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.

Career commitment, proactive personality, and work outcomes: a cross-lagged study

Abstract: Purpose – The aim of this paper is to examine the relationships of career commitment to turnover intention, internal networking, job embeddedness, and turnover, and whether proactive personality moderates these relationships. Design/methodology/approach – Data were collected at two points in time, spaced by a six-month interval, from a sample of employees working in diverse organizations (n=312 at Time 1 and n=186 at Time 2). Hypotheses were tested using moderated multiple (linear and logistic) regression analyses. Findings – Career commitment was positively related to Time 1 turnover intention, with this relationship being stronger at high levels of proactivity. Proactive personality also interacted with career commitment in predicting Time 2 internal networking and job embeddedness, such that these relationships were significantly positive only at low levels of proactivity. Finally, career commitment was positively related to Time 2 turnover, but this relationship was not moderated by proactive personal...

A Survey of Automated Conjectures in Spectral Graph Theory

Abstract: During the last three decades, the computer has been widely used in spectral graph theory. Many results about graph eigenvalues were first conjectured, and in some cases proved, using computer programs, such as GRAPH, Graffiti, Ingrid, newGRAPH and AutoGraphiX. This paper presents a survey and a discussion of such results.

Directors' and Officers' Liability Insurance, Corporate Risk and Risk Taking: New Panel Data Evidence on The Role of Directors' and Officers' Liability Insurance

Abstract: In this paper, we develop and estimate models of the determinants of firms' demand for directors' and officers' liability insurance. We add to and validate the existing empirical literature by testing hypotheses regarding the relationship between D&O insurance purchase and firm size, governance characteristics and business risk. Our data set allows us to test both purchase decisions and limits choices. We present separate estimates of these decisions along with estimates that allow for their joint determination and recognize the potential for selection bias in the estimates of coverage limits models. To shed additional light on the role and consequences of D&O insurance we make additional use of the panel structure of our data to examine the effects of D&O insurance purchase on corporate governance.

Underground, upperground et middle-ground : les collectifs créatifs et la capacité créative de la ville

Abstract: Cet article analyse un acteur meconnu des villes creatives : les collectifs creatifs. Les villes creatives s’articulent selon trois strates actives. Les firmes – upperground - absorbent les connaissances emergeant des activites creatives de la ville alors que les acteurs de l’underground explorent et proposent de nouvelles avenues creatives. Dans ce contexte, les collectifs creatifs du middleground assument une fonction d’integration des connaissances et de transfert entre l’underground et l’upperground. A travers quelques exemples empiriques, la nature et les roles de ces collectifs sont discutes, ainsi que les differents modes de relations qu’ils pourraient developper avec les firmes et leurs milieux.

Analysis of Global k-Means, an Incremental Heuristic for Minimum Sum-of-Squares Clustering

Abstract: The global k-means heuristic is a recently proposed (Likas, Vlassis and Verbeek, 2003) incremental approach for minimum sum-of-squares clustering of a set X of N points of R d into M clusters. For k = 2,3,.... M - 1 it considers the best-known set of k - 1 centroids previously obtained, adds a new cluster center at each point of X in turn and applies k-means to each set of k centroids so-obtained, keeping the best k-partition found. We show that global k-means cannot be guaranteed to find the optimum partition for any M ≥ 2 and d > 1; moreover, the same holds for all M > 3 if the new cluster center is chosen anywhere in R d instead of belonging to X. The empirical performance of global k-means is also evaluated by comparing the values it obtains with those obtained for three data sets with N < 150 which are solved optimally, as well as with values obtained by the recent j-means heuristic and extensions thereof for three larger data sets with N ≤ 3038.