User Judgments Research Articles

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This study examined the theory of design psychology affecting users' thinking actions, focusing on the human cognitive system and information processing process. Based on related literature studies such as design, psychology, and brain science, the study was conducted focusing on case analysis of design psychology principles. As a result of the study, first, it was confirmed that the tendency of thinking appeared differently depending on the height of the ceiling, and creativity was promoted in spaces with high ceilings and specific thinking was promoted in spaces with low ceilings. Second, apart from usability, it was found that a design with good aesthetics reinforces users' positive attitudes and explores problem-solving methods with a generous attitude even in the event of a problem. Third, by first presenting positive expressions or experiences before experiencing a situation, it was found to have a positive effect on the user's perception, judgment, behavior, and memory. Fourth, it has been shown that visual stimuli activate physical waves and central nerves, causing psychological changes in human behavior and thoughts. Fifth, design consistency has been shown to affect user behavior and quick decision-making, but family similarity only needs to be interrelated, even if it is not in a fixed form. The significance of this study is to understand that the preference or bias that users judge design is related to design psychology. As a limitation, it failed to deal with the design psychological principles that negatively affect users' thinking formation. Furthermore, it is proposed to be conducted by supplemented and supplemented.

A Study on Immersion and Intention to Pay in AR Broadcasting: Validating and Expanding the Hedonic Motivation System Adoption Mode

With the rapid growth of online entertainment live streaming, how to continuously innovate and achieve long-term sustainability has become a major challenge for the industry. Augmented reality (AR) technology offers users immersive interactive experiences and potentially addresses this challenge. The aim of this study is to explore how AR technology influences key components of user online experience—immersion and intention to pay—using survey data. Building upon the Hedonic Motivation System Adoption Model (HMSAM), this research incorporates aesthetic variables to theoretically expand the model in order to gain a deeper understanding of the mechanisms influencing user behavior. A questionnaire survey was conducted to collect 450 valid samples. Detailed analysis was conducted using structural equation modeling. The findings confirm that aesthetic design significantly impacts users’ judgments of content value and perceived ease of use, generating positive effects at the perceptual level. Additionally, AR applications enhance the quality of user experience, thereby stimulating intrinsic motivations such as curiosity and joy. Further analysis indicates that users’ curiosity and perceived behavioral control directly influence the level of immersion and intention to pay. Overall, the research results offer important insights into industry applications. This study successfully expands the HMSAM theoretically by incorporating aesthetic variables to enhance the explanatory power of user judgment mechanisms. The analytical framework proposed aids in understanding the potential mechanisms of new technologies on customer experience and commercial value creation. The research findings provide guidelines for technological design and marketing strategies of streaming platforms.

Optimizing spatial survey administration adopting RT-GSCS: A statistical perspective on performance metrics

The Real-Time Geo-Spatial Consensus System (RT-GSCS) is a web-based open-source tool designed for the administration of real-time spatial surveys. One distinguishing feature of this system lies in the adoption of the Real-Time Spatial Delphi logic as the main computational algorithm to achieve a convergence of opinions within a group of individuals. The system is available at http://rtgscs.com/ and is particularly instrumental in supporting the decision-making process when spatial data is absent or when user judgments are solicited for territorial actions. Since the release of the beta version, the system has undergone multiple iterations and significant changes, and for this reason, an updated assessment should be conducted. In this paper, we present an in-depth exploration of the latest version of RT-GSCS (v3.0), which includes novel algorithms, statistical analysis, and spatial techniques, optimized for survey administration. The analysis is meticulously conducted by evaluating a comprehensive array of parameters, encompassing performance metrics, spatial elements, security, updates, and privacy assessments. Moreover, a thorough examination of user feedback is presented, enriching the depth of our investigation. This mixed-method approach ensures a robust and updated investigation of the system, facilitating a nuanced comprehension of the process and the attainment of spatial consensus.•Existing spatial survey administration systems cannot facilitate group collaboration for achieving a spatial consensus on the territory in real-time.•The RT-GSCS experienced substantial modifications, necessitating a revised technical evaluation from the initial beta version to its current version.•The proposed method fills the gaps of a technical performance evaluation of the latest version of RT-GSCS, useful for achieving a spatial consensus among a group of individuals with an assessment of the overall performances.

Development of a novel uncertainty model for interval-valued Q-fuzzy soft sets: Application in design-making

In actual life, dealing with uncertain information has become a challenge for researchers who strive day after day to develop more accurate mathematical tools for better dealing with this information. The Q-Fuzzy soft model can process uncertain information in two dimensions by dealing with the subjective judgments of users effectively. Therefore, this article aims to increase the effectiveness of the Q-fuzzy soft model and address the challenges of design-making under uncertain information by proposing a new model called the interval-valued Q-fuzzy soft (IV-Q-FSS) model. Under the IV-Q-FSSs, we discuss strongly set-theory operations such as subset, union of two IV-Q-FSSs, intersection of two IV-Q-FSSs, complement of IV-Q-FSS, AND operation, and OR operation for IV-Q-FSSs, and some operations like the possibility and necessity operations of an IV-Q-FSS. In addition, we hand over numerous properties held up by numerical examples that describe how they toil. Finally, this recently developed model has been successfully trying out in dealing with one of the design-making problems based on hypothetical data for a respiratory disease. This algorithm is built based on the aggregation operator for IV-Q-FSS data to break this issue (i.e., selecting the optimal alternative).

Creative Accounting Practices and the Credibility of Financial Reports: An Empirical Study on the Jordanian Commercial Banks

This study examines the impact of creative accounting practices on the credibility of financial reports in Jordanian commercial banks. It highlights the use of innovative accounting techniques and manipulative strategies employed by firms to present accounting information misleadingly, thereby influencing the judgments of financial statement users. The research aims to investigate the influence of these practices on the reliability of financial reporting in Jordanian commercial banks. A questionnaire was developed and distributed to senior and middle management as well as workers in Jordanian commercial banks, with 51 usable responses out of 80 participants. The findings reveal a negative impact of creative accounting practices on the credibility of financial reports in Jordanian commercial banks. The study concludes by recommending that regulatory bodies in Jordan adopt policies to detect and address creative accounting methods in financial statements and develop deterrent laws with penalties and sanctions for those engaging in harmful techniques in creative accounting.

Seeing is believing: Climate change graph design and user judgments of credibility, usability, and risk

Abstract Psychological science can be used to inform climate science graph design, resulting in more meaningful and useful graphs for communication, especially with non-scientists. In this study, we redesigned graphs from the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5) and compared participant attention and perceptions between original and novel designs with pre-/post-surveys, eye-tracking, graph usability and ranking activities, and interviews. Participants were selected for lower content knowledge and risk perception of climate from a sample of undergraduate students in the southeastern U.S. Here, we demonstrate our robust graph redesign process and the associated impacts on participants’ perceptions of graph usability, graph and scientist credibility, and risk associated with climate change. These findings indicate that interacting with climate change graphs may impact perceptions that are relevant to individuals’ motivation to take action to address climate change across political audiences, and possibly even more so among self-identified Conservatives. Additionally, participants who viewed graphs designed to align with research-informed best practices had greater increases in perceptions of climate scientist credibility and climate change risk, though these contrasts were not statistically significant (p > 0.05). Participants rated redesigned graphs as being more trustworthy, which is critical to successful climate change communication, and our qualitative results provide a possible explanation and initial points of exploration for future research.

Advanced intelligent health advice with informative summaries to facilitate treatment decision-making

PurposeWhether automatically generated summaries of health social media can assist users in appropriately managing their diseases and ensuring better communication with health professionals becomes an important issue. This paper aims to develop a novel deep learning-based summarization approach for obtaining the most informative summaries from online patient reviews accurately and effectively.Design/methodology/approachThis paper proposes a framework to generate summaries that integrates a domain-specific pre-trained embedding model and a deep neural extractive summary approach by considering content features, text sentiment, review influence and readability features. Representative health-related summaries were identified, and user judgements were analysed.FindingsExperimental results on the three real-world health forum data sets indicate that awarding sentences without incorporating all the adopted features leads to declining summarization performance. The proposed summarizer significantly outperformed the comparison baseline. User judgement through the questionnaire provides realistic and concrete evidence of crucial features that remarkably influence patient forum review summaries.Originality/valueThis study contributes to health analytics and management literature by exploring users’ expressions and opinions through the health deep learning summarization model. The research also developed an innovative mindset to design summarization weighting methods from user-created content on health topics.

Preliminary survey report on the clinical validation of in-use electronic sphygmomanometers in China

Objective: To explore the percentage of in-use electronic sphygmomanometers independently validated clinically in China. Methods: We conducted a cross-sectional survey and Beijing, Shenzhen, Shijiazhuang, Datong, and Shihezi were selected according to the geographical location and economic level. In each site, one tertiary hospital, two community health centers, and 20 families with electronic sphygmomanometers in use were chosen. The information of electronic sphygmomanometers including brand, model, manufacturer and production date were obtained by the trained staff. Ten electronic sphygmomanometers from each hospital, five electronic sphygmomanometers from each community health center, and one electronic sphygmomanometer from each family were surveyed, and the user's subjective judgment results and judgment basis on the accuracy of the electronic sphygmomanometer measurement were collected. We searched six registration websites (Medaval, Stride BP, dabl Educational Trust, British and Irish Hypertension Society, American Medical Association and Hypertension Canada) and two research databases (PubMed and CNKI) for the clinical validation status of each electronic sphygmomanometer. Results: A total of 200 electronic sphygmomanometers were investigated in this study, of which only 29.0% (58/200) passed independent clinical validation. When stratified by users, the percentage of being clinical validated was 46.0% (23/50) for electronic sphygmomanometers in hospitals, 42.0% (21/50) for those in community health centers and 14.0% (14/100) for those in home use, respectively, and the proportions between the three groups were significantly difference (P<0.001). Doctors in tertiary hospitals and community health service centers judged the accuracy of electronic sphygmomanometers mainly on the basis of "regular correction" (41.0% (41/100)) and "comparison with other electronic sphygmomanometers" (20.0% (20/100)), while among home users, 41.0% (41/100) were not clear about the accuracy of electronic sphygmomanometers, and 40.0% (40/100) made the judgment by "comparison with the devices in hospitals". Conclusion: The clinical validation of in-use electronic sphygmomanometers in China is low. Most of users, including healthcare professionals, are not aware of clinical validation of electronic sphygmomanometers.

Science, Technology, Society, Environment Training Model to Improve Positive Attitudes of Early Childhood Teachers in the Environment

The lack of positive attitude may be caused by the weak implementation of environment education. One of the ways to solve the problem is by preparing prospective early childhood teachers to have the ability to instill environment concern to their students as early as possible so that the students grow up becoming people who care about the environment. The purpose of this research is to develop training scenarios on environmental education using Science, Technology, Society, Environment (STSE) model integrated into scientific thematic lessons, particularly for prospective early childhood teachers. This research is kind of development research. There were three aspects assessed by these experts namely usability, accuracy, and feasibility employing quantitative and qualitative data analyses. User judgment came from the prospective teachers done through interviews, and the data from these interviews were analyzed quantitatively and qualitatively. The results involved three expert assessments in the field of early childhood, environmental education, and educational technology, although further analysis is required. Subsequent investigation conducted on the subject user group, indicating a positive value of 83.3%, in support of the product. Therefore, the efficiency test results showed the training on environmental education using the Environmental Education Training with STSE model and integrated with scientific thematic lessons, were known to enhance 32.44% of all aspects of the prospective teacher's positive attitudes on the environment.

Calista: A deep learning-based system for understanding and evaluating website aesthetics

Website aesthetics play an important role in attracting users and customers, as well as in enhancing user experience. In this work, we propose a tool that performs automatic evaluation of website aesthetics using deep learning models that display high correlation to human perception. These models were developed using two different datasets. The first dataset was created by employing a rating-based ranking approach and contains user judgments on websites in the form of an explicit numerical value on a scale. Using the first dataset, we developed models following three different approaches and managed to outperform previous works. In addition, we created a new dataset by employing a comparison-based ranking approach, which is a more reliable dataset in the sense that it follows a more “natural” data collection method. In this case, users were asked to compare two websites at a time and choose which is more attractive. Data collection was performed via a web application especially designed and developed for this purpose. In the experiments conducted, we evaluated each model and compared the two data collection methods. This work aims to illustrate the effectiveness of deep learning as a solution to the problem as well as to highlight the importance of comparison-based ranking in order to achieve reliable results. In order to further promote our work, we also developed a tool that scores the aesthetics of a website, simply by providing the website URL. We argue that such a tool will serve as a reliable guide in the hands of designers and developers during the design process.

A sentiment analysis approach to investigate tourist satisfaction towards transport systems: the case of Mount Etna

This paper aims at analysing tourist satisfaction towards transport systems of the touristic destination using online reviews from TripAdvisor, the most well-known online review platform used in the tourism sector for researchers and customers. First, a web scraping method was used to retrieve online reviews available on TripAdvisor related to transport systems, then the Latent Dirichlet Allocation method was implemented to extract the topics of the online reviews, and finally an analysis was performed to find similarities and differences among positive and negative reviews for each topic in order to identify tourist trends towards transport systems. To test the methodology, the case of Mount Etna has been considered, one of the main tourist destinations in Sicily (Italy), located on the east coast of the island. According to the results of our case study, the reviews can be clustered in positive and negative predicted feelings, and, for each of them 2 topics (Topic 0 and Topic 1) are linked. Topics are related to specific tourist experience characteristics and the differences between positive and negative reviews can be ascribed to a different relationship between transport and tourism concepts. Main results show a positive attitude of tourists towards the of transport as an integrated experience of the visit of the touristic location. The proposed approach is able to provide useful information to decision-makers by identifying the possible criticalities and intervention priorities considering user judgment. Besides, this methodology can be easily applied to other tourist venues and can be used by decision-makers to provide better services with the aim of improving the engagement of tourists with the city and increasing profits.

Employers' Speech-Based First Impressions of Cochlear Implant Users.

This study is part of a series on speech-based first impressions of prelingually deaf cochlear implant (CI) users with differing speech intelligibility. Hiring managers with typical hearing (TH) rated CI users and TH young adults on various personality traits and suitability as job applicants after hearing recorded speech samples. Similar to prior peer listeners, managers rated TH speakers more positively than highly intelligible CI users (CI-Hi), with less-intelligible CI users rated even more negatively. Managers for white-collar or high-communication jobs rated CI-Hi lower than did managers of blue-collar or low-communication jobs. Positive attitudes and experience with deafness improved ratings of CI users. While underlining the importance of intelligibility, these patterns also highlight the impact of listeners' underlying attitudes on their initial judgments of CI users, which could affect deaf employment opportunities. The study calls for workplace education about hearing loss and deaf communication to improve deaf inclusion and employment rates.

A New Customer Satisfaction Index for Evaluating Transit Service Quality

In this paper, an index based on customer perspective is proposed for evaluating transit service quality. The index, named Heterogeneous Customer Satisfaction Index, is inspired by the traditional Customer Satisfaction Index, but takes into account the heterogeneity among the user judgments about the different service aspects. The index allows service quality to be monitored, the causes generating customer satisfaction/dissatisfaction to be identified, and the strategies for improving the service quality to be defined. The proposed methodologies show some advantages compared to the others adopted for measuring service quality, because it can be easily applied by the transit operators.

Pilot testing of experimental procedures to measure user's judgment errors in simulated social engineering attacks

Distracted users appear to have difficulties correctly distinguishing between legitimate and malicious emails or search engine results. Additionally, mobile phone users appear to have a more challenging time identifying malicious content due to the smaller screen size and the limited security features in mobile phone applications. Thus, the goal of this research study was to conduct a pilot test and validate a set of field experiments based on Subject Matter Experts (SMEs) feedback to assess users’ judgment when exposed to two types of simulated social engineering attacks: phishing and Potentially Malicious Search Engine Results (PMSER), based on the interaction of the environment (distracting vs. non-distracting) and type of device used (mobile vs. computer). This paper provides the results from the pilot test we conducted using recruited volunteers consisting of 10 participants out of 20 volunteers invited. Due to COVID-19 restrictions, all interactions in this pilot testing were conducted remotely. These restrictions somewhat limited our ability to control the testing environment to ensure a completely non-distractive environment during these parts of the study; however, a significant attempt was made to ensure such a non-distractive environment was genuinely adhered to during that part of the study. Our initial pilot testing results indicate that the findings were counterintuitive for the Phishing Intelligence Quotient (IQ) tests. In contrast, results of the PMSER were intuitive with improved detection on a computer compared to mobile. We conclude with a discussion on the study limitations and further research.

What is Beautiful is Secure

Visual appeal has been shown to influence perceptions of usability and credibility, and we hypothesize that something similar is happening with user judgments of website security: What is beautiful is secure . Web certificates provide reliable information about a website’s level of security, presented in browser interfaces. Users should use this to inform their trust decisions online, but evidence from laboratory studies and real-world usage suggests that they do not. We conducted two studies—one in lab, and one online—in which participants view and interact with websites with high and low visual appeal, and various security levels, and then make security-related judgments. In both studies, participants consistently rated visually appealing websites as more secure, and indicated they would be more likely to enter sensitive information into visually appealing websites—even when they were less secure. Our results provide evidence that users rely on visual appeal when making security and trust decisions on websites. We discuss how these results may be used to help users.

Automated surface texture analysis via Discrete Cosine Transform and Discrete Wavelet Transform

Surface roughness and texture are critical to the functional performance of engineering components. The ability to analyze roughness and texture effectively and efficiently is much needed to ensure surface quality in many surface generation processes, such as machining, surface mechanical treatment, etc. Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) are two commonly used signal decomposition tools for surface roughness and texture analysis. Both methods require selecting a threshold to decompose a given surface into its three main components: form, waviness, and roughness. However, although DWT and DCT are part of the ISO surface finish standards, there exists no systematic guidance on how to compute these thresholds, and they are often manually selected on case by case basis. This makes utilizing these methods for studying surfaces dependent on the user's judgment and limits their automation potential. Therefore, we present two automatic threshold selection algorithms based on information theory and signal energy. We use machine learning to validate the success of our algorithms both using simulated surfaces as well as digital microscopy images of machined surfaces. Specifically, we generate feature vectors for each surface area or profile and apply supervised classification. Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95%. We also compare our results with Gaussian filtering (GF), and show that while GF results for areas can yield slightly higher accuracies, our results outperform GF for surface profiles. We further show that our automatic threshold selection has significant advantages in terms of computational time as evidenced by decreasing the number of mode computations by an order of magnitude compared to the heuristic thresholding for DCT.

Effect of Sign-recognition Performance on the Usability of Sign-language Dictionary Search

Advances in sign-language recognition technology have enabled researchers to investigate various methods that can assist users in searching for an unfamiliar sign in ASL using sign-recognition technology. Users can generate a query by submitting a video of themselves performing the sign they believe they encountered somewhere and obtain a list of possible matches. However, there is disagreement among developers of such technology on how to report the performance of their systems, and prior research has not examined the relationship between the performance of search technology and users’ subjective judgements for this task. We conducted three studies using a Wizard-of-Oz prototype of a webcam-based ASL dictionary search system to investigate the relationship between the performance of such a system and user judgements. We found that, in addition to the position of the desired word in a list of results, the placement of the desired word above or below the fold and the similarity of the other words in the results list affected users’ judgements of the system. We also found that metrics that incorporate the precision of the overall list correlated better with users’ judgements than did metrics currently reported in prior ASL dictionary research.

Comparing Unstructured Adaptive Mesh Solutions for the High Lift Common Research Airfoil

Discretization error is a common source of uncertainty in computational fluid dynamics (CFD) analyses. Traditional means of controlling discretization error through fixed-mesh refinement studies have proved to be difficult particularly when modeling complex geometries and flowfields. One reason for this is that mesh generation in today’s production CFD workflow is often a labor-intensive process that is heavily dependent on user judgment. Unstructured mesh adaptation is known to be an efficient way to control discretization errors in CFD. Adaptive methods replace user-based decision-making with automated processes that optimize a mesh to reduce discretization error. This paper compares the application of multiple solution adaptive techniques in combination with multiple flow solvers to solve for the flowfield about a 2-D airfoil section of the NASA High-Lift Common Research Model. By driving the adaptive mesh processes to a similar level of mesh convergence, the ability to achieve consistent results between multiple adaptive techniques and flow solvers is demonstrated. Mesh convergence for the various adaptive mesh approaches is compared, identifying potential areas for improvement and providing mesh generation guidance for future workshops.

Tool acceptance and acceptability: insights from a real tool use activity.

The issue of tool adoption has been the subject of many investigations, which focus either on acceptability (evaluating intention to use, a priori) or acceptance (evaluating real tool use, a posteriori). There are many criteria in the literature explaining why a tool is accepted or rejected by users, but behavioral observations are rare. This work aims to study the relationship between acceptability and acceptance and to find out if there is a hierarchy between the criteria that lead a user to use a particular tool. We exposed participants to eight xylophones varying according to three criteria: Ease of use, Utility, and Aesthetics. We assessed acceptability and judgment of participants about xylophones with questionnaires, based on tool use observation in a video session, and after a short-term use (Experiment 1); we also measured acceptance after long-term use of five sessions during which participants learned to play xylophone (Experiment 2). The results suggested that previous exposure to the tool influenced the judgment of the user, indicating a difference between acceptability and acceptance and between observation and use of a tool. The results also indicate differences in the hierarchy of criteria. In the acceptability phase, user judgments are guided by Ease of use. However, during the acceptance phase, the Utility criterion has the greatest influence, whether in terms of tool preference, or time spent using tools.

Information Credibility Evaluation in Presence of Users’ Safety in New Retailing

Understanding users’ safety perception of the credibility of web-based information has become increasingly important in the context of new retailing. This study extends the existing literature by exploring the factors influencing information credibility in the context of new retailing. Based on the technology acceptance model and the rational behavior theory, a theoretical model for the assessment of information credibility in new retailing was developed. We analyzed the factors influencing users’ safety preference toward information communication procedures and information credibility in new retailing based on two aspects: perceived information quality and user judgment motivation. The reliability and validity of the model measure were analyzed, and structural equation modeling was used to test the model hypotheses. The following results were obtained: (1) Authenticity, accuracy, and practicability positively affected the perceived information quality of new retailing information; (2) User judgment motivation had a positive impact on information users’ safety preference and information credibility; (3) Users’ safety preference positively affected information credibility; (4) Information acquisition, social interaction, and self-identity positively affected the perceived credibility of new retailing information.