Purpose -The purpose of this paper is to examine to what extent management practices in safety culture are effective in reducing workplace injuries. Design/methodology/approach -Management practices are an important component of an organization's safety climate. The study was conducted in a major industrial zone in Malaysia. The management practices examined in this study were reward, training, management commitment, communication and feedback, hiring practices, and employee participation. In total, 68 companies participated in this study; the injury data provided by the companies were for three years and 24 musculoskeletal injuries were examined. Findings -The multiple correlation was at 0.43 and the R 2 was 0.19. A significant linear relationship between management practices subscale and injury rates was obtained (F ¼ 2:28, p ¼ 0:14). Rewards, management commitment, feedback and selection were found to reduce injury rates. However, only feedback (t ¼ 22:98) and employee participation (t ¼ 2:01) were significantly predicting the injury rates. Implementing good management practices does reduce injury rates. Research limitations/implications -Nevertheless, a positive relationship for participation with injury rates suggests that a re-examination of these practices is necessary, as it may not be effective in reducing injury rates. Practical implications -It may be posited that although frequency counts of injuries in the workplace do provide some indication of the extent of workplace injury; nevertheless they do not provide a true account of the severity of these injuries. Originality/value -This paper presents empirical findings on the relationship between management practices and injury rates in the industrial sector in Malaysia

This paper examines the relationship between OCBs and turnover intention. The sample consisted of 557 non manager employees across 63 hotels in Malaysia. Managers' ratings of employees' OCBs were collected and matched with 557 employees' self ratings of turnover intention and demographic. Factor analysis of OCB items revealed a new dimension of OCB, labeled as patience, together with four common dimensions -helping behavior, conscientiousness, sportsmanship and civic virtue. Consistent with cognitive consistency theory, the results show that helping behavior, Asian Social ScienceJune, 2009 109 conscientiousness, patience and civic virtue were significantly and negatively related to turnover intention. Moreover, the OCB dimensions explained a meaningful portion (25%) of the turnover intention measure. The results indicated that gender moderated the relationship between helping behavior and turnover intention and this relationship was stronger for females than males. These results point to the importance of considering behavioral factors in effort to predict and manage employee turnover.

Data on job performance were collected for 1,053 male (N = 461) and female (N = 592) rubber tappers from nine different estates in Malaysia, and analysed for age, sex and tenure differences. Job performance data were based on output measures derived from production records over a 12‐month period. Since the type of terrain varied considerably across plantations, terrain was classified as either ‘hilly’ or ‘undulating’ and the effects of terrain type on performance were also examined. The relationship between age and total output took the form of an inverted U. Partial correlation analyses indicated that tenure, rather than age, was the main determinant of job performance. More experienced rubber tappers were found to perform better than less experienced rubber tappers. Women had significantly higher output levels than did men. Tappers working on estates with predominantly hilly terrains produced significantly higher levels of output than did tappers working on estates with predominantly undulating terrains. Neither sex nor tenure interacted significantly with terrain. These results are discussed in relation to previous research concerned with group differences in job performance.

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With the hyperconnectivity and ubiquity of the Internet, the fake news problem now presents a greater threat than ever before. One promising solution for countering this threat is to leverage deep learning (DL)-based text classification methods for fake-news detection. However, since such methods have been shown to be vulnerable to adversarial attacks, the integrity and security of DL-based fake news classifiers are under question. Although many works study text classification under the adversarial threat, to the best of our knowledge, we do not find any work in literature that specifically analyzes the performance of DL-based fake-news detectors under adversarial settings. We bridge this gap by evaluating the performance of fake-news detectors under various configurations under black-box settings. In particular, we investigate the robustness of four different DL architectural choices-multilayer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN) and a recently proposed Hybrid CNN-RNN trained on three different state-of-the-art datasets-under different adversarial attacks (Text Bugger, Text Fooler, PWWS, and Deep Word Bug) implemented using the state-of-the-art NLP attack library, Text-Attack. Additionally, we explore how changing the detector complexity, the input sequence length, and the training loss affect the robustness of the learned model. Our experiments suggest that RNNs are robust as compared to other architectures. Further, we show that increasing the input sequence length generally increases the detector's robustness. Our evaluations provide key insights to robustify fake-news detectors against adversarial attacks.INDEX TERMS fake news detection, deep neural networks, adversarial attacks, adversarial robustness.