# Effect of Sustainable Supply Chain and Management Practices on Supply Chain Performance: The Mediating Roles of Ethical Dilemmas and Leadership Quality

Muhammed Sameer Uddin, Omaima Eltahir Babikir Mohamed, John Ebert

<figure><img src="image_1.png" /></figure>

## To cite this version:

Muhammed Sameer Uddin, Omaima Eltahir Babikir Mohamed, John Ebert. Effect of Sustainable Supply Chain and Management Practices on Supply Chain Performance: The Mediating Roles of Ethical Dilemmas and Leadership Quality. International Journal of Innovative Research and Publications, 2024, pp.10 - 29. 10.51430/ijirp.2024.410.002. hal-04972905

HAL Id: hal-04972905

https://hal.science/hal-04972905v1

Submitted on 2 Mar 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_2.png" /></figure>

Distributed under a Creative Commons Attribution - ShareAlike 4.0 International License
## Article

# Effect of Sustainable Supply Chain and Management Practices on Supply Chain Performance: The Mediating Roles of Ethical Dilemmas and Leadership Quality

Muhammed Sameer Uddin<fnref n="1" />*, Omaima Eltahir Babikir Mohamed<fnref n="2" />, John Ebert<fnref n="3" />

Received: 25 October 2024 / Accepted: 12 November 2024 / Published online: 15 December 2024

## Abstract

The objective of this study is to examine the influence of Sustainable Supply Chain (SSC) and Supply Chain Management Practices (SCMP) on Supply Chain Performance (SCP). It seeks to explain how organizations might utilize SSC and SCMP to improve performance by promoting ethical practices and good leadership. An empirical analysis was performed utilizing data gathered from 350 respondents from enterprises across several industries in Bangladesh through a quota sampling strategy. We used Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the direct and indirect effects. Ethical Dilemma (ETD) and Leadership (LED) quality were used as mediating variables. The results indicate that SSC and SCMP have a beneficial effect on SCP, with LED quality as a significant mediator. However, less directly impactful, ETD creates an essential framework for ethical practices inside the supply chain. The Importance-Performance Map highlights LED quality and SCMP as critical elements to improving SCP, suggesting that firms must prioritize these areas for optimal performance. The study's weaknesses include its cross-sectional methodology and concentration on a small geographic area, which may impact generalization. Future research may investigate SSC's long-term effects on SCP and future technologies' influence on ethical decision-making in global supply chains. This work enhances SSC literature by highlighting the significance of LED quality and ETDs as mediators in the link between SSC, SCMP and SCP.

**Keywords:** Sustainable Supply Chain, Supply Chain Performance, Leadership Quality, Ethical Dilemmas, Management Practices, Structural Equation Modeling

## To cite this article:

Uddin, M. S., Mohamed, O. E. B., & Ebert, J. (2024). Effect of Sustainable Supply Chain and Management Practices on Supply Chain Performance: The Mediating Roles of Ethical Dilemmas and Leadership Quality. *International Journal of Innovative Research and Publications, 4*(10), 13-32.
https://doi.org/10.51430/IJIRP.2024.410.002

<figure><img src="image_3.png" /></figure>

<footnote n="1">¹School of Business and Technology, Saint Mary's University of Minnesota, Winona, MN 55987, USA</footnote>

<footnote n="2">²Bank Rakyat School of Business & Entrepreneurship, Universiti Tun Abdul Razak, Kuala Lumpur, Malaysia</footnote>

<footnote n="3">³Department of Resource Analysis, Saint Mary's University of Minnesota, Winona, MN 55987, USA</footnote>

<footnote>*Corresponding Email: msameerbd@gmail.com</footnote>
**Introduction**

The increasing importance of sustainability in business has resulted in a growing interest in SSC. SSC denotes incorporating environmental and social factors into supply chain management strategies (Eggert & Hartmann, 2023). This integration seeks to mitigate adverse environmental and societal effects while enhancing the long-term success of individual companies and the overall supply chain (Mursidah & Fauzi, 2022). Companies are seeing increasing demand from many stakeholders, such as customers, employees, investors, governments, and NGOs, to implement SSC practices across their supply chains (Narayanan et al., 2019).

Despite an expanding corpus of research on SSC, comprehension of the intricate interplay of factors affecting the link between SCMP and SCP still needs to be improved (Eggert & Hartmann, 2023). This is especially significant when examining the impact of ethical problems and leadership attributes, which are vital in determining how organizations address sustainability and engage with their supply chain partners (Agyabeng-Mensah et al., 2020; Baliga et al., 2020; Vidal et al., 2023).

An ETD in supply chain management (SCM) and SSC signifies a situation where decision-makers face conflicting ethical norms or values while managing the complexities of supply networks (Busch et al., 2023; Kelling et al., 2021). The challenges often arise due to the interactions between parties involved, like suppliers and manufacturers, as well as customers and regulatory bodies. Each person holds values and goals of their own accord. On the side, LED plays a role in facilitating the execution of eco-friendly supply chain procedures. Managers who demonstrate forward-thinking perspectives, skillful communication, collaboration, ethical judgment, and flexibility are inclined to encourage practices within their companies. Leaders can significantly enhance the performance and resilience of supply chains in today's intricate and dynamic business environment by fostering a culture of sustainability and engaging stakeholders (Eggert & Hartmann, 2023).

This study will examine the effects of SSC and SCMPs on SCP, focusing on the mediating roles of ETD and LED quality. Comprehending these mediating aspects is essential for formulating successful ways to establish and oversee sustainable supply chains (Saha et al., 2020).

For example, ETD concerning working conditions and equitable labor practices among lower-tier suppliers can profoundly influence a company's reputation and, subsequently, its performance (Baliga et al., 2020; Vidal et al., 2023). In the same way, the quality of LED, especially the presence of sustainable leadership, can help create a culture of accountability and dedication to sustainability. This can make SCMP work better and lead to better performance (Jensen et al., 2020; Lin et al., 2020).

This study aims to provide a deeper understanding of how companies can utilize SSC and SCMP to improve their overall performance by addressing the following questions:

1. To what extent do SSC and SCMP directly impact SCP?

2. To what extent does ETD mediate the relationship between SSC and SCP?

3. To what extent does LED quality mediate the relationship between SCMP and SCP?

This study will give managers important ideas for getting rid of these complicated relationships in order to adopt and manage SSC well. The data will help make plans to deal with ETDs and improve LED qualities that are necessary to meet performance and sustainability goals.
# Literature Review

## Theoretical Background

This research suggests a complex interplay between sustainability practices, ethical considerations, leadership, and a firm's overall performance. This exploration uses the Resource-Based View (RBV) as a theoretical lens.

RBV theory states that a firm's unique resources and capabilities are the main factors determining its competitive advantage and performance (Razzak, 2023; Shahzad et al., 2022). The research indicates that SSC and SCMP are valuable resources that, when managed efficiently, can result in enhanced SCP.

SSC and SCMP encompass a broad spectrum of activities to minimize adverse social, environmental, and economic impacts throughout the supply chain. Examples include green warehousing, logistics optimization, ethical sourcing, fair labor practices, and waste reduction (Agyabeng-Mensah et al., 2020; Baah et al., 2022; Eggert & Hartmann, 2023; Mehdikhani & Valmohammadi, 2019). On the other hand, SCP can be measured through various metrics, including financial performance (profitability, return on investment), operational efficiency, market share, and stakeholder satisfaction (Saha et al., 2020; Shahzad et al., 2022; Zanon et al., 2021). Social and environmental performances are also critical dimensions of overall firm performance, reflecting a commitment to the triple bottom line (Saha et al., 2020; Shahzad et al., 2022).

RBV emphasizes the importance of intangible resources like leadership quality and ethical decision-making capabilities (Cherré & Lemieux, 2024). These characteristics are unique which can provide term competitive benefits. It underscores the importance of companies cultivating and utilizing their strengths to oversee sustainability initiatives. This could involve investing in employee training, building strong relationships with suppliers, integrating sustainability into strategic planning, and fostering a culture of ethical leadership (Chen et al., 2021; Lin et al., 2020; Mehdikhani & Valmohammadi, 2019).

The mediating role of LED quality emphasizes how effective LED is in navigating these ETDs and driving the successful implementation of sustainable practices. Leaders are crucial in establishing the tone, making ethical decisions, motivating employees, and collaborating with supply chain partners (Awan & Khan, 2021; Chen et al., 2021; Lin et al., 2020; Mehdikhani & Valmohammadi, 2019).

The importance of ETD in overseeing sustainability efforts brings attention to the challenges and complexities in execution. Concerns about material source, labor practices, and environmental impact need investigation (Winter & Knemeyer 2013). Balancing cost-effectiveness with ethical labor practices and sustainable materials selection can be difficult. This study examines how ETDs and LED quality impact enterprises' sustainable practices under proficient leadership.

# Conceptual Framework and Development of Hypotheses

## Conceptual Framework

The conceptual model illustrates the interaction among the dependent variables (SCP), mediating variables (LED quality and ETD), and independent variables (SSC and SCMP). The arrows indicate the influence flow from SSC management approaches to leadership quality, ethical challenges, and ultimately to SCP. Utilizing the RBV theory, we propose and evaluate a model to determine how SSC practices and effective SCM, mediated by LED quality and ETD considerations, can be utilized by firms to enhance performance, as illustrated in Figure 1.
<figure><img src="image_4.png" /><figcaption>Figure 1: Conceptual Framework</figcaption></figure>

## Hypotheses Development

In this section, the hypotheses are organized and discussed in a thematic order that aligns with the study's variables and conceptual framework. Grouping hypotheses by theme rather than numerical sequence allows for a more coherent and structured exploration of the relationships being investigated.

## Impact of Sustainable Supply Chain on Supply Chain Performance

SSC plays a crucial role in shaping ETD considerations, enhancing LED quality, and improving SCP. Integrating sustainability into supply chain practices raises ethical concerns, as businesses recognize that their social and environmental responsibilities extend throughout the supply chain (Mehdikhani & Valmohammadi, 2019; Winter & Knemeyer, 2013). This increased responsibility frequently raises ethical issues such as striking a balance between cost-effectiveness and fair labor standards. For instance, there is a financial trade-off when buying eco-friendly products because they are better for the environment but are sometimes more expensive.

Addressing these ETD challenges requires explicit guidelines, open communication, robust monitoring, and supplier development programs to foster transparency, fair labor standards, and environmental conservation (Agyabeng-Mensah et al., 2020; Mehdikhani & Valmohammadi, 2019; Patience Okpeke Paul et al., 2024; Vidal et al., 2023; Winter & Knemeyer, 2013). By utilizing SSC, enterprises may exhibit corporate social responsibility, so augmenting their reputation and credibility.

SSC also strengthens LED by challenging leaders to adopt practices that align with sustainability principles (Vidal et al., 2023). Leaders must champion ethically sourcing, fair labor standards, and green initiatives such as optimized logistics (Agyabeng-Mensah et al., 2020). Effective SSC-LED involves developing a sustainable vision, directing ethical decision-making, involving stakeholders, empowering employees, and fostering continuous improvement (Mehdikhani & Valmohammadi, 2019). This LED strategy promotes sustainability throughout the firm and establishes a unified ethical framework along the supply chain.

Numerous studies highlight a positive correlation between SSC and SCP (Agyabeng-Mensah et al., 2020; Chen et al., 2021; Mehdikhani & Valmohammadi, 2019). SSC reduces
costs through resource efficiency and waste reduction, enhancing profitability (Agyabeng-Mensah et al., 2020). Operational efficiency enhances sustainable practices implement lean management and optimized logistics, increasing productivity (Kumar & Goswami, 2019). SSC improves brand reputation by implementing ethical sourcing, equitable labor standards, and social responsibility, hence augmenting customer loyalty (Narayanan et al., 2019). SSC improves company reputation, aligns with sustainability goals, and lessens ecological effects (Vidal et al., 2023).

SSC includes creating a positive knock-on effect throughout the supply chain in addition to mitigating negative effects. SSC gives companies a competitive edge and helps create a more sustainable future by promoting ethical decision-making, responsible LED, and enhanced performance. Considering this, the following hypotheses are proposed:

**H1:** Sustainable Supply Chain has a significant effect on Ethical Dilemma.

**H2:** Sustainable Supply Chain has a positive effect on Leadership Quality.

**H3:** Sustainable Supply Chain has a positive effect on Supply Chain Performance.

## Impact of Supply Chain Management Practices on Ethical and Leadership Factors

SCM includes tactics to maximize the movement of products, data, and funds throughout the supply chain (Bani Hani, 2022). Collaboration, enhanced communication, solid partnerships, and information sharing are examples of critical practices. Sharing knowledge promotes openness and trust, which helps to avoid moral dilemmas brought on by false information (Li et al., 2006; Zhu et al., 2013). Effective communication channels among stakeholders address ethical concerns proactively, reducing misunderstandings that could lead to ethical breaches (Wijaya, 2023). Building robust internal and external relationships encourages ethical accountability, as partners are more likely to uphold standards when they have mutual interests (Amjad et al., 2022; Rozar et al., 2015). Collaborative practices, such as joint problem-solving, support a culture of ethical decision-making (Walker, 2013; Zhu et al., 2013).

Effective SCMPs can also reduce ETDs by enhancing transparency and fostering accountability. When supply chain partners share information and communicate openly, it promotes ethical decision-making (Habib et al., 2021; Yildiz Çankaya & Sezen, 2019). Strong relationships and collaboration further create accountability, making partners more likely to report unethical actions and work together to resolve issues (Beske-Janssen et al., 2015; Sokhetye, 2023).

Additionally, SCMPs impact LED quality by empowering leaders to make informed, collaborative decisions and fostering ethical LED within organizations (Aslam et al., 2021; Habib et al., 2020). Leaders prioritizing ethical SCMPs inspire integrity across the organization, improving SCP (Soyege et al., 2023). Organizations with solid SCMPs often achieve better performance metrics and sustainability by aligning actions with ethical values (Abdel-Baset et al., 2022; Freihat et al., 2024; Tukamuhabwa et al., 2023).

In summary, SCMPs significantly influence ETD, LED quality, and SCP. Hence, the following hypotheses are suggested as follows:

**H4:** Supply Chain Management Practice has a positive effect on Ethical Dilemma.

**H5:** Supply Chain Management Practice has a positive effect on Leadership Quality.

**H6:** Supply Chain Management Practice has a direct positive effect on Supply Chain Performance.
## The Role of Leadership Quality in Supply Chain Performance

The importance of LED quality in raising SCP is becoming more widely acknowledged. Successful leaders motivate groups, encourage cooperation, and propel critical projects in line with company objectives. According to research, SCP is greatly increased by transformational leadership, which is characterized by vision and team member support. For example, Haq & Aslam (2023) found that entrepreneurial LED improves supply chain resilience, suggesting that proactive leaders are more suited to manage uncertainty and raise performance measures. Moreover, the quality of LEDs functions as a mediator in the interaction among several supply chains and performance results. According to Almatarneh et al. (2022), a powerful LED improves supply chain integration and has a good effect on performance. According to their findings, LED is an essential component that makes supply chain strategies easier to execute and improves results (Almatarneh et al., 2022). Furthermore, Chatterjee et al. (2024) underlined how LED moderates the link between supply chain management and performance, so showing that businesses with effective LED receive higher degrees of integration, which is vital for performance optimization.

Furthermore, putting procedures like information exchange, teamwork, and quality control into practice requires an efficient LED. According to Mrad and Boujelbène (2023), LED quality has an impact on the adoption of sustainable practices, which are essential for improved operational and environmental results (Habib et al., 2022). Similarly, Zhou et al. (2023) found that high SCP requires a culture of cooperation and continual improvement, which is fostered by strong LED (Zhou et al., 2023).

LED quality has a critical role in driving SCP. LED quality needs to be included in SCM plans if companies want to increase performance metrics and get sustained competitive advantages. Hence, the following hypotheses are proposed:

**H7:** Leadership Quality has a positive effect on Supply Chain Performance.

**H8:** The effect of Supply Chain Management Practice on Supply Chain Performance is mediated by Leadership Quality.

**H9:** The effect of Sustainable Supply Chain Management on Supply Chain Performance is mediated by Leadership Quality.

## The Role of Ethical Dilemmas in Supply Chain Performance

Conflicts of interest, difficulties preserving openness, and dishonesty play roles in supply networks' ETDs. These conundrums might affect SCP and compromise judgment. According to Kabeer and Tufiq (2024), ETDs might result in less-than-ideal choices that damage operational efficiency and company reputation. Their study stresses that incorporating ethics into supply chain strategies mitigates risks tied to unethical practices (Kabeer & Tufiq, 2024). Research shows that ETDs can negatively affect SCP by increasing inefficiencies, costs, and potential reputational damage. Giving ethical practices top priority improves customer loyalty and lowers operational risk, according to Mogre et al. (2021; Picasso et al., 2023). According to Kelling et al. (2020), ethical frameworks enable resilience and performance by helping one negotiate problems. They propose that open policies help businesses satisfy more consumer needs and legal and market expectations (Kelling et al., 2021).

SCM and SSC also shape ethical decision-making by embedding sustainable practices into supply chains. Mursidah and Fauzi (2022) emphasize the need for alignment between operational strategies and ethical standards, highlighting that sustainable practices promote more robust compliance with ethics. Ghadge et al. (2020) add that sustainable procurement practices improve supplier relationships, positively impacting SCP (Ghadge et al., 2020).

Case studies confirm these conclusions, as shown by Kabeer and Tufiq's (2024) study in India's food and beverage sector, where ETDs in labor and sourcing upset supply lines.
Strong ethical frameworks are important; businesses that aggressively handle ETDs are more likely to maintain performance and competitive advantage. Hence, the following hypotheses are suggested as follows:

**H10:** Ethical Dilemma has a positive effect on Supply Chain Performance.

**H11:** The effect of Supply Chain Management Practice on Supply Chain Performance is mediated by Ethical Dilemma.

**H12:** The effect of Sustainable Supply Chain on Supply Chain Performance is mediated by Ethical Dilemma.

## Methodology

### Structural Equation Modeling

In this research, structural equation modeling (SEM) approach was used to analyze the causal relationships among constructs and to test the complicated relationship between the observed and latent variables, as well as the relationship between two or more latent variables (Tripathy et al., 2011, 2012). This method is a combination of multiple regression and factor analysis. Also, this study used SPSS and SmartPLS software to analyze the data gathered and test the hypotheses. In similar research within SSC, SCMP, LED, and ETD issue measurement, Partial Least Squares Structural Equation Modeling (PLS-SEM) has been widely utilized due to its effectiveness in analyzing complex relationships between variables. Studies such as those by Agustine & Nawangsari, (2020); Ali & Anwar, (2021); Beka Be Nguema et al. (2020); Hartono et al. (2021); Mukhsin et al. (2022); Tamara Jevtić (2024) employed PLS-SEM, gathering data through self-administered questionnaires with sample sizes ranging from 100 to 395 respondents.

According to Hair et al. (2011), to use a covariance base analysis (CB-SEM) method, some assumptions such as univariate and multivariate normalization, large sample size, theoretical testing and confirmation, reflective structures, and confirmatory study must exist. Henseler et al. (2010) noted that the CB-SEM approach to data analysis includes two stages. Analyzing the measurement model is the first step and examining the structural connections between latent constructs are the second.

## Data Collection and Measurement Scale Development

To address the research hypotheses, a survey-based empirical study was carried out in a randomly selected 350 manufacturing and service organizations from various industry sectors in Bangladesh. Quota sampling methods were applied to select the respondents from the organizations. The population under investigation in this study was associated with the supply chain of businesses that were actively involved in the automobile, food, apparel, chemical, agricultural, and service sectors. The questionnaires were distributed through a research data collection agency via Google link and e-mail to respondents requesting that they respond within one month.

In order to minimize self-report bias in the data, the respondents were informed that their names and the organizations' names were not needed for the study. Also, as A. Beehr et al. (2024) suggested, a reminder letter through the communications channels was sent to increase the response rate after the deadline. Finally, after about two months of 425 distributed questionnaires, 350 were returned. In total, 350 statistically valid questionnaires were gathered, resulting in a response rate of about 82.35 percent.

The items were scored applying a five-point Likert scale that went from strongly disagree (1) to strongly agree (5) (Kabeer & Tufiq, 2024).
## Non-Response Bias

Five senior executives from manufacturing and service companies as well as five academics were pre-tested on the questionnaire to make sure the phrasing and structure of the questions fit. As the order of the questions can affect the response and to correct this effect, half of the questionnaires with one order and the other half with another order were distributed (Presser, 2010). This leads to many more reliable and actionable results. Based on the recommendation of A. Beehr et al. (2024) conducted a non-response bias test (wave analysis).

Also, to minimize the possibility of common method variance, multiple-item scales were first used to measure the constructs and scattered questions about the independent and dependent variables throughout the questionnaire. Second, Harman's single-factor test revealed that the most of the variance in the measurements could not be explained by any one element or a general factor. Third, the effects of a single unmeasured latent method factor added to our measurement model were analyzed by loading all items originating from the same informant onto the method variable and its respective latent variable. The comparison of standardized parameter estimates with and without the method variable showed that the significance of the substantive relationship was not affected. Therefore, based on the results, common method bias is not a concern (Al Halbusi et al., 2021). Also, following Prajogo et al. (2008) and Valmohammadi's (2017) study, we checked any differences in the four composite scores between small- and medium-sized enterprises and large firms via multivariate analysis of variance. The composite scores of each construct were measured by calculating their factor scores from the principal component analysis. The two groups of varying-sized companies exhibited no statistically significant variation according to the findings. Therefore, it is appropriate to run the analyses using the sample as a whole.

## Data Analysis and Results

### Analysis Methods

The research employed the PLS-SEM methodology, utilizing PLS 4.1.0.1 software for the measurement model and PROCESS software for the analysis of the structural model to evaluate the theoretical framework/conceptual (Figure 1)(J. Hair et al., 2020). PLS-SEM is especially appropriate for exploratory research, as it is more conducive to theory-building than to theory-testing (Hair et al., 2019). Conversely, CB-SEM is more appropriate for bigger samples and normally distributed data, as it accommodates measurement errors; but it is less effective for lower sample sizes and non-normal data distributions (Hair et al., 2016). Multiple studies have confirmed model fit metrics specifically for PLS-SEM, which also elucidate the predictive significance of the model (Henseler et al., 2016).

### Descriptive Statistics

Descriptive statistics for the constructs show that there are no missing values and that the sample size for each variable is 350. Response consistency is suggested by the comparatively low standard deviations and averages, which range from 3.76 (SSC) to 4.27 (SCP). Skewness values are within the acceptable range for normality, although LED shows mild negative skewness (-1.271), and SCP has mild positive skewness (0.365). Kurtosis values are mostly near zero, indicating an approximately normal distribution, except for LED, which shows a higher kurtosis (2.301), suggesting a peaked distribution. Overall, the data meets the normality assumptions for further analysis.

### Measurement Model Assessment

The model was assessed to determine the validity and reliability of the constructs using discriminant validity, convergent validity, internal consistency reliability, and indicator reliability. The average variance extracted (AVE) was applied to evaluate convergent validity,
while indicator reliability was determined using the factor loadings (Henseler et al., 2015). We applied composite reliability (CR) and Cronbach's alpha to test internal consistency (Benitez et al., 2019). The thresholds used for factor loadings, Cronbach's alpha, CR, and AVE were >0.70, >0.70, >0.70, and >0.50, respectively as mentioned in Table 1 (Henseler et al., 2015).

<table><caption>Table 1: Measurement Model Decision Criteria</caption><thead><tr><td>Estimation</td><td>Objectives</td><td>Decision Criteria</td></tr></thead><tbody><tr><td>Cronbach's Alpha α (CA)</td><td>To assess the quality of an instrument and how well the items are positively correlated to each other</td><td>Less than 0.6 = Poor.<br>0.6 – 0.7 = Acceptable.<br>0.7 – 0.8 = Good.<br>0.9 or more = Excellent</td></tr><tr><td>Composite Reliability (CR)</td><td>To assess the reliability and internal consistency of the measured variables</td><td>Minimum of 0.60 or 0.7</td></tr><tr><td>Indicator Loading</td><td>To measure the indicator's variance by the underlying latent variable</td><td>More or equal to 0.70</td></tr><tr><td>Average Variance Extracted (AVE)</td><td>A test to verify if all the items are correlated</td><td>More or equal to 0.50</td></tr><tr><td>Cross Loadings</td><td>To test the degree to which a construct is distinct from the other constructs within the model</td><td>Indicator loadings must be greater than cross loadings</td></tr><tr><td>Fornell-Larcker Criterion</td><td>To test the discriminant validity of the measurement model</td><td>AVE of latent variables must be greater than any squared correlation of latent/common variable</td></tr></tbody></table>

Source: J. Hair et al. (2018) and Uma & Bougie (2016)

<table><caption>Table 2: Mean, Standard Deviation and Correlations</caption><thead><tr><td>Constructs</td><td>Mean</td><td>SD</td><td>ETD</td><td>LED</td><td>SCM</td><td>SCP</td><td>SSC</td></tr></thead><tbody><tr><td>ETD</td><td>3.80</td><td>0.68</td><td><strong>0.76</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>LED</td><td>4.00</td><td>0.79</td><td>0.18</td><td><strong>0.86</strong></td><td></td><td></td><td></td></tr><tr><td>SCM</td><td>3.79</td><td>0.71</td><td>0.15</td><td>0.54</td><td><strong>0.84</strong></td><td></td><td></td></tr><tr><td>SCP</td><td>4.27</td><td>0.48</td><td>0.28</td><td>0.84</td><td>0.61</td><td><strong>0.88</strong></td><td></td></tr><tr><td>SSC</td><td>3.76</td><td>0.70</td><td>0.10</td><td>0.51</td><td>0.67</td><td>0.55</td><td><strong>0.76</strong></td></tr></tbody></table>

Discriminant validity was evaluated by applying the Fornell-Larcker criterion. Each construct's AVE (square root AVE) exceeded the correlations with other constructs, supporting discriminant validity. For example, SCP's AVE square root is 0.88, higher than its correlations with ETD (0.28), LED (0.84), SCM (0.61), and SSC (0.55), indicating good discriminant validity (see Table 2).

From Table 3, the results for CR show values ranging from 0.844 to 0.932, with the lowest CR observed for SSC (0.844) and the highest for SCP (0.932), confirming the internal consistency reliability across constructs. Cronbach's alpha values range from 0.754 for SSC to 0.902 for SCP, all exceeding the recommended threshold of 0.70, thereby establishing strong internal consistency for all constructs.

For convergent validity, the AVE values were as follows: ETD (0.579), LED (0.746), SCM (0.701), SCP (0.773), and SSC (0.574). All AVE values are above the 0.50 threshold, indicating good convergent validity. The indicator loadings for each construct also meet the recommended threshold, with the lowest loading at 0.739 (ETD-1) and the highest at 0.924
(SCP-1).

<table><caption>Table 3: Constructs Factor Loadings, Validity, and Reliability</caption><thead><tr><th>Items</th><th>Outer loadings</th><th>t-value</th><th>***CA</th><th>**CR</th><th>*AVE</th></tr></thead><tbody><tr><td><strong>Ethical Dilemmas (ETD)</strong></td><td></td><td></td><td>0.76</td><td>0.846</td><td>0.579</td></tr><tr><td>ETD-1 - Conflicts of interest</td><td>0.739</td><td>10.136</td><td></td><td></td><td></td></tr><tr><td>ETD-2 - Ethical challenges</td><td>0.761</td><td>13.621</td><td></td><td></td><td></td></tr><tr><td>ETD-3 - Transparency and Honesty</td><td>0.79</td><td>13.407</td><td></td><td></td><td></td></tr><tr><td>ETD-4 - Fair Treatment</td><td>0.755</td><td>13.515</td><td></td><td></td><td></td></tr><tr><td><strong>Leadership (LED)</strong></td><td></td><td></td><td>0.886</td><td>0.921</td><td>0.746</td></tr><tr><td>LED-1 - High ethical standards</td><td>0.855</td><td>41.537</td><td></td><td></td><td></td></tr><tr><td>LED-2 - Effective decision-makers</td><td>0.836</td><td>44.248</td><td></td><td></td><td></td></tr><tr><td>LED-3 - Encourage talent management</td><td>0.85</td><td>41.68</td><td></td><td></td><td></td></tr><tr><td>LED-4 - Foster good organizational culture</td><td>0.912</td><td>74.576</td><td></td><td></td><td></td></tr><tr><td><strong>Supply Chain Management (SCM)</strong></td><td></td><td></td><td>0.858</td><td>0.904</td><td>0.701</td></tr><tr><td>SCM-1 - Information sharing</td><td>0.831</td><td>48.911</td><td></td><td></td><td></td></tr><tr><td>SCM-2 - Improved communication</td><td>0.834</td><td>35.066</td><td></td><td></td><td></td></tr><tr><td>SCM-3 - External and internal relationship</td><td>0.856</td><td>43.229</td><td></td><td></td><td></td></tr><tr><td>SCM-4 - Improved collaboration</td><td>0.827</td><td>34.017</td><td></td><td></td><td></td></tr><tr><td><strong>Supply Chain Performance (SCP)</strong></td><td></td><td></td><td>0.902</td><td>0.932</td><td>0.773</td></tr><tr><td>SCP-1 - Meets customer demand</td><td>0.924</td><td>97.387</td><td></td><td></td><td></td></tr><tr><td>SCP-2 - Responding to changes</td><td>0.855</td><td>40.333</td><td></td><td></td><td></td></tr><tr><td>SCP-3 - Operational performance</td><td>0.888</td><td>57.502</td><td></td><td></td><td></td></tr><tr><td>SCP-4 - Financial performance</td><td>0.848</td><td>44.738</td><td></td><td></td><td></td></tr><tr><td><strong>Sustainable Supply Chain (SSC)</strong></td><td></td><td></td><td>0.754</td><td>0.844</td><td>0.574</td></tr><tr><td>SSC-1 - Environmental sustainability</td><td>0.768</td><td>25.39</td><td></td><td></td><td></td></tr><tr><td>SSC-2 - Social responsibility activities</td><td>0.76</td><td>23.986</td><td></td><td></td><td></td></tr><tr><td>SSC-3 - Innovation in technology</td><td>0.747</td><td>24.927</td><td></td><td></td><td></td></tr><tr><td>SSC-4 - Cost optimization</td><td>0.756</td><td>25.024</td><td></td><td></td><td></td></tr></tbody></table>

Note: *AVE: Average variance extracted; **CR: Composite Reliability;***CA: Cronbach's alpha

Finally, a VIF value below 5 is typically considered acceptable (Hair et al., 2019; Senthilnathan, 2019), indicating that multicollinearity is not severely biasing the model's estimates.

## Assessment of Structural Model

The structural model assessment included evaluating the effect size, explaining variance, and determining the predictive relevance of the exogenous variables (SSC and SCMP) on the endogenous variables (ETD and LED) and SCP, as well as the overall model fit (see Table 4).
<table><caption>Table 4: Structural Model Decision Criteria</caption><thead><tr><td>Estimation</td><td>Objectives</td><td>Decision Criteria</td></tr></thead><tbody><tr><td>Collinearity</td><td>To examine the level of collinearity between Construct specified in the structural model</td><td>Variance Inflation Factor (VIF) value Below 5 (VIF<5)</td></tr><tr><td>Path Coefficient</td><td>To evaluate hypothesized relationships between construct</td><td>Two-tail test with t value at 1.645 or higher</td></tr><tr><td>Coefficient of Determination (R<sup>2</sup> value)</td><td>The coefficient represents combined effects of exogenous latent variables on the endogenous variable</td><td>0.25 = Weak<br>0.50 = Moderate<br>0.75 = High</td></tr><tr><td>Effect Size (f<sup>2</sup> value)</td><td>To access effect of a specific exogenous variables on an endogenous variable</td><td>0.02 = Small<br>0.15 = Medium<br>0.35 = Large</td></tr><tr><td>Predictive Relevance (Q<sup>2</sup> value)</td><td>To estimate the structural model's predictive relevance for a particular construct</td><td>Q<sup>2</sup> = model has predictive relevance<br>Q<sup>2</sup> = model has lack of predictive relevance</td></tr></tbody></table>

Source: J. Hair et al. (2018) and Uma & Bougie (2016)

The variance explained (R²) was assessed using the 0.25, 0.50, and 0.75 thresholds, representing small, moderate, and substantial levels, respectively (J. F. Hair et al., 2012). The R² values for ETD (2.2%), LED (33.2%), and SCP (75.2%) suggest that the model has weak explanatory power for ETD, moderate explanatory power for LED, and substantial explanatory power for SCP. This indicates that the model strongly explains the variance in supply chain performance while it has a limited explanatory effect on ethical dilemmas.

The effect sizes (f² values) for SSC and SCM on the endogenous variables show varied impacts. Specifically, the effect sizes are:

* SSC has a minimal effect size on SCP (f² = 0.014) and a weak effect on LED (f² = 0.062).

* SCM demonstrates a weak effect size on LED (f² = 0.103) and a minimal effect on SCP (f² = 0.053), while showing almost no effect on ETD (f² = 0.013).

The predictive relevance was assessed using Stone-Geisser's Q² values. The Q² values of 0.009 for ETD, 0.318 for LED, and 0.397 for SCP indicate weak predictive relevance for ETD, moderate predictive relevance for LED, and substantial predictive relevance for SCP. These Q² values suggest that the model has good predictive ability, especially in predicting supply chain performance.

The model fit indices include:

* SRMR values of 0.069 for the saturated model and 0.071 for the estimated model, both below the threshold of 0.08, indicating a good model fit.

* The d_ULS and d_G values of 0.996 and 0.576 for the saturated model and 1.073 and 0.581 for the estimated model, respectively, support model adequacy.

* The Chi-square values for the saturated and estimated models are 1174.239 and 1165.721, respectively.

* The NFI values of 0.747 for the saturated model and 0.749 for the estimated model also suggest an acceptable model fit.
<figure><img src="image_5.png" /><figcaption>Figure 2: Final Measurement and Structural Model</figcaption></figure>

## Hypothesis Testing

The analysis' findings (see Table 5) show that, at the 5% level, hypotheses H2, H3, H4, H5, H6, H7, H8, H9, and H10 are statistically significant. However, hypotheses H1, H11, and H12 are not statistically supported.

The analysis reveals that ETD significantly and positively influences SCP (H10, $\beta$ = 0.128, t = 5.154, p < 0.05). Furthermore, LED shows a strong positive and significant effect on SCP (H7, $\beta$ = 0.685, t = 19.106, p < 0.05), confirming the hypothesis.

SCMP significantly influences both ETD and LED, supporting hypotheses H4 and H5, respectively (H4, $\beta$ = 0.153, t = 2.15, p < 0.05; H5, $\beta$ = 0.355, t = 5.735, p < 0.05). SCM also demonstrates a direct positive impact on SCP (H6, $\beta$ = 0.163, t = 3.561, p < 0.05).

On the other hand, SSC does not have a significant effect on ETD, as indicated by H1 ($\beta$ = -0.005, t = 0.074, p = 0.941), which is not supported. However, SSC does positively impact LED Quality and SCP, supporting hypotheses H2 and H3, respectively (H2, $\beta$ = 0.274, t = 4.191, p < 0.05; H3, $\beta$ = 0.081, t = 2.096, p < 0.05).
<table><caption>Table 5: Hypothesis Testing</caption><thead><tr><th colspan="6">Direct Effect</th></tr><tr><th>Structural Path</th><th>Hypothesis</th><th>Original sample (O)</th><th>T statistics (|O/STDEV|)</th><th>P values</th><th>Result</th></tr></thead><tbody><tr><td>SSC -&gt; ETD</td><td>H1</td><td>-0.005</td><td>0.074</td><td>0.941</td><td>Not Supported</td></tr><tr><td>SSC -&gt; LED</td><td>H2</td><td>0.274</td><td>4.191</td><td>0</td><td>Supported</td></tr><tr><td>SSC -&gt; SCP</td><td>H3</td><td>0.081</td><td>2.096</td><td>0.036</td><td>Supported</td></tr><tr><td>SCM -&gt; ETD</td><td>H4</td><td>0.153</td><td>2.15</td><td>0.032</td><td>Supported</td></tr><tr><td>SCM -&gt; LED</td><td>H5</td><td>0.355</td><td>5.735</td><td>0</td><td>Supported</td></tr><tr><td>SCM -&gt; SCP</td><td>H6</td><td>0.163</td><td>3.561</td><td>0</td><td>Supported</td></tr><tr><td>LED -&gt; SCP</td><td>H7</td><td>0.685</td><td>19.106</td><td>0</td><td>Supported</td></tr><tr><td>ETD -&gt; SCP</td><td>H10</td><td>0.128</td><td>5.154</td><td>0</td><td>Supported</td></tr><tr><th colspan="6">Indirect Effect (Mediation Analysis)</th></tr><tr><td>SCM -&gt; LED -&gt; SCP</td><td>H8</td><td>0.243</td><td>5.365</td><td>0</td><td>Supported</td></tr><tr><td>SSC -&gt; LED -&gt; SCP</td><td>H9</td><td>0.188</td><td>4.107</td><td>0</td><td>Supported</td></tr><tr><td>SCM -&gt; ETD -&gt; SCP</td><td>H11</td><td>0.02</td><td>1.937</td><td>0.053</td><td>Not Supported</td></tr><tr><td>SSC -&gt; ETD -&gt; SCP</td><td>H12</td><td>-0.001</td><td>0.073</td><td>0.942</td><td>Not Supported</td></tr></tbody></table>

The study also examines the indirect (mediation) effects. The results indicate that LED Quality partially mediates the effect of SCMP on SCP (H8, $\beta$ = 0.243, t = 5.365, p < 0.05), supporting H8. Additionally, LED Quality also mediates the relationship between SSC and SCP, supporting H9 ($\beta$ = 0.188, t = 4.107, p < 0.05).

However, the mediation effects of ETD in the relationships between SCM and SCP (H11, $\beta$ = 0.02, t = 1.937, p = 0.053) and between SSC and SCP (H12, $\beta$ = -0.001, t = 0.073, p = 0.942) are not statistically significant, hence not supporting H11 and H12.

These findings show the important roles of both SCMP and SSC in enhancing SCP via LED Quality since they reveal the direct and indirect interactions among SSC, SCMP, ETD, LED Quality, and SCP.

### Importance-Performance Map Analysis

With the highest importance (0.685) and performance (73.656) scores, the IPM emphasizes LED Quality's need to improve SCP. However, SCMP has a significant influence (importance score of 0.425), and its performance (62.037) indicates room for development to increase SCP even more. With moderate importance (0.268) and performance (65.012), SSC supports sustainability goals but remains secondary to LED and SCMP in driving SCP. ETD has a lower direct impact on SCP (importance score of 0.128) but shows solid performance (70.097), indicating that while ethical practices are well-managed, they serve more as a supporting foundation. The IPM suggests that organizations should enhance SCMP while maintaining high standards in LED and SCP practices.
<figure><img src="image_6.png" /><figcaption>Figure 3: Importance-Performance Map Analysis</figcaption></figure>

Discussion

This study aimed to explore the impact of SSC and SCMPs on SCP, with LED quality and ETD as mediating factors. The results add to the growing body of research on SSC and ETD issues in modern supply chain frameworks. They stress how important SSC and SCMP are for achieving optimal SCP.

Our findings demonstrate that SSCs positively influence SCP by fostering cost efficiency, operational effectiveness, and improved brand reputation, which aligns with previous research, indicating that SSCs mitigate environmental issues while enhancing overall performance through waste reduction and resource efficiency (Agyabeng-Mensah et al., 2020; Vidal et al., 2023). Including SSC in business processes helps companies satisfy legal requirements, attract consumers who care about the environment, and realize long-term financial gains. These results imply that companies trying to keep a competitive advantage while satisfying ethical and environmental criteria must have SSC policies.

LED quality emerged as a significant mediating factor in the relationship between SCMPs and SCP. Effective LED promotes an ethical and collaborative culture that enhances SCP, supporting findings from recent studies emphasizing transformational and ethical leadership's role in SCP (Awan & Khan, 2021; Chen et al., 2021). Leaders who give ethical issues and sustainability top priority help to create a society in which operational objectives complement social obligations. This strategy creates a strong and flexible supply chain in addition to improving internal and outside stakeholder ties. Thus, companies should invest in leadership training programs that emphasize ethical decision-making and sustainability to maximize SCP outcomes.

ETDs play a crucial role in SCP, as they often arise from conflicts between cost-saving measures and ethical standards in areas such as labor practices, transparency, and environmental responsibility. The findings line with research showing that managing ethical dilemmas through transparency and accountability systems could lower reputational risk and raise SCP (Kabeer & Tufiq, 2024). Clear ETDs help businesses to guarantee that supply chain activities follow both corporate values and external regulatory criteria. Moreover, ethical frameworks encourage suppliers and partners to uphold similar standards, strengthening the entire supply chain network.
**Strategic Recommendations**

This paper recommends that companies deliberately use SSC and SCMPs to improve SCP. Organizations should first include SSC ideas all over the supply chain, prioritizing environmentally friendly logistics, waste reduction, and sustainable procurement. Second, using planned development projects and motivating LED quality will ensure leaders are ready to negotiate ETDs and promote an accountable and transparent culture. Third, applying technologically advanced solutions—such as supply chain visibility tools—may help to increase information flow and reduce ethical questions with opaque behavior.

**Future Research Directions**

Future studies could build on this work by looking at the long-term implications of SSC on SCP and investigating the impact of developing technologies such as block-chain and artificial intelligence on ethical supply chain decision-making. Furthermore, considering how various ethical standards affect global supply chains enables one to better grasp the difficulties in preserving ongoing ethical behavior.

**Limitations**

The findings of this study need to be taken into account while recognizing limitations that apply here the study's cross-sectional design restricts the ability to evaluate cause-and-effect relationships over a period. Secondly, the research data was collected from a particular country and industrial setting, constraining the applicability of the findings to other situations. Third, depending so much on self-reported data could cause response bias since participants, especially about ethical issues—may have given socially desired responses. Finally, although the study looks at LED quality and ETD as mediating elements, other possible mediators and moderators—such as organizational culture or technological innovation—were not investigated and might offer more information in the following studies.

**Conclusions**

This study considers how SSC and SCMP affect SCP in detail, with LED brightness and ETD playing a role as mediators. By combining concepts from the SSC and SCMP models to examine how sustainability and management practices affect the supply chain as a whole, this study adds to the body of literature. The results show that although ETDs, although less important directly, are a necessary basis for ethical supply chain operations, LED quality is crucial in improving SCP.

This study has significant theoretical and managerial consequences. First, it adds to our theoretical knowledge of SSC by showing how LED quality and ETDs act as middlemen between SSC practices and SCP. The result shows that companies need to put money into LED development projects that encourage everyone in the supply chain to make decisions that are ethical and sustainable.

From a manager's point of view, the study shows how important SSC and SCMP are as key factors for reaching goals for better performance and longevity. The study says that supply chain managers put LED and ETD standards at the top of their list of priorities to make their supply chains more resilient and flexible. Adding sustainability practices to SCM not only makes operations run more smoothly, but it also improves brand loyalty and image.

Furthermore, this study also stresses how important it is to align SCMP with SSC concepts in order to promote an all-around approach to SCM. ETD practices and strong LED can help build a transparent and accountable culture, which can give business long-term benefits over its competitors. It is suggested that managers create systems that help with ethical behavior, reducing waste, and sustainable sources. These practices will enable organizations to mitigate risks associated with ethical dilemmas while maximizing supply
chain performance.

Thus, the study helps us understand the role of SSC and SCMP in SCP. It also shows how important LED and ETDs are in modern supply lines. That way, businesses can not only improve their performance measures, but they can also make sure they can grow and stay strong in a market that is becoming more competitive.

## References

A. Beehr, T., Kim, M., & W. Armstrong, I. (2024). How much do survey response rates affect relationships among variables? *International Journal of Social Research Methodology*, 27(1), 63–86. https://doi.org/10.1080/13645579.2022.2113663

Abdel-Baset, M., Chang, V., & Gamal, A. (2022). Evaluation of the green supply chain management practices: A novel neutrosophic approach. *Computers in Industry, 108 C*, 210–220. https://doi.org/https://doi.org/10.1016/j.compind.2019.02.013

Agustine, T. D., & Nawangsari, L. C. (2020). The Effect of Compensation and Work Loads Towards Intension of Turnover with Work Satisfaction as a Variable Mediation in Clinic Employees of PT Nayaka Era Husada Branch of Bekasi. *International Journal of Innovative Science and Research Technology*, 5(7), 1304–1312. https://doi.org/10.38124/ijisrt20jul714

Agyabeng-Mensah, Y., Ahenkorah, E., Afum, E., Dacosta, E., & Tian, Z. (2020). Green warehousing, logistics optimization, social values and ethics and economic performance: the role of supply chain sustainability. *International Journal of Logistics Management*, 31(3), 549–574. https://doi.org/10.1108/IJLM-10-2019-0275

Al Halbusi, H., Williams, K. A., Ramayah, T., Aldieri, L., & Vinci, C. P. (2021). Linking ethical leadership and ethical climate to employees’ ethical behavior: the moderating role of person–organization fit. *Personnel Review*, 50(1), 159–185. https://doi.org/10.1108/PR-09-2019-0522

Ali, B. J., & Anwar, G. (2021). Strategic leadership effectiveness and its influence on organizational effectiveness. *International Journal of Electrical, Electronics and Computers*, 6(2), 11–24. https://doi.org/10.22161/eec.62.2

Almatarneh, Z., Jarah, B. A. F., & Jarrah, M. A. AL. (2022). The role of management accounting in the development of supply chain performance in logistics manufacturing companies. *Uncertain Supply Chain Management*, 10(1), 13–18. https://doi.org/10.5267/j.uscm.2021.10.015

Amjad, A., Abbass, K., Hussain, Y., Khan, F., & Sadiq, S. (2022). Effects of the green supply chain management practices on firm performance and sustainable development. *Environmental Science and Pollution Research*, 29(44), 66622–66639. https://doi.org/10.1007/s11356-022-19954-w

Aslam, J., Saleem, A., Khan, N. T., & Kim, Y. B. (2021). Factors influencing blockchain adoption in supply chain management practices: A study based on the oil industry. *Journal of Innovation and Knowledge*, 6(2), 124–134. https://doi.org/10.1016/j.jik.2021.01.002

Awan, U., & Khan, S. A. R. (2021). Mediating role of sustainable leadership in buyersupplier relationships: An supply chain performance: An empirical study. *Logforum*, 17(1), 97–112. https://doi.org/10.17270/J.LOG.2021.546

Baah, C., Opoku Agyeman, D., Acquah, I. S. K., Agyabeng-Mensah, Y., Afum, E., Issau, K., Ofori, D., & Faibil, D. (2022). Effect of information sharing in supply chains: understanding the roles of supply chain visibility, agility, collaboration on supply chain performance. *Benchmarking*, 29(2), 434–455. https://doi.org/10.1108/BIJ-08-2020-0453

Baliga, R., Raut, R., & Kamble, S. (2020). The effect of motivators, supply, and lean management on sustainable supply chain management practices and performance: Systematic literature review and modeling. *Benchmarking*, 27(1), 347–381. https://doi.org/10.1108/BIJ-01-2019-0004

Bani Hani, J. (2022). the Influence of Supply Chain Management Practices on Supply Chain Performance: the Moderating Role of Information Quality. *Business, Management and Economics Engineering*, 20(1), 152–171. https://doi.org/10.3846/bmee.2022.16597

Beka Be Nguema, J. N., Bi, G., Ali, Z., Mehreen, A., Rukundo, C., & Ke, Y. (2020). Exploring the factors influencing the adoption of supply chain finance in supply chain effectiveness: evidence from manufacturing firms. *Journal of Business and Industrial Marketing*, 36(5),
706-716. https://doi.org/10.1108/JBIM-01-2020-0047

Benitez, J., Henseler, J., Castillo, A., & Schuberth, F. (2019). How to perform and report an impactful analysis using partial least squares: Guidelines for confirmatory and explanatory IS research. *Information and Management*. https://doi.org/https://doi.org/10.1016/j.im.2019.05.003

Beske-Janssen, P., Johnson, M. P. ., & Schaltegger, S. (2015). “20 years of performance measurement in sustainable supply chain management – what has been achieved?” *Supply Chain Management: An International Journal*, 20(6). https://doi.org/http://dx.doi.org/10.1108/SCM-06-2015-0216

Busch, G., Spiller, A., & Kühl, S. (2023). Review: Ethical responsibilities and transformation strategies of focal companies in the meat supply chain: the implementation dilemma. *Animal*, 17, 100915. https://doi.org/10.1016/j.animal.2023.100915

Chatterjee, S., Chaudhuri, R., & Vrontis, D. (2024). Examining the Impact of Adoption of Emerging Technology and Supply Chain Resilience on Firm Performance: Moderating Role of Absorptive Capacity and Leadership Support. *IEEE Transactions on Engineering Management*, 71(January), 10373-10386. https://doi.org/10.1109/TEM.2021.3134188

Chen, L., Jia, F., Li, T., & Zhang, T. (2021). Supply chain leadership and firm performance: A meta-analysis. *International Journal of Production Economics*, 235(March), 108082. https://doi.org/10.1016/j.ijpe.2021.108082

Cherré, B., & Lemieux, N. (2024). To Overcome Ethical Dilemmas in an Organization: Paradox and Ethics of Freedom Can Help. *Business Ethics and Leadership*, 8(2), 76-91. https://doi.org/10.61093/bel.8(2).76-91.2024

Eggert, J., & Hartmann, J. (2023). Sustainable supply chain management – a key to resilience in the global pandemic. *Supply Chain Management*, 28(3), 486-507. https://doi.org/10.1108/SCM-10-2021-0463

Freihat, L., Al-Qaaida, M., Huneiti, Z., & Abbod, M. (2024). Green Human Resource Management/Supply Chain Management/Regulation and Legislation and Their Effects on Sustainable Development Goals in Jordan. *Sustainability (Switzerland)* , 16(7). https://doi.org/10.3390/su16072769

Ghadge, A., Er Kara, M., Moradlou, H., & Goswami, M. (2020). The impact of Industry 4.0 implementation on supply chains. *Journal of Manufacturing Technology Management*, 31(4), 669-686. https://doi.org/10.1108/JMTM-10-2019-0368

Habib, M. A., Balasubramanian, S., Shukla, V., Chitakunye, D., & Chanchaichujit, J. (2022). Practices and performance outcomes of green supply chain management initiatives in the garment industry. *Management of Environmental Quality: An International Journal*, 33(4), 882-912. https://doi.org/10.1108/MEQ-08-2021-0189

Habib, M. A., Bao, Y., & Ilmudeen, A. (2020). The impact of green entrepreneurial orientation, market orientation and green supply chain management practices on sustainable firm performance. *Cogent Business and Management*, 7(1). https://doi.org/10.1080/23311975.2020.1743616

Habib, M. A., Bao, Y., Nabi, N., Dulal, M., Asha, A. A., & Islam, M. (2021). Impact of strategic orientations on the implementation of green supply chain management practices and sustainable firm performance. *Sustainability (Switzerland)*, 13(1), 1-21. https://doi.org/10.3390/su13010340

Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012). An assessment of the use of partial least squares structural equation modeling in marketing research. *Journal of the Academy of Marketing Science*, 40(3), 414-433. https://doi.org/10.1007/S11747-011-0261-6

Hair, J., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. *Journal of Business Research*, 109(August 2019), 101-110. https://doi.org/10.1016/j.jbusres.2019.11.069

Hair, J., Sarstedt, M., Ringle, C., & Gudergan, S. (2011). PLS-SEM : Indeed a silver bullet PLS-SEM : Indeed a Silver Bullet. February 2014. https://doi.org/10.2753/MTP1069-6679190202

Hair, J., Sarstedt, M., Ringle, C., & Gudergan, S. (2018). *Advanced Issues in Partial Least Squares Structural Equation Modeling*. https://tore.tuhh.de/handle/11420/4073

Hair, Ringle, C. M., Sarstedt, M. & G., Ringle, C. M., Sarstedt, M. &, & Gudergan. (2019). *When to use and how to report the results of PLS-SEM*. December. https://doi.org/10.1108/EBR-11-
2018-0203

Hair, Sarstedt, M., Matthews, L. M., & Ringle, C. M. (2016). *Identifying and treating unobserved heterogeneity with FIMIX-PLS: part I – method*. March 2016. https://doi.org/10.1108/EBR-09-2015-0094

Haq, M. Z. ul, & Aslam, H. (2023). Impact of entrepreneurial leadership on supply chain performance: the mediating role of supply chain resilience. *Journal of Manufacturing Technology Management, 34*(5), 694–712. https://doi.org/10.1108/JMTM-10-2022-0376

Hartono, R., Efendi, E., & Nurwati, E. (2021). The Effect of Compensation and Motivation on Employee Performance with Job Satisfaction as Intervening Variables at Hotel XYZ, in Jakarta. *Majalah Ilmiah Bijak, 18*(1), 153–166. https://doi.org/10.31334/bijak.v18i1.1309

Henseler, J., Chin, W. ., Wang, J., & Vinzi, V. E. (2010). *Handbook of Partial Least Squares*. Springer. http://www.springer.com/series/7286

Henseler, J., Hubona, G., & Ray, P. A. (2016). *Using PLS path modeling in new technology research : updated guidelines*. 116(1), 2–20. https://doi.org/10.1108/IMDS-09-2015-0382

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. *Journal of the Academy of Marketing Science, 43*(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8

Jensen, M., Potočnik, K., & Chaudhry, S. (2020). A mixed-methods study of CEO transformational leadership and firm performance. *European Management Journal, 38*(6), 836–845. https://doi.org/10.1016/j.emj.2020.05.004

Jevtić, T. (2024). The effects of the compensation system on job satisfaction and turnover intention of employees in the Republic of Serbia. *STRATEGIC MANAGEMENT, XX*(x), 003-0xx.
https://doi.org/DOI: 10.5937/StraMan2300063J

Kabeer, M., & Tufiq, M. A. (2024). The effect of a green supply chain on a company’s success. *South Asian Journal of Operations and Logistics, 3*(2), 332–344.
https://doi.org/10.57044/sajol.2024.3.2.2445

Kelling, N. K., Sauer, P. C., Gold, S., & Seuring, S. (2021). The Role of Institutional Uncertainty for Social Sustainability of Companies and Supply Chains. *Journal of Business Ethics, 173*(4), 813–833. https://doi.org/10.1007/s10551-020-04423-6

Kumar, G., & Goswami, M. (2019). Sustainable supply chain performance, its practice and impact on barriers to collaboration. *International Journal of Productivity and Performance Management, 68*(8), 1434–1456. https://doi.org/10.1108/IJPPM-12-2018-0425

Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Subba Rao, S. (2006). The impact of supply chain management practices on competitive advantage and organizational performance. *Omega, 34*(2), 107–124. https://doi.org/10.1016/j.omega.2004.08.002

Lin, W. L., Yip, N., Ho, J. A., & Sambasivan, M. (2020). The adoption of technological innovations in a B2B context and its impact on firm performance: An ethical leadership perspective. *Industrial Marketing Management, 89*(December 2018), 61–71.
https://doi.org/10.1016/j.indmarman.2019.12.009

Mehdikhani, R., & Valmohammadi, C. (2019). Strategic collaboration and sustainable supply chain management: The mediating role of internal and external knowledge sharing. *Journal of Enterprise Information Management, 32*(5), 778–806. https://doi.org/10.1108/JEIM-07-2018-0166

Mukhsin, M., Taufik, H. E. R., Ridwan, A., & Suryanto, T. (2022). The mediation role of supply chain agility on supply chain orientation-supply chain performance link. *Uncertain Supply Chain Management, 10*(1), 197–204. https://doi.org/10.5267/j.uscm.2021.9.008

Mursidah, S., & Fauzi, A. M. (2022). Sustainable sugarcane supply chain performance assessment: A review and research agenda. *IOP Conference Series: Earth and Environmental Science, 1063*(1). https://doi.org/10.1088/1755-1315/1063/1/012039

Narayanan, A. E., Sridharan, R., & Ram Kumar, P. N. (2019). Analyzing the interactions among barriers of sustainable supply chain management practices: A case study. *Journal of Manufacturing Technology Management, 30*(6), 937–971. https://doi.org/10.1108/JMTM-06-2017-0114

Nsisong Louis Eyo-Udo, Agnes Clare Odimarha, & Olaniyi Olufemi Kolade. (2024). Ethical Supply Chain Management: Balancing Profit, Social Responsibility, and Environmental Stewardship.
*International Journal of Management & Entrepreneurship Research*, **6**(4), 1069-1077.
https://doi.org/10.51594/ijmer.v6i4.985

Patience Okpeke Paul, Jane Osareme Ogugua, & Nsisong Louis Eyo-Udo. (2024). Sustainable procurement practices: Balancing compliance, ethics, and cost-effectiveness. *International Journal of Scientific Research Updates*, **8**(1), 027-036.
https://doi.org/10.53430/ijsru.2024.8.1.0044

Picasso, F. G., Biazzin, C., Paiva, E. L., & Beal Partyka, R. (2023). Socially responsible supply chain initiatives and their outcomes: a taxonomy of manufacturing companies. *Supply Chain Management*, **28**(1), 90-106. https://doi.org/10.1108/SCM-11-2020-0578

Prajogo, D. I., McDermott, P., & Goh, M. (2008). Impact of value chain activities on quality and innovation. *International Journal of Operations and Production Management*, **28**(7), 615-635. https://doi.org/10.1108/01443570810881785

Presser, J. A. K. and S. (2010). Question and Questionnaire Design. Handbook of Survey Research. In *European Journal of Biochemistry* (2nd ed.). Emerald Group Publishing Limited.
https://doi.org/10.1111/j.1432-1033.1976.tb10115.x

Razzak, M. R. (2023). Mediating effect of productivity between sustainable supply chain management practices and competitive advantage: evidence from apparel manufacturing in Bangladesh. *Management of Environmental Quality: An International Journal*, **34**(2), 428-445.
https://doi.org/10.1108/MEQ-01-2022-0022

Rozar, N. M., Mahmood, W. H. W., Ibrahim, A., & Razik, M. A. (2015). A Study of Success Factors in Green Supply Chain Management in Manufacturing Industries in Malaysia. *Journal of Economics, Business and Management*, **3**(2), 287-291.
https://doi.org/10.7763/joebm.2015.v3.196

Saha, R., Shashi, Cerchione, R., Singh, R., & Dahiya, R. (2020). Effect of ethical leadership and corporate social responsibility on firm performance: A systematic review. *Corporate Social Responsibility and Environmental Management*, **27**(2), 409-429.
https://doi.org/10.1002/csr.1824

Senthilnathan, S. (2019). Usefulness of Correlation Analysis. SSRN Electronic Journal.
https://doi.org/10.2139/ssrn.3416918

Shahzad, M. A., Iqbal, T., Jan, N., & Zahid, M. (2022). The Role of Transformational Leadership on Firm Performance: Mediating Effect of Corporate Sustainability and Moderating Effect of Knowledge-Sharing. *Frontiers in Psychology*, **13**(July), 1-15.
https://doi.org/10.3389/fpsyg.2022.883224

Sokhetye, A. (2023). Sustainable Supply Chain Management Practices. *Acta Carolus Robertus*, **13**(2), 3-10. https://doi.org/10.33032/acr.4875

Soyege, O. O., Makinde, G. O., & Akinlabi, B. H. (2023). Green Supply Chain Management and Organizational Performance of Fast-Moving Consumer Goods Firms in Lagos Nigeria. *International Journal of Entrepreneurship*, **6**(2), 1-20. https://doi.org/10.47672/ije.1517

Tripathy, S., Ray, P. K., & Sahu, S. (2011). Performance measurement of R&D is a vaccine for innovation capability: evidence from Indian manufacturing organisations. *International Journal of Electronic Transport*, **1**(1), 76. https://doi.org/10.1504/ijet.2011.043115

Tripathy, S., Ray, P. K., & Sahu, S. (2012). Factors governing R&D practices in Indian manufacturing firms: structural equation modelling. *International Journal of Modelling in Operations Management*, **2**(1), 45. https://doi.org/10.1504/ijmom.2012.043960

Tukamuhabwa, B., Mutebi, H., & Kyomuhendo, R. (2023). Competitive advantage in SMEs: effect of supply chain management practices, logistics capabilities and logistics integration in a developing country. *Journal of Business and Socio-Economic Development*, **3**(4), 353-371.
https://doi.org/10.1108/jbsed-04-2021-0051

Uma, S., & Bougie, R. (2016). *Research Methods for Business: A Skill-Building Approach* (Seventh Ed). WILEY.

Valmohammadi, C. (2017). Customer Relationship Management, Innovation and Performance. *International Journal of Innovation Science*, **1-34**.
https://doi.org/https://doi.org/10.1108/IJIS-02-2017-0011

Vidal, N. G., Spetic, W., Croom, S., & Marshall, D. (2023). Supply chain stakeholder pressure for the adoption of sustainable supply chain practices: examining the roles of entrepreneurial and
sustainability orientations. *Supply Chain Management*, 28(3), 598-618.
https://doi.org/10.1108/SCM-08-2021-0370

Walker, A. T. and H. (2013). Theories in sustainable supply chain management: A structured literature review. *Int e r Na Tional Jour Nal of Physical Dis t Ribu Tion & Logi s Tics Man a Geme n T*, 45, 16-42. https://doi.org/< h t t p://dx.doi.org/10.1108/IJPDLM-05-2013-0106 >

Wijaya, O. Y. A. (2023). The role of supply chain management in entrepreneurial activities and product innovation on SMEs performance. *Uncertain Supply Chain Management, 11*(2), 443-450. https://doi.org/10.5267/j.uscm.2023.3.005

Winter, M., & Knemeyer, A. M. (2013). Exploring the integration of sustainability and supply chain management: Current state and opportunities for future inquiry. *International Journal of Physical Distribution and Logistics Management*, 43(1), 18-38.
https://doi.org/10.1108/09600031311293237

Yildiz Çankaya, S., & Sezen, B. (2019). Effects of green supply chain management practices on sustainability performance. *Journal of Manufacturing Technology Management, 30*(1), 98-121. https://doi.org/10.1108/JMTM-03-2018-0099

Zanon, L. G., Marcelloni, F., Gerolamo, M. C., & Ribeiro Carpinetti, L. C. (2021). Exploring the relations between supply chain performance and organizational culture: A fuzzy grey group decision model. *International Journal of Production Economics*, 233(January), 108023.
https://doi.org/10.1016/j.ijpe.2020.108023

Zhou, Y., Zhang, Y., Wahab, M. I. M., & Goh, M. (2023). Channel leadership and performance for a closed-loop supply chain considering competition. *Transportation Research Part E: Logistics and Transportation Review*, 175(September 2022), 103151.
https://doi.org/10.1016/j.tre.2023.103151

Zhu, Q., Sarkis, J., & Lai, K. hung. (2013). Institutional-based antecedents and performance outcomes of internal and external green supply chain management practices. *Journal of Purchasing and Supply Management*, 19(2), 106–117. https://doi.org/10.1016/j.pursup.2012.12.001