Computer Science Students’ Perception Of Attractiveness Of Prospective Employers: Identifying The Factors Of Attractiveness And The Use Of Social Media In The Context Of The Czech Republic



Year of publication 2017
Type Article in Proceedings
MU Faculty or unit

Faculty of Economics and Administration

Keywords Resource-Based View; Employer Attractiveness; Social Media; Corporate Reputation
Description Human Resources have become essential when speaking about a competitive advantage in the marketplace nowadays. Brand and corporate reputation are crucial when talented and highly-skilled job seekers consider applying for a job as they have often several options. The boom of the internet has opened new opportunities for companies/employers to lead their branding campaigns and advertise job vacancies. This paper reacts to the call of Sivertzen, Nilsen and Olafsen (2003) to test their model with a different group of respondents because the industry the respondents belonged in may have a substantial impact on the responses and relationships of the model. The original research model identifies important factors organizations should focus on when building their employer brand online and it investigates the relationships that connect dimensions of employer attractiveness with variables measuring corporate reputation, the use of social media and intentions to apply for a job. Their study explores whether the perception of employers by potential employees based on the social media promotion influences their intentions to apply for a job in a company. In the research, an electronic questionnaire distributed to computer science students in the Czech Republic was used, where responses are related to well-known Czech computer science companies. The model is replicated not only within a different industry but also in different geographical and cultural terms compared to the original paper. The proposed model was tested by a structural equation modelling in R statistical software. The purpose of this paper is to verify the validity of the original model, to extend the context of its application and to re-evaluate the limits of the model to increase the possibility of generalization. On the one hand, the results indicate positive relationships between variables of the model. On the other hand, we need to take into consideration weak fitting statistics and a low number of observations which limited the validation of the proposed model.

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