Quali-quantitative Classification of Project Management Serious Games (2014-2026)

Description

This dataset supports the systematic classification and analysis of serious games (SGs) used in project management (PM) education. It contains qualitative and quantitative data for 35 PM serious games identified through a PRISMA-compliant systematic literature review of Scopus and Web of Science (integrated by snowballing) covering peer-reviewed publications from 2014 to 2026. The dataset is structured as a single Excel workbook containing two sheets. The first sheet (Legend) provides a detailed description of all variables, including admitted values, coding rules, and operational definitions. The second sheet (Quali-Quantitative Table) contains one row per game and is organised into four blocks of variables: I) Bibliographic identifiers: author(s), year of publication, title, source, DOI, and database indexing (Scopus and/or Web of Science). II) Qualitative descriptors: nineteen variables characterising each game's structural and contextual features, organised across four analytical dimensions: (i) bibliographic context (country of origin, database indexing); (ii) game structure (player configuration, team size, game mode, delivery format, game type, AI integration, duration); (iii) role dynamics (participant roles, PM role configuration, NPC presence and roles); and (iv) application context (target audience, project sector, technical domain cluster). III) Knowledge Area (KA) intensity scores: ten ordinal variables, one per PMBOK Sixth Edition Knowledge Area in standard order (Integration, Scope, Time, Cost, Quality, HR, Communications, Risk, Procurement, Stakeholder Management). Each KA is scored 0–5 using a rubric developed for this study: 0 = KA entirely absent; 1–5 = progressively deeper operationalisation of the KA within the game's mechanics, from minimal presence (1) to full simulation of professional-grade processes (5). The complete rubric is provided in the Appendix of the associated publication. IV) Derived metrics: two summary variables computed from the KA scores: Coverage (percentage of KAs engaged, i.e., scored above 0) and Intensity (sum of KA scores divided by the maximum achievable score across engaged KAs, expressed as a fraction). The dataset is intended to support replication of the study's analyses, extension of the classification to new games, and use of the KA intensity framework as a design or curriculum alignment instrument.

Institutions

• University of Parma

  Emilia-Romagna, Parma
• University College London

  England, London

Categories

Licence