A User Experience–Based Evaluation Model for AI-Enabled Digital Booking Engines in Tourism

Since the emergence of scholarly big data, there have been several efforts for web-based services such as digital library search engines (DLSEs). However, much of the design and specifications of an accessible, usable, scalable, and sustainable DLSE have not been well represented and discussed in the literature. We argue that these four characteristics are essential to providing a high-quality service for scholarly big data from both the user and developer’s perspectives. This paper reviews the design, implementation, and operation experiences, and lessons of CiteSeerX, a real-world digital library search engine. We analyze the strengths and weaknesses of the current design, and proposed a new design with a revised architecture, enhanced hardware, and software infrastructure. The Alpha version of the new design has been implemented and tested. The new system replaces MySQL and Apache Solr with a single instance of Elasticsearch, which plays a dual role of data storage and search. Another major improvement is the integration of extraction and ingestion, which significantly boosts document ingestion speed. The web application is re-engineered to enhance the user experience by applying a learning-to-rank model and offering more refined search tools. The system is also improved in many other aspects. We believe the design considerations and experience can benefit researchers and engineers who plan, design, and upgrade future systems with comparable scales and functionalities.

An online system has become a priority for organisations or companies in many countries, as it allows many processes to be conducted via online platforms, which contributes to profit gain. There are different types of user experience (UX) evaluation models that have been proposed to guide the measurement and development process. However, most of these models only have dimensions, and there is no guidance for UX measurement on online systems. The lack of evaluation models for online system measurement requires further investigation. This paper aims to identify the gaps in UX evaluation models, and develop a conceptual UX evaluation model for online systems. The method used in this study includes reviewing several literatures and shortlisting the relevant publications on UX and online systems. After that, the gaps were identified from the existing UX evaluation model in the relevant publications based on the ISO standard. Then, the study identified the important components of UX, and proposed a new conceptual UX evaluation model for online systems. The results of the study are the identification of the gaps in existing UX evaluation models, and the development of a new conceptual UX evaluation model that is specifically for online systems. Therefore, the results help in considering UX dimensions, criteria, and metrics and potential UX components for evaluation and measurement. The paper contributes to system developers, designers, and also researchers for future UX evaluation model development for online systems. Future studies could use the reviewed UX evaluation models to identify relevant dimensions of online systems, and hence improve the model that they will develop. The findings may also be beneficial to organisations that own online systems by providing guidelines on important dimensions involved in their UX-based evaluations.

With the rapid development of e‐commerce technology, cross‐channel consumption has become the mainstream mode of contemporary consumers. However, there are several problems of cross‐channel consumption such as inconsistency of online and offline channel information and service, disfluency of channel switching which have brought adverse effects on user experience. The question arises here as to what factors influence user experience and how to build a scientific and effective evaluation index system. Different from previous studies based on sellers, this paper used grounded theory to analyze and summarize the evaluation index system of user experience under cross‐channel consumption from the perspective of consumers. We summarized and refined four first level indexes which are “online platform attribute, offline entity attribute, channel switching attribute, and individual demand” and 13 second level indexes which are “platform operation, platform information, platform service, platform promotion, product quality, service quality, environment quality, channel consistency, channel switching cost, channel switching fluency, psychological expectation, personal interests and individual needs.” Then, we used BP neural network to build the evaluation model and trained and simulated the performance of the sample. The results show that the evaluation model has a good generalization ability and can effectively evaluate user experience under cross‐channel consumption. Finally, implications and limitations are also discussed. This study helps to enrich the theoretical research on user experience and consumer behavior. It also provides targeted basis for in‐depth analysis of cross‐channel consumption behavior, establishment of user experience evaluation index system, and improving user experience and multichannel management of physical stores.

Flow experience is often considered as an important standard of ideal user experience (UX). Till now, flow is mainly measured via self-report questionnaires, which cannot evaluate flow immediately and objectively. In this paper, we constructed a physiological evaluation model to evaluate flow in virtual reality (VR) game. The evaluation model consists of 5 first level indicators and their respective second level indicators. Then, we conducted an empirical experiment to test the effectiveness of partial indicators to predict flow-experience. Most results supported the model and revealed that heart rate (HR), interbeat interval (IBI), heart rate variability (HRV), low frequency HRV, high frequency HRV and respiratory rate (RR) are all effective indicators in predicting flow-experience. Further researches should be conducted to improve the evaluation model and conclude practical implications in UX and VR game design.

Flow experience is often considered as an important standard of ideal user experience (UX). Till now, flow is mainly measured via self-report questionnaires, which cannot evaluate flow immediately and objectively. In this paper, we constructed a physiological evaluation model to evaluate flow in virtual reality (VR) game. The evaluation model consists of five first-level indicators and their respective second-level indicators. Then, we conducted an empirical experiment to test the effectiveness of partial indicators to predict flow experience. Most results supported the model and revealed that heart rate, interbeat interval, heart rate variability (HRV), low-frequency HRV (LF-HRV), high-frequency HRV (HF-HRV), and respiratory rate are all effective indicators in predicting flow experience. Further research should be conducted to improve the evaluation model and conclude practical implications in UX and VR game design.

This paper proposes an enhanced Non-Dominated Sorting Genetic Algorithm -II algorithm to optimize IoT service composition by incorporating national energy consumption requirements and user experience, areas often overlooked in traditional models that primarily focus on time, cost, and quality. The original NSGA-II algorithm is prone to premature convergence and local optima issues during population iteration. To address these limitations, we introduce a novel evaluation model and improve the elite retention strategy of the NSGA-II algorithm. The improved algorithm balances exploration and exploitation through dynamic crowding distance adjustment and adaptive selection pressure, enhancing diversity and avoiding local optima. Experimental results demonstrate that the I-NSGA algorithm not only reduces running time by 5.916% but also achieves a smoother Pareto surface, indicating a more optimal distribution of solutions. The novelty of this approach lies in its comprehensive inclusion of energy consumption and user experience, the timeliness in addressing emerging IoT optimization challenges, and the relevance to current IoT service composition needs. This validates the effectiveness and advancement of the proposed model and algorithm, providing a robust and efficient solution for IoT service composition optimization.

Technical innovation provides new ways to upgrade the future cockpit systems of smart cars. It caused that auto companies equipped more functions of SCS (smart cockpit systems) with wonderful visual and tactile effects aiming to enhance the humanization of their interactive systems and achieve better interaction effects. However, the increased functionality of the SCS did not show significant effect on enhancing the UX (User Experience), for the reason of the chaotic functional logic, the lack of perception of the system and friendly human-computer interaction. To resolve these problems, this paper aims to present a new method for the upgrade of new SCS design on UX research that can analyze problems systematically and evaluate the variability of perceptual degree. This research proposes a human affairs centered design framework and evaluation model for managing UX within SCS. The elements of the affair can be abstracted on seven levels: subjects, objects, time, space, message, interaction and meaning. Since the new SCS is more inclined to human-robot interaction (HRI), both users and systems can be treated as subjects and objects simultaneously, which indicates that the framework contains dual subject and dual object. Furthermore, the evaluation model on the elements on these levels is composed of a horizontal dimension: perceptual degree of design, and a vertical dimension: variability of perceptual degree. The horizontal dimension is used to evaluate the various elements in the affair based on the products in the design phase, while the vertical dimension is based on the result of UX research. The hypothesis is that the framework can help the design and valuation of UX focusing on humanization and perception within the smart cockpit systems’ design. Validation occurred through a field study performed in a smart car in the design phase, where the basic functions of the prototype has already been realized. Preliminary results validate the usability of the framework and efficiency of the method, thus laying the ground for further research and discussions.

The software sharing platform in the Internet provides great convenience for the promotion, application and communication of software (especially source software). But there inevitably exists the problem of software quality on the open Internet platform. How the users choose software to download and use becomes a new challenge for software sharing platforms. Aimed at the above problems and challenges, the internal relations between the data collected on platform and experience of user are analyzed. And then a software popularity recommendation method based on evaluation model is presented. The method constructs two evaluation indexes based on the collected data on the platform, including attention-degree and satisfaction-degree; solves the problem of small sample data's influence on the accuracy of evaluation model by using the Wilson interval model and makes a tradeoff between the recommendation results of old and new software by using Newton cooling law. The experimental results show that the software popularity recommendation method based on evaluation model helps users to screen for software, which can effectively improve the service performance of software sharing platform.

Digital advertisement enables potential consumers to picture what they might experience when they use an advertised product. Creating effective user experience (UX) images in digital advertisement helps companies promote their products and attract specific audiences while allowing the designer of the product to communicate their vision to viewers. To increase the efficacy of advertising while simplifying the design processes, it is important to establish the designer’s own visualization of the product to communicate to buyers. In this research, we conducted an evaluative experiment by analyzing the objective features of 80 video advertisements for home appliances to create an evaluative prediction model for the 24 defined types of UX. The results revealed that UX can be conveyed through digital media and that objective features, which are mainly related to people’s appearance, visual, audio, and content, can influence this. Each UX prediction model was created with different features and might be used as a reference in creating digital content advertisements that convey the UX.

With the rapid progress of science and technology, more and more Internet products emerge. User experience is a kind of purely subjective feeling established by users in the process of using the product. Through the analysis of Nielsen’s ten usability principles and five elements of user experience, this paper make the four elements integrated into the three dimensions of content, interaction and vision, which are different perspectives of user perception and evaluation of user experience of Internet products. According to the above, the CIV evaluation model of Internet products mobile terminal product user experience is established. The CIV model proposed in this paper is suitable for the optimization design of online products. According to this evaluation model, products that meet user requirements can be iterated.

Software can be improved through user experience metrics, so it is very important to choose the right measurement method. Through the desktop research and other research methods, the user experience research methods and model results are summarized. With software A and B as the research object, according to the product form and the characteristics of the business, the user experience evaluation model commonly used in the industry is used to measure the two software products by means of usability test and questionnaire data from the various attributes and indicators of the evaluation model, so as to the rationality and feasibility of the evaluation model and provide Suggestions for improvement as the research object.

This paper explores the problem of user experience evaluation, in particular the quantitative evaluation of group user experience, in smart spaces. First, the classification and definition of four different categories of user groups are proposed, and the notion of group user experience is introduced. Second, we analyze the quantitative evaluation of group user experience for different types of user groups and establish an evaluation model for group user experience. Particularly, we employ two quantitative social metrics, user rating and user attention duration, as the main criteria for evaluating user experience. Other social factors, such as group interaction and the diversity of group members, are also taken into account to form a general quantitative evaluation model of group user experience for different user groups. Finally, we evaluate the effectiveness of the proposed model with preliminary experiments in a smart museum.

Opinion mining for user experience evaluation model using kernel-naive bayes classification algorithm

This paper explores the problem of user experience evaluation, in particular the quantitative evaluation of group user experience, in the ubiquitous computing environments. Firstly, the classification and definition of four different categories of user groups are proposed and the notion of group user experience is introduced. Secondly, we analyze the quantitative evaluation of group user experience for different user groups and establish a uniform evaluation model for group user experience. Particularly, we employ two quantitative metrics, user rating and user attention duration, as the main criteria for evaluating user experience. At the same time, the intercommunication and differences among group members in the capacity of information acquisition, the degree of correlation with other members and the weight of impact to the overall group user experience are taken into account to form a general quantitative evaluation model of group user experience for different user groups. Finally, we evaluate the effectiveness of the proposed model with preliminary experiments.

The rapid evolution of artificial intelligence (AI) and multimodal interaction technologies is reshaping automotive design, demanding new frameworks that prioritize user experience (UX) and market applicability. This conceptual study proposes an integrative framework that combines AI-driven personalization, multimodal interface design (e.g., voice, gesture, and touch), and real-time UX evaluation mechanisms. Drawing upon human-centered design principles and theories of user acceptance, the framework addresses current gaps in adaptive, intelligent vehicle interface systems. It further outlines strategic pathways for deployment in diverse market environments through an evaluation model that accounts for technological scalability, cultural preferences, and demographic diversity. The study concludes by identifying key directions for future research, particularly emphasizing cross-cultural UX testing across various vehicle types and user groups. The proposed framework contributes to both academic discourse and industry practice, offering a foundation for the next generation of intelligent, user-centric automotive systems.