Abstract

The effort-reward imbalance model allows us to see disparity in effort and reward and how this can be a predictor for a variety of constructs. The present study seeks to gather data utilizing the ERI modal in the nursing profession. Previous research has utilized the ERI model but methods for gathering data were not quick and efficient. This study seeks to utilize a database called Glassdoor to rapidly and effectively gather data. The researchers are interested in seeing the likelihood of nurses to recommend their company to a friend based on perceived effort and rewards. The sample included a random selection of 40 reviews from 40 randomly selected hospitals. To collect these random samplings, we used an excel random generator formula. We selected the 40 hospitals based on the corresponding number of the excel random generator and utilized the same method to select the 40 reviews. Sample words were developed through reviewing previous research. The frequency of each type of word was summed to create a numerical variable for effort and reward. Not only was the actual content of the review assessed, but the overall rating the user gave on Glassdoor for each particular variable was also used as reference to maintain accuracy. Bivariate correlations were conducted on the data to determine the strength of the effort-likelihood to recommend relationship and the reward-likelihood to recommend relationship. Results indicated that nurses who reported putting more effort into their company, were significantly more likely to recommend their company to a friend. Results also indicated that nurses who reported more rewards such as raises, compensation, and benefits were significantly more likely to recommend their company to a friend.

Thesis Completion

2020

Semester

Spring

Thesis Chair

Horan, Kristin

Co-Chair

Jex, Steve

Degree

Bachelor of Science (B.S.)

College

College of Sciences

Department

Psychology

Degree Program

Industrial/Organizational Psychology

Language

English

Access Status

Open Access

Release Date

5-1-2020

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