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The datafication of talent: how technology is advancing the science of human potential at work
Abstract
This article reviews three innovations that not only have the potential to revolutionize the way organizations identify, develop and engage talent, but are also emerging as tools used by practitioners and firms. Specifically, we discuss (a) machine-learning algorithms that can evaluate digital footprints, (b) social sensing technology that can automatically decode verbal and nonverbal behavior to infer personality and emotional states, and (c) gamified assessment tools that focus on enhancing the user-experience in personnel selection. The strengths and limitations of each of these approaches are discussed, and practical and theoretical implications are considered.
... People analytics has the potential to transform the way organisations identify, develop, manage and control their workforce (Chamorro-Premuzic et al., 2017;Huselid, 2018). The term "people analytics" does not refer to a technology, but to a novel, quantitative, evidence-based, and data-driven approach to manage the workforce McAfee et al., 2012). ...
... Thereby, algorithms are thought to increase fairness, transparency and objectivity (Jabagi et al., 2020;Martin-Rios et al., 2017;Sharma & Sharma, 2017). Secondly, people analytics is believed to predict, modify, and manage current and future human behaviour, particularly through systematically analysing and exploring historical data (Chamorro-Premuzic et al., 2017;Gal et al., 2017;Isson & Harriott, 2016). The assumption is that current and future behaviour can be explained and predicted from the analysis of past actions and their consequences (Sivathanu & Pillai, 2019;N. ...
... Overall, 53 studies have highlighted such opportunities, addressing the following three sub-themes: opportunities and businessoriented benefits, employee-oriented benefits, and assumptions underpinning people analytics. A common thread in this theme is the potential people analytics has to transform the way organisations identify, develop and evaluate their talent (Chamorro-Premuzic et al., 2017;Fernandez & Gallardo-Gallardo, 2020;Martin-Rios et al., 2017). The underlying rationale of many studies seems to be the belief that people analytics can generate actionable insights for all stages of the employee life cycle to improve operational and strategic firm performance (Levenson, 2018;Minbaeva, 2018;L. ...
Technological advances in the field of artificial intelligence (AI) are heralding a new era of analytics and data-driven decision-making. Organisations increasingly rely on people analytics to optimise human resource management practices in areas such as recruitment, performance evaluation, personnel development, health and retention management. Recent progress in the field of AI and ever-increasing volumes of digital data have raised expectations and contributed to a very positive image of people analytics. However, transferring and applying the efficiency-driven logic of analytics to manage humans carries numerous risks, challenges, and ethical implications. Based on a theorising review our paper analyses perils that can emerge from the use of people analytics. By disclosing the underlying assumptions of people analytics and offering a perspective on current and future technological advancements, we identify six perils and discuss their implications for organisations and employees. Then, we illustrate how these perils may aggravate with increasing analytical power of people analytics, and we suggest directions for future research. Our theorising review contributes to information system research at the intersection of analytics, artificial intelligence, and human-algorithmic management.
... As the world of human resource management (HRM) moves to take advantage of the power of big data and HR analytics (Angrave et al., 2016), artificial intelligence (AI) is becoming a key trend affecting hiring decisions (AON, 2021;Statista, 2020), and in 2019 the AI in recruitment market was worth an estimated $580 million (IndustryARC, 2020). Within this new AI-based approach to hiring, the use of AI-based technologies to evaluate candidate interview performance has been attracting interest (Chamorro-Premuzic et al., 2017). AI evaluations, or AIEs, rely on asynchronous video interviews (AVIs), where candidates record videos of themselves answering pre-determined interview questions. ...
... AI evaluations, or AIEs, rely on asynchronous video interviews (AVIs), where candidates record videos of themselves answering pre-determined interview questions. Their responses are then evaluated by AI based technology, such as machine learning algorithms (Chamorro-Premuzic et al., 2017;Suen et al., 2019;Woods et al., 2020). Responses may be evaluated not only for verbal content, but also linguistic patterns, nonverbal behavior, physiological reactions, and other non-verbal cues (Chamorro-Premuzic et al., 2017;Woods et al., 2020). ...
... Their responses are then evaluated by AI based technology, such as machine learning algorithms (Chamorro-Premuzic et al., 2017;Suen et al., 2019;Woods et al., 2020). Responses may be evaluated not only for verbal content, but also linguistic patterns, nonverbal behavior, physiological reactions, and other non-verbal cues (Chamorro-Premuzic et al., 2017;Woods et al., 2020). ...
In search of greater resource savings and efficiencies, companies are turning to new technologies in the interview process, such as artificial intelligence evaluation (AIE). However, little is known about candidate reactions to this new tool. We identified outstanding questions regarding reactions to AIE arising from justice and signaling theories and conducted interviews with 33 professionals to understand their perceptions of AIE use in selection. Participants raised issues related to all four types of justice and indicated that there is a signaling effect of AIE use. Despite acknowledging the superior objectivity of AIE, participants expressed a desire for the maintenance of human elements in the evaluation process, seemingly preferring ‘the devil they know’ (human biases and intuition) rather than the one they do not (AIE algorithm). This result is explored through the lens of uncertainty reduction, discussing theoretical implications for justice and signaling theories, and providing implications for the implementation of AIE in the selection process.
... Yet, personality assessment usually occurs early in the screening process, making it prohibitively costly to replace self-reports with interviewer trait assessments. Thus, using people analytics to automate interviewer-based personality assessments on a large scale holds potential to improve the utility of hiring outcomes (Chamorro-Premuzic et al., 2017). ...
... This study contributes to the selection and management literatures in several ways. First, this study answers calls to investigate alternatives to self-report personality measures (Morgeson et al., 2007) and technologies for automatically scoring applicant KSAOs (Chamorro-Premuzic et al., 2017). The scientific study of such technologies can ensure HR and management science keeps pace with and remains relevant to management practice. ...
Organizations are increasingly relying on people analytics to aid human resources decision-making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organizations’ decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee’s self-reported (automatic personality recognition) and interviewer-rated personality traits (automatic personality perception). We use 10-fold cross-validation to test the algorithms’ accuracy for predicting Big Five personality traits across both rating sources. The cross-validated accuracy for predicting self-reports was lower than large-scale investigations using language in social media posts as predictors. The cross-validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self-reports. We discuss implications for future research and practice.
... Only 6.52% of the studies did not mention the resource they used to collect the DF. (Benson & Filippaios, 2010;Chamorro-Premuzic et al., 2017;Chretien et al., 2015;Cladis, 2018;Simon Cleveland et al., 2016;Eberlin, 2018;Grover & Mark, 2017;Gruzd, 2009;Hwong et al., 2017;Kosinski et al., 2016;Laleh & Shahram, 2018;Martin et al., 2018;Nechaev et al., 2017;Osborne & Connelly, 2015, 2016Parks et al., 2018;Polignano et al., 2017;Rossetti et al., 2016Rossetti et al., , 2015Shafie et al., 2015;Kulkarni Varsha & Monica, 2018;Vivakaran & Maraimalai, 2019;Ye et al., 2017;Youyou et al., 2015) 52,17 ...
... Abdelnour-Nocera et al., 2015;Azcona et al., 2019;Benson & Filippaios, 2010;Chamorro-Premuzic et al., 2017;Galimova et al., 2019; Hoel & Xiao, 2018b;Hwong et al., 2017;Kosinski et al., 2016;Leon Urrutia et al., 2017;Pardo et al., 2015; Paredes et al.and Digital Life (DF management, DF healing, digital awareness development, digital privacy, cyberbullying, digital presence, digital inequality, digital skills, effects of using digital technologies, legislation on life and digital presence) (Buchanan et al., 2017; Cladis, 2018; Simon Cleveland et al.(creation of student profiles, studies of personality traits, emotionality and/or empathy)(Burr & Cristianini, 2019;Gelbard et al., 2018;Grover & Mark, 2017;Hinds & Joinson, 2019;Laleh & Shahram, 2018;Nechaev et al., 2017;Polignano et al., 2017;Schoedel et al., 2018;Ye et al., 2017;Youyou et al., 2015) 21,74% ...
Digital footprints (DF) offer relevant information about educational activities and processes related to strategies of academic assessment, identification of skills and psychological traits of students, and permanence and dropout trends, etc. This study analyzes scientific evidence on the use of DF in education, and shows the results of a systematic mapping of literature carried out based on the analysis of documents published in the last fifteen years (2005–2019) in two databases: Scopus and Web of Science (WoS). Findings reveal that educational research on DF is focused on learning analytics, the study of digital presence and psychometric modeling. Likewise, the article reports on the scarce investigation of DF in Massive Open Online Courses (MOOCs) environments and highlights the multiple meanings of DF as an action and as a service, beyond the generalized conception of data. These findings suggest the importance of preparing educational institutions to implement DF use and management practices in order to favor processes that range from DF curation, to cognitive evaluation, digital equity, prediction of school success and/or failure, etc. Hence, DF meanings (data, action and service) could aid to generate proper administration and policy-making actions, placed at the service of all students and their educational communities.
... Optimize talent management measures. A number of studies (n = 8) have studied game design elements in talent management measures (Buil et al., 2019;Chamorro-Premuzic et al., 2017;Georgiou et al., 2019;Tansley et al., 2016). Recent literature deals with the use of gamification in assessment centers and talent identification (e.g. ...
... Besides, the studies identified in this literature review focus on the circumstances under which gamification works in the specific area of application (e.g. Abedi et al., 2018;Chamorro-Premuzic et al., 2017;Dubey et al., 2016), rather than on "how" it works in particular contexts. This is in contrast to the findings of Nacke and ...
A firm's entrepreneurial orientation (EO) is its propensity to act proactively, innovate, take risks, and engage in competitive and autonomous behaviors. Prior research shows that EO is an im-portant factor for new ventures to overcome barriers to survival and fostering growth, measured by annual sales and employment growth rates. In particular, individual-level EO (IEO) is an important driver of a firm’s EO. The firm’s ability to exploit opportunities appearing in the mar-ket and to achieve superior performance depends on the employees’ skills and experiences to act and think entrepreneurially. The main objective of this dissertation is to investigate how and when employees engage in entrepreneurial behaviors at work. Building on three essays, this dissertation takes an interdisciplinary approach to employee entrepreneurial behaviors in new ventures, encompassing both entrepreneurship and gamification research. The first main contri-bution proposed in this field is a more nuanced understanding of how employee entrepreneurial behaviors help young firms cope with growth-related, organization-transforming challenges (i.e., changes in organizational culture that accompany growth, the introduction of hierarchical structures, and the formalization of processes). When new ventures grow, employees’ IEO tends to manifest in introducing technological innovations and business improvements rather than in actions related to risk-taking. Second, this dissertation reveals the relevance of self-efficacy for entrepreneurial behaviors and explores how gamification can enhance employee entrepreneurial behaviors in new ventures. Based on these findings, this dissertation contributes to EO research by highlighting the role of IEO as a building block for EO pervasiveness. This research further develops our knowledge on the use of gamification in new ventures. This cu-mulative dissertation is structured as follows. Part A is an introduction to the study of entrepre-neurial behaviors. Part B contains the three essays.
... Since the world population is increasing rapidly, the classification of human resource with respect to talent is a very important task. Despite being a hot research topic, the talentanalytics side of human resource management is still in its infancy (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). In sports, the academic research for the talent identification procedures is gaining a notable ground(K. ...
... Many studies suggested that apart from being identified, the talent can also be upgraded (Mallillin, Collantes, Go, Platoon, & Villegas, 2007). As a result, this area became a rapid research interest (Chamorro-Premuzic et al., 2017;Elferink-Gemser, Visscher, Lemmink, & Mulder, 2007;K. Johnston et al., 2018). ...
Talent Hunt for sports has always been of great concern. The research interest in the domain of sports talent identification is on an increasing curve. The conventional approaches to identify the talent are being modelled into the scientific models using various analytical and mathematical computational techniques. This paper aims to review some of the talent identification models and projects the current perspective of the Sports talent identification (TiD) computational techniques. Articles from a timeframe of 1995-2020 were systematically selected in accordance with the PRISMA guidelines. We remain focused on the computational methodology being employed in the TiD models. The review delivers the findings and highlights some of the inherent issues that are not being addressed by the existing TiD models.
... Yet, personality assessment usually occurs early in the screening process, making it prohibitively costly to replace self-reports with interviewer trait assessments. Thus, using people analytics to automate interviewer-based personality assessments on a large scale holds potential to improve the utility of hiring outcomes (Chamorro-Premuzic et al., 2017). ...
... This study contributes to the selection and management literatures in several ways. First, this study answers calls to investigate alternatives to self-report personality measures (Morgeson et al., 2007) and technologies for automatically scoring applicant KSAOs (Chamorro-Premuzic et al., 2017). The scientific study of such technologies can ensure HR and management science keeps pace with and remains relevant to management practice. ...
Organizations are increasingly relying on people analytics to aid human resources decision-making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organizations’ decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee’s self-reported (automatic personality recognition) and interviewer-rated personality traits (automatic personality perception). We use 10-fold cross-validation to test the algorithms’ accuracy for predicting Big Five personality traits across both rating sources. The cross-validated accuracy for predicting self-reports was lower than large-scale investigations using language in social media posts as predictors. The cross-validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self-reports. We discuss implications for future research and practice.
... Reasons for this development include higher efficiency and independence from time and space when conducting interviews (Blacksmith et al., 2016). Additionally, providers of automatically evaluated interviews promise that the reduction of human influence on applicants' rating (i.e. through automatic evaluation of the interview performance) leads to more fairness and objectivity (Chamorro-Premuzic et al., 2017). ...
... Not only does practice push the use of automatically evaluated interviews (cf., Chamorro-Premuzic et al., 2017), there is also research suggesting that automatic evaluation might predict performance. For instance, Naim et al. (2015) demonstrated that there is a relation between automatically evaluated applicant behavior and interview performance rated by humans. ...
Purpose: Due to recent advancements in Artificial Intelligence, automatic evaluation of job interviews has become an alternative for assessing interviewees. Therefore, questions arise regarding applicant reactions and behavior when algorithms automatically evaluate applicants’ interview responses. This study tests arguments from previous research suggesting that applicants whose interviews will be automatically evaluated may use less impression management (IM), but could react more negatively to the interview.
Methodology: Participants (N = 124; primarily German students) took part in an online mock interview where they responded to interview questions via voice recordings (i.e., an asynchronous interview). Prior to the interview, half of them were informed that their answers would be evaluated automatically (vs. by a human rater). After the interviews, participants reported their honest and deceptive IM behavior as well as their reactions to the interview.
Findings: Participants in the automatic evaluation condition engaged in less deceptive IM, felt they had fewer opportunities to perform during the interview, and provided shorter interview answers.
Research implications: The findings of this study suggest a tradeoff between IM behavior and applicant reactions in technologically advanced interviews. Furthermore, the results indicate that automatically evaluated interviews might affect interview validity (i.e., because of less deceptive IM) and influence interviewees’ response behavior.
Practical implications: Hiring managers might hope that automatically evaluated interviews decrease applicants’ use of deceptive IM. However, the results also challenge organizations to pay attention to negative effects of automatic evaluation on applicant reactions.
Originality: This study is the first empirical study investigating the impact of automatically evaluated interviews on applicant behavior and reactions.
... Such a method of predicting individuals' future behaviour based on their past behaviour is consistent with the concepts of traditional talent recruitment principles. An increasing number of scholars have started to discuss ways of discovering talent [12] or predicting human behaviour [13] using SM information. Relevant theories mainly concern the authenticity of SM content, the consistency of SM assessments, and the results of such assessments to verify their effectiveness. ...
... Therefore, we proposed that the overall judgement of SM information by well-trained HR managers, based on certain standards of the job position as well as the context, is sufficient to predict individuals' willingness to work and specific behavioural intentions. Therefore, our conclusion supported and emphasized the outcomes of SM assessors' ability and training [12]. ...
Individuals have a large amount of personal information on social media (SM), which provides companies with new opportunities for talent selection. However, researchers’ understanding of the effectiveness of assessments based on SM is relatively ambiguous, and the conclusions of empirical studies remain controversial. The Realistic Accuracy Model provides theoretical and methodological support for the application of SM information in zero-acquaintance contexts. Accordingly, we collected and matched 160 sets of Chinese SM assessment (other-assessment) and employee self-assessment data. Through a two-step data analysis, we conducted a consistency check and verification of behavioural predictions. The results suggested that in terms of general suitability, as well as knowledge, skills, abilities, and other characteristics (KSAOs), the other-assessments and self-assessments were consistent. Furthermore, the general suitability and KSAOs of the other-assessments were predictive of behavioural intention (i.e., openness to change). This study empirically tested the accuracy of SM talent assessments, and finally, the research limitations and future trends were discussed.
... Interviews are one of the most widely used (Buehl & Melchers, 2018;McCarthy et al., 2017), yet time and resource intensive, selection tools. Not surprisingly, technology designed to automate the interpretation of verbal and nonverbal behaviors has attracted interest (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). Most research to date on this subject has evaluated applicant reactions to technology once they have entered the selection process (e.g., Langer et al., 2017), ignoring the potential effects on the makeup of the applicant pool. ...
... When signals are not yet well established, the two sides must go through a process of interpreting signals, validating this interpretation and taking into account any adaptations from either side before a steady state is reached where something can be safely interpreted as a valid signal of a given characteristic (Bangerter et al., 2012). With respect to the use of AI-based evaluation technology, companies may initially aim to make the process more standardized, objective, and costefficient or to signal financial fitness, efficiency, objectivity, or a culture of innovation (Chamorro-Premuzic et al., 2017). Alternatively, the adoption of this technology may itself be an adaptation that increases cheating costs in response to impression management and faking behaviors (Bangerter et al., 2012;Buehl & Melchers, 2018). ...
This study investigates how information provided prior to the application stage of the selection process affects application intentions toward the job and organization. Existing research has focused on applicants who have already entered into the selection process; however, information revealed prior to application may cause candidates to self-select themselves out of the process. Utilizing a randomized experimental design, participants read a job ad specifying that their prerecorded interviews would be reviewed by a human or an artificial intelligence-based evaluator. The results show increased intentions to apply and pursue the job in the human evaluation condition.
... Optimize talent management measures. A number of studies (n = 8) have studied game design elements in talent management measures (Buil et al., 2019;Chamorro-Premuzic et al., 2017;Georgiou et al., 2019;Tansley et al., 2016). Recent literature deals with the use of gamification in assessment centers and talent identification (e.g. ...
... Besides, the studies identified in this literature review focus on the circumstances under which gamification works in the specific area of application (e.g. Abedi et al., 2018;Chamorro-Premuzic et al., 2017;Dubey et al., 2016), rather than on "how" it works in particular contexts. This is in contrast to the findings of Nacke and Deterding (2017), who found that research on gamification is shifting from exploring systems, designs, and architectures to investigating the effects of gamified systems. ...
Gamification has recently been presented as a promising opportunity to improve human resource management (HRM) practices and tools. However, while the number of publications on gamification has been increasing in recent years, an overview of the current landscape of HRM-related literature of gamification is missing so far. Intending to support and ease the understanding of prior research findings in this field, this article conducts a systematic literature review. This study contributes to the field of human resources research by examining 45 research papers, aiming to explore areas of application and outcomes of the use of gamification in HRM. Propositions are outlined along with elaborating risks and approaches on how to mitigate the risks of using game design elements in HRM.
... Similarly, the underlying criteria for the prediction of job performance may not be derived from scientific research programs [63,64]. Moreover, ML algorithms predict future human behavior based on historical data, ignoring novel patterns and parameters [65]. Therefore, predictions are often proven wrong because of changes in the overarching ecosystem [66,67]. ...
The use of artificial intelligence (AI) technologies in organizations’ recruiting and selection procedures has become commonplace in business practice; accordingly, research on AI recruiting has increased substantially in recent years. But, though various articles have highlighted the potential opportunities and ethical risks of AI recruiting, the topic has not been normatively assessed yet. We aim to fill this gap by providing an ethical analysis of AI recruiting from a human rights perspective. In doing so, we elaborate on human rights’ theoretical implications for corporate use of AI-driven hiring solutions. Therefore, we analyze whether AI hiring practices inherently conflict with the concepts of validity, autonomy, nondiscrimination, privacy, and transparency, which represent the main human rights relevant in this context. Concluding that these concepts are not at odds, we then use existing legal and ethical implications to determine organizations’ responsibility to enforce and realize human rights standards in the context of AI recruiting.
... In the current study, we chose a personnel selection task which means that trustors will evaluate trustworthiness in regard to their goals, standards, and values in relation to the respective personnel selection task. We decided for personnel selection as it reflects one of the pioneering areas of management where automated systems based on methods from the areas of artificial intelligence and machine learning have quickly become interesting to automate or augment tasks (Chamorro-Premuzic et al., 2017;Oswald et al., 2020;Raghavan et al., 2020). Furthermore, personnel selection reflects a context where people fulfill complex, ethically sensitive decisions, and where automated systems already are a viable options for decision-support (Campion et al., 2016). ...
Introducing automated systems based on artificial intelligence and machine learning for ethically sensitive decision tasks requires investigating of trust processes in relation to such tasks. In an example of such a task (personnel selection), this study investigates trustworthiness, trust, and reliance in light of a trust violation relating to ethical standards and a trust repair intervention. Specifically, participants evaluated applicant preselection outcomes by either a human or an automated system across twelve personnel selection tasks. We additionally varied information regarding imperfection of the human and automated system. In task rounds five through eight, the preselected applicants were predominantly male, thus constituting a trust violation due to a violation of ethical standards. Before task round nine, participants received an excuse for the biased preselection (i.e., a trust repair intervention). Results showed that participants initially perceived automated systems to be less trustworthy, and had less intention to trust automated systems. Specifically, participants perceived systems to be less able, and flexible, but also less biased – a result that was sustained even in light of unfair bias. Furthermore, in regard to the automated system the trust violation and the trust repair intervention had weaker effects. Those effects were partly stronger when highlighting imperfection for the automated system. We conclude that it is crucial to investigate trust processes in relation to automated systems in ethically sensitive domains such as personnel selection as insights from classical areas of automation might not translate to application contexts where ethical standards are central to trust processes.
... Health Innovation; Digital Technology; Digital Innovation; Technological Innovation, Technology Innovation [38]; [39]; [40]; [41]; [42]; [43]; [44]; [45]; [46]; [47]; [48]; [49]; [50]; [51]; [52]; [53]; [54]; [55]; [56]; [57]; Human, Human [58] Digital Transformation [59]; [24]; [60]; [61] [62]; [36]; [63] On Scopus, the most cited papers with the keywords "digital leadership" and "innovations" are 'Leadership, capabilities, and technological change: The transformation of NCR in the electronic era' by [1] that has been cited 216 times. The second most cited paper is a paper by [64] titled 'The next 20 years: How customer and workforce attitudes will evolve' cited 178 times. ...
... Job candidates submit a resume for nearly every application knowing that recruiters will use this information for (pre-) selecting candidates (Cole, Feild, & Stafford, 2005;Thoms et al., 1999). Through the ongoing digitalization of selection processes (Chamorro-Premuzic et al., 2017;Novac & Ciochinȃ, 2018), an important, yet neglected factor that might crucially affect how applicants design their resume and how hiring managers react to information from resumes is the resume format. ...
Resumes are a ubiquitous first hurdle in hiring processes. Applicants' resume fraud behavior and applicants' reactions to selection methods can therefore influence all subsequent selection stages. In addition to classical resumes, professional social media resumes and blockchain resumes emerge as alternative resume formats. In two online studies, this paper investigates whether differing characteristics of classical, social media, and blockchain resumes affect applicant fraud behavior and reactions (e.g., perceived fairness) to the resume formats. We further investigate if differing reactions consequently influence perceived organizational attractiveness of the hiring organization using the respective resume format. In a between-subjects design, Study 1 examined potential applicants' resume fraud behavior and reactions towards the resume formats. Study 2 parallels Study 1 in a sample of actual human resource managers. In both studies, the resume format had negligible effects on expected fraud behavior, with participants expecting only slightly more fraud behavior in social media resumes. In both samples, the novel resume formats triggered less favorable reactions and led to lower organizational attractiveness, calling for caution when considering novel resume formats for hiring. Finally, exploratory findings revealed that the processes through which the novel resume formats negatively affected organizational attractiveness differed between applicants and human resource managers.
... Third, there is a similarity between the mental abilities to be successful at a video game and those related to cognitive ability. Last, job applicants favor playing games (Chamorro-Premuzic et al. 2017). ...
... Assessment at scale can bring considerable long-term benefits, even if slightly less valid than other approaches (Chamorro-Premuzic et al., 2017). Our study provides some initial evidence suggesting that AVI-PAs may validly assess some traits, but the evidence is mixed and many questions remain unanswered. ...
Organizations are increasingly adopting automated video interviews (AVIs) to screen job applicants despite a paucity of research on their reliability, validity, and generalizability. In this study, we address this gap by developing AVIs that use verbal, paraverbal, and nonverbal behaviors extracted from video interviews to assess Big Five personality traits. We developed and validated machine learning models within (using nested cross-validation) and across three separate samples of mock video interviews (total N = 1,073). Also, we examined their test–retest reliability in a fourth sample (N = 99). In general, we found that the AVI personality assessments exhibited stronger evidence of validity when they were trained on interviewer-reports rather than self-reports. When cross-validated in the other samples, AVI personality assessments trained on interviewer-reports had mixed evidence of reliability, exhibited consistent convergent and discriminant relations, used predictors that appear to be conceptually relevant to the focal traits, and predicted academic outcomes. On the other hand, there was little evidence of reliability or validity for the AVIs trained on self-reports. We discuss the implications for future work on AVIs and personality theory, and provide practical recommendations for the vendors marketing such approaches and organizations considering adopting them.
... LinkedIn profiles) (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). This, along with the acknowledgment that "Facebook data has also been used to infer dark side[sic] personality traits, such as psychopathy[…], and narcissism […]"(Chamorro-Premuzic et al., 2017, ...
Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as “digital psychiatry.” This article focuses on the increasing use of digital psychiatry outside of clinical settings, in the following sectors: education, employment, financial services, social media, and the digital wellbeing industry. We analyze the ethical challenges of deploying digital psychiatry in these sectors, emphasizing key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and wellbeing in information societies.
... With the high mobility of talent, and the increment of job seekers through this channel, it becomes important for the recruitment team to seek intelligent ways for person-job fitting to adapt the right job seekers to the right positions. This crucial task for job recruitment person-job fit has been thoroughly studied as candidate matching [1,[15][16][17], job recommendations [13,18], job transitions [19][20][21] and other methods for talent identification [22]. ...
LinkedIn is a social medium oriented to professional career handling and networking. In it, users write a textual profile on their experience, and add skill labels in a free format. Users are able to apply for different jobs, but specific feedback on the appropriateness of their application according to their skills is not provided to them. In this work we particularly focus on applicants of the project management branch from information technologies—although the presented methodology could be extended to any area following the same mechanism. Using the information users provide in their profile, it is possible to establish the corresponding level in a predefined Project Manager career path (PM level). 1500+ experiences and skills from 300 profiles were manually tagged to train and test a model to automatically estimate the PM level. In this proposal we were able to perform such prediction with a precision of 98%. Additionally, the proposed model is able to provide feedback to users by offering a guideline of necessary skills to be learned to fulfill the current PM level, or those needed in order to upgrade to the following PM level. This is achieved through the clustering of skill qualification labels. Results of experiments with several clustering algorithms are provided as part of this work.
... Datafication, a term introduced to popular usage by Mayer-Schonberger and Cukier (2013), can be understood as the conversion of aspects of peoples' unstructured everyday experience into a structured format that can be analysed in a formal system. Datafication is becoming pervasive in modern society, for example in health (Ruckenstein & Dow Schüll, 2017), urban planning (Tenney & Sieber, 2016), human resources (Chamorro-Premuzic, Akhtar, Winsborough & Sherman, 2017), justice (Smith, Bennett Moses & Chan, 2017). New technologies result in data being created, often as a by-product of their use. ...
Increasingly educational providers are being challenged to use their data stores to improve teaching and learning outcomes for their students. A common source of such data is learning management systems which enable providers to manage a virtual platform or space where learning materials and activities can be provided for students to engage with. This study investigated whether data from the learning management system Moodle can be used to predict academic performance of students in a blended learning further education setting. This was achieved by constructing measures of student activity from Moodle logs of further education courses. These were used to predict alphabetic student grade and whether a student would pass or fail the course. A key focus was classifiers that could predict likelihood of failure from data available early in the term. The results showed that classifiers built on all course data predicted student grade moderately well (accuracy= 60.5%, kappa = 0.43) and whether a student would pass or fail very well (accuracy= 92.2%, kappa=0.79). However, classifiers built on the first six weeks of data did not predict failing students well. Classifiers trained on the first ten weeks of data improved significantly on a no-information rate (p<0.008) though more than half of failing students were still misclassified. The evidence indicates that measures of Moodle activity on further education courses could be useful as part of on an early-warning system at ten weeks.
... LinkedIn profiles) (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). This, along with the acknowledgment that "Facebook data has also been used to infer dark side[sic] personality traits, such as psychopathy[…], and narcissism […]"(Chamorro-Premuzic et al., 2017, ...
... Turning to the use of digital psychiatry to support hiring practices, it has been noted that some firms are exploring how AI can improve HR-analytics by mining sources such as social media (e.g. LinkedIn profiles) (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). This, along with the acknowledgment that "Facebook data has also been used to infer dark side [sic] personality traits, such as psychopathy […], and narcissism […]" (Chamorro-Premuzic et al., 2017, p. 14), raises substantial ethical risks related to privacy and discriminatory profiling. ...
Common mental health disorders are rising globally, creating a strain on public healthcare systems. This has led to a renewed interest in the role that digital technologies may have for improving mental health outcomes. One result of this interest is the development and use of artificial intelligence for assessing, diagnosing, and treating mental health issues, which we refer to as 'digital psychiatry'. This article focuses on the increasing use of digital psychiatry outside of clinical settings, in the following sectors: education, employment, financial services, social media, and the digital well-being industry. We analyse the ethical risks of deploying digital psychiatry in these sectors, emphasising key problems and opportunities for public health, and offer recommendations for protecting and promoting public health and well-being in information societies.
... Computer scientists and engineers are developing artificial intelligence systems that 'understand' social situations by modeling, analyzing, and synthesizing non-verbal communication (Vinciarelli et al., 2009). Real-world applications are already in use to detect "deception" and "truth" (Elkins et al., 2012) in face-to-face situations, which in turn can be applied to decision-making on the "hireability" of candidates during job interviews (Chamorro-Premuzic et al., 2017;Nguyen et al., 2014). ...
Wearable sensors are becoming increasingly popular in organizational research. Although validation studies that examine sensor data in conjunction with established social and psychological constructs are becoming more frequent, they are usually limited for two reasons: first, most validation studies are carried out under laboratory settings. Only a handful of studies have been carried out in real-world organizational environments. Second, for those studies carried out in field settings, reported findings are derived from a single case only, thus seriously limiting the possibility of studying the influence of contextual factors on sensor-based measurements. This article presents a validation study of expressive and instrumental ties across nine relatively small R&D teams. The convergent validity of Bluetooth (BT) detections is reported for friendship and advice-seeking ties under three organizational contexts: research labs, private companies, and university-based teams. Results show that, in general, BT detections correlated strongly with self-reported measurements. However, the organizational context affects both the strength of the observed correlation and its direction. Whereas advice-seeking ties generally occur in close spatial proximity and are best identified in university environments, friendship relationships occur at a greater spatial distance, especially in research labs. We conclude with recommendations for fine-tuning the validity of sensor measurements by carefully examining the opportunities for organizational embedding in relation to the research question and collecting complementary data through mixed-method research designs.
... Direct applications in the employment and human resources context are still emerging. Potential uses include collecting and analyzing digital records (e.g., gleaned from social media) to supplement traditional psychometric tests in evaluating talent and predicting work-related outcomes (Chamorro-Premuzic, Akhtar, Winsborough, & Sherman, 2017). ...
Privacy in the workplace is a pivotal concern for employees and employers. Employees expect to be in control of the personal information and access they provide to the organization. Employers, however, expect extensive information regarding their employees as well as extensive access to employees’ presence. The chasm between these two often competing expectations has been magnified by regulatory and technological trends. We begin the review by integrating viewpoints from multiple disciplines to disentangle definitions of privacy and to delineate the privacy contexts of information privacy and work environment privacy. We then identify the key stakeholders of privacy in the workplace and describe their interests. This discussion serves as a platform for our stakeholders’ privacy calculus model, which in turn provides a framework within which we review empirical findings on workplace privacy from organizational research and related disciplines and from which we identify gaps in the existing research. We then advance an extensive research agenda. Finally, we draw attention to emerging technologies and laws that have far-reaching implications for employees and employers. Our review provides a road map for researchers and practitioners to navigate the contested terrain of workplace privacy.
... Assessment at scale can bring considerable long-term benefits, even if slightly less valid than other approaches (Chamorro-Premuzic et al., 2017). Our study provides some initial evidence suggesting that AVI-PAs may validly assess some traits, but the evidence is mixed and many questions remain unanswered. ...
Organizations are increasingly adopting automated video interviews (AVIs) to screen job applicants despite a paucity of research on their reliability, validity, and generalizability. In this study, we address this gap by developing AVIs that use verbal, paraverbal, and nonverbal behaviors extracted from video interviews to assess Big Five personality traits. We developed and validated machine learning models within (using nested cross-validation) and across three separate samples of mock video interviews (total N = 1,073). Also, we examined their test–retest reliability in a fourth sample (N = 99). In general, we found that the AVI personality assessments exhibited stronger evidence of validity when they were trained on interviewer-reports rather than self-reports. When cross-validated in the other samples, AVI personality assessments trained on interviewer-reports had mixed evidence of reliability, exhibited consistent convergent and discriminant relations, used predictors that appear to be conceptually relevant to the focal traits, and predicted academic outcomes. On the other hand, there was little evidence of reliability or validity for the AVIs trained on self-reports. We discuss the implications for future work on AVIs and personality theory, and provide practical recommendations for the vendors marketing such approaches and organizations considering adopting them.
... With reference to soft skills assessments, technological advancements such as Big data, big spatial data [13], Artificial Intelligence and Data Mining techniques have emerged revolutionizing the way organizations identify, develop and engage talents. [14] reported talent assessment in the human resource world is shifting from the traditional methods of assessments to a range of new, novel, efficient tools and techniques for evaluating employee behavior making them less intuitive, more evidence based and data driven. [15] study revealed that with the development in machine learning algorithms, computer based personality judgments are more accurate than those made by humans. ...
Emerging tools such as Game Based Assessments have been valuable in talent screening and matching soft skills for job selection. However, these techniques/models are rather stand alone and are unable to provide an objective measure of the effectiveness of their approach leading to mismatch of skills. In this research study, we are proposing a Theoretical Hybrid Model, combining aspects of Artificial Intelligence and Game Based Assessment in profiling, assessing and ranking graduates based on their soft skills. Firstly, an Intelligent Controller is used to extract and classify the graduate skill profile based on data findings extracted using traditional assessment methods of self-evaluation and interview. With motivation and engagement as a competitive difference, an existing Game Based Assessment (OWIWI) is then used to assess the soft skills of these graduates hence generating a Graduate Profile based on results of the game. Moving forward, a ranking technique is then applied to match the profile to selected job requirements based on soft skills required for the job and the graduate strength. Finally, a comparison analysis is concluded based on the soft skills profile obtained before employment (pre-employment) and objective measure feedback of soft skills obtained after employment (post-employment) to provide a validity check to study the effectiveness of the overall Hybrid Model. Specifically, data obtained from this study can be useful in solving issues of unemployment due to mismatch of soft skills at the Higher Learning Institution level.
... There is great diversity in the possible designs of HR-A in practice. In personnel recruitment, for example, artificial intelligence software could be used as a complement to traditional interviews, not only to analyze the application documents in advance, but also to analyze the personality of the applicants by evaluating, say, nonverbal behavior or voice, thus providing additional data about the candidate (Chamorro-Premuzic et al., 2017). In the context of stress and burnout prevention, fitness trackers and smart keyboards could be used to collect data on employees' sleep patterns, heart rates, and keystrokes, with the goal of promoting employee health and preventing overwork. ...
... Second, text mining has been used to improve matching candidates for specific jobs. [13], [15] and third, analyzing the performance of employees [16], [17]. Beyond this broad classification, text mining of job advertisements has been addressed in different studies, loosely adhering to the clustering by Pejic-Bach et al. [8]. ...
The use of online job advertisement has made them an important source of quantitative information about the innovation system. This data offers significant opportunities to study trends, transitions in the job markets and skill demands. In this study, we have utilized the job ads data of a major Finnish job market platform to investigate the emergence of AI-related jobs. More than 480 000 job advertisements during 2013-2020 was used to create insight on skills transitions, particularly focusing on artificial intelligence related skills. A glossary of AI-related skills was created and applied to the job data to identify the relatedness spectrum of ads to AI using a three-tier system. By incorporating sectoral firm-level information, we explored the variation in AI-related skills demand over time and sectors. Our study presents a systematic way to utilize job advertisement data for detecting demand trends for specific skills.
... Artificial intelligence (AI) and the use of computer algorithms play an increasingly pervasive role in daily life (Binns, 2018;Bjerring & Busch, 2021;Crawford & Calo, 2016;de Laat, 2018;Helbing et al., 2017). The use of AI has become ever more influential in decisions made in fields as diverse as the healthcare field (Esteva et al., 2017;Martinez-Martin, 2019), employment decisions (Chamorro-Premuzic et al., 2017), money lending (Prince et al., 2019), education (Holstein et al., 2018), and the judicial system, including law enforcement (Angwin et al., 2016;Buolamwini & Gebru, 2018;Chouldechova, 2017;Garvie, 2019;Garvie et al., 2016;Lum & Isaac, 2016;Veale et al., 2018). Face recognition technology (FRT) is a type of AI, the use of which comes with both societal benefits along with moral pitfalls. ...
This paper presents a novel philosophical analysis of the problem of law enforcement’s use of biased face recognition technology (FRT) in liberal democracies. FRT programs used by law enforcement in identifying crime suspects are substantially more error-prone on facial images depicting darker skin tones and females as compared to facial images depicting Caucasian males. This bias can lead to citizens being wrongfully investigated by police along racial and gender lines. The author develops and defends “A Liberal Argument Against Biased FRT,” which concludes that law enforcement use of biased FRT is inconsistent with the classical liberal requirement that government treat all citizens equally before the law. Two objections to this argument are considered and shown to be unsound. The author concludes by suggesting that equality before the law should be preserved while the problem of machine bias ought to be resolved before FRT and other types of artificial intelligence (AI) are deployed by governments in liberal democracies.
... In addition, because games can provide surrogate "digital" expertise in place of expert "human" assessors, serious games are being explored and evaluated as a means to provide more valid assessments of these "real world" outcomes in ways that could be more cost effective compared to traditional assessment approaches such as pen and paper questionnaires and direct assessment by an expert [30]. Also, gamification might increase engagement levels which in turn might lead to retention and motivation during the process of selection as well as better predictions about person-job fit [31]. Serious games provide a context for measuring and assessing a broader range of skills and constructs compared to traditional assessment approaches. ...
In the new context of transitional, mobilised and globalised labour markets, an urgent need has emerged for meaningful assessment tools, methods and techniques to measure and recognize the workers’ skills. This paper aims to present a novel approach in skill assessment developed under NADINE H2020 project, the indirect skill assessment, as an alternative or supplement to the traditional selection methods. AI technology is utilised to facilitate the indirect skill assessment via two agnostic content serious games (Tetris and 2048), based on the evaluation of an individual’s performance in playing a game outside a situational judgement test (SJT) context. Novel datasets have been developed, comprised of game sessions and the corresponding skill assessments of the players through validated psychometric questionnaires, which were the basis for the algorithm training that would provide the estimation of a player’s skillset. The trained neural models for both games proved to have strong skill assessment capabilities, indicating that there is indeed a correlation between a person’s action sequence and his/her different skills.
Fortschritte in der Psychometrie, insbesondere die Anwendung von maschinellem Lernen, ermöglichen die Entwicklung neuartiger Assessment-Modalitäten. Spiel- und Video-Assessments bieten nicht nur eine verbesserte Erfahrung für die TeilnehmerInnen und kürzere Testzeiten gegenüber traditionellen Fragebögen, sie kommunizieren auch ein positives Arbeitgeberimage. Dies ist bei Assessments von Führungskräften besonders relevant. Dieser Beitrag erläutert die theoretischen Hintergründe von neuartigen Assessments und zeigt deren psychometrischen Qualitäten anhand von Hirevue Spiel und Video-Assessments. Validität und Fairness werden beschrieben, um interessierten Anwendern Anhaltspunkte für die Evaluation neuartiger Assessments zu bieten.
With advances in technology, new innovative methods for evaluating teamwork skills are emerging, however little research has been done into students’ reactions to such innovative assessments in an educational setting. This study investigated the reactions of undergraduate students to a high-fidelity behavioral simulation assessment for teamwork skills and explored some of the factors behind those reactions. 168 undergraduate students completed a simulation assessment and filled out surveys of reactions, perceptions, and personality. The results of a structural equations model indicate that reactions were positively related to perceived scenario realism, characters (chatbots) realism and design clarity.
Organizational scholars increasingly realize the importance of a dark personality in the workplace. Although a great deal has been learned in terms of the utility of dark personality for the prediction of workplace outcomes, the field has yet to consolidate in terms of which models and measures best reflect the nature of dark personality traits. To facilitate this discussion, the present review examines and evaluates both established and emergent models and measures of dark personality. Further, to inform future research, it also summarizes evidence concerning methodological issues that have been shown to impact levels of dark traits or to moderate their relationships with work outcomes. Finally, the paper considers the implications of widespread practices in the field of dark personality and makes recommendations for future theorizing, research practices, and implementation.
span>In the last few years, all companies have been interested in the analysis of data related to Human Resources and have focused on human capital, which is considered as the major factor influencing the company’s development and all its activities at all levels of human resource policies. Data analysis (HR analytics) will significantly improve business profitability over the next years.We started with an extensive survey of different human resources problems and risks reported by HR specialists, then a comprehensive review of recent research efforts on computer science techniques proposed to solve these problems and finally focusing on suggested artificial intelligence methods. This review article will be an archive and a reference for computer scientists working on HR by summarizing the IT solutions already made in human resources for the period between 2008 and 2018. It aims to present clearly the issues that HR researchers face and for which computer scientists seek solutions. It summarizes at the same time the recent and different methods, IT approaches and tools already used by highlighting those using artificial intelligence.</span
We present initial structural validity evidence for a serious game designed for personnel selection and classification for cybersecurity roles in the US Air Force (USAF). Based on literature review and input from USAF cybersecurity subject‐matter‐experts, we targeted six constructs for assessment. We describe the development process used to build a game to assess individual differences in these constructs, while also being engaging and motivating for players. We attend to the challenge of avoiding an overall game performance factor that dominates variance of multiple constructs scored from the same gameplay episodes and report steps taken to enhance discriminant validity of the scores. We apply factor analysis and item response theory models to develop scores that are reliable, show discriminant validity, and show modest education/gender group differences. • We developed a serious game to assess six competencies relevant for the US Air Force cyber occupations. • We employed strategies to enhance discrimination validity of the six scores. • We discuss future validation plans for this assessment and implications for design of other serious games. We developed a serious game to assess six competencies relevant for the US Air Force cyber occupations. We employed strategies to enhance discrimination validity of the six scores. We discuss future validation plans for this assessment and implications for design of other serious games.
Companies increasingly deploy artificial intelligence (AI) technologies in their personnel recruiting and selection process to streamline it, making it faster and more efficient. AI applications can be found in various stages of recruiting, such as writing job ads, screening of applicant resumes, and analyzing video interviews via face recognition software. As these new technologies significantly impact people’s lives and careers but often trigger ethical concerns, the ethicality of these AI applications needs to be comprehensively understood. However, given the novelty of AI applications in recruiting practice, the subject is still an emerging topic in academic literature. To inform and strengthen the foundation for future research, this paper systematically reviews the extant literature on the ethicality of AI-enabled recruiting to date. We identify 51 articles dealing with the topic, which we synthesize by mapping the ethical opportunities, risks, and ambiguities, as well as the proposed ways to mitigate ethical risks in practice. Based on this review, we identify gaps in the extant literature and point out moral questions that call for deeper exploration in future research.
The objective of this study is to explore the potential of a new method for assessing teamwork skills—a virtual high-fidelity behavioral simulation. In this paper, we describe the development and validation of a chatbot-driven simulation for assessing teamwork skills of university students. We present the results of a criterion validation study using a sample of 215 undergraduate students. The simulation was found to significantly predict peer ratings following a live team-based exercise, over and above a teamwork situational judgment test, and a personality inventory. The mean score for women on the simulation was significantly higher than for men. Limitations and future directions are discussed.
Practitioner points
• Teamwork skills are considered essential, but are difficult to assess well.
• Advancements in technology provide opportunities for assessing teamwork skills via virtual high-fidelity behavioural simulations.
Selection methods are commonly used in talent acquisition to predict future job performance and to find the best candidates, but questionnaire-based assessments can be lengthy and lead to candidate fatigue and poor engagement, affecting completion rates and producing poor data. Gamification can mitigate some of these issues through greater engagement and shorter testing times. One avenue of gamification is image-based tests. Although such assessments are starting to gain traction in personnel selection, few studies describing their validity and psychometric properties exist. The current study explores the potential of a five-minute, forced-choice, image-based assessment of the Big Five personality traits to be used in selection. Study 1 describes the creation of the image pairs and the selection of the 150 best-performing items based on a sample of 300 respondents. Study 2 describes the creation of machine-learning-based scoring algorithms and tests of their convergent and discriminate validity and adverse impact based on a sample of 431 respondents. All models showed good levels of convergent validity with the IPIP-NEO-120 (openness r = .71, conscientiousness r = .70, extraversion r = .78, agreeableness r = .60, and emotional stability r = .70) and were largely free from potential adverse impact. The implications for recruitment policy and practice and the need for further validation are discussed.
HR analytics is an effort and a new methodology for more objectively and effective evidence-based decision-making with the desire of data science that has blown into the HRM field. The interest in HR analytics, which started in the early 2000s, has emerged with the advancement of technologies such as artificial intelligence and big data analytics, and many discussions are already underway abroad. On the other hand, there are not enough research related to HR analytics in Korea, so further discussions are required. Therefore, this study summarizes and proposes the research trends and implications of HR analytics through the following three steps, and proposes a framework of HR analytics that future researchers can base on.
First, in the first step, we review the literature of HR analytics studies and derive their implications. As a result of the study, three implications were obtained, each of which is that a sufficient number of conceptual research related to HR analytics has been made, that empirical research is insufficient, and that a more advanced methodology is urgently needed.
In the second step, based on the review of the literature, we will examine the current position of HR analytics and what advanced methodologies are needed for HR analytics in the future. As a result of the research, it can be seen that HR analytics remains at the stage of transitioning from technical analysis to predictive analysis in the business analytics value model, and advanced methodologies such as machine learning are needed to move to prediction and prescription analysis in the future.
Finally, we propose a framework for future researchers based on all the previous contents. This framework, as the final result of this study, includes the process of HR analytics using machine learning, the key questions for each process, and the corresponding research issues.
The lack of sufficient big data-based approaches impedes the development of human resource management (HRM) research and practices. Although scholars have realized the importance of applying a big data approach to HRM research, clear guidance is lacking regarding how to integrate the two. Using a clustering algorithm based on the big data research paradigm, we first conduct a bibliometric review to quantitatively assess and scientifically map the evolution of the current big data HRM literature. Based on this systematic review, we propose a general theoretical framework described as “Inductive (Prediction paradigm: Data mining/Theory building) vs. Deductive (Explanation paradigm: Theory testing)”. In this framework, we discuss potential research questions, their corresponding levels of analysis, relevant methods, data sources and software. We then summarize the general procedures for conducting big data research within HRM research. Finally, we propose a future agenda for applying big data approaches to HRM research and identify five promising HRM research topics at the micro, meso and macro levels along with three challenges and limitations that HRM scholars may face in the era of big data.
This paper takes an exploratory approach to analyze reactions to game-based assessments (GBAs) by examining users' reviews of GBA mobile applications. In this study, we explore 3146 user reviews and 1253 comments from 10 GBA applications found on the two most popular mobile application distribution platforms using a natural language processing tool. Findings suggest that candidates generally perceive GBAs as novel and have varying reactions to specific game, assessment, and application elements. As this study contributes to the limited body of research available on candidates' reactions to GBA mobile applications, findings and research directions are discussed to expand our understanding of this growing area of assessment.
Practitioner points
• Game-based assessment (GBA) mobile applications are generally viewed by applicants as novel and interesting, and as a favorable method of assessment.
• However, some users are hesitant to place full trust in GBA mobile applications, perceiving them as less face-valid than traditional assessment methods.
• Users also react negatively to technical issues such as mobile application glitches or crashes.
• Organizations seeking to use GBA mobile applications in their employee selection system should carefully design the assessment by selecting psychometrically valid and useful game elements, and should also incorporate user-tested feedback.
Advances in personal devices and communication technologies have changed the learning landscape. Gamification is the use of technology to promote and induce the user's internal motivation by exploiting the game's characteristics. Recently, mobile learning applications introduce various gamification strategies to provoke users' voluntary participation. However, empirical evidence on how to attract users and influence sustained usage is limited. This study establishes a theoretical basis for designing learning applications and discusses its impact on business. A series of gamification strategies such as Competition, Challenge, Compensation, Relationship, and Usability, were applied to a company's Mobile Social Learning Platform (MSLP). A survey result of 293 users from South Korea was used for the advanced mediation model analysis. The result showed that Challenge, Relationship, and Usability had affected Flow and Continuous usage intention. This paper argues that the user's usage intention will have a positive effect on voluntary learning. It also provides a significant implication for organizational effectiveness through the development and application of mobile learning platform.
In the past 20 years, the study of dark personality has seen a surge of interest among both academic researchers and practitioners. Although the research to date has documented that dark personality characteristics are important predictors of workplace behaviors and outcomes, there remain considerable challenges in the field in terms of both theorizing and assessment. The current chapter reviews the history of dark personality, competing models of dark traits, evidence of how and when dark personality impacts organizational outcomes and both current and emerging trends in dark personality assessment. We then suggest potential avenues for future theoretical development as well as for measurement and research design.
Algorithmic decision-making is becoming increasingly common as a new source of advice in HR recruitment and HR development. While firms implement algorithmic decision-making to save costs as well as increase efficiency and objectivity, algorithmic decision-making might also lead to the unfair treatment of certain groups of people, implicit discrimination, and perceived unfairness. Current knowledge about the threats of unfairness and (implicit) discrimination by algorithmic decision-making is mostly unexplored in the human resource management context. Our goal is to clarify the current state of research related to HR recruitment and HR development, identify research gaps, and provide crucial future research directions. Based on a systematic review of 36 journal articles from 2014 to 2020, we present some applications of algorithmic decision-making and evaluate the possible pitfalls in these two essential HR functions. In doing this, we inform researchers and practitioners, offer important theoretical and practical implications, and suggest fruitful avenues for future research.
Purpose
The purpose of this study is to describe the development and psychometric properties of a novel game- and video-based assessment of social attributes. Despite their increasing adaption, little research is available on the suitability of games and video analytics for measuring noncognitive attributes in the selection context.
Design/methodology/approach
The authors describe three novel assessments and their psychometric properties in a sample of 1,300 participants: a game-based adaptation of an Emotion Recognition Task, a chatbot-based situational judgment test for emotion management and a video-based conscientiousness assessment.
Findings
The novel assessments show good to moderate convergent validity for Emotional Recognition ( r = 0.42), Emotion Management ( r = 0.39) and Conscientiousness ( r = 0.21). The video-based assessment demonstrates preliminary predictive validity for self-reported work performance. Novel game-based assessments (GBAs) are perceived as better designed and more immersive than traditional questionnaires. Adverse impact analysis indicates small group differences by age, gender and ethnicity.
Research limitations/implications
Predictive validity findings need to be replicated using objective measures of performance, such as performance ratings by supervisors and extended to the GBAs. Adverse impact should be evaluated using a real-life applicant pool and extended to additional groups.
Practical implications
Evidence for the psychometric validity of novel assessment formats supports their adoption in selection and recruitment. Improved user experience and shortened assessment times open up new areas of application.
Originality/value
This study gives first insights into psychometric properties of video- and game-based assessments of social attributes.
Talent assessment continues to evolve at a rapid pace. A demanding and dynamic business environment and the advent of exponential technologies places renewed importance on conducting talent assessment protocols in a manner that strengthens the psychological contract between employers and employees. This chapter provides a framework for conducting contemporary talent assessments in an inclusive, ethical and responsible manner. It focuses on the candidate experience as a key part of the talent assessment process and incorporates the learning of modern Talent Management principles in delivering impactful assessment journeys. The chapter has three broad focus areas: Firstly, it evaluates the expansion of modern talent assessment and the effect this has on the experience of psychological contract. Secondly, it provides a framework for conducting talent assessment in a manner that supports and expands the psychological contract. Thirdly, the role of technology in scaling talent assessment is evaluated, considering specifically potential risks and opportunities for the psychological contract in digitally enabled assessment approaches. Practical recommendations are provided.
Describing the current state of gamification, Chamorro-Premuzic, Winsborough, Sherman, and Hogan (2016) provide a troubling contradiction: They offer examples of a broad spectrum of gamification interventions, but they then summarize the entirety of gamification as “the digital equivalent of situational judgment tests.” This mischaracterization grossly oversimplifies a rapidly growing area of research and practice both within and outside of industrial–organizational (I-O) psychology. We agree that situational judgment tests (SJTs) can be considered a type of gamified assessment, and gamification provides a toolkit to make SJTs even more gameful. However, the term gamification refers to a much broader and potentially more impactful set of tools than just SJTs, which are incremental, versatile, and especially valuable to practitioners in an era moving toward business-to-consumer (B2C) assessment models. In this commentary, we contend that gamification is commonly misunderstood and misapplied by I-O psychologists, and our goals are to remedy such misconceptions and to provide a research agenda designed to improve both the science and the practice surrounding gamification of human resource processes.
Almost 20 years after McKinsey introduced the idea of a war for talent, technology is disrupting the talent identification industry. From smartphone profiling apps to workplace big data, the digital revolution has produced a wide range of new tools for making quick and cheap inferences about human potential and predicting future work performance. However, academic industrial–organizational (I-O) psychologists appear to be mostly spectators. Indeed, there is little scientific research on innovative assessment methods, leaving human resources (HR) practitioners with no credible evidence to evaluate the utility of such tools. To this end, this article provides an overview of new talent identification tools, using traditional workplace assessment methods as the organizing framework for classifying and evaluating new tools, which are largely technologically enhanced versions of traditional methods. We highlight some opportunities and challenges for I-O psychology practitioners interested in exploring and improving these innovations.
Online social media is changing the personnel recruitment process. Until now, resumes were among the most widely used tools for the screening of job applicants. The advent of inexpensive sensors combined with the success of online video platforms has enabled the introduction of a new type of resume, the video resume. Video resumes can be defined as short video messages where job applicants present themselves to potential employers. Online video resumes represent an opportunity to study the formation of first impressions in an employment context at a scale never achieved before, and to our knowledge they have not been studied from a behavioral standpoint. We collected a dataset of 939 conversational English-speaking video resumes from YouTube. Annotations of demographics, skills, and first impressions were collected using the Amazon Mechanical Turk crowdsourcing platform. Basic demographics were then analyzed to understand the population using video resumes to find a job, and results showed that the population mainly consisted of young people looking for internship and junior positions. We developed a computational framework for the prediction of organizational first impressions, where the inference and nonverbal cue extraction steps were fully automated. Results demonstrated that automatically predicting first impressions up to a certain level was a feasible task, with up to 27% of the variance explained for extraversion, and up to 20% for social and communication skills.
This article summarizes the practical and theoretical implications of 85 years of research in personnel selection. On the basis of meta-analytic findings, this article presents the validity of 19 selection procedures for predicting job performance and training performance and the validity of paired combinations of general mental ability (GMA) and the 18 other selection procedures. Overall, the 3 combinations with the highest multivariate validity and utility for job performance were GMA plus a work sample test (mean validity of .63), GMA plus an integrity test (mean validity of .65), and GMA plus a structured interview (mean validity of .63). A further advantage of the latter 2 combinations is that they can be used for both entry level selection and selection of experienced employees. The practical utility implications of these summary findings are substantial. The implications of these research findings for the development of theories of job performance are discussed.
People's values provide a decision-making framework that helps guide their everyday actions. Most popular methods of assessing values show tenuous relationships with everyday behaviors. Using a new Amazon Mechanical Turk dataset (N = 767) consisting of people's language, values, and behaviors , we explore the degree to which attaining " ground truth " is possible with regards to such complicated mental phenomena. We then apply our findings to a corpus of Face-book user (N = 130, 828) status updates in order to understand how core values influence the personal thoughts and behaviors that users share through social media. Our findings suggest that self-report questionnaires for abstract and complex phenomena, such as values, are inadequate for painting an accurate picture of individual mental life. Free response language data and language modeling show greater promise for understanding both the structure and content of concepts such as values and, additionally, exhibit a predictive edge over self-report questionnaires.
In this article, we show how the use of state-of-the-art methods in computer science based on machine perception and learning allows the unobtrusive capture and automated analysis of interpersonal behavior in real time (social sensing). Given the high ecological validity of the behavioral sensing, the ease of behavioral-cue extraction for large groups over long observation periods in the field, the possibility of investigating completely new research questions, and the ability to provide people with immediate feedback on behavior, social sensing will fundamentally impact psychology.
The challenges associated with managing talent in modern labor markets are a constant source of discussion among academics and practitioners, but the literature on the subject is sparse and has grown somewhat haphazardly. We provide an overview of the literature on talent management-a body of work that spans multiple disciplines-as well as a clear statement as to what defines talent management. The new themes in contemporary talent management focus on (a) the challenge of open labor markets, including issues of retention as well as the general challenge of managing uncertainty, (b) new models for moving employees across jobs within the same organization, and (c) strategic jobs for which investments in talent likely show the greatest return. We review the conceptual and practical literature on these topics, outline the evolution of talent management over time, and present new topics for future research.
The relations between video-game experience and cognitive abilities were examined in the current study. In two experiments, subjects performed a number of working memory, fluid intelligence, and attention-control measures and filled out a questionnaire about their video-game experience. In Experiment 1, an extreme-groups analysis indicated that experienced video-game players outperformed nonplayers on several cognitive-ability measures. However, in Experiments 1 and 2, when analyses examined the full range of subjects at both the task level and the latent-construct level, nearly all of the relations between video-game experience and cognitive abilities were near zero. These results cast doubt on recent claims that playing video games leads to enhanced cognitive abilities. Statistical and methodological issues with prior studies of video-game experience are discussed along with recommendations for future studies.
© The Author(s) 2015.
The Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous complicity of internet companies and the international controversies that followed provide a perfect segue into con- temporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and networks to a C21st focus on data, currently crystallized in ‘‘Big Data.’’ Big Data intensifies certain surveillance trends associated with information technology and networks, and is thus implicated in fresh but fluid configurations. This is considered in three main ways: One, the capacities of Big Data (including metadata) intensify surveillance by expanding interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size, qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity’s predilection for certain definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and foreclose surveillance.
he Snowden revelations about National Security Agency surveillance, starting in 2013, along with the ambiguous complicity of internet companies and the international controversies that followed provide a perfect segue into con- temporary conundrums of surveillance and Big Data. Attention has shifted from late C20th information technologies and networks to a C21st focus on data, currently crystallized in ‘‘Big Data.’’ Big Data intensifies certain surveillance trends associated with information technology and networks, and is thus implicated in fresh but fluid configurations. This is considered in three main ways: One, the capacities of Big Data (including metadata) intensify surveillance by expanding interconnected datasets and analytical tools. Existing dynamics of influence, risk-management, and control increase their speed and scope through new techniques, especially predictive analytics. Two, while Big Data appears to be about size, qualitative change in surveillance practices is also perceptible, accenting consequences. Important trends persist – the control motif, faith in technology, public-private synergies, and user-involvement – but the future-orientation increasingly severs surveillance from history and memory and the quest for pattern-discovery is used to justify unprecedented access to data. Three, the ethical turn becomes more urgent as a mode of critique. Modernity’s predilection for certain definitions of privacy betrays the subjects of surveillance who, so far from conforming to the abstract, disembodied image of both computing and legal practices, are engaged and embodied users-in-relation whose activities both fuel and foreclose surveillance.
Purpose
– The purpose of this paper is to identify the elements of a LinkedIn profile that hiring professionals focus on most, and then examine LinkedIn profiles in terms of these identified elements across different industries.
Design/methodology/approach
– The methodology was comprised of two phases. In the first phase, researchers interviewed hiring professionals to determine their usage of LinkedIn. In the second phase, LinkedIn group member profiles from three industries – HR, sales/marketing and industrial/organizational (I/O) psychology – were compared on the 21 variables identified in Phase 1 ( n =288).
Findings
– χ 2 and ANOVA tests showed significant differences with respect to ten of the LinkedIn variables in how people presented themselves across the three groups. There were also several gender differences found.
Research limitations/implications
– A general limitation was the use of a qualitative research approach. A limitation of Phase 1 was that only a small sample of New York City-based hiring professionals was interviewed. Perhaps a wider, more diverse sample would have yielded different variables. In terms of Phase 2, it is possible that just utilizing the second connections of the researchers limited the generalizability of findings.
Practical implications
– User unwillingness to fully complete the LinkedIn profile suggests that it may not have replaced the traditional resume yet. Sales/marketing professionals were more likely than HR and I/O psychology professionals to complete multiple aspects of a LinkedIn profile. Women were also less likely than men to provide personal information on their profiles.
Originality/value
– Most of the empirical research on social networking sites has focussed on Facebook, a non-professional site. This is, from the knowledge, the first study that systematically examined the manner in which people present themselves on LinkedIn – the most popular professional site used by applicants and recruiters worldwide.
Recent technological developments are reshaping the state of consulting, and consulting psychology is no exception. Although demand for consulting is likely to grow over the next few years, the knowledge base and tool sets most commonly used by consulting psychologists are being commoditized, while the gap between science and practice seems to be widening. Consulting psychologists can respond to these trends in 4 major ways: a) reconnect with academia to bridge the gap between science and practice; b) focus less on problem-solving and more on problem-identification; c) build wider collaborative networks and practice to share data and crowd-source knowledge; d) engage with new technologies. (PsycINFO Database Record (c) 2014 APA, all rights reserved)
Understanding the basis on which recruiters form hirability impressions for a job applicant is a key issue in organizational psychology and can be addressed as a social computing problem. We approach the problem from a face-to-face, nonverbal perspective where behavioral feature extraction and inference are automated. This paper presents a computational framework for the automatic prediction of hirability. To this end, we collected an audio-visual dataset of real job interviews where candidates were applying for a marketing job. We automatically extracted audio and visual behavioral cues related to both the applicant and the interviewer. We then evaluated several regression methods for the prediction of hirability scores and showed the feasibility of conducting such a task, with ridge regression explaining 36.2% of the variance. Feature groups were analyzed, and two main groups of behavioral cues were predictive of hirability: applicant audio features and interviewer visual cues, showing the predictive validity of cues related not only to the applicant, but also to the interviewer. As a last step, we analyzed the predictive validity of psychometric questionnaires often used in the personnel selection process, and found that these questionnaires were unable to predict hirability, suggesting that hirability impressions were formed based on the interaction during the interview rather than on questionnaire data.
Using Latent Semantic Analysis, we quantified the semantic representations of Facebook status updates of 304 individuals in order to predict self-reported personality. We focused on, besides Neuroticism and Extraversion, the Dark Triad of personality: Psychopathy, Narcissism, and Machiavellianism. The semantic content of Facebook updates predicted Psychopathy and Narcissism. These updates had a more ''odd'' and negatively valanced content. Furthermore, Neuroticism, number of Facebook friends, and frequency of status updates were predictable from the status updates. Given that Facebook allows individuals to have major control in how they present themselves and draw benefits from these interac-tions, we conclude that the Dark Triad, involving socially malevolent behavior such as self-promotion, emotional coldness, duplicity, and aggressiveness, is manifested in Facebook status updates.
We analyzed 700 million words, phrases, and topic instances collected from the Facebook messages of 75,000 volunteers, who also took standard personality tests, and found striking variations in language with personality, gender, and age. In our open-vocabulary technique, the data itself drives a comprehensive exploration of language that distinguishes people, finding connections that are not captured with traditional closed-vocabulary word-category analyses. Our analyses shed new light on psychosocial processes yielding results that are face valid (e.g., subjects living in high elevations talk about the mountains), tie in with other research (e.g., neurotic people disproportionately use the phrase 'sick of' and the word 'depressed'), suggest new hypotheses (e.g., an active life implies emotional stability), and give detailed insights (males use the possessive 'my' when mentioning their 'wife' or 'girlfriend' more often than females use 'my' with 'husband' or 'boyfriend'). To date, this represents the largest study, by an order of magnitude, of language and personality.
This article provides a qualitative review of the trait perspective in leadership research, followed by a meta-analysis. The authors used the five-factor model as an organizing framework and meta-analyzed 222 correlations from 73 samples. Overall, the correlations with leadership were Neuroticism = -.24, Extraversion = .31, Openness to Experience = .24, Agreeableness = .08, and Conscientiousness = .28. Results indicated that the relations of Neuroticism, Extraversion, Openness to Experience, and Conscientiousness with leadership generalized in that more than 90% of the individual correlations were greater than 0. Extraversion was the most consistent correlate of leadership across study settings and leadership criteria (leader emergence and leadership effectiveness). Overall, the five-factor model had a multiple correlation of .48 with leadership, indicating strong support for the leader trait perspective when traits are organized according to the five-factor model.
Meta-analysis was used to aggregate results from studies examining the relationship between intelligence and leadership. One hundred fifty-one independent samples in 96 sources met the criteria for inclusion in the meta-analysis. Results indicated that the corrected correlation between intelligence and leadership is.21 (uncorrected for range restriction) and.27 (corrected for range restriction). Perceptual measures of intelligence showed stronger correlations with leadership than did paper-and-pencil measures of intelligence. Intelligence correlated equally well with objective and perceptual measures of leadership. Additionally, the leader's stress level and the leader's directiveness moderated the intelligence-leadership relationship. Overall, results suggest that the relationship between intelligence and leadership is considerably lower than previously thought. The results also provide meta-analytic support for both implicit leadership theory and cognitive resource theory.
We conduct an evidence-based review using an integrative synthesis of published peer-reviewed literature on Human Resource analytics (HR Analytics). Our search of several publication databases identified 60 articles on this topic, however only 14 articles were in quality peer-reviewed journals. Our review of these articles addresses the following 5 questions: (1) What is HR Analytics (how has the concept definition evolved)? (2) How does HR Analytics work (what are the processes)? (3) Why does HR Analytics work (what theories explain cause-effect relationships, antecedents, and consequences)? (4) What does HR Analytics produce (what are the outcomes)? (5) What is required for HR Analytics to succeed (what are the moderators of the analytics-outcome relationships)? We conclude that despite evidence linking the adoption of HR Analytics to organizational performance that adoption of HR Analytics is very low and academic research, and therefore, evidence on this topic is sparse. We offer potential explanations for this paradox and suggest avenues for future research.
Popular video games often provide people with the option to play characters that are good or evil in nature, and yet, little is known about how individual differences in personality relate to the moral and ethical alignments people chose in their digital representations. We examined whether participants' pre-existing levels of moral disengagement and Big 5 scores predicted the alignments they selected for their avatar in video game play. Results revealed that men, relative to women, were more likely to play "bad guys" and that moral disengagement predicted this finding. Agreeableness and conscientiousness mediated the relationship between moral disengagement and alignment such that those higher in these two traits were more likely to play good characters.
Researchers have suggested that video games can be used to measure cognitive abilities (e.g., Boot, 2015). To that end, we wanted to test the feasibility of developing a measure of fluid intelligence within a commercially-available video game. We created a 15 chamber (i.e., problems to be solved) test called the “Portal 2 Test Battery” using the Puzzle Creator within the popular problem-solving video game Portal 2. In study 1, 35 video game players (VGPs) who had experience playing Portal 2 completed this test and Ravens Advanced Progressive Matrices (RAPM), a validated measure of fluid intelligence, revealing a strong relationship (r = .65) between the two. In study 2, 100 participants, both VGPs and non-video game players (nVGPs) completed the Portal 2 Test Battery, RAPM, and the Bochumer Matrices Test (BOMAT), another validated measure of fluid intelligence. Structure equation modeling revealed a strong relationship between the latent variables fluid intelligence and portal ability (r = .78). Additionally, the reliability of our test was acceptable in both studies (α = .80 and α = .76). These results provide the first evidence that it may be feasible to create a measure of fluid intelligence using the Puzzle Creator within Portal 2.
This engaging and thought-provoking text introduces the main techniques, theories, research and debates in personnel selection, helping students and practitioners to identify the major predictors of job performance as well as the most suitable methods for assessing them. Tomas Chamorro-Premuzic and Adrian Furnham provide a comprehensive, critical and up-to-date review of the constructs we use in assessing people – intelligence, personality, creativity, leadership and talent – and explore how these help us to predict differences in individuals' performance. Covering selection techniques such as interviews, references, biographical data, judgement tests and academic performance, The Psychology of Personnel Selection provides a lively discussion of both the theory behind the use of such techniques and the evidence for their usefulness and validity. The Psychology of Personnel Selection is essential reading for students of psychology, business studies, management and human resources, as well as for anyone involved in selection and assessment at work.
Social media profiles are telling examples of the everyday need for disclosure and concealment. The balance between concealment and disclosure varies across individuals, and personality traits might partly explain this variability. Experimental findings on the relationship between information disclosure and personality have been so far inconsistent. We thus study this relationship anew with 1,313 Facebook users in the United States using two personality tests: the big five personality test and the self-monitoring test. We model the process of information disclosure in a principled way using Item Response Theory and correlate the resulting user disclosure scores with personality traits.We find a correlation with the trait of Openness and observe gender effects, in that, men and women share equal amount of private information, but men tend to make it more publicly available, well beyond their social circles. Interestingly, geographic (e.g., residence, hometown) and work-related information is used as relationship currency, in that, it is selectively shared with social contacts and is rarely shared with the Facebook community at large.
Conclusions about the validity of cognitive ability and personality measures based on meta-analyses published mostly in the past decade are reviewed at the beginning of this article. Research on major issues in selection that affect the use and interpretation of validation data are then discussed. These major issues include the dimensionality of personality, the nature and magnitude of g in cognitive ability measures, conceptualizations of validity, the nature of the job performance domain, trade-offs between diversity and validity, reactions to selection procedures, faking on personality measures, mediator and moderator research on test–performance relationships, multilevel issues, Web-based testing, the situational framing of test stimuli, and the context in which selection occurs.
This study examines employers’ and workers’ sensemaking about cybervetting—employers’ use of online information for personnel selection. Analysis of 89 employer and applicant interviews suggest a shift in the social contract—the implicit expectations for how personnel selection and employment relationships should work. Results suggest an extension of new proactive transparency expectations from organizations to workers, the implicit acceptance of which points to the emergence of a digital social contract. The digital social contract prescribes normative expectations for workers’ digital visibility, thereby extending the times and contexts within which employment evaluations and career management occur. Using a communicative perspective to address research gaps on everyday ethics, practices, and technologies of personnel selection, contributions include introducing and explicating the digital social contract, documenting the extension of new transparency expectations to individuals, and explicating ethical and practical implications of new technologies and information visibility on contemporary personnel selection.
Judging others' personalities is an essential skill in successful social living, as personality is a key driver behind people's interactions, behaviors, and emotions. Although accurate personality judgments stem from social-cognitive skills, developments in machine learning show that computer models can also make valid judgments. This study compares the accuracy of human and computer-based personality judgments, using a sample of 86,220 volunteers who completed a 100-item personality questionnaire. We show that (i) computer predictions based on a generic digital footprint (Facebook Likes) are more accurate (r = 0.56) than those made by the participants' Facebook friends using a personality questionnaire (r = 0.49); (ii) computer models show higher interjudge agreement; and (iii) computer personality judgments have higher external validity when predicting life outcomes such as substance use, political attitudes, and physical health; for some outcomes, they even outperform the self-rated personality scores. Computers outpacing humans in personality judgment presents significant opportunities and challenges in the areas of psychological assessment, marketing, and privacy.
Technology and psychology are reshaping the search for the best employees
The use of online social networking is a commonplace occurrence. A recent trend is for employers and recruiters to utilize social networking sites to screen candidates as part of the hiring process. This study examines student attitudes and awareness of these practices.
The advances in automatic facial expression recognition make possible to mine and characterize large amounts of data, opening a wide research domain on behavioral understanding. In this paper, we leverage the use of a state-of-the-art facial expression recognition technology to characterize users of a popular type of online social video, conversational vlogs. First, we propose the use of several activity cues to characterize vloggers based on frame-by-frame estimates of facial expressions of emotion. Then, we present results for the task of automatically predicting vloggers' personality impressions using facial expressions and the Big-Five traits. Our results are promising, specially for the case of the Extraversion impression, and in addition our work poses interesting questions regarding the representation of multiple natural facial expressions occurring in conversational video.
Despite the evidence that social video conveys rich human personality information, research investigating the automatic prediction of personality impressions in vlogging has shown that, amongst the Big-Five traits, automatic nonverbal behavioral cues are useful to predict mainly the Extraversion trait. This finding, also reported in other conversational settings, indicates that personality information may be coded in other behavioral dimensions like the verbal channel, which has been less studied in multimodal interaction research. In this paper, we address the task of predicting personality impressions from vloggers based on what they say in their YouTube videos. First, we use manual transcripts of vlogs and verbal content analysis techniques to understand the ability of verbal content for the prediction of crowdsourced Big-Five personality impressions. Second, we explore the feasibility of a fully-automatic framework in which transcripts are obtained using automatic speech recognition (ASR). Our results show that the analysis of error-free verbal content is useful to predict four of the Big-Five traits, three of them better than using nonverbal cues, and that the errors caused by the ASR system decrease the performance significantly.
In the 20 years since frameworks of employment interview structure have been developed, a considerable body of empirical research has accumulated. We summarize and critically examine this literature by focusing on the 8 main topics that have been the focus of attention: (a) the definition of structure; (b) reducing bias through structure; (c) impression management in structured interviews; (d) measuring personality via structured interviews; (e) comparing situational versus past-behavior questions; (f) developing rating scales; (g) probing, follow-up, prompting, and elaboration on questions; and (h) reactions to structure. For each topic, we review and critique research and identify promising directions for future research. When possible, we augment the traditional narrative review with meta-analytic review and content analysis. We concluded that much is known about structured interviews, but there are still many unanswered questions. We provide 12 propositions and 19 research questions to stimulate further research on this important topic.
Video games are often framed as sites of play and entertainment. Their transformation into work platforms and the staggering amount of work that is being done in these games often go unnoticed. Users spend on average 20 hours a week in online games, and many of them describe their game play as obligation, tedium, and more like a second job than entertainment. Using well-known behavior conditioning principles, video games are inherentlywork platforms that train us to become better gameworkers. And thework that is being performed in video games is increasingly similar to the work performed in business corporations. The microcosm of these online games may reveal larger social trends in the blurring boundaries between work and play.
We show that easily accessible digital records of behavior, Facebook Likes, can be used to automatically and accurately predict a range of highly sensitive personal attributes including: sexual orientation, ethnicity, religious and political views, personality traits, intelligence, happiness, use of addictive substances, parental separation, age, and gender. The analysis presented is based on a dataset of over 58,000 volunteers who provided their Facebook Likes, detailed demographic profiles, and the results of several psychometric tests. The proposed model uses dimensionality reduction for preprocessing the Likes data, which are then entered into logistic/linear regression to predict individual psychodemographic profiles from Likes. The model correctly discriminates between homosexual and heterosexual men in 88% of cases, African Americans and Caucasian Americans in 95% of cases, and between Democrat and Republican in 85% of cases. For the personality trait "Openness," prediction accuracy is close to the test-retest accuracy of a standard personality test. We give examples of associations between attributes and Likes and discuss implications for online personalization and privacy.
More Americans now play video games than go to the movies (NPD Group, 2009). The meteoric rise in popularity of video games highlights the need for research approaches that can deepen our scientific understanding of video game engagement. This article advances a theory-based motivational model for examining and evaluating the ways by which video game engagement shapes psychological processes and influences well-being. Rooted in self-determination theory (Deci & Ryan, 2000; Ryan & Deci, 2000a), our approach suggests that both the appeal and well-being effects of video games are based in their potential to satisfy basic psychological needs for competence, autonomy, and relatedness. We review recent empirical evidence applying this perspective to a number of topics including need satisfaction in games and short-term well-being, the motivational appeal of violent game content, motivational sources of postplay aggression, the antecedents and consequences of disordered patterns of game engagement, and the determinants and effects of immersion. Implications of this model for the future study of game motivation and the use of video games in interventions are discussed. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Meta-analysis of the cumulative research on various predictors of job performance showed that for entry-level jobs there was no predictor with validity equal to that of ability, which had a mean validity of .53. For selection on the basis of current job performance, the work sample test, with mean validity of .54, was slightly better. For federal entry-level jobs, substitution of an alternative predictor would cost from $3.12 (job tryout) to $15.89 billion/year (age). Hiring on ability had a utility of $15.61 billion/year but affected minority groups adversely. Hiring on ability by quotas would decrease utility by 5%. A 3rd strategy—using a low cutoff score—would decrease utility by 83%. Using other predictors in conjunction with ability tests might improve validity and reduce adverse impact, but there is as yet no database for studying this possibility. (89 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
This paper is organized around a model of job performance in which personality and ability constructs (KSASOs) are seen as leading to procedural and declarative knowledge and motivation which, in turn, lead to task proficiency and contextual and adaptive behavior. These latter individual performance variables are thought to have implications for a set of distal variables, many of which can be conceptualized as organizational level constructs (e.g., social responsibility and litigation). Our sense is that job analyses are means of developing performance models. Some important and relatively new developments in this area include the job information tool known as O*NET. Adding distal variables to a performance model means that we must consider levels of analysis issues. Literature on applicant reactions to selection procedures and the implications of those reactions is discussed as is the notion that withdrawal behavior is a general concern that includes tardiness, absenteeism, counterproductive behavior, and turnover. Still other new developments include the use of technology in measurement, use of our procedures in other cultures, and the critical consideration of time in our analyses of the relationship of various variables with individual differences. Finally, the notion that performance itself is multidimensional is being addressed as a result of recent theorizing about its nature.
The present paper provides a conceptual and empirical examination regarding the relevance of the construct curiosity for work-related outcomes. Based on a review and integration of the literature regarding the construct itself, the construct is conceptually linked with performance in the work context. Using a confirmatory research strategy, a sample (N = 320) with requirements that reflected this conceptual link was chosen. Results from a concurrent validation study confirmed the hypothesis regarding the significance of curiosity for job performance (r = .34). Furthermore, incremental validity of curiosity above twelve cognitive and non-cognitive predictors for job performance suggest that curiosity captures variance in the criterion that is not explained by predictors traditionally used in organizational psychology. It is concluded that curiosity is an important variable for the prediction and explanation of work-related behavior. Furthermore, given the dramatic changes in the world of work, the importance is likely to raise, rather than to decline, which has important implications for organizational theories and applied purposes, like personnel selection.
Video games constitute a popular form of entertainment that allows millions of people to adopt virtual identities. In our research, we explored the idea that the appeal of games is due in part to their ability to provide players with novel experiences that let them "try on" ideal aspects of their selves that might not find expression in everyday life. We found that video games were most intrinsically motivating and had the greatest influence on emotions when players' experiences of themselves during play were congruent with players' conceptions of their ideal selves. Additionally, we found that high levels of immersion in gaming environments, as well as large discrepancies between players' actual-self and ideal-self characteristics, magnified the link between intrinsic motivation and the experience of ideal-self characteristics during play.
Previous studies have found systematic associations between personality and individual differences in word use. Such studies have typically focused on broad associations between major personality domains and aggregate word categories, potentially masking more specific associations. Here I report the results of a large-scale analysis of personality and word use in a large sample of blogs (N=694). The size of the dataset enabled pervasive correlations with personality to be identified for a broad range of lexical variables, including both aggregate word categories and individual English words. The results replicated category-level findings from previous offline studies, identified numerous novel associations at both a categorical and single-word level, and underscored the value of complementary approaches to the study of personality and word use.