Culture d'entreprise

Résumé

La culture d'entreprise est un ensemble de connaissances, de valeurs et de comportements qui facilitent le fonctionnement d’une entreprise en étant partagé par la plupart de ses membres. Selon Elliot Jacques cet ensemble doit être appris et admis de façon le plus souvent implicite par les nouveaux membres pour qu'ils soient acceptés dans l'entreprise. « La culture caractérise l'entreprise et la distingue des autres, dans son apparence et, surtout, dans ses façons de réagir aux situations courantes de la vie de l'entreprise ». La culture d’entreprise a cette étrange qualité d’être la chose la plus partagée et la moins formalisée. Dans le monde de la formalisation, l’informel nous échappe alors qu’il constitue la vraie différenciation d’avec les concurrents. Les composantes de la culture d'entreprise De nombreuses composantes sont identifiables, citons par exemple :

histoire de l'entreprise (personnalités importantes, fondateurs, évolution des produits…) ;
rites ;

Source officielle

https://fr.wikipedia.org/wiki/Culture_d'entreprise

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Publications associées (25)

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Concepts associés (22)

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Cours associés (8)

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Séances de cours associées (7)

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Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

In this dissertation, I develop theory and evidence to argue that new technologies are central to how firms organize to create and capture value. I use computational methods such as reinforcement learning and probabilistic topic modeling to investigate three topics: the automation of routines, the organization of artificial intelligence (AI), and the evaluation of technology risk. Overall, I argue that new technologies are not a panacea for the firm but require deliberate strategic planning to manage the potential downsides of myopic automation, AI interdependencies, and the disclosure of technology risks.In the first essay, I argue that while automation can increase productivity by reducing the costs of coordinating individuals, the automation of routines can also incur an indirect opportunity cost due to slow adaptation to environmental change. I develop a reinforcement learning simulation to model the impact of automation on the returns from the division of labor in dynamic environments and to show how automation incurs opportunity costs through lost learning and slow adaptation. Moreover, automation can be suboptimal when it brings about myopic behavior, i.e. high returns from the division of labor in the short term, but negative returns in the long term. Given the simulation results, I argue that firms need dynamic routines to simultaneously balance learning and automation. I open-source the simulation platform as OrgSim-RL on GitHub.In the second essay, I argue that a data-driven culture - what I define as a Data Clan - can help to coordinate complex interdependencies between AI components within a firm. I analyze in-depth semi-structured interview data with a hierarchical stochastic block model (hSBM) and hand-coding to find that managers focus primarily on building a strong culture and establishing high-quality data assets when allocating resources to AI initiatives. Given the results, I inductively develop implications for theory and argue that the emergence of a Data Clan can be a governance mechanism to reduce coordination frictions and build a competitive advantage in the age of AI.In the third essay, I argue that investors require a higher initial return to take on more technology risk disclosure during an IPO. I quantify the magnitude of disclosed risk and the risk disclosure topics based on a latent Dirichlet allocation (LDA) topic model of IPO prospectus text and find a return-for-risk association between text-based technology risk disclosure and underpricing. The study also finds evidence that owning granted patents is associated with a lower return-for-risk association, suggesting that intellectual property allows the disclosure of risk without losing the competitive advantage. I open-source the code for quantifying risk disclosure as RiskyData-LDA on GitHub.In summary, this dissertation develops theory and finds evidence across three essays to argue that leveraging new technologies requires deliberate strategic planning to manage potential downsides of new technologies, such as the opportunity costs of automation, coordination costs, and costs associated with raising capital. The results suggest three mitigating solutions: dynamic routines to balance learning and automation, a Data Clan to improve coordination, and disclosure through patents to reduce underpricing.

EPFL2022

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Fostering Sustainable Innovation Within Organizations

Peter Michael Vogel

Over the past years many corporate business leaders have started to shift their strategy from a pure profit seeking one towards a balance in simultaneously striving to achieve economic, environmental and social goals (Elkington 1998; Preuss 2007; Roth 2009). As a result, challenges on the sustainability agenda have emerged as a new source of opportunities for innovation and competitive advantage (Fichter 2006; Hockerts 2008; Hansen et al. 2009). Research has shown that entrepreneurs are the main drivers of innovation, economic growth and social change (Audretsch 2002); hence, organizations try to adopt entrepreneurial approaches in order to spur their own innovativeness (Hamel 1999; Ireland et al. 2009). However, as recent publications have discussed, the promotion of entrepreneurship is a difficult and multifaceted issue requiring the consideration of dynamic processes describing the interplay of multiple external factors, local conditions and the individual innovators (Isenberg 2010; Krueger 2012; Vogel 2013). These difficulties are particularly distinct when discussing sustainable innovation, as risk-related reluctance in instigating this kind of innovation can still be observed among corporate leaders (Hall 2002). If established companies plan to take part in creating tomorrow’s economy, it will be necessary for them to challenge prevailing assumptions about innovation processes (Hamel 1999). The purpose of this chapter is to investigate the main success factors of entrepreneurial ecosystems and discuss ways how to assimilate these in an organizational context.

Springer2014

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, , , , , , ,

Parkinson’s disease (PD) is characterized by the accumulation of misfolded and aggregated α-synuclein (α-syn) into intraneuronal inclusions named Lewy bodies (LBs). Although it is widely believed that α-syn plays a central role in the pathogenesis of PD, the processes that govern α-syn fibrillization and LB formation remain poorly understood. In this work, we sought to dissect the spatiotemporal events involved in the biogenesis of the LBs at the genetic, molecular, biochemical, structural, and cellular levels. Toward this goal, we further developed a seeding-based model of α-syn fibrillization to generate a neuronal model that reproduces the key events leading to LB formation, including seeding, fibrillization, and the formation of inclusions that recapitulate many of the biochemical, structural, and organizational features of bona fide LBs. Using an integrative omics, biochemical and imaging approach, we dissected the molecular events associated with the different stages of LB formation and their contribution to neuronal dysfunction and degeneration. In addition, we demonstrate that LB formation involves a complex interplay between α-syn fibrillization, posttranslational modifications, and interactions between α-syn aggregates and membranous organelles, including mitochondria, the autophagosome, and endolysosome. Finally, we show that the process of LB formation, rather than simply fibril formation, is one of the major drivers of neurodegeneration through disruption of cellular functions and inducing mitochondria damage and deficits, and synaptic dysfunctions. We believe that this model represents a powerful platform to further investigate the mechanisms of LB formation and clearance and to screen and evaluate therapeutics targeting α-syn aggregation and LB formation.

2020

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Technology Strategy in Dynamic Environments: A Computational Analysis of the Automation of Routines, the Organization of AI, and the Evaluation of Technology Risk

EPFL2022