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Despite the considerable hype about how Big Data Analytics (BDA) can transform businesses and advance their capabilities, recognising its strategic value through successful adoption is yet to be appreciated. The purpose of this paper is to focus on the process-level value-chain realisation of BDA adoption between SMEs and large organisations.

Resource-based theory offered the lens for developing a conceptual BDA process-level value chain adoption model. A combined two-staged regression-artificial neural network approach has been utilised for 369 small, medium (SMEs) and large organisations to verify their critical value chain process-level drivers for successful organisational adoption of BDA.

The findings revealed that organisational BDA adoption success is driven predominantly by product—and service-process-level value, with distinctive discrepancies dependent on the organisation’s size. Large organisations primarily embrace BDA for their external value chain dimensions, while SMEs encompass its internal value chain cues. As such, businesses will be advised to acknowledge their organisational dynamics and precise size to develop the right strategies to adopt BDA successfully.

The study advances the understanding of the role of internal and external value chain drivers in influencing how BDA can be successfully adopted in SMEs and large organisations. Thus, appreciating the organisation’s unique attributes, including its size, will need to be carefully examined. By investigating these elements, this research has shed new light on how developing such innovative capabilities and competencies must be carefully crafted to help create a sustainable competitive advantage.

For an organisational positioning, acknowledging the role of internal and external value chain drivers is critical for implementing the right strategies for adopting BDA. For larger businesses, resources for innovation often can be widely available compared to SMEs. As such, they can manage their costs and associated risks resourcefully. By considering the identified value-chain-related adoption success factors, businesses should be better positioned to assess their competencies while being prepared to adopt BDA.

The study offers the research and business community empirical-based insights into the strategies needed to successfully adopt big data in an organisation from a process-level value chain perspective.

The purpose of this paper is to help in providing a better understanding of the application of blockchain technology in the context of the banking and finance sectors. The aim is to outline blockchain's benefits, opportunities, costs, risks as well as challenges of the technology in the context of banking and finance services.

Careful examination of the extant literature, including utilising relevant academic-based research databases has been carried out. It covered reviewing various research contributions published in peer-reviewed journals, academic reports, as well as technical reports to help in identifying related benefits, opportunities, costs and risks.

The findings reveal that there are limited contributions in utilising blockchain in the banking and finance sectors when compared with other sectors. As such, the study highlighted the relevant perspective of benefits, opportunities, costs and risks within such sectors.

This study helps in offering a focal point to banking and financial sector managers and decision-makers for realising the value and offerings of blockchain technology as well as associated strategies and programmes.

This study highlights the need for a holistic understanding of the various aspects of cost, benefits, risk and opportunities to create blockchain applications that work for banking and finance sectors

The purpose of this paper is to analyse the research design, methodologies and approaches utilised in electronic government (e‐government) research studies published in the last decade (2000‐2012).

A profiling approach is employed to analyse 114 (out of 3,934 from 2000 to 2012) e‐government publications (with specific focus on user satisfaction) including examining variables such as country of research, sample size, type(s) of respondents, data collection methods, and statistical tools and techniques employed.

Among the research design, methodologies and approaches adopted in the extant e‐government research studies, the quantitative research based approach supported by statistical analysis was the most dominant approach applied by authors in the last decade. Few studies were found to use qualitative‐based approaches such as case studies and interviews.

The prime value and uniqueness of this research lies in presenting the type of research design, methodologies and approaches used in studies that focus on issues surrounding user satisfaction in e‐government research. This has been achieved by synthesising existing publications in Electronic Government: An International Journal (EG), Electronic Journal of Electronic Government (EJEG), International Journal of Electronic Government Research (EJEGR), Transforming Government: People, Process and Policy (TG:PPP), Government Information Quarterly (GIQ), Public Administration Review (PAR), Public Administration: An International Quarterly (PA), Journal of Public Administration Research and Theory (JPART), and Local Government Studies (LGS).