IDEAS home Printed from https://ideas.repec.org/a/arp/tjssrr/2018p188-193.html
   My bibliography  Save this article

Modeling of Economic Effects of commercialization of High-Tech Developments at Small Innovative Enterprises of Polymer Profile

Author

Listed:
  • I. L. Beilin*

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • V. V. Khomenko

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • N. M. Yakupova

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • E. I. Kadochnikova

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • D. D. Aleeva

    (Kazan Federal University, Institute of Management, Economics and Finance)

Abstract

On the basis of the approximating polynomial, a three-factor model for managing the sustainability of an innovative chemical project is presented in the context of economic uncertainty. Economic uncertainty in the chemical sector can be caused by intra- and external economic and political, investment, innovative, opportunistic, commercial, raw materials, industry and other factors. In the developed model, isoline levels show simultaneously a better ratio of the three economic characteristics of the innovation project across the entire range of the planning matrix, and also provide the ability to predict the net present value and return on the project’s capital. Since the end of the 20th century, in the international business environment, it has been thought that a company can gain an advantage in its industry, outrunning competitors, offering superior products or being a price leader. It was accepted as a fact that a company can compete only in two of these three areas. Historically, the product life cycle began when the company (usually the market leader) first introduced its new offerings to the market. Then competitors offered similar products of higher quality, then companies appeared on the market offering similar quality at a more attractive price. Japanese manufacturers such as Toyota and Sony have shown that companies can compete in all three strategies simultaneously and become industry leaders. Traditional business has realized that "faster", "better", "cheaper" are not the only variables that consumers weigh when making purchasing decisions. To dominate the industry, organizations must constantly create innovation and remain flexible, able to confidently pursue strategic initiatives, including alliances, acquisitions, outsourcing and global expansion. Companies also need funds to consolidate their business during economic downturns, using cost-effective new tools for integrating business processes. To achieve high results, the executive management must first have control processes and accurate information to make informed decisions to adjust and restructure the strategic course. After making decisions, projects to optimize business processes require the company to study and use opportunities to reduce costs, cycle time, improve the level of service or product quality.

Suggested Citation

  • I. L. Beilin* & V. V. Khomenko & N. M. Yakupova & E. I. Kadochnikova & D. D. Aleeva, 2018. "Modeling of Economic Effects of commercialization of High-Tech Developments at Small Innovative Enterprises of Polymer Profile," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 188-193:5.
  • Handle: RePEc:arp:tjssrr:2018:p:188-193
    as

    Download full text from publisher

    File URL: https://www.arpgweb.com/pdf-files/spi5.43.188.193.pdf
    Download Restriction: no

    File URL: https://www.arpgweb.com/journal/7/special_issue/12-2018/5/4
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    2. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    3. Christian Richter Østergaard & Eunkyung Park, 2015. "What Makes Clusters Decline? A Study on Disruption and Evolution of a High-Tech Cluster in Denmark," Regional Studies, Taylor & Francis Journals, vol. 49(5), pages 834-849, May.
    4. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yongqi Feng & Haolin Zhang & Yung-ho Chiu & Tzu-Han Chang, 2021. "Innovation efficiency and the impact of the institutional quality: a cross-country analysis using the two-stage meta-frontier dynamic network DEA model," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3091-3129, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    2. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    3. Mousavi, Mohammad M. & Ouenniche, Jamal & Xu, Bing, 2015. "Performance evaluation of bankruptcy prediction models: An orientation-free super-efficiency DEA-based framework," International Review of Financial Analysis, Elsevier, vol. 42(C), pages 64-75.
    4. Õie Renata Siimon & Oliver Lukason, 2021. "A Decision Support System for Corporate Tax Arrears Prediction," Sustainability, MDPI, vol. 13(15), pages 1-23, July.
    5. Hyunjung Nam & Won Gyun No & Youngsu Lee, 2017. "Are Commercial Financial Databases Reliable? New Evidence from Korea," Sustainability, MDPI, vol. 9(8), pages 1-23, August.
    6. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    7. I. L. Beilin* & V. V. Khomenko & N. M. Yakupova & E. I. Kadochnikova & D. D. Aleeva, 2018. "Managing the Production Program of a Small Innovative Chemical Enterprise in the Face of Changing Demand," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 175-180:5.
    8. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
    9. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    10. repec:ctc:sdimse:dime19_03 is not listed on IDEAS
    11. du Jardin, Philippe, 2015. "Bankruptcy prediction using terminal failure processes," European Journal of Operational Research, Elsevier, vol. 242(1), pages 286-303.
    12. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Post-Print halshs-01281948, HAL.
    13. Alessandra Amendola & Marialuisa Restaino & Luca Sensini, 2010. "Variabile Selection in Forecasting Models for Corporate Bankruptcy," Working Papers 3_216, Dipartimento di Scienze Economiche e Statistiche, Università degli Studi di Salerno.
    14. Zeineb Affes & Rania Hentati-Kaffel, 2016. "Predicting US banks bankruptcy: logit versus Canonical Discriminant analysis," Documents de travail du Centre d'Economie de la Sorbonne 16016, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    15. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    16. Ana GARCÍA-GALLEGO & María-Jesús MURES-QUINTANA, 2016. "Principal Components And Canonical Correlation Analyses As Complementary Tools. Application To The Processing Of Financial Information," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 50(4), pages 249-266.
    17. Lukason, Oliver & Laitinen, Erkki K., 2019. "Firm failure processes and components of failure risk: An analysis of European bankrupt firms," Journal of Business Research, Elsevier, vol. 98(C), pages 380-390.
    18. Tomasz Korol, 2019. "Dynamic Bankruptcy Prediction Models for European Enterprises," JRFM, MDPI, vol. 12(4), pages 1-15, December.
    19. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    20. Nora Muñoz-Izquierdo & María-del-Mar Camacho-Miñano & María-Jesús Segovia-Vargas & David Pascual-Ezama, 2019. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence," IJFS, MDPI, vol. 7(2), pages 1-23, April.
    21. Theodore Metaxas & Athanasios Romanopoulos, 2023. "A Literature Review on the Financial Determinants of Hotel Default," JRFM, MDPI, vol. 16(7), pages 1-19, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arp:tjssrr:2018:p:188-193. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Managing Editor (email available below). General contact details of provider: http://arpgweb.com/?ic=journal&journal=7&info=aims .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.