Abstract

While technology adoption has emerged as a critical enabler for enterprise competitiveness, effectiveness, and sustainability, the adoption of technologies continues to vary across developing and fragile economies. This study investigates the barriers to technological adoption among enterprises operating in the Democratic Republic of the Congo (DRC), which has significant infrastructural and institutional constraints. Based on the Technology–Organisation–Environment (TOE) framework, this study used a quantitative research approach through a cross-sectional descriptive survey design. The data was gathered from 200 enterprise owners and managers using structured Likert-scale questionnaires through email, WhatsApp, and limited hard-copy distribution. Descriptive and inferential statistical analysis including correlation and multiple regression were used to evaluate the proposed hypotheses. Results indicate that infrastructural and financial resources, human capital, organisational readiness, and institutional barriers have significant impact on technology adoption and organisational readiness and infrastructure proved to be the strongest predictors. The study has theoretical implications through extending the TOE framework to a fragile institutional landscape while also offering practical implications from an evidence-based perspective for policymakers and enterprise managers who address the issue of digital adoption. From the outcomes, the study also supports policy implications such as the necessity of coordinated investments towards infrastructure, skill development, and regulatory support for the advancement of the rapid implementation of technologies in Congolese enterprises.

Keywords

Technological adoption; TOE framework; Enterprise digitalisation;Developing economies; Democratic Republic of the Congo

Introduction

Technology adoption has emerged as the most important factor influencing the competitiveness, efficiency and sustainability of business in the contemporary environment. The modern economy is more globalized than ever, and digital technologies help firms improve business processes, support decision-making processes, and access larger markets. Venkatesh et al. (2022) explain that adopting technology is no longer a luxury but a strategic imperative, if organisational survival and growth are to be achieved. However, the rate of technology adoption is still uneven, with organizations in emerging economies playing catch-up to developed ones. According to [1], it is important to consider context-specific barriers to explain the ongoing difference in technological uptake. As such, investigating the constraints that are imposed on enterprises in less studied contexts in places such as the Democratic Republic of the Congo (DRC) becomes increasingly important.

The business climate of the Democratic Republic of the Congo is a peculiar and complex one comprising of vast wealth in economic terms, but with severe structural and institutional constraints. While Congolese companies are found amidst growing markets, they are limited in their capacity to access technological breakthroughs. As [10] notes, enterprises operating in such fragile and emerging economies find it difficult to adopt new technologies, often because of systemic constraints rather than simply management resistance alone. Key impediments identified are weak infrastructure, limited access to finance, and regulatory uncertainty which are challenges of enterprise development in the Congolese context. Nambisan et al. (2020) assert that studies on technology adoption in developing economies should take these contextual realities into account to produce meaningful insights. This emphasizes the importance of studying the barriers to technological adoption in Congolese enterprises.

Literature indicates that infrastructural constraints represent one of the leading challenges of technology development in developing countries. Ease of access to electricity, internet connectivity and digital platforms are some of the critical requirements for effective technology use. [15] claims that inadequate infrastructure creates greater operational risk and makes firms less attractive towards investment in digital technologies. Likewise, Zhang and Chen (2021) contend that small businesses operating in such low-infrastructure scenarios are more likely to face technology failure and thus feel sceptical towards further adoption. In the DRC, challenges in infrastructural deficiency are most noticeable on a large scale, for both urban and rural enterprises. These roadblocks not only restrict the uptake but also grow the digital divide between Congolese firms and those globally.

Stigma and financial constraints on enterprises are also complicating the challenges of technology implementation. However, the expense involved in obtaining, implementing and keeping digital technologies alive is still a big pull for smaller (and less well-sized) companies. Adegbite et al. (2022) suggest that limited access to affordable financing reduces firms’ ability to invest in innovation in its entirety. Especially in developing countries, enterprises often depend on internal funding not enough for scaling technology investment. Mwangi and Brown (2020) show that lack of certainty about returns on technology investments discourages adoption. Such financial constraints, particularly in the Congolese enterprise industry, can support conservative investment and postpone digital transformation efforts.

And aspects of the human capital constraint also factor crucially in determining technology adoption results. The availability of human capital, digital literacy and managerial capacity determines an organisation’s adoption and use of technology. García-Morales et al. (2021) suggest that without technologically competent staff, firms were not able to achieve successful adoption outcomes. Additionally, Dubey et al. (2020) highlight the need for continual training to drive organisational learning as the key to maintaining digital transformation. In the context of Congolese society, skill shortage and restricted availability of specialized training programs decrease companies’ capacity to fully take advantage of technological innovations. These human capital bottlenecks thus serve as, in fact, both direct and indirect barriers to adoption.

At an institutional state level, organisational level, institutional and regulatory environment are other influential factors that affect technological adoption decisions beyond the organisational factors. A supportive government policy, clear regulatory frameworks, and stable institutional arrangements act as facilitators of firms investing in technology. [14] eludes that low-quality institutions make the uncertainty higher and the perceived threat of innovations greater. Similarly, Scott (2022) suggests that regulatory inefficiencies serve to reduce the long-term technological investment in emerging market countries. In the DRC, policy inconsistency and regulatory complexity remain hurdles to overcome for business enterprises seeking to use new technologies. Grasping how these institutional and financial barriers interact will help devise feasible strategies to promote technology adoption on the part of Congolese enterprises.

Methodology

A quantitative research methodology was used by this study to empirically investigate the barriers to technological adoption in Congolese businesses. In particular, quantitative methods were suitable to answer the objectives that focused on testing hypotheses and measuring the relationships between variables in a numerical manner. Creswell and [3] describe how quantitative methodologies allow generalisation of findings from a sample to a wider group of individuals by employing statistical analysis. The approach permitted the objective measurement of infrastructural, financial, human capital, and institutional constraints. Finally, it increased the reliability and replicability of findings.

Data were collected through a cross-sectional descriptive survey design during one point in time. Saunders et al. (2023) advocate these designs in order to investigate relationships without making any changes in variables. The design accordingly offered a standard and efficient method for evaluating barriers to technology adoption. The target population consisted of enterprises engaged in various sectors in the Democratic Republic of the Congo. At 200 respondents, the number was considered sufficient for statistical analysis and hypothesis testing. According to [18], samples greater than or equal to 150 are adequate for conducting statistically meaningful studies. The researcher employed a structured Likert-scale questionnaire distributed via a mixed-mode approach to collect data.

Saunders et al. (2023) report that online surveys save money and are appropriate for geographically dispersed populations. To cater for inclusivity, hard-copy questionnaires were sent to respondents lacking internet access. [18] says that the data should be collected in mixed mode to minimize non-response bias. For hypothesis testing, multiple regression analysis was used to examine the predictive effectiveness of independent variables on technological adoption. The accepted and rejected hypotheses were based on p-values <0.05 as was mentioned by Hair et al. (2023). The regression analyses provided empirical evidence of the impact of each barrier.

Results and Discussion

H1: Infrastructural constraints, particularly unreliable electricity supply and limited internet connectivity, have a significant negative effect on technological adoption in Congolese enterprises.

Table 1: Correlations
Infrastructural constraints Technology Adoption
Infrastructural constraints Pearson Correlation 1 .229**
Sig. (2-tailed) .005
N 150 150
Technology Adoption Pearson Correlation .229** 1
Sig. (2-tailed) .005
N 150 150

**. Correlation is significant at the 0.01 level (2-tailed).

The correlation results presented in Table 1 indicate a statistically significant relationship between infrastructural constraints and technology adoption among Congolese enterprises (r = 0.229, p = 0.005). Although the magnitude of the correlation is moderate, the positive and significant coefficient suggests that infrastructural conditions particularly unreliable electricity supply and limited internet connectivity are systematically associated with variations in technology adoption levels. This finding implies that deficiencies in basic infrastructure constitute a measurable barrier to the effective uptake of technology. Consequently, the null hypothesis for H1 is rejected, and the alternative hypothesis is accepted, confirming that infrastructural constraints significantly influence technology adoption in Congolese enterprises

Table 2: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 4.532 1 4.532 8.216 .005b
Residual 81.628 148 .552
Total 86.160 149

  1. Dependent Variable: Technology Adoption

  2. Predictors: (Constant), Infrastructural constraints

The ANOVA results in Table 2 demonstrate that infrastructural constraints significantly explain variance in technology adoption (F = 8.216, p = 0.005). The statistically significant F-statistic confirms that the regression model is well-fitted and that infrastructural constraints jointly contribute to predicting technology adoption outcomes. This finding strengthens the evidence that infrastructural barriers are not merely correlated with but actively shape technology adoption decisions within Congolese enterprises.

Table 3: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.223 .332 9.695 .000
Infrastructural constraints .237 .083 .229 2.866 .005

  1. Dependent Variable: Technology Adoption

The regression coefficients in Table 3 reveal that infrastructural constraints exert a statistically significant effect on technology adoption (β = 0.229, t = 2.866, p = 0.005). The positive standardized coefficient indicates that improvements in infrastructural conditions are associated with increased levels of technology adoption. This result provides empirical confirmation that infrastructural reliability is a key enabling factor for enterprise-level technological advancement.

H2: Financial constraints, including limited access to capital and high costs associated with technological investment, significantly hinder technological adoption among Congolese enterprises.

Table 4: Correlations
Financial constraints Technology Adoption
Financial constraints Pearson Correlation 1 .291**
Sig. (2-tailed) .000
N 150 150
Technology Adoption Pearson Correlation .291** 1
Sig. (2-tailed) .000
N 150 150

**. Correlation is significant at the 0.01 level (2-tailed).

Table 4 shows a statistically significant correlation between financial constraints and technology adoption (r = 0.291, p < 0.001). The strength of this relationship suggests that limited access to capital and high technology-related costs meaningfully restrict enterprises’ ability to adopt new technologies. This finding highlights financial capacity as a critical barrier in resource-constrained environments. Therefore, the null hypothesis for H2 is rejected, and the alternative hypothesis is accepted, indicating that financial constraints significantly hinder technology adoption in Congolese enterprises

Table 5: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 7.291 1 7.291 13.681 .000b
Residual 78.869 148 .533
Total 86.160 149

  1. Dependent Variable: Technology Adoption

  2. Predictors: (Constant), Financial constraints

The ANOVA statistics in Table 5 confirm that financial constraints significantly predict technology adoption (F = 13.681, p < 0.001). The high level of statistical significance indicates that financial limitations account for a meaningful proportion of the observed variance in technology adoption. This result underscores the structural importance of financial access and affordability in shaping enterprise innovation outcomes.

Table 6: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.905 .344 8.433 .000
Financial constraints .299 .081 .291 3.699 .000

a. Dependent Variable: Technology Adoption

The regression analysis in Table 6 indicates that financial constraints have a significant and positive effect on technology adoption outcomes (β = 0.291, t = 3.699, p < 0.001). The standardized coefficient suggests that financial capacity is one of the stronger predictors among the examined barriers. This finding confirms that enterprises with fewer financial limitations are more likely to adopt technological innovations.

H3: Human capital constraints, such as inadequate digital skills and insufficient technical expertise, have a significant negative influence on technological adoption in Congolese enterprises.

Table 7: Correlations
Human Capital constraints Technology Adoption
Human Capital constraints Pearson Correlation 1 .249**
Sig. (2-tailed) .002
N 150 150
Technology Adoption Pearson Correlation .249** 1
Sig. (2-tailed) .002
N 150 150

**. Correlation is significant at the 0.01 level (2-tailed).

As shown in Table 7, human capital constraints are significantly correlated with technology adoption (r = 0.249, p = 0.002). This statistically significant relationship indicates that inadequate digital skills and limited technical expertise constrain enterprises’ capacity to integrate new technologies effectively. The result suggests that human capital readiness remains a pivotal determinant of successful technological adoption. Therefore, the null hypothesis is rejected, and H3 is accepted, demonstrating that human capital constraints significantly influence technology adoption in Congolese enterprises.


Table 8: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 5.340 1 5.340 9.779 .002b
Residual 80.820 148 .546
Total 86.160 149

  1. Dependent Variable: Technology Adoption

  2. Predictors: (Constant), Human Capital constraints

The ANOVA results in Table 8 reveal a statistically significant regression model (F = 9.779, p = 0.002), confirming that human capital constraints significantly explain variation in technology adoption. This finding indicates that differences in skills availability and technical competence are systematically linked to adoption outcomes.

Table 9: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 3.169 .323 9.824 .000
Human Capital constraints .233 .075 .249 3.127 .002

a. Dependent Variable: Technology Adoption

Table 9 shows that human capital constraints significantly predict technology adoption (β = 0.249, t = 3.127, p = 0.002). The positive coefficient implies that enterprises with stronger human capital foundations exhibit higher adoption levels. This result empirically confirms that skill deficits and technical knowledge gaps act as substantive barriers to technology uptake.

H4: Organisational readiness, reflected through managerial support and an innovation-oriented organisational culture, has a significant positive effect on technological adoption in Congolese enterprises.

Table 10: Correlations
Organisational readiness Technology Adoption
Organisational readiness Pearson Correlation 1 .480**
Sig. (2-tailed) .000
N 150 150
Technology Adoption Pearson Correlation .480** 1
Sig. (2-tailed) .000
N 150 150

**. Correlation is significant at the 0.01 level (2-tailed).

The correlation analysis in Table 10 reveals a strong and statistically significant relationship between organisational readiness and technology adoption (r = 0.480, p < 0.001). This result indicates that managerial support and innovation-oriented organisational culture play a crucial enabling role in technology adoption. The strength of the correlation suggests that organisational readiness is one of the most influential determinants examined in the study. Accordingly, the H4 is accepted, confirming a positive effect of organisational readiness on technology adoption.

Table 11: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 19.825 1 19.825 44.231 .000b
Residual 66.335 148 .448
Total 86.160 149

  1. Dependent Variable: Technology Adoption

  2. Predictors: (Constant), Organisational readiness

Table 11 presents a highly significant ANOVA result (F = 44.231, p < 0.001), demonstrating that organisational readiness explains a substantial proportion of variance in technology adoption. The magnitude of the F-statistic highlights the robustness of the model and underscores the central role of organisational factors in adoption decisions.

Table 12: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.179 .303 7.193 .000
Organisational readiness .461 .069 .480 6.651 .000

a. Dependent Variable: Technology Adoption

The regression coefficients in Table 12 indicate that organisational readiness is a strong and statistically significant predictor of technology adoption (β = 0.480, t = 6.651, p < 0.001). This result suggests that enterprises with supportive leadership and innovation-friendly cultures are significantly more likely to adopt new technologies. Given its comparatively high standardized coefficient, organisational readiness emerges as the most influential factor in the model.

H5: Institutional and regulatory constraints, including policy inconsistency and weak institutional support, significantly reduce the likelihood of technological adoption in Congolese enterprises.

Table 13: Correlations
Institutional and Regulatory Constraints Technology Adoption
Institutional and Regulatory Constraints Pearson Correlation 1 .422**
Sig. (2-tailed) .000
N 150 150
Technology Adoption Pearson Correlation .422** 1
Sig. (2-tailed) .000
N 150 150

**. Correlation is significant at the 0.01 level (2-tailed).

Table 13 shows a statistically significant correlation between institutional and regulatory constraints and technology adoption (r = 0.422, p < 0.001). This finding suggests that policy inconsistency and weak institutional support substantially affect enterprises’ technology adoption decisions. The strength of the correlation underscores the importance of the broader institutional environment in shaping innovation outcomes. Consequently, H5 is accepted, confirming the significant role of institutional and regulatory barrier.

Table 14: ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 15.373 1 15.373 32.141 .000b
Residual 70.787 148 .478
Total 86.160 149

  1. Dependent Variable: Technology Adoption

  2. Predictors: (Constant), Institutional and Regulatory Constraints

The ANOVA results in Table 14 confirm that institutional and regulatory constraints significantly predict technology adoption (F = 32.141, p < 0.001). The statistical significance of the model indicates that institutional factors meaningfully explain differences in adoption levels across enterprises. This reinforces the argument that weak regulatory frameworks can hinder technological diffusion.

Table 15: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) 2.382 .319 7.478 .000
Institutional and Regulatory Constraints .406 .072 .422 5.669 .000

a. Dependent Variable: Technology Adoption

Table 15 demonstrates that institutional and regulatory constraints significantly influence technology adoption (β = 0.422, t = 5.669, p < 0.001). The substantial standardized coefficient indicates that regulatory and institutional conditions are among the strongest predictors of technology adoption. This finding confirms that policy stability and institutional support are critical enablers of enterprise innovation.

The results indicate that the infrastructure limitations have a major impact on the technology implementation by Congolese businesses, as indicated by the moderate but significant statistical linkages. Unstable power supply and inadequate internet availability continue to be critical barriers that reflect findings in other Sub-Saharan African settings (Ebrahim & Van den Berg, 2024; Preprints.org, 2025). Such shortcomings limit access to cloud computing, digital platforms, and online services critical for operational effectiveness. Findings further support those of ICT adoption models which highlight connectivity and technological readiness as fundamental factors to successful adoption. Failure to build adequate infrastructure leads instead to restricted access and quality of digital use. This further suggests that the foundation of ICT investment is necessary to speed enterprise transformation in digital.

Financial constraints were also found to be a significant predictor of limited technology adoption, reinforcing the extant literature on cost-related barriers in developing economies. There is limited access to formal credit mechanisms and high costs for adoption reduce firms’ ability to invest in digital systems (Ebrahim & Van den Berg, 2024; Preprints.org, 2025). This is supported by the findings of Ghanaian SMEs, which identified finance as an impediment to competitiveness and growth of the digital market (Quaye et al., 2024). From a resource-based perspective, firms with low financial assets struggle to obtain and install necessary technologies. In the Congolese situation, the shortages are related to lack of financing, which adds to deficiencies in infrastructure and also skills. This vicious circle undercuts the overall technological readiness and hinders digital advancement.

Furthermore, human capital and organisational readiness were also established as critical determinants of adoption outcomes. Digital literacy and technical proficiency have significantly limited use of technological systems, which aligns with global technology acceptance research (Noriega Del Valle et al., 2024; Ebrahim & Van den Berg, 2024). Insufficient skills weaken perceived usefulness and hinder the adoption processes in turn. Organisational readiness through leadership support, strategy, and an innovation-oriented culture also substantially improves adoption capacity (Nyanga & Eresia-Eke, 2025; Preprints.org, 2025). Institutional and regulatory constraints further govern adoption through uncertainty and high transaction costs (Quaye et al., 2024). Policies on ICT and limited state support discourage investment in digital innovation. Policy frameworks and organisational capacity need to be strengthened for the adoption of sustainable technologies.

Conclusion

This paper aimed to systematically explore the main obstacles to technological adoption in Congolese companies, and to empirically assess the ways in which infrastructural, financial, human capital, organisational, and institutional factors constrain and influence adoption results. In addition, with strong empirical support and support of prior theories and empirical research, the research took a quantitative perspective to provide sound evidence based on enterprise-based outcomes. The results imply that technology uptake (in the Congolese context) is far more complex and not simply a single constraint but depends on the interaction of internal firm capabilities and, in particular, external environmental conditions. Infrastructural inadequacies, insufficient financial resources and human capital deficits were all deemed as substantial barriers that can significantly impair enterprises in adopting and appropriately implementing technology, whereas organisational readiness emerged as an essential facilitator that is a critical factor in reinforcing or reducing the latter conditions. The research further clarified that fragile institutional and regulatory structures still hinder technology-related investments and innovation. The study provides a broader and deeper contextual understanding as to why technology adoption remains uneven among Congolese businesses, making some of the evidence-based contribution of the paper towards policy in a more comprehensive view of understanding why technological adoption is still uneven throughout the developing-country business environment and how that research helps inform policy making, organizational decision-making by managers and subsequent, potentially transformative research on digital transformation in developing economies.


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