Purpose – The assessment of patient safety culture (PSC) is a major priority for healthcare providers. It is
often realized using quantitative approaches (questionnaires) separately from qualitative ones (patient safety
culture maturity model (PSCMM)). These approaches suffer from certain major limits. Therefore, the aim of the
present study is to overcome these limits and to propose a novel approach to PSC assessment.
Design/methodology/approach – The proposed approach consists of evaluating PSC in a set of healthcare
establishments (HEs) using the HSOPSC questionnaire. After that, principal component analysis (PCA) and
K-means algorithm were applied on PSC dimensional scores in order to aggregate them into macro dimensions.
The latter were used to overcome the limits of PSC dimensional assessment and to propose a
quantitative PSCMM.
Findings – PSC dimensions are grouped into three macro dimensions. Their capitalization permits their
association with safety actors related to PSC promotion. Consequently, a quantitative PSC maturity matrix was
proposed. Problematic PSC dimensions for the studied HEs are “Non-punitive response to error”, “Staffing”,
“Communication openness”. Their PSC maturity level was found underdeveloped due to a managerial style
that favors a “blame culture”.
Originality/value – A combined quali-quantitative assessment framework for PSC was proposed in the
present study as recommended by a number of researchers but, to the best of our knowledge, few or no studies
were devoted to it. The results can be projected for improvement and accreditation purposes, where different
PSC stakeholders can be implicated as suggested by international standards.
Keywords Patient safety culture, PCA, Macro dimensions, HSOPSC questionnaire, Maturity model

Modern approaches to Occupational Health and Safety have acknowledged the important contribution that continuous improvements to working conditions can make to the motivation of employees, their subsequent performance, and therefore to the competitiveness of the company. Despite this fact, organizational change initiatives represent a path less traveled by employees. Specialized literature has drawn on the fact that employees’ satisfaction presents both the foundation and catalyst for effective implementation of improvements to working conditions. Method: This paper conceptualizes the alignment of employees through measurement of job satisfaction and uses the Bayesian Network to assess the influence of human factors, particularly the cognitive, emotional, and behavioral aspects. Toward this aim, the Bayesian Network is evaluated through a cross-validation process, and a sensitivity analysis is then conducted for each influential dimension: emotional, cognitive, and behavioral. Results: The results reveal that these three dimensions are interrelated and have a direct influence on job satisfaction and employees’ alignment during the organization change. Further, they suggest that the best strategy for enhanced alignment and smooth conduct of organizational changes is simultaneous enhancement of the three dimensions. Practical applications: This study shows the influence of emotional, cognitive, and behavioral dimensions on job satisfaction and employees’ alignment during the organizational change. Further, it makes it clear how separate or combined improvements in these dimensions impact the alignment of employees what allows developing efficient and effective strategies for a successful change implementation and sustained alignment.

Purpose – The increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.
Design/methodology/approach – The starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.
Findings – The performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE,MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.
Originality/value – The performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

Abstract:

Purpose – Decoupling of pressures ranging from regulatory compliance and stakeholders expectations to business competitiveness and sustainability, companies need to designing their environmental strategies with a broader consideration of these influences. This paper aims at developing a dynamic alignment model to enhance the environmental performance that considers the influential pressures based on multi-criteria decision making process.

Design/Methodology/Approach: Authors have proposed a dynamic model for the alignment of the environmental performance based on a hybrid multi-criteria decision-making approach combining the analytic hierarchy process and technique for order preference by similarity to ideal solution. This model considers contemporary strategic dynamism of the environmental performance and provides a methodology to assist companies prioritizing the environmental aspects based on the influential pressures and deciding on the enhancement pathways.

Findings: The proposed model based on a hybrid Multi-Criteria Decision-Making process allows prioritizing the environmental aspects considering the allocated weights to the alignment-triggered pressures and draw the way to develop different pathways to improve the alignment.

Practical Implications: The proposed Dynamic Alignment Model presents an instrument for the continuous alignment of the environmental performance and an effective management of changes and contributes to minimize gaps and divergences. 

Originality/Value: In this paper, the environmental performance has been approached through the contemporary strategic dynamism with the deployment of the multi-criteria decision making techniques to yield an alignment framework for the environmental decision that combines the internal and external approaches for an effective and sustainable improvement of the environmental performance.

Keywords: Alignment, Decision Making, Environmental Performance, Hybrid Model, Multi-Criteria.

The ageing of the Algerian oil and gas (O&G) installations has led to many incidents. Such installations are over 30
years old (life cycle) and still in operation. To deal with this O&G crucial problem, the Algerian authorities have
launched a rehabilitation and modernization schedule of these installations. Within the framework of this program,
many audit operations are initiated to elaborate a general diagnosis of the works to be performed while optimizing
production. In other words, industrial ageing risks shall be controlled. In the process safety management (PSM)
context, the aim of this paper is to study ageing problem of the Algerian industrial installations through proposed
indicators. Their prioritization adjusted by TOPSIS method which allows identification of ageing control solutions of
Algerian onshore fields.