Causal Study Design for Digital Showroom Adoption: A Targeted Narrative Synthesis and Protocol for Survey, Text, and Scenario-Based Inference

1. Introduction and evidence scope

Digital showroom adoption refers to the organizational decision to introduce a digitally mediated product presentation environment in which customers interact with products, configurations, or demonstrations through online, immersive, or hybrid interfaces. For a causal study, the relevant question is not only whether firms intend to adopt such systems, but which antecedent conditions alter the probability of moving from intention to deployment and sustained use. This protocol addresses that design problem. It offers a targeted narrative synthesis of adjacent adoption evidence rather than a systematic review, because the aim is methodological transfer: the purpose is to borrow defensible modeling choices from nearby literatures and adapt them to a showroom setting with explicit assumptions and testable rules.

The study question is: which organizational, technological, and market conditions causally shape digital showroom adoption, and through what pathways do attitudes, perceived usefulness, and external pressure translate into deployment? The protocol treats adoption as a process with stages and recommends measuring each stage separately. That choice responds to a common weakness in adoption research: a single intention score can conceal delayed implementation, partial rollout, or superficial use. The manuscript therefore distinguishes antecedent measurement, causal estimation, and scenario analysis as separate tasks.

The evidence scope is bounded. Survey-based studies provide transferable approaches for modeling adoption correlates, mediation, moderation, and structural relationships; text analytics offers a way to measure latent attitudes from open responses; construction-technology adoption work shows how TOE and DOI factors can be combined in an organizational adoption model; Bayesian networks support scenario testing under uncertainty; and digital-twin frameworks show how to compare hypothetical interventions in a virtual environment. These methods are adjacent to digital showroom adoption and informative for design, but none by itself is sufficient for causal inference in the target setting. [1] [2] [3] [4] [5]

2. Transferable methods from adjacent adoption research

The closest survey-based templates come from studies that estimate adoption intention with multivariable models, moderated paths, and mediation. In one study of AI adoption in Romanian medical management and pharmaceutical marketing, the authors combined adapted TAM/UTAUT items, organizational-readiness measures, correlation analysis, robust regression, bootstrapped indirect effects, moderation, clustering, and exploratory classification. Their main methodological lesson is not the reported coefficients themselves, but the value of separating construct coherence from causal proof: cross-sectional associations can support measurement refinement, yet they do not establish temporal effects or intervention response. [1]

A second transferable pattern is the use of PLS-SEM in a digital-marketing adoption context. A tourism study modeled how word of mouth, trust, content characteristics, emotional connection, and brand awareness influence consumer perception and then purchase intention. Its contribution for the present protocol is structural: it shows how a mediated chain can be specified when attitudes are proximal outcomes and behavioral intention is distal. The limitation, which matters here, is that a single cross-sectional wave cannot distinguish perception as a cause from perception as a consequence of prior exposure. [2]

A construction-technology adoption study provides the most direct template for the current application because it integrates Technology-Organization-Environment and Diffusion of Innovation factors. It identifies technological competence, top management support, organizational readiness, external support, environmental orientation, and relative advantage as adoption determinants and analyzes them with PLS-SEM. For a digital showroom study, this combination is useful because showroom adoption depends on internal capability and external market pull, but it needs a stricter causal design than a one-time survey alone. [3]

A causal Bayesian-network study of AI-enabled BIM digital twins adds a different lesson: intervention analysis can be separated from descriptive association. That work used structured expert elicitation, hierarchical Bayesian pooling, and a Bayesian network to examine hypothetical adoption and policy scenarios, explicitly noting that all quantitative results were belief-based rather than performance data. This distinction is important for digital showroom research because scenario comparison can be valuable even when randomized rollout data are unavailable, provided that the analyst does not mistake expert beliefs for observed effects. [4]

Finally, a digital-twin framework for parking management shows how a model can be used for what-if simulation across alternative interventions. Its contribution is procedural rather than domain-specific: the framework uses observed data, predictive components, and a simulation engine to compare hypothetical strategies before real-world implementation. For showroom adoption, this suggests a pre-intervention simulator that can evaluate changes in training, vendor support, and customer-facing content design before a firm commits to full deployment. [5]

3. Research question and conceptual model

The protocol proposes the following main causal question: under what conditions does a digital showroom become adopted, implemented, and routinized within a firm? The corresponding conceptual model has five blocks. First, antecedent conditions include technological competence, top management support, organizational readiness, external support, relative advantage, and customer-facing market pressure. Second, mediating perceptions include usefulness, ease of integration, expected customer engagement, and perceived risk. Third, a text-derived attitude layer captures language that signals enthusiasm, uncertainty, resistance, or strategic alignment. Fourth, implementation processes include pilot launch, staff training completion, content refresh cadence, and CRM integration. Fifth, outcomes include adoption status, sustained use, sales-process integration, and qualitative decision quality. [3] [2] [1] [6]

This model is intentionally staged. Adoption is not treated as a binary endpoint that appears at the moment of software purchase. A firm may approve a digital showroom budget, pilot the platform, abandon it after a weak trial, or scale it across units. A causal design that collapses these states into a single yes/no variable risks mixing different mechanisms. The protocol therefore specifies separate dependent variables for launch, persistence, and routinization, while retaining a higher-level binary summary for comparability.

The model also allows for moderation. The effect of perceived relative advantage is expected to vary by customer digital readiness; the effect of external support is expected to vary by firm size and integration complexity; and the effect of organizational readiness is expected to vary by content-update intensity. These are proposed hypotheses, not verified findings. They are included because adoption processes often depend on interaction between internal readiness and external demand rather than on additive main effects alone. [3] [1]

4. Operational definitions and measurement design

The protocol uses a mixed-measure design with survey items, administrative indicators, and open-text responses. The survey component measures latent constructs on ordinal scales. The administrative component captures observable milestones such as platform procurement, onboarding completion, training attendance, content updates, and integration with e-commerce or CRM systems. The open-text component collects short responses from managers and relevant staff about barriers, expected value, and implementation concerns. Text responses are not treated as decoration; they are used to refine construct definitions and detect tensions between stated readiness and actual sentiment. [6] [1]

Define launch L at the first verified availability to intended users. Define observed use U at each wave by a documented business workflow using showroom outputs. Sustained adoption A=1 requires verified launch and qualifying use at two consecutive scheduled observed waves. A verified failure of a required condition yields A=0; missing launch or use information yields A unavailable, not zero. Report attrition and missingness by arm with worst-case bounds. This operational persistence criterion does not establish organizational routinization, which requires separate measurement. The wave interval and minimum use evidence are fixed before enrollment. Purchase or onboarding alone is not adoption. A firm that closes or loses access is recorded as such under the registered endpoint rule, rather than removed after its assigned intervention becomes inconvenient.

Text responses are coded as supportive, ambivalent, resistant or strategic-neutral using a frozen guide and blinded adjudication. Their proportions are descriptive auxiliary measures. A disagreement between survey readiness and text attitude creates a discrepancy flag for interpretation, not an automatic numerical down-weighting of readiness. No resistant-response percentage is treated as a validated causal adjustment rule. A pilot may examine measurement properties, but its findings cannot retrospectively alter a confirmatory endpoint. Latent construct scores and their uncertainties are reported separately from administrative adoption evidence. This preserves the distinction between staff perceptions, measurement assumptions and actual sustained system use, rather than forcing them into a single unvalidated readiness coefficient. [6]

To preserve construct validity, each latent domain should have at least three indicators where feasible, but the measurement model should allow for sparse indicators when a construct is naturally narrow. Items should be phrased to separate capability from aspiration. For example, technological competence should ask whether staff can configure and maintain showroom functions, not merely whether they support digitization in principle. Likewise, top management support should ask about resource allocation and decision permission, not generic approval. [3] [1]

5. Proposed causal estimation strategy

The primary causal design is a firm-cluster randomized trial of a fixed training-and-integration package versus software-only access. Eligible firms have already procured the same showroom platform before randomization. This prevents purchase from becoming a treatment-induced eligibility criterion. Block assignment by sector, size and baseline digital maturity, and freeze the package before launch. The primary estimand is the intention-to-treat risk difference in sustained adoption at the registered follow-up. Identification requires assignment integrity, consistency of the package and an explicit no-between-firm-interference assumption; spillovers are recorded and examined separately. No firm is reclassified by its received training dose in the primary comparison. A matched observational study, if used instead, is reported as association unless its stronger identification assumptions are defended independently. [1] [4]

Collect baseline antecedents before assignment, log package delivery during implementation, and assess adoption on the fixed wave schedule after launch. Retain all randomized firms and disclose endpoint coverage. The software-only group is contemporaneous and defined before exposure, not selected from firms that subsequently failed to adopt. Use the registered cluster assignment for uncertainty estimation. Baseline measurement checks precede treatment; a baseline variable is not a valid negative-control outcome merely because it concerns accounting or another apparently unrelated process. Differential outcome capture and cross-firm staff sharing are explicit threats requiring sensitivity analysis. Measurement models may describe readiness but cannot replace the assigned intervention in the primary causal contrast.

The main causal estimand is the average effect of exposure to the showroom intervention package on the probability of sustained adoption. Secondary estimands include indirect effects through perceived usefulness and customer engagement, and moderated effects by organizational readiness. For a binary endpoint, a hypothetical risk-difference expression can be written as: ACE = P(Y=1 | do(T=1)) - P(Y=1 | do(T=0)), where Y is sustained adoption and T is exposure to the intervention package. This quantity is not yet estimated here; it defines the causal target for a future study.

Latent measurement models and Bayesian-network scenarios are optional exploratory extensions. Their item loadings, graph, conditional probability tables and elicited priors must be specified before a reproducible implementation can be evaluated. A scenario based on expert belief is a hypothesis generator rather than an estimated causal effect. The primary package trial does not require this overlay. Mediation through usefulness or engagement likewise requires additional post-treatment confounding assumptions and cannot be inferred simply from structural-model arrows. Register any such extension separately, with independent validation data. The proposal reports neither fitted latent coefficients nor observed showroom-adoption effects. [4] [5]

6. Comparator design, outcome measures, and falsifiable propositions

The primary comparator is no-showroom adoption during the observation window. A stronger design adds a second comparator: firms that adopt a conventional static product catalog but not a digital showroom. This allows separation of generic digitization effects from showroom-specific effects. If feasible, a third comparator can include firms that purchased showroom software but did not train staff or integrate the system into sales workflows, because that group helps identify whether implementation capacity, rather than procurement alone, drives adoption success.

Outcome measures should be aligned with the causal stages. For launch, the outcome is whether the firm deploys a showroom in the observation period. For persistence, the outcome is whether use remains active across two or more waves. For routinization, the outcome is whether showroom outputs appear in regular sales, configuration, or customer-support routines. A complementary outcome is decision quality, operationalized as the consistency of showroom use with documented sales or presentation processes. These measures avoid reducing adoption to a single attitude score.

The protocol proposes four falsifiable propositions. First, organizational readiness will have a positive effect on sustained adoption after controlling for firm size and sector. Second, perceived relative advantage will be mediated by perceived usefulness. Third, the positive effect of top management support will be larger in firms with lower initial digital maturity. Fourth, open-text resistance signals will predict dropout from pilot to routinized use even when survey readiness is high. These propositions are specific enough to fail, which is necessary for causal design. [3] [1] [6]

7. Validation design, failure cases, and limits of generalization

Validation should proceed in layers. First, temporal validation: measure antecedents before exposure and outcomes afterward. Second, construct validation: test whether survey readiness and text-derived attitudes converge but are not redundant. Third, sensitivity analysis: vary assumptions about unmeasured confounding and compare results across alternative specifications. Fourth, inspect pre-assignment measurements for baseline coding or timing errors. A proposed negative-control outcome requires a separately justified absence of an intervention pathway; unrelated-looking accounting measures are not automatically valid controls. These diagnostics cannot prove the assumptions behind a causal estimate. [1] [6]

Likely failure cases include symbolic adoption, where a firm installs a showroom but does not use it; selection bias, where firms with stronger digital culture are more likely to volunteer into the study; and construct leakage, where enthusiasm for digital transformation is mistaken for showroom-specific readiness. Another failure case is overfitting the text layer to a small corpus of open responses, which can make attitude refinement unstable. A further limitation is that scenario simulation can appear precise even when it rests on weak priors. The protocol therefore treats Bayesian-network outputs as decision-support scenarios, not as empirical forecasts. [4] [5] [6]

Generalization is bounded by institutional context. A showroom may be adopted for different reasons in retail, manufacturing, real estate, or B2B settings, and the relative weight of customer engagement versus operational efficiency may differ across sectors. The model should therefore be used as a transferable design, not as a universal parameter set. Cross-sector replication is necessary before strong general claims are made. [3] [1]

8. Conclusion

This protocol establishes a causal study design for digital showroom adoption that is more specific than an intention survey and more restrained than an unvalidated forecasting claim. Conceptually, it defines adoption as a staged process, specifies observable and latent variables, introduces a text-derived attitude layer for construct refinement, and combines mediation-based survey modeling with Bayesian scenario analysis. The protocol also clarifies a comparator structure and a set of falsifiable propositions. [1] [2] [6] [4] [5] [3]

What remains untested is substantial: the causal effect of any particular showroom intervention package, the stability of the proposed operational rule across sectors, and the relative importance of readiness, management support, customer pressure, and text-measured attitudes in actual adoption trajectories. The next empirical step is a preregistered longitudinal study using the protocol’s measurement and validation sequence, with results reported separately from this design paper.

References

1. Boruga, Veronica Madalina; Bratu, Melania Lavinia; Puenea, George; Popa, Daniel; Popa, Cristina Annemari; Bogdan, Iulia Georgiana; Savencu, Cristina Elena (2026). Organizational Readiness, Perceived Usefulness, and Determinants of Artificial Intelligence Adoption in Romanian Medical Management and Pharmaceutical Marketing. Healthcare. 14(12), 1714. doi:10.3390/healthcare14121714.
2. Babu, Md. Asaduzzaman; Urmi, Hafsa Akter; Tofayel, Abzal Hosen; Sabuj, Md. Mehedul Islam; Alam, Md Tamjid Ul (2025). The impact of influencer marketing in the tourism industry: A digital marketing perspective. PLOS One. 20(12), e0338423. doi:10.1371/journal.pone.0338423.
3. Alrasheed, Khaled A.; Alotaibi, Abdulaziz; Baarimah, Abdullah O.; Alawag, Aawag Moshen; Almujibah, Hamad; Ramu, Madhusudhan Bangalore (2026). BIM-Supported green building adoption in the construction industry. PLOS One. 21(9), e0356894. doi:10.1371/journal.pone.0356894.
4. Albtoush, Faten; Al-Btoosh, Ja’far A. Aldiabat; Rawashdeh, Taiseer Mustafa; Al-Btoush, Mohammed A. K. A. (2026). Assessing the impact of AI-enabled BIM digital twins on construction risks: a Bayesian Network of Jordanian expert beliefs. Frontiers in Artificial Intelligence. 9, 1846388. doi:10.3389/frai.2026.1846388.
5. Piccialli, Francesco; Amitrano, Sara; Cerciello, Donato; Borrelli, Anna; Prezioso, Edoardo; Canzaniello, Marzia (2025). A digital twin framework for urban parking management and mobility forecasting. Nature Communications. 16(1), 9400. doi:10.1038/s41467-025-65306-w.
6. Jabbar, Asif; Yuan, Jingbo; Al-Shamasneh, Ala’a R.; Rekik, Siwar (2026). BERT based sentiment analysis of consumer hesitancy toward solar energy adoption. Scientific Reports. 16(1), 8397. doi:10.1038/s41598-026-38604-6.