ANOVA F-test Research Articles

The capacity to adapt: Documenting the relationship between stressors and probable depression, anxiety, and posttraumatic stress at two time points during the COVID-19 pandemic

BackgroundPsychological adaptability, or the reduction of psychiatric symptoms in the context of ongoing stressors, is well-documented. The present study assessed relationships between COVID-19 related stressors and depression, anxiety, and post-traumatic stress (PTS) during April and July 2020. MethodsPrevalence of, and changes in, symptom severity levels in April vs. July were measured with ANOVA F-tests. Logistic regressions were used to assess the odds of probable diagnosis. ResultsSymptom distributions skewed lower in July, as compared to April for all three diagnostic categories. From April to July, prevalence of probable anxiety and depression decreased across all levels of stress, prevalence of PTS increased for high stress, and decreased for medium and low stress levels. In July, only high stress related to higher odds of probable diagnoses, as compared to April when both medium and high stress did. LimitationsDue to use of cross-sectional self-report data, the present findings could not establish causality between variables, and provide probable, rather than clinical, diagnoses. ConclusionFindings emphasize adaptability phenomena during COVID-19 and highlight the nuanced impact of ongoing stress.

Relationship Between Gender, Training Level and Goal Orientation Achievement Motivation: Implications for Learners and Faculty

Achievement goal orientation (GO) theory describes Mastery (M), one's intrinsic drive for competency for the sake of competency, and performance approach (PAP), a drive for competency by displaying competency, which are both adaptive. In learners motivated by performance avoid (PAV), showing competency by avoiding appearing incompetent dominates (maladaptive). The aim of this study was to determine differences in GO by gender and training (PGY) level. A prospective, multi-institutional cohort of general surgery trainees participated in a cross-sectional study (2020-2021). Participants completed a 10-item instrument (the Goal Orientation in Surgical Trainees, GO-ST) measured on a 5-pointLikert scale (1 = never,3 = weekly,5 = daily). Student's t-tests and ANOVA F-test were used as appropriate. A total of 144/164 trainees participated (87.8%). The sample was 40.0%(n = 56) female and 57.9%(n = 81) male; 21.3%(n = 30) were PGY1, 22.0%(n = 31) PGY2, 24.8%(n = 35) PGY3, 18.4%(n = 26) PGY4, 13.5%(n = 19) PGY5. There were no significant differences in mean scale scores by gender for Mastery (3.3 vs 3.5; p = 0.17), or PAP (3.7 vs 3.5; p = 0.10), but mean PAV scores were significantly higher for females (3.6 vs 3.3; p = 0.04). While there were no significant differences in mean Mastery and PAP scale scores by training level (p = 0.44; p = 0.31), there was a significant difference in PAV scores (p < 0.01). The frequency of PAV feelings decreased over 5 years. Only PAV motivation differed by gender and training level. Understanding the psychology of motivation with this framework can aid both residents and programs in re-focusing on more adaptive learning strategies and supporting trainees in their transition to master surgeons.

Feature extraction from MRI ADC images for brain tumor classification using machine learning techniques

BackgroundDiffusion-weighted (DW) imaging is a well-recognized magnetic resonance imaging (MRI) technique that is being routinely used in brain examinations in modern clinical radiology practices. This study focuses on extracting demographic and texture features from MRI Apparent Diffusion Coefficient (ADC) images of human brain tumors, identifying the distribution patterns of each feature and applying Machine Learning (ML) techniques to differentiate malignant from benign brain tumors.MethodsThis prospective study was carried out using 1599 labeled MRI brain ADC image slices, 995 malignant, 604 benign from 195 patients who were radiologically diagnosed and histopathologically confirmed as brain tumor patients. The demographics, mean pixel values, skewness, kurtosis, features of Grey Level Co-occurrence Matrix (GLCM), mean, variance, energy, entropy, contrast, homogeneity, correlation, prominence and shade, were extracted from MRI ADC images of each patient. At the feature selection phase, the validity of the extracted features were measured using ANOVA f-test. Then, these features were used as input to several Machine Learning classification algorithms and the respective models were assessed.ResultsAccording to the results of ANOVA f-test feature selection process, two attributes: skewness (3.34) and GLCM homogeneity (3.45) scored the lowest ANOVA f-test scores. Therefore, both features were excluded in continuation of the experiment. From the different tested ML algorithms, the Random Forest classifier was chosen to build the final ML model, since it presented the highest accuracy. The final model was able to predict malignant and benign neoplasms with an 90.41% accuracy after the hyper parameter tuning process.ConclusionsThis study concludes that the above mentioned features (except skewness and GLCM homogeneity) are informative to identify and differentiate malignant from benign brain tumors. Moreover, they enable the development of a high-performance ML model that has the ability to assist in the decision-making steps of brain tumor diagnosis process, prior to attempting invasive diagnostic procedures, such as brain biopsies.

Analyzing ANOVA F-test and Sequential Feature Selection for Intrusion Detection Systems

An Intrusion Detection System (IDS) helps the computer system notify an admin when an attack is coming to a network. However, some problems may delay this process, such as a long time caused by several features in the captured data to classify. One of the optimization approaches is to select those critical features. It is intended to increase performance and reduce computational time. This research evaluates feature selection methods using the ANOVA F-test and Sequential Feature Selection (SFS), whose performance is measured using some metrics: accuracy, specificity, and sensitivity over NSLKDD, Kyoto2006, and UNSW_NB15 datasets. Using that approach, the performance increases, on average, by more than 10% for multiclass; and about 5% for binary class. It can be inferred that an optimal number of features can be obtained, where the best features are selected by SFS. Nevertheless, this method still needs to be improved before being implemented in a real system. Keywords: Network security, Network infrastructure, Intrusion Detection System, Data Security, Information Security.

A new test for comparing J independent groups by using one-step M-estimator and bootstrap-t

It is well known that the conventional method for comparing j independent groups, one-way ANOVA F-test, depends on normal theory assumptions. In this study, a new test is proposed which is based on a one-step M-estimator and a bootstrap-t method. ANOVA F-Test, a test which compares J population’s M-measures of location by using Schrader and Hettmansperger test statistic, a test which also compares M-measures of location by using Liu-Singh’s limiting p-values notion with a percentile bootstrap approach and the proposed test were compared in terms of saving nominal Type I error via Monte Carlo simulations. It was found that the proposed test compares well under normality and homogeneity and it was much more preferable than those three tests under non-normality and heterogeneity of variances.

The COVID-19 Pandemic: Capital Importation to Banking Business

This paper investigated the decline of capital importation to Nigeria and specifically banking business in Nigeria in the pre-COVID-19 pandemic era and during the COVID-19 pandemic era. The investigation became necessary considering the high prevalence of the COVID-19 pandemic in most of the top countries with capital exportation into Nigeria and the sharp decline in the importation of capital. Bringing, liquidity and stability of the banking business into concern. Data on capital importation were extracted from the CBN Statistical Bulletin and analyzed using a test for equality of means with both the Anova and Welch F-tests. We found that there is statistically significant difference in the averages of the capital importation to the banking business and Nigeria as a whole between the two periods of concern. We recommend that this [the reduction in the capital importation] should be monitored intermittently to ensure survival, stability, liquidity and enhanced share premiums of the banks. Also, the regulating authorities should regulate towards promotion of liquidity and stability in the financial system by permitting certain policies to take effect, such as transforming from Plc. to HoldCo structure.

Arduino based air temperature, humidity and air quality monitoring system

This study is conducted to create a better platform for Taguig City University Library where students can access it in just a few clicks using their mobile phones, desktop, or any smart devices. Innovation is inevitable, as the world progress to advancements school services like libraries should also innovate to provide accessible and better learning experiences to students. Library Cloud Storage of Published Thesis and Research Paper with Laravel Security in Taguig City University is created to provide Taguig City University its own online library that will benefit not just the students but also the library staff and faculty members.The ANOVA F-TEST was used in this study to see if there was a significant difference between the three groups of respondents' perceptions of the system's efficiency and effectiveness. The generated result of ANOVA computation to get the significant difference at 0.05 level of significance of the assessment of the three groups of respondents for the criteria set by ISO 25010 namely functional suitability, compatibility, usability, security, and maintainability of the system. The computed value is F and the F crit is the tabulated value. The F value is less than the F critical value that leads to the acceptance of the null hypothesis that is "There is no significant difference between the assessment of the three groups", which means that the three groups of respondents have almost the same assessment on the feature of the system.

Library cloud storage of published thesis and research paper with laravel security in taguig city university

This study is conducted to create a better platform for Taguig City University Library where students can access it in just a few clicks using their mobile phones, desktop, or any smart devices. Innovation is inevitable, as the world progress to advancements school services like libraries should also innovate to provide accessible and better learning experiences to students. Library Cloud Storage of Published Thesis and Research Paper with Laravel Security in Taguig City University is created to provide Taguig City University its own online library that will benefit not just the students but also the library staff and faculty members.The ANOVA F-TEST was used in this study to see if there was a significant difference between the three groups of respondents' perceptions of the system's efficiency and effectiveness. The generated result of ANOVA computation to get the significant difference at 0.05 level of significance of the assessment of the three groups of respondents for the criteria set by ISO 25010 namely functional suitability, compatibility, usability, security, and maintainability of the system. The computed value is F and the F crit is the tabulated value. The F value is less than the F critical value that leads to the acceptance of the null hypothesis that is "There is no significant difference between the assessment of the three groups", which means that the three groups of respondents have almost the same assessment on the feature of the system.

Arduino based air temperature, humidity and air quality monitoring system

&#x0D; &#x0D; &#x0D; This study is conducted to create a better platform for Taguig City University Library where students can access it in just a few clicks using their mobile phones, desktop, or any smart devices. Innovation is inevitable, as the world progress to advancements school services like libraries should also innovate to provide accessible and better learning experiences to students. Library Cloud Storage of Published Thesis and Research Paper with Laravel Security in Taguig City University is created to provide Taguig City University its own online library that will benefit not just the students but also the library staff and faculty members.The ANOVA F-TEST was used in this study to see if there was a significant difference between the three groups of respondents' perceptions of the system's efficiency and effectiveness. The generated result of ANOVA computation to get the significant difference at 0.05 level of significance of the assessment of the three groups of respondents for the criteria set by ISO 25010 namely functional suitability, compatibility, usability, security, and maintainability of the system. The computed value is F and the F crit is the tabulated value. The F value is less than the F critical value that leads to the acceptance of the null hypothesis that is "There is no significant difference between the assessment of the three groups", which means that the three groups of respondents have almost the same assessment on the feature of the system.&#x0D; &#x0D; &#x0D;

Library cloud storage of published thesis and research paper with laravel security in taguig city university

This study is conducted to create a better platform for Taguig City University Library where students can access it in just a few clicks using their mobile phones, desktop, or any smart devices. Innovation is inevitable, as the world progress to advancements school services like libraries should also innovate to provide accessible and better learning experiences to students. Library Cloud Storage of Published Thesis and Research Paper with Laravel Security in Taguig City University is created to provide Taguig City University its own online library that will benefit not just the students but also the library staff and faculty members.The ANOVA F-TEST was used in this study to see if there was a significant difference between the three groups of respondents' perceptions of the system's efficiency and effectiveness. The generated result of ANOVA computation to get the significant difference at 0.05 level of significance of the assessment of the three groups of respondents for the criteria set by ISO 25010 namely functional suitability, compatibility, usability, security, and maintainability of the system. The computed value is F and the F crit is the tabulated value. The F value is less than the F critical value that leads to the acceptance of the null hypothesis that is "There is no significant difference between the assessment of the three groups", which means that the three groups of respondents have almost the same assessment on the feature of the system.

A novel reference model for dental scanning system evaluation: analysis of five intraoral scanners.

PURPOSEThe aim of this in vitro study was to investigate the accuracy (trueness and precision) of five intraoral scanners (IOS) using a novel reference model for standardized performance evaluation.MATERIALS AND METHODSFive IOSs (Medit i500, Omnicam, Primescan, Trios 3, Trios 4) were used to digitize the reference model, which represented a simplified full-arch situation with four abutment teeth. Each IOS was used five times by an experienced operator, resulting in 25 STL (Standard Tessellation Language) files. STL data were imported into 3D software (Final Surface®) and examined for inter- and intra-group analyses. Deviations in the parameter matching error were calculated. ANOVA F-test and Kruskal-Wallis test were applied for inter-group comparisons (α = .05); and the coefficient of variation (CV) was calculated for intra-group comparisons (in % ± SD).RESULTSPrimescan (matching error value: 0.015), Trios 3 (0.016), and Trios 4 (0.018) revealed comparable results with significantly higher accuracy compared to Medit i500 (0.035) and Omnicam (0.028) (P < .001). For intra-group comparison, Trios 4 demonstrated the most homogenous results (CV 15.8%).CONCLUSIONThe novel reference model investigated in this study can be used to assess the performance of dental scanning technologies in the daily routine setting and in research settings.

Spark based Framework for Supervised Classification of Hyperspectral Images

The advancement of remote sensing sensors acquired large amount of image data easily. Primary aspects of big data, such as volume, velocity, and variety, are represented in the acquired images. Furthermore, standard data processing approaches have different limits when dealing with such large amounts of data. As a result, good machine learning-based algorithms are required to process the data with higher accuracy and lower computational efficiency. Therefore, we propose ANOVA F-test based spectral feature selection method with a distributed implementation of this machine learning algorithm on Spark. Experimental results are obtained using the bench mark datasets acquired using AVIRIS and ROSIS sensors. The performance of Spark MLlib based supervised machine learning techniques are evaluated using the criteria viz., accuracy, specificity, sensitivity, F1-score and execution time. Added to that, we compared the execution time between distributed processing and processing with single processor. The results reveal that the proposed strategy significantly cuts down on analytical time while maintaining classification accuracy.

An Aggregated Mutual Information Based Feature Selection with Machine Learning Methods for Enhancing IoT Botnet Attack Detection.

Due to the wide availability and usage of connected devices in Internet of Things (IoT) networks, the number of attacks on these networks is continually increasing. A particularly serious and dangerous type of attack in the IoT environment is the botnet attack, where the attackers can control the IoT systems to generate enormous networks of “bot” devices for generating malicious activities. To detect this type of attack, several Intrusion Detection Systems (IDSs) have been proposed for IoT networks based on machine learning and deep learning methods. As the main characteristics of IoT systems include their limited battery power and processor capacity, maximizing the efficiency of intrusion detection systems for IoT networks is still a research challenge. It is important to provide efficient and effective methods that use lower computational time and have high detection rates. This paper proposes an aggregated mutual information-based feature selection approach with machine learning methods to enhance detection of IoT botnet attacks. In this study, the N-BaIoT benchmark dataset was used to detect botnet attack types using real traffic data gathered from nine commercial IoT devices. The dataset includes binary and multi-class classifications. The feature selection method incorporates Mutual Information (MI) technique, Principal Component Analysis (PCA) and ANOVA f-test at finely-granulated detection level to select the relevant features for improving the performance of IoT Botnet classifiers. In the classification step, several ensemble and individual classifiers were used, including Random Forest (RF), XGBoost (XGB), Gaussian Naïve Bayes (GNB), k-Nearest Neighbor (k-NN), Logistic Regression (LR) and Support Vector Machine (SVM). The experimental results showed the efficiency and effectiveness of the proposed approach, which outperformed other techniques using various evaluation metrics.

Reproducibility of effects of the homeopathic dilutions 14x - 30x of gibberellic acid on growth of Lemna gibba L.

Background: Reproducibility of investigations in homeopathy is still challenging. Duckweed (Lemna gibba L.), a monocotyledonous waterplant which mostly reproduces vegetatively and therefore builds genetically identical clones, may be a suitable test system for standardised trials. &#x0D; Aims: This study investigated if formerly observed effects of gibberellic acid 14x – 30x on growth of Lemna gibba were reproducible. &#x0D; Methododology: Duckweed was grown in dilutions of gibberellic acid (14x–30x) as well as once succussed (c1) and unsuccussed (c0) water control. Area-related growth rate for day 0–7 was determined by a computerised image analysis system. Three series including five independent blinded and randomised experiments each were carried out in the same way as in the original study. Only time and conductor of experiments were modified. System stability was controled by three series of systematic negative control (SNC) experiments with the same set-up, but distilled and autoclaved water was used as the only test substance. According to the series with gibberellic acid, each serie of SNC experiments included five experiments. Full two-way ANOVA (α = 5%) was used for statistical analysis. Independent variables were treatment and experiment number, dependent variable was r(area) for day 0–7. Data of each experiment was normalised to its mean value to allow a better comparison between experiments. Only if the global ANOVA F-test was significant (p &lt; 0.05) we compared the investigated groups with Fisher`s LSD test (protected Fisher`s LSD).&#x0D; Results: No specific effects of agitated dilutions of gibberellic acid were found in the first two replication series (p=0.263 and p=0.062). In the third serie with gibbous Lemna gibba L. we observed a significant effect (p=0.009) of the homeopathic treatment, however growth was increased in contrast to decreasing in the former study. Variability in experiments with gibberellic acid 14x – 30x was lower than in SNC experiments. The stability of the experimental system was verified by the SNC experiments. &#x0D; Conclusions: When designing new studies to investigate reproducibility, different physiological states of the test organism must be considered. Variability might be an interesting parameter to investigate effects of homeopathic remedies in basic research.

Attitudes of Couples attending Family Counselling Clinic in Sulaimani City towards the Premarital Screening and Genetic Counseling Programme

Background and objectives: Premarital screening is an effective method for monitoring, reducing, and avoiding genetic abnormalities and certain viral infections. This study aimed to explore the attitudes of couples who attended a family counselling clinic in Sulimani City towards the premarital screening and genetic counselling programme. Methods: A cross-sectional, descriptive-analytic study was carried out on 310 couples attending the family counselling clinic in Sulaimani City. The statistical analysis was performed by using descriptive and inferential statistical data analysis including frequency, percentage, mean and stranded deviation, independent samples T-Test and one way ANOVA (F-test). Results: The study findings revealed that the majority of the study participants were 20-30 years old. In terms of attitudes, 73.55% of the couples had a favourable attitude toward premarital screening and genetic counselling. There were statistically significant differences between couples' attitudes and age (p=0.012), educational level (p=0.013), and consanguinity (P &lt; 0.001), but there were no statistically significant differences between couples' attitudes and gender (p=0.49), employment (p=0.785), residential area (0.116), and family history of hereditary blood disease (p=0.45) because of p-value &gt;0.05. Conclusion: Couples' attitudes about premarital screening and genetic counselling programmes are positive.

Baseline assessment of patient safety culture in primary care centres in Kuwait: a national cross-sectional study

BackgroundAssessments of the culture surrounding patient safety can inform healthcare settings on how their structures and processes impact patient outcomes. This study investigated patient safety culture in Primary Health Care Centres in Kuwait, and benchmarked the findings against regional and international results. This study also examined the association between predictors and outcomes of patient safety culture in these settings.MethodsThis cross-sectional quantitative study used the Medical Office Survey on Patient Safety Culture. The study was targeted at staff of all the Primary Health Care Centres in Kuwait with at least one year of experience. Data were analysed using SPSS 23 at a significance level of ≤ .05. Univariate (means, standard deviations, frequencies, percentages) and bivariate (chi-squared tests, student t-tests, ANOVA F-tests, Kruskal–Wallis tests, Spearman’s correlation) analyses provided an overview of participant socio-demographics and the association between patient safety culture composites and outcomes. We undertook a multivariate regression analysis to predict the determinants of patient safety culture. Results were benchmarked against similar local (Kuwait, 2014), regional (Yemen, 2015) and international (US, 2018) studies.ResultsThe responses of 6602 employees from 94 centres were included in the study, with an overall response rate of 78.7%. The survey revealed Teamwork (87.8% positive ratings) and Organisational Learning (78.8%) as perceived areas of strength. Communication about Error (57.7%), Overall Perceptions of Patient Safety and Quality (57.4%), Communication Openness (54.4%), Owner/Managing Partner/Leadership Support for Patient Safety (53.8%) and Work Pressure and Pace (28.4%) were identified as areas requiring improvement. Benchmarking analysis revealed that Kuwait centres are performing at benchmark levels or better on four and six composites when compared to international and regional findings, respectively. Regression modelling highlighted significant predictions regarding patient safety outcomes and composites.ConclusionsThis is the first major study addressing the culture of patient safety in public Primary Health Care Centres regionally. Improving patient safety culture is critical for these centres to improve the quality and safety of the healthcare services they provide. The findings of this study can guide country-level strategies to develop the systems that govern patient safety practices.

An Improved Multiple Features and Machine Learning-Based Approach for Detecting Clickbait News on Social Networks

The widespread usage of social media has led to the increasing popularity of online advertisements, which have been accompanied by a disturbing spread of clickbait headlines. Clickbait dissatisfies users because the article content does not match their expectation. Detecting clickbait posts in online social networks is an important task to fight this issue. Clickbait posts use phrases that are mainly posted to attract a user’s attention in order to click onto a specific fake link/website. That means clickbait headlines utilize misleading titles, which could carry hidden important information from the target website. It is very difficult to recognize these clickbait headlines manually. Therefore, there is a need for an intelligent method to detect clickbait and fake advertisements on social networks. Several machine learning methods have been applied for this detection purpose. However, the obtained performance (accuracy) only reached 87% and still needs to be improved. In addition, most of the existing studies were conducted on English headlines and contents. Few studies focused specifically on detecting clickbait headlines in Arabic. Therefore, this study constructed the first Arabic clickbait headline news dataset and presents an improved multiple feature-based approach for detecting clickbait news on social networks in Arabic language. The proposed approach includes three main phases: data collection, data preparation, and machine learning model training and testing phases. The collected dataset included 54,893 Arabic news items from Twitter (after pre-processing). Among these news items, 23,981 were clickbait news (43.69%) and 30,912 were legitimate news (56.31%). This dataset was pre-processed and then the most important features were selected using the ANOVA F-test. Several machine learning (ML) methods were then applied with hyper-parameter tuning methods to ensure finding the optimal settings. Finally, the ML models were evaluated, and the overall performance is reported in this paper. The experimental results show that the Support Vector Machine (SVM) with the top 10% of ANOVA F-test features (user-based features (UFs) and content-based features (CFs)) obtained the best performance and achieved 92.16% of detection accuracy.

Computational identification of multiple lysine PTM sites by analyzing the instance hardness and feature importance

Identification of post-translational modifications (PTM) is significant in the study of computational proteomics, cell biology, pathogenesis, and drug development due to its role in many bio-molecular mechanisms. Though there are several computational tools to identify individual PTMs, only three predictors have been established to predict multiple PTMs at the same lysine residue. Furthermore, detailed analysis and assessment on dataset balancing and the significance of different feature encoding techniques for a suitable multi-PTM prediction model are still lacking. This study introduces a computational method named ’iMul-kSite’ for predicting acetylation, crotonylation, methylation, succinylation, and glutarylation, from an unrecognized peptide sample with one, multiple, or no modifications. After successfully eliminating the redundant data samples from the majority class by analyzing the hardness of the sequence-coupling information, feature representation has been optimized by adopting the combination of ANOVA F-Test and incremental feature selection approach. The proposed predictor predicts multi-label PTM sites with 92.83% accuracy using the top 100 features. It has also achieved a 93.36% aiming rate and 96.23% coverage rate, which are much better than the existing state-of-the-art predictors on the validation test. This performance indicates that ’iMul-kSite’ can be used as a supportive tool for further K-PTM study. For the convenience of the experimental scientists, ’iMul-kSite’ has been deployed as a user-friendly web-server at http://103.99.176.239/iMul-kSite.

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data.

Artificial intelligence in healthcare can potentially identify the probability of contracting a particular disease more accurately. There are five common molecular subtypes of breast cancer: luminal A, luminal B, basal, ERBB2, and normal-like. Previous investigations showed that pathway-based microarray analysis could help in the identification of prognostic markers from gene expressions. For example, directed random walk (DRW) can infer a greater reproducibility power of the pathway activity between two classes of samples with a higher classification accuracy. However, most of the existing methods (including DRW) ignored the characteristics of different cancer subtypes and considered all of the pathways to contribute equally to the analysis. Therefore, an enhanced DRW (eDRW+) is proposed to identify breast cancer prognostic markers from multiclass expression data. An improved weight strategy using one-way ANOVA (F-test) and pathway selection based on the greatest reproducibility power is proposed in eDRW+. The experimental results show that the eDRW+ exceeds other methods in terms of AUC. Besides this, the eDRW+ identifies 294 gene markers and 45 pathway markers from the breast cancer datasets with better AUC. Therefore, the prognostic markers (pathway markers and gene markers) can identify drug targets and look for cancer subtypes with clinically distinct outcomes.

The intention of South Africans to engage in collaborative consumption: The case of Uber

Orientation: The idea of collaborative consumption is developing among consumers, where underutilised or excess resources, skills and money are shared among peers for compensation. Collaborative consumption involves sharing, and as such, Uber, an app-based transport service enables those with spare vehicle capacity to provide a fee-based transport solution.Research purpose: This study investigates the intention to use this service in a South African context, specifically through the Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) because of the suitability of these models in this context. Differences between regular and seldom users are also examined.Motivation for the study: With the widespread use of Uber in South Africa, understanding the factors contributing to the intention to use Uber requires examination.Research design, approach and method: Data were collected using a quantitative survey among panel respondents of South Africans, using electronic data collection. The research instrument comprised pre-existing items measured on a 6-point Likert scale. A total of 367 usable responses were received. Data analysis included testing for differences in means (t-test, Satterthwaite-Welch t-test, Anova F-test, Welch F-test) as well as regression analysis (by means of multiple regressions and two-stage least squares).Main findings: The findings indicate that South Africans intend to use the service largely due to their positive attitude towards the service, the perceived usefulness (PU) of the app and perceived behavioural (PB) control associated with the service. The value of the app has been highlighted in this research, and the importance of its functioning is evident.Practical/managerial implications: The importance of providing a positive service experience impacts the intention to use the service in the future. This emphasises the importance of the service itself and the functioning of the app.Contribution/value-add: Understanding the intention to use this service enables further development of this app and associated services.