Purpose: Robotic Process Automation (RPA) is a digital transformation tool that demonstrated tremendous growth in research output as well as its application in the past decade. This study attempts to identify essential research gaps and proposes future research agendas in the field by analyzing publishing trends, major stakeholders (authors, countries, affiliations, journals), key clusters and evolving themes by mapping the most recent research (2016–2022) in the field.Design/methodology/approach: Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) was used to retrieve a total of 244 publications from the Web of Science (WOS) database for this analysis. The study then uses the open-source R program bibliometrix to conduct bibliometric analysis. A variety of tools and methods including collaboration network, word dynamics, co-occurrence network, thematic map and strategy map were utilized.Findings: The analysis reveals the most influential stakeholders (country: the USA, author: Arai K, affiliation: Christ Deemed University), main clusters of intellectual structure (process mining, digital transformation, blockchain, information systems) and the evolution of themes (model innovation, artificial intelligence, big-data, design science and user acceptance) in the subject.Originality/value: This study uses bibliometric analysis to provide a comprehensive overview of RPA literature which unravels the conceptual structure of the stream and proposes a research agenda for the future. Based on the growth of themes and the strategy map, this study may assist entrepreneurs and practitioners in determining field priorities for strategizing process innovation.

With the continuous development of business intelligence technology, the application research of decision support systems (DSSs) is deepening. In China, the work in this area started relatively late, and there are few DSS research cases to assist in marketing decision-making. Currently, marketing decision support systems have shortcomings in data integration, historical data, query functions, and data analysis. This article analyzes the characteristics of marketing decision-making, discusses the application of data warehouse, OLAP, and data mining technology in marketing decision support systems, and designs a marketing decision support system based on data mining technology. The system uses a BP neural network to conduct data mining marketing forecasting. A three-layer network model for marketing prediction is established, with sales time, product price, and customer purchasing power as network inputs and output as the sales volume of a certain type of product in different locations. The test results show that the average absolute percentage error of this method is 15.13%, and the prediction accuracy is high. Research shows that with the continuous development of data mining technology, the system cannot only help users conduct scientific and reasonable marketing decision-making analyses, making the marketing decision-making process more scientific and reasonable, but also can bring new ideas to enterprise decision-makers, promoting the continuous improvement and progress of the system.

Recently, the phenomenon of population ageing and its impact on the insurance industry has garnered increasing global attention. However, a notable gap in scholarly research persists in understanding the nuanced effects of ageing on consumer behaviour and insurance purchase intentions. This study maps the current academic evidence on how ageing influences individual consumers' insurance decisions. Using a scoping review methodology aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for scoping reviews and Joanna Briggs Institute guidelines, 44 articles out of 1082 from four databases—Web of Science, Scopus, ScienceDirect, and Emerald Insight—are reviewed. The results reveal a rising interest in this research area, with China emerging as a significant contributor. The focus is predominantly on Theory of Planned Behavior, quantitative methods, questionnaire survey, regression analysis, older population, and general health insurance. Variables capturing the impact of ageing, beyond demographic information, include family-related, risk-related, and expectation-related factors. This study highlights the current state of research on ageing's effect on insurance purchase intentions and offers valuable insights and directions for future research.