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Photo of Liat Levontin
Liat Levontin
Associate Professor
Citizen Scientists’ Motivations

Understanding volunteers’ motivations to participate in Citizen Science (CS) projects
is essential for these projects’ effective management and success. Many studies have
investigated citizen scientists’ motivations, but only a few have used a theory-based
approach to provide a standardized methodology to measure CS motivations. The current
research aims to take the literature a step further by developing and applying a general,
standardized, theory-based framework of CS motivation and a CS motivation scale
(CSMS) that can be used to assess volunteers’ motivations across diverse CS projects. The
CSMS comprises 58 items corresponding to 15 motivational categories. It is grounded
in Schwartz’s theory of basic human values, while incorporating the wealth of empirical
knowledge on citizen scientists’ motivations. We administered the scale to three separate
samples of either Dutch or Hebrew-speaking participants who volunteered for three CS
projects. Analysis of participants’ ratings of their motivations supported our theoretical
framework, showing that 13 of the scale’s 15 motivational categories fell into 4 higher-order motivations, which correspond to Schwartz’s theory of values: openness to change,
self-enhancement, continuity (conservation), and self-transcendence. Results further
provide concrete insights into CS participation behavior, showing that certain motivations
(including help with research, benevolence, and self-direction) were consistently among
the most important motivators for participation across CS projects. Finally, we found
that prioritizing certain motivations can also predict participation behavior (e.g., duration
of participation and willingness to participate in additional volunteering activities). The
CSMS is a new tool that can be applied across projects spanning diverse domains and
populations, advancing and standardizing the growing literature on CS motivations.

The psychology of AI

People increasingly rely on Artificial Intelligence (AI) based systems
to aid decision-making in various domains and often face a choice
between alternative systems. We explored the effects of users’ perception of AI systems’ warmth (perceived intent) and competence (perceived ability) on their choices. In a series of studies, we manipulated AI systems’ warmth and competence levels. We show that,
similar to the judgments of other people, there is often primacy for warmth over competence. Specifically, when faced with a choice between a high-competence system and a high-warmth system, more participants preferred the high-warmth system. Moreover,
the precedence of warmth persisted even when the high-warmth system was overtly deficient in its competence compared to an alternative high competence-low warmth system. The current research proposes that it may be vital for AI systems designers to consider and communicate the system’s warmth characteristics to
its potential users

The psychology of AI

Consumers and marketers alike benefit from reviews as they reflect
reviewers’ opinions and experiences with a product or service.
Technological developments of artificial intelligence (AI) systems
designed to assist writers in text composition might bias what consumers
write in their reviews. The current research explores the effects of using
AI-generated text suggestions on reviews and its potential to interfere
with the benefits of reviews. Three studies examine how writing hotel
and restaurant reviews with (vs. without) the assistance of text
suggestions influences review writers, reviews texts, and reviews
readers. Results show that almost 40% of writers who were offered
suggestions indeed used them. Text suggestion usage was associated
with more informal, confident writing. However, contrary to intuition, it
had limited effects on reviewers’ perceptions of their reviews, although
suggestion usage was negatively related to reviewers’ efficiency while
writing. Importantly, it had no impact on readers’ perceptions of the
reviews and the writers of the reviews. We discuss the implications of
using AI-generated text suggestions in consumers’ writing and directions
for future research.