Business Failure Research Articles

A state-of-the-art appraisal of bankruptcy prediction models focussing on the field’s core authors: 2010–2022

PurposeThe primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.Design/methodology/approachThe authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.FindingsThis paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.Research limitations/implicationsThe authors identify several gaps in the literature which this research does not address but could be the focus of future research.Practical implicationsThe authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.Social implicationsBankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.Originality/valueTo the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Leveraging business failure to drive eco‐innovation adoption: An integrated conceptual framework

AbstractDespite a growing body of research on business failure and eco‐innovation, these two streams of research have developed in isolation, thus lacking an organizing framework to account for how businesses can utilize peer companies' failures as a source of eco‐innovation. This study addresses this gap in the current literature by advancing an integrated conceptual framework that illuminates failure as a catalyst for learning and the mechanisms through which organizations can enhance their environmental innovation efforts and competitiveness. The study proposes a multidimensional 2 × 2 typology encompassing dimensions of process and product eco‐innovation, alongside two underlying factors of business failure. These factors further elucidate the mechanisms through which organizations can learn from others' failures, ultimately becoming more resilient and adaptable in the face of new challenges. The implications of this analysis for future research and practice will be further examined, shedding light on promising domains for learning from failure and fostering innovation.

Comparative Analysis of Machine Learning Models for Bankruptcy Prediction in the Context of Pakistani Companies

This article presents a comparative analysis of machine learning models for business failure prediction. Bankruptcy prediction is crucial in assessing financial risks and making informed decisions for investors and regulatory bodies. Since machine learning techniques have advanced, there has been much interest in predicting bankruptcy due to their capacity to handle complex data patterns and boost prediction accuracy. In this study, we evaluated the performance of various machine learning algorithms. We collect comprehensive data comprising financial indicators and company-specific attributes relevant to the Pakistani business landscape from 2016 through 2021. The analysis includes AdaBoost, decision trees, gradient boosting, logistic regressions, naive Bayes, random forests, and support vector machines. This comparative analysis provides insights into the most suitable model for accurate bankruptcy prediction in Pakistani companies. The results contribute to the financial literature by comparing machine learning models tailored to anticipate Pakistani stock market insolvency. These findings can assist financial institutions, regulatory bodies, and investors in making more informed decisions and effectively mitigating financial risks.

Efficiency of Working Capital & Assets Management in the Function of SMEs Bankruptcy Prediction

Research Question: This research has been conducted with the aim of determining whether it is possible to generate a model that can reliably predict bankruptcy of Serbian small and medium-sized enterprises (SMEs) using Working Capital Management (WCM) and Asset Management (AM) efficiency ratios. Motivation: Motive for this research is the fact there are not many business failure prediction models related to SMEs. In addition, existing models are not focused on efficiency of the two above mentioned categories. Also, previously developed models, especially traditional and ground-breaking ones, are not necessarily aligned with accounting procedures and economic environment of all countries, which indicates the need to develop a model that is adapted for Serbian territory. Idea: The idea is to develop a model that has the ability to predict whether an entity has a tendency to initiate bankruptcy proceedings in the next year. This is useful both for external stakeholders and for SMEs’ owners themselves, as it allows them to better manage resources. Data: The research was conducted on a sample of 100 Serbian SMEs. Data for the calculation of ratio indicators is available on the Business Registers Agency webpage. Tools: The research was conducted as a combination of financial and statistical analysis instruments. Ratio indicators were used for financial analysis part, while statistical analysis was conducted in SPSS program v.26 and includes logistic (binary) regression. Findings: Results of the research indicate that AM efficiency indicators are suitable for SMEs bankruptcy prediction modelling, but also indicate that WCM ratios don’t have great contribution in bankruptcy prediction for Serbian SMEs. A model that has a classification accuracy of 79% has been developed. Contribution: This research empirically tests how selected ratio indicators support SMEs bankruptcy prediction, and therefore can be beneficial for all SMEs stakeholders, but also other researches since the research methodology is explained in details.

The Liquefied Natural Gas (LNG) Puzzle: A Singular and Misunderstood Commodity in World Trade

The ocean‐going trade in liquefied natural gas (LNG) is more than half a century old. The first US LNG project began commercial operation in 1971. World trade in LNG arose as countries and regions with warm climates, small populations, and no ready application for such a fuel as natural gas, like Algeria, Qatar, and Western Australia, sought to monetize their local natural resources by building large export facilities (usually at state expense) targeting industrial countries with cold winters and without indigenous fossil fuels, like Japan, Korea, parts of Europe, and New England. But great costs, complexities, paradoxes, and business failures have always accompanied the international LNG trade. Age‐old rules of thumb for other commodities in world trade do not apply to LNG.

고령친화형 서비스 융합의 시니어 주거단지 개발 방안 연구

This study is to find ways to respond to Korea’s low-growth and aging issues with suggesting a future residential complex model through advanced domestic and international cases. The 50+ community shows a new vision for building a preemptive housing system, spreading private-led service supply, and further creating an “Aging In Place” environment.
 Active seniors in their 50s and 60s are the generations not only with relatively large assets, but also with poor economic and residential conditions due to divorce, business failure, and unemployment. It is also necessary to develop community-based social support service system to support housing, stable income and leisure activities, and create premium housing services to fulfill their unmet needs and trendy lifestyles especially for active seniors with high income.
 In the United States, the senior housing business is expanding centering on large private construction companies’ franchises. In addition, various service infrastructures, such as 50+ community, independent living, assisted living, nursing home, and dementia care home, are built together in a huge complex according to the consumer’s requirements. In Europe, senior citizens live in flat, apartments, condos, share homes, or houses and use community-based social welfare service and programs. The famous European retirement communities are usually surrounded by the beautiful natural environment with luxurious resort facilities.
 The 50 plus community is a preemptive model that can contribute to the creation of the “Aging In Place” environment in Korea. It is also necessary to develop a new ageing-friendly residential complex model for encouraging investments of the private sectors. A private-led 50+ community model is to be differentiated from the public-centered model which mainly focus on supporting daily living services for super-aged, low-income, and disabled seniors. The preemptive new model may be triggering a new senior housing business and solve the social problems in this super-aged society.

TECHNOLOGY ADOPTION, SELF-EFFICACY AND ENTREPRENEURIAL BUSINESS SUCCESS

Background and Purpose: The success and demise of a business venture continuously fascinates the attention of many. Specifically, when it relates to micro-entrepreneurs as they are very vulnerable to business failures. Despite many struggles, there are few who succeed. Intrigued with this scenario, the researchers aim to look for factors that lead to the success of micro-entrepreneurs in their business ventures. Following the Social Cognitive Theory and earlier research, the present study predicts that technology adoption and self-efficacy will influence entrepreneurial business success. Additionally, this study posits that self-efficacy mediates the relationship between technology adoption and entrepreneurial business success.
 
 Methodology: The research employed a survey technique using a quantifiable method. The researchers distributed 350 questionnaires to low-income micro-entrepreneurs in Kelantan, Malaysia. Two hundred forty-five people responded, but only 240 data proceeded for further analysis. The research used SPSS and AMOS version 26 to analyse the data gathered.
 Findings: Results indicated that both variables, namely entrepreneurial self-efficacy and technology adoption, significantly influenced business success. Additionally, the results indicated that entrepreneurial self-efficacy significantly mediated the relationship between technology adoption and entrepreneurial business success.
 
 Contributions: The discoveries provide valuable insights to policy makers, poverty agencies, micro-entrepreneurs and the likes in formulating strategies and intervention programs for entrepreneurial success. Indeed, successful entrepreneurs are crucial in elevating the social status of the poor and eradicating national poverty. In addition, it realises the Sustainable Development Goals’ agendas that all states in Malaysia are free from poverty by 2030.
 
 Keywords: Business success factors, micro entrepreneurs, self-efficacy, technology adoption.
 
 Cite as: Ariffien, M. A., Zakaria, M., Syed Yusuf, S. N., & Ahmad, J. H. (2023). Technology adoption, self-efficacy and entrepreneurial business success. Journal of Nusantara Studies, 8(TI), 257-276. http://dx.doi.org/10.24200/jonus.vol8issTIpp257-276

Business failure and entrepreneur experiences of passion

This article examines how passion affects an entrepreneur’s business failure experiences. Our study explores the link between the type of passion an entrepreneur exhibits and the effect this has on the entrepreneurs’ attitudes and reactions to business failure. We analyse the way in which passion type informs entrepreneurs identification with their business, and the entrepreneurial process. Entrepreneurs who experienced harmonious passion maintained an emotional distance from their business failure. Harmoniously passionate entrepreneurs had a rational perspective and were reflective, self-aware, adaptive and future oriented. Entrepreneurs who experienced obsessive passion, were defensive and reactionary about their business failure. Obsessively passionate entrepreneurs attached contingencies and experienced increased stress and conflict. Our findings suggest promising opportunities for future research on the interplay between heterogeneous passions, adaptive/maladaptive entrepreneurial action and regulated goal pursuit.

Data on Black, Indigenous, and People of Color (BIPOC)-Owned Businesses: What's Out There and What Can Be Done?

Decades into the digital era, publicly available data on Black, indigenous, and people of color (BIPOC) owned businesses are still remarkably lacking. In this commentary piece, the authors cover recent literature on the importance of BIPOC-owned businesses for equitable growth and BIPOC wealth-building. The authors then examine the sources of data that are available – public and private – for tracking the success or failure of such businesses. A BIPOC business database was constructed from a variety of sources and used by the authors to examine the potential pitfalls of such exercises. The authors close with a call for better data collection on BIPOC businesses.

From Failing Forward to Thriving

Drawing on entrepreneurial cognition and experiential learning theories, this article explores the complexities of entrepreneurial learning from business failure and its reflections on the entrepreneurial ecosystem. Qualitative research was conducted through individual in-depth interviews and focus groups, and data were subjected to content analysis using NVivo software. The analysed data relate the nature of the failure to its reflections on entrepreneurial learning and the most relevant contents used in the resurgence of the entrepreneur. How the entrepreneurial ecosystem absorbs and recycles learning from failure impacting new business creation was analysed. This study explored emerging economies’ themes based on the triangulation of data from startups from different industries and maturity levels from the perspective of founding partners, directors and employees and incubator and accelerator managers. The article offers a vision of entrepreneurial learning due to exceptional failure. The results presented in this paper demonstrate that learning from a critical event profoundly impacts the performance of entrepreneurial ecosystems and influences their vocation and the accession of new ventures to the industries analysed. These results encourage research in areas such as learning to model or replicating the proposed approach in other ecosystems. In practical terms, the findings can support policymakers in identifying localised factors that can be leveraged to produce macro-level change by identifying appropriate incentives for social networks and experiential knowledge sharing. The article presents a new perspective on entrepreneurial learning. It offers evidence that micro-learning strategies adopted and developed after critical events impact the performance of the entrepreneurial ecosystem related to the creation and diffusion of new ventures.

How COVID-19 Is Facilitating the Digital Transformation of Luxury Retailing Industry in ChinaTaking Louis Vuitton as an Example

In recent years, COVID-19 poses an undeniable threat to the world economy. There is an increase in the global unemployment rate, supply chain disruptions, and business failures in several industries, especially in China. Faced with this difficulty, an increasing number of organizations are transforming their platforms from physical stores to e-commerce and online platforms, hence boosting their digitalization penetration. This paper explores how COVID-19 stimulates the digital transformation of the luxury retailing industry in China, using Louis Vuitton as an example. The purpose of this paper is to determine the possible pattern around all luxury retailers to see if digitalization is better and more sustainable for their commercial operations. This paper will employ literature analysis as its methodology. This paper reveals that the digital change surrounding the luxury brand is not solely a result of COVID-19, while COVID-19 does speed this transformation, and that social media platforms also play a significant role in China.

Survival Strategies in the Economic Recession at Firm Level: The Case of Large Scale Construction Companies in Sri Lanka

As a form of business, construction contracting is heavily sensitive to recessions in the economy. As in many other countries, the Sri Lankan construction industry has also suffered a high proportion of business failures due to the current economic recession. Hence, the aim of this research is to delineate the commercial strategies adopted by construction companies in Sri Lanka to endure short term survival in the recession. A comprehensive literature survey was carried out to reveal these strategies in different contexts. A questionnaire survey was subsequently administered among the large scale contractors to gauge their responses using the Mean Item Score (MIS) and the Relative Importance Index (RII). The study concludes that Cut non-essentials and dispense the indispensable, De-scaling/De-scoping, Minimizing wastage, Getting clear on basic costs and adjust the profit margin with a sense and Slowing down the progress are among the first five strategies found to have been prioritized at contracting firms. Along with 43 strategies, the study recommends that there is a need for firms to network and collaborate to share resources, in order to survive in business during the fierce conditions of the economy.

Words Matter: Measuring the Co-Operative Identity Crisis

Identity is at the core of a rich body of business communications research, spanning studies on organizational identity, branding, and corporate social responsibility. This work has, however, neglected the question of corporate identity from the perspective of co-operatives—democratically-controlled businesses owned and controlled by their users—and the existential challenge posed by an operating environment often hostile to the business model. At the same time, the question of identity permeates the scholarly organizational and co-operative literature, shaping studies into co-operative identity crises, isomorphism, and from a transactions-cost economics perspective, the co-operative lifecycle. Bridging these literatures, we develop a first-ever conceptual dictionary of terms that we associate with co-operative and investor-owned firms (IOFs). Using text-as-data techniques, we apply the dictionary to a 15-year sample of credit union (a type of co-operative) and bank (IOFs) annual report texts. The resulting model ranks credit unions and banks on a co-op versus IOF firm scale and identifies credit unions that may be at risk of losing their identity because of their use of IOF language. To validate our results, we employ a variety of strategies, including novel machine learning models. Generally, these strategies support the findings from our dictionary model but also suggest the model may not be picking up on some creeping isomorphic pressures on credit unions to conform to IOF language. We conclude by noting that identity questions have important real-world implications, noting potential legal and public policy implications (e.g., loss of preferential tax measures) and pointing to literature that associates co-operative “identity crises” with business failures and demutualizations which, in turn, can lead to higher consumer prices.

Employee-resourcing strategies in family-owned small restaurants in South Africa: Do environmental influences matter?

The small restaurant business (SRB) sector is applauded for promoting economic growth and employment creation in developing economies. Despite the spirited efforts by developing nations to capacitate the sector to sustainably contribute to economic growth, it is often flawed by a high inflation failure rate, especially in South Africa. The failure rates have been attributed, among many other factors, to the inability of small restaurant businesses to respond to environmental uncertainties with appropriate employee-resourcing strategies. The literature search shows that there is a dearth of research on the nexus between environmental influences and employee-resourcing strategies in South African small restaurant businesses. To address this gap, the study attempts to interrogate employee-resourcing strategies among family-owned small restaurants through the prism of environmental influences. A quantitative research approach was used, and the ex post facto research design was adopted. A convenient sample of 221 owners/managers of family-owned accommodations and Small Restaurant Businesses was conveniently selected. Structural Equation Modelling (SEM) was employed to confirm the congruence between observed and reproduced data in testing the hypothesized relationships. The inquiry established a positive relationship between internal environmental influences and employee-resourcing strategies in SRBs. Additionally, a positive and strong relationship between the external environment and employee-resourcing strategies was also established. Considering the vitality of small businesses in driving today’s economies and employment growth, this study recommends that owners/managers identify internal and external forces that attenuate small business failures. The study also emphasizes that SRBs must understand their external and internal environments for more productive employee-resourcing strategies.

Does tax increment financing affect business survival and failure in Chicago, Illinois?

ABSTRACT This study examines how and to what extent tax increment financing (TIF) affects changes in business operations—the risk of businesses moving away from their original locations in Chicago, Illinois. Using a Cox proportional hazard regression with multi-year business license datasets, the study finds that TIF districts are not a favorable environment for existing businesses that opened prior to the actual TIF-funded investments, leading to more business closures than their counterparts outside of the districts; on the other hand, new businesses that opened after the TIF investments are better equipped to survive in and around TIF districts than the comparable areas. The study provides a better understanding of the survival and exit patterns of businesses induced by public tax policy, as well as of what factors are ascribed to such business market dynamics and transformations. Findings from this study will yield valuable reference data for both planners and policymakers in decision-making processes for future economic development and corresponding tax policy initiatives.

EXPLORING THE ENTREPRENEURIAL INTENTION OF GENERATION Y PEOPLE IN THAILAND

The main purpose of this study is to analyse the factors affecting and exploring entrepreneurial intention of generation Y people in Thailand with particular attention to demographics, personal planned behaviours, and contextual factors, those factors will be the key tools that may encourage entrepreneurship. This study based on interview the person who born in 1980 to 1997 called generation Yers. The results showed they have a high expectation of being a self-employee rather that working in a well-known organisation and be able to understand the idea how these young entrepreneurs identify business opportunities and overcome challenges through self-attitudes toward unforeseen failure in business. Yers believe that they are the huge wave of movement to challenge and develop a globalisation throughout creativity and innovation. This study also shown the result of relationship among demographics, personal planned behaviours, and other contextual details that are the main key tools were the determinants of Thai entrepreneurial intention.

THE INFLUENCE OF EXCHANGEABILITY, LEVERAGE, INFLATION, AND INTEREST RATE ON FINANCIAL PROBLEMS AMONG MANUFACTURING COMPANIES LISTED ON INDONESIA STOCK EXCHANGE DURING 2018-2021

Many companies are facing financial difficulties and some are failed into bankruptcy. One of the categories of financial distress is business failure which is mostly caused by economic and financial factors. This research provides empirical evidence regarding internal and external factors that affect the financial distress of several companies. The sample consists of 25 manufacturing companies listed on the Indonesia Stock Exchange (IDX) during the period of 2018-2021. This study used multinomial logit regression analysis with liquidity and leverage as internal variable factors, inflation, and interest rates as external variable factors of the companies. The data had used secondary data derived from financial statements. The results showed that Exchangeability and Leverage had significant effects on Financial Distress meanwhile Interest Rates and Inflation had not influenced the Financial Distress significantly.”

Research on Product Selection Model for Small E-Commerce Stores Based on Improved RFM Model

Traditionally, merchants determine whether to enter or exit the market based on the product life cycle. However in the era of e-commerce, the needs of customers are constantly changing, and the life cycle of products is very short. If a merchants accidentally enters the market during the recession period, it may lead to business failure. Therefore, stores must evaluate the value of their products in real time, grasp the trend of product value, and adjust their business direction in a timely manner to ensure the sustainability of their operations. The RFM model is a customer value evaluation model, which includes three important indicators: purchase proximity R, purchase frequency F, and purchase amount M. Products also have indicators such as proximity of purchase, frequency of purchase, and amount of purchase. Therefore, in this article, we propose a products value evaluation model based on the RFM model. In the model, we use the entropy weight method to assign weights to various indicators. Through empirical calculation, analysis, and testing of the business data of sample stores, we confirm that the model is effective in Product Selection for small e-commerce stores.

A Novel Political Optimizer-Based Feature Selection with an Optimal Machine Learning Model for Financial Crisis Prediction

In today’s digital environment, business intelligence advances make it difficult to stay competitive and up to date on business trends. Decision-making in the financial industry is increasingly being powered by big data and machine learning. A decision-making process may be thought of as any sequence of processes that an individual goes through in order to select the option or course of action that is most suitable to meet their needs and necessities. The ability to anticipate the onset of a financial crisis is a significant economic phenomenon. A nation’s economic development and strength can be gauged by its capacity to provide an accurate assessment of the number of failed firms and the frequency with which they fail. The economics of the globe have been ravaged by recent global crises like as the COVID-19 pandemic and other recent environmental, financial, and economic disasters, which have marginalized efforts to construct a maintainable economy and civilization. The health and growth of a nation’s economy can be determined by precisely estimating the number of enterprises that will fail and the number that will succeed. Historically, there have been numerous strategies for constructing a successful financial crisis prediction (FPC) method. Effectively predicting business failures is a gauge of a country’s economic health. Several strategies are available for effective FCP. Classification performance, forecast accuracy, and legality are insufficient for practical use. Several of the suggested methods work for some issues. The specific dataset is not expandable. To improve classification, design a good prediction model adaptable to several datasets. An effective financial crisis prediction method (FPC) requires the right qualities. ML models can also be used to classify a company’s financial health. This research presents political optimizer-based feature selection (POFS) with optimal cascaded deep forest (OCDF) for FCP in big data environments. Hadoop Map Reduce handles huge datasets. POFS reduces computing complexity by handling feature selection. POFS is an original FCP algorithm categorization using OCDF. SFO is used to optimize CDF model parameters. A thorough simulation study was performed to evaluate POFS performance on benchmark datasets OCDFs. The results confirmed the POFS-OCDF method’s superiority over state-of-the-art approaches. With an outstanding sensitivity of 0.912, specificity of 0.953, accuracy of 0.944, F-score of 0.930, and Matthews correlation coefficient (MCC) of 0.912, the proposed POFS-OCDF technique has shown optimum results. The experimental results demonstrated that the POFS-OCDF technique outperformed other recently developed strategies on a variety of criteria. As previously stated, Sunflower optimization (SFO) is also used to tune the Cascaded Deep Forest (CDF) parameters. A detailed simulation analysis is performed based on the benchmark dataset to evaluate the higher classification efficiency of the POFS-OCDF technique. The invention of the POFS algorithm for FCP exemplifies the work’s originality.

Leadership Succession Planning Contributions Towards Family-Owned Business Performance and Sustainability in South Africa

Worldwide, family business has gained prominence as one of the critical vehicles for creating employment and hence made a contribution in the efforts to eradicate poverty. However, most family business lack of leadership succession planning, which eventually lead to low levels of performance and lack of sustainability. The purpose of this study was to investigate the relationship between leadership succession planning, performance, and sustainability of family businesses. A qualitative methodological approach was utilized where participants were selected using purposive and snowball sampling methods. Semi-structured interviews were used in collecting data and the collected data was analyzed thematically. The study found that leadership succession planning is linked to sustainability and general performance within family businesses. Conversely, it was found that levels of sustainability and failure of family businesses are related to their inability to come up with effective succession leadership planning. This finding confirms the vital role that leadership succession planning plays in enhancing the performance and sustainability of a family business.