The turf is always greener: Predicting decommitments in college football recruiting using Twitter data

Bigsby, Kristina Gavin; Ohlmann, Jeffrey W.; Zhao, Kang

doi:10.1016/j.dss.2018.10.003

Cited by 16 publications

(7 citation statements)

References 21 publications

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“…In the rest of this review, we focus mostly on the case of pre-hire predictions of recruitment success. Note that our methodology approaches hiring from the point of view of recruiters, as opposed to other methodologies that examine the perspective of candidates (for example, how they browse or select relevant job positions [29][30][31]).…”

Section: Functional Dimensionmentioning

confidence: 99%

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

172

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In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods.We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

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Section: Functional Dimensionmentioning

confidence: 99%

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Pessach

Singer

Avrahami

et al. 2020

Decision Support Systems

172

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“…Binary logistic regression is a generalised linear model that estimates the probability of a binary outcome based on one or more independent predictors and can be used as a classifier by setting a decision rule. This method offers the advantages of fast training time and interpretability and is suitable for problems where a linear relationship between the predictors and log odds can be assumed (Bigsby et al, 2019). The target level for logistic regression was set to 1 (the respondent realises SNSs screening).…”

Section: Discussionmentioning

confidence: 99%

Cybervetting prospective employees of SMEs

Rózsa¹,

Kmecová²

2020

Journal of International Studies

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Cybervetting has become an integral part of HR practices, even though it is generally perceived negatively, as an invasion into employees' privacy. In response, scholars have begun investigations of the ways social networks impact HRM in organizations. In this paper, we present and discuss the results of our exploration into whether SMEs will be screening job applicants. We have used binary logistic regression to build the model based on the predictors: company's size, HR formality score, company's age and turnover. The units of the analysis in this study are SMEs operating in Czech and Slovak Republics. Random sample technique was applied to select 665 respondents. The resulting model contained only Formality Score (FS) as a predictor with the association to SCR. We can claim that inclination to social networks' screening in SMEs decreases as FS rises. The study also reveals that social networks screening is widely spread among SMEs in both countries under study. The study extends the research on networks' screening and expands the knowledge about how SMEs are replacing formalised HR practices. Finally, it offers recommendations for future work that can enhance knowledge in social networks' impact on SMEs.

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“…The turf is always greener: Predicting decommitments in college football recruiting using Twitter data (Bigsby et al 2019) Social media metrics based on inlinks and outlinks. Data Generation, Model Generation Machine learning based predictive models to predict decommitments.…”

Section: Data Generation Model Generation Machine Learning Model To Detect Intergroup Prejudicementioning

confidence: 99%

“…In the area of social network analysis, data analytics approaches have been used for explaining behavioral patterns (Hassan Zadeh et al 2019), and for developing explanatory models based on network mining techniques (Wang et al 2015). Explanatory models and theory can also be used for developing actionable models that can detect and predict user attitudes (Dutta et al 2018) and user actions (Bigsby et al 2019).…”

Section: Data Generation Model Generation Machine Learning Model To Detect Intergroup Prejudicementioning

confidence: 99%

Leveraging Data Analytics for Behavioral Research

et al. 2019

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Behavioral research in information systems employing quantitative methods has traditionally relied on mainly survey-based approaches to gather subjective user data. With new advances in technology such as mobile computing, wearable devices, and social media, along with computational capabilities, organizations are in a position to leverage objective data in addressing IT issues typically addressed in behavioral research. In this paper, we propose a framework for envisioning how data analytics may be leveraged in conducting behavioral research. Particularly, the framework is explained using examples from extant research along two broad avenues, namely 'data analytics for data generation' and 'data analytics for model generation.' The article also serves as an introduction to the special issue of the Information Systems Frontiers (ISF) journal on the topic of 'Data Analytics in Behavioral Research.

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The turf is always greener: Predicting decommitments in college football recruiting using Twitter data

Cited by 16 publications

References 21 publications

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

Cybervetting prospective employees of SMEs

Leveraging Data Analytics for Behavioral Research

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