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This study aims to examine the impact of board characteristics on firm performance while also exploring the influential mechanisms that help Chinese listed companies establish effective boards of directors and strengthen their corporate governance mechanisms.

This paper uses machine learning methods to investigate the predictive ability of the board of directors' characteristics on firm performance based on the data from Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges in China during 2008–2021. This study further analyzes board characteristics with relatively strong predictive ability and their predictive models on firm performance.

The results show that nonlinear machine learning methods are more effective than traditional linear models in analyzing the impact of board characteristics on Chinese firm performance. Among the series characteristics of the board of directors, the contribution ratio in prediction from directors compensation, director shareholding ratio, the average age of directors and directors' educational level are significant, and these characteristics have a roughly nonlinear correlation to the prediction of firm performance; the improvement of the predictive ability of board characteristics on firm performance in state-owned enterprises in China performs better than that in private enterprises.

The findings of this study provide valuable suggestions for enriching the theory of board governance, strengthening board construction and optimizing the effectiveness of board governance. Furthermore, these impacts can serve as a valuable reference for board construction and selection, aiding in the rational selection of boards to establish an efficient and high-performing board of directors.

The study findings unequivocally demonstrate the superiority of nonlinear machine learning approaches over traditional linear models in examining the relationship between board characteristics and firm performance in China. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. Within the suite of board characteristics, director compensation, shareholding ratio, average age and educational level are particularly noteworthy, consistently demonstrating strong, nonlinear associations with firm performance. The study reveals that the predictive performance of board attributes is generally more robust for state-owned enterprises in China in comparison to their counterparts in the private sector.

The purpose of this paper is to identify and analyse the readmission risk prediction tools reported in the literature and their benefits when it comes to healthcare organisations and management.

Readmission risk prediction is a growing topic of interest with the aim of identifying patients in particular those suffering from chronic diseases such as congestive heart failure, chronic obstructive pulmonary disease and diabetes, who are at risk of readmission. Several models have been developed with different levels of predictive ability. A structured and extensive literature search of several databases was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analysis strategy, and this yielded a total of 48,984 records.

Forty-three articles were selected for full-text and extensive review after following the screening process and according to the eligibility criteria. About 34 unique readmission risk prediction models were identified, in which their predictive ability ranged from poor to good (c statistic 0.5–0.86). Readmission rates ranged between 3.1 and 74.1% depending on the risk category. This review shows that readmission risk prediction is a complex process and is still relatively new as a concept and poorly understood. It confirms that readmission prediction models hold significant accuracy at identifying patients at higher risk for such an event within specific context.

Since most prediction models were developed for specific populations, conditions or hospital settings, the generalisability and transferability of the predictions across wider or other contexts may be difficult to achieve. Therefore, the value of prediction models remains limited to hospital management. Future research is indicated in this regard.

This review is the first to cover readmission risk prediction tools that have been published in the literature since 2011, thereby providing an assessment of the relevance of this crucial KPI to health organisations and managers.

This paper aims to study whether the industry indexes predict the evolution of the broad stock market in the USA.

The study uses industry indexes to predict the equity premium in the USA. It considers several types of predictive models: constant coefficients and constant volatility, drifting coefficients and constant volatility, constant coefficients and stochastic volatility and drifting coefficients and stochastic volatility. The models are estimated through the particle learning algorithm, which is suitable for dealing with the problem that an investor faces in practice, given that it allows the investor to revise the parameters as new information arrives. The individual forecasts are combined based on their past performance.

The results reveal that models exhibit significant predictive ability. The models with constant volatility exhibit better performance, at the statistical level, but the models with stochastic volatility generate higher gains for a mean–variance investor.

This study’s findings are valuable not only for finance researchers but also for private investors and mutual fund managers, who can use these forecasts to improve the performance of their portfolios.

To the best of the knowledge of the author, this is the first paper that uses particle learning and combination of forecasts to predict the equity premium in the USA based on industry indexes. The study shows that the models generate valuable forecasts over the long time span that is considered.

What does it mean to be a “smart” negotiator? Few scholars have paid much attention to this question, a puzzling omission given copious research suggesting that cognitive ability (the type of intelligence commonly measured by psychometric tests) predicts individual performance in many related contexts. In addition to cognitive ability, other definitions of intelligence (e.g., emotional intelligence) have been proposed that theoretically could influence negotiation outcomes. Aiming to stimulate renewed attention to the role of intelligence in negotiation, we develop theoretical propositions linking multiple forms of intelligence to information acquisition, decision making, and tactical choices in bargaining contexts. We outline measurement issues relevant to empirical work on this topic, and discuss implications for negotiation teaching and practice.

The purpose of this paper is to operationalize the construct of work interruptions resiliency (WIR) and develop a measure assessing the extent to which employees report resiliency in resumption of work activities post-interruption (Study 1), and to further examine WIR’s nomological net, specifically its predictive relations with important employee-level outcomes (Study 2).

Study 1 utilized subject matter experts and data from 274 employees from a range of industries for scale development. Study 2 utilized 365 registered nurses from a hospital network to confirm and extend the findings from Study 1 within a relevant, dynamic job type.

Study 1 yielded a psychometrically sound measure for WIR comprised of four factors (typical, critical, external, sensory). Validity was evidenced via negative correlations with cognitive demand and Type A personality, and positive correlations with conscientiousness. Study 2 expanded WIR’s nomological net by evidencing its predictive relations with employees’ role clarity, autonomy support, role breadth self-efficacy, and evidence-based practice adoption intentions.

This research introduces WIR and develops a measure for assessment, providing validity evidence and establishing an initial nomological net for WIR upon which further research can rely and build.

The work interruptions resiliency construct and measure have the potential to impact selection and training, particularly in job types wherein poor recovery from interruptions can yield detrimental consequences.

Work interruptions compromise productivity and result in errors. It is therefore crucial that organizations assess the extent to which employees are resistant to the detrimental effects of such disruptions (Study 1) and understand the nature of WIR’s predictive relations with important employee-level outcomes (Study 2).

Grounding the research in the stimulus-organism-resource (S-O-R) framework, this study aims to address the research gap of explaining and predicting the relationship between price discounts, interactivity and professionalism on college students’ purchasing intention in live-streaming shopping. It also attempts to understand if trust plays the role of mediator in the effect of these relationships.

This study collected data using a questionnaire protocol adapted and refined from the original scales in existing studies. The partial least squares structural equation modeling was used to analyze data collected from 258 college students in China. Other than assessing the path model’s explanatory power, this study examined the model’s predictive power toward predicting new cases using PLS predict.

Results indicated that all three predictors have a positive significant relationship with trust, while only price discounts demonstrate a significant relationship with purchase intention. Simultaneously, the mediation results provide support to the S-O-R framework demonstrating that external factors (professionalism, interactivity and price discounts) can arouse organism (trust), which in return, generate a behavioral outcome (purchase intention).

This study is the first few studies that focus on college students’ behavioral responses in an online shopping environment. At the same time, this is the first study supplement the explanatory perspective with a predictive focus, which is of particular importance in making sound recommendations on managerial decision-making.

The purpose of this paper is to determine whether the original and the revised versions of the existing prediction models are the best tools for assessing the going concern assumption of a firm in the creditor-oriented regime.

The analysis begins from estimating the classification accuracy of the original versions of the bankruptcy, going concern and liquidation prediction models. At the second step, the revised versions of the aforesaid existing prediction models are developed. At the third step, the accounting-based going concern prediction model is proposed by using multiple discriminant analysis for the creditor-oriented regime. The sample contains the financial ratios of manufacturing firms for the period 1997–2014.

The finding indicates that the five discriminatory variables, which belong to “income statement” and “statement of financial position,” of the proposed model are not only useful for evaluating the going concern assumption of a firm, but also give aid for evaluating the financial fraud risk of a firm as compared to the original and revised versions of the prediction models that are developed for the debtor-oriented regime.

The external validity of the proposed prediction model can be tested on the large data sets of the countries where the liquidation provisions are a part of their local corporate law.

The proposed accounting prediction model will be helpful for the internal and external auditors in order to determine the going concern assumption at planning, performing and evaluation stages.

The proposed accounting-based going concern prediction model is based on liquidated firms.

This paper presents a theory of the determinants of the methodology of economics. Following the useful distinction made by Papendreou (1958), theory is defined as comprising both a model and a probability statement as to the empirical validity of the model. Accordingly, first a model of the determinants of methodology of economics is presented and, then as an illustration, the model is applied to explain the major issues which have been debated in the recent literature on methodology. The paper concludes with a brief and simplified analysis of the evolution of methodology and some testable hypotheses are presented about the future course of the methodology of economics.

Auditors are required to perform analytical procedures during the planning and concluding phases of the audit. Such procedures typically use data aggregated at a high level. The authors investigate whether artificial neural networks, a more sophisticated technique for analytical review than typically used by auditors, may be effective when using high level data.

Data from companies operating in the dairy industry were used to train an artificial neural network. Data with and without material seeded errors were used to test alternative techniques.

Results suggest that the artificial neural network approach was not significantly more effective (taking into account both Type I and II errors) than traditional ratio and regression analysis, and none of the three approaches provided more overall effectiveness than a purely random procedure. However, the artificial neural network approach did yield considerably fewer Type II errors than the other methods, which suggests artificial neural networks could be a candidate to improve the performance of analytical procedures in circumstances where Type II error rates are the primary concern of the auditor.

The authors extend the work of Coakley and Brown (1983) by investigating the application of artificial neural networks as an analytical procedure using aggregated data. Furthermore, the authors examine multiple companies from one industry and supplement financial information with both exogenous industry and macro-economic data.

Bradford formula (index) or factor (BF) was originally designed for use as part of the overall investigation and management of absenteeism. Work ability index (WAI) is an instrument that has been used to evaluate work ability. The purpose of this paper is to evaluate retrospectively, the properties of the WAI, the BF and their combination – the sickness absence probability factor – in predicting future sickness absence.

Data on sickness absences of shipyard employees for the period 2002-2006 were utilized for the calculation of the relevant BFs. The Greek version of the WAI questionnaire was also used. The sickness absence probability factor was calculated by summing up the scores of the two other tools, after transforming them into categorical variables.

Increased BF values are positively and strongly correlated to increased sickness absenteeism levels in the following years (p<0.001), especially for the immediate following years. WAI score is also strongly negatively correlated to absence. The combination of BF and WAI acted even better.

The use of tools, like the BF and the suggested sickness probability factor, should be considered by occupational health personnel in order to act proactively on sickness absenteeism, since they were found to be related to future absenteeism. Actions should follow health and safety rules and ethics and should be undertaken by competent health personnel.