Attention Issues in Spatial Information Systems: Directing Mobile Users' Visual Attention Using Augmented Reality

Biocca, Frank; Owen, Charles B.; Tang, Arthur; Bohil, Corey J.

doi:10.2753/mis0742-1222230408

Cited by 87 publications

(45 citation statements)

References 7 publications

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“…Other studies have shown that recall of information can be inhibited (Drolet & Luce, 2004;Nasco & Bruner, 2009). Biocca, Owen, Tang, and Bohil (2007) have found that overwhelming amounts of information makes it harder to comprehend complex messages and Palfrey and Gasser (2008) found that information overload can cause people to use sensory filters to cope with the sheer amount of information, making it impossible to pay attention to most messages (Hill & Moran, 2011). Furthermore, psychologists have linked text messaging and instant messaging with selfreported symptoms of depression and social anxiety among college undergraduates (Becker, Alzahabi, & Hopwood, 2013).…”

Section: Social Media Fatiguementioning

confidence: 98%

Too much Facebook? An exploratory examination of social media fatigue

Bright

Kleiser

Grau

2015

Computers in Human Behavior

374

332

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a b s t r a c tSocial media usage levels continue to climb generating copious amounts of content. As more people crowd social media (e.g. Facebook), and create content, some research points to the existence of a concept called social media fatigue. Social media fatigue is defined as a user's tendency to back away from social media participation when s/he becomes overwhelmed with information. Lang's (2000) limited capacity model is used to understand the role of information overload for social media fatigue. This research examines the concept of social media fatigue and its proposed antecedents: social media efficacy, helpfulness, confidence and privacy concerns. Using confirmatory regression, this research determined that privacy concerns and confidence have the greatest predictive value for social media fatigue. This paper has theoretical implications for not only LCM but also other technology acceptance models such as TAM and UTAUT and UTAUT2. It also has implications for those trying to engage with online audiences and their subsequent reactions to that attempt at engagement. Several future research ideas are explored as well.

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Section: Social Media Fatiguementioning

confidence: 98%

Too much Facebook? An exploratory examination of social media fatigue

Bright

Kleiser

Grau

2015

Computers in Human Behavior

374

332

View full text Add to dashboard Cite

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“…The proper development of the user interface for AR solutions, including speech recognition features, has been addressed by Ajanki et and Caruso et al (2015). Finally, the development of MAR solutions has been discussed by Biocca et al (2007), Mourtzis et al (2013), Verbelen et al (2014), Han and Zhao (2015), and Kim and Lee (2016).…”

Section: Technical Papersmentioning

confidence: 99%

Augmented reality technology in the manufacturing industry: A review of the last decade

2019

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The aim of this article is to analyze and review the scientific literature relating to the application of Augmented Reality (AR) technology in industry. AR technology is becoming increasingly diffuse, due to the ease of application development and the widespread use of hardware devices (mainly smartphones and tablets) able to support its adoption. Today, a growing number of applications based on AR solutions are being developed for industrial purposes. Although these applications are often little more than experimental prototypes, AR technology is proving highly flexible and is showing great potential in numerous areas (e.g., maintenance, training/learning, assembly or product design) and in industrial sectors (e.g., the automotive, aircraft or manufacturing industries). It is expected that AR systems will become even more widespread in the near future.The purpose of this review is to classify the literature on AR published from 2006 to early 2017, to identify the main areas and sectors where AR is currently deployed, describe the technological solutions adopted, as well as the main benefits achievable with this kind of technology.ARTICLE HISTORY

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“…Previous research (Rech et al, 2007) has shown that users respond positively to highlighting as a means of assistance (although Rech et al, 2007, referred to highlighting of unused parts). Finally, highlighting is also known (Biocca et al, 2007) to be a very useful tool for drawing users' attention to the interface, thus it could be effective for providing assistance to users while they are using some interface and are busy trying to complete their task.…”

Section: Adaptive Menus and Toolbarsmentioning

confidence: 99%

Highlighting items as means of adaptive assistance

Antwarg

Lavie

Rokach

et al. 2013

Behaviour & Information Technology

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Providing adaptive help during interaction with the system can be used to assist users in accomplishing their tasks. We propose providing guidance by highlighting the steps required for performing a task that the user intends to complete according to the prediction of a system. We present a study aimed at examining whether highlighting intended user steps in menus and toolbars as a means of assisting users in performing tasks is useful in terms of user response and performance. We also examined the effects of different accuracy levels of the relevancy of the provided help and the help format on user response and performance. An experiment was conducted in which 64 participants performed tasks using menus and toolbars of a simulated email application. Participants were offered a highlighted guidance of the required steps in varying levels of accuracy (100%, 80%, 60%, and no guidance). Our results support the benefits of highlighted help both in user performance times and in user satisfaction from receiving such assistance. Users found the assistance necessary and helpful and by the same token not unduly intrusive. Additionally, users felt that such assistance generally helped in reducing performance time on tasks. We did not find a significant difference when users receiving help at 80% accuracy was compared to those receiving help at 100% accuracy; however, such a difference does appear for 2 those receiving 60% accuracy. In such cases we found that the user's satisfaction level, perceived usefulness and trust in the system decreased while their notion of perceived intrusiveness increased. We conclude that assisting users by highlighting the required steps is useful so long as the minimal accuracy level is higher than 60%.Our study has implications on the implementation of highlighting next steps as a means of adaptive help and on integrating probability-based algorithms such as intention prediction to adaptive assistance systems.

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Attention Issues in Spatial Information Systems: Directing Mobile Users' Visual Attention Using Augmented Reality

Cited by 87 publications

References 7 publications

Too much Facebook? An exploratory examination of social media fatigue

Too much Facebook? An exploratory examination of social media fatigue

Augmented reality technology in the manufacturing industry: A review of the last decade

Highlighting items as means of adaptive assistance

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