Search results

The 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.

The 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.

This 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.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The 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.

Bankruptcy 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.

To 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.

The purpose of this paper is to study the optimization of the geographical location of a network of points of sale, so that each retailer can have access to a potential geographic market. In addition, the authors study the importance of the distance variable in the commercial viability of a point of sale and a network of points of sale, analysing if the best location for each point (local optimum) is always the best location for the whole (global optimum).

Location-allocation models are applied using p-median algorithms and spatial competition maximization to analyse the actual journeys of 64,740 car buyers in 1240 postal codes using a geographic information system (GIS) and geomarketing techniques.

The models show that the pursuit of individual objectives by each concessionaire over the collective provides poorer results for the whole network of points of sale when compared to coordinated competition. The solutions provided by the models considering geographic and marketing criteria permit a reduction in the length of journeys made by the buyers. GIS allows the optimal control of market demand coverage through the collaborative strategies of the supplying retailers, in this case, car dealerships.

The paper contributes to the joint research of geography and marketing from a theoretical and practical point of view. The main contribution is the use of information on actual buyer journeys for the optimal location of a network of points of sale. This research also contributes to the analysis of the correlation between the optimum local and optimum global locations of a commercial network and is a pioneering work in the application of these models to the automotive sector in the territorial area of the study.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

The purpose of this study is to describe the development of market based accounting research (MBAR) published in Indonesia for 10 years. This study attempts to explain the topics of MBAR, research method used, the variables, between-variable relationship formed, and the units analysis used in MBAR. This research uses qualitative-descriptive method to create descriptive models of MBAR articles published in accounting journals that have been accredited with minimum grade of B. The analysis of 109 MBAR articles of five accounting journals shows that 10 MBAR themes are still potential. Among three methods in MBAR, the multivariate association study is dominant. Some papers use intervening and moderating model to explore the relationship between accounting data and capital market reaction. The results for each theme are described in a research map that shows the relationship between variables (constructs) of MBAR from three units of analysis. This paper finds some implications to MBAR research agenda in the future, especially for meta-analysis research and triangulation research, due to many inconsistencies of the MBAR findings in Indonesia. In addition, accounting standard research topic is still promising in the moment of accounting standards transition.

As family and nonfamily businesses differ in how they do business, the focus of this manuscript is on understanding how strategy-level models can be misinterpreted if family involvement is not considered. Thus, in this manuscript, the focus is on understanding the extent to which strategic orientations (market orientation and technology orientation, which reflect strategic approach), strategic performance metric focus (financial-based, optimization-based and market-based, which reflect strategy evaluations) and strategic audacity (which reflects boldness in envisioning and delivering strategic outcomes) play a role in driving firm performance – in family businesses vs nonfamily businesses. Understanding how these drivers impact performance differently in family vs nonfamily businesses enables companies to better direct their strategic efforts.

After presenting theoretical concepts, authors use regression analysis on a sample of companies in a developing European Union (EU) country (n = 282) to evaluate the impact of strategic orientation, strategic performance metric focus and strategic audacity on firm performance separately in three samples: the full sample (consisting of both family and nonfamily-owned firms), sample of family businesses and the sample of nonfamily businesses.

The role of strategic orientation, strategic audacity and focal goals in driving firm performance differs depending on the company type (family vs nonfamily). In the case of nonfamily businesses, strategic audacity and technology orientation with the focus on efficiencies and markets are driving firm performance. In the case of family businesses, both market and technology orientation are important drivers of performance; the focus on financial and market indicators of performance is positively impacting performance, while the focus on efficiency indicators is diminishing the performance of family businesses. Thus, results show that of the performance drivers for family businesses, some are insignificant (strategic audacity), while some even have a negative impact (focus on optimization-based measures of performance) on family businesses' performance. Moreover, results show that some of the drivers of performance in case of family businesses (market orientation and focus on financial-based measures of performance) are not drivers of outstanding performance in the case of nonfamily businesses.

Best practices differ for family vs nonfamily businesses. In case of family businesses, comparing them to nonfamily businesses, market orientation and the focus on financial-based measures of performance have a greater impact on firm performance, while, at the same time, family businesses should refrain focusing on pursuing optimization-based measures of performance as such pursuit drives down their performance. Understanding the drivers of performance specific to family businesses will enable such firms to better navigate contexts characterized by ambiguity and uncertainty.

The manuscript evaluates how models, generally researched in the overall firm metrics, differ between family businesses and nonfamily businesses, thus delivering new insights into the important marketing concepts.

This study aims to measure impact of several firm-specific factors on alternative measures of leverage. The authors also aim to study impact of the subprime crisis on such associations.

The authors utilized an unbalanced panel data of 973 firm-year observations on 47 UK listed non-financial firms for the years 1990–2019. Book-based and market-based long-term and total leverage measures have been used as explained variables. The explanatory variables are profitability, size, two measures of growth, asset tangibility, non-debt tax shields, firm age and product uniqueness. Fixed effect and random effect models with clustered robust standard errors have been utilized for data analysis. To find the effect of subprime crisis, original dataset was split to create pre-crisis and post-crisis datasets.

The authors find that profitability significantly reduces leverage while firms having more tangible assets use significantly more debt in capital structure. Firm size and non-debt tax shield have statistically insignificant positive impact on leverage. Having more unique products reduces use of external debt, albeit insignificantly. Growth, when measured as market-to-book ratio, has inconsistent impact, whereas capital expenditure insignificantly reduces leverage. Age is found to be an insignificant predictor of leverage. After the subprime crisis, firms started relying more on internal fund instead of external debt, more particularly short-term debt. Having more collateral is gradually becoming more important for availing external debt.

Data limitations restrict generalization of the findings.

This is one of the pioneering attempts to show how subprime crisis altered the theoretical domain of capital structure research in the UK.