Modelling the Determinants of the Levels of Digital Technology Adoption among SMEs in Tanzania

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Anchored in the Technology Acceptance Model (TAM) and the Technology–Organisation–Environment (TOE) framework, this study addresses an empirical gap by conceptualising DTA as a multilevel outcome. Using a cross-sectional design, quantitative data were collected from 390 SMEs in the Dar es Salaam, Arusha, and Mbeya regions. We analysed the data using descriptive statistics and an ordered logistic regression. The study findings indicate that most SMEs remain in the early stages of digital transformation: 41.8% exhibit low adoption, 41.3% moderate adoption, and only 16.9% high adoption. The regression results indicate that perceived usefulness strongly drives progression to higher adoption levels, whereas competitive pressure mainly stimulates adoption at lower and moderate levels. Female-owned SMEs are more likely to achieve higher adoption levels than male-owned SMEs. Contrary to expectations, financial resources alone do not predict higher adoption, whereas the number of employees and adequate infrastructure are significantly associated with remaining at a low level of adoption. SMEs with adequate infrastructure are more likely to progress beyond low levels of adoption and consolidate their use at moderate levels. However, infrastructure alone is insufficient to drive adoption at the highest levels. This study advances the digital adoption literature by analysing SMEs’ progression from low to high DTA. It also extends the TAM and TOE frameworks in the African context and offers new insights into gender differences in technology adoption. Digital technology adoption Level of Adoption SMEs Determinants Technology Acceptance Model Technology-Organisation-Environment framework Figures Figure 1 Figure 2 1. Introduction The pervasive and accelerating integration of digital technology has become a defining feature of the contemporary global economy, fundamentally reshaping how firms operate, compete, and create value. Within this transformation, the Fourth Industrial Revolution has intensified the role of digital technology across all sectors in production, marketing, and distribution (Javaid et al., 2024 ; Lee et al., 2018 ). This shift has transformed production systems, reshaped marketing and distribution channels, and enhanced firm performance (Amin et al., 2025 ; Dimoso & Utonga, 2024 ; Sang & Anh, 2025 ). The COVID-19 pandemic accelerated this digital shift, compelling firms to adopt digital tools to sustain operations amid unprecedented disruptions (Amankwah-Amoah et al., 2021 ; Reuschl et al., 2022 ). Small and Medium Enterprises (SMEs), in particular, have derived substantial benefits from adopting digital payment systems, internal operations management solutions, and digital marketing platforms (Bindeeba et al., 2025 ; Ihenyen et al., 2025 ; Mushi, 2024 ; Myataza et al., 2024 ). As a result, these advancements not only enhance operational efficiency but also position SMEs for greater market competitiveness in a digitally driven economy. The adoption of digital payment tools includes integrating mobile money wallets, contactless payments, and online transactions (Abdulai et al., 2024 ). The adoption of digital marketing platforms encompasses the use of social media and other online marketing tools, including search engine optimisation and paid advertising platforms such as Google Ads (Dwivedi et al., 2020 ; Herhausen et al., 2020 ). Additionally, adopting internal operations management solutions involves integrating financial and operational management tools, including accounting systems (Xero, QuickBooks), Excel tools, mobile applications, procurement solutions, resource management systems, and project management platforms (Lutfi et al., 2022 ; Mujalli et al., 2024 ; Ugbebor et al., 2024 ). These tools enable SMEs to reduce transaction costs, expand market access, improve operational efficiency, and optimize resource allocation, thereby enhancing performance and sustainability (United Nations Conference on Trade and Development [UNCTAD], 2022; Organisation for Economic Co-operation and Development [OECD], 2021). Beyond immediate efficiency gains, digital technology supports data-driven decision-making, allowing SMEs to respond more effectively to changing market conditions and customer preferences (Kraus et al., 2021 ; Vial, 2021 ). Consequently, digitally engaged SMEs are better equipped to achieve sustained growth and competitive advantages. While digital technology presents substantial opportunities for SMEs, a considerable challenge persists in how these businesses, particularly in developing economies, can effectively utilise these tools for their benefit (OECD, 2021; UNCTAD, 2022). This challenge is especially pronounced in the Tanzanian agro-processing sector (Nkwabi et al., 2019 ), where SMEs are integral to value addition, employment generation, and strengthening rural–urban economic linkages. Within this sector, the fruit juice processing industry has grown steadily owing to rising incomes, urbanisation, and the availability of diverse fruit varieties (Issa et al., 2021 ; Musaazi et al., 2023 ). As competition intensifies within the digital economy, SMEs face increasing pressure to enhance performance and sustainability by improving service quality, ensuring traceability, strengthening distribution efficiency, and complying with market standards. In this context, digital tools, such as internal operations management systems, digital payment platforms, and online marketing tools, have emerged as significant facilitators (Vial, 2019 ). SMEs need to benefit from these substantial resources of the digital economy. However, despite their potential, there is limited empirical evidence documenting digital technology adoption among fruit juice processing SMEs in Tanzania, and little is known about the determinants of the level of its adoption. This constraint restricts SMEs’ ability to scale operations, access markets, and sustain their competitiveness in an increasingly digitised economy. The characteristics of emerging economies further shape digital technology adoption among Tanzanian SMEs (OECD, 2021; UNCTAD, 2022). Although rising mobile penetration and government-led industrial initiatives have created new opportunities, SMEs continue to face challenges related to infrastructure quality, access to finance, technological skills, and regulatory compliance (Centre for Policy Research and Advocacy [CPRA], 2022 ; Nkwabi et al., 2019 ; Tonya & Samwel, 2025 ). Therefore, promoting effective digital technology adoption among SMEs is closely aligned with Tanzania’s national development goals. This includes the National Development Vision 2050 and the Industrialisation Strategy, which emphasise SME-led growth, technological upgrading, and value addition in agro-processing. The existing literature identifies a broad set of determinants that shape SMEs’ digital technology adoption. At the technological level, perceived security, perceived usefulness, and ease of use consistently influence adoption decisions, as firms are more likely to integrate digital tools that enhance efficiency and are relatively easy to implement (Davis, 1989 ; Faiz, 2023 ; Venkatesh et al., 2003 ; Vial, 2019 ). Organisational factors such as firm size, gender, digital readiness, managerial competence, human capital, and financial capacity also play a central role, with larger and better-resourced SMEs demonstrating a higher propensity to adopt digital technologies (Alam et al., 2022 ; Alhakimi & Albashiri, 2023 ; Machado et al., 2021 ; Omrani et al., 2022 ; Sharma et al., 2021 ). Environmental conditions, including competitive pressure, availability of digital infrastructure, access to external support services, and government policies, further shape adoption outcomes (Shahadat et al., 2023 ; Akerejola et al., 2019 ; Ullah et al., 2023 ; Barros, 2023 ; Shahadat et al., 2023 ). This body of work demonstrates that digital adoption among SMEs is a complex and context-dependent process. Despite this potential contribution, empirical research has offered limited attention to the levels of digital technology adoption. This represents a critical gap, as digital transformation among SMEs is inherently incremental, progressing from basic digital tools to more advanced and integrated systems. A binary or multinomial view of adoption fails to capture the distinct challenges and opportunities at different levels of digital adoption. Without distinguishing between low, moderate, and high levels of adoption, policymakers and support organisations struggle to design interventions that effectively guide SMEs along their digital transformation pathways, often resulting in misaligned strategies and suboptimal outcomes. In the Tanzanian context, specifically within the fruit juice processing industry, evidence on how various factors shape the levels of digital technology adoption remains notably scarce. Addressing this gap, the present study examines the determinants of digital technology adoption among SMEs in Tanzania’s fruit juice processing industry. To comprehensively examine this matter, this study is grounded in the Technology Acceptance Model (TAM) and Technology–Organisation–Environment (TOE) frameworks. TAM provides insights into how perceived usefulness and perceived ease of use influence firms’ engagement with digital technology and their willingness to deepen its integration into their operations and processes (Davis, 1989 ; Venkatesh et al., 2003 ). Complementarily, the TOE framework incorporates organisational capacities and environmental conditions (Baker, 2012 ; Tornatzky & Fleischer, 1990 ), offering a multidimensional lens to explain variations in digital maturity across SMEs. Thus, these frameworks provide a theoretical foundation for examining the determinants of low, moderate, and high digital technology adoption among SMEs. Accordingly, this study seeks to answer the following research question: RQ: What are the factors influencing the varying levels of digital technology adoption among fruit juice processing SMEs in Tanzania? This study contributes to the literature in several ways. First, it advances existing research by moving beyond binary or multiple adoption measures to examine the progression of digital technology adoption across different levels. Second, it extends the application of TAM and TOE by demonstrating their explanatory power in the African SME context, particularly regarding adoption levels. Third, by examining the role of gender in shaping digital technology adoption across different levels, this study offers novel insights into how socio-demographic factors influence digital transformation trajectories in developing economies. The remainder of the manuscript is organised as follows: Section 2 presents the materials and methods; Section 3 reports the results; Section 4 discusses the findings; and Section 5 concludes with the key policy implications, contributions, limitations, and future research directions. 2. Materials and Methods 2.1 The Study Context This study employs a quantitative, cross-sectional research design to analyse the determinants of the level of digital technology adoption among SMEs in the fruit juice processing industry in Tanzania. The cross-sectional approach was chosen to leverage existing variations in SME characteristics at a single point in time, rather than tracking changes over time. This approach enables an efficient and comparative analysis of digital adoption behaviour. The study was conducted in six districts across three regions in mainland Tanzania: Ilala, Kinondoni, Ubungo, and Temeke in Dar es Salaam; Arusha District in the Arusha Region; and Mbeya District in the Mbeya Region. These locations were purposively selected because of their high concentration of fruit juice processing SMEs and their strategic importance within urban economies, particularly in relation to employment creation, food system support, and rural–urban market integration. The inclusion of these locations broadened the study’s analytical scope. Dar es Salaam, as the commercial capital, provides potential markets and relatively mature digital infrastructure; Arusha is significant for its agricultural and regional trade environment; and Mbeya offers insights into a high-production agricultural corridor. This geographical focus enabled the study to capture varying levels of economic activity and digital readiness pertinent to fruit juice processing SMEs in the region. We used a multistage sampling method. First, the regions of Dar es Salaam, Arusha, and Mbeya were purposively selected. The districts of Ilala, Ubungo, Temeke, and Kinondoni were selected in Dar es Salaam. Similarly, the districts of Arusha (Arusha region) and Mbeya (Mbeya region) were purposively selected for their roles as SME activity hubs and their distinct economic development, infrastructure, and urbanisation profiles. This approach captured diverse socioeconomic and geographic settings that could influence SMEs’ access to digital tools. Furthermore, five wards were randomly chosen from each district. Selecting five wards provided sufficient geographic coverage while accounting for practical fieldwork constraints. This method increases the likelihood of capturing variations in SME operations and digital technology use within districts while keeping costs, logistics, and time manageable. Finally, 13 SMEs were randomly selected from each ward, yielding a total sample size of 390. The sample size was determined using a variable-based method that relies on the number of independent variables in the model (Tundui & Tundui, 2025 ). The method recommends at least five observations per independent variable to ensure sufficient statistical power. With 10 independent variables in our analytical model (Table 1 ), we used 39 observations per variable. This approach significantly exceeded the threshold, thereby enhancing the statistical accuracy, robustness, and generalisability. Data collection was conducted through direct interactions using a structured questionnaire administered by trained enumerators from November 2024 to April 2025. The enumerators completed the questionnaire by asking respondents questions. The questionnaire was developed based on an extensive literature review and expert consultations. Initially prepared in English, the questionnaire was subsequently translated into Swahili to enhance clarity and accessibility for respondents. The questionnaire included sections addressing SME characteristics, digital tool utilisation, adoption determinants, and performance outcomes. Prior to the primary survey, the instrument underwent pilot testing with 30 SME owner-managers in Dar es Salaam to evaluate its clarity, relevance, reliability, and validity. Feedback from the pilot study led to minor refinements to improve the wording and relevance of the questions. The refined questionnaire was then administered to the selected SMEs, with each administration lasting approximately 40 min. All questionnaires were completed anonymously, informed consent was obtained from all participants, and the study adhered to established ethical guidelines to ensure confidentiality and to protect respondents’ rights. 2.2 Study Variables 2.2.1 The Response Variable The dependent variable in this study was the level of digital technology adoption. Digital adoption can be operationalised in several ways, including counting the number of digital tools implemented (Zhu & Kraemer, 2005 ), evaluating the combination or bundle of tools adopted, or calculating the proportion of available tools integrated into firm operations (Ardito et al., 2018 ). In line with these approaches, this study measures adoption across three key domains: digital marketing, digital payment, and internal operations management, reflecting the multidimensional nature of SME digitalisation (Bharadwaj et al., 2013 ). An SME is considered to have adopted a domain if it uses any tool within that domain, and the total adoption score is calculated as the number of domains adopted, with a maximum score of three. This domain-based approach allows for capturing adoption while maintaining simplicity and interpretability, which is particularly important for SMEs with heterogeneous technology portfolios (Oliveira & Martins, 2011 ; Rogers, 2003 ). To quantify adoption levels, each SME was assigned a score on a 0–3 scale: 0 = no adoption; 1 = low adoption; 2 = moderate adoption; and 3 = high adoption. This ordinal scoring system aligns with previous research on SME technology adoption, providing a reliable and practical measure of digitalisation that accommodates varying levels of tool use across domains (Zhu & Kraemer, 2005 ; Battisti et al., 2015). 2.2.2 Predictor Variables We employed the TAM and TOE frameworks, along with empirical literature, to conceptualise the explanatory variables for this study. These variables are factors theorised to influence the level of digital tools adoption in SMEs as follows: - Gender was operationalised as a binary variable reflecting the gender of the principal decision-maker in the SME. A value of “1” represents male owners, while “0” corresponds to female owners. This variable was used to assess whether gender influenced decisions regarding digital technology adoption within SMEs. Conceptualising gender as a determinant of technology adoption aligns with previous research on how gender influences business decisions regarding technology use and innovation (Alam et al., 2022 ; Alhakimi & Albashiri, 2023 ; Orser & Riding, 2018 ). Financial capacity is a binary variable that assesses whether an SME has sufficient financial resources to invest in digital tools. SMEs coded as "1" possess adequate financial resources to support the acquisition, implementation, and maintenance of digital tools, while those coded as "0" face financial constraints that limit their ability to invest in such resources. This variable aligns with the TOE framework, which highlights financial resources as an important factor influencing technology adoption. Previous research has emphasised the role of financial capacity in enabling or hindering digital adoption in SMEs (Omrani et al., 2022 ; Sagala & Őri, 2024 ; Sharma et al., 2021 ). Digital readiness was operationalised as a binary factor that captures the extent to which an SME is prepared for digital adoption. A value of "1" indicates that the SME has the necessary organisational mindset, fundamental skills, and preliminary structures to adopt digital tools effectively, while "0" reflects a lack of readiness. This variable is aligned with the TOE framework, which considers organisational readiness a crucial factor in digital technology adoption. Previous studies have highlighted the importance of digital readiness for the successful implementation of digital technologies in SMEs (Machado et al., 2021 ; Michelotto & Joia, 2024 ; Omrani et al., 2022 ). Perceived security reflects the context in which SME decision-makers believe that digital tools and systems (such as e-commerce platforms, enterprise software, and AI solutions) are secure in terms of protecting data, privacy, and operations from threats such as unauthorised access, fraud, and data breaches. Perceived security is expected to positively influence SMEs’ intentions to adopt digital tools, whereas a lack of perceived security may deter adoption. This variable was operationalised as a binary variable: 1 = perceived security (the SME perceives digital tools as secure and trustworthy) and 0 = no perceived security (the SME perceives security risks or lacks confidence in the digital tools). Previous research has consistently highlighted the importance of perceived security in shaping attitudes and intentions toward digital technology adoption in SMEs and similar contexts (Alka’awneh et al., 2025 ; Faiz, 2023 ; Wasudawan et al., 2025 ; Giang et al., 2025 ). Perceived usefulness is a well-established determinant of technology adoption in SME research rooted in the TAM framework. According to TAM, perceived usefulness refers to the degree to which decision-makers believe that using a technology improves their performance. Prior studies across various digital technologies have shown that higher perceived usefulness increases the likelihood of adoption decisions and related performance improvements in SMEs (Buvár & Gáti, 2023 ; Amnas et al., 2025 ; Aremu & Arfan, 2023 ; Wasudawan et al., 2025 ). Perceived usefulness has been empirically linked to adoption attitudes, intentions, and performance effects in SMEs and related contexts. Therefore, in this study, perceived usefulness was operationalised as a binary variable indicating whether the SME owner or manager believes that digital tools improve business performance, which leads to their adoption. A value of 1 represented the belief that digital tools are useful, and 0 represented the belief that they are not helpful. The variable perceived ease of use is a binary measure that indicates whether an SME finds digital tools manageable and straightforward. A value of 1 signifies that SMEs consider these tools easy to learn and apply, whereas a value of 0 indicates a perception of complexity or difficulty. This variable is grounded in the TAM framework, which suggests that perceived ease of use is a crucial factor in technology adoption, shaping users' attitudes and intentions toward adopting new technologies. Previous research has consistently demonstrated that higher perceived ease of use increases the likelihood of adoption, especially when digital tools are perceived as intuitive and user-friendly. The operationalisation of this variable aligns with the established frameworks in the SME digital adoption literature (Amnas et al., 2025 ; Aremu & Arfan, 2023 ; Buvár & Gáti, 2023 ; Wasudawan et al., 2025 ). Competitive pressure is a binary factor indicating whether an SME faces competitive forces that encourage digital adoption. SMEs coded as “1” recognise competition as a driver of digital adoption, whereas “0” indicates the absence of competitive dynamics in the market. This variable is based on the TOE framework, which considers competitive pressure a factor that can drive SMEs to adopt new technologies to maintain or enhance their competitive position. Previous studies have highlighted the role of competitive pressure in motivating SMEs to innovate and adopt digital tools to stay ahead in competitive markets (Shahadat et al., 2023 ; Faiz et al., 2024 ). Infrastructure is a binary indicator that captures the availability of essential infrastructure to support the deployment of digital technology. A value of “1” signifies reliable access to infrastructure such as electricity, internet connectivity, and network coverage, whereas “0” indicates infrastructural limitations. This variable is grounded in the TOE framework, where infrastructure availability is a crucial factor influencing the ability to adopt and utilise digital technologies. Research has shown that adequate infrastructure is a critical enabler of digital technology adoption, with the absence of reliable infrastructure often acting as a barrier to implementation (Omrani et al., 2022 ; Akerejola et al., 2019 ; Ullah et al., 2023 ). The variable number of employees is a quantitative measure of the total number of workers employed by an SME. This variable serves as a proxy for SME size, reflecting the operational capacity and potential resource availability for adopting digital technologies. Larger SMEs with more employees may have greater operational capacity and access to resources, which can facilitate the adoption of digital tools. This measure has been used in previous studies to capture firm size as a determinant of technology adoption, with larger firms often being better positioned to invest in and implement new technologies (Barros, 2023 ; Holl & Rama, 2023 ). Government support is conceptualised as the presence of public policies, programs, incentives, and institutional initiatives that enable SMEs to adopt digital tools, including training, infrastructure support, financial incentives, and regulations. Government support plays a crucial role in helping SMEs overcome barriers to digital adoption, particularly in developing and emerging countries. Prior research has shown that externally oriented support from government entities is a significant determinant of SMEs’ digital technology adoption. In this study, government support is measured as a binary variable indicating whether an SME perceives it as available and accessible. A value of 1 signified the presence of government support, and 0 indicated the absence of such support. This approach aligns with the TOE framework, which emphasises the role of external factors, such as government support, in shaping technology adoption (Chen et al., 2021 ; Shahadat et al., 2023 ). 2.3 Analysis Framework To analyse the determinants of the level of digital technology adoption among SMEs, we employed an ordered logistic regression model. The adoption level was defined as an ordered categorical variable with three categories (1–3) representing low, moderate, and high adoption levels. These levels were based on the number of digital domains each SME adopted. This method enabled the creation of a strictly ordered scale that reflects progressively higher degrees of digital technology adoption. Because the outcome was an ordered categorical variable, ordinary least squares could be unsuitable, as it treats the response as continuous and violates the ordinal nature of the data and the assumption of homoscedasticity and normally distributed error (Long & Freese, 2006 ). Count-data regressions (Poisson and Negative Binomial) are appropriate when the dependent variable is an accurate count of events. However, they also rely on distributional assumptions that do not fit ordered categories with meaningful ranks, but instead on precise interval scales (Cameron & Trivedi, 2013 ). For these reasons, and because the adoption score captures ordered categories rather than a count process, we used an ordered logistic (proportional-odds) regression as the primary method. Formally, let Y i denote the observed adoption category for SME i , taking values 1 (low), 2 (moderate), and 3 (high). The ordered logistic model assumes the existence of an unobserved latent variable \(\:{Y}_{i}^{*}\) , representing the underlying propensity for digital technology adoption, such that: $$\:{\varvec{Y}}_{\varvec{i}}^{\varvec{*}}=\:{\varvec{X}}_{\varvec{i}}^{\varvec{{\prime\:}}}\beta\:+{\epsilon\:}_{i}\text{}$$ Where \(\:{\varvec{X}}_{\varvec{i}}^{\varvec{{\prime\:}}}\) is a vector of explanatory variables capturing various determinants, \(\:\beta\:\) is the corresponding parameter vector, and \(\:{\epsilon\:}_{i}\) follows a logistic distribution. The observed categories of \(\:{\varvec{Y}}_{\varvec{i}}\) arise by partitioning ​ \(\:{\varvec{Y}}_{\varvec{i}}^{\varvec{*}}\) according to threshold parameters \(\:{\varvec{\tau\:}}_{1}\) and \(\:{\varvec{\tau\:}}_{2}\) : $$\:{\varvec{Y}}_{\varvec{i}}=\left\{\begin{array}{c}1\:\:\:if\:{\varvec{Y}}_{\varvec{i}}^{\varvec{*}}\le\:{\varvec{\tau\:}}_{1}\:\:\:\:\:\:\:\:\:\:\:\\\:2\:\:\:if\:{\varvec{\tau\:}}_{1}<{\varvec{Y}}_{\varvec{i}}^{\varvec{*}}\le\:{\varvec{\tau\:}}_{2}\\\:3\:\:\:if\:{\varvec{Y}}_{\varvec{i}}^{\varvec{*}}\ge\:{\varvec{\tau\:}}_{2}\:\:\:\:\:\:\:\:\:\:\end{array}\right.$$ This leads to the cumulative logit representation: \(\:\varvec{l}\varvec{o}\varvec{g}\left(\frac{\text{P}\text{r}({\varvec{Y}}_{\varvec{i}}\le\:\varvec{j})}{\text{P}\text{r}({\varvec{Y}}_{\varvec{i}}>\varvec{j})}\right)={\propto\:}_{\varvec{j}}-{\varvec{X}}_{\varvec{i}}^{\varvec{{\prime\:}}}\beta\:\) , j= 1, 2, Where α j are threshold-specific intercepts. The proportional-odds assumption implies that the vector of slope coefficients β remains constant across the cumulative logits, allowing consistent interpretation of the direction and magnitude of each predictor’s effect on the likelihood of being in a higher adoption category. Parameter estimates were obtained via maximum likelihood, and robust standard errors were computed to correct for potential heteroskedasticity and within-group correlation. We conducted several diagnostic procedures to ensure appropriate model specifications. First, the proportional odds (parallel lines) assumption was tested using the Brant test. Where violations were detected, we estimated a generalised ordered logistic model as a robustness check to assess the stability of the results. Second, multicollinearity was examined using the variance inflation factor (VIFs). Third, the model fit was assessed using likelihood ratio tests, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and pseudo-R2 measures. In addition to reporting odds ratios for ease of interpretation, we computed average marginal effects (AMEs) to quantify the change in the predicted probability of each adoption category associated with changes in each variable. These effects provide a more intuitive assessment of effect magnitudes, particularly when evaluating differences in the predicted probability of high adoption across SMEs’ characteristics. The reported results reflect patterns observed in the survey data collected from fruit juice processing SMEs in the Dar es Salaam, Arusha, and Mbeya regions between November 2024 and April 2025, unless otherwise stated. 3. Results 3.1 Demographic Information of the SMEs This study surveyed 390 SMEs operating in the fruit juice processing industry across six districts in Tanzania: Ilala, Kinondoni, Ubungo, Temeke, Arusha, and Mbeya. The sample distribution reveals a pronounced geographic concentration, with approximately 67% of SMEs located in Dar es Salaam, underscoring the region’s position as Tanzania’s primary industrial and commercial hub. The ownership structure is dominated by sole proprietorships (approximately 85%), reflecting a business landscape characterised by individually owned and managed enterprises. The sector also exhibits a strong gender dimension: approximately 59% of SMEs are led by women, underscoring the prominent role of female entrepreneurship in the agro-processing sector. The SMEs surveyed had an average operational age of three years, indicating a heterogeneous mix of nascent and more established entities. Educational attainment among owner-managers was notably high, with approximately 42% having completed secondary education and approximately 49% holding post-secondary or higher education qualifications. This relatively strong human capital base is complemented by small workforce sizes, averaging three employees per SME, which is consistent with the defining characteristics of small-scale enterprises in developing economies. These attributes depict a sector that is young, urban-centred, and predominantly owner-managed, yet supported by comparatively well-educated leadership. Such demographic features provide important context for understanding digital technology adoption patterns, as they shape both SMEs’ capacity and propensity to engage with emerging digital tools. 3.2 Extent of Digital Technology Adoption Figure 1 illustrates the distribution of digital technology adoption across the three domains. Digital payment systems had the highest adoption rate, with approximately 66.2% of SMEs using them. This indicates that payment tools are now more accessible and are considered essential for improving transactions and reducing cash-handling risks. Digital marketing platforms had an adoption rate of approximately 57.2%, indicating the growing recognition of their value in enhancing market visibility. However, many SMEs still lack the skills and strategies needed to use them effectively. Internal operations management tools had the lowest adoption, at approximately 48.7%, suggesting that technologies for managing inventory, customer relations, and administrative tasks are still not fully integrated into many SME operations. Figure 2 shows the level of digital technology adoption among SMEs to capture its extent. The results also reveal that most SMEs are in the early or partial stages of digital transformation: 163 (41.8%) have adopted digital tools in only one domain (low adoption), and 161 (41.3%) have adopted digital tools in two domains (moderate adoption). Only 66 (16.9%) SMEs adopted all three domains (high adoption). These patterns suggest that while digital technologies are increasingly diffusing across the SME sector, adoption remains uneven, focused mainly on specific functional areas rather than being integrated across all business operations, highlighting both progress and ongoing challenges in SME digitalisation. 3.3 Model Evaluation We analysed the determinants of digital technology adoption levels among SMEs and classified the adoption into three categories: low, moderate, and high. Prior to estimation, we assessed multicollinearity among the predictor variables using VIFs. All VIF values were below 1.3, indicating no strong correlations between the predictors (Table 2 ). Then, we estimated a standard ordered logistic regression under the proportional odds (parallel lines) assumption using the oparalell command in STATA. This assumption requires that the effect of each predictor remains consistent across all thresholds of the ordinal outcomes (Brant, 1990 ). Most tests (Wolfe–Gould, Brant, score, and likelihood ratio) were not significant (p > 0.05). However, the Wald test was significant (χ² = 18.40, df = 10, p = 0.049), indicating that at least one predictor may have violated the proportional odds assumption (Table 1 ). Although the model fit indices favoured the standard ordered logistic regression model (AIC = 797.50, BIC = 845.10) over the generalised model (AIC = 800.58, BIC = 887.84), they did not account for threshold-specific violations. Table 1 Parallel Regression (Proportional Odds) Tests Test Chi² df P > Chi² Wolfe–Gould 16.96 10 0.075 Brant 17.52 10 0.064 Score 17.86 10 0.057 Likelihood Ratio 16.92 10 0.076 Wald 18.40 10 0.049 Model Fit Indices Model type AIC BIC Ordered Logit 797.50 845.10 Generalized Logit 800.58 887.84 To accommodate potential violations of the proportionality assumption, we estimated a generalised ordered logistic regression using gologit2 with robust standard errors and the autofit option in STATA. This approach allows predictors that violate the proportional odds assumption to have threshold-specific coefficients, while retaining a single coefficient for variables that satisfy the assumption. The model was statistically significant (Wald χ² (11) = 38.95, p = 0.0001), with a log pseudolikelihood of − 382.997 and a pseudo-R² of 0.047. The significance of the threshold parameters confirmed that the three levels of digital technology adoption could not be combined into a single, ordered structure. The Wald tests for parallel lines for each predictor, as implemented by gologit2's autofit option, indicated that the proportionality assumption was violated for infrastructure availability, confirming the need for a generalised ordered logistic model. Because coefficients in generalised ordered logit models are threshold-specific and differ by a scale factor, their magnitudes are not directly comparable across thresholds. To provide interpretable effect sizes, we computed the average marginal effects using the margins command in STATA. The marginal effects quantified how a marginal change in each predictor variable altered the predicted probability of being at each adoption level. This approach formed the basis for the substantive presentation of results in Table 2 . Table 2 Determinants of the level of digital technology adoption among SMEs Predictor VIF Low Adoption Moderate Adoption High Adoption dy/dx P>|z| dy/dx P>|z| dy/dx P>|z| X 1 1.01 − .0407 0.363 .0162 0.373 .0246 0.360 X 2 1.02 − .1102 0.014 ** .0438 0.018** .0665 0.019** X 3 1.03 .0922 0.040 ** .0871 0.054* − .0556 0.040** X 4 1.07 .0844 0.058 * − .0335 0.061* − .0509 0.065* X 5 1.11 .1386 0.069 * − .0550 0.080* − .0836 0.072* X 6 1.20 − .0208 0.028 ** .0082 0.040** .0125 0.028** X 7 1.02 − .0278 0.534 .0110 0.539 .0167 0.532 X 8 1.06 .0173 0.703 − .0069 0.703 − .0104 0.704 X 9 1.11 .0690 0.138 − .0274 0.146 − .0416 0.142 X 10 1.03 − .1548 0.001 ** .1660 0.000*** − .0112 0.768 Number of obs = 390, LR= -382.99661, chi 2 (11) = 38.95, Prob > chi2 = 0.0001. *, ** and *** = Significant at 10%, 5% and 1% respectively. As shown in Table 2 , the predictors had diverse effects across different adoption levels. For low-level adoption, perceived usefulness (X 2 ), competitive pressure (X 3 ), number of employees (X 6 ), and X 10 were significant at the 5% level, whereas gender (X 4 ) and financial capacity (X 5 ) were marginally significant (p < 0.1). For moderate-level adoption, perceived usefulness (X 2 ), number of employees (X 6 ), and infrastructure availability (X 10 ) remained significant at the 5% level, whereas competitive pressure (X 3 ), gender (X 4 ), and financial capacity (X 5 ) were marginally significant (p < 0.1). For high-level adoption, perceived usefulness (X 2 ), competitive pressure (X 3 ), and number of employees (X 6 ) remained significant at the 5% level, while gender (X 4 ) and financial capacity (X 5 ) were marginal (p < 0.1). Other variables, such as ease of use (X1), readiness for digital adoption (X 7 ), government support (X 8 ), and perceived security of digital tools (X 9 ), were not significant. The threshold-specific effects of infrastructure availability (X 10 ) confirmed that the three adoption levels could not be combined, justifying the use of a generalised ordered logistic regression model. The results are detailed as follows: As shown in Table 2 , the perceived usefulness of digital tools significantly influenced the level of digital technology adoption. Specifically, SMEs that perceived digital tools as applicable were 11.0 percentage points (pp) less likely to be at the low-adoption level, 4.4 pp more likely to be at the moderate-adoption level, and 6.6 pp more likely to reach the high-adoption level, all else being equal. The results also reveal that competitive pressure influences the level of digital technology adoption by SMEs. The study findings indicate that SMEs facing competitive pressure were 9.2 pp more likely to have low adoption and 8.7 pp more likely to have moderate adoption. However, they were 5.6 pp less likely to achieve high adoption levels. Furthermore, Table 2 indicates that the gender of SME owner-managers significantly influences the level of digital technology adoption. Furthermore, compared with female-owned SMEs, male-owned SMEs were 8.4 pp more likely to be at the low-adoption level, but 3.4 and 5.1 pp less likely to be at the moderate and high-adoption levels, respectively, after controlling for other factors. Conversely, the model results show that SMEs with sufficient financial resources were 13.9 pp more likely to remain at the low adoption level and 5.5 and 8.4 pp less likely to reach moderate and high levels of adoption, respectively, when controlling for other factors. Additionally, Table 2 shows that the number of employees has a moderate, yet statistically significant, effect on the level of digital technology adoption in SMEs. SMEs with more employees were 2.1 pp less likely to be at the low adoption level. In comparison, they were 0.8 and 1.3 pp more likely to reach moderate and high adoption, respectively, when other factors were held constant. Furthermore, the results reveal that infrastructure availability among SMEs is a key factor influencing the level of digital technology adoption. SMEs with adequate infrastructure were 15.5 pp less likely to be at the low-adoption level and 16.6 pp more likely to reach moderate adoption. However, its effect on high adoption rates was not significant. 3.4 Robustness Check Several robustness checks were conducted to ensure the reliability of the generalised ordered logistic regression results. Initially, multicollinearity among the predictors was assessed using VIFs; all values were below 1.3, indicating that multicollinearity was not a concern in this study. Subsequently, residual diagnostics confirmed that the error terms followed a logistic distribution, consistent with the model assumptions, and the Wald χ² test supported the overall goodness-of-fit and the log pseudolikelihood (Wald χ²(11) = 38.95, p < 0.001; log pseudolikelihood = − 382.997). Finally, robust standard errors were employed to address potential heteroscedasticity, confirming the stability and reliability of the coefficient estimates. Therefore, these checks enhance confidence in the substantive interpretation of the marginal effects reported in Table 2 . 4. Discussion Perceived usefulness is a key determinant of the level of digital technology adoption in SMEs. SMEs are more likely to adopt digital technologies when they perceive them as applicable, highlighting the importance of perceived benefits in adoption decisions. This indicates that SMEs that clearly recognise the value of digital tools are more likely to move from low to moderate or high adoption levels, suggesting that perceived usefulness influences both initial uptake and the depth of technology adoption. This finding aligns with the Technology Acceptance Model (TAM), which explains adoption behaviour by linking perceived performance benefits to sustained technology use. Empirical studies on SMEs further support this relationship, consistently identifying perceived usefulness as one of the strongest predictors of digital and e-business adoption decisions (Dallocchio et al., 2024 ; Díaz-Arancibia et al., 2024 ; Shahadat et al., 2023 ; Wymer & Regan, 2005 ). More broadly, the technology adoption literature across e-commerce and ICT contexts reinforces the role of perceived usefulness as a fundamental driver of both adoption and continued use (Selase et al., 2019 ; Vu & Nguyen, 2021 ), underscoring its relevance to SME digital transformation. In this context, perceived usefulness not only drives the decision to adopt digital tools but also plays a pivotal role in ensuring their continued use, which is essential for realising the benefits of long-term digital transformation. Competitive pressure exerts a differentiated influence on the level of digital technology adoption by Tanzanian SMEs. While competitive forces encourage initial adoption, they do not necessarily translate into higher adoption levels, as many SMEs remain at low- or moderate-level use. This suggests that competitive pressure primarily stimulates initial or reactive adoption rather than sustained or advanced digital adoption. This pattern is consistent with the environmental dimension of the TOE framework, which posits that external pressures can trigger adoption decisions but are insufficient to drive deeper integration without adequate organisational and technological capabilities (Olfat, 2024 ; Shahadat et al., 2023 ). Empirical evidence similarly shows that SMEs lacking managerial commitment, digital skills, and strategic orientation often adopt digital tools superficially in response to competition (Deku et al., 2024 ; Ollerenshaw et al., 2021 ; Vrontis et al., 2022 ). In line with this view, the present study finds that competitive pressure increases the likelihood of low and moderate adoption while reducing the probability of high adoption, indicating that environmental pressure alone is unlikely to generate transformative digital outcomes without complementary internal capacities. Male-owned SMEs are more likely to remain at low levels of digital technology adoption and are less likely to progress to moderate or high adoption than female-owned SMEs. This suggests that, within this context, female-owned SMEs may be more proactive in integrating digital tools into their business operations. From a TOE perspective, this pattern reflects the role of organisational and managerial characteristics in shaping adoption depth, potentially driven by differences in managerial orientation, risk perception, and openness to innovation. Empirical evidence from SME digitalisation studies supports this contention, showing that female entrepreneurs often leverage digital financial and marketing tools to enhance efficiency, visibility, and customer engagement, particularly in resource-constrained environments (Atarah et al., 2023 ; Salamzadeh et al., 2025 ; Tarmizi et al., 2023 ). Evidence suggests that digital tools may serve as strategic substitutes for limited access to traditional business resources (Ellström et al., 2021 ). The findings of the current study challenge earlier assumptions that male entrepreneurs are inherently more technologically active, underscoring that gendered digital adoption behaviour is highly context-specific and shaped by sectoral, cultural, and institutional factors. SMEs with sufficient financial resources are significantly less likely to adopt digital tools at a higher level. This indicates that financial capacity alone is insufficient to drive SMEs into higher levels of digital technology adoption. This suggests that, although financial capacity may ease access to digital tools, it does not automatically translate into practical or strategic use. Within the TOE framework, this pattern highlights the organisational dimension, where financial resources must be aligned with managerial commitment, strategic prioritisation, and readiness to absorb learning and implementation costs for digital transformation to occur. Empirical studies reinforce the logic that firms with financial capacity but limited digital skills, leadership, or strategic planning often fail to progress beyond basic adoption (Barragan & Becker, 2024 ; Restrepo-Morales et al., 2024 ). In this context, the findings of the current study underpin the TOE proposition that financial resources alone are insufficient to drive higher-level digital adoption without complementary organisational capabilities. The number of employees exerted a modest but statistically significant influence on SMEs’ digital technology adoption. This indicates that SMEs with larger workforces are less likely to remain at low adoption levels and more likely to transition to medium- or high-level adoption. This shows that workforce size enhances internal capacity, enabling SMEs to absorb better, implement, and sustain digital tools. From the TOE framework perspective, this finding highlights the organisational dimension, demonstrating how internal capabilities, such as human resource availability, shape the depth of digital technology adoption. Empirical studies have similarly reported that such SMEs tend to possess greater absorptive capacity, more diverse skill sets, and stronger internal support structures that facilitate technology integration (Cuevas-Vargas et al., 2022 ; Faiz et al., 2024 ). Thus, these findings support the view that workforce size supports not only initial uptake but also progression toward higher levels of digital technology adoption. Adequate infrastructure reduces the likelihood that SMEs remain at low levels of digital adoption and increases the likelihood that they reach moderate levels of adoption. This highlights the foundational role of infrastructure in enabling the uptake of digital tools. However, the absence of a significant effect on high-level adoption indicates that infrastructure alone is insufficient to drive higher digital adoption practices. From the TOE perspective, infrastructure represents a key environmental factor, but achieving higher adoption levels requires complementary organisational capabilities, financial resources, and digital skills. Empirical studies support this view, showing that infrastructure is necessary but insufficient for full technology adoption (Hassan et al., 2023 ; Zamani, 2022 ). This pattern indicates that while infrastructure is a necessary condition for adoption, it may not be sufficient to drive SMEs toward higher levels without complementary internal capabilities such as skilled personnel. 5. Conclusion, Contributions, and Implications 5.1 Conclusion This study analysed the determinants of the level of digital technology adoption among SMEs in the Tanzanian fruit juice processing industry in the Dar es Salaam, Arusha, and Mbeya regions. The findings reveal that most SMEs remain in the early stages of digital transformation, with only a minority fully utilising digital tools across payment, marketing, and operations management. Perceived usefulness strongly drives SMEs toward higher adoption levels, whereas competitive pressure appears to encourage only low- to moderate-level adoption. Female-owned SMEs are more likely to adopt at higher levels than their male-owned counterparts, highlighting the influence of gender on adoption decisions. Financial resources alone do not guarantee progression to higher adoption levels, whereas a larger workforce and adequate infrastructure help reduce the likelihood of low adoption, even if they do not ensure high adoption. These results suggest that a combination of economic capacity, organizational characteristics, perceived benefits, and contextual pressures shapes digital transformation in SMEs. 5.2 Implications In practice, this study highlights the need for technology providers, incubators, and SME support organisations to prioritise demonstrations, training, and use-case evidence that communicate tangible value. The results further show that competitive pressure triggers only low adoption, underscoring the need for SMEs to build internal capabilities, such as digital skills, strategic planning, and managerial support, to achieve higher adoption levels. Additionally, the gendered pattern of adoption suggests that female-owned SMEs may be more motivated to engage with digital technology than male-owned SMEs. Thus, training and digitalisation programs should be sensitive to gendered behavioural differences and provide targeted support for women. The study also shows that financial resources alone are insufficient for digital transformation, underscoring the need for SMEs to invest in organizational readiness, workforce development, and digital competencies. Finally, the positive role of the number of employees and adequate infrastructure highlights the importance of human capital and foundational ICT conditions in supporting digital integration. Policy-wise, the study points to the need for integrated digitalisation strategies that go beyond infrastructure investment. Government and SME development agencies should implement benefit-oriented digital awareness campaigns, enhance digital literacy and managerial capability programs, and design gender-responsive instruments that recognise the strong digital engagement of female entrepreneurs. Financial support programs should be linked to capacity-building requirements to ensure that resources are translated into meaningful digital progress. Policymakers should also address constraints such as digital skills, organisational processes, and human resource limitations, and support SMEs through shared digital experts, advisory services, or subsidised training, which can help close the adoption gaps and advance the national digital transformation agenda. Theoretically, the results of this study reinforce the TAM framework by confirming that perceived usefulness is one of the most significant determinants of digital technology adoption, particularly at moderate and higher levels of adoption. For the TOE framework, the findings strengthen the idea that environmental pressures, such as competition, act as triggers but do not guarantee deep adoption without the requisite organisational capabilities. Gender’s influence further extends the organisational dimension of the TOE framework by revealing that managerial characteristics are meaningful predictors of adoption patterns. 5.3 Contributions The results of this study make several significant contributions to theory and practice. By examining digital adoption as a multilevel outcome, this study provides a clear understanding of how SMEs progress from low to high levels of digital technology adoption. These findings reinforce and extend both the TAM and TOE frameworks, showing that perceived usefulness remains the strongest driver of higher-level adoption. Simultaneously, environmental pressures, such as competition, trigger only superficial uptake unless internal capabilities support them. The evidence also challenges prevailing assumptions about gender and financial resources, revealing that female-owned SMEs are more digitally progressive and that financial resources alone do not guarantee digital transformation. Furthermore, the study deepens theoretical understanding of the interaction between technological perceptions, organisational characteristics, and environmental dynamics, while offering practical guidance on how SMEs in developing economies can strengthen their readiness for meaningful digital transformation. 5.4 Limitations and Future Research Directions 5.4.1 Limitations This study has several limitations that should be considered. First, the scope was limited to SMEs in the fruit juice processing industry within three regions of Tanzania: Dar es Salaam, Arusha, and Mbeya. This restricts the generalisability of the findings to other industries and locations in the country. Second, the cross-sectional design of the survey data limits the ability to infer causal relationships among variables. While this study identified associations among factors such as perceived usefulness, infrastructure, and competitive pressure, it could not establish definitive cause-and-effect links. Additionally, cultural influences that may affect the level of digital adoption were not included in the analysis, creating another gap in the literature. Finally, this study does not consider the evolving nature of digital adoption, as the factors influencing it may change over time with technological advancements and shifts in market dynamics. 5.4.2 Future Research Directions Building on the limitations of the current study, future research should use longitudinal data to investigate the long-term impact of various factors on the level of digital technology adoption in SMEs. This method will help understand how these factors influence the extent of adoption over time, especially in the context of technological advancements and market shifts. Additionally, using panel data could provide insights into how factors such as perceived usefulness, infrastructure, and competitive pressure affect the digital adoption process over time. To improve the applicability of the findings, future studies should broaden their scope to include SMEs from diverse sectors in Tanzania and other developing countries. Comparative studies would also help assess whether the determinants of the extent of digital adoption identified in the fruit juice processing industry are applicable in other contexts. Declarations Ethical Approval Mzumbe University granted ethical approval for this study, and permission was obtained from the relevant district authorities. The studies involving human participants were reviewed and approved by Mzumbe University. The ethical approach adopted for this study was designed to respect the dignity, rights, and welfare of all participants, ensuring that the research was conducted responsibly and ethically. Consent to Participate All participants provided informed written consent and voluntarily participated in the study. They were assured that their involvement was entirely voluntary and that they could withdraw at any time without consequences. Consent to publication All participants were informed that the collected data would be used solely for academic research and publication. Written informed consent to publish anonymised data was obtained from all participants. Clinical Trial Number Not Applicable. Competing interests The authors declare no competing interests. Funding NA Author Contribution CRediT : Dickson Utonga : Conceptualisation, methodology, literature review, data collection, formal analysis, interpretation of findings, writing – original draft, and review and editing. Charles Stephen Tundui : Supervision, review, and editing. Eliaza Mkuna : Supervision, review, and editing. All authors read and approved the final manuscript. Data Availability The dataset is available from the corresponding author upon reasonable request. References Abdulai MG, Dary SK, Domanban PB. 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Digital transformation during a pandemic: Stretching the organizational elasticity. J Bus Res. 2022;144:1320–32. https://doi.org/10.1016/j.jbusres.2022.01.088 . Rogers EM. Diffusion of innovations. Free; 2003. Sagala GH, Őri D. Toward SMEs' digital transformation success: a systematic literature review. IseB. 2024;22(4):667–719. https://doi.org/10.1007/s10257-024-00682-2 . Salamzadeh A, Hadizadeh M, Salamzadeh Y. Digital empowerment of women entrepreneurs in emerging markets: bridging the gender gap for environmental sustainability. Int J Organizational Anal. 2025. https://doi.org/10.1108/ijoa-01-2025-5120 . Sang TM, Anh NQ. Adopting digital technology for sustainable performance: circular economy practice in the transportation sector. Discover Sustain. 2025;6(1). https://doi.org/10.1007/s43621-025-00973-9 . Selase AM, Selase AE, Ayishetu A-R, Comfort AD, Stanley A, Ebenezer G-A. Impact of Technology Adoption and Its Utilization on SMEs in Ghana. Int J Small Medium Enterprises. 2019;2(2):1–13. https://doi.org/10.46281/ijsmes.v2i2.382 . Shahadat MMH, Nekmahmud M, Ebrahimi P, Fekete-Farkas M. Digital technology adoption in SMES: What technological, environmental, and organisational factors influence in emerging countries? Global Bus Rev. 2023. https://doi.org/10.1177/09721509221137199 . Sharma M, Luthra S, Joshi S, Kumar A. Implementing challenges of artificial intelligence: Evidence from the public manufacturing sector of an emerging economy. Government Inform Q. 2021;39(4):101624. https://doi.org/10.1016/j.giq.2021.101624 . Tarmizi R, Ayu Sanjaya YP, Sunarya PA, Septiani N. Harnessing Digital Platforms for Entrepreneurial Success: A Study of Technopreneurship Trends and Practices. Aptisi Trans Technopreneurship (ATT). 2023;5(3):278–90. https://doi.org/10.34306/att.v5i3.360 . Tonya EM, Samwel E. Challenges facing the growth of small and medium enterprises in Tanzania: A case of Mbeya’s Mwanjelwa market. Afr J Acc Social Sci Stud. 2025;6(2):157–71. https://doi.org/10.4314/ajasss.v6i2.9 . Tornatzky LG, Fleischer M. The processes of technological innovation. Lexington Books; 1990. Tundui CS, Tundui HP. Cornerstones of a cathedral: The influence of adolescent aspirations on future entrepreneurial successes. J Small Bus Enterp Dev. 2025;32(4):962–80. https://doi.org/10.1108/jsbed-10-2024-0517 . Ugbebor F, Adeteye M, Ugbebor J. Automated Inventory Management Systems with IoT Integration to Optimize Stock Levels and Reduce Carrying Costs for SMEs: A Comprehensive Review. J Artif Intell Gen Sci (JAIGS). ISSN 2024;3006–4023(1):306–40. https://doi.org/10.60087/jaigs.v6i1.257 . 6 . Ullah I, Khan M, Rakhmonov DA, Bakhritdinovich KM, Jacquemod J, Bae J. Factors affecting digital marketing adoption in Pakistani small and medium enterprises. Logistics. 2023;7(3):41. https://doi.org/10.3390/logistics7030041 . United Nations Conference on Trade and Development. (2022). Digital economy report 2021: Cross-border data flows and development—For whom the data flow . UNCTAD. Venkatesh N, Morris N, Davis N, Davis N. User acceptance of Information Technology: toward a unified view1. MIS Q. 2003;27(3):425–78. https://doi.org/10.2307/30036540 . Vial G. Understanding digital transformation: A review and a research agenda. J Strateg Inf Syst. 2019;28(2):118–44. https://doi.org/10.1016/j.jsis.2019.01.003 . Vial G. (2021). Understanding digital transformation. In Managing Digital Transformation (pp. 13–66). https://doi.org/10.4324/9781003008637-4 Vrontis D, Chatterjee S, Chaudhuri R. Adoption of Digital Technologies by SMEs for Sustainability and Value Creation: Moderating Role of Entrepreneurial Orientation. Sustainability. 2022;14(13):7949. https://doi.org/10.3390/su14137949 . Vu NH, Nguyen NM. Development of small-and medium-sized enterprises through information technology adoption persistence in Vietnam. Inform Technol Dev. 2021;28(3):585–616. https://doi.org/10.1080/02681102.2021.1935201 . Wasudawan K, Weissmann MA, Nwobodo S. Small and medium-sized enterprises perceived trust towards social media: applying the extended technology acceptance model. J Asia Bus Stud. 2025;19(4):1085–103. https://doi.org/10.1108/jabs-12-2024-0701 . Wymer S, Regan E. Factors Influencing e-commerce Adoption and Use by Small and Medium Businesses. Electron Markets. 2005;15(4):438–53. https://doi.org/10.1080/10196780500303151 . Zamani SZ. Small and Medium Enterprises (SMEs) facing an evolving technological era: a systematic literature review on the adoption of technologies in SMEs. Eur J Innov Manage. 2022;25(6):735–57. https://doi.org/10.1108/ejim-07-2021-0360# . Zhu K, Kraemer KL. Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Inform Syst Res. 2005;16(1):61–84. Additional Declarations No competing interests reported. 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15:18:39","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":204638,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8483750/v1/63d7f24cb00d1c2ab13a95c0.html"},{"id":99627104,"identity":"453250f8-a1a9-4adc-8872-f39bd90aa4e8","added_by":"auto","created_at":"2026-01-06 15:18:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":41796,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Rate of Adoption of Digital Tools by Domains\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8483750/v1/6ab7f45d33ab4c0645a40a62.png"},{"id":99627105,"identity":"8805d63e-7cd8-407c-8c91-b6aa4379553f","added_by":"auto","created_at":"2026-01-06 15:18:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":39603,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Level of Digital Technology Adoption Among SMEs\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8483750/v1/099290571548372760265283.png"},{"id":99804519,"identity":"34c62050-2173-4c0a-a9dd-8a82dc28cea8","added_by":"auto","created_at":"2026-01-08 14:13:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":983651,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8483750/v1/21d108c7-13fb-49b2-9797-cbbf7191a56f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Modelling the Determinants of the Levels of Digital Technology Adoption among SMEs in Tanzania","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe pervasive and accelerating integration of digital technology has become a defining feature of the contemporary global economy, fundamentally reshaping how firms operate, compete, and create value. Within this transformation, the Fourth Industrial Revolution has intensified the role of digital technology across all sectors in production, marketing, and distribution (Javaid et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Lee et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This shift has transformed production systems, reshaped marketing and distribution channels, and enhanced firm performance (Amin et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Dimoso \u0026amp; Utonga, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sang \u0026amp; Anh, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The COVID-19 pandemic accelerated this digital shift, compelling firms to adopt digital tools to sustain operations amid unprecedented disruptions (Amankwah-Amoah et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Reuschl et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Small and Medium Enterprises (SMEs), in particular, have derived substantial benefits from adopting digital payment systems, internal operations management solutions, and digital marketing platforms (Bindeeba et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ihenyen et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Mushi, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Myataza et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As a result, these advancements not only enhance operational efficiency but also position SMEs for greater market competitiveness in a digitally driven economy.\u003c/p\u003e \u003cp\u003eThe adoption of digital payment tools includes integrating mobile money wallets, contactless payments, and online transactions (Abdulai et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The adoption of digital marketing platforms encompasses the use of social media and other online marketing tools, including search engine optimisation and paid advertising platforms such as Google Ads (Dwivedi et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Herhausen et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, adopting internal operations management solutions involves integrating financial and operational management tools, including accounting systems (Xero, QuickBooks), Excel tools, mobile applications, procurement solutions, resource management systems, and project management platforms (Lutfi et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Mujalli et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ugbebor et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These tools enable SMEs to reduce transaction costs, expand market access, improve operational efficiency, and optimize resource allocation, thereby enhancing performance and sustainability (United Nations Conference on Trade and Development [UNCTAD], 2022; Organisation for Economic Co-operation and Development [OECD], 2021). Beyond immediate efficiency gains, digital technology supports data-driven decision-making, allowing SMEs to respond more effectively to changing market conditions and customer preferences (Kraus et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vial, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Consequently, digitally engaged SMEs are better equipped to achieve sustained growth and competitive advantages.\u003c/p\u003e \u003cp\u003eWhile digital technology presents substantial opportunities for SMEs, a considerable challenge persists in how these businesses, particularly in developing economies, can effectively utilise these tools for their benefit (OECD, 2021; UNCTAD, 2022). This challenge is especially pronounced in the Tanzanian agro-processing sector (Nkwabi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), where SMEs are integral to value addition, employment generation, and strengthening rural\u0026ndash;urban economic linkages. Within this sector, the fruit juice processing industry has grown steadily owing to rising incomes, urbanisation, and the availability of diverse fruit varieties (Issa et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Musaazi et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). As competition intensifies within the digital economy, SMEs face increasing pressure to enhance performance and sustainability by improving service quality, ensuring traceability, strengthening distribution efficiency, and complying with market standards. In this context, digital tools, such as internal operations management systems, digital payment platforms, and online marketing tools, have emerged as significant facilitators (Vial, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). SMEs need to benefit from these substantial resources of the digital economy.\u003c/p\u003e \u003cp\u003eHowever, despite their potential, there is limited empirical evidence documenting digital technology adoption among fruit juice processing SMEs in Tanzania, and little is known about the determinants of the level of its adoption. This constraint restricts SMEs\u0026rsquo; ability to scale operations, access markets, and sustain their competitiveness in an increasingly digitised economy. The characteristics of emerging economies further shape digital technology adoption among Tanzanian SMEs (OECD, 2021; UNCTAD, 2022). Although rising mobile penetration and government-led industrial initiatives have created new opportunities, SMEs continue to face challenges related to infrastructure quality, access to finance, technological skills, and regulatory compliance (Centre for Policy Research and Advocacy [CPRA], \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Nkwabi et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Tonya \u0026amp; Samwel, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Therefore, promoting effective digital technology adoption among SMEs is closely aligned with Tanzania\u0026rsquo;s national development goals. This includes the National Development Vision 2050 and the Industrialisation Strategy, which emphasise SME-led growth, technological upgrading, and value addition in agro-processing.\u003c/p\u003e \u003cp\u003eThe existing literature identifies a broad set of determinants that shape SMEs\u0026rsquo; digital technology adoption. At the technological level, perceived security, perceived usefulness, and ease of use consistently influence adoption decisions, as firms are more likely to integrate digital tools that enhance efficiency and are relatively easy to implement (Davis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Faiz, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Vial, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Organisational factors such as firm size, gender, digital readiness, managerial competence, human capital, and financial capacity also play a central role, with larger and better-resourced SMEs demonstrating a higher propensity to adopt digital technologies (Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alhakimi \u0026amp; Albashiri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Machado et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Omrani et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Environmental conditions, including competitive pressure, availability of digital infrastructure, access to external support services, and government policies, further shape adoption outcomes (Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Akerejola et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Barros, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This body of work demonstrates that digital adoption among SMEs is a complex and context-dependent process.\u003c/p\u003e \u003cp\u003eDespite this potential contribution, empirical research has offered limited attention to the levels of digital technology adoption. This represents a critical gap, as digital transformation among SMEs is inherently incremental, progressing from basic digital tools to more advanced and integrated systems. A binary or multinomial view of adoption fails to capture the distinct challenges and opportunities at different levels of digital adoption. Without distinguishing between low, moderate, and high levels of adoption, policymakers and support organisations struggle to design interventions that effectively guide SMEs along their digital transformation pathways, often resulting in misaligned strategies and suboptimal outcomes. In the Tanzanian context, specifically within the fruit juice processing industry, evidence on how various factors shape the levels of digital technology adoption remains notably scarce.\u003c/p\u003e \u003cp\u003eAddressing this gap, the present study examines the determinants of digital technology adoption among SMEs in Tanzania\u0026rsquo;s fruit juice processing industry. To comprehensively examine this matter, this study is grounded in the Technology Acceptance Model (TAM) and Technology\u0026ndash;Organisation\u0026ndash;Environment (TOE) frameworks. TAM provides insights into how perceived usefulness and perceived ease of use influence firms\u0026rsquo; engagement with digital technology and their willingness to deepen its integration into their operations and processes (Davis, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Venkatesh et al., \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Complementarily, the TOE framework incorporates organisational capacities and environmental conditions (Baker, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tornatzky \u0026amp; Fleischer, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e1990\u003c/span\u003e), offering a multidimensional lens to explain variations in digital maturity across SMEs. Thus, these frameworks provide a theoretical foundation for examining the determinants of low, moderate, and high digital technology adoption among SMEs.\u003c/p\u003e \u003cp\u003eAccordingly, this study seeks to answer the following research question:\u003c/p\u003e \u003cp\u003eRQ: What are the factors influencing the varying levels of digital technology adoption among fruit juice processing SMEs in Tanzania?\u003c/p\u003e \u003cp\u003eThis study contributes to the literature in several ways. First, it advances existing research by moving beyond binary or multiple adoption measures to examine the progression of digital technology adoption across different levels. Second, it extends the application of TAM and TOE by demonstrating their explanatory power in the African SME context, particularly regarding adoption levels. Third, by examining the role of gender in shaping digital technology adoption across different levels, this study offers novel insights into how socio-demographic factors influence digital transformation trajectories in developing economies. The remainder of the manuscript is organised as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the materials and methods; Section \u003cspan refid=\"Sec8\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results; Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e4\u003c/span\u003e discusses the findings; and Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e concludes with the key policy implications, contributions, limitations, and future research directions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 The Study Context\u003c/h2\u003e \u003cp\u003eThis study employs a quantitative, cross-sectional research design to analyse the determinants of the level of digital technology adoption among SMEs in the fruit juice processing industry in Tanzania. The cross-sectional approach was chosen to leverage existing variations in SME characteristics at a single point in time, rather than tracking changes over time. This approach enables an efficient and comparative analysis of digital adoption behaviour. The study was conducted in six districts across three regions in mainland Tanzania: Ilala, Kinondoni, Ubungo, and Temeke in Dar es Salaam; Arusha District in the Arusha Region; and Mbeya District in the Mbeya Region. These locations were purposively selected because of their high concentration of fruit juice processing SMEs and their strategic importance within urban economies, particularly in relation to employment creation, food system support, and rural\u0026ndash;urban market integration. The inclusion of these locations broadened the study\u0026rsquo;s analytical scope. Dar es Salaam, as the commercial capital, provides potential markets and relatively mature digital infrastructure; Arusha is significant for its agricultural and regional trade environment; and Mbeya offers insights into a high-production agricultural corridor. This geographical focus enabled the study to capture varying levels of economic activity and digital readiness pertinent to fruit juice processing SMEs in the region.\u003c/p\u003e \u003cp\u003eWe used a multistage sampling method. First, the regions of Dar es Salaam, Arusha, and Mbeya were purposively selected. The districts of Ilala, Ubungo, Temeke, and Kinondoni were selected in Dar es Salaam. Similarly, the districts of Arusha (Arusha region) and Mbeya (Mbeya region) were purposively selected for their roles as SME activity hubs and their distinct economic development, infrastructure, and urbanisation profiles. This approach captured diverse socioeconomic and geographic settings that could influence SMEs\u0026rsquo; access to digital tools. Furthermore, five wards were randomly chosen from each district. Selecting five wards provided sufficient geographic coverage while accounting for practical fieldwork constraints. This method increases the likelihood of capturing variations in SME operations and digital technology use within districts while keeping costs, logistics, and time manageable. Finally, 13 SMEs were randomly selected from each ward, yielding a total sample size of 390. The sample size was determined using a variable-based method that relies on the number of independent variables in the model (Tundui \u0026amp; Tundui, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The method recommends at least five observations per independent variable to ensure sufficient statistical power. With 10 independent variables in our analytical model (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), we used 39 observations per variable. This approach significantly exceeded the threshold, thereby enhancing the statistical accuracy, robustness, and generalisability.\u003c/p\u003e \u003cp\u003eData collection was conducted through direct interactions using a structured questionnaire administered by trained enumerators from November 2024 to April 2025. The enumerators completed the questionnaire by asking respondents questions. The questionnaire was developed based on an extensive literature review and expert consultations. Initially prepared in English, the questionnaire was subsequently translated into Swahili to enhance clarity and accessibility for respondents. The questionnaire included sections addressing SME characteristics, digital tool utilisation, adoption determinants, and performance outcomes. Prior to the primary survey, the instrument underwent pilot testing with 30 SME owner-managers in Dar es Salaam to evaluate its clarity, relevance, reliability, and validity. Feedback from the pilot study led to minor refinements to improve the wording and relevance of the questions. The refined questionnaire was then administered to the selected SMEs, with each administration lasting approximately 40 min. All questionnaires were completed anonymously, informed consent was obtained from all participants, and the study adhered to established ethical guidelines to ensure confidentiality and to protect respondents\u0026rsquo; rights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study Variables\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 The Response Variable\u003c/h2\u003e \u003cp\u003eThe dependent variable in this study was the level of digital technology adoption. Digital adoption can be operationalised in several ways, including counting the number of digital tools implemented (Zhu \u0026amp; Kraemer, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), evaluating the combination or bundle of tools adopted, or calculating the proportion of available tools integrated into firm operations (Ardito et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In line with these approaches, this study measures adoption across three key domains: digital marketing, digital payment, and internal operations management, reflecting the multidimensional nature of SME digitalisation (Bharadwaj et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). An SME is considered to have adopted a domain if it uses any tool within that domain, and the total adoption score is calculated as the number of domains adopted, with a maximum score of three. This domain-based approach allows for capturing adoption while maintaining simplicity and interpretability, which is particularly important for SMEs with heterogeneous technology portfolios (Oliveira \u0026amp; Martins, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Rogers, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To quantify adoption levels, each SME was assigned a score on a 0\u0026ndash;3 scale: 0\u0026thinsp;=\u0026thinsp;no adoption; 1\u0026thinsp;=\u0026thinsp;low adoption; 2\u0026thinsp;=\u0026thinsp;moderate adoption; and 3\u0026thinsp;=\u0026thinsp;high adoption. This ordinal scoring system aligns with previous research on SME technology adoption, providing a reliable and practical measure of digitalisation that accommodates varying levels of tool use across domains (Zhu \u0026amp; Kraemer, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Battisti et al., 2015).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Predictor Variables\u003c/h2\u003e \u003cp\u003eWe employed the TAM and TOE frameworks, along with empirical literature, to conceptualise the explanatory variables for this study. These variables are factors theorised to influence the level of digital tools adoption in SMEs as follows: -\u003c/p\u003e \u003cp\u003eGender was operationalised as a binary variable reflecting the gender of the principal decision-maker in the SME. A value of \u0026ldquo;1\u0026rdquo; represents male owners, while \u0026ldquo;0\u0026rdquo; corresponds to female owners. This variable was used to assess whether gender influenced decisions regarding digital technology adoption within SMEs. Conceptualising gender as a determinant of technology adoption aligns with previous research on how gender influences business decisions regarding technology use and innovation (Alam et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Alhakimi \u0026amp; Albashiri, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Orser \u0026amp; Riding, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFinancial capacity is a binary variable that assesses whether an SME has sufficient financial resources to invest in digital tools. SMEs coded as \"1\" possess adequate financial resources to support the acquisition, implementation, and maintenance of digital tools, while those coded as \"0\" face financial constraints that limit their ability to invest in such resources. This variable aligns with the TOE framework, which highlights financial resources as an important factor influencing technology adoption. Previous research has emphasised the role of financial capacity in enabling or hindering digital adoption in SMEs (Omrani et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Sagala \u0026amp; Őri, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sharma et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDigital readiness was operationalised as a binary factor that captures the extent to which an SME is prepared for digital adoption. A value of \"1\" indicates that the SME has the necessary organisational mindset, fundamental skills, and preliminary structures to adopt digital tools effectively, while \"0\" reflects a lack of readiness. This variable is aligned with the TOE framework, which considers organisational readiness a crucial factor in digital technology adoption. Previous studies have highlighted the importance of digital readiness for the successful implementation of digital technologies in SMEs (Machado et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Michelotto \u0026amp; Joia, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Omrani et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePerceived security reflects the context in which SME decision-makers believe that digital tools and systems (such as e-commerce platforms, enterprise software, and AI solutions) are secure in terms of protecting data, privacy, and operations from threats such as unauthorised access, fraud, and data breaches. Perceived security is expected to positively influence SMEs\u0026rsquo; intentions to adopt digital tools, whereas a lack of perceived security may deter adoption. This variable was operationalised as a binary variable: 1\u0026thinsp;=\u0026thinsp;perceived security (the SME perceives digital tools as secure and trustworthy) and 0\u0026thinsp;=\u0026thinsp;no perceived security (the SME perceives security risks or lacks confidence in the digital tools). Previous research has consistently highlighted the importance of perceived security in shaping attitudes and intentions toward digital technology adoption in SMEs and similar contexts (Alka\u0026rsquo;awneh et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Faiz, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wasudawan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Giang et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePerceived usefulness is a well-established determinant of technology adoption in SME research rooted in the TAM framework. According to TAM, perceived usefulness refers to the degree to which decision-makers believe that using a technology improves their performance. Prior studies across various digital technologies have shown that higher perceived usefulness increases the likelihood of adoption decisions and related performance improvements in SMEs (Buv\u0026aacute;r \u0026amp; G\u0026aacute;ti, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Amnas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aremu \u0026amp; Arfan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wasudawan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Perceived usefulness has been empirically linked to adoption attitudes, intentions, and performance effects in SMEs and related contexts. Therefore, in this study, perceived usefulness was operationalised as a binary variable indicating whether the SME owner or manager believes that digital tools improve business performance, which leads to their adoption. A value of 1 represented the belief that digital tools are useful, and 0 represented the belief that they are not helpful.\u003c/p\u003e \u003cp\u003eThe variable perceived ease of use is a binary measure that indicates whether an SME finds digital tools manageable and straightforward. A value of 1 signifies that SMEs consider these tools easy to learn and apply, whereas a value of 0 indicates a perception of complexity or difficulty. This variable is grounded in the TAM framework, which suggests that perceived ease of use is a crucial factor in technology adoption, shaping users' attitudes and intentions toward adopting new technologies. Previous research has consistently demonstrated that higher perceived ease of use increases the likelihood of adoption, especially when digital tools are perceived as intuitive and user-friendly. The operationalisation of this variable aligns with the established frameworks in the SME digital adoption literature (Amnas et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Aremu \u0026amp; Arfan, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Buv\u0026aacute;r \u0026amp; G\u0026aacute;ti, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wasudawan et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCompetitive pressure is a binary factor indicating whether an SME faces competitive forces that encourage digital adoption. SMEs coded as \u0026ldquo;1\u0026rdquo; recognise competition as a driver of digital adoption, whereas \u0026ldquo;0\u0026rdquo; indicates the absence of competitive dynamics in the market. This variable is based on the TOE framework, which considers competitive pressure a factor that can drive SMEs to adopt new technologies to maintain or enhance their competitive position. Previous studies have highlighted the role of competitive pressure in motivating SMEs to innovate and adopt digital tools to stay ahead in competitive markets (Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Faiz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInfrastructure is a binary indicator that captures the availability of essential infrastructure to support the deployment of digital technology. A value of \u0026ldquo;1\u0026rdquo; signifies reliable access to infrastructure such as electricity, internet connectivity, and network coverage, whereas \u0026ldquo;0\u0026rdquo; indicates infrastructural limitations. This variable is grounded in the TOE framework, where infrastructure availability is a crucial factor influencing the ability to adopt and utilise digital technologies. Research has shown that adequate infrastructure is a critical enabler of digital technology adoption, with the absence of reliable infrastructure often acting as a barrier to implementation (Omrani et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Akerejola et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe variable number of employees is a quantitative measure of the total number of workers employed by an SME. This variable serves as a proxy for SME size, reflecting the operational capacity and potential resource availability for adopting digital technologies. Larger SMEs with more employees may have greater operational capacity and access to resources, which can facilitate the adoption of digital tools. This measure has been used in previous studies to capture firm size as a determinant of technology adoption, with larger firms often being better positioned to invest in and implement new technologies (Barros, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Holl \u0026amp; Rama, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGovernment support is conceptualised as the presence of public policies, programs, incentives, and institutional initiatives that enable SMEs to adopt digital tools, including training, infrastructure support, financial incentives, and regulations. Government support plays a crucial role in helping SMEs overcome barriers to digital adoption, particularly in developing and emerging countries. Prior research has shown that externally oriented support from government entities is a significant determinant of SMEs\u0026rsquo; digital technology adoption. In this study, government support is measured as a binary variable indicating whether an SME perceives it as available and accessible. A value of 1 signified the presence of government support, and 0 indicated the absence of such support. This approach aligns with the TOE framework, which emphasises the role of external factors, such as government support, in shaping technology adoption (Chen et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Analysis Framework\u003c/h2\u003e \u003cp\u003eTo analyse the determinants of the level of digital technology adoption among SMEs, we employed an ordered logistic regression model. The adoption level was defined as an ordered categorical variable with three categories (1\u0026ndash;3) representing low, moderate, and high adoption levels. These levels were based on the number of digital domains each SME adopted. This method enabled the creation of a strictly ordered scale that reflects progressively higher degrees of digital technology adoption. Because the outcome was an ordered categorical variable, ordinary least squares could be unsuitable, as it treats the response as continuous and violates the ordinal nature of the data and the assumption of homoscedasticity and normally distributed error (Long \u0026amp; Freese, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Count-data regressions (Poisson and Negative Binomial) are appropriate when the dependent variable is an accurate count of events. However, they also rely on distributional assumptions that do not fit ordered categories with meaningful ranks, but instead on precise interval scales (Cameron \u0026amp; Trivedi, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For these reasons, and because the adoption score captures ordered categories rather than a count process, we used an ordered logistic (proportional-odds) regression as the primary method.\u003c/p\u003e \u003cp\u003eFormally, let \u003cem\u003eY\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e denote the observed adoption category for SME \u003cem\u003ei\u003c/em\u003e, taking values 1 (low), 2 (moderate), and 3 (high). The ordered logistic model assumes the existence of an unobserved latent variable \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Y}_{i}^{*}\\)\u003c/span\u003e\u003c/span\u003e, representing the underlying propensity for digital technology adoption, such that:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{Y}}_{\\varvec{i}}^{\\varvec{*}}=\\:{\\varvec{X}}_{\\varvec{i}}^{\\varvec{{\\prime\\:}}}\\beta\\:+{\\epsilon\\:}_{i}\\text{}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{X}}_{\\varvec{i}}^{\\varvec{{\\prime\\:}}}\\)\u003c/span\u003e\u003c/span\u003e is a vector of explanatory variables capturing various determinants, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the corresponding parameter vector, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{i}\\)\u003c/span\u003e\u003c/span\u003e follows a logistic distribution. The observed categories of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}_{\\varvec{i}}\\)\u003c/span\u003e\u003c/span\u003e arise by partitioning ​ \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{Y}}_{\\varvec{i}}^{\\varvec{*}}\\)\u003c/span\u003e\u003c/span\u003e according to threshold parameters \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\tau\\:}}_{1}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\tau\\:}}_{2}\\)\u003c/span\u003e\u003c/span\u003e:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{\\varvec{Y}}_{\\varvec{i}}=\\left\\{\\begin{array}{c}1\\:\\:\\:if\\:{\\varvec{Y}}_{\\varvec{i}}^{\\varvec{*}}\\le\\:{\\varvec{\\tau\\:}}_{1}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\\\\\:2\\:\\:\\:if\\:{\\varvec{\\tau\\:}}_{1}\u0026lt;{\\varvec{Y}}_{\\varvec{i}}^{\\varvec{*}}\\le\\:{\\varvec{\\tau\\:}}_{2}\\\\\\:3\\:\\:\\:if\\:{\\varvec{Y}}_{\\varvec{i}}^{\\varvec{*}}\\ge\\:{\\varvec{\\tau\\:}}_{2}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\end{array}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThis leads to the cumulative logit representation:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{l}\\varvec{o}\\varvec{g}\\left(\\frac{\\text{P}\\text{r}({\\varvec{Y}}_{\\varvec{i}}\\le\\:\\varvec{j})}{\\text{P}\\text{r}({\\varvec{Y}}_{\\varvec{i}}\u0026gt;\\varvec{j})}\\right)={\\propto\\:}_{\\varvec{j}}-{\\varvec{X}}_{\\varvec{i}}^{\\varvec{{\\prime\\:}}}\\beta\\:\\)\u003c/span\u003e \u003c/span\u003e, \u003cem\u003ej=\u003c/em\u003e1, 2,\u003c/p\u003e \u003cp\u003eWhere \u003cb\u003eα\u003c/b\u003e\u003csub\u003e\u003cb\u003ej\u003c/b\u003e\u003c/sub\u003e are threshold-specific intercepts. The proportional-odds assumption implies that the vector of slope coefficients \u003cb\u003eβ\u003c/b\u003e remains constant across the cumulative logits, allowing consistent interpretation of the direction and magnitude of each predictor\u0026rsquo;s effect on the likelihood of being in a higher adoption category.\u003c/p\u003e \u003cp\u003eParameter estimates were obtained via maximum likelihood, and robust standard errors were computed to correct for potential heteroskedasticity and within-group correlation. We conducted several diagnostic procedures to ensure appropriate model specifications. First, the proportional odds (parallel lines) assumption was tested using the Brant test. Where violations were detected, we estimated a generalised ordered logistic model as a robustness check to assess the stability of the results. Second, multicollinearity was examined using the variance inflation factor (VIFs). Third, the model fit was assessed using likelihood ratio tests, the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and pseudo-R2 measures. In addition to reporting odds ratios for ease of interpretation, we computed average marginal effects (AMEs) to quantify the change in the predicted probability of each adoption category associated with changes in each variable. These effects provide a more intuitive assessment of effect magnitudes, particularly when evaluating differences in the predicted probability of high adoption across SMEs\u0026rsquo; characteristics. The reported results reflect patterns observed in the survey data collected from fruit juice processing SMEs in the Dar es Salaam, Arusha, and Mbeya regions between November 2024 and April 2025, unless otherwise stated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Demographic Information of the SMEs\u003c/h2\u003e \u003cp\u003eThis study surveyed 390 SMEs operating in the fruit juice processing industry across six districts in Tanzania: Ilala, Kinondoni, Ubungo, Temeke, Arusha, and Mbeya. The sample distribution reveals a pronounced geographic concentration, with approximately 67% of SMEs located in Dar es Salaam, underscoring the region\u0026rsquo;s position as Tanzania\u0026rsquo;s primary industrial and commercial hub. The ownership structure is dominated by sole proprietorships (approximately 85%), reflecting a business landscape characterised by individually owned and managed enterprises. The sector also exhibits a strong gender dimension: approximately 59% of SMEs are led by women, underscoring the prominent role of female entrepreneurship in the agro-processing sector. The SMEs surveyed had an average operational age of three years, indicating a heterogeneous mix of nascent and more established entities. Educational attainment among owner-managers was notably high, with approximately 42% having completed secondary education and approximately 49% holding post-secondary or higher education qualifications. This relatively strong human capital base is complemented by small workforce sizes, averaging three employees per SME, which is consistent with the defining characteristics of small-scale enterprises in developing economies. These attributes depict a sector that is young, urban-centred, and predominantly owner-managed, yet supported by comparatively well-educated leadership. Such demographic features provide important context for understanding digital technology adoption patterns, as they shape both SMEs\u0026rsquo; capacity and propensity to engage with emerging digital tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Extent of Digital Technology Adoption\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the distribution of digital technology adoption across the three domains. Digital payment systems had the highest adoption rate, with approximately 66.2% of SMEs using them. This indicates that payment tools are now more accessible and are considered essential for improving transactions and reducing cash-handling risks. Digital marketing platforms had an adoption rate of approximately 57.2%, indicating the growing recognition of their value in enhancing market visibility. However, many SMEs still lack the skills and strategies needed to use them effectively. Internal operations management tools had the lowest adoption, at approximately 48.7%, suggesting that technologies for managing inventory, customer relations, and administrative tasks are still not fully integrated into many SME operations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the level of digital technology adoption among SMEs to capture its extent. The results also reveal that most SMEs are in the early or partial stages of digital transformation: 163 (41.8%) have adopted digital tools in only one domain (low adoption), and 161 (41.3%) have adopted digital tools in two domains (moderate adoption). Only 66 (16.9%) SMEs adopted all three domains (high adoption). These patterns suggest that while digital technologies are increasingly diffusing across the SME sector, adoption remains uneven, focused mainly on specific functional areas rather than being integrated across all business operations, highlighting both progress and ongoing challenges in SME digitalisation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Model Evaluation\u003c/h2\u003e \u003cp\u003eWe analysed the determinants of digital technology adoption levels among SMEs and classified the adoption into three categories: low, moderate, and high. Prior to estimation, we assessed multicollinearity among the predictor variables using VIFs. All VIF values were below 1.3, indicating no strong correlations between the predictors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Then, we estimated a standard ordered logistic regression under the proportional odds (parallel lines) assumption using the \u003cem\u003eoparalell\u003c/em\u003e command in STATA. This assumption requires that the effect of each predictor remains consistent across all thresholds of the ordinal outcomes (Brant, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Most tests (Wolfe\u0026ndash;Gould, Brant, score, and likelihood ratio) were not significant (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the Wald test was significant (χ\u0026sup2; = 18.40, df\u0026thinsp;=\u0026thinsp;10, p\u0026thinsp;=\u0026thinsp;0.049), indicating that at least one predictor may have violated the proportional odds assumption (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Although the model fit indices favoured the standard ordered logistic regression model (AIC\u0026thinsp;=\u0026thinsp;797.50, BIC\u0026thinsp;=\u0026thinsp;845.10) over the generalised model (AIC\u0026thinsp;=\u0026thinsp;800.58, BIC\u0026thinsp;=\u0026thinsp;887.84), they did not account for threshold-specific violations.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParallel Regression (Proportional Odds) Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eChi\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;Chi\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWolfe\u0026ndash;Gould\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e17.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e17.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.057\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLikelihood Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWald\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e18.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit Indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eAIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBIC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOrdered Logit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e797.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e845.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneralized Logit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e800.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003e887.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo accommodate potential violations of the proportionality assumption, we estimated a generalised ordered logistic regression using gologit2 with robust standard errors and the autofit option in STATA. This approach allows predictors that violate the proportional odds assumption to have threshold-specific coefficients, while retaining a single coefficient for variables that satisfy the assumption. The model was statistically significant (Wald χ\u0026sup2; (11)\u0026thinsp;=\u0026thinsp;38.95, p\u0026thinsp;=\u0026thinsp;0.0001), with a log pseudolikelihood of \u0026minus;\u0026thinsp;382.997 and a pseudo-R\u0026sup2; of 0.047. The significance of the threshold parameters confirmed that the three levels of digital technology adoption could not be combined into a single, ordered structure. The Wald tests for parallel lines for each predictor, as implemented by gologit2's autofit option, indicated that the proportionality assumption was violated for infrastructure availability, confirming the need for a generalised ordered logistic model. Because coefficients in generalised ordered logit models are threshold-specific and differ by a scale factor, their magnitudes are not directly comparable across thresholds. To provide interpretable effect sizes, we computed the average marginal effects using the margins command in STATA. The marginal effects quantified how a marginal change in each predictor variable altered the predicted probability of being at each adoption level. This approach formed the basis for the substantive presentation of results in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDeterminants of the level of digital technology adoption among SMEs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eLow Adoption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eModerate Adoption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eHigh Adoption\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edy/dx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003edy/dx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edy/dx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eP\u0026gt;|z|\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.019**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.054*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.040**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.061*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.065*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e5\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.1386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.069\u003cb\u003e*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.080*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.072*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e6\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.040**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.028**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e7\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.0110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.0167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e8\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e9\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.0690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eX\u003c/b\u003e\u003csub\u003e\u003cb\u003e10\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.1548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.1660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.0112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNumber of obs\u0026thinsp;=\u0026thinsp;390, LR= -382.99661, chi\u003csup\u003e2\u003c/sup\u003e(11)\u0026thinsp;=\u0026thinsp;38.95, Prob\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u0026thinsp;=\u0026thinsp;0.0001. *, ** and *** = Significant at 10%, 5% and 1% respectively.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the predictors had diverse effects across different adoption levels. For low-level adoption, perceived usefulness (X\u003csub\u003e2\u003c/sub\u003e), competitive pressure (X\u003csub\u003e3\u003c/sub\u003e), number of employees (X\u003csub\u003e6\u003c/sub\u003e), and X\u003csub\u003e10\u003c/sub\u003e were significant at the 5% level, whereas gender (X\u003csub\u003e4\u003c/sub\u003e) and financial capacity (X\u003csub\u003e5\u003c/sub\u003e) were marginally significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). For moderate-level adoption, perceived usefulness (X\u003csub\u003e2\u003c/sub\u003e), number of employees (X\u003csub\u003e6\u003c/sub\u003e), and infrastructure availability (X\u003csub\u003e10\u003c/sub\u003e) remained significant at the 5% level, whereas competitive pressure (X\u003csub\u003e3\u003c/sub\u003e), gender (X\u003csub\u003e4\u003c/sub\u003e), and financial capacity (X\u003csub\u003e5\u003c/sub\u003e) were marginally significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). For high-level adoption, perceived usefulness (X\u003csub\u003e2\u003c/sub\u003e), competitive pressure (X\u003csub\u003e3\u003c/sub\u003e), and number of employees (X\u003csub\u003e6\u003c/sub\u003e) remained significant at the 5% level, while gender (X\u003csub\u003e4\u003c/sub\u003e) and financial capacity (X\u003csub\u003e5\u003c/sub\u003e) were marginal (p\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Other variables, such as ease of use (X1), readiness for digital adoption (X\u003csub\u003e7\u003c/sub\u003e), government support (X\u003csub\u003e8\u003c/sub\u003e), and perceived security of digital tools (X\u003csub\u003e9\u003c/sub\u003e), were not significant. The threshold-specific effects of infrastructure availability (X\u003csub\u003e10\u003c/sub\u003e) confirmed that the three adoption levels could not be combined, justifying the use of a generalised ordered logistic regression model. The results are detailed as follows:\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the perceived usefulness of digital tools significantly influenced the level of digital technology adoption. Specifically, SMEs that perceived digital tools as applicable were 11.0 percentage points (pp) less likely to be at the low-adoption level, 4.4 pp more likely to be at the moderate-adoption level, and 6.6 pp more likely to reach the high-adoption level, all else being equal. The results also reveal that competitive pressure influences the level of digital technology adoption by SMEs. The study findings indicate that SMEs facing competitive pressure were 9.2 pp more likely to have low adoption and 8.7 pp more likely to have moderate adoption. However, they were 5.6 pp less likely to achieve high adoption levels.\u003c/p\u003e \u003cp\u003eFurthermore, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that the gender of SME owner-managers significantly influences the level of digital technology adoption. Furthermore, compared with female-owned SMEs, male-owned SMEs were 8.4 pp more likely to be at the low-adoption level, but 3.4 and 5.1 pp less likely to be at the moderate and high-adoption levels, respectively, after controlling for other factors. Conversely, the model results show that SMEs with sufficient financial resources were 13.9 pp more likely to remain at the low adoption level and 5.5 and 8.4 pp less likely to reach moderate and high levels of adoption, respectively, when controlling for other factors.\u003c/p\u003e \u003cp\u003eAdditionally, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that the number of employees has a moderate, yet statistically significant, effect on the level of digital technology adoption in SMEs. SMEs with more employees were 2.1 pp less likely to be at the low adoption level. In comparison, they were 0.8 and 1.3 pp more likely to reach moderate and high adoption, respectively, when other factors were held constant. Furthermore, the results reveal that infrastructure availability among SMEs is a key factor influencing the level of digital technology adoption. SMEs with adequate infrastructure were 15.5 pp less likely to be at the low-adoption level and 16.6 pp more likely to reach moderate adoption. However, its effect on high adoption rates was not significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Robustness Check\u003c/h2\u003e \u003cp\u003eSeveral robustness checks were conducted to ensure the reliability of the generalised ordered logistic regression results. Initially, multicollinearity among the predictors was assessed using VIFs; all values were below 1.3, indicating that multicollinearity was not a concern in this study. Subsequently, residual diagnostics confirmed that the error terms followed a logistic distribution, consistent with the model assumptions, and the Wald χ\u0026sup2; test supported the overall goodness-of-fit and the log pseudolikelihood (Wald χ\u0026sup2;(11)\u0026thinsp;=\u0026thinsp;38.95, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; log pseudolikelihood\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;382.997). Finally, robust standard errors were employed to address potential heteroscedasticity, confirming the stability and reliability of the coefficient estimates. Therefore, these checks enhance confidence in the substantive interpretation of the marginal effects reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePerceived usefulness is a key determinant of the level of digital technology adoption in SMEs. SMEs are more likely to adopt digital technologies when they perceive them as applicable, highlighting the importance of perceived benefits in adoption decisions. This indicates that SMEs that clearly recognise the value of digital tools are more likely to move from low to moderate or high adoption levels, suggesting that perceived usefulness influences both initial uptake and the depth of technology adoption. This finding aligns with the Technology Acceptance Model (TAM), which explains adoption behaviour by linking perceived performance benefits to sustained technology use. Empirical studies on SMEs further support this relationship, consistently identifying perceived usefulness as one of the strongest predictors of digital and e-business adoption decisions (Dallocchio et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; D\u0026iacute;az-Arancibia et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wymer \u0026amp; Regan, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). More broadly, the technology adoption literature across e-commerce and ICT contexts reinforces the role of perceived usefulness as a fundamental driver of both adoption and continued use (Selase et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Vu \u0026amp; Nguyen, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), underscoring its relevance to SME digital transformation. In this context, perceived usefulness not only drives the decision to adopt digital tools but also plays a pivotal role in ensuring their continued use, which is essential for realising the benefits of long-term digital transformation.\u003c/p\u003e \u003cp\u003eCompetitive pressure exerts a differentiated influence on the level of digital technology adoption by Tanzanian SMEs. While competitive forces encourage initial adoption, they do not necessarily translate into higher adoption levels, as many SMEs remain at low- or moderate-level use. This suggests that competitive pressure primarily stimulates initial or reactive adoption rather than sustained or advanced digital adoption. This pattern is consistent with the environmental dimension of the TOE framework, which posits that external pressures can trigger adoption decisions but are insufficient to drive deeper integration without adequate organisational and technological capabilities (Olfat, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shahadat et al., \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Empirical evidence similarly shows that SMEs lacking managerial commitment, digital skills, and strategic orientation often adopt digital tools superficially in response to competition (Deku et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ollerenshaw et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Vrontis et al., \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In line with this view, the present study finds that competitive pressure increases the likelihood of low and moderate adoption while reducing the probability of high adoption, indicating that environmental pressure alone is unlikely to generate transformative digital outcomes without complementary internal capacities.\u003c/p\u003e \u003cp\u003eMale-owned SMEs are more likely to remain at low levels of digital technology adoption and are less likely to progress to moderate or high adoption than female-owned SMEs. This suggests that, within this context, female-owned SMEs may be more proactive in integrating digital tools into their business operations. From a TOE perspective, this pattern reflects the role of organisational and managerial characteristics in shaping adoption depth, potentially driven by differences in managerial orientation, risk perception, and openness to innovation. Empirical evidence from SME digitalisation studies supports this contention, showing that female entrepreneurs often leverage digital financial and marketing tools to enhance efficiency, visibility, and customer engagement, particularly in resource-constrained environments (Atarah et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Salamzadeh et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Tarmizi et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Evidence suggests that digital tools may serve as strategic substitutes for limited access to traditional business resources (Ellstr\u0026ouml;m et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The findings of the current study challenge earlier assumptions that male entrepreneurs are inherently more technologically active, underscoring that gendered digital adoption behaviour is highly context-specific and shaped by sectoral, cultural, and institutional factors.\u003c/p\u003e \u003cp\u003eSMEs with sufficient financial resources are significantly less likely to adopt digital tools at a higher level. This indicates that financial capacity alone is insufficient to drive SMEs into higher levels of digital technology adoption. This suggests that, although financial capacity may ease access to digital tools, it does not automatically translate into practical or strategic use. Within the TOE framework, this pattern highlights the organisational dimension, where financial resources must be aligned with managerial commitment, strategic prioritisation, and readiness to absorb learning and implementation costs for digital transformation to occur. Empirical studies reinforce the logic that firms with financial capacity but limited digital skills, leadership, or strategic planning often fail to progress beyond basic adoption (Barragan \u0026amp; Becker, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Restrepo-Morales et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In this context, the findings of the current study underpin the TOE proposition that financial resources alone are insufficient to drive higher-level digital adoption without complementary organisational capabilities.\u003c/p\u003e \u003cp\u003eThe number of employees exerted a modest but statistically significant influence on SMEs\u0026rsquo; digital technology adoption. This indicates that SMEs with larger workforces are less likely to remain at low adoption levels and more likely to transition to medium- or high-level adoption. This shows that workforce size enhances internal capacity, enabling SMEs to absorb better, implement, and sustain digital tools. From the TOE framework perspective, this finding highlights the organisational dimension, demonstrating how internal capabilities, such as human resource availability, shape the depth of digital technology adoption. Empirical studies have similarly reported that such SMEs tend to possess greater absorptive capacity, more diverse skill sets, and stronger internal support structures that facilitate technology integration (Cuevas-Vargas et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Faiz et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, these findings support the view that workforce size supports not only initial uptake but also progression toward higher levels of digital technology adoption.\u003c/p\u003e \u003cp\u003eAdequate infrastructure reduces the likelihood that SMEs remain at low levels of digital adoption and increases the likelihood that they reach moderate levels of adoption. This highlights the foundational role of infrastructure in enabling the uptake of digital tools. However, the absence of a significant effect on high-level adoption indicates that infrastructure alone is insufficient to drive higher digital adoption practices. From the TOE perspective, infrastructure represents a key environmental factor, but achieving higher adoption levels requires complementary organisational capabilities, financial resources, and digital skills. Empirical studies support this view, showing that infrastructure is necessary but insufficient for full technology adoption (Hassan et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Zamani, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This pattern indicates that while infrastructure is a necessary condition for adoption, it may not be sufficient to drive SMEs toward higher levels without complementary internal capabilities such as skilled personnel.\u003c/p\u003e"},{"header":"5. Conclusion, Contributions, and Implications","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Conclusion\u003c/h2\u003e \u003cp\u003eThis study analysed the determinants of the level of digital technology adoption among SMEs in the Tanzanian fruit juice processing industry in the Dar es Salaam, Arusha, and Mbeya regions. The findings reveal that most SMEs remain in the early stages of digital transformation, with only a minority fully utilising digital tools across payment, marketing, and operations management. Perceived usefulness strongly drives SMEs toward higher adoption levels, whereas competitive pressure appears to encourage only low- to moderate-level adoption. Female-owned SMEs are more likely to adopt at higher levels than their male-owned counterparts, highlighting the influence of gender on adoption decisions. Financial resources alone do not guarantee progression to higher adoption levels, whereas a larger workforce and adequate infrastructure help reduce the likelihood of low adoption, even if they do not ensure high adoption. These results suggest that a combination of economic capacity, organizational characteristics, perceived benefits, and contextual pressures shapes digital transformation in SMEs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Implications\u003c/h2\u003e \u003cp\u003eIn practice, this study highlights the need for technology providers, incubators, and SME support organisations to prioritise demonstrations, training, and use-case evidence that communicate tangible value. The results further show that competitive pressure triggers only low adoption, underscoring the need for SMEs to build internal capabilities, such as digital skills, strategic planning, and managerial support, to achieve higher adoption levels. Additionally, the gendered pattern of adoption suggests that female-owned SMEs may be more motivated to engage with digital technology than male-owned SMEs. Thus, training and digitalisation programs should be sensitive to gendered behavioural differences and provide targeted support for women. The study also shows that financial resources alone are insufficient for digital transformation, underscoring the need for SMEs to invest in organizational readiness, workforce development, and digital competencies. Finally, the positive role of the number of employees and adequate infrastructure highlights the importance of human capital and foundational ICT conditions in supporting digital integration.\u003c/p\u003e \u003cp\u003ePolicy-wise, the study points to the need for integrated digitalisation strategies that go beyond infrastructure investment. Government and SME development agencies should implement benefit-oriented digital awareness campaigns, enhance digital literacy and managerial capability programs, and design gender-responsive instruments that recognise the strong digital engagement of female entrepreneurs. Financial support programs should be linked to capacity-building requirements to ensure that resources are translated into meaningful digital progress. Policymakers should also address constraints such as digital skills, organisational processes, and human resource limitations, and support SMEs through shared digital experts, advisory services, or subsidised training, which can help close the adoption gaps and advance the national digital transformation agenda.\u003c/p\u003e \u003cp\u003eTheoretically, the results of this study reinforce the TAM framework by confirming that perceived usefulness is one of the most significant determinants of digital technology adoption, particularly at moderate and higher levels of adoption. For the TOE framework, the findings strengthen the idea that environmental pressures, such as competition, act as triggers but do not guarantee deep adoption without the requisite organisational capabilities. Gender\u0026rsquo;s influence further extends the organisational dimension of the TOE framework by revealing that managerial characteristics are meaningful predictors of adoption patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Contributions\u003c/h2\u003e \u003cp\u003eThe results of this study make several significant contributions to theory and practice. By examining digital adoption as a multilevel outcome, this study provides a clear understanding of how SMEs progress from low to high levels of digital technology adoption. These findings reinforce and extend both the TAM and TOE frameworks, showing that perceived usefulness remains the strongest driver of higher-level adoption. Simultaneously, environmental pressures, such as competition, trigger only superficial uptake unless internal capabilities support them. The evidence also challenges prevailing assumptions about gender and financial resources, revealing that female-owned SMEs are more digitally progressive and that financial resources alone do not guarantee digital transformation. Furthermore, the study deepens theoretical understanding of the interaction between technological perceptions, organisational characteristics, and environmental dynamics, while offering practical guidance on how SMEs in developing economies can strengthen their readiness for meaningful digital transformation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Limitations and Future Research Directions\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e5.4.1 Limitations\u003c/h2\u003e \u003cp\u003eThis study has several limitations that should be considered. First, the scope was limited to SMEs in the fruit juice processing industry within three regions of Tanzania: Dar es Salaam, Arusha, and Mbeya. This restricts the generalisability of the findings to other industries and locations in the country. Second, the cross-sectional design of the survey data limits the ability to infer causal relationships among variables. While this study identified associations among factors such as perceived usefulness, infrastructure, and competitive pressure, it could not establish definitive cause-and-effect links. Additionally, cultural influences that may affect the level of digital adoption were not included in the analysis, creating another gap in the literature. Finally, this study does not consider the evolving nature of digital adoption, as the factors influencing it may change over time with technological advancements and shifts in market dynamics.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e5.4.2 Future Research Directions\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eBuilding on the limitations of the current study, future research should use longitudinal data to investigate the long-term impact of various factors on the level of digital technology adoption in SMEs. This method will help understand how these factors influence the extent of adoption over time, especially in the context of technological advancements and market shifts. Additionally, using panel data could provide insights into how factors such as perceived usefulness, infrastructure, and competitive pressure affect the digital adoption process over time. To improve the applicability of the findings, future studies should broaden their scope to include SMEs from diverse sectors in Tanzania and other developing countries. Comparative studies would also help assess whether the determinants of the extent of digital adoption identified in the fruit juice processing industry are applicable in other contexts.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e \u003cp\u003eMzumbe University granted ethical approval for this study, and permission was obtained from the relevant district authorities. The studies involving human participants were reviewed and approved by Mzumbe University. The ethical approach adopted for this study was designed to respect the dignity, rights, and welfare of all participants, ensuring that the research was conducted responsibly and ethically.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003cp\u003eAll participants provided informed written consent and voluntarily participated in the study. They were assured that their involvement was entirely voluntary and that they could withdraw at any time without consequences.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to publication\u003c/strong\u003e \u003cp\u003eAll participants were informed that the collected data would be used solely for academic research and publication. Written informed consent to publish anonymised data was obtained from all participants.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial Number\u003c/h2\u003e \u003cp\u003eNot Applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNA\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCRediT : Dickson Utonga : Conceptualisation, methodology, literature review, data collection, formal analysis, interpretation of findings, writing \u0026ndash; original draft, and review and editing. Charles Stephen Tundui : Supervision, review, and editing. Eliaza Mkuna : Supervision, review, and editing. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe dataset is available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdulai MG, Dary SK, Domanban PB. Adoption of digital payment platforms and trade credit activities among informal firms in Ghana. Heliyon. 2024;10(11):e32302. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.heliyon.2024.e32302\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2024.e32302\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkerejola WO, Okpara EU, Ohikhena P, Emenike PO. Availability of infrastructure and adoption of point of sales of selected small and medium enterprises (SMEs) in Lagos State, Nigeria. 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Inform Syst Res. 2005;16(1):61\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Digital technology adoption, Level of Adoption, SMEs, Determinants, Technology Acceptance Model, Technology-Organisation-Environment framework","lastPublishedDoi":"10.21203/rs.3.rs-8483750/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8483750/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analysed the determinants of the levels of digital technology adoption (DTA) among SMEs in the fruit juice processing industry in Tanzania. Anchored in the Technology Acceptance Model (TAM) and the Technology\u0026ndash;Organisation\u0026ndash;Environment (TOE) framework, this study addresses an empirical gap by conceptualising DTA as a multilevel outcome. Using a cross-sectional design, quantitative data were collected from 390 SMEs in the Dar es Salaam, Arusha, and Mbeya regions. We analysed the data using descriptive statistics and an ordered logistic regression. The study findings indicate that most SMEs remain in the early stages of digital transformation: 41.8% exhibit low adoption, 41.3% moderate adoption, and only 16.9% high adoption. The regression results indicate that perceived usefulness strongly drives progression to higher adoption levels, whereas competitive pressure mainly stimulates adoption at lower and moderate levels. Female-owned SMEs are more likely to achieve higher adoption levels than male-owned SMEs. Contrary to expectations, financial resources alone do not predict higher adoption, whereas the number of employees and adequate infrastructure are significantly associated with remaining at a low level of adoption. SMEs with adequate infrastructure are more likely to progress beyond low levels of adoption and consolidate their use at moderate levels. However, infrastructure alone is insufficient to drive adoption at the highest levels. This study advances the digital adoption literature by analysing SMEs\u0026rsquo; progression from low to high DTA. It also extends the TAM and TOE frameworks in the African context and offers new insights into gender differences in technology adoption.\u003c/p\u003e","manuscriptTitle":"Modelling the Determinants of the Levels of Digital Technology Adoption among SMEs in Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-06 15:18:31","doi":"10.21203/rs.3.rs-8483750/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-09T06:30:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-06T04:01:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-06T04:01:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-12-30T18:35:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46ac7717-28ac-454a-b553-05800dbf83f9","owner":[],"postedDate":"January 6th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-12T12:31:40+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-06 15:18:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8483750","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8483750","identity":"rs-8483750","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}