Journal of International Trade, Logistics and Law, Vol. 8, Num. 2, 2022, 191-200 191 THE ROLE OF SUPPLY CHAIN TRANSPARENCY AMONG SUPPLY CHAIN ANALYTICS CAPABILITIES Elif CANBAZ Dinçer Lojistik A.Ş., İstanbul, Turkey Sümeyye ÇİÇEK VURAL Yıldız Technical University, İstanbul,Turkey Cemil ÇELİK Dinçer Lojistik A.Ş., İstanbul, Turkey Murat ÇEMBERCİ Yıldız Technical University, İstanbul,Turkey Received: October 30, 2022 Accepted: November 29, 2022 Published: December 01, 2022 Abstract: Nowadays, to take advantage the huge amounts of data collected, companies have started to adapt analytics tools to useful insight their operations and their service level. Such companies attempt to learn from collected data about current and historical processes to define future operational decisions. Even though the importance of supply chain analytics and data-driven decision-making capabilities is recognized by companies, there is a lack of empirical studies. This study demonstrates the role of supply chain transparency (SCT) in the relation between analytics capabilities. The data were gathered from the online survey and were analyzed with partial least squares structural equation modeling (PLS-SEM) using smart-PLS. The results suggest that the existence of significant relation between analytical capabilities in plan, source and make, deliver and the mediating effect of supply chain transparency on these relationships. Keywords: Supply Chain Analytics Capabilities, Supply Chain Transparency, Big Data Analytics 1. Introduction In today's highly competitive business environment, firms are being challenged by the ongoing globalization. The globalization leads to the rapid changes on customer demands and markets, business models (Chae et al., 2014). These consequences also increase the volume of generated data day by day. Research and literature about the data analytics are still in early stages. Up to date, various terms were used to define analytic capabilities that enable companies to analyze data to obtain useful insights. These terms could be listed as follows, big data analytics, supply chain analytic capabilities, business analytics (Srinivasan and Swink, 2018, Fisher et al., 2012, Agarwal and Dhar, 2014). As a result, most of the companies are planning to boost the information technology structure with Big Data solutions (Dubey et al., 2021, Raj Kumar Reddy and Gunasekaran, 2021, Davenport, 2001). Big data refers to high- volume, high-velocity, and high-variety data sets also known as 3Vs (Jha et al., 2020). These voluminous real-time and almost real-time data are regularly generated in different forms and from individual sources, such as communications (social media interactions, search engines), business (online transactions, reports, billing data), internet of things (sensors) (Jha et al., 2020, Agrawal et al., 2021, Chen et al., 2012). Over the years, data-driven decisions have received much attention in all field of business management so big data analytics provide to increase the quality of services while reducing operational costs (Moldagulova et al., 2020, Arunachalam et al., 2018). Big data impact on various businesses including retail and eCommerce, finance, advertising, logistics and transportation, natural resources (Addo-Tenkorang and Helo, 2016, Wamba et al., 2017). Among them, Logistics and Elif CANBAZ & Sümeyye ÇİÇEK VURAL & Cemil ÇELİK & Murat ÇEMBERCİ 192 Transportation sectors are the most advantageous of the using analytics capabilities due to its dynamic processes (Borgi et al., 2017, Ittmann, 2015, Lekić et al., 2021). B2B companies like logistics require not only faster delivery but also more operational transparency. Transparency in the supply chain (SCT) is the ability to track movement of products from the manufacturer to the end-user. The main purpose is to enhance and strengthen the supply chain by ensuring that all stakeholders, including the customers, are easily accessed with product-related data (Shafiq et al., 2020). On the other hand, there is a lack of study that analytics capabilities can improve supply chain transparency as well as firm performance (Zhu et al., 2018). Herein, this study determines the role of supply chain transparency in the relation between analytics capabilities. Findings were based on a qualitative analysis of data collected via online 5 point-Likert survey (1=strongly disagree to 5=strongly agree). Structural equation modeling was performed with a sample of 100 survey participants from different companies. 2. Conceptual Framework This study investigates potential use of supply chain analytics capabilities in various areas of supply chain operational reference model (SCOR, APICS 2017) which includes planning, making, delivery and sourcing (Trkman et al., 2010, Zhu et al., 2018, Chae and Olson, 2013). Trkman et al. (2010) determined these areas of analytics capability as independent variables and explained their impact on supply chain performance. Zhu et al. (2018) examined the analytics capabilities in planning indirectly impacts on supply chain transparency via analytics capabilities in source, make, and delivery (Zhu et al., 2018). Li and co-workers investigated the positive impact on both supply chain quality performance and business performance of five decision areas (Plan, Source, Make, Deliver, and Return) of the SCOR model (li et al., 2011). Zhou and co-workers examined the structure of supply chain processes on the SCOR model, and they empirically validate this model. Following relationships were suggested; the plan process positively impact on the source, make, and deliver processes and the mediating effect of each other (Zhou et al., 2011). Lockamy and McCormack investigated the effects of SCOR metrics on supply chain performance, their study suggested that the planning process has a great importance to all other areas (Lockamy and McCormack, 2004). In this research, we propose a framework of analytics capabilities in planning directly impacts on other processes (analytics capabilities in source, make, and delivery) through Supply Chain Transparency. All things considered; this study attempts to determine how supply chain transparency effect on the relationship between analytics capabilities. By examining this question, this study represents supply chain analytics capabilities can employ to enhance supply chain performance (Davis-Sramek et al., 2010, Chae and Olson, 2013). Trkman et al. (2010) and Zhu et al. (2018) divide supply chain analytics capabilities for four categories, in this paper these categories were referred as “analytics capability in plan (ACP),” “analytics capability in source (ACS),” “analytics capability in make (ACM),” and “analytics capability in deliver (ACD)”(Zhu et al., 2018, Trkman et al., 2010). The other variable is Supply Chain Transparency (SCT). 2.1. Analytics Capability in Plan ACP addresses to forecast customer demands and market trends. Demand prediction and supply planning play a vital role in the field of planning process (Chae and Olson, 2013). Planning is the major activity also refers to scheduling, finance, distribution (Kusrini et al., 2019b). Decisions in the planning phase could affect the whole supply chain processes, either positive or negative (li et al., 2011). Supply chain planning process should use the real- time and historical information to balance supply and demand, according to (Narasimhan and Kim, 2001) and (Fawcett et al., 2011) information sharing could improve supply chain management and integration. Literature suggests that the planning process is a critical for supply chain to achieve a firm’s strategic goals (Zhou et al., 2011). 2.2. Analytics Capability in Source ACS refers to the measure supplier performance, supplier agreements, how to manage our inventory and handle our relationship between suppliers (Hasibuan et al., 2018). ACS phase is a process that provide products and services to acquire cover the planned or actual demand (Gamoura et al., 2019, Kusrini et al., 2019a, Fosso Wamba et al., 2015). Source is critical especially for manufacturing firms. Academic researchers suggest that the long-term relationships The Role of Supply Chain Transparency Among Supply Chain Analytics Capabilities 193 with suppliers should be established (Chen and Paulraj, 2004, Prahinski and Benton, 2004). A better relationship with supplier lead to more accurate information and quality inputs (Zhou et al., 2011). 2.3. Analytics Capability in Make ACM includes production processes, managing the finished-products network, equipment, and transportation (Trkman et al., 2010, Kusrini et al., 2019b). ACM step covers the entire processes of transforming the demand of the end user into the product. Moreover, ACM includes employee skills, knowledge and technology management skills for improve to customer satisfaction and competitiveness (li et al., 2011). 2.4. Analytics Capability in Deliver ACD is process related to the distribution of services to the customer, also includes warehouses, orders, transportation. This step valuable for firm performance measurement due to its direct relationship with customer demands (Trkman et al., 2010, Zhu et al., 2018). ACD includes management systems such as transport, warehouse, order, and demand (Stewart, 1997). Capability of sharing real-time information about the products with the stakeholders and customers, as well as increasing visibility, has a vital role supply chain management (Zhou et al., 2011). 2.5. Supply Chain Transparency Supply chain transparency covers the process information of the product from raw material to the end user (Montecchi et al., 2021, Bai and Sarkis, 2020). Source planning capabilities provide firms to evaluate supplier performance. To take advantage of analytical capabilities in source planning can boost supply chain transparency and enhance the relation with suppliers (Wang et al., 2016). In addition, inserted transparency into production process can enhance the operational efficiency (Egels-Zandén et al., 2015). 3. Research Model and Hypotheses Figure 1. A Research framework of Supply Chain Analytics Capabilities In order to measure the concepts used in the research model, some scales in the literature were adapted. To measure analytics capability in plan, source, make and deliver, and supply chain transparency, the scales adopted from (Zhu et al., 2018) were used. Supply chain planning must involve the exact demand for the product or services. Proper Elif CANBAZ & Sümeyye ÇİÇEK VURAL & Cemil ÇELİK & Murat ÇEMBERCİ 194 planning reduces any uncertainties or misunderstandings for source planning, production planning and deliver (Lockamy and McCormack, 2004, Zhu et al., 2018). Supply chain transparency is a way in which analytics capabilities in plan impacts analytics capabilities in source, make, deliver. Both the SCOR model and the academic researchers suggest there is a relationship among the supply chain analytics capabilities (Lockamy and McCormack, 2004, Zhou et al., 2011). Effective planning processes influence the implementation of source, make and delivery processes and could enhance the supply chain transparency. Thus, following hypotheses were proposed: H1: There is a positive relationship between ACP and ACS H2: There is a positive relationship between ACP and ACM H3: There is a positive relationship between ACP and ACD H4: There is a positive relationship between ACP and SCT Although H1, H2, H3 directly from the SCOR model, mediating effect of SCT on these relationships can contribute the literature. Even if information sharing is easier ever than before, transparency remains challenging for the supply chain management. On the other hand, recent studies showed that transparency can improve supply chain performance under the right conditions (Kauppila et al., 2020, Ahmed and Omar, 2019, Sodhi and Tang, 2019, Montecchi et al., 2021). H5: SCT mediates the relationship between ACP and ACS H6: SCT mediates the relationship between ACP and ACM H7: SCT mediates the relationship between ACP and ACD 3.1. The Analysis and Results In this study, data were analyzed with partial least squares structural equation modeling (PLS-SEM) using smartPLS (v. 4.0.8.3.) software to test the research model. PLS-SEM can analyze multiple relationship at the same time and allows working with non-normally data and smaller sample size. For all these reasons, PLS-SEM was preferred for the analysis of the study. A two-step analysis process was followed to test the model. In the first step, the validity and reliability of the constructs were evaluated with the measurement model. Then, the hypotheses were examined in the structural model, which is the second step. 3.2. Measurement Model To evaluate the measurement model, the reliability and validity of the model and constructs were examined according to the procedure recommended by (Hair et al., 2013). Firstly, factor loadings, Cronbach's alpha and composite reliability values were computed for each construct. According to (Hair et al., 2013) loadings should be above 0.70 and loadings below 0.40 should be removed from the model. As Table 1 shows, the majority of factor loadings were above 0.70, except for ACP7 (0.665), ACM16 (0.684) and ACM17 (0.646), which were still greater than 0.40. Next, internal consistency reliability was measured through Cronbach’s alpha and composite reliability values. All constructs have Cronbach’s alpha and composite reliability values above the threshold of 0.70 (Table 1). These results confirm internal consistency reliability and indicator reliability. Table 1: Validity and Reliability of Constructs Constructs Items Loadings α CR AVE Analytics Capability in Plan (ACP) 0.806 0.866 0.564 ACP1 0.782 ACP2 0.793 ACP3 0.752 ACP5 0.757 ACP7 0.665 Analytics Capability in Source (ACS) 0.749 0.819 0.604 ACS12 0.711 The Role of Supply Chain Transparency Among Supply Chain Analytics Capabilities 195 ACS13 0.878 ACS15 0.731 Analytics Capability in Make (ACM) 0.846 0.893 0.629 ACM16 0.684 ACM17 0.646 ACM19 0.815 ACM20 0.897 ACM21 0.889 Analytics Capability in Deliver (ACD) 0.845 0.897 0.686 ACD23 0.840 ACD24 0.801 ACD25 0.931 ACD26 0.727 Supply Chain Transparency (SCT) 0.885 0.921 0.744 SCT34 0.903 SCT35 0.821 SCT37 0.875 SCT38 0.848 Notes: α = Cronbach’s alpha, CR = composite reliability, AVE = average variance extracted Average variance extracted (AVE) values were calculated and all values were found greater than 0.50, indicating that constructs have acceptable convergent validity (Table 1) (Hair et al., 2013). The discriminant validity was evaluated by both Heterotrait-Monotrait Ratio (HTMT) (Henseler et al., 2015), and Fornell and Larcker Criterion (Fornell and Larcker, 1981). All the HTMT values of constructs were below the threshold level of 0.85 (Table 2). Looking at Table 3 for Fornell and Larcker criterion, AVEs’ square root values (values in diagonal) were higher than respective inter-correlations (values in off-diagonal), which meets the criterion for discriminant validity. Consequently, both HTMT and Fornell-Larcker Criterion results confirm discriminant validity. Table 2: Heterotrait–monotrait ratio (HTMT) ACD ACM ACP ACS SCT ACD ACM 0.698 ACP 0.735 0.694 ACS 0.552 0.588 0.602 SCT 0.623 0.566 0.585 0.357 Table 3: Fornell-Larcker Criterion ACD ACM ACP ACS SCT ACD 0.828 ACM 0.593 0.793 ACP 0.619 0.588 0.751 ACS 0.498 0.544 0.567 0.777 Elif CANBAZ & Sümeyye ÇİÇEK VURAL & Cemil ÇELİK & Murat ÇEMBERCİ 196 SCT 0.549 0.495 0.503 0.404 0.862 Note: Bold elements show square-root of the AVE 3.3. Structural Model PLS SEM was performed to test research hypothesis. A bootstrapping procedure with 5000 bootstrap subsamples was applied using SmartPLS (v. 4.0.8.3.) To assess collinearity, Variance Inflation Factor (VIF) values were examined and all the VIF values were found below the threshold of 5. This signified there is no multi-collinearity problem with the model. R² value is a measure for predictive power. It represents the amount of variance in the dependent variable explained by all the independent variable linked to it (Hair et al., 2013). The R² values for ACD, ACM, ACS, and SCT are 0.459, 0.399, 0.340, and 0.253, respectively. According to (Chin, 1998), R² values described as follows: substantial (0.67), moderate (0.33) and weak (0.19). The effect size (f2), another measure to evaluate the model, was also examined (Table 4). Effect size indicate the change in the R2 when a specified construct removed from the model (Hair et al., 2013). (Cohen, 1988) classified the effect size as small (0.02), medium (0.15) and large (0.35). After the evaluation of the structural model, both direct and indirect paths were tested. As shown in Table 4, all the direct paths of the model H1, H2, H3 and H4 are statistically supported, and ACP is positively related to ACS (β = 0.438, p<0.01), ACM (β = 0.456, p<0.01), ACD (β = 0.460, p<0.01) and SCT (β = 0.503, p<0.01). Table 4: Hypothesis results Hypothesis Paths β Std. Dev. T statistics P values f2 Results H1 ACP -> ACS 0.483 0.174 2.773 0.006 0.268 Supported H2 ACP -> ACM 0.456 0.155 2.950 0.003 0.255 Supported H3 ACP -> ACD 0.460 0.145 3.170 0.002 0.292 Supported H4 ACP -> SCT 0.503 0.107 4.685 0.000 0.339 Supported To test the mediation effect of SCT the bootstrapping method was conducted. The test results of indirect paths H5, H6 and H7 are expressed in Table 5. According to findings SCT mediates the relationship between ACP and ACM (β = 0.133, p<0.10), and also ACP and ACD (β = 0.159, p<0.05). H6 and H7 are supported according to these results. However, H5 is rejected, because the mediation effect of SCT between ACP and ACS is not significant. Table 5: Mediation Effect Total Effect Direct Effect Path β P values β P values ACP -> ACS 0.559 0.002 0.483 0.006 ACP -> ACM 0.590 0.000 0.456 0.003 ACP -> ACD 0.619 0.000 0.460 0.002 Indirect Effect Hypothesis and Paths β Std. dev T statistics P values BI [2,5%; 97,5%] Results H5 ACP -> SCT-> ACS 0.076 0.068 1.130 0.259 -0.101 – 0.188 Not Supported H6 ACP -> SCT-> ACM 0.133 0.075 1.768 0.077 0.003 – 0.298 Supported H7 ACP -> SCT -> ACD 0.159 0.075 2.117 0.034 0.029 – 0.325 Supported 4. Conclusion The results represent that ACP is valuable for companies in implementing supply chain analytics in source, make, and deliver with mediating effect of supply chain transparency (Zhu et al., 2018). This study provides insights into the relationship between supply chain analytics capabilities (ACP, ACD, ACM, ACS) taking into consideration of SCT. As with all survey-based studies, this study has some limitations. One of the main explanations is companies who survey participants are still in the early stages of deploying analytics capabilities into their operations. The companies The Role of Supply Chain Transparency Among Supply Chain Analytics Capabilities 197 were in Turkey and operating in different sectors. So, future research is encouraged to be data collected from different countries but the same sectors. Accuracy and consistency of results could be increased. To sum up, the results suggest that: ACP has direct and positive effect on analytics capabilities in source, make, and deliver; SCT is positively mediates the relationship between them. Nevertheless, it does not support to mediating effect on relationship between ACP and ACS. The planning phase represents to enhance a whole strategy for the supply chain management, while the source, make and deliver phases specialize in the major needs for performing that plan. Appendix Supply Chain Analytics Capabilities are evaluated using a 5-point Likert ranging from 1 (strongly disagree) to 5 (strongly agree) ACP Analytics capability in plan adapted from (Zhu et al., 2018) ACP1 Does your organization have the analytic capability to measure supply chain performance? ACP2 Does your organization have the analytic capability to measure the impact of different supply chain strategies on supply chain performance? ACP3 Does your organization use adequate analysis tools to inform decisions? ACP4 Does your organization have the analytic capability to assess customer profitability? ACP5 Does your organization have the analytic capability to assess product profitability? ACP6 Does your organization have the analytic capability to assess variability of demand for your products or services? ACP7 Does your organization use mathematical methods (statistics such as time series analysis) to forecast demand? ACP8 Is a forecast developed for each product or service in your organization? ACP9 Is a forecast developed for each customer in your organization? ACP10 Does your demand management process make use of customer information? ACP11 Is demand forecast accuracy measured in your organization? ACS Analytics capability in source adapted from (Zhu et al., 2018) ACS12 Does your organization have the analytic capability to support assessment and documentation of supplier relationships? ACS13 Does your organization have the analytic capability to support sharing of planning and scheduling information with suppliers? ACS14 Does your organization have the analytic capability to support collaboration with your suppliers to develop sourcing requirements? ACS15 Does your organization have the analytic capability to measure and share supplier performance information? ACM Analytics capability in make adapted from (Zhu et al., 2018) ACM16 Does your organization have the analytic capability to support internal integration? ACM17 Does your organization have the analytic capability to support updating supplier lead times monthly? ACM18 Does your organization have the analytic capability to support the use of constraint-based management methodologies? ACM19 Does your organization have the analytic capability to measure levels of production planning adherence? ACM20 Does your organization have the analytic capability to support scheduling among sales, manufacturing, and distribution? ACM21 Does your organization have the analytic capability to support the integration of your customers’ planning and scheduling information with yours? ACM22 Does your organization have the analytic capability to enabling production planning at the “item” level of detail? ACD Analytics capability in deliver adapted from (Zhu et al., 2018) ACD23 Does your organization have the analytic capability to track the percentage of completed customer orders delivered on time? ACD24 Does your organization have the analytic capability to measure “out of stock” situations? Elif CANBAZ & Sümeyye ÇİÇEK VURAL & Cemil ÇELİK & Murat ÇEMBERCİ 198 ACD25 Does your organization have the analytic capability to support assessment and documentation of distribution network relationships? ACD26 Does your organization use mathematical tools to assist in distribution planning? ACD27 Does your organization have the analytic capability to capture distribution management process measures? ACD28 Does your organization have the analytic capability to assess the performance of distribution network participants? SCT Supply Chain Transparency adopted from (Zhu et al., 2018) SCT31 Our suppliers provide us with operational plans (e.g. distribution plans, production plans) regarding the products they produce for us. SCT32 Our major suppliers provide us with detailed product design information SCT33 Our major suppliers collect operations information (e.g.: batch size, run quality, transfer quality, buffer stock, available machines, machine breakdown time) SCT34 Our major suppliers share their operations information with us SCT35 Our major suppliers collect planning and design information (e.g.: current planning and design performance, operations performance, resource utilization, rework and scrap level, level of work progress) SCT36 Our major suppliers share their planning and design information with us SCT37 Our major suppliers collect strategic information (e.g.: new orders, product demand, internal and external expertise, teachability, culture, government regulations) SCT38 Our major suppliers share their strategic information with us References ADDO-TENKORANG, R. & HELO, P. T. 2016. Big data applications in operations/supply-chain management: A literature review. Computers & Industrial Engineering, 101, 528-543. AGARWAL, R. & DHAR, V. 2014. Editorial —Big Data, Data Science, and Analytics: The Opportunity and Challenge for IS Research. Information Systems Research, 25, 443-448. AGRAWAL, T. K., KUMAR, V., PAL, R., WANG, L. & CHEN, Y. 2021. Blockchain-based framework for supply chain traceability: A case example of textile and clothing industry. Computers & Industrial Engineering, 154, 107130. AHMED, W. & OMAR, M. 2019. Drivers of Supply Chain Transparency and its effects on Performance Measures in the Automotive Industry: Case of a Developing Country. International Journal of Services and Operations Management, 33, 159-186. ARUNACHALAM, D., KUMAR, N. & KAWALEK, J. P. 2018. Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice. Transportation Research Part E: Logistics and Transportation Review, 114, 416-436. BAI, C. & SARKIS, J. 2020. A supply chain transparency and sustainability technology appraisal model for blockchain technology. International Journal of Production Research, 58, 2142-2162. BORGI, T., ZOGHLAMI, N. & ABED, M. Big data for transport and logistics: A review. 2017 International Conference on Advanced Systems and Electric Technologies (IC_ASET), 14-17 Jan. 2017 2017. 44-49. CHAE, B. & OLSON, D. 2013. Business analytics for supply chain: A dynamic-capabilities framework. International Journal of Information Technology & Decision Making, 12, 9-26. CHAE, B., OLSON, D. & SHEU, C. 2014. The impact of supply chain analytics on operational performance: a resource-based view. International Journal of Production Research, 52, 4695-4710. CHEN, H., CHIANG, R. H. L. & STOREY, V. C. 2012. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, 36, 1165-1188. CHEN, I. & PAULRAJ, A. 2004. Towards a Theory of Supply Chain Management. Journal of Operations Management, 22, 119-150. CHIN, W. W. 1998. The partial least squares approach for structural equation modeling. Modern methods for business research. Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers. COHEN, J. 1988. Statistical Power Analysis for the Behavioral Sciences Hillsdale, NJ: Lawrence Erlbaum Associates, Publishers. The Role of Supply Chain Transparency Among Supply Chain Analytics Capabilities 199 DAVENPORT, T. H. E. A. 2001. Data to Knowledge to Results: Building an Analytic Capability. California Management Review, 43, 117-138. DAVIS-SRAMEK, B., GERMAIN, R. & IYER, K. 2010. Supply chain technology: the role of environment in predicting performance. Journal of the Academy of Marketing Science, 38, 42+. DUBEY, R., GUNASEKARAN, A., CHILDE, S. J., FOSSO WAMBA, S., ROUBAUD, D. & FOROPON, C. 2021. Empirical investigation of data analytics capability and organizational flexibility as complements to supply chain resilience. International Journal of Production Research, 59, 110-128. EGELS-ZANDÉN, N., HULTHÉN, K. & WULFF, G. 2015. Trade-offs in supply chain transparency: the case of Nudie Jeans Co. Journal of Cleaner Production, 107, 95-104. FAWCETT, S., WALLIN, C., ALLRED, C., FAWCETT, A. & MAGNAN, G. 2011. Information Technology as an Enabler of Supply Chain Collaboration: A Dynamic-Capabilities Perspective. Journal of Supply Chain Management, 47, 38-59. FISHER, D., DELINE, R., CZERWINSKI, M. & DRUCKER, S. 2012. Interactions with big data analytics. Interactions, 19, 50-59. FORNELL, C. & LARCKER, D. F. 1981. Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18, 39-50. FOSSO WAMBA, S., AKTER, S., EDWARDS, A., CHOPIN, G. & GNANZOU, D. 2015. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. GAMOURA, S., DERROUICHE, R., DAMAND, D. & BARTH, M. 2019. Insights from big Data Analytics in supply chain management: an all-inclusive literature review using the SCOR model. Production Planning and Control, 1-27. HAIR, J. F., HULT, G. T. M., RINGLE, C. & SARSTEDT, M. 2013. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), SAGE Publications. HASIBUAN, A., ARFAH, M., PARINDURI, L., HERNAWATI, T., SULIAWATI, HARAHAP, B., SIBUEA, S. R., SULAIMAN, O. K. & PURWADI, A. 2018. Performance analysis of Supply Chain Management with Supply Chain Operation reference model. Journal of Physics: Conference Series, 1007, 012029. HENSELER, J., RINGLE, C. M. & SARSTEDT, M. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43, 115-135. ITTMANN, H. W. 2015. The impact of big data and business analytics on supply chain management. Journal of Transport and Supply Chain Management; Vol 9, No 1 (2015)DO - 10.4102/jtscm.v9i1.165. JHA, A. K., AGI, M. A. N. & NGAI, E. W. T. 2020. A note on big data analytics capability development in supply chain. Decision Support Systems, 138, 113382. KAUPPILA, O., VALIKANGAS, K. & MAJAVA, J. 2020. Improving supply chain transparency between a manufacturer and suppliers: a triadic case study. Management and Production Engineering Review, vol. 11. KUSRINI, E., CANECA, V., HELIA, V. & MIRANDA, S. 2019a. Supply Chain Performance Measurement Usng Supply Chain Operation Reference (SCOR) 12.0 Model : A Case Study in A A Leather SME in Indonesia. IOP Conference Series: Materials Science and Engineering, 697, 012023. KUSRINI, E., CANECA, V. I., HELIA, V. N. & MIRANDA, S. 2019b. Supply Chain Performance Measurement Usng Supply Chain Operation Reference (SCOR) 12.0 Model : A Case Study in A A Leather SME in Indonesia. IOP Conference Series: Materials Science and Engineering, 697, 012023. LEKIĆ, M., ROGIĆ, K., BOLDIZSÁR, A., ZÖLDY, M. & TÖRÖK, Á. 2021. Big Data in Logistics. Periodica Polytechnica Transportation Engineering, 49, 60-65. LI, L., SU, Q. & CHEN, X. 2011. Ensuring supply chain quality performance through applying the SCOR model. International Journal of Production Research - INT J PROD RES, 49, 33-57. LOCKAMY, A. & MCCORMACK, K. 2004. Linking SCOR planning practices to supply chain performance: An exploratory study. International Journal of Operations & Production Management, 24, 1192-1218. MOLDAGULOVA, A., SATYBALDIYEVA, R. & KUANDYKOV, A. 2020. Application of Big Data in Logistics. Proceedings of the 6th International Conference on Engineering & MIS 2020. Almaty, Kazakhstan: Association for Computing Machinery. Elif CANBAZ & Sümeyye ÇİÇEK VURAL & Cemil ÇELİK & Murat ÇEMBERCİ 200 MONTECCHI, M., PLANGGER, K. & WEST, D. C. 2021. Supply chain transparency: A bibliometric review and research agenda. International Journal of Production Economics, 238, 108152. NARASIMHAN, R. & KIM, S. 2001. Information System Utilization Strategy for Supply Chain Integration. Journal of Business Logistics, 22, 51-75. PRAHINSKI, C. & BENTON, W. C. 2004. Supplier evaluations: communication strategies to improve supplier performance. Journal of Operations Management, 22, 39-62. RAJ KUMAR REDDY, K. & GUNASEKARAN, A. 2021. Developing a blockchain framework for the automotive supply chain: A systematic review. Computers & Industrial Engineering, 157, 107334. SHAFIQ, A., AHMED, M. U. & MAHMOODI, F. 2020. Impact of supply chain analytics and customer pressure for ethical conduct on socially responsible practices and performance: An exploratory study. International Journal of Production Economics, 225, 107571. SODHI, M. S. & TANG, C. S. 2019. Research Opportunities in Supply Chain Transparency. Production and Operations Management, 28, 2946-2959. SRINIVASAN, R. & SWINK, M. 2018. An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective. Production and Operations Management, 27, 1849-1867. STEWART, G. 1997. Supply‐chain operations reference model (SCOR): the first cross‐industry framework for integrated supply‐chain management. Logistics Information Management, 10, 62-67. TRKMAN, P., MCCORMACK, K., DE OLIVEIRA, M. P. V. & LADEIRA, M. B. 2010. The impact of business analytics on supply chain performance. Decision Support Systems, 49, 318-327. WAMBA, S. F., NGAI, E. W. & RIGGINS, F. 2017. Transforming operations and production management using big data and business analytics: future research directions. EMERALD GROUP PUBLISHING LTD HOWARD HOUSE, WAGON LANE, BINGLEY BD16 1WA, W …. WANG, G., GUNASEKARAN, A., NGAI, E. W. T. & PAPADOPOULOS, T. 2016. Big data analytics in logistics and supply chain management: Certain investigations for research and applications. International Journal of Production Economics, 176, 98-110. ZHOU, H., BENTON JR, W. C., SCHILLING, D. A. & MILLIGAN, G. W. 2011. Supply Chain Integration and the SCOR Model. Journal of Business Logistics, 32, 332-344. ZHU, S., SONG, J. & HAZEN, B. 2018. How Supply Chain Analytics Enables Operational Supply Chain Transparency: An Organizational Information Processing Theory Perspective. International Journal of Physical Distribution & Logistics Management, 48.