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 efficiencydriven 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.

Artificial intelligence (AI) systems in the workplace increasingly substitute for employees’ tasks, responsibilities, and decision-making. Consequently, employees must relinquish core activities of their work processes without the ability to interact with the AI system (e.g., to influence decision-making processes or adapt or overrule decision-making outcomes). To deepen our understanding of how substitutive decision-making AI systems affect employees’ professional role identity and how employees adapt their identity in response to the system, we conducted an in-depth case study of a company in the area of loan consulting. We qualitatively analyzed more than 60 interviews with employees and managers. Our research contributes to the literature on IS and identity by disclosing mechanisms through which employees strengthen and protect their professional role identity despite being unable to directly interact with the AI system. Further, we highlight the boundary conditions for introducing an AI system and contribute to the body of empirical research on the potential downsides of AI.

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PurposeMedical record-derived comorbidity measures such as the Charlson Comorbidity Index (CCI) do not predict functional limitations or quality of life (QoL) in the chronically ill. Although these shortcomings are known since the 1980s, they have been largely ignored by the international literature. Recently, QoL has received growing interest as an end-point of interventional trials in Nephrology. The aim of this study is to compare a patient-reported comorbidity measure and the CCI with respect to its validity regarding QoL.MethodsThe German Self-Administered Comorbidity Questionnaire (SCQ-G) was completed by 780 adult end-stage renal disease-patients recruited from 55 dialysis units throughout Germany. Acceptance was evaluated via response rates. Content validity was examined by comparing the typical comorbidity pattern in dialysis patients and the pattern retrieved from our data. Convergent validity was assessed via kappa statistics. Data was compared to the CCI. Linear associations with QoL were examined (criterion validity).ResultsThe SCQ-G was very well accepted by dialysis patients of all ages (response rate: 99%). Content validity can be interpreted as high (corresponding comorbidity items: 73.7%). Convergent validity was rather weak (.27≤ρ≤.29) but increased when comparing only concordant items (.39≤ρ≤.43). With respect to criterion validity, the SCQ-G performed better than the CCI regarding the correlation with QoL (e.g., SF-12-physical: SCQ-G total score: ρ = -.49 vs. CCI: ρ = -.36).ConclusionsThe patient-reported measure proved to be more valid than the external assessment when aiming at insights on QoL. Due to the inclusion of subjective limitations, the SCQ-G is more substantial with respect to patient-centered outcomes and might be used as additional measure in clinical trials.

Artists make vital contributions to our society and lay the foundations for billion-dollar industries. However, these artists consistently struggle to acquire sufficient funding for their projects and their livelihood. New technology-supported possibilities for funding artists and their projects have emerged in recent years. Initial Coin Offering (ICO) is a novel form of reward-based tokenized crowdfunding. Although ICOs are promising as a way to fund artistic projects, they lack widespread adoption in the creative and cultural industry (CCI). Based on 35 qualitative in-depth interviews, we identify four barriers that hinder the funding of artistic projects through ICOs: legal shortcomings, investment restrictions, lack of consumer interest, and intermediaries’ resistance. Our research contributes to cultural finance and funding literature by disclosing barriers that impede a promising form of financing artistic projects. Further, we outline possible solutions to overcome them. We also contribute to the research about ICOs by showing that rather than reducing investment risks, these offerings merely shift them.