PREDICTING CONTINUANCE INTENTION AND USE OF MOBILE SHOPPING APPS WITH PLS-SEM AND NECESSARY CONDITION ANALYSIS IN TANDEM

The adoption of multi-channel strategies by retailers, encompassing both online and offline modalities, has fundamentally transformed consumer shopping behaviors, resulting in a significant increase in mobile shopping. Nonetheless, the long-term success of mobile shopping apps is heavily dependent on consumers’ continuous use. Regrettably, the existing studies on this area remain vague and require further exploration. This study aims to close this gap by examining Malaysian consumers’ continuance intention and continuance use of mobile shopping through the lens of ISSM in the context of mobile shopping apps. We utilised a quantitative approach and successfully collected 369 responses from Klang Valley, using purposive sampling techniques. The predictive hypotheses developed were validated using Partial Least Square – Structural Equation Modelling (PLS-SEM) and were further supplemented with Necessary Condition Analysis (NCA) to determine must-have factors based on necessary logic. The PLS-SEM results confirmed the association between continuance intention and continuance use. The findings revealed that service quality and system quality are associated with satisfaction, but not information quality. Additionally, service quality, system quality, and information quality, together with satisfaction, trust and incentive have a positive significant influence on Malaysians’ mobile shopping app continuance intention. However, the NCA results indicated that system quality is the sole must-have factor that contributes to satisfaction. Meanwhile, service quality, system quality, information quality, satisfaction, trust, and incentive are the must-have predictors for mobile shopping app continuance intention. The study sheds light on previously unexplored aspects namely, continuance intention and continuance use, within the framework of the ISSM in the context of mobile shopping. By doing so, it offers more refined and actionable insights to improve consumer experience and foster sustained engagement with mobile shopping apps. Simultaneously, it contributes to the advancement of knowledge regarding the foundational constructs and their interconnectedness, thereby enhancing theoretical development in this domain

A review of the simple view of reading: Decoding and linguistic comprehension skills of low-progress readers

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Unraveling COVID-19: A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS.

PurposeRoutinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD.Patients and methodsWe conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services.ResultsWe aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed.ConclusionWe constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance

Unraveling COVID-19:A Large-Scale Characterization of 4.5 Million COVID-19 Cases Using CHARYBDIS

PURPOSE: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) Characterizing Health Associated Risks and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. PATIENTS AND METHODS: We conducted a descriptive retrospective database study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11th June 2020 and are iteratively updated via GitHub. We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19, and 113,627 hospitalized with COVID-19 requiring intensive services. RESULTS: We aggregated over 22,000 unique characteristics describing patients with COVID-19. All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts and are readily available online. Globally, we observed similarities in the USA and Europe: more women diagnosed than men but more men hospitalized than women, most diagnosed cases between 25 and 60 years of age versus most hospitalized cases between 60 and 80 years of age. South Korea differed with more women than men hospitalized. Common comorbidities included type 2 diabetes, hypertension, chronic kidney disease and heart disease. Common presenting symptoms were dyspnea, cough and fever. Symptom data availability was more common in hospitalized cohorts than diagnosed. CONCLUSION: We constructed a global, multi-centre view to describe trends in COVID-19 progression, management and evolution over time. By characterising baseline variability in patients and geography, our work provides critical context that may otherwise be misconstrued as data quality issues. This is important as we perform studies on adverse events of special interest in COVID-19 vaccine surveillance