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Sentiment analysis has been rapidly employed for business decision support. New data mining researchers are yet to have an adequate understanding of the various applications of sentiment analysis while utilising social media data. As a result, it is critical to define the data mining and text analytics research trend holistically using existing literature. The study explores sentiment analysis research for its application in transforming social media data and identifies relevant research aspects through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the content and thematic analysis. Findings suggested that key purposes of the sentiment analysis are mainly related to innovation, transparency, and efficiency. Our review also highlights the distinctiveness of sentiment analysis for synthesising social media information to investigate various features, including the knowledge-domain map that detects author collaboration networks in the past.

Blockchain has become a widely used information system technology recently because of its effectiveness as an intermediary-free platform. While the use of blockchain in various fields, such as finance, supply chains, healthcare, education, and energy consumption, is increasingly enabling the development of Internet-enabled “distributed databases,” there are not many exploratory studies available to provide an understanding of how the field is progressing. Therefore, it is imperative to explore the status quo of blockchain technology in the finance sector, particularly highlighting how blockchain architectures can aid the finance sector to gain competitive advantage. This systematic literature review analyzes the content of the 50 most relevant articles and professional industry reports through peer-reviewed relevant academic literature in the finance sector from 2008 to 2022 to identify several possible features of blockchain research in the financial sector. This study highlighted the dimensions of blockchain technology, blockchain in finance, its competitive advantages, the current status of finance, and various challenges that keep the implementation of blockchain-based financial information systems at the initial stage. We identified three main areas that require research attention in order for blockchain technology to become the “next-generation networks” that will revolutionize the financial sector.

Businesses use sentiment analysis in different ways for generating market or consumer insights to improve their performance. Existing sentiment analysis studies related to social media research have been significant due to the fact of their rapidly growing potential, as this type of analysis of social media content represents immense benefits. It is imperative to explore the need for such analysis to delineate the trend holistically for knowledge landscaping purposes. The study addresses the research gap through a comprehensive bibliometric review of 523 research articles published in the Scopus database (between 2018 and 2022) to discern the performance, content, and thematic analysis. We adopt an automated bibliometric study approach using the R-tool- biblioshiny for generating and presenting outcomes. Finding points on the vital usages of sentiment analysis such as innovation, transparency, and improved efficiency. It also highlights the uniqueness of sentiment analysis for synthesizing social media content to examine various aspects such as the knowledge-domain map that detects author collaboration networks as well as journal relationships.

Academic research to the utilization of artificial intelligence (AI) has been proliferated over the past few years. While AI and its subsets are continuously evolving in the fields of marketing, social media and finance, its application in the daily practice of clinical care is insufficiently explored. In this systematic review, we aim to landscape various application areas of clinical care in terms of the utilization of machine learning to improve patient care. Through designing a specific smart literature review approach, we give a new insight into existing literature identified with AI technologies in the clinical domain. Our review approach focuses on strategies, algorithms, applications, results, qualities, and implications using the Latent Dirichlet Allocation topic modeling. A total of 305 unique articles were reviewed, with 115 articles selected using Latent Dirichlet Allocation topic modeling, meeting our inclusion criteria. The primary result of this approach incorporates a proposition for future research direction, abilities, and influence of AI technologies and displays the areas of disease management in clinics. This research concludes with disease administrative ramifications, limitations, and directions for future research.

Reducing student attrition in tertiary education plays a significant role in the core mission and financial well-being of an educational institution. The availability of big data source from Learning Management System (LMS) can be analysed to help with the attrition issues. This study aims to use an integrated Design science research (DSR) methodology to develop and evaluate a Big Data Analytical Solution (BDAS) embedded in Educational Decision Support System as an educational artefact. The BDAS as DSR artefact harnesses the outcome of the application of Artificial Intelligence(AI) based approaches on the dataset collected from student interaction with LMS to train a predictive model to predict potential students at risk. Identifying students at risk helps to take timely intervention in the learning process to improve student academic progress to increase the retention rate. To evaluate the performance of the predictive model, we compare the accuracy of the collection of representational Artificial Intelligence algorithms in the literature. The BDAS aims not to replace any existing practice but to support educators to implement a variety of pedagogical practices to improve students’ academic performance.

Block chain has become a popular distributed ledger technology, which assists to store data in a secured, tamper proofed and immutable manner. In recent years, banks and remittance institutes have shown their interests increasingly to investigate the application design issues related to this disruptive technology, for instance, by evaluating the competitive advantage of using the applications over the tradition banking and remittance industry. Gaining opportunities of the block chain, however, is a challenge for international remittance due to the lack of relevant knowledge of business integration, security, regulatory requirements, technological improvements in mining hardware requirements and social trust.It is imperative to develop application design understanding that may be assistive to gain the actual benefit of the block chain in remittance management. Aiming to offer new knowledge for academics, researchers, business owners, government, FinTech companies and general public users elaborating interplaying business, technical and social requirements, this paper presents a design research approach that guides the development of a novel block chain-oriented remittance model for Australian remittance companies. This paper reports on a preliminary design which can be considered as a first proof-of-concept prototype. We identify key requirements of designing a secured, faster, user-friendly and responsive options for delivering secured transparency of financial transactions for stakeholders, which are to be complying with Know Your Customer (KYC) and Anti-Money Laundering (AML) regulations.

Recent studies report doubling numbers of deaths due to dementia. With such an escalating mortality rate related to cognitive decline diseases, like dementia, timely information on contributing factors and knowledge discovery from evidence-based repositories is warranted. A large amount of scholarly knowledge extracted from research findings on dementia can be understood only using human intelligence for arriving at quality inferences. Due to the unstructured data presented in such a massive dataset of scientific articles available online, gaining insights from the knowledge hidden in the literature is complex and time-consuming. Hence, there is a need for developing a knowledge management model to create, query and maintain a knowledge repository of key elements and their relationships extracted from scholarly articles in a structured manner. In this paper, an innovative knowledge discovery computing model to process key findings from unstructured data from scholarly articles by using the design science research (DSR) methodology is proposed. The solution caters to a novel composition of the cognitive script of crucial knowledge related to dementia and its subsequent transformation from unstructured into a structured format using graph-based next-generation infrastructures. The computing model contains three phases to assist the research community to have a better understanding of the related knowledge in the existing unstructured research articles: (i) article collection and construction of cognitive script, (ii) generation of Cypher statements (a knowledge graph query language) and (iii) creation of graph-based repository and visualization. The performance of the computing model is demonstrated by visualizing the outcome of various search criteria in the form of nodes and their relationships. Our results also demonstrate the effectiveness of visual query and navigation highlighting its usability.

BACKGROUND Clinicians rely on their health organization’s knowledge services for clinical information retrieval however studies identified barriers or pain points that prevent them from accessing research from resource providers. OBJECTIVE Our study objective is to develop a feasible implementation design theory for health organizations to remove barriers to evidence-based clinical information retrieval, and improve Evidence-Based Practice, specifically the research evidence component. METHODS Literature from 2010 to 2020 was reviewed to define problems or pain points in evidence-based clinical information retrieval. Recommendations from literature are used to define purpose and scope. Design Science Research is used to complete three projects in a research stream, with the solutions evaluated iteratively and incrementally. Cloud services such as Web-Scale Discovery, Content Management System, Federated Access, Global Knowledgebase, and Document Delivery are unified as a holistic solution and evaluated using case studies by conducting in depth interviews with health sciences information managers. Frameworks and theories such as design thinking, systems thinking, and user-oriented theory of information need are adopted to construct a design theory. RESULTS Anatomy of a design theory is presented by developing its eight structural components to generalize results from the research stream as mature knowledge. The holistic solution’s implementation feasibility is evaluated as testable propositions to remove barriers to clinical information retrieval CONCLUSIONS Design theory provides practical knowledge for health sciences information managers to source existing components and integrate them as a holistic solution at their health organizations. Design theory also contributes to academic knowledge by addressing pain points and ‘call to action’ identified during literature review.

Business intelligence (BI) has proliferated due to its growing application for business decision support. Research on organizational factors may offer significant use in BI implementation. However, a limited number of studies focus on organizational factors for revealing adverse impacts on effective decision support. The aim of this theoretical study is to conduct a literature analysis to identify organizational factors relevant to BI implementation. Through a systematic literature review, a qualitative content analysis on 49 relevant sample articles for generating themes inductively is adopted to reveal organizational factors. Findings suggest two contexts: information management that integrates factors such as technological capability and personnel capability and organizational context that integrates factors such as organizational capability, managerial decision, and organizational culture for facilitating embedding information management capability for BI implementation in businesses. It is hoped that these contextual understanding can be useful for further BI implementations.

Research in Smart City development has been proliferated over the past few years, which focused heavily on various supporting service sectors, such as healthcare. However, little effort has been made to design health surveillance support systems, which is also important for the advancement of public healthcare monitoring as an essential smart city initiatives. From an information system (IS) design perspective, this paper introduces a social media-based health surveillance supporting method, which can automatically extricates relevant online posts for health symptom management and prediction. We describe and demonstrate an IS design approach in this paper for hay-fever prediction solution concept based on Twitter posts. This concept can be applicable to fully functional solution design by relevant practitioners in this field.

This article reviews methods to the development of a decision support system (DSS) solution for small business owners/managers. The main objective of designing the DSS artefact is to support the strategic decision-making for achieving competitive advantages in the business-to-consumer (B2C) e-commerce environment. Many researchers in the DSS domain utilised various methods to design of information systems (IS) artefact, mostly intended for large businesses. Researchers have paid much attention to the business environment as a knowledge source for DSS design and development for the small business strategic decision support needs. User-centred design (UCD) principles were adopted for DSS development. Prior a novel DSS development, multiple case studies were carried out for understanding the user needs and system requirements. Also, knowledge was sourced from the external business environment via the analysis of small business website features against their overseas competitors. The findings suggested developing a DSS solution for small business needs.

Supplemental material, Appendix_14_June_2019-JHI for Methodologies for designing healthcare analytics solutions: A literature analysis by Shah J Miah, John Gammack and Najmul Hasan in Health Informatics Journal

Todays’ businesses are required to have control over the big volume of data, transaction information and records that are rapidly generated through various sources, such as networked sensors, Internet sites, smart devices, and industrial machines. This ‘big data’ are significant to process, store, manipulate and communicate for its various strategic and operational purposes. The pattern, growth or declining facts/rates of the big data are important for developing business strategies, improving management and operational business decision making. Although through various individual interactions the big data are continuously created or re-created by offline and online activities, the actual solution design by offering power of analytics have not been discussed at a greater extent over the past in academic outlets. In this paper, we introduce a combined requirement of developing cloud-based analytics system to handle, retrieve and manipulate the big data for improving decision making in supply chain management. The main emphasis in the study goes after outlining a conceptual analytics approach for meeting the decision support needs in a hypothetical supply chain industry problem-domain, specially focusing on decision support application for various individual chain managers.