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Scientists are increasingly crossing the boundaries of the professional system by involving the general public (the crowd) directly in their research. However, this crowd involvement tends to be confined to empirical work and it is not clear whether and how crowds can also be involved in conceptual stages such as formulating the questions that research is trying to address. Drawing on five different “paradigms” of crowdsourcing and related mechanisms, we first discuss potential merits of involving crowds in the formulation of research questions (RQs). We then analyze data from two crowdsourcing projects in the medical sciences to describe key features of RQs generated by crowd members and compare the quality of crowd contributions to that of RQs generated in the conventional scientific process. We find that the majority of crowd contributions are problem restatements that can be useful to assess problem importance but provide little guidance regarding potential causes or solutions. At the same time, crowd-generated research questions frequently cross disciplinary boundaries by combining elements from different fields within and especially outside medicine. Using evaluations by professional scientists, we find that the average crowd contribution has lower novelty and potential scientific impact than professional research questions, but comparable practical impact. Crowd contributions outperform professional RQs once we apply selection mechanisms at the level of individual contributors or across contributors. Our findings advance research on crowd and citizen science, crowdsourcing and distributed knowledge production, as well as the organization of science. We also inform ongoing policy debates around the involvement of citizens in research in general, and agenda setting in particular.

With permission of Elsevier

The case tells the story of a project at Frontelco (a major telco company in disguise) aiming at defining and piloting a business model (Network as a Service, NaaS) in response to the advancement of a new technology (5G). It is written from the perspective of a “trusted advisor” who had been invited by the protagonist to provide methodological support to the project team. The team, which primarily represents the perspective of product management, spends significant time and effort on developing a methodical approach to their own work, leaving the substantive issue (business model innovation) to be defined only vaguely, under the dominant influence of a few team members. In the follow up to the case we learn that by the time concerns emerge that the team does not seem to have developed any presentable output, they find out that their key competitor has already made significant progress and the technology department, their “internal rival”, has also moved ahead with a concept that allows them to claim ownership for a major corporate-level project.

To study the effect of congestion on the fundamental trade-off between diagnostic accuracy and speed, we empirically test the predictions of a formal sequential testing model in a setting where the gathering of additional information can improve diagnostic accuracy, but may also take time and increase congestion as a result. The efficient management of such systems requires a careful balance of congestion-sensitive stopping rules. These include diagnoses made based on very little or no diagnostic information, and the stopping of diagnostic processes while waiting for information. We test these rules under controlled laboratory conditions, and link the observed biases to system dynamics and performance. Our data shows that decision makers (DMs) stop diagnostic processes too quickly at low congestion levels where information acquisition is relatively cheap. But they fail to stop quickly enough when increasing congestion requires the DM to diagnose without testing, or diagnose while waiting for test results. Essentially, DMs are insufficiently sensitive to congestion. As a result of these behavioral patterns, DMs manage the system with both lower-than-optimal diagnostic accuracy and higher-than-optimal congestion cost, underperforming on both sides of the accuracy/speed trade-off.

Wilo SE, headquartered in Dortmund, Germany, was a leading supplier of pumps and pump systems for the building services, water management and industrial sectors, which was doing business in more than 50 countries. Based on current global developments in politics, economy, and society, Oliver Hermes, President and CEO of Wilo Group, saw a de-coupling of three tectonic plates centered in China, USA, and Western Europe. At the Wilo management conference in September 2020 Hermes informed stakeholders about the decision to establish a 2nd headquarters in Beijing within the next few months, and possibly to open a 3rd headquarters in the following year in the USA. He stated:

“With the opening of a 2nd headquarters I want to set a sign. Wilo has to keep up with current global developments – whether we like them or not. A stronger regionalization of Wilo is necessary to continue our global success.”

The case text introduces the key measures to establish the 2nd headquarters in China.

Product proliferation occurs in supply chains when manufacturers respond to diverse market needs by trying to produce a range of products from a limited variety of raw materials. In such a setting, manufacturers can establish market responsiveness and/or cost efficiency in alternative ways. Delaying the point of the proliferation helps manufacturers improve their responsiveness by postponing the ordering decisions of the final products until there is partial or full resolution of the demand uncertainty. This strategy can be implemented in two different ways: (1) redesigning the operations so that the point of proliferation is swapped with a downstream operation or (2) reducing the lead times. To establish cost efficiency, manufacturers can systematically reduce their operational costs or postpone the high-cost operations. We consider a multi-echelon and multi-product newsvendor problem with demand forecast evolution to analyze the value of each operational lever of the responsiveness and the efficiency. We use a generalized forecast-evolution model to characterize the demand-updating process, and develop a dynamic optimization model to determine the optimal order quantities at different echelons. Using anonymized data of Kordsa Inc., a global manufacturer of advanced composites and reinforcement materials, we show that our model outperforms a theoretical benchmark of the repetitive newsvendor model. We demonstrate that reducing the lead time of a downstream operation is more beneficial to manufacturers than reducing the lead time of an upstream operation by the same amount, whereas reducing the upstream operational costs is more favorable than reducing the downstream operational costs. We also indicate that delaying the proliferation may cause a loss of profit, even if it can be achieved with no additional costs. Finally, a decision typology is developed, which shows effective operational strategies depending on product/market characteristics and process flexibility.

After earlier waves of privatization, local governments have increasingly taken back control of local service provisions in some sectors and countries and instead started providing those services themselves (reverse privatization). Using a unique panel dataset on the mode of service provision for solid waste collection for German municipalities that cover the years 2003, 2009, and 2015, we investigate the motives for reverse privatization. Our results show that -- in deciding whether to insource or not -- municipalities react to the cost advantages of private suppliers as well as to the competitive environment and municipal activity: There is more switching to insourcing in concentrated markets and in markets with horizontally or vertically related public services. Local interest groups influence this decision as well.

© 2022 Springer

We study strategic interactions in markets where firms sell to consumers both directly and via a competitive channel (CC), such as a price comparison website or marketplace, where multiple sellers’ offers are visible at once. We ask how a CC’s size influences market outcomes. A bigger CC means more consumers compare prices, increasing within-channel competition. However, such seemingly pro-competitive developments can raise prices and reduce consumer surplus by weakening between-channel competition. We also use the model to study relevant active policy issues including price clauses, integrated ownership structures, and access to consumers’ purchase data.

Research projects that actively involve ‘crowds’ or non-professional ‘citizen scientists’ are attracting growing attention. Such projects promise to increase scientific productivity while also connecting science with the general public. We make three contributions. First, we argue that the largely separate literatures on ‘Crowd Science’ and ‘Citizen Science’ investigate strongly overlapping sets of projects but take different disciplinary lenses. Closer integration can enrich research on Crowd and Citizen Science (CS). Second, we propose a framework to profile projects with respect to four types of crowd contributions: activities, knowledge, resources, and decisions. This framework also accommodates machines and algorithms, which increasingly complement or replace professional and non-professional researchers as a third actor. Finally, we outline a research agenda anchored on important underlying organisational challenges of CS projects. This agenda can advance our understanding of Crowd and Citizen Science, yield practical recommendations for project design, and contribute to the broader organisational literature.

Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners.