Artificial intelligence in Small and Medium Enterprises – An Empirical Analysis of Critical Factors

Samuel Wandeto Mathagu
Kirinyaga University, Kerugoya, Kenya
Correspondence to: wandetos@gmail.com

DOI: https://doi.org/10.70389/PJS.100009

Abstract
Introduction
Literature Review
Conceptual Framework
Research Methodology
Data Analysis and Results
Discussion
Implications
Conclusions and Future Research
References

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: No industry funding
• Conflicts of interest: N/a
• Author contribution: Samuel Wandeto Mathagu – Conceptualization, Writing – original draft, review and editing
• Guarantor: Samuel Wandeto Mathagu
• Provenance and peer-review:
  Commissioned and externally peer-reviewed
• Data availability statement: N/a

Keywords: adoption, artificial intelligence, critical factors, small and medium enterprises, technology infrastructure

Peer-review
Received: 23 July 2024
Revised: 17 August 2024
Accepted: 19 August 2024
Published: 2 September 2024

Introduction

Digital technologies are fundamentally transforming the business process and strategies. The emerging technologies are contributing to the businesses, including small and medium enterprises (SMEs) to the efficiently conduct their operations, overcome inherent challenges, collaborate with relevant stakeholders, and improve their overall performance [1]. Artificial intelligence (AI) is among the technologies with high potential of positively transforming the business processes and overall supply chain. Its integration into the business has gained significance, due to its advantage in offering solutions of improved production control and optimization [2]. However, according to [3] the new potential of artificial intelligence is majorly realized by big and multinational enterprises. It is only in the recent few years that SMEs are increasingly recognizing and harnessing the potential of artificial intelligence solutions. As well, OECD [4] highlights that the rate of artificial intelligence adoption among SMEs remain quite low globally, even with consideration of huge inherent benefits. They argue that SMEs adoption of this technology is inhibited by their small scale and resource constraints.

The fourth industrial revolution digitalization effort have emphasized the importance of SMEs to embrace digital technologies [5]. AI is a pivotal technology for the SMEs with ability to offer transformative benefits [6], which could enhance their operation efficiency and market competitiveness. According to [7] SMEs could harness the AI technology to drive productivity, innovation and strategic decisions, aspects that are majorly constrained by limited resources. This potential is critical, considering that SMEs are backbone of many economies, supporting significant proportion of employment and production [8]. AI could help SMEs automate and optimize operations, as well as embrace new business models. Despite the great potential and benefits of AI to SMEs and economy, appropriate adoption and incorporation in business operations is challenging [9]. SMEs experience various barrier in adoption of advanced technologies such as financial constraints, lack of technical expertise, and return on investment uncertainties. Additionally, most AI technologies are complex, and require substantial upfront investment, both in hardware and software. Continuous training of employees is necessary for technical capabilities [10].

Considering the potential benefits of AI use by SMEs and the inherent challenges and barriers, it is crucial to understand the factors that influence AI the adoption and integration in SMEs. This is crucial for development of strategies that could facilitate a smoother adoption, implementation and broader utilization of AI technologies. Knowing these factors could help the SMEs enterprises, industry stakeholders and policy makers develop targeted interventions to overcome the existing barriers. The objective of this study, therefore, is to investigate and explore the critical factors that determine the adoption and implementation of AI in SMEs. An empirical analysis is conducted under a case study of United Kingdom.

Previous studies as made a significant attempt to investigate the aspect of AI in businesses, including SME. For instance, [7] conducted a systematic review of the state of the art of AI on SMEs. They highlighted some barriers of AI application as inadequate infrastructure, high cost, and lack of knowledge. Another study by [11] compared the adoption of AI between Germany and Chinese healthcare sector. Their findings indicated highlighted the challenges and opportunities of the AI investment in healthcare. Another study by [12] evaluated the factors that influence the implementation of AI in SMEs supply chain management. They highlighted external pressure factors, complexity, and compatibility issues as the major factors influencing the AI implementation. A study by [13] evaluated the adoption of AI in Danish SMEs. They highlighted that AI allows SMEs in the process of improving performance, increase productivity as well as reduce downtime. Bunte [14] investigated why it is hard to find AI in SMEs, with an intention of proposing practices that can promote it. The study underlined that there is great need for external supports to SMEs in matters AI.

It is evident that many studies have investigated about the benefits and barriers of AI adoption in SMEs. There is no adequate attention regarding the interplay between technological capabilities and organization readiness in SMEs. As a result, there is need for comprehensive research on the critical factors of AI adoption among SMEs, a gap that is addressed by this research. By leveraging a comprehensive dataset and robust analytical methods, this study contributes to the existing literature by offering deeper insights into the technological, organizational, and environmental determinants of AI adoption.

Literature Review

Theoretical Framework

Adoption of technology often involves disruption to the existing work processes and practices. However, understanding the factors that influence the adoption and integration of new technologies in business is critical in improving the innovation adoption processes. AI technology adoption, similar to other technologies can be explained through the TOE framework.

Technology Organization Environment Framework (TOE) Model

In this research, the Technology, Organization, And Environment (TOE) model was adopted to give the theoretical background of the study. The TOE model articulates that the adoption of new technology is based on three principal components including, technology, organization and environment [15]. The Technological component in new technology adoption considers the technological concerns of the new technology to the organization [16]. For instance, small and medium-scale enterprises are concerned with the security of their operations. Technological data among SMEs hold important data on customers, suppliers and other business processes, and any compromise will lead to serious security breaches. Similarly, the other technological concern includes the reliability of the new technology. SMEs operate in competitive environments and reliable technology will increase their competitive advantage.

The organizational component influencing the adoption of new technology, under the TOE framework, includes aspects such as firm size. SMEs with more resources are likely to enjoy economies of scale and have a higher chance of adopting new technology compared to SMEs with limited resources [17]. Firm scope is the other organizational component that influences new technology adoption. Firm scope involves the range of activities of a firm. For instance, a business with several business partners in various geographical regions is likely to benefit from new technology adoption compared to a business located in one region. The environmental component impacting the adoption of new technologies includes aspects such as competitive pressure. Often, SMEs operating in highly competitive environments are likely to be motivated by the competitive pressure to adopt new technologies and improve their competitive advantage [18]. Similarly, the aspect of regulatory influence is the other motivating factor under the environmental component for firms to adopt new technology. For instance, requirements by regulatory authorities for the adoption of specialized standards may be a motivating factor for the adoption of new technologies despite experiencing higher transaction costs.

Empirical Literature and Hypothesis

Various previous empirical research indicates the adoption of new technologies in Small and medium-sized organizations based on The Technological, Organizational and Environmental (TOE) framework.

Technological infrastructure

The study by [19], analysed the influence of technological infrastructure on the adoption of AI in SMEs. While AI technologies come with the capabilities of complex tasks associated with human intelligence by computer-controlled robots, there is a need to involve communities and other capabilities, the research determined that technological infrastructure is an environmental component whose unavailability can jeopardize the AI integration by SMEs. Similarly, the study by [20], affirms that technological infrastructure reflects the SMEs’ attitudes towards technological innovation and can influence their readiness to adopt AI technologies. The technological infrastructure among SMEs ranges from information technologies, capabilities, communities and processes. A well-organized infrastructure with SMEs thus has the potential to foster adoption of the new AI technologies and fuelling growth. Among SME businesses, the component of technology infrastructure presents a positive influence on the organization’s attitudes towards AI technology adoption. This hypothesis was proposed as a result:

H1: Technology Infrastructure has a positive and significant influence on AI technology use by SMEs.

Regulation: Regulation policies influence the level of adoption of new technology among SMEs. The study by [21], articulates that regulation policies by governments often influence the SMEs’ readiness to adopt AI technologies. For instance, organizational obligations, including tax obligations are likely to influence SME’s decisions to adopt AI and attain lenient regulations and technological competence. Supportive regulatory policies play a significant role in facilitating the quick adoption of AI technologies among SMEs. [22] asserts that government support through favorable regulations helps SMEs to implement technology diffusion in their businesses. This analysis led to the development of the following hypothesis:

H2: Regulation has a positive and significant influence on AI technology use by SMEs.

Complexity: Complexity is a technological component impacting the adoption of AI in SMEs. According [24], complexity involves the level of difficulty in learning and understanding new technology. New technology is often perceived to be complex and may involve difficulties among the human resources of the business to learn and und understand the new processes. Where the SMEs find AI technology complex, the ease of adoption would be difficult. Often, AI technologies involve complex processes that are like human activities, done by robots, and this complexity is associated with the negative impact on innovation and adoption of AI among SMEs. The study by [24] affirms the rate of adoption of AI among SMEs can be reduced where the business management finds the AI technologies difficult to understand and implement. The organization’s management is tasked with the role of promoting innovation and facilitating the organization to emerging technologies. Thus, the component of complexity presents a negative effect on the SMEs’ adoption of AI technology. This led to the following hypothesis:

H3: Complexity has a positive and significant influence on AI technology use by SMEs.

Perceived Relative Advantage: The relative advantage in the adoption of new technology involves the extent the benefits are considered to be beneficial over the existing systems. Research by [25] articulates that existing research portrays the use of AI as beneficial in improving organizational efficiency and adaptability. The positive perceived relative advantage thus exerts a positive impact on the adoption of AI infrastructure among SMEs. The Outcomes of the research by [26] on the external factors influencing AI adoption, indicated that the perceived relative advantage of AI technologies had a positive impact on its adoption among SMEs. The likelihood of AI innovation adoption increases with increased perceived relative advantage. As a result, the following hypothesis was proposed.

H4: Perceived relative advantage has a positive and significant influence on AI technology use by SMEs.

Resources: Organizational resources including information technology, human resources and finance, influence the level of technological adaptation in SMEs. According to a study by [27] technological knowledge within the organization plays a critical role in the business’s ease of adopting new technology. Institutional technological knowledge involves the human resource’s technological expertise. A higher technological knowledge among SME human resources leads to a greater potential for businesses to adopt the use of AI technologies. According to [28], organizational resources are often limited; thus, the greater perceived benefit of the new technology is likely to influence its adoption. The greater perceived benefits of AI technologies in relation to SME resources have a positive impact on the adoption of the technologies. The following hypothesis were proposed as a result:

H5: Resources has a positive and significant influence on AI technology use by SMEs.

Management Support: The top management is part of the organizational component that impacts the adoption of new technology in SMEs. The study by [29] articulates that the top management in organizations understands the importance of new technology and facilitates the conditions for the adoption of emerging technologies. For instance, the top management facilitates the conditions including improving the technological infrastructure and improving the degree of employee perception of new technologies. The nature of management support in SMEs thus has both positive and negative effects on AI technology adoption. Research [30] asserts that openness towards new technology adoption is facilitated by the top management, and management support plays a critical role in the organization’s openness to technology adoption and innovation. Thus, management support plays a role in various aspects including technology acceptance, and organizational perceived benefits of the new technologies. The following hypothesis was developed:

H6: Management support has a positive and significant influence on AI technology use by SMEs.

Conceptual Framework: From the theoretical literature discussed above (Technology-Organization-Environment) and the analysis of the previous literature, a conceptual framework was developed. The framework comprised of six independent variables (Technology infrastructure, Regulations, Complexity, Perceived relative advantage, Resources, Management support) and one dependent variable namely the AI adoption by SMEs. The conceptual framework is summarized in the Figure 1 below.

Research Methodology

Sample and Sampling

The population of this study were the SMEs having their operations in the United Kingdom. United Kingdom was considered suitable because it is among the leading countries in Europe in terms of AI adoption, particularly among SMEs. As well, there are many initiatives from the private and public sector promoting AI in SMEs. A representative sample was necessary from which the data was collected. Primary data was used, collected from the SMEs staff using a structured survey instrument. The representative sample comprised of SMEs CEOs, founders, managers and IT professionals, who were aware of the adoption and implementation of AI in their enterprises. Convenience sampling technique was adopted, to sample the respondents with the following inclusion criteria; i) their enterprises implement AI in its operations, ii) they are aware of AI aspects. The sample size was determined using the sample to item ratio technique. The recommended ratio of 1:10 was adopted [31], where the study has 35 items (questions for all the latent variables), which required a minimum sample size of 350 responses.

Instruments and Data Collection

Primary data was collected from the sample respondents for the study. A structured closed ended questionnaire was adopted for data collection. The questionnaire contained several sections. The first section captured the demographic characteristics of the respondents such as age, gender, education levels and awareness of the AI. The section contained questions/items for each of the study latent variables. The questions were developed following the 5-Point Likert Scale, where 1 = strongly disagree to 5 = strongly agree. The survey instrument was pre-tested with a small group of SME staff to ensure clarity and reliability.

Data was collected by hosting the questionnaire on the SurveyMonkey.com and inviting respondents through email and social media (Facebook) advertisement. The respondents were requested to answer the questions and submit the questionnaire. A total of 456 responses were received. Upon cleaning of the data, the usable responses for the study remained 413, which was above the minimum required threshold. The data analysis involved various techniques. The first analysis was the descriptive statistics of the respondents’ demographic characteristics. The second analysis was evaluation of the fitness of the study variables using reliability and validity analysis. The model fitness was also tested using confirmatory factor analysis (CFA). The structural equation modelling (SEM) was used to test the hypothesis of the study, by evaluating the relationship between the study variables. SEM was considered suitable because it allows for the examination of complex relationships among multiple variables, and simultaneous analysis of direct and indirect effects among the independent and dependent variables.

Data Analysis and Results

Demographic Analysis

The analysis of the demographic characters is presented in Table 1 below. The gender indicated that majority were female comprising of 55% while male was 45%. Considering the age of the variables, majority were those aged between 18-27 years (59.8%) followed by those aged between 44-59 years (21.1%). The least age was 79-99 being the least at 1.7%. the highest level of education of the respondents was evaluated, where majority being those with undergraduate level (51.1%) followed by those with secondary level (35.1%), and then those with post graduate degree (13.3%). The number of employees in these SMEs was evaluated, where majority were those employing 1-49 employees (74.3%) followed by those with employees above 50 (25.7%). The last aspect that was evaluated was the work experience of the respondents where majority indicated that they had 0-4 years’ work experience comprising 56.4%, followed by those who had more than 20 years’ experience (20.3%).

Table 1: Descriptive statistics of demographic characters.

Variable	Categories	Frequency (n)	Percent (%)
Gender
	male	186	45
	female	227	55
Age		Frequency (n)	Percent (%)
	18-27 years	247	59.8
	28-43 years	58	14
	44-59 years	87	21.1
	60-78 years	13	3.1
	79-99 years	7	1.7
Highest education level		Frequency (n)	Percent (%)
	Secondary School	145	35.1
	Undergraduate degree	213	51.5
	Postgraduate degree	55	13.3
No of Employees		Frequency (n)	Percent (%)
	1-49	307	74.3
	>50	106	25.7
Work Experience		Frequency (n)	Percent (%)
	0-4 years	233	56.4
	5-10 years	43	10.4
	11-15 years	18	4.4
	16-20 years	35	8.5
	>20 years	84	20.3

Model Fitness Analysis

The other analysis that was conducted was the model fitness, reliability and analysis of the study constructs. The evaluation was carried out by running CFA. The first analysis was the model fitness evaluation. From the fitness index tests, PCMIN/DF was 2.582, which satisfied the required threshold of <5.0. The RMR was 0.055, and RMSEA was 0.062, and they met the required threshold of <0.08. Additionally, the CFI was 0.913, TLI was 0.903, and IFI was 0.914. these were within the required threshold of >0.90. the GFI was satisfactory at 0.840 being >0.80 [32, 33, 34, 35]. The CFA model is presented in Figure 2 below.

In addition to the model fitness tests, the reliability and validity analysis of the study was conducted. As summarized in Table 2, validity was evaluated using average variance extracted (AVE) and standardized factor loadings, whose required threshold is >0.50. The reliability was evaluated using Cronbach’s alpha and composite reliability (CR) whose required threshold >0.70. for the results of the regression weight, the valued ranged from 0.641 to 0.814. For the CR, the values ranged from 0.807 to 0.892. For the values of AVE, the ranged from 0.517 to 0.655. the values for the Cronbach’s alpha ranged from 0.732 to 0.894. These values were all within the required threshold, which indicated that both the reliability and validity requirements were satisfied.

Table 2: Reliability and Validity Analysis.

		Std. Regression Weights	CR	AVE	Cronbach’s alpha
AIA	AIA1	0.696	0.830	0.694	0.732
	AIA2	0.739
	AIA3	0.689
	AIA4	0.729
	AIA5	0.657
Comp	Comp1	0.817	0.859	0.549	0.865
	Comp2	0.736

	Comp3	0.755
	Comp4	0.684
	Comp5	0.707
Msu	Msu1	0.652	0.842	0.517	0.847
	Msu2	0.733
	Msu3	0.68
	Msu4	0.756
	Msu5	0.767
PRA	PRA1	0.782	0.873	0.579	0.756
	PRA2	0.705
	PRA3	0.743
	PRA4	0.775
	PRA5	0.797
Reg	Reg1	0.674	0.807	0.655	0.809
	Reg2	0.724
	Reg3	0.667
	Reg4	0.641
	Reg5	0.665
Res	Res1	0.788	0.892	0.624	0.894
	Res2	0.817
	Res3	0.76
	Res4	0.814
	Res5	0.769
TInf	TInf1	0.74	0.884	0.605	0.749
	TInf2	0.742
	TInf3	0.818
	TInf4	0.816
	TInf5	0.768

Hypothesis Evaluation

The hypothesis of the study was conducted by evaluating the relationship between the study variables. The results indicated that technology infrastructure has a positive and significant influence on AI adoption decision (β = 0.109, p = 0.000), supporting hypothesis 1. The results further indicated that regulation has a positive and significant influence on AI adoption decision (β = 0.389, p = 0.000), supporting hypothesis 2. Complexity was found to have an insignificant and negative influence on AI adoption decision (β = -0.002, p = 0.932), rejecting hypothesis 3. Perceived relative advantage was found to have a positive and significant influence on AI adoption decision (β = 0.349, p = 0.000), supporting hypothesis 4. Resources was found to have a positive and significant influence on AI adoption decision (β = 0.057, p = 0.028), supporting hypothesis 5. Management was found to have a positive and significant influence on AI adoption decision (β = 0.185, p = 0.000), supporting hypothesis 6. The results are summarized in Table 3 and Figure 3.

Table 3: Empirical analysis of Hypothesis.

Hypothesis	Path Relationship			Beta	S.E.	C.R.	P
H1	TInf	à	AIA	.109	.030	3.607	***
H2	Reg	à	AIA	.389	.049	7.958	***
H3	Comp	à	AIA	-.002	.029	-.085	.932
H4	PRA	à	AIA	.349	.039	8.885	***
H5	Res	à	AIA	.057	.026	2.191	.028
H6	Msu	à	AIA	.185	.035	5.344	***
NB: TInf = technology infrastructure; Reg = regulation; Comp = complexity; PRA = perceived relative advantage; Res = resources; Msu = management support; AIA = AI adoption decision

Discussion

The findings presented above shows some significant results, which provides a valuable insight regarding the factors that influence AI adoption by SMEs. In this section, these findings are discussed with comparison to previous results. The regulation was found to have highest influence in the AI adoption by SMEs. The results showed that if regulations improve by one unit, AI adoption would improve by 0.389 units. Regulatory framework is therefore important in shaping technological adoption. A clear and supportive regulatory framework enables the reduction of risk and uncertainties of new technologies, encouraging its adoption [36]. In UK, having an explicit framework that adequately addresses data privacy, security, and ethical considerations is critical towards cultivating a conducive environment for SMEs to thrive in terms of AI adoption and integration in their business operations [37].

Perceived relative advantage was the second most influential critical factor. From the empirical results, if perceived relative advantage improved by one unit, AI adoption by SMEs improves by 0.349 units. If the users have a feeling that the concerned AI technology would offer superior and significant advantage as compared to the current system, their attitude towards the technology would be improved. As suggested by [38], the relative advantage such as improved efficiency, enhanced decision-making, and competitive advantage enhanced technology adoption. This study echoes the findings of [39], that SMEs are more likely to adopt technologies that they perceive as advantageous and beneficial to their operations. To encourage AI adoption by SMEs in UK, the proponents would need to demonstrate tangible benefits of AI to their businesses.

Management support was another critical factor of influence on the AI adoption by SMEs. These results suggested that management support plays a critical role in offering the necessary vision, commitment, and resources for successful adoption and technology implementation. In the context of AI adoption, the management support initiatives that would be important include endorsing AI initiatives, allocating budgets, and fostering a culture that embraces technological innovation in SMEs [40]. Active and supportive leaders are crucial technological progress of the company.

Additionally, technological infrastructure has a significant influence on AI adoption by SMEs. Technological infrastructure implies the hardware, software, and network capabilities that enable seamless AI deployment and usage. These aspects form the backbone of the of the digital capabilities within an organization. This positive relationship implies that SMEs that have a better technological capabilities and resources are positioned to capable of adopting AI technologies. This aligns with [41] argument that technological infrastructure capacity enhances the support of the computational capabilities and data requirements which ware necessary for AI adoption. For effective AI adoption, this study suggests that investment and maintenance of robust technological foundation is crucial. Additionally, resources were found to have a significant influence on the AI adoption by SMEs. It implies that availability of adequate resources is critical for absorption of new technologies [42]. Among the important factors include financial resources, human, and technical resources. The findings of this study suggest that SMEs that have significant resources are well positioned to invest significantly in AI technologies. They are well positioned to cover the associated costs and the required ongoing maintenance. It is critical to note that complexity was found to have an insignificant influence on AI adoption by SMEs in UK.

Implications

The findings of this study suggest important theoretical and managerial contributions, which offer insights interested stakeholders on the aspects AI adoption by SMEs in UK. Considering the theoretical implications, this study leverages the Technology-Organization- Environment (TOE) framework in exploiting the AI adoption among SMEs. This theory expands the understanding of the critical factors that influence the AI integration. This study cements TOE applicability in the SMEs adoption of SMEs. It is therefore a relevant and robust framework in its application in the context of technology landscape. TOE has three context – technology, organizational and environmental. The aspects of the three contexts were proved critical to consider in SMEs AI adoption. Considering the technology context, this study found perceived relative advantage as a significant factor. Considering the organizational context, this study indicated the resources and management support as significant factors that influence the AI adoption among SMEs in UK. It implies that internal organizational factors are critical in driving AI implementation. The environmental context relevance was supported by significance of technological infrastructure and regulation in influencing AI adoption decisions.

Several managerial implications were suggested. First, this study recommends the importance of SMEs investment in technological infrastructure. Prioritizing and establishing a robust technological infrastructure would cultivate a strong technological foundation to support AI adoption decisions. Secondly, the strong technological infrastructure should be aligned with effective regulatory support and compliance. This recommendation is two-fold. The policy makers should concentrate on making clear and supporting standards for AI adoption. As well, the SMEs should prioritize understanding the laid policies such as data privacy, security, and ethical considerations. This would lead to a conducive environment and reduce uncertainties in AI adoption. This research further recommends that in bid to encourage AI adoption decision, the SMEs managers should highlight the tangible relative advantage and benefits of AI to their CEOs and stakeholders. These could include improved efficiency, decision-making, and overall enhancement in business performance. Lastly, this study recommends the importance of adequate resource allocation as a priority. AI integration requires significant resources in terms of finance and human resource. As a result, strategic allocation of resources is critical as far as AI adoption decision in SMEs is concerned.

Conclusions and Future Research

From the study on the artificial intelligence and SMEs findings and discussions, several conclusive remarks could be highlighted. Several factors are critical in influencing the AI adoption decision among UK SMEs. According to the level of significance, the critical influencers of AI adoption include regulations, perceived relative advantage, management support, technology infrastructure, and resources. Complexity was not a significant factor. This study holds that in that order of priority, these aspects should be considered when SMEs are considering adopting AI in their businesses. The study recommended that Technology- Organization-Environment (TOE) framework three contexts – technology, organizational and environment are critical to consider. The study further recommended that to have an effective AI adoption and implementation decisions, SMEs should consider investing in technological infrastructure, consider regulatory support and compliance, highlight the relative advantage and benefits, and mobilize resources. The first limitation is the geographical limitation.

The study’s focus on the UK limits the generalizability of the findings to other regions. This is because different countries have varying levels of AI adoption, regulatory environments, and technological infrastructures, which might influence AI adoption in SMEs differently. Secondly, the study employs a cross-sectional research design, which captures data at a single point in time. This approach limits the ability to assess the long-term effects of AI adoption on SMEs or to observe how adoption patterns might change over time. Considering that the study was conducted on all SMEs in UK, this study recommends future research to consider sector-specific research. Additionally, the study recommends the future studies to adjust the TOE model and include additional variables such as SMEs size and organizational maturity. Lastly, future research can consider a comparative study between SMEs and larger enterprises to identify the unique challenges and opportunities that SMEs face in AI adoption.

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Cite this article as:
Mathagu SW. Artificial intelligence in Small and Medium Enterprises – An Empirical Analysis of Critical Factors. Premier Journal of Science 2024:1;100009

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