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Preparing for scaling: A study on founder role evolution
No full textOne of the major entrepreneurial challenges faced by scaling firms involves changing their internal organization. Our study focuses on a particular aspect of internal organizing—namely, how founder roles evolve in preparation for scaling. By means of an in-depth case study and a combination of data collection methods, we study the evolution of formal and informal founder roles. For both types of roles, we identify a founder-driven and an interaction-driven phase, during which founder and/or joiner role-crafting take place. Through both types of role-crafting, founder roles are (re)shaped. Particularly unique to our study is that we identify three scaling-specific paths through which the role-crafting of joiners shapes founders' roles. Specifically, founders experience a role efficiency increase as they take over some of the joiner-introduced role behaviors, or a role set decrease as joiners take over some of their (formal or informal) roles. We further point to the importance of psychological safety and value fit for successful joiner role-crafting to occur and for founder roles to change following founder-joiner interactions. Our study adds to the literatures on scaling and entrepreneurship as well as to role theory and role-crafting literature
The Adoption of MBA Programs in Germany: An Institutional Perspective
No full textThis study examines the adoption of MBA programs by higher education institutions in Germany. Using arguments from neo-institutional theory and imprinting theory, we propose that private ownership, mimetic processes, and founding period are likely to impact MBA adoption. In an empirical analysis of 86 German universities over the period 1999–2015, we show that private universities are more likely to offer MBA programs. For public universities, we find that prior adoption by other universities and an early foundation period (prior to World War II) positively influence MBA adoption. Interaction analyses show that the positive impact of prior adoption by other universities is attenuated by organizational status and augmented by the regional density of academic institutions. Our findings thus elucidate the major role of institutional factors for the diffusion of MBA programs among German universities
Managing physical assets: a systematic review and a sustainable perspective
No full textContemporary organisations recognise the need for Anthropocene disruptions and transform their business models, restructure their operations, and re-engineer their supply chains to attain greater sustainable objectives and a strong ESG (environmental-social-governance) proposition. Indeed, physical asset management shifted from the negative image associated with asset failure, expensive maintenance, and decommissioning to an enabler of sustainability that allows us to create and capture value from extended lifetime, renewed functions, and increased awareness. In this direction, this study follows a systematic reviewing process enabled by text analytics methods to identify the means and approaches to build a sustainable perspective for physical asset management. Our key contributions and insights are supported by statistics and key features extracted from over 2800 journal articles. We particularly emphasise the research footprint, the evolution, and research trends of the two most asset-intensive sectors (i.e. construction and energy) represented as barriers and enablers of sustainable development. Lacking a unified perspective of the field, this study proposes a conceptual framework that adopts an asset-within-a-system perspective, recognises the links between the stakeholders and holistically integrates the extracted research trends. The knowledge provided here positions physical asset management as a key resource in achieving competitive advantage in the framework of sustainable development
JOB Co: Making Mentor 2.0 Agile
No full textIn 2019, the director of the newly minted Digital Services Lab at JOB Co. found himself on the brink of a crucial meeting with the company's chief executive officer. The focal point of their impending discussion rested on the stagnation of progress in the Mentor 2.0 project—an initiative conceived to embody an agile paradigm in digital transformation, championed by an inventive Scrum team. Despite the project's noble intentions, the journey towards agility had proven to be riddled with formidable challenges for the team. The director, acutely aware that the destiny of Mentor 2.0 was intricately intertwined with the prosperity of the Lab, the linchpin of JOB Co.'s digital metamorphosis, grappled with the necessity of explaining the situation accurately. The imminent meeting stood as a decisive moment for him to carefully consider the most effective strategy. He understood that the future path of the Mentor 2.0 project held significant implications for the Lab—a pivotal force guiding JOB Co.'s digital evolution
Relative Valuation with Machine Learning
No full textABSTRACT We use machine learning for relative valuation and peer firm selection. In out‐of‐sample tests, our machine learning models substantially outperform traditional models in valuation accuracy. This outperformance persists over time and holds across different types of firms. The valuations produced by machine learning models behave like fundamental values. Overvalued stocks decrease in price and undervalued stocks increase in price in the following month. Determinants of valuation multiples identified by machine learning models are consistent with theoretical predictions derived from a discounted cash flow approach. Profitability ratios, growth measures, and efficiency ratios are the most important value drivers throughout our sample period. We derive a novel method to express valuation multiples predicted by our machine learning models as weighted averages of peer firm multiples. These weights are a measure of peer–firm comparability and can be used for selecting peer‐groups.Accepted by Haresh Sapra. We are grateful for the insightful comments from the editorand two anonymous referees. We thank participants at the Department of Accounting andFinance seminar series at the University of Auckland (2019 and 2020), the Quantitative Meth-ods in Finance conference (Sydney, 2019), the 32nd Australasian Finance and Banking Con-ference (Sydney, 2019), the 24th and 25th Annual New Zealand Finance Colloquium (NZFC,Auckland, 2020, and Tauranga, 2021), and an event organized by the Institute of FinanceProfessionals New Zealand Inc. (INFINZ, 2020) for their valuable comments. An earlier ver-sion of this paper was awarded the INFINZ Best Paper in Investments award at the 24th NewZealand Finance Colloquium. We thank Pedro Barroso, Henk Berkman, Steven Cahan, Grif-fin Geng, Maryam Hasannasab, David Hay, Paul Healy, Stephen Kean, Michael Keefe, RobertKnechel, Nick Nguyen, Peter Philips, Paul Rouse, Derek Snow, Charl de Villiers, JilnaughtWong, and Norman Wong for insights and suggestions. Any remaining errors are our own.An online appendix to this paper can be downloaded at https://www.chicagobooth.edu/jar-online-supplement
Vertrouwen is een levend ding: koester het!
No full textWaarom mislukken zoveel digitaliseringsprojecten? Het heeft veel te maken met vertrouwen, zo concludeerde een panel van academici en managers tijdens de Vlerick HR Day. Op de achttiende Vlerick HR Day van 14 juni boog een panel onder leiding van professor Karlien Vanderheyden (Vlerick Business School) zich over de vraag hoe je medewerkers kunt motiveren om een nieuwe digitale technologie te omarmen
Regularization oversampling for classification tasks: To exploit what you do not know
No full textIn numerous binary classification tasks, the two groups of instances are not equally represented, which often implies that the training data lack sufficient information to model the minority class correctly. Furthermore, many traditional classification models make arbitrarily overconfident predictions outside the range of the training data. These issues severely impact the deployment and usefulness of these models in real life. In this paper, we propose the boundary regularizing out-of-distribution (BROOD) sampler, which adds artificial data points on the edge of the training data. By exploiting these artificial samples, we are able to regularize the decision surface of discriminative machine learning models and make more prudent predictions. Next, it is crucial to correctly classify many positive instances in a limited pool of instances that can be investigated with the available resources. By smartly assigning predetermined nonuniform class probabilities outside the training data, we can emphasize certain data regions and improve classifier performance on various material classification metrics. The good performance of the proposed methodology is illustrated in a case study that consists of both benchmark balanced and imbalanced classification data sets
A comparison of activity ranking methods for taking corrective actions during project control
No full textMonitoring and controlling projects in progress is key to support corrective actions in case of delays and to deliver these projects timely to the client. Various project control methodologies have been proposed in literature to include activity variability in the project schedule and measure the performance of projects in progress. Much of these studies rely on a schedule risk analysis to rank activities according to their time sensitivity and expected impact on the total project duration. This paper compares two classes of activity ranking methods to improve the corrective action process of projects under uncertainty. Each method ranks activities based on certain criteria and places the highest ranked activity in a so-called action set that is then used to take certain corrective actions. The first method is the analytical based ranking method which relies on exact or approximate analytical calculations to provide a ranking of activities. This analytical ranking method will be compared with a second simulation-based ranking method that relies on Monte Carlo simulations to measure the sensitivity of each activities. Results on a set of artificial projects show that the analytical ranking method and one specific simulation-based ranking outperform all other methods, not only for predicting the contribution of actions on the expected project duration and its variability, but also in the efficiency of the project manager’s control
Antecedents of the intention to adopt crowdsourcing for innovation in government: Findings from Belgium and the Netherlands
No full textCrowdsourcing is a form of IT-enabled open innovation that has received increased attention in recent years. However, the use of crowdsourcing in public innovation is still in its early stages. To understand the adoption of crowdsourcing in government, this article investigates the antecedents of the intention to adopt crowdsourcing in government organizations. The adoption intention is conceptualized as a rationalistic, goal-directed decision that is driven by multiple strategic intents but tempered by transaction costs. Three strategic intents (accessing complementary knowledge, enhancing organizational legitimacy, and reducing innovation costs) and two transaction costs (codification costs, and broadcasting costs) are hypothesized as antecedents to the adoption intention. Data (n = 205) from municipalities in Belgium and the Netherlands shows that the adoption intention can be explained by the influence of the political executive, the pursuit of organizational legitimacy, and transaction costs associated with broadcasting. Accessing complementary knowledge, reducing innovation costs, and codification costs are not significant predictors. The findings suggest that crowdsourcing is viewed as a tool for political alignment and legitimation