{"title":"Exploring Factors Influencing Adoption of Blockchain in Accounting Applications using Technology–Organization–Environment Framework","authors":"Sujata Seshadrinathan, Shalini Chandra","doi":"10.58729/1941-6679.1477","DOIUrl":"https://doi.org/10.58729/1941-6679.1477","url":null,"abstract":"","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85077570","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}
{"title":"FinTech in Information Systems Research: A 2010-2020 Review of the AIS Senior Scholars’ Basket","authors":"Peter Haried, Ye Han, David A. Annino","doi":"10.58729/1941-6679.1489","DOIUrl":"https://doi.org/10.58729/1941-6679.1489","url":null,"abstract":"","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77200470","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}
Robert Schumaker, Michael A. Veronin, Trevor Rohm, R. Dixit, Shadi A. Aljawarneh, J. Lara
{"title":"An Analysis of Covid-19 Vaccine Allergic Reactions","authors":"Robert Schumaker, Michael A. Veronin, Trevor Rohm, R. Dixit, Shadi A. Aljawarneh, J. Lara","doi":"10.58729/1941-6679.1521","DOIUrl":"https://doi.org/10.58729/1941-6679.1521","url":null,"abstract":"","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81945285","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}
Robert P. Schumaker, Michael A. Veronin, Trevor Rohm, M. Boyett, R. Dixit
We use a data driven approach on a cleaned adverse drug reaction database to determine the reaction severity of several covid-19 drug combinations currently under investigation. We further examine their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine are associated with increased adverse drug event severity versus other drug combinations already not recommended by NIH treatment guidelines, 2. hydroxychloroquine/azithromycin are associated with lower adverse drug event severity among older populations, 3. lopinavir/ritonavir had lower adverse reaction severity among toddlers and 4. the combination of azithromycin, hydroxychloroquine and tocilizumab is safer than its component drugs. While our approach does not consider drug efficacy, it can help prioritize clinical trials for drug combinations by focusing on those with the lowest reaction severity and thus increase potential treatment options for covid-19 patients.
{"title":"A Data Driven Approach to Profile Potential SARS-CoV-2 Drug Interactions Using TylerADE","authors":"Robert P. Schumaker, Michael A. Veronin, Trevor Rohm, M. Boyett, R. Dixit","doi":"10.58729/1941-6679.1504","DOIUrl":"https://doi.org/10.58729/1941-6679.1504","url":null,"abstract":"We use a data driven approach on a cleaned adverse drug reaction database to determine the reaction severity of several covid-19 drug combinations currently under investigation. We further examine their safety for vulnerable populations such as individuals 65 years and older. Our key findings include 1. hydroxychloroquine/chloroquine are associated with increased adverse drug event severity versus other drug combinations already not recommended by NIH treatment guidelines, 2. hydroxychloroquine/azithromycin are associated with lower adverse drug event severity among older populations, 3. lopinavir/ritonavir had lower adverse reaction severity among toddlers and 4. the combination of azithromycin, hydroxychloroquine and tocilizumab is safer than its component drugs. While our approach does not consider drug efficacy, it can help prioritize clinical trials for drug combinations by focusing on those with the lowest reaction severity and thus increase potential treatment options for covid-19 patients.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"97 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82686906","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}
This paper addresses the automatic generation of persuasive content to influence users’ attitude and behaviour. Our research extends current approaches by leveraging individuals’ social media profiles and activity to personalize the persuasive content. Unlike most other implemented persuasive technology, our system is generic and can be adapted to any domain where collections of electronic text are available. Using the Yale Attitude Change approach, we describe: the multi-layered Pyramid of Individualization model; the design, development, and validation of integrated software that can generate individualized persuasive content based on a user’s social media profile and activity. Results indicate the proposed system can create personalized information that (a) matches readers’ interests, (b) is tailored to their ability to understand the information, and (c) is supported by trustable sources.
{"title":"The Design, Development and Validation of a Persuasive Content Generator","authors":"Sam Khataei, M. Hine, A. Arya","doi":"10.58729/1941-6679.1460","DOIUrl":"https://doi.org/10.58729/1941-6679.1460","url":null,"abstract":"This paper addresses the automatic generation of persuasive content to influence users’ attitude and behaviour. Our research extends current approaches by leveraging individuals’ social media profiles and activity to personalize the persuasive content. Unlike most other implemented persuasive technology, our system is generic and can be adapted to any domain where collections of electronic text are available. Using the Yale Attitude Change approach, we describe: the multi-layered Pyramid of Individualization model; the design, development, and validation of integrated software that can generate individualized persuasive content based on a user’s social media profile and activity. Results indicate the proposed system can create personalized information that (a) matches readers’ interests, (b) is tailored to their ability to understand the information, and (c) is supported by trustable sources.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82965409","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}
MBA has become one of the most popular and vital professional degrees internationally. The MBA program admission process’s essential task is to choose the best analysis tools to accurately predict applicants’ academic performance potential based on the evaluation criteria in making admission decisions. Prior research finds that the Graduate Management Admission Test (GMAT) and undergraduate grade point average (UGPA) are common predictors of MBA academic performance indicated by graduate grade point average (GGPA). Using a sample of 250 MBA students enrolled in a state university with AACSB accreditation from Fall 2010 to Fall 2017, we test and compare the effectiveness of artificial neural networks (ANNs) against traditional statistical methods of ordinary least squares (OLS) and logistic regression in MBA academic performance prediction. We find that ANNs generate similar predictive power as OLS regression in predicting the numerical value of GGPA. By dichotomizing GGPA into categorical variables of “successful” and “marginal,” we identify that ANNs offer the most reliable prediction based on total GMAT score and UGPA while logistic regression delivers superior performance based on other combinations of the predictors. Our findings shed light on adopting ANNs to predict academic performance potential with a strong implication in MBA admissions to select qualified applicants in a competitive environment.
{"title":"A comparison of artificial neural networks and the statistical methods in predicting MBA student’s academic performance","authors":"Ojoung Kwon, Harry Xia, Serin Zhang","doi":"10.58729/1941-6679.1485","DOIUrl":"https://doi.org/10.58729/1941-6679.1485","url":null,"abstract":"MBA has become one of the most popular and vital professional degrees internationally. The MBA program admission process’s essential task is to choose the best analysis tools to accurately predict applicants’ academic performance potential based on the evaluation criteria in making admission decisions. Prior research finds that the Graduate Management Admission Test (GMAT) and undergraduate grade point average (UGPA) are common predictors of MBA academic performance indicated by graduate grade point average (GGPA). Using a sample of 250 MBA students enrolled in a state university with AACSB accreditation from Fall 2010 to Fall 2017, we test and compare the effectiveness of artificial neural networks (ANNs) against traditional statistical methods of ordinary least squares (OLS) and logistic regression in MBA academic performance prediction. We find that ANNs generate similar predictive power as OLS regression in predicting the numerical value of GGPA. By dichotomizing GGPA into categorical variables of “successful” and “marginal,” we identify that ANNs offer the most reliable prediction based on total GMAT score and UGPA while logistic regression delivers superior performance based on other combinations of the predictors. Our findings shed light on adopting ANNs to predict academic performance potential with a strong implication in MBA admissions to select qualified applicants in a competitive environment.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"145 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79584778","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}
Jian Lin, Naveed Saleem, Vance Etnyre, G. Gercek, Nanfei Sun
The technology of self-driving cars will inevitably change the industry of taxis and ride-sharing cars that provide important commercial ground transportation services to travelers, tourists and local residents. There is no doubt that new techniques, business models and strategies will be needed to follow the use of self-driving cars. This paper focuses on a forward-looking research topic that route commercial, vacant self-driving vehicles so that the values to both businesses and passengers are improved. Importance of solutions to the new problem is discussed. We also propose a novel design which simulates behaviors of ants in nature to the vehicles. The goal of the system is to obtain an overall balance between the demands of using the services from the passengers and availability of the vehicles in all service areas. The system not only uses historical data to make decisions, it also responds promptly for demands appeared dynamically.
{"title":"An Ant-based Intelligent Design for Future Self-driving Commercial Car Service Strategy","authors":"Jian Lin, Naveed Saleem, Vance Etnyre, G. Gercek, Nanfei Sun","doi":"10.58729/1941-6679.1422","DOIUrl":"https://doi.org/10.58729/1941-6679.1422","url":null,"abstract":"The technology of self-driving cars will inevitably change the industry of taxis and ride-sharing cars that provide important commercial ground transportation services to travelers, tourists and local residents. There is no doubt that new techniques, business models and strategies will be needed to follow the use of self-driving cars. This paper focuses on a forward-looking research topic that route commercial, vacant self-driving vehicles so that the values to both businesses and passengers are improved. Importance of solutions to the new problem is discussed. We also propose a novel design which simulates behaviors of ants in nature to the vehicles. The goal of the system is to obtain an overall balance between the demands of using the services from the passengers and availability of the vehicles in all service areas. The system not only uses historical data to make decisions, it also responds promptly for demands appeared dynamically.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77941120","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}
R. Smit, Jeroen van Yperen Hagedoorn, P. Versteeg, P. Ravesteijn
{"title":"The Soft Skills Business Demands of the Chief Information Security Officer","authors":"R. Smit, Jeroen van Yperen Hagedoorn, P. Versteeg, P. Ravesteijn","doi":"10.58729/1941-6679.1522","DOIUrl":"https://doi.org/10.58729/1941-6679.1522","url":null,"abstract":"","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74267162","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}
C. C. Lee, Sinéad G. Ruane, Hyoun Sook Lim, Ruoquing Zhang, Heechang Shin
The goal of our research study is to develop a hybrid instrument built on the revised Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, which is reliable in predicting the behavioral intention to use the Uber ridesharing app. It focuses on extending the UTAUT2 in collaborative consumption, particularly from a consumer and ridesharing-app perspective. Our proposed framework, UTAUT-CC, preserves existing UTAUT2 constructs – performance expectancy, effort expectancy, social expectancy, and facilitating conditions. It also retains demographic moderating variables of age and gender, while maintaining some of the key integral relationships depicted in those models. We integrated three new constructs deemed relevant in linking collaborative consumption and a sharing economy – price, trust, and convenience. We incorporated elements of online and offline services (O2O) together from respective perspectives of mobile technology and ridesharing. Our overall model explained 70.5% of the variance of behavioral intention of Uber. We concluded the paper by exploring actionable implications for practitioners and scholars.
{"title":"Exploring the Behavioral Intention to Use Collaborative Commerce: A Case of Uber","authors":"C. C. Lee, Sinéad G. Ruane, Hyoun Sook Lim, Ruoquing Zhang, Heechang Shin","doi":"10.58729/1941-6679.1545","DOIUrl":"https://doi.org/10.58729/1941-6679.1545","url":null,"abstract":"The goal of our research study is to develop a hybrid instrument built on the revised Unified Theory of Acceptance and Use of Technology (UTAUT2) framework, which is reliable in predicting the behavioral intention to use the Uber ridesharing app. It focuses on extending the UTAUT2 in collaborative consumption, particularly from a consumer and ridesharing-app perspective. Our proposed framework, UTAUT-CC, preserves existing UTAUT2 constructs – performance expectancy, effort expectancy, social expectancy, and facilitating conditions. It also retains demographic moderating variables of age and gender, while maintaining some of the key integral relationships depicted in those models. We integrated three new constructs deemed relevant in linking collaborative consumption and a sharing economy – price, trust, and convenience. We incorporated elements of online and offline services (O2O) together from respective perspectives of mobile technology and ridesharing. Our overall model explained 70.5% of the variance of behavioral intention of Uber. We concluded the paper by exploring actionable implications for practitioners and scholars.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84674755","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}
Restaurants are using innovative ways to engage with consumers using Food Ordering Apps (FOAs). The purpose of this article is to identify the influence of consumers’ eSatisfaction, perceived value, trust, and eLoyalty on eWOM for FOAs in India. The study also verifies if eWOM for FOAs varies based on age, gender, family size, order value, and online shopping experience. The research followed a multi-stage approach. In the first stage, an extensive literature review was conducted to identify the various factors that lead to eWOM. In the second stage, a survey was distributed among the FOA users. 375 responses were obtained. In the third stage, a binary multivariate logistic regression was used to evaluate the predictive power of the proposed research model. The results indicated that eLoyalty, trust, and perceived value were statistically significant in predicting consumers’ intent to spread eWOM for FOAs. eSatisfaction, age, gender, order value, shopping experience, and family size were insignificant in shaping the customer’s intention to spread eWOM for FOAs. The findings of this study can be used to understand the factors that influence users to spread eWOM for FOAs. Managers in food delivery business can use the findings from this study to address the most relevant constructs shaping eWOM as it has an impact on the survival prospects and profitability of the business.
{"title":"What leads consumers to spread eWOM for Food Ordering Apps?","authors":"Brinda Sampat, K. C. Sabat","doi":"10.58729/1941-6679.1480","DOIUrl":"https://doi.org/10.58729/1941-6679.1480","url":null,"abstract":"Restaurants are using innovative ways to engage with consumers using Food Ordering Apps (FOAs). The purpose of this article is to identify the influence of consumers’ eSatisfaction, perceived value, trust, and eLoyalty on eWOM for FOAs in India. The study also verifies if eWOM for FOAs varies based on age, gender, family size, order value, and online shopping experience. The research followed a multi-stage approach. In the first stage, an extensive literature review was conducted to identify the various factors that lead to eWOM. In the second stage, a survey was distributed among the FOA users. 375 responses were obtained. In the third stage, a binary multivariate logistic regression was used to evaluate the predictive power of the proposed research model. The results indicated that eLoyalty, trust, and perceived value were statistically significant in predicting consumers’ intent to spread eWOM for FOAs. eSatisfaction, age, gender, order value, shopping experience, and family size were insignificant in shaping the customer’s intention to spread eWOM for FOAs. The findings of this study can be used to understand the factors that influence users to spread eWOM for FOAs. Managers in food delivery business can use the findings from this study to address the most relevant constructs shaping eWOM as it has an impact on the survival prospects and profitability of the business.","PeriodicalId":55883,"journal":{"name":"International Journal of Information Technology and Management","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76123411","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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