Positioning big data analytics capabilities towards financial service agility

PurposeEnterprises are increasingly taking actionable steps to transform existing business models through digital technologies for service transformation such as big data analytics (BDA). BDA capabilities offer financial institutions to source financial data, analyse data, insight and store such data and information on collaborative platforms for a quick decision-making process. Accordingly, this study identifies how BDA capabilities can be deployed to provide significant improvement for financial services agility.Design/methodology/approachThe study relied on survey data from 485 banking professionals' perspectives with BDA usage, IT capability development and financial service agility. The PLS-SEM technique was used to evaluate the underlying relationship and the applicability of the research framework proposed.FindingsBased on the empirical test from this study, distinctive BDA usage grounded on the concept of IT capability viewpoint proof that financial service agility could be enhanced provided enterprises develop technical capabilities alongside other relevant resources.Practical implicationsThe study further highlights the need for financial service managers to identify BDA technologies such as data mining, query and reporting, data visualisation, predictive modelling, streaming analytics, video analytics and voice analytics to focus on financial knowledge gathering and market observation. Financial managers can also deploy BDA tools to develop a strategic road map for data management, data transferability and knowledge discovery for customised financial products.Originality/valueThis study is a useful contribution to the burgeoning discussion with emerging technologies such as BDA implication to improving enterprises operations.

Social Media Big Data Analytics for Demand Forecasting

Social media big data offers insights that can be used to make predictions of products' future demand and add value to the supply chain performance. The paper presents a framework for improvement of demand forecasting in a supply chain using social media data from Twitter and Facebook. The proposed framework uses sentiment, trend, and word analysis results from social media big data in an extended Bass emotion model along with predictive modelling on historical sales data to predict product demand. The forecasting framework is validated through a case study in a retail supply chain. It is concluded that the proposed framework for forecasting has a positive effect on improving accuracy of demand forecasting in a supply chain.

The Effect of the plant Adiantum philippense Extracts on Biofilms Formation and Adhesion to Shigella flexneri: A Predictive Modelling Approach

In the quest for novel bioactive metabolites, which can also be used as therapeutic agents, Adiantum philippense (A. philippense), an ethnomedically important fern, has become a fascinating herb. In this study, the predictive mathematical modelling of A. philippense crude extract was tested against Shigella flexneri, a common food pathogen for its phytochemical constituents, antagonistic ability, and effect on bacterial adhesion and biofilm formation was calculated. Various kinetics models such as Von Bertalanffy, Baranyi-Roberts, modified Schnute, Modified Richards, Modified Gompertz, Modified Logistics and latest Huang were used to get values for the above kinetic constants or parameters. modified Gompertz of the entire model was found to be the best model with the highest adjusted R2 value and lowest RMSE value. The accuracy and bias factors values were close to unity (1.0). The maximum specific growth rate (mmax (h-1) for S. flexneri treatment with A. philippense extract was significantly much lower (p<0.05) with a value of 0.292 (95% confidence interval of 0.254 to 0.331) compared to control with a value of 0.540 (95% confidence interval of 0.481 to 0.599) indicating potential biofilm inhibition.

Assessment of physician training and prediction of workforce needs in paediatric cardiac intensive care in the United States

Objective: To assess the training and the future workforce needs of paediatric cardiac critical care faculty. Design: REDCap surveys were sent May−August 2019 to medical directors and faculty at the 120 US centres participating in the Society of Thoracic Surgeons Congenital Heart Surgery Database. Faculty and directors were asked about personal training pathway and planned employment changes. Directors were additionally asked for current faculty numbers, expected job openings, presence of training programmes, and numbers of trainees. Predictive modelling of the workforce was performed using respondents’ data. Patient volume was projected from US Census data and compared to projected provider availability. Measurements and main results: Sixty-six per cent (79/120) of directors and 62% (294/477) of contacted faculty responded. Most respondents had training that incorporated critical care medicine with the majority completing training beyond categorical fellowship. Younger respondents and those in dedicated cardiac ICUs were more significantly likely to have advanced training or dual fellowships in cardiology and critical care medicine. An estimated 49–63 faculty enter the workforce annually from various training pathways. Based on modelling, these faculty will likely fill current and projected open positions over the next 5 years. Conclusions: Paediatric cardiac critical care training has evolved, such that the majority of faculty now have dual fellowship or advanced training. The projected number of incoming faculty will likely fill open positions within the next 5 years. Institutions with existing or anticipated training programmes should be cognisant of these data and prepare graduates for an increasingly competitive market.