Role thinking: Standing in other people’s shoes to forecast decisions in conflicts

When forecasting decisions in conflict situations, experts are often advised to figuratively stand in the other person’s shoes. We refer to this as “role thinking”, because, in practice, the advice is to think about how other protagonists will view the situation in order to predict their decisions. We tested the effect of role thinking on forecast accuracy. We obtained 101 role-thinking forecasts of the decisions that would be made in nine diverse conflicts from 27 Naval postgraduate students (experts) and 107 role-thinking forecasts from 103 second-year organizational behavior students (novices). The accuracy of the novices’ forecasts was 33% and that of the experts’ was 31%; both were little different from chance (guessing), which was 28%. The small improvement in accuracy from role-thinking strengthens the finding from earlier research that it is not sufficient to think hard about a situation in order to predict the decisions which groups of people will make when they are in conflict. Instead, it is useful to ask groups of role players to simulate the situation. When groups of novice participants adopted the roles of protagonists in the aforementioned nine conflicts and interacted with each other, their group decisions predicted the actual decisions with an accuracy of 60%.     

Computational suite of models with heterogeneous agents II: Multi-country real business cycle models

This paper describes the second model considered in the computational suite project that compares the performance of different numerical algorithms. It is a multi-country model in which countries face different productivity shocks. Solving such models is a challenging numerical problem unless the number of countries is small. The solutions are functions of a large set of arguments and the functional forms are unknown. Moreover, the solution procedures have to deal with high-dimensional integration problems.     

An integrated fuzzy mathematical model and principal component analysis algorithm for forecasting uncertain trends of electricity consumption

This paper introduces an integrated algorithm for forecasting electricity consumption (EL) based on fuzzy regression, time series and principal component analysis (PCA) in uncertain markets such as Iran. The algorithm is examined by mean absolute percentage error, analysis of variance (ANOVA) and Duncan Multiple Range Test. PCA is used to identify the input variables for the fuzzy regression and time series models. Monthly EL in Iran is used to show the superiority of the algorithm. Moreover, it is shown that the selected fuzzy regression model has better estimated values for total EL than time series. The algorithm provides as good results as intelligent methods. However, it is shown that the algorithm does not require utilization of preprocessing methods but genetic algorithm, artificial neural network and fuzzy inference system require preprocessing which could be a cumbersome task to deal with ambiguous data. The unique features of the proposed algorithm are three fold. First, two type of fuzzy regressions with and without preprocessed data are prescribed by the algorithm in order to minimize the bias. Second, it uses PCA approach instead of trial and error method for selecting the most important input variables. Third, ANOVA is used to statistically compare fuzzy regression and time series with actual data.     

Behavioural Orientations of Non‐Profit Boards as a Factor in Entrepreneurial Performance: Does Governance Matter?

Relatively little is understood about factors triggering entrepreneurial behaviour within organizations not driven by profit motives. Governance plays an important role in non‐profits, particularly boards of directors. Integrating resource‐based theory and entrepreneurial orientation research, we examine the influence of non‐profit boards as strategic resources shaping the organization's entrepreneurial orientation and performance. In particular, we focus on the non‐profit board's underlying behavioural orientations, or the extent to which the board is strategic, activist, conservative, and cohesive. Findings from a cross‐sectional survey on arts and culture organizations demonstrate that three of these behavioural orientations impact levels of entrepreneurship occurring within non‐profits. Higher levels of entrepreneurship affect social performance, but not financial performance.     

Performance trends in the construction industry worldwide: an overview of the turn of the century

This paper presents an exploratory study to assess the efficiency level of construction companies worldwide, exploring in particular the effect of location and activity in the efficiency levels. This paper also provides insights concerning the convergence in efficiency across regions. The companies are divided in three regions (Europe, Asia and North America), and in the three main construction activities (Buildings, Heavy Civil and Specialty Trade). We analyze a sample of 118 companies worldwide between 1995 and 2003. Data envelopment analysis is used to estimate efficiency, and the Malmquist index is applied for the evaluation of productivity change. Both methods were complemented by bootstrapping to refine the estimates obtained. A panel data truncated regression with categorical regressors is used to explore the impact of location and activity in the efficiency levels. The results reveal that the efficiency of North American companies is higher than the European and Asian counterparts. Other important conclusion points to a convergence in efficiency levels across regions as in North America productivity remains stable, whereas in Asia and Europe productivity improves.     

An empirical assessment of the cross-national measurement validity of graded paired comparisons

The popular use of graded paired comparisons in empirical studies assessing consumers’ preferences, and the potential effect of cross-national differences in (extreme) response styles on the quality of graded paired comparison data, supply ample reasons for an empirical verification of the cross-national validity of such scales. Using data from a cross-national margarine brand study including fourteen different nations (N = 4,560), we found sufficient statistical evidence for cross-national bias due to existing cross-national differences in extreme responses. However, the low values reported for effect size measures (intra-class correlation coefficient, R ² value) indicated that the impact of the cross-national bias is marginal. The findings from our study provided empirical support for the hypothesis that graded paired comparison data can be meaningfully compared across nations.