The Productivity Paradox: How Sustainable Supply Chain Management Mediates the Link Between Enablers and Productivity

Abstract

Global environmental and sustainability concerns are increasingly pressuring industries in all developing economies to align their supply chain operations with ecological, social, and economic responsibilities. This study investigates the extent to which Sustainable Supply Chain Management (SSCM) enablers are influencing firm-level productivity in a developing economy, and how effectively the practices of SSCM mediate this relationship. This research aims to determine the extent to which Sustainable Supply Chain Management (SSCM) enablers influence firm-level productivity in a developing economy, and how effectively SSCM practices mediates this relationship. Building on the Diffusion of Innovation (DOI) theory, the research adopts a well-structured design and employs Structural Equation Modeling (SEM) to test the designed conceptual framework. The findings show that, while direct effects of enablers on productivity are limited, SSCM practices play a critical mediating role in translating these enablers into measurable performance-based improvements. The study contributes theoretical insights by extending DOI theory into the pharmaceutical supply chain context and offers practical guidance for managers and policymakers in developing economies by seeking to enhance competitiveness through sustainable practices.

1. Introduction

The productivity paradox, often overlooked in decision-making processes connected to the adoption of sustainable practices, poses a critical challenge in aligning organizational efforts with the triple bottom line that emphasizes people, planet, and profit, and it is increasingly gaining momentum as a focal point for academic research [1,2,3]. Therefore, scholars are focusing on the application of Sustainable Supply Chain Management (SSCM) across different areas, such as engineering, artificial intelligence, management, computer science, and environmental science [2,4,5,6]. Although productivity, SSCM practices, and their enabling factors have been central to industrial growth since the inception of scientific management, their integration within SSCM discourse remains underexplored, specifically in the setting of developing economies [7,8].

Furthermore, the dilemma between productivity and SSCM enablers becomes more pronounced in developing countries, as there has been less exploration of the mechanisms for technology adoption, enhanced collaboration, and sustainable principles across supply chains [3,9,10,11]. While SSCM is widely recognized for enhancing its long-term competitiveness, it can paradoxically lead to short-term productivity challenges, particularly in developing economies [2,3,12,13,14]. This phenomenon, often termed as the “productivity paradox,” refers to the situations where firms adopt different sustainable practices, such as greener technologies and ethical sourcing, but may fail to see immediate or proportional gains in cases of operational efficiency [2,13,15,16]. Conversely, SSCM implementation demands resource reallocation, the retraining of staff, or new compliance mechanisms that initially slow down processes instead of optimizing these issues [11,17]. Likewise, [13] reported that, in Bangladesh, firms adopting sustainability principles struggle with measurable productivity gains due to fragmented infrastructure and due to a lack of integration between sustainability goals and because of operational routines. Similarly, [2] found that, without aligning SSCM enablers with productivity-focused metrics, performance gains for organizations in any industry remain limited.

SSCM includes social, environmental, and economic aspects in conventional supply chain operations to perpetuate value on a long-term basis within the supply chain network [14,18,19]. Furthermore, its importance lies in its promotion of operational resilience, regulatory compliance, and stakeholder satisfaction, especially in industries facing increasing sustainability demands. However, developing countries still face multiple challenges in implementing SSCM, such as the absence of policy congruence, the shortage of resources, and weak infrastructures [2,18,19]. Similarly, productivity in sustainable supply chains is the capacity of firms to efficiently transform inputs into outputs while being in compliance with environmental and social standards [15,16]. It is not just about running efficiently; it is about utilizing resources to their full potential, lessening waste, and enhancing quality in sustainable frameworks [13,15]. In SSCM, productivity serves as a crucial indicator of whether sustainable practices are truly translating into tangible firm-level gains [2].

In SSCM, practices consistently serve as a mediator, as they translate the relationship between SSCM enablers and productivity in order to attain tangible firm-level performance [20,21]. Ref. [13] demonstrates in Bangladesh’s apparel industry that, while the direct correlation among SSCM practices and the competitive edge was weak, all indirect effects became statistically substantial when productivity was shown as a mediator among the variables. Similarly, Ref. [6] confirms that SSCM initiatives combined with dynamic capabilities affect performance via productivity improvements. Moreover, it is necessary to study SSCM practices as a mediator for developing economies because they are not adopted here properly due to various impediments such as a lack of policy or guidelines, a lack of consumer awareness, differences in resistance, and financial challenges [10,14].

Given these issues, there is a pressing need for research focusing on the pharmaceutical industry. Unlike the RMG or apparel industries in Bangladesh, which have been widely studied, the pharmaceutical sector is under-researched despite being one of the fastest growing and most export-oriented industries. Its distinct regulatory environment, global compliance pressures, and reliance on quality-sensitive supply chains make it an ideal context for the exploration of SSCM enablers and their link to productivity [2,10,13]. So, we formulate an RQ for this study that is as follows: to what extent do Sustainable Supply Chain Management (SSCM) enablers influence firm-level productivity in a developing economy, and how effectively do SSCM practices mediate this relationship? This study uses a model based on DOI theory where enablers are fitted properly in this study. Applying DOI theory provides insights into why and how SSCM enablers facilitate or hinder the diffusion of sustainable practices within this pharmaceutical industry [10,17]. The mediating role of SSCM-related practices aligns with DOI’s key attributes, such as these terms’ compatibility, trialability, and observability, by demonstrating how enablers can translate into performance outcomes through adoption mechanisms. Productivity is positioned here as the pivotal construct that can capture whether the diffusion of SSCM practices can generate measurable organizational benefits, by offering a deeper conceptual linkage between terms such as DOI theory, SSCM enablers, and firm-level performance [1,21]. So, the objectives are as follows: RO1: To identify and empirically validate the key enablers of SSCM in the pharmaceutical industry of Bangladesh. RO2: To examine the mediating role of SSCM practices in the relationship between SSCM enablers and productivity in the pharmaceutical industry of Bangladesh.

• Theory Insights: This research has extended the application of DOI theory beyond the traditional innovation-focused sectors into the pharmaceutical supply chain, aligning with a novel theoretical lens to understand how sustainability-enabling factors diffuse and interact within complex industry ecosystems.
• Industry Insights: This study plays a crucial role in industry-specific dynamics, organizational capacity, and regulatory environments in cases of shaping innovation adoption in resource-constrained economies, aligning with calls from different scholars for more embedded, context-sensitive applications of DOI [22,23].
• Managerial Insights: Similarly, this study brings practical insights to pharmaceutical supply chain managers and policymakers by identifying SSCM practices as the key pathway linking strategic enablers to productivity. In addition, it shows that investments in policy, technology, finance, and human resources only improve firm performance when embedded within sustainable practices.

The study is mainly structured into six sections, the first being the Introduction, where the RO and RQ are precisely defined. Second, we have developed theoretical underpinnings related to DOI and SSCM power enablers, focusing on their connection and then finally ending by forming the basis for the development of the hypothesis. Third, we present the methodology for this study. Fourth, the study is designed through an analysis of the research data. The fifth section focuses on the discussion and conclusion, and finally the last section focuses on the contributions, limitations, and future study directions.

2. Literature Review

2.1. Theoretical Lens and Sustainable Supply Chain Management (SSCM) Enablers and Productivity

During the technology advancement and globalization era, different businesses, organizations, and industries are simultaneously facing adverse effects on environmental and human well-being, including the exhaustion of natural resources, the prevalence of social inequities, and, to a certain degree, disparities in income distribution [10,24]. To counter these issues and protect future generations, sustainability has become increasingly important for all industries [1]. Now, to minimize environmental impact, companies are focusing on sustainability in supply chain operations [25]. More specifically, SSCM connects SCM with sustainability to improve the sustainability of supply networks [26].

DOI theory explores the manifestations of sustainability in SCM processes where relative advantage, trialability, observability, complexity, and compatibility are presumed to be key aspects of assessing the acceptance of SSCM [23]. Similarly to adoption factors, enablers are often used in the profitability of an organization or industry [10]. We aimed to build the connection between enablers and the firm level productivity through the propositions of DOI theory. Furthermore, from the viewpoint of productivity, according to DOI theory, organizations will be found to adopt the practices of SSCM if they think that those practices are a comparably superior idea and good for the firm’s productivity or efficiency [27,28]. That is, the efficient use of resources is productivity, which can be enhanced by sustainable practices [29,30,31]. For example, reduced waste and energy efficiency can bring about cost reductions and increased productivity. This study is grounded in the known Diffusion of Innovation (DOI) theory, which provides a lens for understanding the fact that SSCM enablers diffuse into organizational practices and lead to productivity-based outcomes. In comparison, the Resource-Based View (RBV) emphasizes the firm-specific resources and capabilities as drivers of competitive advantages [32,33]. While RBV highlights the role of internal assets such as technology and human capital, DOI uniquely can explain how innovations spread across all firms and industries. This study uses DOI theory because it can capture the process of how sustainability practices are diffused and have been adopted in resource-constrained settings. RBV and Institutional Theory emphasize static firm resources in cases of institutional pressures, whereas DOI highlights dynamic attributes (compatibility, observability, trialability) that explain why pharmaceutical firms in Bangladesh often hesitate or delay in SSCM adoption despite institutional encouragement. In this sense, DOI does not replace RBV or Institutional Theory, but complements them by focusing on diffusion mechanisms rather than the static conditions [32,33,34,35,36]. To clarify the theoretical mechanisms properly, we have explicitly mapped each DOI attribute to the SSCM enablers through testable propositions: for example, “relative advantage” corresponds to financial enablers by framing SSCM as cost-saving and productivity-enhancing; “compatibility” links with human resource enablers, as the alignment with existing skills and training determines their adoption; “observability” relates to environmental enablers, where visible ecological outcomes reinforce commitment; “trialability” maps onto technology enablers, where the pilot testing of sustainable technologies can reduce uncertainty; and “complexity” connects with policy/regulation, where regulatory simplification makes adoption less burdensome. This systematic alignment can demonstrate that DOI is not used as a loose label, but as a mechanism-level framework explaining why and how different enablers can translate into SSCM adoption. The theory that can be employed to explain the adoption of SSCM practices in an organization is the Diffusion of Innovation (DOI) theory. Relative advantage, compatibility, and trialability with respect to SSCM practices are also perceived as important [27,28]. DOI can be particularly valuable in scenarios where there are adoption barriers, such as those faced by developing countries [29,30]. While RBV focuses on factor-specific assets, DOI is rather a type of variable that helps to explain adoption rates spread through the process of diffusion within an organization for it to be involved in this case.

Therefore, SSCM implementation improves performance since, in principle, it is triggered by the enablers of DOI theory, namely top management support, government regulation, and technology adoption, among others, that can enhance productivity objectives [29,37,38]. Firms can improve productivity by implementing more effective processes, eliminating waste, and making their supply chain operations more effective [2]. Improved processes, waste reduction, and improved supply chain operations can drive a firm to be more productive [31,39]. Figure 1 illustrates the theoretical ground of this study, based on a focused review of peer-reviewed papers directly addressing SSCM enablers and performance outcomes. These papers were selected using the screening criteria or keywords related to our paper. But we have selected only peer-reviewed articles with an explicit focus on SSCM enablers, productivity, or DOI-related frameworks. The selection ensured a broad level of relevance, though it does not cover the entire SSCM literature. Also, Figure 2 addresses the methodological ground where the existing studies have relied more on the quantitative forms.

In SSCM, enablers are regarded as elements supporting the evaluation and implementation of sustainable practices in SCM, and they can become the forces that help the organizations implementing SSCM strategies [7,40,41,42]. There have been significant studies conducted investigating and examining the enablers of SSCM in different industries and countries (see

Table 1. Summary of SSCM-based studies.

Authors	Theory	Objective	Industry	Country Name and Type	Findings
[10]	Diffusion of Innovation (DOI)	To construct a conceptual model using the DOI theoretical framework, fitting enablers such as policy, technology, environment, human resource, and finance into DOI theory.	RMG	Bangladesh (Developing)	The study found that the environment and policy enablers have influenced SSCM significantly, while finance, technology, and HR enablers are found to be non-significant because of the lack of fund and resource availability for the adoption of technology in developing country context.
[4]	N/A	To adapt SSCM by analyzing the interactions among various enablers to convert the SC into a fully sustainable system.	Not Specified	Gulf (Developed)	According to the findings, some enablers of SSCM had a higher power of driving and lower dependence. And so, researchers needed a strategic focus on them, while some of the enablers had a high dependence.
[2]	Resource-Based View (RBV)	To integrate the dual theoretical framework of the SSCM practices view and RBV to develop a set of hypotheses concerning the environmental, social, and economic dimensions of SSCM sustainability practices’ effect on productivity and CA.	Export-oriented Fashion–Apparel Manufacturing	Bangladesh (Developing)	The study has found a direct relationship among the enablers of SSCM practices (social sustainability dimensions) and an organization’s competitive advantages, while economic and environmental SSCM were found as non-significant. Meanwhile, the mediating role of productivity found that social, economic, and environmental SSCM had a significant relationship with competitive advantages.
[42]	N/A	To find out the enablers of social sustainability in SC.	Leather	Bangladesh (Developing)	The findings suggest that safety and health practices within the workplace can be a significant factor for achieving SSC goals, closely considering the significance of wages and benefits for employees.
[1]	N/A	To analyze the critical success factors in SC that leverage sustainability.	Electronic	India (Developing)	This study concluded that government legislations and policies are significant enablers of SSCM. Here, the policy enabler had a significant impact on the other variables.
[43]	Resource-Based View (RBV)	To explore the impact that top/middle management support and purchasing strategy have on SSCM practices, as well as its influence on competitive advantage.	Not Specified	Colombia (Developing)	According to the study, the support of top and mid-level management was necessary to adopt SSCM practices. Organizations can obtain competitive advantages and enhance their capacity with the use of Social Supply Chain Practices.
[44]	N/A	To investigate the enablers which are effective for the implementation of SSCM.	Textile	Germany. Sweden, Norway, America, Canada (Developed)	The study found that the effectiveness of SSCM needed to be integrated with corporate strategies and organizational objectives and achieved by the goals of departments. However, key factors included informational connection and cross-functional teams for SSCM.

). The studies of [1,10,43] have classified SSCM enablers using terms such as policy, environment, technology, human resources, and finance categories, linking them through DOI theory to explain all adoptions [44,45,46]. However, prior research has revealed mixed findings, with some enablers such as finance and technology found to be non-significant in cases of developing country contexts due to resource constraints, institutional barriers, and a lack of technical capacity. These contradictions highlight the need for deeper investigation, particularly in sectors where contextual limitations strongly shape all adoptions [1,10,47]. Again, this reflects a key theoretical gap: although SSCM’s mediating role is widely acknowledged [2,48,49], most studies have examined either developed economies or the apparel industry in Bangladesh, leaving the pharmaceutical sector almost untouched. Existing reviews also tend to list the enablers (See

Table 2. Enablers of SSCM characterized by DOI theory.

Enablers	Items	Adoption of DOI Theory	Sources
Policy	Commitment of Top-Level Management	Compatibility	[1,7,10,47,48,49,50]
	Safety and Health Guidelines
	Regulatory Policies by Government
Technology	Innovation and Development through Research	Relative advantage
	Intelligence Usage on Technologies, Resources, and Processes
	Lean-Manufacturing Usage
Environment	Eco-Friendly Labeling and Packaging	Trialability
	Reverse Supply Chain Usage
	Eco-friendly Collaboration
Finance	Sustainable Purchasing	Complexity
	Adequate Funding
Human Resource	Training	Observability
	Specialized Human Knowledge
	Cultural Influences Adoption

) and outcomes without probing the paradox of why sustainability adoption sometimes reduces short-term productivity. By positioning the productivity paradox as a central level of the construct, this study advances in novelty: it not only validates the mediation but also situates SSCM within the productivity-based debate, thereby extending the literature from descriptive enumeration to conceptual clarification. Furthermore, this work explicitly contrasts its results with earlier levels of RBV- and institutional-based studies, clarifying how our DOI-driven explanation departs from and contributes to the ongoing SSCM discourse.

In the literature, productivity measures the efficiency with which the inputs to the activities of a business are converted into outputs with a minimal waste of resources [48]. It is a slippery concept, with its measures evolving according to the context [2,15,51]. The success measurement process is based on the achievement of a successful project that is defined by goals and/or targets such as the quantity of created units, time, and labor hours, as well as other things that can be calculated on the basis of subjective data [16]. The productivity indicators are presented in

Table 3. Indicators of productivity.

	Indicators	Sources
Productivity	Capacity Utilization System	[2,15,16,52]
	Level of Financial Productivity
	Extent of Market Growth
	Extent of Market Productivity Reputation

.

From previous studies, some indicators of SSCM were found to be investigated and applied to environmental, social, and financial dimensions. These can be helpful for the organizations to enhance the overall SSCM in their firms [50,53]. In

Table 4. Indicators of SSCM.

	Indicators	References
SSCM	Waste Minimization	[1,10,11,47,54]
	Contamination Reduction
	Energy Optimization
	Working Environment
	Cost Minimization
	Competitive Advantage and Operational Efficiency

, the indicators of SSCM are shown.

It has been noted that there is a different focus for SSCM in developed and developing countries, with research in all the advanced economies emphasizing the enhancement of existing practices, while studies in developing economies highlight the adoption of barriers and success factors [2,3,13]. Research in advanced countries is mostly directed towards an enhancement of the existing practices of SSCM and the correlation of enablers of SSCM. Conversely, in the context of developing countries, it is important to search for the success and inhibiting factors of adoption of SSCM [42,55]. Our study contributes to the SSCM literature by adopting DOI theory as the underlying lens for the study. Little or no theoretical underpinning is to be found underlying many SSCM studies, except for the Resource-Based View (RBV) theory employed in some of the studies [2,56], and a study was found using DOI theory [10] for their investigation; hence, we have used DOI theory for this research to understand SSCM adaptation in the Bangladeshi pharmaceutical industry. This framework allows for the systematic study of how different enablers shape the diffusion and adoption of SSCM practices in a developing economy. Through this method, we can understand the specific ways in which developing economies experience the adoption process differently than developed ones. This theoretical viewpoint fills a gap in the current literature, offering implications for further research as well as practical applications, specifically in regions with similar economic contexts.

2.2. Development of Hypothesis

Productivity has been investigated in different studies where some of the previous research has shown the impacts of SSCM enablers on productivity [2,15,16,57]. The relationship between enablers and SSCM practices was examined in some of the previous studies. According to [10], the enablers of SSCM (e.g., Policy, technology, environment, finance, human resource, etc.) have influenced SSCM in different contexts.

2.2.1. Policy Category Enabler

The enablers of the policy category include top management commitment, governmental policy, and compliance with organizational health and safety standards. These elements align the organization with SSCM, integrating the values, norms, and needs of the organization [54]. Policy enablers like top management commitment and government regulations align with the DOI attribute of compatibility, as they ensure that all of the sustainability initiatives are consistent with organizational norms and external institutional requirements. A clear vision and a leadership initiative are keys to resolve all environmental challenges [58,59,60] and incorporating sustainability within industries and organizations [1]. This indicates that, other than financial considerations, occupational health and safety compliances are part of the sustainability of and organization and ultimately receive respect, due to their taking care of employees of a society by assuring the social, emotional, and physical safety of employees, which is reflected in the literature [10,61,62]. Strong top management ownership and adherence to government regulations and health and safety rules have been positively associated with the overall long-run productivity of a firm [16,30,39]. These policy-based actions enhance an organization’s alignment with sustainability objectives, resulting in greater resource efficiency and productivity [2,15]. However, we then proposed this:

H1a. 

There is a direct effect of policy enablers on productivity.

H2a. 

There is a direct effect of policy enablers on SSCM practices.

2.2.2. Technology Category Enabler

Elements of this enabler include research and development (R&D), sustainable manufacturing practices (SMP), and lean manufacturing techniques (IBM SPSS 27.0). Technology enablers, including R&D, sustainable manufacturing, and lean practices, align with the DOI attribute of relative advantage, as they highlight the superior efficiency and competitiveness gained from adopting innovation in relation to SSCM solutions. Businesses must receive benefits from the utilization of the technological innovations for the practical approach of SSCM in the era of technology and Industry 4.0 [2,10]. Firms that have made investments in research and technological advancements that prioritize sustainability are more well positioned to comply with SSCM requirements and gain advantages over competitors in adopting SSCM [14,23]. Technology enablers help to increase financial performance through competitive advantages, improved productivity, improved quality, reduced time, and improved productivity [10,23]. Technological developments (e.g., R&D, sustainable manufacturing practices (SMP), and lean manufacturing) give firms competitive advantages, which increase the productivity [27,37,38]. By embracing new technologies, enterprises are able to rationalize operations, increase quality, minimize production duration, and improve performance in general [13]. Accordingly, we predict the following:

H1b. 

There is a direct effect of technology enablers on productivity.

H2b. 

There is a direct effect of technology enablers on SSCM practices.

2.2.3. Environment Category Enabler

The environment enabler category includes items such as green packaging and labeling, green collaboration with partners, and reverse logistics [1,10] aligning with DOI attributes of trialability and observability. These practices allow organizations to experiment with visibility, which can demonstrate sustainable innovations before their full-scale adoption. Organizations have adopted various practices to address these issues related to sustainability, including obtaining green partners for sector players, the initiation and use of reverse logistics, and green labeling and packaging [63,64]. Based on [23] and Diffusion of Innovation (DOI) theory, the attributes of an innovation that can influence its acceptance rate include relative advantages, compatibility, observability, and trialability, as well the relatability of an innovation, which can lead to its adoption. Environmental enablers such as green labeling, eco-packaging, and reverse logistics are also consistent with sustainability goals, and save costs and improve operations [13,31,39]. They also facilitate the efficient use of resources and reduce waste, which in turn has a marked impact on the productivity of an organization [16,31,37]. From these, we suggest the following:

H1c. 

There is a direct effect of environment enablers on productivity.

H2c. 

There is a direct effect of environment enablers on SSCM practices.

2.2.4. Finance Category Enabler

It is evident that the support of funding and sustainable purchasing are also preconditions to the financial side of SSCM [1,10]. There are many finance enablers, including sustainable procurement and funding, aligning with the DOI attribute of complexity. They reflect how resource availability can reduce or increase the perceived level of difficulty of adopting SSCM practices. For this, organizations should have the necessary financial support to infuse the infrastructure for research and the knowledge bank related to sustainable initiatives of production and dissemination; yet, this issue is also important enough [21,65]. Green buying involves purchasing eco-friendly items [63]. It also enables the cooperation between firms and suppliers who can develop green products [21,66]. Sustainable procurement and adequate resources are also important in the operationalization of SSCM practices [15,31,39]. Sufficient funding allows firms to concentrate on green technologies; however, in developing economies, barriers such as the misallocation of resources, corruption, and a lack of technical capacity often limit the effective utilization of financial enablers [2,13,15,38]. Therefore, we expect the following:

H1d. 

There is a direct effect of finance enablers on productivity.

H2d. 

There is a direct effect of finance enablers on SSCM practices.

2.2.5. Human Resource Category Enabler

For successful SSCM implantation, training, cultural items, and human knowledge and experiences are required [10]. Human resource enablers, including training, knowledge, and cultural support, align with the DOI attribute of observability, as the outcomes of skilled employees in implementing all SSCM practices are directly visible in organizational performance. Orientation improves all of the skills of employees, allowing them to meet the organization green responsibilities effectively, and also resolving the health and safety issues inside the organizations [1,17]. Moreover, due to training, the creation of norms, and expertise, SSCM practices become increasingly visible and demonstrable [10,23,67]. Human expertise is crucial for facilitating practices that achieve SSCM goals, with effective SSCM execution contingent on practices adopted under the auspices of capable experts [8,10,22]. Investing in the training and development of specialized human resources provides employees with the requisite skills to practice SSCM [2,27,37]. This stimulates productivity by promoting a sustainable culture and effectiveness [16,39]. We suggest the following based on these observations:

H1e. 

There is a direct effect of human resource enablers on productivity.

H2e. 

There is a direct effect of human resource enablers on SSCM practices.

Firms caring for the environment are regarded as good corporate citizens and achieve operational efficiency [20]. The adoption of eco-friendly SCM policies enhances reputation and operational efficiency, resulting in the values of the company increasing [65]. The productivity of an organization can be connected with SSCM enablers [2].

2.3. SSCM Practices as Mediator

Social sustainability in SSCM also encompasses the just treatment of supply chain partners, which includes making fair payments, offering favorable terms, providing supplier capacity-building, and collaborating in demand forecasting to help avoid excess inventory and waste [53]. However, it can be related with the productivity of an organization [2]. Therefore, we propose the following:

H3. 

There is a direct effect of SSCM practices on productivity.

The environment and policy enablers have a significant influence on SSCM, whereas finance, technology, and HR enablers are regarded here as non-significant in some contexts due to barriers such as resource constraints, lack of infrastructure, and institutional barriers [10]. These boundary conditions underscore that, while positive hypotheses are proposed properly, their effectiveness may depend on the contextual limitations that are mediated through SSCM practices [10,68]. To fill the gap within the existing literature, we have considered all SSCM practices as mediating variables from the previous literature. From a DOI perspective, enablers can provide the initial conditions for adoption, but SSCM practices represent the actual observable adoption-based processes through which compatibility, trialability, and observability are realized properly. Thus, the transition from direct to indirect effects is theoretically grounded in the alignment of DOI, as enablers may not directly improve productivity unless translated into concrete practices. Hence, SSCM practices are likely to mediate the relationships between the enablers and productivity. So, researchers need to explore the correlation among enablers and productivity with the mediating role of SSCM practices individually, and develop the following hypothesis:

H4a. 

SSCM practices mediate the effect of policy enablers on productivity.

Research in the field has indicated that policy enablers, including top management commitment and regulation of government, positively affect SSCM adoption [15,39]. In particular, organizational alignment with sustainable goals encouraged by policy support increases the efficiency of operations and thereby productivity [13,37].

H4b. 

SSCM practices mediate the effect of technology enablers on productivity.

The use of technology such as R&D, lean manufacturing, and sustainability manufacturing practices has also been indicated to have a direct impact on productivity [2,13,39]. Nevertheless, researchers stress that the integration of environmental sustainability initiatives, such as waste minimization and energy efficiency, would have to be adopted by companies within their supply chain management [13,37,69].

H4c. 

SSCM practices mediate the effect of environment enablers on productivity.

Green enablers such as green labeling and eco-packaging have been extensively documented to lower the operational costs and enhance the overall efficiency [2,29,31]. They also fall into sustainability and can contribute to a better use of the company’s resources [13,16].

H4d. 

SSCM practices mediate the effect of finance enablers on productivity.

Resource support, including funding and sustainable purchasing, is an important financial enabler for the practice of SSCM [13,39]. Access to capital grants enables investment in green technology, infrastructure, and supply chain innovation [15,27].

H4e. 

SSCM practices mediate the effect of human resource enablers on productivity.

Human resource enablers, including employee training and skills, also have a mediating influence on successfully implementing SSCM practices [2,27,38]. Skilled employees provide a competitive advantage, as human resource enablers improve productivity through SSCM practices such as green training, ethical procurement, and collaborative process improvements that enhance both efficiency and sustainability [15,27].

2.4. Conceptual Framework

To test the mediation, SSCM practices, not productivity, were used as the mediator variable in the enabler–productivity relationship, consistent with the conceptual framework (Figure 3). Here, the researchers’ goal is to test whether or not SSCM practices play the role of mediator in enhancing productivity, meaning whether or not the effects of enablers increased productivity due to the mediation of SSCM practices.

3. Materials and Methods

3.1. Instrument Design

We adopted the positivist philosophy and an objectivist epistemological stance to measure the strength of enablers in SSCM practices, conceptualized as the observable phenomena suitable for quantitative analysis, based on Saunders’ Research Onion (Figure 4). Therefore, quantitative data collection used a cross-sectional design for gathering data from respondents and assessing the acceptability of the proposed conceptual framework [70,71]. Although cross-sectional surveys have limitations in cases of establishing a temporal sequence for mediation, this approach was selected due to practical constraints in the pharmaceutical industry, where longitudinal access to the same firms is not feasible. We interpret mediation effects as associative, not causal, consistent with the received recommendations from social science methodology in constrained contexts [2,72,73]. We used a structured questionnaire for data collection using Google Forms to ensure clarity and ease of data entry. The instrument was designed in four distinct sections: (i) respondent demographic profile, (ii) enablers of SSCM, (iii) productivity of the organization, and (iv) assessment of SSCM. Amongst all the items, 24 items to measure variables were arranged through the usage of a 5-point Likert scale [10,74]. Amongst all the items, to check the mediator variable, we have used the term which is known as SSCM. Then, four items were also adapted to assess SSCM; finally, five items were adapted. All the items were adapted and designed in the questionnaire, where the ranges for answers were (1 = Strongly Disagree to 5 = Strongly Agree), except for the SSCM assessment [40,75,76].

3.2. Pre-Test, Pilot Survey, and Validation of Research Instrument

The questionnaire was validated through expert review before conducting the actual data collection following positivist research standards utilized in prior research to assure the validity and reliability of the instrument [77,78]. To mitigate common method bias (CMB), dependent and independent items were separated in the case of the survey design. Harman’s single-factor test showed that no single factor is explained >50% variance (47.4%), indicating limitations to the CMB [79,80]. Since the HTMT ratio is for discriminant validity, CMB was further checked using VIF (<5 across constructs), confirming a low risk level [2,81,82,83].

Details of the expert profile are presented in

Table 5. Experts face validity.

Designation	Qualification	Experience (In Years)
Professor	Ph.D. in Sustainable Supply Chain Management	18
Assistant Professor	Ph.D. in Pharmaceutical Supply Chain	11
Associate Professor	Ph.D. in Operations and Supply Chain Strategy	12
Head of Procurement and Logistics	BBA and MBA in Supply Chain Management	8
Managing Director (Pharma)	BBA and MBA in Pharmaceutical Management	8
Supply Chain Manager	BSc in Industrial and Production Engineering	11
Planning and Regulatory Manager	BSc in Pharmaceutical Engineering	10

. Subsequently, to conduct a pilot survey, the revised questionnaire was sent to 74 people, as those people were within the reach of researchers, and, from them, 49 respondents were able to give their responses. However, only 25 valid responses were received, among them 10 males and 15 females. Based on the feedback from the experts, some small changes were made to improve items of the questionnaire for precise clarity. Subsequently, the final revised questionnaire was sent to 510 respondents, and 174 valid responses were collected between March 2024 to January 2025. The response rate was 34.11%, better than previous studies in the earlier social science studies [79]. Furthermore, we have applied snowball sampling to improve response rate, as recommended by [80].

3.3. Sample and Data Collection with Regression Model

In Bangladesh, the pharmaceutical industry employs approximately 150,000 workers, of which around 6.2% are managerial employees, yielding a target population of 9300 [24]. The required sample size of 174 was determined by using G*Power software (Version 3.1), with effect size = 0.15, α = 0.05, and power = 0.95, ensuring all the statistical adequacy for regression and SEM analysis [84,85]. Respondents were chosen using purposive and snowball sampling, primarily supply chain managers, procurement officers, and production planners. Non-response bias was assessed by comparing early vs. late responses; no significant differences were observed here (p > 0.05), suggesting minimal bias. Participants were contacted through professional networks, industry associations, and email invitations. Secondly, although the sample included 174 valid responses, these were concentrated in the cases of managerial positions within a limited number of companies. This raises concerns about representativeness, and the results may not fully capture the perspectives of non-managerial employees or other industries. Future research could expand the coverage to multiple sectors and hierarchical levels.

The questionnaire was distributed in English, the official business language in the Bangladeshi pharmaceutical sector. A bilingual expert team verified clarity, and back-translation procedures were applied ultimately to ensure semantic accuracy. Minor wording adjustments were made for local comprehension, such as replacing “sustainable procurement” with “green purchasing” to match the industry terminology. Examples were provided in the survey here (e.g., “eco-labeling” explained as “environment-friendly packaging”).

The instrument drew the measurement items from validated prior studies. SSCM enablers were measured with 24 items (policy: 3, technology: 3, environment: 3, finance: 2, human resources: 3). Productivity was measured with four items, and SSCM practices were measured with six items. All items used a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). There are many sources, such as [1,7,10,47,48,49,50].

Thus, multiple linear regression with mediation analysis was used not only for its statistical appropriateness but also for its ability to quantify relationships, test mediation hypotheses, and provide clear interpretation aligned with the objectives of this study. However, the models developed are shown in

Table 6. Regression model.

Step	Analysis	Formula
Step 1	Conduct a simple regression analysis with X predicting Y to test for path c alone, Y = β0 + β1X + ϵ	Productivity = β0 + β1 × Policy + β2 × Technology + β3 × Environment + β4 × Finance + β5 × Human Resource + ϵ
Step 2	Conduct a simple regression analysis with X predicting M to test for path a, M = β0 + β1X + ϵ	SSCM = β0 + β1 × Policy + β2 × Technology + β3 × Environment + β4 × Finance + β5 × Human Resource + ϵ
Step 3	Conduct a simple regression analysis with M predicting Y to test the significance of path b alone, Y = β0 + β1M + ϵ	Productivity = β0 + β1 × SSCM + ϵ
Step 4	Conduct a multiple regression analysis with X and M predicting Y, Y = β0 + β1X + B2M + ϵ	Productivity = β0 + β1 × Policy + β2 × Technology + β3 × Environment + β4 × Finance + β5 × Human Resource + β6 × SSCM + ϵ

. Table 6 now presents the PLS-SEM path coefficients, significance levels, and bootstrapped t-values, replacing all of the earlier Baron–Kenny style steps to avoid confusion about the estimation strategy [83]. Data was analyzed using the SmartPLS 4 and SPSS 27. SmartPLS was chosen due to its suitability for mediation analysis and non-normal data distributions. Bootstrapping with the 5000 resamples tested hypothesis significance. SPSS was used for descriptive statistics and multicollinearity diagnostics. The analysis had three stages; first of all, (i) measurement model validation (convergent/discriminant validity, reliability), then (ii) structural model assessment (β, t, p, R², f², VIF), and finally (iii) mediation analysis of SSCM practices [80,81,86,87,88,89]. Alongside bootstrapping procedures, model fit indices (e.g., SRMR = 0.061, NFI = 0.921) were also assessed, here confirming acceptable fit. Reporting both effect sizes and the model fit values improves transparency and reproducibility.

4. Results

This analysis used data from 174 respondents of varying age groups, gender, designation, and experience level, as per

Table 7. Demographic profiles for respondents.

Characteristics	Categories	Frequencies	%
Age	25 to 35	51	29.3%
	36 to 45	48	27.6%
	46 to 55	39	22.4%
	Above 55	36	20.7%
Gender	Male	88	50.6%
	Female	86	49.4%
Designations	Procurement Officer	30	17.2%
	Quality Control Manager	26	14.9%
	Logistics Coordinator	24	13.8%
	Production Manager	28	16.1%
	Planning Manager	24	13.8%
	Senior Supervisor	20	11.5%
	Others	22	12.6%
Experience	4 to 6 Years	37	21.3%
	7 to 10 Years	39	22.4%
	10 to 12 Years	51	29.3%
	Above 12 Years	47	27.0%

.

4.1. Analysis of the Measurement Model

The measurement model (Figure 5) is evaluated for this study by following convergent validity (CV) and discriminant validity (DV).

Table 8. Convergent validity.

Constructs	Items	Mean	SD	LOM	α	rho_a	rho_c	AVE
Policy	PL1	3.778	1.168	0.825	0.839	0.840	0.903	0.757
	PL2	3.747	1.095	0.862
	PL3	3.879	1.066	0.870
Technology	TEC1	3.909	1.111	0.930	0.866	0.868	0.918	0.789
	TEC2	3.798	1.223	0.914
	TEC3	3.960	1.072	0.867
Environment	EVT1	3.899	1.040	0.842	0.735	0.739	0.883	0.790
	EVT2	3.879	1.066	0.878
	EVT3	3.899	1.159	0.882
Finance	FN1	3.808	1.203	0.890	0.825	0.836	0.919	0.850
	FN2	3.859	1.045	0.849
Human Resource	HRE1	3.768	1.136	0.850	0.828	0.828	0.897	0.744
	HRE2	3.798	1.128	0.865
	HRE3	3.939	1.081	0.879
SSCM	SSCM1	3.879	1.066	0.857	0.922	0.923	0.939	0.721
	SSCM2	3.848	1.122	0.850
	SSCM3	3.838	1.134	0.867
	SSCM4	3.848	1.226	0.872
	SSCM5	3.899	1.159	0.918
	SSCM6	3.768	1.162	0.874
Productivity	PTY1	3.949	1.058	0.800	0.816	0.872	0.891	0.732
	PTY2	3.909	1.164	0.842
	PTY3	3.828	1.129	0.807
	PTY4	3.838	1.042	0.849

depicts the results of CV, with outer model loadings, Composite Reliability (CR), Average Variance Extracted (AVE), and finally Cronbach Alpha (α), and these range from 0.735 to 0.919, indicating the acceptable range of internal consistency or reliability according to recent studies [90,91]. AVE is used here to measure convergent validity; also, the LOM is higher than 0.6, indicating a level of acceptance of convergent validity and indicating that the constructs are measuring what they are intended to [69].

In recent studies [89], it is suggested to use the HTMT ratio, also known as the Heterotrait and Monotrait ratio, for measuring the discriminant validity. If the HTMT ratio has a range exceeding 0.85, then it indicates that different constructs are highly correlated, suggesting potential issues with construct uniqueness and measurement validity [90]. For this study,

Table 9. HTMT ratio.

	Environment	Finance	Human Resource	Policy	Productivity	SSCM	Technology
Environment
Finance	0.754
Human Resource	0.604	0.763
Policy	0.707	0.623	0.677
Productivity	0.749	0.599	0.626	0.718
SSCM	0.040	0.675	0.721	0.598	0.717
Technology	0.023	0.684	0.679	0.649	0.652	0.697

shows that all the HTMT ratios are lower than 0.85, indicating that discriminant validity has been ensured for all the constructs, so every construct is unique enough from the other constructs.

Common method bias (CMB) is an issue while designing surveys in data collection systems [40,91,92]. In cases of designing questionnaires, the instructions can lead to the respondent answering in a biased direction, resulting in shared common variations among the questionnaire’s construct measurements and a common method bias from that [93]. The study follows cross-sectional time horizon-based data collection obtained by using a questionnaire, and it might indicate CMB presence for our study. Mitigation of CMB is needed and so that we have differentiated both dependent and independent variables, and we noticed two issues from CMB checking. First, we have used Structural Equation Modeling (SEM). Here, to detect high or low correlations between constructs, the HTMT ratio showed results < 0.8, indicating low correlations and a lower potential CMB [81,94]. Second, we conducted Harman’s one-factor test, and our findings revealed individual factors for this study explaining 47.4% of the total variance, which is less than 50%; therefore, CMB is not present for this study.

4.2. Analysis of the Structural Model

Results of the Hypothesis Testing

Direct Effects: The structural model is evaluated by certain metrics: firstly, R square, β-value, t-value, and p-value. Those are obtained by bootstrapping with resamples of 5000, as per [69]. A predictive relevance measurement is also needed, and it is assessed by effect size F-Square [93]. The p-value and f-value together can give results about the determination of significant or insignificant relations, and also about the magnitude of the effect [80,89].

Table 10. Hypotheses testing (direct effect).

Hypotheses	Relations	β-Value	SD	t-Value	p Values	Decision	R2	F2	VIF
H-1a	Policy -> Productivity	−0.133	0.118	1.085	0.278	Not supported	0.678	0.071	4.335
H-1b	Technology -> Productivity	0.023	0.093	0.245	0.807	Not supported		0.052	2.772
H-1c	Environment -> Productivity	0.149	0.108	1.391	0.164	Not supported		0.226	4.16
H-1d	Finance -> Productivity	0.142	0.090	1.576	0.115	Not supported		0.071	2.807
H-1e	Human Resource -> Productivity	0.108	0.084	1.262	0.207	Not supported		0.046	2.998
H-3	SSCM -> Productivity	0.674	0.132	5.081	0.000	Supported		0.22	4.306
H-2a	Policy -> SSCM	0.394	0.075	5.198	0.000	Supported	0.714	0.269	4.147
H-2b	Technology -> SSCM	0.159	0.059	2.697	0.007	Supported		0.119	3.407
H-2c	Environment -> SSCM	0.155	0.059	2.694	0.007	Supported		0.161	3.804
H-2d	Finance -> SSCM	0.129	0.064	2.083	0.037	Supported		0.183	3.555
H-2e	Human Resource -> SSCM	0.196	0.062	3.121	0.002	Supported		0.219	3.468

is attached, with information on β, t, p, the collinearity issue, R square, and f square. For this study, the direct relationships between the independent variables (policy, technology, environment, finance, human resource) and the dependent variable (productivity), as well as the mediator or indirect (SSCM), were examined. The direct results indicate that policy does not have a significant direct effect on productivity (β = −0.133, t = 1.085, p = 0.278), shown in Figure 6. Policy, technology, environment, finance, and human resource enablers did not have significant direct effects on the productivity. This aligns with prior studies in the cases of developing economies, where limited resources and institutional barriers reduce the direct influence of all the enablers on firm performance. SSCM practices had a strong positive effect on productivity, consistent with the evidence that sustainability-oriented practices enhance efficiency and performance [2,12,33]. Likewise, enablers significantly predicted the SSCM practices, confirming findings from earlier studies that all of the organizational policies, regulations, financial resources, and skilled human capital can drive SSCM adoption [2,3,13].

Similarly, technology’s direct impact on productivity is non-significant (β = 0.023, t = 0.245, p = 0.807). The environment variable also shows a non-significant direct relationship with productivity (β = 0.149, t = 1.391, p = 0.164). Finance does not exhibit a significant direct effect on productivity (β = 0.142, t = 1.576, p = 0.115). Human resource similarly shows a non-significant direct impact on productivity (β = 0.108, t = 1.262, p = 0.207). In contrast, SSCM demonstrates a significant positive direct effect on productivity (β = 0.674, t = 5.081, p < 0.001). Regarding the direct effects on SSCM, policy has a significant positive impact (β = 0.394, t = 5.198, p < 0.001). Technology also shows a significant positive effect on SSCM (β = 0.159, t = 2.697, p = 0.007). The environment variable exhibits a significant positive relationship with SSCM (β = 0.155, t = 2.694, p = 0.007). Finance has a significant positive effect on SSCM (β = 0.129, t = 2.083, p = 0.037). Human resource also demonstrates a significant positive impact on SSCM (β = 0.196, t = 3.121, p = 0.002).

Productivity’s R² value is 0.678, which implies that around 67.8% of the variation in productivity is accounted for by the model, since R² is expected to indicate the extent of the variation in the dependent variable that can be described by the variation in the independent variable [89,90,91]. The R² value for SSCM is 0.714, indicating that about 71.4% of the variance in SSCM is accounted for by the independent variables. Ref. [80] proposed thresholds of 0.25, 0.50, and 0.75 for very weak, weak, and moderate explanatory power, so this study possesses a moderate explanatory power as per the R² value. Then, Cohen’s f² values of 0.02, 0.15, and 0.35 are conventionally interpreted as small, medium, and large effect sizes, respectively, so for this study f² can be interpreted as a medium effect size, as every f² value is greater than 0.02 [72,89]. Variance Inflation Factor (VIF) values for all paths are below 5, implying no significant multicollinearity issues for this study [92,93,94].

Indirect Effects: In statistical terms, full mediation occurs when the statistical relation between an IV and a DV is fully mediated by a mediator variable, such that the direct path from the IV to the DV is no longer significant when the mediator is included [93]. This is also assessed by comparing the strength and significance of the direct effect before and after adding the mediator to the model [2,62,86]. LOM (Loading of Measurement) and SD (Standard Deviation) are used throughout by replacing SD. If the direct effect decreases and becomes non-significant for all the constructs, then it reflects that the mediator completely mediates the correlation among independent and dependent variables [2,15,62,79]. The indirect effects were examined for our study to assess the mediating role of SSCM in the relationship between the independent variables or constructs and productivity. The mediation analysis indicates that policy influences productivity via SSCM (β = 0.267, t = 3.359, p = 0.001). Technology also affects the productivity indirectly via SSCM (β = 0.107, t = 2.451, p = 0.014). Similarly, environment has an indirect effect on the productivity through SSCM (β = 0.104, t = 2.387, p = 0.017). Finance also has an indirect effect on productivity via SSCM (β = 0.085, t = 2.081, p = 0.037). Human resource also exhibits an indirect effect on productivity through SSCM shown in

Table 11. Hypotheses testing (indirect effect).

Hypotheses	Relations	β-Value	SD	t-Value	p Values	Decision
H-4c	Policy -> SSCM -> Productivity	0.267	0.078	3.359	0.001	Accepted with Full Mediation
H-4a	Technology -> SSCM -> Productivity	0.107	0.044	2.451	0.014	Accepted with Full Mediation
H-4b	Environment -> SSCM -> Productivity	0.104	0.044	2.387	0.017	Accepted with Full Mediation
H-4d	Finance -> SSCM -> Productivity	0.085	0.043	2.081	0.037	Accepted with Full Mediation
H-4e	Human Resource -> SSCM -> Productivity	0.133	0.053	2.43	0.015	Accepted with Full Mediation

(β = 0.133, t = 2.430, p = 0.015). The analysis shows that SSCM practices mediate the relationship between all the SSCM enablers and productivity. To strengthen causal inference, we have included different control variables such as firm size, age, export intensity, ownership type, and regulatory exposure in the structural model. Additionally, alternative models were tested properly to examine reverse causality and omitted variable bias, confirming that the mediation effect of SSCM is robust. To test the robustness of this overall conclusion, both partial mediation and full mediation models were compared. The partial mediation model showed no significant direct effects of enablers on the productivity (p > 0.05), while the indirect effects through SSCM practices remained significant enough. This comparison confirms that full mediation is the most appropriate level of interpretation.

5. Discussion

This study’s variables, analysis, and alignment with the literature deliver key insights into the interconnected role of the enablers in shaping SSCM outcomes within Bangladesh’s pharmaceutical industry, which is a rapidly developing yet underexplored sector. By adopting the recognized theory of DOI and employing SEM, the findings offer both theoretical richness and a clear empirical path on how SSCM functions as a critical performance conduit in cases of resource-constrained environments. We remain cautious in our interpretation of these results, as the cross-sectional design cannot confirm a strict causality. Thus, our claims are limited to associative patterns, and further longitudinal studies are needed to establish the temporal ordering. The empirical results of this study have suggested that enablers such as policy, finance, human resource, environment, and technology do not individually exhibit much direct impact on productivity. While our model applies linear mediation due to data- and design-based constraints, we recognize paradoxical dynamics in the literature: SSCM adoption may temporarily reduce the productivity because of adjustment costs, training burdens, or regulatory compliance, before enhancing the productivity level in the longer run. We therefore frame our findings as capturing the “short-term associative level of pathway,” while encouraging future research to test the curvilinear, lagged, or trade-off effects across performance dimensions. By explicitly positioning our study as an initial step in this paradox debate, we aim to enhance clarity.

However, through SSCM practices, their indirect effects become strong and even statistically significant, validating the full mediating role of SSCM practices in the connection between SSCM enablers and productivity. These findings affirm the emerging consensus that sustainability-related enablers alone do not generate performance-related gains unless integrated into a cohesive level of mood in the operational model, such as SSCM practices as a mediating construct [10,55,94].

Among the enablers aligned with the first objective of this study, policy and environment demonstrated a stronger influence on SSCM, aligning with the earlier literature that emphasizes top management support and compliance with health, safety, and environmental regulations as critical to fostering sustainability [5,7,10,14,39]. The impact of environment over the relationship of variables underscores the value of reverse logistics, green labeling, and eco-packaging, which are necessary for pharmaceutical firms aspiring to align with international sustainability standards. Conversely, the findings also highlight that technology, finance, and human resource enablers did not directly influence productivity. This observation mirrors patterns observed in cases of other developing economies, where lean practices like green financing or skilled human capital are often limited because of the prohibitive cost, lack of access, and institutional readiness [2,11,57,61]. These findings suggest further exploration into contextual limitations that hinder the enabler efficacy despite the theoretical potential.

This type of productivity paradox arises when SSCM implementation gives rise to a short-term decline in productivity associated with new technology adoption and training and compliance. But, in the long term, those are efficient payoffs. The organizations must bear this minor sacrifice in mind and focus on the overall profitability, especially in resource-constrained settings and emerging economies.

Aligning with the second specific objective, this study further adds to the SSCM literature by validating SSCM practices as mediators and introducing SSCM as a contextually justified and operationally relevant construct. By empirically connecting the enablers to firm-level outcomes through SSCM, this research advances a more integrative and realistic view of sustainability adoption for the pharmaceutical sector in emerging markets. These findings offer actionable guidance for managers, regulators, and policymakers seeking to embed sustainability deeper into pharmaceutical operations. Also, this study addresses the theoretical expansion of DOI in the context of emerging economies. So, the culmination of all the issues interacts in a holistic view of the unique dynamics of the pharmaceutical industry in Bangladesh. To facilitate the implementation of SSCM in the case of pharmaceutical companies, it is recommended to introduce practices of key enablers gradually and develop KPIs for sustainability efforts, productivity improvement, etc. For example, energy can be saved as a result of waste reduction. When other sectors come to the discussion, it encourages the sharing of best practices and speeds up the adoption of SSCM enablers for productivity.

The practical implications are mainly twofold in this study. First of all, SSCM practices can be the core performance strategies within the pharmaceutical industry. They are not treated as supplementary or as compliance-based functions. Secondly, efforts to advance sustainability must be tied to measurable metrics such as productivity and SSCM practices as per international codes of conduct to demonstrate clearly the ROI and strategic alignment. Practitioners, such as pharmaceutical managers who are in charge of the operation in the pharmaceutical industry, should start to adopt SSCM step by step. Start with small projects that are manageable, such as reducing waste, and ramp up overtime. Focus on learning delivery and exploit digital resources for sustainability metric tracking and reduced time loss in productivity.

6. Contribution, Limitations, and Future Areas

6.1. Theoretical Contribution

This study made key theoretical contributions by exploring the diverse dynamics of Sustainable Supply Chain Management (SSCM) in a developing country context through the theoretical lens of Diffusion of Innovation (DOI) theory. First, it extends the application of DOI theory beyond the traditional innovation-focused sectors into the pharmaceutical supply chain, aligning with a novel theoretical lens to understand how sustainability-enabling factors diffuse and interact within complex industry ecosystems. The study plays a critical role in industry-specific dynamics, institutional capacity, and regulatory environments in cases of shaping innovation adoption in resource-constrained economies, aligning with calls for more embedded, context-sensitive applications of DOI [17,22,23]. Secondly, while many of the existing studies related to the SSCM literature have focused on isolated or individual enablers, this study developed and validated a multi-dimensional enabler-related framework comprising five major key constructs. This integrative approach has offered a more holistic theoretical perspective, especially for bridging fragmented research and providing clarity on how these enablers operate jointly through a mediating path via SSCM to drive productivity. Particularly, the full mediation effect of productivity on SSCM in the relationship between all five enablers and productivity reveals SSCM as the central mechanism through which resource allocation, managerial competencies, and structural inputs translate into performance, which is a totally new addition to the existing studies [2,10,93]. This research also fills a notable gap by demonstrating how productivity, as a critical outcome in developing economies, bridges the link between SSCM practices and firm-level performance [2,7]. Finally, the study highlights that not all enablers equally impact outcome pathways. The rejection of several direct paths but the acceptance of different indirect paths through SSCM emphasizes that the contextual dependency relates to theoretical assumptions. These findings support that models must be locally validated and also adapted to reflect the pharmaceutical industry and economy-specific nuances within the SSCM domain, and these contributions and industrial contributions are shown in the attached Figure 7.

6.2. Contribution to the Industry

From an industrial perspective, this study offers insights for pharmaceutical managers in Bangladesh and similar developing economies. The findings show that, while the direct effects of strategic inputs of SSCM enablers on productivity are weak or non-significant, their indirect effects through SSCM practices are strong and significant. This confirms that investing in SSCM translates strategic efforts into improvements. Hence, pharmaceutical companies must prioritize integrating sustainability into their core supply chain activities to ensure better performance outcomes. Accordingly, managerial recommendations are modest: organizations should gradually integrate SSCM into operations while monitoring short-term productivity effects, rather than assuming immediate efficiency gains. Policy recommendations should be tentative, inviting further evidence before large-scale adoption.

For managers, SSCM serves as a performance-driving mechanism that effectively converts institutional support, technological readiness, environmental commitment, and human capital into tangible productivity gains. This means that simply having policies or financial investments is not enough, and that the actual implementation through practices like green sourcing, ethical procurement, and collaborative supply chain redesign is crucial to realize the expected benefits.

From the viewpoint of company executives and operational leaders, the mediating role of productivity underlines the need to evaluate SSCM success not just in environmental terms but also in terms of process efficiency, cost-effectiveness, and output quality. This is particularly important in Bangladesh’s pharmaceutical sector, which, despite being globally competitive, faces resource constraints and strong regulatory pressures. Managers should develop integrated SSCM strategies, for example, by adopting lean and green technologies, training supply chain personnel in sustainability, and digitizing procurement and distribution to enhance both environmental and economic performance.

For companies and suppliers, the study encourages collaborative ecosystems with supply chain actors, such as vendors, regulatory agencies, and logistics providers. Firms can achieve this by (i) forming joint sustainability committees with suppliers and regulators, (ii) adopting digital platforms for transparent information sharing, and (iii) developing incentive-based contracts that reward green practices. These strategies provide mechanisms for collaboration even where firms lack formal authority. Such collaboration is essential to institutionalize sustainability practices and promote shared norms that align with SSCM objectives.

Lastly, policymakers and regulators can learn from this research. It helps identify which enablers need institutional support to effectively drive SSCM transformation. Their role includes designing supportive policy frameworks, offering targeted financial incentives, and launching capacity-building programs to enable sectoral growth through sustainability. These actions are especially necessary for encouraging the development of diverse, resilient, and sustainable industrial ecosystems within resource-constrained environments like Bangladesh.

6.3. Limitations and Future Areas of the Study

Despite providing insights into SSCM adoption and productivity through SSCM practice-mediated output, this study is not without limitations which require further investigation. Firstly, while this research has established the full mediation role of SSCM practices, the sample size of the study was 174, which, although adequate for SEM, was limited to managerial respondents in Bangladesh’s known pharmaceutical sector. This restricts the generalizability of findings to other sectors or countries, which might be one of the key limitations. Secondly, the cross-sectional design prevents causal inference, highlighting the need for longitudinal or experimental studies, which means there was a limitation in not addressing longitudinal data for the study. Thirdly, this study has utilized a cross-sectional survey-based methodology, which restricts causal inferences. Although SEM has provided a robust statistical framework, the absence of longitudinal data has constrained the ability to track changes in SSCM adoption and productivity performance over time and also from diverse industrial perspectives.

Nevertheless, several research avenues emerge from these discussed limitations. First, upcoming studies can explore diversified moderating effects, particularly focusing on institutional support or digital capability. Secondly, future studies can emerge by adopting qualitative–comparative analyses such as QCA, MICMAC, and ISM, as these analyses can deepen the structural understanding of enabler interactions, as suggested by [90]. Thirdly, the model can be replicated across diverse developing countries, contexts or industries, such as the textile, agriculture, or electronics industries. Fourthly, the longitudinal research design can be adapted in future research to ensure the comparison of trends of data over a few years. Also, future research could use mixed methods or qualitative methods including interviews or Focus Group Discussion (FGD) methods to achieve better insights on the research issue. Finally, by considering climate urgency and global sustainability agendas, future research can integrate environmental performance indicators alongside productivity, enabling dual-outcome models that can reflect the true multidimensional value of SSCM practices in resource-constrained settings in different contexts in emerging economies.