HEC Paris

About: HEC Paris is a education organization based out in Jouy-en-Josas, France. It is known for research contribution in the topics: Market liquidity & Entrepreneurship. The organization has 584 authors who have published 2756 publications receiving 104467 citations. The organization is also known as: Ecole des Hautes Etudes Commerciales & HEC School of Management Paris.

Organizational and Epistemic Change: The Growth of the Art Investment Field

Abstract: What can studying the creation of knowledge tell us about how new technical fields emerge and develop? This paper shows how a knowledge community may be necessary to support the legitimacy of new products that undergo performance evaluation before purchase. Using historical and ethnographic data covering half a century, we review the growth of the art investment field through an epistemic cultures lens. Technical knowledge about the financial characteristics of art has been developed alongside practical knowledge about how best to structure investment ventures. Investment venture success has been determined by legitimacy as much as by profitability, given durable expectations about the evaluation and monitoring of investments. The growth of knowledge, practices and tools was thus a necessary condition for the recognition of artwork as an asset class. Crucially, the epistemic cultures approach highlights deepening knowledge, resources and professional expertise, and their development through experimentation, failures and negative knowledge. This shows accounting issues contributing to technical field legitimacy and emergence, such as the role of knowledge production, valuation practices and receptive environments, and the distinction between legitimate investments that can be valued and investment venture profitability.

Institutional work between dissemination and fidelity: Insights from the case of Beyond Budgeting

Abstract: This paper analyses the evolution of Beyond Budgeting as a theoretical concept and as a phenomenon in practice. We draw from diffusion theory to explain why Beyond Budgeting did not have the same success as other management accounting ideas, such as Activity-Based Costing or the Balanced Scorecard. Our particular focus is thereby on the way in which the identity of Beyond Budgeting was defined – namely in terms of a comprehensive management model rather than a particular tool or technique. We demonstrate how some proponents of Beyond Budgeting actively defended this identity and, consequently, abandoned the label “Beyond Budgeting” because of its misalignment with that identity. Others distanced themselves from the idea of a full management model and rather focused on the promotion of particular components of Beyond Budgeting. Both strategies ultimately did not favour diffusion of the concept as such. We use these observations to reflect upon the trade-off between a concept’s identity and its plasticity, and we explain why, in some cases, this trade-off may be difficult, or even impossible, to overcome.

Sampling Designs for Recovering Local and Global Characteristics of Social Networks

Abstract: The trajectories of social processes (e.g., peer pressure, imitation, and assimilation) that take place on social networks depend on the structure of those networks. Thus, to understand a social process or to predict the associated outcomes accurately, marketers would need good knowledge of the social network structure. However, many social networks of relevance to marketers are large, complex, or hidden, making it prohibitively expensive to map out an entire social network. Instead, marketers often need to work with a sample (i.e., a subgraph) of a social network. In this paper we evaluate the efficacy of nine different sampling methods for generating subgraphs that recover four structural characteristics of importance to marketers, namely, the distributions of degree, clustering coefficient, betweenness centrality, and closeness centrality, which are important for understanding how social network structure influences outcomes of processes that take place on the network. Via extensive simulations, we find that sampling methods differ substantially in their ability to recover network characteristics. Traditional sampling procedures, such as random node sampling, result in poor subgraphs. When the focus is on understanding local network effects (e.g., peer influence) then forest fire sampling with a medium burn rate performs the best, i.e., it is most effective for recovering the distributions of degree and clustering coefficient. When the focus is on global network effects (e.g., speed of diffusion, identifying influential nodes, or the “multiplier” effects of network seeding), then random-walk sampling (i.e., forest-fire sampling with a low burn rate) performs the best, and it is most effective for recovering the distributions of betweenness and closeness centrality. Further, we show that accurate recovery of social network structure in a sample is important for inferring the properties of a network process, when one observes only the process in the sampled network. We validate our findings on four different real-world networks, including a Facebook network and a co-authorship network, and conclude with recommendations for practice.

What are axiomatizations good for

Abstract: Do axiomatic derivations advance positive economics? If economists are interested in predicting how people behave, without a pretense to change individual decision making, how can they benefit from representation theorems, which are no more than equivalence results? We address these questions. We propose several ways in which representation results can be useful and discuss their implications for axiomatic decision theory.