Digitalizing sustainability in Pakistan’s textile sector: An investigation of lean digital transformation adoption
Masooma Zahra et al.
What the paper says
Rapid digital technology adoption is reshaping manufacturing worldwide, yet Pakistan’s textile industry faces challenges – outdated practices, high innovation costs, and limited lean operations – that constrain competitiveness and sustainability. Addressing this gap, the study adapts the Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, extending it with lean thinking and sustainability concerns to identify drivers of digital transformation. Data were gathered via a cross-sectional survey of 124 stakeholders from small, medium, and large textile industries in Punjab, Pakistan, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings indicate that effort expectancy, performance expectancy, facilitating conditions, innovation cost, lean thinking, and social influence significantly predict behavioural intention toward adopting digital technologies. Moderation analysis reveals that firm size amplifies the effects of performance expectancy, lean thinking, facilitating conditions, innovation cost, and social influence on adoption intention, while sustainability concern does not show a significant impact. Moreover, behavioural intention robustly forecasts actual adoption behaviour. These results underscore the need for robust digital infrastructure and integrated lean management practices, together with cost-effective strategies, to drive digital transformation in Pakistan’s textile sector. Policy recommendations urge targeted interventions that enhance digital competencies and provide tailored support for small- and medium-sized enterprises, thereby boosting industrial competitiveness and sustainable growth.
5 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.41 × 0.4 = 0.16 |
| M · momentum | 0.63 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.