Incorporating Supply Chain Strategies into Organizational Excellence: The Moderating Role of Supply Chain Dynamism in an Export Sector of an Emerging Economy

Abstract

Nowadays, the emphasis on sustainable performance highlights the necessity for resilience and innovation in tackling environmental and economic concerns within supply chain operations. Therefore, this study investigates the impact of six supply chain management practices (SCMPs) on organizational performance (OP) and environmental sustainability performance (ESP), along with the moderating role of supply chain dynamism. This research was conducted within medium and large export manufacturing firms in Jordan’s Garment, Textile, and Leather (GTL) sector, a pivotal export industry critical to the country’s economy. Data were gathered from 204 managers, employing an online self-administered questionnaire, using a quantitative research approach. The hypotheses were examined via structural equation modeling (SEM) through the SmartPLS software4. The findings reveal that ESP was significantly influenced by strategic supplier partnership and postponement. Additionally, the level of information sharing and internal lean practices were found to have a dual impact on both OP and ESP. Supply chain dynamism acted as a significant moderator only in the relationship between postponement and both OP and ESP. This study fills a significant gap in the GTL context in developing economies for export manufacturing firms that contribute to the current literature. What makes it original is its consideration of supply chain dynamism as a moderating variable and its context in an important sector for Jordan’s economy. In conclusion, the results present valuable implications for practitioners on developing custom SCMPs for sustainable and operational performance objectives in the dynamic supply chain context. Future studies should adopt probability sampling methods to improve the generalizability of the findings. Further, the findings should be confirmed by conducting a study on other exporting sectors or geographical areas to gain additional perspectives on the relationships between SCMPs, OP, and ESP.

1. Introduction

Supply chain management (SCM) has emerged as a strategic approach to enhancing organizational performance (OP) and gaining a competitive advantage in an era where global business competition has shifted from individual firms to entire supply chains (Attia & Salama, 2018). This shift is driven by factors such as global sourcing, increasing quality and time-based competition, and growing environmental uncertainty (Dahinine et al., 2024). Additionally, heightened concerns over climate change and sustainability have influenced customer preferences, compelling corporations to adopt environmentally sustainable practices and comply with global environmental standards (De et al., 2020; Iqbal et al., 2022; Jum’a et al., 2021). Scholars have emphasized that organizations with strong environmental sustainability performance (ESP) benefit from improved reputations, brand equity, and market positioning, reinforcing the importance of integrating sustainability into SCM strategies (Al-Sheyadi et al., 2019; Rasheed et al., 2023).

To achieve efficient SCM, firms must implement effective supply chain management practices (SCMPs), which include a set of coordinated activities aimed at optimizing supply chain efficiency and performance (Li et al., 2006). Prior studies highlight that SCMPs enhance logistics operations, supply chain (SC) efficiency, and overall firm performance (Fredendall et al., 2016; Gandhi et al., 2017). Additionally, SCMPs contribute positively to ESP by fostering resource optimization and minimizing environmental footprints (Jum’a et al., 2021; Rasheed et al., 2023).

Consequently, SCMPs should be designed to achieve dual objectives: strengthening OP by enhancing supply chain competitiveness and addressing environmental responsibilities through sustainable business practices (Le, 2020). Moreover, stakeholders’ concerns regarding the environmental impact of production activities have driven firms to integrate green initiatives into their supply chains (Jum’a et al., 2023). With increasing consumer awareness of issues such as resource depletion and carbon emissions, companies are adopting sustainable practices, including a reduced reliance on fossil fuels and greener supply chain strategies (Yosef et al., 2023; Jum’a, 2023).

In dynamic business environments, supply chains must adapt to external pressures, such as legal, social, economic, and political changes, to maintain competitiveness. Supply chain resilience, characterized by flexibility and responsiveness, plays a crucial role in achieving this adaptability (Boute et al., 2012; Isnaini et al., 2020). Contingency theory posits that firms face varying degrees of uncertainty and must tailor their strategies accordingly, emphasizing the importance of context-specific SCMP implementation (Gingersberg & Venkatraman, 1985; Lee et al., 2016). Among the factors influencing SC effectiveness, supply chain dynamism (SCD) is particularly relevant, as it represents the rate of unpredictable changes within supply chain elements (Billah et al., 2023). Understanding the moderating role of SCD in SCM-performance relationships can provide deeper insights into how firms navigate external uncertainties.

Despite the recognized benefits of SCM, research on SCMPs has been largely concentrated in developed economies, overlooking their impact in developing countries where SCM adoption is still evolving (M. G. M. Yang et al., 2011; Yosef et al., 2023; Jum’a et al., 2024). Many multinational corporations have relocated their manufacturing operations to developing nations to capitalize on lower labor costs and tax incentives (Coyle et al., 2015; Tukamuhabwa et al., 2017). However, while these regions contribute significantly to global supply chains, studies on SCMPs in these contexts remain limited (Lautier, 2024; Wan et al., 2022). Addressing this research gap is crucial for developing tailored SCM strategies that account for the specific challenges and opportunities faced by firms in these regions.

The manufacturing sector is a key pillar of Jordan’s economy, contributing approximately 17.3% of GDP and serving as the second-largest economic driver after the service sector (Jordan Chamber of Industry, 2023). Export-oriented firms, particularly those in the Garment, Textile, and Leather (GTL) sector, play a critical role in the country’s economic growth (Ramadhani et al., 2018). Despite competition from other industries, such as chemicals, mining, and agriculture, the GTL sector has remained a dominant force, surpassing JOD 1.4 billion in export value in recent years (Jordan Chamber of Industry, 2023). Moreover, the sector aligns with green trade principles, enhancing its attractiveness to investors. Government-driven initiatives, such as the establishment of Qualified Industrial Zones (QIZs), have further pressured firms to adopt improved SCMPs to maintain competitiveness (Holzberg, 2023). However, research indicates that SCM adoption in Jordan remains limited, with many firms lacking awareness and the implementation of effective SCMPs (Alzubi & Akkerman, 2022). Furthermore, studies on sustainability improvements highlight the need for policies that promote sustainable manufacturing technologies, particularly in developing economies (Montiel-Hernández et al., 2024; Jum’a et al., 2024; Yosef et al., 2023).

Given this context, there is a critical need to examine how SCMPs contribute to both OP and ESP in Jordan’s GTL sector. This study fills a significant research gap by investigating the effectiveness of SCMPs in a developing economy, offering insights into how these practices influence performance outcomes under varying levels of SCD. Specifically, this research addresses the following research questions: (1) How do SCMPs influence OP? (2) How do SCMPs impact ESP? (3) Does SCD moderate the relationship between SCMPs and OP/ESP?

This study’s novelty lies in its examination of six key SCMPs—strategic supplier partnership (SSP), customer relationship management (CRM), internal lean practices (ILPs), level of information sharing (LIS), quality of information sharing (QIS), and postponement (PP)—within Jordan’s export-oriented GTL sector. By integrating SCD as a moderating variable, this research extends contingency theory and provides empirical evidence on how external uncertainties shape SCM–performance relationships.

Unlike previous studies that primarily focus on developed economies, this study contextualizes SCM practices in a developing market, offering a holistic framework for building resilient and sustainable supply chains. The findings contribute to both the academic literature and managerial practice, equipping supply chain professionals with actionable strategies to optimize performance in dynamic business environments.

This paper is structured as follows: Section 2 represents a literature review from which the research objectives, questions, hypotheses, and conceptual model were developed. Section 3 includes the research methodology, whereas the findings and discussion are represented in Section 4. Finally, in Section 5, the study’s conclusion, future recommendations, and limitations are represented.

2. Literature Review

2.1. SCMPs’ Dimensions

Implementing SCMPs effectively is crucial for firms to not only sustain their competitive edge in the international market but to enhance their overall performance (Baisa et al., 2023). A key step toward achieving efficient SCMPs is recognizing their significance. Many studies have examined and empirically validated various SCMP aspects (

Table 1. SCMPs dimensions.

Dimensions	Description
Strategic Supplier Partnership (SSP)	Organizations must move away from transactional arm’s length interactions and toward long-lasting ones (Huang et al., 2014).
Customer Relationship Management (CRM)	Activities related to customer engagement should be managed, aiming to build lasting relationships and enhance customer experience through strong client service (Chen & Wu, 2016; Jum’a et al., 2021).
Internal Lean Practices (ILPs)	This is the process of getting rid of all the time and materials that are lost throughout the production process. A philosophy, work ethic, method, management idea, value, attitude, or ethos can all be considered lean (Li et al., 2006; Iranmanesh et al., 2019; Jum’a et al., 2022).
Level of Information Sharing (LIS)	The degree of supply chain partners in exchanging sensitive and private–classified information (Jum’a et al., 2021; Li et al., 2006).
Quality of Information Sharing (QIS)	Accuracy, timeliness, trustworthiness, sufficiency, and dependability of the information shared across all parties in the supply chain (Nazifa & Ramachandran, 2019).
Postponement (PP)	This refers to the methods firms use during manufacturing processes to cope with hurdles in the supply chain to avoid risks and, in the meantime, enhance organizational performance (Simão et al., 2016).

Source: developed by author.

). By reviewing the literature, it was found that the top practices used in investigating the SCMPs–performance nexus in developing and emerging economies mainly encompass some or all of five dimensions, representing the upstream side of the SC through SSP, the downstream side of the SC through CRM, as well as internal SC processes through PP and those involving all SC members through LIS and QIS. However, ILP, a critical dimension for achieving sustainability, was not addressed as part of SCMPs’ construct in these studies, despite being recognized alongside the five previously mentioned dimensions in the pioneering work of Li et al. (2005), who established the framework for defining the dimensions of SCMPs. ILPs have only been included in a limited number of studies, like Hashim et al. (2020) and Khalil et al. (2019), leaving them largely underexplored.

In the context of ESP, the lean concept has been investigated in most studies either independently or in synergy with green supply chain management (GSCM) (Bandehnezhad et al., 2012; Dieste & Panizzolo, 2019; Iranmanesh et al., 2019). This represents a notable gap in empirical research, particularly given the increasing emphasis on sustainability in SCM. Therefore, by including ILP, this study aims to offer a complete comprehension of SCMPs and their influence on the performance of organizations and the environment within a developing country, such as Jordan. For this study, six practices were conceptualized, aligning with the findings of Li et al. (2006). A brief discussion of these dimensions is provided in Table 1.

2.2. Organizational Performance (OP) and Environmental Sustainability Performance (ESP)

As per Fraser (2006), linking SC operations to financial metrics is crucial for improving both business performance and operational efficiency. Therefore, experts are increasingly interested in examining the influence of SCMPs on firm performance, recognizing their growing importance, as indicated by previous studies, like Li et al. (2006). Organizational performance is a comprehensive term that allows businesses to evaluate their success by demonstrating how well their financial and market objectives are met (Li et al., 2006; Roh et al., 2022). This study used the same indicators proposed by many studies, such as M. G. M. Yang et al. (2011), Maaz and Ahmad (2022), and Jum’a et al. (2021), to assess the OP of GTL export manufacturing firms in Jordan, as they have been adopted in recent studies in developing regions. For instance, Maaz and Ahmad (2022) applied these same measures while also incorporating non-financial metrics related to customer satisfaction.

The primary goal of businesses has always been economic; however, the escalating climate catastrophe is pressuring businesses to increase their commitment to ESP, with an eye toward being carbon-neutral to meet modern societal expectations (Khan et al., 2021; Roh et al., 2022; Svensson et al., 2016). Therefore, businesses should prioritize the use of SCMPs to improve overall performance while also contributing to a greener environment (Aslam et al., 2021; Jum’a, 2023).

An environmental impact is characterized by the international standard ISO 14001 as any change to the environment caused by human actions, regardless of being beneficial or detrimental (García Alcaraz et al., 2022; Rasheed et al., 2023). As per Chardine-Baumann and Botta-Genoulaz (2014), sustainability performance is divided into three primary pillars, environmental, economic, and social, all of which are important and relevant to operations and SCM. However, to the best of the authors’ knowledge, among these dimensions, economic and environmental factors are more prioritized in sustainability assessments, as evidenced by the research conducted by Marshall et al. (2015) and Montiel-Hernández et al. (2024). This focus supports the emphasis of this study on organizational and environmental performance. This study uses the same performance metrics adopted by Al-Ghwayeen and Abdallah (2018) on environmental sustainability, who investigated common issues, including efforts to reduce air emissions, waste minimization, decreased solid waste generation, a reduced use of hazardous materials, lower environmental accident rates, and the overall improvement of a company’s environmental situation.

2.3. Hypothesis Development

2.3.1. SCMPs’ Influence on Organizational Performance

Numerous studies have empirically examined the impact of various SCMPs within specific manufacturing sectors or across multiple sectors in different geographical locations, all with the common goal of improving organizational outcomes, as demonstrated in

Table 2. Summary of SCM-related studies.

Authors	SSP	CRM	LIS	QIS	PP	ILPs	Time to Market	Customer Satisfaction	Outsource
Saad et al. (2024)	X	X	X	X				X
Hassan (2023)	X	X		X	X
Rasheed et al. (2023)	X	X	X	X	X
Hejazi (2022)	X	X	X		X
Al-Madi et al. (2021)	X	X	X	X	X
Jum’a et al. (2021)	X	X	X	X	X
Keawkunti et al. (2020)	X	X	X	X	X		X
Hashim et al. (2020)	X	X	X	X		X
Utami et al. (2019)	X	X	X
Khalil et al. (2019)	X		X	X	X	X
Quynh and Huy (2018)	X	X	X	X
Salleh (2017)	X		X		X
Attia and Salama (2018)	X	X	X
Hussain et al. (2014)	X		X		X				X
Mwale (2014)	X	X	X	X	X	X

Source: developed by author.

.

According to Hussain et al. (2014), firms’ performance in Pakistan’s consumer goods manufacturing sector was influenced solely by SSP. In contrast, Mwale (2014) and Salleh (2017) reported positive results in Kenya and Malaysia, respectively. Likewise, the findings of Hashim et al. (2020), in Pakistan’s textile industry, found that all five conceptualized practices, including SSP, CRM, LIS, QIS, and ILPs, had a significant positive impact on OP. However, Hassan (2023) found different results within the same industry context, revealing that only QIS sharing and SSP were significant.

In addition, Jum’a et al. (2021) investigated the impact of SSP, CRM, LIS, QIS, and PP in multiple manufacturing sectors in Jordan. The results reveal that CRM, LIS, and QIS did not prove to be significant effects on financial performance, which contrasts with the results of Al-Madi et al. (2021), who found that all five practices positively influenced the performance of Jordanian medical device manufacturers.

Keawkunti et al.’s (2020) study found that both CRM and PP positively impact the business performance of the pharmaceutical industry in Thailand. Conversely, Hejazi (2022) found that PP had no significant influence on OP of the food manufacturing sector in Saudi Arabia. Quynh and Huy (2018) found that only CRM and QIS had a significant impact on OP in Vietnam, whereas the findings of Utami et al. (2019) show that all SCM practices had a considerable and positive effect on both financial and economical sustainability. These results are inconsistent with those of Khalil et al. (2019), whose study found that only QIS, PP, and ILPs had a significant impact on organizational performance.

Unlike earlier studies that focused on the impact of individual SCMPs on OP, Rasheed et al. (2023) and Attia and Salama (2018) examined the overall impact of SCMPs on OP. All three studies found positive and significant results, with Rasheed et al. (2023) reporting improvements in the performance of textile manufacturers in Pakistan, Jum’a et al. (2021) covering multiple sectors in Jordan, and Attia and Salama (2018) focusing on the food industry in Saudi Arabia.

In examining the existing literature on the relationship between SCMPs and OP, several key insights and gaps become evident, including inconsistent findings across the literature, wherein, overall, SCMPs have a positive and direct impact on OP. Moreover, insufficient attention has been paid to investigating the impact of ILPs as a part of SCMPs on manufacturing firms’ performance. Therefore, the following hypotheses were developed:

H1: 

SSP has a positive, significant impact on OP.

H2: 

CRM has a positive, significant impact on OP.

H3: 

LIS has a positive, significant impact on OP.

H4: 

QIS has a positive, significant impact on OP.

H5: 

ILPs have a positive, significant impact on OP.

H6: 

PP has a positive, significant impact on OP.

2.3.2. SCMPs’ Influence on Environmental Sustainability Performance

The focus of the existing studies in terms of ESP was investigated in relation to lean practices and green supply chain management practices with limited attention on SCMPs (Jum’a et al., 2022). For instance, Al-Ghwayeen and Abdallah (2018) conducted an empirical study in Jordanian manufacturing firms to assess the impact of GSCM on environmental performance, showing a positive and considerable impact.

In addition, Kosasih et al. (2023) explored the incorporation of lean–green practices in Indonesia’s manufacturing industry. Findings indicate that lean practices directly enhance green practices and sustainability performance. Moreover, Bandehnezhad et al. (2012) investigated the impact of ILPs as a standalone construct on environmental performance in Malaysian manufacturing firms, incorporating dimensions such as process and equipment, manufacturing planning and control, HRM, SSP, and CRM. Results show that all practices have a significant impact on EP, except for supplier relationships and manufacturing planning and control.

Similarly, Iranmanesh et al. (2019) conceptualized the same practices to investigate their impact on sustainable performance. Their findings, based on manufacturing firms in Malaysia, indicate that process and equipment, SSP, and CRM have a positive and significant effect on sustainable performance. Likewise, Dieste and Panizzolo (2019) used a multiple-case-study technique to evaluate the transformation journey of lean practices in three Italian manufacturing organizations and their direct influence on environmental performance over a period of five years. The study’s findings show that implementing lean techniques improves environmental performance by reducing waste, increasing resource usage, and enhancing environmental sustainability.

Notwithstanding the findings of previous studies, which propose a direct positive impact of ILPs on environmental performance, some argue that lean techniques alone may not have a substantial impact on environmental results. For example, M. G. M. Yang et al. (2011) studied three interrelated sub-dimensions of lean manufacturing and discovered that implementing lean practices by themselves does not directly and significantly improve environmental performance.

Moreover, Jum’a et al., 2021 and Rasheed et al., 2023 were one of the earliest studies to investigate the direct and indirect relationship between SCMPs on environmental sustainability. The study by Jum’a et al. (2021) revealed that all SCM practices, except for SSP, had a significant positive impact on ESP, whereas Rasheed et al. (2023) found that SCMPs’ overall impacts on environmental impacts were significant.

In conclusion, a notable gap in the literature involves a scarcity of studies investigating the impacts of SCMPs on environmental performance. Therefore, based on the research completed by Dieste and Panizzolo (2019), Hashim et al. (2020), Jum’a et al. (2021), and Rasheed et al. (2023), the following assumptions were developed:

H7: 

SSP has a positive, significant impact on ESP.

H8: 

CRM has a positive, significant impact on ESP.

H9: 

LIS has a positive, significant impact on ESP.

H10: 

QIS has a positive, significant impact on ESP.

H11: 

ILPs have a positive, significant impact on ESP.

H12: 

PP has a positive, significant impact on ESP.

2.3.3. Supply Chain Dynamism (SCD) as Moderator

SCD has been investigated in various studies as a moderator. For example, Lee et al. (2016) revealed that the link between supply chain integration (SCI) and logistics performance in the South Korean manufacturing industry is notable. Their findings confirm that firms with a high level of SCD show better capabilities in linking SCI with logistics performance. Additionally, the intricate relationship among artificial intelligence (AI), supply chain collaboration (SCC), supply chain performance (SCP), supply chain resilience (SCR), and adaptive capabilities (ACs) was examined in a global setting by Belhadi et al. (2024). Results show that SCD moderates the relationship between AI and AC, AI and SCP, and AI and SCC. This means that the dynamic environment of SCs can positively influence firms’ adaptive and information processing capabilities as well as SCC.

Also, Billah et al. (2023) discovered that strategic corporate diplomacy (SCD) effectively influences the connection between IoT and sustainability performance as well as IoT and supply chain coordination (SCC). In a separate investigation, Isnaini et al. (2020) concluded that SCD has a beneficial impact on the association between sustainable SCMPs and sustainability performance in the Indonesian restaurant industry. Ali et al. (2024) investigated the impact of SCR and digital supply chains (DSCs) on SC sustainability. Results show that SCD positively moderates the relationship with SCR and DSCs. As stated by Zailani and Rajagopal (2005), products with a short lifecycle require a fast adaption to market changes, a continuous assessment of market conditions, and the adoption of flexible strategies to preserve their competitive edge.

This aligns with the scope of this study, where the GTL sector is known for its fast-paced changes. Based on previous studies, it was found that SCD remains underexplored in the context of developing countries, like Jordan. Therefore, the following hypotheses were developed to fill this gap:

H13a–H13f: 

SCD moderates the relationship between SCMPs (SSP, CRM, LIS, QIS, ILPs, and PP) and OP, respectively.

H14a–H14f: 

SCD moderates the relationship between SCMPs (SSP, CRM, LIS, QIS, ILPs, and PP) and ESP, respectively.

Table 2 provides a summary of the main studies that investigated SCMPs.

2.4. Conceptual Framework

After reviewing the previous literature comprehensively, it is evident that only a limited number of studies have examined the connection between SCMPs and OP/ESP. To the authors’ knowledge, no study has yet investigated SCD as a contingency factor in Jordan. As a result, the following conceptual framework was developed to address this gap in the literature (see Figure 1).

3. Materials and Methods

3.1. Sampling and Data Collection

In this study, the total population consisted of export-oriented manufacturing firms from GTL sectors. To ensure this study’s relevance, the sample was limited to medium and large firms where SCMPs are implemented. The targeted participants included higher managers (e.g., CEOs, presidents, and vice presidents); middle managers (e.g., procurement, marketing, production, logistics, and accounting); and first-line managers (e.g., supervisors and related positions). Respondents were purposefully selected to ensure accurate responses to the research questions, employing a non-probability purposive sampling technique (Saunders et al., 2019). This approach emphasized the importance of involving upper management specialists who possess in-depth knowledge of SCM processes and can provide credible answers.

Since this study explores SCMPs in a developing market context, a purposive sampling technique was used, as a comprehensive sampling frame for all experts in the field was not available. This non-probability sampling approach was chosen to target senior respondents who specialize in SCM and who possess sufficient knowledge to accurately answer the questionnaire. Additionally, the GTL sector is considered a niche market within the Jordanian textile manufacturing industry, further justifying the use of purposive sampling. One potential limitation of purposive sampling is selection bias, as participants were chosen based on specific criteria rather than through random selection. Consequently, the findings may not fully represent the broader population, which could impact the extent to which the results can be applied in different contexts. However, even though this sampling method may limit the generalizability of the results, its findings contribute to the existing literature by examining SCM practices in the Jordanian GTL sector and might be replicated in similar contexts.

A questionnaire was used to collect primary data, as this facilitates the exploration of relationships among variables. A self-administered questionnaire was distributed through various online channels to increase the chances of participation. A total of 204 surveys were returned out of the 400 distributed, resulting in a 51% response rate.

Structural equation modeling (SEM) analysis depends on a sufficient sample size to guarantee statistical power and model stability; thus, this study should focus on how the selected sample size fits accepted recommendations in the literature. Based on model complexity and estimated parameter counts, earlier studies, such as Jum’a et al. (2023), indicate a minimum of 200 respondents, which is typically regarded as acceptable for conducting SEM analysis. To mitigate common method biases in this study, several procedural and statistical remedies were implemented. First, during the survey design, we ensured respondent anonymity and assured them that there were no right or wrong answers, reducing the likelihood of social desirability bias. Statistically, Harman’s single-factor test was conducted to assess whether a single factor accounted for most of the variance in the data. The results indicate that no single factor explained a significant portion of the variance, suggesting that CMBs were not a major concern. Furthermore, we used the full collinearity approach to detect any potential bias, confirming that variance inflation factor (VIF) values remained below the recommended threshold of less than 5, further supporting the absence of substantial CMBs. These steps collectively enhanced the methodological rigor of this study, ensuring that the findings are not significantly influenced by systematic biases.

3.2. Measurement Scale

The measuring items in this study were adopted and, to some extent, carefully adapted from other researchers and various sources, as shown in

Table 3. Constructs and measurement items.

Dimensions	No. of Items	References
SSP	7	(Jum’a et al., 2021; Attia & Salama, 2018; Hassan, 2023; Saad et al., 2024)
CRM	6	(Hassan, 2023; Saad et al., 2024; Jum’a et al., 2021)
PP	3	(Jum’a et al., 2021; Hassan, 2023; Rasheed et al., 2023)
LIS	6	(Al-Madi et al., 2021; Saad et al., 2024; Jum’a et al., 2021)
QIS	6	(Jum’a et al., 2021; Hassan, 2023; Rasheed et al., 2023)
ILPs	7	(Bandehnezhad et al., 2012; Dieste & Panizzolo, 2019; Mwale, 2014)
OP	5	(M. G. M. Yang et al., 2011; Maaz & Ahmad, 2022; Jum’a et al., 2021)
ESP	6	(Al-Ghwayeen & Abdallah, 2018; Jum’a et al., 2023; Jum’a et al., 2024)
SCD	4	(Billah et al., 2023; Belhadi et al., 2024; Lee et al., 2016)

Source: developed by author.

. The questionnaire was structured into three major sections: A, B, and C. Section A collected participants’ general information. Section B contained questions about the independent variables, while section C presented the dependent variables.

In the second and third sections, a 5-point Likert scale was used, where participants were asked to rate their views on a 5-point rating scale ranging from 1 (strongly disagree) to 5 (strongly agree).

3.3. Sample Characteristics

The findings presented in

Table 4. Demographic profiles of respondents.

Category	Subcategory	Count	(%)
Gender	Male	173	84.8
	Female	31	15.2
Position	First-line managers	15	7.4
	Middle managers	50	24.5
	Top managers	139	68.1
Firm size—No. of employees	Medium (20–100)	67	32.8
	Large (above 100)	137	67.2

Source: developed by author.

indicate that the sample predominantly comprised male respondents (84.8%), with females representing 15.2%. Most participants held top management positions (68.1%), followed by middle managers (24.5%) and first-line managers (7.4%). Most respondents were affiliated with large firms employing over 100 individuals (67.2%), while medium-sized firms accounted for 32.8%.

4. Results and Discussion

Structural equation modeling (SEM) was conducted using SmartPLS version 4 to evaluate the proposed conceptual model and hypotheses, as detailed by Sarstedt et al. (2021) and Richter et al. (2016). The measurement model analysis stage involved employing various techniques, including a demographic analysis, descriptive statistics, validity and reliability assessments, and tests for multicollinearity.

4.1. Descriptive Statistics

To analyze the data, composite scores were calculated for each latent construct by combining the responses to their respective measurement items, as shown in

Table 5. Descriptive statistics.

Construct	Mean	Std. Dev.
SSP	4.00	0.677
CRM	4.03	0.794
ILPs	3.94	0.869
LIS	4.01	0.970
LIQ	3.99	0.778
PP	3.35	0.922
OP	3.42	1.157
ESP	3.38	1.031
SCD	3.16	0.980

Source: developed by author.

. CRM recorded the highest mean score (M = 4.03; SD = 0.794), as shown in Table 5, reflecting strong positive feedback. Conversely, SCD had the lowest mean score (M = 3.16; SD = 0.980). The remaining variables reported mean scores falling between these two extremes.

4.2. Multicollinearity Diagnosis

Multicollinearity develops when one predictor variable has a direct relationship with another set of predictor variables. The variance inflation factor (VIF) is a metric used to detect multicollinearity in independent variables. According to Hair et al. (2019), the VIF should be less than 5. The findings confirm that all VIF values met these criteria, indicating the absence of significant multicollinearity, as demonstrated in

Table 6. Results of multicollinearity test.

Construct (SCM Practices)	Collinearity Statistics
	OP/ESP
SSP	1.484
CRM	1.733
ILPs	1.658
LIS	2.005
QIS	2.049
PP	1.181

Source: developed by author.

.

4.3. Scale Validity and Reliability

Factor loadings and the Average Variance Extracted (AVE), along with Discriminant Validity, assessed through the Fornell and Larcker criterion, were utilized to validate the constructs. Internal reliability was evaluated using Cronbach’s alpha and Composite Reliability (CR). According to Hair et al.’s (2019) guidelines, factor loading values for the measurement items should exceed 0.70, and AVE values should be above 0.50 for the constructs to be considered valid. Additionally, all Cronbach’s alpha and composite reliability values must surpass the 0.70 threshold to be deemed acceptable. As demonstrated in

Table 7. Constructs’ validity and internal reliability.

Construct	Items	Factor Loadings	Cronbach’s Alpha	Composite Reliability	AVE
SSP	SSP1	0.706	0.938	0.948	0.728
	SSP2	0.894
	SSP3	0.935
	SSP4	0.836
	SSP5	0.942
	SSP6	0.764
	SSP7	0.941
CRM	CRM1	0.968	0.973	0.978	0.881
	CRM2	0.957
	CRM3	0.935
	CRM4	0.864
	CRM5	0.956
	CRM6	0.948
ILPs	ILP1	0.890	0.957	0.965	0.798
	ILP2	0.869
	ILP3	0.787
	ILP4	0.935
	ILP5	0.939
	ILP6	0.952
	ILP7	0.870
LIS	LIS1	0.964	0.982	0.986	0.932
	LIS2	0.970
	LIS3	0.980
	LIS4	0.968
	LIS5	0.947
QIS	QIS1	0.876	0.965	0.971	0.850
	QIS2	0.930
	QIS3	0.920
	QIS4	0.921
	QIS5	0.954
	QIS6	0.929
PP	PP1	0.924	0.912	0.944	0.849
	PP2	0.948
	PP3	0.891
OP	OP1	0.951	0.981	0.985	0.929
	OP2	0.978
	OP3	0.973
	OP4	0.967
	OP5	0.950
ESP	ESP1	0.961	0.989	0.991	0.946
	ESP2	0.971
	ESP3	0.974
	ESP4	0.977
	ESP5	0.984
	ESP6	0.969
SCD	SCD1	0.974	0.941	0.931	0.773
	SCD2	0.714
	SCD3	0.977
	SCD4	0.824

Source: developed by author.

, the independent variables used in this study were confirmed to be both valid and reliable. Discriminant validity is confirmed when the square root of AVE exceeds the corresponding inter-construct correlations. The results, as shown in

Table 8. Discriminant validity.

	CRM	ESP	ILPs	QIS	LIS	OP	PP	SCD	SSP
CRM	0.939
ESP	0.329	0.973
ILPs	0.365	0.447	0.893
LIQ	0.385	0.307	0.497	0.922
LIS	0.309	0.444	0.486	0.549	0.966
OP	0.219	0.441	0.427	0.347	0.619	0.964
PP	0.153	0.274	0.040	0.046	0.125	0.072	0.921
SCD	−0.227	−0.159	0.061	−0.060	0.047	−0.064	0.138	0.879
SSP	0.359	0.410	0.368	0.248	0.398	0.218	0.012	−0.029	0.853

Source: developed by author.

, validate the discriminant validity of the constructs, demonstrating that each construct is distinct and measures a unique aspect of the model.

The findings indicate an SRMR of 0.063 and an NFI of 0.819, indicating that the model was well-fitted, as SRMR values less than 0.09 and NFI values greater than 0.80 are generally considered acceptable (Hair et al., 2019). The links between the constructs’ hypotheses and predictive ability assessments are being studied. The structural model in SmartPLS was executed using the bootstrapping method with 5000 sub-samples, as shown in Figure 2. The analysis determined that the model is responsible for approximately 59% of the variance in OP and 56.2% of the variance in ESP.

This figure illustrates the structural relationships between SCMPs, OP, ESP, and the moderating role of SCD. The path coefficients and significance levels highlight that LIS and ILPs have the strongest positive effects on both OP and ESP, while PP is significantly moderated by SCD. These results visually reinforce the statistical findings, providing a clearer understanding of the key relationships within the model.

4.4. Hypothesis Testing

The findings from the direct and indirect hypotheses of the conceptual model are presented in

Table 9. Direct and indirect effects of the model.

Hyp. No.	Path Description	T Statistics	p-Values	Results
H1	SSP -> OP	1.298	0.194	Rejected
H2	CRM -> OP	0.077	0.938	Rejected
H3	LIS -> OP	6.935	0.000	Accepted
H4	QIS -> OP	0.032	0.975	Rejected
H5	ILPs -> OP	2.088	0.037	Accepted
H6	PP -> OP	0.472	0.637	Rejected
H7	SSP -> ESP	2.599	0.009	Accepted
H8	CRM -> ESP	0.247	0.805	Rejected
H9	LIS -> ESP	2.760	0.006	Accepted
H10	QIS -> ESP	0.479	0.632	Rejected
H11	ILPs -> ESP	2.891	0.004	Accepted
H12	PP -> ESP	3.727	0.000	Accepted
Indirect Effect Results of the Model
Hyp. No.	Path Description	T statistics	p-values	Results
H13f	SCD x PP -> OP	2.310	0.021	Accepted
H14f	SCD x PP -> ESP	2.988	0.003	Accepted

Source: developed by author.

. Among the hypotheses tested, several were accepted based on their statistically significant p-values (≤0.05). Specifically, hypotheses H3 (LIS -> ESP; p = 0.006); H5 (ILPs -> OP; p = 0.037); H7 (SSP -> ESP; p = 0.009); H9 (LIS -> ESP; p = 0.006); H11 (ILPs -> ESP; p = 0.004); and H12 (PP -> ESP; p = 0.000) demonstrated significant relationships between their respective constructs. Conversely, hypotheses H1 (SSP -> OP; p = 0.194); H2 (CRM -> ESP; p = 0.805); H4 (QIS -> OP; p = 0.975); H6 (PP -> OP; p = 0.637); H8 (CRM -> OP; p = 0.938); and H10 (QIS -> ESP; p = 0.632) were rejected due to non-significant p-values. Furthermore, this study revealed that among all SCMPs, LIS had the most substantial impact on OP, evidenced by a T statistic of 6.935. Additionally, PP emerged as having the most significant impact on ESP among SCM practices, with a T statistic of 3.727.

Regarding the moderation impact, SCD was tested between overall SCM practices and OP/ESP. Results show that hypotheses H13f (p = 0.021) and H14f (p = 0.003) were accepted due to significant p-values. Therefore, this study revealed a significant moderation effect of SCD on the relationship between PP and OP/ESP.

The moderation analysis revealed that SCD had a significant moderating effect only on the relationship between PP and both OP and ESP, while its interaction with other SCMPs did not yield significant results. A slope analysis for the moderating effect of SCD on the relationship between PP and both OP and ESP revealed that at higher levels of SCD, the positive impact of PP on performance outcomes is more pronounced. This suggests that in highly dynamic supply chain environments, firms that adopt PP strategies experience greater improvements in OP and ESP compared to those operating in more stable conditions, as shown in Figure 3 and Figure 4.

4.5. Discussion

The results show that SSP and CRM, as proposed in H1 and H2, respectively, did not significantly affect OP, contradicting the findings of several prior studies. For instance, Hejazi (2022) emphasized that OP is positively influenced by both SSP and CRM. Similarly, Al-Madi et al. (2021), Hashim et al. (2020), and Utami et al. (2019) reported these practices as critical contributors to OP success.

The differing results for SSP and CRM on OP may stem from the nature of the export-oriented firms operating under the QIZ agreements in Jordan, where key decisions are heavily influenced by foreign purchasers. This limits firms’ autonomy in SSP and CRM, potentially restricting their impact on OP. Additionally, according to Danese and Romano (2011) and Yusuf and Shehu (2017), the impact of customer integration on organizational performance may vary, depending on the amount of supplier integration. This comment stresses this study’s findings, which reveal a negligible association between SSP and OP.

In contrast, the results show that OP is significantly influenced by LIS, as formulated in H3. This finding aligns with those of Jum’a et al. (2021) and Salleh (2017), who concluded that higher levels of information sharing enhance supply chain visibility, enable better planning, and optimize resources, thereby contributing to better business performance and a competitive advantage. Although OP was positively influenced by the quantity aspect of information (LIS), the results show that QIS, as claimed in H4, did not exhibit a significant impact on OP, deviating from the findings of Hassan (2023) and Keawkunti et al. (2020). This suggests that when firms lack access to high-quality, timely, and reliable data, their ability to drive operational improvements may be reduced, thereby diminishing the impact of QIS on performance.

Regarding the impact of internal practices on OP, ILPs, as claimed in H5, demonstrated positive performance outcomes, aligning with the findings of Hashim et al. (2020) and Khalil et al. (2019). In contrast, PP, as predicted in H6, showed no significant impact on OP. Although postponement is designed to manage risks by delaying final product assembly until customer orders are received, this approach did not lead to performance improvements. One possible explanation for this result is the need for higher levels of education and expertise for effective implementation, as highlighted by Hussain et al. (2014). Moreover, since firms operate under rigid buyer-driven models, the potential benefits of postponement may not be translated into improvements in organizational performance.

In the context of ESP, the results show that SSP, as claimed in H7, revealed a significant positive relationship with ESP. SSP can reduce waste, save energy, and enhance environmental outcomes through effective collaboration and knowledge sharing, as claimed by the findings of Bandehnezhad et al. (2012) and Iranmanesh et al. (2019).

CRM, as proposed in H8, was found to have no significant impact on ESP in this study. These findings are inconsistent with those of Jum’a et al. (2021) and Iranmanesh et al. (2019), who identified CRM as a significant contributor to ESP. This could be attributed to the fact that sustainability policies are dictated by buyers and enforced externally through supplier selection rather than direct customer engagement, thereby limiting the impact of CRM on sustainability efforts.

This study also found that LIS, as predicted in H9, significantly influences environmental outcomes. These results are consistent with Jum’a et al. (2021), who concluded that LIS facilitates accurate demand forecasting, reducing waste and overproduction, thereby contributing to improved environmental performance.

QIS, as claimed in H10, was found to have an insignificant impact on ESP. According to Li et al. (2006), longer and more complex supply chains are prone to information distortion, which can negatively impact information quality. This complexity in the GTL supply chain could also explain the discrepancies in QIS’s influence on ESP observed in this study.

This study reveals that ESP is positively influenced by both ILPs and PP, as predicted in H11 and H12, respectively. The findings align with those of Bandehnezhad et al. (2012) and Iranmanesh et al. (2019), who emphasized that adopting lean practices is critical for achieving environmental benefits. While firms may not experience performance improvements from PP in terms of OP, this practice can still enhance ESP by ensuring compliance with global sustainability requirements and reducing environmental impacts. The positive effect of PP is further supported by B. Yang et al. (2005), who highlighted that postponement minimizes excessive inventory in the early supply chain stages, preventing overproduction, reducing waste, conserving energy, and aligning with sustainable business practices.

This study shows insignificant results for supply chain dynamism on organizational or environmental performance in the Jordanian GTL sector. The SCD items capture volatility in customer preferences, process changes, and competitive dynamics. Unlike industries with greater autonomy, many strategic decisions for major export-oriented enterprises operating under QIZ agreements are heavily influenced by the specifications and criteria of foreign purchasers, limiting the direct influence of supply chain dynamism on performance outcomes. Therefore, these fluctuations do not necessarily lead to improved performance. The absence of a significant link between SCD and ESP further suggests that environmental sustainability efforts in this sector are not internally driven.

The indirect impact of SCD as a moderator between SCMPs and both OP and ESP showed inconsistent results compared to previous studies (Ali et al., 2024; Isnaini et al., 2020; Lee et al., 2016; Billah et al., 2023). Among the six practices, only PP was moderated by SCD, influencing both OP and ESP, as claimed in H13f and H14f, respectively. This suggests that in the GTL sector, not all SCMPs benefit from dynamism. Instead, practices that inherently offer flexibility, like PP, become more critical as supply chain uncertainty increases.

The findings indicate that as SCD rises, flexible practices, like PP, become more important. PP enhances OP and ESP in dynamic environments by allowing firms to delay production and final assembly based on real-time demands. Despite this sector’s nature, PP serves as an adaptive mechanism, helping firms manage external uncertainties while maintaining compliance with buyer expectations. This explains why SCD strengthens PP’s impact on performance, even though SCD alone does not directly improve OP or ESP.

5. Theoretical and Practical Implications

5.1. Theoretical Implications

This study makes several important theoretical contributions to the field of SCM by providing a nuanced understanding of how SCMPs influence both ESP and OP, particularly in the context of a developing economy. While prior research has predominantly focused on developed markets, this study addresses a critical gap by examining SCMPs within Jordan’s export-oriented GTL sector, offering valuable insights into the unique challenges and opportunities faced by firms in this setting. By analyzing six key SCMPs, this study enhances theoretical perspectives on their differential impact on performance outcomes, revealing that not all practices contribute equally to OP and ESP.

Additionally, this study advances contingency theory by exploring the moderating role of SCD in shaping the effectiveness of SCMPs. The findings indicate that SCD significantly moderates the relationship between PP and both OP and ESP, highlighting the importance of adaptability in highly dynamic supply chain environments. This contributes to the theoretical discourse on supply chain resilience, demonstrating that firms operating in volatile markets can achieve greater performance benefits by strategically leveraging postponement. This study also challenges conventional assumptions by showing that SCD does not significantly moderate other SCMPs, suggesting that some practices remain stable in their impact regardless of environmental uncertainty.

Moreover, this research extends the literature on sustainable SCM by emphasizing the dual objectives of economic competitiveness and environmental responsibility. It reinforces the growing recognition that integrating sustainability into supply chain strategies is not only a regulatory or ethical necessity but also a performance-enhancing factor within the GTL sector in Jordan.

5.2. Practical Implications

SC managers must actively drive firms toward stronger environmental sustainability commitments by integrating structured implementation strategies. The findings highlight that LIS significantly enhances both OP and ESP, emphasizing the need for firms to establish real-time data-sharing mechanisms and transparent communication channels across the supply chain. To achieve this, firms should invest in digital solutions, such as cloud-based SCM platforms, blockchain for traceability, and AI-driven analytics, ensuring seamless and accurate information flow. Additionally, training programs for employees on data management and digital tools should be prioritized to enhance LIS capabilities and improve decision-making efficiency.

ILPs also play a crucial role in improving OP and ESP by streamlining internal processes and minimizing waste. Manufacturers should adopt lean methodologies, such as JIT inventory systems, kaizen initiatives, and cross-functional team collaboration, to optimize production efficiency and sustainability. Implementing energy-efficient production techniques, automation in material handling, and waste reduction initiatives will further enhance operational performance while supporting environmental goals.

Moreover, PP was found to significantly influence OP and ESP when moderated by SCD, suggesting its effectiveness in high-velocity, high-uncertainty industries, like fast-moving consumer goods. To leverage PP effectively, managers should implement flexible production systems, late-stage customization, and demand-driven inventory replenishment strategies to minimize risks associated with market volatility. Investing in modular production systems and digital twins for real-time scenario planning can further enhance firms’ ability to adapt to fluctuating demand conditions.

In addition, the negative impact of SSP on OP suggests that while sustainable supplier partnerships can drive long-term environmental benefits, they may not immediately enhance operational outcomes. Firms should engage suppliers in joint sustainability initiatives, such as green material sourcing, waste reduction programs, and carbon footprint monitoring, to ensure that sustainability efforts align with supply chain efficiency. Supplier evaluation metrics should incorporate ESG (Environmental, Social, and Governance) compliance standards, ensuring that sustainability-driven partnerships also contribute to long-term performance improvements.

Furthermore, the findings indicate that CRM and QIS do not significantly impact OP or ESP, suggesting that these practices require strategic realignment. Firms should redefine their CRM strategies to better integrate sustainability objectives, focusing on customer engagement initiatives that promote eco-friendly products and responsible consumption. For QIS, firms must enhance data accuracy and relevance through advanced data validation techniques and AI-driven quality assessments, ensuring that shared information adds real value to supply chain decision-making.

Finally, firms must move beyond viewing SCD as merely a performance determinant and instead adopt adaptive supply chain strategies to mitigate environmental volatility. Given the moderating role of SCD on PP, firms should develop resilient supply chain models incorporating predictive analytics, scenario-based risk management, and agile response systems. Leveraging IoT-enabled monitoring, digital supply networks, and responsive logistics strategies will allow companies to proactively adjust to environmental uncertainties, improve decision-making, and align supply chain operations with sustainability goals.

6. Conclusions, Future Research, and Limitations

The results of this research indicate that two dimensions, LIS and ILPs, have a dual positive and significant impact on organizational and environmental outcomes. These findings suggest that manufacturing firms aiming to excel in both economic and environmental aspects should focus on prioritizing these practices into their operations. Based on the findings, given the enormous benefits of ILPs and LIS for OP, future research should investigate the specific processes by which these activities improve performance in various circumstances. Furthermore, PP requires more flexibility in response to external changes, as its effectiveness in enhancing organizational and sustainability performance was moderated in a highly dynamic environment.

Future research should enhance the generalizability of the findings by adopting probability sampling methods, such as simple random sampling, stratified sampling, or cluster sampling, to ensure a more representative sample of firms across different sectors and regions. This approach would provide a broader perspective on the impact of SCMPs on OP and ESP, making the results more applicable to diverse business environments. Additionally, given that this study relies solely on quantitative data, future research should incorporate qualitative approaches, such as in-depth interviews or case studies, to explore the contextual factors influencing SCMP adoption and effectiveness. This mixed-methods approach could uncover managerial perspectives, industry-specific challenges, and best practices that are not easily captured through survey data.

Moreover, while this study focuses on environmental outcomes within ESP, sustainability is a multidimensional construct that also includes social and economic factors. Future research should consider integrating these dimensions to provide a more holistic view of sustainability performance. For instance, examining how SCMPs contribute to social responsibility initiatives, fair labor practices, or economic resilience would offer a more comprehensive understanding of their long-term impact. Researchers could also explore the role of government policies, market regulations, and industry-specific sustainability standards in shaping firms’ supply chain strategies.

Furthermore, longitudinal studies could provide valuable insights into the long-term effects of SCMPs on performance outcomes, allowing researchers to track changes over time and assess the sustainability of implemented practices. Future research should also examine the moderating role of technological advancements, such as artificial intelligence, blockchain, and the Internet of Things (IoT), in optimizing supply chain processes and enhancing sustainability performance. These practical recommendations would help bridge the gap between theory and real-world applications, offering valuable insights for both academics and practitioners in the field of supply chain management.