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Data Sharing or Analytics Sharing ?
No full textNational audienceData combination and analytics can generate valuable insights for firms and society as a whole. Firms can seize these opportunities by joining platforms that either allow them to access the data contributed by other firms or provide the result of the analytics performed on such data, depending on whether the platform adopts ''data sharing'' or "analytics sharing" technologies. The former technology enables firms to exploit their data endowment together with the data contributed by others, whereas the latter offers advantages in terms of privacy and security by reducing data transmission. We present a model that allows us to study the economic and managerial incentives generated by these technologies for both firms and a platform. First, we find that the platform chooses analytics sharing only when the security advantage of this technology is sufficiently large. Second, we show that analytics sharing results in a higher total data contribution than data sharing under general and reasonable conditions. Third, we determine the optimal data-combination technology from the perspective of consumers and discuss potential misalignments between the platform's and consumers' preferred technology. Our findings carry relevant policy and managerial implications, offering a pathway to enhance both data provision and security
Une classification des télétravailleurs évoluant entre autonomie et contrôle : vers un ré-enchantement de l’expérience collaborateur ?
No full textInternational audienc
Réexaminer la rigidité des coûts : une méta-analyse
No full textInternational audienceThis paper aims to build on the rich body of existing knowledge on cost stickinessto produce more accurate information and add different perspectives on multipleaspects of cost stickiness, unveiling avenues for novel research. Utilizing ameta-analysis approach, we aggregate findings from 85 papers and 122 studies toprovide robust results and effect size estimates. Our analysis reveals that labor costs,operating costs, and selling, general, and administrative costs are sticky, whereasthe cost of goods sold is not. We identify that asset intensity reinforces cost stickiness,while employee intensity has no significant impact. Additionally, cost stickinessdecreases when companies face consecutive decreases in activity levels but isunaffected by economic growth at the country level. The meta-analyses conductedin this paper lead to higher accuracy in interpreting findings from previous empiricalresearch and add clarity around the existing mixed evidence on the occurrence andspecific determinants of cost stickiness. Our findings emphasize the complexity ofcost behavior and highlight the importance of considering specific determinants andcost categories. Future research should further explore these dimensions to provide amore nuanced understanding of cost stickiness across different contexts.L’objectif de cet article est d’exploiter le riche corpus de connaissances sur la rigiditédes coûts pour produire des connaissances plus précises et apporter de nouvellesperspectives sur plusieurs aspects de la rigidité des coûts, et ainsi proposerdes perspectives de recherche. À partir de 85 articles et 122 études, nous adoptonsune approche méta-analytique de la rigidité des coûts pour présenter des résultatsrobustes et des estimations de la taille d’effet. Notre analyse révèle que les coûts demain-d’oeuvre, les coûts d’exploitation et les frais de vente, généraux et administratifssont rigides, contrairement au coût des marchandises vendues. Nous constatons quel’intensité capitalistique accroît la rigidité des coûts, alors que l’intensité de maind’oeuvren’a pas d’impact significatif. En outre, la rigidité des coûts diminue lorsqueles entreprises sont confrontées à des baisses successives de leur niveau d’activité. Enrevanche, la croissance économique nationale n’exerce aucune influence sur la rigiditédes coûts. Les méta-analyses réalisées dans cet article permettent d’interpréter avec plus de précision les résultats des précédents travaux empiriques et de clarifiercertains éléments contradictoires concernant la rigidité des coûts et ses déterminantsspécifiques. Nos résultats mettent en évidence la complexité du comportement descoûts et l’importance de prendre en compte des déterminants et des catégories decoûts spécifiques. Il serait intéressant que de futures recherches approfondissent cesdimensions afin d’obtenir une vision plus nuancée de la rigidité des coûts dans différents contextes
Intelligence artificielle (IA) et territoires : nihil novi sub sole ?
No full textInternational audienceIn the history of digital transformation, the deployment of Artificial Intelligence (AI) is often presented as a major new step forward. AI is seen as both a "transition" in itself and an accelerator of the economic, social and environmental transitions that regions are undergoing. But what is the reality? What is truly "new under the sun" in terms of innovation? This article contributes to this debate.Dans l’histoire de la transformation numérique, le déploiement de l’Intelligence Artificielle (IA) est souvent présenté comme une nouvelle étape majeure. L’IA serait à la fois « transition » en tant que tel, et accélérateur des transitions économiques, sociales et environnementales que connaissent les territoires. Qu’en est-il véritablement ? Qu’est-ce qu’il y a vraiment de « nouveau sous le soleil » de l’innovation ? Cet article contribue à cette réflexion
Understanding Repair in the Circular Economy: A Comprehensive Literature Review
No full textInternational audienc
The Performative Effect of Marketing Metrics: Investigating the Sensemaking Process of Social Media Managers
No full textInternational audienc
How We Learned to Stop Worrying and Love AI: Analyzing the Rapid Evolution of Generative Pre-Trained Transformer (GPT) and Its Impacts on Law, Business, and Society
No full textInternational audienceBy March 2023, The Wall Street Journal reports that, “Bill Gates said he believes artificial intelligence [AI] is the most revolutionary technology he has seen in decades, on par with computers, cell phones, and the internet.” According to Mr. Gates blog post, “The development of AI is as fundamental as the creation of the microprocessor, the personal computer, the Internet, and the mobile phone… Entire industries will reorient around it. Businesses will distinguish themselves by how well they use it.” Questions provided by developments in artificial intelligence raise many novel issues of law and problems that will soon need to be addressed by policy makers and courts. Early to recognize these challenges, Professor Lawrence Solum asked the question, “Could an artificial intelligence become a legal person?” Professors Felten, Raj, and Seamans write, “consumer adoption of generative AI tools has been rapid. For example, ChatGPT is the fastest growing consumer application in history, having reportedly reached 100 million active users within two months of its public launch.” By the end of March 2023, “More than 1,000 technology leaders and researchers, including Elon Musk, have urged artificial intelligence labs to pause development of the most advanced systems, warning in an open letter that A.I. tools present ‘profound risks to society and humanity.”’ The U.S. National Security Commission on Artificial Intelligence (NSCAI) warns, “America is not prepared to defend or compete in the AI era. This is the tough reality we must face. And it is this reality that demands comprehensive, whole-of-nation action.” Just like the compelling necessity to deal efficiently with cybersecurity threats, AI governmental crafted policy will require, “Committed partners in industry, academia, and civil society. And America needs to enlist its oldest allies and new partners to build a safer and freer world for the AI era.”Those readers new to this topic may be surprised to consider that machine learning and AI has been influencing our culture for several decades now. Prominent examples include consideration of user indicated preferences revealed in such applications as Facebook, Google, and Instagram. As a consumer searches for specific product information, like EV cars for example, the programs will “learn” from these inquiries and return future relevant information to the user. Other examples include those verbal questions asked of Amazon’s Alexa, Apple’s Siri, or Google’s voice assistant applications. The self-driving function pioneered-by and available now on Tesla automobiles is another useful example of “machine learning” from a very large data set. The promise of AI’s beneficial impact is significant. For example, the National Artificial Intelligence Research Resource Task Force (NAIRR) reports, “New AI and AI-driven discoveries and capabilities hold the potential to drive practical solutions to address critical global challenges such as food production, climate change, poverty, and cancer. We have only started to scratch the surface of what is possible, and cannot afford to miss out on seizing the opportunity for leveraging AI to serve the public good.
Technology shocks, directed technical progress and climate change
No full textNational audienceTechnical progress is considered a key element in the fight against climate change. It may take the form of technological breakthroughs, that is, shocks that induce significant leaps in the stock of knowledge. We use an endogenous growth framework with directed technical change to analyze the climate impact of such shocks. Two production subsectors coexist: one subsector is fossil-based, using a non-renewable resource, and yields carbon emissions; the other subsector uses a clean, renewable resource. At a given date, the economy benefits from an exogenous technology shock.We fully characterize the general equilibrium and analyze how the shock modifies the economy’s trajectory. The overall effect on carbon emissions basically depends on the substitutability between the production subsectors, the initial state of the economy, and the nature and size of the shock.We notably show that green technology shocks induce higher short-term carbon emissions when the two subsectors are gross complements, but also in numerous cases when they are gross substitutes
L'intelligence artificielle générative dans l'éducation entrepreneuriale : un levier d'évaluation de la posture d'accompagnateur
No full textNational audienceThis paper explores the integration of generative artificial intelligence (GAI) in entrepreneurial education (EE) and its impact on assessing students' skills, particularly their role as mentors. its impact on assessing students' mentoring posture. It examines how GAI can enhance active pedagogies in entrepreneurship by simulating interactions with virtual entrepreneurs and contributing to experiential learning. The study is based on an experiment conducted using the TIPS tool, which allows students to interact with an entrepreneur simulated by GAI.The study also highlights the complementarity between summative assessment (based on the final deliverable) and formative assessment (analysis of interactions during the learning process). GAI enables insight into the "black box" of the learning process by tracking interactions and identifying students' skills, particularly in terms of critical analysis and mentoring posture. Finally, the paper discusses the limitations and paradoxes related to the use of GAI in educational assessment, particularly concerning the standardization of feedback and the technologization of evaluation processes. Despite these limitations, GAI proves to be a valuable tool for assessing the mentoring posture of students.Cette communication explore l'intégration de l'intelligence artificielle générative (IAG) dans l'éducation entrepreneuriale (EE) et son impact sur l'évaluation de la posture d'accompagnateur des étudiants. Il s'intéresse à la manière dont l'IAG peut enrichir les pédagogies actives en entrepreneuriat, en simulant des interactions avec des entrepreneurs virtuels et en contribuant à l'apprentissage expérientiel. Il s'appuie sur une expérimentation menée avec l'outil TIPS, qui permet aux étudiants d'interagir avec un entrepreneur simulé par une IAG. L'étude met également en évidence la complémentarité entre évaluation sommative (basée sur le rendu final) et évaluation formative (analyse des interactions pendant le processus d'apprentissage). L'IAG permet d'entrer dans la "boîte noire" du processus d'apprentissage en suivant les interactions et en identifiant les capacités des étudiants, notamment en termes d'analyse critique et de posture d'accompagnateur. Enfin, la communication discute des limites et paradoxes liés à l'usage de l'IAG dans l'évaluation pédagogique, notamment en ce qui concerne la standardisation des retours et la technicisation du processus d'évaluation. Malgré ces limites, l'IAG se révèle être un levier d'évaluation de la posture d'accompagnateur
How can global city attributes explain international strategic alliance formation?
No full textInternational audienceThis paper investigates the importance of microlocation factors in FDI. Combining the literature on international business and economic geography, we focus on how the spatial determinants, measured as global city attributes, affect international strategic alliance formation patterns. Methodologically, we investigate this through a configurational analysis of American companies having created international strategic alliances during 2015 and 2019. We identify three types of city clusters where American partners have created international alliances. The identified city clusters are explained further by firm, industry, and national factors. Based on the findings, we conclude that spatial attributes should be included when understanding international alliance formation