Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082738
Fazlyn Petersen
The use of business simulation games in education is increasing as it allows students to apply theoretical concepts. It encourages critical skills for Information Systems students such as problem-solving, critical thinking and creativity. However, there may be a steep learning curve for students with limited skills and experience. The steep learning curve may lead to anxiety. This research used positivism and added anxiety as a variable to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative data were collected from 224 third-year Information Systems students via an online survey. Data were analysed using Partial Least Squares (PLS) Structured Equation Modelling. This research produced unexpected results, as effort expectancy and facilitating conditions did not influence behavioural intention. The influence of anxiety was also not negative. These findings are contrary to the literature. Debriefing, tutor support and training videos may also have reduced the initial anxiety experience, and this may influence the future usage of business simulation games.
{"title":"Impact of anxiety on students' behavioural intention to use business simulation games","authors":"Fazlyn Petersen","doi":"10.1109/ICTAS56421.2023.10082738","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082738","url":null,"abstract":"The use of business simulation games in education is increasing as it allows students to apply theoretical concepts. It encourages critical skills for Information Systems students such as problem-solving, critical thinking and creativity. However, there may be a steep learning curve for students with limited skills and experience. The steep learning curve may lead to anxiety. This research used positivism and added anxiety as a variable to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Quantitative data were collected from 224 third-year Information Systems students via an online survey. Data were analysed using Partial Least Squares (PLS) Structured Equation Modelling. This research produced unexpected results, as effort expectancy and facilitating conditions did not influence behavioural intention. The influence of anxiety was also not negative. These findings are contrary to the literature. Debriefing, tutor support and training videos may also have reduced the initial anxiety experience, and this may influence the future usage of business simulation games.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114171795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082742
H. Studiawan, Mhd. Fadly Hasan, B. Pratomo
In digital forensics, the sequence of all events in a forensic image needs to be analyzed. Building a forensic timeline is one of the possible techniques. Naturally, the forensic timeline contains several standard entities, such as date, time, and host name. Another field provided by a forensic timeline is a message, which is a brief description of an event. To assist investigators in analyzing the incident, a more detailed identification of the entity name is needed from the message text. In this paper, rule-based entity recognition is proposed. We also discuss the advantages and disadvantages of this technique. Experimental results show that the entities in the message column have been annotated successfully.
{"title":"Rule-based Entity Recognition for Forensic Timeline","authors":"H. Studiawan, Mhd. Fadly Hasan, B. Pratomo","doi":"10.1109/ICTAS56421.2023.10082742","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082742","url":null,"abstract":"In digital forensics, the sequence of all events in a forensic image needs to be analyzed. Building a forensic timeline is one of the possible techniques. Naturally, the forensic timeline contains several standard entities, such as date, time, and host name. Another field provided by a forensic timeline is a message, which is a brief description of an event. To assist investigators in analyzing the incident, a more detailed identification of the entity name is needed from the message text. In this paper, rule-based entity recognition is proposed. We also discuss the advantages and disadvantages of this technique. Experimental results show that the entities in the message column have been annotated successfully.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124744363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082727
Nondumiso Sihlangu, R. Millham
Chronic diabetes results from the body's inability to produce adequate insulin. It is an incurable but treatable disease. The experiment conducted in this study aims to analyze different machine learning methods like Stochastic Gradient Descent, Support Vector Machine, Logistic Regression, and CN2 Rule using the Orange data mining software and use them for diabetes prediction based on the PIMA Indian Diabetes Dataset. Utilizing different performance criteria like Accuracy, Precision, Recall, and F1-Score, these approaches were examined and evaluated. The best outcome was obtained by CN2 Rule Induction, achieving an accuracy score of 80.7% which shows that this method is the most suitable for diabetes prediction compared to the other three models.
{"title":"Analysis of machine learning methods to determine the best data analysis method for diabetes prediction","authors":"Nondumiso Sihlangu, R. Millham","doi":"10.1109/ICTAS56421.2023.10082727","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082727","url":null,"abstract":"Chronic diabetes results from the body's inability to produce adequate insulin. It is an incurable but treatable disease. The experiment conducted in this study aims to analyze different machine learning methods like Stochastic Gradient Descent, Support Vector Machine, Logistic Regression, and CN2 Rule using the Orange data mining software and use them for diabetes prediction based on the PIMA Indian Diabetes Dataset. Utilizing different performance criteria like Accuracy, Precision, Recall, and F1-Score, these approaches were examined and evaluated. The best outcome was obtained by CN2 Rule Induction, achieving an accuracy score of 80.7% which shows that this method is the most suitable for diabetes prediction compared to the other three models.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131883523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082725
Emmanuel Tuyishimire, Carine Pierrette Mukamakuza, Aimable Mbituyumuremy, T. Brown, Didier Iradukunda, Ofentse Phuti, Happiness Rose Mary
Malaria is a long-standing disease and one of the top life-threatening diseases, yet its treatment has not changed, while the world has already embraced the Fourth Industrial Revolution (4IR). A wave of research on digitizing monitoring mechanisms of such a deadly disease has surfaced. Automated malaria screening is one of the detection processes which are gaining popularity in the research domain. However, the process needs to be coupled with other processes aiming a nationally or regionally contextualised malaria monitoring system. This paper proposes a digital malaria monitoring system in the context of an African country or region. One advantage of such a digital system is that is enables a novel disease spread forecasting model based on the dynamics of different malaria types. The architecture of the diagnosis system is described, and the disease spread model is mathematically modelled in terms of a SPITR (Susceptible-Protected-Infected-Treated-Recovered) epidemic model which is further analysed. The forecasting model is expressed and analysed whereas experiments are conducted using a Monte Carlo simulation method.
{"title":"IT-Aided Forecasting Model for Malaria Spread for the Developing World","authors":"Emmanuel Tuyishimire, Carine Pierrette Mukamakuza, Aimable Mbituyumuremy, T. Brown, Didier Iradukunda, Ofentse Phuti, Happiness Rose Mary","doi":"10.1109/ICTAS56421.2023.10082725","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082725","url":null,"abstract":"Malaria is a long-standing disease and one of the top life-threatening diseases, yet its treatment has not changed, while the world has already embraced the Fourth Industrial Revolution (4IR). A wave of research on digitizing monitoring mechanisms of such a deadly disease has surfaced. Automated malaria screening is one of the detection processes which are gaining popularity in the research domain. However, the process needs to be coupled with other processes aiming a nationally or regionally contextualised malaria monitoring system. This paper proposes a digital malaria monitoring system in the context of an African country or region. One advantage of such a digital system is that is enables a novel disease spread forecasting model based on the dynamics of different malaria types. The architecture of the diagnosis system is described, and the disease spread model is mathematically modelled in terms of a SPITR (Susceptible-Protected-Infected-Treated-Recovered) epidemic model which is further analysed. The forecasting model is expressed and analysed whereas experiments are conducted using a Monte Carlo simulation method.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128865940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082741
M. Bisheko, G. Rejikumar
Although the use of mobile technology in Indian agriculture has been the subject of an expanding corpus of research, little is known about how farmers perceive the potential of these technologies to improve agricultural sustainability. In light of this observation, and given the low adoption rates of agricultural mobile apps in India, this study seeks to gain a deeper understanding of farmers' perceptions of how this app helps to improve agricultural sustainability. In-depth interviews were used to collect data from 20 farmers, and 4 major themes were derived from the content analysis of the interview data. The findings of this study indicate that the majority of respondents (94%) perceived that the Kisan Suvidha App has the potential to improve agricultural sustainability. In their opinion, this app is very instrumental in promoting agricultural sustainability due to its ability to facilitate the effective exchange of agricultural information between extension service providers and farmers and link farmers with markets and other actors in the food value chains.
{"title":"A study on farmers' perceptions about the scope of the Kisan Suvidha App in improving agricultural sustainability","authors":"M. Bisheko, G. Rejikumar","doi":"10.1109/ICTAS56421.2023.10082741","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082741","url":null,"abstract":"Although the use of mobile technology in Indian agriculture has been the subject of an expanding corpus of research, little is known about how farmers perceive the potential of these technologies to improve agricultural sustainability. In light of this observation, and given the low adoption rates of agricultural mobile apps in India, this study seeks to gain a deeper understanding of farmers' perceptions of how this app helps to improve agricultural sustainability. In-depth interviews were used to collect data from 20 farmers, and 4 major themes were derived from the content analysis of the interview data. The findings of this study indicate that the majority of respondents (94%) perceived that the Kisan Suvidha App has the potential to improve agricultural sustainability. In their opinion, this app is very instrumental in promoting agricultural sustainability due to its ability to facilitate the effective exchange of agricultural information between extension service providers and farmers and link farmers with markets and other actors in the food value chains.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"228 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115105822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082722
Lwando Nkuzo, Malusi Sibiya, E. Markus
Human error, fatigue, and negligence cause the majority of road accidents. Modern automobiles are outfitted with advanced driver assistance systems (ADASs) to help drivers and other vehicle occupants improve safety, enforce the law, and provide comfort. The purpose of this paper is to identify research gaps by highlighting the challenges of computer vision-based application techniques in modern automobiles for safety purposes. This study will also highlight publicly available datasets that can be used for research purposes. As a guideline, the study uses the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2020) protocol. Our study drew on seventy sources of literature, fifty of which focused on modern car applications (Lane, Pedestrian, and Traffici sign detection) and 20 on publicly available datasets. Using search criteria, the literature was mined in Google Scholar and IEEE Explore. The boolean operators and keywords listed below were employed. The inclusion and exclusion criteria used in the study are detailed in Section II. To understand the research gaps between the presented applications and the availability of public datasets, a comparison analysis was performed. Deep learning techniques are more accurate and robust than traditional computer vision techniques, according to the results. The results also show that there are available public datasets. The study, however, was restricted to English papers, lane, pedestrian, and traffic sign applications. Other languages and applications could be future research topics.
{"title":"Computer Vision-based Applications in Modern Cars for safety purposes: A Systematic Literature Review","authors":"Lwando Nkuzo, Malusi Sibiya, E. Markus","doi":"10.1109/ICTAS56421.2023.10082722","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082722","url":null,"abstract":"Human error, fatigue, and negligence cause the majority of road accidents. Modern automobiles are outfitted with advanced driver assistance systems (ADASs) to help drivers and other vehicle occupants improve safety, enforce the law, and provide comfort. The purpose of this paper is to identify research gaps by highlighting the challenges of computer vision-based application techniques in modern automobiles for safety purposes. This study will also highlight publicly available datasets that can be used for research purposes. As a guideline, the study uses the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA 2020) protocol. Our study drew on seventy sources of literature, fifty of which focused on modern car applications (Lane, Pedestrian, and Traffici sign detection) and 20 on publicly available datasets. Using search criteria, the literature was mined in Google Scholar and IEEE Explore. The boolean operators and keywords listed below were employed. The inclusion and exclusion criteria used in the study are detailed in Section II. To understand the research gaps between the presented applications and the availability of public datasets, a comparison analysis was performed. Deep learning techniques are more accurate and robust than traditional computer vision techniques, according to the results. The results also show that there are available public datasets. The study, however, was restricted to English papers, lane, pedestrian, and traffic sign applications. Other languages and applications could be future research topics.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126128807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082746
T. T. Oladimeji, P. Kumar, Mohamed K. Elmezughi
Millimeter wave technology has experienced propagation loss due to the obstacles such as furniture, walls, and people etc. Due to their incredibly tiny wavelengths of millimeter waves, the high-frequency bands have been found to suffer significantly from the effects of wireless propagation. Hence, it is crucial to anticipate the path loss accurately. The close-in (CI) path loss model's high-ordering reliance on the separation between the antennas of the transmitter and the receiver on a logarithmic scale is examined in this work. In this paper, the performance of the third-order CI model for the frequency bands 28 and 38 GHz is analyzed. Two different antenna polarizations are used in both the line of sight (LOS) as well as non-line of sight (NLOS) scenarios in an interior corridor. The outcome demonstrates a significant drop in the values of the shadow fading standard deviation with increasing model rank. The results also show that the third order CI model gives better performance as compared to the standard CI model for predicting the path loss.
{"title":"Performance Analysis of High Order Close-In Path Loss Model at 28 and 38 GHz","authors":"T. T. Oladimeji, P. Kumar, Mohamed K. Elmezughi","doi":"10.1109/ICTAS56421.2023.10082746","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082746","url":null,"abstract":"Millimeter wave technology has experienced propagation loss due to the obstacles such as furniture, walls, and people etc. Due to their incredibly tiny wavelengths of millimeter waves, the high-frequency bands have been found to suffer significantly from the effects of wireless propagation. Hence, it is crucial to anticipate the path loss accurately. The close-in (CI) path loss model's high-ordering reliance on the separation between the antennas of the transmitter and the receiver on a logarithmic scale is examined in this work. In this paper, the performance of the third-order CI model for the frequency bands 28 and 38 GHz is analyzed. Two different antenna polarizations are used in both the line of sight (LOS) as well as non-line of sight (NLOS) scenarios in an interior corridor. The outcome demonstrates a significant drop in the values of the shadow fading standard deviation with increasing model rank. The results also show that the third order CI model gives better performance as compared to the standard CI model for predicting the path loss.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128491381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082752
Deshalin Naidoo, Timothy T. Adeliyi
Students at risk in universities are becoming a rising global issue. These are students who have a high likelihood of dropping out of their respective academic programs. Due to the importance and impact on students, if interventions are not implemented, research on students at risk is garnering widespread attention in the literature. Early identification of these at-risk students is essential for intervention to lessen the likelihood of dropout. On a virtual learning environment dataset, this study compared Adaboost with five other machine learning algorithms, including Random Forest, Logistic Regression, Support Vector Machine, and Decision Trees, to detect students at risk. This study focused on training and evaluating the six machine learning models adopted, employing performance evaluation metrics such as F1 score, Confusion Matrix, Recall, Precision, ROC, error rate, and accuracy. Adaboost was found to be the top-performing algorithm, having the highest accuracy, F1 score, Precision, and Recall.
{"title":"Analysing University at-Risk Students in a Virtual Learning Environment using Machine Learning Algorithms","authors":"Deshalin Naidoo, Timothy T. Adeliyi","doi":"10.1109/ICTAS56421.2023.10082752","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082752","url":null,"abstract":"Students at risk in universities are becoming a rising global issue. These are students who have a high likelihood of dropping out of their respective academic programs. Due to the importance and impact on students, if interventions are not implemented, research on students at risk is garnering widespread attention in the literature. Early identification of these at-risk students is essential for intervention to lessen the likelihood of dropout. On a virtual learning environment dataset, this study compared Adaboost with five other machine learning algorithms, including Random Forest, Logistic Regression, Support Vector Machine, and Decision Trees, to detect students at risk. This study focused on training and evaluating the six machine learning models adopted, employing performance evaluation metrics such as F1 score, Confusion Matrix, Recall, Precision, ROC, error rate, and accuracy. Adaboost was found to be the top-performing algorithm, having the highest accuracy, F1 score, Precision, and Recall.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126606940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082750
O. Aroba, Thuthukani Xulu, Nonsikelelo N. Msani, Thuso T. Mohlakoana, Experience E. Ndlovu, Simphiwe M. Mthethwa
Solid waste management has become a significant concern in environmental issues. This can be a problem, especially in cities where the population is quickly developing, and the sum of waste produced is expanding like never before. Programs for innovative city waste can help raise proficiency, diminish costs, and improve the aesthetics of open places as cities endeavor to oversee waste in public regions effectively. This study enhances intelligent waste systems by developing innovative technologies and software as additional tools for collection. This research demonstrates how the SQERT model, a periodic trend analysis report specific to projects, will be used to assess the intelligent waste management system and the proposed software technology. Furthermore, A software prototype visualization was created to demonstrate and show how the software system will look and its functionalities to improve the waste collection system.
{"title":"The Adoption of an Intelligent Waste Collection System in a Smart City","authors":"O. Aroba, Thuthukani Xulu, Nonsikelelo N. Msani, Thuso T. Mohlakoana, Experience E. Ndlovu, Simphiwe M. Mthethwa","doi":"10.1109/ICTAS56421.2023.10082750","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082750","url":null,"abstract":"Solid waste management has become a significant concern in environmental issues. This can be a problem, especially in cities where the population is quickly developing, and the sum of waste produced is expanding like never before. Programs for innovative city waste can help raise proficiency, diminish costs, and improve the aesthetics of open places as cities endeavor to oversee waste in public regions effectively. This study enhances intelligent waste systems by developing innovative technologies and software as additional tools for collection. This research demonstrates how the SQERT model, a periodic trend analysis report specific to projects, will be used to assess the intelligent waste management system and the proposed software technology. Furthermore, A software prototype visualization was created to demonstrate and show how the software system will look and its functionalities to improve the waste collection system.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133924854","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-03-01DOI: 10.1109/ICTAS56421.2023.10082721
D. Malanga, W. Chigona
This study aimed to explore how user expectations promote or inhibit community health workers (CHWs) intentions to continue using Cstock, as a case study of mobile health (mHealth) in Malawi. To achieve the objectives of the study, a research model is proposed based on the expectation confirmation model, complemented by other theories from the extant literature. The study employed an interpretive case study design, utilising a deductive approach to theory. The study used semi-structured interviews, field notes and observations to gather data. Twelve CHWs were sampled purposively to participate in the study at Chitipa district health facility in Malawi. The study confirmed that to some extent information quality, system quality, and service quality were pre-acceptance expectations that impacted the CHWs on their future benefits and satisfaction with mHealth. The study also noted that users of Cstock formed expectations from trainings, workshops, seminars, and briefings organised by the Cstock vendor/provider, officials from the Ministry of Health and Chitipa district health facility. Most importantly, the study found that satisfaction and post-usage usefulness impacted CHWs to continue using mHealth despite the available alternatives. The findings demonstrate user expectations' role in continuance intention towards the use of mHealth in Malawi. The study's implication points to policymakers and research practitioners the need to focus on understanding user expectations that could promote the successful adoption of mHealth in Africa and beyond.
{"title":"User Expectations and Continuance Intention of mHealth among Community Health Workers in Malawi *","authors":"D. Malanga, W. Chigona","doi":"10.1109/ICTAS56421.2023.10082721","DOIUrl":"https://doi.org/10.1109/ICTAS56421.2023.10082721","url":null,"abstract":"This study aimed to explore how user expectations promote or inhibit community health workers (CHWs) intentions to continue using Cstock, as a case study of mobile health (mHealth) in Malawi. To achieve the objectives of the study, a research model is proposed based on the expectation confirmation model, complemented by other theories from the extant literature. The study employed an interpretive case study design, utilising a deductive approach to theory. The study used semi-structured interviews, field notes and observations to gather data. Twelve CHWs were sampled purposively to participate in the study at Chitipa district health facility in Malawi. The study confirmed that to some extent information quality, system quality, and service quality were pre-acceptance expectations that impacted the CHWs on their future benefits and satisfaction with mHealth. The study also noted that users of Cstock formed expectations from trainings, workshops, seminars, and briefings organised by the Cstock vendor/provider, officials from the Ministry of Health and Chitipa district health facility. Most importantly, the study found that satisfaction and post-usage usefulness impacted CHWs to continue using mHealth despite the available alternatives. The findings demonstrate user expectations' role in continuance intention towards the use of mHealth in Malawi. The study's implication points to policymakers and research practitioners the need to focus on understanding user expectations that could promote the successful adoption of mHealth in Africa and beyond.","PeriodicalId":158720,"journal":{"name":"2023 Conference on Information Communications Technology and Society (ICTAS)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128005286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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