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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

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.

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.

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.

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.

The proposed approach provides a set of indicators which allow managers to identify strategic priorities, thus facilitating decision making and strategic planning.

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.

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.

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.

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.

The exemplificative study provides evidences useful for standard setters to improve their comprehension about the inclination of companies in disclosing information on goodwill impairment.

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.

This paper contributes to the field of public services’ performance measurement systems by proposing a benchmarking-based methodology that improves the effective use of big and open data in analyzing and evaluating efficiency, for supporting internal decision-making processes of public entities.

The proposed methodology uses data envelopment analysis in combination with a multivariate outlier detection algorithm—local outlier factor—to ensure the proper exploitation of the data available for efficiency evaluation in the presence of the multidimensional datasets with anomalous values that often characterize big and open data. An empirical implementation of the proposed methodology was conducted on waste management services provided in Italy.

The paper addresses the problem of misleading targets for entities that are erroneously deemed inefficient when applying data envelopment analysis to real-life datasets containing outliers. The proposed approach makes big and open data useful in evaluating relative efficiency, and it supports the development of performance-based strategies and policies by public entities from a data-driven public sector perspective.

Few empirical studies have explored how to make the use of big and open data more feasible for performance measurement systems in the public sector, addressing the challenges related to data quality and the need for analytical tools readily usable from a managerial perspective, given the poor diffusion of technical skills in public organizations. The paper fills this research gap by proposing a methodology that allows for exploiting the opportunities offered by big and open data for supporting internal decision-making processes within the public services context.

This paper aims to highlight the degree of diffusion and intensity of use of knowledge management systems (KMSs) among small and medium enterprises (SMEs) in Brazil and apply a taxonomy that synthesizes the strategies of use of KMSs by the SMEs. In addition, it seeks to better understand the processes, practices and technologies of KM by SMEs, pointing improvements in the KMS of Brazilian SMEs and contributing to obtain better results in the search for efficiency and innovation.

Based on a literature review on KMSs used by SMEs, an empirical investigation was conceived, developed and conducted through online questionnaires involving 49 selected SMEs operating in several sectors.

This paper reinforces the results of the previous work of Cerchione and Esposito (2017) that point to the existence of a reciprocal relationship between KM-Tools and KM-Practices: one reinforces the other and vice versa. On the other hand, it indicates a difference in the behavior of Brazilian companies in relation to the sample of Italian companies studied by Cerchione and Esposito (2017), which presented a negative correlation between the general differentiation index and the general use intensity index of SMEs, while the Brazilian ones showed a positive correlation.

The study points out the need for greater dissemination of practices and tools to support knowledge management (KM), as well as greater support for the implementation and effective use of these practices and tools within the organizational context of SMEs.

This paper identifies the main practices and tools to support KM used by Brazilian SMEs, indicating the need for investments in employee training and acquisition of tools.

SMEs represent an important part of the generation of jobs and income in Brazil. Initiatives that lead to the successful implementation of tools and practices to support KM can increase the efficiency and productivity of these organizations.

This paper applies in a sample of Brazilian companies the taxonomy proposed by Cerchione and Esposito (2017) combining strategies of SMEs for the use of KMSs.

Communication within a public organization is a fundamental aspect considering its contribution both to organizational well-being and – as highlighted by recent literature – to the improvement of relations with external users. This paper aims at analyzing the relevance of different dimensions of communication and the relationship between communication and citizens' satisfaction.

The study is based on data collected through questionnaires (303; 86%) filled in by the public personnel of an Italian local government to verify the communication dimensions relevance. The authors applied the OLS method to test the relationship between communication dimensions and citizens' satisfaction (deriving from the municipality's customer satisfaction survey system, which collected 3,708 questionnaires).

The authors show that four of the five communication dimensions considered are particularly relevant and that two of them (“interpersonal communication” and “organizational communication”) positively influence the level of satisfaction of users of local public services (citizens' satisfaction), also countering the negative perception of certain sectors (e.g. taxes and local taxation, traffic police). The conclusion also highlights some limitations of the work.

The study brings new insights into the impact of communication (as an element of public employee well-being) on citizen satisfaction, leading to some useful implications for public managers.

The effect of digital transition on knowledge management (KM) processes is becoming relevant for companies operating in different industries and the body of literature examining this impact is rapidly growing. This paper aims to critically analyse the literature on the impact of digital transition on KM by rethinking the SECI model proposed by Nonaka and proposing the WISED model for the digital knowledge-creating company.

The systematisation of existing studies on the topic and the analysis of the evolution of knowledge creation process in the era of digital transition was carried out through a bibliometric approach.

According to the traditional epistemological and ontological dimensions and considering the innovative KM processes identified by this study (i.e. webification, informalisation, systematisation, explicitation and digitalisation), the results highlight how the proposed WISED model can be adopted by organizations to manage knowledge through the use of digital technologies.

Digital transition seems to open up new horizons that can expand the potential use of the WISED model for organisations and society.