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This paper empirically investigates the effects of banks triggering contingent capital instruments by studying liability management exercises, which bear comparable regulatory capital effects. These actions create core tier one capital by crystalizing losses on hybrid debt holders. Banks' use of liability management exercises, and the market reaction to them, are consistent with these exercises relaxing a regulatory capital constraint. The created value mainly accrues to debt holders, and does not generate a negative signal. Liability management exercises prove effective at improving bank capitalization levels. These findings strengthen the case for contingent capital instruments as an alternative to raising bank capital requirements.

How do we, as management researchers, develop novel theoretical contributions, and potentially break new ground, in management studies? To address this question, we review previous methodological work on theorizing and advance a typology of the reasoning processes that underlie theoretical contributions and significant advances in management studies. This typology consist of various types of analogical and counterfactual reasoning that range from focused thought experiments aimed at prodding existing theory in the direction of alternative assumptions, constructs and hypotheses to more expansive efforts around inducing new theoretical models and alternative explanations. With this typology we detail the mechanisms behind the formation of novel theoretical contributions and we illustrate the currency of our typology with a review of twenty-four major theoretical breakthroughs in management studies. We conclude the paper with a discussion of the implications of this typology for our collective efforts in building, elaborating and expanding theory in management studies.

Warm-glow refers to other-serving behavior that is valuable for the actor per se, apart from its social implications. We provide axiomatic foundations for warm-glow by viewing it as a form of preference for larger choice sets driven by one's desire for freedom to act selfishly. Specifically, an individual who experiences warm-glow values the availability of selfish options even if she plans to act unselfishly. Our theory accommodates the empirical findings on motivation crowding out and provides clear-cut predictions for empirically distinguishing between warm-glow and other motivations for prosocial behavior, a task of obvious importance for policy. The choice behavior implied by our theory subsumes Riker and Ordeshook (1968) on voting and Andreoni (1989, 1990) on the provision of public goods.

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.

International audience; We advocate for more tolerance in the manner we collectively address categories and categorization in our research. Drawing on the prototype view, organizational scholars have provided a 'disciplining' framework to explain how category membership shapes, impacts, and limits organizational success. By stretching the existing straightjacket of scholarship on categories, we point to other useful conceptualizations of categories - i.e. the causal-model and the goal-based approaches of categorization - and propose that depending on situational circumstances, and beyond a disciplining exercise, categories involve a cognitive test of congruence and a goal satisfying calculus. Unsettling the current consensus about categorical imperatives and market discipline, we suggest also that audiences may tolerate more often than previously thought organizations that blend, span, and stretch categories. We derive implications for research about multi-category membership and mediation in markets, and suggest ways in which work on the theme of categories in the strategy, entrepreneurship, and managerial cognition literatures can be enriched.

We derive several popular systemic risk measures in a common framework and show that they can be expressed as transformations of market risk measures (e.g., beta). We also derive conditions under which the different measures lead to similar rankings of systemically important financial institutions (SIFIs). In an empirical analysis of US financial institutions, we show that (1) different systemic risk measures identify different SIFIs and that (2) firm rankings based on systemic risk estimates mirror rankings obtained by sorting firms on market risk or liabilities. One-factor linear models explain most of the variability of the systemic risk estimates, which indicates that systemic risk measures fall short in capturing the multiple facets of systemic risk.

Firms that are slow in the execution of investment projects often incur substantial revenue losses. However, accelerating investments generally results in higher investment costs. In this paper, we integrate this investment speed tradeoff in a reduced-form model of project development to create an empirical proxy for firm speed. We examine how deviations from industry-average speed in the execution of large investments in oil and gas facilities worldwide from 1996 to 2005 impact firm value, as measured by Tobin's q. We find that there is substantial variation in investment speed among firms in the oil and gas industry. Using a linear correlated random parameter model to account for unobserved firm heterogeneity, we show that faster firms have higher firm value when speed results from firms' dynamic capabilities. On average, accelerating a firm's investments by 5% (or 1 month) below the industry norm due to dynamic capabilities increases market value by $214.3 million. Additionally, we show that the effect of speed on firm value varies widely among firms and is amplified by good corporate governance but often mitigated by the level of firms' debt.