Measuring Machine Companionship: Scale Development and Validation for AI Companions

TL;DR

This paper develops and validates a new measurement scale for AI companions, capturing the positive, ongoing, and coordinated human-AI relationships, addressing a gap in the conceptualization of machine companionship.

Contribution

It introduces a novel scale for measuring machine companionship, validated through empirical analysis, and identifies two key factors: Eudaimonic Exchange and Connective Coordination.

Findings

Two factors of machine companionship identified: Eudaimonic Exchange and Connective Coordination.

Scale validation confirmed the factors' expected functions across two samples.

Deviations suggest two templates: socioinstrumental and autotelic MC.

Abstract

The mainstreaming of companionable machines--customizable artificial agents designed to participate in ongoing, idiosyncratic, socioemotional relationships--is met with relative theoretical and empirical disarray, according to recent systematic reviews. In particular, the conceptualization and measurement of machine companionship (MC) is inconsistent or sometimes altogether missing. This study starts to bridge that gap by developing and initially validating a novel measurement to capture MC experiences--the unfolding, autotelic, positively experienced, coordinated connection between human and machine--with AI companions (AICs). After systematic generation and expert review of an item pool (including items pertaining to dyadism, coordination, autotelicity, temporality, and positive valence), N = 467 people interacting with AICs responded to the item pool and to construct validation measures. Through exploratory factor analysis, two factors were induced: Eudaimonic Exchange and Connective Coordination. Construct validation analyses (confirmed in a second sample; N = 249) indicate the factors function largely as expected. Post-hoc analyses of deviations suggest two different templates for MC with AICs: One socioinstrumental and one autotelic.