| Popis: |
Data visualisation reveals patterns and provides insights that lead to actions from management, thereby playing a mediating role in the relationship between the internal resources of a firm and its financial performance. In this chapter, contingent resource-based theory is applied to the analysis of big data, treating its visualisation as a mode of interdisciplinary communication. In service industries in general and the legal industry in particular, big data analytics (BDA) is emerging as a decision-making tool for management to achieve competitive advantage. Traditionally, data scientists have delved into data armed with a hypothesis, but increasingly they explore data to discern patterns that lead to hypotheses that are then tested. These big data analytics tools in the hands of data scientists have the potential to unlock firm value and increase revenue and profits, through pattern identification, analysis, and strategic action. This exploratory mode of working can increase complexity and thereby diminish efficient management decision-making and action. However, data pattern visualisation reduces complexity, as it enables interdisciplinary communication between data scientists and managers through the translation of statistical patterns into visualisations that enable actionable management decisions. When data scientists visualise data patterns for managers, this translates uncertainty into reliable conclusions, resulting in effective management decision-making and action. Informed by contingent resource theory and viewing these primary and secondary resources as independent variables and performance outcomes such as revenue and profitability as dependent variables, a conceptual framework is developed. The contingent resource-based theory highlights capabilities emerging from the interrelationship between primary and secondary resources as being central to competitiveness and profitability. Data decision-making systems are viewed as a primary resource, while complementary resources are (1) their completeness of vision (i.e., strategy and innovation) and (2) their ability to execute (i.e., operational capabilities). Data visualisation is therefore crucial as a resource facilitating actionable decisions by management, which in turn enhances firm performance. The balance between expert agents' self-reliance and central control, the entity's values, task attributes, and risk appetite all moderate the type of data visualisation produced by data scientists. |
