Analysis Tools Research Articles

Associative linking for collaborative thinking: Self-organization of content in online Q&A communities via user-generated links.

Virtual collaborative Q&A communities generate shared knowledge through the interaction of people and content. This knowledge is often fragmented, and its value as a collective, collaboratively formed product, is largely overlooked. Inspired by work on individual mental semantic networks, the current study explores the networks formed by user-added associative links as reflecting an aspect of self-organization within the communities' collaborative knowledge sharing. Using eight Q&A topic-centered discussions from the Stack Exchange platform, it investigated how associative links form internal structures within the networks. Network analysis tools were used to derive topological indicator metrics of complex structures from associatively-linked networks. Similar metrics extracted from 1000 simulated randomly linked networks of comparable sizes and growth patterns were used to generate estimated sampling distributions through bootstrap resampling, and 99% confidence intervals were constructed for each metric. The discussion-network indicators were compared against these. Results showed that participant-added associative links largely led to networks that were more clustered, integrated, and included posts with more connections than those that would be expected in random networks of similar size and growth pattern. The differences were observed to increase over time. Also, the largest connected subgraphs within the discussion networks were found to be modular. Limited qualitative observations have also pointed to the impacts of external content-related events on the network structures. The findings strengthen the notion that the networks emerging from associative link sharing resemble other information networks that are characterized by internal structures suggesting self-organization, laying the ground for further exploration of collaborative linking as a form of collective knowledge organization. It underscores the importance of recognizing and leveraging this latent mechanism in both theory and practice.

Analysis of the Research Status of Traditional Chinese Medicine Pension Industry Based on Bibliometrics

At present, the problem of population aging in China is becoming increasingly prominent, and the combination of traditional Chinese medicine and elderly care is becoming a hot topic. In this paper, VOSviewer was used to statistically and visually analyze the literature related to traditional Chinese medicine pension in CNKI database from 2013 to 2023. Firstly, we collected and sorted out the research literature on the Chinese medicine pension industry in recent years, and then used bibliometric methods to quantitatively analyze the quantity, quality and research fields of the literature. The study found that the number of research literature on the elderly care industry of traditional Chinese medicine has increased year by year, the research quality has been continuously improved, and the research field has gradually expanded. At the same time, we also found that the research hotspots of traditional Chinese medicine for the elderly care industry are mainly focused on elderly care services, traditional Chinese medicine health preservation, rehabilitation treatment, etc (Ge & Xu, 2023). In addition, this paper also puts forward the shortcomings and development directions of the research on the Chinese medicine pension industry in China and hopes that this study can provide useful reference and guidance for the development of the Chinese medicine pension industry. Based on the China National Knowledge Infrastructure (CNKI) database as the main data source, this paper uses the topic search method in advanced search to search the CNKI database, and confirms that “Chinese medicine for the elderly” is used to read a large number of literature and understand the health care of traditional Chinese medicineAs the theme, the document format was set to see the full text only and Chinese and English extensions, the search time range was set from January 1th, 2013 to December 31th, 2023, the document type was set to academic journals, and the journal source category was set to all journals, a total of 149 articles were obtained, excluding news, publicity, interviews, correspondence and other literature that did not meet the research theme, and finally a total of 149 articles were obtained by manual screening 126 articles in Chinese. VOSviewer is an open-source network analysis tool, mainly used for scientific literature analysis, which can help users carry out visual analysis and network construction and is suitable for analyzing the research status of a certain field. In this paper, the VOSviewer visual analysis tool was used to process and analyze the literature data of 126 academic journals included in CNKI from 2013 to 2023, and the knowledge map of Chinese medicine pension in China was drawn, so as to visually analyze the author cooperation, hot topics and cutting-edge trends of Chinese medicine pension research in China.

Implementation of Solar Salt Properties Into ARIANT and Simulation of Pressurized Loss of Forced Circulation in a High Temperature Gas-Cooled Small Modular Reactor

Abstract Small modular reactors (SMRs) are actively being considered for use in Canada. Some proposed SMRs can make use of solar salt as an intermediate coolant for a heat storage system. The development of thermalhydraulic simulation tools is one of the key capabilities needed to examine the performance of SMRs and license this class of reactors. This article summarizes the implementation of molten solar salt fluid properties into the algorithm for analysis of network thermalhydraulics (ARIANT) and uses this code to simulate a high temperature gas-cooled SMR with helium and solar salt as its primary and secondary coolants during a pressurized loss of forced circulation (PLOFC) event. This work demonstrates the ability of ARIANT to simulate transient events in a two-loop reactor system consisting of helium and solar salt as coolants and helps to establish ARIANT as a tool for SMR analysis.

Bibliometric and visual analysis of heavy metal health risk assessment: development, hotspots and trends

Due to the widespread presence and harmfulness of heavy metals in the environment, scholars around the world have evaluated the exposure characteristics and health risks of heavy metals. To understand the status, hotspots, and development treads of heavy metal health risk assessment research, we used bibliometric analysis tools to conduct scientometric analysis of the literature related to the health risk assessment of heavy metals in the Web of Science database from 2000 to 2022. The analysis results indicate that research related to heavy metal health risk assessment is rapidly developing in both developed and developing countries. China’s significant international influence in this field is worth noting, as there are many publications and highly cited documents related to China. France and other developed countries also play an important role in this field due to their high centrality and strong bursts. The results of co-citation cluster analysis and keyword co-occurrence analysis indicate that in the past two decades, the primary research domains and hotspots of heavy metal health risk assessment have been the study of heavy metals in soil, dust, drinking water, vegetables, fish, and sediment. There is a specific focus on bioaccumulation, bioavailability, source apportionment, and spatial distribution of heavy metals. The main types of heavy metals studied are lead, cadmium, mercury, and zinc. The results of the bursts keywords analysis suggest that future research trends may focus more on the health risks of heavy metals in different functional areas of cities.

Comparative study of riemann integral and lebesgue integral in calculus

The aim of this research paper is to provide a comprehensive comparison between Riemann integral and Lebesgue integral. Integration is described as the inverse process of differentiation, which is used to determine the original function. Riemann integration is a specific type of definite integral applied to find the exact area under a function graph between two limits in a closed interval. Lebesgue integration, on the other hand, provides a more generalized framework for integration theory. Integrals are essential in mathematical modelling and analysis tools. Studying and comparing the similarities and differences between these two integration methods can help us better understand the essence and properties of integrals, so as to more accurately apply integration methods to solve practical problems. This paper provides a systematic analysis of the basics, definitions, concepts, and properties of Riemann integral and Lebesgue integral. Reasoning, proofs, and examples are consolidated to explain the properties and characteristics of these two integral methods. Finally, this paper explores the strengths and limitations of each integration methods, summarising their advantages and applicability to practical problems and providing insights into their respective computational methods and applicability in different contexts.

Evaluating the Barriers to Blockchain Adoption in the Energy Sector: A Multicriteria Approach Using the Analytical Hierarchy Process for Group Decision Making

The blockchain has been proposed for use in various applications in the energy field. Although the blockchain has technical strengths, several obstacles affect the application of the technology in energy services. The scope of this study is to highlight and prioritise the most important barriers to such applications. The first step in this direction is specifying the potential areas of the implementation of blockchain technology in the energy sector. Two useful tools for market analysis were used: Political, Economic, Social, Technological, Legal and Environmental, PESTLE Analysis, and Strengths, Weaknesses, Opportunities and Threats, SWOT Analysis, which examine external and internal factors, respectively. Thus, a list of the most important elements hindering the incorporation of the blockchain in the energy sector was extracted. The detected barriers were classified and ranked by energy and IT experts using the multicriteria method, “Analytical Hierarchy Process for Group Decision Making”. The results reveal that legal barriers relating to the complexities of deficiencies of regulations are the most significant, while technological barriers, especially those related to security issues, are also important. Sociopolitical barriers related mainly to lack of trust in blockchain, as well as economic concerns such as high upfront costs, are less influential but should still be considered. The conclusions of the conducted research have the potential to guide market actors in their endeavours to modernise energy systems through the use of the blockchain, assisting them in designing the most appropriate market strategies.

A Novel Approach for Mixed-Methods Research Using Large Language Models: A Report Using Patients' Perspectives on Barriers to Arthroplasty.

Mixed-methods research is valuable in health care to gain insights into patient perceptions. However, analyzing textual data from interviews can be time-consuming and require multiple analysts for investigator triangulation. This study aims to explore a novel approach to investigator triangulation in mixed-methods research by employing a large language model (LLM) for analyzing data from patient interviews. This study compared the thematic analysis and survey generation performed by human investigators and ChatGPT-4, which uses GPT-4 as its backbone model, using data from an existing study that explored patient perceptions of barriers to arthroplasty. The human- and ChatGPT-4-generated themes and surveys were compared and evaluated based on their representation of salient themes from a predetermined topic guide. ChatGPT-4 generated analogous dominant themes and a comprehensive corresponding survey as the human investigators but in significantly less time. The survey questions generated by ChatGPT-4 were less precise than those developed by human investigators. The mixed-methods flowchart proposes integrating LLMs and human investigators as a supplementary tool for the preliminary thematic analysis of qualitative data and survey generation. By utilizing a combination of LLMs and human investigators through investigator triangulation, researchers may be able to conduct more efficient mixed-methods research to better understand patient perspectives. Ethical and qualitative implications of using LLMs should be considered.

An analytical approach to engineer multistability in the oscillatory response of a pulse-driven ReRAM

A nonlinear system, exhibiting a unique asymptotic behaviour, while being continuously subject to a stimulus from a certain class, is said to suffer from fading memory. This interesting phenomenon was first uncovered in a non-volatile tantalum oxide-based memristor from Hewlett Packard Labs back in 2016 out of a deep numerical investigation of a predictive mathematical description, known as the Strachan model, later corroborated by experimental validation. It was then found out that fading memory is ubiquitous in non-volatile resistance switching memories. A nonlinear system may however also exhibit a local form of fading memory, in case, under an excitation from a given family, it may approach one of a number of distinct attractors, depending upon the initial condition. A recent bifurcation study of the Strachan model revealed how, under specific train stimuli, composed of two square pulses of opposite polarity per cycle, the simplest form of local fading memory affects the transient dynamics of the aforementioned Resistive Random Access Memory cell, which, would asymptotically act as a bistable oscillator. In this manuscript we propose an analytical methodology, based on the application of analysis tools from Nonlinear System Theory to the Strachan model, to craft the properties of a generalised pulse train stimulus in such a way to induce the emergence of complex local fading memory effects in the nano-device, which would consequently display an interesting tuneable multistable oscillatory response, around desired resistance states. The last part of the manuscript discusses a case study, shedding light on a potential application of the local history erase effects, induced in the device via pulse train stimulation, for compensating the unwanted yet unavoidable drifts in its resistance state under power off conditions.

Individual Learning Plan (ILP) Effectiveness: EFL Islamic Students’ Writing Skill Enhancement

This study investigates the efficacy of implementing Individual Learning Plan (ILP) to enhance the writing skills of eleventh-grade students at , (Madrasah Aliyah Swasta) Guppi Mamuju, an Islamic private senior high school in West Sulawesi, Indonesia. Conducted through a pre-experimental approach, the research population comprised eleventh-grade students from MAS Guppi Mamuju, totaling one class. Utilizing total sampling, one experimental class of eight students was selected. The study employed a writing test as the primary research instrument, assessing various aspects including content, organization, vocabulary, language use, and mechanics using Jacobs' scoring scale. Data analysis was conducted using Microsoft Excel's Automatic Data Analysis tool. The results indicated a notable improvement in students' writing ability following the implementation of ILP, as evidenced by the significant increase in mean scores from the pre-test (57.00) to the post-test (70.63), with a range of 13.37 points. Statistical analysis using the t-test supported the rejection of the Null Hypothesis (H0) and acceptance of the Alternative Hypothesis (H1). In conclusion, the integration of ILP effectively enhanced the writing skills of eleventh-grade students at MAS Guppi Mamuju.

Multichannel multiscale increment entropy and its application in roller bearing fault diagnosis

As a new index to measure the complexity of time series, increment entropy, which takes into account the fluctuation directions and amplitude of time series, has better performance than the traditional entropy analysis methods such as sample entropy and permutation entropy. However, the increment entropy value of time series at single scale cannot completely reflect the dynamic change of time series. In this paper, the multichannel multiscale increment entropy (MMIE) is proposed by introducing the coarse-graining and multichannel analysis tools of time series to explore the complexity of multichannel time series over multiple different scales. MMIE considers the dynamic relationships between multichannel data and the relevant cross-channel variations that are often overlooked in single-channel analysis, thus achieving a full utilization of state information. Finally, an MMIE-based fault diagnosis method is proposed for roller bearing. The analysis results of the simulation signals and the measured data of roller bearings indicate that MMIE performs better than MMDE, MMPE, and MMSE approaches in time costing and fault identification rates.

Design and implementation of breast cancer prediction system based on machine learning

With the increasing incidence of breast cancer worldwide, early diagnosis and treatment of breast cancer has become the key to improving patient survival and quality of life. As a powerful data analysis tool, machine learning is increasingly widely used in the medical field, especially in disease prediction and assisted diagnosis. This paper aims to design and implement a machine learning-based breast cancer prediction system to improve the early diagnosis rate of breast cancer and reduce medical costs. Through an in-depth analysis of the global incidence of breast cancer and the current application of machine learning in the medical field, this study clarified the importance of breast cancer prediction and the problems existing in the existing prediction system. This paper further discusses the theoretical basis of machine learning in breast cancer detection, evaluates the advantages and disadvantages of commonly used machine learning algorithms, and reviews the latest research progress in this field at home and abroad. In the part of system design and implementation, the architecture design, data flow and processing process of the prediction system, as well as the method of data preprocessing and feature selection are introduced in detail. In addition, this paper also constructs a machine learning model suitable for breast cancer prediction, and carries out systematic implementation and testing through the actual development environment. In the discussion section, the applicability of machine learning model in breast cancer prediction is analyzed, the causes of model inefficiency are discussed, and the corresponding solutions are proposed. Finally, the paper summarizes the content of the full text, points out the limitations of the research, and puts forward the direction of future research. The results of this study not only provide a new technical means for the early diagnosis of breast cancer, but also provide valuable experience for the application of machine learning in the medical field.

Chronic Stress Indicator: A Novel Tool for Comprehensive Stress Analysis.

Extensive research has highlighted the strong association between chronic stress and negative health outcomes. This relationship is influenced by various factors, including sociobehavioral, environmental, and genetic and epigenomic forces. To comprehensively assess an individual's stress levels, we propose the development of the Chronic Stress Indicator (CSI), a novel comprehensive multifaceted tool that incorporates key biological, anthropometric, behavioral, and socioeconomic factors. The objective of this study is to assess the effectiveness of the CSI compared to Allostatic Load (AL), a type of chronic stress, in identifying health issues related to stress. The objective of this research is to evaluate the performance of the Chronic Stress Indicator (CSI) versus Allostatic Load (AL) in detecting adverse health outcomes within the U.S. demographic aged 20-49. The information used for this study was sourced from the National Health and Nutrition Examination Survey (NHANES), carried out from 2001 to 2004. Logistic regression modeling was employed to calculate odds ratios and confidence intervals. The Wilcoxon rank-sum test was employed to assess differences in means, whereas the chi-square test, accompanied by Cramer's V statistic, was used to examine the association among categorical variables. Additionally, the relationship between continuous variables was analyzed using Pearson's correlation coefficient. Our association tests show that the length of occupation activity and health status were among the strongest associations to CSI risk. Based on our logistic regression models, age and sex were found to be significant factors in determining AL. We also found that age, smoking, and longest occupation activity were significant factors of CSI risk. These findings suggest a need for individuals to limit smoking as it may lead to higher overall stress despite its common use as a coping mechanism for stress. We should also review the level of occupational activity a job has before continuously working on it as this may also lead to higher cumulative stress.

Use of GIS and RS Tools for Analysis of Land Use Land Cover and Land Surface Temperature: A Case Study of District Khairpur, Pakistan

Land use and land cover (LULC) monitoring plays an important role in sustainable planning and management of resources. This research focuses on analysing historical LULC changes over the Khairpur district of Sindh province of Pakistan. Remote Sensing and Geographic Information System (RS & GIS) tools have been employed using the maximum likelihood algorithm of the supervised classification method. Temporal data of the latest high-resolution satellite imagery provided by NASA GLCF (Global Land Cover Facility), Landsat 8 Operational Land Imager (OLI), and Landsat 4, 5 Thematic Mapper (TM) for 2001, 2010, 2015, and 2020 were acquired from Earth Explorer of United States Geological Survey (USGS). The study area is classified into four major classes namely Barren Land, Vegetation, Built-up areas, and Water Bodies. Analysis revealed that for 2001 and 2005 there was no change in barren land area, whereas in 2010 slight decrease in barren land was found which further decreased for the analysis period of 2020, overall barren land class decreased at a rate of 07% (i.e., 74% to 67%) during the analysis period. Also, vegetation and water bodies classes experienced variations throughout the analysis period, however built-up areas showed constant increase throughout the analysis period where built-up area classes increased from 9% to 15%. Whereas land surface temperature (LST) throughout the analysis period showed increasing trends, for both high ranges and low ranges. It was analysed that the areas having dominancy of the barren land class experienced the highest temperature ranges whereas low temperature ranges were discernible in the areas having vegetation covers. It can be concluded from the study that barren land covers the major area of the study area and there is an increase in built-up areas which can further enhance the LST in the study area.

A 30 YEAR (1991-2020) RETROSPECTIVE MORTALITY REVIEW OF GIRAFFIDS (GIRAFFA SPP. AND OKAPIA JOHNSTONI) IN MANAGED CARE.

Identifying common causes of mortality in zoo giraffe (Giraffa spp.) and okapi (Okapia johnstoni) provides an opportunity to help improve welfare and population management for these endangered species. Mortality reports from 1,024 giraffe and 95 okapi in zoos were compiled from the Species 360 Zoological Information Management Software (ZIMS) utilizing the Morbidity & Mortality Analysis tool. Thirty years of mortality reports (1991-2020) were evaluated to help identify trends and evaluate the impacts, if any, of changes over time in husbandry and management practices. The most common causes of death for giraffe from 1991 to 2015 were neonatal issues (234/845, 27.7%), trauma (213/845, 25.2%), noninfectious disease (190/845, 22.5%), and infectious disease (188/845, 22.2%). In comparison, the most common causes of mortality for giraffe from 2016 to 2020, were noninfectious disease (78/179, 43.6%), trauma (39/179, 21.8%), neonatal issues (39/179, 21.8%), and infectious disease (17/179, 9.5%). The most common cause of death for okapi from 1991 to 2015 were neonatal issues (29/64, 45.3%), infectious disease (13/64, 20.3%), noninfectious disease (11/64, 17.2%), and trauma (10/64, 15.6%). In comparison, the most common cause of death for okapi from 2016 to 2020 was noninfectious disease (15/31, 48.4%), neonatal issues (8/31, 25.8%), and infectious disease (5/31, 16.1%). The results suggest that zoo giraffids have had a relative decrease in mortality from infectious diseases in recent years, whereas death from noninfectious causes has increased significantly. Trauma-related giraffe mortalities and neonatal mortality in both giraffe and okapi, although decreasing in prevalence between time periods, continue to be important causes of death in zoos. This is the first descriptive mortality review for the Giraffidae family and provides data on potential giraffe and okapi health issues that zoos could proactively address.

Preparatory activity and the expansive null-space.

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

THE EFFECT OF INFLUENCER MARKETING, VIRAL MARKETING AND CUSTOMER ENGAGEMENT ON PURCHASE INTENTION AT BLOODS STORE IN PURWOKERTO

This research investigates the multifaceted influence of contemporary marketing methodologies, namely influencer marketing, viral marketing, and customer engagement, on consumers' purchase intentions. This research used SmartPLS statistical analysis tool version 3.0. This research uses an online questionnaire with a total of 150 respondent from students of the Faculty of Economics and Business at Jenderal Soedirman University, University of Muhammadiyah Purwokerto, and State Islamic University Prof. KH. Saifuddin Zuhri Purwokerto that have purchase intention in Blood Store Purwokerto. Based on the findings of this research, it shows that influencer marketing, viral marketing, and customer engagement has a positive and significant impact on the purchase intention. By understanding the synergistic effects of these marketing approaches, businesses can strategically tailor their campaigns to optimize customer engagement and enhance purchase intentions, ultimately fostering greater success in today's competitive market landscape.This research investigates the multifaceted influence of contemporary marketing methodologies, namely influencer marketing, viral marketing, and customer engagement, on consumers' purchase intentions. This research used SmartPLS statistical analysis tool version 3.0. This research uses an online questionnaire with a total of 150 respondent from students of the Faculty of Economics and Business at Jenderal Soedirman University, University of Muhammadiyah Purwokerto, and State Islamic University Prof. KH. Saifuddin Zuhri Purwokerto that have purchase intention in Blood Store Purwokerto. Based on the findings of this research, it shows that influencer marketing, viral marketing, and customer engagement has a positive and significant impact on the purchase intention. By understanding the synergistic effects of these marketing approaches, businesses can strategically tailor their campaigns to optimize customer engagement and enhance purchase intentions, ultimately fostering greater success in today's competitive market landscape.

SWOT Analysis: Implementation of National Education Standard Policy to Improve the Quality of High School Graduates

The competence of quality graduates cannot be separated from the relationship between each educational standard that is a reference for management, governance, and education. The purpose of this analysis is to examine the implementation of national education standard policies as guidelines for improving the quality of education governance based on the quality of graduates who are ready to compete in the world of work both nationally and at the global level. The approach used in this analysis is descriptive-qualitative, which involves processing data obtained through literature studies in the form of reference books, scientific journals, official documents, digital sites, and official information that can be accessed online. The results of the study on the topic above conclude that SWOT analysis can be used as an alternative management analysis tool to identify aspects of strengths, challenges, opportunities, and threats to improve the quality and competence of graduates in accordance with the policies of national education standards. The results of the SWOT analysis recommend alternative strategies that can be developed and followed up as a form of implementation for improving the quality of graduates.

Developing a mobile application with geo-analytic tools to perform real estate valuation within smart cities

Urban land management policies and applications require comprehensive information about real estate development dynamics and value criteria. In this study, geo-analytic tools and a mobile app were produced for residential property valuation in smart cities. Geo-analytical tools were automated to process and use datasets extracted from the databases and ‘Smart Real Estate’ mobile app using data sets produced by the developed geo-analytics tools shares map services automatically. In this way, users can get real-time location data via web services by triggering geographic analysis tools at certain intervals. It can manage and analyse big geographic data to share smart city services about real estate to citizens.

Exploring the Dynamic Relationship: How Exchange Rates Drive Company Valuation through Foreign Portfolio Investments

This study aims to analyze the influence of economic growth and exchange rates on firm value with foreign portfolio investment as an intervening variable at PT Bank Central Asia Tbk. for the period 2011-2020. Quarterly secondary data from 2011-2020 were utilized. The data were then processed using EViews 11 Version analysis tool. The analysis employed descriptive statistics, linear tests, and classical assumption tests. Hypothesis testing was conducted using Partial Test (T-Test). The results of the partial test (T-Test) indicate that economic growth, exchange rates, and foreign portfolio investment insignificantly influence firm value. However, regarding the intervening variable, foreign portfolio investment does not mediate the influence of economic growth (GDP) on firm value, either directly or indirectly, and foreign portfolio investment also does not directly mediate the influence of exchange rates on firm value. Nonetheless, exchange rates directly affect foreign portfolio investment, thus reinforcing the relationship between exchange rates and firm value. Therefore, the fifth hypothesis is accepted.

Evaluating Students’ User Experience on Student Management Information Systems

Studies evaluating students’ UX of applications that will influence their continuance use of educational systems in Higher Educational Institutions (HEIs) have not been sufficiently addressed in the African region, specifically in Ghana. Thus, conducting a study on students’ UX of systems in HEIs will enhance students’ interest in continuing to use such systems. Therefore, this study examines students’ user experience (UX) and how it impacts their continued use of the Student Management Information System (SMIS). The study proposed a research model by integrating user experience questionnaire (UEQ) constructs with continuance intention to use. The study adopted an online questionnaire to collect data from 415 students at Koforidua Technical University (KTU). The partial least square-structural equation model (PLS-SEM) method was used to evaluate the proposed model’s reliability, validity, and relationship among the constructs. The UEQ data analysis tool was used in analysing the data. The study’s findings showed that attractiveness, perspicuity, efficiency, stimulation, and novelty significantly influenced students’ continuance intention to use the SMIS. However, dependability did not significantly affect students’ continuance intention to use the SMIS. Also, the study’s findings on the benchmark results showed that attractiveness, perspicuity, and stimulation were categorized as good, while dependability, efficiency, and novelty were categorized as excellent. These findings offer valuable insights for UX designers and developers aiming to create engaging and intuitive SMIS solutions. Also, it will provide students feedback on system UX that can be incorporated into future release updates. Furthermore, this research contributes significantly to the understanding of student UX with SMIS in developing country HEIs and its impact on continued usage.