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Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.

The entrepreneurship ecosystem is one of the most important elements determining the conditions for the development of modern enterprises. The structure and tools of the system are the subjects of research and analysis by practitioners and researchers dealing with the issues of entrepreneurship. The main objective of these studies is to search for the optimal use of the potential and opportunities of the enterprise sector in relation to current socio-economic problems.

The purpose of the chapter is to present and discuss the possibilities of the impact of the entrepreneurship ecosystem on the development of enterprises in Poland.

The analysis includes directions, support instruments and an assessment of the effectiveness of activities related to supporting entrepreneurship in Poland. To prepare the chapter, a review and analysis of literature, analysis of programs and documents as well as an analysis of statistical data describing the development of entrepreneurship in Poland were used.

Entrepreneurial ecosystems are an essential factor determining the development of entrepreneurship in Poland. There are visible problems related to the implementation of an effective entrepreneurial ecosystem. The experience and adaptation processes of institutions and organisations related to its functioning make the system and its elements work correctly.

Energy consumption in transportation accounted for over 29% of total final consumption (TFC) of energy and 65% of global oil usage, and it is highly connected to mobility. Mobility is essential for access to day-to-day activities such as education, leisure, healthcare, business activities, and commercial and industrial operations. This study examines the energy consumption for the transport industry, and the level of renewable energy development in some selected Sub-Saharan African (SSA) nations. This study relied on previous publications of government, reports and articles related to the subject matter. Vehicle ownership is fast increasing, particularly in cities. Still, it begins at a relatively low level because the area is home to countries with the lowest ownership rates worldwide. In its current state, the energy sector faces significant challenges such as inadequate and poorly maintained infrastructure, dealing with increasing traffic congestion in cities, large-scale imports of used vehicles with poor emission standards that affect air quality in cities, a lack of safe and formally operated public transportation systems, and inadequate consideration for women and disabled mobility needs. Motorcycle and tricycle are dominating the rural areas, accounting for a substantial amount of this growth. Aviation is the largest non-road user of energy, and this trend is predicted to continue through 2040 as Gross Domestic Product (GDP) grows and urbanisation expands. This study revealed the energy consumption for the transport industry, and the level of renewable energy development in some selected SSA. Rail and navigation lag behind current global levels. The usage of biofuel and rail transport was recommended.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.