Assessing Smartphone Ease of Use and Learning from the Perspective of Novice and Expert Users: Development and Illustration of Mobile Benchmark Tasks

Abstract

Joe Valacich was the accepting Senior Editor. Galletta, D. F., & Dunn, B. K. (2014). Assessing smartphone ease of use and learning from the perspective of novice and expert users: Development and illustration of mobile benchmark tasks, THCI, 6(4), 74-91. Dennis F. Galletta Katz Graduate School of BusinessUniversity of Pittsburgh galletta@katz.pitt.edu Brian Kimball Dunn Price College of BusinessUniversity of Oklahoma bkdunn@ou.edu Original ResearchAssessing usability of device types with novel function sets that are adopted by diverse user groups requires one to explore a variety of approaches. In this paper, we develop such an approach to assess usability of smartphone devices. Using a three-stage Delphi-method study, we identify sets of benchmark tasks that can be used to assess usability for various user types. These task sets enable one to evaluate smartphone platforms from two perspectives: ease of learning (for those unfamiliar with smartphone use) and ease of use (for experienced users). We then demonstrate an approach for using this task set by performing an exploratory study of both inexperienced smartphone users (using a convenience sample) and experienced users (using the keystroke model). Our exploration illustrates the methodology for using such a task set and, in so doing, reveals significant differences among the leading smartphone platforms between novice and expert users. As such, we provide some preliminary evidence that ease of use is indeed significantly different from ease of learning. IntroductionNew types of multi-function computing devices with novel interface types and standards have risen in prominence over the last several years. In particular, smartphones have become a ubiquitous phenomenon. As of 2014, 71 percent of all Americans own smartphones (Nielsen, 2014a), and 85 percent of all mobile phone owners opt to upgrade to smartphones (Nielsen, 2014b). Further, the IDC has predicted that, by 2017, 70.5 percent of all connected devices in the world will be smartphones (Columbus, 2013).With such devices becoming the de facto standard for both interpersonal communication and online connectivity, it has become increasingly important for researchers and practitioners alike to understand the dynamics behind their adoption and use. Most smartphone interfaces differ significantly from those of personal computers (PCs), largely due to their small size and the rarity of PC touchscreens. Further, Norman and Nielsen (2010) report that designers of smartphone and tablet interfaces have ignored years of accepted interface design standards by introducing designs that are confusing and foreign to users. Interestingly, as touchscreen PCs proliferate, the trend toward making computer interfaces resemble tablet interfaces could make operating a PC confusing, too, which would require many people to re-learn how to use them.Instead of requiring mouse movements and keystrokes, smartphones rely on a new set of inputs such as swipes, pinches, voice-to-text transcriptions, and double-taps. Further, s...

Opposing Effects of Response Time in Human–Chatbot Interaction

“…However, we believe that the distinction between novice users and experienced users is appropriate for two main reasons. First, understanding the difference between novice and experienced users, rather than the difference between users with different levels of experience, is particularly important in the context of adoption and use (Thompson et al 1994;Taylor and Todd 1995;Galletta and Dunn 2014). Moreover, recent chatbot studies specifically highlight the difficulties novice users face when making the ''transition'' to becoming experienced users (e.g., Jain et al 2018;Muresan and Pohl 2019).…”

Exploring adoption of augmented reality smart glasses: Applications in the medical industry

“…Ease of learning is usually combined with PEoU, although these are two different but related concepts (Galletta and Dunn, 2014). In the literature, ease of learning has generally been measured through the PEoU construct, and has mostly appeared as a significant antecedent to PEoU when inexperienced users are subjected to research (Gefen and Straub, 2000).…”

“…The intuitiveness of any studied system is evaluated by means of an ease of learning factor. When people can easily understand the technology and remember how the system works, it is considered as easy to learn (Galletta and Dunn, 2014). In this research, as ARSG is introduced to healthcare professions who have not used these devices, a significant ease of learning effect is expected.…”