Aversion Analysis

In this paper : a) the consumer’s problem is studied over two periods, the second one involving S states, and the consumer being endowed with S+1 incomes and having access to N financial assets; b) the consumer is then representable by a continuously differentiable system of demands, commodity demands, asset demands and desirabilities of incomes (the S+1 Lagrange multiplier of the S+1 constraints); c) the multipliers can be transformed into subjective Arrow prices; d) the effects of the various incomes on these Arrow prices decompose into a compensation effect (an Antonelli matrix) and a wealth effect; e) the Antonelli matrix has rank S-N, the dimension of incompleteness, if the consumer can financially adjust himself when facing income shocks; f) the matrix has rank S, if not; g) in the first case, the matrix represents a residual aversion; in the second case, a fundamental aversion; the difference between them is an aversion to illiquidity; this last relation corresponds to the Drèze-Modigliani decomposition (1972); h) the fundamental aversion decomposes also into an aversion to impatience and a risk aversion; i) the above decompositions span a third decomposition; if there exists a sure asset (to be defined, the usual definition being too specific), the fundamental aversion admits a three-component decomposition, an aversion to impatience, a residual aversion and an aversion to the illiquidity of risky assets; j) the formulas of the corresponding financial premiums are also presented.

The contribution of enterprise systems to core capabilities: the case of asset lifecycle management in the utilities sector

The desire to obtain competitive advantage is a motivator for implementing Enterprise Resource Planning (ERP) Systems (Adam & O’Doherty, 2000). However, while it is accepted that Information Technology (IT) in general may contribute to the improvement of organisational performance (Melville, Kraemer, & Gurbaxani, 2004), the nature and extent of that contribution is poorly understood (Jacobs & Bendoly, 2003; Ravichandran & Lertwongsatien, 2005). Accordingly, Henderson and Venkatraman (1993) assert that it is the application of business and IT capabilities to develop and leverage a firm’s IT resources for organisational transformation, rather than the acquired technological functionality, that secures competitive advantage for firms. Application of the Resource Based View of the firm (Wernerfelt, 1984) and Dynamic Capabilities Theory (DCT) (Teece and Pisano (1998) in particular) may yield insights into whether or not the use of Enterprise Systems enhances organisations’ core capabilities and thereby obtains competitive advantage, sustainable or otherwise (Melville et al., 2004). An operational definition of Core Capabilities that is independent of the construct of Sustained Competitive Advantage is formulated. This Study proposes and utilises an applied Dynamic Capabilities framework to facilitate the investigation of the role of Enterprise Systems. The objective of this research study is to investigate the role of Enterprise Systems in the Core Dynamic Capabilities of Asset Lifecycle Management. The Study explores the activities of Asset Lifecycle Management, the Core Dynamic Capabilities inherent in Asset Lifecycle Management and the footprint of Enterprise Systems on those Dynamic Capabilities. Additionally, the study explains the mechanisms by which Enterprise Systems sustain the Exploitability and the Renewability of those Core Dynamic Capabilities. The study finds that Enterprise Systems contribute directly to the Value, Exploitability and Renewability of Core Dynamic Capabilities and indirectly to their Inimitability and Non-substitutability. The study concludes by presenting an applied Dynamic Capabilities framework, which integrates Alter (1992)’s definition of Information Systems with Teece and Pisano (1998)’s model of Dynamic Capabilities to provide a robust diagnostic for determining the sustained value generating contributions of Enterprise Systems. These frameworks are used in the conclusions to frame the findings of the study. The conclusions go on to assert that these frameworks are free - standing and analytically generalisable, per Siggelkow (2007) and Yin (2003).