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The purpose of this paper is to focus around the literatures of the resource‐based firm and cognitive mental models, explores the dynamic linkages between cognitive models, resources and firm performance in the context of the insurance industry.

In a real‐life example drawn from the insurance industry, a process‐based simulation model is developed to explore the linkages between managerial mental models, resources and performance. It represents resources as endogenous flows and mental models and resource constraints as exogenous parameters. This allows, for example, the impact of heterogeneity in mental models, on such factors as the time path of resource allocations, resources and capabilities, and ultimately performance, to be studied in two firms (business units) in the insurance industry.

In general, heterogeneity in mental models leads to differences in performance in the long run. This finding is reinforced by the presence of resource constraints. Facing strategic change, however, it is often difficult for senior managers to overcome the influence of well‐established managerial mental models or recipes which create cognitive inertia and, in turn, hinder performance improvements.

There are few empirical studies which explore the impact of changes in mental models and resource constraints on firm‐performance and resource allocation decisions.

This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources.

The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques.

The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective.

The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field.

Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories.

The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.