Gurumurthy Kalyanaram on Interpretation of Collective Bargaining Agreement (CBA)

There are many lawsuits arising out of disputes in interpretation of the collectively bargained agreements.  Unions and employers work hard to craft CBAs, but lawsuits emerge even in cases of carefully designed CBAs.  In this essay, Gurumurthy Kalyanaram reports on this important matter.

Gurumurthy Kalyanaram – Law, Lawsuit and Including Politics

Gurumurthy Kalyanaram Lawsuit Continue reading

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Gurumurthy Kalyanram Professor, City University of New York

Dr Gurumurthy Kalyanaram is a professor at City University of New York. Concurrently, he is also serving as Visiting Professor at Tata Institute of Social Sciences, and editing the Management Review.

Gurumurthy Kalyanaram

See:

http://zicklin.baruch.cuny.edu/faculty/marketing/faculty/part-time-faculty

http://zicklin.baruch.cuny.edu/faculty/marketing/faculty/part-time-faculty-bio

Dr. Gurumurthy Kalyanaram is a distinguished professor with a long record of academic achievements and honors.  He received his B.E. degree in engineering in 1978 from the University of Madras, India, and his M.B.A. in 1983 from the University of Texas.  He earned his Ph.D from the Massachusetts Institute of Technology in 1989 in the field of Management Science, where he won the prestigious Harold Lobdell award.

Gurumurthy Kalyanaram

Dr Gurumurthy Kalyanaram was a Visiting Scholar at The Kennan Institute, The Woodrow Wilson International Center for Scholars, Washington, D.C., during 1990 to 1994.  He was also a Fellow at the Center for Russian and East European Studies, University of Pittsburgh, during 1995 to 1997.

Dr Gurumurthy Kalyanaram has served as a tenured professor at The University of Texas at Dallas.  He served at UT Dallas from 1988 to 2001.  During his tenure at the University of Texas, he was Chairman of the Department of Marketing, Senior Director of the Master’s Programs, Founding Director of the Cohort MBA Program, Chair of the Master’s Program Committee, and Faculty Liaison for External Affairs.

Gurumurthy Kalyanaram NYIT

He has also served as a tenured professor at NYIT, and served as academic director of MBA programs in New York and globally (including Canada, China and Middle-East).  He also led the research efforts of the faculty as the Faculty Research Director.

See:

http://www.gkalyan.com

http://scholar.google.com/citations?user=oB2_JR4AAAAJ&hl=en

Dr Gurumurthy Kalyanaram – A Model to Forecast the US Presidential Elections Employing Favorability Measure Abstract

Forecasting the US Presidential elections has drawn the attention of scholars from many disciplines including Economics, Political Science, and Sociology.  These scholars have built elaborate models to forecast the outcome of US presidential elections.  The models have incorporated economic variables (e.g., GDP, personal income, inflation), demographic variables (race, religion, geography, sex, age) and a host of other variables such as job approval, favorability, voter enthusiasm, and voter partisanship.

However, we posit that a simple model which employs only favorability measure can predict the outcome of the elections as well or better than most complex models.  We postulate that the percentage of vote that a candidate for the US Presidency will secure in the elections will be an average of the favorability measure of the candidate in the last six months preceding the elections.  We further postulate that the favorability model will forecast the percentage of vote better than a comparable simple model: the job approval model.

Our favorability model is based on powerful theory.  Researchers (Belk 2012) have argued that such measures as attitude, intent, and persuasion are relatively poor predictors of behavior.  A better predictor is behavior itself or a close surrogate of behavior.  We argue that favorability measure is such a variable.

We demonstrate the predictive validity of our model using data for the 2012 US presidential elections, and the past presidential elections.

We also present coherent and powerful theoretical underpinning for this model.