Carbon Risk Management: A Comparative Case Study of Two Companies within the Australian Energy Sector

By Shaun Butterworth, Nava Subramaniam, Michelle M. S. Phang

The purpose of this study is to understand how regulatory uncertainty associated with the Australian Federal government initiatives towards establishing a carbon pricing scheme has affected the perceptions and management of risks related to carbon emission reduction at the organisational level. The present study, based on evidence from archival and interview data from two Australian energy sector firms, provides insights into how managers perceived various risks associated with the Renewable Energy Trading (RET) scheme and the delay in the proposed Carbon Pollution Reduction Scheme (CPRS), and how such risks were seen to impact different features of their organisations’ management control system (MCS). Our findings indicate that regulatory uncertainty and the strategic stance undertaken by organizations affect a variety of internal uncertainties related to financial, information processing and organisational values, which in turn impact risk mitigation and performance management strategies.

EVA™, EPS, ROA and ROE as Measures of Performance in Australian Banks: A Longitudinal Study

By Gregory Laing, Kirsty Dunbar

This research empirically tests the relative and incremental information content of performance measures EVA, EPS, ROA and ROE against the Market Value of the shares in the four major Australian banks. Data was obtained from database sources for the purpose of calculating the EVA and extracting the EPS, ROA, ROE and Share Market Value of the four major banks in Australia for the years 2003 to 2011.

Two null hypotheses were derived from the literature and tested using the Multiple Linear Regression method. In contrast to results commonly reported, no significant relationship was found between EVA and the market value in terms of relevant information content. However, the incremental information content of EVA was found to be significant in the model tested. This study did not test the different models for calculating EVA which future research may investigate. Future research may also examine more performance measures in the model.

Mismatch in the Design of Performance Measures: A Solution for Managing Conflicting Organisational Goals

By VG Sridharan, Jane Bui

This paper examines the economic rationale behind an unusual field study finding with the help of agency theory. Why would a firm evaluate employees on one measure but reward them on the basis of another conflicting measure? The paper adopts Yin’s (1994) scientific case study approach to identify practical reasons from a division of a single firm.

The analysis points to the firm’s inevitable need to control two potential opportunism sources namely, information-hiding and delaying (or ‘go-slow’) as the primary reasons for the deliberate design of such mismatching measures. Finally, the firm exploits the economic power of the customer satisfaction measure, whose subjectivity and informal design helps offset the negative effects of the goal conflict.

While the extant literature identifies two types of measurement mismatch, this paper documents the prevalence and management of a third type namely, deliberate design of mismatching measures for conflicting goals.

Impact of Extreme Decisions and Extreme Probabilities on Attribute Framing Effects

By William A. Kerler III, Christopher D. Allport, A. Scott Fleming

This study examines attribute framing in a capital budget decision-making scenario.  In an experiment utilising 183 participants, we test the impact of extreme probabilities on the investment decision making process.

Results indicate that the presence of attribution frames may be problematic in certain decision-making scenarios and that framing effects may be stronger and more persistent than prior theory suggests.

This research adds to the literature through an extension of the understanding of the attribute framing phenomenon on capital budgeting decisions.  it is important in that it highlights the potential for sub-optimal decision-making in that attribute framing is not easily moderated, even when utilising extreme probabilities in the capital spending approval process.