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The curse of self-presentation: looking for career patterns in online CVs

Published: 15 January 2020 Publication History

Abstract

Climbing the career ladder to a senior executive position is a long and complex process that, nevertheless, many people are trying to master. Over the last decades, the number of people providing their CVs on professional online social networks, such as LinkedIn is growing. New methods of pattern detection raise the question of whether online CVs provide insights into career patterns and paths. The respective hypothesis is that online CVs map people's careers and therefore build the ideal data set to detect career patterns. To test this hypothesis, 100.006 online CVs were downloaded and preprocessed. This paper presents initial results of one educational and one internship variable. Whereas a higher degree positively predicts career level, having made an internship negatively relates to career level. These results reveal that rather than objectively mirroring people's career trajectories, online career platforms provide selective information. The information of online CVs and the respective career level is intermingled, i.e. people with a high career level present different parts of their careers than people on lower levels. Furthermore, self-presentational effects might have an impact. The effect on similar research and possible implications are discussed.

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  • (2020)When Imprecision Improves Advice: Disclosing Algorithmic Error Probability to Increase Advice Taking from AlgorithmsHCI International 2020 - Posters10.1007/978-3-030-50726-8_66(504-511)Online publication date: 10-Jul-2020

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cover image ACM Conferences
ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
August 2019
1228 pages
ISBN:9781450368681
DOI:10.1145/3341161
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 15 January 2020

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Author Tags

  1. career research
  2. data mining
  3. human generated data
  4. professional online social network

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ASONAM '19 Paper Acceptance Rate 41 of 286 submissions, 14%;
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Cited By

View all
  • (2023)Methodology and Empirical StrategyMapping Digital Skills in Cultural and Creative Industries in Italy10.1007/978-3-031-26867-0_3(43-93)Online publication date: 30-Mar-2023
  • (2020)When Imprecision Improves Advice: Disclosing Algorithmic Error Probability to Increase Advice Taking from AlgorithmsHCI International 2020 - Posters10.1007/978-3-030-50726-8_66(504-511)Online publication date: 10-Jul-2020

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