Discriminated by an algorithm: a systematic review of discrimination and fairness by algorithmic decision-making in the context of HR recruitment and HR development

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.

Success factors of academic journals in the digital age

Since the early 1990s, when digitalisation began to open new opportunities for disseminating information, many academic journals started to introduce online services. However, while some studies suggest that online availability and free access to journal articles are positively connected to the number of citations an article receives, little is known about whether being an early adopter of digital services provides journals with a (long-term) competitive advantage in times of digital change. We use data from SSCI-listed management journals to examine which journals pioneered the introduction of digital services, to what extent first-mover advantages can be identified, and which journal characteristics are associated with citation-based performance indicators. Our results show that lower ranked journals were the first to introduce digital services and were beneficiaries of the digital age. Furthermore, we find a negative correlation between general submission fees and journal performance and that the top-performing journals of our sample are those of non-commercial publishers. Our analysis of the relationship between journal performance and the provision of open access contradicts previous studies, as we find no positive correlations between performance and open access on the journal level.

Determinants and consequences of clawback provisions in management compensation contracts: a structured literature review on empirical evidence

The goal of this systematic literature review is to provide a detailed understanding of the determinants and consequences of clawback provisions in management compensation contracts, motivated by the increasing global regulatory, practical, and academic importance of implementing this new corporate governance tool. We identify 44 empirical (archival and experimental) studies on this topic and review them based on an agency-theoretical framework. Our review of empirical clawback research offers insights into this growing field and supports future researchers in developing new research questions. Our main results are as follows. First, we still know very little about the drivers of clawback adoption, as prior research concentrated on the consequences of clawback provisions. Second, many studies indicate that clawbacks lead to better earnings quality (reduced re-/misstatements), better pay-for-performance sensitivity, increased firm performance, greater value relevance, and lower overinvestment. While there are indications that clawbacks may serve an incentive-alignment function for managers, their contribution may be dependent on other corporate governance mechanisms, e.g. board composition, as significant moderators. We develop a research agenda with detailed recommendations for future research from methodological and content perspectives. We expect that the research activity in this field regarding the European capital market will increase in light of the EU shareholder rights Directive of 2017.

An ethical intuitionist account of transparency of algorithms and its gradations

To make evaluations about the morally relevant impacts of algorithms, transparency is needed. This paper lays out discussion of algorithms and transparency in an explicitly moral analysis with a special focus on the domain of justice and security. The paper provides an account of the moral import of transparency, defined itself as an instrumental value denoting a state of affairs conducive to acquisition of knowledge about some X. A normative account of transparency is outlined relying on an intuitionist framework rooted in the works of Ross and Robert Audi. It will be argued that transparency can be derived as a subsidiary (prima facie) principle from other duties including beneficence and justice and that it is groundable in the value of knowledge. Building on this foundation, the paper examines transparency and duty conflict with a special focus on algorithms in justice and security, recognising that complete transparency can be impossible where duties conflict. It is argued that as a subsidiary (prima facie) principle, transparency is overridable but ineradicable, which is to say that sufficiently justifiable reasons for secrecy or opacity can licence limiting transparency, that is, there may be occasion where full transparency is not our final duty.

Betting the farm and playing it safe? Hyper-core self-evaluation in decisions when managers are winning and losing

Core self-evaluation summarizes a decision maker’s self-worth. This key personality trait has been shown to lead to extreme performance consequences of either winning or losing big. We suggest that these extreme performance outcomes may partly rest in how core self-evaluation affects executive’s perception and evaluation of risk in choices under uncertainty. We conducted a choice experiment building on the original prospect theory experiments with 97 executives, in which we measured the effect of core self-evaluation on risk behavior. As a robustness test, we replicated and validated our findings with a larger sample of 111 executives. Building on the tenets of prospect theory, we show that decision makers with high levels of core self-evaluation are less loss averse. Surprisingly, this effect differs depending on whether gains or losses are highlighted in the decision. For gains, higher levels of core self-evaluation are associated with behaviors that are closer to risk neutrality. For losses, however, we find that higher levels of core self-evaluation further enhance the risk-seeking behavior of decision makers. These findings contribute towards understanding the effects of core self-evaluation in the work environment as well as in the decision process and provide an additional lens for studying how the personality of executives affects choices under uncertainty.

The social and environmental drivers of corporate credit ratings: international evidence

We provide evidence of the exogenous impact of environmental and social performance components on credit ratings in North America, Europe, and Asia. In particular, the product innovation dimension is clearly identified as being the dominating driver of credit ratings within the environmental performance in every subsample region. In the social performance dimension, the extent of diversity is a main driver for firms in North America and Europe, but due to cultural reasons, not in Asia. Our results show that the risk mitigation view holds for all significant corporate social or environmental performance variables, but the magnitude of impact differs regionally.

Strategic decision-making under ambiguity: a new problem space and a proposed optimization approach

The existence of ambiguity presents a challenge to decision-makers as it eliminates the ability to apply standard optimization approaches, such as those based on calculating the objective expected values of alternative actions. In reality, ambiguity arises in most strategically important decisions in some form because of the genuine limits on the decision-maker’s rationality and on the information available about the alternatives and the future. To address that reality, we define such problems as strategic decision-making under ambiguity where choices over resource investments must be made in competitive environments where possible outcomes and their payoffs are known ex ante, but the probabilities of such outcomes are unknowable ex ante. We outline a multi-step, logical approach for addressing such problems in theory with the goal of providing an improved basis for practical decisions that should increase organizational performance.

On the current state of combining human and artificial intelligence for strategic organizational decision making

Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI, other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

A contingency lens on cloud provider management processes

Traditional ways of managing information technology (IT) service providers are no longer applicable as companies use more and more services provisioned in the cloud. Therefore, organizations are looking for new ways to manage their relationship with cloud providers. The shift from IT-as-a-product to IT-as-a-service puts clients in a continued dependency on cloud service providers (CSPs), making provider management a critical factor for companies’ success. In this paper, we (1) identify cloud-specific challenges in managing CSPs, (2) develop a corresponding process framework for CSP management, and (3) discuss and extend this framework. Our final cloud management framework comprises ten processes for effective CSP management based on a literature study and twelve expert interviews. Furthermore, we unpack three major contingency factors, i.e., client–provider ratio, specificity, and service delivery model, which influence the reasonability and configuration of the cloud management processes. Drawing on two specific cases from our interview study, we explicate the contingency factors’ influence. Thus, our paper contributes to cloud sourcing research by deepening the understanding of client–provider relationships and by introducing a viable CSP management instrument contingent on three salient factors of cloud service provisioning.