An Algorithm for Induction of Decision Rules Consistent with the Dominance Principle
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Rough Set Analysis of Preference-Ordered Data
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
The Influence of Relational Demography and Guanxi: the Chinese Case
Organization Science
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Combined rough set theory and flow network graph to predict customer churn in credit card accounts
Expert Systems with Applications: An International Journal
Exploring the preference of customers between financial companies and agents based on TCA
Knowledge-Based Systems
Dominance-based rough set model in intuitionistic fuzzy information systems
Knowledge-Based Systems
Flow Graphs and Intelligent Data Analysis
Fundamenta Informaticae - Contagious Creativity - In Honor of the 80th Birthday of Professor Solomon Marcus
Evaluation of the decision performance of the decision rule set from an ordered decision table
Knowledge-Based Systems
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This study compares the relative role of impression management tactics and the other form of supervisor-subordinate guanxi (s-s g) in predicting supervisor-rated employee performance. Empirical data were collected from 175 supervisor-subordinate dyads working full-time in Taiwanese organisations. Specifically, this study uses the Dominance-based Rough Set Approach (DRSA) to formulate employee social skills by generating ''if-then'' decision rules. Then, flow network graphs are applied to represent employee decision rules. The results indicate that the personal-life inclusion of supervisor-subordinate guanxi matters more than various impression management tactics in achieving high performance ratings. Additionally, employees avoiding engaging in the supplication tactic may face in low performance ratings. The findings have implications for many of the decision rules that influence performance ratings from supervisors.