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stages, while feature-specific sentiment mapping highlights areas requiring improvement
in future iterations.
The findings contribute to the growing body of literature on technology acceptance
by demonstrating how sentiment analysis can enhance traditional acceptance models
with real-world usage data. Additionally, this research provides practical insights for
manufacturers, retailers, and policymakers engaged in promoting sustainable
consumption patterns in the technology sector. The methodology developed here offers a
replicable framework for analyzing consumer sentiment across various technology
products, with potential applications in quality assurance, product development, and
marketing strategy optimization.
2. Literature review
2.1. Technology acceptance in smart wearables
The adoption of wearable technology has been extensively examined through the
lens of technology acceptance models, with Davis's (1989) Technology Acceptance Model
(TAM) serving as a foundational framework. TAM posits that Perceived Usefulness (PU)
and Perceived Ease of Use (PEOU) are primary predictors of user acceptance. However,
research by Chuah et al. (2016) demonstrates that traditional IT acceptance models
require extension when applied to wearable devices, as aesthetics and wearability
emerge as equally critical factors alongside functionality. This is particularly relevant for
Meta Glasses, where the Ray-Ban partnership explicitly addresses the fashion dimension
that hindered earlier smart glasses adoption.
The Unified Theory of Acceptance and Use of Technology (UTAUT) extends TAM by
incorporating Social Influence and Facilitating Conditions as key determinants of adoption
(Tamilmani et al., 2017). For smart glasses, social acceptance represents a unique
challenge, as the "Glasshole" stigma associated with Google Glass demonstrates that
public perception of privacy violations can override functional benefits. Current devices
attempt to mitigate this through LED recording indicators, though studies by Hoyle et al.
(2014) indicate that bystander discomfort persists regardless of technical safeguards,
creating tension between user utility and social acceptability.
2.2. User experience dimensions in smart eyewear
User satisfaction in smart eyewear operates across functional and hedonic
dimensions. Functional factors include audio quality, battery life, and connectivity
stability, all of which contribute to baseline PEOU in TAM frameworks (Chismar & Wiley-
Patton, 2005). The shift to open-ear audio in Meta Glasses introduces design tradeoffs
between user clarity and sound leakage, directly impacting perceived privacy and social
acceptability. Battery constraints inherent to the form factor create friction in daily
usage patterns, with user tolerance highly sensitive to whether devices sustain a full day
of typical activity.
Hedonic factors, particularly the unique value proposition of first-person
perspective capture, represent a distinct advantage over smartphone photography.
UTAUT2 emphasizes Hedonic Motivation as a key determinant of consumer technology
acceptance (Hwang & Lee, 2018), with immersive experiences driving adoption beyond
purely utilitarian considerations. The emotional value of POV content creation has been
identified in user reviews as a differentiating feature that smartphones cannot replicate
authentically, suggesting that experiential benefits may outweigh functional limitations
for certain user segments.

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