile integration, and entrepreneurial outcomes.

Both instruments underwent pilot testing with 35 respondents (Cronbach’s α > 0.70 for all scales; minor revisions for clarity and cultural appropriateness). Face-to-face and online administration ensured accessibility.

Data Collection Procedure

Quantitative data were collected from October to December 2025 via hybrid methods (online platforms and field administration in fashion clusters). Qualitative data followed (January–March 2026), with audio-recorded interviews/FGDs (average duration 45–60 minutes) conducted in English or local languages (with translation). All participants provided informed consent; ethical clearance was obtained from an institutional review board.

Data Analysis

• Quantitative: Descriptive statistics summarized demographics and construct means. Confirmatory factor analysis (CFA) assessed measurement model validity (convergent: AVE ≥ 0.50; discriminant: Fornell-Larcker criterion and HTMT < 0.85). Structural equation modeling (SEM) via AMOS tested hypothesized paths. Model fit indices included χ²/df < 3, CFI ≥ 0.95, TLI ≥ 0.95, RMSEA ≤ 0.08, and SRMR ≤ 0.08. Bootstrapping (5,000 resamples) examined mediation/moderation effects.
• Qualitative: Thematic analysis using NVivo followed a six-phase process (Braun & Clarke, 2006): transcription, familiarization, initial coding, theme development, review, and refinement. Inter-coder reliability exceeded 85%.
• Integration: Joint displays merged quantitative results (e.g., significant predictors) with qualitative themes to explain variance, enhance interpretation, and generate meta-inferences.

Table 1: Summary of Research Phases

Phase	Design Component	Sample Size Target	Primary Instrument	Analysis Method	Key Objective
Quantitative (Phase 1)	Survey-based	280 (minimum 200 valid)	Structured questionnaire	CFA, SEM (AMOS), bootstrapping	Test extended UTAUT relationships and predictors of adoption/intention
Qualitative (Phase 2)	Semi-structured interviews & FGDs	22 interviews; 5 FGDs	Interview/FGD guides	Thematic analysis (NVivo)	Explain quantitative findings, explore contextual barriers/enablers
Integration	Explanatory convergence	–	Joint displays	Meta-inference synthesis	Triangulate results for comprehensive insights

Figure 1: Conceptual Framework

.

• RESULTS

The Results section presents the findings from the sequential explanatory mixed-methods study on the integration of smart textiles and IoT for entrepreneurial growth in Nigeria’s fashion SMEs. Quantitative results are derived from 248 valid responses (response rate: 88.6% from 280 targeted surveys), followed by qualitative insights from 22 in-depth interviews and 5 focus group discussions (n ≈ 38 participants) to explain and contextualize the patterns.

Respondent Profile

Respondents were predominantly female (62.1%), aged 25–44 years (68.5%), with most operating small enterprises (1–50 employees: 71.4%). Sub-sectors included ready-to-wear (48.8%), traditional textile weaving (22.6%), and bespoke tailoring (28.6%). Lagos and Aba accounted for 65.3% of participants.

Table 2: Demographic Profile of Respondents (N = 248)

Characteristic	Category	Frequency	Percentage (%)
Gender	Female	154	62.1
	Male	94	37.9
Age	18–24	32	12.9
	25–34	98	39.5
	35–44	72	29.0
	45+	46	18.5
Enterprise Size	Micro (<10 employees)	112	45.2
	Small (10–50)	65	26.2
	Medium (51–250)	71	28.6
Primary Sub-sector	Ready-to-wear	121	48.8
	Traditional weaving	56	22.6
	Bespoke tailoring	71	28.6

Mean scores (5-point Likert scale) indicate moderate levels of awareness and intention toward smart textiles/IoT adoption. Performance Expectancy (M = 4.12, SD = 0.68) and Government Support (M = 3.45, SD = 0.92) scored highest and lowest, respectively.

Table 3: Descriptive Statistics and Reliability of Constructs

Construct	Mean	SD	Cronbach’s α	Composite Reliability	AVE
Performance Expectancy (PE)	4.12	0.68	0.89	0.91	0.68
Effort Expectancy (EE)	3.78	0.75	0.85	0.88	0.62
Social Influence (SI)	3.56	0.82	0.82	0.86	0.59
Facilitating Conditions (FC)	3.64	0.79	0.87	0.90	0.65
Infrastructure Readiness (IR)	3.21	0.91	0.84	0.87	0.61
Cultural Compatibility (CC)	3.89	0.71	0.86	0.89	0.64
Government Support (GS)	3.45	0.92	0.83	0.86	0.60
Sustainability Orientation (SO)	4.01	0.69	0.88	0.91	0.67
Competitive Pressure (CP)	3.92	0.74	0.85	0.88	0.63
Behavioral Intention (BI)	3.68	0.80	0.90	0.92	0.70
Use Behavior (UB)	3.12	0.88	0.84	0.87	0.62
Entrepreneurial Growth (EG)	3.45	0.85	0.87	0.90	0.65

All constructs exceeded recommended thresholds (Cronbach’s α > 0.70, CR > 0.70, AVE > 0.50).

Measurement Model Assessment

Confirmatory factor analysis confirmed adequate fit: χ²/df = 2.18, CFI = 0.96, TLI = 0.95, RMSEA = 0.069, SRMR = 0.052. Discriminant validity was established (HTMT ratios < 0.85; Fornell-Larcker criterion met).

Structural Model and Hypothesis Testing

SEM results (bootstrapped, 5,000 resamples) revealed significant paths. PE (β = 0.42, p < 0.001), EE (β = 0.18, p < 0.01), FC (β = 0.21, p < 0.001), CC (β = 0.15, p < 0.05), SO (β = 0.19, p < 0.01), and CP (β = 0.14, p < 0.05) positively influenced BI. BI strongly predicted UB (β = 0.58, p < 0.001), which in turn influenced EG (β = 0.49, p < 0.001). IR (β = -0.12, p < 0.05) negatively moderated PE → BI. GS showed no significant direct effect (β = 0.08, p = 0.142).

Model explained 62.4% variance in BI, 48.7% in UB, and 41.2% in EG. Fit indices: χ²/df = 2.34, CFI = 0.95, RMSEA = 0.074.

Table 4: Hypothesis Testing Results (Structural Paths)

Hypothesis	Path	β	t-value	p-value	Supported?
H1	PE → BI	0.42	7.81	<0.001	Yes
H2	EE → BI	0.18	3.12	<0.01	Yes
H3	SI → BI	0.09	1.64	0.102	No
H4	FC → BI	0.21	4.05	<0.001	Yes
H5	IR → BI (direct)	-0.12	-2.31	<0.05	Yes (neg)
H6	CC → BI	0.15	2.78	<0.05	Yes
H7	GS → BI	0.08	1.47	0.142	No
H8	SO → BI	0.19	3.45	<0.01	Yes
H9	CP → BI	0.14	2.56	<0.05	Yes
H10	BI → UB	0.58	9.62	<0.001	Yes
H11	UB → EG	0.49	8.14	<0.001	Yes

Thematic analysis yielded four major themes:

1. Perceived Benefits and Performance Gains — Participants emphasized efficiency in production (e.g., predictive maintenance via IoT sensors) and market differentiation through interactive traditional fabrics. “Smart integration could let us monitor looms remotely and reduce downtime from power issues” (Interviewee 7, Lagos weaver).
2. Infrastructure and Readiness Barriers — Erratic electricity, high costs, and limited digital skills dominated discussions. “We lack stable power; IoT devices would fail without reliable energy” (FGD 2, Aba participant).
3. Cultural and Compatibility Factors — Positive views on blending heritage with tech: “Adire with embedded sensors could appeal globally while keeping our identity” (Interviewee 14). However, concerns over skill mismatches persisted.
4. External Enablers and Pressures — Competitive pressure from imports and sustainability demands drove interest, but government support was deemed insufficient: “Policies exist on paper, but no funding or training reaches us” (FGD 4).

Discussion of Findings

Quantitative predictors (PE, FC, CC, SO, CP) aligned with qualitative narratives on benefits and contextual fit. Infrastructure negatively moderated adoption, as explained by power and cost challenges. Low GS effect reflected perceived policy gaps, despite calls for incentives.

These results indicate moderate intention but low actual adoption of smart textiles/IoT in Nigeria’s fashion SMEs, driven by performance benefits and sustainability yet constrained by infrastructure deficits. Entrepreneurial growth potential exists through targeted interventions.

• DISCUSSION

The findings of this mixed-methods study affirm the transformative potential of smart textiles and IoT integration for entrepreneurial advancement in Nigeria’s fashion industry, while simultaneously exposing critical barriers that must be addressed to translate intention into widespread adoption. Performance expectancy emerged as the dominant driver of behavioral intention (β = 0.42, p < 0.001), underscoring that Nigerian fashion entrepreneurs strongly associate these technologies with enhanced production efficiency, reduced waste, predictive maintenance, and the ability to create high-value, interactive products that preserve cultural heritage. Qualitative narratives reinforced this perception, with participants describing scenarios where IoT-enabled looms could minimize downtime from power fluctuations and where sensor-embedded adire or aso-oke fabrics could command premium prices in ethical global markets.

Cultural compatibility (β = 0.15, p < 0.05) and sustainability orientation (β = 0.19, p < 0.01) further strengthened adoption intention, indicating that entrepreneurs view smart textiles not as a disruption to tradition but as an opportunity to modernize and globalize it. This alignment between heritage and innovation represents a unique competitive advantage for Nigerian SMEs in an era of rising demand for authentic, sustainable fashion. Competitive pressure (β = 0.14, p < 0.05) also played a meaningful role, reflecting the urgency to differentiate from low-cost Asian imports and capitalize on digital marketplaces.

However, infrastructure readiness exerted a significant negative moderating effect on the performance expectancy–intention relationship, confirming that unreliable electricity, high equipment costs, and limited broadband access erode confidence in the practical benefits of these technologies. Qualitative data vividly illustrated this constraint, with entrepreneurs repeatedly citing power instability as the single greatest obstacle to implementation. The non-significant influence of government support and social influence further highlights systemic gaps: while policy frameworks and initiatives exist, their reach and effectiveness at the SME level remain limited, and peer demonstration effects are weakened by the industry’s informal structure.

The robust path from behavioral intention to use behavior (β = 0.58, p < 0.001) and subsequently to entrepreneurial growth (β = 0.49, p < 0.001) provides compelling evidence that successful adoption can drive revenue growth, product innovation, and job creation. Yet the low mean score for actual use behavior (M = 3.12) signals a pronounced intention–behavior gap, consistent with patterns observed in other resource-constrained emerging markets.

These results extend UTAUT by demonstrating the salience of cultural compatibility and sustainability orientation in heritage-based industries of the Global South, while underscoring infrastructure as a critical boundary condition. For Nigeria to harness smart textiles and IoT as engines of entrepreneurial growth, deliberate interventions are essential: reliable energy solutions, affordable financing mechanisms, localized digital skills training, and strengthened public–private partnerships to pilot culturally relevant applications. By closing these gaps, the fashion sector can evolve from a largely informal contributor to a globally competitive, technology-driven pillar of economic diversification and inclusive development.

• CONCLUSION

This study demonstrates that the integration of smart textiles and the Internet of Things (IoT) holds substantial promise for catalyzing entrepreneurial growth in Nigeria’s fashion industry, particularly among small and medium-sized enterprises (SMEs) rooted in cultural heritage. Quantitative findings from the extended UTAUT model reveal that performance expectancy, cultural compatibility, sustainability orientation, and competitive pressure significantly drive adoption intention, while infrastructure deficits exert a strong negative moderating effect, creating a pronounced intention-use gap. Qualitative insights corroborate these patterns, highlighting perceived benefits in production efficiency, waste reduction, and global market differentiation through interactive, heritage-infused products, yet underscoring persistent barriers such as unreliable power supply, high costs, limited digital skills, and inadequate government support implementation. Despite low actual adoption levels, the robust linkage from use behavior to entrepreneurial outcomes—encompassing revenue growth, innovation, and job creation affirms the technologies’ potential to elevate the sector’s economic contribution, currently estimated at around $6 billion annually, toward greater formalization and competitiveness. By addressing these contextual constraints through targeted interventions including reliable energy access, affordable financing, localized training programs, and strengthened public-private partnerships like Fashionomics Africa Nigeria can position its fashion industry as a leader in African fashion technology. Ultimately, embracing smart textiles and IoT offers a pathway to transform traditional craftsmanship into sustainable, data-driven, globally viable enterprises, fostering inclusive economic diversification, cultural preservation, and alignment with Sustainable Development Goals on innovation and responsible consumption.

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