Analysis of disclosure determinants: a local-relation approach

Purpose – In the literature on determinants of disclosure, scholars generally tend to investigate the
existence of relations in “global” terms by considering the whole range of observed values pertaining to both
dependent and independent variables involved in the descriptive model. Despite the different methodologies
used coherently to this approach, a hypothesis can be only accepted or rejected entirely. This paper aims to
contribute to the literature by proposing a data-driven method based on smooth curves, which allow scholars
to detect the existence of local relations, significant in a limited interval of the dependent variable.
Design/methodology/approach – The employment of smooth curves is simplified by conducting a
study on goodwill disclosure. The model derived by the adoption of the locally weighted scatterplot
smoothing (LOWESS) curves may provide an accurate description about complex relations between the
extent of disclosure and its expected determinants, whose shape is not completely captured by traditional
statistic techniques.
Findings – The model based on LOWESS curves provided a comprehensive description about the
complexities characterizing the relationship between disclosure and its determinants. The results show that in
some cases, the extent of disclosure is influenced by multi-faceted local relations.
Practical implications – The exemplificative study provides evidences useful for standard setters to
improve their comprehension about the inclination of companies in disclosing information on goodwill
impairment.
Originality/value – The adoption of smooth curves is coherent with an inductive research approach,
where empirical evidence is generalized and evolves into theoretical explanations. The method proposed is
replicable in all the field of studies, when extant studies come to unclear and contradicting results as a
consequence of the complex relations investigated.