Publication database

In this short paper, we discuss the impact of data analytics in services and delineate future research directions for the field. After illustrating how data analytics are transforming different service sectors, we consider the provision of data analysis as a service in its own right. We discuss how the very nature of data and certain features of the machine learning method give rise to new issues and pitfalls for the management of these services, which delineates as many future research directions. We also discuss the co-production of services by humans and machines, and call for more research on responsible data analytics services to tackle some of the most pressing ethical issues in our societies.

Copyright © 2020, INFORMS

Crowdsourcing—asking an undefined group of external contributors to work on tasks—allows organizations to tap into the expertise of people around the world. Crowdsourcing is known to increase innovation and loyalty to brands, but many organizations struggle to leverage its potential, as our research shows. Most often this is because organizations fail to properly plan for all the different stages of crowd engagement. In this paper, we use several examples to explain these challenges and offer advice for how organizations can overcome them.

The Federal Chancellery recently finished its first draft of the revised Foreign Intelligence Service Law (BND-Gesetz) that has become necessary subsequent to the judgment of the Federal Constitutional Court in May of 2020. While the draft bill contains numerous improvements, some crucial provisions pertaining to the treatment of journalists and their trusted sources remain insufficient. The article analyses some of the problems.

GitLab is a software company that works “all remote” at the scale of more than 1000 employees located in more than 60 countries. GitLab has no physical office and its employees can work from anywhere they choose. Any step of the organizational life of a GitLab employee (e.g., hiring, onboarding and firing) is performed remotely, except for a yearly companywide gathering. GitLab strongly relies on asynchronous coordination, allowing employees to work anytime they want. After highlighting some of the main practices implemented by GitLab to effectively work all remotely and asynchronously, I asked renowned organizational scientists their thoughts on this interesting case and to question the generalizability of the all remote asynchronous model. Understanding whether and under what conditions this model can succeed can be of guidance for organizational designers that are now considering different remote models in response of the COVID-19 shock and its aftermath.

Over the past two decades, the progressing digital transformation has brought along a growing number of challenges in the context of security: internet crime, cyberwar and espionage, surveillance and autonomous weapons systems. While increased security measures seem indispensable, they need to be weighed against individual human rights guarantees. This chapter provides an overview of the pertinent questions.

The World Health Organization seeks effective ways to alert its member states about global pandemics. Motivated by this challenge, we study a public agency’s problem of designing warning policies to mitigate potential disasters that occur with advance notice. The agency privately receives early information about recurring harmful events and issues warnings to induce an uninformed stakeholder to take preemptive actions. The agency’s decision to issue a warning critically depends on its reputation, which we define as the stake- holder’s belief regarding the accuracy of the agency’s information. The agency faces then a trade-off between eliciting a proper response today and maintaining its reputation in order to elicit responses to future events.
We formulate this problem as a dynamic Bayesian persuasion game, which we solve in closed form. We find that the agency sometimes strategically misrepresents its advance information about a current threat in order to cultivate its future reputation. When its reputation is sufficiently low, the agency downplays the risk and actually downplays more as its reputation improves. By contrast, when its reputation is high, the agency sometimes exaggerates the threat and exaggerates more as its reputation deteriorates. Only when its reputation is moderate does the agency send warning messages that fully disclose its private information.
Our study suggests a plausible and novel rationale for some of the false alarms or omissions observed in practice. We further test the robustness of our findings to imperfect advance information, disasters without advance notice, and heterogeneous receivers.

Copyright © 2020, INFORMS

How can a company commit to maximizing stakeholder value while maintaining financial performance? Companies increasingly have the ambition to provide stakeholder value to their owners and shareholders, employees, consumers, suppliers, partners, the environment, and future generations. However, such companies often face difficulties in demonstrating the value they bring to stakeholders, due to the lack of universal methods for assessing their impact. Besides the practical need to develop a method for impact valuation, we researched the existing literature and discovered the lack of a holistic method to evaluate all impacts of a company using a common currency with flexible adaptations at different levels. We developed a new method called Sustainable Business Value (SBV) to address these gaps and enable companies to evaluate their impacts. We tested the SBV in two pilots. The SBV method differs from currently used methods, including sustainability reporting, sustainability rating and indices, and sustainability accounting. SBV can be used for decision-making, portfolio management, benchmarking, stakeholder communication, investor communication, and business development and also provides a comprehensive perspective of a company’s impact across six standardized dimensions. However, further development and standardization of proxies and cross-industry standards are needed.

Problem description: Classical models of queueing systems with rational and strategic customers assume queues to be either fully visible or invisible while service parameters are known with certainty. In practice, however, people only have “partial information” on the service environment in the sense that they are not able to fully discern prevalent uncertainties. This is because assessing possible delays and rewards is costly as it requires time, attention, and cognitive capacity which are all limited. On the other hand, people are also adaptive and endogenously respond to information frictions. Methodology: We develop an equilibrium model for a single-server queueing system with customers having limited attention. Following the theory of rational inattention, we assume that customers optimize their learning strategies by deciding the type and amount of information to acquire and act accordingly while internalizing the associated costs. Results: We establish the existence and uniqueness of a customer equilibrium and delineate the impact of service characteristics and information costs. We numerically show that when customers allocate their attention to learn uncertain queue length, limited attention of customers improves throughput in a congested system that customers value reasonably highly, while it can be detrimental for less popular services that customers deem rather unrewarding. This is also reflected in social welfare if the firm's profit margin is high enough, although customer welfare always suffers from information costs. Managerial implications: Our results shed light on optimal information provision and physical design strategies of service firms and social planners by identifying service settings where they should be most cautious for customers' limited attention. Academic/practical relevance: We propose a microfounded framework for strategic customer behavior in queues that links beliefs, rewards, and information costs. It offers a holistic perspective on the impact of information prevalence (and information frictions) on operational performance and can be extended to analyze richer customer behavior and complex queue structures, rendering it a valuable tool for service design.