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This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

This study aims to contribute to the debate on goodwill accounting by examining the information content of impairment losses recognized in half-yearly reports. Half-yearly reports provide a suitable context to examine the effectiveness of the impairment process. Due to IFRIC 10 requirements, indeed, managers may have incentives to avoid recognizing impairment losses at the interim reporting date.

The study adopts an archival approach. Based on the traditional Ohlson’s model (1995), it explores the information content of half-yearly impairment losses in the European context over the period 2007–2017.

Findings confirm the relevance of half-yearly reports and suggest that half-yearly impairment losses are significantly associated with stock prices. In particular, investors positively value companies that recognized goodwill impairment losses at the interim reporting date.

The study contributes to the academic debate on goodwill and the effectiveness of the impairment procedure. In particular, it provides empirical evidence on the recognition of goodwill write-offs when it is possible to avoid the impairment test in the absence of indications of impairment.

Findings of this study can support the current debate on accounting for goodwill also in the light of the recent proposals of the IASB on the need to improve the effectiveness of the impairment test.

This study provides original empirical evidence on the goodwill impairment test in half-yearly reports, extending previous research that typically examines this issue in annual reports.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

Developing a reliable cost estimate at the early stage of construction projects is challenging due to inadequate project information. Most of the information during this stage is qualitative, posing additional challenges to achieving accurate cost estimates. Additionally, there is a lack of tools that use qualitative project information and forecast the budgets required for project completion. This research, therefore, aims to develop a model for setting project budgets (excluding land) during the pre-conceptual stage of residential buildings, where project information is mainly qualitative.

Due to the qualitative nature of project information at the pre-conception stage, a natural language processing model, DistilBERT (Distilled Bidirectional Encoder Representations from Transformers), was trained to predict the cost range of residential buildings at the pre-conception stage. The training and evaluation data included 63,899 building permit activity records (2021–2022) from the Victorian State Building Authority, Australia. The input data comprised the project description of each record, which included project location and basic material types (floor, frame, roofing, and external wall).

This research designed a novel tool for predicting the project budget based on preliminary project information. The model achieved 79% accuracy in classifying residential buildings into three cost_classes ($100,000-$300,000, $300,000-$500,000, $500,000-$1,200,000) and F1-scores of 0.85, 0.73, and 0.74, respectively. Additionally, the results show that the model learnt the contextual relationship between qualitative data like project location and cost.

The current model was developed using data from Victoria state in Australia; hence, it would not return relevant outcomes for other contexts. However, future studies can adopt the methods to develop similar models for their context.

This research is the first to leverage a deep learning model, DistilBERT, for cost estimation at the pre-conception stage using basic project information like location and material types. Therefore, the model would contribute to overcoming data limitations for cost estimation at the pre-conception stage. Residential building stakeholders, like clients, designers, and estimators, can use the model to forecast the project budget at the pre-conception stage to facilitate decision-making.

This study explores whether the unique organizational form of family firms helps to mitigate the negative effects caused by the announcement of product recalls.

The authors use an event study, for a sample of 2,576 product recalls in the United States (US) automobile industry, between January 2010 and June 2021.

The authors found that stock market's reaction to a product recall announcement is less negative for family firms. This superior performance is partially driven by the family firms' long-term investment horizons and higher strategic emphasis on product quality. However, the relationship between family ownership and cumulative abnormal returns around product recall announcements is nonlinear as the impact of family ownership starts by being positive but becomes negative for higher levels of family ownership. The authors also find that family firm's chief executive officer (CEO) and managerial ownership influence positively the stock market reaction to product recall announcements.

This work has several implications for family firms' management as well as for investors and financial analysts. First, as higher managerial ownership is associated with a greater emphasis on product quality, decreasing stock market losses when a product recall occurs, family firms should consider increasing equity-based compensation. Second, as there seems to exist an optimal proportion of family ownership, family firms should consider the risks of increasing too much their ownership share. Third, investors and financial analysts can use the results in the study to help them in their investment and trading decisions in the stock market.

The authors extend the knowledge of product recalls by studying the under-researched role of the flexible, internally focused culture of family businesses on the stock market reaction to product recalls.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

The purpose of this study is to quantify the impact of electricity power outages on the local housing market in South Africa.

This study uses the autoregressive distributive lag (ARDL) and quantile autoregressive distributive lag (QARDL) models on annual time series data, for the period 1971–2014. The interest rate, real income and inflation were used as control variables to enable a multivariate framework.

The results from the ARDL model show that real income is the only factor influencing housing price over the long run, whereas other variables only have short-run effects. The estimates from the QARDL further reveal hidden cointegration relationship over the long run with higher quantile levels of distribution and transmission losses raising the residential price growth.

Overall, the findings of this study imply that the South African housing market is more vulnerable to property devaluation caused by power outages over the short run and yet remains resilient to loadshedding over the long run. Other macro-economic factors, such as real income and inflation, are more influential factors towards long-run developments in the residential market.

To the best of the authors’ knowledge, this is the first study to examine the empirical relationship between power outages and housing price growth.

This paper aims to investigate the impact of political stability on corruption by drawing upon a sample encompassing both developed and developing European and Asian countries.

The dataset, sourced from the Refinitiv database, spans from July 2014 to May 2022. Panel data techniques, specifically pooled estimation and dynamic panel data [generalized method of moments (GMM)] are employed. The analysis encompasses both fixed and random effects models to capture country-specific cross-sectional effects. To validate our findings, we perform a robustness test by including in the investigation four control variables, namely poverty, type of governance, economic freedom and inflation. To test heterogeneity, the dataset is further divided into two distinct subsamples based on the countries’ locations.

Empirical findings substantiate that political stability (viewed as the risk of government destabilization) has a positive and significant impact on corruption in all analyzed samples of European and Asian countries, though some differences are observed in various subsamples. When we take into account the control variables, these analysis results are robust.

This research provided a panel data analysis with GMM, while other empirical methodologies could also be used, like the difference-in-difference approach. However, our results should be validated by extending the time and the sample to a worldwide sample and using alternative measures of corruption and political stability. Moreover, our focus was on a linear and unidirectional relationship between the considered variables, but it would be interesting to test in our further research a non-linear and bidirectional correlation between them. Furthermore, we have introduced in the robustness test only four economic variables, but to consolidate our findings, we plan to include socioeconomic and demographic variables in future studies.

These outcomes imply that authorities should be aware of the necessity of implementing anti-corruption policies designed to establish effective agencies and enforcement structures for combating systemic corruption, to improve the political environment and the quality of institutions and to apply coherent economic strategies to accelerate economic growth because higher political stability and sustainable development determine a decrease in levels of corruption.

At the microeconomic level, the survival of organizations may be in danger from new types of corruption and money laundering. Therefore, in order to prevent financial harm, the top businesses worldwide should respond to instances of corruption through strengthened supervisory procedures. This calls for the creation of a mechanism inside the code of conduct where correct reporting of suspected situations of corruption would have a prompt procedure to be notified of. To avoid corruption in operational procedures, national plans and policies should be developed by government officials, executives and legislators on a national level, as well as by senior management and the board of directors on an organizational level. This might lower organizations' extra corruption-related expenses, assure economic growth and improve global welfare.

A novel feature of our research resides in its broad examination of a sizable sample of European and Asian countries regarding the nexus between corruption and political stability. The paper also investigates a less explored topic in economic literature, namely the impact of political stability on corruption. Furthermore, the study depicts policy recommendations, outlining effective and reasonable measures aimed at improving the political landscape and combating corruption.

This study aims to determine the effect of bank ownership on the credit risk of Indonesian Islamic local banks (ILBs).

This study uses the system generalized method of moments (GMM) estimation technique with a sample of 155 Islamic local banks in Indonesia from 2012 to 2019.

The results show that commissioner board (D.COW) ownership has a negative effect on credit risk. This indicates that an increase in the number of shares of Islamic local banks owned by the commissioner board reduces credit risk. On the other hand, government ownership (D.GOW), the Sharia supervisory board (D.SOW) and the director board (D.DOW) do not affect credit risk.

The government, Sharia supervisory board and director board need opportunities to easily own more Islamic local bank shares. Therefore, the provisions regarding the share ownership rights of the government, Sharia supervisory board and director board need to be improved to increase their role in reducing credit risk.

Previous researchers have not studied the effect of government ownership, the commissioner board, the Sharia supervisory board and the ownership of directors on credit risk at the ILB in Indonesia.

Reducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.

In this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.

The results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.

The suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.

The research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.