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The aim is to explore the potential of a hybrid genetic algorithm‐partial least squares (GA‐PLS) modeling approach to understand the important promotional spending variables that influence physicians' prescribing habits and to help leverage managers' insight to plan better spend on their promotional activities.

A GA was used as a variable‐selecting tool, and PLS analysis was employed for correlating these variables with the observed variation in the volume of prescriptions. This approach is illustrated using database from a marketing consultant on four major brands in the US antibiotic universe.

Good statistical models were derived that permit simpler and faster computational prediction of the effects of physician‐directed promotion. All final models established had r² values ranging from 0.835 to 0.922 and cross‐validated r² (q²) values ranging from 0.791 to 0.911 whereas the mean absolute percentage error (MAPE) values were confined within 5 percent range on averaging all brand models. Further, thorough statistical analyses revealed the usefulness of promotional spending as a variable and the robustness of GA‐PLS as a correlation tool.

Modeling frame was comprised of only antibiotic category, which may limit its general utility.

Managers can become more adept at interpreting the effects of promotion on prescribing behaviors of physicians and are able to build predictive models that would help identify where and how their curious blend of promotional cocktail would yield the highest future returns. Moreover, if the impact of individual promotional spending element can be measured, then this is perhaps a testament to the way the efficacy of interventions to reduce the harmful consequences of pharmaceutical marketing could be validated given a growing number of public beliefs that physician‐directed promotion has grown too heavy‐handed and is undermining medical professionalism.

This area of research has not received much attention in the pharmaceutical marketing literature until recent years, and hopefully this study will stimulate some interest.

What follows is a series of reports by leading scholars on the very successful Second Asian Pacific Interdisciplinary Research in Accounting (APIRA) Conference held at the Osaka City University, Japan, 4‐6 August, 1998. This conference was held in association with Accounting, Auditing > Accountability Journal (AAAJ) and followed on from the inaugural APIRA network conference held in Sydney in 1995

The purpose of this paper is to focus on conditional cooperation and investigate whether the difference in contributions between Partners and Strangers designs in linear public goods experiments can be explained by differences in beliefs.

The author conducted linear public goods experiments by using Partners and Strangers designs with belief eliciting their group member’s contributions.

The author shows that the difference in the magnitude of the responsiveness of contribution to belief (i.e. the marginal contribution to belief) creates different contribution levels in Partners and Strangers designs.

The presented results imply that having a strategic motive increases contributions by increasing the magnitude of the responsiveness of contribution to belief rather than by raising belief level.

The main claim of this paper is that “marginal contribution to belief” rather than “belief level” causes the difference in contribution levels between Partners and Strangers. This is the first proven evidence of a difference in belief between Partners and Strangers.