Sociology of Media & Technology eJournal最新文献

筛选

英文中文

How to Build an Effective Professional Network on Linkedin: Some Data-Driven Insights 如何在Linkedin上建立一个有效的专业网络:一些数据驱动的见解

Sociology of Media & Technology eJournal Pub Date : 2021-05-03 DOI: 10.2139/ssrn.3838442

Yinyin Yu, Guillaume Saint-Jacques, P. Matsiras

{"title":"How to Build an Effective Professional Network on Linkedin: Some Data-Driven Insights","authors":"Yinyin Yu, Guillaume Saint-Jacques, P. Matsiras","doi":"10.2139/ssrn.3838442","DOIUrl":"https://doi.org/10.2139/ssrn.3838442","url":null,"abstract":"In 2019, LinkedIn announced a new initiative towards closing the Network Gap as part of our commitment to offer equal opportunity to equal talents. Our Executive Chairman, Jeff Weiner, asked people to take the Plus One Pledge and help someone outside their network who may not have access to the same resources as they do. The Professional Network Strength Checklist builds upon existing work we have done on understanding the effect of one's professional network on his/her career mobility. We used a novel three-step approach to identify the top network features with the largest impact on the career mobility of members who are otherwise similar in demographics, education, and work experiences, and translated them into actionable recommendations. Specifically, we found that, compared to similar peers, members with at least 13 connections from companies other than their current employer are 22.9% faster in transitioning to their next job than those who do not. Members who are a part of at least one group on LinkedIn are 8.6% faster in transitioning to their next job than those who are not a member of any groups. And members who follow at least 5 organizations on LinkedIn are 7.1% faster in transitioning to their next job than those who do not. In addition to our insights, we believe that our methodology can be helpful to data science practitioners who are interested in working with network or duration data, as well as those who are interested in marrying causal inference techniques and ML techniques in deriving insights from observational data.","PeriodicalId":445375,"journal":{"name":"Sociology of Media & Technology eJournal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133393347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Personal Brands of ESports Athletes: An Exploration of Evaluation Mechanisms 电子竞技运动员个人品牌:评价机制探索

Sociology of Media & Technology eJournal Pub Date : 2019-12-10 DOI: 10.2139/ssrn.3501522

Ilya Musabirov, D. Bulygin, Ekaterina Marchenko

引用次数: 3