Sami Ben Jabeur

Visual artificial intelligence (AI) is beginning to enter entrepreneurship research but adoption remains scarce, even as recent advances in vision-language models and AI-assisted coding are substantially lowering the technical barriers to entry. Combined with the abundance of visual data now available, visual AI opens new opportunities to study questions previously out of reach, while also raising methodological and ethical challenges. This paper provides a structured roadmap for using visual AI in entrepreneurship research: We map visual data sources, outline visual AI approaches, identify research insights, and propose an ethics audit. Ultimately, we illustrate the application of this visual AI pipeline with an example study: a facial image classification model distinguished between entrepreneur and non-entrepreneur portrait images in a platform-specific dataset with 79.5% accuracy, far outperforming human experts, raising important methodological and ethical questions. Plain English Summary: Entrepreneurial life is inherently visual, from pitch decks and startup logos to founder emotions in fundraising presentations, workspaces, product designs, and satellite maps of innovation hubs, yet this rich visual panorama has been largely ignored by researchers. This paper provides a step-by-step guide for using visual AI in entrepreneurship research: how to find and select visual data, which AI methods fit which research questions, and how to build in ethical safeguards from the start. Such research comes with real ethical challenges around privacy, consent, algorithmic bias, and the risk of reinforcing existing biases and stereotypes, all of which the paper addresses throughout. To show what this looks like in practice, we include an example study in which an AI model classified professional portrait images as belonging to entrepreneurs or non-entrepreneurs with 79.5% accuracy, far outperforming human experts. This result may reflect differences in professional self-presentation and platform-specific conventions that are imperceptible to the human eye, but it also illustrates how easily such findings can be misinterpreted or taken out of context in ways that raise serious ethical concerns. The key implication for researchers is that visual AI can open genuinely new empirical territory, but responsible use demands that ethical considerations are treated as part of the method, not an afterthought.

Dans la conjoncture économique actuelle un nombre croissant de firmes se trouvent confrontées à des difficultés économiques et financières qui peuvent, dans certains cas, conduire à la faillite. En principe, les difficultés ne surviennent pas brutalement, en effet, avant qu’une entreprise soit déclarée en faillite, elle est confrontée à des difficultés financières de gravité croissante : défaut de paiement d’une dette, insolvabilité temporaire, pénurie de liquidité, etc. L’identification des causes de la défaillance n’est pas évidente, puisqu’on ne saurait énumérer de manière limitative les facteurs qui la provoquent. Les causes sont multiples et leur cumul compromet d’autant plus la survie de l’entreprise. L’importance de ce phénomène et son impact sur l’ensemble de l’économie justifie le besoin de le comprendre, l’expliquer en analysant les causes et les origines. L’objectif de notre étude est de classer les entreprises en difficulté selon leur degré de viabilité et de comprendre les causes de la dégradation de leur situation. Nous effectuerons une comparaison entre trois modèles (Analyse discriminante linéaire, le modèle Logit et la régression PLS) ce qui nous permettra à partir des taux de bon classement obtenus, de choisir le meilleur modèle tout en précisant l’origine et les causes de ces défaillances.

Extreme changes in the weather conditions can lead to operational inefficiencies and physical degradation of a firm’s assets. As a consequence, firms with a weak capacity to adapt to such changes can be confronted with financial difficulties. Using explainable artificial intelligence modelling (XAI), we examine the performance of variables assessing climate change risks and the magnitude of the climate change phenomenon in predicting corporate failure. The experimental findings, which are supported by real-world datasets from France, show that climate change risk (CCR) and climate change magnitude (CCM) variables combined with accounting ratios can better predict the firm’s outcome, which is either survival or liquidation. The findings based on the XGBoost model, which incorporates the CCR and CCM variables, demonstrate high predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.995. By relying upon such variables, the Extremely Randomized Trees (ERT) model also reveals a strong bankruptcy prediction capacity, achieving an AUC of 0.999 for the transport sector and 0.996 for the industrial sector. Additionally, the effects of CCM variables are heterogeneous across sectors. The liquidation risk of trade firms tends to be higher in French counties exposed to a low degree of humidity, higher temperatures, and strong winds. Transport and industrial firms benefit from a low likelihood of failure in counties with more precipitations that can prevent the risk of droughts or heat waves.

This paper examines the influence of various ambidexterity tactics on green innovation among corporate investors. It explores how these investors might improve their environmental performance by acquiring green innovations through ambidextrous corporate venture capital (CVC) investments. The study demonstrates that 126 American corporate investors can successfully leverage green innovation by making ambidextrous investments in entrepreneurial enterprises. Results show that within‐period balance between exploration and exploitation is associated with higher green innovation. Corporate investors who pursue dynamic ambidexterity or a combination of dynamic and static ambidexterity show enhanced outcomes in green innovation. This research demonstrates a clear connection between the green innovation of corporate investors and their enhanced environmental performance, particularly in terms of reducing greenhouse gas (GHG) emissions. The study contends that ambidextrous CVC investments serve as a gateway for corporate investors to elevate their environmental performance through the strategic acquisition of green innovation. This research provides significant insights for company managers, regulators, and government bodies, delivering practical knowledge for establishing environmentally friendly practices in corporate investment strategies.

Tripling the world's installed renewable power capacity by 2030 is one of the global pledges at the COP28 UN Climate Change Conference to keep the 1.5°C goal within reach. Green finance (GRF), financial development (FD) and energy taxes (ENTAX) are expected to play a crucial role in achieving this goal. However, little empirical evidence has been provided on this topic at the European Union (EU) level. Therefore, this paper fills this lacuna by estimating the combined effects of these three variables on Installed Clean Energy Capacity (ICEC) in 14 European countries during the 2007–2021 period. Due to non‐normal data distribution, the Method of Moment Quantile Regression (MMQR) has been employed. The findings assert that GRF and ENTAX are positively associated with ICEC, whereas FD hinders investment in ICEC. However, energy taxes are more effective than green finance in promoting ICEC. A deeper analysis shows that the positive impact of GRF is higher when countries have a higher level of ICEC. In comparison, the positive effect of ENTAX in stimulating ICEC is higher when countries have a lower level of ICEC. Additionally, FD exhibits a U‐shaped influence on ICEC. Based on these results, this research suggests that accelerating the energy transition in the EU requires expanding both green finance and energy taxes. Furthermore, a reform of the current structure of the financial system is needed.