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

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.

A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction

“…Market-based measures of interconnectedness have been used to investigate the relationship between financial stability and interconnectedness, with extensive literature reviews discussing various financial network models [41]. Additionally, qualitative approaches through literature reviews have been used to systematically analyze the network formed by financial distress literature in specific sectors [42]. Furthermore, network analysis has been utilized to predict bank distress and to provide support for measures of interconnectedness in early-warning models, aiming to capture the interconnectedness among financial entities that could trigger the formation of contagion channels [43], [44].…”

“…In the context of specific sectors, such as the textile and garment industry, network analysis has been employed to predict financial distress and its impact, aiming to determine the effect of profitability, liquidity, and solvency on financial distress in these sectors [49], [42]. Furthermore, network analysis has been applied to predict financial distress in various sectors, including infrastructure, utilities, transportation, and retail trade, using financial ratios and macroeconomic variables as independent variables [50], [51], [45], [52].…”