Abstract
Job seekers are used to looking at job postings published on the main websites like Glassdoor and Google Jobs. Typically, an online job posting provides a piece of text that describes the job in a more qualitative way. Most job seekers, who would have to view hundreds of postings every day, tend to pay attention to the explicit information exposed by the textual description, such as required skills, salary, and benefits, which are information that the author wishes to convey to the job seekers directly. However, this would lead to overlook of a large part of implicit information which is hidden deeper in the linguistic characteristics of the textual description, such as readability of the text, status of the employer, and domain-unrelated concerns of the text. These implicit aspects of the job description can give job seekers knowledge into the job culture and personal characteristics of future colleagues, helping them to prepare for job interviews more efficiently, and integrate into future job environment more smoothly. Using text mining methods, this study extracts various types of implicit information/knowledge of a collection of more than 24 thousand job postings and depicts the implicit characteristics of IT-related jobs compared to non-IT jobs. Analysis results show that IT-related and non-IT job descriptions have distinct profiles in terms of implicit characteristics.
Recommended Citation
Han, Yuzhang and Palvia, Prashant, "Unraveling Implicit Knowledge in Information Technology Jobs" (2019). AMCIS 2019 Proceedings. 2.
https://aisel.aisnet.org/amcis2019/is_leadership/is_leadership/2
Unraveling Implicit Knowledge in Information Technology Jobs
Job seekers are used to looking at job postings published on the main websites like Glassdoor and Google Jobs. Typically, an online job posting provides a piece of text that describes the job in a more qualitative way. Most job seekers, who would have to view hundreds of postings every day, tend to pay attention to the explicit information exposed by the textual description, such as required skills, salary, and benefits, which are information that the author wishes to convey to the job seekers directly. However, this would lead to overlook of a large part of implicit information which is hidden deeper in the linguistic characteristics of the textual description, such as readability of the text, status of the employer, and domain-unrelated concerns of the text. These implicit aspects of the job description can give job seekers knowledge into the job culture and personal characteristics of future colleagues, helping them to prepare for job interviews more efficiently, and integrate into future job environment more smoothly. Using text mining methods, this study extracts various types of implicit information/knowledge of a collection of more than 24 thousand job postings and depicts the implicit characteristics of IT-related jobs compared to non-IT jobs. Analysis results show that IT-related and non-IT job descriptions have distinct profiles in terms of implicit characteristics.