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arxiv: 2606.07551 · v1 · pith:D4L4L3ZQnew · submitted 2026-05-21 · 💻 cs.CY · cs.HC· cs.RO

Astro, I'm Home! Investigating Factors that Influence the Acceptance of Home Robots Using Supervised Machine Learning

Katrin Fischer , Essence Wilson , Steffie Kim , Dmitri Williams This is my paper

Pith reviewed 2026-06-30 16:45 UTC · model grok-4.3

classification 💻 cs.CY cs.HCcs.RO
keywords home robotssocial robotstechnology acceptanceUTAUT2Lasso regressionRidge regressionintention to usesupervised machine learning
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The pith

Performance expectancy, social influence, and hedonic motivation best predict intention to use home social robots in the UTAUT2 framework.

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

The paper uses Lasso and Ridge regression on survey data to test which factors from the UTAUT2 model drive people's stated intention to adopt home social robots. It finds that performance expectancy, social influence, and hedonic motivation are the strongest and most consistent predictors, while also flagging usability, trust, and competence as additional useful variables. A sympathetic reader would care because clearer knowledge of these drivers could guide robot design and marketing so that new home robots match what people actually value. The work is exploratory and centers on variable selection rather than building a complete revised model.

Core claim

Within the original UTAUT2 framework, performance expectancy, social influence, and hedonic motivation emerged as the strongest and most consistent predictors of intention to use the technology. In addition, usability, trust, and competence were identified as promising variables in a model predicting intention to use.

What carries the argument

Regularization techniques (Lasso and Ridge regression) applied to UTAUT2 survey responses to isolate the strongest predictors of intention to use home robots.

If this is right

  • Designers should emphasize clear performance benefits when developing home robots.
  • Social proof and enjoyment features should receive priority in product positioning.
  • Future acceptance models for home robots should incorporate usability, trust, and competence alongside the core UTAUT2 constructs.
  • Regularization methods can help identify the most relevant variables when extending technology-acceptance frameworks to new device categories.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the identified predictors generalize, marketing campaigns could focus on demonstrating task performance and fun rather than technical specifications alone.
  • The same analysis approach could be applied to other emerging home technologies such as smart appliances to test whether the same three UTAUT2 factors dominate.
  • Real deployment trials that replace self-reported intention with logged usage data would provide a direct test of whether the survey-based predictors hold outside laboratory conditions.

Load-bearing premise

The survey responses accurately reflect real future behavior and the sample represents typical potential home-robot users without major bias.

What would settle it

A follow-up study that measures the same predictors then tracks whether participants actually buy or regularly use a home robot would falsify the claim if the measured predictors show no relation to observed adoption.

Figures

Figures reproduced from arXiv: 2606.07551 by Dmitri Williams, Essence Wilson, Katrin Fischer, Steffie Kim.

Figure 1
Figure 1. Figure 1: The Home Robot Astro. [1], and TAM [13]. Moreover, research indicates that a robot’s trustworthiness characteristics (i.e. ability, benevolence, and integrity), beyond contributing to the development of trust [34], directly affect robot acceptance [20]. Third, people’s first impressions are informed by assessments of warmth and competence, which influence the evaluation of upcoming interactions [12]. Both … view at source ↗
Figure 2
Figure 2. Figure 2: Study Overview. 3.2 Measures Demographic Variables. Demographic variables included participants’ age (measured by birth year), gender (female, male, other), education (in years), race (White/Caucasian, Black African American, Native American, Asian, Native Hawaiian/Pacific Islander, other, prefer not to say), and familiarity with robots (7-point Likert scale). UTAUT2 Variables. The dependent variable, beha… view at source ↗
Figure 3
Figure 3. Figure 3: Best Subset Selection. 4.1 Best Subset Selection Best subset selection was run and to identify additional variables predicting behavioral intention. The latent variable behavioral intention (α = 0.79, M = 3.88, SD = 0.80) was entered as dependent variable. Performance expectancy (PE; α =0.69, M = 3.91, SD = 0.76) and social influence (SI; α = 0.75, M = 3.83, SD = 0.76) are the strongest predictors from the… view at source ↗
Figure 4
Figure 4. Figure 4: Lasso Regression Results. In the best model selected by Lasso regression, performance expectancy (PE), social influence (SI), and hedonic motivation (HM; α = 0.70, M = 4.03, SD = 0.69) are the strongest UTAUT variables. Usability and trust are the strongest predictors among the added variables of interest. The coefficients for effort ex￾pectancy (EE; α = 0.68, M = 4.03, SD = 0.60), ability (Ab), benevolenc… view at source ↗
Figure 5
Figure 5. Figure 5: Ridge Regression Results. Performance expectancy (PE), social influence (SI) and hedonic motivation (HM) are three strongest UTAUT predictors (closely followed by habit and price). Usability, trust and competence (Comp; α = 0.83, M = 5.52, SD = [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
read the original abstract

The use of social robots in home environments is on the rise. This exploratory study applies regularization techniques (e.g., Lasso and Ridge regression) to investigate variables and identify new models of technology acceptance in the context of social robots. Within the original UTAUT2 framework, performance expectancy, social influence, and hedonic motivation emerged as the strongest and most consistent predictors of intention to use the technology. In addition, usability, trust, and competence were identified as promising variables in a model predicting intention to use.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The manuscript reports an exploratory study applying Lasso and Ridge regression to UTAUT2-based survey data (extended with usability, trust, and competence items) to identify predictors of intention to use home social robots. It concludes that performance expectancy, social influence, and hedonic motivation are the strongest and most consistent predictors within the original framework, while usability, trust, and competence emerge as promising additional variables.

Significance. If the regression results prove robust, the work offers a data-driven extension of UTAUT2 to the domain of home social robots and illustrates how regularization can manage multicollinearity in acceptance surveys. The explicit use of supervised ML for variable selection is a methodological strength that could be replicated in related technology-acceptance studies.

major comments (2)
  1. [Methods] Methods: the manuscript does not report the survey sample size or demographic composition, which is load-bearing for evaluating whether the identified predictors (performance expectancy, social influence, hedonic motivation) are stable or sensitive to small-n artifacts.
  2. [Analysis] Analysis/Results: no description is given of the procedure for selecting the regularization parameter lambda (cross-validation folds, grid search, or stability selection), nor are coefficient stability metrics or out-of-sample error rates provided; without these the claim that certain variables are 'strongest and most consistent' cannot be assessed.
minor comments (1)
  1. [Abstract] Abstract: adding one sentence on sample size and validation approach would allow readers to gauge the strength of the reported predictors without consulting the full text.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our exploratory study. We address each major comment below and indicate the revisions that will be incorporated.

read point-by-point responses

  1. Referee: [Methods] Methods: the manuscript does not report the survey sample size or demographic composition, which is load-bearing for evaluating whether the identified predictors (performance expectancy, social influence, hedonic motivation) are stable or sensitive to small-n artifacts.

    Authors: We agree that the sample size and demographic composition are necessary to assess the stability of the identified predictors. In the revised manuscript we will add a dedicated subsection in the Methods reporting the exact survey sample size and a full demographic breakdown of respondents. revision: yes

  2. Referee: [Analysis] Analysis/Results: no description is given of the procedure for selecting the regularization parameter lambda (cross-validation folds, grid search, or stability selection), nor are coefficient stability metrics or out-of-sample error rates provided; without these the claim that certain variables are 'strongest and most consistent' cannot be assessed.

    Authors: We acknowledge that explicit details on lambda selection and supporting metrics are required to substantiate the claims. The revised manuscript will describe the cross-validation procedure used to select lambda and will report coefficient stability metrics together with out-of-sample error rates. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper performs exploratory regularized regression (Lasso/Ridge) on survey responses to identify which UTAUT2 constructs and extensions best predict intention to use. Reported predictors are direct outputs of coefficient shrinkage on the collected data; no equation or claim reduces a result to its own inputs by definition, no self-citation chain is load-bearing, and no fitted parameter is relabeled as an independent prediction. The analysis is therefore self-contained empirical modeling.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Abstract-only review limits visibility into parameters and assumptions; standard ML regularization and UTAUT2 validity are presumed without independent verification in the provided text.

free parameters (1)
  • Regularization parameter lambda
    Controls penalty strength in Lasso and Ridge models and is typically selected via cross-validation or grid search during fitting.
axioms (1)
  • domain assumption UTAUT2 constructs validly and reliably measure technology acceptance factors for social robots
    The study applies the framework directly to identify predictors without additional validation of its constructs in this domain.

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