LinkedIn(to) Job Opportunities: Experimental Evidence from Job Readiness Training

Wheeler, Laurel; Garlick, Robert; Johnson, Eric M.; Shaw, Patrick; Gargano, Marissa

doi:10.1257/app.20200025

Cited by 24 publications

(13 citation statements)

References 51 publications

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“…On the basis of the MHN and the ERG theory, plenty of previous research related to community services has been referred to systematically classify residents’ demands. Community services in smart communities can be split into seven categories according to existing literature (i.e., smart business service [ 38 , 39 , 40 , 41 , 42 , 43 ], smart property service [ 38 , 39 , 40 , 44 , 45 , 46 , 47 , 48 ], smart emergency service [ 38 , 39 , 45 , 46 , 49 , 50 ], smart medical care service [ 38 , 45 , 51 , 52 ], smart elderly care service [ 38 , 39 , 53 , 54 , 55 , 56 , 57 ], smart communication service [ 39 , 40 , 45 , 58 ], and smart government service [ 40 , 44 , 58 , 59 , 60 , 61 , 62 ]), and there are a variety of services within each category [ 38 , 44 , 58 ]. A list of these services is provided in Table 1 (Detailed information about different types of community services can be seen in Supplementary File S1 ).…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Chen

Wang

et al. 2023

IJERPH

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Smart community enables a sustainable and livable community future, in which residents’ demands play an important role in its success. Though great efforts have been made to encourage residents’ participation in the implementation of smart communities, inefficient service supply still exists. Thus, this study aimed to classify residents’ demands for community services in smart communities and to explore relevant influencing factors based on the developed conceptual framework. Data from 221 respondents in Xuzhou city of China were analyzed by using binary logistic regression. The results indicated that more than 70% of respondents had demands for all community services in smart communities. Moreover, the demands were influenced by distinct factors, including sociodemographic characteristics, living characteristics, economic characteristics, and individual attitude characteristics. The types of community services in smart communities are clarified and fresh insights are provided into associated factors related to residents’ demands for these services in this study, through which enhanced provision of community services and effective implementation of smart communities can be achieved.

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Section: Literature Review and Hypothesesmentioning

confidence: 99%

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Chen

Wang

et al. 2023

IJERPH

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“…Wheeler et al [52], in the study, point out that online professional networking platforms are widely used and may help workers to search for and obtain jobs. They run the first randomized evaluation of training work seekers to join and use one of the largest platforms, LinkedIn.…”

Section: According To Marin and Nilămentioning

confidence: 99%

“…Also, they say Training increases the end-of-program employment rate by 10 percent, and this effect persists for at least 12 months. The available employment, platform use, and job search data suggest that employment effects are explained by work seekers using the platform to acquire information about prospective employers and perhaps by work seekers accessing referrals and conveying information to prospective employers on the platform [52].…”

Section: According To Marin and Nilămentioning

confidence: 99%

Employment in the modern era through understanding business social networks

Knežević¹,

Vukolić²,

Gajić³

et al. 2022

Trend poslovanju

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The importance of research on the impact of social networks on the employment is an increasingly current topic for research at the global level. LinkedIn is increasingly present by companies, businesses offering jobs as well as people looking for a business opportunity. The aim of the research was to determine the extent to which the LinkedIn network has an impact on employment in the Republic of Serbia and to investigate the attitudes of users of this network. The survey was conducted from January to March 2022 on a sample of 244 respondents, users of the LinkedIn network. SPSS software was used, 26.00, and the obtained data were analyzed by descriptive statistics. To determine the structure of the questionnaire and the percentage of variance, an exploratory factor analysis was performed together with a higher order factor analysis, in order to obtain the desired number of factors. The authors used multiple regression analysis to confirm the significance of the predictors. The results indicate that the greatest importance is in predicting the use of the Linkedin network when hiring workers and their feelings when using this network. The importance of work is reflected in the recognition of the importance of the social network LinkedIn in hiring workers in order to better recruit professionals.

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“…With over 875 million users, LinkedIn is the world's largest professional networking platform 1 . Although the large user base engaging with LinkedIn as part of their career strategy makes it an important platform to study in its own right, recent work also shows that online networking behaviour on LinkedIn influences job opportunities for users (Rajkumar et al 2022;Wheeler et al 2022).…”

Section: Introductionmentioning

confidence: 99%

Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn

Kalhor,

Gardner,

Weber

et al. 2024

ICWSM

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Online professional social networking platforms provide opportunities to expand networks strategically for job opportunities and career advancement. A large body of research shows that women’s offline networks are less advantageous than men’s. How online platforms such as LinkedIn may reflect or reproduce gendered networking behaviours, or how online social connectivity may affect outcomes differentially by gender is not well understood. This paper analyses aggregate, anonymised data from almost 10 million LinkedIn users in the UK and US information technology (IT) sector collected from the site’s advertising platform to explore how being connected to Big Tech companies (‘social connectivity’) varies by gender, and how gender, age, seniority and social connectivity shape the propensity to report job promotions or relocations. Consistent with previous studies, we find there are fewer women compared to men on LinkedIn in IT. Furthermore, female users are less likely to be connected to Big Tech companies than men. However, when we further analyse recent promotion or relocation reports, we find women are more likely than men to have reported a recent promotion at work, suggesting high-achieving women may be self-selecting onto LinkedIn. Even among this positively selected group, though, we find men are more likely to report a recent relocation. Social connectivity emerges as a significant predictor of promotion and relocation reports, with an interaction effect between gender and social connectivity indicating the payoffs to social connectivity for promotion and relocation reports are larger for women. This suggests that online networking has the potential for larger impacts for women, who experience greater disadvantage in traditional networking contexts, and calls for further research to understand differential impacts of online networking for socially disadvantaged groups.

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LinkedIn(to) Job Opportunities: Experimental Evidence from Job Readiness Training

Cited by 24 publications

References 51 publications

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City

Employment in the modern era through understanding business social networks

Gender Gaps in Online Social Connectivity, Promotion and Relocation Reports on LinkedIn

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