Empirical identification of skills gaps between chief information officer supply and demand: a resource-based view using machine learning

Stuart Barnes, Richard N. Rutter, Ariel I. La Paz, Eusebio Scornavacca

Research output: Contribution to journalArticlepeer-review

Abstract

Purpose: The role of emerging digital technologies is of growing strategic importance as it provides significant competitive advantage to organisations. The chief information officer (CIO) plays a pivotal role in facilitating the process of digital transformation. Whilst demand continues to increase, the supply of suitably qualified applicants is lacking, with many companies forced to choose information technology (IT) or marketing specialists instead. This research seeks to analyse the organisational capabilities required and the level of fit within the industry between CIO requirements and appointments via the resource-based view. Design/methodology/approach: Job postings and CIO curriculum vitae were collected and analysed through the lens of organisational capability theory using the machine learning method of Latent Dirichlet Allocation (LDA). Findings: This research identifies gaps between the capabilities demanded by organisations and supplied by CIOs. In particular, soft, general, non-specific capabilities are over-supplied, while rarer specific skills, qualifications and experience are under-supplied. Practical implications: The research is useful for practitioners (e.g. potential CIO candidates) to understand current market requirements and for companies aiming to develop internal training that meet present and future skill gaps. It also could be useful for professional organisations (e.g. CIO Forum) to validate the need to develop mentoring schemes that help meet such high demand and relative undersupply of qualified CIOs. Originality/value: By applying LDA, the paper provides a new research method and process for identifying competence requirements and gaps as well as ascertaining job fit. This approach may be helpful to other domains of research in the process of identifying specific competences required by organisations for particular roles as well as to understand the level of fit between such requirements and a potential pool of applicants. Further, the study provides unique insight into the current supply and demand for the role of CIO through the lens of resource-based view (RBV). This provides a contribution to the stream of information systems (IS) research focused on understanding CIO archetypes and how individual capabilities provide value to companies.

Original languageEnglish (US)
Pages (from-to)1749-1766
Number of pages18
JournalIndustrial Management and Data Systems
Volume121
Issue number8
DOIs
StatePublished - 2021
Externally publishedYes

Keywords

  • CIO
  • Demand
  • Job fit
  • LDA
  • Machine learning
  • Supply

ASJC Scopus subject areas

  • Management Information Systems
  • Industrial relations
  • Computer Science Applications
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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