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The purpose of this paper is to determine the agility level of a manufacturing system along different timelines.

The fuzzy synthetic extents of agile manufacturing enablers (AMEs), on the basis of their importance, are determined. Then they are integrated with their performance ratings along different timeline for calculating the Fuzzy Agile Manufacturing Index (FAMI). Euclidean distances of FAMI from predetermined agility levels are mapped to determine the agility level of the manufacturing system along different timeline.

The proposed methodology was implemented in an Indian manufacturing organization to determine its agility level. It was concluded from the obtained results that there was significant improvement in the agility level along the timeline.

The weights of the AMEs are assumed to be constant along the timeline.

The supply chain mangers can easily apply this methodology in their respective manufacturing organizations to assess and determine the agility level. This proposed approach will show the direction to check the performance of agility and evaluate the evolution of agility in their respective manufacturing organizations.

The combination of fuzzy synthetic extent of weights and average fuzzy performance ratings of AMEs to calculate the FAMI along the timeline considering judgments of multiple experts is a unique contribution.

This paper provides more insight into the assumption of human capital theory that the productivity of job-related training is driven by the improvement of workers’ skills. We analyze the extent to which training and informal learning on the job are related to employee skill development and consider the heterogeneity of this relationship with respect to workers’ skill mismatch at job entry. Using data from the 2014 European Skills and Jobs Survey, we find – as assumed by human capital theory – that employees who participated in training or informal learning show greater improvement of their skills than those who did not. The contribution of informal learning to employee skill development appears to be larger than that of training participation. Nevertheless, both forms of learning are shown to be complementary. This complementarity between training and informal learning is related to a significant additional improvement of workers’ skills. The skill development of workers who were initially underskilled for their job seems to benefit the most from both training and informal learning, whereas the skill development of those who were initially overskilled benefits the least. Work-related learning investments in the latter group seem to be more functional in offsetting skill depreciation than in fostering skill accumulation.

This paper aims to examine how gender differences influence students’ reactions to the use of the annotatable multimedia e-reader (AME). To reach this aim, we develop an AME where various annotation tools are provided to help students learn English in-class and after-class.

An empirical study was conducted with 63 fifth-grade students from an elementary school. A pre-test and post-test were used to identify their prior knowledge and learning achievement, respectively. A questionnaire was applied to identify participants’ perceptions towards the AME.

The results show that students’ post-test scores are significantly related to after-class behaviour, instead of in-class behaviour. Females prefer to use the text annotation and teachers’ voice, but it is voice annotation that is beneficial to improve their learning achievement. Conversely, males prefer to use the text-to-speech only, but it is text annotation that is helpful to improve their learning achievement. Additionally, the ease of use affects males’ intention to use the AME to learn English after-class while it has no effects on females.

This study not only shows the importance of gender differences but also demonstrates the essence of after-class learning behaviour. More importantly, a framework is proposed to support designers to develop e-readers that can accommodate the preferences of females and males.

The authors examine the influence of powerful political corporate appointments on the usage of firm bribery channels. Party Secretaries within Chinese state-owned enterprises (SOEs) may simultaneously hold top management positions, thereby endowing powerful firm-level decision rights on those appointees, hereafter referred to as powerful dual role Party Secretaries.

This study employs panel data analysis with industry and year fixed effects. The authors use a sample of 1,143 Chinese SOEs listed on the Shanghai and Shenzhen Stock Exchanges from 2004 to 2015.

The authors find that powerful dual role Party Secretaries are associated with greater bribery channel usage. Following the ongoing anticorruption campaign, SOEs with the powerful appointments significantly reduce their usage of both transparent (entertainment and travel costs) and opaque bribery (abnormal management expenses) channels. However, in general, Chinese SOEs respond to the anticorruption shock by switching from the more transparent to the opaquer bribery channel.

The authors contribute to the ongoing debate of politicians on corporate boards by examining the relatively unexplored area of government appointed top management and their influence on bribery at the firm level.

Taking a career capital approach, this paper addresses the issue of “pipeline block” frequently experienced by women seeking career advancement. Focusing on the Arab Middle East (AME) region, the authors take a contextually relevant multi-level approach to examine these issues.

The study uses a qualitative, interview-based approach, drawing on data obtained from women leaders from the AME region. Drawing on Bourdieu's capital-field-habitus framework, we explore how women in the AME developed career capital in particular organisational fields.

The findings show the importance of human and social capital, as well as the influence of habitus for women's career advancement in specific fields. The study also highlights the unique contribution of cultural capital in helping women to navigate organisational fields where it is necessary to both challenge, and conform to, traditional norms.

Limitations of the study include assumptions of homogeneity across countries of the AME, whereas differences are known to exist. Future research should consider these contextual differences, and also include a study of women who were not successful in gaining career advancement.

The study’s multi-level approach highlights practical implications for women, organisations and society. For organisations, the authors propose some context-relevant coaching strategies that can help women to attain leadership positions.

The study’s multi-level approach highlights practical implications for women, organisations,and society. Focusing on organisations, the authors propose some context-relevant coaching strategies that can help women to attain advancement in their careers.

The study demonstrates originality in the findings by showing how women overcome the pipeline block in relation to their career advancement. The use of the Bourdieusian framework, an in-depth qualitative approach, and the AME context also add to the study's originality.

The purpose of this article is to suggest that doctoral student socialization is a gendered process.

This article uses a qualitative case study methodology, studying engineering students in one university department.

The author considers how various norms and practices, including competition and hierarchy along with overt objectification of women, point to the masculine nature of the discipline.

Although stage models of socialization are helpful in that they provide an outline of students’ various tasks as they progress through their doctoral programs, they can account neither for the culture of disciplines nor for the identities of students who populate them. The author suggests that students in engineering are prepared to embrace competition and hierarchy, norms that point to a gendered disciplinary culture. Although, certainly, particular interests will lead students to pursue different majors, the discipline serves to reinforce culture.

The purpose of this paper is to discuss the visualizer interface that has been developed for the first phase of an automatic meaning extraction (AME) system.

AME system was developed to automatically extract concepts and their relations across texts from all domains of knowledge. One challenge for the developer is to create interface tools that help the users use the system. This paper describes a visualizer interface that can map the concepts and relations in the form of two‐dimensional graph or network.

Using this visualizer, users can maximize the use of AME system by allowing the visualization of the concepts' networks results. Users can search for a concept and view the relationships of the concept to other concepts. Those relationships can be traced back to the source sentences in the original documents through the “Show Text” function.

This visualizer is useful in solving the problem of visualizing the relationships between concepts across varied domains of knowledge. The extraction of relationships in the AME system is based upon a unique connector‐based relation extraction. It is particularly appropriate for target users such as the researcher, educators and learners. The visualizer implements the Java Universal Network/Graph Framework to provide a few functions that enable users to manipulate the concepts graph.

The aim of this paper is to give empirical evidence of the fundamental mechanisms underlying the resource systemics: time compression diseconomies, asset mass efficiency, and interconnectedness of assets. It assumes that time, resource properties and interactions are the critical elements leading to accumulation of idiosyncratic resources, firm performance and survival. Results from a Cox regression on a simulated dataset confirm the protective effects of time compression diseconomies, asset mass efficiency, and interconnectedness of assets against firm's death.

This study aims to develop a regression-based machine learning model to predict housing price, determine and interpret factors that contribute to housing prices using different data sets available publicly. The significant determinants that affect housing prices will be first identified by using multinomial logistics regression (MLR) based on the level of relative importance. A comprehensive study is then conducted by using SHapley Additive exPlanations (SHAP) analysis to examine the features that cause the major changes in housing prices.

Predictive analytics is an effective way to deal with uncertainties in process modelling and improve decision-making for housing price prediction. The focus of this paper is two-fold; the authors first apply regression analysis to investigate how well the housing independent variables contribute to the housing price prediction. Two data sets are used for this study, namely, Ames Housing dataset and Melbourne Housing dataset. For both the data sets, random forest regression performs the best by achieving an average R² of 86% for the Ames dataset and 85% for the Melbourne dataset, respectively. Second, multinomial logistic regression is adopted to investigate and identify the factor determinants of housing sales price. For the Ames dataset, the authors find that the top three most significant factor variables to determine the housing price is the general living area, basement size and age of remodelling. As for the Melbourne dataset, properties having more rooms/bathrooms, larger land size and closer distance to central business district (CBD) are higher priced. This is followed by a comprehensive analysis on how these determinants contribute to the predictability of the selected regression model by using explainable SHAP values. These prominent factors can be used to determine the optimal price range of a property which are useful for decision-making for both buyers and sellers.

By using the combination of MLR and SHAP analysis, it is noticeable that general living area, basement size and age of remodelling are the top three most important variables in determining the house’s price in the Ames dataset, while properties with more rooms/bathrooms, larger land area and closer proximity to the CBD or to the South of Melbourne are more expensive in the Melbourne dataset. These important factors can be used to estimate the best price range for a housing property for better decision-making.

A limitation of this study is that the distribution of the housing prices is highly skewed. Although it is normal that the properties’ price is normally cluttered at the lower side and only a few houses are highly price. As mentioned before, MLR can effectively help in evaluating the likelihood ratio of each variable towards these categories. However, housing price is originally continuous, and there is a need to convert the price to categorical type. Nonetheless, the most effective method to categorize the data is still questionable.

The key point of this paper is the use of explainable machine learning approach to identify the prominent factors of housing price determination, which could be used to determine the optimal price range of a property which are useful for decision-making for both the buyers and sellers.

The purpose of this paper is to identify and assess the key performance indicators (KPIs) for the maintenance of public hospital buildings in the Gaza Strip.

Four KPIs were identified and evaluated in this paper: building performance indicators (BPI), maintenance efficiency indicators (MEI), annual maintenance expenditure (AME) and urgent repair request indicator. Twenty-one buildings in 13 public hospitals in Gaza Strip Governorate were taken as the sample of this study.

The results indicated that the European Gaza hospital has the highest BPI score (81.66) and the Dorra hospital has the lowest BPI score (68.26). The findings revealed that the average AME for all hospitals was $13.8/m2 which is considered to be below the standard level of expenditure. The MEI for Gaza public hospital buildings was found to be equal to 0.3 which indicated low level of maintenance expenditure.

Unavailability of certain data, lack of maintenance documentation and comparison difficulty between the Gaza Strip and Israel due to political, cultural and financial situation were some of the limitations of this study.

The Ministry of Health (MoH) can utilize the results of this study and consider it as benchmarking for maintenance management in public hospital buildings. This can improve the current maintenance situation which ultimately will improve the health-care situation in Palestine. The Palestinian MoH should look for external funding to increase the AME, as well as aim at increasing the MEI.

The health-care situation in Palestine will be improved.

This study is considered the first study to identify and assess the KPIs in the Gaza Strip. KPIs will assist the MoH to compare the actual and estimated performance in terms of effectiveness, efficiency and quality of workmanship.