Research Productivity, h-index, of faculty is predicted on their job-satisfaction, persistence, optimism, self-discipline, motivation, and procrastination. Never has been a better answer than H-Index in the history of science to the question of how to quantify the cumulative productivity, accomplishments, impact, and relevance of a researcher’s scientific work. Multiple Prediction design of correlational research method was adopted in the investigation. Faculty in natural sciences in universities around the world constituted the population. A multistage random sample of 180 faculty, 30 from each continent 7 or 8 from each of 24 universities, and 4 universities from each of 6 continents made the sample. Results showed statistically significant 21 correlation coefficients among the seven variables. The six independent variables taken together, significantly predicted research productivity [F (6, 173) = 72.379, p < .01, R2 = .715]. Each of persistence, optimism, self-discipline, and procrastination unilaterally predicted research productivity significantly. Neither job-satisfaction nor motivation singlehandedly predicted research productivity. Multiple regression equation was created for the prediction of research productivity from the six independent variables. Predicted values and residuals for each participant were tabulated.
Keywords: Continents in the world, Correlational research method., Faculty, H-index; Multiple prediction, Job Satisfaction, Motivation, Optimism, Persistence, Procrastination, Research productivity, Research productivity h-index, Self-discipline
The purpose of this study is to investigate whether there is consistency among the measure of earnings quality. So far, there is no agreed definition of earnings quality in accounting and finance arena. We used secondary data draw from Prowess data base for companies listed in Bombay stock exchange from 2006 to 2012. We employed non-parametric test using spearman rank correlation to investigate the consistency among earnings quality measures. We used five commonly used measures of earnings quality persistence, predictability, smoothness, earnings surprise and accrual quality (Penman, & Zhang 2002; Francis et al. 2004; Abdelghany 2005, Dechow et al. 2010). We find in general there is no completely consistency among the measures of earnings quality. Evidence from this study suggests that analyst, investors and market participants should not use one measure of earnings quality since one measure of earnings quality cannot complement other measure of earnings quality. We therefore request analyst to use more than one measure. In case of inconsistency when more than one measure of earnings quality is used further analysis is inevitable.