Metaverse Adoption Barrier Analysis for CEMAC Construction

Analytical results from a multi-criteria decision-making study on barriers to metaverse adoption in the CEMAC construction sector. The dataset includes SF-DEMATEL, ISM, and MICMAC outputs from 13 domain experts across six Central African countries. It was authored by Maximino Gambat and published in April 2026.

Use Cases

• Identify key causal barriers using the 'Cause/Effect classification' and 'causal direction (Rᵢ−Cᵢ)' columns from the SF-DEMATEL results.
• Construct a hierarchical model of barrier relationships using the 'ISM five-level hierarchy partitioning' data.
• Analyze the influence and dependence of barriers via the 'MICMAC classification by driving and dependence power'.
• Validate the expert consensus using the reported 'Cronbach's α = 0.847' from the panel profile.

Strengths

• Data derived from 13 domain experts covering all six CEMAC member states, ensuring regional representation.
• Analysis identifies 287 significant causal links from a possible 600, based on a defined threshold α = 0.412.
• Includes nine novel CEMAC-specific barrier constructs not found in prior literature.

Limitations

• The dataset is very small at 17.3 KB, containing analytical results rather than raw survey or observational data.
• Findings are based on a limited panel of 13 experts, which may not capture the full diversity of industry perspectives.
• Temporal coverage is a single snapshot from the study period, with no longitudinal tracking of barrier evolution.

Provenance

Source

figshare, authored by Maximino Gambat.

Collection Method

Data generated via a hybrid Spherical Fuzzy DEMATEL and ISM methodology applied to expert judgments.

Time Range

Study conducted prior to April 2026; specific data collection period not provided.

Freshness

Published in April 2026, representing a recent analysis.

Geography

Covers the six CEMAC member states: Cameroon, Chad, Central African Republic, Republic of Congo, Equatorial Guinea, and Gabon.