Recherche académique à emlyon business school
Not a member yet
3264 research outputs found
Sort by
Mean-Variance Efficient Large Portfolios : A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem
No full textInternational audienceWe revisit in this article the Two-Fund Separation Theorem as a simple technique for the Mean-Variance optimization of large portfolios. The proposed approach is fast and scalable and provides equivalent results of commonly used ML techniques but, with computing time differences counted in hours (1 minute versus several hours). In the empirical application, we consider three geographic areas (China, US, and French stock markets) and show that the Two-Fund Separation Theorem holds exactly when no constraints are imposed and is approximately true with (realistic) positive constraints on weights. This technique is shown to be of interest to both scholars and practitioners involved in portfolio optimization tasks.<br /
Specifying the role of religion in entrepreneurial action : A cognitive perspective
No full textInternational audienceResearch on the relationship between religion and entrepreneurship has produced mixed findings. We argue such equivocal findings are partly the result of under-specification of the role of religion in entrepreneurial action. To address this issue, we build on the process perspective of entrepreneurial cognition by simultaneously incorporating mental representations and cognitive resources. Specifically, we theorize a cognitive process that incorporates both framing effects of opportunity cues and religious belief integration based on sanctification into the assessment of feasibility and desirability of entrepreneurial action. Through two within-subject experiments, we find (i) positively framed opportunity cues yield more favorable assessments of entrepreneurial action than negatively framed opportunity cues, and (ii) religious belief integration moderates the relationship between framing and assessments of entrepreneurial action, enhancing perceived feasibility and desirability when information framing is negative. We discuss the implications of our model to research the theological turn of entrepreneurship and a cognitive perspective of entrepreneurial action.<br /
Anticipatory shipping versus emergency shipment : Data-driven optimal inventory models for online retailers
No full textInternational audienceThe inventory levels of pickup points play an important role for the same-day or next-day pickup and delivery services. The previous inventory optimisation research usually makes an assumption about demand distribution, does not use the real dataset or consider shipping strategies for this problem. In this study, we introduce a new strategy, mixture of anticipatory and emergency shipping, and propose forecasting-optimisation integrated approach to optimise multi-items' inventories in each pickup point based on big data analysis. We explore a real dataset including 23,808,261 records with 54 pickup points and 4018 items. We first cluster the dataset based on the distances between pickup points and the warehouse, then, implement the forecasting-optimisation integrated algorithms to select the more profitable strategy for each group. The result indicates that compared with the original algorithms, our proposed approach can effectively increase the profits, particularly, the novel algorithm, Long Short-Term Memory networks – Quantile Regression, performs better. Additionally, we find that the 100% anticipatory shipping is not necessarily superior to emergency shipment, when the pickup point is farther from the warehouse, the advantage of emergency shipment is more significant. However, the mixture of anticipatory and emergency shipping can contribute to higher profits for online retailers.<br/
Exploring the Role of AI in B2B Customer Journey Management : Towards an IPO Model
No full textInternational audienceArtificial intelligence (AI) is becoming a pervasive technology and companies are increasingly urged to adopt and implement it in order to thrive and innovate. While much has been researched on the role AI can play in business-to customer (B2C) settings, a research gap exists when it comes to business-to-business (B2B), which is characterized by higher complexity, increasing number of players, and larger volumes of data. In such a context, an emerging issue deals with the possibility of understanding how companies can leverage AI to aptly manage the customer journey. This aspect touches on many organizational activities, so managers must be aware of existing solutions, process implications, and potential outcomes. In this article, we present an empirical study of 61 Indian B2B companies and provide an input-process-output (IPO) framework to help managers understand and profit from AI solutions while effectively managing the customer journey. We add value to literature on using AI to improve the customer journey in B2B settings. The developed IPO model can be used as a roadmap for introducing AI in managing customer journeys as well as a strategic instrument for organizational change and design.<br /
Mind the conversion risk: contingent convertible bonds as a transmission channel of systemic risk
No full textInternational audienc
Does battery management matter? Performance evaluation and operating policies in a self-climbing robotic warehouse
No full textInternational audience"Our research is motivated by battery management in a new self-climbing robotic (SCR) system. The SCR system fully depends on battery-powered robots for tote movements. Therefore, battery management plays an important role and considerably impacts the system performance. This paper investigates the decision of battery charging technology (fast charging versus slow charging) taking into account the battery degradation, the battery charging policy (priority charging policy and dedicated charging policy), and the optimal number of chargers in the system. The paper also optimizes battery management in the SCR system by establishing semi-open queuing networks (SOQNs). The analytical models are solved by the approximate mean value analysis and are validated by simulation models. We find several interesting managerial insights: (1) In the operational policies, although fast charging can decrease the throughput time, we find a new condition when slow charging outperforms fast charging in robotic warehouses. (2) The priority charging policy is more cost-effective than the dedicated charging policy. (3) We also find a decision tool to determine the optimal number of chargers to satisfy the maximum allowed throughput time with the minimum cost."<br /
Citizen Knowledge and the Debate on Information in Welfare Economics in Perspective: Beyond the True-False and the Positive-Normative Entanglements
No full textInternational audienceThis paper shows how the debate on information in Welfare Economics is enriched from the perspective of Lisa Herzog’ s thesis on citizen knowledge, and conversely. First, the two sources of information for welfare enhancing public decisions, individual utilities and knowledge, need articulated justification, insofar as knowledge may be used to revise individual utilities. The process of preference revisions implicitly assumes the coincidence between knowledge and truth, but there are compelling arguments why this assumption should be debated. Second, public decisions are ultimately based on an additional third component of information: collective ethical norms. They are decisive, but their legitimacy is conditional to their transparency in the debate between experts and citizens. Transparency on which knowledge is judged relevant hence constitutes a minimal condition for the design of democratic infrastructures involving public decision making
Up-to-date challenges in entrepreneurship quantitative research : what we have accomplished, the challenges that remain, and the new challenges on the block
No full textInternational audienceEntrepreneurship research has grown exponentially over the last decades, in part thanks to the development of quantitative datasets and new methods of data collection and analysis. Indeed, entrepreneurship scholars are now better equipped than ever to do quantitative research. However, such development does not solve a few fundamental issues with conducting entrepreneurship research, and it even brings a couple of additional challenges to the field. In this chapter, I discuss the main challenges associated with the fact that entrepreneurship fits poorly with the assumption of normality, that it is a broad concept and a non-linear, path-dependent process. I also discuss two sets of relatively newer challenges related to: (1) our increased statistical power and analytic sophistication, and (2) the asymmetry between gains and losses.<br/
AI Technologies for Information Systems and Management Science : Proceedings of 6th International Conference on Information Systems and Management Science (ISMS) 2023 - Volume 1
No full textInternational audienceThis book explores the integration of artificial intelligence into various facets of information systems and management. It delves into machine learning, natural language processing, and computer vision applications, illustrating how these technologies revolutionize decision-making, optimization, and data analysis. Through case studies and theoretical frameworks, the book elucidates the transformative potential of AI in enhancing organizational efficiency and strategic planning, making it an essential reading for professionals and researchers navigating the intersection of AI and business. This book also highlights the efforts to build ethical norms and frameworks for AI adoption in MIS, as well as data privacy and security considerations.<br/
