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It is assumed that the production system responds to planned demand at the end of the expected life of each individual piece of equipment and unplanned demand triggered by equipment failures. The difficulty of controlling this type of production system resides in the variable nature of the remanufacturing process. In practice, remanufacturing operations for planned demand can be executed at different rates, referring to different component replacement and repair strategies. A sub‐optimal control policy in which inventory thresholds trigger the use of different execution modes has been formulated in previous research to address this problem when unplanned demands are processed under an exponential time distribution. The aim of this study is to extend this control policy to more realistic unplanned demand arrival and processing times distributions.

The proposed approach is based on a combination of analytical modeling, simulation experimentation and regression analysis. The model was validated by comparing the obtained simulation results with those obtained under an exponential processing time distribution.

The results demonstrate that the structure of optimal control can be approximated by the sub‐optimal multiple hedging point policy with non‐significant cost variations.

The simulation results demonstrate that hedging point control policies could be applicable to a wide variety of complex remanufacturing problems in which analytical solutions are not easily obtained.

The paper extends the concept of hedging point policy to the control of real‐word repair and remanufacturing operations. Once calculated, the sub‐optimal policy parameters can be simply implemented by practitioners through the definition of stock‐level parameters.

The purpose of this article is to consider a corrective and preventive maintenance model with a view to both minimizing cost and maximizing system availability.

The proposed experimental multiobjective approach combines a simulation model and a statistical method to determine the best system parameters. The desirability function is used to convert a multiresponse problem into a maximization problem with a single aggregate measure. The model examined is based on a m identical machines system subject to unpredictable breakdown and repair, and the maintenance strategy used is based on the existing block‐replacement policy, which consists in replacing components upon failure or preventively, at scheduled intervals (T). Spare part inventory management is based on the (S, Q) model, whereby an order is placed when the replacement stock level drops below a given safety threshold level (S). At that time, a replacement part quantity (Q) is ordered, and is received after a stochastic lead time (τ).

The proposed model jointly minimizes the overall maintenance cost and maximizes system availability using a multiobjective optimization desirability function.

The multiobjective model can be used in a real manufacturing environment to help business decision makers determine the best compromise system parameters and adjust them to obtain desired response variables (overall production cost and system availability).

The proposed model allows the simultaneous optimization of two response variables, and determines the best system parameter compromise between the system cost minimization and the system availability maximization.

The purpose of this paper is to investigate the optimal production policy and maintenance strategy for leased equipment under a lease contract with warranty periods. In order to have steady revenue, the lessor (owner) of the equipment may provide guaranty periods to encourage the lessee to sign a lease contract with a longer lease period.

Under this production/maintenance scheme, the mathematical model of the expected total cost is developed and the optimal production planning and the corresponding optimal maintenance policy are derived by choosing the optimal warranty periods for the lessee in order to minimize the total cost.

The influence of the production rates variation in the equipment degradation is considered by an increased failure rate according to both time and production rates. The impact of warranty periods on optimal maintenance planning will be studied thereafter. Finally, numerical examples are given to illustrate the analytical study and the effects of the warranty periods variation during the lease periods on the maintenance policy and consequently on the total cost.

The paper proposes a new idea of production and maintenance coupling in the leasing aspect. This study shows that it has a novelty and originality relative to this type of problem which considers and proposes a new maintenance strategy for leasing contract. This originality characterized by the influence of two factors on the equipment maintenance strategy. First factor is the influence of the production variation production rates on the machine degradation degree that is new in the literature charactering by analytical equation that shows the evolution of the machine failure rate according to its use (which is in our case the production rate for each period) respecting in the same time the continuity of the equipment reliability for a period to another.

Grounded in the socio-emotional selectivity theory, the purpose of this paper is to develop a people recommender and social matching system that better serves the information needs of older people on social networking sites or services (SNSs).

The paper uses systems development as a design science research methodology to construct a conceptual framework and then design and prototype a recommender system.

The research demonstrates that it is possible to exploit Google Maps-based interfaces, coupled with historical geo-temporal information, to develop a recommender system on SNSs that can empower older adults to reconnect with past acquaintances.

The proposed system is an advanced prototype that has been tested using simulated data sets as opposed to real-life data involving actual end-users through field studies.

When examined through the lenses of socio-emotional and neighborhood theories, this research opens new opportunities to develop supportive social networks for older people.

The paper promotes a better social engagement and contributes to the mental and physical health of older people, which can act as a shield against loneliness, anxiety and depression.

The paper uses Google Maps interfaces and the concept of geo-temporal proximity indices to build an “elder-friendly” recommender system that can assist older people to reconnect with past friends, neighbors and colleagues.

The paper aims to re-interpret the role of corporate social responsibility (CSR) in limiting the extreme practices in earnings management (EM) by using evidence from large UK companies.

The study has used content analysis and disclosure index to measure the level of CSR. The authors measured EM based on discretionary accruals by using cross-sectional version of the modified Jones model.

The findings of this study reveal that companies with a higher commitment to CSR activities are less likely to manage earnings through accruals.

This study shed more light on the potential impact of CSR on earnings management in the context of the UK. Prior research on the impact of CSR on earnings management has used exclusively CSR scores, provided by CSR score indices. The manual measurement used in this study for CSR (disclosure index/content analysis) is considered to provide a more detailed and precise measure.

This study aims to investigate whether threshold effects exist in the relationship between board gender diversity and firm performance.

This study applies the panel smooth threshold regression model (PSTR) to a sample of 284 non-financial French firms listed on Euronext Paris over the period 2009–2017. Firm performance proxies are Tobin’s Q and return on assets (ROA). The board gender diversity is measured by the percentage of women participation in board.

Threshold effects in the relationships between board gender diversity and firm performance measures are found. For Tobin’s Q, the model has one threshold at the 34.17% of women directors’ appointment on boards. Similarly, for ROA, the model has one threshold at the 38.28% of women presence in the board. The results show that above the estimated threshold, women directorship has a positive impact on firm performance. However, below the threshold, there is a neutral relationship. The findings support the critical mass view that a minimum of one third women representation in board is compulsory to achieve the desired effect of gender diversity.

This study’s finding provides useful insights to managers, investors and policymakers. Managers and investors can identify the adequate board gender diversity levels that enhance firm performance. Policymakers are divided on whether mandatory or voluntary board gender quota should be adopted. This study suggests that a quota of one third can be used in policy implementation.

Unlike prior studies that consider the relationship between firm performance and board gender diversity as linear, to the best of the authors’ knowledge, this study is the first to investigate the threshold effects in this relationship using a new econometric approach.

This paper aims to shed light on an essential role that “female directors” on boards of companies in sub-Saharan Africa play towards corporate financial performance enhancement. The study observes how board gender diversity moderates the relationship between earnings management (EM) and financial performance of firms in sub-Saharan Africa from a dynamic perspective.

The study’s sample comprises 105 companies listed on the respective stock markets of nine sub-Saharan African countries. The data are collected from annual reports over the period 2007–2019, a total of 1,166 firm-year observations. Panel data models are used in the analyses.

The study finds that the performance effect of EM is contingent on board diversity and this finding persists even after controlling for dynamic endogeneity, simultaneity and unobserved time-invariant heterogeneity inherent in the EM and performance relationship.

The findings should be understood within the context that, only available annual reports and audited financial statements that were filed with respective capital markets of the nine surveyed countries are used as source of information.

The current study is unique, in that, it is the first panel multi-cross-country investigation within Africa to introduce gender diversity in the study of the relationship between EM and firm performance. It therefore extends the agency theory by using gender diversity as a moderating variable in the EM–firm performance nexus.

This paper aims to determine whether Islamic banks (IBs) and conventional banks (CBs) in Tunisia are distinguishable from one another based on financial characteristics during the 2005–2014 period covering the 2008 global financial crisis (GFC) and the 2011 Tunisian revolution.

For the comparison between IBs and CBs, 11 hypotheses are formulated to distinguish between the two types of banks. The authors use a univariate analysis based on the multi-dimension figures investigation and a multivariate one based on the robust OLS technique for panel linear regression with mixed effects.

Bank-specific factors, dummy and dummy interacting variables indicate that there are differences between Islamic and conventional bank behavior. Both methods show that IBs are more liquid, more profitable and riskier than CBs. Post-2011 Tunisian revolution, small IBs (small CBs) are more (less) solvent, large IBs are more stable and both types of banks are more liquid, which explain why Tunisian governments have relay on bank system to cover budget deficits post-2011 revolution.

In investigating the feature of IBs and CBs from the Tunisian context, the authors take into account the effect of two abnormal events (2008 GFC and 2011 Tunisian revolution) on IBs through interaction variables.

A novel approach using artificial neural networks (ANNs) for predicting temperature profiles evaluated 27 wells in the Gulf of Mexico. Two artificial neural network models were developed that predict the temperature of the flowing fluid at any depth in flowing oil wells. Back propagation was used in training the networks. The networks were tested using measured temperature profiles from the 27 oil wells. Both neural network models successfully mapped the general temperature‐profile trends of naturally flowing oil wells. The highest accuracy was achieved with a mean absolute relative percentage error of 6.0 per cent. The accuracy of the proposed neural network models to predict the temperature profile is compared to that of existing correlations. Many correlations to predict temperature profiles of the wellbore fluid, for single‐phase or multiphase flow, in producing oil wells have been developed using theoretical principles such as energy, mass and momentum balances coupled with regression analysis. The Neural Network 2 model exhibited significantly lower mean absolute relative percentage error than other correlations. Furthermore, in order to test the accuracy of the neural network models to that of Kirkpatrick’s correlation, a mathematical model was developed for Kirkpatrick’s flowing temperature gradient chart.