What if the most famous digital technologies do not all help SMEs in the same way? In Saudi SMEs, artificial intelligence and big-data analytics were linked to stronger technological capabilities, while internet of things and blockchain were not.

Digital transformation offers Small and Medium-Sized Enterprises, or SMEs, opportunities to improve sustainability, although individual technologies may show different patterns of association with organizational capability development. The study examines two capability-formation pathways in Saudi SMEs, drawing on the Resource-Based View and Dynamic Capabilities Theory.

One pathway connects Internet of Things, Blockchain, Artificial Intelligence, and Big Data Analytical Capabilities with Technological Capabilities. The other connects Digital, Innovativeness, and Management Capabilities with Eco-Innovation.

Using survey data from 280 SME respondents, the analysis finds that Artificial Intelligence and Big Data Analytical Capabilities are positively associated with Technological Capabilities, while Internet of Things and Blockchain Capabilities show no significant independent relationships.

Digital transformation has become a strategic priority for Small and Medium-Sized Enterprises seeking to improve competitiveness, resilience, and long-term sustainable performance. SMEs also contribute substantially to employment and innovation. SMEs contribute to national economic development, but they often face financial constraints, shortages of specialised skills, limited digital infrastructure, and organizational resistance when adopting advanced technologies.

These constraints make it important to distinguish acquiring a digital technology from the organizational capability to deploy it effectively. Prior research has reported positive effects of digital technologies on operational efficiency, value creation, innovation, and sustainability.

However, much of this evidence examines technologies independently, models direct effects on performance, or treats digital technologies as a broadly interchangeable bundle. That leaves uncertainty about differentiated capability formation: AI, IoT, Blockchain, and BDA may not contribute equally to a firm’s ability to integrate and reconfigure technologies, particularly in resource-constrained SMEs.

A second unresolved issue concerns the pathway from organizational capabilities to sustainable performance. Digital infrastructure and skills, an innovation-oriented culture, and managerial coordination may enable Eco-Innovation, while Technological Capabilities may provide a separate route to sustainable performance.

Existing studies provide limited evidence on these two parallel mechanisms within one model, and they rarely distinguish technology-specific deployment capabilities from broad Digital Capabilities and realized firm-wide Technological Capabilities. The Resource-Based View treats technology-specific and organizational capabilities as heterogeneous firm resources that may create value when they are effectively combined and deployed.

Dynamic Capabilities Theory explains why resource possession is insufficient: firms must integrate, coordinate, and reconfigure resources in response to changing market and sustainability requirements. Here, Dynamic Capabilities Theory provides the organizing logic linking resource inputs, capability formation, and performance outcomes, rather than directly measuring sensing, seizing, and transforming microfoundations.

Dynamic Capabilities Theory complements the Resource-Based View by focusing on how firms deploy and reconfigure resources under changing conditions. The study uses Dynamic Capabilities Theory across three analytical layers: technology-specific and organizational resources; the formation of Technological Capabilities and Eco-Innovation; and Sustainable Performance.

The microfoundations of sensing, seizing, and transforming are not modeled as separate latent variables, so the study does not claim to test those microfoundations directly. AI, IoT, Blockchain, and BDA capabilities capture deployment within a specific technology domain.

Digital Capabilities capture cross-cutting infrastructure, employee skills, integration routines, and adaptability across technologies. Innovativeness Capabilities capture idea generation, experimentation, and openness to new solutions, whereas Management Capabilities capture leadership support, coordination, and resource allocation.

Technological Capabilities represent the realized, firm-wide ability to integrate and reconfigure multiple technologies across business processes. Eco-Innovation represents the implementation of environmentally beneficial products, processes, and practices.

Figure one presents a layered capability-based model. Technology-specific resources—IoT, Blockchain, AI, and big data analytical capabilities—feed into Technological Capabilities, while Digital, Innovativeness, and Management Capabilities feed into Eco-innovation; both pathways point toward SME sustainable performance.

The authors use this structure to distinguish specialized technology deployment from broader infrastructure, experimentation, leadership, and coordination capabilities. Technical capabilities are the ability to deploy, configure, and reconfigure multiple technologies across processes to support operational performance, innovation, and adaptive decision-making.

This higher-order construct is different from the four technology-specific markers that capture deployment within a technology. The Resource-Based View indicates that heterogeneous technological resources create diverse levels of value, while their contribution depends on routines of integration and reconfiguration.

The hypotheses therefore examine heterogeneous relationships of IoT, Blockchain, AI, and BDA capabilities with technical capabilities rather than assuming the technologies function as a cohesive bundle. Eco-Innovation refers to new or modified products, processes, technologies, and managerial practices that reduce environmental impact while improving resource efficiency and organizational performance.

In this study, Eco-Innovation is modeled as a realized sustainability-oriented capability. Digital Capabilities provide broad infrastructure, skills, and integration routines; Innovativeness Capabilities provide experimentation and openness to new solutions; and Management Capabilities provide leadership, coordination, and resource allocation.

The Resource-Based View treats these as heterogeneous organizational resources, while Dynamic Capabilities Theory explains how they may be deployed and reconfigured to implement environmentally beneficial change. The study adopted a cross-sectional quantitative design to examine associations among technology-specific resources, organizational capabilities, Technological Capabilities, Eco-Innovation, and Sustainable Performance in Saudi SMEs.

The model was organized using the Resource-Based View and Dynamic Capabilities Theory and analyzed with Partial Least Squares Structural Equation Modeling in SmartPLS four. The target population comprised SMEs operating in Saudi Arabia across manufacturing, logistics, industrial services, technology and IT services, retail, and related sectors.

SMEs were identified using the classification criteria of the Saudi Small and Medium Enterprises General Authority, Monsha’at. The study focused on firms with experience of digital transformation or sustainability-related activities because respondents needed sufficient exposure to evaluate the constructs.

The final questionnaire contained 43 indicators across ten constructs, including IoT Capabilities, Blockchain Capabilities, Artificial Intelligence Capabilities, Big Data Analytical Capabilities, Digital Capabilities, Innovativeness Capabilities, Management Capabilities, Technological Capabilities, Eco-Innovation, and SMEs’ Sustainable Performance.

All items used a five-point Likert scale from one, strongly disagree, to five, strongly agree. The data were analyzed in SmartPLS four using a two-stage Partial Least Squares Structural Equation Modeling procedure: assessment of the reflective measurement model followed by assessment of the structural model.

The measurement model used indicator loadings, Cronbach’s alpha, composite reliability, average variance extracted, HTMT, and the Fornell–Larcker criterion. The structural model used standardized path coefficients, five-thousand-resample bootstrapping, R squared, adjusted R squared, and effect size f squared.

Direct and indirect estimates are interpreted as associations because the cross-sectional design does not establish temporal precedence, and the manuscript does not claim out-of-sample predictive performance because PLSpredict results are not reported. The study gathered 280 valid responses from SME managers, senior executives, decision-makers, and experienced employees operating across multiple industrial sectors in Saudi Arabia.

SMEs were classified according to Saudi SME Authority, or Monsha’at, criteria based on organizational size and operational scale. The demographic profile indicates that the sample includes knowledgeable and experienced respondents from diverse Saudi SMEs, making it suitable for examining technological capabilities, eco-innovations, and sustainable performance within the study context.

Approximately 53.9 percent of the variance in Technological Capabilities is explained by IoT Capabilities, Blockchain Capabilities, Artificial Intelligence Capabilities, and Big Data Analytical Capabilities. This finding indicates that digital technological enablers significantly contribute to strengthening technological capabilities within SMEs.

Digital Capabilities, Innovativeness Capabilities, and Management Capabilities jointly explain 51.9 percent of the variance in eco-innovation practices. These findings suggest that organizational readiness, managerial support, and innovation-oriented capabilities play important roles in facilitating sustainability-oriented innovation activities within SMEs.

Technological Capabilities and Eco-Innovation jointly explain 58.3 percent of the variance in Sustainable Performance. This indicates substantial explanatory relevance within the observed sample. Because no PLSpredict results are reported, the R squared value is not presented as evidence of out-of-sample predictive validity.

Not all hypothesized relationships were statistically significant. Artificial Intelligence Capabilities demonstrated the strongest positive relationship with Technological Capabilities, with a beta of zero point six zero nine, a t value of ten point six seven five, and a p value below zero point zero zero one.

Big Data Analytical Capabilities also showed a significant positive relationship with Technological Capabilities, with a beta of zero point one seven one, a t value of two point three nine three, and a p value of zero point zero one seven. These findings suggest that AI-enabled automation, prediction, and decision support, together with data-analytics capabilities, contribute to the development of broader technological capabilities in Saudi SMEs.

IoT Capabilities demonstrated a small negative but statistically nonsignificant relationship with Technological Capabilities, with a beta of negative zero point zero five four and a p value of zero point three two one. Blockchain Capabilities also showed a small and nonsignificant relationship with Technological Capabilities, with a beta of zero point zero three three and a p value of zero point four seven two.

Therefore, the first and second hypotheses were not supported. IoT and blockchain capabilities did not independently explain Technological Capabilities after accounting for the other technology-specific resources included in the model. Seven of the nine hypotheses were supported.

AI Capabilities and Big Data Analytical Capabilities were significant antecedents of Technological Capabilities, whereas IoT and Blockchain Capabilities were not. Digital, Innovativeness, and Management Capabilities were significantly associated with Eco-Innovation, while both Technological Capabilities and Eco-Innovation demonstrated significant positive relationships with SMEs’ Sustainable Performance.

The figure’s arrows make the model’s structure especially clear: four technology-specific resources feed into technological capabilities, while three organizational capabilities feed into eco-innovation. The displayed path coefficients range from negative zero point zero five four to zero point six zero nine, and the two arrows entering sustainable performance are labeled zero point five one seven and zero point three two three.

Future longitudinal studies could examine these relationships over time. Each SME was represented by one respondent, which may introduce personal perception and common-method bias. Purposive sampling limits how broadly the findings can be applied to all Saudi SMEs, and information about firms that did not participate was unavailable, so nonresponse bias could not be assessed.

Future research should use more representative samples, collect data from multiple respondents and objective sources, measure relevant institutional conditions, and compare findings across countries and sectors. The central takeaway is that sustainable performance is associated with distinct capability pathways: artificial intelligence and big-data analytics connect through technological capabilities, while organizational capabilities connect through eco-innovation.

The results matter because digital transformation is not one interchangeable bundle.