Revisiting Review Depth in Search for Helpful Online Reviews

This study investigates online review features that constitute review depth and assess their impacts on review helpfulness. It develops a model capturing the moderating effects of heuristic and systematic cues of an online review on the relationship between review length and its helpfulness. In particular, this study examines the moderating effects of price, product type, review readability and the presence of two-sided arguments. For testing the model, a dataset of 568,454 reviews from 256,059 different reviewers on Amazon.com were analyzed. The variables were operationalized using test processing techniques and relationships were empirically tested using regression and machine learning models. The results highlight significant moderating effects of review readability and the presence of two-sided arguments on the relationship between review length and its helpfulness. However, the results did not confirm the moderating effects of price and product type. This article discusses the significant implications for a better understanding of review depth and helpfulness in e-commerce platforms

On the Complexity of Health Data Protection-in-Practice: Insights from a Longitudinal Qualitative Study

Digitalization of healthcare presents opportunities for improving the quality of healthcare services and promises economic benefits. However, the success of digital health and the benefits cannot be actualized without considering health data protection practices in the process of healthcare service delivery. Despite the criticality of protecting health data in the system use lifecycle (from recording to consuming and taking informed actions), there is a paucity of research to investigate this complex phenomenon. Using longitudinal qualitative data on a state-wide digital health transformation project, we contextually theorize the practices for protecting health data. Our study reveals five types of health data protectionin-practice, namely data minimization, informal encoding, accuracy, improving cyber-awareness, and appropriate access management. Our results provide new insights into information system use (especially, effective use), and highlight practices that can improve health data protection

Socio-technical Challenges to the Effective Use of Health Information Systems (IS) and Data Protection: A Contextual Theorization of the Dark Side of IS Use

Information Systems (IS) research on health IS use has suffered from a positivity bias – largely focusing on upside gains rather than the potential dark side of usage practices. Exploring the dark side and failures in health IS use, such as shortcomings in data privacy and cybersecurity, can provide useful insights for research, practice, and policy. Through qualitative analyses of three datasets collected between 2015 and 2021, we theorize challenges to the effective use of IS and data protection in Australian health services. We propose a contextualized theory of ‘health records misuse’ with two overarching dimensions: data misfit and improper data processing. We explain sub-categories of data misfit: availability misfit, meaning misfit, and place misfit, as well as sub-categories of improper data processing: improper interaction and improper data recording and use. Our findings demonstrate how health records misuse arises from socio-technical systems, and impacts health service delivery and patient safety

Towards explaining user satisfaction with contact tracing mobile applications in a time of pandemic: a text analytics approach

This research project investigates the critical phenomenon of the post-adoption use of Contact Tracing Mobile Applications (CTMAs) in a time of pandemic. A panel data set of customer reviews was collected from March 2020 to June 2021. Using sentiment analysis, topic modeling and dictionary-based analytics, 10,337 reviews were analyzed. The results show that after controlling for review sentiment and length, user satisfaction is associated with users’ perception of utilitarian benefits of CTMA, their CTMA-specific privacy concerns, and installation and use issues. Our methodological approach (using various text analysis techniques for analyzing public feedback) and findings (influential factors on consumers’ satisfaction with CTMA) can inform the design and deployment of the next generation of CTMAs for managing future pandemics

Exploring the Relationship between Influencers’ Sentiment and Cryptocurrency Fluctuation through Microblogs

Scholars and practitioners increasingly recognize the importance of microblogs in capturing eWord of Mouth (eWoM) and their predictive power for cryptocurrency markets. This research in progress paper examines the extent to which microblog messages are related to bitcoin fluctuation. Building on information systems and finance literature, we examine the interactions between influencers’ extreme sentiment and the bitcoin fluctuation using natural language processing techniques and hypothesis testing. Our preliminary results show when influencers express extreme sentiment, in favour or against bitcoin, it is less likely that their tweets are related to future bitcoin fluctuation. However, when their extreme tweets are in-depth and unique, this negative relationship is moderated. Overall, our findings reveal that influencers’ sentiment is an important predictor in determining bitcoin fluctuation, but not all tweets are of equal impact. This study offers new insights into social media and its role in the cryptocurrency market

Re-examining the Status of IT in IT Research - An Update on Orlikowski and Iacono (2001)

Nearly 10 years ago, Orlikowski and Iacono examined the conceptualization of Information Technology in Information Systems Research (ISR) articles published in 1990s, and found that the majority of these articles were not thoroughly engaged with IT artifact. They proposed that IS researchers should start to theorize about the IT artifact and employ rich conceptualizations of IT. In order to assess the field’s response to Orlikowski and Iacono’s recommendations, and obtain an up-to-date image of the contemporary IS research, we carried out a similar analysis on a recent set of articles, i.e. the full set of papers published in the last three years of ISR, Management Information Systems Quarterly (MISQ), and Journal of the Association for Information Systems (JAIS). Our results reveal no drastic progress in terms of deeper engagement with IT artifact; 30% of the articles in our set are virtually mute about the artifact, and only 10% are employing an ensemble view of IT. Nevertheless, there are informative discrepancies between patterns in our results and those in the original study, and noticeable differences among the three journals. Implications of these findings for future research will be discussed

On Justification: Legislating a Digital First Artifact

The \u27digital first\u27 paradigm and its ontological reversal proposition bring new risks and implications for governing and regulating digital technologies. This article reports the findings from a qualitative study of the justifications used in legislating a \u27digital first\u27 artifact: Australia’s COVIDSafe contact tracing app. We build on justification theory (‘orders of worth’ framework) and use deductive qualitative analysis for examining 74 parliamentary records of proceedings (Hansards) in 2020 and 2021. The findings are structured in 38 empirical themes and 15 conceptual categories, which pertain to five orders of worth used in justifying the actors’ positions. This research unpacks the complexities of the justifications invoked in the legislative debates and sheds light on the novel and important yet understudied practices of governing ‘digital first’ artifacts

Iterative Seed Word Generation for Interactive Topic Modelling: a Mixed Text Processing and Qualitative Content Analysis Approach

Topic models have great potential for helping researchers and practitioners understand the electronic word of mouth (eWoM). This potential is thwarted by their purely unsupervised nature, which often leads to topics that are not entirely explainable. We develop a novel method to iteratively generate seed words to guide the interactive topic models. We assess the validity and applicability of the proposed method by investigating the critical phenomenon of Contact Tracing Mobile Applications (CTMAs) post-adoption during a time of the COVID-19 pandemic. The results show that constructs developed through our interactive topic modeling can capture primary research variables related to the phenomenon. Compared to existing topic modeling methods, our approach shows superior performance in explaining users’ satisfaction with CTMAs