HEC Montréal

We examine whether athletes’ performance on low-level circuits can predict success on a top-level circuit to assist endorsement preemption decisions. As athletes compete during their inaugural season on the second-tier tour, their tournament performances provide cumulative information that allow us to make weekly predictions about the probability of players’ future success on the first-tier tour. Combining several high-caliber data sets, including information for every player from every PGA TOUR tournament during the 2002–2019 period, we employ 106 success predictors using random forests, a machine learning algorithm. Our results allow us to identify the endorsers whose future value will be highest on the first-tier tour of the PGA TOUR many months, or in some case years, before athletes achieve it. We argue that predicting athlete’s probability of success can enhance the value of athlete endorsements because it helps identify scarce talent before its market price skyrockets.

Purpose This study examines how the adoption of artificial intelligence (AI) is reshaping the HR function in large organisations. Addressing the predominance of normative accounts and limited empirical evidence in the AI–HRM literature, it explores how AI influences HR roles, the strategic positioning of the HR function, employee well-being and the professional future of HR specialists. Design/methodology/approach The authors used a multi-case qualitative research design to examine AI adoption and impacts in the HR function, studying five large financial services organisations in France and Canada with similar AI–HRM maturity. Data were collected through semi-structured interviews with HR analytics leaders overseeing AI–HR applications, HR professionals and line managers. Findings The study highlights certain trends and identifies two trajectories underpinning the AI–HR interaction: an “AIHR” model, prioritising automation and cost-minimisation through AI, and potentially compromising employee well-being and deprofessionalising the HR function; and an “HAIR” model, placing greater emphasis on maintaining a human-centric, well-being-oriented approach, one in which “shepherding” of AI applications by HR specialists helps to entrench HR professionalism and to minimise perversities. Practical implications The choice between an AIHR or HAIR model requires periodic assessment of HR sub-functions and evolving HR roles. Under an HAIR model, closer collaboration between AI developers and HR professionals is essential to reduce opacity and mistrust, requiring the development of HR “fusion skills” and the implementation of a formal AI–HRM strategy encompassing governance, data, training, communication and stakeholder engagement. Originality/value The fast-paced rise of AI in HRM is reshaping the activities and roles of the HR function, with significant implications for organisational performance, employee well-being and the future of the HR profession. Despite growing interest and limited empirical research on the AI–HR interaction, this study explores how AI is being implemented in the HR function and its impacts on HR roles and the tensions they entail.

Interest in the social dimensions of the circular economy (CE) has grown over the last decade, but empirical research on the quality of circular jobs remains scarce, hindering efforts to progress towards a just circular transition. Scholars highlight the risk that the circular transition could deepen existing inequalities, with a growing body of research calling for further investigation into its impacts on labor market transformations and its embedment with social and solidarity economy (SSE) as a potential solution towards circular justice. Building on the emergence of this new paradigm, this article contributes to the nascent literature on the social and solidarity circular economy (SSCE) by presenting an exploratory study of job satisfaction. Applying both quantitative and qualitative methods, it aims to characterize work in the SSCE and to open avenues for future research aimed at identifying the specific characteristics of work within this model. It offers two main conclusions. First, the perceived quality of circular jobs is heterogeneous because it depends on the type of organization, circular strategy and job position. Second, it remains difficult to clearly isolate characteristics that are specific to work within the SSCE, as many of its features appear to be derived from those traditionally associated with the CE and the SSE respectively. Focusing on job satisfaction and quality of work, this article provides empirical data to reinforce the emerging yet predominantly conceptual literature on the SSCE.

Background/Objectives: Standardized reporting of astigmatism outcomes is essential for comparability, reproducibility, and interpretation of refractive surgery studies. Vectorial analyses based on established metrics are increasingly required by major journals, yet no freely available tool exists for generating publication-ready vector analysis tables with statistical comparisons. This study presents AstigMETRICS, a standalone application for automated calculation, formatting, and statistical comparison of standard vector metrics in refractive surgery. Methods: AstigMETRICS was developed in MATLAB and compiled as a standalone executable requiring no programming knowledge. The software accepts preoperative, intended, and postoperative astigmatism data in spreadsheet format for both refractive and corneal measurements. It calculates seven standard vector metrics following the Alpins method: the target-induced astigmatism (TIA), surgically induced astigmatism (SIA), difference vector (DV), correction index (CI), magnitude of error (ME), angle of error (AE), and index of success (IOS). Statistical comparisons are performed automatically using appropriate parametric or nonparametric tests for paired and unpaired study designs, with p-values and Cohen's d effect sizes reported. Results: AstigMETRICS generates standardized tables reporting the means, standard deviations, and clinically relevant proportions (percentage of eyes with an ME within ±0.50 D or ±1.00 D, and an AE within ±15°). Three simulated datasets were created to validate the software functionality across common surgical scenarios: a contralateral eye laser vision correction, toric phakic IOL implantation, and cataract surgery with toric IOLs. The output tables are displayed in standardized format and saved as high-resolution TIFF images suitable for publication. The software is freely available and a download link is provided in this article. Conclusions: AstigMETRICS enables clinicians and researchers to perform standardized, reproducible astigmatism vector analyses with built-in statistical comparisons. This freely available tool simplifies outcome reporting and improves methodological consistency in refractive surgery research.

This paper integrates insights from the literature on invention networks, gender, and the sociological literature to analyze differences in how firms participate in man-led and woman-led invention networks. We contribute to the current debate on whether clustering or boundary-spanning network properties are more important for invention by introducing gender as an important factor. We empirically test our hypotheses on a sample of more than 30,000 firms from around the world over time using OECD REGPAT global patent data. Our findings indicate that different network properties are important for firm invention in woman-led and man-led innovation networks. In man-led invention networks, firms strongly benefit from being in a boundary-spanning position and are negatively affected by clustering, whereas in woman-led invention networks, boundary spanning has a less pronounced positive effect, and clustering has a positive rather than negative effect. Our findings have substantial implications for firms and policymakers interested in invention and contribute to the studies of gender and invention networks. • Investigates how gender shapes the impact of network positions on invention outcomes. • Finds that clustering and boundary spanning affect invention differently across team genders. • Shows that cohesion can enhance invention performance depending on team composition. • Constructs innovation networks from global patent data. • Provides guidance for firms aiming to design inclusive, innovation-oriented collaborations.

This study examines how green credit policies (GCP) influence corporate social responsibility (CSR) in pollution-intensive firms in China, using China’s 2012 “Green Credit Guidelines” as a quasi-natural experiment. Through a difference-in-differences (DID) model analyzing 3627 publicly listed Chinese companies from 2010 to 2020, we identify key challenges in aligning financial incentives with environmental goals. Our findings reveal that financial constraints and differences in total factor productivity (TFP) may limit the effectiveness of GCP in promoting CSR initiatives among highly polluting firms. However, this inhibitory effect decreases once TFP surpasses a threshold, suggesting that greater productivity can mitigate these disincentives. Interestingly, we find that GCP have a negative impact on CSR in highly polluting firms, underscoring the need for more refined green credit policies to encourage sustainable corporate practices. These insights have significant implications for transition economies facing similar environmental challenges and contribute to broader global sustainability strategies.

Abstract Due to the long duration of Carbon Capture and Storage projects and the uncertainty of the captured $$\text {CO}_{2}$$ amounts, making decisions about the construction of transport infrastructure in the early stages is challenging. The objective of this study is to propose a stochastic programming model to optimize the deployment of infrastructure, specifically pipelines and ships, to transport $$\text {CO}_{2}$$ from industrial sources to sequestration sites. The proposed model accounts for uncertainties in the capture (or supply) of $$\text {CO}_{2}$$ and is tested using an illustrative case study of the Humber cluster in the UK. The constructed scenario trees incorporate the risk of potential closure and reactivation of the capture facilities, allowing decision makers to make more robust first stage decisions about the type and capacity of transport infrastructure to construct. The experiments in this paper examine changes in optimal investment decision for a range of uncertainties. For example, the trade-off between investment in pipelines and ships is influenced by assumptions about the potential for ships to relocate to other regions if CCS projects close and key policy decisions (e.g. availability of upfront funding for infrastructure investment). For our illustrative case study, pipelines are preferred in cases with lower probabilities of closure in later periods when sufficient budget is available. When a higher probability of project closure is considered or discount rate is increased to typical commercial rates, our model indicates that investment in ships will dominate.