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The purpose of this paper is to analyze the construct validity of learning transfer system inventory (LTSI) for use in Portugal. Furthermore, it also aims to analyze whether LTSI dimensions differ across individual variables such as gender, age, educational level and job tenure.

After a rigorous translation process that included forward and backward translation, subjective evaluations of the translated items, and pilot testing, the Portuguese version of LTSI was completed by 484 trainees, who attended different kinds of training courses. Two separate exploratory factor analyses were run to analyze the specific and general domains covered by LTSI. An initial analysis with the validated items and a second one with the validated and research items were also completed.

The results of the initial factor analyses showed a 16‐factor structure that accurately paralleled the 16‐factor structure of the original English version and revealed that this one is the most parsimonious factor structure. Statistically significant differences in some LTSI dimensions were found across gender and education‐level variables.

Data were collected through self‐reported measures using a single instrument applied immediately after the training, which might have affected the internal validity of the study. Although this study covered a wide variety of trainees' business areas and types of training, it might be possible that some types of organizations and training were not included in the analysis, thus limiting the generalization of the results to those contexts.

Based on the results of this study, Portuguese companies are now able to develop their training evaluation practices and to assess the training transfer factors through a valid and reliable instrument.

The paper expands LTSI validity in Europe, demonstrating that it has construct validity for use in Portugal.

The purpose of this research is to examine the construct and predictive ability of a German version of the Learning Transfer Systems Inventory (GLTSI), an instrument designed to assess a constellation of 16 factors known to influence the transfer of training in work settings.

The survey data for this study was collected from 569 employees working in 17 private sector organizations in Germany. This data was used to test the construct validity of the GLTSI using principle components analysis (common factor analysis). The predictive validity of the instrument was tested against individual and organizational level outcome measures using hierarchical stepwise multiple regression.

Exploratory factor analysis of the GLTSI produced a 16 factor solution that was a highly consistent original English version of the LTSI. The results of the regression analysis showed that a subset of GLTSI scales were significant predictors of measures of individual transfer results and organizational performance.

The study relies on self‐report data for both the independent and dependent variables and, although there is no evidence suggesting this was a problem, factors such as common method variance or social desirability could have influenced the correlations in this study in unknown ways. Certainly future criterion‐related validation research with the GLTSI using outcome measures that do not rely on self‐reports would be valuable. Future research with the GLTSI should also seek to modify certain items or add new items to enhance the emic or culture‐specific nature of specific scales.

This study contributes the study and practice of human resource development by furthering the dissemination of analytical tools across linguistic settings and enhancing the potential for the cross‐cultural study of learning transfer and training effectiveness.

The purpose of this study is to contribute to the understanding of factors that affect training effectiveness. According to the literature, social support, perceived content validity, transfer design, the motivation to improve work through learning and positive transfer, contribute to the effectiveness of training.

The sample used consisted of 202 employees with ages between 18 and 60 years, working for an insurance company where they had training for a period of three months.

The results show a relationship between perceived content validity and transfer design, as well as with the motivation to improve work through learning. A mediating role of social support was also evident in this relationship. Finally, the authors highlight the findings of the relationship between motivation to improve work through learning and positive transfer.

These findings contribute to the literature by demonstrating how the role of social support can increase training effectiveness in organizations.

This study explored the ability of liking and two forms of rater‐ratee similarity to predict multi‐source proficiency ratings of managerial competencies. Findings from the regression model that included all raters indicated that technical proficiency, rater‐ratee liking, demographic and attitudinal similarity about work all were significant predictors of proficiency ratings. For these ratings, attitudinal similarity was the strongest predictor of ratings after technical proficiency. Liking, attitudinal and demographic similarity combined increased explained variance by about one‐third over technical proficiency. Regression models, run for each rating source separately, suggested that supervisors, subordinates and peers may weigh performance and interpersonal elements differently, when making performance ratings.

Online learning continues to grow year after year and majority of the growth is seen in public institutions of higher education. The purpose of this book chapter is to acquaint the readers to group-based learning in an online environment as an innovative practice for engaged learning. Diverse facets of group-based learning are discussed such as role of instructors, role of learners, challenges of group-based learning and finally role of social media in designing such group-based learning projects. A comprehensive prototype application of a group-based learning project walks the readers through applying group-based learning in their respective courses at the undergraduate and/or graduate level. Finally some instructor designed rubrics for assessing group-based learning are shared for effective assessment of a group-based learning project.

Fringe, or “non‐wage”, benefits typically form an important part of the compensation package provided for employees, having grown considerably during the 20th century. One of the more traditional types of benefit, of interest in this article, is the provision of goods and services to employees at a price below that which they would normally expect to pay. More specifically, we are interested in the sale, at a discount, of a company's own products, rather than the provision of other goods and services, such as meals or private health insurance, at low rates made possible by company subsidy or the exploitation of its buying power or facilities. While a company may sometimes sell discontinued lines or damaged stock to its employees, our focus is on the sale of normal products. Our primary purpose is to show how, with an understanding of cost and revenue relationships, the problem of setting the rate of discount can be approached. The analysis draws on and extends previous work on shareholder concessions.

The purpose of this paper is to propose a comprehensive version of a hybrid autoregressive integrated moving average (ARIMA), and artificial neural networks (ANNs) in order to yield a more general and more accurate hybrid model for exchange rates forecasting. For this purpose, the Kalman filter technique is used in the proposed model to preprocess and detect the trend of raw data. It is basically done to reduce the existing noise in the underlying data and better modeling, respectively.

In this paper, ARIMA models are applied to construct a new hybrid model to overcome the above-mentioned limitations of ANNs and to yield a more general and more accurate model than traditional hybrid ARIMA and ANNs models. In our proposed model, a time series is considered as a function of a linear and nonlinear component, so, in the first phase, an ARIMA model is first used to identify and magnify the existing linear structures in data. In the second phase, a multilayer perceptron is used as a nonlinear neural network to model the preprocessed data, in which the existing linear structures are identified and magnified by ARIMA and to predict the future value of time series.

In this paper, a new Kalman filter based hybrid artificial neural network and ARIMA model are proposed as an alternate forecasting technique to the traditional hybrid ARIMA/ANNs models. In the proposed model, similar to the traditional hybrid ARIMA/ANNs models, the unique strengths of ARIMA and ANN in linear and nonlinear modeling are jointly used, aiming to capture different forms of relationship in the data; especially, in complex problems that have both linear and nonlinear correlation structures. However, there are no aforementioned assumptions in the modeling process of the proposed model. Therefore, in the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be generally guaranteed that the performance of the proposed model will not be worse than either of their components used separately. In addition, empirical results in both weekly and daily exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models.

In the proposed model, in contrast to the traditional hybrid ARIMA/ANNs, it can be guaranteed that the performance of the proposed model will not be worse than either of the components used separately. In addition, empirical results in exchange rate forecasting indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid ARIMA/ANNs models. Therefore, it can be used as an appropriate alternate model for forecasting in exchange ratemarkets, especially when higher forecasting accuracy is needed.

Discusses British Rail’s (BR’s) organizational transformation during the 1980s and in particular the position of R.B. Reid, chairman of the British Railways Board (BRB) during that period. As a career railwayman, Reid was an atypical choice to chair the BRB. Considers how Reid brought his professional knowledge and experience to bear in carrying through arguably one of the most fundamental processes of change and organizational development that BR has experienced in the past 40 years.

This article discusses the question of risk in the lives of people who are supported by human services. It responds to the way in which risk, as it has traditionally been approached by these services, imposes a barrier to social inclusion and to an interesting and productive life. The article proposes an alternative person‐centred risk process. We argue that, by beginning with a focus on who the person is, their gifts and skills, and offering a positive vision of success, it could be possible to avoid the implied aversion to any form of risk embedded in the traditional approaches and attitudes.