Skip to main navigation
Skip to search
Skip to main content
University of Arizona Home
Home
Profiles
Departments and Centers
Scholarly Works
Activities
Grants
Datasets
Prizes
Search by expertise, name or affiliation
Experimental evidence for agency models of salesforce compensation
Mrinal Ghosh
, George John
Research output
:
Contribution to journal
›
Article
›
peer-review
34
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Experimental evidence for agency models of salesforce compensation'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Insurance
100%
Salesforce Compensation
100%
Agency Model
100%
Risk Agent
85%
Expected Utility
57%
Validity Threats
57%
Risk-neutral
28%
Explicit Solution
28%
Effort Level
14%
Model Prediction
14%
Salary
14%
Small Gap
14%
Wages
14%
Utility Function
14%
Decision Aid
14%
Treatment Difference
14%
Ordering Behavior
14%
Agent Behavior
14%
Internal Validity
14%
Output Uncertainty
14%
Game Theory
14%
Lack of Support
14%
Experimental Task
14%
Large Gap
14%
Common Knowledge
14%
Experimental Stimulus
14%
Laboratory Design
14%
Stylized Facts
14%
Compensation Programs
14%
Verifiability
14%
Model Failure
14%
Utility Maximizing
14%
Student Subjects
14%
Manipulation Check
14%
Principal Behaviours
14%
Comprehension Problems
14%
Risk Neutrality
14%
Agent Perception
14%
Principals' Perceptions
14%
Knowledge Assumptions
14%
Sales Compensation
14%
Computer Science
Experimental Evidence
100%
Explicit Solution
100%
Utility Function
50%
Model Failure
50%
Common Knowledge
50%
Specific Agency
50%
Alternative Explanation
50%
Model Prediction
50%
Related Validity
50%
Internal Validity
50%
Psychology
Decision Aid
100%
Predisposition
100%
Game Theory
100%
Economics, Econometrics and Finance
Salespeople
100%
Verifiability
12%