a b s t r a c tThe purpose of this research is to investigate dividend policy, including its impact on share prices of transportation providers and related service companies, by comparing generalized regression neural networks with conventional regressions. Our results using regressions reveal that for Europe and for the US and Canada the market-to-book-value, as a surrogate for growth opportunities, fulfils expectations of pressures on dividends leading to a negative association with dividend yields in accordance with the pecking order theory. Neural network analysis indicates a clear role for growth opportunities for the US and Canada pointing to an underlying confidence on the part of transportation companies in their own internal policies. Finally, risk is rewarded especially in Europe.

PurposeThe purpose of this study is to investigate the incidence of target gearing behaviour in firms as well as the drivers of such behaviour.Design/methodology/approachThe paper employs a triangulation approach across three methodological phases: a questionnaire survey, logistic regression modelling of firm data, and interviews with finance directors. The results are then discussed under the key themes of gearing optimality, valuation issues, external drivers, the finance life‐cycle, the impact of risk, and the relationship between gearing and corporate strategy.FindingsThe results reveal that the majority of firms engage in targeting, though targets are subject to fairly frequent revision as both external and internal drivers evolve. Important external drivers include macroeconomic variables and analysts' views, whereas important internal drivers include income gearing and profitability.Practical implicationsGiven the range and variety of drivers, target gearing evidently represents a complex strategic decision for finance directors. The paper provides a benchmark perspective for finance directors when determining their firm's gearing strategy.Originality/valueThe innovation of the paper is the study of target gearing across three methods, the results of which are then triangulated to provide a deeper understanding of both the quantifiable and qualitative drivers of gearing. This provides a far broader insight into the real‐world determination of gearing strategy than a conventional empirical approach.