This study provides a robust analysis of environment-adjusted airline efficiency under a dual-disposability assumption for undesirable output. It employs also a two-stage methodology to investigate the complex and heterogeneous relationships between airline operational profiles and their environmental efficiency.Technical efficiency is estimated in the first step using a Data Envelopment Analysis (DEA) based on the Directional Distance Function (DDF) under both assumptions of the technological characterization of gas emissions, contractible versus non-contractible. In the second step, to analyze the factors influencing the estimated efficiency scores, a Tobit second-stage regression is employed.Results show that efficiency scores under strong disposability are systematically and significantly higher than those obtained under weak disposability, with an average difference of 11.9 percentage points. The findings also indicate a positive and statistically significant relationship between the LCC variable and technical efficiency, implying that airline groups with LCC subsidiaries achieve higher efficiency levels. Based on their specific trade-off between operational and environmental inputs, the analysis of technical efficiency elasticities classifies airlines into five groups. These range from “Strong Proactive Environmental Performers” to “Low Efficiency High-Cost” groups, whose efficiency improvements are structurally linked to either an optimization or a deterioration of Greenhouse Gas (GHG) emissions performance. The technical efficiency elasticity with respect to revenue share and to fuel efficiency are parallel across all airlines, while the elasticity for GHG consistently exhibits the opposite sign.We provide one of the few systematic comparisons of efficiency scores under weak versus strong disposability. This comparison allows us to quantify the systematic contraction in measured efficiency that occurs when greenhouse gas emissions are modeled as a technologically costly output rather than a costless byproduct.
PurposeSustainable entrepreneurship (SE) is gaining momentum as an innovative pathway for tackling global environmental challenges and fostering sustainable community development. Underpinned by the theory of planned behavior, this study aims to identify the main determinants influencing Higher Education students' intentions to undertake SE (a behavior essential for community-level transformation) while also examining the moderating role of perceived feasibility.Design/methodology/approachEmpirical data were collected from 280 university graduates in Tunisia, providing a critical "global insight" into youth engagement in sustainable practices within an emerging economy context.FindingsData analysis using partial least squares structural equation modelling (PLS-SEM) shows that several factors significantly and positively predict sustainable entrepreneurial intention (SEI): environmental values (a psychological factor), green consumption commitment (a sustainable behavior lever), environmental citizenship (a community engagement factor) and education for sustainable entrepreneurship (an innovative educational pathway). These intentions subsequently affect sustainable entrepreneurial behavior, and the relationship is significantly moderated by perceived feasibility.Originality/valueThis research provides an original contribution by developing and expanding the literature on SE by identifying specific educational and psychological antecedents that empower youth, a key demographic for community change, to pursue sustainable ventures. Furthermore, it is among the rare studies to investigate the moderating role of perceived feasibility in the transition from sustainable entrepreneurial intention to concrete sustainable entrepreneurial behavior. This approach brings a novel perspective on how youth entrepreneurship can foster sustainable community development. It offers direct, evidence-based insights for managers and practitioners to design innovative strategies and educational programs that stimulate the sustainable entrepreneurial actions and mindsets necessary for community development.
Using a sample of 9,798 firms from 91 countries during the period from 2007 to 2018, this study investigates the relationship between greenwashing and investment efficiency. We argue that firms engaging in greenwashing exhibit higher information asymmetry by misrepresenting their environmental actions, which distorts investor perceptions and weakens managerial oversight, leading to opportunistic behavior and inefficient investment decisions. Moreover, greenwashing erodes trust among diverse stakeholders, raising financial risks and constraining resource access, thereby further impairing investment efficiency. Employing robust panel data regressions, we find consistent evidence that greenwashing significantly decreases investment efficiency, primarily by fostering underinvestment. These results remain robust to various sensitivity tests and endogeneity controls. Furthermore, the negative impact of greenwashing is more pronounced during economic crises and for firms facing severe financial constraints or operating in countries with weak legal frameworks. These findings underscore the significant role of greenwashing in influencing firm risk management and investment decision-making.
Pervasive computing environments are designed to operate efficiently in diverse contexts, delivering personalized recommendations at any time, anywhere, and for any purpose. However, because they handle sensitive personal data, privacy becomes a critical concern. With personalized recommendation systems becoming integral to daily life, achieving a balance between robust privacy protection and system effectiveness presents substantial challenges. This survey offers a comprehensive analysis of privacy-preserving methodologies developed for pervasive recommender systems (PRS). We rigorously review the recent state of research, identifying key privacy challenges inherent in recommendation frameworks, particularly those arising from malicious attacks such as data breaches, unauthorized access, and adversarial manipulations. We examine existing techniques and solutions designed to mitigate these threats, such as encryption, anonymization, federated learning, blockchain, and differential privacy, evaluating their strengths and limitations in pervasive environments. Additionally, we discuss how improving context awareness through emerging technologies such as edge/fog computing, blockchain, and federated learning can provide promising pathways toward decentralized, distributed computing models. Finally, we outline future research directions that aim to develop robust and scalable solutions that protect user privacy without compromising the performance of recommendation algorithms.
PurposeThe paper aims to examine the return spillover effects between G7 stock markets, investors' FEAR and pandemic-related CORONA FEAR while analyzing their correlation, volatility influence and dynamic interconnections.Design/methodology/approachThe study uses daily data from January 2, 2019, to April 2021. Two novel sentiment indices (FEAR and CORONA FEAR) are constructed from Google Trends following Da et al. (2015). The analysis relies on the dynamic conditional correlation generalized autoregressive conditional heteroskedasticity model to capture dynamic correlations and the time-varying parameter vector autoregression (TVP-VAR) framework to assess time-varying connectedness across markets and fear indices.FindingsBoth FEAR and CORONA FEAR show long-run dynamic correlations with G7 stock markets. Uncertainty and pandemic fear gradually affect asset prices. TVP-VAR results reveal synchronization between fear sentiment and volatility across G7 markets. Both indices act as net volatility receivers, indicating stock markets are the main triggers of fear. Findings highlight the psychological impact of crises on investors and provide policy insights to reduce fear-driven reactions and enhance financial stability.Originality/valueThe paper introduces two new fear sentiment indices, FEAR and CORONA FEAR, derived from Google Trends data, and integrates them into financial spillover analysis, highlighting their role in shaping market volatility and investor behavior during crises.
