Abstract
Purpose
This study aims to establish a comprehensive framework for adopting mobile-based artificial intelligence (AI) services in Tanzanian manufacturing small and medium enterprises (SMEs).
Design/methodology/approach
The methodology involved conducting a literature review and using the combination of Mobile Services Acceptance Model and Innovation Diffusion Theory (IDT) as a theoretical foundation. This synthesis delves into the current knowledge on technology adoption, organizational behavior and innovation diffusion, creating a solid conceptual basis. Expert review was used for framework validation to ensure the framework's accuracy.
Findings
This study shows that the factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs include perceived usefulness, perceived ease of use, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, power distance, compatibility, observability and trialability.
Research limitations/implications
The framework provides valuable insights tailored to Tanzanian sociocultural and economic nuances. However, its generalizability is limited due to its specificity to Tanzanian manufacturing SMEs.
Practical implications
The framework outlined in this research provides SME leaders, policymakers and technology implementers with valuable guidance to make informed decisions during the adoption process.
Originality/value
This study introduces a novel lens for understanding technology adoption. This study's focus on the Tanzanian context and its nuanced examination of contributing factors add to its originality and practical significance.
Keywords
Citation
Ishengoma, F. and John, E. (2024), "Factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs", Vilakshan - XIMB Journal of Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/XJM-11-2023-0214
Publisher
:Emerald Publishing Limited
Copyright © 2024, Fredrick Ishengoma and Elia John.
License
Published in Vilakshan - XIMB Journal of Management. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode.
1. Introduction
The evolving domain of global business emphasizes the pivotal role played by small and medium enterprises (SMEs) in fostering economic growth, generating employment and contributing to overall industrial development (Mandari et al., 2021). As the backbone of many economies, SMEs recognize the importance of leveraging emerging technologies to increase operational efficiency, simplify processes and compete in a dynamic marketplace (Mushi et al., 2023). In this context, the advent of artificial intelligence (AI) has emerged as a transformative force, providing unprecedented opportunities for SMEs to transform their operations (Wei and Pardo, 2022).
AI is a branch of computer science dedicated to development of intelligent machines capable of executing jobs that need human intelligence. These machines can analyze data, learn from experiences, make decisions and solve problems much like humans do. AI uses algorithms, machine learning methods and extensive datasets to acquire knowledge, enhance performance and adjust to new situations. AI is applied across various industries, such as health-care transportation, gaming, finance and SMEs (Lada et al., 2023; Luo et al., 2022).
AI has proved to provide several advantages to SMEs. These range from enhancing competitiveness and adapting to evolving markets, improving operational efficiency, decision-making processes and customer experiences. Additionally, they enable SMEs to leverage data analytics, automation and predictive capabilities to drive growth and innovation, gaining insights into market trends, optimizing resource allocation and identifying new business opportunities, empowering SMEs to stay relevant in the digital age, overcome resource constraints and achieve sustainable growth in a rapidly changing business landscape (Lemos et al., 2022).
The widespread adoption of smartphones in Tanzania offers the potential environment for using mobile-based AI services. In the meantime, the advancement of AI is reshaping traditional practices in various disciplines, including SMEs (Hansen and Bøgh, 2021). The impact of AI on manufacturing is becoming increasingly apparent, changing how businesses operate and positioning them on the frontier of technology integration. For Tanzanian manufacturing SMEs, the integration of AI offers a potential transformation in terms of productivity and competitiveness.
To take advantage of these developments, SMEs need a structured framework that matches the contextual environment because adopting a general framework may not reflect Tanzanian manufacturing SMEs' unique socioeconomic and technical nuances (Shaikh et al., 2021). This need for specificity is emphasized by acknowledging that technology is not universally appropriate work. Tanzanian manufacturing SMEs face challenges and opportunities that differ from those in more developed countries. Thus, a framework that uniquely captures the Tanzanian environment is crucial.
Furthermore, the current body of literature does not adequately consider the unique difficulties and opportunities in developing nations favoring mobile-based AI services in more developed economies. More empirical studies and theoretical frameworks tailored to the particular conditions of developing economies like Tanzania are needed, as evidenced by the need for more literature. This study aims to address the following research questions in light of the arguments above:
What are the key factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs?
How can a practical framework be developed to guide the strategic integration of mobile-based AI services in Tanzanian manufacturing SMEs?
This study improve the understanding of the dynamics of technology adoption in Tanzanian manufacturing SMEs at the nexus of smartphones and AI services. The study fills a critical gap in the literature by identifying the main factors influencing the adoption of these technologies, laying the groundwork for informed decision-making within the SME sector. The study also offers a framework tailored to Tanzanian SMEs' socioeconomic and technological landscape, going beyond acknowledging challenges.
2. Literature review
2.1 Small and medium enterprises and technological advancements in Tanzania
SMEs constitute an essential and dynamic sector of Tanzania's economy, significantly boosting employment, income generation and economic growth (Gamba, 2019). A key component of modern business landscapes is how technology influences SMEs' growth (Msuya et al., 2017). SMEs struggle to adopt technology due to resource scarcity, technical naivety and a lack of awareness (Wei and Pardo, 2022). However, in recent times, the situation has been transforming. SMEs in various industries have realized the advantages of incorporating technology into their operations and are signing up.
Initiatives that have been put forward to promote technological integration reflect the Tanzanian government's recognition of SMEs' crucial role in economic development (Kabanda and Brown, 2017). In the meantime, Tanzania's manufacturing SMEs have the opportunity to adopt cutting-edge technologies like AI services using mobile phones. This is due to the widespread use of mobile technology and the steady increase of bespoke AI solutions designed for SMEs, which open up exciting prospects for innovation and efficiency.
2.2 Mobile-based artificial intelligence services
Mobile-based AI services represents AI technologies using the mobile platforms (smartphones and tablets) to execute tasks that require human intelligence with the support of machine learning algorithms, natural language processing and data analytics (Hwang et al., 2021; Singh et al., 2021; Manser et al., 2021). AI mobile-based services encompass many applications and functionalities that leverage AI technology to enhance user experiences, automate tasks and provide personalized services on mobile devices (Luo et al., 2022). Some common examples of AI mobile-based services in SMEs include transaction execution (Khan et al., 2023), customer support chatbots and personalized product recommendations (Sharma et al., 2022). The services provided by mobile-based AI to enable innovative and intelligent manufacturing include predictive maintenance, quality control, supply chain optimization and customer and decision-making support for manufacturing small and medium-sized enterprises, which can be operated on a mobile device (Lee and Chen, 2022).
The generic architecture of mobile-based AI services typically comprises several vital components, including hardware, software, cloud integration and security. The hardware layer consists of smartphones and tablets equipped with processing capabilities to run AI algorithms in a local context or leverage the power of cloud-based AI services (Banafaa et al., 2023). Such devices can include dedicated AI chips or optimize essential processors for machine learning tasks like pattern recognition. Another important part of hardware is hardware sensors such as cameras, microphones and global positioning system (GPS) modules, which acquire real-time data from a physical environment or user input (Seng et al., 2022).
The software layer also comprises tools for AI operations on mobile devices. AI frameworks and libraries tailored to mobile environments enable end users to develop and run AI models for data preprocessing, inference and result visualization (Cebollada et al., 2021; Luo et al., 2022). Built-in AI frameworks from android and iOS mobile operating systems support AI development and offer the convenience and assurance of the platform's security standards (Luo et al., 2022). Cloud integration, offloading tasks for computation and storage capacities to remote platforms, is vital to the software, allowing end users to fully use devices and process data (Mohammed et al., 2023).
Tanzania SMEs have the potential to harness the benefits of mobile-based AI services as it can assist in improving production and competitive practices strategically. Mobile-based AI applications offer solutions that can be customized to the circumstances of SMEs (Pelekamoyo and Libati, 2023). Manufacturing firms that use mobile-based AI can reconfigure production using workflow management and resource allocation to enhance enterprise efficiency. In particular, automated applications can assist in carrying out routine tasks such as scheduling and inventory management through data-based observation.
Moreover, using technical data, AI-driven quality-control systems can enable greater precision in a product's design and characteristics and ensure consistently high quality and iterative improvements in stability (Abrokwah-Larbi and Awuku-Larbi, 2023). Because particular variations in production can occur due to raw material differences, quality-control systems fueled by mobile AI using computer vision embedded within production processes can identify defects in real-time and prevent elements that do not meet quality standards from entering the supply chain. Through such initiatives, errors in manufacturing can be minimized and both customer satisfaction and companies' brand profile bolstered. In Tanzania, several SMEs could also benefit from AI-enabled predictive maintenance systems. Such tools that use sensors and information from IoT devices to identify maintenance hazards, reduce downtime and extend assets' lifespans can help SMEs conserve resources and enhance operational efficiency.
2.3 Mobile services acceptance model
Models have been put forth to explain the complex dynamics of user acceptance in the extensive literature on technology adoption. The Technology Acceptance Model (TAM), developed by Davis (1989) and an improvement of the Theory of Reasoned Action put forth by (Fishbein and Ajzen, 1975), is a strong contender in this area. TAM has become the leading model for examining how technology is used, providing vital measurements and insights into how users interact with new technologies. TAM's, however, fall short when used with mobile technologies. With the limitations of TAM in representing the adoption of mobile technologies and services, Gao et al.'s (2008) study instigated a shift in thinking with the Mobile Service Acceptance Model (MSAM). By incorporating some of the established constructs from TAM, such as Perceived Usefulness (PU), Perceived Ease of Use (PEoU) and Intention to Use, MSAM lays a good foundation for the model while adding extra constructs to help explain the unique challenges of mobile technologies (Figure 1).
While numerous scholars advocate for using TAM-based extension models across various research contexts, the exploration of MSAM remains lacking in AI-based mobile services in manufacturing SMEs. Studies argue that more research is required to investigate the applicability of TAM and its extensions outside the context in which it has been examined and validated (Sargolzaei, 2017).
MSAM is relevant in explaining the adoption of mobile-based AI services in Tanzanian manufacturing SMEs, especially given the significant rise in mobile phone adoption among these enterprises (Mushi et al., 2018; Swallehe, 2021). With the widespread availability and increasing penetration of mobile devices in Tanzania, SMEs are uniquely positioned to leverage mobile platforms for AI applications (Mushi et al., 2018). MSAM's focus on PU and PEoU provides a framework for understanding how SMEs perceive the value and usability of mobile-based AI services.
2.4 Innovation diffusion theory
Innovation Diffusion Theory (IDT) identifies five critical attributes that influence the adoption of an innovation: relative advantage, compatibility, complexity, trialability and observability (Rogers, 1983). Relative advantage refers to the degree to which an innovation is perceived as better than the existing solutions (Gharaibeh et al., 2020). Compatibility is the extent to which an innovation is consistent with the existing values, past experiences and needs of potential adopters (Wang and Lin, 2019). In other words, compatibility is the ability of the technology to fit within the lifestyle of potential adopters.
Complexity refers to how difficult the innovation is to understand and use. It refers to the degree to which an innovation is perceived as difficult to understand and use (Wang and Lin, 2019). In environments where technical expertise and resources are limited (such as in the context of Tanzanian manufacturing SMEs), the perceived complexity of AI technologies can be a significant barrier to adoption. If mobile-based AI services are seen as overly complicated, requiring extensive training or substantial changes to existing workflows, SMEs are likely to be hesitant (Ben Hamadi and Fournès, 2023). The complexity can deter decision-makers who fear that their workforce may struggle to use the new technology effectively, potentially leading to disruptions in operations and productivity.
Trialability is the degree to which an innovation can be tested on a limited basis. Trialability also play vital roles, as SMEs are more likely to adopt these services if they can see tangible, positive results from limited trials and observe the benefits realized by others in their industry (Mamun, 2018). Trialability allows SMEs to test mobile-based AI services on a small scale before full implementation, reducing perceived risks and uncertainties. Rogers (1983) argued that trial of innovation reassures the adopter and reduces risks and uncertainty associated with adopting the technology. Also, it has been found that the likelihood of adopting an innovative technology will be increased if users are given the opportunity to experiment with the technology prior to adoption (Ben Hamadi and Fournès, 2023).
Observability pertains to the degree to which the results and benefits of an innovation are visible to others (Rogers, 1983). In the Tanzanian context, where SMEs are often cautious about adopting new technologies due to financial and operational constraints, seeing the tangible benefits of mobile-based AI services in peer organizations can be a powerful motivator (Mamun, 2018). For example, if an SME successfully implements AI-driven solutions to streamline its production process, reduce costs, or improve product quality, other SMEs observing these successes are more likely to consider adopting similar technologies (Shetty and Panda, 2020). The visibility of these benefits can significantly reduce uncertainty and provide a compelling case for investment in AI services. Moreover, observability contributes to building trust and reducing the perceived risks associated with new technologies.
In this study, IDT (Figure 2) complements the MSAM in elucidating the adoption of mobile-based AI services in Tanzanian manufacturing SMEs. However, due to the correlation between the relative advantage of IDT and PU of MSAM, as well as the correlation between the complexity of IDT and the PEoU of MSAM, relative advantage and complexity will be excluded from the model.
3. Methodology
The study conducted a systematic literature review following the specifications of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2015 checklist to ensure transparency and rigor at each review step (Moher et al., 2015).
Inclusion criteria for this review are limited to peer-reviewed articles, conference proceedings and books written in English from 2013 to 2023. The review covers studies on browsing mobile-based AI, factors influencing manufacturing SMEs' adoption of technology and all related Tanzanian factors. This article adopted the SME definition from Tanzania's SME development policy (United Republic of Tanzania (URT), 2003), which describes an SME as a business entity with fewer than 100 employees and a capital investment not above Tshs 800m. Exclusion criteria were used to exclude non-peer-reviewed articles, outdated works and materials not in English.
Searches were conducted across highly regarded electronic databases covering information systems and technology adoption literature. These databases included Scopus, Web of Science and Institute of Electrical and Electronics Engineers (IEEE) Xplore. The search strategies were formed using synonyms plus controlled vocabulary terms in various combinations connected by Boolean operators. The research keywords include “adoption,” “mobile-based AI services,” “mobile-based artificial intelligence,” “technology adoption,” “mobile-AI adoption,” “success factors,” “framework,” “manufacturing SMEs,” “adoption factors” and “Tanzania.”
Titles and abstracts identified through the database searches were independently assessed for eligibility against two reviewers' specified inclusion and exclusion criteria. Full-text screening of potentially relevant articles was conducted to determine eligibility. Any disagreements that arose between the reviewers were discussed and resolved. A standardized form was formulated to extract data from selected studies, which mainly captured the study metadata and key research outcomes on adopting mobile-based AI services among Tanzanian manufacturing SMEs.
The methodological quality of the studies in the review was systematically assessed to evaluate the rigor with which they were implemented. The methodological rigor of each of the included studies was examined. For example, the methodological rigor of qualitative studies was assessed in terms of the level of clarity in their research questions, the appropriateness of the data collection methods and the rigor with which the data analysis was conducted. For quantitative studies, the rigor with which the study was conducted was assessed in terms of the robust nature of the study design, clarity on how the sample size was determined and which statistical methods were used.
Data synthesis applied thematic synthesis to identify and interpret findings from the included studies. This allowed for identifying themes and patterns permeating the entire body of literature on determinants influencing adoption of mobile-based AI services in Tanzanian manufacturing SMEs. Data synthesis involved coding and labeling the segments extracted from the full text and abstracts of the included studies through the NVivo software package to organize and explore these themes systematically.
Expert evaluation was used to determine the validity of the proposed framework, using ten experts selected from various fields (Table 1). The experts were given a questionnaire for their initial survey. It examined multiple aspects of the framework and used a scoring system ranging from 1 to 5, where 1 indicated low validity and 5 signified high validity. The scoring system was used to evaluate different aspects of the framework, including theoretical foundation, applicability to Tanzanian manufacturing SMEs, impact on SME operations and feasibility of implementation. Table 1 tabulates the details of the experts involved in the validation process.
Figure 3 depicts the methodology used in this study.
4. Findings
4.1 Factors for the adoption of mobile-based artificial intelligence services in manufacturing services
The analysis encompassed diverse studies aiming to discern the success factors influencing the adoption of mobile-based AI services in manufacturing SMEs. The values associated with these success factors were quantified using content analysis methodology. In identifying critical success factors (CSFs), this study considered all factors with over 25% occurrence in the literature. The resulting compilation of success factors for adopting characterized AI services in manufacturing SMEs is presented in Table 2.
The literature review findings reveal 11 significant factors for the adoption of mobile-based AI services in manufacturing SMEs relevant to the Tanzanian context, namely, PU, PEoU, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, observability, trialability and compatibility.
4.1.1 Perceived usefulness.
The technology adoption in emerging economies, including Tanzania, underscores the enduring relevance of TAM's core construct – PU, in influencing the acceptance and integration of innovative technologies (Davis, 1989; Gao et al., 2008; Chale and Mbamba, 2015). Literature supports that technologies that demonstrate their usefulness by improving productivity, efficiency and overall operational effectiveness are more likely to be adopted by manufacturing SMEs (Chale and Mbamba, 2015; Lubua and Semlambo, 2017; 2021; Mandari et al., 2021).
4.1.2 Perceived ease of use.
Within the domain of technology adoption, the PEoU holds great significance as it addresses the potential challenges arising from limited technical expertise and resources (Davis, 1989; Gao et al., 2008). Studies emphasize that manufacturing SMEs are more likely to adopt mobile-based AI services with intuitive and user-friendly interfaces requiring minimal training (Davis, 1989; Gobin-Rahimbux et al., 2017; Lubua and Semlambo, 2017; Shah et al., 2020; Tongora and Ndume, 2020; Mandari et al., 2021).
4.1.3 Context.
The importance of mobile-based AI services in manufacturing SMEs cannot be emphasized enough. These businesses operate in distinct environments with unique challenges and requirements. AI services must align with contextual factors such as the nature of manufacturing processes, supply chain complexities and market dynamics. Understanding the manufacturing context makes it possible to effectively tailor the technology to address the specific needs and intricacies of SME operations (Ndesaulwa et al., 2017; Lubua and Semlambo, 2017; Msuya et al., 2017; Kabanda and Brown, 2017).
4.1.4 Personal initiatives and characteristics.
The success of implementing mobile-based AI services in manufacturing SMEs heavily relies on individuals' personal initiatives and characteristics. Employee attitudes, leadership support and a culture that promotes innovation and technology adoption are crucial factors. The willingness of individuals to adapt to change and the potential of these services impacts the overall success of the adoption process (Mpunga, 2016; Anderson, 2017; Ndesaulwa et al., 2017; Ishengoma et al., 2018; Mdasha et al., 2018).
4.1.5 Trust.
Literature shows that adopting technologically advanced services such as mobile-based AI services in SMEs heavily relies on trust, often built through personal relationships (Amoako, 2018). Establishing trust is especially important in SMEs where relationships are more personal. Tanzanian SMEs, for example, are more likely to trust AI solutions that can demonstrate proven reliability, robust security measures and positive outcomes (Kabanda and Brown, 2017; Mabula et al., 2020; Nkwabi and Fallon, 2020).
4.1.6 Infrastructure.
The feasibility of implementing mobile-based AI services in manufacturing SMEs heavily relies on their existing technological infrastructure (Ndesaulwa et al., 2017; Lubua and Semlambo, 2017; Msuya et al., 2017). SMEs with strong and flexible infrastructure can integrate technological advancements such as AI without significant challenges. Literature supports that the success of integrating AI services relies heavily on the adaptability of the technology to the existing infrastructure constraints, making infrastructure a crucial determinant in the assessment of the practicality and viability of incorporating technological advancements (Kuzilwa and Nyamsogoro, 2016; Kabanda and Brown, 2017; Nkwabi and Fallon, 2020; Shah et al., 2020; Tongora and Ndume (2020); Ye and Tekka, 2020).
4.1.7 Cost.
The cost is a significant factor for SMEs as financial resources are usually limited in emerging African economies like Tanzania. In Tanzania, citizens, business firms and SMEs are cost-conscious and typically consider the return on investment and cost-effectiveness of incorporating AI services into their operations (Ishengoma et al., 2018). Moreover, literature shows that SMEs in African developing economies, such as Tanzania, are more likely to adopt affordable solutions (Marwa, 2014; Mohamed and Mnguu, 2014; Kuzilwa and Nyamsogoro, 2016; Mpunga, 2016; Anderson, 2017; Ndesaulwa et al., 2017; Mdasha et al., 2018; Gamba, 2019; Tongora and Ndume (2020); Bigambo et al., 2023).
4.1.8 Mobility.
Literature supports that the prevalence of mobility among the SME workforce significantly contributes to creating an optimal environment for adopting technologically advanced innovations such as mobile-based AI services (Donner and Escobari, 2010; Aikaeli, 2012; Chale and Mbamba, 2015; Kabanda and Brown, 2017; Ndekwa, 2017; Ishengoma et al., 2018; Tongora and Ndume (2020)). Furthermore, it includes SME employees 'mobility, allowing them to access mobile AI services wherever they are.
4.1.9 Observability.
In the context of Tanzanian SMEs, where trust and familiarity strongly influence adoption decisions, observable benefits serve as compelling evidence of a technology’s value. Ndesaulwa et al. (2017) highlight that when SMEs can observe tangible improvements in productivity or operational efficiency, they are more likely to overcome initial skepticism and invest further in such technologies. This also fosters a positive perception among employees, stakeholders and potential adopters, reinforcing the technology's credibility and encouraging broader acceptance (Ye and Tekka, 2020). Moreover, literature underscore that observable benefits are critical in enhancing confidence and reducing perceived risks associated with technological investments (Msuya et al., 2017; Kabanda and Brown, 2017). SMEs that showcase the benefits such as cost savings, enhanced product quality or streamlined operations, are more likely to attract adoption.
4.1.10 Trialability.
Trialability facilitates a gradual approach to technology adoption, allowing SMEs to mitigate risks and uncertainties associated with new technologies (Rogers, 1983; Mamun, 2018; Ben Hamadi and Fournès, 2023). In Tanzania, where SMEs often operate within resource-constrained environments, the ability to experiment with AI solutions enables businesses to assess feasibility and adaptability without significant upfront investment (Shah et al., 2020). Studies have emphasized that trialability fosters learning and adaptation within SMEs, enabling them to tailor AI technologies to specific operational needs and workflows (Ben Hamadi and Fournès, 2023; Shah et al., 2020). This allows businesses to identify and address challenges or compatibility issues early on, enhancing the likelihood of successful integration (Wang and Lin, 2019).
4.1.11 Compatibility.
Studies argue that compatibility reduces resistance to change by ensuring that new technologies fit seamlessly into current operational practices and organizational culture (Wang and Lin, 2019). In Tanzania, where SMEs often prioritize stability and continuity, AI solutions that align with established workflows are more likely to be perceived as valuable and worth investing in (Tongora and Ndume, 2020). Moreover, compatibility fosters long-term sustainability, as technologies that resonate with SMEs’ values and operational priorities are more likely to yield enduring benefits and support continuous growth (Mdasha et al., 2018; Gharaibeh et al., 2020; Bigambo et al. (2023)). Thus, in the Tanzanian context, ensuring compatibility between mobile-based AI services and SMEs’ existing frameworks is essential for overcoming adoption barriers.
The study's conceptual framework is depicted in Figure 4.
4.2 Framework validation
The validation process concentrated on four essential dimensions: the theoretical foundation, the applicability to Tanzanian manufacturing SMEs, the anticipated impact on SME operations and the feasibility of implementation. During the validation phase, experts engaged in discussions to refine and enhance the proposed model. After extensive deliberations, a consensus emerged among the experts to incorporate an additional construct, “power distance,” into the framework. This decision acknowledged Tanzania's cultural values, particularly its high-power distance, which various scholarly works have substantiated (Hofstede, 1980, 2019; Ishengoma, 2022).
Despite its potential significance, “power distance” was not included in the summary of factors derived from the literature review due to limited support from existing studies and its failure to meet the predetermined threshold. This omission can be attributed to the dominance of literature from Western, Asian and Arab regions, where the relevance of “power distance” is less pronounced. The theoretical basis of the framework, however, received commendable evaluations, with most experts (80%) rating it as a 4 or 5, indicating a well-founded theoretical foundation.
Experts expressed strong confidence in the proposed framework's applicability to manufacturing SMEs in Tanzania. Eight out of ten experts gave positive ratings between 3 and 5, indicating that the framework is well-suited to the SME environment and capable of addressing the unique requirements and challenges faced by Tanzania's manufacturing SMEs. The framework validation results are summarized in Table 3.
The framework was assessed and most experts rated it 4 or 5, indicating that it effectively promotes positive change and enhances SMEs' performance. Furthermore, experts' assessments reflect a positive outlook on the feasibility of implementation, with values ranging from 3 to 5. This indicates that the experts consider the framework to be both logically and practically feasible. Figure 5 depicts the validated framework for mobile-based services in Tanzanian manufacturing SMEs.
5. Discussion
The study findings underscore the importance of PU and PEoU, thus fitting with findings from earlier studies (Davis, 1989; Mandari et al., 2021). The context in Tanzania emphasizes the significance of these factors, highlighting their real-world effects on decision-making within SMEs aiming for operational effectiveness. The results of this study highlight the need for contextual alignment, which echoes the study by (Chau and Tam, 2000) argument about the significance of integrating technology with context.
The personal initiatives of individuals within these enterprises play a significant role in determining the technological trajectory of Tanzanian manufacturing SMEs. Thus, adopting mobile-based AI services is successfully facilitated by employee attitudes toward innovation and technology and a general organizational culture that promotes embracing new technologies. Trust is crucial in SMEs where relationships are more personal, like Tanzania. Moreover, the role of infrastructure underscores the importance of technological infrastructure in facilitating adoption, emphasizing that the success of integrating technologically advanced services, such as mobile-based AI services, depends on the technology's adaptability to the existing infrastructure constraints (Shaikh et al., 2021).
The economic justification for technology adoption resonates with the emphasis on cost considerations. Tanzania's economic landscape is characterized by a strong focus on the cost-conscious practices of its citizens, which aligns with the argument that financial factors play a decisive role in the adoption decision. Furthermore, the significance of mobility corresponds to studies that emphasize mobility's importance in the acceptance of technology, especially in developing nations (Gao et al., 2008). The high rate of mobile device penetration in Tanzania and the extensive use of mobile technology establishes an environment conducive to the seamless integration of mobile-based AI services. The mobility factor aligns with the prevailing mobile-centric landscape in African developing countries, thereby reinforcing its crucial role in the decision-making process of manufacturing SMEs when considering the adoption of mobile-based AI services.
Furthermore, power distance notably impacts technology adoption in Tanzanian SMEs, characterized by sociocultural landscapes emphasizing respect for authority and hierarchical structures. Decision-making procedures involving the adoption of cutting-edge technologies frequently use hierarchical channels, where the perceived authority of decision-makers is intricately linked to the acceptance or resistance to change.
A strong sense of compatibility also emerged as a driver of the uptake of mobile-based AI services among the Tanzanian manufacturing SMEs. Findings from other studies have also revealed that technologies more likely to be adopted successfully are those that are compatible with the organization’s existing structures and infrastructures (Ye and Tekka, 2020; Mandari et al., 2021). Moreover, compatibility with traditional systems, as argued by Chau and Tam (2000), can further facilitate the process of adoption by making technological solutions make sense in supporting the values and practices of the organization. However, on the contrary, Maduku (2021) argued that overly rigid commitment to traditional systems might hamper innovations, which would block the adoption of disruptive technologies.
Trialability is another factor influencing adoption of mobile-based AI services in Tanzanian SMEs. Similar studies have found that the ability to trial technologies on a small scale reduces perceived risks and allows for informed decision-making (Wang and Lin, 2019; Shah et al., 2020; Ben Hamadi and Fournès, 2023). However, a contrasting perspective was highlighted the study by Li et al. (2022), which indicate that trialability may not fully capture the complexities of technology integration across different operational contexts. This limitation suggests that while trialability mitigates initial risks and provides valuable insights, it may not comprehensively predict long-term scalability or operational impacts.
The study also found that observability influences the mobile-based AI services among Tanzanian manufacturing SMEs. Similar studies have found that when the benefits of a new technology are easily visible to potential adopters, the likelihood of its adoption increases (Wang and Lin, 2019; Gharaibeh et al., 2020; Tongora and Ndume, 2020; Bigambo et al., 2023). This visibility provides tangible proof of the technology's effectiveness, reducing uncertainty and enhancing trust among users. In the Tanzanian context, demonstrating observable benefits of mobile-based AI services, such as improved efficiency and cost savings, plays a critical role in persuading SMEs to adopt these innovations.
The study has one limitation, the findings might not be generalizable to other contexts. Tanzania's distinctive sociocultural and economic landscape and technological infrastructure introduce factors that might not directly affect SMEs in different areas or industries. Tanzania's business environment is shaped by cultural norms, economic conditions and infrastructural challenges, which influence the adoption in ways that may not be applicable elsewhere. For instance, power distance, which is notably high in Tanzania has the potential to significantly impact technology adoption in ways that differ from contexts with lower power distance.
6. Conclusion, implications and future work
This study has provided a framework for adopting mobile-based AI services in Tanzanian manufacturing SMEs. The successful integration of mobile-based AI services largely depended on perceived usefulness, perceived ease of use, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, observability, trialability and compatibility. The results of this study have important implications.
6.1 Theoretical implications
The results of this study have important theoretical implications for upcoming technological adoption research projects, especially in the context of manufacturing SMEs in developing countries like Tanzania. The findings of the study have confirmed that in developing countries, the adoption of mobile-based AI services can be constrained and influenced by several factors related to market forces, sociocultural readiness and government institutions. Therefore, by examining the complex interactions between these factors from the two models (i.e. MSAM and IDT) and their changing dynamics over time, scholars and researchers can expand on the identified factors influencing the adoption of mobile-based AI services.
6.2 Practical implications
Policymakers need to develop targeted initiatives and support programs to facilitate the seamless adoption of mobile-based AI services, thereby advancing the technological advancement of Tanzanian SMEs. Moreover, the study also emphasizes the importance of considering cultural and contextual factors like power distance when considering technology adoption processes. The emphasis on power distance highlights the need for customized strategies that fit SMEs 'current hierarchical structures, ensuring a more efficient and culturally sensitive integration of technological innovations. Furthermore, the study informs that investing on ICT infrastructure, particularly mobile networks and internet availability, is crucial. More specifically, this entails enhancing access to high-speed internet and coverage among manufacturing SMEs can greatly improve feasibility and effectiveness of mobile-based AI services.
6.3 Social implications
Mobile-based AI services 'successful adoption in manufacturing SMEs has the potential to boost productivity, enhance employment opportunities and promote economic sustainability. This highlights the need to develop inclusive technological strategies considering Tanzania's diverse sociocultural landscape and encouraging equitable access and benefits for various social groups. A strategy that considers social, cultural and economic factors in addition to technical ones is necessary for integrating technological innovations into the fabric of Tanzanian SMEs.
Future research should consider several specific avenues to enhance understanding and application of mobile-based AI services in diverse contexts:
To gain deeper insights into the sustainability and evolving dynamics of technology adoption, it is recommended that future studies adopt longitudinal research designs. Long-term studies should focus on tracking the implementation and impact of mobile-based AI services over extended periods, such as 5–10 years. This approach will provide valuable data on how these technologies adapt to changing business environments and evolving user needs, thereby offering a comprehensive view of their long-term benefits and challenges.
Expanding research to include the application of AI in cottage industries presents a promising area for future inquiry. Given the significant role that small-scale, informal industries play in many developing economies, investigating how AI technologies can be tailored and adopted in these contexts could provide insights into enhancing productivity and innovation at the grassroots level.
Conducting comparative studies involving SMEs from different countries beyond Tanzania would offer a broader perspective on the dynamics of technology adoption. Such studies should analyze similarities and differences in technology integration, taking into account varying socioeconomic, cultural and infrastructural contexts.
By addressing these areas, future research can contribute to a more nuanced understanding of technology adoption in various contexts and enhance the practical application of mobile-based AI services across different sectors and regions..
Figures
Figure 1.
Mobile services acceptance model
Figure 2.
Innovation diffusion theory (IDT)
Figure 3.
Methodology used in this study
Figure 4.
Conceptual framework for the adoption of mobile-based AI services in manufacturing services
Figure 5.
Framework for adopting mobile-based AI services in manufacturing SMEs
Experts information
| Expert | Role | Organization | Location |
|---|---|---|---|
| Expert 1 | Business and economic strategist | Institute of Rural Development Planning (IRDP) | Dodoma, Tanzania |
| Expert 2 | Technology integration specialist | Tech Innovators Ltd. | Mwanza, Tanzania |
| Expert 3 | SME growth consultant | Growth Solutions Inc. | Arusha, Tanzania |
| Expert 4 | Industrial tech researcher | Tanzania Investment left | Dar es Salaam, Tanzania |
| Expert 5 | Business expansion manager | Tanzania Chamber of Commerce, Industry and Agriculture | Dar es Salaam, Tanzania |
| Expert 6 | ICT policy analyst | Ministry of Information, Communication and Information Technology | Dodoma, Tanzania |
| Expert 8 | SMEs researcher | The University of Dodoma | Dodoma, Tanzania |
| Expert 9 | Financial tech analyst | National Bank of Commerce | Dodoma, Tanzania |
| Expert 10 | Tech-enabled supply chain specialist | The University of Dar es Salaam | Dar es Salaam, Tanzania |
Source: Authors’ work
Success factors emanating from existing literature review
| SN | CSF | Supported factor theme | Reference |
|---|---|---|---|
| 1 | Perceived usefulness (PU) | Technologies that demonstrate their usefulness by improving productivity, efficiency and overall operational effectiveness are more likely to be adopted by manufacturing SMEs | Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Chale and Mbamba (2015), Lubua and Semlambo (2017), Mandari et al. (2021) |
| 2 | Perceived ease of use (PEoU) | The ease with which manufacturing SMEs can interact with and seamlessly incorporate these services into their daily tasks determines their adoption | Davis (1989), Gao et al. (2008), Gobin-Rahimbux et al. (2017), Lubua and Semlambo (2017), Shah et al. (2020), Tongora and Ndume (2020), Mandari et al. (2021) |
| 3 | Context | AI services must align with contextual factors such as the nature of manufacturing processes, supply chain complexities and market dynamics | Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017) |
| 4 | Personal initiatives and characteristics | The adoption of mobile-based AI services in SMEs relies on personal initiatives and characteristics of individuals within the manufacturing SMEs | Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), (Ishengoma, 2022) |
| 5 | Trust | Adopting technologically advanced services, such as mobile-based AI services in SMEs, heavily relies on trust | Mwangi et al. (2014), Chale and Mbamba (2015), Kabanda and Brown (2015, 2017), Amoako (2018), Gamba (2019), Mabula et al. (2020), Nkwabi and Fallon (2020) |
| 6 | Infrastructure | The technological infrastructure within manufacturing SMEs is a vital determinant of the feasibility of adopting mobile-based AI services | Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Ndesaulwa et al. (2017), Lubua and Semlambo (2017), Msuya et al. (2017), Kabanda and Brown (2017), Nkwabi and Fallon (2020), Shah et al. (2020), Tongora and Ndume (2020), Ye and Tekka (2020) |
| 7 | Cost | In African developing economies, SMEs are more likely to adopt solutions that offer tangible benefits while being affordable and functional | Marwa (2014), Mohamed and Mnguu (2014), Kuzilwa and Nyamsogoro (2016), Mpunga (2016), Anderson (2017), Ndesaulwa et al. (2017), Ishengoma et al. (2018), Mdasha et al. (2018), Gamba (2019), Tongora and Ndume (2020), Bigambo et al. (2023) |
| 8 | Mobility | The prevalence of mobility among the SME workforce significantly contributes to creating an optimal environment for adopting technologically advanced innovations such as mobile-based AI services | Donner and Escobari (2010), Aikaeli (2012), Chale and Mbamba (2015), Kabanda and Brown (2017), Ndekwa (2017), Ishengoma et al. (2018),Tongora and Ndume (2020) |
| 9 | Observability | The degree to which the results and benefits of an innovation are visible to others | Ndesaulwa et al. (2017), Msuya et al. (2017), Kabanda and Brown (2017), Ye and Tekka, 2020, Mandari et al. (2021) |
| 10 | Triability | The degree to which an innovation can be tested on a limited basis | Rogers (1983), Mamun (2018), Ndesaulwa et al. (2017), Wang and Lin (2019), Shah et al. (2020), Ben Hamadi and Fournès (2023) |
| 11 | Compatibility | The extent to which an innovation is consistent with the existing values, past experiences and needs of potential adopters | Rogers (1983), Mdasha et al. (2018), Wang and Lin (2019), Gharaibeh et al. (2020), Tongora and Ndume (2020), Bigambo et al. (2023) |
Source: Authors’ work
Framework validation results
| Expert | Theoretical foundation | Applicability to Tanzanian manufacturing SMEs | Impact on SME operations | Feasibility of implementation | Overall validity |
|---|---|---|---|---|---|
| Expert 1 | 4 | 4 | 4 | 4 | 4 |
| Expert 2 | 5 | 4 | 4 | 5 | 4.5 |
| Expert 3 | 5 | 4 | 4 | 5 | 4.5 |
| Expert 4 | 4 | 3 | 4 | 3 | 3.5 |
| Expert 5 | 4 | 4 | 4 | 4 | 4 |
| Expert 6 | 5 | 5 | 5 | 4 | 4.75 |
| Expert 7 | 3 | 3 | 4 | 3 | 3.25 |
| Expert 8 | 4 | 4 | 4 | 4 | 4 |
| Expert 9 | 4 | 4 | 3 | 4 | 3.75 |
| Expert 10 | 4 | 4 | 5 | 4 | 4.25 |
Source: Authors’ work
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Further reading
Ishengoma, F., Mselle, L. and Mongi, H. (2019), “Power distance and users behavior towards the adoption of m-government services in Tanzania”, International Journal of Open Information Technologies (INJOIT), Vol. 7 No. 8, pp. 66-72.