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The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

Some analysts have claimed that the volatility of an asset is caused solely by the random arrival of new information about the future returns from the asset. Others have claimed that volatility is mainly caused by trading. In any case it is a common belief that volatility is of great importance in finance and it is the factor that plays the most important role in determining option prices. This paper discusses the development of a decision support system (D‐TIFIS) for options trading based on volatility forecasting. In order to evaluate the system, data were used from the Greek FTSE/ASE 20 stock index as well as at the money call and put prices on the specific index.

The paper aims to focus on the role that cosmopolitanism and, in particular, “the cosmopolitan disposition” (Woodward et al., 2008) plays in the process of entrepreneurial business by transnational business elites in Dubai.

Adopting a relational perspective based on Bourdieu and Wacquant’s (1992) Reflexive Sociology, as well as an inductive design, the authors conducted 30 semi-structured interviews focusing on both expatriates and Emiratis (locals/nationals) who displayed key features of a transnational business elite.

The findings indicate that the cosmopolitan disposition is an asset for transnational business elites when they venture in the context of Dubai.

The findings would have to be further replicated in similar contexts, i.e. other major cities displaying similar cosmopolitan features with Dubai. A theoretical framework that calls for further study of transnational entrepreneurship via the lens of cosmopolitan disposition and Bourdieuan “habitus” is proposed.

The research outlines cosmopolitan skills for a transnational business elite which are required when entrepreneurial ventures are developed in the context of a city like Dubai.

Cosmopolitanism and transnational entrepreneurship change cities like Dubai around the world constantly. Therefore, this study aims at achieving a better understanding of these changes and the ways in which they occur.

Studies on transnational entrepreneurship have already adopted Bourdieu’s theory (1977/1986), but this is the first time the cosmopolitan perspective and disposition is researched using this approach.

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

Given a time‐series, what is the best partitioning of the state space in order to obtain reasonable values for the block entropies? The purpose of this paper is to provide a simple answer (an algorithm), although approximative, in connection with symbolic dynamics and statistical properties of 1‐d maps on the interval.

The logistic map is examined as an archetype of a Complex System with different behaviors, namely: periodicity, order‐to‐chaos period‐doubling transition, weak chaos, parametric intermittent chaos, developed chaos and fully developed chaos. For the logistic map the generating partition is known, and allows comparison with other prescriptions in the literature. The partitioning of the phase space with the easy generated bipartition induced by the mean value of a curve in the plane, gives results in good agreement (roughly up to a 20 per cent difference) with the results of the generating partition, if the trajectory of the system is in parametric intermittent chaos and in developed chaos (DC). In the case of fully developed chaos (FDC), the agreement is perfect.

The authors confirm that a statistical partitioning is almost equivalent with the exact partitioning for the logistic map.

The paper updates previous results and proposes a better understanding on the partitioning for symbolic dynamics.

Intermittent demand appears sporadically, with some time periods not even displaying any demand at all. Even so, such patterns constitute considerable proportions of the total stock in many industrial settings. Forecasting intermittent demand is a rather difficult task but of critical importance for corresponding cost savings. The current study aims to examine the empirical outcomes of three heuristics towards the modification of established intermittent demand forecasting approaches.

First, optimization of the smoothing parameter used in Croston's approach is empirically explored, in contrast to the use of an a priori fixed value as in earlier studies. Furthermore, the effect of integer rounding of the resulting forecasts is considered. Lastly, the authors evaluate the performance of Theta model as an alternative of SES estimator for extrapolating demand sizes and/or intervals. The proposed heuristics are implemented into the forecasting support system.

The experiment is performed on 3,000 real intermittent demand series from the automotive industry, while evaluation is made both in terms of bias and accuracy. Results indicate increased forecasting performance.

The current research explores some very simple heuristics which have a positive impact on the accuracy of intermittent demand forecasting approaches. While some of these issues have been partially explored in the past, the current research focuses on a complete in‐depth analysis of easy‐to‐employ modifications to well‐established intermittent demand approaches. By this, the authors enable the application of such heuristics in an industrial environment, which may lead to significant inventory and production cost reductions and other benefits.

Despite the growing importance of online word-of-mouth (WOM) with regard to television (TV) ratings, it is usually excluded from early prediction models. The purpose of this paper is to investigate the role of online WOM in TV ratings predictions, focussing on whether the incorporation of online WOM could improve predictions of TV ratings, and extracts meaningful rules for decision-making.

The author uses online WOM as a potential predictive variable in the TV ratings prediction model. The author matches a list of programs based on TV ratings for the movie channel with internet user reviews and TV ratings information from Yahoo! Movies (YM) and XYZ Company. The data set includes 71 movies, for which the data were analyzed with a hybrid model.

Grey relational analysis shows that online WOM is a useful ex ante determinant of TV ratings. As a predictive variable, it plays an essential role in enhancing TV ratings predictions. The experimental results also indicate that the proposed model surpasses other listed methods in terms of both accuracy and reduction of variables, while the proposed procedure yields a set of easily understandable decision rules that facilitate the interpretation of TV ratings information.

This paper identifies critical predictors of TV ratings and suggests that online WOM messages are a credible source. A hybrid model is developed to illustrate an intelligent prediction system for TV ratings.

The study demonstrates the effectiveness of online WOM and its impact on TV ratings. It offers an intelligent prediction system for TV ratings with practical implications for managers within the TV industry.

Policy implementation strategies (PIS) are schemes designed by a government with an aim of hitting targets or attaining objectives set out by a policy. Forecasting by analogies (FBA) has been shown to be successful in situations of high uncertainty and low quantitative data as is that of PIS effectiveness forecasts. What is more, a structured approach to FBA helps the expert structure his thoughts in an organized manner before making a prediction, which is hypothesized to improve accuracy. This paper aims to discuss these issues.

This research suggests a semi‐structured analogies (S‐SA) approach for such a task. A pilot experiment was carried to test the performance of the S‐SA approach in the hands of semi‐experts when compared with unaided judgment (UJ).

The results of the experiment showed that for this level of expertise, there is no statistical evidence to suggest any approach is better than the other. Possible explanations of this result is that analogy recall of experts was hindered by four constructs: information, complexity, worldview, and expertise. It was concluded that the structured analogies approach for forecasting PIS effectiveness must be investigated further by means of a study involving “true experts”.

The sample sizes were small.

Forecasting PIS effectiveness is seen as an important tool for deciding upon which PIS to ultimately adopt (as there may be many available!) and this then has important implications for governmental budgeting.

The paper offers further insight into the performance of a structured analogies approach to forecasting PIS effectiveness in the hands of individuals with a mid‐level of expertise.

A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

The study's purpose is to examine market returns around dividend announcements that contrast with a pattern of prior dividend announcements.

The paper identifies firms that have a smooth dividend pattern of once-a-year dividend increases but at some point break that pattern and announce an unchanged dividend. The sample design allows the opportunity to investigate the market reaction to unchanged dividend announcements when an increase was likely to have been expected.

The results indicate that failing to increase the dividend is associated with significantly positive abnormal returns that are greater in magnitude for more entrenched dividend-increase records, supporting a contrast-effect hypothesis.

The results indicate that dividends are interpreted not only relative to the immediate dividend amount but also how the decision contrasts with dividends over a prolonged period. This finding suggests that the information content of the announcement of an unchanged dividend can vary according to the prior dividend pattern.