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Academy of Management Proceedings
No full textMedia holding companies face a fundamental tension between leveraging economies of scale through content sharing and maintaining distinctive brand identities across their portfolios. While content sharing across outlets offers clear operational benefits, it risks diluting unique brand voices that attract loyal audiences. This study examines how atypicality — standing out among similar entities, both in terms of content characteristics and network position — affects this trade-off. Drawing on 32 months of data from a European media conglomerate encompassing 1.5M articles shared across a network of 17 news and magazine brands, our results confirm that increased content adoption volume generally reduces content performance. Atypicality of the content and the way brands adopt articles from each other (i.e., content-level and network-level atypicality) can turn this into an advantage. We find that relying on more atypical content adoption strategies can help media organizations offset the negative effects of increased adoption volume. These findings advance our understanding of how atypicality functions in consumption- and experience-based digital markets and offer practical insights for media organizations balancing efficiency with differentiation
Synchronizing production and delivery in flow shops with time-of-use electricity pricing
No full textManufacturing supply chains (SC) shift from traditional make-to-stock systems to make-to-order (MTO) systems in order to coordinate the production and distribution of complex and highly customized products. Despite the need for advanced scheduling approaches when coordinating such MTO-based SCs, there is little research focussing on practical settings that include variable processing speeds, sequence-dependent setup times (SDST) and time-of-use (TOU) electricity prices. However, these settings are important since they influence the energy consumption and the associated electricity costs have an impact on the decision-making process. Furthermore, there is also an increasing concern regarding green production in manufacturing such that the energy consumption cannot be ignored in decision making. In this study, we investigate a bi-objective energy-efficient permutation flow shop scheduling problem in MTO-based SC (EPFSPSC) with the conjoint objectives of minimising the cost of inventory, delivery, tardiness and electricity costs for production. In order to solve this problem, a genetic algorithm-based memetic algorithm is proposed and its effectiveness is demonstrated against a well-known benchmark approach. This research aims to assist production managers in making integrated production and distribution decisions, while simultaneously considering all associated costs and ensuring green manufacturing.(Fonds voor wetenschappelijke onderzoek|12A4222N
Understanding Decision‐Making to Tackle Complexity in Open Innovation Labs in Government
No full textThis article examines the decision‐making processes in open innovation labs (OI‐labs) in government. Through a qualitative single case study, we explore how the use of causal and effectual reasoning, as dichotomous logics, evolves over time and is manifested in the form of organizational practices to tackle temporal, relational, and cultural complexity. The findings reveal three episodes: the conceptualizing of the lab (predominantly causation), the building of the lab (predominantly effectuation), and the sustaining of the lab (hybrid causation–effectuation). Moreover, shifts in the logic are aimed at addressing different types of complexity, and over time, a hybrid logic emerges
AI-Driven Fleet Cost Forecasting: A Case Study in Logistics
No full textThis item is suitable for undergraduate, postgraduate and executive education courses.In early 2023, Maureen Philips, CEO of Actbel Logistics, faces ballooning cost volatility after pivoting from bulk chemicals to temperature‑controlled vaccine freight during COVID‑19. With 600 trucks, two distinct Business Units (Healthcare and Chemicals) and an impending ETS2 EUR0.15 /L carbon surcharge, the legacy unit-cost method is no longer capable of delivering accurate, forward-looking cost estimates. Maureen asks her Finance team to build a machine‑learning (ML) engine on three years of detailed truck‑month financials (≈ EUR185 million costs) and 1.2 million trip records to deliver granular, real‑time €/km forecasts. Students must (i) explore & combine the datasets provided, (ii) discover natural cost segments, (iii) benchmark supervised machine learning (regression) models, (iv) interpret feature importance with SHAP, and (v) translate analytics into contract‑pricing and fleet‑mix recommendations
Breaking Through Only to Break up: A Cross‐Country Analysis of the Speed of Advancement and Exit of Female Executives
No full textABSTRACT We examine the speed of advancements and exits of female executive directors vis‐à‐vis comparable men. In line with recent research, we suggest that women are likely to experience an apparent gender‐based advantage in the form of lower age at the time of their first‐ever executive director appointment. However, we argue that this advantage may be transitory. Appointed women also experience faster exits from these positions, with age partially mediating the differential speed of exits between male and female executive directors. We also contend that these effects are contingent on countries' local gender norms (especially women's economic participation) such that lower gender parity leads to even lower ages at appointments and faster exits for female executive directors. Results based on 15,202 unique rookie executive directors from 33 countries between 2002 and 2015 largely support these predictions
Volume Flexibility at Responsive Suppliers in Reshoring Decisions: Analysis of a Dual Sourcing Inventory Model
No full textWe investigate how volume flexibility, defined by a sourcing cost premium beyond a base capacity, at a local responsive supplier impacts the decision to reshore supply. The buyer also has access to a remote supplier that is cheaper with no restrictions on volume flexibility. We show that with unit lead time difference between both suppliers, the optimal dual sourcing policy is a modified dual base‐stock policy with three base‐stock levels S 1 f , S 2 f , and S s . The replenishment orders are generated by first placing a base order from the fast supplier of at most k units to raise the inventory position to S 1 f , if that is possible. After this base order, if the adjusted inventory position is still below S 2 f , additional units are ordered from the fast supplier at an overtime premium to reach S 2 f . Finally, if the adjusted inventory position is below S s , an order from the slow supplier is placed to bring the final inventory position to S s . Surprisingly, in contrast to single sourcing with limited volume flexibility, a more complex dual sourcing model often results in a “simpler” policy that replaces demand in each period. The latter allows analytical insights into the sourcing split between the responsive and the remote supplier. Our analysis shows how increased volume flexibility at the responsive supplier promotes the decision to reshore operations and effectively serves as a cost benefit. It also shows how investing in base capacity or additional volume flexibility act as strategic substitutes
Digital at heart- How to lead the human centric digital transformation
No full textThe much-needed digital transformation organisations and companies must make creates tension and uncertainty for many. After all, more than finding and applying the latest technologies, it is essential to streamline internal processes and adapt the organization to new ways of working. Success largely depends on the willingness of all employees to participate in this process and, therefore to what extent organizations succeed in transforming the mindset and culture within the company. This book will teach you how to place people first in a digital transformation process. It allows you to look at the relationship between people and technology in a new way and helps get all employees on board confidently
Gender and racial minorities on corporate boards: How board faultlines and CEO‐minority director overlap affect firm performance
No full textAbstract Research Summary In this article, we examine the multidimensional and multilevel nature of diversity in the context of corporate boards. Using the concept of faultlines, we argue that when gender and racial background aligns with human capital attributes of board members, faultlines may be formed with negative implications for firm performance. However, the potential negative impact of faultlines can be alleviated by overlaps in the characteristics of the CEO and minority directors. Specifically, we find that higher overlaps in tenure and personal range of functional experiences help overcome some of the disadvantages that minority directors face and moderate the relationship between board faultline strength and firm performance. Empirical tests using 14 years data on 262 firms belonging to S&P500 index largely support our theoretical ideas. Managerial Summary Boards often suffer from unhealthy team dynamics. In this article, we explore how alignment of board members' attributes may lead to potential subgroup formation within boards. Specifically, we examine how, under existing pressures to increase demographic diversity on corporate boards, alignment of human capital characteristics with gender and racial minority status may lead to the formation of board faultlines that negatively influence firm performance. Our results suggest that the CEO plays a pivotal role in overcoming negative consequences of board faultlines by utilizing shared tenure on board and common functional experiences with minority board members. Our research suggests that board selection needs to focus beyond scrutinizing individual‐level human capital and instead understand alignments of directors' profiles that enable optimal board functioning
IRAF-BRB: An explainable AI framework for enhanced interpretability in project risk assessment
No full textIn high-stakes project risk assessment, balancing predictive accuracy with interpretability is critical to fostering stakeholder trust and supporting well-informed decision-making. This study presents the Interpretable Risk Assessment Framework with Belief Rule-Based Systems (IRAF-BRB), an Explainable AI (XAI) framework specifically designed to improve transparency, accountability, and accuracy in risk assessment. IRAF-BRB combines Interpretive Structural Modeling (ISM) to map and analyze interdependencies among risk factors with an optimized Belief Rule-Based (BRB) model. A modified Differential Evolution Covariance Matrix Self-Adaptation (DECMSA) algorithm is employed to enhance the predictive power of the BRB model while preserving interpretability, ensuring that stakeholders can both trust and understand the model’s outputs. By transforming complex risk data into intuitive visualizations, the IRAF-BRB framework enables project managers to identify key risk drivers and anticipate cascading effects, leading to proactive risk mitigation. Experimental results demonstrate that IRAF-BRB reduces Mean Squared Error (MSE) to 4.09 e − 4 in predicting risk levels for high-rise construction projects, outperforming traditional BRB models such as Differential Evolution-based BRB (DE-BRB) ( 8.29 e − 4 ) and Particle Swarm Optimization-based BRB (PSO-BRB) ( 2.53 e − 3 ) . The statistical significance of these results was confirmed via a two-sample t-test ( p < 0.05 ) , establishing IRAF-BRB as a reliable and effective tool for accurate and interpretable risk assessment