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Construction companies are increasingly adopting sensing technologies like laser scanners, making it necessary to upskill the future workforce in this area. However, limited jobsite access hinders experiential learning of laser scanning, necessitating the need for an alternative learning environment. Previously, the authors explored mixed reality (MR) as an alternative learning environment for laser scanning, but to promote seamless learning, such learning environments must be proactive and intelligent. Toward this, the potentials of classification models for detecting user difficulties and learning stages in the MR environment were investigated in this study.

The study adopted machine learning classifiers on eye-tracking data and think-aloud data for detecting learning stages and interaction difficulties during the usability study of laser scanning in the MR environment.

The classification models demonstrated high performance, with neural network classifier showing superior performance (accuracy of 99.9%) during the detection of learning stages and an ensemble showing the highest accuracy of 84.6% for detecting interaction difficulty during laser scanning.

The findings of this study revealed that eye movement data possess significant information about learning stages and interaction difficulties and provide evidence of the potentials of smart MR environments for improved learning experiences in construction education. The research implication further lies in the potential of an intelligent learning environment for providing personalized learning experiences that often culminate in improved learning outcomes. This study further highlights the potential of such an intelligent learning environment in promoting inclusive learning, whereby students with different cognitive capabilities can experience learning tailored to their specific needs irrespective of their individual differences.

The classification models will help detect learners requiring additional support to acquire the necessary technical skills for deploying laser scanners in the construction industry and inform the specific training needs of users to enhance seamless interaction with the learning environment.

The study aims to pioneer an innovative approach for the evaluation of government portal websites (GPWs) by introducing an eye-tracking-based method. The research meticulously pinpoints and analyses the distinct usability issues and challenges that users encounter while navigating and interacting with GPWs.

This study devises an eye-tracking-based GPW usability evaluation approach, which focuses on the major functions (i.e. government information disclosure, government services and interactive responses) of GPWs. An Entropy Weighted Technique for Order Preference by Similarity to an Ideal Solution (EW-TOPSIS) method is employed to process eye-tracking indicator results for deriving GPW usability results.

The proposed approach is demonstrated to assess the usability of 12 GPWs in pilot smart cities in China, and it is found that most GPWs have lower-than-average usability. GPWs with low usability require more cognitive load that exhibit increased fixation and saccade. The comparisons among the GPW usability results from (1) the eye-tracking experiment, (2) questionnaire surveys and (3) the ready-made performance evaluation report validate the effectiveness of eye-tracking-based GPW usability evaluation.

The work contributes to shifting the GPW usability evaluation approach from a subjective judgment paradigm to an objective paradigm, as well as provides implications for enhancing GPW usability, including improving search function, reducing website complexity and prioritizing user needs.

Design-thinking (DT) in education has attracted significant interest from practitioners and academics, as it proffers new-age thinking to transform learning processes. This paper synthesises extant literature and identifies the current intellectual frontiers.

First, a systematic-literature-review was undertaken employing a robust process of selecting papers (from 1986 to 2022) by reading titles, abstracts and keywords based on a required criterion, backward–forward chaining and strict quality evaluations. Next, a bibliometric analysis was undertaken using VOSviewer. Finally, text analysis using RStudio was done to trace the implications of past work and future directions.

At first, we identify and explain 12 clusters through bibliometric coupling that include “interdisciplinary-area”, “futuristic-learning”, “design-process” and “design-education”, amongst others. We explain each of these clusters later in the text. Science, Technology, Engineering, Arts and Mathematics (STEAM), management education, design and change, teacher training, entrepreneurship education and technology, digital learning, gifted education and course development) Secondly, through co-word-analysis, we identify and explain four additional clusters that include “business education and pedagogy”, “content and learning environment”, “participants and outcome” and finally, “thinking-processes”. Based on this finding, we believe that the future holds a very positive presence sentiment for design thinking and education (DT&E) in changing the 21st century learning.

For investigating many contemporary challenges related to DT&E, like virtual reality experiential learning, sustainability education, organisational learning and management training, etc. have been outlined.

Academics may come up with new or improved courses for the implementation of DT in educational settings and policymakers may inculcate design labs in the curricula to fortify academic excellence. Managers who would employ DT in their training, development and policy design, amongst others, could end up gaining a competitive advantage in the marketplace.

This study conducted a comprehensive review of the field, which to our limited knowledge, no prior studies have been done so far. Besides, the study also outlines interesting research questions for future research.

This study aims to propose and assess a model with the main elements influencing Brazilian agtech scalability with the aim of supporting the scalability process of startups that find themselves in the stage of initial conception of the business model.

The research was conducted using the design science research (DSR) method. The data for assessing the proposed model were collected through in-depth interviews. The answers were analyzed quantitatively, using descending hierarchical classification (DHC), correspondence factor analysis (CFA), and level of agreement; and qualitatively, using content analysis.

Considering the Brazilian context, the objective, environmental, structural, and evaluative dimensions presented positive characteristics for the main criteria analyzed (operational viability, generality, clarity, adaptation to the reality studied, completeness, consistency, comprehensibility, and structural simplicity). Specific improvements were proposed in all the criteria analyzed.

The artifact can be considered a strategic guide for agtechs that have yet to overcome the barrier of initial conception of the business model. The model enables the identification of the main problems that agtechs can encounter in their life cycle, as well as seeking solutions in advance.

The purpose of this study is to understand user perceptions and misconceptions regarding security tools. Security and privacy-preserving tools (for brevity, the authors term them as “security tools” in this paper, unless otherwise specified) are designed to protect the security and privacy of people in the digital environment. However, inappropriate use of these tools can lead to unexpected consequences that are preventable. Hence, it is significant to examine why users do not understand the security tools.

The authors conducted a qualitative study with 40 participants in the USA to investigate the prevalent misconceptions of people regarding security tools, their perceptions of data access and the corresponding impact on their usage behavior and data protection strategies.

While security vulnerabilities are often rooted in people’s internet usage behavior, this study examined user’s mental models of the internet and unpacked how the misconceptions about security tools relate to those mental models.

Based on the findings, this study offers recommendations highlighting the design aspects of security tools that need careful attention from researchers and industry practitioners, to alleviate users’ misconceptions and provide them with accurate conceptual models toward the desired use of security tools.

The paper aims to discuss error detection and correction in Kashmiri carpet weaving (KCW), mediated by cryptographic code, Talim which is held to guarantee accurate information transference from designing to weaving, even after hundred years. Yet, carpets often show errors on completion.

Human factors analysis revealed error emergence, detection and correction in this practice whose task domains are distributed over large geographies (from in-premises to several kilometers) and timescales (from days to decades). Using prospective observation method, production process of two research carpets from their design, coding and weaving was observed while noting the errors made, identified and corrected by actors in each phase.

The errors were found to emerge, identified and corrected during different phases of designing, coding and weaving while giving rise to fresh errors in each phase, due to actors’ normal work routines.

In view of this, usual branding of “weaver-error” behind flawed carpet turns out to be misplaced value judgment passed in hindsight.

Properly allocating an organization's activities within a building is vital to reducing the relational complexity arising from process–environment interactions. Multiple relationships are mapped, and certain interferences are only identified after these have been processed. The method/software employed for this task is Mapping Activity Environment Allocation (MAEA). However, data input and interpretation of results depend on the usability conditions of the organization's agents. This paper presents MAEA's usability test results.

Test sessions and interviews were carried out with seven agents registered at a University Hospital. Participants were instructed to think aloud during its use, and immediately afterward, responded to semi-structured interviews. Test sessions were audio recorded and screen captured.

Participants found the software easy to use and pointed out valuable implications for professional and academic use. In addition to relationship, priority and parallelism data, customized visualizations were created, including organizational charts, flowcharts and activity flow routes on the floor plan.

MAEA's simplicity allows non-designers to conduct evidence-based assessments and decisions. It allows designers to test their proposals during the programming and outline proposal stages.

A more detailed definition of design requirements from the beginning increases the conditions to successfully achieve project goals.

The ability to map the allocation of activity-spaces in the pre-design phase of building architecture allows for early identification of interactions, aiding in the development of more robust project requirements during programming.

Numerous design methods are available to facilitate digital innovation processes in user interface design. Nonetheless, little guidance exists on their appropriate selection within the design process based on specific situations. Consequently, design novices with limited design knowledge face challenges when determining suitable methods. Thus, this paper aims to support design novices by guiding the situational selection of design methods.

Our research approach includes two phases: i) we adopted a taxonomy development method to identify dimensions of design methods by reviewing 292 potential design methods and interviewing 15 experts; ii) we conducted focus groups with 25 design novices and applied fuzzy-set qualitative comparative analysis to describe the relations between the taxonomy's dimensions.

We developed a novel taxonomy that presents a comprehensive overview of design conditions and their associated design methods in innovation processes. Thus, the taxonomy enables design novices to navigate the complexities of design methods needed to design digital innovation. We also identify configurations of these conditions that support the situational selections of design methods in digital innovation processes of user interface design.

The study’s contribution to the literature lies in the identification of both similarities and differences among design methods, as well as the investigation of sufficient condition configurations within the digital innovation processes of user interface design. The taxonomy helps design novices to navigate the design space by providing an overview of design conditions and the associations between methods and these conditions. By using the developed taxonomy, design novices can narrow down their options when selecting design methods for their specific situations.

This study aims to explore the evidence-based usage patterns of higher education commission (HEC) subscribed e-journal databases in the university digital library used by the scholarly community and the academics’ online searching behaviour at a higher education institution in Pakistan.

The study used an explanatory sequential mixed methods approach. Raw transaction log data were collected for quantitative analysis, and the interview technique was used for qualitative data collection and thematic analysis.

Log analysis revealed that HEC subscribed databases were used significantly, and among those, scholarly databases covering various subjects were more frequently used than subject-specific society-based databases. Furthermore, the users frequently accessed the needed e-journal articles through search engines like Google and Google Scholar, considering them sources of free material instead of the HEC subscribed databases.

It provides practical implications for examining the evidence-based use patterns of e-journal databases. It suggests the need for improving the access management of HEC databases, keeping in view the usage statistics and the demands of the scholars. The study may also help create market venues for the publishers of scholarly databases by offering attractive and economical packages for researchers of various disciplines in developing and underdeveloped countries. The study results also guide the information professionals to arrange orientation and information literacy programs to improve the searching behaviour of their less frequent users and enhance the utilization of these subscribed databases.

The study is part of a PhD project and, to the best of the authors’ knowledge, is the first such work in the context of a developing country like Pakistan.

The aim of this article is to map the use of AI in the user experience (UX) design process. Disrupting the UX process by introducing novel digital tools such as artificial intelligence (AI) has the potential to improve efficiency and accuracy, while creating more innovative and creative solutions. Thus, understanding how AI can be leveraged for UX has important research and practical implications.

This article builds on a systematic literature review approach and aims to understand how AI is used in UX design today, as well as uncover some prominent themes for future research. Through a process of selection and filtering, 46 research articles are analysed, with findings synthesized based on a user-centred design and development process.

The authors’ analysis shows how AI is leveraged in the UX design process at different key areas. Namely, these include understanding the context of use, uncovering user requirements, aiding solution design, and evaluating design, and for assisting development of solutions. The authors also highlight the ways in which AI is changing the UX design process through illustrative examples.

While there is increased interest in the use of AI in organizations, there is still limited work on how AI can be introduced into processes that depend heavily on human creativity and input. Thus, the authors show the ways in which AI can enhance such activities and assume tasks that have been typically performed by humans.