Abstract
The goal of this article is to analyse the relationship between rental income and capitalisation rates when real estate value is assessed in parishes of the Lisbon and Porto municipalities. Based on housing market values in euros per square metre during the 2006-2009 period, the income approach was used to compare the two main types of apartments (i.e. B2, or two-bedroom, and B1, or one-bedroom) in Lisbon and Porto. We used the capital asset pricing model to calculate the risk measure. The cluster analysis was used to group the Lisbon and Porto parishes according to their rental income and capitalisation rates. Regressions were used to model both geographical markets. Clear differences were found between Porto and Lisbon, the results being more robust for the Porto municipality in regard to B2 apartments. Moreover, rental income is inversely proportional to capitalisation rates for B1 apartments for Porto parishes, which means that there is an initial overinvestment.

Abstract
In recent years, in the services market, we have witnessed a growing importance of concepts such as service quality, satisfaction, word-of-mouth and complaint behaviour. The proposed conceptual model aimed to examine the existing relationships among these dimensions in the context of health clubs. Data was collected through questionnaires and analysed using structural equations modelling (SEM) to simultaneously test all the relationships in the model. Overall, the results suggest that quality is assessed through staff, programme and facilities evaluation, and that service quality is crucial for both satisfaction and word-of-mouth generation. Customers do not complain to third parties, i.e., to external parties that are not involved, but have some influence on the service provider even when low quality is delivered or they are dissatisfied. © 2016 Inderscience Enterprises Ltd.

Abstract
Purpose - The importance of innovation as a generator of competitive advantage and the collaborative nature of this process are recurring themes in the literature. This paper aims to contribute to the improvement of knowledge about the relationship between supply chains and the innovation process by means of a systematic literature review. Methodology/approach - The method used consists of the identification, selection, analysis and synthesis of existing research on the subject and aims to ensure that the review is transparent, auditable and replicable. This paper presents the analysis of 94 papers from 37 journals and the major contributions are explored. Findings - The identification and analysis of relevant articles showed the complexity, timeliness and the wide-ranging character of the theme. The analysis of articles allowed the identification of facilitators of the innovation process, as well as five approaches applicable to supply chains to drive the innovation process. From these analyses, a model synthesising the main practices identified for improving innovation performance is presented. Research limitations/implications - When carrying out literature reviews, the selection of articles might be considered subjective. To circumvent this limitation, the papers have been assessed by three researchers. Practical implications - The results presented can be applied in the decision-making process by managers in the areas of innovation and supply chain. Originality/value - This paper synthesises knowledge involving the relationships between supply chains and the innovation process. The analysis is based on quantitative and qualitative criteria.

Abstract
In this work, a cross-validation procedure is used to identify an appropriate Autoregressive Integrated Moving Average model and an appropriate state space model for a time series. A minimum size for the training set is specified. The procedure is based on one-step forecasts and uses different training sets, each containing one more observation than the previous one. All possible state space models and all ARIMA models where the orders are allowed to range reasonably are fitted considering raw data and log-transformed data with regular differencing (up to second order differences) and, if the time series is seasonal, seasonal differencing (up to first order differences). The value of root mean squared error for each model is calculated averaging the one-step forecasts obtained. The model which has the lowest root mean squared error value and passes the Ljung-Box test using all of the available data with a reasonable significance level is selected among all the ARIMA and state space models considered. The procedure is exemplified in this paper with a case study of retail sales of different categories of women's footwear from a Portuguese retailer, and its accuracy is compared with three reliable forecasting approaches. The results show that our procedure consistently forecasts more accurately than the other approaches and the improvements in the accuracy are significant.

Abstract
The progress in medical science and the decline of altruistic behavior of couples introduced to the world the ageing problem. The existence of ageing is more experienced by developed countries. Researchers and policy makers are constantly trying to find ways to study the impacts of ageing since the issue is unprecedented in our history. However, the majority of the literature focus more on immediate mechanisms such as public expenditures and somehow neglected the influence of ageing on manufacturing sector. Thus, through panel data, we studied the influence of ageing on manufacturing sectors. The empirical study was carried out on six developed countries namely Japan, Germany, Italy, Greece, Finland and Portugal that have high ageing population. Our results suggest that the growth of the old age group over 65 years old will have significantly negative influence on percentage contribution of manufacturing to the GDP of these countries. Moreover, the results also demonstrate that a country with a higher proportion of old age group over working group will face fall in the manufacturing.

Abstract
Aggressive marketing causes rapid changes in consumer behavior and some significant impact in the retail business. In this context, the sales forecasting at the SKU level can help retailers to become more competitive by reducing inventory investment and distribution costs. Sales forecasts are often obtained combining basic univariate forecasting models with empirical judgment. However, more effective forecasting methods can be obtained by incorporating promotional information, including price, percentage of discount (direct discount or loyalty card discount), calendar events and weekend indicators not only from the focal product but also from its competitors. To deal with the high dimensionality of the variable space, we propose a two-stage LASSO regression to select optimal predictors and estimate the model parameters. At the first stage, only focal SKUs promotional explanatory variables are included in the Autoregressive Distributed Lag model. At the second stage, the in-sample forecast errors from the first stage are regressed on the explanatory variables from the other SKUs in the same category with the focal SKU, and to use that information more effectively three different approaches were considered: select the five top sales SKUs, include all raw promotional information, and preprocess raw information using Principal Component Analysis. The empirical results obtained using daily data from a Portuguese retailer show that the inclusion of promotional information from SKUs in the same category may improve the forecast accuracy and that better overall forecasting results may be obtained if the best model for each SKU is selected.