With increased globalization and labor mobility, human resource reallocation across firms, industries and regions has become the new norm in labor markets. The emergence of massive digital traces of such mobility offers a unique opportunity to understand labor mobility at an unprecedented scale and granularity. While most studies on labor mobility have largely focused on characterizing macro-level (e.g., region or company) or micro-level (e.g., employee) patterns, the problem of how to accurately predict an employee's next career move (which company with what job title) receives little attention. This paper presents the first study of large-scale experiments for predicting next career moves. We focus on two sources of predictive signals: profile context matching and career path mining and propose a contextual LSTM model, NEMO, to simultaneously capture signals from both sources by jointly learning latent representations for different types of entities (e.g., employees, skills, companies) that appear in different sources. In particular, NEMO generates the contextual representation by aggregating all the profile information and explores the dependencies in the career paths through the Long Short-Term Memory (LSTM) networks. Extensive experiments on a large, real-world LinkedIn dataset show that NEMO significantly outperforms strong baselines and also reveal interesting insights in micro-level labor mobility.
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The Impact of Ideology Misfit on Open Source Software Communities and Companies
Corporate involvement in open source software (OSS) communities has increased substantially in recent years. Often this takes the form of company employees devoting their time to contribute code to the efforts of projects in these communities. Ideology has traditionally served to motivate, coordinate, and guide volunteer contributions to OSS communities. As employees represent an increasing proportion of the participants in OSS
communities, the role of OSS ideology in guiding their commitment and code contributions is unknown. In this research, we argue that OSS ideology misfit has important implications for companies and the OSS communities to which their employees contribute, since their engagement in such communities is not necessarily voluntary. We conceptualize two different types of misfit: OSS ideology under-fit, whereby an employee embraces an OSS ideology more than their coworkers or OSS community do, and OSS ideology overfit, whereby an employee perceives that their coworkers or OSS community embrace the OSS ideology more strongly than the employee does. To develop a set of hypotheses about the implications of these two types of misfit for employee commitment to the company and commitment to the OSS community, we draw on selfdetermination theory. We test the hypotheses in a field study of 186 employees who participate in an OSS
community. We find that OSS ideology under-fit impacts the company and the community in the same way: it decreases employee commitment to the company and commitment to the OSS community. In contrast, we find that OSS ideology over-fit increases commitment to the company but decreases commitment to the OSS community. Finally, we find that employees’ commitment to their company reinforces the impact of their commitment to the OSS community in driving ongoing code contributions. This provides a holistic view of OSS ideology and its impacts among an increasingly pervasive yet understudied type of participant in OSS research. It provides insights for companies that are considering assigning their employees to work in OSS communities as well as for OSS communities that are partnering with these companies.
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- Award ID(s):
- 1633083
- NSF-PAR ID:
- 10108572
- Date Published:
- Journal Name:
- Management information systems quarterly
- Volume:
- 42
- Issue:
- 4
- ISSN:
- 2162-9730
- Page Range / eLocation ID:
- 1069-1096
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.25300/MISQ/2018/14242
