Conceptualising and Modelling E-Recruitment Process for Enterprises through a Problem Oriented Approach

Authors: Alamro, S., Dogan, H., Cetinkaya, D., Jiang, N. and Phalp, K.

http://eprints.bournemouth.ac.uk/31403/

Journal: Information

Volume: 9

Issue: 11

Pages: 269

DOI: 10.3390/info9110269

Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a failed e-recruitment project conducted in military domain which was used as a case study. After reviewing the various challenges faced in that project through a number of related research domains, this research focused on two major problems: (1) the difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective, and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment; (2) British Army Regular Enlistment; and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study shows that POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives.

This data was imported from Scopus:

Authors: Alamro, S., Dogan, H., Cetinkaya, D., Jiang, N. and Phalp, K.

http://eprints.bournemouth.ac.uk/31403/

Journal: Information (Switzerland)

Volume: 9

Issue: 11

eISSN: 2078-2489

DOI: 10.3390/info9110269

© 2018 by the authors. Internet-led labour market has become so competitive it is forcing many organisations from different sectors to embrace e-recruitment. However, realising the value of the e-recruitment from a Requirements Engineering (RE) analysis perspective is challenging. This research was motivated by the results of a failed e-recruitment project conducted in military domain which was used as a case study. After reviewing the various challenges faced in that project through a number of related research domains, this research focused on two major problems: (1) the difficulty of scoping, representing, and systematically transforming recruitment problem knowledge towards e-recruitment solution specification; and (2) the difficulty of documenting e-recruitment best practices for reuse purposes in an enterprise recruitment environment. In this paper, a Problem-Oriented Conceptual Model (POCM) with a complementary Ontology for Recruitment Problem Definition (Onto-RPD) is proposed to contextualise the various recruitment problem viewpoints from an enterprise perspective, and to elaborate those problem viewpoints towards a comprehensive recruitment problem definition. POCM and Onto-RPD are developed incrementally using action-research conducted on three real case studies: (1) Secureland Army Enlistment; (2) British Army Regular Enlistment; and (3) UK Undergraduate Universities and Colleges Admissions Service (UCAS). They are later evaluated in a focus group study against a set of criteria. The study shows that POCM and Onto-RPD provide a strong foundation for representing and understanding the e-recruitment problems from different perspectives.

This data was imported from Web of Science (Lite):

Authors: Alamro, S., Dogan, H., Cetinkaya, D., Jiang, N. and Phalp, K.

http://eprints.bournemouth.ac.uk/31403/

Journal: INFORMATION

Volume: 9

Issue: 11

ISSN: 2078-2489

DOI: 10.3390/info9110269

The data on this page was last updated at 05:01 on March 20, 2019.