We introduce a machine learning approach that accurately captures disclosure quality by quantifying the thematic content of climate reporting in annual reports. We then use our approach to analyze firms’ climate reporting practices in the context of the widespread European Non-Financial Reporting Directive. For a large sample of annual reports from 2010 to 2020, we find that firms significantly changed their climate reporting narratives in the periods following the announcement and implementation of the mandate. Using the Task Force on Climate-Related Financial Disclosures’ framework as the benchmark for high-quality climate reporting, we show that these changes correspond with improvements in disclosure quality. We further show that the comparability of reporting improves over time and provide first descriptive evidence of a more pronounced comparability change in the years following the implementation of the mandate. Overall, our results highlight the validity of our model and provide further descriptive evidence on the disclosure impact of nonfinancial reporting regulation. Our study also adds to the growing body of research applying machine learning to analyze information from annual reports.

Monitoring 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.

Purpose – When is manufacturing in high-cost environments and more profitable for multinational manufacturers and when in low-cost environments? While the literature offers many cues to answer this question, too little empirical research directly addresses this. In this study, we quantitatively and empirically investigate the financial effect of companies’ production footprint in low-cost and high-cost environments for different types of production networks. Design/methodology/approach – Using the data of 770 multinational manufacturing companies, we analyze the relationship between production footprints and profitability during four calendar semesters in 2018 and 2019 (N 5 2,940), investigating the moderating role of companies’ production network type. Findings – We find that companies with networks distinguished by both high levels of product complexity and process sophistication profit the most from producing to a greater extent in high-cost countries. For these companies, shifting production to low-cost countries would be associated with negative performance implications. Practical implications – Our findings suggest that the production geography of companies should be attuned to their network type, as defined by the companies’ process sophistication and product complexity. Manufacturing in low-cost countries is not always the best choice, as doing so can adversely affect profits if the products are highly innovative and the production processes are complex. Originality/value – We contribute to the scarce empirical literature on managing global production networks and provide a data-driven analysis that contributes to answering some of the enduring questions in this critical area

We study inventory control with volume flexibility: A firm can replenish using period-dependent base capacity at regular sourcing costs and access additional supply at a premium. The optimal replenishment policy is characterized by two period-dependent base-stock levels but determining their values is not trivial, especially for non-stationary and correlated demand. We propose the Lookahead Peak-Shaving policy that anticipates or peak shaves orders from future peak-demand periods to the current period, thereby matching capacity and demand. Peak shaving anticipates future order peaks and partially shifts them forward. This contrasts with conventional smoothing, which recovers the inventory deficit resulting from demand peaks by increasing later orders. Our contribution is three-fold. Firstly, we use a novel iterative approach to prove the robust optimality of the Lookahead Peak-Shaving policy. Secondly, we provide explicit expressions of the period-dependent base-stock levels and analyze the amount of peak shaving. Finally, we demonstrate how our policy outperforms other heuristics in stochastic systems. Most cost savings occur when demand is non-stationary and negatively correlated, and base capacities fluctuate around the mean demand. Our insights apply to several practical settings, including production systems with overtime, sourcing from multiple capacitated suppliers, or transportation planning with a spot market. Applying our model to data from a manufacturer reduces inventory and sourcing costs by 6.7%, compared to the manufacturer's policy without peak shaving.

This commentary advances evidence-based propositions for interventions targeting the stalled side of the gender revolution: gender balanced roles in the home domain. Such interventions should be approached in a multi-level frame, from (1) socialization; to (2) family-level interventions; (3) organisational policies; and (4) societal policy/governance levels. Please refer to the Supplementary Material section to find this article's Community and Social Impact Statement.

Introduction: Currently, the healthcare sector is under tremendous financial pressure, and many acknowledge that a dramatic shift is required as the current system is not sustainable. Furthermore, the quality of care that is delivered varies strongly. Several solutions have been proposed of which the conceptual framework known as value-based healthcare (VBHC) is further explored in this study for psoriasis. Psoriasis is a chronic inflammatory skin disease, which is associated with a high disease burden and high treatment costs. The objective of this study is to investigate the feasibility of using the VBHC framework for the management of psoriasis. Methods and analysis This is a prospective clinical study in which new patients attending the psoriasis clinic (PsoPlus) of the Ghent University Hospital will be followed up during a period of 1 year. The main outcome is to determine the value created for psoriasis patients. The created value will be considered as a reflection of the evolution of the value score (ie, the weighted outputs (outcomes) divided by weighted inputs (costs)) obtained using data envelopment analysis. Secondary outcomes are related to comorbidity control, outcome evolution and treatment costs. In addition, a bundled payment scheme will be determined as well as potential improvements in the treatment process. A total of 350 patients will be included in this trial and the study initiation is foreseen on 1 March 2023. eEthics and dissemination This study has been approved by the Ethics Committee of the Ghent University Hospital. The findings of this study will be disseminated by various means: (1) publication in one or more peer-reviewed dermatology and/or management journals, (2) (inter)national congresses, (3) via the psoriasis patient community and (4) through the research team’s social media channels.

Workers that master multiple skills increase the flexibility and the working range of teams in organizations. Efficient multi-skilled team formation or workforce composition is therefore paramount for the organization’s success. In this paper, we study various multi-skilled workforce formation problems that are complementary to problems in the scheduling literature. The goal of these problems is to design a set of multi-skilled workers (or resources) that can fulfill a certain skill demand. More specifically, we investigate the complexity of problems that minimize the skill availability or the size of the workforce. Next, we look at the impact of specific skill and worker characteristics on the complexity of these problems. We propose a set of fixed individual multi-skilled workforce problems, in which the number of available skills per skill type or the number of mastered skills per worker is defined upfront. Furthermore, we introduce and discuss the complexity of fixed total multi-skilled workforce problems in which either the total skill availability or the workforce size is fixed and the other quantity is minimized. We conclude this paper by applying the presented problems to real-life projects and by performing computational experiments that analyze the empirical hardness of the multi-skilled workforce problems.

Although research on leader identity has been ongoing for twenty years, there are still considerable gaps in the literature, in particular related to its potential drawbacks. This dissertation employs social identity theory to examine the effects of having or lacking a leader identity on managers, non-managers, and their surroundings. While our research strengthens existing research on the benefits of having a leader identity, it also presents an argument for a more nuanced approach, acknowledging that having a leader identity may also lead to unwanted outcomes. These findings not only enhance our understanding of leader identity but also contribute to the broader leadership and organizational behavior literatures, including research streams on leadership behavior, performance, well-being and impression management.

While organizational frontlines in the digital age involve complex interactions between consumers, autonomous technology (AT), and frontline workers, research so far largely focuses on the effect of AT on either the consumer or the worker. Bridging the fields of marketing and organizational behavior, we develop the Consumer–Autonomous Technology–Worker (CAW) framework, which reflects the implications of consumer–worker–AT interactions. We consider that AT can be consumer-facing, such as service robots, or worker-facing, such as AT-enabled knowledge-based systems supporting a worker’s decision-making. Drawing on illustrative interviews in hospitality contexts with workers who co-work with robots and the consumers served, we develop research propositions that highlight avenues for future research. We expect consumer–worker relations to strengthen when AT augments instead of replaces the worker. Human leadership is critical for consumers’ and workers’ acceptance of AT, while AT anthropomorphism is less critical in the presence of a human worker.

Earned Value Management (EVM) is a project monitoring and control technique that enables the forecasting of a project’s duration. Many EVM metrics and project duration forecasting methods have been proposed. However, very few studies have compared their accuracy and stability. Design/methodology/approach – This paper presents an exhaustive stability and accuracy analysis of deterministic EVM project duration forecasting methods. Stability is measured via Pearson’s, Spearman’s and Kendall’s correlation coefficients while accuracy is measured by Mean Squared and Mean Absolute Percentage Errors. These parameters are determined at ten percentile intervals to track a given project’s progress across 4,100 artificial project networks with varied topologies. Findings – Findings support that stability and accuracy are inversely correlated for most forecasting methods, and also suggest that both significantly worsen as project networks become increasingly parallel. However, the AT þ PD-ESmin forecasting method stands out as being the most accurate and reliable. Practical implications – Implications of this study will allow construction project managers to resort to the simplest, most accurate and most stable EVM metrics when forecasting project duration. They will also be able to anticipate how the project topology (i.e., the network of activity predecessors) and the stage of project progress can condition their accuracy and stability. Originality/value – Unlike previous research comparing EVM forecasting methods, this one includes all deterministic methods (classical and recent alike) and measures their performance in accordance with project duration.