‘Quality’ part-time work in Australian organizations: implications for HRD

Part-time work is an international phenomenon. Many western countries show similar patterns to Australia, with a significant increase in part-time work in the last twenty to twenty-five years. In Australia, part-time work has developed some unique features, particularly as it often involves lower wages and poor conditions. Part-time employees in Australia are often excluded from human resource development (HRD) activities such as training and career tracks provided to full-time workers. The experience of particular Australian organizations illustrates possible avenues for quality part-time work. This paper examines practical examples of quality part-time work in eight Australian organizations across five industries (nursing, hospitality, retail, local government and the legal profession). Using case study research, the paper discusses the dimensions of quality part-time work in different industry and occupational contexts from an HRD perspective.     

Ariadne’s thread: Cape Verdean women in transnational webs

This article is about Cape Verdean women who make a living through engaging in regular transnational petty trade. They buy consumer goods in West African countries, in Portugal and elsewhere in Europe, in Brazil and in the USA, to sell back in Cape Verde, either in open‐air markets or in their own shops. Women perform most of this kind of trade and they operate on the border between the formal and informal economies. Drawing on interview material, the article shows how their self‐presentation as autonomous self‐reliant economic agents relates to gender and family roles and to a strategic balance between personal freedom and embeddedness in multi‐territorialized networks.     

Generalized poisson regression for positive count data

This paper suggests a flexible parametrization of the generalized Poisson regression, which is likely to be particularly useful when the sample is truncated at zero. Suitable specification tests for this case are also studied. The use of the models and tests suggested is illustrated with an application to the number of recreational fishing trips taken by households in Alaska     

Sharing of Resources Within the Family and the Economics of Household Decision Making

Over the last 3 decades, economic models have been developed that recognize that potentially conflicting interests may shape household decisions and the sharing of resources within families. This article provides an overview of how decision making within households has been modeled within economics, presents the main benefits and limitations of those models, and critically assesses their usefulness to researchers from other disciplines interested in the within‐ household distribution of resources. The main focus is on the theory, empirical application, and results of the currently dominant collective models, but the authors also look at developments that led up to them and some subsequent extensions and alternative approaches. Given the weight placed by policymakers and others on economic and quantitative evidence, it is incumbent on researchers of all disciplines to understand the achievements and limitations of the models used, explicitly or implicitly, to produce such evidence and the assumptions that lie behind them.     

Risk‐Based Input‐Output Analysis of Influenza Epidemic Consequences on Interdependent Workforce Sectors

Outbreaks of contagious diseases underscore the ever‐looming threat of new epidemics. Compared to other disasters that inflict physical damage to infrastructure systems, epidemics can have more devastating and prolonged impacts on the population. This article investigates the interdependent economic and productivity risks resulting from epidemic‐induced workforce absenteeism. In particular, we develop a dynamic input‐output model capable of generating sector‐disaggregated economic losses based on different magnitudes of workforce disruptions. An ex post analysis of the 2009 H1N1 pandemic in the national capital region (NCR) reveals the distribution of consequences across different economic sectors. Consequences are categorized into two metrics: (i) economic loss, which measures the magnitude of monetary losses incurred in each sector, and (ii) inoperability, which measures the normalized monetary losses incurred in each sector relative to the total economic output of that sector. For a simulated mild pandemic scenario in NCR, two distinct rankings are generated using the economic loss and inoperability metrics. Results indicate that the majority of the critical sectors ranked according to the economic loss metric comprise of sectors that contribute the most to the NCR's gross domestic product (e.g., federal government enterprises). In contrast, the majority of the critical sectors generated by the inoperability metric include sectors that are involved with epidemic management (e.g., hospitals). Hence, prioritizing sectors for recovery necessitates consideration of the balance between economic loss, inoperability, and other objectives. Although applied specifically to the NCR, the proposed methodology can be customized for other regions.     

Integration of the information systems in the production process: a case study

This paper evaluates the execution of an ERP – enterprise resource planning in a production process. The changes that were made to processes to make them compatible with ERP modules and adaptations that were required by the ERP implementation are presented. The presented study is different from the normal patterns of a vertical implementation, since its application is not direct. In order to use the standard modules of the ERP system, the internal processes were embellished and the data collection steps were integrated into the production process (production orders, inventory, production, daily consumption, daily output). The case study shows that the ERP implementation caused an improvement of the processes in the areas adjacent to production (purchasing, logistics, distribution, supply and sales). In conclusion, this paper presents the main advantages and disadvantages of the ERP system implementation, as well as some considerations and recommendations for the future ERP implementations.     

Copula, marginal distributions and model selection: a Bayesian note

Copula functions and marginal distributions are combined to produce multivariate distributions. We show advantages of estimating all parameters of these models using the Bayesian approach, which can be done with standard Markov chain Monte Carlo algorithms. Deviance-based model selection criteria are also discussed when applied to copula models since they are invariant under monotone increasing transformations of the marginals. We focus on the deviance information criterion. The joint estimation takes into account all dependence structure of the parameters’ posterior distributions in our chosen model selection criteria. Two Monte Carlo studies are conducted to show that model identification improves when the model parameters are jointly estimated. We study the Bayesian estimation of all unknown quantities at once considering bivariate copula functions and three known marginal distributions.     

A multiple group item response theory model with centered skew-normal latent trait distributions under a Bayesian framework

Very often, in psychometric research, as in educational assessment, it is necessary to analyze item response from clustered respondents. The multiple group item response theory (IRT) model proposed by Bock and Zimowski [12] provides a useful framework for analyzing such type of data. In this model, the selected groups of respondents are of specific interest such that group-specific population distributions need to be defined. The usual assumption for parameter estimation in this model, which is that the latent traits are random variables following different symmetric normal distributions, has been questioned in many works found in the IRT literature. Furthermore, when this assumption does not hold, misleading inference can result. In this paper, we consider that the latent traits for each group follow different skew-normal distributions, under the centered parameterization. We named it skew multiple group IRT model. This modeling extends the works of Azevedo et al. [4], Bazán et al. [11] and Bock and Zimowski [12] (concerning the latent trait distribution). Our approach ensures that the model is identifiable. We propose and compare, concerning convergence issues, two Monte Carlo Markov Chain (MCMC) algorithms for parameter estimation. A simulation study was performed in order to evaluate parameter recovery for the proposed model and the selected algorithm concerning convergence issues. Results reveal that the proposed algorithm recovers properly all model parameters. Furthermore, we analyzed a real data set which presents asymmetry concerning the latent traits distribution. The results obtained by using our approach confirmed the presence of negative asymmetry for some latent trait distributions.