Exposure Detection Applications Acceptance: The Case of COVID-19

Nurses' continuance intention to use electronic health record systems: The antecedent role of personality and organisation support

Nurses play a crucial role in the adoption and continued use of Electronic Health Records (EHRs), especially in developing countries. Existing literature scarcely addresses how personality traits and organisational support influence nurses' decision to persist with EHR use in these regions. This study developed a model combining the Five-Factor Model (FFM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore the impact of personality traits and organisational support on nurses' continuance intention to use EHR systems. Data were collected via a self-reported survey from 472 nurses across 10 public hospitals in Jordan and analyzed using a structural equation modeling approach (Smart PLS-SEM 4). The analysis revealed that personality traits, specifically Openness, Experience, and Conscientiousness, significantly influence nurses' decisions to continue using EHR systems. Furthermore, organisational support, enhanced by Performance Expectancy and Facilitating Conditions, positively affected their ongoing commitment to EHR use. The findings underscore the importance of considering individual personality traits and providing robust organisational support in promoting sustained EHR usage among nurses. These insights are vital for healthcare organisations aiming to foster a conducive environment for EHR system adoption, thereby enhancing patient care outcomes.

Effect of compassion fatigue on emotional labor in female nurses: Moderating effect of self-compassion

Emotional labor is common in nursing but may be affected by the mental state of nurses. This study explored the effect of compassion fatigue on emotional labor and whether self-compassion moderates this effect of compassion fatigue.

METHODS

A two-stage survey design with a convenience sample. Participants were female nursing staff recruited from emergency departments, intensive care units, ward nursing units, and outpatient departments of medical centers, regional hospitals, and district hospitals in Taiwan. A total of 300 questionnaire copies in each of the first and second stages were distributed, and 272 pairs of responses were retrieved (valid response rate = 91%). The reliability and validity of the questionnaire were tested, and confirmatory factor analysis was conducted with AMOS 21. The proposed hypotheses were verified using hierarchical regression conducted with SPSS version 25.0.

RESULTS

This study revealed that compassion fatigue positively predicted surface acting (β = 0.12, p < 0.05) and negatively predicted deep acting (β = -0.18, p < 0.01) and expression of genuine emotions (β = -0.31, p < 0.01). In addition, self-compassion negatively moderates the relationships between compassion fatigue and surface acting (β = -0.12, p < 0.05), and positively moderates the relationships between compassion fatigue and expression of genuine emotions (β = 0.15, p < 0.01).

CONCLUSIONS

To avoid excessive consumption of emotional resources, nurses with high compassion fatigue may employ surface acting by engaging in emotional labor without making an effort to adjust their feelings. Nurses need also be sympathized with, and such sympathy can come from hospitals, supervisors, colleagues, and, most crucially, the nurses themselves. Hospital executives should propose improvement strategies that can prevent the compassion fatigue on nurses, such as improving nurses' self-compassion.

Use of Digital COVID-19 Exposure Notifications at a Large Gathering: Survey Analysis of Public Health Conference Attendees

BACKGROUND

WA Notify was Washington State's smartphone-based COVID-19 digital exposure notification (EN) tool, which was used to help limit the spread of COVID-19 between November 30, 2020, and May 11, 2023. Following the 2022 Washington State Public Health Association Annual Conference, attendees who had WA Notify activated began receiving ENs alerting them to a possible COVID-19 exposure during the conference. A survey was emailed to all conference attendees to measure WA Notify adoption, mechanisms through which attendees received ENs, and self-reported engagement in protective behaviors postexposure.

OBJECTIVE

This study aimed to learn more about the experiences of WA Notify adopters and nonadopters who may have been exposed to COVID-19 at a large group gathering.

METHODS

A web-based survey administered through REDCap (Research Electronic Data Capture; Vanderbilt University) was sent to all attendees of the Washington State Public Health Association conference. Self-reported demographic information and characteristics of respondents were summarized. Regression models were used to estimate relative risks to compare WA Notify adoption and testing behaviors between groups.

RESULTS

Of the 464 total registered attendees who were sent the survey, 205 (44%) responses were received; 201 eligible attendees were included in this analysis. Of those, 149 (74%) respondents reported having WA Notify activated on their phones at the time of the conference. Among respondents with WA Notify activated, 54% (n=77) reported learning of their potential exposure from a WA Notify EN. Respondents who reported that they did not have WA Notify activated and learned of their potential exposure via the event-wide email from conference organizers were 39% less likely to test for COVID-19 compared to respondents with WA Notify activated who learned of their potential exposure from the email (relative risk 0.61, 95% CI 0.40-0.93; P=.02), and this gap was even larger when compared to respondents who learned of their exposure from a WA Notify EN. The most commonly cited reason for not having WA Notify activated was privacy concerns (n=17, 35%), followed by not wanting to receive ENs (n=6, 12%) and being unaware of WA Notify (n=5, 10%).

CONCLUSIONS

Digital EN systems are an important tool to directly and anonymously notify close contacts of potential exposures and provide guidance on the next steps in a timely manner. Given the privacy concerns, there is still a need for increasing transparency surrounding EN technology to increase uptake by the public if this technology were to be used in the future to slow the spread of communicable diseases.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

Predicting m-health acceptance from the perspective of unified theory of acceptance and use of technology

Addressing the growing popularity of mobile health (m-Health) technology in the health industry, the current study examined consumers' intention and behaviour related to the usage of digital applications based on the unified theory of acceptance and use of technology (UTAUT). In particular, this study quantitatively assessed the moderating role of perceived product value and mediating role of intention to use m-Health application among Indonesians. This study adopted a cross-sectional design and collected quantitative data from conveniently selected respondents through an online survey, which involved 2068 Telegram users in Indonesia. All data were subjected to the analysis of partial least square- structural equation modeling (PLS-SEM). The obtained results demonstrated the moderating effect of perceived product value on the relationship between intention to use m-Health application (m-health app) and actual usage of m-Health app and the mediating effects of intention to use m-Health app on the relationships of perceived critical mass, perceived usefulness, perceived convenience, perceived technology accuracy, and perceived privacy protection on actual usage of m-Health app. However, the intention to use m-Health app did not mediate the influence of health consciousness and health motivation on the actual usage of m-Health app. Overall, this study's findings on the significance of intention to use m-Health app and perceived product value based on the UTAUT framework serve as insightful guideline to expand the usage of m-Health app among consumers.

Modeling Consumer Acceptance and Usage Behaviors of m-Health: An Integrated Model of Self-Determination Theory, Task-Technology Fit, and the Technology Acceptance Model

Although mobile health (m-health) has great potential to reduce the cost of medical care and improve its quality and efficiency, it is not widely accepted by consumers. In addition, there is still a lack of comprehensive insight into m-health acceptance, especially among consumers with different demographic characteristics. This study aimed to explore the factors affecting consumers' acceptance and usage behaviors of m-health and to examine whether their roles differ by demographic characteristics. A comprehensive m-health acceptance model was proposed by integrating factors from the Self-Determination Theory, Task-Technology Fit, and Technology Acceptance Model. Survey data were collected from 623 Chinese adults with at least 6 months of m-health usage experience and analyzed using structural equation modeling techniques. Multi-group analyses were performed to assess whether the model relationships were different across gender, age, and usage experience. The results indicated that relatedness and competence were significant motivational antecedents of perceived ease of use. Task-technology fit and the perceived ease of use significantly affected the perceived usefulness. The perceived ease of use and perceived usefulness were significant determinants of consumer usage behaviors of m-health and together explained 81% of its variance. Moreover, the relationships among autonomy, perceived usefulness, and usage behaviors of m-health were moderated by gender. Consumer usage behaviors of m-health were affected by factors such as self-motivation (i.e., relatedness and competence), technology perceptions (i.e., perceived ease of use and perceived usefulness), and task-technology fit. These findings provide a theoretical underpinning for future research on m-health acceptance and provide empirical evidence for practitioners to promote the better design and use of m-health for healthcare activities.

The influence of accounting information system adoption on business performance amid COVID-19

The previous decade witnessed the dynamic progress that information systems (ISs) brought about in business performances. In this regard, an effective and efficient organization reflects heightened performance through the use of financial systems like the Accounting Information System (AIS) as the system automates the processes and improves efficiencies. In the current times, AIS has been the reason behind the optimum performance of businesses, with past studies evidencing its successful role dependent on critical success factors. Hence, the primary aim of this study is to evaluate AIS through the use of De Lone and Mc Lean's information sys-tem model (DM ISM) among Sudanese banks. The system focuses on critical factors including information quality, system quality, service quality, system usage and user satisfaction and their effects on the performance of banks in Sudan. Accordingly, this study made use of self-administered survey questionnaire to collect data from 103 AIS user, after which PLS-SEM was employed for data validation. The findings supported the significant effects of system and information quality on system usage but not services quality. Also, AIS use was found to significantly affect the performance of business. The study contributed to literature concerning IS in light of AIS benefits determinants, and it validated the proposed model among firms in Sudan. In effect, the study has both theoretical and practical significance, and it provided limitations, implications and future studies recommendations.

The Roles of Trust and Its Antecedent Variables in Healthcare Consumers' Acceptance of Online Medical Consultation during the COVID-19 Pandemic in China

Online medical consultation (OMC) is generating considerable interest among researchers and practitioners due to the mandatory quarantine measures implemented during the COVID-19 pandemic in China. However, the acceptance rate of OMC has declined over time. This paper aims to empirically investigate OMC acceptance using a proposed research model by integrating the technology acceptance model (TAM) with trust and its antecedent variables. A quantitative self-administered cross-sectional survey was conducted to collect data from 260 healthcare consumers. A partial least squares structural equation modeling method was used to examine the data. Results revealed that healthcare consumers' behavioral intention was influenced by attitudes, while perceived usefulness and trust significantly influenced behavioral intention through attitude as a mediator. In addition, perceived risk, perceived privacy protection, network externalities, cognitive reputation, and interactivity directly influenced trust. Overall, the research model explained 50% of the variance in attitude and 71% of the variance in behavioral intention. The study's findings should provide useful insights into making effective design, development, and implementation decisions for OMC services.

Empowering Self-Efficacy by Using Patient Empowerment among Chronic Obstructive Pulmonary Disease: Pre-Post-Test Study

Patient empowerment is increasingly acknowledged as a milestone of high-quality patient-centered care. This study was conducted using COPD Self-Efficacy Scale to determine the effectiveness of the patient empowerment intervention program among chronic obstructive pulmonary disease patients on self-efficacy. We employed an interventional design with a pre-test and post-test. Sixty COPD patients comprised the final sample of the study. The current study revealed significant improvement in overall self-efficacy factors among most participants. Statistically significant positive correlations were found between the total self-efficacy post-empower intervention model scores concerning age, sex, work, educational level, and marital status. The study's findings revealed that the patient empowerment intervention program positively affected COPD patients' self-efficacy.

The Use of a Technology Acceptance Model (TAM) to Predict Patients' Usage of a Personal Health Record System: The Role of Security, Privacy, and Usability

Personal health records (PHR) systems are designed to ensure that individuals have access and control over their health information and to support them in being active participants rather than passive ones in their healthcare process. Yet, PHR systems have not yet been widely adopted or used by consumers despite their benefits. For these advantages to be realized, adoption of the system is necessary. In this study, we examined how self-determination of health management influences individuals' intention to implement a PHR system, i.e., their ability to actively manage their health. Using an extended technology acceptance model (TAM), the researchers developed and empirically tested a model explaining public adoption of PHRs. In total, 389 Saudi Arabian respondents were surveyed in a quantitative cross-sectional design. The hypotheses were analysed using structural equation modelling-partial least squares (SEM-PLS4). Results indicate that PHR system usage was influenced by three major factors: perceived ease of use (PEOU), perceived usefulness (PU), and security towards intention to use. PHR PEOU and PHR intention to use were also found to be moderated by privacy, whereas usability positively moderated PHR PEOU and PHR intention to use and negatively moderated PHR PU and PHR intention to use. For the first time, this study examined the use of personal health records in Saudi Arabia, including the extension of the TAM model as well as development of a context-driven model that examines the relationship between privacy, security, usability, and the use of PHRs. Furthermore, this study fills a gap in the literature regarding the moderating effects of privacy influence on PEOU and intention to use. Further, the moderating effects of usability on the relationship between PEOU, PU, and intention to use. Study findings are expected to assist government agencies, health policymakers, and health organizations around the world, including Saudi Arabia, in understanding the adoption of personal health records.

The role of uncertainty and affect in decision-making on the adoption of AI-based contact-tracing technology during the COVID-19 pandemic

Objective

This study explores how negative affect, perceived net equity, and uncertainty influence the public's privacy decision-making regarding the adoption of contact-tracing technology based on artificial intelligence (AI) during the COVID-19 pandemic.

Methods

Four hundred and eighteen adults in the US participated in the study via Amazon Mechanical Turk in August 2020. Statistical analyses were performed using the PROCESS macro. Indirect effects and their significance were estimated using bias-corrected bootstrap confidence intervals (CIs) with resampling set to n = 5000.

Results

Perceived net equity was positively associated with low levels of perceived uncertainty regarding a COVID-19 contact-tracing application and intention to adopt it. Low levels of perceived uncertainty were positively associated with intentions to adopt such an application, thereby suggesting that a perceived level of uncertainty mediates the association between perceived net equity and adoption intentions. Anxieties regarding AI technology and COVID-19 risks both moderate the associations among perceived net equity, perceived level of uncertainty, and intentions to adopt the contact-tracing technology.

Conclusions

Our findings highlight how the differing sources of emotion influence the associations among rational judgment, perceptions, and decision-making about new contact-tracing technology. Overall, the results suggest that both rational judgments and affective reactions to risks are important influencers of individuals' perceptions and privacy-related decision-making regarding a new health technology during the pandemic.

Promoting Healthcare Workers' Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human-Computer Trust

Artificial intelligence (AI)-assisted diagnosis and treatment could expand the medical scenarios and augment work efficiency and accuracy. However, factors influencing healthcare workers' adoption intention of AI-assisted diagnosis and treatment are not well-understood. This study conducted a cross-sectional study of 343 dental healthcare workers from tertiary hospitals and secondary hospitals in Anhui Province. The obtained data were analyzed using structural equation modeling. The results showed that performance expectancy and effort expectancy were both positively related to healthcare workers' adoption intention of AI-assisted diagnosis and treatment. Social influence and human-computer trust, respectively, mediated the relationship between expectancy (performance expectancy and effort expectancy) and healthcare workers' adoption intention of AI-assisted diagnosis and treatment. Furthermore, social influence and human-computer trust played a chain mediation role between expectancy and healthcare workers' adoption intention of AI-assisted diagnosis and treatment. Our study provided novel insights into the path mechanism of healthcare workers' adoption intention of AI-assisted diagnosis and treatment.

Knowledge, Practice, Compliance, and Barriers toward Ventilator-Associated Pneumonia among Critical Care Nurses in Eastern Mediterranean Region: A Systematic Review

BACKGROUND

Ventilator-associated pneumonia (VAP) has been identified as a serious complication among hospitalized patients and is associated with prolonged hospitalizations and increased costs. The purpose of this study was to examine the knowledge, practices, compliance, and barriers related to ventilator-associated pneumonia among critical care nurses in the eastern Mediterranean region.

METHODS

The PRISMA guidelines guided this systematic review. Four electronic databases (EMBASE, MEDLINE (via PubMed), SCOPUS, and Web of Science) were used to find studies that were published from 2000 to October 2021.

RESULTS

Knowledge of ventilator-associated pneumonia was the highest outcome measure used in 14 of the 23 studies. The review results confirmed that nurses demonstrated low levels of knowledge of ventilator-associated pneumonia, with 11 studies assessing critical care nurses' compliance with and practice with respect to ventilator-associated pneumonia. Overall, the results showed that most sampled nurses had insufficient levels of compliance with and practices related to ventilator-associated pneumonia. The main barriers reported across the reviewed studies were a lack of education (N = 6), shortage of nursing staff (N = 5), lack of policies and protocols (N = 4), and lack of time (N = 4).

CONCLUSIONS

The review confirmed the need for comprehensive interventions to improve critical care nurses' knowledge, compliance, and practice toward ventilator-associated pneumonia. Nurse managers must address barriers that impact nurses' levels of knowledge, compliance with, and practices related to ventilator-associated pneumonia.

The Role of Personality and Top Management Support in Continuance Intention to Use Electronic Health Record Systems among Nurses

This study examines nurses' Continuance Intention (CI) to use electronic health records (EHRs) through a combination of three conceptual frameworks: the Unified Theory of Acceptance and Use of Technology (UTAUT), the theory of expectation-confirmation (ECT), and the Five-Factor Model (FFM). A model is developed to examine and predict the determinants of nurses' CI to use EHRs, including top management support (TMS) and the FFM's five personality domains. Data were collected from a survey of 497 nurses, which were analyzed using partial least squares. No significant relationship was found between TMS and CI. The study revealed that performance expectancy significantly mediated the influences of two different hypotheses of two predictors: agreeableness and openness to testing CI. A significant moderating impact of conscientiousness was found on the relationship between performance expectancy and CI and the relationship between social influence and CI. The findings of this study indicated that rigorous attention to the personality of individual nurses and substantial TMS could improve nurses' CI to use EHRs. A literature gap was filled concerning the mediating effects of performance expectancy on the FFM-CI relationship, and the moderation effects of Conscientiousness on UTAUT constructs and CI are another addition to the literature. The results are expected to assist government agencies, health policymakers, and health institutions all over the globe in their attempts to understand the post-adoption use of EHRs.

Acceptance, Attitudes, and Barriers of Vaccine Booster Dose among Nursing Students: A Multicounty Survey

AIM

This study investigated the acceptance and attitudes of nursing students toward the COVID-19 vaccine booster dose in two Gulf Cooperation Countries and the potential influencing factors for taking a COVID-19 vaccine booster dose.

BACKGROUND

The world is still battling coronavirus because of the emerged of variants and protection against COVID-19 has waned over time. Vaccination is a powerful and effective method of reducing the outbreak of COVID-19 and decreasing the loss of lives.

DESIGN

This research was a survey using a cross-sectional design.

METHODS

The study's sample was two nursing colleges. The study tool was adopted according to recent information concerning the COVID-19 vaccine published by the World Health Organization. Data was collected through an online survey during March to April 2022.

RESULTS

A total of 216 nursing students completed the survey, of which 69.4 % (n = 150) were male students and more than half of the participants were from Saudi Arabia (55.1%, n = 119). Two-thirds of the students (75.5%, n = 161) reported that they agreed to receive a COVID-19 vaccine booster. The total attitude scores for the students ranged from 28 to 35, with a mean score of 15.8 (SD = 2.5), representing 73% of the highest possible score, with 79.3% were classified as 'positive attitude toward booster dose of COVID-19. Vaccine booster might cause infection, vaccine booster ineffective, worried about adverse effects, and not safe were major barriers influencing the acceptance of the COVID-19 vaccine booster.

CONCLUSION

Nursing students revealed high acceptance rates related to COVID-19 vaccine booster. However, more attention should be paid from nursing educators to barriers influencing the acceptance of the COVID-19 vaccine booster. Preparing nursing students with positive attitude of COVID-19 vaccine booster is very important to patient and community safety.

IMPLICATIONS FOR NURSING MANAGEMENT

Nursing educators and managers must make an effort to educate the nursing students regarding safety and effectiveness from COVID-19 vaccine booster and ensure that is necessary to reduce their perception of the injury of COVID-19 infection.

All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the "Delta" virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

Evaluating the D&M IS Success Model in the Context of Accounting Information System and Sustainable Decision Making

The Accounting Information System (AIS) is generally employed for the automation of operational processes and the enhancement of company efficiencies, but more recently, AIS developments have had a great effect on the process of sustainable decision-making among organizations. However, previous research on the AIS and its capabilities attributed its success to critical success factors. Therefore, in the current work, De Lone and Mc Lean’s Information System (D&M IS) Success Model is evaluated in terms of AIS in Jordanian organizations. The current study primarily aimed to determine the influence of system quality, service quality, information quality, system use and user satisfaction on AIS use, which is argued to eventually enhance the quality and sustainability of decision-making. The study employed a quantitative approach using a self-administered questionnaire for data collection involving 101 decision-makers who are familiar with AIS usage. Following the collection of data, it was validated using Structural Equation Modeling (SEM)—PLS. Based on the obtained results, system quality and information quality significant affected system use, but service quality did not. In turn, AIS was found to have a significant effect on user satisfaction. Furthermore, system use and user satisfaction had positive effects on AIS, which eventually affected the sustainability of decision-making, representing the net AIS benefits. The study contributes to existing IS literature, particularly in the field of determining the factors that influence the AIS net benefits, with the proposed model validated in Jordanian organizations using AIS. The study can be used as a guide to shed light on the importance of AIS and it also provides implications, limitations and opportunities for future studies.

Actual Use of Mobile Learning Technologies during Social Distancing Circumstances: Case Study of King Faisal University Students

The most current highly infectious disease, which has become a global health challenge permeating entire sectors of society, is COVID-19. In the education sector, the transmission of COVID-19 has been curbed through the closure of institutions and the facilitation of online learning. The main objective of this study was to propose an integrated model of the unified theory of acceptance and use of technology combined with the DeLone and McLean model, to examine the influence of quality features, namely, performance expectancy (PE), effort expectancy (EE), facilitating conditions (FC), and social influence (SI), on the intentions and satisfaction of users toward mobile learning (m-learning) use in the context of Saudi learning institutions. The study obtained m-learning user data using an online questionnaire, after which the data were exposed to partial least squares structural equation modeling to test the proposed research model. The findings supported the influence of PE, EE, and FC on intention toward m-learning use but did not support the significant influence of SI. Moreover, system, intention, and user satisfaction were found to positively and significantly influence m-learning-system usage, with system, information, and service quality being top drivers of such user intention and satisfaction. The results reflect the required information concerning the strategies of higher institutions to enhance m-learning-system acceptance among students, with general implications for learning acceptance and usage.