When do others matter? The impact of dyadic social relationships on fairness judgments

This dissertation reports two studies that examine the influence of social context factors in the formation of individuals’ fairness judgments about their organization. In the first study, I investigated the extent to which individuals are susceptible to social influences from others in forming justice judgments. Examining this in terms of congruence in perceptions of justice between two individuals at the dyad level, I found that individuals are influenced by certain specific members of their immediate social networks more than others. Using a sample in an Indian bank, I found that individuals had perceptions about their organization that were similar to others who were central in the expressive and instrumental networks, as well as those who occupied structurally similar positions in the network as themselves. I also found that negative ties and negative network characteristics were negatively related to similarity in perceptions. Such dyad level influence processes, when aggregated at the group level, could lead to contagion in perceptions about the organization. In the second study, I studied how people react to (in)equity issues in specific resource allocation situations based on relational considerations. 196 employees in a small Indian bank provided responses to a work related vignette/scenario study. I found that people tended to perceive reward situations as more fair, even when they received an unfavorable outcome, if a positively related comparison other received a better outcome. On the other hand, I also found that individuals were more critical in their fairness judgments when they received an unfavorable outcome when the comparison other was negatively related. Positive and negative emotions mediated this relationship. Practical implications and directions for future research are discussed

Soft information fusion of correlation filter output planes using Support Vector Machines for improved fingerprint verification performance

Reliable verification and identification can be achieved by fusing hard and soft information from multiple classifiers. Correlation filter based classifiers have shown good performance in biometric verification applications. In this paper, we develop a method of fusing soft information from multiple correlation filters. Usually, correlation filters are designed to produce a strong peak in the correlation filter output for authentics whereas no such peak should be produced for impostors. Traditionally, the peak-to-sidelobe-ratio (PSR) has been used to characterize the strength of the peak and thresholds are set on the PSR in order to determine whether the test image is an authentic or an impostor. In this paper, we propose to fuse multiple correlation output planes, by appending them for classification by a Support Vector Machine (SVM), to improve the performance over traditional PSR based classification. Multiple Unconstrained Optimal Tradeoff Synthetic Discriminant Function (UOTSDF) filters having varying degrees of discrimination and distortion tolerance are employed here to create a feature vector for classification by a SVM, and this idea is evaluated on the plastic distortion set of the NIST 24 fingerprint database. Results on this database provide an Equal Error Rate (EER) of 1.36% when we fuse correlation planes, in comparison to an average EER of 3.24% using the traditional PSR based classification from a filter, and 2.4% EER on fusion of PSR scores from the same filters using SVM, which demonstrates the advantages of fusing the correlation output planes over the fusion of just the peak-to-sidelobe-ratios (PSRs)