Upper Echelons Theory and the Digital Leap: Expert-Coded Leadership and Organizational Digitalization

This dataset accompanies the study “Upper Echelons Theory and the Digital Leap: Expert-Coded Leadership and Organizational Digitalization.” It provides a unique expert-coded view of how CEO digital expertise, top management team (TMT) diversity, and integration mechanisms influence digital innovation outcomes in leading global technology firms.

The dataset covers 12 firm-years (2023–2024) from six representative companies at the forefront of digital transformation: Intel, NVIDIA, Huawei, Tencent, SAP, and ASML. Three independent coders contributed to the evaluation process: (1) the current CEO of a Shenzhen software company (aggressive stance), (2) a retired U.S. semiconductor CEO (conservative stance), and (3) a European scholar specialized in Upper Echelons Theory (moderate stance). This triangulated approach reduces bias and improves construct validity.

Variables include:

CEO Digital Expertise: five dimensions (human capital depth, track record of digital value creation, boundary spanning with IT leaders, governance of digital/data/AI risk, and centralization stance).

TMT Diversity: size, functional heterogeneity (Blau index), gender diversity.

Integration Mechanisms: presence of CIO/CDO/CTO roles, digital councils, and ecosystem partnerships.

Digital Innovation Outcomes: digital patent families, product/feature releases, digital revenue share, process digitization milestones.

Controls: firm age, size, R&D intensity, industry volatility.

Contents:
The dataset consists of three main workbooks (Coder A, B, C), each containing:

Coding_Sheet: ratings per firm-year, including formulas for indices.

TMT_Roster: member-level data (local and common names, function category, gender, source URL).

Roster_Calcs: auto-calculated measures of TMT size, Blau index, and share female.

Rubric & Instructions: coding anchors and coder-specific guidance.

Usefulness:
This dataset is valuable for scholars in strategic management, leadership, and digital transformation. It offers a replicable, transparent method to operationalize Upper Echelons Theory beyond demographics and surveys, enabling the study of substantive expertise and team integration. Researchers can reuse the coding rubric, adapt the roster approach to other industries, or extend the methodology with automated coding (e.g., NLP, machine learning). The multi-rater structure also allows for intercoder reliability analysis, an uncommon but critical step in leadership research.

This dataset directly supports the empirical analysis presented in the associated article, offering open and replicable materials for future comparative, cross-industry, and longitudinal studies.