Explaining the Impact of Enterprise Resource Planning on Company Performance with the Mediating Role of Supply Chain Management (Case Study: Saipa Yadak Company)PirizadehBehroozTaghizadeh-YazdiMohammadrezaPirizadehAliDepartment of Management, Islamic Azad University Ahar, Ahar, Iran.Department of Production and Operations Management, University of Tehran, Tehran, Iran.Department of Industrial Engineering, University of Tehran, Tehran, Iran.Literature Review
The integration of ERP systems with SCM has become a focal point in recent research, particularly for its impact on organizational performance. ERP systems streamline organizational processes, enhancing efficiency and decision-making, while SCM optimizes the flow of goods, information, and finances across supply chain partners. Recent studies (2018 and later) consistently highlight SCM’s mediating role in amplifying ERP’s effects on financial, operational, and environmental performance outcomes.
Ata et al. [13] investigated ERP’s impact on organizational performance in Jordanian manufacturing firms, finding that ERP adoption enhances operational efficiency and financial outcomes. The study emphasized that robust SCM practices, enabling better coordination with suppliers and customers, are critical for realizing ERP’s full potential, suggesting SCM as a mediator for performance metrics like cost reduction. Similarly, Yang and Su [14] used Structural Equation Modeling (SEM) to demonstrate that ERP systems improve supply chain integration in South Korean firms, with SCM mediating the positive impact on organizational performance through enhanced inventory management and delivery times.
Setyadi [15] explored the interplay of ERP, Total Quality Management (TQM), and SCM in Indonesian manufacturing firms, revealing that ERP, when combined with TQM, strengthens SCM practices, leading to improved business performance. SCM’s mediating role was evident in reducing supply chain disruptions and enhancing process efficiency. The environmental aspect of SCM, particularly Green Supply Chain Management (GSCM), was examined by Sheng et al. [16], who found that ERP systems integrated with GSCM practices in Chinese firms reduce environmental risks and enhance sustainability performance, positively impacting financial and reputational outcomes.
More recent studies further validate these findings. Linda et al. [17] employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to confirm a significant positive relationship between ERP and SCM in Indonesian firms, with SCM mediating the impact on profitability and customer satisfaction. Santoso et al. [18] proposed a model integrating ERP and SCM, emphasizing SCM’s role in enabling real-time data sharing and collaborative decision-making, which enhances operational efficiency and market competitiveness. Feng et al. [19] focused on green SCM, finding that sustainable SCM practices significantly amplify ERP’s positive effects on organizational performance, particularly in cost savings and ecological efficiency.
These studies collectively underscore SCM’s critical mediating role in translating ERP’s technological capabilities into tangible performance benefits. However, gaps persist in understanding how contextual factors, such as industry type or organizational size, influence this mediation. Additionally, the integration of GSCM with ERP in specific sectors, such as automotive spare parts (e.g., Saipa Yadak Company), remains underexplored. This study builds on this literature by examining the ERP-SCM-performance relationship in the context of Saipa Yadak Company, contributing to both theoretical and practical insights.
Research Hypotheses
I. ERP has a significant impact on the organizational performance of Saipa Yadak Company’s dealerships . II. ERP has a significant impact on the SCM of Saipa Yadak Company’s dealerships .
III. SCM has a significant impact on the organizational performance of Saipa Yadak Company’s dealerships . IV. SCM, as a mediating variable, enhances the positive impact of ERP on the organizational performance of Saipa Yadak Company.
Conceptual Model
Enterprise resource planning based on the model by Spathis and Constantinides [1], Organizational Performance based on Maletic [2], and SCM based on the study by Turulja [20].
This figure is shown in the PDF version of the article.
Research Methodology
The present study falls within the category of applied research. In terms of its objective, it is classified as applied research, aimed at utilizing the findings to address issues within Saipa Yadak Company. Regarding the data collection method (Research design), the study is descriptive (Describing the subject, phenomenon, or variable without manipulating or intervening in the situation, status, or role of variables) and correlational (Seeking to identify the existence of relationships between variables, though not necessarily establishing cause-and-effect relationships) .
Statistical Population
The statistical population of the study consists of employees of Saipa Yadak Company, totaling approximately 760 individuals, as confirmed by the organization’s human resources department. The sampling method used is convenience sampling .
Sample size
Based on the sample size formula for structural equation modeling and considering that the research questionnaire comprises 61 items, the minimum sample size is 305, and the maximum is 610. In this study, 340 questionnaires were distributed. After collection, 315 questionnaires were returned, and due to some being incomplete, 310 questionnaires were analyzed .
Data collection methods
To gather information for the theoretical framework, literature review, and identification of components, both library and field research methods were employed. Accordingly, two types of data collection tools were used: document and record analysis, and questionnaires, as described below :
Document and Record Analysis: To compile information for the theoretical framework and literature review, relevant resources, articles, and books were utilized .
Questionnaires
ERP questionnaire: designed and validated by Spathis and Constantinides [1], this questionnaire consists of 22 closed-ended items based on a five-point Likert scale. It covers dimensions such as process integration, inventory management, production planning, process control, and workflow management.
Organizational performance questionnaire: based on Maletic’s [2] standard model, this questionnaire assesses organizational performance through the following components: financial and market performance (5 items), quality performance (4 items), innovation performance (3 items), environmental performance (4 items), and social performance (3 items).
SCM questionnaire: this questionnaire comprises 20 items and four subscales: organizational infrastructure (4 items), information technology (7 items), decision support systems (4 items), and inter-organizational relationships (5 items). It is designed to evaluate SCM with respect to organizational infrastructure .
Types of research variables.
This table is shown in the PDF version of the article.
Data Analysis Method
After data collection, descriptive statistical techniques are initially employed to describe the data. Descriptive statistics, including minimum, maximum, mean, variance, skewness, and kurtosis, will be presented for the research variables. Subsequently, the research hypotheses will be tested using SPSS and SmartPLS software . Findings: In this section, the hypotheses are examined using the SmartPLS software.
Structural model of the study with factor loading coefficients.
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Structural model of the study with T_value.
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Model Fit
To evaluate the model fit, the measurement model fit, structural model fit, and overall model fit are assessed .
Cronbach’s Alpha and composite reliability
In accordance with the data analysis algorithm in PLS, after evaluating the factor loadings of the items, Cronbach’s alpha and composite reliability coefficients are calculated and reported. The results are presented in the table below.
Results of Cronbach’s Alpha and composite reliability for the study’s latent variables.
This table is shown in the PDF version of the article.
Given that the acceptable threshold for Cronbach's alpha and composite reliability is 0.7, and based on the findings presented in the above table, these criteria have been adequately met for the latent variables. Therefore, the reliability of the study can be confirmed as satisfactory.
Convergent validity
The second criterion for assessing the fit of measurement models is convergent validity, which examines the degree of correlation between each construct and its respective questions (Indicators).
Results of convergent validity for the study’s latent variables.
This table is shown in the PDF version of the article.
Given that the acceptable threshold for Average Variance Extracted (AVE) is 0.5, and according to the findings presented in the above table, this criterion has been adequately met for the latent variables. Consequently, the convergent validity of the study is confirmed as satisfactory.
R-Square (R²) criterion
The second criterion for evaluating the fit of a structural model in a study is the R² coefficients associated with the endogenous (Dependent) latent variables of the model. R² is a measure that indicates the effect of an exogenous variable on an endogenous variable, with threshold values of 0.19, 0.33, and 0.67 considered as benchmarks for weak, moderate, and strong R² values, respectively. As shown in Fig. 3, the R² values for the endogenous constructs of the study have been calculated. Based on these threshold values, the adequacy of the structural model’s fit can be confirmed.
Results of the R² criterion for endogenous constructs.
This table is shown in the PDF version of the article.
Overall model fit
Goodness Of Fit (GOF) criterion: To evaluate the overall model fit, the GOF criterion is used. Threshold values of 0.01, 0.25, and 0.36 are designated as indicators of weak, moderate, and strong GOF, respectively. This criterion is calculated using the following formula:
is derived from the average of the communality values of the latent variables in the study.
Communality and R² values for the study’s variables.
This table is shown in the PDF version of the article.
Results of the Overall Model fit.
This table is shown in the PDF version of the article.
Based on the obtained GOF value of 0.379, the overall model fit is confirmed to be highly satisfactory.
Structural Model FitSignificance Coefficients (t-values)
According to Fig. 3, the research hypotheses are confirmed as statistically significant at a 95% confidence level, given that the t-values obtained are greater than 1.96.
Results of direct relationships and significance coefficients for the research model hypotheses.
This table is shown in the PDF version of the article.
Additionally, the direct and indirect relationships between the research variables in the final research model, which indicate the extent of direct and indirect effects of the variables on each other, are presented in Table
Results of Research Hypotheses
Hypothesis 1. ERP affects the organizational performance of Saipa Yadak Company’s agency.
This hypothesis was measured using 22 questionnaire items related to ERP and 19 items related to organizational performance. The standardized coefficient (Path coefficient) between the two variables (ERP and organizational performance) was β = 0.326. The significance coefficient (t-statistic) between these variables was t = 5.383 (Greater than the absolute value of 1.96), indicating a statistically significant relationship. Therefore, the null hypothesis (H₀) is rejected, and the alternative hypothesis (H₁) is confirmed, suggesting that ERP significantly affects the organizational performance of Saipa Yadak Company.
Hypothesis 2. ERP affects the SCM of Saipa Yadak Company.
This hypothesis was measured using 22 questionnaire items related to ERP and 20 items related to SCM. The standardized coefficient (Path coefficient) between the two variables (ERP and SCM) was β=0.560. The significance coefficient (t-statistic) between these variables was t=12.188 (Greater than the absolute value of 1.96), indicating a statistically significant relationship. Therefore, the null hypothesis (H₀) is rejected, and the alternative hypothesis (H₁) is confirmed, suggesting that ERP significantly affects the SCM of Saipa Yadak Company. These findings are consistent with the results of Alvandi and Anzari [21].
Hypothesis 3. SCM affects the organizational performance of Saipa Yadak Company.
This hypothesis was measured using 20 questionnaire items related to SCM and 19 items related to organizational performance. The standardized coefficient (path coefficient) between the two variables (SCM and organizational performance) was β = 0.427. The significance coefficient (t-statistic) between these variables was t=6.868 (greater than the absolute value of 1.96), indicating a statistically significant relationship. Therefore, the null hypothesis (H₀) is rejected, and the alternative hypothesis (H₁) is confirmed, suggesting that SCM significantly affects the organizational-organizational performance of Saipa Yadak Company. These findings are consistent with the results of Nazeri et al. [22].
Hypothesis 4. SCM (As a mediating variable) enhances the positive effect of ERP on the organizational performance of Saipa Yadak Company.
Based on the results obtained in Chapter 4, the z-value from the Sobel test was 5.623, which is greater than 1.96. This indicates that, at a 95% confidence level, SCM (As a mediating variable) significantly enhances the positive effect of ERP on the organizational performance of Saipa Yadak Company.
Recommendations Based on Research FindingsRecommendations for Hypothesis 1 (ERP and Organizational Performance)
Greater attention should be paid to the quality of recruitment, internal transfers, promotions, and annual employee evaluations within the studied organization. Emphasis should also be placed on employee training, career path planning, compensation and benefits, welfare provisions, discipline, delegation of responsibilities, and fostering positive relationships between employees and managers.
The establishment of a dedicated human resources deputy position is recommended to enhance cohesion and prevent fragmentation in the company’s human resource management practices.
Recommendations for Hypothesis 2 (ERP and SCM)
Given the significant impact of collaboration on competitive advantage and organizational performance, it is recommended to foster a sincere and cooperative atmosphere among the components of the supply chain. Developing long-term programs to establish sustained communication with supply chain components, rather than relying on short-term or intermittent interactions, is advised.
Recommendations for Hypothesis 3 (SCM and Organizational Performance)
Increased focus on marketing and a more precise understanding of customer needs through various methods is deemed essential. Efforts should be made to enhance this aspect.
It is recommended to cultivate a sincere collaborative environment among supply chain components. Incorporating feedback from SCM components to improve programs and activities, as well as engaging with customers to obtain feedback, are common and effective practices.
Conclusion
This study investigated the impact of ERP on organizational performance, with SCM as a mediating factor, in the context of Saipa Yadak Company. The findings confirm that ERP significantly enhances organizational performance, as evidenced by a standardized path coefficient of β = 0.326 and a t-statistic of 5.383 (p<0.05), rejecting the null hypothesis (H₀) and supporting the alternative hypothesis (H₁). Additionally, ERP positively influences SCM (β=0.560, t=12.188, p<0.05), which in turn significantly affects organizational performance (β=0.427, t=6.868, p< 0.05). The mediating role of SCM was substantiated by the Sobel test, yielding a z-value of 5.623 (p < 0.05), indicating that SCM significantly strengthens the relationship between ERP and organizational performance at a 95% confidence level.
The study’s structural model demonstrated robust fit, supported by high Cronbach’s Alpha (0.789–0.884), composite reliability, convergent validity (AVE), and R² values, indicating that the constructs reliably capture the hypothesized relationships. Notably, SCM’s mediating effect underscores its importance in translating ERP’s technological capabilities into tangible performance benefits, such as improved operational efficiency, cost reduction, and market competitiveness.
For Saipa Yadak Company, these findings suggest that strategic investments in ERP systems, coupled with effective SCM practices, can significantly enhance organizational performance, particularly in the competitive automotive spare parts industry. The integration of green SCM practices, as supported by literature [23], could further amplify these benefits by addressing environmental concerns and improving sustainability outcomes. However, the study has limitations. The findings are context-specific to Saipa Yadak, limiting generalizability to other industries or regions. Additionally, the reliance on questionnaire-based data may introduce response biases, and the cross-sectional design restricts insights into long-term effects. Future research should explore longitudinal designs to assess the sustained impact of ERP and SCM integration. Moreover, investigating contextual factors, such as organizational size or industry type, and incorporating green SCM practices in the automotive sector could provide deeper insights into optimizing performance.
In conclusion, this study contributes to the literature by empirically validating the mediating role of SCM in the ERP-organizational performance relationship, offering practical implications for Saipa Yadak and similar firms. By aligning ERP and SCM strategies, organizations can achieve superior performance, reinforcing the strategic importance of integrated systems in today’s dynamic business environment.
Acknowledgments
The authors would like to express their sincere appreciation to all individuals whose valuable comments and support contributed to the successful completion of this study.
Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
Data Availability
The data supporting the findings of this study are available from the corresponding author upon reasonable request.
Refrences
[1] Spathis, C., & Constantinides, S. (2004). Enterprise resource planning systems’ impact on accounting processes. Business process management journal, 10(2), 234–247.
https://doi.org/10.1108/14637150410530280 [2] Maletič, M., Maletič, D., Dahlgaard, J. J., & Gomišček, B. (2009). Modeling organizational performance indicators. International journal of production economics, 122(1), 104–119.
https://doi.org/10.1016/j.ijpe.2009.02.017 [3] Momudi, J., Purreza, N., & Torbati, A. (2013). Strategic coordination of human resource management by strategic dynamic map. Human resources management, Imam Hussein (AS) comprehensive university, 3, 69-90. (In Persian). https://journals.ihu.ac.ir/article_11753.html [4] Mohammad Reza Fathi, Hossein Safari, A. F. (2013). Integration of graph theory and matrix approach with fuzzy ahp for equipment selection. Journal of industrial engineering and management, 6(2), 477–494. https://doi.org/10.3926/jiem.403 [5] Wright, P. M., & McMahan, G. C. (1992). Theoretical perspectives for strategic human resource management. Journal of management, 18(2), 295–320. https://doi.org/10.1177/014920639201800205 [6] Moghadam, M. B., & Tabriz, A. A. (2009). Statistical quality control. Saffar.
https://daneshnegar.com/fa/Product/4817 [7] Kaplan, R. S., & Norton, D. P. (2009). The balanced scorecard: Translating strategy into action. Harvard business review press.
https://books.google.com/books/about/The_Balanced_Scorecard.html?id=lxTJHAAACAAJ [8] Sadeghian, S. H., & Rahimnia, F. (2011). Strategic planning and the success of export companies. (In Persian). Organizational resource management research. https://ensani.ir/fa/article/231774 [9] Sadeghifar, J., Tofighi, S., Roshani, M., Toulideh, Z., Mohsenpour, S., & Jafari, M. (2017). An assessment of implementation and evaluation phases of strategic plans in Iranian hospitals. SAGE open medicine, 5, 2050312117736227. https://doi.org/10.1177/2050312117736227 [10] Sharma, R. (2014). Enterprise resource planning: Concepts and practice. New delhi: PHI learning pvt. ltd. https://b2n.ir/ps3720 [11] Hervani, A. A., Helms, M. M., & Sarkis, J. (2005). Performance measurement for green supply chain management. Benchmarking: An international journal, 12(4), 330–353.
https://doi.org/10.1108/14635770510609015 [12] Carter, C., Kale, R., & Grimm, C. (2000). Environmental purchasing and firm performance: An empirical investigation. Transportation research part e: Logistics and transportation review, 36, 219–228. https://doi.org/10.1016/S1366-5545(99)00034-4 [13] Ata, M., Al-Momani, B., Omar, M., & Al-Momani, N. (2017). The impact of using enterprise resource planning (ERP) systems on organization’s performance in jordanian industry companies. European journal of business and management, 9(24), 72–85.
https://www.iiste.org/Journals/index.php/EJBM/article/view/38245 [14] Yang, C., & Su, Y. (2009). The relationship between benefits of ERP systems implementation and its impacts on firm performance of SCM. Journal of enterprise information management, 22(6), 722–752. https://doi.org/10.1108/17410390910999602 [15] Setyadi, A. (2019). Role of enterprise resource planning and total quality management in supply chain organizational performance. International journal of supply chain management, 8(2), 1–6.
https://doi.org/10.59160/ijscm.v8i2.3000 [16] Sheng, X., Chen, L., Yuan, X., Tang, Y., Yuan, Q., Chen, R., … & Liu, H. (2023). Green supply chain management for a more sustainable manufacturing industry in China: A critical review. Environment, development and sustainability, 25(2), 1151–1183. https://doi.org/10.1007/s10668-022-02109-9 [17] Linda, M. R. S., Thabrani, G., Suhery, S., Yonita, R., Ravelby, T. A., Pujani, V., & Hasan, A. (2022). Organizational Performance: The Role of Enterprise Resource Planning and Supply Chain Management. AFEBI management and business review, 7(1), 47–56.
https://doi.org/10.47312/ambr.v7i01.558 [18] Santoso, R. W., Siagian, H., Tarigan, Z. J., & Jie, F. (2022). Assessing the benefit of adopting ERP technology and practicing green supply chain management toward operational performance: An evidence from Indonesia. Sustainability, 14(9), 49–44. https://doi.org/10.3390/su14094944 [19] Feng, T., Qamruzzaman, M., Sharmin, S. S., & Karim, S. (2024). Bridging environmental sustainability and organizational performance: The role of green supply chain management in the manufacturing industry. Sustainability, 16(14), 5918. https://doi.org/10.3390/su16145918 [20] Turulja, L., Celjo, A., Pejić Bach, M., & Bajgoric, N. (2024). ERP systems, knowledge management and organizational performance. Integrating erp systems and knowledge management: Improving information system adoption and enhancing business performance (pp. 75–91). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-68922-2_4 [21] Fazlinezhad, A., Alvandi Azari, M., & Bigdellu, L. (2016). Severe hypoplasia of posterior mitral valve leaflet presented with atypical chest pain: A case report. Razavi journal of medicine, 5(1), 22–23.
https://doi.org/10.30483/rijm.2016.118354 [22] Nazeri, A., Nosratpur, M., Asakare, Sh. (2017). The impact of supply chain quality management on performance due to rule of innovation. Iranian journal of management studies, 10(4), 189–206.
https://dor.isc.ac/dor/20.1001.1.17350794.1396.22.85.3.6 [23] Ahmad, A., Ikram, A., Ikram, A., Rehan, M. F., & Ahmad, A. (2022). Going green: Impact of green supply chain management practices on sustainability performance. Frontiers in psychology, 13.