We also identified the factors that are critical to older adults but did not appear in the existing models. Finally, we applied the existing vocabulary to our model to comply with the conventional terms in the field.

Again following grounded theory practices from [33], we compared the model that emerged from our data with existing theoretical models of technology acceptance to determine differences and similarities between them.

Triangulating the empirical findings from our preliminary results with the existing theoretical models, we proposed an extension of the existing theoretical models that explains the technology acceptance behavior of our participants who were aged 60 or over.

Consolidating our preliminary findings with the existing models, we propose an extended technology acceptance model for older adults illustrated in Figure 3. Extending to the predecessor theories, our tentative model introduces the perceived effort of learning a new technology as an obstacle for older adults' technology acceptance, which has not been reported in any studies of younger adults' technology acceptance.

Using TAM, UTAUT, and several other works as theoretical underpinning, Renaud and Biljon proposed a model to explain older adults' mobile phone adoption.

Although many researchers have sought to understand and predict technology acceptance behavior, there has been relatively less effort to build a theoretical model for older adults, with one exception (STAM).

Extending the original TAM and consolidating the constructs of several other existing models, Venkatesh et al. proposed the Unified Theory of Acceptance and Use of Technology (UTAUT) [37].

Azjen's theory of planned behavior [1, 2] posits that a specific behavior is the result of an intention to carry it out, and that intention is determined by attitudes, norms, and the perception of control over the behavior. Drawing upon this theory of planned behavior, Davis et al. developed the technology acceptance model (TAM) [10].

Then, by triangulating our empirical findings with existing theoretical models from the literature, we found out that the existing models of technology adoption require new theory components to be able to describe technology adoption processes of our participants.

Technology acceptance has been widely studied, and several models have been proposed and tested [10, 37]. However, the HCI literature lacks a comprehensive explanation of technology acceptance among older adults.

Established theories of human cognition describe how exposure to variation and consistency within prescribed structures can help people more robustly form mental models of a phenomenon, e.g., how an LLM behaves. Specifically, in line with Variation Theory [35], the features we instantiate identify patterns of consistency (Figure 1d, "Exact Matches"), variation (Figure 1c, "Unique Words"), or both (Figures 1a, 1b, "Positional Diction Clustering (PDC)"—a novel algorithm we introduce in this paper). In line with Analogical Learning Theory [13], PDC highlights analogous text across LLM responses, i.e., positionally consistent and similar in diction, such that users can see emergent relationships.

The revealed variation within these analogous cross-document relationships can invite the user’s engagement.

This is the essence of comparative close reading, a dialectical activity that requires repeated deep engagement with the texts to reveal new insights.

By definition, sensemaking and other dialectical activities necessitate engagement.

nobody told it what to do that's that's the kind of really amazing and frightening thing about these situations when Facebook gave uh the algorithm the uh uh aim of increased user engagement the managers of Facebook did not anticipate that it will do it by spreading hatefield conspiracy theories this is something the algorithm discovered by itself the same with the capture puzzle and this is the big problem we are facing with AI

because of feelings of belonging and obligation to the community.

Content organization refers to features that require little effort from the user and that help fellow users receive useful information about the content. These features include the “like” button and options such as ratings (star ratings or a numerical scale) or tagging content with user-suggested keywords.

n Saul Carliner’s LessonsLearned from Museum Exhibit Design, exhibit design is broken into three main stages(2003). The “idea generator,” “exhibit designer, and “idea implementer,”leads each phase respectively (Carliner, 2003). The idea generator determines the main concepts or themes and chooses the content of the exhibit. Then, the exhibit designer takes the concept to prepare physical designs for the new gallery, creating display cases and deciding wall and floor coverings for the overall ambiance. Lastly, the idea implementer brings together everything to create the exhibit. The implementer collects any missing pieces for the gallery, ensures conservation of displayed pieces, and oversees all parts of the assembly(Carliner, 2003)

it is not art itself that people are turning from. People are turning away “from the traditional delivery mechanisms” (Cohen, 2013)