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.

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.

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.

Revenue management is a complex operational planning process involving predictive and prescriptive analytics. As real-world implementations strongly rely on the joint outcomes from both algorithms and analysts, we consider the revenue management system as an example of symbiotic analytics systems. This paper presents insights from a field study observing a natural experiment in revenue management. As a firm updates its automated revenue management systems, it also updates the related processes and the corresponding organizational structure. We use this opportunity to examine the multilevel use of symbiotic analytics systems based in a field study and explore the implications for the design of future systems. Specifically, we identify two different perspectives on the revenue management process. In the functional view, jobs are organized sequentially with a high degree of system-oriented specialization. The process view organizes jobs in a parallel structure, differentiating two perspectives on demand. Depending on what view the firm implements, different structural fault lines turn the communication and training of analysts into keystones of the planning process. Furthermore, as we point out, even implementing more sophisticated algorithms and redesigning planning processes and organization do not seem to reduce the relevance of human analysts.

Motivated by recent developments to deploy collaborative robots in industrial production systems, we investigate the assembly line balancing problem with collaborative robots. The problem is characterized by the possibility that human and robots can simultaneously execute tasks at the same workpiece either in parallel or in collaboration. For this novel problem type, we present a mixed-integer programming formulation for balancing and scheduling of assembly lines with collaborative robots. The model decides on both the assignment of collaborative robots to stations and the distribution of workload to workers and robotic partners, aiming to minimize the cycle time. Given the high problem complexity, a hybrid genetic algorithm is presented as a solution procedure. Based on extensive computational experiments, the algorithm reveals promising results in both computational time and solution quality. Moreover, the results indicate that substantial productivity gains can be utilized by deploying collaborative robots in manual assembly lines. This holds especially true for a high average number of robots and tasks to be assigned to every station as well as a high portion of tasks that can be executed by the robot and in collaboration.

Theories of path dependence and incumbent inertia assume that self-reinforcing mechanisms lead to highly persistent and eventually inefficient institutional solutions. The resulting lock-in is likely to threaten the viability of an organization. While path dependence theory was initially developed as a market-based approach, it has more recently been transferred to institutional settings and in particular to hierarchies. Some critics doubt, however, its applicability to hierarchical organizations. The major argument states that asymmetric power structures in organizations differ significantly from symmetric coordination modes and autonomous evolutionary dynamics. Hierarchical authority is assumed to be stronger and to rule out emergent autonomous dynamics. This reasoning raises the question whether hierarchical structures are actually strong enough to suppress (deliberately) the power of evolutionary self-reinforcing organizational dynamics, or vice versa. To our knowledge, there are no studies to date examining in detail these reverse dynamics. In this paper, we build on simulations to study these competing dynamics and possible conditions that favor one view or the other. We suggest using agent-based simulation and modeling, conceiving of institutional change as an interdependent multi-level process that can be analyzed numerically. The results indicate that in most situations self-reinforcing organizational dynamics can actually overrule hierarchical authority, whilst in some other situations formal authority proves to be stronger.

Organizations today operate in an increasingly complex and turbulent world in which unexpected events are omnipresent. Thus, they need to develop resilience capabilities to manage unexpected disruptions, maintain high performance, and even thrive and grow. In research, there are preliminary indications that diversity could play an important role in the development of resilience in organizations. However, although there are numerous studies on both resilience and diversity, the connection between the two constructs remains largely unexplored. Our paper aims to narrow this research gap by answering the following questions: What role does diversity play in the development of organizational resilience? What does this mean for resilience-enhancing diversity management? To answer these questions, we link existing research on elements of organizational resilience and outcomes of diversity in organizations. By developing a theoretical framework, formulating propositions, and discussing implications for further research, this paper provides a foundation for future empirical research. Moreover, it offers useful insights into the successful management of organizational resilience.

Emerging smart manufacturing technologies combine physical production networks with digital IT systems, resulting in complex smart factory networks, which are especially vulnerable to IT security risks, such as IT component non-availabilities. Companies must employ extensive IT security measures to secure their production facilities. However, complex network structures and inherent dependencies of smart factory networks complicate corresponding investment decisions and increase the need for appropriate decision support. We develop a risk assessment model that supports companies in the investment decision-making process regarding IT security measures by identifying and evaluating the most critical areas of the information network while considering the underlying production network. For this purpose, IT availability risks are quantified by means of graph theory, matrix notation, and value-at-risk. Our model provides a structured approach and considers network structures and interdependencies. The insights gained by our model present a profound economic basis for investment decisions on IT security measures. By applying our model in an exemplary real-world setting, we analyze various IT security measures and their risk reduction effect.

This study derives a conceptual framework for examining parallel and differential influences of organizational pride in employees’ efforts versus abilities on proactivity. Data from a field survey (N = 1218) confirm our theoretical model. Organizational pride in employees’ efforts and organizational pride in employees’ abilities both had positive indirect effects on proactive behaviors via affective organizational commitment. Yet, whereas organizational pride in employees’ efforts additionally had a direct positive effect on individual and team member proactivity, organizational pride in employees’ abilities showed a direct negative effect on proactive behaviors for the self, the team, and the organization including a behavioral measurement of employees’ provision of ideas for improvement. These findings contribute to the nascent literature on organizational pride by indicating towards employees as source of organizational pride, highlighting potential negative effects of organizational pride, and introducing the differentiation between employees’ efforts and abilities.

Stakeholder influences on sustainable supply chain management (SSCM) are of increasing interest for researchers to take into account economic, environmental, and social risks. While extant literature on stakeholder influences or risks in SSCM concentrates on selected issues, a comprehensive review of both stakeholder and risk constructs is missing. Hence, this paper examines stakeholder influences and risks in SSCM, as addressed by conceptual frameworks, empirical studies, and formal models to shed light on the trends and gaps in qualitative and quantitative SSCM research. Based on a content analysis of systematically selected journal publications, the commonalities and differences between the research designs are identified. The findings suggest that the integration of economic risks prevails over the consideration of environmental and social risks. Qualitative studies frequently focus on customers or multiple stakeholders that trigger SSCM and relate to supply, demand, and particularly reputational risks. In contrast, quantitative models rather concentrate on formalizing governmental triggers and operational risks. Thus, mutual stimuli between conceptual, empirical, and model-based SSCM research and their implications for future research directions are derived.