Exploring the impact of university-driven supplier development interventions on supplier performance: a case of the garment industry

Seyed Pendar Toufighi (Department of Innovation and Technology, Global Sustainable Production Section, University of Southern Denmark, Odense, Denmark)
Jan Vang (Department of Innovation and Technology, Global Sustainable Production Section, University of Southern Denmark, Odense, Denmark)
Kannan Govindan (Department of Technology and Innovation Danish Institute for Advanced Study, Center for Sustainable Supply Chain Engineering, University of Southern Denmark, Odense, Denmark)
Min Zar Ni Lin (Department of Innovation and Technology, Global Sustainable Production Section, University of Southern Denmark, Odense, Denmark)
Amanda Bille (Department of Innovation and Technology, Global Sustainable Production Section, University of Southern Denmark, Odense, Denmark)

International Journal of Productivity and Performance Management

ISSN: 1741-0401

Article publication date: 20 November 2024

Issue publication date: 16 December 2024

342

Abstract

Purpose

The purpose of this study is to investigate the effectiveness of university-driven knowledge transfer initiatives in enhancing the capabilities and performance of local suppliers in the garment industry. By focusing on the impact of UDIs in Myanmar, this research aims to provide empirical evidence on how these initiatives can foster supplier development and performance improvement through targeted capability enhancement strategies.

Design/methodology/approach

This study utilizes a combination of surveys and an experimental design to evaluate the impact of university-driven supplier development interventions (UDIs) based on Lean principles in Myanmar’s garment industry. Nine garment suppliers were assessed before and after the UDI program. The research employed partial least squares structural equation modeling (PLS-SEM) to analyze the direct, indirect and mediating effects of UDIs on supplier performance, focusing on the role of supplier capability enhancement as a mediating factor.

Findings

The study found that the UDI program significantly improved supplier capabilities, which in turn led to enhanced performance. The analysis revealed partial mediation, indicating that while UDIs directly impact supplier performance, their effect is significantly amplified through the enhancement of supplier capabilities. These findings highlight the critical role of targeted capability development in achieving substantial performance improvements among local suppliers.

Originality/value

This research contributes to the literature by providing empirical evidence on the effectiveness of university-driven supplier development initiatives in a developing country context. It validates the indirect role of UDIs in boosting supplier performance via capability enhancement, emphasizing the importance of industry-specific and capability-focused development strategies. The findings underscore the value of structured knowledge transfer programs in supporting local suppliers, offering practical insights for policymakers and educational institutions aiming to enhance industrial performance through strategic interventions.

Keywords

Citation

Toufighi, S.P., Vang, J., Govindan, K., Lin, M.Z.N. and Bille, A. (2024), "Exploring the impact of university-driven supplier development interventions on supplier performance: a case of the garment industry", International Journal of Productivity and Performance Management, Vol. 73 No. 11, pp. 355-384. https://doi.org/10.1108/IJPPM-06-2024-0405

Publisher

:

Emerald Publishing Limited

Copyright © 2024, Seyed Pendar Toufighi, Jan Vang, Kannan Govindan, Min Zar Ni Lin and Amanda Bille

License

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode


1. Introduction

As a significant contributor to manufacturing output and employment in Myanmar, the garment industry is critical to the country’s economy (Bae et al., 2021). Although the country is home to numerous suppliers, they face many challenges that hamper their competitiveness in the global markets (Hetzenauer et al., 2023). The lack of skilled labor has led to low productivity in garment factories due to inefficient processes and inconsistent quality (Karami et al., 2021; Habib et al., 2022). It is also important to note that suppliers have not adopted sustainability best practices as widely as they should, increasing environmental and social risks (Phan et al., 2020). Due to these issues, growth potential is limited, and there are fewer opportunities for attracting orders from large international brands (LeBaron et al., 2022).

Universities are well-situated through knowledge transfer programs that develop local capabilities to support industry development (Leydesdorff and Etzkowitz, 1998; Lundvall et al., 2011; Long and Young, 2016). Several prior studies have demonstrated the positive effects of Lean manufacturing principles on performance metrics when applied correctly (Flores et al., 2021; van Dun and Wilderom, 2021; Habib et al., 2022; Hoque and Maalouf, 2022). Continuous improvement, value stream mapping, elimination of bottlenecks, and just-in-time production are four principles of Lean manufacturing that aim at eliminating waste (Womack et al., 2007; Soltan and Mostafa, 2015; Åhlström et al., 2021). Companies can enhance their operations by optimizing operations, streamlining workflows, and redesigning workstations by 5S (Prince and Kay, 2003). Universities can use tailored interventions to facilitate meaningful transformation for local garment suppliers by introducing Lean concepts and tools (Hoque et al., 2020; Flores et al., 2021).

Dynamic capability theory (DCT) is particularly relevant to this study as it emphasizes the importance of an organization’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments (Jia et al., 2023; Kähkönen et al., 2023). Unlike the Resource-based view (RBV), which is often critiqued for its static nature, DCT acknowledges the dynamic nature of resources and capabilities, making it a more suitable theoretical framework for examining the enhancement of suppliers’ capabilities through university-driven interventions (Vanpoucke et al., 2014; Gu et al., 2021). By adopting DCT, this study positions itself to better explore how suppliers can adapt and evolve their capabilities in response to the interventions introduced. The goal of this study is to determine the impact of university-led supplier development interventions (Oliva, 2019; Hasle and Vang, 2021a, b; Chandrasekaran et al., 2023; Gao et al., 2023), illustrated by the case of a UDI aimed at introducing Lean principles within the Myanmar garment industry. It responds to recent calls for more intervention-based research (Oliva, 2019; Chandrasekaran et al., 2023). Suppliers were guided to apply certain factory tools and techniques in a structured training module. The purpose of the intervention was to address issues that undermine competitiveness, like low levels of Productivity (Longoni and Cagliano, 2015; Latif and Vang, 2021; Habib et al., 2022), inconsistent quality levels, and long lead times (Phan et al., 2020) and poor working conditions (Hasle and Vang, 2021).

1.1 Developing the research question

Despite the growing body of literature on supplier development and the benefits of lean manufacturing, there is a notable gap in empirical research focused on the effectiveness of university-driven interventions in enhancing supplier capabilities, particularly in developing countries like Myanmar. The existing literature highlights the critical role of knowledge transfer and internal capabilities in driving innovation across various industries, including manufacturing and the garment sector. Awan et al. (2021) explore how export firms in Pakistan organize knowledge management to enhance product innovation, emphasizing the importance of buyer-driven knowledge transfer and absorptive capacity (Awan et al., 2021). Similarly, Awan, Arnold and Gölgeci (2021)investigate the impact of knowledge acquisition and environmental investments on green innovation, finding that buyer-driven activities significantly influence both product and process innovations, particularly when firms invest in environmental management (Awan et al., 2021). These studies underscore the importance of external knowledge resources and internal capabilities in achieving innovation outcomes. Toufighi et al. (2024) examine the role of participative leadership and cultural factors in fostering knowledge-sharing behavior within supplier development initiatives in the garment industry, revealing that leadership effectiveness and cultural dimensions play crucial roles in facilitating knowledge flow and employee engagement (Toufighi et al., 2024). Linking these insights to our research, which investigates the impact of university-driven supplier development interventions in Myanmar’s garment industry, we see a consistent theme: external knowledge transfer, whether from buyers or universities, enhances internal capabilities that drive performance improvements. Our study builds on this literature by focusing on how UDIs enhance supplier capabilities and performance, particularly in a developing country context, thereby contributing to the broader discourse on the mechanisms through which external interventions influence organizational outcomes.

This study seeks to provide both theoretical and empirical insight into the benefits of leveraging university expertise to support knowledge transfer initiatives within the context of intervention research by assessing the impact of the program on key metrics, including productivity, quality, lead time, and adoption of social standards (Oliva, 2019) and drawing theoretical implications for concerning the role of universities in the supplier development field. In addition, the long-term goal is to improve supplier performance to attract orders and contribute to the advancement of the industry in general (Krause and Ellram, 1997; Jia et al., 2023; Vang et al., 2023). This research aims to fill the knowledge gap regarding university involvement’s practical applications and outcomes in industry collaborations, especially within developing Asian contexts (Lundvall et al., 2011). Myanmar’s garment industry is important to the country, and the findings suggest strategies to support the industry’s continued growth. This research makes several significant contributions. Firstly, it addresses a gap in empirical research by investigating the effectiveness of university-led supplier development programs, which has received limited attention in the literature. The study utilizes Lean principles and university-driven supplier development interventions (UDIs) to examine how such interventions affect supplier performance in Myanmar’s garment industry. Secondly, the research contributes by demonstrating that the enhancement of supplier capability, facilitated through the UDI, plays a mediating role in improving supplier performance. This finding highlights the importance of focusing on supplier capability enhancement as a means to achieve positive performance outcomes. Based on the provided research contributions, the following research questions (RQs) were formulated:

RQ1.

To what extent does a university-driven supplier development intervention influence supplier capability enhancement in the garment industry?

RQ2.

How does supplier capability enhancement influence supplier performance in the context of a university-led intervention?

RQ3.

How does a university-driven supplier development intervention influence supplier performance in the garment industry?

RQ4.

To what extent does supplier capability enhancement mediate the relationship between a university-driven supplier development intervention and supplier performance?

The study comprises a literature review in Section 2, methodology in Section 3, results and discussion in Sections 4 and 5, and the conclusion with future directions in Section 6.

2. Literature review and background

2.1 Supplier development and its significance in SCM

Supply chain management (SCM) practices have undergone a strategic shift from selecting vendors on a transactional basis and relying on arms-length measures for developing suppliers (for example, market pressure, certification processes, monitoring) (Krause and Ellram, 1997) to increasingly cultivating long-term, collaborative relationships with key suppliers through supplier development (Dyer and Singh, 1998; Humphreys et al., 2004; Dyer et al., 2018; Bonatto et al., 2020) with especially lead supplier. According to recent literature, the focal firm and its suppliers benefit from this approach, which results in enhanced capability and overall performance for both parties (Modi and Mabert, 2007). Supplier development interventions are employed to facilitate stricter control measures within the supplier’s organization to improve quality (Sillanpää et al., 2015). As a result, defects are reduced, and product consistency is improved (Benton et al., 2020; Latif et al., 2023). In addition to streamlining processes, collaborative efforts can identify and eliminate waste reduction opportunities, potentially resulting in more favorable pricing structures, and ultimately resulting in cost reductions (Bai and Satir, 2020). The collaboration between companies and suppliers on production planning and logistics also results in the optimization of delivery times, the reduction of bottlenecks, and the continuity of materials, which ultimately leads to shorter lead times (Subramaniam et al., 2020).

A supplier development program led to joint product development initiatives by promoting knowledge sharing and co-creation (Lawson et al., 2015). Supplier development must ensure adherence to these standards across the supply chain, especially in today’s globalized environment, where ethical and sustainability concerns are paramount (Longoni et al., 2013; Veldman et al., 2023). A well-documented fact is that supplier development plays an important role in a dynamic supply chain landscape (Govindan et al., 2021). Multiple factors lead to increased competitive advantage for global buyers and other MNCs when a supplier base becomes more robust. With the assistance of supplier development programs, higher quality products can be produced, faster delivery times may be accomplished through collaborative planning, and the possibility of lowering costs through process optimization and better pricing practices can be achieved (Adesanya et al., 2020; Giannakis et al., 2020).

Supplier development plays a crucial role in modern supply chain management, shifting from transactional vendor selection to long-term, collaborative relationships that enhance both supplier capabilities and overall performance (Kovalevskaya et al., 2024). This strategic shift is driven by the need for tighter control measures, streamlined processes, and collaborative efforts to reduce defects, improve product consistency, and lower costs. Supplier development programs also facilitate joint product development and knowledge sharing, contributing to resilience in the face of supply chain disruptions (Govindan and Jha, 2024).

2.2 University-driven intervention’s role in supplier development

As a tool for supplier development, interventions that lead to long-term improvements in performance are challenging due to tensions between productivity-enhancing and decent work logic (Hasle and Vang, 2021). This is, among others, because most research within supply development is case research (Krause and Ellram, 1997). However, the dynamic nature of many supply chains today makes it imperative for companies to develop their suppliers (Masoomi et al., 2022). At the same time, knowledge and practices related to SCM are increasingly being advanced by universities (Belhadi et al., 2024). To accomplish this, we see great potential in university-developed training interventions (UDIs) and see it as a necessity to examine UDIs that offer suppliers of, for instance, ready-made garments the opportunity to learn about quality control, lean manufacturing, and sustainable production practices. (Yu et al., 2018). Using university-based technical assistance, these programs enable suppliers to adopt new technologies and improve operational efficiency (Li, 2020).

The role of universities in societal and economic development is studied by exploring how university initiatives influence traditional crafts development, knowledge exchange mechanisms, and co-creation activities in the textile sector of the Northeast region of India (Meetei et al., 2024). The role of universities as contextual determinants of technological entrepreneurship was analyzed, revealing a positive influence through their supply of talented human capital, with this impact shaped by various regional factors (Zapata-Huamaní et al., 2024). The analysis extends the role of universities beyond the entrepreneurial triple helix, demonstrating that a mission approach can effectively encompass more than just science, technology, and economic outcomes by highlighting universities as safe convening spaces that bring together local actors to design and deliver a micro-missions approach (Henderson et al., 2023). SCM and operations management (OM) can be used to understand the impact of university-driven supplier development interventions, e.g. by introducing lean principles. To optimize efficiency and reduce waste in a production process, Lean principles are a set of practices and tools (Rosin et al., 2020; Gusmao Brissi et al., 2022). A supply chain can benefit from applying Lean principles in the context of supplier development by improving the efficiency and effectiveness of suppliers, thereby improving the entire supply chain’s performance (Powell and Coughlan, 2020; Latif et al., 2023). Research on this topic has significant implications for academia as well as industry. A university-driven supplier development intervention can be viewed from an academic perspective as a valuable resource for developing a better educational program and curriculum for supply chain management and operations management (Kim, 2015), developing research programs combining relevance and rigor (Oliva, 2019) in addition to impacting suppliers through applied intervention research (Hasle and Vang, 2021a, b).

University research needs to identify supply chain challenges specific to the garment industry, such as labor practices or environmental impact (Hamja et al., 2019; Habib et al., 2021). In the garment industry, universities significantly improve supplier performance by developing innovative solutions that benefit both companies and suppliers (Pournader et al., 2020). UDIs provide valuable insights into broader supply chain management practices, and they are especially relevant for lower-tier suppliers not prioritized by global buyers’ supplier development programs. This is because these interventions provide valuable insights that contribute to developing a more efficient and sustainable garment industry (Villena and Gioia, 2018). University-driven supplier development interventions offer a unique approach to enhancing supplier capabilities, particularly in developing regions where local suppliers face significant challenges. Universities can play a pivotal role in fostering innovation and collaboration within supply chains by providing technical assistance, facilitating knowledge transfer, and introducing advanced production practices. These interventions are particularly effective in addressing industry-specific issues such as quality control, lean manufacturing, and sustainability, ultimately leading to improved supplier performance and competitiveness.

2.3 Theoretical framework and hypothesis development

The theoretical framework of this research is anchored in DCT, which serves as the primary lens through which the impact of UDIs on supplier performance is examined. DCT emphasizes the importance of an organization’s ability to integrate, build, and reconfigure internal and external competencies to address rapidly changing environments (Teece et al., 1997). In the context of this study, DCT is particularly relevant because it recognizes that suppliers in the garment industry must continuously adapt to evolving market demands, technological advancements, and sustainability requirements. By applying DCT, this research explores how UDIs can enhance suppliers’ dynamic capabilities, enabling them to respond more effectively to these external pressures and, consequently, improve their performance.

The second key element of the theoretical framework involves the role of Lean principles as a vehicle for enhancing dynamic capabilities within supplier development. Lean principles, which focus on waste reduction, continuous improvement, and process optimization, are introduced to suppliers through UDIs as part of the university-led interventions. These principles are not only operational tools but also serve as mechanisms for developing the dynamic capabilities of suppliers. By adopting Lean practices, suppliers can become more flexible, responsive, and efficient, thus better positioning themselves to meet the challenges of the global garment industry. The integration of Lean principles within the DCT framework allows this research to investigate how operational improvements contribute to the broader goal of capability enhancement and sustained performance improvements.

Finally, the theoretical framework incorporates the concept of university-driven interventions as a catalyst for capability development. Universities, through their knowledge transfer programs, play a critical role in bridging the gap between academic knowledge and practical industry application. This research posits that UDIs provide suppliers with the necessary skills, knowledge, and tools to enhance their dynamic capabilities. The study hypothesizes that these interventions lead to measurable improvements in supplier performance by fostering innovation, improving operational efficiency, and enhancing sustainability practices. The framework thus links the dynamic capabilities of suppliers, facilitated by Lean principles and university interventions, to the ultimate outcome of improved supplier performance, providing a comprehensive understanding of the mechanisms through which these interventions achieve their intended impact.

As a result of the literature review, we have developed our proposed model addressing the research gap. In Figure 1, a conceptual framework is presented.

Universities facilitate the development of suppliers’ capabilities through supplier development interventions. DCT serves as the theoretical foundation for this research, emphasizing the importance of an organization’s ability to adapt, integrate, and reconfigure internal and external competencies to address rapidly changing environments (Teece et al., 1997). In the context of university-driven supplier development interventions, DCT suggests that these interventions act as external resources that can significantly enhance the dynamic capabilities of suppliers. When universities engage in supplier development through training programs, knowledge transfer, and collaborative efforts, they provide suppliers with the tools necessary to adapt to new challenges, innovate, and improve their operational efficiencies. Therefore, it is hypothesized that university-driven interventions directly contribute to the enhancement of supplier capabilities by equipping them with the dynamic capabilities needed to thrive in competitive markets. Based on these findings, the university will be able to enhance the capabilities of its suppliers when it initiates and implements a supplier development initiative. The intervention aims to improve the suppliers’ skills, expertise, and overall capabilities through training programs, knowledge transfer, or other collaborative efforts, e.g. joint introduction of participatory leadership. As a result of the literature, it is anticipated that university-driven interventions will enhance suppliers’ capabilities in a direct and positive manner, emphasizing the potential contribution of educational institutions to the development of their suppliers.

H1.

University-driven supplier development intervention positively influences supplier capability enhancement.

According to DCT, the development and enhancement of dynamic capabilities are crucial for achieving superior performance in volatile and complex environments (Eisenhardt and Martin, 2000). Suppliers with enhanced capabilities are better positioned to reconfigure their resources, improve their processes, and respond to changing market demands effectively. By acquiring new skills, knowledge, and technologies through university-driven interventions, suppliers are expected to demonstrate improved performance outcomes, such as increased efficiency, higher quality products, and greater competitiveness. The dynamic process of capability enhancement enables suppliers to continuously adapt to market changes, ultimately leading to better overall performance. Consequently, supplier capability enhancement improves supplier performance as a dynamic process by enabling adaptability and responsiveness to market demands. According to this study, suppliers will likely demonstrate improved efficiency, quality, and competitiveness when they acquire new skills, knowledge, and resources from university-driven interventions. Based upon the hypothesis, there is a direct and positive relationship between supplier capability enhancement and supplier performance, emphasizing the necessity of building and strengthening supplier capabilities and skills to affect the organization’s overall performance positively.

H2.

Supplier capability enhancement positively influences supplier performance.

The third hypothesis explores the direct impact of university-driven interventions on supplier performance, independent of changes in supplier capabilities. DCT supports the notion that external resources, such as those provided by universities, can directly influence organizational outcomes by fostering innovation and providing critical support systems (Teece, 2007). Universities, through their expertise, reputation, and credibility, can directly impact supplier performance by offering strategic guidance, facilitating the adoption of best practices, and providing access to new technologies. This direct influence underscores the importance of UDIs as standalone factors that can drive significant improvements in supplier performance, regardless of internal capability changes. According to the findings, universities provide valuable external support and guidance to suppliers through their credibility, reputation, or specific knowledge transfer (Geuna and Muscio, 2009). A positive impact on supplier performance is implied by an intervention rather than relying on changes in supplier capabilities. A university-driven initiative may directly impact supplier performance, emphasizing its importance as a standalone factor in improving supplier performance.

H3.

University-driven supplier development intervention directly and positively influences supplier performance.

Finally, the dynamic capabilities perspective also suggests that the relationship between external interventions and performance outcomes is often mediated by the development of internal capabilities (Teece et al., 1997; Helfat, 2009). In the context of this study, it is proposed that the impact of university-driven interventions on supplier performance is mediated by the enhancement of supplier capabilities. As universities introduce new knowledge and technologies, suppliers develop dynamic capabilities that allow them to better leverage these resources, thereby improving their performance. This hypothesis posits that the enhancement of supplier capabilities is a key mechanism through which UDIs exert their influence on performance outcomes, highlighting the critical role of continuous capability development in achieving long-term success. It has been suggested in the literature that the intervention indirectly affects the performance of suppliers by improving their capabilities. The university-driven intervention improves supplier capabilities, positively impacting overall supplier performance. As a result of these findings, it is possible to identify the mechanisms through which the university intervention exerts its impact on supplier performance, providing insight into the potential mediation role of supplier capability enhancement in the relationship between university intervention and supplier performance.

H4.

Supplier capability enhancement mediates the relationship between the University-driven supplier development intervention and supplier performance.

2.4 Research gap

The operation management processes in manufacturing have been the subject of research by several researchers compared to other processes in other sectors (Gawankar et al., 2017; Kamal et al., 2021). Multiple research studies have examined the conceptual model behind this investigation (Arráiz et al., 2013; Hasle and Vang, 2021a, b). An empirical study of supplier performance in the garment sector was conducted based on the framework presented in this paper and aimed to fill a knowledge gap by identifying the determinants of university-driven supplier development interventions (Hasle and Vang, 2021a, b). The researchers employed the structural equation modeling (SEM) in their existing empirical studies on supplier development (Table 1).

To conclude this literature review, we noticed how interventions aimed at enhancing the performance and capabilities of suppliers are typically referred to as supplier development interventions (Krause and Ellram, 1997; Saghiri and Mirzabeiki, 2021; Vang et al., 2023). These interventions aim to build collaborative relationships, improve supplier efficiency, and enable mutual growth among supply chain partners (Subramaniam et al., 2020), especially for lead suppliers. As outlined, universities are likely to play an important role in this development of suppliers, with an emphasis on their ability to foster innovation and collaboration in the supply chain. Several empirical case studies and systematic approaches have been identified to enhance buyer-supplier relationships (Shamsollahi et al., 2021). To explore research gap, we analyzed over 200 articles using VOS viewer software, which maps research networks. Figure 1 illustrates the main themes such as “interventions,” “market,” “buyer-supplier relationships,” and “supplier development.” Blue terms relate to “market” and “supply chain,” green terms to “knowledge” and “interventions,” and red terms to broader concepts like “research” and “collaboration.” Figure 2 shows that university-driven interventions are an emerging field in supplier development.

The literature on UDIs and their impact on supply chain performance reveals a significant focus on various industries and geographic contexts, yet also exposes several limitations and gaps that this study aims to address. The majority of the existing studies (e.g. Nakandala et al., 2023; Oubrahim et al., 2023; Manikas et al., 2022) have concentrated on manufacturing and electronics supply chains, leaving other critical sectors like garments underexplored. Only a limited number of studies, such as Hasle and Vang (2021a), have focused on the garment industry, which is crucial in many developing countries, including Myanmar. The garment sector is unique due to its labor-intensive nature, complex global supply chains, and significant socio-economic implications, particularly in regions where it is a major contributor to employment and economic development. The lack of comprehensive research on garment supply chains highlights the need for targeted studies that address the specific challenges and dynamics of this industry. The studies listed in Table 1 predominantly examine supply chains in more developed or rapidly developing regions such as Australia, Morocco, Turkey, Malaysia, the UAE, and China. These regions have relatively more advanced infrastructure, regulatory frameworks, and access to resources, which can influence the effectiveness of UDIs and their impact on supplier performance. Conversely, there is a notable lack of research in less developed or emerging economies, where the challenges and outcomes of UDIs may differ significantly due to varying economic conditions, levels of industrial maturity, and access to education and training resources. The present study, which focuses on Myanmar, addresses this geographic gap by examining the effects of UDIs in a developing country context, contributing valuable insights into how these interventions function in less developed environments. The Table 1 indicates a varied focus on key variables such as university-driven interventions, supplier capability enhancement (SCE), and supplier performance (SP). While several studies (e.g. Nakandala et al., 2023; Khan et al., 2022; Hasle and Vang, 2021a) have explored the relationship between UDIs and SCE or SP, very few have comprehensively examined all three variables simultaneously. The present study distinguishes itself by integrating these three critical variables within a single research framework, providing a holistic analysis of how UDIs influence both the enhancement of supplier capabilities and their subsequent impact on performance. This comprehensive approach not only fills a gap in the literature but also contributes to a more nuanced understanding of the mechanisms through which UDIs can drive sustainable improvements in supply chain performance.

The existing literature reveals several key gaps that this study aims to address. First, there is a lack of focused research on the garment industry, particularly within the context of developing economies like Myanmar. While studies have extensively explored the manufacturing and electronics sectors, the unique challenges and opportunities in garment supply chains have been relatively neglected. Second, the geographic concentration of research in more developed regions leaves a void in understanding how UDIs operate and impact supply chains in less developed countries. Third, the existing studies often examine university-driven interventions, supplier capability enhancement, and supplier performance in isolation, rather than in an integrated manner. This study addresses these gaps by investigating the impact of UDIs on supplier capability enhancement and performance within the garment industry in Myanmar, providing a comprehensive analysis that contributes to both theory and practice in the field of supply chain management.

3. Methodology

3.1 Study population, sample, and unit of analysis

This study investigates the impact of university-driven supplier development interventions that introduce Lean principles into Myanmar’s garment industry. The study population consists of garment factories operating in Myanmar, particularly those engaged in export-oriented production for European and Japanese markets. The sample for this study comprises nine garment factories, selected through a purposive sampling method, focusing on factories that demonstrated a willingness to participate, relevance to the intervention objectives, and representativity in terms of size and ownership structure. The unit of analysis is the individual factory, with data collected at the factory level to assess the effectiveness of the interventions. Specifically, the study focuses on mid-level management within these factories, who were directly involved in the implementation of Lean practices. This focus allows the research to examine how these managers’ capabilities and the factories’ overall performance were influenced by the interventions. The selected factories were all export-oriented and varied in ownership, including both local and foreign investors, ensuring that the findings would be generalizable across different ownership structures within the industry. Table 2 displays the demographic characteristics of the respondents. Most (40% of respondents) have less than one year of job experience. Female operator responses (95%) exceeded male replies.

3.2 Research design and data collection methods

The study employs a longitudinal research design, where data were collected after the intervention to capture changes in supplier capabilities and performance. This design allows for the observation of cause-and-effect relationships between the university-driven interventions and the resulting changes in the factories. While the research is intervention-based, it does not follow a quasi-experimental design because no control groups were established, and the study focused solely on the outcomes within the treated group. Instead, the design is better described as an intervention-based longitudinal study, which systematically tracks changes over time in the participating factories.

The data collection process involved both quantitative and qualitative methods. A pretested questionnaire, based on validated scales from previous studies, was distributed to workers and managers in the selected factories. The survey aimed to measure key variables such as supplier capability enhancement (SCE), supplier performance (SP), and the impact of university-driven interventions (UDI). A total of 520 questionnaires were distributed, with 472 completed responses collected, yielding a final sample size of 459 after excluding incomplete responses. The questionnaire was carefully translated and culturally adapted to ensure accuracy and relevance to the local context. Additionally, qualitative data were collected through follow-up interviews and focus group discussions with 57 frontline workers. These qualitative methods provided deeper insights into the processes and challenges associated with the implementation of Lean practices in the factories, complementing the quantitative data and aiding in triangulation to ensure the robustness of the findings.

Several validated scales for occupational health and safety – OHS (Aghaei et al., 2018; COPSOCIII, 2019) leadership/speaking up (Detert and Edmondson, 2011) were used to develop the questions. According to (Hasle et al., 2016), intervention protocols for the Bangladesh garment industry (Maalouf, 2015) were adapted to the local industrial and cultural context through open-ended interviews with experts and information. The postmortem analysis of data collection methods and surveys used in previous related interventions in Myanmar’s garment industry also inspired productivity scales (DMDP, 2020). Additionally, the questionnaire was translated back and forth to ensure cultural accuracy (Cuervo-Cazurra et al., 2016). Workers in Myanmar were asked questions concerning productivity, occupational health, safety (OHS), and leadership. For productivity, the subdimensions were divided into productivity perceived improvements, occupational safety, ergonomics, leadership, and active listening. There is a 0.88% response rate for analysis. A PLS-SEM analysis requires a minimum sample size of ten times, which meets the minimum criterion of the ten-time rule (Kabir et al., 2021; Seow et al., 2022). A Likert scale of 1–5 assessed each response, with 1 denoting strongly disagree and 5 denoting strongly agree. The factors, their sub-factors, the items, and the sources are displayed in Table 3.

3.3 Data analysis techniques

To analyze the data, the study employs a three-step statistical approach: exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM). These methods were chosen to rigorously assess the relationships between the study variables and to validate the proposed conceptual model. EFA was conducted to identify the underlying structure of the variables and to refine the measurement model by determining the most relevant constructs. This step helped in reducing the data to a more manageable number of factors, which were then used in subsequent analyses. CFA was used to test the validity and reliability of the factors identified in the EFA. This step involved evaluating the measurement model’s fit using various goodness-of-fit indices to ensure that the constructs accurately represented the underlying data. SEM was employed to test the hypothesized relationships between the university-driven interventions, supplier capability enhancement, and supplier performance. SEM allows for the simultaneous assessment of multiple relationships, making it an ideal tool for testing complex models. Given that the study’s primary goal is prediction and understanding the direct and indirect effects of the interventions, Partial Least Squares SEM (PLS-SEM) was chosen over covariance-based SEM due to its robustness in handling smaller sample sizes and non-normal data distributions. These analytical methods were selected to ensure the replicability of the study by other researchers. Each step of the analysis was carefully documented, with detailed descriptions provided for the modeling procedures and the software used. This transparency ensures that the study’s findings can be independently verified and replicated in future research.

3.4 Justification for the chosen approach

The longitudinal research design was selected for its ability to capture the dynamic changes in supplier capabilities and performance over time. Unlike quasi-experimental designs, this approach does not require control groups, which can be challenging to establish in real-world settings where interventions are applied universally within the study context. The use of PLS-SEM is justified by its suitability for exploratory research and its ability to handle complex models with multiple dependent variables, which is essential for testing the multifaceted impacts of the university-driven interventions. Several recent studies have employed similar methodologies in different contexts, providing a foundation for the approach used in this research. For instance, Gao et al. (2023) utilized a longitudinal study to assess the impact of Lean practices in manufacturing settings, while Oliva (2019) discussed the theoretical contributions of intervention-based research in supply chain management. These references underscore the validity of the chosen research design and analytical techniques, aligning this study with current best practices in the field.

4. Result

4.1 Exploratory factor analysis

We determined their fundamental characteristics using our set of variables to examine their fundamental aspect. UDI with SCE as a mediating influence was investigated through qualitative and quantitative methods. Three constructs of the model were assessed in a two-stage process. Three components were identified as potential measurement indicators by the principal component analysis. A factor analysis was conducted to determine the result’s significance since the Kaiser-Meyer-Olkin (KMO) measure (0.962) was substantially higher than the required critical value of 0.6. (Hair et al., 2017). Furthermore, Bartlett’s Test of Sphericity (BTS) results were significant (p < 0.01), which indicated that all the criteria for exploratory factor analysis were met. Table 4 illustrates the results.

4.2 The measurement model analysis

Three-step statistical approaches have been used to test hypotheses: exploratory factor analysis, confirmatory factor analysis, and structural equation modeling (SEM). This method identifies the relationships between model variables using a statistical method known as exploratory factor analysis. A more transparent construct model can be developed by analyzing the nature and pattern of constructs and choosing fewer constructs from a wide range of latent constructs (Williams et al., 2013; Hair et al., 2017). Using the confirmatory factor technique, the components are improved and validated to overcome the limitations of the experimental factor technique.

SEM uses confirmatory factors to provide greater clarity by using various goodness-of-fit measures to assess the validity of the predicted construct model (Chan, 2020). Because PLS-SEM was developed initially for prediction, it is recommended over covariance-based SEM software (e.g. AMOS). PLS-SEM is less sensitive to sample size than multivariate standard sample data; therefore, it is unnecessary to have multivariate standard sample data (Sarstedt et al., 2020). Moreover, the measurement model does not violate the normality assumptions for the sample data when it measures kurtosis and skewness. The quality of the measuring model is evaluated by utilizing multiple measurements. SEM with factor analysis is employed with a university-driven supplier development intervention in Myanmar’s garment industry to evaluate suppliers’ performance.

Exploratory factors, confirmatory factors, and SEM-integrated techniques justify the initial data analysis. Investigating causal relationships uses a structural equation modeling (SEM) component. Mediation often enhances an explanation of the causal relationship between the antecedent and the dependent variable (Lowry and Gaskin, 2014).

Table 5 shows that all constructs have values greater than 0.50. Factor loading values were also evaluated for reliability. There is a strong indication of reliability in all items with loadings greater than or equal to 0.5. (Hair et al., 2017). Figure 3 illustrates the confirmatory factor analysis results and Structural equation. The Composite Reliability (CR) and Cronbach’s Alpha (CB) values were utilized to evaluate the constructions’ reliability, as depicted in Table 6. The construct’s internal reliability must meet the CB and CR cutoff value of 0.70 (Nunnally, 1978). It can be seen that the reliability of the questionnaire based on the criterion of the structural capability coefficient is within the acceptable range, and the reliability of the questionnaire questions is confirmed (see Figure. 4).

To examine the constructions’ reliability, we utilized a twofold approach. It examines convergent and discriminant validity. The rho coefficient is also used to measure the internal reliability of structures. The rho coefficient is also called the Dillon-Goldstein coefficient. As (Chin, 1998) believes, the rho coefficient is more reliable than CB. If the value of this coefficient is more than 0.6, internal reliability is confirmed (Chin, 1998). Discriminant validity was evaluated using the Fornell-Larcker criterion and the Heterotrai–Monotrait (HTMT) ratio, as recommended by recent studies (Hair et al., 2019). According to Tables 5 and it can be seen that the reliability of the questionnaire based on the Rho criterion is within the acceptable range, and the internal stability of the questionnaire questions is confirmed. A construct’s Average Variance Extracted (AVE) must be more than 0.50 to be convergent and valid (Fornell and Larcker, 1981; Barclay and Smith Jr, 1995; Chan, 2020). The AVE findings in Table 6 illustrate that all constructs have AVE values over 0.30. As a result, the validity of all constructs is convergent.

To assess discriminant validity, we applied the Fornell-Larcker matrix (Fornell and Larcker, 1981). Table 6 contains the results of the investigation. According to Figure 3, The measurement model describes 0.389 variations for the supplier capability enhancement and 0.097 variances for supplier performance. Since such percentages are larger than and near 10%, the measurement model has significant and sufficient predictive power (Zhao et al., 2022). Furthermore, the HTMT ratio was calculated in Table 7 to provide additional evidence of discriminant validity. HTMT values below 0.85 indicate that constructs are distinct from one another (Henseler et al., 2015). The HTMT ratios for all constructs were below this threshold, further confirming the discriminant validity of the measurement model. As AVE is a pretty conservative measure, experts recommend that the use of CR alone is adequate to conclude convergent validity (Fornell and Larcker, 1981; Malhotra, 1996; Malhotra, 1996, p. 702; Fornell and Larcker, 1981). Also, (Hair, 2014b) recommended the use of indicator reliability (outer loadings (OLs) > 0.7) and AVE to measure convergent validity.

The inclusion of both the Fornell-Larcker criterion and the HTMT ratio in the analysis provides a more rigorous validation of the constructs’ discriminant validity, aligning with best practices in structural equation modeling (Hair et al., 2019). The comprehensive approach taken in this study ensures that the results are both reliable and valid, offering valuable insights for academics and practitioners interested in the role of educational institutions in supply chain management.

4.3 Structural model analysis

The predictive correlation index, or Q2, measures the model’s predictive ability. Based on this criterion, introduced by (Geisser, 1975), the predictive power of a model can be determined for endogenous constructs. It can be concluded that a positive value of the Q2 index indicates that the model is well-fitted and has substantial predictive power (Hair, 2014a).

Table 8 presents values for Q2. In addition to the coefficient of determination values, examining and evaluating changes in the effect size (f2) of exogenous hidden variables is important (Table 9). Exogenous variables can be used to affect endogenous variables by utilizing effect size. (Cohen, 1988) describes three types of effect sizes: weak, medium, and strong, based on values of 0.02, 0.15, and 0.35.

According to Table 10, effect sizes less than 0.02 are not considered significant.

According to Table 11, our study validates two hypotheses that suggest UDI and SCE have a significant impact on SP (SE = 0.624, p < 0.000), as well as SCE and UDI have an impact on SP (SE = 0.312, p < 0.000). SCE plays a mediation role between UDI and SP, as demonstrated in Table 11.

The standardized root mean square residual (SRMR) is proposed by (Henseler et al., 2014) as an appropriate criterion for preventing the undesirable aspects of a model. A conservative model fit can be achieved with values less than 0.2 (Hu and Bentler, 1998). A SRMR value of less than 0.08 indicates a very good model fit, and a value of more than 0.2 indicates a poor model fit. After fitting the model, considering the SRMR criterion, the software output indicated a value of 0.108, indicating that the model was appropriately fitted. RMS coefficients (RMS-theta) are another promising criterion for approximating model fit (Lohmöller, 1989). There is evidence that (Henseler et al., 2014) offer evidence that the RMS-theta can distinguish well-specified models from those that are poorly specified and that models with values less than 0.12 are considered to be well-fit (Henseler et al., 2016). A general fit and appropriateness of the model have been presented in Table 12.

The findings confirm the pivotal role of university-driven supplier development interventions in enhancing supplier capabilities, which, in turn, positively impacts supplier performance. The lack of a direct relationship between UDIs and supplier performance underscores the importance of dynamic capability development as a mediating factor. This study contributes to the understanding of how external interventions catalyze internal capability development, which is critical for improving performance in the garment industry of a developing country like Myanmar.

5. Discussion

Based on the analysis, UDIs positively impact supplier capabilities, with a path coefficient of 0.624 and a T-value higher than 1.96. Additionally, SCE significantly enhances SP, indicated by a path coefficient of 0.312 and a T-value greater than 1.96. This supports the hypothesis that UDIs improve supplier capabilities, which in turn significantly boost supplier performance. This aligns with studies suggesting the effectiveness of such initiatives depends on the industry context (Sarkar and Mohapatra, 2006; Weigelt, 2013). Due to industry-specific challenges, enhanced capabilities may not directly improve supplier performance in Myanmar’s garment industry (Bae et al., 2021). Myanmar’s context significantly impacts supplier development. Discrepancies with existing literature suggest tailoring programs to Myanmar’s specific needs. Future research should examine factors affecting capability enhancement and performance in Myanmar’s garment industry for better understanding.

With a path coefficient of (0.015) and a T-value below 1.96, UDI shows no impact on SP, indicating a negative effect. This contradicts existing literature highlighting the positive impact of university-driven supplier development programs (Ma et al., 2023). Some scholars argue that such interventions facilitate knowledge transfer and skill development within supplier networks (Hasle and Vang, 2021b; Hoque et al., 2020; Lorentz et al., 2021). The positive relationship may also be attributed to the collaborative nature of UDI, fostering stronger ties between academia and industry, ultimately improving SP.

These findings are relevant for universities and industry, showcasing the effectiveness of SD programs. They can help universities attract collaborations, boost competitiveness, and support sustainable transitions. Investing in university partnerships for SD enhances SP, offering practical insights for academia and industry. Encouraging these partnerships can improve SP, making UDI programs strategic investments for long-term benefits.

Indirect effect analysis in PLS examined SCE as a mediator between UDI and SP. The significant mediating effect (effect = 0.195, T-value = 4.677) supported H4. SCE acts as a mediator alongside the direct impact of UDIs on SP, indicating a nuanced relationship beyond direct influence. Supplier capability enhancement indirectly influences SP according to the mediation hypothesis, attributing UDI’s positive effects on SP to improved supplier capabilities. This highlights the crucial role of supplier capabilities in determining supplier performance, as emphasized in numerous studies (Modi and Mabert, 2007; Sarstedt et al., 2020; Tseng, 2014; Ziggers and Henseler, 2009). The UDI aims to enhance supplier capabilities through interventions like training, technology transfer, and collaborations. This positively impacts various aspects of supplier performance, including efficiency, quality, innovation, and competitiveness.

Testing the sequential relationships between UDI, SCE, and SP is crucial to provide empirical support for the mediation hypothesis. If the UDI-SCE and SCE-SP paths are significant, and the direct UDI-SP path diminishes when considering SCE, it suggests a mediating role of supplier capability enhancements. This mediation analysis has practical implications, directing the design of more effective supplier development programs. Understanding how UDI influences supplier performance through capability enhancement is valuable for academia, industry, and government authorities.

5.1 Theoretical and policy implications

The findings of this study have significant theoretical implications, particularly for the application of DCT in supply chain management. The results confirm that UDIs can serve as external resources that enhance the dynamic capabilities of suppliers, enabling them to adapt and respond to market demands more effectively. This contributes to the broader understanding of how educational institutions can play a strategic role in industry development, particularly in developing countries where resource constraints are prevalent. From a policy perspective, the study suggests that universities should be encouraged to engage more actively in supplier development programs, especially in regions where local industries face significant challenges. Policymakers can leverage these insights to design initiatives that foster stronger collaborations between universities and industries, with a focus on capability building as a means to improve competitiveness and sustainability. The findings also indicate that such programs should be tailored to the specific needs and contexts of the industries they aim to serve, as the effectiveness of UDIs can vary significantly depending on external factors.

Despite its limitations, this research contributes to understanding the positive outcomes of university-driven interventions, paving the way for future exploration in this area. Knowledge transfer, skill development, and resource allocation are critical components of supplier development programs initiated by universities (Hasle and Vang, 2021b). UDI has practical implications, suggesting tangible improvements in suppliers’ operational efficiency, product quality, and innovation capabilities. These outcomes are strategically important for both universities and suppliers. Managers leverage these insights to optimize supplier development strategies aligned with UDI’s positive effects on supplier performance (Hoque et al., 2020). However, in the Myanmar garment industry, unique contextual factors like regulatory challenges, infrastructure limitations, and market dynamics may influence the expected link between capabilities development and performance outcomes, contrary to some existing literature.

5.2 Managerial implications

The study underscores the impact of UDIs in SCE and SP, advocating for strategic partnerships with universities for targeted training. Collaboration with Western universities and lower-tier suppliers can add value, but managers must navigate industry-specific challenges and relationship dynamics. Customized strategies tailored to local contexts are essential for effective SD.

SCE bridges UDIs and performance. Managers should value both immediate UDI benefits and long-term capabilities. Empirical support aids in advocating academic partnerships for holistic supplier growth. Such collaborations strategically enhance suppliers. Future research should explore industry contexts for effective interventions, balancing theory, and practicality in operations and SCM (Oliva, 2019). The study reveals UDIs’ positive impact on lower-tier suppliers in developing countries, addressing sustainability challenges and improving competitiveness. It underscores the potential of intervention-based research for both academia and practical SC solutions (Oliva, 2019; Raworth and Kidder, 2009; Wilhelm and Villena, 2021).

For managers, this study highlights the importance of strategic partnerships with universities to enhance supplier capabilities. The findings suggest that while the direct impact of UDIs on performance may be limited, the long-term benefits of capability enhancement are significant. Managers should therefore prioritize collaborations that focus on developing the skills and knowledge of their suppliers, as this can lead to sustained improvements in performance.

Furthermore, the study indicates that customized strategies are essential for effective supplier development, particularly in industries with unique challenges like Myanmar’s garment sector. Managers should work closely with academic institutions to design interventions that are tailored to the local context, taking into account factors such as regulatory environments, infrastructure, and market dynamics. By doing so, they can maximize the benefits of UDIs and support the long-term growth and competitiveness of their suppliers.

5.3 Limitations and future research directions

While this study provides valuable insights into the impact of UDIs on supplier performance, it is not without limitations. First, the research was conducted in Myanmar, a developing country with specific challenges that may not be generalizable to other contexts. Future studies should explore the effectiveness of UDIs in different geographic regions and industries to validate and expand upon these findings. Second, the study focused primarily on certain aspects of the supplier development framework, such as capability enhancement and performance. Future research could benefit from a broader examination of the factors influencing supplier development, including the role of technology adoption, innovation, and supply chain integration. Additionally, the confidentiality and data integrity issues associated with UDI should be investigated to understand their impact on adoption decisions.

Future research could also explore the sustainability aspects of UDIs, particularly in terms of their long-term effects on supply chain performance. Studies might investigate the impact of other types of UDIs on sustainable performance, without the mediation effect of SCE, to identify alternative pathways for improving supplier performance. Moreover, the use of mixed-method approaches, including qualitative and quantitative research, could provide a more comprehensive understanding of the complex dynamics involved in supplier development. The influence of other UDIs on the sustainable performance of a given supply chain may be investigated without the mediation effect of SCE since such relationships were recognized as research gaps.

6. Conclusion

The study examined a university-led supplier development intervention in Myanmar’s garment industry, revealing the effectiveness of leveraging university expertise and knowledge transfer to enhance supplier performance. This intervention significantly strengthened supplier capabilities through structured training programs introducing lean principles. Enhanced capabilities positively influenced supplier performance outcomes, emphasizing the importance of capability development initiatives. Understanding the nuances of direct versus mediated effects is crucial for designing optimal interventions. Context-specific factors may moderate the benefits of university-industry collaboration, highlighting the need for adaptive and industry-aware supplier development models. Leveraging university expertise in knowledge transfer and supplier competitiveness can foster competitiveness, while a contextualized approach enables the exploration of contingency factors and refinement of intervention strategies.

The findings reveal that while UDIs do not directly influence supplier performance, they significantly enhance supplier capabilities, which in turn lead to improved performance outcomes. This emphasizes the importance of focusing on capability development as a key mechanism for driving performance improvements in challenging industry contexts. The study’s results have important implications for both theory and practice. They contribute to the understanding of how external interventions, such as those provided by universities, can enhance the dynamic capabilities of suppliers, enabling them to better respond to market demands. The findings also suggest that policymakers and managers should prioritize capability development in their supplier development strategies, particularly in developing countries where local industries face significant challenges.

In conclusion, this research underscores the need for adaptive and context-specific supplier development models that leverage university expertise in knowledge transfer and capability building. By fostering strategic collaborations between academia and industry, such models can help improve supplier performance and support the long-term growth and competitiveness of local industries. Future research should continue to explore the nuances of these relationships, particularly in different geographic and industry contexts, to build a more comprehensive understanding of the factors that drive successful supplier development.

Figures

The proposed conceptual model

Figure 1

The proposed conceptual model

Co-occurrence map of UDI and SP

Figure 2

Co-occurrence map of UDI and SP

Measurement model

Figure 3

Measurement model

Structural significant diagram

Figure 4

Structural significant diagram

Studies associated with the research

NoAuthorsSupply chain type Variables Country
UIDSCESP
1Nakandala et al. (2023)Manufacturing Australian
2Oubrahim et al. (2023)Manufacturing Morocco
3Yavuz et al. (2023) Turkey’s
4Lee et al. (2022)Food Malaysia
5Manikas et al. (2022)Manufacturing UAE
6Khan et al. (2022)Electronics Malaysian
7Li et al. (2022)Construction China
8Nayal et al. (2021)Agricultural India
9AlNuaimi et al. (2021)Energy UAE
10Júnior et al. (2022)Agricultural China
11Hasle and Vang, (2021a, b)Garment Bangladesh
12Present studyGarmentMyanmar

Source(s): Author’s own work

Sample profile and the respondents’ demographic information

n%
Gender
Male255
Female43495
Age Group
<20419
20–2416536
25–2913429
30–346514
>=355412
Education
Primary school and below5111
Middle school22850
High school15133
Tertiary (Including university student)286
Duration at current factory
<1 Year18340
1–2 Years6414
2–3 Years7617
>3 Years13630
Position
Sewing operator37682
Helper/Ironer7115
Others102

Source(s): Author’s own work

Constructs related to the questions

AbbreviationVariableQuestion
UDIUniversity-driven interventionDo you have difficulties using/following 5S techniques and implementing 5S processes?
Do you think implementing/following 5S processes/procedures helps solve the productivity challenges of your production line and OHS?
Will you share your experience with 5S intervention and technique with colleagues from the current factory in the coming three months?
Will you share your experience with ergonomics issues (working posture) with colleagues from the current factory in the coming 3 months?
Do you believe raising your concern or asking your spouse (wife/husband) questions is appropriate?
Is it appropriate to raise your concerns or ask your parents questions regarding their instruction or guidelines?
Is it appropriate to raise your concern or ask monks or religious leaders questions regarding their instruction or guidelines?
SP.Supplier performanceIn the last month, have you discussed/shared in the meeting/discussion led by your line leader/supervisor/All super and shared your difficulties (or) views? (In front of others/public space)
In the last month, have you discussed/shared with your line leader/supervisor and shared your difficulties (or) your views? (Face-to-face/private space)
5S implementation questions?
SCESupplier capability enhancementHave you viewed your line leader’s leadership style and forward-thinking, identifying potential challenges that could arise (visionary)?
Are you always open to listening to new ideas/suggestions regarding your line leader’s leadership style?
Have you viewed your line supervisor’s leadership style and forward-thinking, foreseeing challenges that could occur (visionary)?
Do you assess your line supervisor’s leadership style and relationship with you while consistently remaining open to listening to new ideas/suggestions?
Do you share your knowledge and experiences of sewing with other colleagues?
Will you join the online knowledge-sharing session on 5S implementation and OHS issues (including working posture) organized by ESAM of SDU (via Viber group or Zoom)?
Will you join the online knowledge-sharing session on 5S implementation and OHS issues (including working posture), which will be organized by the Myanmar Garment Manufacturers Association (MGMA) or other organizations (via Viber group or Zoom)?

Source(s): Author’s own work

Bartlett’s test of sphericity and Kaiser-Meyer-Olkin (KMO)

KMO and Bartlett’s test
Kaiser-Meyer-Olkin measure of sampling adequacy 0.962
Bartlett’s test of sphericityApprox. chi-square 20520.940
df 2080
Sig 0.000

Source(s): Author’s own work

Standardized factor loading

FactorsSub-factorsLoading
University driven intervention (UDI)UDI-20.781
UDI-40.777
UDI-50.885
UDI-70.930
Supplier capability enhancement (SCE)SCE-10.788
SCE-20.924
SCE-30.843
SCE-40.908
SCE-70.855
SCE-80.669
Supplier performance (SP.)SP-10.575
SP-20.820
SP-30.761

Source(s): Author’s own work

Reliability and validity measures

CBrho_ACRAVEUDISCESP
UDI0.9420.9430.9490.6120.782
SCE0.9690.9700.9710.5310.6240.728
SP0.9360.9390.9420.3230.2100.3210.568

Note(s): Values in diagonal are square roots of AVE

Source(s): Author’s own work

The result of the HTMT ratio

SCESCE-1SCE-2SCE-3SCE-4SCE-7SCE-8SPSP-1SP-2SP-3UDIUDI-2UDI-4UDI-5UDI-7
SCE
SCE-10.837
SCE-20.8440.826
SCE-30.8410.5760.744
SCE-40.8390.7020.8250.756
SCE-70.7810.6230.7960.7710.829
SCE-80.3620.5290.7170.5860.6410.807
SP0.2380.2760.2860.3380.2690.4380.407
SP-10.2380.1790.0570.3120.2380.3160.1500.688
SP-20.3570.1070.3000.4080.3160.4300.4330.8310.482
SP-30.2380.3080.2750.1000.0980.2560.3230.7990.1000.448
UDI0.6430.3050.5190.5750.7210.7350.6420.2820.3650.2690.151
UDI-20.6410.3550.5470.6570.6330.6410.5780.2410.3090.2140.1400.839
UDI-40.4900.2200.3740.4210.5850.5840.4900.2100.2400.2240.1390.8430.594
UDI-50.6490.3250.4920.6500.6960.7190.6210.3060.4090.2840.1570.8270.8400.837
UDI-70.6080.3040.4810.5230.6930.7030.6090.2690.3460.2490.1660.8100.7330.8210.829

Source(s): Author’s own work

Predictive relevance: Q² value

FactorsSSOSSEQ2 (=1-SSE/SSO)
SCE137.70111.560.190
SP160.65155.820.030

Source(s): Author’s own work

Effect size (f2)

FactorsUDISCESP
UDI***0.6370.001
SCE******0.066
SP*********

Source(s): Author’s own work

Results of hypotheses testing

HypothesesS. Et-valuep-valueAccept/Reject
H1UDISCE0.62423.4950.000Accepted
H2SCESP0.3125.4530.000Accepted
H3UDISP0.0150.2610.794Rejected

Source(s): Author’s own work

Specific indirect effect

Indirect Effectst Statisticsp-valuesAccept/Reject
H4UDISCESP0.1955.3450.000Accepted

Source(s): Author’s own work

Model fit

CriteriaValueStatus
SRMR0.108ACCEPTED
RMS THETA0.111ACCEPTED

Source(s): Author’s own work

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Acknowledgements

The research project behind the paper was funded by the DANIDA Danish Fellowship Centre (Project ID: Project no 19-M08-SDU; Overcoming barriers to improving OHS among SMEs in Myanmar). There are no conflicts of interest pertaining to the paper. The effort of Esam Group for engagement in data collection is recognized. The researchers also appreciate the collaboration of the local suppliers, global buyers, and employees at the factories. The project did not entail any collaboration with the Myanmar government.

Corresponding author

Seyed Pendar Toufighi can be contacted at: spto@iti.sdu.dk

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