Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 357 Impact of Network Factors on Supply Chain Performance: A Case of Textile Sector of Pakistan Nadir Munir Hassan a , Muhammad Nauman Abbasi b a Assistant Professor, Department of Business Administration, Air University, Multan, Pakistan Email: nadir.magsi@aumc.edu.pk b Professor, Institute of Management Sciences, Bahauddin Zakaraiya University, Multan, Pakistan Email: abbasimna@bzu.edu.pk ARTICLE DETAILS ABSTRACT History: Accepted 30 July 2021 Available Online September 2021 This study elaborates the importance of network perspective in driving performance outcomes especially in the context of agriculture (Textile) supply chains. The impact of network factors (i.e. actors, resources, and activities) on overall supply chain performance have been explored. By deploying survey, a two-stage cluster sampling was used to attain study objectives. The Textile firms from Punjab and Sindh were selected for data collection. Through a structured questionnaire, 482 responses were generated and analyzed using PLS-SEM. The findings of the study confirmed that Network Actors (Textile Firms), the activities they perform, and the resources they have, generate a significant and positive impact on supply chain performance. The study recommends the need for understanding the role of integrative initiatives between the studied variables, i.e. network factors. Further, it is argued that integrated Network Factors can generate a significant impact on Supply Chain Performance. © 2021 The authors. Published by SPCRD Global Publishing. This is an open access article under the Creative Commons Attribution- NonCommercial 4.0 Keywords: Network perspective, Supply Chain Performance, Supply Chain Integration, Agriculture (Textile) Supply Chain JEL Classification: R41, L25, L29 DOI: 10.47067/reads.v7i3.378 Corresponding author’s email address: abbasimna@bzu.edu.pk 1. Introduction In the recent era, increasing interest in technological adoption and digitization at large has raised the need for mindful synchronization between firms, and supply chains. Specifically for agro- based industries which are striving to compete in the market for better profits and also significantly contributing to the overall GDP of the country, through increased reach to international markets. In the given era, businesses are heading towards saturated and complex markets, and the competition has moved beyond firms, and now it’s between supply chains. The concept of looking onto firms, from the perspective of supply chain management isn’t new (Ahmed, Munir, & Sameer, 2020). Whilst, consistent development and up gradation of theories and new practices reflect that supply chains are increasingly prone to disruption these days. To tackle the issue of disruption, inconsistencies, and to enhance performance, there is an ever Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 358 rising need for mindfully aligning the chains processes (Khan & Qianli, 2017). It is believed that better integration across chains can significantly enhance chain performance (Green, McGaughey, & Casey, 2006; Ho, Au, & Newton, 2002). For example, Covid-19 has been one such recent environmental phenomenon that has reaffirmed practitioners, researchers and scholars, to look into countering strategies, managing natural and supernatural uncertainties across value chains. This is the era in which there is an ever rising need for thoughtful relationship management within and across the chains. It is need of the hour to realize that, in the given competitive business environment, chains and firms can hardly operate in isolation. Firms should not undermine the significance of integrating across chains, from raw material acquisition to service delivery to end consumers (Farooq & O’Brien, 2012; Mungan, Yu, & Sarker, 2010). While seeking collaboration, one needs to be mindful of the fact that experts have started seeing supply chains as a complex network of manufacturing, processing and resource delivery (Aslam & Amos, 2010; Zhou, Tu, Han, Xu, & Ye, 2017), who’s operation management require network-based optimization. Likewise, the sequence of flow in SCN management deals with the allocation of material, information and processes to achieve multiple objectives (Ke, Huang, & Gao, 2018; Wang, Hu, & Zhou, 2017; Zhang et al., 2019). For the given reason there exists a dire need for firms (among networks) to develop strong relationships to seek appropriate performance outcomes. Accordingly, the study was initiated to “examine the influence of network factors (i.e. Actors, Resources, and Activities) on supply chain performance”. The Textile Industry of Pakistan is targeted to verify the phenomenon under investigation due to its significant contribution to GDP and global markets, i.e. total export of the Pakistan textile sector is 9.6 million US dollars and this makes around 8.5 percent of Pakistan's GDP (Bhutto & Jamal, 2020). In reality, the Textile Supply Chains (TSC) are comparatively complex because of number of reasons including: demand variability, environmental concerns, product variety, lack of product standardization, and seasonality (Majumdar & Sinha, 2019). Despite many studies have explored the significance of integrated network perspective still studies have failed to provide an executable solution and hence remained inconclusive. Therefore, an attempt has been made to understand the role of network factors and to measure their impact on supply chain performance especially in the context of Textile Supply Chains. 2. Literature Review With constant increase in operational technicalities for organizations, reduced life cycles of products, and ever rising need for upgraded customer service made researchers and managers to focus on supply chain management (Davis, 1993). Since 1990s, supply chain view point have been at the epicenter of firms looking to provide their stakeholders with services, in line with the market trends. Resultantly they have been eyeing managing their operations in line with best supply chain practices (e.g. (Stevens, 1989). SCM has been defined as the set of tools and techniques used for managing supply chain activities for effective coordination to improve overall supply chain performances (Kusi-Sarpong, Sarkis, & Wang, 2016; Liu, Bai, Liu, & Wei, 2017). It’s reasoned that efficient management of supply chains depends on; functional attributes, resources at hand, and demand fulfillment (Heckmann, Comes, & Nickel, 2015). Ideally, all concerning philosophies of SCM consider all supply chain functions for SC strategy formulation, in pursuit of enhancing business performance (Borges & Vieira, 2014; Muysinaliyev & Aktamov, 2014). Earlier literature on supply chain still contains contradictory viewpoints, despite number of research initiatives to understand multiple aspects of supply chain, and very scarce performance outcomes of SCM have yet been explored (Bala, 2014; Oualid, Mocan, Dumitrache, & Amine, 2016). Therefore, number of scholars have propagated the need for detailed insights into the effect of relationship between firms, the Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 359 activities they perform, and the resource they have, on SC performance outcomes. The significance of SC relations from the viewpoint of the network perspective is not hidden anymore. A network is a system shaped by core factors (e.g. actors, activities and resources) that join the number of linked firms (L. Jraisat, 2016b). A network is defined as a set of links among constellations of actors (Jarillo, 1988; Ritter & Gemünden, 2004; Sanzo et al., 2003) and these links make connections with each other to provide functions in a two-way process for overall firms’ performance. Network perspective helps in providing with a workable framework for evaluating the business dynamics, wherein, firms (as actors) aimed to have better control and resource allocation over vertical supply chains including production, logistics and marketing activities (Mikkola, 2008). Herein, the management of network relationships can be viewed as a primary driver of both B2B and B2C markets (Grönroos, 2011; C. Harland, Zheng, Johnsen, & Lamming, 2004; Lazzarini, Chaddad, & Cook, 2001). Unfortunately, lack of coordination, synchronization and integration among network factors is regarded as one of the vital reason behind deterioration of SC performance. In networks, actors (firms) connect each other functionally to bring suppliers, producers, logistic providers and consumers together. The key actor creates a position that enhances strategic transaction and firms’ performance (Sanzo, Santos, Vázquez, & Álvarez, 2003) and is being flexible to be oriented by customer preferences (Dalvi & Kant, 2015). Authors like, (C. M. Harland, 1996b; Mikkola, 2008; Ritter & Gemünden, 2004) have recommended, business stature (e.g. Investment with partners, firms reputation, and leadership), Social alignment (i.e. Socializing and developing a certain integrative bond with channel members across value chain). Business Position (i.e. leadership position in the industry backed the reputation), Social bonds (e.g. Social activities and friendships with other members of the supply chains), Image (e.g. family name, and recognition intensity of the firm), & relationship partners (e.g. partners of new products) to illustrate the role of actors in a network perspective. Similarly, effective resources allocation and utilization, is always considered as a hallmark to attain competitive advantage, hence, wise allocation of resources especially within network perspective leads to remarkable gains within and across the chain (Mikkola, 2008). The extent to which resources are shared notably influence the performance of the firm and their chain members (Christensen & Klyver, 2006). Literature suggests four classifications of resource that includes; Physical resources (e.g. facilities that have the ability to assist the flow of information, both within and across the organizations), financial resources (e.g. staff related assistance, and other facilitations) and informational resources (i.e. information sharing both inside and beyond firms among channel members) (Ritter, 1999). Similarly, SC activities play their part in converting resources into meaningful form, in order to encourage actors to attain their objectives and fortify the interconnectivity within the supply chain networks. Moreover, activities have an important role in improving coordination, cooperation & collaboration between actors (Bourlakis & Bourlakis, 2005). SC activities majorly include; exchange activities (i.e. flow of information, products, personnel and money), planning activities (i.e. analyzing the internal networks and thinking over the future course of action), organizing activities (e.g. assembling and allocating the available resources) and controlling activities (i.e. checking mechanisms through staff management and information exchange) (Ritter, 1999). This is where networks create various exchanges to reach out the required resources and through mindful leadership, access to prompt information, which could help in making the right decisions for improved businesses and SC performance. Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 360 Interestingly, SC performance is a multidimensional construct, hence, measured differently by different authors. For example, Maestrini et a. (2018) stated it as “a set of metrics used to quantify both the efficiency and effectiveness of actions.” While, Nugraha and Hakimah (2019) studied SC performance in the context of supplier partnership, customer relationship, information sharing, and technology adoption. Sudusinghe and Seuring (2020) (for textile & apparel supply chains) have measured SC performance considering social and economic sustainability as performance parameters. Resultantly, high performing supply chains usually depend on their cutting edge abilities of their channel members, and on their endeavors to establish relationships across supply chain networks (Michalski, Montes-Botella, & Narasimhan, 2018). In line with the stated discussion, the given hypotheses have been formulated for empirical verification; H1: Network actors (SC firms) have a significant impact on SC performance. H2: Network resources have a significant impact on SC performance. H3: Network activities have a significant impact on SC performance. 3. Research Methodology A structured questionnaire was used to test the hypotheses. While, a partial least squares structural equation modeling (PLS-SEM), widely recommended for management research (Kaufmann & Gaeckler, 2015), was applied to investigate and testify the phenomenon under investigation. Grounded in the literature, a framework has been established to investigate the direct relationship between network factors (i.e. Network Actors, Resources, and Activities) and SC performance. The theoretical framework comprises of one broader IV (i.e. Network Perspective), divided into three aspects (i.e. Network Factor), termed as AC, RS, & AV, while, SC performance (SCP) is observed as DV. Smart PLS 3 Ringle, Da Silva, and Bido (2015) along with bootstrapping technique were utilities to observe the significance level of assumed relationships. Initially, framework was used to gather first- order constructs, followed by estimation of structural model, and second-order constructs estimation (Joseph F Hair, Risher, Sarstedt, & Ringle, 2019). 3.1 Population and Sampling Technique: The Textile Sector of Pakistan was selected for data collection and empirical verification of the phenomenon under investigation due to its economic significance. Unfortunately, the Textile sector is stagnant for a long, when related industries in neighboring countries like, India, and Bangladesh have shown reasonable growth. This increases the need to probe the factors that hinder SC performance in this sector. Literature also emphasizes on increasing attention towards the implementation of integrational strategies which can greatly influence SC performance and can enhance the competitiveness in Textile supply chains (Verma et al., 2020). The target population was comprised of Textile Units. Official representative authorities/bodies, i.e. All Pakistan Textile Mills Association (APTMA) and All Pakistan Bedsheets and Upholstery Manufacturers Association (APBUMA) were requested to provide a list(s) of Textile Units operating in these regions. The list(s) includes 1776, registered Textile Units, involved in spinning, weaving, processing (knitting), dyeing, printing, garment manufacturing and filament yarn manufacturing. The listed firms were divided into four geographical clusters (i.e. Punjab, Sindh, Baluchistan and Khyber Pakhtunkhwa). Furthermore, using multi-stage, cluster probability sampling was applied to consider only ‘Registered Textile Units’ operating in Punjab (i.e. largest cotton producer) and Sindh (i.e. seaport for export purposes). Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 361 Table 1:- Cluster sampling with proportionate technique Details into Clusters Firms in Cities Total Textile Firms Rule of Thumb (Multiply by 5/ 10 Max) Sample Size Req. Morgan Table Error: 5% Req. Sample from Each Cluster (city wise) Questionnaire Distributed (Organization wise) Total= 726 Response Received (Organization wise) Total= 493 City wise Response Rate (in %age) Overall response= 68% Punjab Multan 228 1776 200/ 390 322 50 97 64 66 Lahore 384 1776 200/ 390 322 84 146 104 71 Faisalabad 443 1776 200/ 390 322 97 178 136 76 Sindh Karachi 431 1776 200/ 390 322 94 197 116 59 Others 290 1776 200/ 390 322 64 108 73 68 Managers representing Marketing, Operations and Production and Supply Chain departments were selected as a respondent. Practically, bigger sample size is preferable to avoid non-response bias (Sekaran & Bougie, 2003). However, due to the prevailing pandemic (Covid-19), data collection remained quite challenging, yet it produced good output with a useable response of 482 (i.e. 68%), out of 726 distributed questionnaires. The demographic details are presented in Table-2: Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 362 Table 2:- Demographic specifications of the Respondents 3.2 Operationalization of the Measurement Instruments To measure ‘Network Actors’, 4-Items scale suggested by C. M. Harland, (1996b); L. Jraisat, (2016b); Mikkola, (2008); Ritter & Gemünden, (2004), while, for ‘Network Resources’ 4-Items scale by L. Jraisat, (2016b) and Ritter, (1999) have been adopted. Similarly, 4-items scale as suggested by L. Jraisat, (2016b); Ritter, (1999); Ritter & Gemünden, (2004) were adapted to measure the construct of ‘Network Activities’. Finally, 11-Item scale of SC performance has been adopted from (Green Jr, Whitten, & Inman, 2008). The responses were measured on Likert scale, ranging from 1 to 7, wherein, 1 stands for strongly disagree and 7 stand for strongly agree. DEMOGRAPHICS DESCRIPTION RESPONSE RATE % OF RESPONSES Gender Male 408 84.64 Female 74 15.35 Age Less Than 30Yrs 64 13.27 30-40yrs 298 61.82 41-50yrs 84 17.42 51-60yrs 36 7.49 Above 61yrs 0 Qualification College Level 4 0.8 Under Graduate 326 67.63 Post Graduate 152 31.57 Position Top Management / Director / CFO / Company Secretary 62 12.86 General Manager / Sr. Manager 214 44.39 Production In charge / Mill Manager 206 42.75 Operational Sector Spinning 221 44.60 Weaving 121 25.10 Knitting 27 5.5 Dyeing and Finishing 84 16.42 Garments 40 8.38 Company Location Multan 77 16 Lahore 1070 22 Faisalabad 123 25 Karachi 131 27 Others 44 10 Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 363 Table 3:- Variables, Items & Cronbach Alpha VARIABLES NUMBER OF ITEMS CRONBACH ALPHA Network Actors My firm has a good image and a leadership position. 0.785 My firm keeps doing of social events and friendships with other actors. My firm is considered as a pioneer and family business recognized in the chain. My firm creates relationships with new actors. Network Resources My firm offers information exchange inside the firm and between firms. 0.719 My firm’s top management has financial support offered for staff and new development. My firm’s staff are trained by local and/or international experts. My firm provides infrastructures for harvesting and post- harvesting. Network Activities There are exchanges of goods, services, money, information and personnel between my firm & its partners. 0.747 There are analyses of network quality and resources and network environment by my firm. There are formal and/or informal agreements between my firm & its partners. There are controls of the network output by my firm. SC Performance My firm’s primary Supply chain has the ability to deliver Zero- Defect products to final customers. 0.881 My firm’s primary Supply chain has the ability to deliver value added services to end customers. My firm’s primary Supply chain has the ability to eliminate late, damaged, and incomplete orders to final customers. My firms primary Supply chain has the ability to quickly respond, to and solve problems, of final customers. My firm’s primary Supply chain has the ability to deliver products precisely on time to final customers. My firm’s primary Supply chain has the ability to deliver precise quantities to final customers. My firm’s primary Supply chain has the ability to deliver shipment of variable size on frequent basis to final customers. My firm’s primary Supply chain has the ability to minimize total product cost to final customers. My firm’s primary Supply chain has the ability to minimize all types of waste throughout the supply chain. My firms primary Supply chain has the ability to channel safety stock throughout the supply chain My firms primary Supply chain has the ability to deliver smaller lot sizes and shipping case sizes to final customers Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 364 4. Results Considering the significance of common method bias in data (Bagozzi, 2011), the current study used (un-rotated) factor analysis with 23 items of the constructs, reflected that none of the factors accounted for more than 50% of the variance. Output revealed 36% of total variance accounted by a single factor that reflects the absence of common method bias. Furthermore, in order to check the reliability and suitability of the data used, data screening was managed. Based on the recommendations of Joseph et al (2010), out of 493 responses, 11 responses were deemed invalid and hence eliminated. The descriptive scores (mean value) remained in between 5.52 to 6.36 and (standard deviation) 1.08 to 1.79. Structural models were looked into after ensuring the reliability and validity of the variables under consideration, followed by measuring the relationships between latent variables. Smart PLS 3.0 by Ringle et al., (2015) was used to determine causal links among the constructs in these theoretical models, and to evaluate the outer model (measurement model) and the inner model (structural model). This study adopts a two-step process; one is an assessment of measurement model and second one measurement of structural model (Joseph F Hair, Ringle, & Sarstedt, 2013; Hair Jr et al., 2014). Further, Confirmatory Factor Analysis (CFA) was employed and out of 23 items, 06 were deleted with loadings lesser than the cutoff value of 0.50. All variables have AVE and composite reliability above the threshold value of 0.50 (Fornell & Larcker, 1981; Hair Jr et al., 2014), while, the values of Cronbach’s Alpha (above then threshold level, i.e. 0.7) reflects internal consistency. The discriminant validity was analyzed by using AVE as recommended by (Fornell & Larcker, 1981). The comparison among the latent constructs as explained in Table 4 & 5 summarize the square root of AVE of the constructs; i.e. Network Actors (AC) = 0.821; Network Resources (RS) = 0.799; Network Activities (AV) = 0.813; and SC Performance (SCP) = 0.74. Table 4:- Loadings, Composite Reliability and AVE CONSTRUCTS ITEMS OUTER LOADINGS VIF COMPOSITE RELIABILITY AVERAGE VARIANCE EXTRACTED (AVE) Network Actor AR1 0.774 1.288 0.861 0.674 AR3 0.853 1.921 AR4 0.834 1.873 Network Activities AV1 0.801 1.358 0.854 0.661 AV2 0.787 1.602 AV3 0.849 1.617 Network Resource RS1 0.787 1.408 0.841 0.638 RS2 0.774 1.396 RS3 0.834 1.419 SC Performance SCP1 0.674 1.774 0.906 0.548 SCP2 0.694 1.991 SCP3 0.718 1.752 SCP4 0.827 2.69 SCP5 0.749 2.446 SCP6 0.824 2.877 SCP7 0.741 2.179 SCP8 0.678 1.672 Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 365 Table 5:- Discriminant Validity Matric FORNEL LARCKER CRITERION Network Activities Network Actor Network Resource SC Performance Network Activities 0.813 Network Actor 0.576 0.821 Network Resource 0.56 0.562 0.799 SC Performance 0.598 0.641 0.67 0.74 The measurement model of the given study is as follows: Figure 1:- Measurement Model 4.1 Assessment of Significance of the Structural Model Direct Relationships This section elaborates both the structural (inner model) & measurement models (outer model), and direct relationships as emphasized by (Joseph F Hair et al., 2010). By making use of the criteria suggested by Hair Jr et al. (2014), the t – value greater than 1.64 is considered to be as significant, which helps in upcoming decisions concerning the formulated hypothesis. Based on recommended t-value, all of the (03) direct hypothesis, were accepted. The structural model for the given direct relationships are as follows: Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 366 Figure 2:- Structural Model Direct Relationships The results presented in Figure 2, generated with the help of Smart-PLS 3.2.8 (Ringle et al., 2015), illustrate the path p-value, t-value, coefficient value and the standard errors. Based on these standard values the hypothesis decision has been made regarding significance level of each hypothesis. While, Table 6 indicates that those hypothesis which were supported in this current research have a p- value of less than 0.05. Table 6:- Hypothesis Testing DIRECT HYPOTHESIS BETA STANDARD DEVIATION T STATISTICS P VALUES DECISION Activities -> SC Performance 0.187 0.044 4.301 0.000 Supported Actors -> SC Performance 0.286 0.042 6.754 0.000 Supported Resources -> SC Performance 0.439 0.044 10.060 0.000 Supported 5. Discussions The outcomes of this study, empirically verify the impact of network perspective (i.e. actors, resources, and activities), on SC performance. Though, the outcomes are contextual and was specifically tested for Textile Supply Chains, still it adds newness and valuable insights to the existing body of knowledge. By considering, multi-factors network factors, fundamental question like, why firms, when aligning their resources and activities with channel partners (as a network) can help in enhancing SC performance in developing nations, such as Pakistan. However, the topic pursued for empirical outcomes is just a beginning in understanding the use of network perspective in TSC. Empirical outcomes have reflected a positive significant relationship between all three network factors (i.e. actors, resources, and activities) on SC performance. It supports Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 367 the idea of channel members across agriculture (textile) chains exchanging, sharing, and coordinating their resources and activities with each other. The results authenticate that channel members need to come closer to each other and to strengthen their relations for better SC performance. The outcomes of the given study, conjoin with the outcomes and suggestions of the previous research studies, propagating a positive relationship between network factors and business performance outcomes (Håkansson, Havila, & Pedersen, 1999; C. M. Harland, 1996b; Kahiya & Dean, 2014; Mikkola, 2008). It is important to note that practically, actors across Textile Supply Chains are subject to number of procedural and bureaucratic contingencies, which hinder their smooth alignments across value chains. Similarly, SC activities across agriculture (Textile) chains are mostly managed at an individual level, but if managed in a coordinated manner across firms and chains, will help in managing the issues of both shortages and surplus of output produced. Lastly, resources aren’t shared as such across chains, which causes the compromised performance outcomes. So, if resources will also be aligned across channel members will takes the burden off from all textile units, and compromised ones in particular and help them in contributing towards overall supply chain performance. Practically, the study brings to fore, some of the future research related questions. Future studies can look into the possibilities of, how to integrate network factors in a better planned manner, for achieving the overall SC performance (Duffy, 2008; Dyer & Nobeoka, 2000; Le, Thi Nguyen, & Van Nguyen, 2013). Researchers can also pursue the longitudinal research approach, to develop a better idea on how, integration among network factors across industries takes place, and also on how it helps achieving the overall SC performance. Furthermore, there has been a recent uprising on, use of multiple extent of supply chain integration across value chains, because of varying needs of industries. Future researchers can check the relationship between multiple extents of SCI along network factors, in generating overall chains performance. Future studies can also introduce certain mediating (i.e. supply chain integration dimensions) and moderating variables (i.e. environmental complexity, environmental dynamism & environmental uncertainty etc.) in through the prism of interpretive lenses like Resource Based View, A-R-A model, and Contingency Theory to address the issues of multiple extents of integration, and contextual nature of value chains networks, across the globe. Likewise, all industries are contextual in nature, and are usually subject to specific uncertainties, the role of moderating variables (e.g. environmental uncertainty, environmental dynamism, & role of culture etc.) can help researchers in developing a better understanding on how supply chains are better managed (Wiengarten, Li, Singh, & Fynes, 2019). Lastly, the given study has implications for firms operating within agriculture (textile) sector of Pakistan and beyond. By considering chains as networks of actors, their resources and activities, firms can take a step towards improving the overall performance. Proper consideration in policy making on network orientation both at dyadic and chain level will be of great utility. It is believed that outcomes of this study, once implemented in true letter and spirit, will also help other industries and manufacturing sectors of Pakistan to enhance their SC performance. Keeping prevailing pandemic (Covid-19) and geo- strategic dynamics in mind, it will help Pakistan to increase its textile exports, which eventually will takes the nation in achieving its larger goals of import substitution, ultimately enhancing firms, supply chain and national level performance. Review of Economics and Development Studies, Vol. 7 (3) 2021, 357-370 368 References Ahmed, R., Munir, R., & Sameer, M. (2020). Impact of supply chain integration on firm performance: Evidence from manufacturing sector of Pakistan. 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