| Abstract | Selekcijski intervju najčešće je korištena metoda procjene ličnosti kandidata za različite poslove, usprkos tome što kandidati često tijekom njega iskrivljavaju svoje odgovore. Dodatni problem predstavlja to što stručnjaci u području odabira osoblja nisu vješti u otkrivanju lažiranja. Neki su istraživači predložili korištenje većeg broja ponašajnih pokazatelja kako bi se odgovorilo na ovaj izazov, iako su istraživanja u ovom području još dosta rijetka. Cilj ovog rada bio je istražiti mogućnost otkrivanja lažiranja odgovora u selekcijskom intervjuu putem multimodalnog pristupa, odnosno putem ponašajnih indikatora iz četiri šire kategorije: paraverbalnog, verbalnog i neverbalnog ponašanja te izraza lica. Dodatno, istražili smo i usporedili u kojoj se mjeri dijelovi intervjua u kojima su se sudionici predstavljali iskreno, odnosno kao idealan kandidat, mogu uspješno klasificirati logističkom regresijom i umjetnim neuronskim mrežama. U ovom istraživanju sudjelovao je prigodan uzorak od 102 studenta, odnosno osobe koje su diplomirale unatrag godinu dana. Oni su sudjelovali u zamišljenom selekcijskom intervjuu za poziciju voditelja pozivnog centra, pri čemu su snimani kamerom. Intervju se sastojao od dva bloka: u jednom zadatak sudionika bio je predstaviti se iskreno, a u drugom kao idealni kandidat za radno mjesto. Redoslijed blokova i pitanja bio je uravnotežen. Kako bismo dodatno motivirali sudionike na što uvjerljivije lažiranje njihovih odgovora, naveli smo da će 10 % najuvjerljivijih dobiti nagradni bon od 500 kn u jednoj vele-drogeriji. Na kraju intervjua prikupili smo podatke o nekim kontrolnim varijablama te varijablama vezanim uz učinkovitost eksperimentalne manipulacije. Parovi nezavisnih opažača kodirali su pojedine kategorije ponašanja na videosnimkama. Dodatno, za analizu izraza lica koristili smo i aplikaciju OpenFace. Provjera eksperimentalne manipulacije pokazala je da je ona bila uspješna, ali ne u potpunosti. Što se tiče prosječnih razlika u ponašajnim pokazateljima između dvije situacije odgovaranja, dobili smo značajne multivarijatne efekte za paraverbalno i verbalno ponašanje te za izraze lica, neovisno o tome je li se radilo o ljudskim opažačima ili o OpenFaceu. Logistička regresija i neuronske mreže pokazale su se podjednako uspješnima u klasifikaciji blokova intervjua, pri čemu je stopa točnih klasifikacija iznosila 65% za oba modela. |
| Abstract (english) | Introduction The selection interview is the most frequently used method of personnel assessment for various jobs, despite its susceptibility to response distortion. Unfortunately, existing research indicates that experts in personnel selection are no better than chance in their detection of response distortion, irrespective of their interviewing experience. As one of the potential solutions for this challenge, some authors have proposed investigating the behavioural indicators of faking during the interview. According to various general theories and models of deception (i.e., Buller & Burgoon, 1996; DePaulo et al, 2003; Ekman, 2002; Walczyk et al, 2014), differences in behavioural cues should mirror the differences in psychological processes between honesty and deception. More precisely, deception should, at least under some circumstances, be more cognitively taxing, accompanied by more rigid behaviour and by less authentic facial expressions. Although there is an abundance of papers whose authors investigated the possibility of deception detection via behavioral cues in the context of forensic and cognitive psychology, research on this topic is still very scarce when it comes to the selection interview. Therefore, the aim of this study was to explore the possibility of detection of faking in the selection interview through a multimodal approach, based on paraverbal, verbal and nonverbal cues, and facial expressions. Additionally, it was examined whether it is possible to detect response distortion with two different algorithms: one based on logistic regression and the other on artificial neural networks. Methodology: In total, a convenience sample of 102 students/recent graduates (71% female, Mage=24.2, SDage=3.08) participated in a video-recorded mock structured selection interview for the position of call centre manager. There was a total of 16 interview questions that measured the dimensions of extraversion and honesty/humility. The interview consisted of two blocks: in one block participants had to answer honestly, while in another block their task was to present themselves as an ideal candidate for the position. The order of interview questions and blocks was balanced across participants and every question appeared just in one block. As an incentive to distort their answers effectively, participants were told that 10% of those with the most convincing self-presentations in faking condition would receive an award coupon of approximately 70 euros. At the end of the interview, some control variables and variables related to the experimental manipulation check were measured. Two independent coders analysed cues from every behavioural category. Additionally, facial expressions were analysed with the OpenFace application. Results and discussion: The manipulation check indicated that participants answered less honestly, used more faking strategies, experienced more cognitive load and fear of detection, and controlled their behaviour more when presenting themselves as ideal candidates. They achieved higher scores on extraversion and honesty/humility while faking as well. Interestingly, participants reported higher levels of motivation to present themselves convincingly in the honest condition. Regarding the differences in behavioural cues between honest vs. ideal candidate conditions, significant multivariate effects were obtained for paraverbal, verbal and facial expressions (both for human coders and OpenFace) categories. In the context of the paraverbal behaviour, participants demonstrated more filled pauses and fewer speech errors while presenting themselves as ideal candidates. Regarding the verbal behavior, faking was accompanied by more first-person pronouns and fewer terms indicating uncertainty. In the facial expressions’ category, both the human coders and the OpenFace data indicate that, overall, the face was more rigid and “frozen” during faking. Additionally, an interesting pattern emerged: during the faking condition action units implicated in eyebrow raising (AU1 and AU2) were more active, while during the honest responding, there was more activation in action units related to smiling in general (AU10, AU12, and AU14), and especially to its honest, authentic variant (AU6). There was no significant multivariate effect for the nonverbal behavior in differentiating honest and faking conditions. The pattern of results described above is, at least to some degree, in line with some of the general models and theories of deception. A higher frequency of filled pauses and less activity in the face during the faking condition could indeed indicate that it was more cognitively taxing for participants to present themselves as ideal candidates, compared to responding honestly. Alternatively, this pattern of results could also have emerged as an unplanned consequence of overcontrolling one’s behaviour during the faking, in accordance with hypotheses derived from a self-presentational perspective (DePaulo et al., 2003) and interpersonal deception theory (Buller & Burgoon, 1996). On the other hand, as evidenced by their results in the experimental manipulation check, participants didn’t experience intense emotions during the interview, limiting their utility as an exploratory mechanism beyond behavioral differences between faking and honest responding. As for the classification of the interview conditions (honest vs. fake responding), the same levels of performance were achieved both with logistic regression and artificial neural networks, with 65% sensitivity, specificity, and overall accuracy rates on the test data. These rates are only slightly lower than the ones achieved on the training data, indicating a good generalizability of our models’ performances to a new, previously unseen dataset. Additionally, these results are comparable to some previous findings in the field (e.g., Culbertson et al., 2016). Conclusion: It was demonstrated in this study that response distortion in the selection interview could be detected by its unique behavioural signature, but not all modalities and their cues were equally useful. Although statistically large effect sizes were obtained for various measures of the experimental manipulation, in an absolute and practical sense the psychological differences between conditions could have been more pronounced. This could have at least partly contributed to the absence of a larger number of significant behavioural cues. Regarding the classification of faking/honest responding, when investigating different algorithms, future researchers should remember that more powerful models are not necessarily more accurate. |
