Purpose While the dynamic capabilities perspective is the most cited strategic theory in the information systems field of research, little effort has been made to review and integrate the associate literature of this perspective in the field. Accordingly, this paper aims to systematically analyze the information systems literature on dynamic capabilities and provide a holistic understanding of the topical composition and trend of dynamic capabilities studies in information systems research. Design/methodology/approach Using latent Dirichlet allocation as the text analysis algorithm, the author conducted a topic modeling of the dynamic capabilities corpus in the information systems field of research to quantitatively review, summarize and classify the prior literature. The review covered 191 articles published on dynamic capabilities between 1998 and 2018 in pioneering information systems journals and conference proceedings. Findings In accordance with the topic modeling results, the topical composition of the dynamic capabilities corpus in information systems research dominantly includes seven themes titled T1. Information systems value, T2. Information systems change, T3. Digitalization, T4. Information systems agility, T5. Big data, T6. Information systems innovation and T7. Information systems alignment. Also, the overall and topical trend of dynamic capabilities studies in the information systems field of research were revealed. The trends indicated that the investigated domain and its prominent sub-domains have generally had positive productivity over the past years. Originality/value The current study contributes to the domain by developing knowledge and improving literature on dynamic capabilities in information systems research, discovering the main topics of interest for information systems researchers to deploying the dynamic capabilities perspective in their studies, and prioritizing the future information systems research on dynamic capabilities based on the identified trends of topics.

Purpose Although the business model field of study has been a focus of attention for both researchers and practitioners within the past two decades, it still suffers from concern about its identity. Accordingly, this paper aims to clarify the intellectual structure of business model through identifying the research clusters and their sub-clusters, the prominent relations and the dominant research trends. Design/methodology/approach This paper uses some common text mining methods including co-word analysis, burst analysis, timeline analysis and topic modeling to analyze and mine the title, abstract and keywords of 14,081 research documents related to the domain of business model. Findings The results revealed that the business model field of study consists of three main research areas including electronic business model, business model innovation and sustainable business model, each of which has some sub-areas and has been more evident in some particular industries. Additionally, from the time perspective, research issues in the domain of sustainable development are considered as the hot and emerging topics in this field. In addition, the results confirmed that information technology has been one of the most important drivers, influencing the appearance of different study topics in the various periods. Originality/value The contribution of this study is to quantitatively uncover the dominant knowledge structure and prominent research trends in the business model field of study, considering a broad range of scholarly publications and using some promising and reliable text mining techniques.

No abstract

Improving the Intensive Care Unit (ICU) management network and building cost-effective and well-managed healthcare systems are high priorities for healthcare units. Creating accurate and explainable mortality prediction models helps identify the most critical risk factors in the patients’ survival/death status and early detect the most in-need patients. This study proposes a highly accurate and efficient machine learning model for predicting ICU mortality status upon discharge using the information available during the first 24 hours of admission. The most important features in mortality prediction are identified, and the effects of changing each feature on the prediction are studied. We used supervised machine learning models and illness severity scoring systems to benchmark the mortality prediction. We also implemented a combination of SHAP, LIME, partial dependence, and individual conditional expectation plots to explain the predictions made by the best-performing model (CatBoost). We proposed E-CatBoost, an optimized and efficient patient mortality prediction model, which can accurately predict the patients’ discharge status using only ten input features. We used eICU-CRD v2.0 to train and validate the models; the dataset contains information on over 200,000 ICU admissions. The patients were divided into twelve disease groups, and models were fitted and tuned for each group. The models’ predictive performance was evaluated using the area under a receiver operating curve (AUROC). The AUROC scores were 0.86 [std:0.02] to 0.92 [std:0.02] for CatBoost and 0.83 [std:0.02] to 0.91 [std:0.03] for E-CatBoost models across the defined disease groups; if measured over the entire patient population, their AUROC scores were 7 to 18 and 2 to 12 percent higher than the baseline models, respectively. Based on SHAP explanations, we found age, heart rate, respiratory rate, blood urine nitrogen, and creatinine level as the most critical cross-disease features in mortality predictions.

This study aimed at providing an overview of research themes and collaborations in the digital transformation scholarship. The methods of co-word analysis, co-author analysis, and network analysis were employed to network-analyze the keywords, countries, and institutions of 2820 research articles published on the digital transformation topic and indexed by the Web of Science database. Our main results indicated that researchers have mostly focused on three aspects of the digital transformation phenomenon including Technological and Industrial View, Organizational and Managerial View, and Global and Social View. Also, it was realized that Technology, Sustainability, Big Data, Information and Communications Technology, Innovation, Industry 4.0, Artificial Intelligence, Business Model, Social Media, and Digitization are the most recurring themes in this field of research. Besides, Small and Medium-Sized Enterprises, Blockchain, Machine Learning, Knowledge Management, and Sustainable Development were respectively identified as the five hottest issues in the digital transformation scholarship. The contribution of our study highlights that European countries and specially the institutions of northern Europe have had better performance in the research collaborations in digital transformation.