Standardized Questionnaires for User Experience Evaluation: A Systematic Literature Review

Oreiro, Ignacio Díaz; Herrera, Gustavo López; Quirós, Luis Quesada; Blanco, Luis Guerrero

doi:10.3390/proceedings2019031014

Cited by 65 publications

(45 citation statements)

References 20 publications

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“…Among the questionnaire-based methods, we selected two: user experience questionnaire (UEQ) [ 15 ] and AttrakDiff [ 14 ]. We chose these methods because they are two of the most well-known and used standardized UX evaluation questionnaires [ 24 ], employed in several studies [ 13 , 25 , 26 , 27 , 28 ] and contexts, from the evaluation of educational platforms [ 7 , 29 , 30 ] to the evaluation of human-robot interaction [ 31 ]. Additionally, they were designed based on the same theoretical UX model proposed by Hassenzahl [ 32 ] and are comparable [ 15 ].…”

Section: Methodsmentioning

confidence: 99%

Are UX Evaluation Methods Providing the Same Big Picture?

Nakamura

Ahmed

Redmiles

et al. 2021

Sensors

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The success of a software application is related to users’ willingness to keep using it. In this sense, evaluating User eXperience (UX) became an important part of the software development process. Researchers have been carrying out studies by employing various methods to evaluate the UX of software products. Some studies reported varied and even contradictory results when applying different UX evaluation methods, making it difficult for practitioners to identify which results to rely upon. However, these works did not evaluate the developers’ perspectives and their impacts on the decision process. Moreover, such studies focused on one-shot evaluations, which cannot assess whether the methods provide the same big picture of the experience (i.e., deteriorating, improving, or stable). This paper presents a longitudinal study in which 68 students evaluated the UX of an online judge system by employing AttrakDiff, UEQ, and Sentence Completion methods at three moments along a semester. This study reveals contrasting results between the methods, which affected developers’ decisions and interpretations. With this work, we intend to draw the HCI community’s attention to the contrast between different UX evaluation methods and the impact of their outcomes in the software development process.

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Section: Methodsmentioning

confidence: 99%

Are UX Evaluation Methods Providing the Same Big Picture?

Nakamura

Ahmed

Redmiles

et al. 2021

Sensors

View full text Add to dashboard Cite

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“…AttrakDiff, UEQ, and meCUE are the most used questionnaires in current academic research. Díaz-Oreiro et al (2019) have provided a systematic literature review for these standardized questionnaires, noting that AttrakDiff is the most popular by far since it was first to market in 2003, although UEQ has outperformed it in 2017 and 2018. On the other hand, meCUE remains a relative newcomer.…”

Section: Mecue 20 Questionnairementioning

confidence: 99%

“…On the other hand, meCUE remains a relative newcomer. According to Díaz-Oreiro et al (2019), these methods are typically complemented with others. They report that, in over 60% of cases, between one and five additional methods were used.…”

Section: Mecue 20 Questionnairementioning

confidence: 99%

Teleguidance-based remote navigation assistance for visually impaired and blind people—usability and user experience

Chaudary

Pohjolainen

Aziz³

et al. 2021

Virtual Reality

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This paper reports the development of a specialized teleguidance-based navigation assistance system for the blind and the visually impaired. We present findings from a usability and user experience study conducted with 11 blind and visually impaired participants and a sighted caretaker. Participants sent live video feed of their field of view to the remote caretaker’s terminal from a smartphone camera attached to their chest. The caretaker used this video feed to guide them through indoor and outdoor navigation scenarios using a combination of haptic and voice-based communication. Haptic feedback was provided through vibrating actuators installed in the grip of a Smart Cane. Two haptic methods for directional guidance were tested: (1) two vibrating actuators to guide left and right movement and (2) a single vibrating actuator with differentiating vibration patterns for the same purpose. Users feedback was collected using a meCUE 2.0 standardized questionnaire, interviews, and group discussions. Participants’ perceptions toward the proposed navigation assistance system were positive. Blind participants preferred vibrational guidance with two actuators, while partially blind participants preferred the single actuator method. Familiarity with cane use and age were important factors in the choice of haptic methods by both blind and partially blind users. It was found that smartphone camera provided sufficient field of view for remote assistance; position and angle are nonetheless important considerations. Ultimately, more research is needed to confirm our preliminary findings. We also present an expanded evaluation model developed to carry out further research on assistive systems.

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“…The AttrakDiff is a widely recognized online accessible questionnaire [87] to estimate UX, which is based on the UEQ research framework. The pragmatic qualities (PQ) of AttrakDiff correlate with the dependability, efficiency, and perspicuity scale of UEQ while hedonic-stimulation quality (HQ-S) has a correlation with novelty and stimulation scales of UEQ.…”

Section: • Utilization Of Extended Wellness Man-mentioning

confidence: 99%

“…The AttrakDiff provides a limited free online service (only 20 users) to investigate the pragmatic, hedonic, and attractiveness qualities of applications [89]. It consists of 28 contrasting adjective-pairs, which are clustered into PQ, HQ (Identity, Stimulation), and ATT [87].…”

Section: • Utilization Of Extended Wellness Man-mentioning

confidence: 99%

Towards User-Centric Intervention Adaptiveness: Influencing Behavior-Context Based Healthy Lifestyle Interventions

et al. 2020

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In the era of digital well-being, smart gadgets are the unobtrusive sources of acquiring information. A variety of personalized wellness applications support self-quantification based recommendations to provide wellness status for achieving personalized targets. However, these applications are unable to promote the induction of new healthy habits and thus are not too much effective for long term as users tend to loose their interest. Thus, we have proposed a methodology for User-Centric Adaptive Intervention based on behavior change theory for maintaining end-users' interest. The methodology consists of four steps: (1) quantification of behavior based on contributing factors governed by expert-driven rules; (2) behavior-context based mapping for the identification of behavior status of the user; (3) selection of appropriate way of intervention to get fruitful outcomes; and finally (4) feedback based evaluation on the basis of recorded activities and questionnaires for satisfaction. A comprehensive healthy behavior index-based quantification supports the machine learning-based prediction model for behavior-context mapping. Furthermore, the evaluation is performed through implicit and explicit feedback analysis along with the accuracy of the behavior-context prediction model through multiple scenarios to cover comprehensive situations. The ensemble classifier suggests the accuracy of 98.02% for the behavior-context prediction model, which is higher than the other classifiers. The gain in behavior change is drawn from implicit feedback, which depicts that behavior context-based methods have improved the adaptation in behavior at a steady pace for the long term. The explicit feedback from 99 end-users of wellness application based on the proposed methodology obtained Good and Desired status for widely used System Usability Score and AttrakDiff tools respectively.

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Standardized Questionnaires for User Experience Evaluation: A Systematic Literature Review

Cited by 65 publications

References 20 publications

Are UX Evaluation Methods Providing the Same Big Picture?

Are UX Evaluation Methods Providing the Same Big Picture?

Teleguidance-based remote navigation assistance for visually impaired and blind people—usability and user experience

Towards User-Centric Intervention Adaptiveness: Influencing Behavior-Context Based Healthy Lifestyle Interventions

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