Digital transformation in the economics of complexity: the role of predictive models in strategic management

Abstract

Purpose This paper aims to investigate the role of predictive models in the learning and decision-making processes of strategic management. The rapid advancement of digitalisation has contributed to increasing the complexity of the worldwide economy and led to various new competitive dynamics. Design/methodology/approach To achieve this purpose, a literature review has been carried out and a predictive model based on Watson, an IBM supercomputer, is presented as a qualitative process model. Findings Specific insights derived from a review of the literature highlight organisations' need to modify their decision- and strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy. Predictive models, acting as filters, transform data into informative knowledge that decision-makers can interpret based on individual domain knowledge. Originality/value From a theoretical point of view, this paper contributes to the field of digital transformation by proposing the economics of complexity as a paradigm through which to observe and study the issue of predictive models in strategic management. Additionally, the authors analyse the phenomenon from a cognitive perspective, defining the new learning dynamics of digital transformation and the social learning cycle triggered by big data and predictive models. From a managerial and policy-making point of view, this suggests the need to re-shape traditional education contents and dynamics and foster skills that are multi-disciplinary, multi-domain, multi-empathic, multi-interaction and multi-communication between people and things.

Strategic Management in the Digital Age: A Review of Decision-Making Frameworks

“…Predictive models in strategic management use data analytics and machine learning to inform strategic decisions. These models forecast future trends and market changes, providing data-driven insights for decision-making (Iscaro et al, 2022). They are utilized in various industries to anticipate market changes and customer behavior, requiring advanced technical expertise and significant data processing capabilities.…”

“…This framework is best for sectors like education where technology enhances planning and execution. On the other hand, Predictive Models in Strategic Management use data analytics for forecasting and decisionmaking (Iscaro et al, 2022). This approach is more applicable in industries where data-driven insights are crucial, though it demands advanced technical expertise.…”

“…They are particularly suitable for industries facing digital disruption or those needing to modernize their operations. These approaches require a holistic view of the organization, making them more resource-intensive (Bughin et al, 2021;Deep, 2023;Iscaro et al, 2022;Zehir et al, 2020).…”

“…The advent of digital technologies has transformed the business landscape, presenting both unprecedented challenges and opportunities. From the integration of artificial intelligence and big data analytics to the increasing emphasis on sustainability and cybersecurity, the contours of strategic management are being redrawn (Goi et al, 2023;Iscaro et al, 2022;Kumar & Mallipeddi, 2022). In this dynamic environment, the ability of businesses to adapt, innovate, and make informed decisions is crucial for longterm success.…”

Guest editorial: Digital transformation, strategic management and entrepreneurial process: dynamics, challenges and opportunities

“…The seventh article, “Digital transformation in the economics of complexity: the role of predictive models in strategic management” by Iscaro et al (2022), explores the role of predictive models in the learning and decision-making processes of strategic management. The authors suggest that organizations' need to modify their decision/strategy-making processes, which are increasing in speed and frequency, thus also leading to the formulation of emergent and trigger event strategies based on the identification of conditions that require the revision of all or part of the firm's strategy.…”