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The purpose of this study is shed light on the underlying forces behind entrepreneurship within a regional innovation system (RIS) in a remote rural region. The authors examine the following questions: Which are the main underlying forces behind the entrepreneurial process in a rural RIS characterized by traditionally low-tech, small-scale businesses? How can the development of a low-tech regional innovation system be conceptualized?

The design of the study is based on entrepreneurship theory. Data analysis followed practices used in phenomenography, a research approach used to analyse and identify commonalities and variations in populations' perceptions of a certain phenomenon. Data are composed using semi-structured interviews and a database composed of company information of all firms in the population.

A proactive mobilization of regional stakeholders and resources can be an important driving force behind the entrepreneurial process and generation of a rural RIS. Innovation can be generated within low-tech industries turning the rural context into an asset. An RIS in a remote rural context can be initiated and orchestrated by regional authorities, but knowledge brokering and orchestration can also be managed by networks of small-scale businesses brought together by mutual benefit and common interests.

Regional innovation systems theory is most often used to study high-tech industries. But by combining regional innovation systems with rural entrepreneurship and entrepreneurship context theory is a fruitful avenue to understand the role of rural entrepreneurship in regional development, even in remote and peripheral regions. Innovation does not need to entail high-tech international environments; it can appear as the result of efforts in low-tech industries in rural and remote environments. The authors’ findings need to be scrutinized; therefore, the authors call for more research on regional innovation systems in rural environments.

It is possible for regional authorities to orchestrate a development process through the actions of a strong regional agent but also by supporting the creation of networks of small businesses that are built on trust and common interests.

This study contributes to the literature with a new perspective to the study of entrepreneurship and of regional innovation systems. Entrepreneurship research with focus on rural contexts most often highlight limits to entrepreneurship and see entrepreneurship as “just running a business”. A perspective that starts from innovation and innovative behaviour, despite the rural context and embedded resources, helps to generate new knowledge that can enrich the understanding of entrepreneurship and also be the foundation for more precise business development policies in rural settings.

This chapter discusses the evolution of online trading, its application in various market structures, and its benefits and potential concerns. Computers were first used in electronic communication networks among brokers and dealers to make trades and for informational purposes. Online brokers became popular with retail investors as the internet spread. Online trading comes with various trading protocols and order types. It enables traders to automate trading decisions and process data more easily using charting tools and customized programs connected to the broker's infrastructure. Electronic trading allows for greater centralization but can also be accompanied by market fragmentation. Market regulation has affected market structure and is still evolving. Centralization allows for more competitive prices and reduces search costs. Decentralized markets could cope better with asymmetric information.

This chapter introduces what a digital ID is, why it is important, how it works, the design choices, as well as how central banks can collaborate with other stakeholders in promoting digital ID infrastructures for use in digital financial services.

The history of online trading began in the 1960s with the emergence of electronic communication networks, which allowed the electronic execution of trades outside traditional exchanges. The internet revolution led to the development of online brokerage platforms such as E*Trade and Schwab, enabling non-institutional investors to participate in the digital trading revolution. These platforms have evolved to serve the retail investor market, eventually adapting to mobile-first and commission-free models, significantly lowering the barriers to entry for financial markets. Platforms like Robinhood and other fintech firms have rapidly gained market share by offering services and products previously unavailable, such as commission-free trades, mobile trading, and novel products such as fractional shares and cryptocurrency investing. This chapter provides an overview of the history of online trading. It also introduces several new developments in fintech and the online trading industry and discusses various controversies and future implications of new technologies.

Financial technologies form the heart of considerable disruptive innovation. Fintech is the emerging financial infrastructure for modern business. Big data are the feedstock for artificial intelligence (AI) that drives many fintech sectors – start-up finance, commodities and investment instrumentation, payment systems, currencies, exchange markets/trading platforms, market-failure response forensics, underwriting, syndication, risk assessment, advisory services, banking, financial intermediaries, transaction settlement, corporate disclosure, and decentralized finance. This chapter demonstrates how analyzing big data, largely processed through cloud computing, drives fintech innovations, scholarship, forensics, and public policy. Despite their apparent virtues, some fintech mechanisms can externalize various social costs: flawed designs, opacity/obscurity, social media (SM) influences, cyber(in)security, and other malfunctions. Fintech suffers regulatory lag, the delay following the introduction of novel fintechs and later assessment, development, and deployment of reliable regulatory mechanisms. Big data can improve fintech practices by balancing three key influences: (1) fintech incentives, (2) market failure forensics, and (3) developing balanced public policy resolutions to fintech challenges.

This study aims to explore the potential application of blockchain technology in Lean Six Sigma (LSS) project through a proposed blockchain-LSS (BLSS) model. The proposed model can tackle real-time problems in information sharing, transparency and traceability in every stage of the LSS project.

The scoping review approach is used to develop the integrated model of the BLSS approach for operational excellence. The proposed model is validated through expert’s input, which is collected by a questionnaire survey method.

The prime function of the proposed BLSS model is the information sharing among the project team and real-time monitoring, transparency, traceability and immutability in the Define-Measure-Analyze-Improve-Control phase. The proposed model also consists the information about the role of blockchain features at each phase of the LSS project. The project team and industry employees can trace the success of the project at every moment, resulting in trust buildup and the elimination of fake data. Moreover, there would be no disputes among various sections/shops of the plant and employees to share the real information.

This paper provides guidelines to practitioners and managers for integrating the LSS approach and blockchain. The blockchain helps managers and practitioners in better data traceability and transparency, monitoring of data as well as more sustainable LSS project management.

To the best of the authors’ knowledge, this is the first research attempt that developed an integrated model of blockchain and LSS approach to maintaining the immutable records of assets in projects and targeted Industry 4.0.

Due to the rapid growth of digital economy, improving employees’ creativity is becoming essential to optimizing the development of organizations. This study investigates how enterprise social media can enhance employee creativity and develops an integrated model based on communication visibility and social capital theories.

A two-stage questionnaire was conducted on full-time employees with enterprise social media experience. The first round of this study distributed 1,048 questionnaires and collected 639 valid sample data. A month later, the second survey was sent to the first valid respondents, with 421 valid sample data collected within a week.

Results show that visibility has a positive influence on employee creativity, in which expertise recognition and network recognition play a mediating role. The findings also indicate that bridging social capital positively moderates the effect of visibility on expertise recognition, and bonding social capital positively moderates the effect of visibility on network recognition.

This study contributes to a better understanding of the benefits of enterprise social media by uncovering the mechanism and theoretical boundary of the effect of visibility on employee creativity.

In recent years, investing with robo-advisors has gained momentum and is seen as a simplifying approach for individual investors to participate in financial markets. This chapter contributes to a better understanding of the concept of a robo-advisory and its implications for private investors by discussing its past, present, and future. It explores key issues, like cost-efficiency, historical performance, and automation levels, based on research and industry insights. Moreover, this chapter examines a robo-advisor's benefits, limitations, and challenges, like behavioral biases, regulation, and risk profiling. Finally, the importance of the ongoing megatrends of AI and green investing is examined concerning a robo-advisory.