Skip to content
Research Article Open access CC BY 4.0

Textile and Garment Sector Financial Distress and Its Prediction: A Systematic Indonesian Literature Review

. Muhani, Subur Karyatun, . Molina, . Elwisam, Kadek Wiweka

Journal of Economics, Management and Trade · pp. 7–18 · Published 14 Jun 2022

10.9734/jemt/2022/v28i730421

Abstract

Aims: The purpose of this study is to examine the conceptualization of financial distress research in the textile and apparel industries, particularly in terms of research scope and methodology. Furthermore, this article attempts to systematically analyze the network formed by these literatures. Methodology: In this study, a qualitative approach was used through the literature review method, with 41 specific articles about financial distress in the textile and garment sector serving as the research corpus and drawn from the Litmaps database. To interpret and describe the frequency patterns and relationships visualized using RStudio and Gephi devices, text mining, network analysis, and content analysis were used. Results: This study discovers that a frequently discussed issue is the influence of financial variables, both dependent and independent, on the prediction of financial distress or vice versa, using various quantitative approaches and models of financial distress. This claim is supported by the findings of a systematic analysis, which reveals a positive correlation between global cloud output and network analysis. Implication/Applications: The corpus aspect of this research is limited, and the research scope is limited to the Indonesian context. Future research with broader literature sources and different types of company sectors is highly anticipated. This literature review can provide a comprehensive framework for researchers and practitioners who are interested in cases of financial distress. The Originality of the Study: Furthermore, this is a recent study that conducts a systematic review of the literature on financial distress in Indonesian textile and garment companies.

Financial distress textile and garment sector literature review systematic network analysis

References (44)

  1. 1 Social and Economic Networks [DOI]
  2. 2 METODE PENELITIAN KUANTITATIF DAN KUALITATIF
  3. 3 Financial Distress and Corporate Performance [DOI]
  4. 4 Cultural tourism: A review of recent research and trends [DOI]
  5. 5 Text Mining Infrastructure in R [DOI]
  6. 6 Big data in tourism research: A literature review [DOI]
  7. 7 Financial distress, reorganization, and organizational efficiency [DOI]
  8. 8 A Guide to Writing the Dissertation Literature Review [DOI]
  9. 9 20 years of research on virtual reality and augmented reality in tourism context: A text-mining approach [DOI]
  10. 10 Tourism Research on Sustainability: A Bibliometric Analysis [DOI]
  11. 11 The early stages of financial distress [DOI]
  12. 12 Doing a Literature Review [DOI]
  13. 13 Estimating financial distress likelihood [DOI]
  14. 14 AntConc: design and development of a freeware corpus analysis toolkit for the technical writing classroom [DOI]
  15. 15 A Survey of Text Mining in Social Media: Facebook and Twitter Perspectives [DOI]
  16. 16 The Essence of Research Methodology: A Concise Guide for Master and PhD Students in Management Science [DOI]
  17. 17 Using Text Mining Techniques for Extracting Information from Research Articles [DOI]
  18. 18 Sentiment analyses of multilingual tweets on halal tourism [DOI]
  19. 19 A Text Mining and Multidimensional Sentiment Analysis of Online Restaurant Reviews [DOI]
  20. 20 Financial distress in the hospitality industry during the Covid-19 disaster [DOI]
  21. 21 Tourism information technology research trends: 1990-2016 [DOI]
  22. 22 Making sense of customer service experiences: a text mining review [DOI]
  23. 23 Research in Sustainable Tourism: A Longitudinal Study of Articles between 2008 and 2017 [DOI]
  24. 24 Intelligent Natural Language Processing: Trends and Applications [DOI]
  25. 25 Industry specific financial distress modeling [DOI]
  26. 26 Predicting financial distress of the South Korean manufacturing industries [DOI]
  27. 27 Corporate Financial Distress [DOI]
  28. 28 Improving access to health information for older migrants by using grounded theory and social network analysis to understand their information behaviour and digital technology use [DOI]
  29. 29 Factors Influencing Customers' Satisfaction and Dissatisfaction with Hotels: A Text-Mining Approach [DOI]
  30. 30 THE CHANGES IN FOOD CONSUMPTION BEHAVIOR: A RAPID OBSERVATIONAL STUDY OF COVID-19 PANDEMIC [DOI]
  31. 31 Sentiment Analysis of Movies on Social Media using R Studio [DOI]
  32. 32 Word Cloud of Online Hotel Reviews in Myanmar for Customer Satisfaction Analysis [DOI]
  33. 33 EKSPLORATORI TUJUAN MANAJEMEN KEUANGAN BISNIS HIJAU
  34. 34 The Models of Financial Distress [DOI]
  35. 35 EXPLORING THE EFFECT OF CORPORATE SOCIAL RESPONSIBILITY, LEVERAGE, AND INTELLECTUAL CAPITAL ON FINANCIAL PERFORMANCE (EMPIRICAL EVIDENCE FROM BANKING SECTOR COMPANIES PERIOD 2015-2019) [DOI]
  36. 36 Indonesia Tourism Enterprises' Stock Returns Research: Looking Back and Moving Forward [DOI]
  37. 37 ANALISIS PENGARUH STOCK SPLIT TERHADAP LIKUIDITAS SAHAM DAN RETURN SAHAM
  38. 38 Systematic Approaches to a Successful Literature Review [DOI]
  39. 39 A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques [DOI]
  40. 40 UNIVERSITAS MUHAMMADIYAH SURAKARTA
  41. 41 GEPHI: Introduction to Network Analysis and Visualisation
  42. 42 Income Loss and Financial Distress during COVID-19: The Protective Role of Liquid Assets [DOI]
  43. 43 2016 5th IIAI International Congress on Advanced Applied Informatics (IIAI-AAI) [DOI]
  44. 44 IPCC 2005. Proceedings. International Professional Communication Conference, 2005. [DOI]

Cited by 3

Turnaround success for textile and garment companies in the COVID-19 pandemic era

Mita Indah Suci Wulandari, Fidiana Fidiana · VNU University of Economics and Business · 2024

Showing 2 of 3 known citations — external sources report more than can currently be individually listed.

Article metrics

Real usage data collected on this platform.

44

Page views

0

PDF downloads

13

Outbound clicks

3

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.