Silvana Revellino

Purpose: This paper clarifies the circumstances under which merging Intellectual Capital (IC) and the Balanced Scorecard (BSC) becomes helpful in enhancing strategic performance management in turbulent environments characterized by increasing value co-creation with customers and partners. Design/methodology/approach: This paper proposes an integration based on theory by combining (1) the IC perspective of narrating, visualising, and quantifying knowledge resources, and the BSC logic of strategy maps and measure cascades, and situates their complementarity in the Red Queen perspective of competition-driven learning and co-evolution. Findings: The paper contends that IC tools, under severe turbulence, tend to lead strategy formation by moulding the firm's knowledge narrative and renewal of its capabilities, while the BSC is most useful for execution and alignment once the strategic intent stabilises. The paper shows how value co-creation magnifies the importance of relational capital and stretches measurement and control to the firm's periphery. This paper presents an integrative framework and a few propositions for empirical testing. Originality/value: The paper contributes by (a) providing an evolutionary explanation for IC-BSC integration, (b) linking strategic management accounting to value co-creation through relational capital, and (c) specifying boundary conditions (turbulence, knowledge intensity, ecosystem embeddedness) for when IC should lead and the BSC should follow.

This paper analyses the relations between intellectual capital (IC) and innovation. It links interest in the macro-effects of intellectual capital, typically found in cross-sectional studies on the effects of intellectual capital, to micro-studies of the performativity of intellectual capital. The former literature suffers from a lack of attention to the mechanisms that produce innovation, and the latter suffers from its focus on stabilising decisions in uncertain situations. The paper draws on the notion of perlocutionary performativity, which, in addition to suggesting that IC provokes effects, underlines that particular directions of these effects are uncertain, if not unknown. To show the mechanism through which perlocutions work, the paper draws on Butler’s (1993; 1997) distinction between citability and ex-citability. According to this perspective, a citation of an IC corpus of expressions (citations, references, information) may be transformed, by being circulated (re-cited) and brought into a new potentially innovative arrangement, something which goes beyond (ex-cites) the cited reference. Over time, IC citations provoke innovation. Such a relation can be traced as a bushy form of innovation, which develops from a set of IC citations that have some durability in being reproduced regularly. The paper shows, through the analysis of two decades of reporting from Autostrade, that IC is both a set of disciplined citations of a particular kind of use value, a set of obligations to invest along this use value, and an unpredictable capitalisation of items of innovation.

In recent decades, floods have caused significant loss of human life as well as interruptions in economic and social activities in affected areas. In order to identify effective flood mitigation measures and to suggest actions to be taken before and during flooding, microscale risk estimation methods are increasingly applied. In this context, an implemented methodology for microscale flood risk evaluation is presented, which considers direct and tangible damage as a function of hydrometric height and allows for quick estimates of the damage level caused by alluvial events. The method has been applied and tested on businesses and residential buildings of the town of Benevento (southern Italy), which has been hit by destructive floods several times in the past; the most recent flooding occurred in October 2015. The simplified methodology tries to overcome the limitation of the original method—the huge amounts of input data—by applying a simplified procedure in defining the data of the physical features of buildings (e.g., the number of floors, typology, and presence of a basement). Data collection for each building feature was initially carried out through careful field surveys (FAM, field analysis method) and subsequently obtained through generalization of data (DGM, data generalization method). The basic method (FAM) allows for estimating in great detail the potential losses for representative building categories in an urban context and involves a higher degree of resolution, but it is time-consuming; the simplified method (DGM) produces a damage value in a shorter time. By comparison, the two criteria show very similar results and minimal differences, making generalized data acquisition most efficient.