This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons. org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright © 2022 The Author(s). Published by Vilnius Gediminas Technical University *Corresponding author. E-mail: jayyash@uj.edu.sa Business, Management and Economics Engineering ISSN: 2669-2481 / eISSN: 2669-249X 2022 Volume 20 Issue 1: 152–171 https://doi.org/10.3846/bmee.2022.16597 THE INFLUENCE OF SUPPLY CHAIN MANAGEMENT PRACTICES ON SUPPLY CHAIN PERFORMANCE: THE MODERATING ROLE OF INFORMATION QUALITY Jehad BANI HANI * Department of Health Services and Hospitals Management, Collage of Business, University of Jeddah, Jeddah, Kingdom of Saudi Arabia Received 25 February 2022; accepted 21 April 2022 Abstract. Purpose – The main aim of this investigation is to introduce a framework for combining information quality concerning with contextual and representational relevance, and intrinsic accura- cy in supply chain management practices to develop supply chain performance in Saudi companies. Research methodology – Inferential technique used to examine the interplay between constructs of the study. SEM was run in order to assess and estimate the causal relationship among variables. Findings  – The significant effect of information quality on SCP as well as the significant effect of SCMPs on SCP. Furthermore, it was found that the information quality could significantly moderate the interplay between SCP and SCMPs. Research limitations – This study conducted on 150 Saudi manufacturing companies during the year 2021, in future, the findings can be adapted for other sectors of the Saudi economy. Practical implications – This study has implications for the industrial managers and key personnel. The study provides strong evidence revealing that higher level of integration between SCMPs and quality of information can lead to enhanced supply chain performance.so the proposed model can be used in the practical activities of service sectors In the future; it is possible to change and/ or add key variables and further expand the field of use of the model. Originality/Value – The study contributed in determining main practices of supply Chan management in industrial sector of Saudi Arabia considering their influence on supply chain performance, as well as this study contributed in showing the engagement of information quality in supply chain management. Keywords: supply chain management practices, supply chain management, information quality, supply chain performance, Saudi manufacturing companies. JEL Classification: L15, L60, M10, M11. Introduction Todays, the intense competition in the business environment demands an increasing con- centration on transmitting values to the clients. The main purpose in most businesses is to http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3846/bmee.2022.16597 https://orcid.org/0000-0002-3624-4782 Business, Management and Economics Engineering, 2022, 20(1): 152–171 153 present kinds of items and services that are considered more precious than those of its com- petitors. Besides client value, the marketplace, where businesses are running today, is widely regarded as being turbulent and complex (Christopher, 2000; Goldman et al., 1995). All types of organizations consider supply chain management (SCM) as a critical strategy, which is mainly employed to increase the competitive position (Li et  al., 2006) as SCM pays more attention to the flows of information, material and cash between customers and suppliers or vice-versa (Wibowo & Sholeh, 2015). Indeed, the firms have recently encountered vari- ous types of problems and threats in the turbulent business and unexpected requirements. Further, the organizations follow an appropriate supply chain strategy (SCS) to use available chances in order to resolve uncertainty. Additionally, the flow of information, materials, knowledge, and cash demands an efficient information system which enables information quality sharing among supply chain partners (i.e., suppliers, distributors, manufacturers, and customers) to reduce the uncertainty and enhance supply chain performance (SCP). Hence, the supply chain needs to design a strategy which conform to the goods, markets, and target clients (Hallavo, 2015). Cigolini et al. (2004) and Li et al. (2006) have noted that the practices of SCM have not received enough attention from researchers in business. This lack of studies can be ascribed to SCM’s interdisciplinary structure as well as its developmental features, which has led to a state of a theoretical distortion from the perspectives of its perceptions. Research on SCM practices that has been conducted in certain countries and industries needs to be interpreted within the particular contexts of these countries along with their distinct characteristics, which can help narrow the gap between SCM application and its theory. The present study aims at empirically examining a framework in order to explore the moderating function of information quality in the interaction between the performance of supply chains and SCM practices of manufacturing companies in Saudi Arabia. Accordingly, SCM practices are referred to as a range of activities that unify all different groups involved in SC including suppliers, distributors, goods producers, and clients in order to boost SC activities and achievements (Barros, 2006; Koh et  al., 2007). The SCM practices examined in this study were designed, reviewed, and validated in the past studies done by Green Jr et al., (2008), Cook et al. (2011), Tan (2002), and Li et al. (2006). These actions consist of six components: Level of Information Sharing, Strategic Supplier Partnership, Customer, Internal Lean Practice, Relationship Management, and Postponement. They are defined as strategic practices, covering both past and future aspects of the SC. As the SC and its function are appealing to the scholars and researchers, this investigation seems to be contributing to the industries and literature. This will be done by examining the SCM practices that result in enhancing SC performance which is related to supply chain in terms of its cost, quality, and lead time as well as the moderating information quality which itself consists of accuracy, timeliness, validity of exchanged information, and adequacy (refer to Li et  al., 2006). After collecting data through the survey, the variables are operationally assessed. Structural equa- tion modelling, widely known as SEM, and inferential statistics were used for assessment and validation of the hypothesized interactions. This study intends to provide scholars, more particularly, producing companies with insights that are connected with the perception of SCM practices and with scope connected to their SCM practices which can prominently 154 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... affect not only their SC but also the entire company. The present investigation therefore can effectively guide Saudi manufacturing organizations in terms of SCM practices. It is also a validated tool to test and conduct SCM practices. The study further develops the theory in academia by extending SCM knowledge among developing nations (Cigolini et  al., 2004; Li et al., 2006; Min & Mentzer, 2004). Consequently, the findings of the current investigation can enable organizations to address the supply chain performance problems through several practical managerial practices. The industrial sector in the Kingdom of Saudi Arabia has witnessed a steady development during the past years. A report issued by the “National Center for Industrial and Mining Information” indicated that the total number of existing factories in the Kingdom of Saudi Arabia until the end of October 2021, reached about 10,216 factories, and the reason for this is to the interest and support that this sector finds from the Saudi government through the es- tablishment of industrial cities in various regions (Modon has established 35 industrial cities, including five “industrial oases” suitable for women’s work, called “Modon oases”). Recently, the Saudi government launched the National Industries Development and Logistics Services Program, which is one of the most important programs of Saudi Vision 2030, which aims to place Saudi Arabia in the ranks of developed and leading countries. The Saudi industrial sector has a sustainable impact, especially since there is a direct relationship between the future of industries and the rate of economic growth, which calls for the adoption of poli- cies that support the level of productivity and competitiveness the sector in particular the manufacturing sector, which is classified as the main engine of growth. The industrial sector in the Kingdom of Saudi Arabia seeks in all its tools to sort out an environment capable of to achieve the requirements of competitiveness in the current and future stage. Accordingly, the present study was carried out to address three research questions as follows: (1) what kinds of SCM practices could be used in Saudi manufacturing organizations? (2) Is there any relationship between the current SCM practices applied by manufacturing companies in Saudi Arabia and their manufacturing performance? and (3) Does information quality have moderating role in the interplay between SCM performance and SCM practices?.. 1. Literature review 1.1. Supply chain practices The term SCM is mostly used to referred to as synchronizing a firm’s procedures with those of its clients and distributors so that the flow of information, servicing offers, and materials could be consistent with customers’ requests (Krajewski et al., 2019). Hence, the main pur- pose of SCM is to handle the rate and speed of information and materials between a sequence of operations that form the “chains” or strands of a supply network (Slack & Brandon-Jones, 2019). Then, supply chain management will have an important role in the structure of sup- ply chain in order to increase its competitive gain and benefits that are offered to the target client (Lysons & Farrington, 2020). Academic scholars and managers of business companies have paid increasing attention to the practices of supply chain management (Bani Hani, 2021; Tan et  al., 2009; Li et  al., 2005; Croom et  al., 2000). Numerous manufacturing firms have found supply chain man- Business, Management and Economics Engineering, 2022, 20(1): 152–171 155 agement practices to play acritical role in generating and keeping a competitive profit at the market (Li et  al., 2005). In the practices of SCM, all levels of the SC including distributors, manufacturers, suppliers, and customers are integrated in order to enhance supply chain performance (Barros, 2006; Koh et  al., 2007). Researchers have given attention to supply chain management performances from different aspects. For instance, Alvarado and Kotzab, (2001) explored core competences, inter-organizational system, and removal of excess in inventory through postponement. Further, Tan et al. (2009) noted that the main practices of SC include exchange of information, integration of supply chain, management of customer services, JIT capabilities, and geographic proximity. Using a system approach, Min and Men- tzer (2004) and Sundram et  al. (2016) classified the supply chain management practices: information sharing, agreed-upon prospects and goals, collaboration, dangers and reward sharing, long-standing association, process integration, and planned leadership in SC. Lee et al. (2007) investigated five performances at supply chain level including strategic supplier partnerships, outsourcing, information sharing, customer relationship, and product modu- larity. Zhou and Benton Jr (2007), in a different study, examined the integration of delivery practice, SC planning, IS, just-in-time (JIT) production in supply chain management. Thatte et  al. (2013) defined the practices of supply chain management in the light of customer relationship, strategic supplier partnership, and information sharing. Chan and Lam (2011) assessed the performances of supply chain practices with respect to customer relationships, strategic supplier partnerships, information technology, information sharing, internal opera- tions, training, innovation performance, and operational performance. Sukati et  al. (2012) hold that supply chain practices are connected with customer-firm relationships, internal firm relationships, and supplier-firm relationships. In examining SCP, Al-Shboul et al. (2017) applied seven dimensions including information sharing, strategic distributor association, client connection management, information quality, postponement, management of total quality, and internal lean performances. The present investigation employs the following parameters to examine SCMP in consumer goods industry in Saudi Arabia: customer rela- tionship (CRM), strategic supplier partnership (SSP), information sharing (IS), internal lean practices (ILP), and postponement (P). 1.2. Supply chain performance In the past decade, many industries have used SCP as a vital source of durable competitive leverage (WH Ip et al., 2011). It is argued that companies have to improve their supply chain performance progressively in order to gain the prosperity and supremacy of the supply chain in the global business (Acar & Uzunlar, 2014). Although some studies have brought supply chain practices and theories to the center of attention, there has not been enough attention to the supply chain performance. As, today firms face various challenges due to intense competition not only between supply chains but also between organizations themselves (Ab- dulameer & Yaacob, 2020), promoting the supply chain performance is not restricted to the organization exclusively. Yet, any factor of downstream or upstream could have a prominent role in enhancing the supply chain performance (Cai et al., 2009). Furthermore, upgrading the supply chain performance does not make a difference for companies that provide services 156 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... or produce goods (Basu et al., 2017). Therefore, it seems necessary to draw more importance to the supply chain performance in companies. Meanwhile, researchers have pointed out that no single measure has been offered to represent the supply chain performance (Saleheen et  al., 2018; Anand & Grover, 2015). Banomyong and Supatn (2011) refer to supply chain performance as a system that accounts for several performance measures connected to the SC members, as well as the coordination and integration of their performances. Several scholars have indicated that development and selection of the appropriate criteria to assess the supply chain performance have always been a challenging task (see Anand & Grover, 2015). This challenge is usually attributed to the hardship of coordinating many organizations that form the supply chain, as well as performances in the firms (Subburaj et al., 2015; Dweekat et al., 2017; Alam et al., 2014). Ahi and Searcy (2015) also highlighted the difficult and challenging aspect of assessing supply chain performance. Additionally, Harland et  al. (1999) puts that the majority of the measurements of conventional actions are directed towards economic landscape. Previous research has offered a wide range of criteria that were used to measure supply chain performance. Some scholars have noted that supply chain performance could be as- sessed with respect to cost, service level, inventory level, throughput efficiency, and supplier performance (Stevens, 1990). Pittiglio, Rabin, Todd, & McGrath (1994) approached the SCP measurement from four different dimensions: cost, customer satisfaction/quality, assets, and time. Spekman et  al. (1998) considered cost reduction and client satisfaction in order to measure the SCP. Beamon (1999) offered qualitative criteria to gauge SCPs including the integration of both information and material, flexibility, performances of suppliers, client satisfaction, and effective management of possible risks. The present study puts more empha- sis on performance measures that could be used in the practical issues encountered by the manufacturing organizations, namely the issues indicated by Shepherd and Günter (2006) such as quality (or reliability), time, cost, innovativeness and flexibility. 1.3. Information quality Information quality is the main part of information management as it is processed and de- veloped in a company. The high quality of information can help improve the making process and it brings the organization a competitive advantage (Azemi et  al., 2017). Information could have high quality provided that it fits the purpose, which can be evaluated by the users of the information only (Bani Hani et  al., 2009; Embury et  al., 2009). Information Quality (IQ) is a multidimensional notion, in which many researchers are interested in order to ex- plore and categorize its aspects. Information quality refers to what extent measurement meth- ods employed to prepare information could reflect what a decision maker intends to find out (information relevance) and whether the given methods have been competently used and results have been truthfully depicted (information credibility or reliability) (Kinney, 2000). Operationally, information quality is defined as information which is useful, good, accurate, and current (Rieh, 2002). Li and Lin (2006) argued that ensuring the information quality could play a significant role in accomplishing an effective supply chain management. The main aspects of information quality include both the subjective and objective parameters of, reliability, bias awareness, accuracy, comprehensiveness, validity, credibility, currency, trust, Business, Management and Economics Engineering, 2022, 20(1): 152–171 157 expertise, transparency, and thoroughness (Laudon & Laudon, 2021; Bani Hani & Awad, 2017; Diakopoulos & Essa, 2008; Li et al., 2006). In this study, the quality of information is measured with respect to four semantic categories as identified by Lee and Levy (2014) and Huang et  al. (1999): (1) Representational that is interpretable, understandable, consistence, and conciseness. (2) Intrinsic accuracy that contain; credibility, objectivity, and reputability. (3) Accessibility that related to accessing information securely and easily. (4) Contextual rel- evance factors that include timeliness, valuableness, information richness, and completeness. 1.4. The influence of SCMP on SCP It has been proved that the SCM is a critical factor influencing the performance of supply chain. However, empirical studies have shown mixed results. For example, some studies have reported that supply chain management performances are to gain and promote practices through supply chain, which demands integrating both internally with the organization and externally with customers and suppliers (Kannan & Tan, 2010; Kim, 2006). Kumar and Kush- waha (2018) investigated the relationship between operational function of the fair price shops and various SCMPs (information technology or IT, CRM, information quality) in India. They found three aspects of practices in SCM with a positive and significant connection with the practical performance. Saragih et  al. (2020) demonstrated that sustainable operational performance can be gained via supply chain practices. Despite the influence of SCMPs on SC performance, supply chain management performances could greatly impact the loyalty and satisfaction of customers, contract design in SC, and pricing rate of supply chain. For example, Prathiba (2020) examined whether customer affiliation, supplier affiliation, and knowledge sharing have any effect on clients’ loyalty and satisfaction. The study found the significant influence of SCMPs on clients’ loyalty and satisfaction. Thus, although it is agreed that supply chain management practices could influence an organization, it seems critical to precisely assess such impact on supply chain performance in an organization (Green et  al., 2006). Further, Alahmad (2021) probed the interplay between SCP and SCMPs in various industries in Saudi Arabia. accordingly, following hypothesis is presented: H1: SCMPs have statistically significant influences SCP. 1.5. Information quality as a moderating construct In modern business, in order to be successful in competitive environment, companies need to concentrate on information which strengthen supply chain performance. Literature abounds with research on the effect of information quality (IQ) on SCP, demonstrating the significant role of IQ and information sharing play in improving the SCPs. A study by Afshan et  al. (2018) in India showed that information quality is directly related to supply chain collabora- tion. This, consequently, results in an improvement in supply chain performance. Al-Shboul et  al. (2017) also revealed a significant interaction between supply chain performances and information quality within manufacturing organizations. Studying retail firms, Gandhi et al. (2017) reported significant impact of information quality and sharing on supply chain per- formances. Kim and Chai (2017) conducted a study among the manufacturing companies in South Korea and revealed that information quality and sharing is significantly connected to 158 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... SCPs. Sahin and Topal (2019) reported that businesses activities could be affected by infor- mation quality and sharing. Thus, as previous studies have indicated, there is a significantly positive connection between SCPs and IQ. The gap in the past studies proposed that the theoretical framework explaining the way information quality is integrated into supply chain management practices in order to assess their impacts on SCP. It seems that the present study is pioneer in developing a research framework as illustrated in Figure 1. Supply Chain Management Practices Supply Chain Performance Information Quality Figure 1. The theoretical framework illustrated the integrating of IQ into SCMPs to improve SCP According to what mentioned above, the following hypotheses are offered: H2: IQ has a statistically significant influence on SCP. H3: IQ plays a significant moderating role between SCMPs and SCP. 2. Methodology 2.1. Participants and collection of data All the general and functional executives in Saudi Arabia manufacturing companies form the population of this study. They have been listed in Jeddah Industrial cities becuase they are included in the Saudi Authority for Industrial Cities and Technology Zones (MODEN). As Rahi et al. (2018) suggested, G-Power Software was used in the prior-power analysis in order to measure the sample size. Rahi et al. (2018) argued that factor analysis should be used in measuring the size of the sample with prior-power analysis. According to the results of the prior-power analysis, at least 245 responses are needed in order to project the productivity of an organization. Previous research demonstrated that a rise in the response rate could help decrease the magnitude of error in the sampling process (e.g., Rahi et  al., 2018), and Rahi et  al. (2020) suggested, according to Rahi and Abd. Ghani (2019), a convenience sampling method is suitable when the list of the respondents is not at hand. The convenience sampling method was adequate to help in engage with actual respondents (Rahi, 2021), and as the purpose of the study was to achieve high veracity from data. Nearly 750 questionnaires were distributed, 150 manufacturing firms volunteered to take part in the research. Each com- pany received five questionnaires, which were filled by general manager (GM), HR manager, finance manager, operations manager, and marketing manager. 500 usable questionnaires were completed and returned with 67% response rate: HR manager (n = 66), GM (n = 113), finance manager (57), operations manager (n  = 140), and marketing manager (n  = 124). Business, Management and Economics Engineering, 2022, 20(1): 152–171 159 The responding companies were working in a wide range of areas such as cartoon and pa- per(18 company), beverages and food (24 company), medical and pharmaceutical products (7 companies), electronic goods (30 company), textiles (26 company), leathers (21 company), ceramic and glass (12 company), and clothing products (12 company). In total, 500 responses were inferentially analyzed. 2.2. The development of the Instrument The study reported in this paper was carried out to examine the moderating role information quality (IQ) plays in the interaction between SCP and SCMPs, considering the executives’ atti- tudes working in Saudi manufacturing companies based in Jeddah Industrial areas as they are listed in the Saudi Authority for Industrial Cities and Technology Zones (MODEN). To do so, we reviewed a wide range of relevant studies on the central measures of SCP, SCMPs, and IQ. A questionnaire was designed according to the key variables used in the past studies. The surveys were sent to Jeddah factories with a notification that they need to be filled out by the executives at different levels. The questionnaire contains four sections. The first part pertains to the respondents’ demographic profiles; the second section was meant to examine SCMPs; and the third section was to measure SCP. The final section included items for evaluating IQ. The questionnaire was developed in such a way that the major key measures could be addressed in the light of previous literature. The main reason for choosing industrial sector is that this sector is more likely to satisfy the major constructs of this investigation. The Likert scales with a range of “1” strongly disagree to “7” strongly agree conforms to Rahi et al. (2018), was used in the questionnaire. 2.3. Evaluating common method variance Data were gathered using a single source, which is based on positivist paradigm. Therefore, measurement of the bias in common method variance seems necessary. Harman’s single fac- tor analysis was employed for the sake of approving that the investigation is exempted from such variance. According to single factor analysis developed by Harman, variance should go beyond 50%, which indicates that the data is bias free (Podsakoff et  al., 2003). The present findings demonstrate that the variance was 19%, thus confirming that this study is exclusive of any variance bias and, therefore, known as a valid structural model. 3. Analysis of data and results Following the quantitative method, inferential technique was employed to examine the in- terplay between constructs of the study. Concerning inferential technique, SEM was run in order to assess and estimate the causal relationship among variables (Rahi et  al., 2020; Bani Hani, 2021). SEM involves estimating both structural model and measurement model (Anderson & Gerbing, 1988; Rahi et  al., 2020) SEM model show the causal interactions between two or more constructs, while the measurement model measures convergent and discriminant validity of the constructs. To run the models, Smart-PLS was employed (Ringle et al., 2015; Rahi, 2017). 160 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... 3.1. Model reliability and validity The internal consistency of the constructs (test reliability) was measured using Cronbach’s Alpha with an acceptance level of 0.60 (Sekaran, 2010). Table 1 displays the “alpha” values of all constructs. The Cronbach’s Alpha indexes greater than 0.7 not only demonstrate the ac- ceptable level of reliability but also indicate that instruments used in the study are of a good internal consistency (Nunnally & Berstein, 1994; Hair et al., 2013). Composite reliability (CR) was chosen to calculate the internal consistency of the model, indicating the amount of measure variance underlying the given trait in every single ordered construct of the model. All the CR values had a range between 0.944 and 0.973, exceeding the suggested minimum level of 0.7 (Gefen et al., 2000; Hair et al., 2013), thus demonstrating an adequate consistency (acceptable reliability) in the model. A confirmatory factor analysis (CFA) was run in order to demonstrate the validity of the model. Discriminate validity is verified provided that the average variance extracted (AVE) exceeds the squared correlations (Fornell & Larcker, 1981). As Table 1 shows, the AVE values ranging from 0.699 to 0.781 were all higher than the level of 0.5 recommended by Bagozzi and Yi (2012) and greater than the squared correlations among the variables (off-diagonal). The measures of average shared variance (ASE) and maximum shared variance (MSV) are found less than that of average variance extracted (AVE), supporting discriminate validity (Hair et  al., 2013) (See Table  1). Regarding all the studied constructs, the values met the recommended threshold levels, then demonstrating the satisfactory convergent validity. As Table 2 reveals the outcomes of the Fornell and Larcker analysis, the measures of AVE (diagonal) were found to be higher than the values of constructs correlation counterparts, thus confirming that the variable is discriminant enough to be used in the assessment of the specific concepts. Table 1. Validity and reliability indices of the proposed model Variable Item Loading Cronbach’s Alpha CR AVE MSV ASV SCMPs Strategic Supplier Partnership-SSP 0.658 0.88 0.957 0.777 0.547 0.379 Customer Relationship-CRM 0.671 Internal Lean Practices-ILP 0.893 Information Sharing-IS 0.950 Postponement-P 0.942 IQ Intrinsic accuracy-IA 0.967 0.84 0.944 0.781 0.589 0.358 Representation-R 0.733 Contextual relevance-CR 0.837 Accessibility-A 0.881 SCP Cost -C 0.977 0.87 0.973 0.699 0.539 0.361 Time-T 0.871 Quality-Q 0.945 Flexibility-F 0.917 Innovativeness-I 0.728 Business, Management and Economics Engineering, 2022, 20(1): 152–171 161 Table 2. Fornell and Larcker’s analysis Variables SSP CRM ILP IS P IQ SCP SSP 0.871 CRM 0.414 0.881 ILP 0.569 0.411 0.882 IS 0.415 0.367 0.406 0.891 P 0.477 0.571 0.467 0.213 0.911 IQ 0.409 0.321 0.658 0.258 0.217 0.897 SCP 0.478 0.351 0.708 0.376 0.053 0.357 0.866 3.2. Goodness-of-fit of the model With reference to Hair et  al. (2013), the “goodness-of-fit” of the model was analyzed using different measures, namely absolute fit, incremental fit, and parsimonious fit. As shown in Table  3, 12 indices, which are widely applied by researchers (Hair et  al., 2013; Cheung & Rensvold, 2002; Bentler, 1990; Marsh et al., 1988), were used in the current study. Table 3. Goodness-of-fit indices of the structural model Measures Value Threshold Absolute fit Chi-square goodness of fit 921.68 p-value 0.000 Degree of freedom 499 Normed chi-square 1.847 ≤3 Goodness-of-Fit Index (GFI) 0342 ≥0.08 0r 0.09 Root mean squared error of approximation (RMSEA) 0.065 ≤0.08 Incremental fit Tucker-Lewis Index (TLI) 0.971 0.95 and above Normal Fit Index (NFI) 0.935 0.9 and above Non-Normal Fit Index (NNFI) 0.917 0.9 and above Comparative Fit Index (CFI) 0.924 0.9 and above Incremental Fit Index (IFI): 0.927 0.9 and above Relative Fit Index (RFI) 0.938 0.9 and above Parsimonious fit Parsimonious Normed Fit Index (PNFI) 0.779 0.5 and above Parsimonious Goodness-of-Fit Index (PGFI) 0.853 0.5 and above AS Table 2 shows, most of “goodness-of-fit” values were found satisfying and acceptable. Thus, these measures illustrate that the proposed model sound well developed and accept- able. The outcomes of the Fornell and Larcker’s analysis revealed that the values of EVA (diagonal) were found greater than those of the constructs’ correlation counterparts, hence verifying that construct measures and discriminates distinct concepts (Table 2). 162 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... 3.3. Descriptive analysis Table 4 displays the detailed descriptive statistics concerning the respondents’ demographics. Table 4. Respondents’ demographics (n = 500) Variable Frequency Percentage Age Less than 30 years 21 0.04 30–40 188 0.38 41–50 117 0.23 More than 50 174 0.35 Education Bachelor 348 0.70 Master 121 0.24 Doctorate 31 0.06 Position General Manager 113 0.23 HR Manager 66 0.13 Operations Manager 140 0.28 Finance Manager 57 0.11 Marketing Manager 124 0.25 Experience Less than 5 years 81 0.16 5 – less than 10 318 0.64 More than 10 101 0.20 3.4. Descriptive analysis of the constructs Table 5 displays the standard deviation and mean in order to answer the research questions of the study. Regarding SCMPs, the standard deviation and the mean values were 0.26 and 3.7, respectively. Concerning information Quality, the values of the standard deviation and the mean were 3.64 and 0.35, respectively. Finally, as for SCP the values of standard devia- tion and the mean were 0.78 and 3.83, respectively. The values of standard deviation show the level of concentration and homogeneity in the data set as the level of dispersion or vari- ability was smaller. Table 5. Standard deviation and mean for items domain Variables Mean STD Rank Degree SCMPs 3.75 0.26 2 High Information quality 3.64 0.35 3 Medium Supply chain performance 3.83 0.78 1 High Business, Management and Economics Engineering, 2022, 20(1): 152–171 163 3.5. Hypotheses testing 3.5.1. Normality test Prior to the linear regression analysis, the assumption of the normal distribution of data needs to be verified. As Table  6 reveals through Shapiro-Wilk test and the level of signifi- cance, it is obvious that all the variables of the study are normally distributed. Since all the significance level values are higher than 0.05 in the table, the null hypothesis, which states “there is no statistically significant difference between the normal distribution and the dis- tribution of the variable values at the significance level (α ≤ 0.05)” is accepted. This finding confirms that the values of the variables have a normal distribution in this study. Table 6. Normal distribution test using Shapiro-Wilk test Variable Shapiro-Wilk test Sig. SCMPs 0.98 0.17 IQ 0.95 0.20 SCP 0.91 0.35 3.5.2. SCNPs Influence on SCP Smart PLS/ bootstrapping technique was used to assess the relevance and significance of the structural model (see Table 6). Supporting hypothesis 1, the results show the statistically significant effect of SCNPs (t  = 13.71, p < 0.001) on SCP (β  = 0.256), thus indicating the acceptance of the alternative hypothesis (H1) that SCMPs influences SCP. As correlation confident value between SCP and SCMPs (R  = 0.53, p < 0.05) show, as long as the Saudi factories pays more attention to SCMPs, there will be an improvement in the SCP. Regarding the determination coefficient of the SCNPs-SCP construct (R2 = 28%), it seems that 28% of the total SCP variance could be interpreted by SCMPs. The residual could be attributed to other variables. 3.5.3. The impact of IQ on SCP Hypothesis 2 was also supported as the findings show the statistically significant influence of information quality on SCP (t = 4.78, p < 0.01, β = 0.227), thus implying the acceptance of the alternative hypothesis (H2) that information quality could impact SCP (Table 6). According to correlation confident value between SCP and IQ (R = 0.55, p < 0.05), it could be said whenever the Saudi companies devote more attention to the quality of information, the supply chain performance will experience an improvement. The determination coefficient of the SCP-IQ construct (R2 = 30%) implies 30% of the total SCP variance could be explained by information quality. The residual could be interpreted in the light of other variables. 164 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... 3.5.4. The moderating role of information quality in the effect of SCMPs on ACP As can been seen from Table 7, the quality of information has a moderating role in the in- fluence of SCMPs on SCP (β = 0.514; t = 9.58; p < 0.001), thus indicating that information quality moderates the influence of SCMPs on SCP (alternative hypothesis 3). Regarding the determination coefficient of the variable (SCMPs-IQ) on SCP (R2 = 35%), it is shown that the determination coefficient has an increase compared to the first model, im- plying that the moderating role of information quality helps explain the SCMPs – SCP model. Table 7. PLS bootstrapping results Hypotheses R R2 Standard bootstrap results Standard error t-value p-value (1-sided) SCMPs – SCP 0.53* 0.28** 0.017 13.71 0.00*** IQ – SCP 0.55* 0.30** 0.013 4.78 0.00** [SCMPs & IQ] – SCP 0.59** 0.35** 0.037 9.58 0.00*** Notes: *p < 0.05; **p < 0.01; ***p < 0.001. 4. Discussion The results showed that the respondents are highly interested in supply chain management practices, information quality, and supply chain performance as appeared in mean values, this is may due to recently growing Saudi government interest in supply chains, in particular logistics services. This result agreed with study by Alahmad (2021), that concluded that sup- ply chain management practices has a direct in influence on supply chain performance. Also, the findings of this study showed that SCMPs correlate with SCP which implies that as long as the Saudi factories pays more attention to SCMPs, there will be an improvement in the SCP. In addition, SCMPs have influence on SCP. The findings also showed significant positive correlation relationship between information quality and SCP, which implies whenever the Saudi companies devote more attention to the quality of information, the supply chain per- formance will experience an improvement, also the findings showed that information quality have influence on SCP. Study of Choy et al. (2004) indicated that high cost will be considered if information cannot be reached effectively with partners in supply chain that may not help to improve supply chain performance. Finally, results showed the Moderating role of Infor- mation Quality in the effect of SCMPs on SCP. Study by Marinagi et  al. (2015) reveals that information quality as an independent variable affects the supply chain performance, the study also reflects the moderating role of information sharing between information quality and supply chain performance. Research work related to SCMPs in the manufacturing organizations find a significant correlation between SCMPs and SCP dimensions (Al-Shboul et al., 2017; Ibrahim & Hamid, 2014; Abdallah et  al., 2014; Karimi & Rafiee, 2014; Kannan & Tan, 2004). Moreover, other many different previous literature agreed with the findings of this study (Abdulameer & Yaacob, 2020; Ortiz & Gomez, 2017) as they have conclude that supply chain management Business, Management and Economics Engineering, 2022, 20(1): 152–171 165 practices has a direct positive influence on different aspects of supply chain performance. The managing of supply chain practices will contribute in reduce the total cost, speed the time, improve quality, increase flexibility, and enhance innovativeness of the supply chain manage- ment, therefore the suppliers may contribute to improve performance (Subburaj et al., 2015; Utami et al., 2019; Kumar & Kushwaha, 2018; Thatte, 2007). Today the competition between companies has become between supply chains (Li et  al., 2006). For this reason, companies should increasingly adopting SCMPs to achieve cost-reductions, speed the time, improv- ing their quality, increasing flexibility, and enhance innovation that lead to enhancing their competitiveness. Conclusions The three SCMPs, SCP aspects, and IQ dimensions adopted in this study were developed, examined and validated in the literature. Data were collected from one hundred and fifty Saudi manufacturing companies using a survey questionnaire distributed to executives and analyzing data using statistical measures developed and used to test the proposed experimen- tal model. This study conducted in the Kingdom of Saudi Arabia from the end of the year 2020 to the end of the year 2021. This study, thus, aims to help, manufacturing companies and researchers to better recognize the scope and activities associated with their supply chain that have a prominent role on the effective performance of the entire company. The study, therefore, provides a useful guidance for Saudi manufacturing companies as well as a vali- dated tool for them to measure and implement supply chain management. This study was an effort to show the influence of SCMPs on SCP. It was also tried to explore whether information quality plays a moderating role between SCP and SCMPs. The study helps grow the body of the literature by specifying the crucial role of information qual- ity in enhancing the impact of SCMPs on SCP on the basis of the managers’ perceptions in Saudi manufacturing firms. The results of the statistical analysis highlights significant inter- play between SCP and SCMPs, that is, an increase in SCMPs could upgrade the level of Saudi factories’ SCP. Moreover, SCMPs significantly influenced SCP. Such a result indicates that a change in SCP could be interpreted in the light of a change in SCMPs. Saudi manufacturing firms are recommended to implement SCMPs to enhance their performance. The results also suggest a significant and positive relationship between IQ and SCP. Thus, this confirms that if more attention is drawn toward quality of information, there will be an improvement in the level of SCP in Saudi companies. Furthermore, the significant influence of IQ on SCP could explain that a change in SCP could be attributed to IQ influence. It was also found that IQ moderates the impact of SCMPs on SCP. Such a moderating role could lead to a change in SCP. More specifically, the moderating role of information quality helps in the perception of the SCMPs – SCP model. Saudi manufacturing firms are recommended to increase the level of interest in information quality as an opportunity to have effective SCP. The results of this study will have important implications and is believed to be very useful for the Saudi industrial sector and benefited for the public sector since both will be aware of the relatively important factors that should be considered in formulating ap- propriate strategies. 166 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... Current years have seen increase in the significance of integration suppliers, manufactur- ers, and customers. Effective integration needs intensive concentration on quality of informa- tion in terms of intrinsic accuracy, representation, contextual relevance, and Accessibility. Effective integration of suppliers into supply chains serves as a key element to many firms to gain competitive advantage. Thus, the present findings could be valuable to all Saudi man- agers at all levels at workplace as integrating the quality of information in the practices of supply chain management will help reduce or eliminate the uncertainty and risks, as a result making an improvement in the supply chain performance. In general, it is an important for every manufacturing company to align its SCMPs with information quality to achieve effective SCP; hence, information quality will be an imperative tool to address the align- ment process for the companies. Finally, the interesting in information quality would enable companies to study the efficacy of SCMPs and their outcomes in achieving effective supply chain performance. Acknowledgements The University of Jeddah, Jeddah, Saudi Arabia, funded this study under grant No. (UJ-21-DR-49). The author, therefore, acknowledge with full thanks the University of Jeddah technical and financial support. References Abdulameer, S. S., & Yaacob, N. A. (2020). The moderating role of information sharing on the relation- ship between lean supply chain and supply chain performance: A conceptual framework. Interna- tional Journal of Supply Chain Management, 9(1), 411–419. Abdallah,  A.  B., Obeidat,  B.  Y., & Aqqad,  N.  O. (2014). The impact of supply chain management practices on supply chain performance in Jordan: The moderating effect of competitive intensity. International Business Research, 7(3), 13–27. https://doi.org/10.5539/ibr.v7n3p13 Acar, A. Z., & Uzunlar, M. B. (2014). The effects of process development and information technology on time-based supply chain performance. Procedia - Social and Behavioral Sciences, 150, 744–753. https://doi.org/10.1016/j.sbspro.2014.09.044 Afshan, N. S., Chatterjee, S., & Chhetri, P. (2018). Impact of information technology and relational aspect on supply chain collaboration leading to financial performance: A study in Indian context. Bench- marking: An International Journal, 25(7), 2496–2511. https://doi.org/10.1108/BIJ-09-2016-0142 Ahi, P., & Searcy, C. (2015). Measuring social issues in sustainable supply chains. Measuring Business Excellence, 19(1), 33–45. https://doi.org/10.1108/MBE-11-2014-0041 Alahmad, Y. (2021). The relationship between supply chain management practices and supply chain performance in Saudi Arabian firms. American Journal of Industrial and Business Management, 11(1), 42–59. https://doi.org/10.4236/ajibm.2021.111004 Alam, A., Bagchi, P., Kim, B., Mitra, S., & Seabra, F. (2014). The mediating effect of logistics integration on supply chain performance: A multi-country study. The International Journal of Logistics Manage- ment, 25(3), 553–580. https://doi.org/10.1108/IJLM-05-2013-0050 Al-Shboul, M. D., Barber, K. D., Garza-Reyes, J. A., Kumar, V., & Abdi, R. (2017). The effect of supply chain management practices on supply chain and manufacturing firms’ performance. Journal of Manufacturing Technology Management, 28(1). https://doi.org/10.1108/JMTM-11-2016-0154 https://doi.org/10.5539/ibr.v7n3p13 https://doi.org/10.1016/j.sbspro.2014.09.044 https://doi.org/10.1108/BIJ-09-2016-0142 https://doi.org/10.1108/MBE-11-2014-0041 https://doi.org/10.4236/ajibm.2021.111004 https://doi.org/10.1108/IJLM-05-2013-0050 https://doi.org/10.1108/JMTM-11-2016-0154 Business, Management and Economics Engineering, 2022, 20(1): 152–171 167 Alvarado, U. Y., & Kotzab, H. (2001). Supply chain management: The integration of logistics in market- ing. Industrial Marketing Management, 30(2), 183–198. https://doi.org/10.1016/S0019-8501(00)00142-5 Anand, N., & Grover, N. (2015). Measuring retail supply chain performance: Theoretical model using key performance indicators (KPIs). Benchmarking: An International Journal, 22(1), 135–166. https://doi.org/10.1108/BIJ-05-2012-0034 Anderson,  J.  C., & Gerbing,  D.  W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 Kumar, A., & Kushwaha, G. S. (2018). Supply cain management practices and operational performance of fair price shops in India: An empirical study. LogForum, 14(1), 85–99. https://doi.org/10.17270/J.LOG.2018.237 Azemi, N. A., Zaidi, H., & Hussin, N. (2017). Quality in organization for better decision-making. Inter- national Journal of Academic Research in Business and Social Sciences, 7(12), 429–437. https://doi.org/10.6007/IJARBSS/v7-i12/3624 Bagozzi, R. P., & Yi, Y. (2012). Specification, evaluation, and interpretation of structural equation mod- els. Journal of the Academy of Marketing Science, 40(1), 8–34. https://doi.org/10.1007/s11747-011-0278-x Bani Hani, J., & Awad, H. (2017). A broad-spectrum orientation of business innovation: An empirical investigation using managers’ attitudes toward the effectiveness of innovation measurement indi- cators in Saudi private hospitals: The case of private hospitals in Jeddah. International Journal of Innovative Research in Engineering & Management, 4(4), 729–734. https://doi.org/10.21276/ijirem.2017.4.4.11 Bani-Hani, J., Al-Ahmad, N., & Alnajjar, F. (2009). The impact of management information systems on organizations performance: Field study at Jordanian universities. Review of Business Research, 9(2), 127–138. Bani Hani, J. (2021). The moderating role of lean operations between supply chain integration and operational performance in Saudi manufacturing organizations. Uncertain Supply Chain Manage- ment, 9, 169–178. https://doi.org/10.5267/j.uscm.2020.10.004 Banomyong, R., & Supatn, N. (2011). Developing a supply chain performance tool for SMEs in Thai- land. Supply Chain Management: An International Journal, 16(1), 20–31. https://doi.org/10.1108/13598541111103476 Barros, C. P. (2006). Efficiency measurement among hypermarkets and supermarkets and the identifica- tion of the efficiency drivers. International Journal of Retail & Distribution Management, 2, 135–154. https://doi.org/10.1108/09590550610649795 Basu, G., Jeyasingam, J., Habib, M., Letchmana, U., & Radhakrishnan, R. (2017). The impact of supply chain management practices on the performance of private universities in Malaysia. International Journal of Supply Chain Management, 6(3), 22–35. Beamon, B. (1999). Measuring supply chain performance. International Journal of Operations & Produc- tion Management, 19(3), 7–12. https://doi.org/10.1108/01443579910249714 Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin, 107, 238–246. https://doi.org/10.1037/0033-2909.107.2.238 Cai, J., Liu, X., Xiao, Z., & Liu, J. (2009). Improving supply chain performance management: A system- atic approach to analyzing iterative KPI accomplishment. Decision. Support System, 46, 512–521. https://doi.org/10.1016/j.dss.2008.09.004 Chan, W., & Lam, C. (2011). Modeling supply chain performance and stability. Industrial Management and Data Systems, 111(8), 1323–1354. https://doi.org/10.1108/02635571111171649 https://doi.org/10.1016/S0019-8501(00)00142-5 https://doi.org/10.1108/BIJ-05-2012-0034 https://doi.org/10.1037/0033-2909.103.3.411 https://doi.org/10.17270/J.LOG.2018.237 https://doi.org/10.6007/IJARBSS/v7-i12/3624 https://doi.org/10.1007/s11747-011-0278-x https://doi.org/10.21276/ijirem.2017.4.4.11 https://doi.org/10.5267/j.uscm.2020.10.004 https://doi.org/10.1108/13598541111103476 https://doi.org/10.1108/09590550610649795 https://doi.org/10.1108/01443579910249714 https://doi.org/10.1037/0033-2909.107.2.238 https://doi.org/10.1016/j.dss.2008.09.004 https://doi.org/10.1108/02635571111171649 168 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... Cheung, G. W., & Rensvold, R. B. (2002). Evaluating goodness-of-fit indexes for testing measurement in- variance. Structural Equation Modeling, 9, 233–255. https://doi.org/10.1207/S15328007SEM0902_5 Christopher, M. (2000). The agile supply chain: Competing in volatile markets. Industrial Marketing Management, 29(1), 37–44. https://doi.org/10.1016/S0019-8501(99)00110-8 Choy, K., Lee, W., & Lo, V. (2004). An enterprise collaborative management system  – a case study of supplier relationship management. Journal of Enterprise Information Management, 17(3), 191–207. https://doi.org/10.1108/17410390410531443 Cigolini, R., Cozzi, M., & Perona, M. (2004). A new framework for supply chain management. Inter- national Journal of Operations & Production Management, 24, 7–41. https://doi.org/10.1108/01443570410510979 Cook,  L.  S., Heiser,  D.  R., & Sengupta, K. (2011). The moderating effect of supply chain role on the relationship between supply chain practices and performance. International Journal of Physical Dis- tribution and Logistics Management, 41(2), 104–134. https://doi.org/10.1108/09600031111118521 Croom, S., Romano, P., & Giannakis, M. (2000). Supply chain management: an analytical framework for critical literature review. European Journal of Purchasing & Supply Management, 6, 67–83. https://doi.org/10.1016/S0969-7012(99)00030-1 Diakopoulos, N., & Essa, I. (2008, November 28–30). An annotation model for making sense of infor- mation quality in online video. In ICPW ‘08: Proceedings of the 3rd International Conference on the Pragmatic Web: Innovating the Interactive (pp. 31–34). https://doi.org/10.1145/1479190.1479195 Dweekat, A. J., Hwang, G., & Park, J. (2017). A supply chain performance measurement approach us- ing the internet of things: Toward more practical SCPMS. Industrial Management & Data Systems, 117(2), 267–286. https://doi.org/10.1108/IMDS-03-2016-0096 Embury,  S.  M., Missier, P., Sampaio, S., Greenwood,  R.  M., & Preece,  A.  D. (2009). Incorporating domain-specific information quality constraints into database queries. Journal of Data and Informa- tion Quality, 1(2), 1–11. https://doi.org/10.1145/1577840.1577846 Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104 Gandhi,  A.  V., Shaikh, A., & Sheorey,  P.  A. (2017). Impact of supply chain management practices on firm performance. International Journal of Retail & Distribution Management, 45, 366–384. https://doi.org/10.1108/IJRDM-06-2015-0076 Gefen, D., Straub, D., & Boudreau, M. (2000). Structural equation modeling and regression: Guidelines for research practice. Communications of the Association for Information Systems, 4. https://doi.org/10.17705/1CAIS.00407 Goldman, S., Nagel, R., & Preiss, K. (1995). Agile competitors and virtual organizations: Strategies for enriching the customer (1st ed.). Wiley. Green Jr, K. W., Whitten, D., & Inman, R. A. (2008). The impact of logistics performance on organiza- tional performance in a supply chain context. Supply Chain Management: An International Journal, 13(4), 317–327. https://doi.org/10.1108/13598540810882206 Green, K. W., McGaughey, R., & Casey, K. M. (2006). Does supply chain management strategy mediate the association between market orientation and organizational performance? Supply Chain Manage- ment, 11, 407–414. https://doi.org/10.1108/13598540610682426 Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2013). Multivariate data analysis (7th ed.). Pearson. Hallavo, V. (2015). Superior performance through supply chain fit: a synthesis. Supply Chain Manage- ment: An International Journal, 20(1), 71–82. https://doi.org/10.1108/SCM-05-2014-0167 https://doi.org/10.1207/S15328007SEM0902_5 https://doi.org/10.1016/S0019-8501(99)00110-8 https://doi.org/10.1108/17410390410531443 https://doi.org/10.1108/01443570410510979 https://doi.org/10.1108/09600031111118521 https://doi.org/10.1016/S0969-7012(99)00030-1 https://doi.org/10.1145/1479190.1479195 https://doi.org/10.1108/IMDS-03-2016-0096 https://doi.org/10.1145/1577840.1577846 https://doi.org/10.1177/002224378101800104 https://doi.org/10.1108/IJRDM-06-2015-0076 https://doi.org/10.17705/1CAIS.00407 https://doi.org/10.1108/13598540810882206 https://doi.org/10.1108/13598540610682426 https://doi.org/10.1108/SCM-05-2014-0167 Business, Management and Economics Engineering, 2022, 20(1): 152–171 169 Harland, C., Lamming, R., & Cousins, P. (1999). Developing the concept of supply strategy. Interna- tional Journal of Operations & Production Management, 19(7), 650–673. https://doi.org/10.1108/01443579910278910 Huang, K.-T., Lee, Y. W., & Wang, R. Y. (1999). Quality information and knowledge. Prentice-Hall. Ibrahim,  S.  B., & Hamid,  A.  A. (2014). Supply chain management practices and supply chain perfor- mance effectiveness. International Journal of Science and Research (IJSR), 3(8), 187–195. Kannan, V. R., & Tan, K. C. (2004). Supplier alliances: Differences in attitudes to supplier and quality management of adopters and non‐adopters. Supply Chain Management: An International Journal, 9(4), 279–286. https://doi.org/10.1108/13598540410550028 Karimi, E., & Rafiee, M. (2014). Analyzing the impact of supply chain management practices on orga- nizational performance through competitive priorities. International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(1), 1–15. https://doi.org/10.6007/IJARAFMS/v4-i1/503 Kim, M., & Chai, S. (2017). The impact of supplier innovativeness, information sharing and strategic sourcing on improving supply chain agility: Global supply chain perspective. International Journal of Production Economics, 187, 42–52. https://doi.org/10.1016/j.ijpe.2017.02.007 Kim, S. (2006). Effects of supply chain management practices, integration and competition on perfor- mance. Supply Chain Management: An International Journal, 11(3), 241–248. https://doi.org/10.1108/13598540610662149 Kinney, W. R. Jr. (2000). Information quality assurance and internal control. McGraw Hill. Koh, L. C. S., Demirbag, M., Bayraktar, E., Tatoglu, E., & Zaim, S. (2007). The impact of supply chain management practices on performance of SMEs. Industrial Management and Data Systems, 107(1), 103–124. https://doi.org/10.1108/02635570710719089 Krajewski, L. J., Malhotra, N. K., & Ritzman, L. P. (2019). Operations management: Processes and supply chains (12th ed.). Pearson. Kumar, A., & Kushwaha, G. (2018). Supply chain management practices and operational performance of fair price shops in India: An empirical study. LogForum, 14(1), 85–99. https://doi.org/10.17270/J.LOG.2018.237 Laudon, K., & Laudon, J. (2021). Essentials of MIS (14th ed.). Pearson. Lee, C., Kwon, I., & Severance, D. (2007). Relationship between supply chain performance and degree of linkage among supplier, internal integration, and customer. Supply Chain Management: An In- ternational Journal, 12(6), 444–452. https://doi.org/10.1108/13598540710826371 Lee, A., & Levy, Y. (2014). The effect of information quality on trust in e-government systems’ trans- formation. Transforming Government: People, Process and Policy, 8(1), 76–100. https://doi.org/10.1108/TG-10-2012-0011 Li, S., & Lin, B. (2006). Accessing information sharing and information quality in supply chain manage- ment. Decision Support Systems, 42(3), 1641–1656. https://doi.org/10.1016/j.dss.2006.02.011 Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Rao, S. S. (2006). The impact of supply chain manage- ment practices on competitive advantage and organizational performance. Omega, 34(2), 107–124. https://doi.org/10.1016/j.omega.2004.08.002 Li, S., Rao,  S.  S., Ragu-Nathan,  T.  S., & Ragu-Nathan, B. (2005). Development and validation of a measurement instrument for studying supply chain management practices. Journal of Operations Management, 23(6), 618–641. https://doi.org/10.1016/j.jom.2005.01.002 Lysons, K., & Farrington, B. (2020). Procurement and supply chain management (10th ed.). Pearson. Marinagi, C., Trivellas, P., & Reklitis, P. (2015). Information quality and supply chain performance: The mediating role of information sharing. Procedia - Social and Behavioral Sciences, 175, 473–479. https://doi.org/10.1016/j.sbspro.2015.01.1225 https://doi.org/10.1108/01443579910278910 https://doi.org/10.1108/13598540410550028 https://doi.org/10.6007/IJARAFMS/v4-i1/503 https://doi.org/10.1016/j.ijpe.2017.02.007 https://doi.org/10.1108/13598540610662149 https://doi.org/10.1108/02635570710719089 https://doi.org/10.17270/J.LOG.2018.237 https://doi.org/10.1108/13598540710826371 https://doi.org/10.1108/TG-10-2012-0011 https://doi.org/10.1016/j.dss.2006.02.011 https://doi.org/10.1016/j.omega.2004.08.002 https://doi.org/10.1016/j.jom.2005.01.002 https://doi.org/10.1016/j.sbspro.2015.01.1225 170 J. Bani Hani. The influence of supply chain management practices on supply chain performance: ... Marsh,  H.  W., Balla,  J.  R., & McDonald,  R.  P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103, 391–410. https://doi.org/10.1037/0033-2909.103.3.391 Min, S., & Mentzer, J. T. (2004). Developing and measuring supply chain management concepts. Journal of Business Logistics, 25, 63–99. https://doi.org/10.1002/j.2158-1592.2004.tb00170.x Nunnally, J. C., & Berstein, I. H. (1994). Psychometric theory. McGraw Hill. Ortiz, A. L., & Gomez, M. (2017). The supply chain management and the supply chain responsiveness in the competitiveness of the agrofood sector: An econometric analysis. International Journal of Industrial and Systems Engineering, 11(17), 2818–2821. Pittiglio, Rabin, Todd, & McGrath. (1994). Integrated supply chain performance measurement: a multi- industry consortium recommendation, Westin Ma. PRTM Consulting, Massachusetts. Podsakoff, P. M., MacKenzie, S. B., Lee, J., & Podsakoff, N. (2003). Common method biases in behav- ioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 Prathiba, S. (2020). Can supply chain management practices influence customer satisfaction and loy- alty? Journal of Supply Chain Management Systems, 9(1). Rahi, S. (2021). Investigating the role of employee psychological well-being and psychological empow- erment with relation to work engagement and sustainable employability. International Journal of Ethics and Systems. https://doi.org/10.1108/IJOES-12-2020-0200 Rahi, S., Ghani, M. A., & Ngah, A. H. (2020). Factors propelling the adoption of internet banking: The role of e-customer service, website design, brand image and customer satisfaction. International Journal of Business Information Systems, 33(4), 549–569. https://doi.org/10.1504/ijbis.2020.105870 Rahi, S., & Abd. Ghani, M. (2019). Integration of expectation confirmation theory and self-determi- nation theory in internet banking continuance intention. Journal of Science and Technology Policy Management, 10(3), 533–550. https://doi.org/10.1108/JSTPM-06-2018-0057 Rahi, S., Ghani, M., & Ngah, A. (2018). A structural equation model for evaluating user’s intention to adopt internet banking and intention to recommend technology. Accounting, 4(4), 139–152. https://doi.org/10.5267/j.ac.2018.3.002 Rahi, S. (2017). Research design and methods: A systematic review of research paradigms, sampling issues and instruments development. International Journal of Economics & Management Sciences, 6(2), 1–5. https://doi.org/10.4172/2162-6359.1000403 Rieh,  S.  Y. (2002). Judgment of information quality and cognitive authority in the web. Journal of the American Society for Information Science and Technology, 53(2), 145–161. https://doi.org/10.1002/asi.10017 Ringle,  C.  M., da Silva, D., & Bido, D. (2015). Structural equation modeling with sampling. Revista Brasileira de Marketing, 13(2), 56–73. https://doi.org/10.5585/remark.v13i2.2717 Sahin, H., & Topal, B. (2019). Examination of effect of information sharing on businesses performance in the supply chain process. International Journal of Production Research, 57(3), 1–14. https://doi.org/10.1080/00207543.2018.1484954 Saleheen, F., Habib, M., & Hanafi, Z. (2018). Supply chain performance measurement model: A litera- ture review. International Journal of Supply Chain Management, 7(3), 70–78. Saragih, J., Tarigan, A., Pratama, I., Wardati, J., & Silalahi,  E.  F. (2020). The impact of total quality management, supply chain management practices, and operations capability on firm performance. Polish Journal of Management Studies, 21(2). https://doi.org/10.17512/pjms.2020.21.2.27 Sekaran, U. (2010). Research methods for business: A skill-building approach (4 ed.). John Wiley & Sons. Shepherd, C., & Günter, H. (2006). Measuring supply chain performance: Current research and future directions. International Journal of Productivity and Performance Management, 55, 242–258. https://doi.org/10.1108/17410400610653219 https://doi.org/10.1037/0033-2909.103.3.391 https://doi.org/10.1002/j.2158-1592.2004.tb00170.x https://doi.org/10.1037/0021-9010.88.5.879 https://doi.org/10.1108/IJOES-12-2020-0200 https://doi.org/10.1504/ijbis.2020.105870 https://doi.org/10.1108/JSTPM-06-2018-0057 https://doi.org/10.5267/j.ac.2018.3.002 https://doi.org/10.4172/2162-6359.1000403 https://doi.org/10.1002/asi.10017 https://doi.org/10.5585/remark.v13i2.2717 https://doi.org/10.1080/00207543.2018.1484954 https://doi.org/10.17512/pjms.2020.21.2.27 https://doi.org/10.1108/17410400610653219 Business, Management and Economics Engineering, 2022, 20(1): 152–171 171 Slack, N., & Brandon-Jones, A. (2019). Operations management (9th ed.). Pearson. Spekman, R., Kamauff Richey, J., & Myhr, N. (1998). An empirical investigation into supply manage- ment: A perspective on partnership. Supply Chain Management, 3(2), 53–67. https://doi.org/10.1108/13598549810215379 Stevens, G. (1990). Integrating the supply chain. International Journal of Physical Distribution and Ma- terials Management, 19(8), 3–8. https://doi.org/10.1108/EUM0000000000329 Subburaj, M., Ramesh Babu, T., & Suresh Subramonian, B. (2015). a study on strengthening the opera- tional efficiency of dairy supply chain in Tamilnadu, India. Procedia - Social and Behavioral Sciences, 189, 285–291. https://doi.org/10.1016/j.sbspro.2015.03.224 Sukati, I., Hamid,  A.  B., Baharum, R., & Yussoff, R. (2012). The study of supply chain management strategy and practices on supply chain performance. Procedia Social and Behavioral Sciences, (40), 225–233. https://doi.org/10.1016/j.sbspro.2012.03.185 Sundram, V. P. K., Chandran, V. G. R., & Bhatti, M. A. (2016). Supply chain practices and performance: The indirect effects of supply chain integration. Benchmarking: An International Journal, 23(6), 1445–1471. https://doi.org/10.1108/BIJ-03-2015-0023 Tan,  K.  C., Kannan,  V.  R., & Leong,  G.  K. (2009) Supply chain management practices as a mediator of the relationship between operations capability and firm performance. International Journal of Production Research, 47(3), 835–855. https://doi.org/10.1080/00207540701452142 Tan, K. C. (2002). Supply chain management: Practices, concerns, and performance issues. Journal of Supply Chain Management, 38(1), 42–53. Thatte, A. A., Rao, S. S., & Ragu-Nathan, T. S. (2013). Impact of SCM practices of a firm on supply chain responsiveness and competitive advantage of a firm. Journal of Applied Business Research (JABR), 29(2), 499–530. https://doi.org/10.19030/jabr.v29i2.7653 Thatte,  A.  A. (2007). Competitive advantage of a firm through supply chain responsiveness and SCM practices (Publication No. 3264621) [Doctoral dissertation, The University of Toledo]. ProQuest Information and Learning Company. Utami,  C.  W., Susanto, H., Septina, F., Sumaji, U.  M.  P., & Pratama, I. (2019). Effect of supply chain management practices on financial and economic sustainable performance of Indonesian SMEs. International Journal of Supply Chain Management, 8(5), 523–535. WH Ip., W., Chan, S., & Lam, C. (2011). Modeling supply chain performance and stability.Industrial Management and Data Systems, 111(8), 1332–1354. https://doi.org/10.1108/02635571111171649 Wibowo, M. A., & Sholeh, M. N. (2015). The analysis of supply chain performance measurement at con- struction project. Procedia Engineering, 125, 25–31. https://doi.org/10.1016/j.proeng.2015.11.005 Zhou, H., & Benton, Jr., W. C. (2007). Supply chain practice and information sharing. Journal of Opera- tions Management, 25(6), 1348–1365. https://doi.org/10.1016/j.jom.2007.01.009 https://doi.org/10.1108/13598549810215379 https://doi.org/10.1108/EUM0000000000329 https://doi.org/10.1016/j.sbspro.2015.03.224 https://doi.org/10.1016/j.sbspro.2012.03.185 https://doi.org/10.1108/BIJ-03-2015-0023 https://doi.org/10.1080/00207540701452142 https://doi.org/10.19030/jabr.v29i2.7653 https://doi.org/10.1108/02635571111171649 https://doi.org/10.1016/j.proeng.2015.11.005 https://doi.org/10.1016/j.jom.2007.01.009