Beyond Intrinsic Motivation: The Role of Autonomous Motivation in User Experience

TL;DR

This work draws on Self-Determination Theory (SDT) to analyse autonomous and non-autonomous patterns of motivation in 497 interaction experiences and identifies five distinct patterns of motivation in technology use associated with significant differences in need satisfaction, affect, and perceived usability.

Abstract

Motivation and autonomy are fundamental concepts in Human-Computer Interaction (HCI), yet in User Experience (UX) research they have remained surprisingly peripheral. We draw on Self-Determination Theory (SDT) to analyse autonomous and non-autonomous patterns of motivation in 497 interaction experiences. Using latent profile analysis, we identify 5 distinct patterns of motivation in technology use -- "motivational profiles" -- associated with significant differences in need satisfaction, affect, and usability. Users' descriptions of these experiences also reveal qualitative differences between profiles: from intentional, purposive engagement, to compulsive use which users themselves consider unhealthy. Our results complicate exclusively positive notions of intrinsic motivation, and clarify how extrinsic motivation can contribute to positive UX. Based on these findings we identify open questions for UX and SDT, addressing "hedonic amotivation" -- negative experiences in activities which are intrinsically motivated but not otherwise valued -- and "design for internalisation" -- scaffolding healthy and sustainable patterns of engagement over time.

Figures (7)

• Figure 1: Motivation as conceptualised by Self-Determination Theory. Organismic Integration Theory (OIT) posits 6 ways in which motivation can be regulated, ranging from the least self-determined and autonomous (amotivation) to the most self-determined and autonomous (integrated regulation and intrinsic motivation). Note: the placement of intrinsic motivation to the right of integrated regulation does not indicate it is more autonomous. Adapted from ryan2017organismicvansteenkiste_fostering_2018.
• Figure 2: The five motivational profiles identified via latent profile analysis. Higher scores on constructs to the left indicate a less autonomous motivational profile, and higher scores on constructs to the right indicate a more autonomous profile. The black points indicate the mean, error bars show standard errors, boxes show standard deviations. Profile membership in LPA is probabilistic, and membership probability is indicated here by opacity (range:$(0.82,0.95)$). am = amotivated, ex = external regulation, ij = introjected regulation, id = identified regulation, it = integrated regulation, im = intrinsic motivation.
• Figure 3: Plots of the UMI data for the 6 motivational regulations, over the whole data set (all), and split by condition (negative, positive). Data provided in \ref{['tab:umi_descriptive']} in \ref{['appA']}. Points show means, error bars show standard error, boxes show standard deviation
• Figure 4: Results for the three basic need measures. Black points indicate means, error bars show standard error, boxes show standard deviation. Green and red lines indicate indicates means for positive and negative' conditions respectively. Asterisks indicate Holm-Bonferroni adjusted p-values: *** $< 0.001$, ** $< 0.01$, * $< 0.05$. See \ref{['lpa_profiles']} for more details supporting interpretation of the chart. Full tables of results, and plots for non-hypothesis variables are provided in \ref{['appA']}.
• Figure 5: Results for the PANAS (affect), UMUX (usability), and self-attribution measures. Black points indicate means, error bars show standard error, boxes show standard deviation. Green and red lines indicate indicates means for positive and negative' conditions respectively. Asterisks indicate Holm-Bonferroni adjusted p-values: *** $< 0.001$, ** $< 0.01$, * $< 0.05$. See \ref{['lpa_profiles']} for more details supporting interpretation of the chart. Full tables of results, and plots for non-hypothesis variables are provided in \ref{['appA']}.