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Purpose This study aims to examine the relationships for the following: supportive work environment, person–organisation fit and employee retention among academic staff in one of the Malaysian public universities. Design/methodology/approach This study used a conceptual framework to assess the direct impacts of supportive work environment (i.e. perceived climate, supervisory relationship, peer group interaction, perceived organisational support), person–organisation fit and employee retention. A self-administered questionnaire was distributed to 225 respondents. Findings The findings present the mediating influence of person–organisation fit on the relationships between supportive work environment and employee retention. The results reveal a direct and positive relationship between supportive work environment and academic staff retention. These results imply that individuals’ perceived towards an organisation can influence their decision to stay at the university. Research limitations/implications This study had filled in the knowledge gap about the role of supportive work environment with person–organisation fit and the relationship for employee retention in Malaysia. Previous research emphasised on organisations’ role in employee retention and engagement in the manufacturing and service industry. Originality/value The findings of this study reveal how a supportive work environment can impact employee retention among academic staff. Specifically, the person–organisation fit describes the relationship between supportive work environment and employee retention.

Abstract-Accuracy is one of the main elements in the disease diagnose. Thus, it is important to select most relevant attributes to generate the optimal accuracy. The objective of this study is to predict more accurately the presence of oral cancer primary stage with reduced number of attributes. Originally, 25 attributes have been identified in order to predict the oral cancer staging. In this study, the integrated diagnostic model with hybrid features selection methods is used to determine the attributes that contribute the most to the diagnosis of oral cancer, which, indirectly, reduces the number of features that are collected from a variety of patient records. Twentyfive attributes have been reduced to 14 features using hybrid feature selection. Subsequently, four classifiers: Updatable Naïve Bayes, Multilayer Perceptron, K-Nearest Neighbors and Support Vector Machine are used to predict the diagnosis of patients with oral cancer. Also, the observations indicate that the Support Vector Machine outperforms other machine learning algorithms after incorporating feature subset selection with SMOTE at preprocessing phases.

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Forensic age estimation methods are biased to sex and population; in general, accuracy is reduced when applied to foreign populations. This study assessed the accuracy of the Suchey–Brooks method in contemporary Malaysian individuals and aimed to formulate population‐specific standards. Multi‐detector computed tomography scans of 355 individuals (165 male; 190 female) of 15–83 years of age were reconstructed using 3D‐volumetric rendering in RadiAnt. Pubic symphyseal phase, bias, inaccuracy, and percentage correct age classifications are examined. Transition analysis was used to develop age estimation standards. High observer agreement (κ = 0.763–0.832) and a positive relationship between age and pubic symphyseal phase (r = 0.884–0.90) were demonstrated. Mean inaccuracies were 8.62 and 8.95 years for males and females, respectively; overall correct classification was 97.8%. Transition ages between phases in males were 18.79, 23.29, 28.85, 43.64, and 61.15 years; in females, the corresponding data were 19.77, 22.53, 32.62, 41.85, and 57.39 years.