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Understanding UX through Implicit and Explicit Feedback

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A special issue of Multimodal Technologies and Interaction (ISSN 2414-4088).

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 48194

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Special Issue Editors

Dr. Bruce Ferwerda

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Guest Editor

Department of Computer Science and Informatics, Jönköping University, Jönköping, Sweden
Interests: psychological modeling; user modeling; personalization; Recommender Systems

Dr. Mark Graus

E-Mail Website
Guest Editor

Maastricht University, Maastricht, The Netherlands
Interests: predictive modeling; user modeling; recommender systems; intelligent systems

Special Issue Information

Dear Colleagues,

This special issue aims to explore the opportunities and challenges of combining implicit and explicit feedback to understand and design user experience (UX) in Human-Computer Interaction (HCI).

Measuring UX is important to understand how successful applications and systems are in reaching their goals. In general, there are two main approaches to measure UX: 1) explicit feedback (i.e., using data measured through surveys, interviews and focus groups) and 2) implicit feedback (i.e., using data describing users’ observable interaction behavior measured through, for example, telemetry). Measuring explicit feedback is costlier, requires user input, and thus relies on smaller scale studies. However, it allows to gain deeper information and understanding about the relationship between user characteristics, their needs and preferences, their behavior and their experience. Although, implicit feedback can be collected automatically, it allows for limited understanding of the relationship between user behavior, user traits and user experience.

Implicit and explicit feedback can be combined to effectively measure and understand UX factors; implicit feedback can facilitate the breadth (by quantitatively indicating how designs influence UX) while explicit feedback can facilitate the depth (by providing insight how user behavior, user characteristics and user experience are related). The combination of these two approaches result in an understanding with a high level of detail with the cost efficiency of quantitative research.

Specific areas in which the combination of implicit and explicit feedback is valuable is in personalized and adaptive systems: systems that adapt itself based on users’ interaction behavior to match their preferences or needs. A prominent direction using this approach is the field of recommender systems in which historical behavioral data (implicit feedback) is used to alter the order of items in a catalog (from highest predicted relevance to lowest predicted relevance), with the goal of helping users to find relevant items more easily or making them consume more items. In this case, implicit feedback (behavior) is used to make inferences about concepts that normally can only be measured through explicit feedback (preferences).

We encourage authors to submit original research articles, case studies, reviews, theoretical and critical perspectives, and viewpoint articles within the domain of HCI on topics including but not limited to:

Deriving metrics for measuring UX from qualitative research
The interplay between user characteristics/user behavior and UX
Combining explicit and implicit feedback for UX Research
Empirical studies incorporating UX factors, user behavior and/or user characteristics (e.g., A/B testing)
Explicit and implicit feedback in personalized/adaptive systems
Implicit feedback for UX design (e.g., data-driven design)
Explicit feedback for UX design (e.g., theory-driven design)

Dr. Bruce Ferwerda
Dr. Mark Graus
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Multimodal Technologies and Interaction is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

User Experience
Human-Computer Interaction
Implicit Feedback
Explicit Feedback
Qualitative UX Research
Quantitative UX Research
Adaptive Systems
Personalized Systems

Published Papers (9 papers)

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Research

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20 pages, 2537 KiB

Open AccessArticle

UUX Evaluation of a Digitally Advanced Human–Machine Interface for Excavators

by Sebastian Lorenz, Jens R. Helmert, Ruben Anders, Christian Wölfel and Jens Krzywinski

Multimodal Technol. Interact. 2020, 4(3), 57; https://doi.org/10.3390/mti4030057 - 20 Aug 2020

Cited by 8 | Viewed by 5834

Abstract

With the evaluation of a next-generation human–machine interface (HMI) concept for excavators, this study aims to discuss the HMI quality measurement based on usability and user experience (UUX) metrics. Regarding the digital transformation of construction sites, future work environments will have to be capable of presenting various complex visual data and enabling efficient and safe interactivity while working. The evaluated HMI focused on introducing a touch display-based interface, providing advanced operation functions and different interaction modalities. The assessment of UUX should show whether the novel HMI can be utilised to perform typical tasks (usability) and how it is accepted and assessed in terms of non-instrumental qualities (user experience, UX). Using the collected data, this article also aims to contribute to the general discussion about the role of UX beyond usability in industrial applications and deepen the understanding of non-instrumental qualities when it comes to user-oriented process and machine design. The exploratory study examines insights into the application of elaborated UUX measuring tools like the User Experience Questionnaire (UEQ) on the interaction with industrial goods accompanied by their rating with other tools, namely System Usability Scale (SUS), Intuitive Interaction Questionnaire (INTUI) and the National Aeronautics and Space Administration (NASA) Task Load Index (NASA-TLX). Four goals are pursued in this study. The first goal is to compare in-depth two different ways of interaction with the novel HMI—namely one by a control pad on the right joystick and one by touch. Therefore, a sample of 17 subjects in total was split into two groups and differences in UUX measures were tested. Secondly, the performances of both groups were tested over the course of trials to investigate possible differences in detail. The third goal is to interpret measures of usability and user experience against existing benchmark values. Fourth and finally, we use the data gathered to analyse correlations between measures of UUX. The results of our study show that the different ways of interaction did not impact any of the measures taken. In terms of detailed performance analysis, both groups yielded differences in terms of time per action, but not between the groups. The comparison of UUX measures with benchmark values yielded mixed results. The UUX measures show some relevant significant correlations. The participants mostly reported enjoying the use of the HMI concept, but several practical issues (e.g., efficiency) still need to be overcome. Once again, the study confirms the urge of user inclusion in product development. Especially in the course of digitalisation, as big scale advancements of systems and user interfaces bring uncertainty for many manufacturers regarding whether or how a feature should be integrated. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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24 pages, 2260 KiB

Open AccessArticle

Adapting a Virtual Advisor’s Verbal Conversation Based on Predicted User Preferences: A Study of Neutral, Empathic and Tailored Dialogue

by Hedieh Ranjbartabar, Deborah Richards, Ayse Aysin Bilgin, Cat Kutay and Samuel Mascarenhas

Multimodal Technol. Interact. 2020, 4(3), 55; https://doi.org/10.3390/mti4030055 - 17 Aug 2020

Cited by 8 | Viewed by 3763

Abstract

Virtual agents that improve the lives of humans need to be more than user-aware and adaptive to the user’s current state and behavior. Additionally, they need to apply expertise gained from experience that drives their adaptive behavior based on deep understanding of the user’s features (such as gender, culture, personality, and psychological state). Our work has involved extension of FAtiMA (Fearnot AffecTive Mind Architecture) with the addition of an Adaptive Engine to the FAtiMA cognitive agent architecture. We use machine learning to acquire the agent’s expertise by capturing a collection of user profiles into a user model and development of agent expertise based on the user model. In this paper, we describe a study to evaluate the Adaptive Engine, which compares the benefit (i.e., reduced stress, increased rapport) of tailoring dialogue to the specific user (Adaptive group) with dialogues that are either empathic (Empathic group) or neutral (Neutral group). Results showed a significant reduction in stress in the empathic and neutral groups, but not the adaptive group. Analyses of rule accuracy, participants’ dialogue preferences, and individual differences reveal that the three groups had different needs for empathic dialogue and highlight the importance and challenges of getting the tailoring right. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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42 pages, 2674 KiB

Open AccessArticle

EUREKATAX: A Taxonomy for the Representation and Analysis of Qualitative Usability Test Data

by Panagiotis Germanakos and Ludwig Fichte

Multimodal Technol. Interact. 2020, 4(2), 22; https://doi.org/10.3390/mti4020022 - 25 May 2020

Viewed by 4107

Abstract

Usability tests serve as an insightful source of feedback for product teams that want to deliver user-centered solutions and enhance the User Experience (UX) of their products and services. However, in many cases, formative usability tests in particular may generate a large volume of qualitative and unstructured data that need to be analyzed for decision making and further actions. In this paper, we discuss a more formal method of analyzing empirical data, using a taxonomy, namely Engineering Usability Research Empirical Knowledge and Artifacts Taxonomy (EUREKATAX). We describe how it can provide guidance and openness for transforming fuzzy feedback statements into actionable items. The main aim of the proposed method is to facilitate a more holistic and standardized process to empirical data analysis while adapting on the solution or context. The main contributions of this work comprise the: (a) definition of the proposed taxonomy which represents an organization of information structured in a hierarchy of four main categories (discover, learn, act, and monitor), eight sub-categories, and 52 items (actions/operations with their respective properties); (b) description of a method, that is expressed through the taxonomy, and adheres to a systematic but modular approach for analyzing data collected from the usability studies for decision making and implementation; (c) formulation of the taxonomy’s theoretical framework based on meticulously selected principles like experiential learning, activity theory: learning by expanding, and metacognition, and (d) extended evaluation into two phases, with 80 UX experts and business professionals, showing on the one hand the strong reliability of the taxonomy and high perceived fit of the items in the various classifications, and on the other hand the high perceived usability, usefulness and acceptability of the taxonomy when put into practice in real-life conditions. These findings are really encouraging, in an attempt to generate comparable, generalizable and replicable results of usability tests’ qualitative data analysis, thereby improving the UX and impact of software solutions. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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17 pages, 453 KiB

Open AccessArticle

Identifying Personas in Online Shopping Communities

by Yu Xu and Michael J. Lee

Multimodal Technol. Interact. 2020, 4(2), 19; https://doi.org/10.3390/mti4020019 - 20 May 2020

Cited by 5 | Viewed by 4013

Abstract

Online shopping communities have emerged amid growing social shopping activities and involve user-centered online platforms that encourage user-generated content and interactions, such as reading and writing reviews, rating products, and sharing shopping experiences. However, similar to other online platforms and communities, online shopping communities face challenges to provide tailored content and support appropriate socialization to engage users and encourage individualized contribution within the communities. To provide unique, personalized support for each individual user, this study developed personas in online shopping communities based on their motivation for participation, as well as reading and posting behaviors. Based on the findings from 20 interviews and focus groups with 24 active online shopping community participants, we developed an online survey on MTurk to investigate the characteristics of the personas and received 194 valid responses. Four persona types emerge after the analysis of both the qualitative and quantitative data—Opportunists, Contributors, Explorers, and Followers. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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19 pages, 2925 KiB

Open AccessArticle

User Experience (UX) in Business, Management, and Psychology: A Bibliometric Mapping of the Current State of Research

by Laura Luther, Victor Tiberius and Alexander Brem

Multimodal Technol. Interact. 2020, 4(2), 18; https://doi.org/10.3390/mti4020018 - 16 May 2020

Cited by 47 | Viewed by 8925

Abstract

User Experience (UX) describes the holistic experience of a user before, during, and after interaction with a platform, product, or service. UX adds value and attraction to their sole functionality and is therefore highly relevant for firms. The increased interest in UX has produced a vast amount of scholarly research since 1983. The research field is, therefore, complex and scattered. Conducting a bibliometric analysis, we aim at structuring the field quantitatively and rather abstractly. We employed citation analyses, co-citation analyses, and content analyses to evaluate productivity and impact of extant research. We suggest that future research should focus more on business and management related topics. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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20 pages, 1374 KiB

Open AccessArticle

PERSEL, a Ready-to-Use PERsonality-Based User SELection Tool to Maximize User Experience Redesign Effectiveness

by Stefano Filippi

Multimodal Technol. Interact. 2020, 4(2), 13; https://doi.org/10.3390/mti4020013 - 22 Apr 2020

Cited by 7 | Viewed by 3509

Abstract

Some literature has already demonstrated the widespread influence of human personality on product design. Nevertheless, most of the existing user experience (UX) design methods and tools do not fully exploit knowledge about user personality in selecting the best participants to maximize the effectiveness of the design efforts. This research tries to fill the gap by introducing PERSEL, the ready-to-use PERsonality-based SELector. PERSEL is a Microsoft Excel workbook, free to download, which allows expression of the objectives (needs) and assessment of the user personality; in turn, PERSEL suggests the best users to be involved in UX redesign activities and in what way, in order to get solutions answering to the needs in the best possible way. A comparison of the solutions generated by the first adoption of PERSEL in the field with those coming from the involvement of users selected without obeying any specific criterion, begins validating the research results, mainly in terms of PERSEL functioning and effectiveness. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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13 pages, 862 KiB

Open AccessArticle

The Effect of Layout and Colour Temperature on the Perception of Tourism Websites for Mobile Devices

by Kiemute Oyibo and Julita Vassileva

Multimodal Technol. Interact. 2020, 4(1), 8; https://doi.org/10.3390/mti4010008 - 13 Mar 2020

Cited by 13 | Viewed by 5790

Abstract

In e-commerce, the user interface design of a website is critical to its success. However, there is limited research on how colour and layout design elements influence the perception of e-commerce websites for mobile devices. To bridge this gap, we conducted an empirical study to investigate, how the layout of information and colour temperature of an e-commerce tourism website for mobile device influence essential Technology Acceptance Model (TAM) user experience (UX) design attributes and intention to use the website. The results of our Partial Least Square Path Modelling (PLSPM) showed that both interface design elements significantly influence perceived aesthetics, perceived enjoyment, perceived usefulness and intention to use. Specifically, layout (list = 0 and grid = 1) positively influences perceived aesthetics and perceived enjoyment, while colour temperature negatively influences perceived usefulness and intention to use. The first finding suggests that in tourism website design for mobile devices, a grid layout of products and services provides a better hedonic user experience than a list layout. Moreover, the second finding suggests that cooler-temperature (blue and green) tourism websites are viewed by users as more useful than warmer-temperature (orange and red) tourism websites. We discuss the implications of these findings in the context of website UX design for mobile devices in the tourism domain. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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22 pages, 4054 KiB

Open AccessArticle

Influence of Adaptive Human–Machine Interface on Electric-Vehicle Range-Anxiety Mitigation

by Antonyo Musabini, Kevin Nguyen, Romain Rouyer and Yannis Lilis

Multimodal Technol. Interact. 2020, 4(1), 4; https://doi.org/10.3390/mti4010004 - 14 Feb 2020

Cited by 3 | Viewed by 5142

Abstract

The electrification of vehicles is without a doubt one of the milestones of today’s automotive technology. Even though industry actors perceive it as a future standard, acceptance, and adoption of this kind of vehicles by the end user remain a huge challenge. One of the main issues is the range anxiety related to the electric vehicle’s remaining battery level. In the scope of the H2020 ADAS&ME project, we designed and developed an intelligent Human Machine Interface (HMI) to ease acceptance of Electric Vehicle (EV) technology. This HMI is mounted on a fake autonomous vehicle piloted by a hidden joystick (called Wizard of Oz (WoZ) driving). We examined 22 inexperienced EV drivers during a one-hour driving task tailored to generate range anxiety. According to our protocol, once the remaining battery level started to become critical after manual driving, the HMI proposed accurate coping techniques to inform the drivers how to reduce the power consumption of the vehicle. In the following steps of the protocol, the vehicle was totally out of battery, and the drivers had to experience an emergency stop. The first result of this paper was that an intelligent HMI could reduce the range anxiety of the driver by proposing adapted coping strategies (i.e., transmitting how to save energy when the vehicle approaches a traffic light). The second result was that such an HMI and automated driving to a safe spot could reduce the stress of the driver when an emergency stop is necessary. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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Review

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24 pages, 5895 KiB

Open AccessReview

When Agents Become Partners: A Review of the Role the Implicit Plays in the Interaction with Artificial Social Agents

by Sanobar Dar and Ulysses Bernardet

Multimodal Technol. Interact. 2020, 4(4), 81; https://doi.org/10.3390/mti4040081 - 22 Nov 2020

Cited by 6 | Viewed by 4656

Abstract

The way we interact with computers has significantly changed over recent decades. However, interaction with computers still falls behind human to human interaction in terms of seamlessness, effortlessness, and satisfaction. We argue that simultaneously using verbal, nonverbal, explicit, implicit, intentional, and unintentional communication channels addresses these three aspects of the interaction process. To better understand what has been done in the field of Human Computer Interaction (HCI) in terms of incorporating the type channels mentioned above, we reviewed the literature on implicit nonverbal interaction with a specific emphasis on the interaction between humans on the one side, and robot and virtual humans on the other side. These Artificial Social Agents (ASA) are increasingly used as advanced tools for solving not only physical but also social tasks. In the literature review, we identify domains of interaction between humans and artificial social agents that have shown exponential growth over the years. The review highlights the value of incorporating implicit interaction capabilities in Human Agent Interaction (HAI) which we believe will lead to satisfying human and artificial social agent team performance. We conclude the article by presenting a case study of a system that harnesses subtle nonverbal, implicit interaction to increase the state of relaxation in users. This “Virtual Human Breathing Relaxation System” works on the principle of physiological synchronisation between a human and a virtual, computer-generated human. The active entrainment concept behind the relaxation system is generic and can be applied to other human agent interaction domains of implicit physiology-based interaction. Full article

(This article belongs to the Special Issue Understanding UX through Implicit and Explicit Feedback)

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