Applying Computer-aided Textual Analysis to Understand Employee Recruitment in Start-up
Disciplines
Social Psychology
Abstract (300 words maximum)
New ventures may be at a disadvantaged position regarding talent attraction due to reasons such as a less established reputation and job seekers’ perceived uncertainty regarding their career trajectory. Employment webpages and job postings are common places where start-up organizations can convey an attractive organizational image in a textual format to job seekers. However, little is known about whether such textual information reflects the actual experience of employees or the effectiveness of such textual information in attracting talent. In this study, we collected texts related to organizational culture, employee benefits, and diversity statements from the websites of 142 new ventures featured in the Forbes 2022 and 2023 lists of AI50 and Fintech 50. We first used computer-aided text analysis (CATA) in R to analyze the organizational culture/value text on their recruitment web page and scored the startups regarding entrepreneurial orientation. We used a web scraper to download employee ratings of each company from Glassdoor.com, where employees rated their employer regarding positive business outlook and specific aspects such as career opportunities. Multiple regression results indicated that entrepreneurial orientation, senior management, and career opportunity scores were positively related to positive business outlook ratings from Glassdoor. We also conducted exploratory text analysis using R to create a visual representation of our data. To test the effectiveness of using texts to increase organizational attraction, we will conduct an online experiment with real job seekers recruited from Prolific. Specifically, we will manipulate textual elements in a recruitment webpage (e.g., types of employee benefits, deep vs surface-level diversity statements, and types of culture/value statements) and test whether the inclusion of certain texts can enhance job seekers’ attraction to the organization and intention to apply for the job. Our findings will have important practical implications for job seekers and talent recruitment strategies in the start-up scene.
Academic department under which the project should be listed
RCHSS - Psychological Science
Primary Investigator (PI) Name
Dianhan Zeng
Applying Computer-aided Textual Analysis to Understand Employee Recruitment in Start-up
New ventures may be at a disadvantaged position regarding talent attraction due to reasons such as a less established reputation and job seekers’ perceived uncertainty regarding their career trajectory. Employment webpages and job postings are common places where start-up organizations can convey an attractive organizational image in a textual format to job seekers. However, little is known about whether such textual information reflects the actual experience of employees or the effectiveness of such textual information in attracting talent. In this study, we collected texts related to organizational culture, employee benefits, and diversity statements from the websites of 142 new ventures featured in the Forbes 2022 and 2023 lists of AI50 and Fintech 50. We first used computer-aided text analysis (CATA) in R to analyze the organizational culture/value text on their recruitment web page and scored the startups regarding entrepreneurial orientation. We used a web scraper to download employee ratings of each company from Glassdoor.com, where employees rated their employer regarding positive business outlook and specific aspects such as career opportunities. Multiple regression results indicated that entrepreneurial orientation, senior management, and career opportunity scores were positively related to positive business outlook ratings from Glassdoor. We also conducted exploratory text analysis using R to create a visual representation of our data. To test the effectiveness of using texts to increase organizational attraction, we will conduct an online experiment with real job seekers recruited from Prolific. Specifically, we will manipulate textual elements in a recruitment webpage (e.g., types of employee benefits, deep vs surface-level diversity statements, and types of culture/value statements) and test whether the inclusion of certain texts can enhance job seekers’ attraction to the organization and intention to apply for the job. Our findings will have important practical implications for job seekers and talent recruitment strategies in the start-up scene.