HEC Montréal

About: HEC Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Vehicle routing problem & Corporate governance. The organization has 1221 authors who have published 5708 publications receiving 196862 citations. The organization is also known as: Ecole des Hautes Etudes Commerciales de Montreal & HEC Montreal.

Consumer reactions to product placement strategies in television sponsorship

Abstract: Reports the results of an experimental study that examines the impact of different strategies of product placement on consumer reactions in the context of television sponsorship. Four factors were manipulated: type of placement, sponsor’s image, type of television program and sponsor‐program congruity. Uses a factorial design comprising these four factors, which was operationalised by means of 12 brief written vignettes. Indicates that strategies of product placement impact differently on consumers’ evaluative and ethical judgements and that their effects interact with the type of television program. Suggests that evaluations of product placement are most negative in the context of mini‐series/drama television programs. Furthermore, product placements that play a passive role and are not clearly expressed within the program are generally perceived as less ethical, especially when they appear in information/services magazines. Also determines that sponsor‐program congruity leads to better ethical and evaluative consumer reactions for all types of television program considered except mini‐series/dramas. Implications for research and practice are derived from these results.

On the Forecasting Accuracy of Multivariate GARCH Models

Abstract: This paper addresses the question of the selection of multivariate GARCH models in terms of variance matrix forecasting accuracy with a particular focus on relatively large scale problems. We consider 10 assets from NYSE and NASDAQ and compare 125 model based one-step-ahead conditional variance forecasts over a period of 10 years using the model confidence set (MCS) and the Superior Predicitive Ability (SPA) tests. Model per- formances are evaluated using four statistical loss functions which account for different types and degrees of asymmetry with respect to over/under predictions. When consid- ering the full sample, MCS results are strongly driven by short periods of high market instability during which multivariate GARCH models appear to be inaccurate. Over rel- atively unstable periods, i.e. dot-com bubble, the set of superior models is composed of more sophisticated specifications such as orthogonal and dynamic conditional correlation (DCC), both with leverage effect in the conditional variances. However, unlike the DCC models, our results show that the orthogonal specifications tend to underestimate the conditional variance. Over calm periods, a simple assumption like constant conditional correlation and symmetry in the conditional variances cannot be rejected. Finally, during the 2007-2008 financial crisis, accounting for non-stationarity in the conditional variance process generates superior forecasts. The SPA test suggests that, independently from the period, the best models do not provide significantly better forecasts than the DCC model of Engle (2002) with leverage in the conditional variances of the returns.

A Comparative Study on Parameter Recovery of Three Approaches to Structural Equation Modeling

Abstract: Traditionally, two approaches have been employed for structural equation modeling: covariance structure analysis and partial least squares. A third alternative, generalized structured component analysis, was introduced recently in the psychometric literature. The authors conduct a simulation study to evaluate the relative performance of these three approaches in terms of parameter recovery under different experimental conditions of sample size, data distribution, and model specification. In this study, model specification is the only meaningful condition in differentiating the performance of the three approaches in parameter recovery. Specifically, when the model is correctly specified, covariance structure analysis tends to recover parameters better than the other two approaches. Conversely, when the model is misspecified, generalized structured component analysis tends to recover parameters better. Finally, partial least squares exhibits inferior performance in parameter recovery compared with ...

Antecedents, moderators and dimensions of country‐of‐origin evaluations

Abstract: Purpose – The purpose of this paper is to provide an in‐depth examination of country‐of‐origin (COO) perceptions of consumers in a multinational setting. It shows how explanatory factors like demographics, familiarity with a country's products, purchase behaviour and psychological variables jointly work to explain consumers' COO perceptions.Design/methodology/approach – This is a quantitative study using a drop‐off and pick‐up survey among three samples of consumers in Canada, Morocco and Taiwan. The final sample size was comprised of 506 male consumers. The data were analyzed using factor analysis to group countries of origin and analyses of variance to relate COO perceptions to the explanatory variables.Findings – The familiarity with products made in a country was the strongest predictor of country perceptions, followed by nationality and the manufacturing process and product complexity dimensions of country evaluation. Canadians had the highest propensity to distinguish between countries of origin on ...