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An integrated model combining ECM and UTAUT to explain users’ post-adoption behaviour towards mobile payment systems
No full textTechnological progression in mobile phones has increased the popularity of mobile payments. Users can shop online through a mobile device, which is time saving and convenient. Mobile payment systems involve ongoing interactions between users and payment providers. The initial acceptance of mobile payment systems has been studied extensively, but few studies have attempted to understand users’ post-adoption behaviour. This study employs an integrated model with the unified theory of acceptance and use of technology (UTAUT) framework and the expectation confirmation model (ECM), along with two additional constructs: perceived security and trust. The empirical results show that the integrated model has a higher predictive power to explain continuance intentions for using mobile payment systems with significant factors of satisfaction, trust, performance expectancy, and effort expectancy. This study confirmed that the UTAUT model could be extended to explain post-adoption behaviour towards mobile payment systems. The study’s findings have theoretical and practical value to further the understanding of pre- and post-adoption behaviour towards mobile payment systems
Preface to the Special Section on the 9th Information Systems Foundations Workshop
Get PDFIntroduction to the Special Section
Telemedicine Healthcare Service Adoption Barriers in Rural Bangladesh
Get PDFThis article investigates potential barriers to telemedicine adoption in centres hosted by rural public hospitals in Bangladesh. Little is known of the barriers related to telemedicine adoption in this context. Analysis of data collected from rural telemedicine patients identified seven broad categories of barriers: lack of organisational effectiveness, information and communication technology infrastructure, quality of care, allocation of resources, health staff motivation, patient satisfaction and trustworthiness. Their significance is explored. This research is based on the quantitative analysis of a data set of 500 telemedicine patients, from rural areas in Bangladesh. A conceptual model showing the interaction of pre-determined classes of barriers was established and hypotheses set up and tested using partial least squares structural equation modelling. Exemplary barriers to telemedicine adoption were identified and confirmed (p<.01) namely, lack of organisational effectiveness, health staff motivation, patient satisfaction, and trustworthiness collectively explaining 62% of the variance in barriers to adoption and providing for the first-time empirical support of their existence. These barriers offer considerable resistance to the adoption and maintenance of current telemedicine projects in rural Bangladesh. Further, lack of information and communication technology infrastructure, allocation of resources and quality of care are indirect barriers affecting successful deployment of telemedicine in rural settings. These findings illuminate adoption impediments faced by existing telemedicine projects and institutionalise favourable policy guidelines to improve Bangladesh’s and similar emerging economies’ healthcare industries. Policy interventions and recommendations are provided, including current research limitations leading to opportunities for future research
Computing, Girls and Education: What we need to know to change how girls think about information technology.
Get PDFDespite significant efforts and many intervention programs over the years to encourage girls to study computing, we continue to see a declining interest. Girls’ lack of engagement with technology at school is resulting in fewer women entering the Information Technology (IT) workforce. Our research investigated whether a long-term intervention program with a specifically designed school-based curriculum could change girls’ minds about computing generally and increase their confidence and interest in an IT career. Qualitative and quantitative data were collected from girls and teachers before, during, and after this program was implemented. A conceptual model of the school-based influences on girls’ attitude was developed from the literature and used to explore the data. Findings from this four-year project added rich insights and resulted in a comprehensive model of ‘Factors that Influence Girls’ Attitude to IT.’ This research demonstrates that a carefully designed IT curriculum, delivered in single-sex classes, reinforced by opportunities to interact with role models, and timetabled in regular class time, can and does change girls’ attitudes to IT. We also found that the students reported improved confidence and increased interest in IT. We posit that our refined model of ‘Factors that Influence Girls’ Attitude to IT’ is a valuable reference tool. Teachers, academics and professionals who are implementing programs to promote IT to girls can use it
Explanations as Discourse: Towards Ethical Big Data Analytics Services
Get PDFBig data analytics uses algorithms for decision-making and targeting of customers. These algorithms process large-scale data sets and create efficiencies in the decision-making process for organizations but are often incomprehensible to customers and inherently opaque in nature. Recent European Union regulations require that organizations communicate meaningful information to customers on the use of algorithms and the reasons behind decisions made about them. In this paper, we explore the use of explanations in big data analytics services. We rely on discourse ethics to argue that explanations can facilitate a balanced communication between organizations and customers, leading to transparency and trust for customers as well as customer engagement and reduced reputation risks for organizations. We conclude the paper by proposing future empirical research directions
Machine Learning, Ethics and Law
Get PDFRecent revelations concerning data firm Cambridge Analytica’s illegitimate use of the data of millions of Facebook users highlights the ethical and, relatedly, legal issues arising from the use of machine learning techniques. Cambridge Analytica is, or was – the revelations brought about its demise - a firm that used machine learning processes to try to influence elections in the US and elsewhere by, for instance, targeting ‘vulnerable’ voters in marginal seats with political advertising. Of course, there is nothing new about political candidates and parties employing firms to engage in political advertising on their behalf, but if a data firm has access to the personal information of millions of voters, and is skilled in the use of machine learning techniques, then it can develop detailed, fine-grained voter profiles that enable political actors to reach a whole new level of manipulative influence over voters. My focus in this paper is not with the highly publicised ethical and legal issues arising from Cambridge Analytic’s activities but rather with some important ethical issues arising from the use of machine learning techniques that have not received the attention and analysis that they deserve. I focus on three areas in which machine learning techniques are used or, it is claimed, should be used, and which give rise to problems at the interface of law and ethics (or law and morality, I use the terms “ethics” and “morality” interchangeably). The three areas are profiling and predictive policing (Saunders et al. 2016), legal adjudication (Zeleznikow, 2017), and machines’ compliance with legally enshrined moral principles (Arkin 2010). I note that here, as elsewhere, new and emerging technologies are developing rapidly making it difficult to predict what might or might not be able to be achieved in the future. For this reason, I have adopted the conservative stance of restricting my ethical analysis to existing machine learning techniques and applications rather than those that are the object of speculation or even informed extrapolation (Mittelstadt et al. 2015). This has the consequence that what I might regard as a limitation of machine learning techniques, e.g. in respect of predicting novel outcomes or of accommodating moral principles, might be thought by others to be merely a limitation of currently available techniques. After all, has not the history of AI recently shown the naysayers to have been proved wrong? Certainly, AI has seen some impressive results, including the construction of computers that can defeat human experts in complex games, such as chess and Go (Silver et al. 2017), and others that can do a better job than human medical experts at identifying the malignancy of moles and the like (Esteva et al. 2017). However, since by definition future machine learning techniques and applications are not yet with us the general claim that current limitations will be overcome cannot at this time be confirmed or disconfirmed on the basis of empirical evidence
Writing, Arguing, Contributing - A Cogent Argumentation Framework for Identifying, Specifying, and Evaluating Research Contribution
Get PDFThe predominant means by which research becomes visible and accessible to the research community is through publication. Generally, publication requires careful framing of the research in relation to existing knowledge. As a contribution to knowledge cannot be self-evident, authors must persuade, through argumentation, the editors, reviewers, and the research community that their work offers a contribution. In Information Systems, the discussion of argumentation is often limited to the logic dimensions of argumentation, namely deductive, inductive, and abductive reasoning. In this paper, we demonstrate that argumentation requires the consideration of three additional dimensions of argumentation: rhetoric, dialectic, and social-institutional. Kuhn’s concept of the disciplinary matrix is introduced as the background toward which a cogent argument is directed and against which contribution is evaluated. We then illustrate the role of argumentation through the example of the seminal paper by Orlikowski and Iacono on the role of IT in Information Systems research. Understanding the importance of argumentation in framing one’s research contribution is critical to authors, editors, and reviewers alike within and beyond Information Systems and its reference disciplines
Blockchain in healthcare
Get PDFBlockchain is treated as a ledger system that manages data and their transactions using time-stamped blocks through cryptography and works in a decentralised manner over the computing network. Although blockchain is originally used as a backbone for the cryptocurrency, Bitcoin, its capabilities and applications have yet to be extended far beyond cryptocurrencies. In this paper, through conducting a latest systematic literature review aiming to produce new source of evidence, we identify potential applications of the blockchain technologies in healthcare. The comprehensive review looks at the professional and academic open-sourced journals published between 2008 to 2019 to recognise the potential of blockchain based approaches in the purpose of healthcare information disseminations, as well as to segregate issues for the implementation and development of blockchain applications. We identify several major application domains that present research opportunities and challenges for the future advancements and directions for the benefits of IS researchers and professionals
Understanding the Factors that Influence the Primary Appraisal of mHealth Tools in Developing Countries: An Exploratory Case-Study in Nigeria
Get PDFShortages of health workers, infrastructural deficiencies, limited access to medical care are just a few of the many barriers to care in developing countries. The integration of smartphones and mobile devices into healthcare systems has been proposed to address some of the physical barriers to care and service delivery. These mHealth solutions extend the reach of medical care into rural areas of developing countries. However, it is not clear how mHealth solutions designed and tested in one developing region can be positively appraised for use in others. This study frames this problem using a coping theory approach based on an exploratory case-study to understand the factors that influence primary appraisal of smartphone-enabled clinical guidelines (mHealth tool) for accessing, classifying and eliciting treatment recommendation for sick children under the age of five by rural healthcare workers (RHCWs). Findings identified a set of factors which are bound as an emerging explanatory positivity model that influence primary appraisal of an mHealth tool in a new context. These factors are the set of individual and social factors that governments, funding bodies and non-governmental organisations should consider before embarking on the introduction of an mHealth tool in rural communities of developing countries. It is envisaged that by understanding the factors that influence primary appraisal, that is, either as an opportunity or a threat, practitioners and organisations will support positive appraisal and minimise the occurrence of negative ones when introducing mHealth tools. These findings have implications for theory, practice, and future research as explained in the concluding section of this paper
Anticipating, avoiding, and alleviating measurement error: A synthesis of the literature with practical recommendations
Get PDFResearchers’ ability to draw inferences from their empirical work hinges on the degree of measurement error. The literature in Information Systems and other behavioural disciplines describes a plethora of sources of error. While it helps researchers deal with them when taking specific steps in the measurement process, like modelling constructs, developing instruments, collecting data, and analysing data, it does not provide an overall guide to help them prevent and deal with measurement error. This paper presents a synthesis of the insights in the literature through a decomposition of the logic of measurement. It shows how researchers can classify sources of error, evaluate their impact, and refine their measurement plans, in terms of specific steps or overall measurement approaches. We hope this will aid researchers in anticipating, avoiding, and alleviating error in measurement, and in drawing valid research conclusions