Disruptive Technologies in Small and Medium Scale Enterprises (SMEs)

2. 2. 2024


Disruptive technologies fundamentally disrupt how organizations and customers engage with products and services (Palmié et al., 2020). Disruptive technologies fall into two major categories: those that enable new business models and those that offer new user experiences (Morkunas et al., 2019). This article focuses on both categories, examining the possible ramifications for businesses and consumers.

In terms of enabling new business models, disruptive technologies have been found to have the potential to enable firms to better serve customers by providing them with more efficient and cost-effective services (Ranta et al., 2021). For example, the emergence of cloud computing has enabled firms to reduce hardware and software costs and provide customers with more flexible and scalable services (Rashid & Chaturvedi, 2019). Similarly, the emergence of mobile technologies has enabled firms to reach a larger customer base and to provide customers with more personalized services (Palmié et al., 2020). Moreover, the rise of blockchain technology creates new opportunities for SMEs by providing a decentralized, secure, and transparent platform for conducting transactions and exchanging information (Polyviou et al., 2019).

Enterprises that employ less than 50 employees fall under the category of a small enterprise, and those with more than 50 but fewer than 250 employees are categorized as medium enterprises (Pichlak & Szromek, 2021). SME success is vital for the economy. The importance of SMEs lies in their ability to yield opportunities for entrepreneurs and to create meaningful jobs, which are generally more satisfying than those in larger companies (Naradda Gamage et al., 2020). SMEs support local economies, keep the money close to home, and support neighborhood and community development (Kawano, 2020). Understanding the factors influencing adopting and integrating disruptive technologies in SMEs has implications for SME leaders, their employees, and the larger communities they support.

In small and medium enterprises (SMEs), technology disruptors create new industries with ground-breaking products that replace existing products and technologies (Frizzo-Barker et al., 2020). Investments in disruptive technologies are at an all-time high, with firms spending an increasing share of their profits and retained earnings on innovation (Fenwick et al., 2018). As a result, there is growing interest in the strategic management of disruptive technologies among management scholars and practitioners alike (Crous, 2017; Fenwick et al., 2018

Examples of Disruptive Technology

            This section provides examples of disruptive technology that has influenced small business operations. Specific types of disruptive technology evaluated are: (a) digital fingerprinting, (b) chatbots, (c) cybersecurity, (d) big data, (e) cloud services, (f) artificial intelligence, (g) advanced virtual reality, (h) 5G technology, and (i) blockchain technology. Each type of disruptive technology is discussed in turn.

Digital Fingerprinting

            Digital fingerprinting, similar to a human fingerprint that identifies a person, is a digital unique identifier that identifies video and audio content on the Internet. Digital fingerprinting allows content owners and digital media publishers to control the distribution of their content (Wang et al., 2016). Digital fingerprinting is also useful in limiting content distribution without the owner’s permission and consent. Digital fingerprints are embedded in audio and video material using watermarking technologies designed to withstand several attacks. According to Bloom et al. (2021), an ideal digital fingerprint should meet several requirements, accurately identifying a media asset, regardless of the compression level, and the ability to withstand distortion and severe interference levels. For audio files, digital fingerprints should be able to withstand extreme levels of pitching, equalization, and background noise, while for video files, they should be able to withstand heavy compression, change of aspect ratios, and flipping.

            The modern internet era has seen increased digital content being shared all over the Internet. According to Chen et al. (2018), 70% of Internet users in the country regularly watch and listen to digital content in videos and audio files. The need for content creators and rights holders to identify copyrighted content as it is being shared cannot be overly stated. Digital creators in the small business industry seek ways to develop business models and protect their digital assets through digital fingerprinting. Advertising and publishing companies are utilizing this new technology to protect and limit the distribution of their content without their prior permission. Small businesses in the digital industry suffer from piracy, hence the need for digital fingerprinting and legal measures to protect small businesses in the content and publishing industry. Peng et al. (2023) note that digital piracy remains challenging for digital content developers.


            With rapid technological advancement, chats have become common among businesses in the 21st century. According to Maudlin (1994), chatbots are computer programs with natural language capabilities that can be configured to converse with human users. In another study, Tintarev et al. (2016) defined chatbots as automated advice-givers that facilitate consumers' decision-making process. The chatbot systems include voice-driven digital assistants such as Sir, Cortana, and Alexa and text-based systems, which are the most common for small businesses embedded in their websites. Projections according to Sheehan et al. (2020) estimate that a quarter of customer services will integrate chatbot technology by 2030.  Several types of chatbots include menu-based chatbots, keyword recognition chatbots, and contextual chatbots (Gupta et al., 2020). Chatbots can be categorized as a group of self-service technologies as they operate without needing a human service agent.

            Chatbots are used widely to create more positive customer experiences as customers spend more time in digital environments than in physical shops. Selamat and Windasari (2021) note that chatbots in the age of artificial intelligence have created effective online customer experiences, such as the provision of personalized recommendation systems and seamless online shopping experiences through virtual shopping assistants. Chatbots are used in many forms in the business world, with the common use being customer support and sales promotion within a business environment. Chatbots are used in small businesses to retain customers as they have a problem retaining and attracting customers (Selamat & Windasari, 2021). Chatbots enhance customer relationship management, which has seen an increase in the number of initiatives that small businesses have taken in technology and marketing to improve customer relations and eventually retain and attract more customers. Despite the benefits of chatbots in businesses, they are resource-extensive, which may represent a challenge for small and medium enterprises.


            Cybersecurity is a critical field for all organizations across all industries. According to Alahmari and Duncan (2020), cybersecurity involves a combination of technologies, practices, and policies to protect and prevent cyber-attacks. Kemmerer (2003) defines cybersecurity as a field comprising largely defensive methods to detect and thwart would-be intruders. Lewis (2006) notes that cybersecurity involves safeguarding computer networks and their information from penetration and malicious damage or disruption. Craigen et al. (2014) proposed that cybersecurity is the organization and collection of resources, processes, and structures used to protect cyberspace and cyberspace-enabled systems from occurrences that misalign de jure from de facto property rights. This field enables organizations to stay informed about new trends in the cyber field, including threats, implementing robust security measures, and having well-planned responses in the event of an attack.

            Cybersecurity is an important field of information security framework to maintain customers' privacy, data consistency, and integrity in e-commerce. Protecting clients’ data improves customer relations and attracts more customers (Craigen et al., 2014). Cybersecurity ensures that small businesses secure customer confidential information and protect their systems from cyber-attacks. Kaneria (2021) notes that small businesses lack the necessary resources and capabilities to mitigate cyber-attacks; hence, they should implement preventive measures to prevent cyber attacks. These preventive measures include antivirus installations, firewalls, and physical and wireless security to prevent the chances of being compromised. Kaneria (2021) further noted that a secure and successful e-commerce security system needs a reliable infrastructure and independent framework.

Big Data

            Data in the current age of technology is everywhere; it is one of the major factors influencing quality decision-making. Data can be gathered anywhere, from phones, tablets, emails, or the Internet of Things. Sadiku et al. (2018) defined big data as huge data that the conventional database system cannot process. They further note that while traditional data can be structured, unstructured, and semi-structured, big data can be characterized by volume, velocity, variety, veracity, and value. De Mauro et al. (2016) noted that big data is an information asset characterized by such a high volume, velocity, and variety that it requires specific technology and analytical methods to transform into value. As noted by Gartner (2014), big data technologies are directed at finding ways to effectively assimilate voluminous and fast-flowing data into the day-to-day conduct of firms. Amado et al. (2018) note that big data indicates data that is in enormous form, which cannot be processed by conventional database systems.

            Businesses have always sought insights and information from big data to make better and smarter decisions. Businesses are the biggest sources of data in the big data market and the biggest beneficiaries of big data (Sundee Bo, 2018). The need for knowledge and information on customers, goods, services, and markets has seen the rise of big data over the years. Big data is applied by both big businesses and small businesses to create and look for growth opportunities. Sadiku et al. (2018) noted that small businesses can apply big data in marketing, as decision-making is important in marketing. Big data is a game changer to small businesses, it is one of the most strategic resources of the 21st century its inclusion in decision-making processes can be a game changer.

Cloud Services

            Cloud services are taking over the business industry with ease and efficiency. Ahmad et al. (2017) defined cloud computing as a philosophy and design concept of computing architecture that aims to separate applications, operating systems, and hardware from each other. Youseff et al. (2008) defined cloud computing as a re-conceptualization and technological advancement rather than disruptive innovation.  There are several methods of deploying cloud services, which include Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (Iaas). SaaS represents an on-demand software delivery model with the highest abstraction level (Ahmad et al., 2017). PaaS is a service delivery model with a medium level of abstraction that helps speed up processes. IaaS is a virtualized platform that provides services, operating systems, and storage for networks to develop, host, and execute applications (Ahmad et al., 2017).

            Cloud computing is becoming one of the most popular technologies in the business industry. There is an increase in small and medium enterprise businesses that are migrating to cloud technology due to its convenience and efficiency in storage, computing resources, and infrastructure resources. This new technology brings new opportunities to the business industry, especially for small businesses (Rimba et al., 2017). Cloud computing is being utilized by small businesses to have greater business continuity. Small businesses are utilizing cloud services as they reduce costs. With cloud services, there is no need for hardware requirements, hence less maintenance and power-related costs. Small businesses are also utilizing cloud services to remotely access information, improving efficiency and smooth running of businesses.

Artificial Intelligence

            Researchers and scholars acknowledge that the fourth industrial revolution is here, and artificial intelligence is at the center of everything. In a broad definition, Artificial Intelligence (AI) can be regarded as the use of algorithms to perform tasks (Sheikh et al., 2023). Nilsson (2009) defines Artificial Intelligence as a technology that functions appropriately and with foresight in the environment. artificial intelligence has gained momentum and popularity as a new innovation in many fields over the years with its ability to perform tasks seamlessly and accurately. Machine learning and deep learning are subsidiaries of artificial intelligence, machine learning needs less human intervention to perform their tasks while deep learning requires heavy data and more human intervention.

            Artificial intelligence is becoming a common phenomenon in all industries, and the business world has not been left behind. One of the biggest challenges affecting small businesses is uncertainty. Nascimento et al. (2020) noted that machine learning is being used in small businesses to reduce uncertainties within the business world. The current digital revolution, which has been accelerated by easy access and connectivity around the world, is being supported by artificial intelligence (Rönnberg & Areback, 2020). Small businesses use AI to predict and forecast market changes and shocks. Companies are using artificial intelligence to predict daily sales and revenue by the businesses which helps make companies more sustainable and stable. Better preparation helps reduce the financial hits that uncertainties in the market can cause.

Advanced Virtual Reality

            Virtual reality has been defined in many ways over the years. However, one of the most prevalent used is the adjective immersive. Virtual reality provides a sense of presence and presence in an environment (Jangra et al., 2022). Lin et al. (2014) defined virtual reality as computer-generated immersive surroundings where participants feel they are part of the simulated world and can interact intuitively with on-screen projects. Virtual reality has been around for a long time, first being felt in the 1960s with the game Sensorama, which introduced multisensory driving experiences to consumers (Wallin, 2017). However, the use of advanced virtual reality has grown in the past years with acceleration in technological advancement.

            Technological advancements and innovations have a tremendous effect on businesses, hence the need to adapt to new technologies. Virtual reality is changing how small businesses operate, and the effects on the industry are being felt (Farah et al., 2019). Virtual reality is revolutionizing product creation among small and innovative businesses with increased interaction during product development (Syed Azman & Ahmad, 2021). Virtual reality is also being used by small businesses to hold virtual meetings to streamline company operations, reducing the cost of physical meetings. Virtual reality also helps small businesses grow their customer base by providing new ways to reach new customers. 

5G Technology

            Mobile wireless communication networks have gone through several developments over the years. From the first generation of wireless networks (1G) to the fourth generation (4G) and LTE, the current fifth generation is the latest wireless network revolution and is exemplary (Khee et al., 2023). 5G refers to the fifth generation of cellular network technologies, which provide a network for mobile devices (Emma & Peng, 2020). Experiments for the fifth generation of mobile networks started in 2009, and by 2017, the network was increasingly becoming popular among mobile phone manufacturers (Rao & Prasad, 2018). The 5G network consists of high-value, fast-speed technology allowing fast and efficient connectivity.

            Small businesses are the biggest beneficiaries of the 5G technology. 5G technology allows interconnectivity among Internet users with faster and more efficient technology (Zhou, 2022). Small businesses can use the Internet for advertising to reach new customers worldwide (Dietrich et al., 2023). With 5G technology, small businesses can improve their technology, production thinking, and service thinking to improve how they serve their customers. 5G technology improves connectivity and traffic among users of social media platforms, hence opening an avenue for advertising their products to many potential customers on the internet (Azadehnia, 2019). 5G can potentially improve relationships and connectivity between small businesses and customers.

3D Printing

3D printing, often known as additive manufacturing, is a prime example of disruptive technology, drastically changing established industrial paradigms. Unlike traditional subtractive manufacturing, which involves carving material from a more significant component, 3D printing builds products layer by layer from digital designs (Kantaros et al., 2022). This disruptive breakthrough can disrupt sectors across the board, breaking down barriers and offering new opportunities. Furthermore, the democratization of production is one of the most remarkable aspects of 3D printing's disruptive impact. Therefore, individuals, small organizations, and even amateurs can use this technology to create complicated and customized things with little setup expenditures (Kantaros et al., 2022).

The accessibility and cost of 3D printers have democratized and localized manufacturing, reducing the need for bulk production and lengthy supply networks (Rayna & Striukova, 2021). Furthermore, 3D printing challenges established conceptions of design and prototyping. It shortens the iterative design process by allowing for the quick fabrication of prototypes, improving innovation cycles, and minimizing time-to-market (Kantaros et al., 2022). This ability to explore and iterate dramatically boosts creativity while lowering the risks associated with traditional product development. Additionally, technology promotes sustainable practices by reducing material waste, enabling on-demand production, and reducing surplus inventory and its environmental impact (Rayna & Striukova, 2021). 3D printing, in essence, epitomizes the spirit of disruptive technology by challenging traditional manufacturing standards, democratizing production, and stimulating creativity across industries (Rayna & Striukova, 2021). The ability of 3D printing to transform production, distribution, and consumption paradigms highlights its significance as a forerunner in the ongoing story of technology disruption.

Quantum Computing

Quantum computing is a paradigm-shifting example of disruptive technology, set to revolutionize computation and push beyond the limits of traditional computer systems (Egger et al., 2020). This novel method of computing, based on quantum mechanics principles, can solve complicated issues that have remained impossible for traditional computers, unleashing a wave of transformation across numerous sectors. Additionally, quantum computing is based on the inherent features of quantum bits, or qubits, which can exist in several states simultaneously due to the phenomenon of superposition (Egger et al., 2020). This characteristic enables quantum computers to process massive amounts of data simultaneously, resulting in an exponential speedup in addressing tasks that would otherwise be unsolvable for classical counterparts. For example, complex calculations that would take classical computers millions of years to complete could potentially be completed in minutes by quantum computers (Egger et al., 2020).

Quantum computing poses a challenge to present encryption systems due to its natural ability to solve problems that underpin encryption quickly, threatening data security as we know it (Egger et al., 2020). On the other hand, it promises to transform optimization problems with applications in supply chain management, financial modeling, drug development, and other areas (Bova et al., 2021). Quantum computers can more efficiently navigate the possibilities, leading to ideal solutions for small business use. However, realizing the potential of quantum computing is challenging because quantum systems are susceptible to external perturbations, necessitating highly regulated conditions to sustain coherence (Bova et al., 2021). This demands revolutionary breakthroughs in technology and error-correcting approaches. However, when these limitations are overcome, quantum computing's disruptive potential becomes more palpable, radically altering our approach to computation, data analysis, and problem-solving across various areas (Bova et al., 2021).


Another example of disruptive technology is blockchain, described in a study by Frizzo-Barker et al. (2020). The authors conducted a study on the influence of blockchain technology on businesses. The authors define Blockchain technology as a decentralized, distributed ledger that provides for safe and transparent record-keeping and transfer of digital assets. They posited that blockchain technology can disrupt existing corporate paradigms while fostering innovation and development.

Frizzo-Barker et al. (2020) commenced by introducing blockchain technology and its potential to disrupt existing business paradigms while also driving innovation and growth. Following that, the researchers examine the data on the influence of blockchain technology on many elements of business, such as supply chain management, financial services, and intellectual property. According to the authors, blockchain technology can disrupt existing corporate paradigms while driving innovation and development. For instance, the authors offered data demonstrating how blockchain technology has increased the efficiency and openness of supply-chain management. Similar to how it was shown in the evaluation, blockchain technology can improve the efficiency and security of financial services (Frizzo-Barker et al., 2020).

Frizzo-Barker et al. (2020) stressed that blockchain technology is currently in its early phases of development and that barriers to wider implementation remain, including regulatory uncertainty and the need for defined protocols. In conclusion, the authors believe that blockchain technology can disrupt established business structures and foster innovation and development, but it also emphasizes the need for more research and addressing barriers to wider use (Frizzo-Barker et al., 2020).

Problem Background


The problem is that the factors influencing business owners, leaders, and managers’ decisions to adopt disruptive technologies are unknown. Technology includes all theoretical and practical knowledge and skills for developing products and services. People, machines, computers, and software are among the many forms of technology. While many disruptive technologies range from learning and artificial intelligence applications to robotics and new streaming models, factors influencing entrepreneurs and their managers in adopting disruptive technologies remain understated in the research (Schoemaker et al., 2018).

Research Methodology

To investigate and determine those factors influencing the participant's decisions on the adoption of disruptive technologies in small and medium-scale enterprises, one central research question and three sub-research questions were devised:

Research Question:

What role does disruptive technology play across the SMEs?

Sub Research Questions:

  1. What factors influence business owners, and managers’ decisions to adopt disruptive technology?
  2. How do employees and leadership impact disruptive technologies in SMEs?

Research Methodology

To address the research questions, a qualitative survey was designed and administered to the participants.


A qualitative survey was administered to 48 small- or medium-sized business owners, leaders, or managers to understand factors influencing the participant's decision to adopt the disruptive technologies. The results of this survey are shown in Figure 1 and the tables below.

Figure 1

Types of Disruptive Technology Used by the Participants

            Most participants (24, 50%) described using cloud-based services to conduct their business. Cloud service providers offer a wide range of services, from software subscriptions to infrastructure delivered over the Internet (Golightly et al., 2022). Twelve participants described using disruptive technology for cybersecurity, and 11 participants used 5G technology, a fifth-generation mobile network. Thus, the participants used a variety of disruptive technologies in their business operations.

Table 1

Factors Influencing Participants’ Decisions to Adopt or Not Adopt the technologies



Response from survey


Enhance operations

“Simplicity and reliability.”


Customer relations

“If we think it will benefit our employees or customers.”


Financial resources

“If the technology doesn’t cost too much.”


Customer relations

“If it helps us stand out and will make our customers happy.”


Financial resources



Financial resources, Customer relations

“Lowering opportunity costs and increasing satisfaction for clients.”


Data security

“We’ll use the technology if it’s going to make the business and data more secure.”


Enhance operations, Financial Resources

“We assess our need versus learning curve and cost.”


Enhance operations, Customer relations

“Our processes are succeeding in satisfying our customers and outperforming our competitors in our niche.”


Enhance operations

“If and how much does it improve our advantage in the marketplace?”


Enhance operations, Customer relations

“If it will make us more efficient and keep our clients safe.”


Enhance operations

“Programmatic needs.”


Skeptical of new technology

“Distrust of said technologies being used properly.”


Enhance operations

“Applicability to our core mission.”


Enhance operations

“Whether they can help make money or not. It has to be profitable.”


Enhance operations

“The Pandemic forced us into the Zoom. Cloud-based technology became the only option for many operations.”


Enhance operations, Financial Resources

“Need and cost.”


Enhance operations

“Efficiency and effectiveness.”


Source: I. Ayoola, generated for this study.


Table 2

Participants’ Strategies for Using Disruptive Technology



Response from survey


Business utility

“We assess new technology for simplicity and reliability while trying to understand if it’ll improve business operations.”


Customer satisfaction

“If it works and the customers like it, then we’ll continue to use it.”


Customer satisfaction

“We use it to make the workplace safer and the customers happier.”


Business utility

“We use new technology if we think it will help improve our business profit margins.”


Customer satisfaction

“If the technology will help us work with customers, we try it.”


Business utility

“We analyze whether improvements to the business are needed and if there’s a technology that can help us with that.”


Business utility

“We do not usually rush to adopt new technologies unless they add value to our current processes.”


Skeptical of new technology

“We apply disruptive technology cautiously and monitor it continually.”


Business utility

“We use whatever technologies we need to keep our clients’ information and health records safe and secure.”


Skeptical of new technology

“We have not adopted many technologies because our budget is small and our needs are few.”


Skeptical of new technology

“We use as little of those as possible while still being able to provide our services.”


Business utility

“We identify uses for ML/AI in the diagnostics space, author and perform IRB-approved studies in patients with diseases to develop advanced diagnostics.”


Research new technology

“We spent time learning about new technology extensively before we adopted it.”


Business utility

“We use whatever technology seems to work for our industry.”


Business utility

“We obtain new technology to increase our business reach and provide the best products and services for growth.”


Skeptical of new technology

“I’m an aggressive follower, not a first adopter.”


Business utility

“We adopt technology if it’s going to make our operations safer, especially when protecting customer information.”


Source: I. Ayoola, generated for this study.


Discussion of the Study’s Findings


The preliminary findings reveal the disruptive technologies participants use in their businesses. Cloud-based services, disruptive technology for cybersecurity, and 5G technology emerged as key technologies these businesses adopt. This indicates a diversity in the adoption of disruptive technologies among SMEs. Furthermore, participants shared their approaches to evaluating the utility of technology for their business operations and ensuring customer satisfaction. Additionally, some participants expressed skepticism towards new technology due to financial constraints associated with being small businesses. This suggests a cautious yet strategic approach to technology adoption among SMEs.

Employees and Leadership


The findings reveal a variety of opinions among participants. Regarding employees, most participants believed that upper management or owners were the primary influencers in adopting disruptive technology. However, some participants indicated a collaborative approach involving employees in researching, identifying, and advocating for new technology. Leadership's role was also varied, with some leaders being open to new approaches and technology while others were more cautious. This suggests that leadership dynamics greatly impact the adoption of disruptive technology.

Strategy and Technology Integration


The analysis of how strategic management affects the adoption of disruptive technologies offers insight into the challenges faced by SMEs when integrating new technologies into their operational plans. The division in participant viewpoints shows the requirement for flexible and adaptive strategic frameworks, with some noting the crucial importance of strategic planning and others highlighting its difficulties in the face of new and revolutionary technology. This suggests that SMEs should foster an organizational culture emphasizing flexible strategy planning that can adapt to the quick changes caused by disruptive technology. As shown in the literature, disruptive technologies often challenge established strategy frameworks due to their unique nature, demanding novel approaches to strategic management (Shang et al., 2019). Thus, SMEs should proactively embrace a forward-thinking and agile approach to strategic management to harness disruptive technologies' potential effectively.

Discussion In the Context of the Academic Literature

The preliminary findings reveal the disruptive technologies SMEs adopt, including cloud-based services, cybersecurity technologies, and 5G networks. These findings resonate with the literature's emphasis on adopting technologies that streamline operations, enhance customer experiences, and enable businesses to remain competitive in rapidly evolving markets (Palmié et al., 2020; Rashid & Chaturvedi, 2019). Additionally, the literature highlights that cloud computing, for example, offers cost-saving benefits and scalability, similar to the participants' reliance on cloud-based services (Rashid & Chaturvedi, 2019). Specifically, Rashid and Chaturvedi (2019) posited in their review of cloud computing characteristics and services that a wide range of applications can be offered due to the Cloud's many fascinating and promising properties, services, and applications. Therefore, the analysis of strategies for embracing disruptive technology is consistent with the literature's awareness of the necessity of assessing technology's utility and impact on business operations and consumer satisfaction (Rashid & Chaturvedi, 2019).


Based on the study's findings and their agreement with current literature, practical advice for SMEs wishing to adopt and integrate disruptive technologies into their operations effectively can be presented. These ideas cover strategic management, employee involvement, leadership, education, and regulatory concerns, all to promote successful technology adoption and capitalize on its potential benefits.

Recommendations for Practice

These recommendations, resulting from the study's findings aligned with current literature, cover strategic management, employee engagement, leadership, collaborations, education, and policy issues. These recommendations help SMEs navigate the challenges and opportunities of adopting disruptive technology, increasing their capacity for innovation and sustainable development. The specific practice recommendations include agile strategic management and employee engagement, leadership as technology champions, and collaborations and partnerships, which will now be discussed.

Agile Strategic Management and Employee Engagement

SMEs should adopt agile strategic management approaches that match the dynamic nature of disruptive technology. To ensure broad insights, cross-functional teams should be involved in strategic planning. Furthermore, SMEs should actively engage staff in technological talks, fostering idea exchange and internal innovation. Employees can be empowered to engage effectively in technology-driven initiatives through regular training sessions and workshops. Additionally, fostering open communication and involving employees in the decision-making process, as supported by Rashid and Chaturvedi (2019) and Schmidthuber et al. (2020), promotes innovation and enhances the quality of technology adoption.

Leadership as Technology Champions

SME leaders should serve as technology champions by remaining current on emerging trends and their potential influence on the business. Continuous education and obtaining professional guidance will help executives make better decisions about technology adoption. Leaders should encourage innovation and learning, encouraging people to experiment with new technology and develop creative solutions. Creating a continuous learning and innovation culture aligns with the studies of Yong et al. (2020) and Melnyk et al. (2019), encouraging employees to engage with new technologies and contribute to the organization's technological evolution.

Collaborations and Partnerships

Partnerships and cooperation with technology providers, industry peers, and research institutes should be sought by SMEs. These alliances provide access to resources, experience, and shared knowledge, allowing for faster technology adoption and risk mitigation. Participation in industry networks and innovation ecosystems keeps SMEs competitive and on the cutting edge of developing technology and best practices. As highlighted by Bublitz et al. (2019) and Schuelke-Leech (2018), the importance of forming collaborations enables SMEs to access expertise and insights that align with emerging technologies, keeping them competitive in rapidly evolving markets.

Recommendations for Small Business Owners


Small business owners might profit from strategic integration methods in the ever-changing field of disruptive technology and benefit from strategic integration techniques in the quickly shifting field of disruptive technologies. These suggestions provide practical approaches for navigating the complicated landscape of technology adoption and efficiently using its potential benefits:

        1. To comprehend developing technologies, stay updated through industry resources, workshops, and seminars.
        2. Assess the relevance and possible influence of these technologies on your business, prioritizing those that correspond with your objectives. Begin with modest pilot projects to test and understand the practical implications.
        3. Involve staff in technology adoption talks, drawing on their expertise to determine how new technologies might improve operations.
        4. Seeking expert assistance from consultants or industry associations can help make better decisions.
        5. Integrate technological decisions into your strategic planning, linking them with long-term goals to guarantee a consistent approach.
        6. Invest in employee training to improve abilities connected to adopted technologies and check their performance regularly. Through shared information, collaborative efforts with peers or providers can facilitate the adoption process.
        7. Prioritize cybersecurity steps to protect your company's and customers' data.

Following these suggestions, small business owners can negotiate the difficulties of disruptive technology adoption, making strategic decisions that foster growth, innovation, and long-term success.

Cost Benefit Analysis

Implementing the recommended strategies for small business owners entails considering the costs and benefits associated with each action. Aligning technology decisions with strategic plans may incur strategic planning costs; nevertheless, the payoff is a cohesive approach that supports long-term business objectives. Employee training incurs expenses, but the benefits of more excellent worker capabilities and increased technology use outweigh the expenditures. Additionally, monitoring technology performance and implementing cybersecurity measures are continual costs, but the rewards of improved technology usage and data security are critical. Collaborative activities may necessitate sharing resources, but the insights gained and potential cost savings make this strategy appealing. Finally, while having a flexible attitude may necessitate resource flexibility, the benefits of remaining competitive and responsive to shifting technological landscapes are enormous.

Recommendations for Future Research

Recommendations for further research emerge from the study's findings while noting its limits and limitations. Broadening the study's sector and geographical contexts could provide a more comprehensive view of disruptive technology uptake. A mixed-methods strategy combining qualitative interviews with quantitative data collection methods may produce more thorough results. Furthermore, triangulating data from numerous sources, including stakeholders other than business owners, could improve the study's credibility (Creswell & Poth, 2018). Additionally, researching the methods SMEs use during technology adoption and managerial mindsets impacting decision-making processes may provide deeper insights into the dynamics at work. These future research endeavors will contribute to a more comprehensive knowledge of disruptive technology uptake within SMEs and practical insights for efficient implementation techniques.


A thorough knowledge of the obstacles, opportunities, and implications of technology adoption in the SME sector has evolved from an in-depth review of the study's findings and their connection with previous literature. The debates and consequences in this study highlight the multidimensional nature of incorporating disruptive technologies. SMEs operate in various situations, resulting in differing viewpoints on the importance of strategic management in this process. Furthermore, the potential of developing technologies like blockchain serves as a reminder of the ongoing need for knowledge distribution and educational programs to help people make educated decisions. The study's practical recommendations provide a road map for SMEs adopting disruptive technology effectively. SMEs can traverse the complexity of technology integration by developing adaptable strategic management, promoting employee involvement, cultivating leadership as technology champions, and forging alliances. These recommendations are founded on empirical data and the insights supplied by a thorough analysis of existing literature. As technology continues to influence business environments, this study provides a prism through which SMEs may traverse the ever-changing panorama of technological innovation, delivering findings that benefit researchers, practitioners, and policymakers alike.






Abdel-Basset, M., Chang, V., & Nabeeh, N. A. (2021). An intelligent framework using disruptive technologies for COVID-19 analysis. Technological Forecasting and Social Change, 163, Article 120431. https://doi.org/10.1016/j.techfore.2020.120431

Ahmad, I., Bakht, H., & Mohan, U. (2017). Cloud computing–a comprehensive definition. Journal of Computing and Management Studies, 1(1), 1–8. https://www.researchgate.net/publication/314072571_Cloud_Computing_-_A_Comprehensive_Definiton

Alahmari, A., & Duncan, B. (2020). Cybersecurity risk management in small and medium-sized enterprises: A systematic review of recent evidence. 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). https://doi.org/10.1109/cybersa49311.2020.9139638

Amado, A., Cortez, P., Rita, P., & Moro, S. (2018). Research trends on big data in marketing: A text mining and topic modeling based literature analysis. European Research on Management a nd Business Economics, 24(1), 1 –7.   https://doi.org/10.1016/j.iedeen.2017.06.002

Attaran, M., & Gunasekaran, A. (2019). Blockchain-enabled technology: the emerging technology set to reshape and decentralize many industries. International Journal of Applied Decision Sciences, 12(4), 424. https://doi.org/10.1504/ijads.2019.102642

Azadehnia, A. (2019). What benefits will 5G be for small and mid-sized companies? [Masters Thesis, Gavle University]. https://www.diva-portal.org/smash/get/diva2:1357459/FULLTEXT01.pdf

Birkinshaw, J., Visnjic, I., & Best, S. (2018). Responding to a potentially disruptive technology: How big pharma embraced biotechnology. California Management Review, 60(4), 74-100. https://doi.org/10.1177/0008125618778852

Bloom, N., Hassan, T. A., Kalyani, A., Lerner, J., & Tahoun, A. (2021). The diffusion of disruptive technologies. National Bureau of Economic Research. https://doi.org/10.3386/w28999

Bongomin, O., Gilibrays Ocen, G., Oyondi Nganyi, E., Musinguzi, A., & Omara, T. (2020). Exponential disruptive technologies and the required skills of industry 4.0. Journal of Engineering, 2020, 1-17. https://doi.org/10.1155/2020/4280156

Bova, F., Goldfarb, A., & Melko, R. G. (2021). Commercial applications of quantum computing. EPJ Quantum Technology, 8(1), 2. https://doi.org/10.1140/epjqt/s40507-021-00091-1

 Bublitz, F. M., Oetomo, A., S. Sahu, K., Kuang, A., X. Fadrique, L., E. Velmovitsky, P., M. Nobrega, R., & P. Morita, P. (2019). Disruptive technologies for environment and health research: an overview of artificial intelligence, blockchain, and internet of things. International Journal of Environmental Research and Public Health, 16(20), 3847. https://doi.org/10.3390/ijerph16203847

Chang, N., Zhang, Y., Lu, D., Zheng, X., & Xue, J. (2020). Is a disruptive technology disruptive? The readiness perspective based on TOE. 2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). https://doi.org/10.1109/IEEM45057.2020.9309849 

Chen, H., Rohani, B. D., & Koushanfar, F. (2018). Deepmarks: A digital fingerprinting framework for deep neural networks. arXiv preprint arXiv:1804.03648. https://arxiv.org/pdf/1804.03648

Craigen, D., Diakun-Thibault, N., & Purse, R. (2014). Defining cybersecurity. Technology Innovation Management Review, 4(10), 13-21. https://doi.org/10.22215/timreview/835

Crous, C. J. (2017). Could disruptive technologies also reform academia? Web Ecology, 17(2), 47-50. https://doi.org/10.5194/we-17-47-2017

Damanpour, F. (2020). Organizational innovation: Theory, research, and direction. Edward Elgar Publishing. https://doi.org/10.4337/9781788117449

De Mauro, A., Greco, M., & Grimaldi, M. (2015, February). What is big data? A consensual definition and a review of key research topics. In AIP conference proceedings (Vol. 1644, No. 1, pp. 97-104). American Institute of Physics. http://big-data-fr.com/wp-content/uploads/2015/02/aip-scitation-what-is-bigdata.pdf

Dietrich, F., Angos Mediavilla, M., Turgut, A., Lackner, T., Jooste, W., & Palm, D. (2023). Feasibility assessment of 5G use cases in Intralogistics. Lecture Notes in Production Engineering, 587-599. https://doi.org/10.1007/978-3-031-15602-1_43

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., Duan, Y., Dwivedi, R., Edwards, J., Eirug, A., Galanos, V., Ilavarasan, P. V., Janssen, M., Jones, P., Kar, A. K., Kizgin, H., Kronemann, B., Lal, B., Lucini, B., … Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, Article 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002

Egger, D. J., Gambella, C., Marecek, J., McFaddin, S., Mevissen, M., Raymond, R., & Yndurain, E. (2020). Quantum computing for finance: State-of-the-art and future prospects. IEEE Transactions on Quantum Engineering, 1, 1-24. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9222275

Emma, X., & Peng, L. (2020). 5G Network: An overview of the pros and cons. International Digital Organization for Scientific Research, 5(2) 40-46, 2020. https://www.idosr.org/wp-content/uploads/2020/05/IDOSR-JSR-52-40-46-2020.pdf

Farah, M. F., Ramadan, Z. B., & Harb, D. H. (2019). The examination of virtual reality at the intersection of consumer experience, shopping journey and physical retailing. Journal of Retailing and Consumer Services48, 136-143. https://laur.lau.edu.lb:8443/xmlui/bitstream/handle/10725/11614/Postprint%20%28The%20examination%29.pdf?sequence=2&isAllowed=n

Fenwick, M., Vermeulen, E. P. M., & Corrales, M. (2018). Business and regulatory responses to artificial intelligence: Dynamic regulation, innovation ecosystems and the strategic management of disruptive technology. Perspectives in Law, Business and Innovation, 81–103. https://doi.org/10.1007/978-981-13-2874-9_4 

Gartner, I. (2014). Gartner says 4.9 billion connected “things” will be in use in 2015. Press release, Barcelona, Spain, 11 November 2014.[Online] Available at: http://www. gartner. com/newsroom/id/2905717.

Golightly, L., Chang, V., Xu, Q. A., Gao, X., & Liu, B. S. (2022). Adoption of cloud computing as innovation in the organization. International Journal of Engineering Business Management, 14, Article 18479790221093992. https://doi.org/10.1177/18479790221093992

Gupta, A., Hathwar, D., & Vijayakumar, A. (2020). Introduction to AI chatbots. International Journal of Engineering Research and Technology, 9(7), 255-258. https://pdfs.semanticscholar.org/f5f4/746acffef08df37f184cb6acc0505362ea9b.pdf

Jangra, S., Singh, G., & Mantri, A. (2022). A systematic review of applications and tools used in virtual reality and augmented reality. ECS Transactions, 107(1), 6781. https://ui.adsabs.harvard.edu/link_gateway/2022ECSTr.107.6781J/doi:10.1149/10701.6781ecst

Kaneria, K. K. (2021). Cyber security in business. ResearchGate , 445(1816021), 1–10. https://doi.org/https://www.researchgate.net/publication/350956356_Cyber_Security_in_Business

Kantaros, A., Diegel, O., Piromalis, D., Tsaramirsis, G., Khadidos, A. O., Khadidos, A. O., ... & Jan, S. (2022). 3D printing: Making an innovative technology widely accessible through makerspaces and outsourced services. Materials Today: Proceedings, 49, 2712-2723. https://doi.org/10.1016/j.matpr.2021.09.074

Karimi, J., & Walter, Z. (2016). Corporate entrepreneurship, disruptive business model innovation adoption, and its performance: The case of the newspaper industry. Long Range Planning, 49(3), 342-360. https://doi.org/10.1016/j.lrp.2015.09.004

Kawano, E. (2020). Solidarity economy: Building an economy for people and planet. In The new systems reader (pp. 285-302). Routledge.

Kemmerer, R. A. (2003). Cybersecurity. Proceedings of the 25th IEEE International Conference on Software Engineering: 705-715. http://dx.doi.org/10.1109/ICSE.2003.120125

Khee, P. C., Ee, F. F., & Chinna, K. (2023). Perception on and the intention to use 5G technology in Malaysian SMEs. Research Square. https://doi.org/10.21203/rs.3.rs-2381162/v1

Kotarba, M. (2018). Digital transformation of business models. Foundations of Management, 10(1), 123-142. https://doi.org/10.2478/fman-2018-0011

Lewis, J. A.  (2006).  Cybersecurity and critical infrastructure protection. Washington, DC: Center for Strategic and International Studies.      http://csis.org/publication/cybersecurity-and-critical-infrastructure-protection

Li, M., Porter, A. L., & Suominen, A. (2018). Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective. Technological Forecasting and Social Change, 129, 285-296. https://doi.org/10.1016/j.techfore.2017.09.032

Maudlin, M. (1994). ChatterBots, TinyMuds, and the Turing Test: Entering the Loebner Prize competition. In Proceedings of the Eleventh National Conference on Artificial Intelligence. AAAI Press. https://cdn.aaai.org/AAAI/1994/AAAI94-003.pdf

Melnyk, L., Dehtyarova, I., Kubatko, O., Karintseva, O., & Derykolenko, A. (2019). Disruptive technologies for the transition of digital economies towards sustainability. Економiчний часопис-XXI, (9-10), 22-30. https://doi.org/10.21003/ea.v179-02

Morkunas, V. J., Paschen, J., & Boon, E. (2019). How blockchain technologies impact your business model. Business Horizons, 62(3), 295-306. https://doi.org/10.1016/j.bushor.2019.01.009

Naradda Gamage, S. K., Ekanayake, E. M. S., Abeyrathne, G. A. K. N. J., Prasanna, R. P. I. R., Jayasundara, J. M. S. B., & Rajapakshe, P. S. K. (2020). A review of global challenges and survival strategies of small and medium enterprises (SMEs). Economies, 8(4), 79. https://doi.org/10.3390/economies8040079

Nascimento, A. M., De Melo, V. V., Muller Queiroz, A. C., Brashear-Alejandro, T., & Meirelles, F. D. (2020). Artificial intelligence applied to small businesses: The use of automatic feature engineering and machine learning for more accurate planning. Revista de Contabilidade e Organizações, 14, e171481. https://doi.org/10.11606/issn.1982-6486.rco.2020.171481

Nilsson, N. J. (2009). The quest for artificial intelligence. Cambridge University Press. https://doi.org/10.1017/cbo9780511819346

Palmié, M., Wincent, J., Parida, V., & Caglar, U. (2020). The evolution of the financial technology ecosystem: An introduction and agenda for future research on disruptive innovations in ecosystems. Technological Forecasting and Social Change, 151, 119779. https://doi.org/10.1016/j.techfore.2019.119779

Peng, S., Li, B., & Wu, S. (2023). Presence of piracy and legal protection: Decisions in the digital goods market under different contracts. European Journal of Operational Research, 309(2), 578-596. https://doi.org/10.1016/j.ejor.2023.01.050

Pichlak, M., & Szromek, A. R. (2021). Eco-innovation, sustainability and business model innovation by open innovation dynamics. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 149. https://doi.org/10.3390/joitmc7020149

Polyviou, A., Velanas, P., & Soldatos, J. (2019). Blockchain technology: financial sector applications beyond cryptocurrencies. Multidisciplinary Digital Publishing Institute Proceedings, 28(1), 7. https://doi.org/10.3390/proceedings2019028007

Ranta, V., Aarikka-Stenroos, L., & Väisänen, J. M. (2021). Digital technologies catalyzing business model innovation for circular economy—Multiple case study. Resources, Conservation and Recycling, 164, Article 105155. https://doi.org/10.1016/j.resconrec.2020.105155

Rashid, A., & Chaturvedi, A. (2019). Cloud computing characteristics and services: a brief review. International Journal of Computer Sciences and Engineering, 7(2), 421-426. https://doi.org/10.26438/ijcse/v7i2.421426

Rayna, T., & Striukova, L. (2021). Assessing the effect of 3D printing technologies on entrepreneurship: An exploratory study. Technological Forecasting and Social Change, 164, Article 120483. https://doi.org/10.1016/j.techfore.2020.120483

Rimba, P., Tran, A. B., Weber, I., Staples, M., Ponomarev, A., & Xu, X. (2017, April). Comparing blockchain and cloud services for business process execution. In 2017 IEEE international conference on software architecture (ICSA) (pp. 257-260). IEEE. http://www.imweber.de/downloads/2017-ICSA-Blockchain-Costmodel--authors_copy.pdf

Rönnberg, H., & Areback, J. (2020). Initiating transformation towards AI in SMEs [Masters Thesis, Lulea University of Technology]. https://www.diva-portal.org/smash/get/diva2:1438217/FULLTEXT01.pdf

Sadiku, N., Adebo, O., & Musa, M. (2018). Big data in business. International Journal of Advanced Research in Computer Science and Software Engineering, 8(1), 160-162. DOI:10.23956/ijarcsse.v8i1.543

Schmidthuber, L., Maresch, D., & Ginner, M. (2020). Disruptive technologies and abundance in the service sector-toward a refined technology acceptance model. Technological Forecasting and Social Change, 155, Article 119328. https://doi.org/10.1016/j.techfore.2018.06.017

Schoemaker, P. J., Heaton, S., & Teece, D. (2018). Innovation, dynamic capabilities, and leadership. California Management Review, 61(1), 15-42. https://doi.org/10.1177/0008125618790246

Selamat, M. A., & Windasari, N. A. (2021). Chatbot for SMEs: Integrating customer and business owner perspectives. Technology in Society, 66, 101685https://doi.org/10.1016/j.techsoc.2021.101685

Sheehan, B., Jin, H. S., & Gottlieb, U. (2020). Customer service chatbots: Anthropomorphism and adoption. Journal of Business Research, 115, 14-24. https://doi.org/10.1016/j.jbusres.2020.04.030

Sheikh, H., Prins, C., & Schrijvers, E. (2023). Artificial Intelligence: Definition and Background. In Mission AI: The New System Technology (pp. 15-41). Cham: Springer International Publishing. https://link.springer.com/chapter/10.1007/978-3-031-21448-6_2

Sundee Bo, K. (2018). Cloud computing for business. International Journal of Advances in Scientific Research and Engineering, 4(7), 150-160. https://doi.org/10.31695/ijasre.2018.32816

Surya, B., Menne, F., Sabhan, H., Suriani, S., Abubakar, H., & Idris, M. (2021). Economic growth, increasing productivity of SMEs, and open innovation. Journal of Open Innovation: Technology, Market, and Complexity, 7(1), 20. https://doi.org/10.3390/joitmc7010020

Thomond, P., Herzberg, T., & Lettice, F. (2003, September). Disruptive innovation: Removing the innovators dilemma. In British Academy of Management Annual Conference: Knowledge into Practice. https://d1wqtxts1xzle7.cloudfront.net/42281106/BAM2003forDisruptit-libre.pdf

Tintarev, N., O'donovan, J., & Felfernig, A. (2016). Introduction to the special issue on human interaction with artificial advice givers. ACM Transactions on Interactive Intelligent Systems (TiiS), 6(4), 1-12. https://www.eventhelpr.com/files/events/2v0rDyN6/attachments/a26-tintarev_1jY3JmXC.pdf

Van Looy, A. (2021). A quantitative and qualitative study of the link between business process management and digital innovation. Information & Management, 58(2), 103413. https://doi.org/10.1016/j.im.2020.103413

Wallin, R. L. (2017). The use of virtual reality to increase efficiency and profitability in different industries and functions: how head-mounted display can bring value to sectors with its competitive attributes [Bachelor’s Thesis, Aalto University of Business]. https://core.ac.uk/download/pdf/84756572.pdf

Wan, F., Williamson, P. J., & Yin, E. (2015). Antecedents and implications of disruptive innovation: Evidence from China. Technovation, 39, 94-104. https://doi.org/10.1016/j.technovation.2014.05.012

World Bank Group. (2019). Disruptive technologies in the credit information sharing industry. https://doi.org/10.1596/31714

Youseff, L., Butrico, M., & Da Silva, D. (2008). Toward a unified ontology of cloud computing. 2008 Grid Computing Environments Workshop. https://doi.org/10.1109/gce.2008.4738443

Zhou, Z. (2022). The impact of 5G on the sustainable development of enterprises. Proceedings of the 2022 6th International Seminar on Education, Management and Social Sciences (ISEMSS 2022), 2174-2181. https://doi.org/10.2991/978-2-494069-31-2_255

Application for study

Interactive online: