Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness

Amir Reza Asadi; Shiva Ghasemi

REVIEW 4 major objections 6 minor 43 references

Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A systematic review of 144 academic and industry records identifies six user-experience factors, four business-readiness factors, and four national-competitiveness factors, and packages them into three maturity-style models with practical…

desk verdict Plausible tri-level metaverse readiness taxonomy, but the n=144 corpus is unreported and the level structure is prefigured by the search terms; useful as a scoping synthesis, not as a grounded-theory result. read the letter →

arxiv 2411.10408 v1 pith:Y5KSCRSG submitted 2024-11-15 cs.HC cs.CY

Amir Reza Asadi , Shiva Ghasemi This is my paper · ORCID

classification cs.HCcs.CY

keywords metaversesystematicliteraturereviewgroundedtheoryuserexperiencebusinessreadinessnationalcompetitivenessextendedrealitymaturitymodel

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the metaverse is best understood as a sociotechnical system that must be studied at three levels at once: the individual user, the business, and the nation. Through a systematic review of 144 academic and industry records and grounded-theory coding, the authors extract six user-experience factors (accessibility, flexibility, gamification, interoperability, social interaction, and user empowerment), four business-readiness factors (digital-twin integration, marketing, product/service development, and resources), and four national-competitiveness factors (policy, service development, human-capital readiness, and sociocultural influence). They assemble these factors into three research models—a Metaverse User Experience Maturity Model, a Metaverse Business Readiness Model, and a Metaverse National Competitiveness Model—each accompanied by a heuristic rubric or checklist intended for practical assessment. If the models are right, designers, business strategists, and policymakers gain concrete, multi-level tools for evaluating where they stand and what to build next as the metaverse develops. The models are presented as provisional: the paper explicitly says the rubrics are not yet validated and future expert interviews are planned.

What carries the argument

The analytical engine is grounded theory applied to a mixed corpus of 144 records. Open coding tags discrete concerns in the literature; axial coding groups those concerns into candidate categories; selective coding consolidates them into the core categories that become the three models. The analysis is organized by a micro-meso-macro lens, with each level corresponding to one research question, and the resulting categories are then expressed as maturity-model rubrics: the UX heuristic rubric rates each user factor on a 1–5 scale, the business checklist offers yes/no readiness questions, and the national checklist organizes readiness into infrastructure, regulatory, economic, human-capital, cultural, and other policy areas. Topic modeling of titles is used as a secondary, descriptive view of the corpus's themes.

What would settle it

Re-run the review with a fully documented protocol: fixed search queries and dates, a complete screening log, and the full list of included records, then check whether the same six user factors, four business factors, and four national factors emerge. If a comparable corpus yields different factor categories, the models are corpus-dependent rather than universal.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the scattered literature on the metaverse converges on a small, ordered set of factors at each scale of analysis. At the individual level, adoption hinges on accessibility, flexibility, gamification, interoperability, social affordances, and user empowerment; at the business level, readiness hinges on digital-twin integration, marketing capability, product and service development, and resources; at the national level, competitiveness hinges on policy frameworks, service development, human-capital readiness, and sociocultural influence. Working from a corpus that deliberately mixes peer-reviewed and grey literature from industry, the authors code these factors using grounded theory and then join them into three layered models, asserting that the levels are mutually reinforcing: good user experience drives business innovation, business innovation drives national economic activity, and government policy shapes the conditions for both. The paper also proposes that these factors can be operationalized as maturity-style rubrics—a heuristic evaluation for UX, a self-assessment checklist for businesses, and a readiness checklist for nations—so that the models are usable before they are fully validated.

Load-bearing premise

The load-bearing premise is that the 144 records gathered from Google Scholar and LinkedIn fairly represent the broader metaverse discourse, since the search steps are reported without screening counts or the full query set and search-engine results change over time.

Editorial extensions

If this is right

User experience researchers can use the six-factor UX maturity rubric to benchmark existing metaverse applications and identify which dimension, such as interoperability or user empowerment, is least mature.
Business strategists can use the four-factor readiness checklist to audit their organization for digital-twin integration, marketing channels, product/service innovation, and resource readiness before committing to metaverse initiatives.
Policymakers can use the national competitiveness checklist to compare readiness across infrastructure, regulation, economic development, human capital, and cultural influence, and to spot gaps in high-speed connectivity investment or workforce training.
Researchers can treat the three models as a variable catalog for designing studies that connect levels, such as testing whether a rise in user empowerment predicts business adoption of metaverse commerce.
The paper's planned expert interviews constitute a direct validation path for the models and their rubrics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The models imply a causal chain—user experience to business innovation to national competitiveness to policy and back to user experience—that the review surfaces but does not test; modeling that loop with actual adoption data would be a natural next step.
The reliance on search-engine results that are personalized and time-varying means replicating the review with a frozen, archived corpus would determine how stable the factor lists are, and such a replication could also reveal whether the grey-literature proportion changes the conclusions.
Because the rubrics are presented as unvalidated, they could be converted into survey instruments and field-tested in specific sectors such as retail or education to see which items discriminate between high- and low-readiness organizations.
The national model's inclusion of cultural influence and virtual citizenship points toward a policy question the paper leaves open: how countries without strong cultural export industries should compete in the metaverse.

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Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the short version. This paper builds three maturity/readiness models (UX, business, national) from a mixed white/grey literature corpus, and the models themselves are sensible and well organized. But the empirical grounding for the claimed systematic review is not visible, and the three-level structure does not really 'emerge' from the data. Treat it as a thorough scoping synthesis, not as a validated grounded-theory derivation.

What's good: the paper engages with existing maturity models (Radoff, Digitopia, Weinberger & Gross), spans academic and industry sources, and produces concrete heuristic rubrics and checklists that are explicitly flagged as unvalidated. The six UX factors, four business factors, and four national factors are a reasonable reading of the current discourse. The LDA topic model is a nice supplementary touch, and the Python script is provided.

The soft spots are real. The paper invokes PRISMA but reports no screening counts, no search dates, no complete query strings, and no list of the 144 included records. The LinkedIn sampling ('first fifty results') is not reproducible, and Google Scholar results are personalized; the corpus cannot be reconstructed. The reference list leaks 'RAYYAN-INCLUSION' annotations, which tells me screening was done in a tool but the audit trail was not reported. On top of that, Table 1's search terms are already grouped into User Experience (Micro), Business Readiness (Meso), and Governance (Macro), so the micro-meso-macro model structure is built into the search strategy. The grounded theory coding then reproduces that scaffolding. The paper acknowledges researcher bias and lack of inter-rater reliability in the Limitations section, which is honest, but the reporting of the corpus itself is the bigger problem.

The abstract also overstates readiness: the 'practical assessment tools' are heuristics and checklists that the authors themselves say are not yet validated.

Who gets value: anyone wanting a compact multi-level checklist of metaverse factors for design, strategy, or policy brainstorming. Not a reliable empirical foundation for further systematic claims.

On peer review: I'd send it to referees, but with a clear request that the authors supply the full PRISMA flow, the exact queries, dates, and the complete list of included records. If that is not possible, they should drop the PRISMA framing and call it a scoping review.

Referee Report

4 major / 6 minor

Summary. This manuscript reports a systematic literature review of 144 white- and grey-literature records, analyzed with grounded theory, to produce three conceptual models: a Metaverse User Experience Maturity Model, a Metaverse Business Readiness Model, and a Metaverse National Competitiveness Model. The authors frame the metaverse as a sociotechnical imaginary and organize the review by micro (user), meso (business), and macro (national) levels, with heuristic rubrics and checklists for practical use. The paper is qualitative and integrative, covering user-level factors such as accessibility, flexibility, gamification, interoperability, social interaction, and empowerment; business factors such as digital twin integration, marketing, product/service development, and resources; and national factors such as policy, service development, human capital readiness, and sociocultural influence.

Significance. If the synthesis is trustworthy, the three models and their associated assessment tools would be a useful contribution to HCI and information science, bridging academic and industry perspectives and giving designers, strategists, and policymakers a structured starting point. The paper is openly self-critical: it acknowledges the interpretive nature of grounded theory, the lack of validation of the proposed rubrics, and the need for inter-rater reliability. The practical appendices are detailed and could seed future empirical work. However, the significance is conditional on the credibility of the systematic review corpus and on whether the model structure genuinely derives from the literature rather than being imposed by the search design.

major comments (4)

[§3.2–3.3, Figure 1] The corpus is not reproducible. The text states that the study collected 'first fifty results query searching on LinkedIn' and searched Google Scholar, but it does not provide search dates, complete query strings, database/platform details, or the PRISMA screening counts (identified, screened, excluded, included). The flow diagram in Figure 1 is not inspectable in the supplied text, and no list of the 144 included records is given. This is load-bearing because the three models are claimed to emerge from this corpus. The reference list contains leaked inclusion annotations such as 'RAYYAN-INCLUSION: "Amir"=¿"Included"' in refs. [46], [52], [56], [105], and [130], which suggest the screening was performed in a tool but the documented screening trail was not reported. Please provide a complete PRISMA flow table, the full search strings with dates, and an appendix listing the 144 records.
[Table 1, §3.1, §3.4] The claim of grounded-theory emergence is undermined by the search-term design. The search terms in Table 1 are already grouped by the three target levels ('User Experience (Micro)', 'Business Readiness (Meso)', 'Governance (Macro)'), and Section 3.1 declares the micro-meso-macro analytical framework before data collection. The coding process in Section 3.4 then produces categories that map neatly onto those three levels. The paper does not explain how the grounded-theory analysis could have revised, rejected, or restructured the three-level typology. Please revise the presentation to acknowledge the hybrid deductive–inductive nature of the study, or provide evidence that the coding was open to alternative level structures.
[§4.3, Table 6] The national-competitiveness model is the least transparently derived. The text says 'The coding processes is presented in table??' and the actual reference to Table 6 is missing. The selective-coding step is described as using WEF terminologies of network readiness and competitiveness, but the open-to-axial-to-selective mapping is less clearly evidenced than in Tables 4 and 5. Additional coding examples or a more explicit trace from the codes to the final four national factors are needed to support the model as an empirical synthesis.
[§4, Table 3] The LDA topic modeling of titles is reported but never connected to the grounded-theory analysis or the three research questions. Table 3 lists keyword sets for five topics, but there is no interpretation of these topics, no coherence measure, and no explanation of how they informed the models. As it stands, the LDA section is a disconnected descriptive addition; either integrate it into the analysis narrative or remove it.

minor comments (6)

[§4.3] The cross-reference to 'table??' should be 'Table 6'.
[§4.2.2] The text '144.2.2Marketing' appears to be a numbering artifact; the stray '14' should be removed.
[References] The leaked 'RAYYAN-INCLUSION' annotations in several reference entries, e.g. refs. [46], [52], [56], [105], and [130], should be removed from the reference list.
[Declarations] 'Data availability: Not Applicable' is not appropriate for a systematic literature review. If the corpus cannot be shared, the authors should state this explicitly and justify the exception; otherwise, a supplementary list of the 144 records is expected.
[Throughout] There are numerous typographical and spacing errors (e.g., 'sociotechnichal' in Section 2, 'guidedbyof', 'glasserian' in Section 5.4). The manuscript needs a full copyedit.
[References [13] and [14]] The same paper by Bibri (2022) appears to be cited twice as separate references; this should be consolidated.

Circularity Check

2 steps flagged · score 4.0 of 10

The three-level model architecture is largely predetermined by the search strategy and WEF terminology, while factor-level content remains independently grounded; no load-bearing self-citation chain.

self definitional [Section 3.2, Table 1; Section 3.1; Section 4 opening]
"The selection of our search terms was driven by the necessity to thoroughly address our research questions. ... The resulting search terms, as outlined in (see Table 1). Table 1 Search Terms User Experience (Micro) Business Readiness (Meso) Governance (Macro)"

The three models reported in Section 4 are named exactly after the three columns of Table 1, and Section 3.1 fixes the 'Micro-Meso-Macro' lens before data collection. Table 2's inclusion/exclusion criteria are likewise pre-grouped under UX, Business, and Country. Selective coding then identifies core categories that cannot deviate from these input groupings, so the highest-level structure of the models is an input assumption rather than an emergent grounded-theory finding.
renaming known result [Section 4.3 (Metaverse National Competitiveness Model)]
"The researchers utilize the World Economic Forum’s terminologies of network readiness and competitiveness to for selective coding of country level factors. This refers to the various drivers that determine a country’s level of capability in utilizing the benefits of transitioning to the metaverse."

For RQ3 the paper does not derive the national-factor taxonomy from open coding; it adopts the WEF 'network readiness and competitiveness' terminology as the selective-coding scheme. The four output factors (Policy, Service Development, Human Capital Readiness, Sociocultural) are then presented as findings, which re-labels an externally defined framework as a grounded-theory output.

full rationale

The paper's analysis pipeline is not formally circular: no equations are fitted to outcomes, no parameter is renamed as a prediction, and the factor-level content (accessibility, flexibility, digital twins, DAO governance, etc.) is tied to specific cited records and shown in coding tables, so much of the content has independent evidentiary support. The circularity is structural and partial: the micro-meso-macro architecture is fixed by the search strategy and inclusion criteria before coding begins, and the national model borrows WEF categories, so the three-model deliverable is partly built from its own inputs. Corpus auditability problems (no search dates, no PRISMA screening counts, Rayyan-inclusion annotations left in references) do not by themselves make the derivation circular, but they prevent an independent check that the n=144 corpus generated the categories. Self-citations are present but not load-bearing for the core models. Overall score 4: partial predetermination of the model structure, with genuine content at the factor level.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

No free parameters or invented entities appear in this qualitative review. The load-bearing assumptions are methodological: the sociotechnical-imaginary framing, the micro-meso-macro lens, the validity of grounded theory on secondary sources, the representativeness of the grey-literature sample, and the usefulness of LDA topic labels.

assumptions (5)

domain assumption The metaverse is best studied as a sociotechnical imaginary of information technology.
Section 2 builds the entire framing on this definition, citing Jasanoff and Kim; if the metaverse is not usefully analyzed this way, the model categories may be misaligned.
domain assumption The micro-meso-macro analytical framework is appropriate for organizing metaverse literature.
Section 3.1 adopts Dopfer, Foster and Potts's micro-meso-macro approach to narrow the search; this pre-commits the review to three levels before coding begins.
domain assumption Grounded theory coding of a secondary literature corpus yields valid conceptual models.
Section 3.4 applies grounded theory to existing research as an accepted method; the authors acknowledge in Section 5.4 that the methodology is interpretive and subject to researcher bias.
domain assumption The inclusion and exclusion criteria in Table 2 and the grey literature sample are sufficient to capture relevant metaverse perspectives.
The criteria exclude non-English, pre-2015, and opinion pieces, and include the first fifty LinkedIn results; if this sample is unrepresentative, the models inherit the bias.
domain assumption LDA topic modeling of titles provides a meaningful holistic view of the corpus.
Section 4 reports five LDA topics from titles only, but the topics are not used to weight or validate the grounded theory models; the authors present them as context.

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Cite this review

Pith. "Pith review of Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness." pith.science (2026). https://pith.science/paper/Y5KSCRSG

@misc{pith2026241110408,
  author       = {Pith},
  title        = {Pith review of: Exploring the Future Metaverse: Research Models for User Experience, Business Readiness, and National Competitiveness},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y5KSCRSG}},
  note         = {Machine review of arXiv:2411.10408}
}

read the original abstract

This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies. Through a systematic literature review, n=144 records were included and by employing grounded theory for analysis of data, we developed three research models, which can guide researchers in examining the metaverse as a sociotechnical future of information technology. Designers can apply the metaverse user experience maturity model to develop more user-friendly services, while business strategists can use the metaverse business readiness model to assess their firms' current state and prepare for transformation. Additionally, policymakers and policy analysts can utilize the metaverse national competitiveness model to track their countries' competitiveness during this paradigm shift. The synthesis of the results also led to the development of practical assessment tools derived from these models that can guide researchers

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