NEOMA Business School

This study focuses on mainland China, covering 30 provinces. To explore the relationship between water scarcity induced by water use, rather than water use volume per se, and economic growth, this study establishes an improved integrated water scarcity footprint ( IWSF ) considering water quantity, quality and environmental flow requirements ( EFR ). Building on a comparison with the conventional water footprint ( WF ) at the sectoral level, the study examines the evolution of decoupling from economic growth, the underlying driving effects, and their heterogeneity across industries. The water footprint results show that, at both the regional and sectoral levels, IWSF is greater than WF in northern provinces, whereas the opposite pattern is observed in southern provinces. Decoupling results show that, 2007–2012, IWSF decoupling was weaker than WF , while the opposite occurred in 2012–2017; Water use intensity was the key determinant of IWSF decoupling patterns; The reduction in water use intensity driven by pollution control was key to achieving strong IWSF decoupling in 2012–2017, with the primary and secondary industries contributing the most. These results indicate that the newly developed IWSF successfully captures the contribution of water pollution to water scarcity and its decoupling, whereas WF , which considers only absolute water withdrawal, cannot. This study highlights the necessity of addressing water scarcity from the perspective of impact-oriented water use. • Proposed an improved water scarcity footprint characterization factor integrating water quantity, water quality, and environmental flow requirement. • Provinces with quality-induced water scarcity showed an "inverted U-shape" pattern from 2007 to 2017. • Northern provinces underestimated, while southern provinces overestimated their water resource impact; regional and industrial IWSF revealed this discrepancy. • Shifts in water use intensity's role from promoter to restrainer caused expansive negative decoupling, driving diverse decoupling patterns. • 2007–2012: Primary industry structure & water use intensity drove decoupling most. 2012–2017: Secondary industry water use intensity became the top contributor.

Purpose The purpose of this paper is to investigate whether corporate governance mechanisms influence greenwashing practices within the Latin American context. Design/methodology/approach The analysis draws on a sample of 376 companies across six Latin American countries, totaling 1,870 firm-year observations between 2016 and 2023. The study examines governance variables such as board size, board independence, board gender diversity, the presence of a CSR committee and ESG-based executive compensation. A multi-method approach is used, combining multiple linear regression for unbalanced panel data with necessary condition analysis. Findings The results show that larger boards and the presence of CSR committees are positively associated with greenwashing. These findings suggest that, in the Latin American context, certain corporate governance structures may unintentionally facilitate misleading sustainability communication toward stakeholders. Practical implications For practitioners, this study underscores the importance of aligning corporate governance with responsible sustainability communication. Limiting board size to approximately eight members may help reduce communication distortions. Originality/value By conceptualizing greenwashing as a form of strategic sustainability communication and empirically demonstrating that specific governance mechanisms influence its occurrence, the study challenges the implicit assumption − largely derived from evidence in developed economies − that formal governance structures uniformly constrain opportunistic behavior. Instead, the results show that governance mechanisms may operate differently in emerging markets, thereby enriching and contextualizing existing theoretical frameworks.

Additive Manufacturing (AM) has been extensively studied for spare parts management. The literature concurs that producing AM spare parts close to the point of use (i.e., on-site) offers both economic and environmental advantages compared to off-site production, particularly when spare parts are produced through printing hubs, which is the current industrial practice. However, this might not always hold true: different countries have different energy costs and emit different amounts of CO<sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2-eq</sub> to produce one kWh. Consequently, it may be preferable to produce spare parts in a country having low energy costs and/or depending less on fossil fuels, even if far from point of use (i.e., off-site). Such decisions should also consider governmental carbon-reduction schemes (e.g., carbon taxes) whereby the more a firm emits, the more it pays. However, there is a lack of research encompassing all these aspects when deciding whether to produce AM spare parts on- or off-site. This work fills this gap, identifying if and when off-site production of AM spare parts is preferable from both economic and environmental perspectives. Contrary to claims in the literature, the results show that off-site production is sometimes preferable, particularly when its unitary energy cost is lower than that of on-site production. Interestingly, due to current low carbon tax values, the on-site/off-site production decision is driven only by economic considerations. Robustness checks confirmed the findings' reliability. Notably, adopting off site AM production, when advantageous, results in substantial savings, as demonstrated numerically and through a case study considering 6,000+ spare parts.

A developer's popularity plays a crucial role in their success within open source software (OSS) communities and their access to sponsorship opportunities. This study seeks to answer the question: which signals have the most predictive power for popularity and sponsorship volume on social coding platforms? Using algorithm-supported abductive theory generation supplemented by qualitative insights from observations and interviews, we arrive at a theory of peer evaluation in OSS communities. We examine a large number of signals and categorize them. The two categories are signaling via self-disclosure through profile signals and signaling via contribution quantity and quality through behavioral signals. The large amount of data available to us allows us to use machine learning techniques to arrive at top-ranking predictors within each category. We generate our theory by finding robust patterns and test our theory using a hold-out sample. Our findings indicate that easily observable credibility-enhancing and approachability-related developer profile signals hold greater predictive importance in shaping popularity. However, harder to observe and more complex behavioral signals show greater predictive importance for sponsorship volume. These results signify that OSS social coding platforms are not meritocratic, as developer self-disclosure significantly influences popularity. In contrast, sponsorship decisions, due to their high cost and irreversibility, depend on within-platform contribution-related signals. This research contributes to a deeper understanding of popularity and sponsorship within peer-to-peer followership networks in OSS communities. Through our research, platforms are better informed about the predictors of popularity and sponsorship and can introduce measures to enhance the meritocratic nature of these communities. Developers who seek influence and sponsorship on the platform can be more strategic about information disclosure and their contributions.

We examine how chairman education influences income-smoothing decision-making in Chinese firms, a context where chairmen hold the most power in their firms. We use a dataset of firms listed in the Shanghai and Shenzhen stock exchanges during the 2001–2017 period and employ the fixed-effect model to account for firm heterogeneity, which may confound our estimation results. We find that chairmen with postgraduate degrees in business-related fields (e.g., business management, finance, accounting, or economics) tend to encourage income smoothing to improve earning informativeness, while those with undergraduate degrees and lower do not. Further analyses suggest that the chair's gender, the chair's compensation, and the presence of state ownership do not influence the newfound relationship. However, analyst coverage plays a role as the mitigating factor that reduces the positive impact of academic chairmen on income smoothing. The findings of this study imply that Chinese firms should require chairmen to have postgraduate qualifications or equivalents in business-related fields. Business schools should pay attention to accounting education to hone students' understanding of earning information. Our findings add new knowledge and emphasize the importance of higher education in forming corporate strategy and decision-making in the unique economy of China.

Latent moderated structural equation (LMS) is one of the most common techniques for estimating interaction effects involving latent variables (i.e., XWITH command in Mplus). However, empirical applications of LMS often overlook that this estimation technique assumes normally distributed variables and that violations of this assumption may lead to seriously biased parameter estimates. Against this backdrop, we study the robustness of LMS to different shapes and sources of nonnormality and examine whether various statistical tests can help researchers detect such distributional misspecifications. In four simulations, we show that LMS can be severely biased when the latent predictors or the structural disturbances are nonnormal. On the contrary, LMS is unaffected by nonnormality originating from measurement errors. As a result, testing for the multivariate normality of observed indicators of the latent predictors can lead to erroneous conclusions, flagging distributional misspecifications in perfectly unbiased LMS results and failing to reject seriously biased results. To solve this issue, we introduce a novel Hausman-type specification test to assess the distributional assumptions of LMS and demonstrate its performance. (PsycInfo Database Record (c) 2025 APA, all rights reserved).