On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

Faliagka, Evanthia; Iliadis, Lazaros; Karydis, Ioannis; Rigou, Maria; Sioutas, Spyros; Tsakalidis, Athanasios K.; Tzimas, Giannis

doi:10.1007/s10462-013-9414-y

Cited by 74 publications

(40 citation statements)

References 16 publications

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“…In our study we have analysed LinkedIn profile information that can be quantified as "use" but we have not taken into account its content. Modern natural language processing techniques have proven to be effective in predicting personality in other social networks, such as Facebook (Schwartz et al, 2013;Youyou, Kosinski, & Stillwell, 2014), and also in developing artificial intelligence systems for recruitment and selection (Faliagka et al, 2014;Faliagka, Tsakalidis, & Tzimas, 2012)). Therefore, these techniques could be used, for example, to analyse how users present themselves in their profile (beyond counting the number of lines of the presentation, as we have done in this study).…”

Section: Limitations Of the Study And Future Researchmentioning

confidence: 99%

LinkedIn “Big Four”: Job Performance Validation in the ICT Sector

Aguado

Andrés

Izquierdo

et al. 2019

Revista De Psicología Del Trabajo Y De Las Organizaciones

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Information technology is dramatically changing society and work relations nowadays. Different ways of connecting through the Internet and the emergence of social networks have provided the means for individuals to contribute and share personal and professional information affecting the way of accessing the job market. In many cases this is in an unstructured mode, although professional websites often require that information is standardized. The quality of the data offered in professional web portals makes it possible to extract more information than in traditional methods (Zide, Elman, & Shahani-Denning, 2014), and additional aspects can be obtained, such as, for example, social capital (Reiners & Alexander, 2013). Moreover, as using these resources has a low cost (Nikolaou, 2014; Roth, Bobko, Van Iddekinge, & Thatcher, 2016), they are an element of great added value for personnel management in different organizational processes (Madia, 2011), such as recruitment, selection, or hiring. Therefore, companies that do not use social networks in their processes for contacting clients and potential employees are missing a huge opportunity. Specifically, the automated analysis of candidates' profiles to determine the adjustment to a position offers a significant efficiency gain in the process (Faliagka et al., 2014). However, it is true that today most organizations with implemented human resource policies use social networks to a greater or lesser extent. One of the most common uses is electronic recruitment, a form of external recruitment based h t t p s : / / j o u r n a l s. c o p m a d r i d. o rg / j wo p

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Section: Limitations Of the Study And Future Researchmentioning

confidence: 99%

LinkedIn “Big Four”: Job Performance Validation in the ICT Sector

Aguado

Andrés

Izquierdo

et al. 2019

Revista De Psicología Del Trabajo Y De Las Organizaciones

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“…Automation of the recruitment process has been consistent when tested for the applicants 'profile, semantically when compared with job requirements based on the linguistic analysis proving better than human recruiters in evaluating the applicants through online recruitment using machine learning algorithms [8]. The artificial intelligence facilitates in the recruitment process of an organization through a modern approach based on the logical reasoning, probability, mathematics, imperfect real-time decisions, game theories can be applied in the recruitment for better performance and to cut down the cost [9].…”

Section: Literature Reviewmentioning

confidence: 89%

Artificial Intelligence: A Technological Prototype in Recruitment

Vedapradha¹,

Ravi²,

Shivakami³

2019

JSSM

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Purpose: The study is conducted to evaluate the adaptability of artificial intelligence in recruitment and to assess the effect of this technology on the performance of the employees. Design/Methodology/Approach: Standard Multiple Linear regression model is used to predict the performance of the employees and one-way ANOVA is used to compare the artificial intelligence based recruitment with performance indicating variables namely reliability, productivity, Automation, Gamification & Training using SPSS. Snowball sampling method has been adopted for a sample size of 440 respondents working in leading recruitment consultancies in urban Bangalore. Findings: There is a greater association between the recruitment and performance variables when artificial intelligence is adopted as it is significant at 0.001 per cent level and productivity being the maximum. However, the impact of implementing gamification for recruitment doesn't have a significant impact on the output due to partial significant effect on the adoption as (p = 0.046 < 0.05). Value of "R" is 0.604 and the coefficient of determination is 0.365. Productivity, Training, Automation & Reliability are the significant predictors of the performance in employees. Originality/Value: Artificial intelligence has emerged as a boon to the recruiters by automating the repetitive tasks, administrative tasks. Intelligent screening helps in automating resume screening, recruiter Chatbots for real-time candidate engagement, and digitization of interviews. This promotes pro-active strategic decision making better by the recruiters.

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“…the specific online social networks indicators) to predict a user's personality (cf. Ortigosa et al 2011Ortigosa et al , 2014Bai et al 2012;Faliagka et al 2014;Kosinski et al 2014).…”

Section: Personality Prediction Enginementioning

confidence: 99%

Predicting user behavior in electronic markets based on personality-mining in large online social networks

Buettner

2016

Electron Markets

115

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Determining a user's preferences is an important condition for effectively operating automatic recommendation systems. Since personality theory claims that a user's personality substantially influences preference, I propose a personality-based product recommender (PBPR) framework to analyze social media data in order to predict a user's personality and to subsequently derive its personalitybased product preferences. The PBRS framework will be evaluated as an IT-artefact with a unique online social network XING dataset and a unique coffeemaker preference dataset. My evaluation results show (a) the possibility of predicting a user's personality from social media data, as I reached a predictive gain between 23.2 and 41.8 percent and (b) the possibility of recommending products based on a user's personality, as I reached a predictive gain of 45.1 percent.

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On-line consistent ranking on e-recruitment: seeking the truth behind a well-formed CV

Cited by 74 publications

References 16 publications

LinkedIn “Big Four”: Job Performance Validation in the ICT Sector

LinkedIn “Big Four”: Job Performance Validation in the ICT Sector

Artificial Intelligence: A Technological Prototype in Recruitment

Predicting user behavior in electronic markets based on personality-mining in large online social networks

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