A review of the Information System Models for Technology Acceptance

Published Conference ProceedingsThe words “acceptance” and “behaviour” have been used interchangeably. The acceptance of any form of technology is determined by the behaviour of the individual towards that technology. Extensive research has been carried out on factors that influence human behaviour. This includes research in mathematics, philosophy, anthropology, information systems theories and many more. In the field of Information Technology and Information systems, there are models that have been developed in an attempt to try and understand technology acceptance. The aim of this paper is to review 6 unique Information Systems models of acceptance (Diffusion of Innovations, Theory of Reasoned Action, Theory of Planned Behaviour, Technology Acceptance Model, Task Technology fit and Unified Theory of Acceptance and use of Technology). The paper defines each of the models, providing past applications and recommending future applications within the context of a university of technology. The aim of this review is to help create awareness among fellow academics about the various acceptance models and their possible usage

Whose Advice Counts More – Man or Machine? An Experimental Investigation of AI-based Advice Utilization

Due to advances in Artificial Intelligence (AI), it is possible to provide advisory services without human advisors. Derived from judge-advisor system literature, we examined differences in the advice utilization depending on whether it is given by an AI-based or human advisor and the similarity of the advice and their own estimation. Drawing on task-technology fit we investigated the relationship between task, advisor and advice utilization. In study A we measured the actual advice utilization within a guessing game and in study B we measured the perceived task-advisor fit for this game. The findings show that compared to human advisors, judges utilize advices of AI-based advisors more when the advice is similar to their own estimation. When the advice is very different to their estimation, the advices are used equally. Concluding, we investigated AI-based advice utilization and presented insights for professionals providing AI-based advisory services

Following the Robot? Investigating Users’ Utilization of Advice from Robo-Advisors

Companies are gradually creating new services such as robo-advisors (RA). However, little is known if users actually follow RA advice, how much the fit of RA to task requirements influences the utilization, how users perceive RA characteristics and if the perceived advisor’s expertise is influenced by the user’s expertise. Drawing on judge-advisor systems (JAS) and task-technology fit (TTF), we conducted an experimental study to measure actual advice-taking behavior in the context of RA. While the perceived advisor’s expertise is the most influential factor on task-advisor fit for RA and human advisors, integrity is a significant factor only for human advisors. However, for RA the user’s perception of the ability to make decisions efficiently is significant. In our study, users followed RA more than human advisors. Overall, our study connects JAS and TTF to predict advice utilization and supports companies in service promotion

Where was COVID-19 first discovered? Designing a question-answering system for pandemic situations

The COVID-19 pandemic is accompanied by a massive “infodemic” that makes it hard to identify concise and credible information for COVID-19-related questions, like incubation time, infection rates, or the effectiveness of vaccines. As a novel solution, our paper is concerned with designing a question-answering system based on modern technologies from natural language processing to overcome information overload and misinformation in pandemic situations. To carry out our research, we followed a design science research approach and applied Ingwersen’s cognitive model of information retrieval interaction to inform our design process from a socio-technical lens. On this basis, we derived prescriptive design knowledge in terms of design requirements and design principles, which we translated into the construction of a prototypical instantiation. Our implementation is based on the comprehensive CORD-19 dataset, and we demonstrate our artifact’s usefulness by evaluating its answer quality based on a sample of COVID-19 questions labeled by biomedical experts

Question answering systems for health professionals at the point of care -- a systematic review

Objective: Question answering (QA) systems have the potential to improve the quality of clinical care by providing health professionals with the latest and most relevant evidence. However, QA systems have not been widely adopted. This systematic review aims to characterize current medical QA systems, assess their suitability for healthcare, and identify areas of improvement. Materials and methods: We searched PubMed, IEEE Xplore, ACM Digital Library, ACL Anthology and forward and backward citations on 7th February 2023. We included peer-reviewed journal and conference papers describing the design and evaluation of biomedical QA systems. Two reviewers screened titles, abstracts, and full-text articles. We conducted a narrative synthesis and risk of bias assessment for each study. We assessed the utility of biomedical QA systems. Results: We included 79 studies and identified themes, including question realism, answer reliability, answer utility, clinical specialism, systems, usability, and evaluation methods. Clinicians' questions used to train and evaluate QA systems were restricted to certain sources, types and complexity levels. No system communicated confidence levels in the answers or sources. Many studies suffered from high risks of bias and applicability concerns. Only 8 studies completely satisfied any criterion for clinical utility, and only 7 reported user evaluations. Most systems were built with limited input from clinicians. Discussion: While machine learning methods have led to increased accuracy, most studies imperfectly reflected real-world healthcare information needs. Key research priorities include developing more realistic healthcare QA datasets and considering the reliability of answer sources, rather than merely focusing on accuracy.Comment: Accepted to the Journal of the American Medical Informatics Association (JAMIA

Antecedents to the effectiveness of game-based learning environments for the Net generation: A game task fit and flow perspective

Purpose: There is a general consensus that games are effective as learning tools. There is however, a lack of knowledge regarding what makes games effective as a learning tool. The purpose of this study is therefore to answer the question: what are the antecedents of an effective game-based learning environment for the Net generation? The Net generation comprises individuals who prefer to learn using games as a tool. Aim: The aim of this dissertation is to develop a conceptual framework that reflects the antecedents of an effective game-based learning environment for the Net generation. The conceptual framework combines the IS Success Model, and the Task-Technology Fit and Flow theory. Method: The study used a quantitative method. Data was collected using an online instrument. The study used 125 participants from mainly the United Kingdom, United States and South Africa. The model was validated using confirmatory factor analysis and tested using multiple regression analysis. Key Findings: The identified antecedents of effectiveness are Game-Task Fit and Flow, where Flow consists of Clear Goals, Feedback and Concentration. Additionally, the Use factor in the model is replaced by Perceived Usefulness. The Conceptual Framework can be used as an evaluation tool for effective game-based learning environments for the Net generation

Examining question-answering technology from the task technology fit perspective

The World Wide Web has become a vital supplier of information for organizations in order to carry on such tasks as business intelligence, security monitoring, and risk assessments. By utilizing the task-technology fit (TTF) theory, we investigate the issue of when open-domain question-answering (QA) technology would potentially be superior to general-purpose Web search engines. Specifically, we argue theoretically and back up our arguments with a user study that the presence of fusion (information synthesis) is crucial to warrant the use of QA. At the same time, many information seeking tasks do not require fusion and, thus, are adequately served by traditional keyword search portals (Google, MSN, Yahoo, etc.). This explains why prior attempts to demonstrate the value of QA empirically were unsuccessful. We also discuss methodological challenges to any empirical investigation of QA and present several solutions to those challenges, validated with our user study. In order to carry our study, we created a novel prototype by following the Design Science guidelines. Our prototype is the first of its kind and is capable of answering list questions, such as What companies own low orbit satellites? or In which cities have illegal methyl-methionine labs been found? This investigation is only a precursor to a full-scale empirical study, but it serves as a medium to overview the state of the art QA technologies and to introduce important theoretical and empirical concepts involved. Although we did not find empirical evidence that one technology is uniformly better than the other, we discovered that once the user accumulates experience using QA, he/she can make an intelligent decision whether to use it for a particular task, which leads to the user to be more productive on average with the same tasks compared to when there is no choice of technology

Examining question-answering technology from the task technology fit perspective

The World Wide Web has become a vital supplier of information for organizations in order to carry on such tasks as business intelligence, security monitoring, and risk assessments. By utilizing the task-technology fit (TTF) theory, we investigate the issue of when open-domain question-answering (QA) technology would potentially be superior to general-purpose Web search engines. Specifically, we argue theoretically and back up our arguments with a user study that the presence of fusion (information synthesis) is crucial to warrant the use of QA. At the same time, many information seeking tasks do not require fusion and, thus, are adequately served by traditional keyword search portals (Google, MSN, Yahoo, etc.). This explains why prior attempts to demonstrate the value of QA empirically were unsuccessful. We also discuss methodological challenges to any empirical investigation of QA and present several solutions to those challenges, validated with our user study. In order to carry our study, we created a novel prototype by following the Design Science guidelines. Our prototype is the first of its kind and is capable of answering list questions, such as What companies own low orbit satellites? or In which cities have illegal methyl-methionine labs been found? This investigation is only a precursor to a full-scale empirical study, but it serves as a medium to overview the state of the art QA technologies and to introduce important theoretical and empirical concepts involved. Although we did not find empirical evidence that one technology is uniformly better than the other, we discovered that once the user accumulates experience using QA, he/she can make an intelligent decision whether to use it for a particular task, which leads to the user to be more productive on average with the same tasks compared to when there is no choice of technology