The causes of gender diversity in Malaysian large firms

The issue of women’s representation at the decision-making level in Malaysia has received special attention from the Government since 2004, the year in which it adopted a policy requiring that 30 % of the posts at the decision-making level in the public sector be filled by women. In 2011, the policy was extended to the private sector where 30 % of listed firms’ board seats are to be allocated to women with 2016 being the deadline for compliance. To this end, this paper aims at examining the factors that determine the appointment of women to the boards of Malaysian large firms. Large firms were chosen in this study because they have the resources and the capacity to adopt the policy more readily than smaller firms. The results reveal that gender diversity is positively associated with board size and the presence of family on the board. That is, the larger the board, the more likely it is that women sit on it. The fact that the presence of women on the board is associated with the presence of one or more family members on the board means that the appointment of women to the board is very much influenced by family ties rather than commercial reasons. The results also reveal a positive association between board independence and the proportion of women directors. Further, it is found that board independence is associated positively with the presence of independent women directors. Finally, the results show that firm performance is negatively associated with gender diversity. That is, firms with low financial performance are more likely to have women on their boards. Hence, taken altogether, the evidence suggests that the appointment of women to the board is very much driven by tokenism and family connection rather than by the business case.     

Success Traps,Dynamic Capabilities and Firm Performance

Dynamic capabilities (DCs) are fundamental to the understanding of differential firm performance. However, the question remains why some firms are better at developing and applying DCs than others. In particular, successful firms have been warned against the tendency to fall into a success or competence trap, where success reinforces exploitation of existing competences and crowds out exploration of new competences, hindering the development of DCs. Therefore, this study examines the effects of success traps on DCs and consequently firm performance, taking into account firm strategy and market dynamism. To facilitate this, our study also identifies the commonalities of DCs across firms. Drawing on survey data from 113 UK high‐tech small and medium‐sized firms, we find that success traps have a significant, strong negative effect on DCs, which in turn have a weak positive effect on firm performance; DCs are manifested through absorptive and transformative capabilities as two common features across firms. We also find that the development and application of DCs is related to internal factors (such as success traps) rather than external factors (such as market dynamism).     

A new three-parameter extension of the log-logistic distribution with applications to survival data

In this article, a new three-parameter extension of the two-parameter log-logistic distribution is introduced. Several distributional properties such as moment-generating function, quantile function, mean residual lifetime, the Renyi and Shanon entropies, and order statistics are considered. The estimation of the model parameters for complete and right-censored cases is investigated competently by maximum likelihood estimation (MLE). A simulation study is conducted to show that these MLEs are consistent in moderate samples. Two real datasets are considered; one is a right-censored data to show that the proposed model has a superior performance over several existing popular models.     

Detection of outliers in high-dimensional data using nu-support vector regression

Support Vector Regression (SVR) is gaining in popularity in the detection of outliers and classification problems in high-dimensional data (HDD) as this technique does not require the data to be of full rank. In real application, most of the data are of high dimensional. Classification of high-dimensional data is needed in applied sciences, in particular, as it is important to discriminate cancerous cells from non-cancerous cells. It is also imperative that outliers are identified before constructing a model on the relationship between the dependent and independent variables to avoid misleading interpretations about the fitting of a model. The standard SVR and the μ-ε-SVR are able to detect outliers; however, they are computationally expensive. The fixed parameters support vector regression (FP-ε-SVR) was put forward to remedy this issue. However, the FP-ε-SVR using ε-SVR is not very successful in identifying outliers. In this article, we propose an alternative method to detect outliers i.e. by employing nu-SVR. The merit of our proposed method is confirmed by three real examples and the Monte Carlo simulation. The results show that our proposed nu-SVR method is very successful in identifying outliers under a variety of situations, and with less computational running time.     

Perturbed nuptiality,delayed fertility: childbirth effects of Covid19

Journal of Population Research - An aspect of the Covid-19 pandemic that merits attention is its effects on marriage and childbirth. Although the direct fertility effects of people getting the...     

Multi-objective multi-verse optimiser for integrated two-sided assembly sequence planning and line balancing

Research in assembly optimisation is presently inclined towards integrative measures. Several benefits of simultaneously optimised Assembly Sequence Planning (ASP) and Assembly Line Balancing (ALB) have been highlighted by researchers to have better solution quality, shorter time-to-market, and minimalised error during planning. Recently, several efforts have been made to realise integrated assembly optimisation. However, none of the published research considered the two-sided assembly line problem. This paper presents an integrated ASP and ALB optimisation in a two-sided assembly environment (2S-ASPLB), which is mainly adopted in automotive assembly process. In this study, the 2S-ASPLB problem was formulated and optimised using Multi-Objective Multi-Verse Optimiser (MOMVO) by considering line efficiency, reorientation penalty, and tool change as the optimisation objectives. The computational experiments were conducted in a few stages, beginning with the identification of the best decoding approach for 2S-ASPLB. Next, the best MOMVO coefficient was studied, followed by comparing MOMVO performance with well-established multi-objective optimisation algorithms. Finally, a case study problem was presented to demonstrate applicability of the proposed model and algorithm in real-life problem. The results indicated that the priority factor (PF) decoding approach had better performance compared with others. Meanwhile, in comparison with well-established algorithms, MOMVO performed better in convergence and solution distribution. The case study results indicated the applicability of proposed 2S-ASPLB model and algorithm to improve line efficiency in assembly line. The main contribution of the research is a new 2S-ASPLB model and optimisation scheme, which can assist manufacturer in designing better assembly layout.

     

SEIR Model to address the impact of face masks amid COVID-19 pandemic

Early in the pandemic of coronavirus disease 2019 (COVID-19), face masks were used extensively by the general public in several Asian countries. The lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Asian countries compared with Western countries suggested that the wider community use of face masks has the potential to decrease transmission of SARS-CoV-2. A risk assessment model named Susceptible, Exposed, Infectious, Recovered (SEIR) model is used to quantitatively evaluate the potential impact of community face masks on SARS-CoV-2 reproduction number (R₀) and peak number of infectious persons. For a simulated population of one million, the model showed a reduction in R₀ of 49% and 50% when 60% and 80% of the population wore masks, respectively. Moreover, we present a modified model that considers the effect of mask-wearing after community vaccination. Interestingly mask-wearing still provided a considerable benefit in lowering the number of infectious individuals. The results of this research are expected to help public health officials in making prompt decisions involving resource allocation and crafting legislation.