Visual impressions of mobile app interfaces

Miniukovich, Aliaksei; Angeli, Antonella De

doi:10.1145/2639189.2641219

Cited by 39 publications

(47 citation statements)

References 41 publications

(110 reference statements)

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“…We based the metrics on the psychological investigations of what people see as complex and unappealing [23], and HCI investigations of webpage aesthetics [33,14,36,25,26]. The results of the present two studies replicated the results of past studies [17,16], which suggests the metrics are solid enough to be presented to the larger CHI audience. In addition to this, we have replicated the phenomenon of consistent and lasting immediate impressions [14,33] on two types of stimuli (webpages and mobile apps) using a between-subjects experimental design.…”

Section: Introductionsupporting

confidence: 55%

“…In this paper, we extend earlier work [17,16], and describe and test in two studies eight GUI aesthetics metrics: visual clutter, color range, number of dominant colors, figureground contrast, contour congestion, symmetry, and the new metrics of grid quality and white space. We based the metrics on the psychological investigations of what people see as complex and unappealing [23], and HCI investigations of webpage aesthetics [33,14,36,25,26].…”

Section: Introductionmentioning

confidence: 93%

“…Past attempts [40,39,26,25,16,17] to automatically account for visual aesthetics of GUIs consisted of two steps: gathering user scores of GUI aesthetics and matching them against computed scores of a set of automatic metrics. The first step reflects our understanding that beauty lies in the eye of the beholder, and involves conducting either carefully orchestrated in-lab user studies [14,33,21,17] or large-scale crowdsourcing studies with thousands of participants [25,25].…”

Section: Related Workmentioning

confidence: 99%

“…The first step reflects our understanding that beauty lies in the eye of the beholder, and involves conducting either carefully orchestrated in-lab user studies [14,33,21,17] or large-scale crowdsourcing studies with thousands of participants [25,25]. The second step uses the averaged user scores as the ground truth data, and tests how well various metrics and algorithms predict the scores.…”

Section: Related Workmentioning

confidence: 99%

“…Despite these studies started from an assumption of a strong complexity-aesthetics link, none of them looked systematically into the underlying determinants of visual complexity, and thus, missed out several potential determinants from their analyses. Earlier work [16,17] gave a more detailed overview of visual complexity determinants and listed eight of them, relevant to HCI (Table 1).…”

Section: Complexity Roots Of Aestheticsmentioning

confidence: 99%

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Computation of Interface Aesthetics

Miniukovich

Angeli

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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People prefer attractive interfaces. Designers strive to outmatch competitors, and create apps and websites that stand out. However, significant expenses on design are unaffordable to small companies; instead, they could adopt automatic tools of interface aesthetics evaluation, a cheaper strategy to good design. This paper describes an important step towards such a tool; it presents eight automatic metrics of graphical user interface (GUI) aesthetics. We tested the metrics in two exploratory studies -on desktop webpages (N = 62) and on iPhone apps (N = 53) -and found them to function on both GUI types and for both immediate (150ms exposure) and deliberate (4s exposure) aesthetics impressions. Our best-fit regression models explained up to 49% of variance in webpage aesthetics and up to 32% (if app genre is considered) of variance in iPhone app aesthetics. These results confirm past results and suggest the metrics are valid and reliable enough to be widely discussed, and possibly, to be embedded in our prospective GUI evaluation tool, tLight.

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Section: Introductionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 93%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Complexity Roots Of Aestheticsmentioning

confidence: 99%

See 3 more Smart Citations

Computation of Interface Aesthetics

Miniukovich

Angeli

2015

Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

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The Impact of Perceived Visual Complexity of Mobile Online Shops on User's Satisfaction

Sohn

Seegebarth

Moritz

2017

Psychology and Marketing

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The use of Internet‐enabled mobile devices (i.e., smartphones and tablets) for shopping purposes has gained in relevance among consumers. As mobile technologies become increasingly powerful, richer modes of presentation (e.g., videos and animated pictures) are used to design mobile online shops resulting in increased visual complexity. Given the importance of fluent mobile web site processing during typically short interactive sessions and the need to bridge a gap in prior research, this article explores the effects of perceived visual complexity on consumers’ perceptions and behavior‐related intentions when using mobile online shops. The findings from two empirical studies (i.e., an online survey and an experimental study) conducted among mobile device‐using online shoppers illustrate that perceived visual complexity negatively influences individuals’ satisfaction with their mobile shopping experience, whereas their channel‐specific satisfaction enhances their loyalty to the online retailer. Customers’ perceived psychological costs (i.e., time and effort costs as well as spatial crowding perceptions) partially mediate the relationship between visual complexity perceptions and satisfaction. As the results of the laboratory experiment show, the mediating role of perceived spatial crowding in the relationship between perceived visual complexity and satisfaction depends on the mobile device individuals use to visit an online shop. Hence, this relationship is mediated by perceptions of spatial crowding only when online shop visitors use a smartphone rather than a tablet.

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Users’ Behavioral Strategies Toward Mobile App Problems: Fight or Flight

İnal

Hacaloğlu

2019

Lecture Notes in Computer Science

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In this paper, we identify two distinct behavioral strategies for dealing with problems encountered in the use of mobile apps -fight or flight. In the fight strategy, individuals do not give up using an app when faced with a problem; rather, they experiment with different ways to cope with that problem, whereas the flight strategy refers to the user's decision to uninstall an app when they encounter a problem and/or their intention to use an alternative app. These strategies were identified from an analysis of documents, which forty-two users reported, and can be used to understand how users deal with encountered problems. The participants were asked to use a mobile app of their choice for one week and report the behavioral strategies they utilized to counter problems they experienced. According to the findings obtained from content analysis, the most reported complaints concerned the categories of interface design, functional error, feature request, and feature removal. The participants who complained about functional errors, frustrating features, and slow application speed stopped using the app (flight behavior) whereas those that were dissatisfied with the interface, a missing feature or the content of the app continued to use the app and tried to overcome the problems (fight behavior).

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Visual impressions of mobile app interfaces

Cited by 39 publications

References 41 publications

Computation of Interface Aesthetics

Computation of Interface Aesthetics

The Impact of Perceived Visual Complexity of Mobile Online Shops on User's Satisfaction

Users’ Behavioral Strategies Toward Mobile App Problems: Fight or Flight

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