Effects of visual simplicity in product design and individual differences in preference of interactive products

Eytam, Eleanor; Lowengart, Oded; Tractinsky, Noam

doi:10.1007/s11846-020-00391-3

Cited by 18 publications

(15 citation statements)

References 63 publications

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“…The Model 1 (doctor: mean 5.25, SD 1.57; robot: mean 4.71, SD 1.59) and Model 2 (doctor: mean 4.96, SD 1.56; robot: mean 4.66, SD 1.50) designs were rated as significantly more instrumental than that of Model 3 (doctor: mean 4.57, SD 1.72; robot: mean 4.45, SD 1.71) in both conditions (both comparisons, P <.001). Similar trends were also reported in studies that examined the effect of model choice on judgments of instrumentality [ 18 , 19 ]. There was a significant difference between aesthetic ratings of Model 2 (mean 4.07, SD 1.60) versus Model 3 (mean 3.82, SD 1.60) but only in the robot condition ( P =.004).…”

Section: Resultssupporting

confidence: 83%

“…Predictably, the most complex model (Model 3 with 12 controls) was regarded as the least instrumental. This pattern of rating simplicity as providing high instrumentality has been noted in previous research [ 18 , 19 ]. Usability experts often advocate simplicity to promote a product’s usability.…”

Section: Discussionsupporting

confidence: 80%

“…Preference assigned to mHealth apps largely depends on the qualities of the UI (eg, [ 17 , 18 ]). Users are interested in how useful an app may be, its easy operation, and its aesthetics [ 18 , 19 ]. In addition, technology in health care often relies on artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

“…Symbolism refers to the associations that the product produces and the meanings it communicates, regardless of its pragmatic goals. The effect of each of these qualities on product preference is mediated by the role of the users [ 18 ], their personal characteristics [ 19 ], and the product itself [ 17 ]. Eytam et al [ 18 ] found that the visual simplicity or complexity of a UI, as reflected by the number of its controls, influences judgments of instrumentality and aesthetics.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we expected instrumentality to be a salient predictor of product preference for applications backed by a human physician and believed to have greater instrumental value. Because noninstrumental attributes are reported in the literature as salient predictors of preference [ 18 , 19 ], we expected aesthetics and symbolism in both application types (with a human physician and with an AI algorithm) to be salient predictors of mHealth apps . Therefore, H3a was that “Instrumentality should be a salient predictor of preference variance for apps that engage a human physician versus an AI algorithm.” H3b was that “Aesthetics should be a salient predictor of preference variance for apps that engage a human physician and those that engage an AI algorithm.” H3c was that “Symbolism should be a salient predictor of preference variance for apps that engage a human physician and those that engage an AI algorithm.”…”

Section: Introductionmentioning

confidence: 99%

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Supporting Management of Noncommunicable Diseases With Mobile Health (mHealth) Apps: Experimental Study

Kela¹,

Eytam²,

Katz³

2022

JMIR Hum Factors

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Background Noncommunicable diseases (NCDs) are the leading global health problem in this century and are the principal causes of death and health care spending worldwide. Mobile health (mHealth) apps can help manage and prevent NCDs if people are willing to use them as supportive tools. Still, many people are reluctant to adopt these technologies. Implementing new apps could result in earlier intervention for many health conditions, preventing more serious complications. Objective This research project aimed to test the factors that facilitate the adoption of mHealth apps by users with NCDs. We focused on determining, first, what user interface (UI) qualities and complexity levels appeal to users in evaluating mHealth apps. We also wanted to determine whether people prefer that the data collected by an mHealth app be analyzed using a physician or an artificial intelligence (AI) algorithm. The contribution of this work is both theoretical and practical. We examined users’ considerations when adopting mHealth apps that promote healthy lifestyles and helped them manage their NCDs. Our results can also help direct mHealth app UI designers to focus on the most appealing aspects of our findings. Methods A total of 347 respondents volunteered to rate 3 models of mHealth apps based on 16 items that measured instrumentality, aesthetics, and symbolism. Respondents rated each model after reading 1 of 2 different scenarios. In one scenario, a physician analyzed the data, whereas, in the other, the data were analyzed by an AI algorithm. These scenarios tested the degree of trust people placed in AI algorithms versus the “human touch” of a human physician regarding analyzing data collected by an mHealth app. Results As shown by the responses, the involvement of a human physician in the application had a significant effect (P<.001) on the perceived instrumentality of the simple model. The complex model with more controls was rated significantly more aesthetic when associated with a physician performing data analysis rather than an AI algorithm (P=.03). Conclusions Generally, when participants found a human touch in the mHealth app (connection to a human physician who they assumed would analyze their data), they judged the app more favorably. Simple models were evaluated more positively than complex ones, and aesthetics and symbolism were salient predictors of preference. These trends suggest that designers and developers of mHealth apps should keep the designs simple and pay special attention to aesthetics and symbolic value.

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Section: Resultssupporting

confidence: 83%

Section: Discussionsupporting

confidence: 80%