Journal of Improvement Management

Journal of Improvement Management

A Comparative Analysis of the Performance of Petrochemical Companies in the Pars Special Economic Energy Zone with the Upstream Documents Governing the Petrochemical Industry

Document Type : Research Article (Original Article)

Authors
Sayed Milad Azimi 1
Gholamreza Ghoudarzi 2
Reza Tahmaseby Bolokabad 3
Mahdi Sadeghi Shahdani 4
Mohammad Hadi Zahedi Vafa 5
1 1. Ph.D. Candidate of Management of International Oil and Gas Contracts, Imam Sadegh University, Tehran, Iran.
2 Professor, Faculty of Islamic Studies and Management, Imam Sadiq University, Tehran, Iran.
3 Ph.D. Candidate of Management of International Oil and Gas Contracts, Imam Sadegh University, Tehran, Iran.
4 Professor, Department of Economic, Faculty of Islamic Studies and Economics, Imam Sadiq University, Tehran, Iran.
5 Associate Professor, Department of Economic, Faculty of Islamic Studies and Economics, Imam Sadiq University, Tehran, Iran.
10.22034/jmi.2024.474359.3127
Abstract
The Pars Special Economic Energy Zone was established to utilize the vast oil and gas resources of the South Pars region and to develop economic activities in the fields of oil, gas, and petrochemicals along the coastal strip of Assaluyeh and the Nayband Gulf. This zone, focusing on attracting investment and enhancing industrial production, hosts numerous petrochemical companies, with their activities being conducted within the framework of the governing policies and upstream documents of the petrochemical industry. The management of this zone, under the responsibility of the Pars Special Economic Energy Zone Organization, requires a precise understanding and assessment of these companies' performance based on the criteria and objectives defined in the upstream documents. In this article, by referring to these documents, key indicators were identified in four areas: production, sales, customers, and human resources, and five key metrics were extracted, including "nominal capacity," "production volume," "utilization rate," "domestic sales value," and "inter-plant sales volume." The performance of 20 petrochemical companies in the region was evaluated and ranked using multi-criteria decision-making (MADM) techniques and data normalization through the Shannon entropy method. Additionally, SAW and TOPSIS techniques were employed for the final assessment. The results indicate a significant performance gap between the companies, with "Pardis Company" ranked first and "Sabalan Industries" ranked last. These findings highlight the importance of aligning company performance with the objectives and indicators defined in the upstream documents and demonstrate that targeted evaluations can effectively improve performance and help achieve the macro objectives of the petrochemical industry.
Keywords
Domestic Sales Value
Inter-Complex Sales
Nominal Capacity
Pars Special Economic Energy Zone
Petrochemical
Utilization Rate
Subjects
امام جمعه زاده, محمد حامد, اکرمی, سعید, ارشادی, محمود. بررسی موانع تأمین مالی پروژه های EPCF در ایران: با نگاهی به تحولات قراردادهای EPCF کشور. بهبود مدیریت, 1399; 14(2): 117-145.
 doi: 10.22034/jmi.2020.113837
زرندی, سعید; عساکره, سجاد; سجاد; افشارپور; محسن (1394): مطالعه ی تطبیقی مدل های کسب و کار پلتفرم های برتر تأمین مالی جمعی در جهان. فصلنامه مدیریت توسعه فناوری 3 (3), صص. 127–150.
نجفی, علی; چیت ساز, احسان  (1400): خوشه بندی فناوری های مورد علاقه سرمایه‌گذاران در تأمین مالی جمعی بر اساس تکنیک های هوش مصنوعی. نخستین کنفرانس بین المللی بلاک چین رمز ارزها و اقتصاد جهانی. تهران. باشگاه نوآوری و فناوری یونسکو
نوروزی, محمد, عاشری, محمدرضا, بلادیان, سید مصطفی. عوامل حیاتی موفقیت در پیاده سازی پروژه های مشارکت عمومی- خصوصی در حوزه انرژی جمهوری اسلامی ایران (مورد مطالعه: صنعت برق). بهبود مدیریت, 1394; 9(3): 83-108.
Agrawal, Ajay; Catalini, Christian; Goldfarb, Avi (2015): Crowdfunding: Geography, social networks, and the timing of investment decisions. In Journal of Economics & Management Strategy 24 (2), pp. 253–274.
Agrawal, Rakesh; Imieliński, Tomasz; Swami, Arun (Eds.) (1993): Proceedings of the 1993 ACM SIGMOD international conference on Management of data. Proceedings of the 1993 ACM SIGMOD international conference on Management of data.
Amendomar, A. D.A. (2015): O crowdfunding de recompensas como alternativa de capital empreendedor para EBTs no Brasil: um estudo descritivo-exploratório. 2015. 227 f. Dissertação (Mestrado em Administração)‐Faculdade de Economia, Administração …
Andreas Kaplan; Michael Haenlein (2019): Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. In Business Horizons 62 (1), pp. 15–25. DOI: 10.1016/j.bushor.2018.08.004.
Best, Jason; Neiss, Sherwood; Swart, Richard; Lambkin, Anthony; Raymond, Sam (2013): Crowdfunding’s potential for the developing world. The World Bank.
Brynjolfsson, Erik; Mcafee, ANDREW (2017): Artificial intelligence, for real. In Harvard business review 1, pp. 1–31.
Chang, C.-L., & Lee, Y.-S. (2023). Predicting the Success Rate of Reward-Based Crowdfunding Campaigns: Evidence from Machine Learning. International Journal of Information & Management Sciences, 34 (3).
Chen, M.-Y., Chang, J.-R., Chen, L.-S., & Shen, E.-L. (2022). The key successful factors of video and mobile game crowdfunding projects using a lexicon-based feature selection approach. Journal of Ambient Intelligence and Humanized Computing, 1–19.
Chen, Ming-Syan; Park, Jong Soo; Yu, Philip S. (Eds.) (1996): Data mining for path traversal patterns in a web environment. Proceedings of 16th International Conference on Distributed Computing Systems. IEEE.
Cheng, Chaoran; Tan, Fei; Hou, Xiurui; Wei, Zhi (2019): Success Prediction on Crowdfunding with Multimodal Deep Learning. In : Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19: International Joint Conferences on Artificial Intelligence Organization, pp. 2158–2164. Available online at https://doi.org/10.24963/ijcai.2019/299.
Chen, X., Ding, H., Fang, S., & Chen, W. (2022). Predicting the Success of Internet Social Welfare Crowdfunding Based on Text Information. Applied Sciences, 12(3), 1572.
Deng, Lingfei; Ye, Qiang; Xu, DaPeng; Sun, Wenjun; Jiang, Guangxin (2022): A literature review and integrated framework for the determinants of crowdfunding success. In Financ Innov 8 (1), p. 955. DOI: 10.1186/s40854-022-00345-6.
Drover, Will; Busenitz, Lowell; Matusik, Sharon; Townsend, David; Anglin, Aaron; Dushnitsky, Gary (2017): A review and road map of entrepreneurial equity financing research: venture capital, corporate venture capital, angel investment, crowdfunding, and accelerators. In Journal of management 43 (6), pp. 1820–1853.
Felipe, Israel José dos Santos; Ferreira, Bruno César Franca (2020): Determinants of the success of equity crowdfunding campaigns. In Rev. contab. finanç. 31 (84), pp. 560–573. DOI: 10.1590/1808-057x202010460.
Kaminski, Jermain C.; Hopp, Christian (2020): Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals. In Small Business Economics 55 (3), pp. 627–649.
Kao, Shu-Chen (2021a): A crowdfunding prediction model: a data-driven approach. In : The 8th Multidisciplinary International Social Networks Conference, pp. 63–70.
Kaplan, Andreas; Haenlein, Michael (2020): Rulers of the world, unite! The challenges and opportunities of artificial intelligence. In Business Horizons 63 (1), pp. 37–50.
Lambert, Thomas; Schwienbacher, Armin (2010): An empirical analysis of crowdfunding. In Social Science Research Network 1578175 (1), p. 23.
Lukkarinen, Anna; Teich, Jeffrey E.; Wallenius, Hannele; Wallenius, Jyrki (2016): Success drivers of online equity crowdfunding campaigns. In Decision Support Systems 87, pp. 26–38.
McCarthy, John; Minsky, Marvin L.; Rochester, Nathaniel; Shannon, Claude E. (2006): A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. In AI magazine 27 (4), p. 12.
Mollick, Ethan (2014): The dynamics of crowdfunding: An exploratory study. In Journal of Business Venturing 29 (1), pp. 1–16.
Moy, N., Chan, H. F., Septianto, F., Mathmann, F., & Torgler, B. (2024). Confidence is Good? too Much, not so Much: Exploring the effects on crowdfunding success. Journal of Business Research, 182, 114711.
Oduro, M. S., Yu, H., & Huang, H. (2022). Predicting the entrepreneurial success of crowdfunding campaigns using model-based machine learning methods. International Journal of Crowd Science, 6(1), 7–16.
Pekar, V., Candi, M., Beltagui, A., Stylos, N., & Liu, W. (2024). Explainable text-based features in predictive models of crowdfunding campaigns. Annals of Operations Research, 1–31.
Peng, Nianjiao; Zhou, Xinlei; Niu, Ben; Feng, Yuanyue (2021): Predicting Fundraising Performance in Medical Crowdfunding Campaigns Using Machine Learning. In Electronics 10 (2), p. 143.
Shi, Jiatong; Yang, Kunlin; Xu, Wei; Wang, Mingming (2021): Leveraging deep learning with audio analytics to predict the success of crowdfunding projects. In The Journal of Supercomputing 77 (7), pp. 7833–7853.
Silva, Lafaiet; Silva, Nádia Félix; Rosa, Thierson (2020): Success prediction of crowdfunding campaigns: a two-phase modeling. In International Journal of Web Information Systems 16 (4), pp. 387–412. DOI: 10.1108/IJWIS-05-2020-0026.
technavio (2022): Crowdfunding Market. Crowdfunding Market Growth, Size, Trends, Analysis Report by Type, Application, Region and Segment Forecast 2022-2026. Available online at https://www.technavio.com/report/crowdfunding-market-industry-service-analysis.
Vismara, Silvio (2019): Sustainability in equity crowdfunding. In Technological Forecasting and Social Change 141, pp. 98–106. DOI: 10.1016/j.techfore.2018.07.014.
Voorbraak, KJPM; Tanrisever, F.; Mahieu, R. J. (2011): Crowdfunding for financing new ventures: consequences of the financial model on operational decisions: Technische Universiteit Eindhoven.
Yang, K.-F., Lin, Y.-R., & Chen, L.-S. (2023). Discovering Critical Factors in the Content of Crowdfunding Projects. Algorithms, 16(1), 51.
Yeh, Jen-Yin; Chen, Chi-Hua (2020): A machine learning approach to predict the success of crowdfunding fintech project. In JEIM 16820 (7-8), p. 1. DOI: 10.1108/JEIM-01-2019-0017.
Yu, Pi-Fen; Huang, Fu-Ming; Yang, Chuan; Liu, Yu-Hsin; Li, Zi-Yi; Tsai, Cheng-Hung (2018): Prediction of crowdfunding project success with deep learning. In : 2018 IEEE 15th international conference on e-business engineering (ICEBE). IEEE, pp. 1–8.
Zhang, Qizhen; Ye, Tengyuan; Essaidi, Meryem; Agarwal, Shivani; Liu, Vincent; Loo, Boon Thau (2017): Predicting startup crowdfunding success through longitudinal social engagement analysis. In : Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1937–1946.
Zhong, C., Xu, W., & Du, W. (2022). Success prediction of crowdfunding campaigns with project network: A machine learning approach. Journal of Electronic Commerce Research, 23(2), 99–11

  • Receive Date 20 August 2024
  • Revise Date 22 December 2024
  • Accept Date 26 December 2024