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Purpose The purpose of this paper is to study how the design of a strategy map can be supported by measures expressing the customers’ perceptions about strategic factors and their related determinants. In particular, managers are provided with a fact-based test useful to revise prior knowledge and beliefs. Design/methodology/approach A case study is used to describe the adoption of the partial least squares path modelling (PLS-PM) approach to structural equation modelling in order to compare competing strategy maps and select the one that best fits customer perceptions. A focus group was organised to design the strategy maps, which were tested through a survey of 600 randomly selected resellers. Findings The empirical-based validation of a causal map by using PLS-PM may effectively stimulate a revision of managers’ collective perceptions about a phenomenon characterised by implicit knowledge, as in the case of customer needs. Research limitations/implications The case-study company operates in a business-to-business environment, and thus only the needs of direct customers have been included in the analysis. Final users’ needs should also be considered, even if different solutions are required for data collection. Practical implications The proposed approach provides a set of indicators which allow managers to identify strategic priorities, thus facilitating decision making and strategic planning. Originality/value In the strategic management literature, few attempts have been made to operationalise the complex and multidimensional latent constructs of a strategy map combining managers’ implicit knowledge and empirical validation in a “holistic” manner. The adoption of PLS-PM is relatively new in testing the accuracy of causal maps.

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.

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