Constructing an 'efficient frontier' of accounting journal quality

This paper reports the construction of an 'efficient frontier' of the perceived quality attributes of academic accounting journals. The analysis is based on perception data from two web-based surveys of Australasian and British academics. The research reported here contributes to the existing literature by augmenting the commonly supported single dimension of quality with an additional measure indicating the variation of perceptions of journal quality. The result of combining these factors is depicted diagrammatically in a manner that reflects the risk and return trade-off as conceptualised in the capital market model of an efficient frontier of investment opportunities. This conceptualisation of a 'market' for accounting research provides a context in which to highlight the complex issues facing academics in their roles as editors, researchers and authors. The analysis indicates that the perceptions of the so-called 'elite' US accounting journals have become unsettled particularly in Australasia, showing high levels of variability in perceived quality, while other traditionally highly ranked journals (ABR, AOS, CAR) have a more 'efficient' combination of high-quality ranking and lower dispersion of perceptions. The implications of these results for accounting academics in the context of what is often seen as a market for accounting research are discussed. © 2006 Elsevier Ltd. All rights reserved.

Organizational capital, product market competition and technical efficiency in Italian cooperatives

This paper tries to identify under which conditions increasing market competition may help cooperatives to improve technical efficiency to guarantee positive profits. This hypothesis is first formalized in a partial equilibrium framework and then is tested on a sample of Italian conventional and cooperative firms, using frontier analysis. Technical efficiency indexes are computed by using the one-stage approach as suggested by Battese and Coelli (1995), where proxies for competition are introduced as determinants of efficiency, along with other exogenous factors accounting for the firms’ heterogeneity. However, the overall impact of increasing competition on efficiency is negative.

The limits of open innovation:openness and (quasi-)markets in the organization of innovation

Recent studies have stressed the importance of ‘open innovation’ as a means of enhancing innovation performance. The essence of the open innovation model is to take advantage of external as well as internal knowledge sources in developing and commercialising innovation, so avoiding an excessively narrow internal focus in a key area of corporate activity. Although the external aspect of open innovation is often stressed, another key aspect involves maximising the flow of ideas and knowledge from different sources within the firm, for example through knowledge sharing via the use of cross-functional teams. A fully open innovation approach would therefore combine both aspects i.e. cross-functional teams with boundary-spanning knowledge linkages. This suggests that there should be complementarities between the use cross-functional teams with boundary-spanning knowledge linkages i.e. the returns to implementing open innovation in one innovation activity is should be greater if open innovation is already in place in another innovation activity. However, our findings – based on a large sample of UK and German manufacturing plants – do not support this view. Our results suggest that in practice the benefits envisaged in the open innovation model are not generally achievable by the majority of plants, and that instead the adoption of open innovation across the whole innovation process is likely to reduce innovation outputs. Our results provide some guidance on the type of activities where the adoption of a market-based governance structure such as open innovation may be most valuable. This is likely to be in innovation activities where search is deterministic, activities are separable, and where the required level of knowledge sharing is correspondingly moderate – in other words those activities which are more routinized. For this type of activity market-based governance mechanisms (i.e. open innovation) may well be more efficient than hierarchical governance structures. For other innovation activities where outcomes are more uncertain and unpredictable and the risks of knowledge exchange hazards are greater, quasi-market based governance structures such as open innovation are likely to be subject to rapidly diminishing returns in terms of innovation outputs.

An efficient knowledge transfer solution to a novel SMDP formalization of a broker's decision problem

This paper introduces a new technique for optimizing the trading strategy of brokers that autonomously trade in re- tail and wholesale markets. Simultaneous optimization of re- tail and wholesale strategies has been considered by existing studies as intractable. Therefore, each of these strategies is optimized separately and their interdependence is generally ignored, with resulting broker agents not aiming for a glob- ally optimal retail and wholesale strategy. In this paper, we propose a novel formalization, based on a semi-Markov deci- sion process (SMDP), which globally and simultaneously op- timizes retail and wholesale strategies. The SMDP is solved using hierarchical reinforcement learning (HRL) in multi- agent environments. To address the curse of dimensionality, which arises when applying SMDP and HRL to complex de- cision problems, we propose an ecient knowledge transfer approach. This enables the reuse of learned trading skills in order to speed up the learning in new markets, at the same time as making the broker transportable across market envi- ronments. The proposed SMDP-broker has been thoroughly evaluated in two well-established multi-agent simulation en- vironments within the Trading Agent Competition (TAC) community. Analysis of controlled experiments shows that this broker can outperform the top TAC-brokers. More- over, our broker is able to perform well in a wide range of environments by re-using knowledge acquired in previously experienced settings.