Information To Users Research Articles

The Evolution Of International Financial Reporting Standards (IFRS) And Its Impact On Global Business Practices

The International Financial Reporting Standards (IFRS) have undergone significant evolution over the years, shaping the financial reporting on a global scale. The journey of IFRS began with the establishment of the International Accounting Standards Committee (IASC) in 1973, which laid the foundation for international accounting harmonization. Over time, the IASC transformed into the International Accounting Standards Board (IASB), which took on the responsibility of developing and issuing IFRS. The adoption of IFRS has become increasingly widespread, with many countries around the world either adopting the standards outright or converging their national standards with IFRS. One of the primary objectives of IFRS is to enhance the comparability, transparency, and reliability of financial reporting across borders. By providing a common set of accounting principles, IFRS facilitates better communication and understanding among stakeholders, including investors, creditors, regulators and other users of financial information. This standardization promotes efficiency in capital markets and encourages cross-border or cross-nation investment and business activities. IFRS is characterized by its principles-based approach, which emphasizes substance over form and allows for greater flexibility in financial reporting. Unlike rules-based standards, which prescribe specific treatments for various transactions, principles-based standards provide a framework for judgment, allowing preparers to alter their financial statements to reflect the economic reality of their transactions. The impact of IFRS extends beyond financial reporting to areas such as taxation, auditing, and regulatory compliance. While the adoption of IFRS has brought many benefits, it has also presented challenges for companies and regulators alike. The evolution of IFRS has had a profound impact on global business practices, promoting greater transparency, comparability, and efficiency in financial reporting. Through an examination of the evolution of IFRS, this research contributes to a deeper understanding of its profound influence on global business practices

Evaluating and Enhancing Canvas Course Website: Prioritizing User‐Centered Design

ABSTRACTCanvas, a learning management system (LMS), has been widely adopted by academic institutions across North America. Despite its robust features, the user experience with Canvas varies significantly. This study examines the design of a Canvas course website, providing evidence‐based guidance for creating user‐centered LMS interfaces. The findings aim to enhance the user experience by addressing issues such as information overload, user control, and accessibility. Design principles and recommendations generated from this study are crucial for our institution‐wide adoption of Canvas, promoting a seamless and supportive learning environment.

Empowering Users with ChatGPT and Similar Large Language Models (LLMs): Everyday Information Needs, Uses, and Gratification

ABSTRACTDisruptive technologies such as ChatGPT and similar Large Language Models (LLMs) have transformed mundane everyday tasks of information users since their debut in late 2022. In this study, we leverage uses and gratifications theory to test a distinct set of motivations that drive users' satisfaction and continued use intentions of ChatGPT and similar large language models. Data were collected using a national online survey of 323 adults residing in the United States. We conducted data analysis using Partial Least Squares (PLS‐SEM) to investigate both direct and indirect impact of factors on users' gratification, thereby influencing the continued utilization of these tools for everyday information seeking. Results show four motivational factors ‐ social influence, trust, personalization, and perceived usefulness ‐ that positively influence users' satisfaction or sense of gratification, impacting their intentions to continue using these tools. This is one of the few early studies of ChatGPT and other LLMs from an information science perspective.

From Fandom to Fadeaway: Unpacking Factors Contributing to Anime Dropout

ABSTRACTThis study explores a case of anime dropout/disengagement to understand media information users. Particularly, this study identifies elements in anime (e.g., Tropes) that prevent audience from continue watching what they had started (i.e., dropout/disengagement). A content analysis of 226 posts from MyAnimeList.net, an online anime database and fan community, suggests that the major elements that motivate audience dropout are Plot/Narrative, Tropes, Artwork/Visual Style, Characters, and Mood. The expectation gap and sunk cost also contribute to disengagement. This study presents a way to enrich anime metadata through analyzing the identified elements. Additionally, this study suggests that understanding anime disengagement may contribute to the search and recommendation of anime.

PECULIARITIES OF INFORMATION AND ANALYTICAL SUPPORT IN DECISION-MAKING WITHIN GOVERNMENT BODIES

The modern world is increasingly entering the era of an information society, where the role of information becomes ever more significant. Information is accumulating rapidly and in large volumes, so quickly that people struggle to process and thoroughly comprehend the incoming information flow, leading to an overall reduction in usable information. In order to process relevant data effectively in the context of professional or other activities today, it is no longer sufficient to rely solely on traditional administrative experience to make informed management decisions. It is essential to incorporate scientific knowledge, comprehensive awareness, analytical and forecasting skills, as well as socio-political responsibilities, with an appropriate recognition of these processes. This is particularly relevant for public administration bodies—both central and regional levels of executive state power—and increasingly for local government authorities, which are gaining more authority as part of the decentralization process. In Ukraine, in particular, the ideology and framework for interaction between the state and society are changing, with an increased use of decentralization mechanisms that delegate specific functions and powers from state bodies (to varying degrees) to the level of territorial communities and even to civil society. The purpose of this article is to define the peculiarities of information and analytical support in the decision-making process within government bodies. The article systematizes approaches to information and analytical support in public administration. It identifies the subjects and objects of the information and analytical support system in state management processes and specifies the goals of such support. It justifies the need to highlight the key elements of the information and analytical support system for public administration. The article also outlines the principles guiding information and analytical activities conducted by government and local authorities. Levels of information and analytical work are established, taking into account the temporal dimension. Furthermore, the article identifies issues with information and analytical support in government bodies in Ukraine and suggests ways to address these challenges.

Development of a T2D App for Elderly Users: Participatory Design Study via Heuristic Evaluation and Usability Testing

Diabetes management applications effectively help patients monitor blood glucose levels and adjust diet and exercise plans. However, most diabetes management apps, including the SugarShift application developed in 2023, use generalized designs that often fail to consider the unique cognitive and physiological characteristics of elderly users, thereby impacting their acceptance and usage. This paper proposes an enhancement for the SugarShift application by introducing a mode specifically designed for elderly users. This proposed enhancement aims to directly involve elderly patients so as to meet their needs better through a participatory design approach. A heuristic evaluation conducted with six experts identified 126 usability issues and 179 heuristic violations in the current version, which has led to the recommendation to develop optimization strategies tailored for elderly users. Subsequent usability testing with 28 elderly patients and six experts emphasized the importance of interface simplicity, logical operation, and interaction quality in enhancing user experience. These factors improve usability, facilitate information processing, and reduce cognitive load. Despite the overall usability of SUS scores, significant challenges still need to be addressed in information display, system feedback, and user interaction. Recommendations for future enhancements include simplifying data entry and presentation, improving readability, and providing timely feedback to enhance usability and user satisfaction, ultimately increasing user retention.

Enhancing Recommendation Diversity and Novelty with Bi-LSTM and Mean Shift Clustering

In the digital age, personalized recommendation systems have become crucial for information dissemination and user experience. While traditional systems focus on accuracy, they often overlook diversity, novelty, and serendipity. This study introduces an innovative recommendation system model, Time-based Outlier Aware Recommender (TOAR), designed to address the challenges of content homogenization and information bubbles in personalized recommendations. TOAR integrates Neural Matrix Factorization (NeuMF), Bidirectional Long Short-Term Memory Networks (Bi-LSTM), and Mean Shift clustering to enhance recommendation accuracy, novelty, and diversity. The model analyzes temporal dynamics of user behavior and facilitates cross-domain knowledge exchange through feature sharing and transfer learning mechanisms. By incorporating an attention mechanism and unsupervised clustering, TOAR effectively captures important time-series information and ensures recommendation diversity. Experimental results on a news recommendation dataset demonstrate TOAR’s superior performance across multiple metrics, including AUC, precision, NDCG, and novelty, compared to traditional and deep learning-based recommendation models. This research provides a foundation for developing more intelligent and personalized recommendation services that balance accuracy with content diversity.

Friend2User : A new CNN based method for user network and content embedding

Nowadays, social networks have become an integral part of modern society, significantly influencing individuals worldwide due to their extensive reach. Consequently, analyzing the data disseminated within these networks in order to identify online communities presents a major challenge for researchers in the data mining field. To address this challenge, we propose, in this paper, a novel deep user embedding framework for community extraction on social networks. Our method leverages the capability of Convolutional Neural Networks (CNNs) to produce abstract representations of users that preserve the semantic information in the data. Specifically, our approach considers both the profile content and the network structure, harnessing the power of unsupervised CNNs. The key concept underlying our proposal is that each user is represented not only by their own content but also by the content of their close friends. We employ a recursive CNN to integrate neighboring users’ content, thereby generating concise and informative user embeddings. The empirical findings obtained by our method demonstrate the effectiveness of our proposed user embeddings in efficiently detecting communities within social networks, particularly in the context of cybersecurity.

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

GNNRI: detecting anomalous social network users through heterogeneous information networks and user relevance exploration

Optimising the review of electronic monitoring information for management of commercial fisheries

Electronic monitoring (EM) systems incorporating cameras and other devices can collect a broad range of data to support fisheries management. We reviewed the data collection capabilities of EM and considered approaches to increasing efficiency, including cost effectiveness, of EM review. EM can provide information on catch, effort, catch handling, bycatch mitigation, fishing gear and operational data, which are relevant for fisheries management including by Regional Fisheries Management Organisations (RFMOs). Methods to increase efficiency and decrease costs of EM review apply from the programme design phase, through data collection and review. At review, costs may be reduced by sampling imagery optimally to meet monitoring objectives. Considering RFMOs as users of EM-collected information, we applied EMoptim, an open-source simulation model developed in R that estimates the amount of EM review necessary to meet one or more user-specified monitoring objectives. EMoptim uses stratification to increase review efficiency and incorporates a function to explore review costs against the monitoring objectives set. We evaluated the amount of EM review needed to estimate catch with specified precision, using fishery data available from the Western and Central Pacific Fisheries Commission. Model outputs show that EM review requirements increase as catch frequency decreases, dispersion of catch events increases, and when more precise catch estimates are required. Geographical stratification reduced the amount of review required for more commonly caught species and when catch events were focused in a limited area. Optimising review rates across multiple monitoring objectives was most effective for more commonly caught species. We highlight opportunities for future use and development of this prototype modelling package.

Adaptation of Parallax Scrolling Effect Technique in LegitCheck Website Interface Development

This research focuses on developing LegitCheck's website interface by applying the Parallax Scrolling Effect technique, which is considered as a potential solution to support the startup's promotional strategy. LegitCheck, as a startup that needs an effective way to introduce its services and business concept to consumers and brands, requires a website that can serve as an attractive promotional medium as well as a comprehensive information center. The purpose of this research is to examine the effectiveness of the Parallax Scrolling Effect technique compared to a website without the effect in increasing the effectiveness of the LegitCheck website as a promotional tool. The research method used includes the design and implementation of the Parallax Scrolling Effect technique on the LegitCheck website, followed by evaluation using the A/B testing method. The results showed that the implementation of the Parallax Scrolling Effect technique provided significant benefits, including improved information accuracy, service quality, user satisfaction, and company net profit. Although there are constraints related to page loading speed, the majority of respondents prefer websites with Parallax Scrolling Effect because it provides a more interactive and engaging experience. The implication of this study is that the use of Parallax Scrolling Effect can be an effective strategy for startups such as LegitCheck to increase interaction with users, strengthen brand image, and increase the effectiveness of the website as a promotional tool, although it is necessary to pay attention to technical aspects such as page loading speed to minimize potential disruptions to the user experience.

Experts’ prediction of item difficulty of multiple-choice questions in the Ethiopian Undergraduate Medicine Licensure Examination

BackgroundThe ability of an expert’s item difficulty ratings to predict test-taker actual performance is an important aspect of licensure examinations. Expert judgment is used as a primary source of information for users to make prior decisions to determine the pass rate of test takers. The nature of raters involved in predicting item difficulty is central to set credible standards. Therefore, this study aimed to assess and compare raters’ prediction and actual Multiple-Choice Questions’ difficulty of the undergraduate medicine licensure examination (UGMLE) in Ethiopia.Method815 examinees’ responses to 200 Multiple-Choice Questions (MCQs) were used in this study. The study also included experts’ item difficulty ratings of seven physicians who participated in the standard settings of UGMLE. Then, analysis was conducted to understand experts’ rating variation in predicting the actual difficulty levels of examinees. Descriptive statistics was used to profile the mean rater’s and actual difficulty value for MCQs, and ANOVA was used to compare the mean differences between raters’ prediction of item difficulty. Additionally, regression analysis was used to understand the interrater variations in item difficulty predictions compared to the actual difficulty. The proportion of variance of actual difficulty explained from rater prediction was computed using regression analysis.ResultsIn this study, the mean difference between raters’ prediction and examinees’ actual performance was inconsistent across the exam domains. The study revealed a statistically significant strong positive correlation between the actual and predicted item difficulty in exam domains eight and eleven. However, a non-statistically significant very weak positive correlation was reported in exam domains seven and twelve. The multiple comparison analysis showed significant differences in mean item difficulty ratings between raters. In the regression analysis, experts’ item difficulty ratings of the UGMLE had 33% power in predicting the actual difficulty level. The regression model also showed a moderate positive correlation (R = 0.57) that was statistically significant at F (6, 193) = 15.58, P = 0.001.ConclusionThis study demonstrated the complex process for assessing the difficulty level of MCQs in the UGMLE and emphasized the benefits of using experts’ ratings in advance. To ensure the exams maintain the necessary reliable and valid scores, raters’ accuracy on the UGMLE must be improved. To achieve this, techniques that align with the evolving assessment methodologies must be developed.

Relevant information from the perspective of sustainable forest management – auditing socio-environmental information and data

Purpose: The study aims to: (1) deepen the theory of defining new concepts such as relevant information and information needs, and (2) examine the essence of financial and non-financial information and its usefulness to forest stakeholders. The research problem was formulated as follows: “Since forest management entities generate and disclose financial and non-financial information, which of these sources of information will be considered most useful (relevant) by information users?” In the context of environmental and social challenges, accounting 5.0 must generate, report, and disclose information according to stakeholders’ expectations. Methodology/approach: The following methods were used: literature analysis and critique, document examination, logical analysis and construction, the Delphi method, and snowballing. The Pearson chi-square independence test and the Kruskal-Wallis test were used. Findings: The most important characteristics of information are reliability and timeliness. The most useful information for stakeholders includes forest maps and conservation information, a “forest dictionary”, and rules for timber sales. Information about campaigns, charitable actions, forest hazards, and forest access restrictions is useful. Research limitations: A limitation of the research is the national sample size of the survey, with a higher percentage of internal forest stakeholders than external stake-holders. Practical implications: The practical implications of the article are based on the collected research material, which can contribute to the development of a standard questionnaire for investigating the information needs of stakeholders in the future. Originality/value: The study is the first empirical research of its kind in Poland and involves the participation of foreign experts. Contributions to social science theory include: (1) an original definition of “relevant information”; (2) a definition of “information needs”; (3) comparative frameworks for the impact areas of the sustainable development concept in correlation with information, relevant information, and stake-holders; (4) a conceptual table of differences between types of information from a sustainability perspective.

Secured fog-based vehicular crowd-sensing protocol by Modified Attribute based Encryption Model

The contemporary fog computing paradigm evolved from traditional cloud computing to give storage and computation resources at the network’s edge. Fog enabled vehicular processing is anticipated to be a fundamental component that may expedite a variety of applications, notably crowd-sensing, when applied to vehicular networks. As a result, the confidentiality and safety of vehicles participating in the crowd-sensing platform are now recognized as critical challenges for smart police and cyber defence. Furthermore, sophisticated access control is essential to meet the requirements of crowd-sensing users of information. This work presents a novel secured fog-based protocol for vehicular crowd sensing utilizing an attribute-based encryption model. The proposed framework incorporates a two-layered fog architecture termed fog layer A and fog layer B. Fog layer A includes data owners (vehicles) and fog nodes, while fog layer B consists of fog nodes integrated with Road Side Units (RSUs). The Transport Triggered Architecture (TTA) governs the retrieval of regular or specialized data as per the request of data users. The data collection procedure varies based on the type of data being processed. Regular data is gathered from data owners, aggregated using the Multiple Kernel Induced in Kernel Least Mean Square (MKI-KLMS) technique, which employs kernel least mean square and hierarchical fractional bidirectional least mean square methods to handle redundancy in data aggregation. Subsequently, the aggregated data is encrypted using the Proposed Fusion Key for Encryption (PFKE) technique, which employs a fusion key for message encryption. Additionally, an enhanced Blowfish algorithm is applied for an extra layer of encryption on the already encrypted data. The encrypted data is then transmitted to the TTA. The decryption process utilizes the same key to retrieve the original message. The Modified Attribute based Encryption Model (MAEM) scheme achieves the highest efficiency of 90.9476.

Exploring the impact of digital distrust on user resistance to e-health services among older adults: the moderating effect of anticipated regret

This study investigates the factors influencing user resistance to e-health services among older adults, focusing on the role of information inequality, digital distrust, low self-efficacy, perceived risk, and social influence. Structured questionnaires were used to gather data from 301 older adult participants, and the structural equation modeling method was used to analyze the results. The results show that digital distrust, perceived risk, and user resistance have a significant positive relationship, underscoring the significance of resolving trust and risk concerns in e-health interventions. The study also finds that the association between digital distrust and user resistance is moderated by anticipated regret, indicating that people who anticipate regretting not adopting electronic health services are less likely to oppose digital technologies despite their distrust. Surprisingly, the study did not find support for the hypothesized relationship between information inequality and user resistance, indicating the need for further exploration of this complex relationship. The study contributes to a better understanding of the factors influencing user resistance to e-health services among older adults, offering valuable insights for policymakers and practitioners seeking to promote the adoption of digital health technologies in aging populations.

Significance Of Information Sources To Integrated Pest Management Usage Among Poultry Farmers In Kwara State, Nigeria

Sources of information on the use of combined pest control measures for a sustainable ecofriendly environment are germane in poultry production. A multistage sampling procedure was used to select 120 respondents using an interview schedule. Data collected on socioeconomic characteristics, sources of information, pest prevalence, constraints, and utilisation of Integrated Pest Management (IPM) in Kwara State were analysed with percentages, frequency, weighted mean score, mean, Chi-square, and Pearson’s Product Moment Correlation at α 0.05. The result shows that respondents were members of the Poultry Association of Nigeria (PAN) (96.7%) with mean age and years of business experience were 43.0±9.9 and 7.3±5.8years respectively. Most of the respondents had tertiary education (84.2%) and produced broilers (51.7%) in four cycles a year (57.5%). The most utilized IPM information sources were from co-farmers (68.3%) and workshops (67.5%); with the most prevalent pest being flies (x̅=1.48) and was low for 54.2% of the respondents. The respondents’ most utilised IPM method was the prompt elimination of dead birds from the flock (x̅=2.86) which was high for 60%. However, respondents’ most prominent constraint to IPM usage was the government’s insufficient pesticide subsidy (x̅=2.69); and was high for 60.0%. A significant relationship existed between respondents’ IPM usage and sources of information like family (X2=0.938; p=0.005) and internet (X2=1.000; p=0.000); years of education attainment (r=0.022; p=0.021), prevalent pest (r=0.577; p=0.051) and constraints to IPM usage (r=0.186; p=0.012). IPM information sources positively influence its utilisation, hence such sources should be empowered by the relevant stakeholders for a sustainable eco-friendly environment.

Metaverse adventures into libraries: what librarians and information users need to know

PurposeThe study explores the transformative potential of the metaverse in libraries, highlighting opportunities for innovative patron engagement and challenges such as digital equity, privacy concerns, and accessibility barriers.Design/methodology/approachThis study employed an interpretive content/document analysis approach, drawing insights from diverse literature sources including Scopus, Web of Science, and Google Scholar.FindingsThe findings indicate that through the integration of immersive technologies like virtual reality (VR) and augmented reality (AR), libraries can enrich their offerings and reach a broader audience. However, addressing challenges in libraries requires proactive measures, including bridging the digital divide and implementing inclusive design practices. Furthermore, cultivating metaliteracy skills among librarians and information users is essential to navigating the complexities of the metaverse responsibly.Originality/valueThe study’s originality lies in its comprehensive exploration of the implications of the metaverse for libraries and information users. Thus, exploring the nexus of immersive technologies, digital equity, and the evolving role of libraries, the research provides a nuanced understanding of the challenges and opportunities presented by the metaverse. The value outlines the key strategies for libraries to thrive in the metaverse by emphasizing the integration of immersive technologies, enhancing digital literacy, fostering collaborative partnerships, prioritizing accessible design, investing in research and innovation, and advocating for inclusive policies, the study equips libraries with a roadmap to fulfill their mission of providing equitable access to information and fostering lifelong learning in the digital age. The insights from the study will inform the strategic planning and decision-making of library administrators, policymakers, and information professionals as they navigate the transformative potential of the metaverse.

Adapting to evidence users: accessibility and incentives

Adapting infrastructure for evidence creation and dissemination involves taking new technological, social, and environmental realities into account. Adaptations should also respond to what hasn’t changed: structural disincentives to evidence use among intended users. This presentation addresses the understudied problem of how administrative data systems go beyond enabling access to information – how data linkages or other infrastructure impacts policymakers or other intended information users. It addresses data sharing and data use, as well as public engagement and involvement. A recent practitioner convening examined how new administrative data infrastructure can catalyze evidence-building, and, specifically explored the incentives and disincentives for stakeholders' use of evidence from evaluations. Participants discussed the multiple functions of evaluations for nonprofits, funders, policymakers, and communities, and shared lessons in capacity building from the recent history of nonprofit & human services evaluation. Preliminary results from this convening will be discussed in terms of their broader implications for the use of information resulting from future large-scale data linkages. Broadly, the convening validated the hypothesis that efforts to increase the availability of evidence often proceed without sufficient consideration of the history of non-uptake of evidence, and that there has been limited acknowledgement of this problem by practitioners of evidence generation. Much work is needed within the evaluation & evidence, and nonprofit sector to address this challenge. Key themes for this work that emerged during the convening will illustrate what might work and what won’t work to address the non-uptake of evidence more broadly.

Comparison of spatiotemporal gait parameter measurements across various emulated foot strike patterns between the Tekscan® Strideway™ pressure sensitive walkway and gold-standard marker-based motion capture

Spatiotemporal gait parameters are commonly used to quantify physical functioning including in populations with atypical foot strike patterns. The current gold standard measurement system for gait analysis is marker-based motion capture and floor-mounted force plates, but it can be expensive and cumbersome to set up. Pressure sensitive walkways are more affordable, quicker to set up, and more portable. Currently available walkways have not been compared to marker-based motion capture when measuring atypical foot strike patterns. The recently developed Tekscan® Strideway pressure sensitive walkway system has not been compared to any gold standard. This study compared measurements of step width, step length, and step time from the Strideway™ system against a gold standard marker-based motion capture (Vicon® Vantage™) and floor-mounted force plate (AMTI®) system. Ten typically ambulating adults, free of injury, emulated five different foot strike patterns recording two-hundred footsteps for each. Results indicate that the Strideway™ compares well to the gold standard especially under typical foot strike patterns. The errors were highest for step width and near-zero for step time. However, the user needs to be aware that errors can substantially increase with certain foot strike patterns such as when the heel does not make contact with the walkway. The results of this study will help inform users about potential limitations when using a pressure sensitive walkway like the Strideway™ for testing populations with typical and atypical strike patterns.

Linking boundary organizations to co-produce actionable knowledge: A case study of ice forecasting for Great Lakes navigation

Abstract Navigating the Great Lakes during icy conditions poses significant safety challenges for the shipping sector. Available ice information is uncertain and fragmented, and navigators must seek out multiple sources for information at the spatial and temporal scales they require, if the information is available at all. Navigators have expressed that they require more highly localized and easily usable information for current and predicted ice conditions to support decision-making. In this study, we seek to meet this information need by applying a boundary organizations chain (BOC) approach to facilitate the co-production of an actionable short-term Great Lakes ice forecast. We focus on two main aspects of this research: 1) producing an actionable decision-support product that meets the needs of Great Lakes ice navigators, and 2) contributing to the knowledge co-production scholarship on BOCs by providing a detailed account of our methods to create a BOC and co-produce an actionable ice forecast. Our results support incorporating existing communities of practice (COPs) into BOCs to enhance the co-production of actionable knowledge, specifically through increasing their complementarity and embeddedness. COPs are informal networks of users that meet voluntarily to share knowledge and develop professional skills, which we found naturally builds the co-production capacities of participants (e.g. embeddedness and complementarity). We also find that COP members are well positioned to disseminate co-produced knowledge across wider user groups.