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Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

This study examined how people assess health information from AI and improve their diagnostic ability to identify health misinformation. The proposed model was designed to test a cognitive heuristic theory in misinformation discernment.

We proposed the heuristic-systematic model to assess health misinformation processing in the algorithmic context. Using the Analysis of Moment Structure (AMOS) 26 software, we tested fairness/transparency/accountability (FAccT) as constructs that influence the heuristic evaluation and systematic discernment of misinformation by users. To test moderating and mediating effects, PROCESS Macro Model 4 was used.

The effect of AI-generated misinformation on people’s perceptions of the veracity of health information may differ according to whether they process misinformation heuristically or systematically. Heuristic processing is significantly associated with the diagnosticity of misinformation. There is a greater chance that misinformation will be correctly diagnosed and checked, if misinformation aligns with users’ heuristics or is validated by the diagnosticity they perceive.

When exposed to misinformation through algorithmic recommendations, users’ perceived diagnosticity of misinformation can be predicted accurately from their understanding of normative values. This perceived diagnosticity would then positively influence the accuracy and credibility of the misinformation.

Perceived diagnosticity exerts a key role in fostering misinformation literacy, implying that improving people’s perceptions of misinformation and AI features is an efficient way to change their misinformation behavior.

Although there is broad agreement on the need to control and combat health misinformation, the magnitude of this problem remains unknown. It is essential to understand both users’ cognitive processes when it comes to identifying health misinformation and the diffusion mechanism from which such misinformation is framed and subsequently spread.

The mechanisms through which users process and spread misinformation have remained open-ended questions. This study provides theoretical insights and relevant recommendations that can make users and firms/institutions alike more resilient in protecting themselves from the detrimental impact of misinformation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2023-0167

Referring to “behavioural finance” and “normative model” theories, this study explores the relative significance of behavioural heuristic biases in the investment decisions of real estate investment trusts (REITs) when compared with the conventional normative decision factors, with an ultimate aim to identify the significant behavioural factors that should be avoided to ensure rational asset acquisitions and market efficiency.

A triangulation approach was adopted. Qualitative multiple case studies were conducted, with four cases selected from Australian and New Zealand REITs across the industry, to identify what normative and behavioural finance factors are involved in investment decisions. This formed the basis for the subsequent expert review survey to explore how significant the behavioural factors were manifested in the judgement when compared with the normative factors.

Three out of four theoretical behavioural factors manifested themselves in the investment decisions: investor sentiment, anchoring factors and overconfidence. The overall impact of these three behavioural factors was that they were as significant as normative factors in investment decisions. The heuristic availability of information was found to have no significant effect on experienced REIT fund managers.

The findings were based on four multiple cases and an expert review survey of six frontline fund managers, which form a baseline upon which further research can be conducted to widen the scope of research to cover all REITs in Australasia so that the results can become more robust to benefit the entire market in the region.

As behavioural factors are significant in the decision-making process, REIT fund managers should raise awareness to avoid the significant behavioural factors identified, in particular investor sentiment, which was found to be the most significant one.

This study confirms the relative significance of behavioural factors in property investment decisions within the context of Australasian REITs and alerts fund managers to the ways they should follow to ensure rational investments and market efficiency. It also extends the scale of existing studies to cover not only Australia but also New Zealand for the benefit of the entire Australasian market.

This paper aims to evaluate the Digital Government Collaborative Platform (DGCP), which facilitates collaborations between the citizens and the government to address environmental issues in Sri Lanka. The DGCP is an artifact developed by the value-sensitive design approach.

The DGCP is evaluated following the Framework for Evaluation in Design Science Research (FEDS). In total, 224 citizens participated in the survey based on the User Experience Questionnaire (UEQ) and open questions about human values embedded in the design. Fifteen government officers were interviewed to enhance the evaluation.

The DGCP received positive evaluations from the citizens and government officers. The platform is attractive, novel and pragmatic, also generating hedonic experiences for the citizens. The users believed that human values are reflected in the DGCP. Furthermore, they shared a few suggestions to improve it.

The paper contributes knowledge to evaluating digital government systems, especially in developing countries. The human-value-centered DGCP was evaluated using multiple methods of quantitative (i.e. UEQ Survey) and qualitative (i.e. qualitative interviews with stakeholders) techniques. Furthermore, the systematic process of DGCP evaluation produces a case-based guideline for evaluating related and similar digital government systems using FEDS.

Adopting a brand prominence perspective, this study aims to examine how textual and visual brand elements influence the success of crowdfunding campaigns for aftermarket offerings.

A Python-based Web scraper was used to collect data from 620 crowdfunding campaigns for aftermarket offerings hosted on Kickstarter. The linguistic inquiry and word count application programing interface was then used to analyze the linguistic aspects of these campaigns. A fixed effects regression model was used to evaluate the hypotheses.

Textual and visual brand prominence are positively associated with campaign success. Further, with some variations, both types of brand prominence augment the positive effects of narrative and graphic design elements on campaign success.

This study makes novel theoretical contributions to the literature on branding, crowdfunding and aftermarket products. The results also provide practical insights to aftermarket enterprises on creating compelling crowdfunding campaigns.

By analyzing the direct and synergistic effects of branding, narrative and graphic design elements on crowdfunding success, this study extends various literature streams and identifies several future research opportunities.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.

Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted.

A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications.

The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment.

The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field.

The purpose of this paper is to gain a better understanding on how metasurfaces behave, in terms of currents in each unit cell. A better knowledge of their behavior could lead to an ad-hoc design for specific applications.

The methodology used is both theoretical and numerical; it is based on circuit theory and on an optimization procedure.

The results show that when the knowledge of the current in each unit cell of a metasurface is needed, the most common approximations currently used are often not accurate. Furthermore, a procedure for the termination of a metasurface, with application-driven goals, is given.

This paper investigates the distribution of the currents in a 2D metamaterial realized with magnetically coupled resonant coils. Different models for the analysis of these structures are illustrated, and the effects of the approximations they introduce on the current values are shown and discussed. Furthermore, proper terminations of the resonators on the boundaries have been investigated by implementing a numerical optimization procedure with the purpose of achieving a uniform distribution of the resonator currents. The results show that the behavior of a metasurface (in terms of currents in each single resonator) depends on different properties; as a consequence, their design is not a trivial task and is dependent on the specific applications they are designed for. A design strategy, with lumped impedance termination, is here proposed.

The purpose of this paper is to share the process, findings and conclusions from one library’s iterative usability study of its website design to inform other libraries as they perform their own assessments.

A task-completion usability study was conducted with eight undergraduate students across two iterations: the first gauged the usability of a redesigned library website and the second gauged the effectiveness of the first iteration’s findings.

We found that users performed better when the site provided multiple access points to the same information, displayed a prominent chat feature, limited the amount of text on a given page and avoided library jargon. Not only was the second round of testing important for confirming that first-round recommendations were effective but also it proved useful in catching a problem with the site that was unintentionally created during the time between tests.

No demographic data were collected during the study, thus hindering our ability to analyze our users through these data points.

This study demonstrates the value of iterative usability testing, especially when untested changes made between site versions may produce usability issues.

This study aims to identify how restaurants can effectively initiate communication via social media to promote ethical dining behaviors. This research investigates the psychological mechanism of how the matching effect of color and a sustainability activity influence customer attitude toward a restaurant and the role of perceived credibility and green image.

Two experimental studies were conducted. Study 1 used a 2 food source (non-sustainable vs sustainable) × 2 color consistency (inconsistent vs consistent) factorial design (n = 231). Study 2 used a 2 food origin (world-famous vs locally renowned) × 2 color consistency (inconsistent vs consistent) factorial design (n = 220).

The results indicate that the matching effect from the marketing effect of sustainability significantly promotes customer attitudes and visit intentions when background color is consistent. An unexpected matching effect was found between a non-sustainable restaurant using world-famous food with its associated color. This research demonstrates a moderation effect of credibility and a mediation effect of green image to explain the ethical decision-making process for customers.

The findings provide suggestions for restaurant marketers to effectively advertise sustainability initiatives and practices using color as a marketing tool via social media.

This research is one of the earliest studies to investigate the effect of color consistency with primary information to demonstrate how consumers respond to restaurant sustainability in social media messages using local food.