Management Research that Makes a Difference:Broadening the Meaning of Impact

The world is undergoing dramatic transformations. Many of the grand societal challenges we currently face underscore the need for scholarly research – including management studies – that can help us best sort out and solve them. Yet, management scholars struggle to produce concrete solutions or to communicate how their research can help to tackle these grand societal challenges. With this editorial, we want to help scholars seeking to ‘make a difference’ by broadening our understanding of what constitutes impactful research. We examine five forms of impact – scholarly, practical, societal, policy, and educational – outlining how scholars can systematically extend or enlarge their research agenda or projects to amplify their impact on the challenges societies face. We suggest that each of these forms of impact has intrinsic value in advancing the scientific enterprise and, together, can help to address key societal problems that reach beyond the immediate and traditional context of business management. With concrete suggestions for getting started on these forms of impact, and possible outputs for each, we hope to stimulate management and organization scholars to think more broadly about the opportunities for making an impact with their research and to begin doing so more often

Here, There and Everywhere: On the Responsible Use of Artificial Intelligence (AI) in Management Research and the Peer‐Review Process

AbstractThis editorial introduces and explains the Journal of Management Studies’ (JMS) new policy on artificial intelligence (AI). We reflect on the use of AI in conducting research and generating journal submissions and what this means for the wider JMS community, including our authors, reviewers, editors, and readers. Specifically, we consider how AI‐generated research and text could both assist and augment the publication process, as well as harm it. Consequentially, our policy acknowledges the need for careful oversight regarding the use of AI to assist in the authoring of texts and in data analyses, while also noting the importance of requiring authors to be transparent about how, when and where they have utilized AI in their submissions or underlying research. Additionally, we examine how and in what ways AI's use may be antithetical to the spirit of a quality journal like JMS that values both human voice and research transparency. Our editorial explains why we require author teams to oversee all aspects of AI use within their projects, and to take personal responsibility for accuracy in all aspects of their research. We also explain our prohibition of AI's use in peer‐reviewers’ evaluations of submissions, and regarding editors’ handling of manuscripts.</jats:p

REQUITE: A prospective multicentre cohort study of patients undergoing radiotherapy for breast, lung or prostate cancer

Purpose: REQUITE aimed to establish a resource for multi-national validation of models and biomarkers that predict risk of late toxicity following radiotherapy. The purpose of this article is to provide summary descriptive data. Methods: An international, prospective cohort study recruited cancer patients in 26 hospitals in eight countries between April 2014 and March 2017. Target recruitment was 5300 patients. Eligible patients had breast, prostate or lung cancer and planned potentially curable radiotherapy. Radiotherapy was prescribed according to local regimens, but centres used standardised data collection forms. Pre-treatment blood samples were collected. Patients were followed for a minimum of 12 (lung) or 24 (breast/prostate) months and summary descriptive statistics were generated. Results: The study recruited 2069 breast (99% of target), 1808 prostate (86%) and 561 lung (51%) cancer patients. The centralised, accessible database includes: physician-(47,025 forms) and patient-(54,901) reported outcomes; 11,563 breast photos; 17,107 DICOMs and 12,684 DVHs. Imputed genotype data are available for 4223 patients with European ancestry (1948 breast, 1728 prostate, 547 lung). Radiation-induced lymphocyte apoptosis (RILA) assay data are available for 1319 patients. DNA (n = 4409) and PAXgene tubes (n = 3039) are stored in the centralised biobank. Example prevalences of 2-year (1-year for lung) grade >= 2 CTCAE toxicities are 13% atrophy (breast), 3% rectal bleeding (prostate) and 27% dyspnoea (lung). Conclusion: The comprehensive centralised database and linked biobank is a valuable resource for the radiotherapy community for validating predictive models and biomarkers. Patient summary: Up to half of cancer patients undergo radiation therapy and irradiation of surrounding healthy tissue is unavoidable. Damage to healthy tissue can affect short-and long-term quality-of-life. Not all patients are equally sensitive to radiation "damage" but it is not possible at the moment to identify those who are. REQUITE was established with the aim of trying to understand more about how we could predict radiation sensitivity. The purpose of this paper is to provide an overview and summary of the data and material available. In the REQUITE study 4400 breast, prostate and lung cancer patients filled out questionnaires and donated blood. A large amount of data was collected in the same way. With all these data and samples a database and biobank were created that showed it is possible to collect this kind of information in a standardised way across countries. In the future, our database and linked biobank will be a resource for research and validation of clinical predictors and models of radiation sensitivity. REQUITE will also enable a better understanding of how many people suffer with radiotherapy toxicity

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Gender Data Gap and its impact on management science — Reflections from a European perspective

As data increasingly inform every aspect of our lives, gender discrimination in the collection and application of female-based data has also risen. Because data are primarily sourced from (white) men, the solutions we design to address global problems are also primarily based on men, i.e. male bodies, male preferences and prototypical male life choices. The Gender Data Gap – referring to the circumstance that most data on which organisational decisions are based appear to be biased in favour of (white) men – describes this very absence of information about aspects of women's lives. In this article, we not only demonstrate how the Gender Data Gap (negatively) impacts society and management science, but also highlight how the gap can be overcome in the long run. Further, we showcase several initiatives, particularly European ones, that suggest opportunities to gradually close the Gender Data Gap.</p

Emotion regulation as risk management for industrial crisis resolution ::an MDP model driven by field data on interpersonal emotion management (IEM)

Existing risk control systems seeking to evaluate and alleviate the negative outcomes of industrial production crises typically fail to consider the role of human factors, such as managers' interpersonal emotion management. Yet, literature in applied psychology shows that leaders elicit very different reactions from others as a function of leaders' abilities to regulate their emotions and those of people around them, particularly in times of crisis. Unfortunately, these two research communities tend to work in silos. Consequently, this research aims at developing a decision-making meta-model for addressing production crises that relies on both management science and applied psychology. A practical production crisis decision model is developed that couples the formalized decision making framework of a Markov Decision Process (MDP) with empirical data on interpersonal emotion management. This model is applied to cases of production crises or disruptions where managers use different levels of Interpersonal Emotion Management (IEM)

What changes after women enter top management teams? A gender-based model of strategic renewal

The question of what changes when women enter upper-echelons teams has long frustrated upper echelons and gender researchers. We build on the dynamic strategic renewal literature, combine it with upper echelons theory insights, and integrate knowledge about female executives' career strategies to theorize how and when female appointments into top management teams (TMTs) cause firms to change their approach to knowledge-related strategic renewal. In doing so, we reconcile the tension among extant mediating processes invoked to explain how female TMT representation might affect strategic decisions: change orientation and risk-taking propensity. Estimating a dynamic ordinary least squares model on panel data from 163 multinationals, we find that following female (but not male) TMT appointments, TMT cognitions shift, becoming more change oriented and less risk seeking. Subsequently, these TMT cognitive shifts cause a decrease in mergers and acquisitions and an increase in research and development. Our model of female TMT appointments as catalysts that cause shifts in TMT cognitions, which, in turn, redirect knowledge-related strategic renewal from a buying to a building approach, is a novel effort at advancing research on women at upper echelons to examine time-dependent, within-firm mechanisms linking women in upper echelons and firm outcomes