Preface

Small and medium-sized enterprises (SMEs) are the backbone of many developing countries, contributing to economic growth and job creation. However, SMEs in these countries face a myriad of challenges that hinder their success, including limited resources, poor infrastructure, and lack of access to financing and technology. In recent years, the convergence of data analytics and artificial intelligence has presented a new set of opportunities for SMEs to navigate these challenges and unlock their potential.

Studies have shown that technologies play a major role in alleviating most of the challenges SMEs face in most developing countries. Studies also indicated that SMEs have shown remarkable resilience and adaptability in navigating the rapidly changing the business landscape. In recent years, the proliferation of disruptive technologies such as, data analytics and artificial intelligence has presented both opportunities and challenges for SMEs in developing countries. While these technologies offer new ways of doing business, SMEs often struggle to navigate the complex landscape of business models and technology adoption.

This book, titled “Business Models and Innovative Technologies for SMEs” aims to provide a comprehensive overview of the challenges and opportunities presented by these disruptive technologies. The book draws on the latest research and real-world case studies to offer practical insights into how SMEs can successfully navigate the changing business landscape. Drawing from real-life case studies and expert insights, the book provides a detailed analysis of the key concepts and theories related to SME business models, data analytics, and artificial intelligence, as well as their practical applications in the context of SMEs in developing countries for promoting the adoption of data-driven strategies.

The book incorporates various SME Business Models, Data Analytics, and Artificial Intelligence in the context of developing countries. It draws its use cases and insights from diverse environment academics, researchers, policymakers, and business practitioners having a core interest in SME development and the intersection of business models, data analytics, and artificial intelligence, to name a few. To be more specific, the book aims to address issues that include the following:

• What are the potential benefits of innovative technologies for SMEs?
• What is the proposed strategy model that can be used to support mobile application development for Small and Medium Enterprises (SMEs) in response to disruptive innovation?
• What are the cybersecurity risks associated with the use of modern digital technologies during the processes of digital transformation and business model innovation in Small and Medium Enterprises (SMEs), and how can appropriate cybersecurity culture be developed to protect SMEs during and after these processes?
• How can innovating SMEs' business models unleash their full innovative potential beyond simply riding on the wave of digital technology?
• How does this study contribute to the field of business model innovation, particularly in the industries that are situated in the developing countries?

The book is written based on the joint efforts of the editor, authors, and reviewers. The book mainly features qualitative, quantitative, and analytic insight into diverse case studies on SME business models, data analytics and artificial intelligence within the framework of the developing countries. The book is divided into seven chapters. This edited volume features the work of experts who bring both academic knowledge and real-world expertise to the table. Each author has made a unique contribution to this collection of writings based on their own studies and professional experiences.

The book is divided into seven chapters, each of which discusses the author's research methodologies and the context in which they were developed. The following provides a brief overview of each chapter.

Chapter 1: Business Model Innovation Mobile Application Development SMEs in Response to Disruptive Innovation

In this chapter, Dr. Francke explained Business model innovation for mobile application development for SMEs in response to disruptive innovation. This chapter proposes a multi- factorial strategy model to support mobile application development SMEs in response to disruptive innovation through business model innovation. The chapter explores the impact of artificial intelligence on various industries and suggests that SMEs in the mobile application sector should collaborate with each other to moderate their independent weaknesses. The research has developed the Disruptive Innovation State Response Model and the Disruptive Innovation Praxis Model, which can be used by development agencies, businesspersons, technologists, venture capitalists, etc. to determine the state of the business and make an appropriate response. The chapter highlights the value of the synergistic relationship of its principles.

Chapter 2: Cybersecurity Culture as a Critical Component of Digital Transformation and Business Model Innovation in SMEs

In this chapter, Prof. Zoran Mitrovic, Prof. Colin Thakur and Dr. Sudhika Palhad explain how Cybersecurity culture as a critical component of digital transformation and business model innovation in SMEs should be created in order to protect the inherent values of digital transformation. This chapter discusses how SMEs create jobs and generate tax revenue which is crucial to the economic growth of many developing countries. To keep up with the times, more and more small and medium-sized enterprises (SMEs) are adopting digital transformation (DT) and business model innovation (BMI) strategies. But if businesses do not take cybersecurity risks associated with new technology seriously, they might end up paying a high price for these inventive breakthroughs that could be disastrous. As a result, the cyber threats posed by the ICT used in DT and BMI procedures are investigated in this conceptual desktop research. It is proposed that SMEs be protected throughout and after the DT and BMI procedures by fostering a culture of adequate cybersecurity.

Chapter 3: Assessing SMEs’ Business Model Innovation Readiness

In this chapter, Dr. Cecil Kgotiane carried out assessment of SME’s business model innovation readiness before, during, and after the COVID-19 pandemic. This chapter titled “Assessing SMEs’ business model innovation readiness”, discusses two major difficulties faced by small and medium-sized enterprises (SMEs) before, during, and after the COVID-19 pandemic: their lack of preparedness to implement new business models and slow adoption of game-changing technologies like Intelligent Analytics (IA). The study focuses on a large number of SMEs in northern Gauteng, South Africa, using literature reviews, questionnaires, and on-site observations. While the study primarily focuses on Asian SMEs, the chapter concludes that the highlighted difficulties are not universal and apply to South African SMEs.

SMEs play a crucial role in society, and if they do not address these difficulties, innovation in service delivery will suffer.

The SME sector offers many possibilities for creative problem-solving, and reinventing business models is a good place to start. Simply riding the wave of digital technology may not be enough for SMEs to fully realize their potential, and they may benefit from innovating business models and adopting technologies like IA. SMEs also contribute to economic growth and need to constantly improve their strategies. Large corporations may also benefit from SMEs' novel business approaches. Improved service and product delivery have pushed society towards digitalization and disruptive technology, with IA being one such technology.

Chapter 4: Digital Transformation in SMEs: Developing Digital Business Model Innovations Based on Artificial Intelligence

In this chapter, Dr. Tlou Maggie Masenya examined the digital transformation in SMEs and developed digital business model innovations that are based on Artificial Intelligence. This chapter discusses how small and medium-sized enterprises (SMEs) are adopting modern technologies, including artificial intelligence (AI), to develop innovative business models and remain competitive in the era of digital transformation. The study reviewed literature on the impact of AI on business innovation and performance in SMEs and found that AI has the potential to revolutionize business processes, practices, and organizational performance. The article recommends that SME managers should find ways to support business innovation processes with AI and other advanced technologies to boost their dynamic capabilities, efficiency, and reduce operational risk. The major goal of every organizational strategy is to enhance the effectiveness and efficiency of operation, which could lead to organizational success. However, digital transformation presents challenges for SMEs, who need to maintain a high-performance work environment to remain competitive.

Chapter 5: Understanding the Affordances of Expert Systems in Improving the Competitiveness of South African Insurance SMEs

In this chapter, Dr. Stevens P. Mamorobela presented the role of expert systems in improving the competitiveness of South African insurance SMEs. Small and medium-sized businesses (SMEs) in South Africa's insurance market are seeking ways to improve their competitiveness. Expert systems, a newly developed technology, are expanding knowledge bases to help businesses offer insurance services more efficiently and with higher quality. However, the potential benefits of expert systems for SMEs in the insurance sector are not well understood in the literature on business model innovation. This chapter reviews the resource-based view model and proposes a model of the affordances of expert systems to help SMEs become more competitive. An explanatory mixed-method research strategy, including questionnaires and semi-structured interviews, was used to investigate the affordances of expert systems in SME insurance firms. The study found that treating the expert system as a valuable, rare, unique, low-cost, and low-risk resource can help SMEs enhance their competitiveness. This research has practical and theoretical implications for the field of business model innovation, particularly for SMEs in the insurance sector.

Chapter 6: Factors Affecting the Adoption of Data as a Service (DaaS) in Small, Medium, and Micro Enterprises (SMMEs)

In this chapter, Ms. Megan Morta and Prof. Osden Jokonya describe a research study that examined factors affecting the adoption of Data as a Service (DaaS) in Small, Medium, and Micro Enterprises (SMMEs). There have not been much research on the variables impacting the adoption of Data as a Service (DaaS) in SMMEs, despite the numerous advantages of embracing cognitive analytics, business model innovation, and data science by SMMEs. Therefore, the purpose of this chapter is to investigate what influences SMMEs to embrace Data as a Service (DaaS). The research used a comprehensive literature review to investigate what influences Small, Medium, and Micro Enterprises (SMMEs) to use DaaS. This research used the Theory of Constraints (TOC) Framework as a lens to investigate barriers to DaaS adoption in SMMEs. According to the findings, SMMEs cite technical concerns including complexity, network capacity, and availability as the most significant barriers to using DaaS. Cost, support, and infrastructure demand were also cited as the most important environmental factors influencing DaaS adoption among SMMEs. Finally, the findings show that consumer demand was deemed the most important environmental element influencing DaaS adoption in SMMEs. Finally, the study's findings indicate that technical, organizational, and environmental variables all have a role in whether or not SMMEs embrace DaaS. The research adds to the existing body of knowledge on the variables that impact the adoption of DaaS in SMMEs, notwithstanding the constraints associated with easy sampling and non- empirical data. Empirical data may be used to address the issues in future investigations.

Chapter 7: Factors Affecting the Adoption of Emerging Technologies to Reduce Food Waste by SMEs in the Food Industry

In this chapter, Ms. Talent Muzondo and Prof. Osden Jokonya confirmed that food waste is a major issue in modern society, with one-third of the world's food supply being lost or squandered every year. This study focuses on small and medium-sized enterprises (SMEs) in the food sector and what influences their adoption of new technology to reduce food waste. The research uses the TOE framework to analyze the factors that affect SMEs' likelihood to use new technology to reduce food waste. The study finds that technical criteria such as complexity, security, usability, cost, and flexibility influence SMEs' decision to adopt new technologies to decrease food waste. The size of an organization and resistance to change are significant organizational factors that influence technology adoption in the food sector. Additionally, IT policy and law are significant environmental factors influencing technology adoption. The study provides insight into the barriers that prevent SMEs in the food sector from adopting new technology to reduce food waste and highlights the need for further research.