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Institution

York University

EducationToronto, Ontario, Canada
About: York University is a education organization based out in Toronto, Ontario, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 18899 authors who have published 43357 publications receiving 1568560 citations.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors examine the relationship between job demands, job resources, and burnout, and examine if burnout could predict both work and health-related outcomes among police officers.

295 citations

Posted Content
TL;DR: In this article, the authors examined the relationship between the quality of corporate governance and information asymmetry in the equity market around quarterly earnings announcements, using the change in market liquidity (i.e., bid-ask spreads and depths) around the announcements as a proxy for information asymmetric.
Abstract: We examine the relationship between the quality of corporate governance and information asymmetry in the equity market around quarterly earnings announcements. We use the change in market liquidity (i.e., bid-ask spreads and depths) around the announcements as a proxy for information asymmetry. We use principal components analysis to identify three factors, board independence, board structure and board activity, that capture the information in the eight individual corporate governance variables we examine. We then use ordinary least squares and two-stage least squares to estimate the relations between market liquidity changes and the following four explanatory variables: directors' and officers' percentage stock holdings, board independence, board structure, and board activity. Our results indicate that changes in bid-ask spreads at the time of earnings announcements are significantly negatively related to board independence, board activity, and the percentage stock holdings of directors and officers. We also find that depth changes are significantly positively related to board structure, board activity, and directors' and officers' percentage stock holdings. Our results are consistent with the hypothesis that firms with higher levels of corporate governance have lower information asymmetry around quarterly earnings announcements.

295 citations

Journal ArticleDOI
TL;DR: The original DEA cross-efficiency concept is generalized to game cross efficiency, where each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate.
Abstract: In this paper, we examine the cross-efficiency concept in data envelopment analysis (DEA). Cross efficiency links one decision-making unit's (DMU) performance with others and has the appeal that scores arise from peer evaluation. However, a number of the current cross-efficiency approaches are flawed because they use scores that are arbitrary in that they depend on a particular set of optimal DEA weights generated by the computer code in use at the time. One set of optimal DEA weights (possibly out of many alternate optima) may improve the cross efficiency of some DMUs, but at the expense of others. While models have been developed that incorporate secondary goals aimed at being more selective in the choice of optimal multipliers, the alternate optima issue remains. In cases where there is competition among DMUs, this situation may be seen as undesirable and unfair. To address this issue, this paper generalizes the original DEA cross-efficiency concept to game cross efficiency. Specifically, each DMU is viewed as a player that seeks to maximize its own efficiency, under the condition that the cross efficiency of each of the other DMUs does not deteriorate. The average game cross-efficiency score is obtained when the DMU's own maximized efficiency scores are averaged. To implement the DEA game cross-efficiency model, an algorithm for deriving the best (game cross-efficiency) scores is presented. We show that the optimal game cross-efficiency scores constitute a Nash equilibrium point.

295 citations

Proceedings ArticleDOI
25 Apr 2007
TL;DR: This paper proposes two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile - they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors.
Abstract: A fundamental problem in wireless sensor networks is localization -- the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -- they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

295 citations

Journal ArticleDOI
TL;DR: In this paper, a two-dimensional typology of dynamic capabilities, grouping them by the life-cycle stage and the timing of expected returns, is presented, and validated and mapped four distinct innovation strategies onto specific sets of product innovation capabilities.

294 citations


Authors

Showing all 19301 results

NameH-indexPapersCitations
Dan R. Littman157426107164
Martin J. Blaser147820104104
Aaron Dominguez1471968113224
Gregory R Snow1471704115677
Joseph E. LeDoux13947891500
Kenneth Bloom1381958110129
Osamu Jinnouchi13588586104
Steven A. Narod13497084638
David H. Barlow13378672730
Elliott Cheu133121991305
Roger Moore132167798402
Wendy Taylor131125289457
Stephen P. Jackson13137276148
Flera Rizatdinova130124289525
Sudhir Malik130166998522
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023180
2022528
20212,675
20202,857
20192,426
20182,137