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The paper aims at extending extant research on sources of divestiture gains by suggesting a novel program‐based perspective on divestitures and analyzing the performance of program divestitures in comparison to single “stand‐alone” divestitures.

Based on event study methodology, the authors analyze the abnormal returns of 160 divestiture announcements within the global insurance industry between 1998 and 2007. In contrast to prior research which relied on ex post statistical clustering to identify transaction programs, ad hoc corporate press releases issued with the divestiture announcements are used to categorize program divestitures.

Empirical results suggest that program divestitures generate higher abnormal returns than stand‐alone divestitures. Further analyses into the sources for these higher gains, however, do not provide support for experience effects as significant explanatory factors. Instead, results suggest that the scheduling of divestitures significantly impacts announcement returns.

The scope and single industry setting of the study suggest future cross‐industry research on the influence of divestiture program characteristics on divestiture performance and the conditions under which these programs improve divestiture performance.

Managers are advised to refrain from piecemeal divestiture behavior lacking clear strategic focus. Instead, they are encouraged to bundle their divestitures as part of a divestiture program with a clear strategic intent and shared business logic.

While prior research on divestitures has treated divestitures as isolated events, the paper directs attention towards the analysis of divestiture programs. Further, experience and timing effects, which have been widely absent from prior divestiture studies, are considered.

To better understand the role of industrial big data in promoting digital transformation, the authors propose a theoretical framework of industrial big-data-based affordance in the form of an illustrative metaphor – what the authors call the “organizational drivetrain.”

This study investigates the effective use of industrial big data in the process of digital transformation based on the technology affordance–actualization theoretical lens. A software platform and services provider with more than 4,000 industrial enterprise clients in China was selected as the case study object for analyzing the digital affordance and actualization driven by industrial big data.

Drawing on a revelatory case study, the authors identify three affordances of industrial big data in the organization, namely developing data-driven customized projects, provisioning equipment-data-driven life cycle services, establishing data-based trust and determining affordance actualization actions driven by technology and market. In addition, the authors reveal the underlying drivetrain mechanisms to advance industrial big data affordance and actualization: stabilizing, enriching and pioneering.

This study builds a drivetrain model on digital transformation by industrial big data affordance actualization. The authors also provide practical implications that can help practitioners to implement digital transformation effectively and extract value from their investment.