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Recruiting the New Generation of Computer Science Talent: Identifying and Addressing Candidate and Recruiter Challenges using Design & AI Recommendations.
KTH, School of Electrical Engineering and Computer Science (EECS).
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Rekrytering av Nya Generationens Talanger Inom Datateknik : Att Identifiera och Hantera Kandidaters och Rekryterares Utmaningar Genom Design & AI Rekommendationer. (Swedish)
Abstract [en]

The use of Information and Communication Technology (ICT) has significantly impacted Human Resource (HR) practices in recent years. With the field still undergoing a digital transition, challenges in providing a positive candidate experience during recruitment processes have emerged, with existing systems proving to have its limitations. While previous research and development has primarily focused on either the candidate or recruiter perspective in isolation, this paper examines both sets of users in order to provide insights on the recruitment process as a whole. The purpose of this study is to identify and address the challenges faced by both candidates and recruiters when recruiting the new generation of computer science talent, providing applicable design and AI recommendations for companies looking to streamline their recruitment processes while also improving candidate experiences. Through combining usability testing methods with semi-structured interviews and Figma prototyping, the findings were that the main reason why candidates experience bad recruitment processes is due to an overload of work among recruiters, and that many of the frustration points are connected between the two sets of users. By improving information architecture, syncing current systems with application websites and implementing a few automation tools, candidates can be provided the experience they demand while recruiters save a significant amount of working days at each recruitment period.

Abstract [sv]

Användandet av Informations- och kommunikationsteknik (ICT) har avsevärt påverkat metoder inom mänskliga resurser (HR) under de senaste å ren. Genom att befinna sig mitt i en digitaliseringsprocess har utmaningar kring att kunna erbjuda positiva kandidatupplevelser under rekryteringsprocesser uppstått, där befintliga system visat sig inte vara tillräckliga. Medan tidigare forskning och utveckling primärt har fokuserat på antingen kandidater eller rekryterares perpektiv i separation, undersöker denna studie båda användargrupperna för att tillhandahålla insikter kring rekryteringen i sin helhet. Syftet med studien är att identifiera de utmaningar som både kandidater och rekryterare ställs inför vid rekrytering av den nya generationens talanger inom datateknik, samt att ge applicerbara design- och AI-rekommendationer för företag som vill effektivisera sina rekryteringsprocesser i samband med att förbättra kandidatupplevelserna. Genom att kombinera metoder för användbarhetstestning med semistrukturerade intervjuer och skissering i Figma, fann denna studie att den största anledningen till varför kandidater får en sämre rekryteringsupplevelse beror på en överbelastning av arbete bland rekryterare, och att många av frustrationspunkterna hänger ihop mellan de två användargrupperna. Genom att förbättra informationsarkitektur, synkronisera befintliga system med ansöknings-webbsidor och att implementera ett par automationsverktyg kan kandidater erbjudas upplevelsen de efterfrågar, samtidigt som rekryterare sparar ett betydande antal arbetsdagar vid varje rekryteringsperiod.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology , 2024. , p. 28
Series
TRITA-EECS-EX ; 2024:496
Keywords [en]
Information and Communication Technology, Human Resources, E-Recruitment, User Experience Design, Usability, Information Architecture, Feedback, Artificial Intelligence
Keywords [sv]
Informations- och kommunikationsteknik, Mänskliga resurser, E-rektrytering, Användarupplevelsedesign, Användbarhet, Informationsarkitektur, Feedback, Artificiell Intelligens
National Category
Computer Sciences Computer and Information Sciences
Identifiers
URN: urn:nbn:se:kth:diva-352305OAI: oai:DiVA.org:kth-352305DiVA, id: diva2:1892786
External cooperation
Exsitec AB
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Available from: 2024-09-27 Created: 2024-08-27 Last updated: 2025-02-18Bibliographically approved

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