Some Advice for Psychologists Who Want to Work With Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdisciplinary collaboration. This article aims to inform psychologists who are interested in working with computer scientists about the potentials of interdisciplinary collaboration, as well as the challenges such as differing terminologies, foci of interest, data quality standards, approaches to data analyses, and diverging publication practices. Finally, we provide recommendations preparing psychologists who want to engage in collaborations with computer scientists. We argue that psychologists should proactively approach computer scientists, learn computer scientific fundamentals, appreciate that research interests are likely to converge, and prepare novice psychologists for a data-oriented scientific future

Some Advice for Psychologists Who Want to Work with Computer Scientists on Big Data

This article is based on conversations from the project “Big Data in Psychological Assessment” (BDPA) funded by the European Union, which was initiated because of the advances in data science and artificial intelligence that offer tremendous opportunities for personnel assessment practice in handling and interpreting this kind of data. We argue that psychologists and computer scientists can benefit from interdiscip

RoboREIT: an Interactive Robotic Tutor with Instructive Feedback Component for Requirements Elicitation Interview Training

[Context] Interviewing stakeholders is the most popular requirements elicitation technique among multiple methods. The success of an interview depends on the collaboration of the interviewee which can be fostered through the interviewer's preparedness and communication skills. Mastering these skills requires experience and practicing interviews. [Problem] Practical training is resource-heavy as it calls for the time and effort of a stakeholder for each student which may not be feasible for a large number of students. [Method] To address this scalability problem, this paper proposes RoboREIT, an interactive Robotic tutor for Requirements Elicitation Interview Training. The humanoid robotic component of RoboREIT responds to the questions of the interviewer, which the interviewer chooses from a set of predefined alternatives for a particular scenario. After the interview session, RoboREIT provides contextual feedback to the interviewer on their performance and allows the student to inspect their mistakes. RoboREIT is extensible with various scenarios. [Results] We performed an exploratory user study to evaluate RoboREIT and demonstrate its applicability in requirements elicitation interview training. The quantitative and qualitative analyses of the users' responses reveal the appreciation of RoboREIT and provide further suggestions about how to improve it. [Contribution] Our study is the first in the literature that utilizes a social robot in requirements elicitation interview education. RoboREIT's innovative design incorporates replaying faulty interview stages and allows the student to learn from mistakes by a second time practicing. All participants praised the feedback component, which is not present in the state-of-the-art, for being helpful in identifying the mistakes. A favorable response rate of 81% for the system's usefulness indicates the positive perception of the participants.Comment: Author submitted manuscrip

Investigating Technologically Advanced Job Interview Approaches

Die technologische Entwicklung stellt Unternehmen vor die Herausforderung informierte Entscheidungen über technologische Lösungen für organisationale Prozesse zu treffen. Besonders auffällig ist das im Falle von Bewerbungsgesprächen, bei denen in der Praxis wenig erforschte Technologien verwendet werden. Infolgedessen können Unternehmen über die Einflüsse technologiegestützter Bewerbungsgespräche (z.B. digitale Interviews) auf den Interviewprozess nur spekulieren. Meine Doktorarbeit soll die Forschung zu technologiegestützen Bewerbungsgesprächen in vier Schritten modernisieren. Erstens entwickle ich eine psychometrisch fundierte Skala zur Messung von Creepiness. Diese soll die Forschung zur Akzeptanz neuer Technologien unterstützen. Zweitens vergleiche ich digitale Interviews mit Videokonferenz-Interviews. Die Ergebnisse zeigen, dass digitale Interviews weniger akzeptiert und dass Bewerbende in digitalen Interviews besser bewertet werden. Drittens antizipiere ich die Zukunft des Bewerbungsgesprächs und untersuche ein algorithmusbasiertes Bewerbungsgespräch. Das algorithmusbasierte Bewerbungsgespräch führte zu negativeren Bewerberreaktionen als ein Videokonferenz-Interview. Im vierten Schritt erweitern zwei weiteren Studien die vorangegangenen Erkenntnisse indem versucht wird, negative Bewerberreaktionen durch Informationen zu technologisch fortschrittlichen Bewerbungsgesprächen vorzubeugen. Die Ergebnisse zeigen eine komplexe Beziehung zwischen Informationen und Akzeptanz. Weiterhin scheinen rechtfertigende Informationen besser als Prozessinformationen zu sein, um Bewerberreaktionen zu verbessern. Zusammengefasst zeigt meine Dissertation, dass die Anwendung neuer Technologien für die Personalauswahl wohl durchdacht sein sollte und dass Forschung zu klassischen Bewerbungsgesprächen möglicherweise nicht auf technologisch fortschrittliche Bewerbungsgespräche übertragbar ist. Schlussendlich ruft meine Dissertation zu weiterer Forschung bezüglich des Einflusses neuer Technologien in der Personalauswahl auf.Technology and its use has an immense effect on our daily lives. For instance, the recent rapid technological evolution has led to a myriad of technological solutions for organizational procedures. This challenges organizations to stay up-to-date and to make informed decisions about implementing and investing in technologically advanced procedures. In the context of job interviews, the technology that is used in practice has outpaced the research on the use of these technologies. As a consequence, researchers and practitioners can only speculate about how modern job interviews (e.g., digital interviews) affect outcomes such as applicant reactions and interview performance ratings. My dissertation therefore aims to update the research on technologically advanced job interviews in four steps. First, I provide a study on the development of a psychometrically sound measure of creepiness as a new perspective on research involving acceptance of technology-based situations. Second, I present a study comparing the emerging interview form of digital interviews with videoconference interviews showing that digital interviews can impair applicants’ reactions but increase applicants’ performance ratings. Third, I attempt to foreshadow the future of job interviewing technology by investigating an algorithm-based job interview with a virtual agent as the interviewer; results showed diminished applicant reactions compared to videoconference interviews. Fourth, two additional studies incorporate the aforementioned findings and attempt to buffer negative applicant reactions with information preceding technologically advanced job interviews. The results indicate a complex relation between information and acceptance and that justification information is better than process information to improve applicant reactions. All things considered, my dissertation implies that careful design is needed for personnel selection technology, that previous research in non-technological job interview settings might not translate to situations including novel technologies, and it calls for further research to investigate the influence of technology on personnel selection.Bundesministerium für Bildung und Forschung, Projekt Empa