IDEAS home Printed from https://ideas.repec.org/a/iaf/journl/y2023i4p81-90.html

Factors Affecting Going Concern of the Transport and Logistics Companies

Author

Listed:
  • Putri Dwi Wahyuni

    (Universitas Mercu Buana, Jakarta, Indonesia)

  • Febrina Mahliza

    (Universitas Mercu Buana, Jakarta, Indonesia)

  • Agustine Dwianika

    (Universitas Pembangunan Jaya, South Tangerang, Indonesia)

Abstract

The COVID-19 pandemic reminded accountants and auditors of one of the most important concepts of modern accounting - the going concern assumption. Going concern refers to a company's ability to make enough money to stay afloat or to avoid bankruptcy. Based on the application of agency theory and signalling theory this study aimed to examine the influence of GCG practices, financial condition and company growth on the going concern of transportation and logistics service sector companies listed on the IDX. This study is associative research with secondary data obtained from the annual report on the IDX website and CGC reports. The data collected for analysis covers the period from 2019-2022. Going concern is proxied by going concern opinion with dummies 1 and 0. GCG practices are measured using disclosure quality based on the CG index covering GCG structures and processes and the performance of its issuers in terms of internal control units, remuneration, and Corporate Secretary. Financial condition is measured using the Altman Z-score prediction model. Corporate growth is proxied by calculating the sales growth ratio based on each corporation's profit/loss statement. The research sample is transportation and logistics service sector companies listed on the IDX in 2019-2022, amounting to 20 companies with a total observation data of 80. The data testing in this study uses logistic regression analysis to determine the predictive power of these financial ratios, which are the most dominant in determining whether a corporation will receive a going concern audit opinion. The researchers found that only financial condition significantly affects the company's going concern in this study. The negative coefficient direction means that transportation and logistics service sector companies that experience poor financial conditions still get a non-going concern opinion where management can convince stakeholders and auditors that they can still maintain their business sustainability. Meanwhile, good corporate governance practices do not significantly affect going concern because investors take into account many other factors to consider the sustainability of the company's business. Company growth also has no significant effect on going concern because companies with profit growth also do not guarantee that the company is free from financial problems that may affect its business continuity.

Suggested Citation

  • Putri Dwi Wahyuni & Febrina Mahliza & Agustine Dwianika, 2023. "Factors Affecting Going Concern of the Transport and Logistics Companies," Oblik i finansi, Institute of Accounting and Finance, issue 4, pages 81-90, December.
  • Handle: RePEc:iaf:journl:y:2023:i:4:p:81-90
    DOI: 10.33146/2307-9878-2023-4(102)-81-90
    as

    Download full text from publisher

    File URL: http://www.afj.org.ua/pdf/1028-faktori-scho-vplivayut-na-bezperervnist-diyalnosti-transportnih-i-logistichnih-kompaniy.pdf
    Download Restriction: no

    File URL: http://www.afj.org.ua/en/article/1028/
    Download Restriction: no

    File URL: https://libkey.io/10.33146/2307-9878-2023-4(102)-81-90?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    2. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    Full references (including those not matched with items on IDEAS)

    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. Barbara Su, 2023. "Banking practices and borrowing firms’ financial reporting quality: evidence from bank cross-selling," Review of Accounting Studies, Springer, vol. 28(1), pages 201-236, March.
    2. Matthew Smith & Francisco Alvarez, 2022. "Predicting Firm-Level Bankruptcy in the Spanish Economy Using Extreme Gradient Boosting," Computational Economics, Springer;Society for Computational Economics, vol. 59(1), pages 263-295, January.
    3. Premachandra, I.M. & Bhabra, Gurmeet Singh & Sueyoshi, Toshiyuki, 2009. "DEA as a tool for bankruptcy assessment: A comparative study with logistic regression technique," European Journal of Operational Research, Elsevier, vol. 193(2), pages 412-424, March.
    4. José Carlos Trejo García & Humberto R�os Bol�var & Francisco Almagro V�zquez, 2016. "Actualización del modelo de riesgo crediticio, una necesidad para la banca revolvente en México," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, vol. 8(1), pages 17-30.
    5. Shaikh, Ibrahim A. & O'Brien, Jonathan Paul & Peters, Lois, 2018. "Inside directors and the underinvestment of financial slack towards R&D-intensity in high-technology firms," Journal of Business Research, Elsevier, vol. 82(C), pages 192-201.
    6. José-Luis Peydró [AP BACKUP – NOW EXTERNAL] & Mikel Bedayo & Raquel Vegas & Gabriel Jiménez & José-Luis Peydró, 2020. "Screening and Loan Origination Time: Lending Standards, Loan Defaults and Bank Failures," Working Papers 1215, Barcelona School of Economics.
    7. repec:ags:aolpei:309925 is not listed on IDEAS
    8. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    9. Ruey-Ching Hwang, 2013. "Forecasting credit ratings with the varying-coefficient model," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1947-1965, December.
    10. Beck, Thorsten & Laeven, Luc, 2006. "Resolution of failed banks by deposit insurers : cross-country evidence," Policy Research Working Paper Series 3920, The World Bank.
    11. Antonio Davila & George Foster & Xiaobin He & Carlos Shimizu, 2015. "The rise and fall of startups: Creation and destruction of revenue and jobs by young companies," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 6-35, February.
    12. Jonas Heese, 2017. "The Role of Overbilling in Hospitals’ Earnings Management Decisions," Harvard Business School Working Papers 18-026, Harvard Business School.
    13. Masahiro Enomoto, 2018. "Effects of Corporate Governance on the Relationship between Accounting Quality and Trade Credit: Evidence from Japan," Discussion Paper Series DP2018-12, Research Institute for Economics & Business Administration, Kobe University, revised Dec 2023.
    14. Ye, Dezhu & Ji, Wenjun & Sun, Nan, 2025. "Corporate diversification strategies and bankruptcy risk: A re-examination based on COVID-19," Journal of Asian Economics, Elsevier, vol. 99(C).
    15. Knyazeva, Anzhela & Knyazeva, Diana, 2012. "Does being your bank’s neighbor matter?," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1194-1209.
    16. Chen, Peimin & Wu, Chunchi, 2014. "Default prediction with dynamic sectoral and macroeconomic frailties," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 211-226.
    17. Barth, Mary E. & Beaver, William H. & Landsman, Wayne R., 2001. "The relevance of the value relevance literature for financial accounting standard setting: another view," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 77-104, September.
    18. Bonfim, Diana, 2009. "Credit risk drivers: Evaluating the contribution of firm level information and of macroeconomic dynamics," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 281-299, February.
    19. Xin Chang & Shi Hua Lin & Lewis H. K. Tam & George Wong, 2010. "Cross‐sectional determinants of post‐IPO stock performance: evidence from China," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 50(3), pages 581-603, September.
    20. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    21. Sanghoon Lee & Keunho Choi & Donghee Yoo, 2020. "Predicting the Insolvency of SMEs Using Technological Feasibility Assessment Information and Data Mining Techniques," Sustainability, MDPI, vol. 12(23), pages 1-17, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • M11 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - Production Management
    • M40 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting - - - General
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

    Statistics

    Access and download statistics

    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:iaf:journl:y:2023:i:4:p:81-90. 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: Serhii Ostapchuk (email available below). General contact details of provider: https://edirc.repec.org/data/iafkvua.html .

    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.