Business Models and Innovative Technologies for SMEs

The central proposition of this chapter is that a multi-factorial strategy model can be evolved to support mobile application development for SMEs in response to disruptive innovation through business model innovation. Artificial Intelligence is often regarded as a significantly disruptive technology impacting many industries today. AI is penetrating many sectors and transforming the tasks performed by computers. This chapter rests on two principles: the discovery of business model innovation and the type of disruption of SMEs in the mobile application sector. The fieldwork for this chapter consists of four phases. The findings and interpretations presented in this chapter imply that the rise of disruptive innovation has sparked technological advancement, which will affect SMEs in South Africa. SMEs should transition to a progressive interdependent modality where they participate using their shared strengths, according to the concept of business model innovation. They could mitigate their weaknesses by working with other mobile app development SMEs. The value of the principles' synergistic relationship has finally been revealed in the chapter. The Disruptive Innovation State Response Model and the Disruptive Innovation Praxis Model were created by this research as responses to its main thesis. A development agency, businessperson, technologist, venture capitalist, etc. could use these models to determine the state that the business recognizes itself in and, employing its use, generate a response that is suitable according to the chapter's final proposition.

Small and medium enterprises (SMEs) are crucial to national and regional development and are significant drivers of job creation and income generation. To remain competitive, SMEs are increasingly adopting Digital Transformation (DT) and Business Model Innovation (BMI) to take advantage of modern digital technologies. However, these transformations can also pose serious cybersecurity risks if organisations do not prioritise cybersecurity threats associated with these modern technologies. Therefore, this conceptual desktop study examines the cybersecurity risks of information and communication technologies (ICT) utilised in DT and BMI processes and recommends fostering an appropriate cybersecurity culture to protect SMEs during and after these transformations. 

This chapter assesses business model innovation readiness for South African SMEs. The assessment is based on the perspective of two challenges that SMEs went through pre-, during, and post-novel coronavirus disease of 2019 (COVID-19). The challenges are about the readiness to innovate the SMEs’ business models and grip disruptive technologies such as Intelligent Analytics (IA). To investigate the challenges identified, the chapter expands on IA. In considering IA, four major areas of IA are synthesized. Importantly, IA is about smarter ways of doing business across different sectors. Society 5.0 and the fourth industrial revolution plus (4IR+) complement IA, as the chapter proposes. By and large, the novel COVID-19 accelerated the adoption of disruptive technologies. The chapter concludes by considering the role of the SMEs’ owner-managers in embracing the technology. 

The strategic goal of most organisations is to improve business operations for successful organisational performance. Digital transformation is a time of swift technological changes posing an ongoing challenge for Small and Medium Enterprises (SMEs) to establish high-performance work systems for organisational performance. Hence SMEs are exploring new technologies to stay competitive and develop innovative business models in the digital transformation era. Artificial Intelligence (AI) technologies may enable SMEs to enhance practices and improve their business model innovation strategies. This chapter examined the existing literature on the digital transformation of SMEs by implementing Artificial Intelligence. The goal was to emphasise how AI can significantly change business processes, practices, and overall organisational performance, ultimately supporting the development of digital business models in SMEs. The study's findings indicated that AI holds great potential for SMEs to enhance their business processes, practices, and overall performance, enabling them to better navigate their increasingly competitive environment. Therefore, it is recommended that managers of SMEs explore opportunities to incorporate AI and other advanced technologies into their business innovation processes. By doing so, SMEs can generate tangible value by strengthening their dynamic capabilities, improving efficiency, and mitigating operational risks.

Small and Medium Enterprises in the insurance industry of South Africa are increasingly seeking new ways to improve competitiveness. Recently, technologies like expert systems are evolving to provide new knowledge to enable organizations to deliver insurance services more effectively and efficiently. However, little information is presented in the studies of business model innovation about the affordances of expert systems in improving the competitiveness of the Small and Medium Enterprises in the insurance industry. Based on the literature review of the Resource-based view model, this chapter develops a model of the affordances of expert systems to guide Small and Medium Enterprises in the insurance industry in their effort to improve competitiveness. An explanatory mixed-method research approach was followed to collect relevant data using a questionnaire and semi-structured interviews to understand the affordances of expert systems in Small and Medium Enterprises in the insurance industry. The results of the study indicate that competitiveness can be improved if the Small and Medium Enterprises manage the expert system in five areas: (i) as a valuable resource; (ii) as a rare resource; (iii) as an inimitable resource; (iv) as a unique organizational resource; and (v) as a low-cost resource. Since the concept of business model innovation is centered on resources that generate value, this study has implications for theory and practice in the field of business model innovation, particularly in the Small and Medium Enterprises operating in the insurance industry.

Although there are several benefits to the adoption of emerging technologies by Small, Medium, and Micro Enterprises (SMMEs), not many studies have been performed on inhibiting Data as a Service (DaaS) adoption factors in SMMEs. This study, therefore, explores Data as a Service (DaaS) adoption factors in Small, Medium, and Micro Enterprises (SMMEs). The study adopted a systematic literature review and TOE framework as a lens to explore the possible factors. The study results suggest that within the technological context, the complexity, network bandwidth, and availability were considered the most important factors that affected the adoption of DaaS in SMMEs. Furthermore, within the organizational context, cost, support, and infrastructure demand were considered the highest factors affecting DaaS adoption in SMMEs. Lastly, within the environmental context, the results indicate that customer demand was considered an environmental factor. In conclusion, the study results suggest that the adoption of DaaS in SMMEs is affected by several TOE factors. Despite limitations associated with convenient sampling and non-empirical data, the study contributes to the body of knowledge on DaaS adoption factors by SMMEs. Further studies may address the mentioned shortcomings by using empirical data.

Consumers have now been able to purchase whatever food they wish in the 21st century. Not all the food that is purchased is consumed, resulting in food waste. About a third of the food produced is wasted in the world. Humans are the main culprits of food waste. The problem is worsening by each day, with different attempts failing to counter the problem. The study investigates factors affecting the adoption of emerging technologies to reduce food waste by SMEs in the food industry. The TOE framework was used for this purpose. The study adopted a systematic literature review to explore the factors affecting the adoption of emerging technologies to reduce food waste by SMEs in the food industry. The results from the study suggest that complexity, security, usability, cost, and flexibility are important technological factors that affect the adoption of emerging technologies. Furthermore, the results from the study suggest that organisation size and organisation resistance are important organisational factors affecting the adoption of emerging technologies to reduce food waste by SMEs in the food industry. Lastly, the results from the study suggest that IT policy and legislation are important environmental factors affecting this technology adoption. Further studies may consider adopting other research methods to explore factors affecting adopting emerging technologies in reducing food waste by SMEs in the food industry.