Entropy-Based Assessment of AI Adoption Patterns in Micro and Small Enterprises: Insights into Strategic Decision-Making and Ecosystem Development in Emerging Economies

Abstract

This study examines patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), with an emphasis on functional diversity across value chain activities. Based on a cross-sectional dataset of 781 enterprises and an entropy-based model, it assesses internal variability in AI use and explores its relationship with strategic perception and dynamic capabilities. The findings reveal predominant partial adoption, alongside high functional entropy in sectors such as mining and services, suggesting an ongoing phase of technological experimentation. However, a significant gap emerges between perceived strategic use and actual functional configurations—especially among microenterprises—indicating a misalignment between intent and organizational capacity. Barriers to adoption include limited technical skills, high costs, infrastructure constraints, and cultural resistance, yet over 70% of non-adopters express future adoption intentions. Regional analysis identifies both the Andean Highlands and Coastal regions as “innovative,” although with distinct profiles of digital maturity. While microenterprises focus on accessible tools (e.g., chatbots), small enterprises engage in data analytics and automation. Correlation analyses reveal no significant relationship between functional diversity and strategic value or capability development, underscoring the importance of qualitative organizational factors. While primarily descriptive, the entropy-based approach provides a robust diagnostic baseline that can be complemented by multivariate or qualitative methods to uncover causal mechanisms and strengthen policy implications. The proposed framework offers a replicable and adaptable tool for characterizing AI integration and informing differentiated support policies, with relevance for Ecuador and other emerging economies facing fragmented digital transformation.

1. Introduction

In a global context shaped by digital transformation, MSEs face increasing pressure to adopt technological solutions that balance operational efficiency, environmental sustainability, and social equity. While artificial intelligence (AI) has proven capable of enhancing productivity and reducing costs at various organizational levels, its adoption varies significantly across regions, sectors, and firm sizes. Ardito et al. [1] and Soomro et al. [2] have documented this disparity in both industrialized and developing economies, where gaps in human capital, infrastructure, and organizational capabilities tend to widen. Thus, while some studies argue that AI may serve as a universal catalyst for inclusive innovation, others warn that its implementation without minimum organizational preparedness may exacerbate pre-existing inequalities and foster technological dependence [3,4]. Recent analyses confirm these tensions in digital marketing and fintech adoption among SMEs [5], as well as in financial applications such as asset management [6].

In this context, a methodological approach is necessary that can capture the non-linear dynamics of technological adoption in structurally heterogeneous environments. Entropy-based analysis represents a suitable tool for identifying emerging AI adoption patterns by measuring organizational variability from a systemic perspective. This approach is especially relevant in developing economies, where technological fragmentation and sectoral diversity necessitate models that are sensitive to dimensions such as sustainability through logistical optimization or environmental impact reduction [2], and social equity through the improvement of working conditions via task automation [3,7]. At the same time, empirical studies in Latin America demonstrate that AI facilitates more accurate planning and forecasting in small businesses [8]. Complementary work highlights the role of big data and AI integration in managerial decision-making and sustainable growth [9,10].

This study contributes to understanding AI integration patterns within the business fabric of developing countries, focusing specifically on Ecuadorian MSEs. Building on prior research [11] and moving beyond the use of unidimensional indicators of technological intensity, it proposes an entropy-based evaluation model that captures internal heterogeneity by region, sector, firm size, value chain activity, and technological maturity level. This approach enables the quantification of functional diversity and the interpretation of findings from a strategic perspective grounded in the dynamic capabilities framework. Consequently, it advances a systemic vision of digital transformation, allowing for the identification of both barriers and enablers in strengthening the local AI adoption ecosystem [1,3]. Broader perspectives, such as the transition toward Industry 5.0, further suggest that SMEs must combine technological adoption with social and environmental objectives [12].

The strategic role that MSEs play in the Ecuadorian economy—where they represent more than 90% of the national business structure—further supports the relevance of this approach [13,14]. In an environment of accelerated digitalization and evolving consumer behavior, AI has the potential to increase efficiency, automate processes, and enable more intelligent decision-making —key capabilities that allow these firms to compete more effectively against large corporations. Despite the existence of some successful cases, the actual adoption patterns of AI remain largely unknown, limiting the ability to design effective intervention strategies.

Based on previous studies and recent evidence regarding sectoral, organizational, and perceptual variability in technological adoption in emerging economies [3,11,15], this study proposes the general hypothesis that AI adoption among Ecuadorian MSEs is not uniform but instead characterized by high functional heterogeneity. Strategic, structural, and perceptual factors—shaped by sector, region, and firm size—determine the extent of this heterogeneity. This hypothesis guides the analysis toward identifying divergent adoption configurations that require differentiated approaches.

2. Theoretical Framework

2.1. Adoption, Capabilities, and Ecosystem Context

Digital transformation through AI is reshaping the competitive landscape of smaller firms, which face both resource constraints and unique opportunities. SMEs often operate with limited budgets, weak digital infrastructures, and restricted access to specialized talent, which limits the development of advanced projects, such as machine learning or natural language processing [2,15]. Nevertheless, their flatter hierarchies and agile decision-making structures can accelerate innovation when combined with strong leadership and a culture open to change [1]. Globally, adoption is uneven: digitally mature firms in regions with robust infrastructure and innovation policies advance toward automation, analytics, and service personalization, while less developed contexts remain at incipient stages due to policy gaps and infrastructural deficits [2]. Sectoral differences are also evident: industries with digital alignment, such as e-commerce and professional services, progress faster than traditional sectors, which struggle to transition to data-driven models [14].

Theories of technology adoption provide important lenses for analyzing these dynamics. The Technology Acceptance Model (TAM) emphasizes perceived usefulness and ease of use, while the Diffusion of Innovations (DOI) highlights sequential adoption stages, progressing from awareness to confirmation. The Technology–Organization–Environment (TOE) model, on the other hand, emphasizes the interaction among contextual, organizational, and technological conditions [16,17,18]. While these frameworks identify relevant determinants, they are insufficient to explain firms’ adaptive capacity in volatile environments. Recent research, therefore, complements them with the dynamic capabilities perspective, which emphasizes the ability of firms to sense, seize, and reconfigure resources in response to change [19,20].

Motivations for AI adoption derive from both internal and external factors. Internally, AI improves productivity, automates repetitive tasks, and compensates for labor shortages [21]. Externally, competitive pressure drives demand for differentiation, personalization, and data analytics [21]. Nonetheless, significant barriers remain, including high costs for licenses, training, and infrastructure [14]; a scarcity of skilled personnel [11]; cultural resistance [13]; and regulatory or ethical uncertainties [13,14]. SMEs often respond with incremental strategies such as pilot projects, SaaS-based solutions, and partnerships with universities or incubators, which provide access to talent and reduce risks [6]. Adoption usually unfolds in phases: (1) awareness and diagnosis, (2) pilot testing, (3) scaling and integration, and (4) continuous evaluation and adjustment [22].

While AI can transform business models, it does not automatically generate sustainable competitive advantage. The increasing accessibility of algorithms and cloud services has led to technological homogenization, making adoption a baseline rather than a differentiator [23]. According to the Resource-Based View, competitive advantage arises when AI is integrated into proprietary processes, intangible assets, or networks that are difficult to imitate [19]. Standard tools, such as chatbots or recommendation systems, are easily replicable, whereas firms that leverage proprietary data, tacit knowledge, or customized user experiences can build unique advantages [24]. AI further strengthens dynamic capabilities by fostering experimentation, co-creation, and organizational learning in uncertain environments [25]. Empirical studies confirm its positive impact on productivity, cost reduction, customer retention, and value innovation [26]. However, risks such as vendor dependence, algorithmic bias, and maintenance costs remain [13], reinforcing the need for strategic alignment and collaborative support among industry, academia, and government.

AI adoption in MSEs should be understood not merely as functional implementations but as part of a broader process of digital transformation, which involves the whole organization in reconfiguring capabilities, structures, and value creation [27]. This view enables us to link the entropy-based measure of functional diversity with the dynamic capabilities framework [19,20]. Although entropy is a static indicator, it provides a valuable lens to capture the distribution of adoption across functions at a given point in time. A higher entropy value suggests that AI adoption is dispersed across several activities, which may reflect experimentation, heterogeneous implementation, or a lack of strategic focus. This pattern does not necessarily indicate a higher level of adoption, but somewhat greater functional diversity. Conversely, low entropy may indicate concentration in a single area, reflecting either early-stage selective adoption or advanced consolidation. The interpretation depends on the mean adoption level across activities to distinguish between technological restraint and strategic focus. Interpreted in this way, functional diversity becomes more than a descriptive metric; it signals the extent to which firms are engaging in adaptation processes and reconfiguring their capabilities [24,25]. Thus, entropy provides a structural snapshot that, when contextualized, offers insight into the dynamic evolution of capabilities.

In emerging economies, firm-level adoption cannot be analyzed in isolation from institutional and ecosystem conditions. Institutions shape the environment through regulation, infrastructure, and incentives that can either support or constrain digital transformation. At the same time, innovation ecosystems—comprising firms, universities, government agencies, and technology providers—create channels for resource sharing and knowledge exchange that individual MSEs could rarely access on their own [28]. For example, government-backed training programs or industry clusters often provide small firms with access to skills, data, and technological platforms that amplify their capacity to experiment with AI. Recent studies confirm that SMEs integrated into such ecosystems are more capable of mobilizing dynamic capabilities and sustaining competitive advantage from digital technologies [29]. In this sense, ecosystem participation complements internal heterogeneity: firms with diverse AI uses are more likely to leverage external partnerships, while those with stronger external linkages can translate dispersed adoption into strategic renewal. Hence, AI adoption should be interpreted as the outcome of both internal capability development and external systemic support, recognizing that long-term competitiveness depends on their interaction.

2.2. Integrated Analytical Lens: Value Chain Meets Dynamic Capabilities

Building on the previous discussion of adoption determinants, barriers, and dynamic capabilities, it is important to integrate these perspectives into a coherent analytical framework. Porter’s [30] value chain provides a valuable lens for mapping how AI can be applied across different business functions, from primary activities (e.g., operations, marketing, logistics) to support activities (e.g., HR, infrastructure, procurement). Recent studies have illustrated these applications, including AI-driven forecasting and optimization in logistics [31], predictive maintenance in operations [32], customer analytics in marketing [33], and algorithmic decision-making in human resources [34]. These examples highlight the potential breadth of adoption across the enterprise.

However, the value chain by itself is essentially a static framework that identifies areas of activity but does not explain how firms reconfigure and adapt these functions over time. The dynamic capabilities perspective complements this limitation by emphasizing the processes through which firms sense opportunities, seize them, and reconfigure resources in response to environmental change [19]. By linking the value chain to dynamic capabilities, AI adoption can be analyzed not only in terms of where it occurs within the organization but also how enterprises adapt and transform these activities to generate competitive advantage. For example, an SME might adopt AI for customer analytics in marketing and sales. However, the long-term advantage arises from how it integrates insights across departments, reconfigures workflows, and develops new competencies [35].

This integrated lens allows us to capture both the breadth of adoption (diversity of applications across the value chain) and the depth of organizational transformation (capability building and strategic renewal). In this sense, our entropy-based measure reflects the diversity of adoption at a functional level. At the same time, the dynamic capabilities framework provides the theoretical foundation to interpret how this diversity may—or may not—translate into sustained competitiveness.

Moreover, combining the value chain with dynamic capabilities aligns with the study’s empirical objectives. The analysis not only identifies functional heterogeneity of AI adoption but also examines whether such dispersion correlates with strategic perceptions and capability development. This integrated framework highlights that the breadth of adoption is not inherently beneficial; its value depends on the organizational capacity to reconfigure activities, mobilize learning, and align adoption with strategic objectives.

By bridging these two perspectives, we establish a theoretical foundation for analyzing the adoption of AI in MSEs. The value chain clarifies the scope of potential adoption areas, while dynamic capabilities explain how firms transform those activities into sources of resilience and advantage. This dual lens also underlines the role of external ecosystems discussed earlier, since reconfiguration often requires access to complementary resources, partnerships, and institutional support beyond firm boundaries. Together, these insights provide the analytical basis for the empirical model presented in the following sections.

2.3. AI Applications Across the SME Value Chain

Artificial intelligence is increasingly integrated across both primary and support activities of the SME value chain, creating benefits that span operational efficiency, innovation, and customer value. In logistics, AI enables demand forecasting, supplier evaluation, and route optimization through the use of machine learning and heuristic algorithms [31,36]. In operations, predictive maintenance and process optimization support continuity and cost reduction [32]. Marketing and sales benefit from clustering, recommendation systems, and campaign optimization tools that enhance personalization and customer engagement [33]. After-sales services increasingly rely on chatbots and sentiment analysis to deliver continuous and adaptive customer support [34].

Support activities also illustrate AI’s transformative potential. In human resources, AI systems streamline recruitment, scheduling, and workforce analytics, while in procurement, predictive models and fraud detection systems strengthen supply chain security [11]. Technology development benefits from AI-assisted design, virtual prototyping, and real-time analytics that accelerate innovation cycles [35]. At the governance level, AI-based dashboards, financial analytics, and responsible AI practices enhance decision-making and stakeholder trust [37,38,39,40,41].

Taken together, these applications demonstrate that AI adoption spans the entire value chain, underscoring its dual role in operational integration and strategic renewal. Rather than focusing on isolated tools, the critical question is how SMEs combine these applications with dynamic capabilities and ecosystem resources to generate sustained advantage. This integrative view provides the bridge to the empirical analysis that follows.

2.4. Objectives, Hypothesis, and Empirical Perspectives

2.4.1. Research Objectives and Hypothesis

Building on the theoretical foundations outlined above, this study aims to achieve three main objectives. First, it seeks to quantify the heterogeneity of AI adoption among Ecuadorian micro and small enterprises, capturing the diversity of functional applications across the value chain. Second, it aims to identify regional and sectoral patterns in adoption, assessing whether geography and industry context shape functional diversity. Third, it investigates potential misalignments between perceived strategic importance and actual adoption breadth, thereby evaluating the consistency between managerial perceptions and organizational practices.

From these objectives, we derive the following general hypothesis:

H1. 

AI adoption among Ecuadorian micro and small enterprises is characterized by high functional heterogeneity, shaped by strategic, structural, and perceptual factors.

Building on these objectives, we further specify two theory-driven hypotheses.

H2a. 

Greater functional diversity in AI adoption (measured through Shannon entropy across value chain activities) is positively associated with higher perceived strategic value (uniqueness, inimitability, and alignment).

H2b. 

Greater functional diversity in AI adoption is positively associated with stronger dynamic capabilities, understood as the ability to integrate, reconfigure, and extend resources in response to change.

These hypotheses are grounded in the Resource-Based View (RBV) [42,43] and the dynamic capabilities framework [19,20]. The RBV highlights that resources capable of sustaining competitive advantage must be valuable, rare, inimitable, and non-substitutable. At the same time, dynamic capabilities explain how firms adapt and reconfigure such resources in turbulent environments. Together, these perspectives provide the theoretical foundation for expecting a link between functional adoption diversity and strategic or capability outcomes. Recent research confirms these connections by examining business model innovation in SMEs under disruptive contexts [44], the role of supply chain risk factors and technological capabilities in firm performance [45], and the application of AI and data mining in organizational processes such as mergers and acquisitions [46].

This subsection provides a clear research anchor that connects the theoretical discussion with the empirical analysis, ensuring alignment between the study’s conceptual foundations, methodological approach, and expected contributions.

2.4.2. Empirical Confirmation and Emerging Perspectives

Recent empirical research supports the theoretical arguments presented in this study by demonstrating how AI enhances SME performance. For instance, AI-driven marketing strategies underpinned by IoT and collaborative decision-making have been shown to improve financial, customer, internal process, and learning outcomes [26]. Similarly, internet entrepreneurial self-efficacy and strategic agility, when combined with AI adoption, facilitate digital transformation in family-owned firms, particularly under proactive leadership [15]. A systematic review by Ayinaddis [3] further highlights the importance of infrastructure, readiness, and regulation—factors captured within the TOE framework—in shaping adoption dynamics. Collectively, these findings confirm that AI functions not only as a technological tool but also as a dynamic capability that fosters adaptability, decision-making efficiency, and innovation in resource-constrained environments.

Other streams of research provide complementary insights: algorithmic marketing offers new ways to optimize decision-making through data-driven strategies [47]. At the same time, the literature on Industry 4.0 illustrates how SMEs can integrate advanced manufacturing and digital technologies to enhance competitiveness [48]. Furthermore, emerging methods such as AutoML and federated learning facilitate the scalable application of AI in resource-constrained settings [49]. Additionally, resilience during the COVID-19 pandemic was supported through AI-enabled marketplaces designed for small businesses [50]. Contextualized applications, such as AIoT-driven edge computing for rural agribusiness [51], also reveal the adaptability of these frameworks to diverse environments.

In parallel, emerging frameworks deepen this perspective. The concept of digital ambidexterity emphasizes how SMEs strike a balance between operational efficiency and innovation, extracting both incremental and transformative value from AI [43]. Likewise, the evolving notion of dynamic digital capabilities incorporates organizational resilience as a core competence, which has become particularly critical in post-pandemic and disruption-prone contexts [43]. At the institutional level, public support for AI research [52] and organizational cybersecurity adoption via machine learning [53] illustrate the environmental and technological factors that influence the translation of AI adoption into long-term strategic outcomes.

In sum, AI integration across value chain activities—from logistics and operations to marketing, HR, and innovation—acts as a lever for both process optimization and strategic renewal. However, genuine transformation requires more than deploying tools: it depends on foresight, internal capability building, and responsible governance. SMEs that align AI adoption with long-term value creation, continuous learning, and social responsibility are best positioned to thrive in an increasingly uncertain and rapidly changing digital economy.

3. Materials and Methods

3.1. Population

This study focuses on MSEs, which dominate Ecuador’s business landscape. According to INEC [54], microenterprises account for 93.2% and small enterprises 5.0%, together comprising 98.2% of registered establishments. Given their central role in employment and productivity, these firms are a key analytical unit. The study focuses on the Andean Highlands (56.51%) and the Coastal regions (40.57%), which together account for 97.08% of all economic units. It excludes the Amazon (2.92%) and the Galápagos Islands due to their low economic weight and logistical limitations. This regional focus ensures contextual relevance and represents the areas with the highest entrepreneurial activity. The population includes all formally registered MSEs operating in these regions.

3.2. Sample Size and Quota Sampling Design

The research team used a quota sampling design to guarantee proportional representation of Ecuadorian MSEs while optimizing logistical resources. The final sample size (n = 1200) was determined to ensure proportional representation across all strata and to allow robust comparisons across population segments. This size exceeds typical thresholds for subgroup analyses, providing sufficient coverage for detailed comparisons by sector, region, and firm size. Oversampling was applied to strata with lower population densities to preserve analytical validity.

The sample was stratified using three control variables:

• Firm size: Microenterprise vs. Small enterprise.
• Geographic area: Andean Highlands vs. Coastal.
• Economic sector: Agriculture, livestock, forestry, and fishing; Mining and quarrying; Manufacturing; Commerce; Construction; and Services.

This segmentation supports a detailed understanding of AI adoption patterns by region, size, and industry, aligning with the study’s systemic approach to analyzing digital transformation in structurally heterogeneous environments.

Although the number of small enterprises (SEs) in the adopting-firms subsample is relatively lower (n = 90) compared to microenterprises, this size is adequate for exploratory comparative analysis. Similar studies on technology and AI adoption in SMEs have employed subgroup sizes of comparable magnitude, particularly when disaggregating by firm size or sector [22,55,56]. Our analytical goal is not statistical generalization at the national level but the identification of comparative patterns across subgroups. In this sense, the MSE sample provides sufficient coverage to reveal adoption profiles while reflecting the structural distribution of Ecuadorian MSEs, where microenterprises overwhelmingly predominate.

While entropy as a distributional measure does not strictly require population-level representativeness, the stratified sampling design enhances the robustness and interpretability of the results. By ensuring that firm size, geographic region, and economic sector are proportionally represented, the entropy indicators capture the actual structural heterogeneity of Ecuador’s business ecosystem rather than being biased toward overrepresented groups. In this sense, stratification enhances the ecological validity of the findings and their policy relevance, as it ensures that the diversity patterns observed in the sample accurately reflect the systemic configuration of the national MSE landscape.

3.3. Quota Allocation Strategy

To ensure subgroup representation, quotas were defined based on a 2 × 2 × 6 stratification (firm size × region × sector), yielding 24 segments with varying population weights. A proportional-to-size allocation was adopted to accurately reflect the population structure and maintain analytical validity across strata.

The research team established a minimum of five surveys per unit to avoid excluding small segments and to ensure their analytical inclusion. This approach pursued two objectives:

• Guarantee minimum representativeness in all strata, particularly important in structurally heterogeneous environments.
• Maintain proportionality by allocating the remaining surveys based on the relative population weights of each stratum.

This hybrid allocation—combining minimum thresholds with proportional distribution—balanced statistical rigor with inclusivity across Ecuador’s diverse MSE landscape.

$${\mathrm{q}}_{\mathrm{i}}=5+\left({\displaystyle \frac{{\mathrm{N}}_{\mathrm{i}}}{\mathrm{N}}}\right)\left(\mathrm{q}\mathrm{s}-5\mathrm{s}\right)$$

(1)

where

q_i = quota assigned to stratum i;

N_i = population size of stratum i;

N = total population;

q_s = total number of surveys to be conducted;

s = number of strata.

3.4. Stratum Quotas and Sampling Considerations

Table 1. Population and Assigned Quota by Stratum.

Firm Size	Microenterprise		Small Enterprise
Sector/Region	Andean Highlands Region	Coastal Region	Andean Highlands Region	Coastal Region
Agriculture, Forestry, Livestock, and Fishing	10,925 (16)	16,192 (21)	1189 (6)	2572 (7)
Mining and Quarrying	548 (6)	522 (5)	155 (5)	132 (5)
Manufacturing Industries	59,759 (63)	35,635 (40)	2395 (7)	1585 (7)
Trade	206,613 (207)	167,983 (169)	9227 (14)	8388 (13)
Construction	13,991 (19)	12,405 (17)	1945 (7)	1564 (7)
Services	321,987 (319)	203,155 (203)	15,318 (20)	12,291 (17)

Note: Quotas are indicated in parentheses.

presents the assigned quotas by stratum, based on the actual distribution of micro and small enterprises across mainland Ecuador. Each stratum received a minimum of five surveys, with the remaining distributed proportionally to population weight, ensuring a total sample size of n = 1200.

Although quota sampling does not allow precise probability-based error estimation, the use of population data and proportional allocation provides sufficient coverage for subgroup comparisons, and any sampling error is expected to be small. While quota sampling introduces certain limitations—such as selection bias, reduced generalizability, and dependency on the accuracy of the official registry—it also offers key advantages: Guarantees coverage of minority subgroups, enabling robust comparative analysis; Optimizes costs, time, and logistics of fieldwork; and Ensures disaggregated representativeness by sector, region, and firm size.

The research team used a quota sampling design to guarantee proportional representation of Ecuadorian MSEs while optimizing logistical resources. The final sample size (n = 1200) was determined to ensure adequate coverage across all 24 strata and to allow robust comparisons by firm size, region, and economic sector.

3.5. Data Collection and Instrument Design

This study adopted a hybrid framework combining Porter’s value chain and the dynamic capabilities approach to analyze AI adoption in MSEs. A structured questionnaire was designed based on previous literature on technology adoption [3,55] and organizational capabilities [19,20], with five main sections:

3.5.1. Firm Profile

Includes three single-choice items on firm size, geographic region, and economic sector. These variables ensure alignment with the sampling design and allow contextual control.

3.5.2. Overall Level of AI Adoption

A single nominal item with three categories (1 = No adoption, 2 = Partial, 3 = Intensive) captures the general level of AI implementation in the firm. This three-level categorization follows prior studies on technology and AI adoption in MSEs [2,22,56], which typically distinguish between non-use, exploratory/partial adoption, and intensive use. Such a classification strikes a balance between analytical clarity and empirical precedent, making it especially suitable for contexts characterized by heterogeneous adoption trajectories.

3.5.3. AI Application in the Value Chain

Respondents indicate AI use across nine value chain activities, using the same three-level adoption scale. Open-ended fields allow descriptions of specific use cases, enriching the qualitative dimension.

3.5.4. Strategic Perceptions of AI

This section assesses how respondents perceive AI adoption in terms of uniqueness, inimitability, and strategic fit. A three-item Likert scale (ranging from 1 = Strongly Disagree to 5 = Strongly Agree) was validated as follows:

•

Cronbach’s alpha = 0.724, acceptable for exploratory use.

•

EFA (KMO = 0.733; Bartlett’s χ² = 3358.06, p < 0.001) yielded a single factor explaining 93.15% of variance.

•

CFA showed high loadings (0.870–0.988); CR = 0.965 and AVE = 0.901, confirming convergent validity.

Despite its brevity, the scale demonstrated strong unidimensionality and internal consistency, making it suitable for strategic perception analysis.

Conceptually, these three items capture how entrepreneurs perceive AI adoption as a potential source of sustained competitive advantage. Specifically, they reflect whether AI is regarded as (a) unique in generating value, (b) inimitable by competitors, and (c) strategically aligned with the firm’s long-term positioning. This construct is grounded in the Resource-Based View (RBV) [43], which emphasizes the role of valuable, rare, inimitable, and non-substitutable resources, and in the dynamic capabilities framework [19,20], which highlights firms’ ability to sense, seize, and reconfigure resources in turbulent environments. Interpreted in this way, the strategic perception score is not merely an attitudinal measure but an indicator of whether firms believe their AI practices can be transformed into defensible and durable advantages.

3.5.5. Dynamic Capabilities

This nine-item scale assesses a firm’s ability to integrate, reconfigure, and extend the use of AI in dynamic environments. Items cover adjustment speed, knowledge flows, and future preparedness, rated on a five-point Likert scale (ranging from 1 = Strongly Disagree to 5 = Strongly Agree). Psychometric analysis showed:

•

Cronbach’s alpha = 0.962

•

Inter-item correlations = 0.593–0.943

•

EFA (KMO = 0.969; Bartlett’s χ² = 7531.76, p < 0.001) confirmed unidimensionality, with one factor (eigenvalue = 6.96) explaining 77.31% of variance.

•

Factor loadings = 0.775–0.943; communalities > 0.60.

•

CFA supported the one-factor model; CR = 0.963, AVE = 0.746.

These results confirm the scale’s reliability, internal coherence, and convergent validity. Experts in business and technology reviewed the instrument to ensure content validity. The research team designed a mixed-format survey, combining closed- and open-ended items, to provide a balanced view of AI adoption practices and strategic capabilities.

3.6. Interviews with Non-Adopting Firms

The research team conducted semi-structured interviews with entrepreneurs from non-adopting firms to gain an understanding of the barriers to AI adoption. The interview guide explored three key areas:

• Reasons for non-adoption include financial, technical, organizational, and training barriers.
• The research team captured future adoption expectations using a five-level probability scale and asked participants to provide narrative justifications for their responses.
• Organizational context, such as firm size, sector, and geographic region.

Interviews lasted about 35 min, conducted in person or via video call, with prior informed consent. All sessions were audio-recorded, transcribed verbatim, and analyzed thematically. The interview guide was tested through a pilot with ten entrepreneurs, allowing adjustments to improve clarity and flow. This qualitative component captured attitudinal nuances and triangulated responses, consistent with recommendations by Bryda and Costa [57] for robust qualitative research in organizational settings.

3.7. Statistical Treatment

The researchers conducted a descriptive analysis to examine the level of AI adoption among micro and small enterprises, considering three contextual dimensions: firm size, geographic region, and economic sector. The objective is to identify patterns of differential adoption—whether partial or intensive—and to characterize firms according to their degree of technological integration. It is important to emphasize that Shannon’s entropy (H) is a measure of functional diversity or dispersion, not of adoption intensity or technological maturity. A high H value indicates that firms’ AI adoption is spread relatively evenly across the three categories (non-adoption, partial, intensive), reflecting heterogeneous or exploratory patterns. A low H value indicates concentration in one or two categories, meaning adoption patterns are more predictable and homogeneous. However, low entropy does not imply low adoption. It may reflect either widespread non-adoption (a sign of technological restraint) or widespread intensive adoption (a sign of advanced integration). Therefore, H alone cannot distinguish between underdevelopment and consolidation.

To avoid misinterpretation, all analyses based on H are systematically complemented with the mean adoption level across the nine value chain activities. For example, a group with low H and a mean adoption score close to 1 is interpreted as functionally restricted or incipient. In contrast, a group with low H and a mean score near three is interpreted as strategically concentrated or mature. This dual-criterion approach ensures that interpretations of low-entropy groups are grounded in both diversity and direction of adoption, preventing erroneous conclusions about technological development.

Entropy thresholds and classification. The entropy thresholds used to classify strategic profiles (Technological Explorers, Incipient Adopters, and Functionally Concentrated) were determined using the Jenks natural breaks optimization method. This empirical clustering technique minimizes within-group variance and maximizes between-group differences, ensuring that the cutoffs reflect actual patterns in the data rather than arbitrary choices. The resulting thresholds—H > 0.85 (high diversity), 0.80 ≤ H ≤ 0.85 (moderate diversity), and H < 0.80 (low diversity)—emerged directly from the sample distribution. These cutoffs are empirically justified and correspond to meaningful shifts in adoption dispersion across the sample. Similar procedures have been applied in organizational and technological adoption contexts, including cloud computing in MSEs [58], diversity in transport systems [59], and smart metering adoption [60]. Thus, the thresholds in this study are both data-driven and grounded in established methodological precedents.

For reference, Shannon’s entropy (H) in this study ranges from 0 (complete concentration in a single adoption category) to ln(3) ≈ 1.099 (maximum dispersion across the three categories: non-, partial, and intensive). In this context, entropy values above 0.85 can be considered high, reflecting heterogeneous or exploratory adoption; values between 0.80 and 0.85 are moderate, signaling partial dispersion; and values below 0.80 are low, indicating more homogeneous patterns. Importantly, “low” entropy does not automatically imply low adoption—it may reflect either widespread non-adoption or consolidated intensive use. To avoid such misinterpretation, all entropy values are systematically interpreted together with the mean adoption score across functions. This dual criterion allows us to benchmark entropy levels qualitatively and situate our thresholds within the methodological precedent of prior organizational and technological studies [58,59,60].

3.8. Classification of Entrepreneurs According to Perception–Behavior Consistency

Building on the entropy thresholds and data-driven cutoffs defined in Section 3.7, we categorize firms into strategic profiles and examine the consistency between their perceptions and behaviors accordingly. The researchers classified entrepreneurs into three groups based on the degree of alignment—or misalignment—between their general perception of AI adoption and their actual implementation across specific value chain activities (see

Table 2. Classification of Firms According to Self-Reported and Actual Levels of Adoption.

Group	Non-Adopters	Partial Adopters	Intensive Adopters
Consistent Respondents	Adoption in 0 activities	Adoption in 3–5 activities	Adoption in ≥6 activities
Overestimators	—	Adoption in <3 activities	Adoption in <6 activities
Underestimators	Adoption in ≥1 activity	Adoption in >5 activities	—

).

The application of Shannon’s entropy to organizational and technological contexts has been increasingly recognized as a valuable tool for measuring functional diversity and heterogeneity in adoption. Initially developed in information theory [61], entropy has been adapted in organizational research to assess technological diffusion and adoption dynamics. For instance, Alismaili et al. [58] applied quantitative metrics to evaluate the heterogeneity of cloud computing adoption in SMEs, while Numair et al. [60] employed entropy-related approaches to analyze the adoption and value of intelligent metering systems in the UK. In addition, Miguel et al. [59] employed entropy-based optimization to assess diversity in transport systems, highlighting its utility in modeling complex technological environments. Complementary studies on AI adoption in SMEs [2,22,55] reinforce the importance of adopting robust quantitative measures to capture uneven adoption trajectories. Collectively, these works demonstrate that entropy serves as a sensitive indicator of organizational adaptability, particularly in contexts characterized by uneven resource endowments and fragmented innovation systems, conditions common in emerging economies.

To assess heterogeneity in AI adoption across business functions, Shannon’s entropy (H) was applied—a metric commonly used in probabilistic distribution analysis. This measure quantifies functional diversity in adoption by capturing the dispersion of responses across three ordinal categories: non-adoption, partial adoption, and intensive adoption. Equation (2) was used:

$$H=-\sum _{\mathrm{i}=1}^{\mathrm{k}}{\mathrm{p}}_{\mathrm{i}}\mathrm{l}\mathrm{n}\left({\mathrm{p}}_{\mathrm{i}}\right)$$

(2)

where p_i represents the probability associated with category i, and k is the number of adoption categories. In our case, k = 3, corresponding to non-adoption, partial adoption, and intensive adoption. Entropy was calculated separately for each of the nine value chain activities, generating a diversity estimate per function. Then, average entropy values were computed by region, firm size, and economic sector, allowing for the identification of broader patterns of technological dispersion.

3.9. Strategic Typology of AI Adoption

To better understand strategic positioning, a dual classification strategy was implemented: one based on individual perception and another based on patterns of functional application.

1.

Individual Perception-Based Classification: Based on three Likert-scale items (scale ranged from 1 = Strongly Disagree to 5 = Strongly Agree) assessing: (a) uniqueness of value; (b) inimitability; and (c) alignment with residual advantage. A composite strategic score (avg_overall) was calculated for each firm and classified as:

• Differentiation Strategy: avg_overall ≥ 3.50;
• Tactical Tool: 2.50 ≤ avg_overall < 3.50;
• Symbolic/Non-Strategic: avg_overall < 2.50.

2.

Functional-Combination Classification: Recurring adoption patterns across the nine value chain functions were analyzed. These combinations were categorized based on the intensity (partial or intensive) and diversity of their applications. Strategic scores were averaged per combination to assess coherence between perception and actual use.

This two-pronged classification reveals potential gaps between perceived strategy and actual AI deployment.

3.10. Correlation Between Strategic Perception and Functional Diversity

To analyze the relationship between functional adoption diversity and perceived strategic value, Spearman’s rank correlation coefficients (ρ) were calculated between each firm’s entropy value and its average overall score. This non-parametric method was selected due to the ordinal nature of the data and lack of multivariate normality.

The analysis was disaggregated by Region (Coastal vs. Andean Highlands), firm size (Micro vs. Small), and economic sector (six sectors).

For each subgroup, the mean entropy, mean strategic score, and corresponding ρ with p-value were reported. This approach enables exploration of whether a higher functional diversity in AI use (as a proxy for technological maturity) correlates with a stronger strategic valuation across different contexts.

3.11. Index of Dynamic Capabilities

The researchers assessed organizational dynamic capabilities using nine items (P1–P9), each measured on a five-point scale (see

Table 3. Items used in the questionnaire.

Code	Item	Scale (1 to 5)
P1	To what extent have your operations changed after using AI?	1 (Almost nothing) to 5 (Very much)
P2	How easy was it to start using AI in your firm?	1 (Extremely difficult) to 5 (Very easy)
P3	When AI is applied in one area, how easy is it to use it in other parts of the firm?	1 (Very complicated) to 5 (Very simple)
P4	If the market changes, how quickly can you adjust your use of AI?	1 (Very slow) to 5 (Very fast)
P5	How easy is it to combine AI with your current processes?	1 (Very difficult) to 5 (Very easy)
P6	How prepared is your firm to adopt new AI tools that may appear in the coming years?	1 (Not prepared at all) to 5 (Fully prepared)
P7	When we implement AI, we record what we have learned to avoid repeating mistakes.	1 (Never) to 5 (Always)
P8	Knowledge about AI flows easily between teams.	1 (Strongly disagree) to 5 (Strongly agree)
P9	In our firm, it is permitted to test AI tools, even if not all of them are effective.	1 (Strongly disagree) to 5 (Strongly agree)

).

These items capture how each firm integrates, reconfigures, and extends its use of AI in response to internal and external changes.

The researchers computed a global index by averaging the nine item scores, assigning values from 1 (very low adaptability) to 5 (very high adaptability). This index was then correlated with the functional entropy metric, allowing for an analysis of whether firms with broader AI implementation patterns also exhibit stronger dynamic capabilities.

4. Results

This section presents the empirical findings regarding the adoption of artificial intelligence (AI) in micro and small enterprises (MSEs) in Ecuador. The analysis focuses on three key contextual dimensions: firm size, geographic region, and economic sector. By examining variations in adoption levels—categorized as non-adoption, partial adoption, and intensive adoption—this section aims to identify distinct behavioral patterns and characterize the degree of technological integration across different business profiles. The results offer insight into both the extent and distribution of AI implementation, providing a foundation for understanding the strategic positioning of MSEs in the digital transformation process.

4.1. AI Adoption by Firm Size

As shown in

Table 4. Distribution of AI adoption levels by firm size.

Firm Size	Non-Adopters	Partial Adopters	Intensive Adopters
Microenterprise	394 (36.3%)	524 (48.3%)	167 (15.4%)
Small enterprise	25 (21.7%)	53 (46.1%)	37 (32.2%)

, adoption patterns differ substantially between micro and small enterprises, revealing important contrasts in their technological capacities. In microenterprises, partial adoption is predominant (48.3%), indicating that most firms engage with AI in limited or exploratory ways, often constrained by resource scarcity and the absence of specialized capabilities. The relatively high rate of non-adoption (36.3%) further reflects structural barriers—financial, organizational, and human capital—that restrict their ability to integrate advanced technologies. Intensive adoption is rare (15.4%), indicating that only a small fraction of microenterprises have the capacity to embed AI deeply into their operations.

Small enterprises, by contrast, exhibit a stronger technological profile, with nearly one-third (32.2%) reporting intensive adoption, signaling a greater readiness to integrate AI as a core component of their business processes. While partial adoption remains high (46.1%), the lower incidence of non-adopters (21.7%) suggests a more favorable environment for the adoption of technology. These patterns suggest that small enterprises are better positioned to overcome adoption barriers and effectively leverage AI. In contrast, microenterprises face persistent limitations that confine them to incremental or fragmented use of the technology.

These findings suggest that small firms are better equipped to adopt more advanced AI integration, likely due to their greater resource endowment and managerial capacity. In contrast, microenterprises display a more polarized pattern, oscillating between no adoption and early-stage use, reflecting underlying heterogeneity in digital preparedness and organizational capabilities.

4.2. AI Adoption by Geographic Region

Partial adoption predominates in both regions, with rates of 46.6% in the Coastal region and 49.2% in the Andean Highlands region. Intensive adoption is slightly higher in the Coastal region (19.2%) than in the Andean (15.4%). Non-adoption rates are similar, at 34.2% and 35.4%, respectively, indicating minimal regional differences in AI adoption. Although slight variations suggest differing progress toward intensive use (see

Table 5. Distribution of AI adoption levels by geographic region.

Region	Non-Adopters	Partial Adopters	Intensive Adopters
Coastal	175 (34.2%)	238 (46.6%)	98 (19.2%)
Andean Highlands	244 (35.4%)	339 (49.2%)	106 (15.4%)

), these rates are comparable.

4.3. AI Adoption by Economic Sector

Sectoral differences are more pronounced. Intensive adoption is highest in the Mining Sector (28.6%), the Construction Sector (28.0%), and the Manufacturing Sector (19.7%). Trade shows the lowest intensive use (12.7%) and the highest non-adoption rate (40.7%). Partial adoption is widespread in Agriculture (54.0%) and Manufacturing (56.4%), indicating transitional phases (see

Table 6. Distribution of AI adoption levels by economic sector.

Sector	Non-Adopters	Partial Adopters	Intensive Adopters
Agriculture, Forestry, Livestock, and Fishing	14 (28.0%)	27 (54.0%)	9 (18.0%)
Trade	164 (40.7%)	188 (46.7%)	51 (12.7%)
Construction	15 (30.0%)	21 (42.0%)	14 (28.0%)
Mining and Quarrying	4 (19.0%)	11 (52.4%)	6 (28.6%)
Manufacturing Industries	28 (23.9%)	66 (56.4%)	23 (19.7%)
Services	194 (37.7%)	264 (47.2%)	101 (18.1%)

). Overall, partial adoption prevails across most firms. However, a larger size and affiliation with sectors such as construction or Mining increase the likelihood of intensive use—an aspect explored further in the following sections.

4.4. General Perception vs. Specific AI Adoption

4.4.1. Comparison Between General Perception and Specific AI Adoption Across the Value Chain

The researchers analyzed 1200 micro and small firms to assess the consistency between entrepreneurs’ self-reported AI adoption and its actual implementation across the value chain. They measured general perception using a 3-point ordinal scale. They compared it with the number of value chain activities where firms had partially or intensively adopted AI (i.e., ≥2 on the same scale).

As shown in

Table 7. Relationship Between AI Adoption and Perceived Adoption Intensity.

General Perception of AI Adoption	N (MSEs)	Average Number of Activities with Adoption
Non-adopter (1)	419	0.00
Partial adopter (2)	577	4.74
Intensive adopter (3)	204	4.78

, non-adopters report no adoption across any activity, confirming a strong alignment between perception and reality. However, partial and intensive adopters show a similar average number of adopted activities (≈5), suggesting that subjective perceptions of “intensive” use do not reflect broader functional implementation.

4.4.2. Classification of Entrepreneurs Based on Consistency Between Perceived and Actual AI Adoption

The researchers classified firms into three groups based on the alignment between general perception and observed AI use across the value chain (see

Table 8. Distribution of Firms by Consistency Classification.

Group	Number of Firms	Percentage
Consistent	924	77.0%
Overestimators	155	12.9%
Underestimators	121	10.1%

). Most firms (77%) show consistency between what entrepreneurs perceive and what they implement. However, 12.9% overestimate their level of adoption, while 10.1% underestimate it. In total, nearly one in four entrepreneurs misjudge their firm’s real degree of AI integration.

4.5. Influence of Contextual Variables on Perception–Practice Consistency

We assessed whether firm size, geographic region, and economic sector influence consistency between perceived and actual AI adoption.

4.5.1. Consistency by Economic Sector and Non-Adoption Patterns

Sectoral consistency in AI adoption perception varies significantly (χ²(10) = 20.02, p = 0.029). Trade shows the highest consistency (80.6%), while Mining and Quarrying records the lowest (52.4%), suggesting that sector-specific dynamics affect perception accuracy (see

Table 9. Consistency by Economic Sector.

Sector	Consistent	Overestimators	Underestimators	Total	% Consistent
Agriculture, Forestry, Livestock, and Fishing	34	7	9	50	68.0%
Trade	325	37	41	403	80.6%
Construction	36	10	4	50	72.0%
Mining and Quarrying	11	4	6	21	52.4%
Manufacturing Industries	85	15	17	117	72.6%
Services	424	75	60	559	75.8%

).

The researchers excluded 419 firms (34.9% of the 1200 surveyed) from the intensity analysis because they reported no AI adoption. However, semi-structured interviews revealed key barriers to adoption, including limited knowledge, skill shortages, high costs, internal resistance to change, and a lack of institutional support.

Despite these challenges, 70.6% of non-adopters expressed a positive disposition toward future adoption (35.6% probable, 35.1% definite). Only 21% showed resistance. This polarization (46.1% at the extremes) suggests defined positions that require differentiated strategies: evidence of value for hesitant firms, education for the undecided, and long-term engagement for the resistant.

Firm size does not significantly affect adoption intention. Microenterprises display both the highest acceptance and resistance. Although small firms appear more favorable (3.75:1 adopter-to-opponent ratio vs. 2.3:1 in microenterprises), chi-square tests confirm no statistically significant association (p > 0.47). These trends reflect sample imbalance rather than structural size effects.

The final analysis focuses on the 781 AI-adopting firms (65.1%), comparing partial vs. intensive users. Results apply only to this subgroup and are not generalizable to the whole MSE population.

4.5.2. Consistency by Geographic Region

As shown in

Table 10. Consistency by Region.

Region	Consistent	Overestimators	Underestimators	Total	% Consistent
Coastal	388	69	54	511	75.9%
Andean Highlands	527	79	83	689	76.5%

, consistency rates are similar between the Coastal (75.9%) and Andean (76.5%) regions. The association is not statistically significant (χ²(2) = 1.56, p = 0.458), suggesting that geographic location does not influence entrepreneurs’ accuracy in perceiving their level of AI adoption.

4.5.3. Consistency by Firm Size

As shown in

Table 11. Firm Size vs. Consistency Classification.

Firm Size	Consistent	Over-Estimators	Under-Estimators	Total	% Consistent
Microenterprise	843	119	123	1085	77.7%
Small enterprise	72	29	14	115	62.6%

, microenterprises exhibit significantly higher consistency (77.7%) compared to small enterprises (62.6%). The association is statistically significant (χ²(2) = 20.29, p < 0.001), indicating that larger firms tend to over- or underestimate their AI use more frequently, potentially due to greater internal complexity or role delegation.

The study previously addressed regional and sectoral effects. Together, these findings confirm that contextual factors condition perception accuracy, substantial size, which appears to modulate self-assessment of technological adoption.

4.6. Analysis of Diversity in AI Adoption Across the Value Chain

The researchers calculated Shannon’s entropy for each of the nine value chain activities to assess the variability of AI adoption across business functions. This metric captures the degree of dispersion across three adoption levels (non, partial, intensive), with higher values indicating greater diversity and lower predictability in usage patterns (

Table 12. Entropy Values by Activity.

Activity	Entropy
Marketing & Sales	0.9072542
Human Resource Management	0.8959472
Outbound Logistics	0.8930108
Procurement	0.8728367
Inbound Logistics	0.8712057
Operations	0.8662440
Service	0.8577435
Firm Infrastructure	0.8507329
Technology Development	0.8398616

).

The classifications presented in

Table 13. Reported AI Applications by Value Chain Activity.

Activity	Reported AI Applications
Marketing and Sales	Customer service automation via chatbots, predictive analytics for segmentation, and personalized commercial strategies.
Operations	Optimization of production processes, real-time inventory management, and anomaly detection in production lines.
Outbound Logistics	Delivery route optimization; predictive planning in the supply chain.
Human Resources	Personnel selection using evaluation algorithms; internal training programs focused on AI-related skills.
Service	Chatbots for automated technical support; customer feedback analysis for continuous improvement.
Technology Development	Data analysis to support product and service innovation; simulations and predictive models in R&D projects.
Firm Infrastructure	Cybersecurity systems for threat detection and fraud prevention, as well as management of cloud-based technological infrastructure.
Procurement	Supplier evaluation, price prediction, and optimization of purchasing decisions.
Inbound Logistics	Inventory planning and control; data analytics to improve storage and material handling efficiency.

,

Table 14. Shannon Entropy by Region.

Region	Average Entropy	Classification
Andean Highlands Region	0.8797	Innovative Region
Coastal Region	0.8620	Innovative Region

and

Table 15. Shannon Entropy by Firm Size.

Firm Size	Average Entropy	Classification
Small Enterprise	0.8721	Technological Explorer
Microenterprise	0.8715	Technological Explorer

derive directly from the thresholds established in Section 3.7. Using the Jenks natural breaks optimization, three interpretive ranges were defined: H > 0.85 = high functional diversity (“Technological Explorer”/“Innovative” profile), 0.80 ≤ H ≤ 0.85 = moderate diversity (“Incipient Adopter”), and H < 0.80 = low diversity (“Functionally Concentrated”). These thresholds were then applied to classify firms, regions, and sectors based on their average entropy values.

The highest entropy values correspond to Marketing, HR, and Outbound Logistics, reflecting a more exploratory or trial-and-error stage of adoption, often associated with innovations such as chatbots, analytics for segmentation, or personnel selection algorithms. In contrast, Technology Development, Firm Infrastructure, and Service show more homogeneous behaviors, possibly due to financial and technical constraints or limited awareness of strategic value in these areas.

Qualitatively, entropy values above 0.85—such as those observed in Marketing (H = 0.907) and Human Resources (H = 0.896)—can be interpreted as indicating high diversity, reflecting dispersed or exploratory adoption, where firms are experimenting with various AI tools. Values in the 0.80–0.85 range, such as Firm Infrastructure (H = 0.851), indicate moderate diversity, suggesting a mix of adoption but with somewhat more concentration. In contrast, values below 0.80 would signal low diversity, where adoption is relatively uniform—either because most firms are not adopting at all, or because adoption is consolidated in intensive use. This reinforces the need to interpret entropy in conjunction with mean adoption scores to distinguish between incipient non-use and strategic consolidation.

These variations highlight that AI integration is uneven across functions. Adoption tends to be broader and more diverse in externally facing or customer-centered activities, while internally focused functions exhibit more cautious or standardized approaches. Although entropy values do not reach the theoretical maximum (~1.585), they offer analytically meaningful insights into the functional heterogeneity of digital transformation.

To complement this analysis, Table 13 presents the most frequently reported AI applications by activity, evidencing how firms operationalize AI in practice.

These findings underscore AI’s dual role as both an operational enabler and a strategic asset. The functional diversity in adoption not only reflects the state of technological integration but also reveals the sectoral priorities, organizational constraints, and internal capabilities that shape the digital transformation of MSEs.

4.6.1. Shannon Entropy by Region

Table 14 shows the average entropy of AI adoption across value chain activities by region. Both the Andean (0.8797) and Coastal (0.8620) regions are classified as Innovative, indicating high functional diversity in AI adoption patterns. This heterogeneity may reflect exploratory behavior, fragmented implementation, or varied strategic priorities across firms.

The Andean Region exhibits slightly greater diversity, suggesting a heterogeneous landscape where advanced adopters coexist with firms still in exploratory phases. This mix reflects active experimentation and dynamic learning. The Coastal Region also exhibits notable variability, albeit slightly more uniform, with the prevalent use of tactical AI tools alongside more conservative firms. Both regions thus demonstrate significant potential for further digital transformation.

4.6.2. Shannon Entropy by Firm Size

Table 15 presents the average entropy values across value chain activities for micro and small enterprises. Both groups fall within the Technological Explorer category, showing similar levels of adoption diversity.

These values reflect a heterogeneous landscape across both firm sizes, where adoption levels vary widely, from non-use to partial and intensive integration. This diversity of practices suggests that digital transformation among Ecuadorian MSEs remains in an early and evolving phase. Rather than a consolidated strategy, AI adoption in MSEs remains exploratory, shaped by uneven capabilities and contexts. The observed diversity underscores the need for differentiated support policies that take into account enterprises’ varied starting points in their technological trajectories.

4.6.3. Shannon Entropy by Economic Sector

Table 16. Shannon Entropy by Economic Sector.

Sector	Average Entropy	Classification
Mining and Quarrying	0.9453	Technological Explorer
Agriculture, Forestry, Livestock, and Fishing	0.9049	Technological Explorer
Services	0.8728	Technological Explorer
Manufacturing Industries	0.8703	Technological Explorer
Trade	0.8579	Technological Explorer
Construction	0.8214	Technological Explorer

presents the average entropy values by sector. All sectors exhibit entropy values above 0.80, indicating a high level of functional diversity in the adoption of AI. The Mining and Quarrying sectors show the highest entropy (0.9453), reflecting the most heterogeneous adoption patterns. This does not imply greater maturity, but somewhat wider variation in how AI is applied across value chain activities.

The Mining and Agriculture sectors show the highest entropy, signaling experimentation with varied AI applications, from predictive maintenance to precision farming. Services and Manufacturing also display broad adoption profiles, combining automation with customer-facing tools—even Trade and Construction, with relatively lower entropy, present notable variability.

This widespread heterogeneity suggests that no sector has yet achieved systematic integration of AI. Instead, they reflect early or transitional stages, where diverse capabilities and use cases coexist. Supporting this trajectory will require sector-specific policies that consolidate emerging practices, foster peer learning, and reduce adoption gaps. It is essential to note that while entropy values across sectors are numerically close, ranging from 0.8214 (Construction) to 0.9453 (Mining and Quarrying), they reveal meaningful differences in adoption patterns. The relatively high entropy in sectors such as Mining, Agriculture, and Services reflects a broad and heterogeneous distribution of AI applications across value chain activities, consistent with an exploratory phase of technological adoption. In contrast, the lower entropy observed in the Construction sector (0.8214) suggests a more homogeneous and functionally concentrated pattern, where firms tend to adopt AI in a narrower set of activities, possibly focused on specific operational functions such as logistics or project management. This divergence underscores the value of entropy as a sensitive metric: even minor numerical differences can signal distinct strategic postures. The case of construction may reflect structural constraints—such as fragmented supply chains, project-based operations, or limited digital readiness—that channel AI adoption toward a more uniform trajectory. Thus, rather than indicating a limitation of the entropy measure, this contrast highlights its ability to differentiate between sectors in terms of functional diversity. While most sectors exhibit profiles of fragmented experimentation, construction emerges as a counterpoint of relative uniformity, suggesting a need for targeted policy interventions to broaden and deepen technological integration.

4.6.4. Shannon Entropy by Region, Firm Size, and Economic Sector

Table 17. Shannon Entropy by Region, Firm Size, and Economic Sector.

Region	Firm Size	Sector	Average Entropy	Most Influential Activity
Andean Highlands	Microenterprise	Agriculture, Forestry, Livestock, and Fishing	0.8885	Outbound Logistics
		Mining and Quarrying	0.8077	Inbound Logistics
		Manufacturing Industries	0.8666	Inbound Logistics
		Trade	0.8711	Marketing & Sales
		Construction	0.7665	Outbound Logistics
		Services	0.8787	Outbound Logistics
	Small Enterprise	Agriculture, Forestry, Livestock, and Fishing	0.4081	Inbound Logistics
		Mining and Quarrying	0.7149	Inbound Logistics
		Manufacturing Industries	0.7152	Operations
		Trade	0.7998	Human Resources
		Construction	0.7783	Human Resources
		Services	0.8010	Operations
Coastal	Microenterprise	Agriculture, Forestry, Livestock, and Fishing	0.9192	Outbound Logistics
		Mining and Quarrying	0.7840	Infrastructure
		Manufacturing Industries	0.8588	Infrastructure
		Trade	0.8296	Technology Development
		Construction	0.7655	Marketing & Sales
		Services	0.8489	Marketing & Sales
	Small Enterprise	Agriculture, Forestry, Livestock, and Fishing	0.7696	Marketing & Sales
		Mining and Quarrying	0.8050	Inbound Logistics
		Manufacturing Industries	0.8269	Service
		Trade	0.8595	Marketing & Sales
		Construction	0.6722	Inbound Logistics
		Services	0.8461	Infrastructure

shows the average Shannon entropy of AI adoption across nine value chain activities, segmented simultaneously by region, firm size, and economic sector. It also identifies the most influential activity in each subgroup.

The combined segmentation reveals that microenterprises in the Andean region exhibit high entropy in sectors such as agriculture (0.8885), services (0.8787), and trade (0.8711), with outbound logistics as a recurring driver of variability, likely due to geographical and infrastructural challenges. Conversely, small enterprises in the same region show more restricted adoption, especially in agriculture (0.4081), with inbound logistics and operations emerging as key functional areas of variation.

In the Coastal region, microenterprises in agriculture (0.9192), manufacturing (0.8588), and services (0.8489) report high functional dispersion, linked to broader experimentation across both core and support activities. For small enterprises, entropy remains high in manufacturing (0.8269), trade (0.8595), and services (0.8461), with strategic activities such as post-sales service, marketing, and infrastructure being key drivers of adoption.

These findings underscore that AI adoption is function-specific, shaped by the interaction between regional conditions, firm size, and sectoral dynamics. The identification of high-entropy activities signals where experimentation is most active and where support policies could have the most significant impact.

Ultimately, this analysis reveals a modular pattern of digital transformation, with firms integrating AI in a fragmented manner across various business functions. Rather than reflecting linear or uniform progress, AI adoption in MSEs appears as a hybrid ecosystem, where traditional operations coexist with exploratory digital initiatives.

It is essential to clarify that, although the results presented in Table 14, Table 15, Table 16 and Table 17 show high entropy values across all groups, the analytical framework is designed to prevent misinterpretation in cases of low entropy. Low entropy values indicate low functional diversity in AI adoption, but they do not indicate whether adoption levels are low or high. A low H could reflect either widespread non-adoption or widespread intensive adoption. Therefore, in any analysis involving low-entropy groups, interpretation is systematically complemented by the mean adoption level across activities to distinguish between functionally restricted and strategically consolidated adoption patterns. For example, in the Coastal region, microenterprises in the ‘Agriculture, Forestry, Livestock, and Fishing’ sector show the highest entropy (H = 0.9192), indicating a highly dispersed pattern of AI adoption across value chain activities. This suggests a phase of exploratory adoption, where AI is being tested in multiple functions, albeit not necessarily deeply or intensively. In contrast, small enterprises in the ‘Trade’ sector exhibit high entropy (H = 0.8595) and are more likely to report intensive adoption in key areas such as marketing and customer service. This contrast underscores the importance of interpreting entropy in conjunction with adoption intensity metrics to distinguish between broad but shallow experimentation and functionally diverse, advanced integration.

4.7. Summary of AI Adoption Entropy Patterns by Region, Firm Size, and Sector

Table 18. Interpretative Summary of AI Adoption Entropy Patterns.

Region	Firm Size	Sectors with Highest Entropy	Most Influential Activity (Frequent)	Technological Profile
Andean Highlands	Microenterprise	Agriculture, Services, Trade	Outbound Logistics	Active exploration focused on distribution
	Small enterprise	Services, Trade	Operations, Human Resources, Inbound Logistics	Targeted exploration in internal processes
Coastal	Microenterprise	Agriculture, Manufacturing, Services	Infrastructure, Technology, Marketing	Broad and systemic exploration
	Small enterprise	Trade, Services, Manufacturing	After-Sales Service, Infrastructure	Functionally diversified exploration

summarizes the main regularities in AI adoption entropy across regions, firm sizes, and sectors, integrating the most frequent value chain activities that influence entropy and the dominant technological profiles.

This synthesis reveals distinct AI adoption patterns shaped by regional context and firm size:

•

Regionally, Coastal firms show more diverse adoption in support activities (e.g., infrastructure, technology, marketing), suggesting a systemic digital strategy. In contrast, Andean firms exhibit higher variability in primary activities, such as logistics and operations, indicating a more tactical focus on operational efficiency.

•

By firm size, microenterprises exhibit broader but less structured adoption, likely driven by intuitive or trial-and-error experimentation. Small enterprises exhibit more targeted integration, particularly in operations, HR, and infrastructure, indicating a strategic, resource-aware approach.

Overall, AI adoption among MSEs is modular and non-linear, forming functionally distinct configurations rather than uniform trajectories. These differences necessitate flexible, context-sensitive policy frameworks that can support diverse digital transformation pathways and promote inclusive, sustainable innovation. Patterns of high entropy suggest active or broad functional engagement with AI, characterized by diverse and non-uniform adoption across value chain activities. These patterns are consistent with exploratory behavior, though not necessarily with higher overall adoption intensity.

Figure 1 maps the AI applications deployed across the nine value chain activities by sector, highlighting sector-specific adoption patterns and strategic use cases.

The heatmap visually confirms that AI adoption among MSEs is not linear or homogeneous but relatively modular and selective, concentrating on activities perceived as high-impact or low-barrier.

The entropy-based values shown reflect intra-sector heterogeneity in AI use across the value chain. In sectors such as agriculture and mining, AI is primarily focused on outbound logistics and infrastructure, highlighting the need for optimization in distribution and operational continuity. By contrast, services, trade, and manufacturing display more functional diversification, with AI applied in marketing, after-sales service, human resources, and tech development, indicating broader experimentation.

Three cross-sectoral “critical nodes” of digital transformation emerge:

•

Marketing and Sales: customer analytics, personalization, campaign automation.

•

Operations: process optimization, quality control, and demand forecasting.

•

Infrastructure: cybersecurity, technological scalability.

These patterns confirm that AI adoption is guided by function-specific logics aimed at improving efficiency, market responsiveness, and resilience, rather than full-chain transformation.

In sum, AI adoption among Ecuadorian MSEs is:

•

Functionally selective;

•

Sectorally diverse;

•

Shaped by regional dynamics.

This calls for targeted public policies and context-sensitive support strategies to foster sector-appropriate, scalable, and sustainable digital transformation.

4.8. Strategic Classification by Firm Size: Perception vs. Functional Reality

The strategic interpretation of AI adoption differs significantly between micro and small enterprises. Based on individual perceptions of AI’s uniqueness, inimitability, and alignment, differentiation strategies predominate—especially among small firms (68.9% vs. 60.9% in microenterprises), as shown in

Table 19. Strategic classification by firm size (individual perception).

Firm Size	Differentiation Strategy	Tactical Tool	Non-Strategic/Symbolic	Total Firms
Microenterprise	421	186	84	691
Small enterprise	62	18	10	90

. However, microenterprises exhibit greater dispersion, with higher shares adopting AI as a tactical tool (26.9%) or in a symbolic/non-strategic manner (12.2%).

However, when analyzing firms’ actual configurations—i.e., the combination of AI uses across the value chain—the number of firms truly following a differentiation strategy drops, particularly among microenterprises. As

Table 20. Strategic classification by firm size (adopted configuration).

Firm Size	Differentiation Strategy	Tactical Tool	Non-Strategic/Symbolic	Total Firms
Microenterprise	395	107	189	691
Small enterprise	53	11	26	90

shows, symbolic use doubles: from 12.2% to 27.4% in micro firms and from 11.1% to 28.9% in small firms.

This gap between perception and actual strategic alignment—especially pronounced in microenterprises—suggests that while many firms aspire to leverage AI for differentiation, limitations in resources, capabilities, or integration hinder effective implementation. The findings underscore the need for capacity-building efforts that bridge the divide between strategic intent and operational reality.

4.9. Strategic Classification by Economic Sector: Contrasts Between Internal Perception and Collective Performance

Sectoral analysis reveals consistent gaps between firms’ self-perceived strategic use of AI and the actual functional configurations they adopt. According to individual perception (

Table 21. Strategic classification by sector (individual perception).

Sector	Differentiation	Tactical	Non-Strategic	Total
Services	225	94	46	365
Trade	146	66	27	239
Manufacturing	56	23	10	89
Agriculture, Forestry, and Fishing	25	7	4	36
Construction	21	9	5	35
Mining and Quarrying	10	5	2	17

), firms in the Services (61.6%) and Trade (61.1%) sectors most frequently classify their AI use as strategic, particularly in customer-facing areas such as marketing and support.

However, when examining adopted configurations (

Table 22. Strategic classification by sector (adopted combination).

Sector	Differentiation	Tactical	Non-Strategic	Total
Services	203	57	105	365
Trade	139	30	70	239
Manufacturing	52	15	22	89
Agriculture, Forestry, and Fishing	29	3	4	36
Construction	16	8	11	35
Mining and Quarrying	9	5	3	17

), strategic classification drops. The Services sector, for instance, sees a rise in symbolic uses (from 12.6% to 28.8%), and Trade shows a similar trend (from 11.3% to 29.3%).

The most significant discrepancies occur in Services and Trade, where many firms claim to use strategic AI, yet rely on combinations that lack the complexity or integration required to generate sustainable competitive advantages. These mismatches highlight the need for sector-specific interventions to align perceptions with actual strategic capabilities.

4.10. Integrated Reflection: Strategic Perception and Functional Reality

The contrast between individual perceptions and actual adoption configurations offers a deeper understanding of AI integration processes among micro and small enterprises (MSEs). While most firms perceive their AI use as strategic—particularly in services, trade, and manufacturing—the analysis of functional combinations reveals a significant misalignment. Many organizations that claim to pursue differentiation adopt fragmented or isolated practices, which do not translate into a systemic competitive advantage.

This misalignment is often interpreted as a capability gap—reflecting limited managerial, technical, or organizational resources to implement coherent AI strategies. While such constraints are clearly present, particularly among microenterprises, it is also plausible that some firms exhibit strategic selectivity rather than strategic overestimation. That is, entrepreneurs may deliberately prioritize high-impact, low-complexity functions (e.g., marketing, sales, and service) as part of a rational, resource-aware digitalization strategy. Indeed, these functions are present in all high-scoring configurations (Table 22), suggesting that they are not only accessible but also perceived as strategically valuable. However, the key insight from the data is that even when selectivity is intentional, it rarely evolves into integrated, cross-functional configurations capable of generating sustained advantage. Thus, while strategic selectivity may explain which functions are adopted, it does not fully account for the persistent gap between perceived strategic impact and the systemic coherence required for actual competitive differentiation. The challenge lies not in the act of selection itself, but in the failure to build dynamic, interconnected capabilities from those challenges.

From a hypothesis-testing perspective, these results indicate that H2a and H2b are not supported: broader functional diversity in AI adoption (entropy) does not correlate significantly with higher perceived strategic value or with stronger dynamic capabilities. Rather than a null finding, this outcome has theoretical relevance. It challenges the widespread assumption—rooted in technology adoption literature—that expanding adoption across multiple functions necessarily translates into strategic differentiation or capability development. Instead, the evidence suggests that strategic value and adaptability emerge from qualitative factors such as the coherence of functional combinations, alignment with business goals, and the firm’s ability to integrate and scale AI-driven practices. This insight refines the Resource-Based View (RBV) and dynamic capabilities perspective by underscoring that what matters is not the breadth of adoption per se, but the extent to which firms can reconfigure and orchestrate resources to build defensible, long-term advantages.

This asymmetry points to a persistent gap between strategic intention and organizational reality, driven by several factors:

•

Technical limitations in integrating AI across value chain functions.

•

Limited capabilities to scale beyond pilot implementations.

•

Overestimation of the strategic value of narrow or symbolic applications.

Microenterprises are particularly susceptible to this misalignment, exhibiting greater disparity between perception and practice. In contrast, small enterprises exhibit higher convergence, suggesting more deliberate implementation. Similarly, sectors with higher adoption volumes—such as services and trade—also show the widest perceptual gaps, signaling the need for targeted support to consolidate their technological trajectories.

These findings highlight that AI adoption in MSEs is not inherently strategic; it only becomes so when configurations are coherent, integrated, and scalable. Thus, public policies should not only promote adoption but also focus on strengthening the technical and organizational conditions that allow AI to function as a trustworthy source of competitive transformation.

This integrated analysis sets the stage for the next section, which examines the specific combinations of AI-enabled functions that firms themselves associate with strategic value. Going beyond the quantity of adopted functions, this approach emphasizes which activities—and in what combinations—generate genuine synergies, contributing to the construction of sustainable advantages.

Table 23. Combinations with the highest perceived strategic impact (avg_overall ≥ 3.0).

Functional Combination Adopted	n	Strategic Average
Inbound Logistics:2; Operations:2; Marketing & Sales:3; Service:2	7	3.71
Inbound Logistics:2; Operations:3; Marketing & Sales:3; Service:2	5	3.67
Operations:2; Marketing & Sales:3; Service:3; Firm Infrastructure:2	5	3.67
Inbound Logistics:2; Operations:3; Marketing & Sales:2; Service:2	5	3.60
Operations:3; Marketing & Sales:2; Service:2	18	3.52
Operations:3; Marketing & Sales:3; Service:2	6	3.50
Operations:2; Marketing & Sales:2; Service:2; Firm Infrastructure:2	10	3.47
Operations:2; Outbound Logistics:2; Marketing & Sales:2; Service:2; Technology Development:2	10	3.47
Operations:3; Marketing & Sales:2; Service:3	6	3.39
Operations:2; Marketing & Sales:2; Service:2; Human Resource Management:2	10	3.37

presents the functional combinations of AI adoption that firms rated as having the highest strategic value—defined by perceived uniqueness, inimitability, and alignment—considering only adopted activities (partial or intensive). All combinations reach an average strategic rating of at least 3.0, reflecting a clear recognition of value in specific adoption patterns.

Several conclusions emerge:

1.

Strategic value derives from cross-functional integration.

The most highly rated combinations include three or more value chain activities, especially core functions like operations, marketing, sales, and services, often complemented by infrastructure or HR. This suggests that strategic differentiation emerges not from isolated implementations but from synergies across interconnected functions.

2.

Marketing, sales, and services are central to strategic configurations.

Marketing and sales are present in every top-rated combination, often with intensive adoption, highlighting their role in generating competitive advantage. Service also appears in all cases, underscoring the strategic importance of customer experience and post-sale engagement.

3.

Partial adoption can still generate strategic impact.

Many combinations involve mixed levels of adoption, showing that strategic configurations do not require complete digitalization. For MSEs, this is encouraging: coherent integration across select functions can yield meaningful value, even under resource constraints.

4.

High-impact configurations are rare but replicable.

Although only a small number of firms currently adopt these configurations, the recurrence of specific structures indicates the potential for replication. These patterns can serve as references for firms aiming to align digital adoption with strategic outcomes.

In short, strategic value in AI adoption does not stem solely from intensity, but from the functional articulation of its benefits. The most effective configurations combine customer-facing innovation with operational optimization and organizational support. This insight reinforces that the shift from perception to actual competitive advantage depends less on the number of digitalized functions and more on their interconnection, coherence, and alignment with business goals.

To assess whether greater functional diversity in AI adoption is associated with higher perceived strategic value, Spearman correlation coefficients were calculated by region and firm size. The results, summarized in

Table 24. Correlation between entropy and strategic score by region.

Region	N	Mean Entropy	Mean Strategic Score	Spearman ρ	p-Value
Coast	445	0.8553	2.9820	0.0194	0.683
Highlands	336	0.8422	3.0040	–0.0326	0.551

and

Table 25. Correlation between entropy and strategic score by firm size.

Firm Size	N	Mean Entropy	Mean Strategic Score	Spearman ρ	p-Value
Microenterprise	691	0.8481	2.9735	–0.0010	0.979
Small enterprise	90	0.8616	3.1296	–0.0189	0.860

, indicate that the correlation values are close to zero and statistically non-significant in all cases (p > 0.05).

At the regional level, the Coast and Highlands exhibit similar levels of functional diversity (mean entropy 0.8553 and 0.8422, respectively) and comparable strategic scores. However, the Spearman coefficients (ρ = 0.0194 and –0.0326) indicate no systematic relationship between the extent of AI integration across functions and the enterprises’ perceived strategic value.

Similarly, no significant correlation emerges when the data are disaggregated by firm size. Although small enterprises report a higher average strategic score (3.13 vs. 2.97), the correlation with entropy remains negligible (ρ = –0.0189). Among microenterprises, the association is virtually null (ρ = –0.0010).

These findings suggest that greater diversity in AI adoption across the value chain does not, by itself, guarantee a higher strategic impact, at least from the perspective of the firms surveyed. Strategic value appears to depend less on the breadth of functional coverage and more on which specific combinations are adopted and how coherently they are aligned with business goals.

This finding directly challenges a prevalent assumption in digital transformation literature—that wider technological adoption across functions is a proxy for organizational maturity and strategic advantage. While intuitive, our results show that functional diversity, as measured by entropy, does not correlate significantly with perceived strategic value (ρ ≈ 0, p > 0.05 across all segments) or with the development of dynamic capabilities. This suggests that broader adoption is not synonymous with more profound transformation. Instead, the data indicate that strategic impact emerges from qualitative factors, including the coherence of functional combinations, alignment with business objectives, and the firm’s ability to integrate and scale AI-driven practices. For instance, Table 23 reveals that high-impact configurations are not the most diverse, but rather those that link customer-facing innovation (e.g., marketing, sales, service) with operational optimization (e.g., operations, infrastructure). These synergistic patterns, even when limited in scope, generate perceived strategic value because they are functionally articulated and operationally viable. In contrast, dispersed adoption across many functions, potentially reflecting experimentation or uncoordinated initiatives, does not automatically translate into competitive differentiation. This distinction is critical: digital maturity may be less about how many functions use AI, and more about how well they work together. The lack of significant correlation, therefore, is not a null result, but a substantive insight, one that redirects attention from adoption breadth to organizational integration as the actual driver of strategic advantage.

Similarly to the regional and firm-size analyses, the sector-level results do not show statistically significant correlations between functional diversity in AI adoption, measured via Shannon entropy, and enterprises’ perceived strategic value. As detailed in

Table 26. Correlation between entropy and strategic score by economic sector.

Sector	N	Mean Entropy	Mean Strategic Score	Spearman ρ	p-Value
Agriculture, Forestry, Livestock, and Fishing	36	0.8488	2.9815	–0.0193	0.911
Mining and Quarrying	17	0.9038	2.8824	–0.3145	0.219
Manufacturing Industries	89	0.8378	2.9663	0.0758	0.480
Trade	239	0.8396	2.9874	–0.0136	0.834
Construction	35	0.8454	2.9619	0.0403	0.818
Services	365	0.8571	3.0091	–0.0063	0.904

, all Spearman coefficients are low and non-significant (p > 0.05). Even in the case of the Mining and Quarrying sector (ρ = –0.3145), where the correlation is moderately negative, the small sample size (n = 17) and high p-value (p = 0.219) preclude drawing firm conclusions.

Taken together, these results do not support the hypothesis that broader or more functionally diverse AI adoption is associated with higher strategic value. Across all segmentation levels—region, firm size, and economic sector—correlation values are low and statistically insignificant.

This outcome suggests that the perceived strategic value of AI is not determined solely by the number of functions digitized or the breadth of technology deployment. Instead, other qualitative and organizational dimensions may play a more decisive role. These may include:

•

The degree of alignment between AI use and business objectives;

•

The firm’s capability to integrate and scale AI solutions across the organization;

•

The depth of internal adoption and knowledge absorption;

•

Firms’ interpretive frameworks, which may not associate multi-function digitization with strategic differentiation.

From a theoretical standpoint, these findings align with the concept of dynamic capabilities, which is defined as an organization’s ability to integrate, build, and reconfigure internal and external competencies in response to rapidly changing environments. In the context of AI adoption, these capabilities are not limited to deploying digital tools across more activities but require organizational learning, structural adaptation, and knowledge orchestration.

The evidence gathered in this study suggests that such dynamic capabilities are still in their early stages among Ecuadorian MSEs. While many firms demonstrate an aspirational orientation toward AI as a strategic lever, the actual configuration of adoption remains fragmented, and the capacity to extract cumulative strategic value from functionally diverse AI use is still developing.

In summary, functional diversity alone is not a sufficient condition for achieving differentiation. What matters more is how firms combine, align, and integrate AI adoption within coherent strategic and organizational architectures. This insight will inform the following section on capability development and policy implications.

Table 27. Summary of results on dynamic capabilities in MSEs.

Indicator	Main Result
Change in operations	58.5% report “moderate” to “very high” change
Ease of AI expansion	Only 33% consider it “simple” or “very simple”
Ease of integration into other areas	33.4% consider it “easy” or “very easy”
Speed of organizational adjustment	25.2% fast; 43.5% normal; 31.3% slow or very slow
Future preparedness	56.5% “moderately prepared”; 18.4% “quite prepared”
Documentation of learning	21.5% “almost always” document; 77.5% do so occasionally or never
Internal knowledge flow	25.4% perceive it as adequate; 34.9% disagree; 39.7% neutral
Organizational tolerance for error	21.1% agree; 47.2% neutral; 31.6% disagree

synthesizes the main findings on the development of dynamic capabilities among micro and small enterprises (MSEs) in the context of AI adoption. The results reflect a mixed scenario, marked by operational transformations on the one hand and persistent organizational rigidities on the other.

A first relevant signal is that AI is producing tangible operational change: 58.5% of firms report a “moderate” to “very high” transformation in their operations. This suggests that technological adoption is not merely symbolic but is altering production and service dynamics—a prerequisite for building adaptive capabilities.

However, these changes do not yet translate into systemic flexibility. Only one-third of firms consider AI expansion or cross-functional integration to be “simple” or “easy.” This indicates that while transformations are occurring, they are often isolated or hard to scale, typical of organizations still developing the internal coordination mechanisms that underpin dynamic capabilities.

The speed of organizational response to AI adoption also reflects an intermediate stage. Just 25.2% of firms report rapid adjustment, while most describe a “normal” pace (43.5%) or even a “slow” pace (31.3%). A similar pattern emerges in future preparedness: although 56.5% feel “moderately prepared,” only 18.4% express high confidence in their adaptive capacity. This suggests that firms are aware of the need for transformation but remain cautious about their ability to execute it effectively.

More critical, however, are the deficits in organizational learning and knowledge management. Only 21.5% of MSEs consistently document learning from AI use, while nearly 80% admit to doing so only occasionally or not at all. Furthermore, just 25.4% affirm that knowledge flows adequately within the organization, suggesting that expertise remains compartmentalized and under-leveraged. The picture is similar regarding tolerance for error—a key ingredient for innovation and experimentation, with only 21.1% affirming that their firm accepts mistakes as part of the learning process.

Taken together, these findings suggest that AI adoption is triggering change, but not yet driving deep, sustained organizational renewal. The current trajectory suggests the incipient development of dynamic capabilities, which is constrained by the limited institutionalization of knowledge, low integration across functions, and cultures that still prioritize control over experimentation.

In short, MSEs are beginning to transform operationally but lack the structural conditions to consolidate adaptive, learning-oriented organizations. Closing this gap will require not only further technological adoption but also the development of management systems, knowledge architectures, and cultural norms that enable continuous innovation and strategic agility.

To examine whether greater functional diversity in AI use (measured by Shannon entropy) is linked to the development of dynamic capabilities, Spearman correlation coefficients were calculated by region and firm size.

At the regional level (

Table 28. Correlation between entropy and dynamic capabilities by region.

Region	N	Mean Entropy	Mean Dynamic Capabilities	Spearman ρ	p-Value
Coast	445	0.8553	2.9031	0.0370	0.437
Highlands	336	0.8422	2.8737	0.0791	0.148

), correlations were low and not statistically significant (ρ = 0.0370 for Coast, ρ = 0.0791 for Highlands; p > 0.05). Similarly, by firm size (

Table 29. Correlation between entropy and dynamic capabilities by firm size.

Firm Size	N	Mean Entropy	Mean Dynamic Capabilities	Spearman ρ	p-Value
Microenterprise	691	0.8481	2.8950	0.0466	0.221
Small enterprise	90	0.8616	2.8556	0.1116	0.295

), no significant associations were found, though a slightly higher coefficient was observed for small enterprises (ρ = 0.1116).

Table 30. Correlation between entropy and dynamic capabilities by economic sector.

Sector	N	Mean Entropy	Mean Dynamic Capabilities	Spearman ρ	p-Value
1 (Agriculture, Forestry, Livestock, and Fishing)	36	0.8488	2.9105	0.1465	0.394
2 (Mining and Quarrying)	17	0.9038	2.9281	0.0928	0.723
3 (Manufacturing Industries)	89	0.8378	2.9963	0.0585	0.586
4 (Trade)	239	0.8396	2.9163	0.0361	0.579
5 (Construction)	35	0.8454	2.7111	−0.0702	0.689
6 (Services)	365	0.8571	2.8612	0.0804	0.125

presents the correlation between AI adoption entropy and the development of dynamic capabilities across economic sectors. In all cases, the Spearman coefficients are low and statistically non-significant (p > 0.05), with one industry—construction—showing a slightly negative, yet inconclusive, correlation.

These results confirm that there is no significant association between the diversity of functional AI adoption and the development of dynamic capabilities, at least in the short term. While broader AI use may support transformation, capability development likely depends more on qualitative organizational factors—such as internal coordination, leadership, or learning culture—than on the breadth of adoption alone. Additionally, dynamic capabilities may require more time to mature following initial implementation efforts.

5. Discussion

This study analyzed the patterns of artificial intelligence (AI) adoption in Ecuadorian micro and small enterprises (MSEs), revealing a highly heterogeneous technological landscape. The diversity observed in the functions where AI is implemented, captured through entropy measures, reflects a fragmented ecosystem, in which exploratory initiatives coexist with minimal or symbolic adoption. This confirms the findings of Ayinaddis [3] and Bettoni et al. [22], who describe MSEs in emerging economies as technologically uneven, constrained by limited resources and structural gaps.

The results also reinforce findings from prior studies focused on the Ecuadorian context. Jara Obregón and Naspud Espinoza [13], Murillo Valverde et al. [14], and Pérez-Campdesuñer et al. [6] demonstrate that AI use is primarily concentrated in customer-oriented functions—especially marketing—utilizing tools such as social media automation, content generation, and market analytics. These studies also report limited use of AI in internal processes, such as finance, logistics, human resources, and quality control. Our analysis confirms this segmentation, but adds a broader perspective by incorporating nine value chain functions, highlighting not only the frequency but also the diversity and intensity of AI adoption across firm sizes and sectors.

The entropy values reveal that sectors such as mining, agriculture, and services are in an active phase of technological exploration, displaying high internal variability. This supports the idea, proposed by Anwar et al. [36] and Ayinaddis [3], that sectors exposed to greater volatility or competitive pressure tend to adopt AI more dynamically. However, our findings also emphasize that even within highly exploratory sectors, adoption is far from homogeneous. Sectoral affiliation alone does not predict technological maturity—firm-level differences play a critical role.

A key contribution of this study is the identification of a significant gap between perception and practice. While most entrepreneurs perceive their use of AI as strategically differentiating, the functional configurations adopted often correspond to symbolic or tactical patterns. Around 23% of the firms either overestimate or underestimate their level of adoption. This perceptual mismatch may be attributed to conceptual ambiguity—especially regarding what constitutes “intensive” or “strategic” use of AI—or to low levels of digital literacy. As noted by Crockett et al. [4] and Le Dinh et al. [21], a clear understanding of AI capabilities and technological management skills is essential for conscious and effective implementation. These findings underscore the importance of complementing self-reported data with objective indicators that enable more accurate assessments of technological integration.

Consistent with the broader literature, the study identifies several structural barriers to AI adoption, including a shortage of qualified personnel [13], high implementation costs [3,62], infrastructural deficiencies [22], a lack of understanding of AI’s strategic potential [14], and resistance to organizational change [4]. These obstacles are not exclusive to Ecuadorian MSEs; instead, they mirror patterns observed in other emerging economies [46,50], suggesting the existence of globally shared constraints that limit the digital transformation of micro and small enterprises (MSEs).

However, the findings are not exclusively negative. A high proportion (70.6%) of non-adopting firms express favorable intentions toward future AI implementation. This supports the observations of Jara Obregón and Naspud Espinoza [13] and Murillo Valverde et al. [14], who report an increasing openness and interest in digital technologies among Ecuadorian entrepreneurs. However, turning this interest into tangible adoption will require targeted interventions, such as tailored training programs, flexible financing models, and support structures that reduce both technical and economic barriers to entry.

Additionally, the analysis reveals that adoption strategies vary by firm size. Microenterprises prioritize affordable, low-complexity tools—such as chatbots and time optimization systems—while small enterprises are more likely to invest in analytics, automation, and cross-functional integration. These patterns underscore the need for differentiated support strategies tailored to firms’ maturity levels and available resources. In this regard, the early proposals by Graubard [63] remain relevant: rather than pushing for complete digitalization, MSEs should follow an incremental approach, developing AI applications progressively, starting with narrow-use expert systems, and scaling based on proven value and internal capabilities. High entropy groups exhibit heterogeneous adoption patterns, consistent with experimentation or fragmented implementation. Low-entropy groups exhibit more predictable usage, but the level of adoption in such cases must be evaluated using the mean adoption score to avoid misinterpreting concentration as a limitation. Moreover, he emphasizes that technological solutions must be adapted to each organization’s structure and strategic goals, avoiding one-size-fits-all tools that often fail in real implementation.

Taken together, the findings of this study contribute to a deeper understanding of the complex and uneven paths through which MSEs engage with AI. The coexistence of aspiration and limitation, of strategic vision and partial execution, paints a realistic picture of digital transformation in small business ecosystems. Recognizing these dynamics is essential for designing public policies and business development programs that move beyond technological optimism and instead focus on building the structural, human, and organizational conditions necessary for sustainable and effective AI integration [64,65].

5.1. Study Limitations

This study has several limitations that should be acknowledged when interpreting its findings. First, the use of a quota and self-selection sampling design—although effective in ensuring structural representativeness by sector, region, and firm size—may introduce selection bias. Firms more interested in or familiar with AI are likely overrepresented, which could influence the results regarding adoption patterns and perceived strategic value. Additionally, although the number of small enterprises (n = 90 adopting firms) is sufficient for descriptive and comparative analysis, it remains relatively modest compared to the microenterprise subgroup. This imbalance reflects the actual structure of Ecuador’s business fabric, but it may constrain the statistical power of some analyses. Future research could benefit from expanding the SE subsample to enhance representativeness and enable more robust inferential comparisons [66,67].

It is also important to note that strict representativeness is not a methodological requirement for entropy measures, which operate on the internal distribution of responses. However, the use of a stratified design in this study enhances the robustness and transferability of the findings by ensuring that the observed diversity patterns accurately reflect the actual structural heterogeneity of Ecuadorian MSEs, rather than being biased toward overrepresented groups [68].

Second, the analysis is restricted to firms that reported partial or intensive AI adoption. As such, the conclusions cannot be generalized to the entire population of MSEs, particularly those that have not yet adopted AI. Excluding non-adopters limits the ability to analyze barriers to entry or the factors delaying technological integration [69].

Third, the data relies on self-reported responses, which may be subject to perceptual bias or inaccuracies, particularly regarding the intensity and strategic impact of AI use. This is especially relevant in contexts with low digital literacy or a limited understanding of AI capabilities, which can affect the validity of strategic self-assessments [70].

Fourth, while the use of Shannon entropy offers an innovative quantitative measure of functional diversity in adoption, its interpretation depends on assumptions about the quality of response distribution and the consistency of categorical definitions. Differences in how respondents interpret functional boundaries or adoption levels may influence entropy calculations and their subsequent implications. Additionally, the definition of adoption categories (non-, partial, intensive) may shape entropy outcomes, and future research could incorporate sensitivity analyses to test the robustness of these categorizations [71].

Finally, the cross-sectional design provides only a snapshot of adoption at a given point in time. Longitudinal studies are needed to trace how adoption breadth and depth evolve across stages and over time.

A further methodological limitation is that the entropy-based approach is primarily descriptive in nature. While it captures heterogeneity and identifies adoption patterns, it does not explain the causal mechanisms underlying these distributions. Future research could address this gap by combining entropy with explanatory approaches, such as Qualitative Comparative Analysis (QCA) to identify causal configurations, multivariate models (e.g., SEM, regression, predictive analytics) to test the simultaneous effect of organizational and environmental factors, or qualitative case studies to uncover managerial and institutional mechanisms not visible in survey data [72].

Beyond sample size and stratification, the external validity of our findings depends on the structural characteristics of the Ecuadorian context—namely, the prevalence of microenterprises, fragmented digitalization, and pronounced regional asymmetries. For this reason, the results should not be generalized mechanically to all countries or industries. Instead, they are most transferable to other emerging economies or sectors that display similar patterns of heterogeneity and gradual adoption. This highlights that generalization depends less on statistical representativeness and more on contextual similarity.

5.2. Practical and Policy Implications

The findings of this study have important implications for designing inclusive and context-sensitive digital transformation strategies in emerging economies.

First, training programs should be differentiated not only by firm size or sector, but also by the digital maturity level and functional area of intended AI application. Customized educational pathways—whether focused on customer-facing functions, internal processes, or strategic management—can improve alignment between organizational needs and technological capabilities.

Second, the development of flexible financing mechanisms is crucial for reducing entry barriers. Public–private models that offer access to AI tools through progressive schemes—such as phased implementation, subscription-based services, or pay-per-use models—can foster experimentation without requiring high upfront investments.

Third, the creation of regional technology transfer centers is recommended. These centers should function as articulation hubs among MSEs, universities, and technology providers, facilitating not only the transfer of tools and know-how but also the co-development of sector-specific solutions. In doing so, they would help close the gap between strategic perception and effective implementation.

Fourth, regulatory and ethical frameworks must take into account the functional heterogeneity observed in AI adoption. Current debates on AI regulation often assume a uniform level of technological sophistication; however, the reality of MSEs in emerging economies is far more diverse. Tailored standards that consider firms’ actual capacities and risks are needed to ensure both feasibility and ethical alignment.

Lastly, the analytical framework proposed, based on the integration of functional entropy and strategic self-assessment, offers a transferable model for diagnosing AI adoption profiles in other national contexts, particularly in emerging economies with similarly heterogeneous productive structures and MSE-dominant ecosystems. However, its applicability is conditioned by several factors: it is most effective where adoption is fragmented and uneven, reflecting early experimentation rather than consolidated integration, as is often the case in more digitally mature economies where recalibration may be necessary. Its validity depends on precise functional categorizations, consistent interpretation of adoption levels (partial vs. intensive), and access to firm-level data across value chain activities. These conditions may not be met in regions with limited survey infrastructure or low digital literacy. Furthermore, the self-assessment component relies on respondents’ ability to accurately perceive and report their strategic posture, which can be influenced by cognitive biases or limited exposure to advanced digital strategies. Therefore, while the framework is adaptable to contexts with structural and institutional profiles like Ecuador’s, its application should be accompanied by contextual validation and, where possible, qualitative triangulation to ensure interpretive accuracy and policy relevance.

Beyond Ecuador, the analytical framework developed here requires adaptation to local contexts. The main parameters to be recalibrated are: (a) the functional categories considered relevant for adoption (e.g., sector-specific value chain activities), (b) the adoption levels defined (e.g., non-use, exploratory, intensive), and (c) the entropy thresholds, which may shift depending on the distribution of responses in a new sample. The contrast observed within Ecuador itself—between the Coastal region, where adoption concentrates in commercial and logistics functions, and the Highlands, where adoption is more aligned with operations and efficiency—illustrates how regional differences guide such adaptation. These examples highlight that generalization is possible but requires tailoring to contextual realities.

Future research could extend this model by tracking the evolution of adoption patterns over time, examining how they relate to performance, innovation, or sustainability outcomes, and identifying the mechanisms that enable successful transitions from perception to strategic integration.

To translate these recommendations into practice, further detail is required regarding implementation. Training programs can be designed in modular formats with differentiated levels (introductory, intermediate, and advanced), tailored to the firm’s size and maturity. For microenterprises, modules should emphasize awareness and low-cost applications, while small enterprises would benefit from more advanced modules on integration, data governance, and scaling. Grouping firms with comparable levels of digital readiness would further enhance learning outcomes. Likewise, the creation of regional technology transfer centers can be operationalized through multi-stakeholder partnerships: local universities can provide technical expertise, chambers of commerce and industry associations can coordinate outreach, and government agencies (at local and central levels) can contribute infrastructure and policy alignment. Financing could follow a hybrid model, combining public funding (e.g., seed grants, innovation vouchers) with private sector contributions (such as membership fees or co-investments), as well as international cooperation programs. Such collaborative governance reduces dependency on a single actor and strengthens the sustainability of the initiative.

6. Conclusions

This study offers a comprehensive and empirically grounded portrait of artificial intelligence (AI) adoption among Ecuadorian micro and small enterprises (MSEs), revealing a landscape marked by high functional heterogeneity, sectoral divergence, and asymmetries between strategic perception and implementation reality. By applying a novel entropy-based framework and integrating subjective assessments of strategic value, the analysis advances our understanding of how AI technologies are being integrated—selectively, partially, and often experimentally—across diverse business contexts.

More broadly, the entropy-based analytical framework is methodologically portable and adaptable to diverse contexts. Its logic—measuring functional diversity of adoption—remains constant. However, its operationalization can be tailored by redefining functional categories, adjusting adoption levels, and recalibrating thresholds to fit specific industries or countries. Lessons from Ecuador’s own internal contrasts—such as the Coastal region, where adoption concentrates in commerce and logistics, versus the Highlands, where adoption is oriented toward operations and efficiency—illustrate how contextual differences shape the application of the framework. Likewise, in highly digitalized economies, additional functions such as data analytics or platform integration may need to be included, while in lower-income contexts, a simplified categorization (e.g., marketing, transactions, customer service) may suffice. This flexibility ensures both the general applicability of the framework and its capacity to be tailored to local realities, while preserving comparability across countries and industries.

First, the entropy indicators confirm that AI adoption does not follow a linear or homogeneous path. Instead, firms exhibit fragmented and functionally selective integration patterns, with activities such as marketing, operations, and service concentrating most of the innovation efforts. These adoption profiles are neither random nor incidental; instead, they reflect sector-specific rationalities, resource constraints, and perceived strategic opportunities. While some firms experiment with advanced tools such as predictive analytics and process automation, others limit their use to basic or symbolic functions. This finding highlights the need for tailored digital transformation strategies, acknowledging the varying capacities and readiness levels among MSEs.

Second, the study identifies a significant gap between how firms perceive their AI adoption and the strategic potential of their actual configurations. While most firms self-classify their adoption as strategic—particularly in sectors such as services and trade—functional analyses reveal that many of these combinations lack the integration and coherence necessary to generate sustainable competitive advantages. This misalignment is especially pronounced among microenterprises, suggesting that organizational capabilities, not just technological access, are key determinants of strategic impact.

Third, from a regional perspective, both the Andean and Coastal zones are classified as “innovative regions” due to the high entropy observed in their value chain configurations. However, their innovation trajectories differ: the Andean region shows a more fragmented and exploratory pattern, while the Coastal region exhibits a more standardized but still heterogeneous integration. These insights support regionally differentiated policy interventions, such as scaling experimental initiatives in the Andes and promoting technological deepening among lagging firms on the Coast.

Fourth, the analysis of strategic configurations confirms that competitive advantage is not derived from isolated AI uses, but from coherent functional combinations that integrate primary and support activities. High-performing configurations frequently combine operations, marketing, service, and firm infrastructure, even when adoption intensity varies across activities. This suggests that strategic articulation, not maximalist digitalization, is the critical factor—a particularly relevant insight for resource-constrained MSEs.

Fifth, the study reveals that dynamic capabilities remain at an early and fragile stage of development. Although many firms report operational changes and moderate preparedness for future adoption, they face structural limitations in knowledge circulation, learning documentation, and tolerance for experimentation. This organizational rigidity hinders the institutionalization of innovation and prevents AI adoption from evolving into a sustained process of renewal.

Finally, the correlations between functional diversity (entropy) and both perceived strategic value and dynamic capabilities are weak and non-significant across all dimensions analyzed (region, size, sector). This suggests that diversity alone is insufficient to generate higher strategic or adaptive capacity, highlighting instead the importance of qualitative factors such as organizational culture, leadership, and alignment with business objectives.

In summary, this study offers three core takeaways: AI adoption in MSEs is real but uneven: partial, sector-specific, and marked by experimentation; Strategic perception often exceeds actual integration, revealing gaps in organizational capability; and Transforming AI from a tactical tool to a source of competitive advantage requires not only technology but also coherence, support, and organizational change.

These findings have direct implications for policy and practice. Future digital strategies should prioritize differentiated training, progressive financing models, and institutional infrastructures that support not only adoption but the consolidation of dynamic capabilities. The proposed methodological framework can also be applied in other emerging economies to map AI trajectories and design more adaptive and inclusive innovation policies.

Future research should therefore build on this descriptive baseline by integrating entropy-based measures with explanatory methods to gain a deeper understanding of the phenomenon. Such combinations would move beyond identifying patterns to uncovering causal pathways and mechanisms, thereby enhancing both the theoretical contributions and the concreteness of policy implications.