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Technological innovation is one of the strongest driving forces in the survival and growth of any organization, including textile and apparel industries. However, technological innovation inherits a wide array of risks due to the uncertainty involved in it. In-depth research reveals the existence of a significant relationship between innovation failures and the approach used to innovate, that is, the organization’s innovator type. However, quantitative evidence supporting this concern is still lacking. Hence, the purpose of this paper is to bridge the existing gap in the literature on effective management of technological innovation risk factors and the innovator type of textile and apparel industries.

The risk factors related to technological innovations are identified under different innovator types. Analytic network process (ANP) has been used to evaluate the contribution of risk factors according to the innovator type of the organization. Data was gathered through the literature review and structured and semi structured interviews with textile and apparel industry experts. The contribution of risk factors was determined through priorities, derived according to the ANP using Super Decision software.

Contribution of risk factors takes different values according to innovator type. This provides comprehensive knowledge on developing a risk management strategy according to the innovator type of the organization. Furthermore, this provides insight into the fact that a generalized risk management strategy will not be effective and sensible for all innovator types.

The findings provide a thorough understanding of developing a customized risk management strategy by determining the “most to least” criticality of risks based on the innovator type of the organization. Furthermore, findings can be used to adopt the most appropriate innovator type based on the organization’s key competencies. Moreover, this guides the organization in making the best use of internal resources during risk management. Furthermore, this provides insight into the risk factors that must be addressed prior to embarking on new innovative approaches.

Technological innovation has become a significant part of textile and apparel industries. To become distinguished from competitors, it is essential to harness the potentials of the organizations in achieving higher speeds, faster delivery times, more precise research and development processes followed by modern manufacturing techniques, lower inventory and lower costs. This paper aims to present the risk factors, root causes and their impact related to technological innovation. This uncovers deeper problems inherent to the technological innovation process while providing insight to develop a more robust risk management strategy in marking the survival and growth of the organizations in the textile and apparel trade.

Literature review and structured interviews with industry experts were conducted to identify the risk factors and root causes associated with technological innovations related to textile and apparel industries. The impact of root causes to the risk factors was determined through priorities, derived according to analytic network process using Super Decision software.

Impact of the root causes on risk factors take different priorities, highlighting the most alarming root causes, which contribute more to the outcome. This provides insight for a more precise decision-making on the order of prioritization of root causes in managing risks involved.

Findings of this research provide insight on the most influential competencies of any textile and apparel organization for a thriving innovation. Profound knowledge on risk factors, root causes and their contribution to the outcome enhances complex decision-making. This ultimately leads to “credible performances’’ of the technological innovation and optimizing the resources available, which bridges the gap that exists in the current literature.

Sewing machine operators face a significant health risk in the form of work-related musculoskeletal disorders (WMSDs). Although the importance of adopting dynamic postures to alleviate musculoskeletal symptoms is researched, a substantial proportion of operators still experience WMSDs. This study aims to fill a gap in the literature by analysing the impact of dynamic posture, as opposed to static ones, on musculoskeletal symptoms among sewing machine operators.

A quantitative research approach was used for this study. Data were gathered from 1,543 sewing machine operators in Sri Lankan textile and apparel manufacturing sector, encompassing seated, standing and dynamic working postures. The participants were selected using stratified random sampling, and data collection was conducted via a pre-tested, interviewer-administered questionnaire based on the extended translated version of the Nordic Musculoskeletal Questionnaire. To analyse the data, a one-way analysis of variance was performed to examine the impact of current working postures on pain and discomfort, considering various demographic variables such as age and gender.

The results indicate that 58.24% of sewing machine operators reported suffering from pain/discomfort due to work activities. Individuals in dynamic postures (21%) reported the least pain (52.7%), while majority of female sewing machine operators declared of having pain/discomfort as a result of work activities they perform. However, foot/ankle pain was common across postures. In summary, the discussion reveals the nuanced relationship between working postures and musculoskeletal pain/discomfort among sewing machine operators.

Enhancing ergonomics through dynamic postures has implications that extend beyond the textile and apparel manufacturing industry, offering valuable insights applicable to a wide range of workplace environments. This study highlights the importance of further research, particularly in assessing the effectiveness of ergonomic interventions designed to reduce musculoskeletal symptoms among sewing machine operators. It also underscores the importance of incorporating anthropometric data, adopting a holistic approach that considers factors such as the work environment, equipment design and worker health, and ensuring a more balanced or gender-specific sample in future studies.