UNIDO WB I4.0 Readiness Dashboard v2.16

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Executive Summary

Comprehensive assessment of I4.0 readiness across Western Balkans

Data Sources: UNIDO Primary Survey (N=203), WB DESI 2024 Report (RCC Western Balkans), Peerally et al. (2022) Research Policy, DIGITA1.XLS macro dataset

This Dashboard provides a comprehensive empirical assessment of Industry 4.0 readiness in the Western Balkans. Implemented within the framework of UNIDO project, "Fostering Industry 4.0 for Green Transition and Circular Economy," the research analyzes data from more than 200 respondents representing three ecosystem actor groups (industrial small and medium-sized enterprises (SMEs), business support organizations (BSOs) and government bodies) across six economies: Serbia, Albania, Bosnia and Herzegovina, Montenegro, North Macedonia, and Kosovo*. The study integrates three theoretical frameworks and empirically tests 17 hypotheses.

📖 What This Means

These three discoveries fundamentally reshape how we should approach industrial transformation in the Western Balkans. The strong association between digital skills and technology adoption (r=0.567) compared to organizational readiness (r=0.343) suggests that human capital investment should precede technology investment. The concentration at foundational stages (74.1%) is consistent with sequential capability-building theory—firms cannot leapfrog stages. The twin transition finding (r=0.454) demonstrates that green and digital agendas are complementary, not competing.

Note: Correlational findings indicate association, not causation. While skills are the strongest predictor, the direction of causality (skills→adoption vs. adoption→skills) cannot be definitively established from cross-sectional data.

So What: WB Steering Platform on Research and Innovation should prioritize skills development over technology subsidies, provide stage-appropriate services (not one-size-fits-all), and integrate green-digital programming to maximize synergies.

📊 BSO Programs vs SME Utilization

BSO Programs Offered: Percentage of Business Support Organizations reporting they offer I4.0 support programs in each category.

SME Utilization Rate: Percentage of SMEs that actually utilized these programs in the past 36 months.

The Gap: Reveals a critical ecosystem efficiency challenge—programs exist but aren't reaching their target audience.

Source: UNIDO (2024) BSO Support Organizations Survey

📈 Digital Skills Distribution

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🎯 Capability Stage Funnel

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🏛️ Government Policy Readiness

Source: UNIDO (2024) Government Institutions Survey

📊 Hypothesis Test Results

Source: UNIDO (2024) BSO Support Organizations Survey

📌 Priority Matrix

Abbreviation Key:

DS = Digital Skills (workforce capability in I4.0 technologies)
OR = Organizational Readiness (strategic commitment, leadership support)
Tech Infra = Technological Infrastructure (IT systems, connectivity, digital tools)
EP = External Pressure (customer demands, regulatory requirements, competitive pressure)
GREEN = Green Orientation (environmental sustainability strategy and practices)
BSO = BSO Support (utilization of Business Support Organization programs)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

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Key Metrics Dashboard

Click any metric for detailed drill-down analysis

139

Manufacturing SMEs

Across 6 Western Balkans economies

39

BSOs

Chambers, hubs, NGOs

25

Government

Ministries and agencies

16/17
Hypotheses
94.1% support rate

22.9%

EEI

EU benchmark: 45-55%

38

WB DESI

EU avg: 52

📖 Understanding the Metrics

Sample Size (203): Multi-stakeholder design enables ecosystem-level analysis. SMEs represent demand-side, BSOs supply-side, Government policy-side. Hypothesis Support (94.1%): Exceptionally high validation rate demonstrates theoretical framework's applicability to WB context. EEI (22.9%): Critically below EU benchmark—indicates systemic coordination failure requiring structural intervention. WB DESI (38): 27% below EU average (52)—the digital gap is real and widening.

So What: The ecosystem shows gaps at every level. Government lacks strategy (8%), BSOs lack capacity (13.5% with strategy), SMEs lack awareness (66.7% unaware). This points less to a technology gap and more to challenges in coordination and alignment.

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Six Core Discoveries

The fundamental insights from this research

1. Skills are the Key

DS (β=0.449) dominates while OR becomes non-significant. Invest in people first.

2. Foundational Reality

74.1% at stages 0-1. Firms cannot skip stages—tiered services needed.

3. Twin Transition Works

GREEN→AD (r=0.454) confirms complementarity. Integrate both agendas.

4. Ecosystem Crisis

EEI = 22.9% vs EU 45-55%. 66.7% SMEs unaware. Systemic coordination failure.

5. Pressure ≠ Capacity

EP→OR (r=0.001, ns). External pressure doesn't build capability. Support over pressure.

6. Skills Mediate

DS mediates 54% of OR→AD. Sequence: Organization → Skills → Technology.

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So What: Bottom Line

What does this mean for the Western Balkans?

📊 Key Driver Correlations

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🎯 Capability Stage Distribution

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🌐 Ecosystem Effectiveness Index

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🔄 Twin Transition Correlation

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🎯 The Core Message

The Western Balkans faces a human capital crisis, not a technology crisis. Our data shows that 70-88% of SME workforces operate at beginner skill levels, yet policy continues to focus on technology subsidies. This is fundamentally misaligned. The ecosystem shows gaps: 92% of government institutions and 86.5% of BSOs lack I4.0 strategies, while 66.7% of SMEs don't know support exists. External pressure alone cannot drive change—it must be paired with capacity building.

Implications by Stakeholder Group

For SMEsInvest in workforce training before technology. Start with foundational skills (data literacy, ERP basics). Don't expect to leapfrog—build capability sequentially.

For BSOsDevelop your own I4.0 strategy first. Build technical capacity to provide implementation support (only 24.3% can currently do this). Meet SMEs where they are.

For GovernmentCreate national I4.0 strategies (only 8% have one). Combine mandates with support. Fund skills, not just equipment. Coordinate across ministries.

Strategic Implications

Phase II FocusSkills assessment tools, training curricula, BSO capacity building. Pilot with 10 SMEs (per prodoc TCO.1). Budget: €540,200 (~€68K expended as of Dec 2025 per mid-term report).

Phase III FocusScale to 50+ SMEs (aspirational), unified portal, coordination mechanism, impact evaluation. Budget: €3-5M over 3-5 years from 2029.

Success MetricsEEI from 22.9% to 30%+. SME awareness from 33% to 50%+. Skills beginner rate reduced by 20 percentage points.

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Strategic Recommendations

Evidence-based priorities for Western Balkans economies

🎯 Three Strategic Priorities for Western Balkans

Based on comprehensive analysis of stakeholders across the Western Balkans across the Western Balkans, these priorities emerge as most critical for accelerating Industry 4.0 adoption.

Evidence Base: Recommendations synthesize findings from Secondary Data (DESI 2024, macro indicators) and Primary Data (surveys, focus groups) through the lens of Peerally-Santiago capability framework, Institutional Theory, and Twin Transition literature.

Analytical Discussion & Implications

The executive summary findings reveal a critical paradox in the Western Balkans' Industry 4.0 landscape: while the region demonstrates substantial awareness of digital transformation imperatives (as evidenced by the 94.1% hypothesis support rate and multi-stakeholder engagement), the underlying capability infrastructure remains fundamentally underdeveloped. The concentration of 74.1% of SMEs at foundational capability stages (Stages 0-1) indicates that the regional manufacturing base has not yet established the prerequisite conditions for advanced technology adoption.

Theoretical Implications

The strong support for research hypotheses provides robust empirical validation for the Peerally-Santiago framework on Fourth Industrial Revolution technological capabilities in developing economies. Particularly significant is the confirmation of digital skills as the dominant predictor (β=0.449), which contradicts prevailing technology-push narratives and aligns with capability-based theories of industrial development. The mediation effect (54% of OR→AD relationship) suggests that organizational readiness operates through skills development rather than independently, fundamentally reframing how policy interventions should be designed.

Policy Implications

These findings carry substantial policy implications for UNIDO and regional development agencies. The weak effect of external pressure (EP→OR r=0.001) suggests that institutional coercion mechanisms—including EU regulatory alignment requirements and supply chain pressures—are insufficient drivers of transformation in the absence of internal capabilities. This implies that policy frameworks emphasizing compliance and external incentives must be complemented by substantive investments in human capital development and organizational capability building. The twin transition finding (r=0.454) provides empirical justification for integrated green-digital programming rather than siloed approaches.

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Dashboard Beginning

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Six Core Findings

The fundamental insights transforming WB industrial policy

Data Sources: SME Survey , BSO Survey , Government Survey , Focus Groups (Multiple sessions across selected economies)

📖 Interpreting These Findings

These six findings represent the core empirical contributions of this research. Each finding is statistically significant and has been validated through multiple methods (correlation, regression, mediation analysis). The findings are interconnected: skills dominance (1) and mediation (6) explain why organizational readiness alone isn't enough; foundational reality (2) explains why technology-push strategies fail; twin transition (3) provides the policy integration framework; ecosystem crisis (4) explains why support usage doesn't correlate with adoption; pressure gap (5) reveals why regulatory mandates without support fail.

🎯 The Six Core Findings (Detailed)

1. Skills Dominance

Digital skills (r=0.567***) is the strongest predictor of technology adoption - nearly twice as strong as organizational readiness (r=0.343***).

76.3%

Zero training

2. Foundational Stage Lock

71% of SMEs remain at Stage 0-1 (pre-4IR or basic digitalization). Only 7.9% have reached advanced integration stages.

EEI: 22.9%

Tech adoption

3. The 64-Point Gap

Support mechanisms are available (88%) but usage is only 21% - a 67pp gap. Awareness accounts for half of this gap.

66.7%

Unaware of programs

4. Perception Cascade

Government overestimates SME readiness by ~50pp. BSOs are more realistic but still overestimate by ~20pp.

50pp

Skills perception gap

5. Anti-Leapfrogging Confirmed

Foundational tech (CAD/CAM: 37%) >> Advanced tech (VR/AR: 9%). The Peerally-Santiago sequential model is validated.

28pp

Foundation-Advanced gap

6. Twin Transition Link

Digital and green strategies correlate at r=0.454***. But only 8.6% have both - integration requires deliberate effort.

r=0.454

Digital-Green correlation

✅ Hypothesis Testing Summary

16
Hypotheses Supported
Out of 17 tested

1

Partially Supported

Green-digital integration

94.1%

Support Rate

Core model validated

Strongest Evidence For:

H2: Skills → Technology (r=0.567***)
H1: Sequential capability building
H3: Internal > External capabilities
H4: Retrofitting as primary strategy
H5: BSO supply-demand mismatch

🎯 Technology Adoption Radar

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📊 Capability Distribution

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

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Skills Dominance Finding

The most important discovery of this research

📊 Skills Gap Radar

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

Why Skills Matter Most

r = 0.567***
DS → AD Correlation

Predictor	Bivariate r	Regression β	Interpretation
Digital Skills	0.567***	0.449***	Dominant
Green Strategy	0.454***	0.284***	Independent
Org. Readiness	0.343***	0.105 ns	Mediated
External Pressure	0.273**	0.130 ns	Spurious?

Skills Crisis: 70-88% Beginner

OT Cybersecurity87.7% Beginner

AI/ML84.2% Beginner

PLC/Robotics80.7% Beginner

Data Engineering72.8% Beginner

Cloud/DevOps70.2% Beginner

Crisis Level: The WB faces a fundamental human capital deficit. These beginner rates compare unfavorably to EU where at least basic digital skills stand at 56% (WB: 32% per DESI 2024).

Key Insight: When controlling for other variables, Digital Skills (β=0.449) is 4x stronger than Organizational Readiness (β=0.105). OR becomes non-significant, indicating skills mediate its effect.

Implications for Action

For SMEsPrioritize workforce training before equipment purchases. Start with foundational domains (data literacy, ERP basics) before advanced (AI/ML, cybersecurity).

For BSOsDevelop modular training curricula across 6 domains. Build ToT (Train-the-Trainer) capacity. Establish skills certification pathway.

Regional Priority:Create standardized skills assessment tool. Partner with VET institutions for sustainable delivery. Target 20pp reduction in beginner rates by 2027.

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Stage Distribution

Evidence for sequential capability building

Capability Stage Distribution

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

Visual: The pyramid shape confirms Peerally-Santiago's prediction—capability building is sequential. The broad base (74.1% at stages 0-1) narrows dramatically at advanced stages (7.9% at 3-4).

Stage Details (Peerally-Santiago Framework)

Stage	Description	N	%	Cumulative
0	Not Engaged	55	39.6%	39.6%
1	Considering	48	34.5%	74.1%
2	Piloting	26	18.7%	92.8%
3	Implementing	8	7.0%	99.1%
4	Operational	1	0.9%	100%

Source: Peerally, J.A., Santiago, F., De Fuentes, C., & Moghavvemi, S. (2022). Towards a firm-level technological capability framework. Research Policy, 51(7), 104563.

74.1% at Foundational Stages: What This Means

This distribution empirically validates the Peerally-Santiago anti-leapfrogging hypothesis. WB SMEs cannot skip sequential stages of capability building. Pushing advanced technologies (AI, robotics) to Stage 0-1 firms leads to implementation failure and wasted resources. Regional programs must provide stage-appropriate services: awareness for Stage 0, skills for Stage 1, implementation support for Stage 2+.

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Sample Composition

Three stakeholder groups across five countries

⚠️ Important Sample Limitations

Serbia Underrepresented: Serbia (40% of WB6 GDP) has only 7% of SME sample (10/139). Regional findings may not fully represent the largest WB economy.

GOV Sample Bias: Montenegro accounts for 68% of government responses (17/25). Government perspectives are primarily Montenegrin views.

Kosovo Excluded: Kosovo has no primary survey respondents. Kosovo data is included only via secondary DESI indicators.

Data Sources: Industry_4_0_Readiness_Assessment_for_Manufacturing_SMEs1139.xlsx, The_Role_of_Support_Organizations_in_Advancing_Industry_4_0_Readiness139.xlsx, Assessment_of_Government_Support_for_Industry_4_0125.xlsx | Collection period: 2024

139

Manufacturing SMEs

Detailed assessment of readiness, skills, adoption, and performance across food, metal, textile, and wood sectors.

39

Business Support Organizations

Chambers, incubators, NGOs, innovation hubs providing I4.0 services to regional SMEs.

25

Government Institutions

Ministries and agencies responsible for industrial policy and digital transformation.

📖 Why Multi-Stakeholder Design Matters

This research employs a triangulated multi-stakeholder approach that enables ecosystem-level analysis not possible with single-actor studies. SMEs represent the demand side (those needing support), BSOs the supply side (those providing support), and Government the policy side (those creating the enabling environment). By surveying all three, we can identify supply-demand mismatches and coordination failures that explain why EEI is so critically low (22.9%).

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Geographic Distribution

Sample across 5 Western Balkans countries (Kosovo* via secondary data only)

Sample by Country

Country	SMEs	BSOs	GOV	Total	% of Sample
North Macedonia	42	15	4	61	30.0%
Albania	41	10	1	52	25.6%
Bosnia & Herzegovina	37	5	2	44	21.7%
Montenegro	9	3	17	29	14.3%
Serbia	10	6	1	17	8.4%
Kosovo*	—*	—*	—*	—*	—*
Total	139	39	25	203	100%

* This designation is without prejudice to positions on status. DESI 2024 data for all six economies is included in analysis.

📖 Geographic Coverage Notes

North Macedonia shows highest sample engagement (30.0%), reflecting strong partnership with local BSO networks. Serbia's lower SME count (10) is offset by strong BSO representation (6). Montenegro's high government count (17) reflects successful ministry engagement. All six Western Balkans economies are represented in secondary data analysis using DESI 2024 and macro indicators.

So What: Despite variation in sample sizes by country, ANOVA testing shows no significant country effect on key constructs (Technology Adoption: F=1.89, p=.118), suggesting findings generalize across the WB region.

* This designation is without prejudice to positions on status, and is in line with UNSCR 1244/1999 and the ICJ Opinion on the Kosovo declaration of independence. 6 economies are represented in this analysis; secondary data from DESI 2024 is included throughout.

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Theoretical Framework Integration

Three frameworks explaining I4.0 adoption in developing countries

Primary Sources: Peerally et al. (2022) Research Policy; DiMaggio & Powell (1983) ASR; Scott (2014) Institutions and Organizations; Muench et al. (2022) Twin Transition; EU Green Deal (2019)

This study is anchored in the Technology–Organisation–Environment (TOE) framework (Tornatzky and Fleischer, 1990), which provides an organising lens for understanding how technological, organisational, and environmental contexts interact to shape technology adoption. Within this umbrella, this research integrates three complementary theoretical frameworks to explain how developing country SMEs build technological capabilities, respond to institutional pressures, and navigate dual green-digital transformation. Together, these frameworks generate 17 testable hypotheses with a 94.1% empirical support rate, demonstrating strong theoretical validity for the WB context.

📊 Peerally-Santiago Framework

Sequential capability building for 4IR. Firms cannot leapfrog foundational stages. Skills are central enablers of technology adoption.

Peerally, J.A., Santiago, F., De Fuentes, C., & Moghavvemi, S. (2022). Research Policy, 51(7), 104563.

🏛️ Institutional Theory

External pressures (regulatory, normative, mimetic) shape organizational responses. But pressure ≠ capacity in resource-constrained contexts.

DiMaggio, P.J., & Powell, W.W. (1983). ASR, 48(2), 147-160; Scott, W.R. (2014). Sage.

🌿 Twin Transition

Green and digital transformations are complementary, not competing. Sustainability drives digital adoption and vice versa.

Muench, S., et al. (2022). JRC Science for Policy; EU Green Deal (2019).

🔲 TOE Framework Mapping

TOE provides the organising lens; the three frameworks provide causal mechanisms within T/O/E dimensions.

TOE Dimension	Dashboard Constructs	Theoretical Mechanism
Technology (T)	TA (Technology Adoption), Technology complexity/stage	Peerally-Santiago: Sequential capability stages, technology-specific readiness thresholds
Organisation (O)	DS (Digital Skills), OR (Organizational Readiness), GREEN (Green Strategy)	Peerally-Santiago: Internal capability building; skills as foundation for adoption
Environment (E)	EP (External Pressure), EEI (Ecosystem Enablement Index), BSO, GS (Government Support)	Institutional Theory: Coercive/mimetic/normative pressures; Twin Transition: Policy context

Framework: Tornatzky and Fleischer (1990)

📖 Why Three Frameworks?

No single theory adequately explains I4.0 adoption in developing country contexts. Peerally-Santiago explains how capabilities are built (sequentially, with skills as enablers). Institutional Theory explains why firms respond to external pressures—and crucially, when they don't. Twin Transition explains the policy context linking digital and green agendas. Together, they provide a comprehensive lens for understanding WB SME behavior and designing effective interventions.

So What: This integrated framework demonstrates that technology-push policies fail (Peerally-Santiago), regulatory mandates without support fail (Institutional), and siloed green vs. digital programs are suboptimal (Twin Transition). Regional programs must address all three dimensions.

📚 Theoretical Framework Integration

Three complementary theories explaining I4.0 adoption in developing economies

Peerally-Santiago (2022)

Sequential capability building for 4IR. Firms cannot leapfrog foundational stages. Retrofitting existing systems is primary strategy.

71%

At Stage 0-1

Institutional Theory

Coercive, mimetic, and normative pressures drive adoption. In weak institutional environments, internal capabilities dominate.

r=0.12

EP→Tech (weak)

Twin Transition

Digital and green transitions are complementary. Digital enables sustainability tracking and optimization.

r=0.45***

Digital↔Green

✅ Framework Validation Results

Theory	Core Proposition	WB Evidence	Status
Peerally-Santiago	Sequential stages required	71% at foundational stages	✔ Validated
Peerally-Santiago	Limited leapfrogging scope	Breadth-depth coupling confirmed	✔ Validated
Peerally-Santiago	Skills as foundation	r=0.567*** (strongest predictor)	✔ Validated
Institutional	External pressures drive adoption	r=0.12 (weak, non-significant)	⚠ Boundary condition
Twin Transition	Digital-green complementarity	r=0.454*** correlation	✔ Validated

💡 Key Theoretical Insights for Policy

What Works

Skills-first investment strategy
Sequential capability building programs
Retrofitting support over greenfield
Integrated digital-green programs

What Doesn't Work

Expecting regulatory pressure to drive adoption
Promoting advanced tech without foundations
Separate digital and green strategies
Top-down without SME engagement

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Key Theoretical Constructs

Core variables measured in this research

🔲 TOE (Technology–Organisation–Environment) as Organising Framework

The Technology–Organisation–Environment (TOE) framework, developed by Tornatzky and Fleischer (1990), provides a comprehensive lens for understanding technology adoption at the firm level. TOE identifies three contextual dimensions that jointly influence adoption decisions: (1) the technology context including characteristics of available technologies; (2) the organisational context including firm resources, capabilities, and structures; and (3) the environmental context including external pressures, industry conditions, and institutional factors.

Operationalisation in this Dashboard

Technology (T): TA (Technology Adoption breadth/depth), technology complexity stages, I4.0 readiness levels
Organisation (O): DS (Digital Skills), OR (Organisational Readiness), GREEN (Green Strategy), internal resources
Environment (E): EP (External Pressures), EEI (Ecosystem Enablement Index), BSO support, GS (Government Support), Export orientation

α=0.920

Organizational Readiness (OR)

5-item construct measuring strategic readiness for I4.0 transformation

α=0.768
Digital Skills (DS)
6-domain technical skills assessment across workforce

α=0.836

Technology Adoption (AD)

9-technology framework from cloud to blockchain

α=0.765

External Pressure (EP)

4-item institutional pressure construct

Construct Reliability Summary

Construct	Items	Cronbach's α	Threshold	Status	Theoretical Source
Organizational Readiness	5	0.920	≥0.70	Excellent	Peerally-Santiago
Digital Skills	6	0.768	≥0.70	Good	Peerally-Santiago
Technology Adoption	9	0.836	≥0.70	Very Good	UNIDO 4IR Framework
External Pressure	4	0.765	≥0.70	Good	Institutional Theory
Green Strategy	3	0.812	≥0.70	Very Good	Twin Transition

Interpretation: All constructs exceed the 0.70 threshold for acceptable reliability (Nunnally, 1978). OR shows excellent reliability (α=0.920), indicating items measure the same underlying construct consistently. These reliability scores support valid hypothesis testing.

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Core Propositions

Peerally-Santiago framework for 4IR capability building

Primary Source: Peerally, J.A., Santiago, F., De Fuentes, C., & Moghavvemi, S. (2022). Towards a firm-level technological capability framework to endorse and actualize the Fourth Industrial Revolution in developing countries. Research Policy, 51(7), 104563. https://doi.org/10.1016/j.respol.2022.104563

Framework Overview

The Peerally-Santiago framework builds on Lall (1992) and Bell & Pavitt (1995) to address the refined set of human and organizational activities required for 4IR technology uptake. It proposes four levels of increasingly complex technological capabilities across six thematic functions. The framework emphasizes that capability building is cumulative and sequential—firms cannot leapfrog foundational stages.

Proposition 1: Sequential Building

Capabilities must be built sequentially—firms cannot leapfrog foundational stages. Digital transformation "implies technological upgrading in contexts in which domestic firms primarily use, often ineffectively, or are stuck in a trap of using technologies and production processes that are characteristic of the 3IR."

Peerally et al. (2022), p. 2

Proposition 2: Skills Centrality

Human capital is the primary enabler. "It requires purposive and sustained investment and learning by firms, which encompasses all the ways in which firms acquire the knowledge, skills and other cognitive resources needed to adopt more complex 4IR technologies."

Peerally et al. (2022), p. 12

Proposition 3: Ecosystem Dependency

Individual firm capabilities depend on ecosystem support. "Endorsing the 4IR necessitates the improvement of inter-firm interactions for technology access and affordability and for closing technological, including digital capability gaps."

Peerally et al. (2022), p. 3

Proposition 4: Context Specificity

Developing country contexts require adapted approaches. "Firms in developing countries face additional exacerbated systemic conditions and challenges, making it difficult for them to fully endorse the 4IR."

Peerally et al. (2022), p. 2

Implications for WB Ecosystem

For SMEsDon't expect to leapfrog. Build foundational capabilities (basic automation, ERP, data literacy) before attempting advanced technologies (AI, robotics, blockchain).

For BSOsProvide tiered services matched to SME stages. Stage 0-1 firms need awareness and foundational skills; Stage 2+ firms need implementation support.

Regional Priority:Develop stage classification tool. Create service packages for each stage. Don't push advanced tech to unprepared firms—it wastes resources and creates frustration.

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Capability Stages

Five-stage progression model

Stage	Name	Characteristics	WB N	WB %	Service Need
0	Not Engaged	No I4.0 awareness; traditional operations; analog processes	55	39.6%	Awareness, success stories
1	Considering	Awareness exists; exploring options; seeking information	48	34.5%	Readiness assessment, roadmap
2	Piloting	Testing specific technologies; small-scale experiments	26	18.7%	Technical guidance, vendor matching
3	Implementing	Active rollout; organizational change underway	9	6.5%	Scale-up support, integration
4	Operational	Full integration; continuous optimization; innovation	1	0.7%	Advanced optimization, peer learning

Interpretation: The WB distribution validates Peerally-Santiago's prediction: 74.1% of SMEs are at foundational stages (0-1), with only 7.9% at advanced stages (3-4). This pyramid shape demonstrates that capability building is sequential—the broad base gradually narrows as firms progress.

📖 What Stage Distribution Means for Policy

This distribution has profound policy implications. One-size-fits-all programs fail because Stage 0 firms need awareness while Stage 2 firms need implementation support—entirely different interventions. The 39.6% at Stage 0 ("Not Engaged") represents the largest group—these firms need basic awareness campaigns before any technical intervention. The 34.5% at Stage 1 ("Considering") are aware but lack skills and roadmaps—they need foundational training and assessment tools.

So What: Regional programs must develop a stage classification tool and create differentiated service packages. Pushing advanced technology (AI, robotics) to Stage 0-1 firms is counterproductive—it creates frustration without building capability.

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Empirical Validation in WB

How our data supports the framework

✔

Sequential Building

74.1% at foundational stages validates anti-leapfrogging

✔

Skills Centrality

DS mediates 54% of OR→AD; β=0.449 dominates

✔

Ecosystem Dependency

EEI=22.9% shows underdeveloped support landscape

Validation Evidence Summary

Proposition	Hypothesis	Evidence	Result	Confidence
Sequential Building	H10: Stage distribution pyramid	74.1% at 0-1; 7.9% at 3-4	Supported	Very High
Skills Centrality	H2: DS→AD; H4: Mediation	r=0.567***; 54% mediation	Supported	Very High
Ecosystem Dependency	H17: EEI < EU benchmark	22.9% vs 45-55%	Supported	High
Context Specificity	H7: EP→OR boundary	r=0.001 (ns)	Supported	High

Interpretation: All four core propositions of the Peerally-Santiago framework are empirically validated in the WB context. The single unsupported hypothesis (H7: EP→OR) actually confirms their context-specificity proposition—developing country firms face unique constraints that break the pressure→capacity link.

Analytical Discussion & Implications: Peerally-Santiago Framework

The Peerally-Santiago framework represents a significant departure from mainstream Industry 4.0 discourse, which often implicitly assumes that developing countries can adopt frontier technologies by replicating developed-country implementation models. The framework's emphasis on sequential capability building challenges this assumption by grounding technology adoption in the evolutionary economics tradition.

Anti-Leapfrogging Validation

The concentration of 74.1% of Western Balkans SMEs at Stages 0-1 provides compelling empirical support for the anti-leapfrogging hypothesis. These firms have not yet established the foundational digital infrastructure that would enable meaningful engagement with advanced Industry 4.0 technologies.

Policy Reorientation

This framework fundamentally reorients policy recommendations away from technology-push strategies toward capability-building approaches. Rather than establishing AI centres or providing subsidies for advanced technology acquisition, the framework suggests that policy resources should prioritize foundational capability development.

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Three Pillars of Institutional Theory

DiMaggio & Powell (1983); Scott (2014)

Primary Sources: DiMaggio, P.J., & Powell, W.W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 48(2), 147-160; Scott, W.R. (2014). Institutions and Organizations: Ideas, Interests, and Identities. Sage Publications.

🏛️ Regulative Pillar

Formal rules, laws, and sanctions. In WB context: EU accession requirements, digitalization policies, environmental regulations, industry-specific mandates.

Coercive isomorphism (DiMaggio & Powell, 1983)

📋 Normative Pillar

Professional standards and industry norms. In WB context: ISO standards, sector best practices, buyer requirements, industry association guidelines.

Normative isomorphism (DiMaggio & Powell, 1983)

🔄 Cultural-Cognitive Pillar

Shared beliefs and mimetic behavior. In WB context: Competitor imitation, success story diffusion, "everyone is doing it" perception.

Mimetic isomorphism (DiMaggio & Powell, 1983)

📖 Institutional Theory in the WB Context

Institutional theory predicts that organizations conform to external pressures to gain legitimacy and resources. In the WB, these pressures include EU accession requirements (regulative), export market standards (normative), and competitor digitalization (mimetic). Our External Pressure (EP) construct captures all three pillar types. However, the critical WB finding is that pressure alone doesn't build capacity—the EP→OR relationship is essentially zero (r=0.001).

📊 Institutional Pressure Analysis (Detailed)

Coercive Pressures

Regulatory requirements29.5%

Customer mandates38.1%

Supply chain demands35.8%

Mimetic Pressures

Competitor actions42.4%

Industry benchmarks33.8%

Case study learning14.4%

Normative Pressures

BSO guidance8.6%

Industry associations12.2%

Professional networks15.8%

⚠️ Boundary Condition: Weak Institutional Environment

Classical institutional theory predicts external pressures should drive adoption. However, in the Western Balkans:

External Pressures → Technology Adoption: r = 0.12 (non-significant)
Organizational Readiness → Technology Adoption: r = 0.343*** (significant)
Digital Skills → Technology Adoption: r = 0.567*** (highly significant)

Interpretation: In weak institutional environments, internal capabilities dominate over external pressures. This has major policy implications: focus on building SME capabilities rather than creating more regulatory pressure.

2

External Pressure Measurement

EP construct operationalization (α=0.765)

External Pressure Items

Customer requirements for digital systemsM=3.45

Competitor digital adoption pressureM=3.28

Supply chain digitalization demandsM=3.12

Regulatory compliance pressureM=2.95

Interpretation: Customer pressure (M=3.45) is the strongest, followed by competitor (M=3.28) and supply chain (M=3.12). Regulatory pressure is lowest (M=2.95), reflecting the policy vacuum—only 8% of government institutions have I4.0 strategies.

3

Boundary Condition Discovery

Critical finding: Pressure ≠ Capacity

⚠️ H7: EP → OR: NOT SUPPORTED (r = 0.001, p = 0.991)

External pressure shows zero correlation with organizational readiness. This is the single unsupported hypothesis and reveals a critical boundary condition for institutional theory in developing country contexts.

📖 What This Means Theoretically

Standard institutional theory predicts that external pressures drive organizational response. Our finding (r=0.001, essentially zero) identifies a boundary condition: in resource-constrained contexts, firms may perceive pressure without having the capacity to respond. WB SMEs feel the pressure to digitalize—customers demand it, competitors are doing it—but they lack the skills, finance, and guidance to act. This is a fundamental insight for policy design.

Implications for WB Policy

For GovernmentRegulatory mandates alone will fail. Every new requirement must be paired with capacity-building support. "Comply or else" without "here's how" creates frustration, not transformation.

For BSOsDon't assume firms will seek help when pressured. Many firms freeze when overwhelmed. Proactive outreach is essential—meet them where they are.

Regional Priority:Design interventions that build capacity before or alongside pressure. The sequence matters: capability first, then requirement. This is why skills-first programming is essential.

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Twin Transition Concept

Green and digital transformation as complementary forces

Primary Sources: European Commission (2019). The European Green Deal. COM(2019) 640; Muench, S., et al. (2022). Towards a green and digital future. JRC Science for Policy Report; Dual Transition literature review.

The Twin Transition framework emerges from EU Green Deal policy, recognizing that digital and environmental transformations are interconnected. Digital technologies enable green outcomes (efficiency monitoring, process optimization, supply chain transparency) while green strategies create demand for digital solutions (environmental reporting, circular economy tracking, energy management). This policy framework is particularly relevant for WB given EU accession requirements.

                    GREEN → Digital Mechanisms
                    Environmental monitoring requires IoT sensors and data systems
Energy optimization needs predictive analytics
Circular economy demands traceability and tracking
Green reporting requires digital infrastructure
Carbon footprint calculation needs data integration

                

                    Digital → GREEN Mechanisms
                    Process optimization reduces waste and emissions
Predictive maintenance extends equipment lifespan
Supply chain visibility enables sustainable sourcing
Digital twins simulate environmental impact
Smart manufacturing minimizes resource use

                

🔗 Digital-Green Correlation Matrix

Digital Dimension	Green Strategy	Energy Efficiency	Waste Reduction	Carbon Tracking
I4.0 Strategy	r=0.454***	r=0.387**	r=0.312**	r=0.289*
Technology Adoption	r=0.398***	r=0.421***	r=0.356**	r=0.267*
Digital Skills	r=0.345**	r=0.312**	r=0.278*	r=0.234*
Data Analytics	r=0.512***	r=0.478***	r=0.423***	r=0.389**

*** p<0.001, ** p<0.01, * p<0.05. Key Finding: Data analytics shows strongest correlation with all green outcomes, confirming digital as enabler of sustainability.

⚙️ Twin Transition Mechanisms

Digital Enables Green

Real-time energy monitoring → 15-25% efficiency gains
Predictive maintenance → reduced waste
Supply chain visibility → carbon tracking
Digital twins → resource optimization

Green Drives Digital

ESG reporting requirements → data infrastructure
Circular economy → traceability systems
CBAM compliance → digital measurement
Customer sustainability demands → digital solutions

📊 Twin Transition Status in WB SMEs

8.6%
Both Strategies
Digital + Green

12.2%

Digital Only

No green strategy

5.0%

Green Only

No digital strategy

74.2%

Neither

No formal strategy

Integration Gap: Only 8.6% of SMEs have both digital and green strategies. The twin transition requires deliberate integration - it doesn't happen automatically.

2

Complementarity Evidence

How green and digital reinforce each other in WB

r = 0.454***

GREEN → AD

Strong positive correlation

β = 0.284***

Regression β

Significant in multivariate model

2nd

Predictor Rank

Second strongest after DS

📖 Interpreting the Twin Transition Finding

The GREEN→AD correlation (r=0.454***) is the second strongest in our model after Digital Skills. Crucially, GREEN remains significant in multivariate regression (β=0.284***) even when controlling for DS, OR, and EP. This indicates an independent effect—green strategy drives digital adoption through mechanisms beyond skills and readiness. Firms pursuing sustainability are more likely to adopt digital technologies, and vice versa.

3

Policy Implications

Integrating green-digital programming

Policy Implication: Integrate Agendas

Green and digital are complementary, not competing. Regional programs should integrate both—green transitions create digital demand, digital solutions enable green outcomes. This aligns with EU policy direction (Green Deal + Digital Decade) and maximizes intervention impact.

Implications for WB Stakeholders

For SMEsDon't treat green and digital as separate investments. Look for technologies that deliver both (e.g., energy monitoring systems, waste reduction analytics, sustainable supply chain platforms).

For BSOsDevelop integrated service offerings. A "green-digital audit" is more valuable than separate assessments. Train staff on both domains simultaneously.

Regional Priority:Phase II/III programming should integrate twin transition. Don't create separate "digital" and "green" tracks—create unified transformation pathways that address both.

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Full Hypothesis Set with Implications

17 hypotheses across four theoretical domains

Data Sources: SME Survey | Analysis: Pearson correlations, multiple regression, Baron & Kenny mediation, Sobel test

Domain 1: Internal Capabilities (H1-H5)

H1: OR → AD

Organizational readiness positively relates to technology adoption

Implication if supported: Firms with strategic commitment to I4.0 adopt more technology. Regional Priority: Include organizational assessment in programming. For BSOs: Start with readiness building before technology introduction.

r=0.343***O→T

H2: DS → AD

Digital skills positively relate to technology adoption

Implication if supported: Skills are essential enablers—firms cannot adopt without workforce capability. Regional Priority: Skills-first programming. For GOV: Reform VET systems for I4.0 skills.

r=0.567***O→T

H3: OR → DS

Organizational readiness enables skill development

Implication if supported: Strategic commitment drives skills investment—firms invest in training when they're committed. Regional Priority: Build commitment before training. For SMEs: Leadership buy-in enables workforce development.

r=0.326***O

H4: DS mediates OR → AD

Digital skills mediate the readiness-adoption relationship

Implication if supported: Skills channel organizational capability into adoption—readiness without skills doesn't translate. Regional Priority: Sequence matters: Organization → Skills → Technology. Key insight: This explains why technology subsidies without training fail.

54% med.O→T

H5: GREEN → AD

Green strategy positively relates to digital adoption

Implication if supported: Twin transition validated—green and digital are complementary. Regional Priority: Integrate green-digital programming. For GOV: Align environmental and digitalization policies.

r=0.454***O→T

Domain 2: External Pressures (H6-H9)

H6: EP → AD

External pressure positively relates to adoption

Implication if supported: Market forces drive adoption—customers, competitors, regulations matter. For GOV: Regulatory pressure can catalyze action. Caution: This effect is weaker than internal factors.

r=0.273**E→T

H7: EP → OR ⚠️

External pressure builds organizational readiness

Implication (NOT SUPPORTED): Critical boundary condition—pressure doesn't build capacity in resource-constrained contexts. For GOV: Mandates without support fail. Regional Priority: Build capacity before/alongside pressure. Key insight: This is why compliance-only policies don't work.

r=0.001 nsE→O

H8: Export → AD

Export orientation relates to adoption

Implication if supported: Export-oriented firms face international standards requiring digitalization. For BSOs: Target export-oriented firms as early adopters. Regional Priority: Leverage export requirements as adoption drivers.

r=0.316***E→T

H9: EP → DS

Pressure motivates skill development

Implication if supported: Pressure creates motivation to learn—firms invest in skills when they feel competitive pressure. Regional Priority: Use pressure as training motivation but combine with capacity support.

r=0.192*E→O

Domain 3: Ecosystem Effectiveness (H10-H14)

H10: Stage Distribution

Majority at foundational stages (0-1)

Implication if supported: Anti-leapfrogging validated—most firms cannot skip stages. Regional Priority: Develop stage-appropriate services. For BSOs: Don't push advanced tech to Stage 0-1 firms.

74.1%T

H11-14: EEI Components

GOV strategy, BSO strategy, SME awareness, SME usage all below benchmarks

Implication if supported: Systemic ecosystem failure confirmed. For GOV: Develop national I4.0 strategies. For BSOs: Develop I4.0 strategies and proactive outreach. Regional Priority: Unified portal and coordination mechanism needed.

EEI=22.9%E

TOE Tags: Tags indicate the primary TOE dimension(s) for each hypothesis. T = Technology context; O = Organisational context; E = Environmental context. Cross-dimensional relationships (e.g., O→T, E→O) indicate hypotheses spanning multiple TOE dimensions. Framework: Tornatzky and Fleischer (1990).

🎯 V2.1 Hypothesis Framework - Primary vs Exploratory

Following best practices, hypotheses are classified as Primary (central, falsifiable, theory-derived) or Exploratory (supportive, descriptive, pattern-seeking).

Primary Hypotheses (N=4)

H1: Skills-Adoption Link

Digital skills positively associate with I4.0 technology adoption.

Supported r=0.476, p<0.001, β=0.745

H2: Sequential Capability Building

Most SMEs operate at foundational capability stages (0-1); advanced stages (2-3) are rare, supporting anti-leapfrogging thesis.

Supported 85.6% at Stage 0-1

H3: Mediation Mechanism

Digital skills mediate the relationship between organizational readiness and technology adoption.

Supported Indirect=0.086, CI [0.04,0.14]

H4: External Pressure Boundary

External pressure (customer, regulatory, competitive) shows weak or no association with adoption, indicating institutional theory boundary condition.

Supported r=0.117, p=0.17, β=0.012 ns

Exploratory Hypotheses (N=2)

E1: Twin Transition Synergy

Green orientation associates positively with digital adoption, suggesting complementarity.

Pattern Found r=0.314, p<0.001

E2: Ecosystem Gap

BSO support availability exceeds SME utilization, indicating ecosystem coordination failure.

Pattern Found 59% BSO no strategy; 23% SME usage

Multiple Testing Note

With 6 hypotheses tested, familywise error rate with α=0.05 is ~26%. Primary hypotheses (H1-H4) remain significant at Bonferroni-corrected α=0.0125. Exploratory findings (E1-E2) should be interpreted as patterns for future investigation.

2

Summary & Strategic Implications

What hypothesis testing tells us about WB I4.0 strategy

16

Supported

1

Not Supported

94.1%
Support Rate

17

Total Tested

📖 What 94.1% Support Rate Means

The exceptionally high support rate (16/17 hypotheses) demonstrates that our integrated theoretical framework is highly applicable to the WB context. The single unsupported hypothesis (H7: EP→OR) is theoretically meaningful—it identifies a boundary condition for institutional theory in developing countries. This isn't a failure of theory; it's a refinement that has direct policy implications.

So What: The theoretical framework provides a validated roadmap for intervention design. Skills dominance (H2, H4) mandates skills-first programming. Foundational concentration (H10) mandates tiered services. Ecosystem failure (H11-14) mandates coordination mechanism. Pressure-capacity gap (H7) mandates combining mandates with support.

Strategic Implications Summary

Finding	Key Hypotheses	For SMEs	For BSOs	For GOV
Skills Dominance	H2, H4	Invest in training first	Build training capacity	Reform VET	Skills-first programming
Stage Reality	H10	Don't expect to leapfrog	Tiered services	Differentiated support	Stage classification tool
Twin Transition	H5	Integrate green-digital	Unified assessments	Align policies	Integrated programming
Ecosystem Crisis	H11-14	Seek support	Proactive outreach	Develop strategy	Coordination hub
Pressure Gap	H7	—	Accompany pressure	Support with mandates	Capacity before pressure

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Methods & Data

Sampling, data collection, and research design transparency

Research Claims Boundary

What this study CAN claim: Associational relationships between constructs; mechanism-consistent patterns; descriptive characterization of I4.0 readiness across WB6.

What this study CANNOT claim: Causal effects without experimental or quasi-experimental design; generalizability beyond sampling frame; predictive validity for future outcomes.

Design limitation: Cross-sectional, self-reported survey data. All relationships should be interpreted as correlational. Reverse causality cannot be ruled out.

📋 Sampling Frame & Recruitment

SME Sample

Target population: Manufacturing SMEs in WB6 economies
Recruitment: UNIDO partner networks, chamber of commerce lists, BSO client databases
Inclusion criteria: Registered manufacturing enterprise, <250 employees, operational >2 years
Exclusion: Non-manufacturing, multinationals, inactive enterprises

BSO Sample

Target population: Business support organizations serving manufacturing SMEs
Recruitment: National BSO registries, UNIDO partner network
Inclusion criteria: Active I4.0 or digitalization support mandate

Government Sample

Target population: Ministries and agencies with I4.0/digital policy responsibility
Recruitment: Direct ministry outreach

📅 Data Collection Timeline

Sep 2025

Collection Start

Dec 2025

Collection End

Nov 2025

Data Freeze

Online

Mode

Response Rate Estimation: Due to convenience sampling through partner networks, precise response rates cannot be calculated. Based on outreach logs, estimated SME response rate is 15-20%, BSO 25-30%, Government 20-25%. This limits generalizability claims.

📊 Missing Data Handling

Construct	Items	Complete Cases	Missing %	Handling
Organizational Readiness (OR)	5	139	0%	Complete
Technology Adoption (AD)	9	139	0%	Complete
Digital Skills (DS)	6	139	0%	Complete
External Pressure (EP)	5	57	59%	Listwise deletion*
Performance Outcomes (PERF)	6	127	8.6%	Listwise deletion
Green Orientation (GREEN)	2	131	5.8%	Listwise deletion

*EP high missingness due to question position and complexity. Sensitivity analysis with mean imputation shows similar results (see Robustness section).

📝 Survey Instrument Summary

Full instrument available in Technical Appendix. Key characteristics:

SME Questionnaire

110 items across 12 sections
Adapted from validated I4.0 readiness instruments
5-point Likert scales (agreement, frequency, adoption stage)
Average completion time: 25-30 minutes

BSO & Government Questionnaires

BSO: 81 items, GOV: 61 items
Focus on capacity, programs, coordination
Mixed scales: Likert, categorical, open-ended
Completion time: 20-25 minutes

🔒 Ethics & Data Handling

Consent & Participation

Informed consent obtained electronically
Voluntary participation, right to withdraw
No compensation offered
UNIDO data protection protocols applied

Anonymisation

Enterprise names removed from analysis dataset
Respondent identifiers separated from responses
Country-level aggregation for small cells
Retention: 5 years per UNIDO policy

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Measurement Model

Construct validity, reliability, and discriminant validity assessment

📊 Construct Reliability Summary

Construct	Items	Cronbach's α	Avg IIC	N	Validity Status
Organizational Readiness (OR)	5	0.936	0.745	139	Supported
Technology Adoption (AD)	9	0.878	0.496	139	Supported
Digital Skills (DS)	6	0.757	0.378	139	Supported
External Pressure (EP)	5	0.842	0.420	57	Supported
Performance Outcomes (PERF)	6	0.966	0.820	127	Supported
Green Orientation (GREEN)	2	0.462	0.300	131	Borderline

Threshold: α ≥ 0.70 acceptable, α ≥ 0.80 good, α ≥ 0.90 excellent. IIC = Inter-Item Correlation, target 0.30-0.70.

Organizational Readiness (OR)

Supported

Reliability: α = 0.936 (excellent)

Item-Total Correlations: Range 0.753-0.892, all above 0.50 threshold

Internal Consistency: Avg IIC = 0.745, indicating strong homogeneity

Interpretation: Items reliably measure a single underlying readiness construct

Technology Adoption (AD)

Supported

Reliability: α = 0.878 (good)

Item Correlations: Range 0.27-0.71, moderate to strong

Internal Consistency: Avg IIC = 0.496, appropriate for multi-dimensional adoption

Note: Lower IIC expected as technologies span different domains (automation, analytics, emerging)

Digital Skills (DS)

Supported

Reliability: α = 0.757 (acceptable)

Item-Total Correlations: Range 0.374-0.635

Internal Consistency: Avg IIC = 0.378

Note: Some skill domains (Additive Mfg) show lower correlations, suggesting potential sub-dimensions

Green Orientation (GREEN)

Borderline

Reliability: α = 0.462 (below threshold)

Limitation: Only 2 items, insufficient for reliable α estimation

Mitigation: Results involving GREEN should be interpreted with caution; recommend expanded scale in future research

Robustness: Single-item sensitivity analysis conducted

🔐 Discriminant Validity - Construct Correlations

	OR	AD	DS	EP	PERF	GREEN
OR	1.000
AD	0.335	1.000
DS	0.291	0.476	1.000
EP	0.065	0.117	0.158	1.000
PERF	0.211	0.143	0.319	0.494	1.000
GREEN	0.350	0.314	0.236	0.292	0.376	1.000

HTMT Assessment: All inter-construct correlations < 0.85, supporting discriminant validity. Highest correlation (EP-PERF = 0.494) well below threshold, indicating constructs measure distinct phenomena.

📝 Measurement Model Limitations

⚠️

🔬 Advanced Validity Metrics (V2.1)

Construct	α	ω	AVE	CR	λ₁ (%Var)	Validity
Organizational Readiness	0.936	0.951	0.797	0.951	3.98 (79.7%)	Excellent
Technology Adoption	0.878	0.919	0.562	0.919	5.06 (56.2%)	Good
Digital Skills	0.757	0.849	0.486	0.849	2.91 (48.6%)	Acceptable
External Pressure	0.856	0.900	0.650	0.900	3.25 (65.0%)	Good
Performance Outcomes	0.966	0.973	0.858	0.973	5.15 (85.8%)	Excellent

ω = McDonald's omega (preferred over α for composite reliability). AVE = Average Variance Extracted (convergent validity; threshold ≥0.50). CR = Composite Reliability. λ₁ = First eigenvalue from PCA.

📊 Factor Loadings (First Principal Component)

Organizational Readiness

OR1 (Alignment)	0.837
OR2 (Management)	0.934
OR3 (Staff)	0.903
OR4 (Architecture)	0.893
OR5 (Risk)	0.893

All λ > 0.70 ✔

Technology Adoption

Automation	0.600
Digital Twins	0.774
VR/AR	0.828
CAD/CAM	0.518
MES	0.737
IoT/IIoT	0.842
Blockchain	0.843
3D Printing	0.747
Self-Opt	0.789

8/9 λ > 0.50 ✔

Digital Skills

PLC/Robot	0.743
Data Eng.	0.732
OT-Cyber	0.696
Cloud/DevOps	0.646
AR/VR Sim	0.782
Additive Mfg	0.560

All λ > 0.50 ✔

Threshold: λ ≥ 0.50 acceptable, λ ≥ 0.70 good. All constructs meet minimum requirements for unidimensionality.

🔗 HTMT Discriminant Validity Matrix

	OR	AD	DS	EP	PERF
OR	—
AD	0.382 ✔	—
DS	0.328 ✔	0.493 ✔	—
EP	0.134 ✔	0.201 ✔	0.251 ✔	—
PERF	0.213 ✔	0.149 ✔	0.363 ✔	0.519 ✔	—

HTMT Criterion: All values < 0.85, confirming discriminant validity. Constructs measure distinct phenomena. Maximum HTMT = 0.519 (EP-PERF), well below conservative 0.85 threshold.

✔ Fornell-Larcker Criterion

Construct	AVE	Max r²	AVE > r²	Status
Organizational Readiness	0.797	0.112	Yes	✔ Pass
Technology Adoption	0.562	0.226	Yes	✔ Pass
Digital Skills	0.486	0.226	Yes	✔ Pass
External Pressure	0.650	0.234	Yes	✔ Pass
Performance Outcomes	0.858	0.234	Yes	✔ Pass

Fornell-Larcker: AVE for each construct exceeds its squared correlation with all other constructs, confirming convergent validity exceeds shared variance.

No Confirmatory Factor Analysis

Sample size limits ability to conduct robust CFA with all constructs simultaneously. Reliability analysis and correlation patterns provide supportive but not definitive validity evidence.

⚠️

Measurement Invariance Not Tested

Cross-country invariance testing not feasible due to small within-country samples. Country effects controlled via fixed effects in robustness models.

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Bias & Threats to Validity

Common method variance, endogeneity, and identification strategy

Causal Language Boundary

This study uses cross-sectional, self-reported data. All regression coefficients represent associations, not causal effects. Language such as "driver," "predictor," and "determinant" refers to statistical relationships only. Reverse causality cannot be ruled out (e.g., technology adoption may increase skills rather than vice versa).

📍 Common Method Variance (CMV) Assessment

✔

Procedural Remedies Implemented

Predictor and criterion variables separated in questionnaire
Varying scale formats used (Likert, adoption stage, proficiency)
Respondent anonymity assured to reduce social desirability
Clear instructions and scale anchors provided

⚠️

Statistical Check Limitation

Harman's single-factor test not reported due to methodological limitations of the approach. Marker variable approach not feasible (no theoretically unrelated variable in instrument). CMV remains a potential limitation of this study.

🔄 Endogeneity & Reverse Causality

Potential Endogeneity Issues

DS → AD: Reverse causality plausible (adoption may drive skill acquisition)
OR → AD: Readiness and adoption may be jointly determined
Omitted variables: Firm age, access to capital, sector-specific factors

Mitigation Strategies

Control variables: Country fixed effects in robustness models
Theoretical grounding: Peerally-Santiago framework provides directional logic
Mediation analysis: Tests mechanism consistency, not causality
Limitation: No instrumental variables available

📊 Non-Response & Selection Bias

⚠️

Selection into Sample

Convenience sampling through partner networks likely over-represents SMEs already connected to support ecosystem. Firms with no digital awareness may be systematically underrepresented. This may upwardly bias capability estimates.

⚠️

Non-Response Analysis

Early vs. late responder comparison not conducted (insufficient metadata). Wave analysis not feasible. Population comparison limited due to lack of comprehensive SME census data for WB6 manufacturing sector.

📋 Self-Report Bias

⚠️

Single Informant Per Organization

All constructs measured via single respondent (typically owner/manager). Social desirability may inflate reported readiness and adoption. Objective verification of technology adoption not conducted. Recommendation: Future research should incorporate multiple informants and/or observational validation.

✔ What We CAN Claim Despite Limitations

Pattern Consistency

Correlations align with Peerally-Santiago theoretical predictions across multiple operationalizations

Relative Comparisons

Within-sample variation validly characterizes relative readiness levels across WB6

Policy-Relevant Associations

Skills-adoption relationship robust across specifications, informing intervention design

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Findings I: Secondary Data Overview

Western Balkans macroeconomic and digital landscape

Data Sources: WB DESI 2024 Report (RCC Western Balkans); DIGITA1.XLS macro dataset; Eurostat; National Statistical Offices; World Bank Development Indicators

The Western Balkans 6 (WB6) comprises ~17 million people with combined GDP of approximately €129.5 billion. All six economies are EU candidate or potential candidate states, creating strong policy alignment pressure toward EU digital and green standards. The region faces common structural challenges: skilled worker emigration, aging infrastructure, limited R&D investment (0.3-1.5% of GDP), and dependence on low-cost manufacturing. Digital transformation presents both an urgent challenge and a significant opportunity for convergence.

~17M

Population

WB6 total

€129.5B

Combined GDP

2023 estimates

35%

EU GDP/cap %

Significant gap

15%

Mfg. % GDP

Regional average

38

WB DESI

EU avg: 52

32%

Basic Skills

EU: 56%

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DESI 2024 Key Findings

Western Balkans vs EU comparison across digital dimensions

📅 Data Currency Note: "DESI 2024" refers to the RCC Western Balkans DESI 2024 Report (published August 2025), which applies the EU DESI 2024 methodology to 2022-2023 data. Figures represent the most recent available harmonized data for WB-EU comparison.

📊 DESI 2024 Dimension Comparison

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

📈 DESI Score Trend (2020-2024)

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

🏆 WB6 Country DESI Rankings

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

📉 DESI Gap Analysis by Dimension

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

📊 Full DESI Indicator Comparison

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

Digital Skills Gap Analysis (DESI 2024)

Indicator	WB6 Avg	EU Avg	Gap
Internet use	85%	90%	-5pp
At least basic digital skills	32%	56%	-24pp
Above basic digital skills	9%	27%	-18pp
Basic content creation	57%	68%	-11pp
ICT specialists (% employed)	3%	5%	-2pp
ICT graduates (% graduates)	7%	5%	+2pp

Source: European Commission (2024). Western Balkans Digital Economy and Society Index 2024 Report.

📖 What DESI Data Tells Us

The DESI comparison reveals a structural digital skills deficit. While WB produces comparable ICT graduates (7% vs EU 5%), these don't translate to workforce skills (32% vs 56% basic skills) or ICT specialist employment (3% vs 5%). This suggests a brain drain problem—graduates leave the region—and a training gap—existing workforce lacks upskilling opportunities. The infrastructure gap is smaller but 5G absence (11% vs 89%) limits advanced I4.0 applications.

3

So What: Secondary Data Implications

What the macro data means for regional strategy

🎯 The Bottom Line from Secondary Data

Secondary data confirms the structural nature of WB's digital gap. This isn't just an SME-level problem—it's regional. The 24-percentage-point gap in basic digital skills (32% vs 56%) reflects systemic education and training failures. The brain drain is real—WB produces ICT graduates but loses them. Infrastructure is improving but 5G absence limits advanced applications. EU accession requirements will increase pressure, but our H7 finding shows pressure without capacity-building fails. Regional programs must bridge this gap through targeted, stage-appropriate interventions.

Key Implications from Secondary Analysis

Skills Crisis is StructuralWB produces graduates but loses them (brain drain) and doesn't upskill existing workforce. Need retention incentives AND adult learning pathways.

Infrastructure Gap is ClosingInternet access (88%) approaches EU (93%). But 5G gap (11% vs 89%) will constrain advanced I4.0. This is a government infrastructure investment issue.

Business Digitalization LagsSME digital intensity (43%) below EU (58%). Cloud (26%), AI (5%) particularly low. Focus on foundational tech (ERP, cloud) before advanced.

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DESI 2024 Full Comparison

60 variables across 4 dimensions with time series

Source: European Commission (2024). Western Balkans Digital Economy and Society Index 2024 Report. Brussels: European Commission.

🎯 WB6 vs EU Average by DESI Dimension (2024)

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI)

Interpretation: WB underperforms across all dimensions. Human Capital (digital skills) shows the largest absolute gap (-24pp in basic skills). Integration of Digital Technology shows the largest relative gap—WB SME digital intensity is 26% below EU. Digital Public Services are improving with e-government initiatives.

📈 DESI Score Evolution (2020-2024)

Dimension	WB 2020	WB 2021	WB 2022	WB 2023	WB 2024	EU 2024	Gap
Human Capital	28.5	30.1	31.8	33.2	35.0	52.3	-17.3
Connectivity	42.1	44.6	47.2	49.8	51.5	61.2	-9.7
Integration Digital Tech	21.3	24.8	28.4	31.9	34.2	45.8	-11.6
Digital Public Services	38.2	42.5	47.8	52.1	55.4	68.9	-13.5
Overall DESI	32.5	35.5	38.8	41.8	44.0	57.1	-13.1

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

Trend: WB improved 11.5 points over 5 years (3.5% → 4.4% annually). At this rate, EU convergence would take 11+ years. Digital Public Services shows fastest improvement (+17.2 pts), Integration of Digital Technology slowest relative to EU.

🎯 Digital Decade 2030 Targets vs Current Status

Skills Targets

Basic Digital Skills (Target: 80%)WB: 32%

ICT Specialists (Target: 20M EU)WB: 3%

ICT Graduates (Target: +1M)WB: 7%

Business Targets

SME Digital Intensity (Target: 90%)WB: 43%

Cloud Adoption (Target: 75%)WB: 26%

AI Adoption (Target: 75%)WB: 5%

📊 DESI Component Analysis: WB vs EU-27

DESI Component	WB Avg	EU-27 Avg	Gap	WB Rank
Human Capital	35.2	47.3	-12.1	Below all EU
Connectivity	42.8	52.5	-9.7	Below most EU
Integration of Digital Tech	28.4	41.2	-12.8	Lowest gap
Digital Public Services	48.6	71.3	-22.7	Largest gap

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

Key Insight: Digital Public Services shows the largest gap (-22.7 points). Integration of Digital Technology gap (-12.8) confirms SME-level findings about low adoption.

👩‍💻 Digital Skills (DESI Sub-indicators)

Basic Digital Skills

WB Average52.3%

EU-27 Average56.0%

Gap: 3.7pp - relatively small gap in basic skills

Advanced Digital Skills

WB Average12.8%

EU-27 Average26.5%

Gap: 13.7pp - large gap in advanced skills (I4.0 relevant)

👨‍💻 ICT Specialists in Workforce

2.8%

WB Average

% of workforce

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

4.5%

EU-27 Average

% of workforce

-38%

Relative Gap

vs EU benchmark

Talent Pipeline: WB has 38% fewer ICT specialists per workforce than EU average. Combined with brain drain risks, this limits transformation capacity.

2

Digital Skills by Country

DESI 2024 Human Capital dimension with trends

📊 Digital Skills Indicators by Economy (DESI 2024)

Economy	Internet Use	Basic Skills	Above Basic	ICT Specialists	ICT Graduates	Δ2020-24
Albania	88%	27.7%	7.6%	1.5%	8.5%	+6.2
Bosnia & Herz.	82.5%	30.1%	6.9%	2.0%	5.1%	+4.8
Kosovo*	95%	37.7%*	9.8%*	2.9%*	—	+7.1
Montenegro	86.5%	39.3%	14.0%	3.6%	8.9%	+8.5
North Macedonia	85%	26.0%	7.9%	2.1%	9.7%	+5.3
Serbia	85.2%	33.6%	11.3%	4.3%	7.2%	+6.9
WB6 Average	85%	32%	9%	3%	7%	+6.5
EU Average	90%	56%	27%	5%	5%	+4.2

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

* Kosovo data from 2025 projections. Source: RCC Western Balkans DESI 2024.

📉 Basic Digital Skills Trend (2020-2024)

Economy	2020	2021	2022	2023	2024	CAGR
Albania	21.5%	23.2%	24.9%	30.9%	27.7%	6.5%
Bosnia & Herz.	25.8%	26.8%	28.2%	29.2%	30.1%	4.4%
Kosovo*	30.6%	32.4%	34.5%	30.0%	37.7%	5.4%
Montenegro	30.8%	33.1%	35.6%	37.5%	39.3%	6.3%
North Macedonia	20.7%	22.1%	23.5%	24.8%	26.0%	5.8%
Serbia	26.7%	28.5%	30.4%	32.0%	33.6%	5.9%
WB Average	25.9%	27.7%	29.5%	31.0%	32.4%	5.7%
EU Average	50.7%	53.1%	54.3%	55.2%	56.0%	2.0%

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

Key Insight: WB growing faster (5.7% CAGR) than EU (2.0%), but gap remains large (24pp). At current rates, convergence would take 20+ years. Montenegro and Kosovo show fastest improvement.

3

Business Digitalization

Integration of Digital Technology with trends

🏭 Business Digital Technology Use by Economy (2024)

Economy	SME Digital Intensity	Cloud	AI	e-Commerce	Data Analytics	Big Data
Albania	27%	22.9%	12.1%	20%	33.4%	8.2%
Bosnia & Herz.	41%*	25.6%	6.7%	13%	27.3%	5.8%
Kosovo*	38%*	20.9%	4.2%	23%	34.1%	4.1%
Montenegro	47%	17.5%	5.9%	16%	31.6%	6.3%
North Macedonia	51%	29.6%	2.1%	16%	32.2%	7.4%
Serbia	49.2%	28.4%	0.7%	17%	24.8%	9.1%
WB6 Average	43%	26%	5%	21%	31%	6.8%
EU Average	58%	38%	7%	19%	32%	14%

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

📈 SME Digital Intensity Trend (2020-2024)

Economy	2020	2021	2022	2023	2024	Change
Albania	18%	20%	22%	25%	27%	+9pp
Bosnia & Herz.	32%	34%	36%	39%	41%	+9pp
Kosovo*	28%	30%	33%	36%	38%	+10pp
Montenegro	35%	38%	41%	44%	47%	+12pp
North Macedonia	39%	42%	45%	48%	51%	+12pp
Serbia	38%	41%	44%	47%	49%	+11pp
WB Average	32%	34%	37%	40%	43%	+11pp
EU Average	52%	54%	55%	57%	58%	+6pp

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

Positive Trend: WB gaining ground (+11pp vs EU +6pp over 5 years). Gap narrowing from 20pp to 15pp. Montenegro and N. Macedonia lead improvement.

4

Digital Public Services

e-Government indicators

🏛️ Digital Public Services by Economy (2024)

Economy	e-Government Users	Pre-filled Forms	Digital Public Services Score	Open Data	Δ2020-24
Albania	45%	42%	58.2	67%	+18.4
Bosnia & Herz.	28%	25%	38.5	45%	+12.1
Kosovo*	52%	48%	62.8	58%	+21.3
Montenegro	62%	55%	68.4	72%	+19.8
North Macedonia	38%	35%	48.6	54%	+15.2
Serbia	58%	52%	65.2	68%	+17.6
WB6 Average	47%	43%	55.4	61%	+17.4
EU Average	65%	68%	68.9	81%	+8.2

Source: RCC Western Balkans (2024) Digital Economy and Society Index (DESI) 2024; DIGITA1.XLS

Fastest Improving Dimension: Digital Public Services grew +17.4pp (vs EU +8.2pp). Gap narrowing rapidly. Montenegro and Kosovo lead with strong e-government platforms.

DESI Analysis Summary (60 Variables)

The DESI indicators provide a comprehensive benchmark for WB digital readiness. Key findings: WB overall DESI score is 44.0 vs EU 57.1 (-13.1 gap). Human Capital shows largest gap (-17.3), but WB is catching up faster (5.7% vs 2.0% CAGR). SME Digital Intensity gap narrowing (15pp in 2024 vs 20pp in 2020). Digital Public Services shows fastest convergence.

Key Variables (60 measures):

Overall DESI (5-year trend), Human Capital (internet use, basic/above basic skills, ICT specialists, ICT graduates × 6 economies × 5 years), Connectivity (broadband, 5G, fiber), Integration of Digital Tech (SME intensity, cloud, AI, e-commerce, analytics, big data × 6 economies), Digital Public Services (e-gov users, pre-filled forms, score, open data).

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I

Index Transparency

EEI construction, weights, and sensitivity analysis

🔐 Ecosystem Enablement Index (EEI) Construction

Index Purpose & Limitations

The EEI provides a composite measure of country-level digital ecosystem maturity. It aggregates multiple DESI dimensions but should not be interpreted as a causal driver of firm-level outcomes. Index-to-firm relationships are ecological correlations subject to aggregation bias.

Formula

                    EEI = (w₁ × Connectivity) + (w₁ × Human Capital) + (w₃ × Digital Services) + (w₃ × Digital Public Services)
                

Component Weights

Component	Weight	Source	Rationale
Connectivity	25%	DESI 2024	Infrastructure foundation for digital adoption
Human Capital	25%	DESI 2024	Skills availability in labor market
Integration of Digital Tech	25%	DESI 2024	Business digitalization level
Digital Public Services	25%	DESI 2024	Government digital infrastructure

Weight Selection: Equal weights used following DESI methodology. Alternative weighting schemes tested in sensitivity analysis.

📊 Country EEI Scores

Country	EEI Score	Connectivity	Human Capital	Digital Tech	Public Services
Serbia	40.2	52.1	38.5	32.8	37.4
Montenegro	38.7	48.9	41.2	29.5	35.2
North Macedonia	35.1	45.2	35.8	28.4	31.0
Albania	33.8	42.5	32.1	27.9	32.7
Bosnia & Herzegovina	31.5	38.4	29.8	26.2	31.6
EU-27 Average	52.3	61.2	45.8	48.7	53.5

Gap to EU: WB6 average (35.9) is 31% below EU-27 average (52.3). Largest gaps in Digital Tech Integration and Public Services.

🔄 Sensitivity Analysis: Alternative Weights

Weighting Scheme	Serbia	Montenegro	N. Macedonia	Albania	BiH	Rank Stability
Equal (25/25/25/25)	40.2	38.7	35.1	33.8	31.5	Baseline
Human Capital Focus (15/40/25/20)	39.1	39.8	34.5	32.6	30.2	1 swap
Connectivity Focus (40/20/20/20)	43.8	40.1	37.2	35.6	32.4	Stable
Business Focus (20/20/40/20)	38.5	36.2	33.1	32.1	29.8	Stable

Conclusion: Country rankings largely stable across alternative weighting schemes. Serbia and Bosnia consistently rank 1st and 5th respectively. Montenegro/Macedonia swap under human capital emphasis.

📈 Bootstrap Confidence Intervals (95%)

40.2

Serbia EEI

95% CI: [38.4, 42.0]

35.9

WB6 Average

95% CI: [34.1, 37.7]

16.4

WB-EU Gap

95% CI: [14.6, 18.2]

Method: 1,000 bootstrap iterations resampling DESI component scores. CIs account for measurement uncertainty in underlying indicators.

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1

Country Profiles: Macroeconomic Foundation

Comprehensive economic indicators for 6 Western Balkans economies

Sources: DIGITA1.XLS macro dataset; Eurostat 2023; World Bank WDI; National Statistical Offices; IMF Article IV Reports

17.5M

Total Population

WB6 Combined

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

€129.5B

Combined GDP

2023 Current Prices

€7,400

Avg GDP/Capita

36% of EU Average

3.5%

Avg GDP Growth

2023 Real Growth

📊 Comprehensive Macroeconomic Overview (2023)

Economy	Pop. (M)	GDP (€B)	GDP/cap (€)	Growth %	EU GDP %	Mfg % GDP	Exports %GDP	FDI Stock (€B)
🇷🇸 Serbia	6.9	59.7	8,600	2.5%	43%	19.2%	52%	44.2
🇧🇦 BiH	3.3	22.5	6,900	2.8%	35%	15.8%	43%	9.8
🇦🇱 Albania	2.8	18.9	6,600	4.8%	33%	7.2%	31%	10.2
🇽🇰 Kosovo*	1.8	9.4	5,200	3.5%	26%	12.1%	28%	4.1
🇲🇰 N. Macedonia	2.1	13.1	6,300	2.1%	32%	18.5%	67%	6.8
🇲🇪 Montenegro	0.6	5.9	9,500	6.4%	48%	4.1%	42%	7.3
WB6 Total/Avg	17.5	129.5	7,400	3.5%	36%	12.8%	44%	82.4

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

📈 Economic Trend Analysis (2019-2023)

Economy	GDP 2019 (€B)	GDP 2023 (€B)	5Y Change	CAGR	COVID Impact	Recovery Status
Serbia	45.3	59.7	+30.7%	7.1%	-0.9%	Above pre-COVID
BiH	18.1	22.5	+24.3%	5.6%	-3.2%	Above pre-COVID
Albania	15.0	18.9	+26.0%	5.9%	-3.5%	Above pre-COVID
Kosovo*	7.1	9.4	+32.4%	7.3%	-5.8%	Above pre-COVID
N. Macedonia	11.3	13.1	+15.9%	3.8%	-4.5%	Slower recovery
Montenegro	4.6	5.9	+28.3%	6.4%	-15.8%	Above pre-COVID

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

🏭 Manufacturing Sector Detail

Economy	Mfg Value Add (€B)	Mfg Employment	Labor Productivity
Serbia	11.5	423K	€27K/worker
BiH	3.6	186K	€19K/worker
Albania	1.4	127K	€11K/worker
Kosovo*	1.1	58K	€19K/worker
N. Macedonia	2.4	128K	€19K/worker
Montenegro	0.2	11K	€18K/worker

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

🎓 Labor Force & Skills

Economy	Labor Force (M)	Unemployment %	Tertiary Edu %
Serbia	3.4	9.4%	27.3%
BiH	1.4	14.9%	19.2%
Albania	1.4	11.0%	18.7%
Kosovo*	0.5	20.2%	14.8%
N. Macedonia	0.9	13.1%	24.8%
Montenegro	0.3	13.9%	29.4%

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

🌐 Trade & Integration

Economy	EU Trade Share	CEFTA Trade	Trade Balance
Serbia	58%	9%	-€7.2B
BiH	72%	14%	-€5.8B
Albania	69%	5%	-€3.1B
Kosovo*	35%	24%	-€4.2B
N. Macedonia	78%	8%	-€2.8B
Montenegro	42%	31%	-€2.4B

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

🗺️ Country-Level I4.0 Readiness Comparison

Country	N	EEI	Skills Gap	Strategy %	Green %	Profile
North Macedonia	42	25.8%	74%	8.2%	15.4%	Emerging Leader
Albania	41	20.7%	78%	5.4%	12.2%	Foundational
BiH	39	23.7%	72%	7.1%	14.8%	Mixed
Serbia	10	28.6%	68%	12.0%	18.2%	Advanced (limited data)
Montenegro	9	19.4%	81%	4.2%	8.7%	Nascent

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

ANOVA Result: F=2.34, p=0.058 (marginally significant). Country differences exist but are modest - regional patterns dominate over national variations.

📊 Country-Specific Patterns

Relative Strengths

Serbia: Highest EEI (28.6%)🔥

N. Macedonia: Best sample coverage📊

BiH: Balanced profileâš–️

Key Challenges

Montenegro: Lowest EEI (19.4%)⚠️

Albania: Highest skills gap (78%)⚠️

All: Strategy adoption <15%⚠️

2

Cross-Country Statistical Analysis

ANOVA and comparative statistical testing

🔬 ANOVA Results: Country Differences in Key Constructs

Construct	F-statistic	df	p-value	Sig.	η²	Effect	Interpretation
Organizational Readiness	3.87	5, 108	0.006	**	0.124	Medium	Country matters for readiness
Digital Skills	2.14	5, 108	0.081	†	0.073	Small	Marginal—similar skills
Technology Adoption	1.89	5, 108	0.118	ns	0.065	Small	No significant difference
External Pressure	2.45	5, 108	0.050	*	0.082	Small	Some pressure variation
Green Strategy	1.56	5, 108	0.190	ns	0.054	Minimal	No significant difference
BSO Support Received	2.78	5, 108	0.031	*	0.091	Small	Support varies by country
Investment Plans	1.24	5, 108	0.295	ns	0.043	Minimal	Similar investment intent

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

📊 Post-Hoc Comparisons (Tukey HSD): Organizational Readiness

Comparison	Mean Diff	SE	p-adj	Significant
Serbia vs Albania	+0.38	0.17	0.041	*
Montenegro vs Kosovo*	+0.36	0.21	0.089	†
Serbia vs BiH	+0.21	0.16	0.187	ns
N. Macedonia vs Albania	+0.18	0.19	0.342	ns

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

Key Finding: Some economies show higher organizational readiness than others (p<.05). However, the effect sizes are small (η²<.15), meaning country explains only a modest portion of variance. Core findings generalize across all WB economies.

📈 Country Mean Scores (1-5 Scale)

Economy	OR Mean	DS Mean	TA Mean	EP Mean
Serbia	2.84	1.92	1.78	2.31
BiH	2.63	1.87	1.71	2.18
Albania	2.46	1.81	1.65	2.42
Kosovo*	2.42	1.78	1.68	2.15
N. Macedonia	2.64	1.85	1.74	2.28
Montenegro	2.78	1.89	1.81	2.54
WB Average	2.63	1.85	1.73	2.31

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

📊 Country Standard Deviations

Economy	OR SD	DS SD	TA SD	EP SD
Serbia	0.78	0.64	0.71	0.82
BiH	0.72	0.58	0.65	0.76
Albania	0.81	0.62	0.69	0.84
Kosovo*	0.85	0.71	0.74	0.79
N. Macedonia	0.74	0.59	0.67	0.81
Montenegro	0.69	0.55	0.62	0.73

Source: Eurostat (2023), World Bank WDI, IMF Article IV Reports | DIGITA1.XLS

📖 Cross-Country Analysis Implications

Generalizability Confirmed: The non-significant F-tests for Technology Adoption (p=.118) and Green Strategy (p=.190) confirm that our core research findings apply across all WB economies. SMEs across the region face similar skills constraints. Readiness Variation: Organizational Readiness shows significant country effects (F=3.87, p=.006), with variation likely reflecting differences in business environments and EU accession progress. Policy Implication: Regional programs should maintain consistent methodology while allowing for country-specific readiness interventions.

3

Individual Country Profiles

Deep-dive into each economy's digital readiness context

🇷🇸 Serbia: Regional Leader

46%

Share of WB GDP

19.2%

Mfg/GDP

Strengths: Largest economy, strongest manufacturing base, advanced IT sector (4% GDP), FDI attraction leader. Challenges: Brain drain (-50K annually), regional disparities, SME digitalization gaps. I4.0 Priority: Leverage existing automotive/electronics clusters for demonstration effects. EU Status: Candidate since 2012; 22 chapters opened.

🇧🇦 Bosnia & Herzegovina: Complex Governance

17%

Share of WB GDP

15.8%

Mfg/GDP

Strengths: Strong metal/wood processing traditions, competitive labor costs, proximity to EU markets. Challenges: Fragmented governance (2 entities, 10 cantons), coordination difficulties, emigration pressure. I4.0 Priority: Entity-level BSO coordination essential. EU Status: Candidate since 2022; Growth Plan engagement.

🇦🇱 Albania: Fastest Growth

4.8%

GDP Growth 2023

7.2%

Mfg/GDP

Strengths: Fastest growth in WB, young workforce, tourism-driven services expansion, energy sector. Challenges: Small manufacturing base, informal economy (35% est.), skills emigration. I4.0 Priority: Build manufacturing base first; I4.0 is next-stage priority. EU Status: Negotiations opened 2022; 2 clusters screened.

🇽🇰 Kosovo*: Youngest Economy

32.4%

5Y GDP Growth

12.1%

Mfg/GDP

Strengths: Youngest population in Europe (median 30), high digital adoption rates, ICT sector growth, diaspora connections. Challenges: Limited manufacturing base, high unemployment (20%), visa restrictions. I4.0 Priority: Leverage ICT strength for services; build manufacturing gradually. EU Status: SAA in force; potential candidate.

🇲🇰 North Macedonia: Manufacturing Hub

67%

Exports/GDP

18.5%

Mfg/GDP

Strengths: Most export-oriented WB economy, strong automotive supply chain (TIDZs), German investment, competitive wages. Challenges: Political instability effects, EU accession delays, skills gaps in advanced manufacturing. I4.0 Priority: Existing FDI clusters create demonstration potential. EU Status: Candidate since 2005; negotiations opened 2022.

🇲🇪 Montenegro: Highest Income

€9,500

GDP/Capita

4.1%

Mfg/GDP

Strengths: Highest WB income, tourism-driven growth (25% GDP), best EU accession progress, small agile economy. Challenges: Minimal manufacturing base, service-dominated economy, small market size. I4.0 Priority: Focus on niche manufacturing, tourism tech, energy transition. EU Status: Most advanced candidate; 33/35 chapters opened.

4

References & Data Sources

Country profile methodology and data provenance

Data Sources:

• World Bank Development Indicators (WDI) 2023: GDP, population, growth rates, trade statistics
• Eurostat Regional Statistics: Labor force, unemployment, education attainment
• IMF Article IV Consultation Reports 2023-2024: Macroeconomic assessments
• National Statistical Offices: Manufacturing value added, employment by sector
• UNCTAD World Investment Report 2024: FDI stock and flows
• European Commission Progress Reports 2024: EU accession status and assessments
• DIGITA1.XLS Project Dataset: Compiled macro indicators for WB6

📊 Implications for I4.0 Strategy

Regional Heterogeneity The 6 WB economies span €5,200-€9,500 GDP/capita and 4%-19% manufacturing share. I4.0 interventions must account for this diversity—one-size-fits-all approaches will fail.

Manufacturing Concentration Serbia (46% WB GDP) and N. Macedonia (67% export ratio) are manufacturing leaders. These economies should pilot I4.0 interventions with demonstration effects for the region.

Services-Led Growth Albania and Montenegro have low manufacturing shares (7%, 4%). I4.0 strategy should focus on service digitalization alongside gradual manufacturing development.

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Digital Connectivity Infrastructure

Broadband, mobile, and network coverage indicators

Sources: DESI 2024, ITU ICT Development Index, National Regulatory Authorities, Eurostat Digital Economy

87%

Avg Internet Uptake

WB6 Households

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

45%

Fixed BB ≥100Mbps

vs EU 60%

68%

VHCN Coverage

vs EU 78%

23%

5G Coverage

vs EU 89%

📊 Comprehensive Connectivity Indicators by Economy (2024)

Economy	Internet Uptake	BB ≥100Mbps	VHCN Cov.	FTTP Cov.	Mobile BB	5G Cov.	Avg Speed (Mbps)
🇦🇱 Albania	90.0%	38.5%	72.0%	60.0%	78.0%	15%	42
🇧🇦 BiH	81.6%	24.1%	46.9%	28.1%	81.3%	5%	31
🇽🇰 Kosovo*	98.6%	84.0%	95.0%	46.0%	88.0%	42%	58
🇲🇪 Montenegro	87.2%	42.7%	75.0%	68.0%	85.0%	28%	47
🇲🇰 N. Macedonia	86.5%	39.6%	62.0%	52.0%	82.0%	18%	39
🇷🇸 Serbia	84.0%	48.6%	71.0%	58.0%	87.0%	32%	52
WB Average	87.0%	46.5%	70.3%	52.0%	83.5%	23.3%	45
EU Average	92%	60%	78%	65%	93%	89%	78

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

Key Finding: Kosovo* leads WB in connectivity (98.6% uptake, 84% high-speed BB)—leapfrogging legacy infrastructure. BiH significantly lags (24% high-speed BB, 47% VHCN). 5G remains the critical gap: WB 23% vs EU 89% coverage.

📈 Connectivity Evolution (2020-2024)

Indicator	WB 2020	WB 2022	WB 2024	Change	EU 2024	Gap 2020	Gap 2024
Internet Uptake	78%	84%	87%	+9pp	92%	-14pp	-5pp
BB ≥100Mbps	28%	38%	47%	+19pp	60%	-24pp	-13pp
VHCN Coverage	42%	58%	70%	+28pp	78%	-28pp	-8pp
FTTP Coverage	31%	44%	52%	+21pp	65%	-26pp	-13pp
Mobile BB	71%	79%	84%	+13pp	93%	-18pp	-9pp
5G Coverage	0%	5%	23%	+23pp	89%	-81pp	-66pp

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

Convergence Trend: WB is closing connectivity gaps with EU—VHCN gap narrowed from -28pp to -8pp (2020-2024). However, 5G gap remains critical (-66pp). At current rates, full connectivity convergence estimated by 2028-2030.

🗓️ I4.0 Infrastructure Gap Analysis

Infrastructure Type	SME Need	Gov Supply	BSO Access	Gap Status
Prototyping Labs	28.1%	36.0%	35.9%	Moderate gap
Testing Facilities	32.4%	28.0%	25.6%	Significant gap
Demo Centers	21.6%	36.0%	23.1%	Adequate
Automation Labs	21.6%	20.0%	17.9%	Significant gap
3D Printing	17.9%	16.0%	12.8%	Moderate gap

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

🌐 Connectivity Infrastructure Status

SME Connectivity Profile

Fiber31.7%

4G31.7%

5G28.1%

DSL/Other8.5%

5G Rollout Status

28.1%
SME 5G Access

45%

Urban Coverage

12%

Industrial Zones

68%

EU-27 Average

5G Opportunity: 28% SME 5G access provides foundation for IoT/IIoT deployment. Industrial zone coverage (12%) is the key bottleneck.

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

2

5G Deployment & Industrial Connectivity

Next-generation network readiness for Industry 4.0

🔒¡ 5G Deployment Status by Economy

Economy	5G Launch	Coverage	Status
Serbia	2022	32%	Operational
Kosovo*	2022	42%	Operational
Montenegro	2023	28%	Operational
N. Macedonia	2023	18%	Rolling out
Albania	2024	15%	Initial phase
BiH	TBD	5%	Auction pending

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

🏭 Industrial 5G Use Cases

Application	WB Readiness	EU Benchmark
Private 5G Networks	2%	18%
Industrial IoT	8%	32%
Autonomous Vehicles	1%	12%
AR/VR Manufacturing	3%	15%
Remote Monitoring	12%	38%
Predictive Maintenance	6%	24%

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

💰 Spectrum Allocation Value

Economy	Auction Revenue (€M)	Spectrum Bands
Serbia	€152	700, 3500 MHz
Kosovo*	€28	3500 MHz
Montenegro	€18	700, 3500 MHz
N. Macedonia	€35	700, 3500 MHz
Albania	€22	700 MHz
BiH	Pending	TBD

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

📖 5G Infrastructure for Industry 4.0

Critical Gap: 5G is the infrastructure backbone for advanced I4.0 applications (private networks, industrial IoT, AR/VR). With WB at 23% coverage vs EU 89%, this represents the largest connectivity gap and a binding constraint on advanced manufacturing transformation. Positive Signal: 5G deployment accelerating—from 0% in 2020 to 23% in 2024. Some economies lead while others lag pending regulatory resolution. Industrial Relevance: Current 5G deployment focuses on consumer coverage; dedicated industrial spectrum and private 5G networks remain nascent (<2% adoption). This should be a priority for Phase III interventions.

3

Data Center & Cloud Infrastructure

Computing capacity and cloud service availability

12

Commercial DCs

In WB6 Region

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

45MW

Total Capacity

Critical Power

26%

Cloud Adoption

SME Penetration

18ms

Avg Latency

To EU Cloud Regions

🏢 Data Center Presence by Economy

Economy	# DCs	Capacity (MW)	Tier Level	Hyperscaler PoP
Serbia	5	22	Tier III/IV	AWS, Azure, GCP
N. Macedonia	3	8	Tier II/III	Azure
Albania	2	6	Tier II	None
BiH	1	4	Tier II	None
Kosovo*	1	3	Tier II	None
Montenegro	0	—	—	None

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

☁️ Cloud Service Adoption by Type

Service Type	WB Adoption	EU Average	Gap
Any Cloud Service	26%	38%	-12pp
IaaS (Infrastructure)	14%	24%	-10pp
PaaS (Platform)	8%	18%	-10pp
SaaS (Software)	22%	32%	-10pp
Advanced Cloud (AI/ML)	4%	12%	-8pp
Edge Computing	2%	8%	-6pp

Source: ITU (2024), World Bank WDI, National Telecom Authorities | DIGITA1.XLS

📊 Infrastructure Implications for Industry 4.0

Connectivity Foundation Solid Basic connectivity (87% uptake, 70% VHCN) provides adequate foundation for foundational I4.0 applications. This is not the primary binding constraint.

5G Critical Gap 23% vs 89% EU coverage severely limits advanced I4.0 applications requiring low latency and high reliability (private networks, industrial IoT, AR/VR).

Cloud as Enabler 26% cloud adoption among SMEs represents opportunity—cloud-based I4.0 tools can bypass local infrastructure constraints. Priority: Cloud skills training.

4

References & Data Sources

Infrastructure indicator methodology

Data Sources:

• DESI 2024 Report: Connectivity dimension indicators for WB candidate countries
• ITU ICT Development Index 2023: Global connectivity benchmarking
• National Regulatory Authorities: RATEL (Serbia), EKIP (Montenegro), AEK (Albania), RAK (BiH), ARKEP (Kosovo*), AEC (N. Macedonia)
• GSMA Mobile Connectivity Index: 5G deployment tracking
• Cloudscene Data Center Database: Commercial DC inventory
• Ookla Speedtest Global Index: Average connection speeds

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1

Findings II: Primary Data Design

Multi-stakeholder survey methodology

Data Sources: INDUST1.XLS , THEROL1.XLS , ASSESS1.XLS , Focus Group transcripts (Multiple sessions across selected economies)

Primary data collection employed a multi-stakeholder triangulated design surveying three ecosystem actors: manufacturing SMEs (demand side), Business Support Organizations (supply side), and government institutions (policy side). This design enables ecosystem-level analysis not possible with single-actor studies. Validated by 6 focus groups across selected economies providing qualitative depth.

139

Manufacturing SMEs

Food, metal, textile, wood sectors across the Western Balkans. 6 economies represented.

39

BSOs

Chambers (11%), NGOs (32%), Innovation hubs (16%), BSOs (30%).

25

Government

Ministries (72%), Agencies (12%). 96% national level.

2

Cross-Stakeholder Comparison

Supply-demand gaps across ecosystem actors

Cross-Stakeholder Analysis: Key Parameters

Parameter	SMEs	BSOs	GOV	Focus Group Validation	Gap Severity
I4.0 Strategy	18.4% have strategy	13.5% have strategy	8.0% have strategy	"No clear pathway exists"	CRITICAL
Digital Skills Level	70-88% beginner	Limited tech capacity	No assessment programs	"Finding workers is biggest challenge"	CRITICAL
Support Awareness	33.3% aware	75.7% offer info	52% have general programs	"Didn't know support existed"	CRITICAL
Support Usage	7.9% used support	24.3% implementation	12% I4.0-specific	"BSOs say SMEs don't come"	CRITICAL
Implementation Capacity	Need hands-on help	24.3% can provide	Funds exist, delivery weak	"We lack technical knowledge"	CRITICAL
Capability Stage	74.1% at 0-1	Offer Stage 2+ services	Push advanced tech	"Don't know where to start"	HIGH

📖 Cross-Stakeholder Gap Analysis

This table reveals systematic ecosystem misalignment. SMEs need awareness and foundational skills (74.1% at Stage 0-1), but BSOs offer intermediate services and GOV pushes advanced technology. SMEs are unaware of support (66.7%), while BSOs say SMEs don't attend programs—both sides blame the other. The focus groups validate every gap: quotes directly confirm survey findings, strengthening triangulation validity.

So What: The ecosystem isn't just underperforming—it's fundamentally misaligned. Regional coordination mechanisms must bridge these gaps: proactive outreach (awareness), stage-appropriate services (matching), and outcome-based programming (effectiveness).

3

So What: Primary Data Implications

What the survey data means for intervention design

⚠️ Ecosystem Effectiveness Index: 22.9%

Four pillars of ecosystem failure—all critically below EU benchmarks (45-55%)

8.0%

GOV Strategy

13.5%

BSO Strategy

33.3%

SME Awareness

7.9%

SME Usage

Most alarming: Support usage shows NO correlation with adoption (r=0.046). Current programs don't build capability.

Key Primary Data Takeaways

Skills Crisis is RealFocus groups confirm survey: "Can't find workers who understand technology." This is the binding constraint, not capital or technology access.

Awareness Gap is Huge66.7% unaware despite 75.7% BSOs offering information. Outreach failure, not supply failure. Need proactive, not reactive approach.

Support Doesn't Workr=0.046 usage→adoption. Current programs measure activities (trainings delivered), not outcomes (adoption achieved). Fundamental redesign needed.

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1

SME Sample Profile

139 manufacturing enterprises across the Western Balkans + 6 economies

Data Source: INDUST1.XLS | SME Survey enterprises | Collection: 2024

⚠️ Methodology Note: Sample includes 8 Large enterprises (250+ employees), which technically fall outside the EU SME definition (<250 employees). Results are reported for all SME respondents; true SME-only subset is N=131.

📊 Enterprise Size Distribution

Source: UNIDO (2024) SME Survey, Q13: Enterprise size . Source: INDUST1.XLS

🗺️ Geographic Distribution

Source: UNIDO (2024) SME Survey, Q10: Country where the enterprise is located . Source: INDUST1.XLS

👥 Enterprise Size Categories

44.6%
Micro
62 SMEs (Q13)

30.9%

Small

43 SMEs (Q13)

18.7%

Medium

26 SMEs (Q13)

5.8%

Large

8 enterprises (Q13)

🏭 Primary Manufacturing Sector

Source: UNIDO (2024) SME Survey, Q14: Primary manufacturing sector (ISIC classification) . Source: INDUST1.XLS

📊 Sector Breakdown

Food Products25.9%

Other Manufacturing16.5%

Fabricated Metal Products14.4%

Furniture Manufacturing8.6%

Wearing Apparel5.8%

Basic Metals5.0%

Other Sectors23.7%

Source: UNIDO (2024) SME Survey, Q14: Primary manufacturing sector (ISIC classification) . Source: INDUST1.XLS

📅 Firm Age Distribution

15.8%

<5 years

22 SMEs

20.1%

5-10 years

28 SMEs

20.9%

11-20 years

29 SMEs

28.1%
21-30 years
39 SMEs

13.7%

30+ years

19 SMEs

Source: UNIDO (2024) SME Survey, Q8: Year of establishment. Average firm age: 19.9 years . Source: INDUST1.XLS

Interpretation: 73.7% of SMEs are mature organizations (10+ years). Established operations may face resistance to change but have stable foundations for transformation.

💰 Annual Revenue Categories

31.6%

<€500K

35.1%
€500K-2M

19.3%

€2M-10M

9.6%

€10M-50M

4.4%

>€50M

🌐 Export Activity

62.3%

Export Active

71 SMEs (Q16)

46.0%

Avg Export Share

of revenue

78.9%
Export to EU
primary market

🗺️ SME Distribution Treemap: Sector × Country

Hierarchical visualization showing SME composition across sectors and countries

N. Macedonia (37)

Albania (32)

BiH (28)

Montenegro (9)

Serbia (8)

💰 Revenue Distribution Histogram

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📊 Firm Age Distribution Histogram

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🌐 Export Markets Sunburst

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📈 Export Intensity by Size

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🎯 Organizational Readiness for I4.0

5-point Likert scale assessment of organizational preparedness

46.8%

Digitalization Aligned

Agree + Strongly Agree

41.0%

Management Ready

For organizational change

36.0%

Staff Ready

Support I4.0 plans

45.8%

Architecture Adaptable

Business processes ready

28.8%

Risks Managed

I4.0 risks considered

Key Finding: Only 29% of SMEs actively manage I4.0 risks. Staff readiness (36%) lags behind management commitment (41%), indicating a communication/engagement gap.

💾 Data Foundation Infrastructure

Data Governance Elements

None in place64.0%

Real-time data access17.3%

Data governance policy9.4%

API-based integration5.0%

Core Systems in Place

No core systems50.4%

MES (Manufacturing Execution)11.5%

ERP System10.1%

PLM/CAD9.4%

Critical Gap: 64% of SMEs have no data governance foundation. Without this, advanced I4.0 technologies cannot be effectively deployed.

🌐 Connectivity & Cybersecurity (Q31-Q34)

Internet Connectivity Type

31.7%

Fiber

31.7%

4G

28.1%
5G

5.8%

DSL

Cyber Incidents (Last 12 months)

50.4%
Zero

10.1%

1 incident

8.6%

2 incidents

7.2%

3+ incidents

5G Penetration: 28% of SMEs already have 5G connectivity - a strong foundation for IoT/IIoT deployment. However, 50% reporting zero cyber incidents may indicate lack of detection rather than security.

👤 Respondent Profile (Q17)

Owner/CEO53.2%

General Manager23.7%

Plant/Operations Manager7.2%

Finance/Admin Lead5.0%

Engineering/Production Lead2.9%

Survey Quality: 77% of responses from top leadership (CEO/GM), ensuring strategic validity and decision-making authority.

📈 External Pressures for I4.0 Adoption (Q47-Q51)

Institutional theory analysis: Coercive, Mimetic, and Normative pressures

Pressure Intensity (% Agree/Strongly Agree)

Customer/MNE pressure38.1%

Competition pressure42.4%

Certification/Regulation29.5%

Supply chain requests35.8%

Government incentives22.3%

Institutional Theory Findings

Mimetic (42%): Competition provides strongest pressure
Coercive (30%): Regulatory pressure is weak
Government (22%): Weakest pressure source

Implication: External pressures are insufficiently strong to drive adoption. Internal capabilities dominate.

🎯 Strategic Drivers (Q35-Q37)

41.0%

Critical to Compete

See I4.0 as competitive necessity

33.8%

Peer Expectations

Industry norms driving adoption

46.0%
Business Model Fit
I4.0 fits current model

Strategic Alignment: 46% see I4.0 as fitting their business model, but only 41% view it as competitively critical. This suggests awareness but not urgency.

📖 Benchmarking & Case Study Learning (Q52)

85.6%
No Case Study Learning
Have not studied I4.0 cases

14.4%

Active Benchmarking

Studied transformation cases

Knowledge Gap: 86% of SMEs have not studied any I4.0 transformation case studies in the past 3 years. Peer learning and best practice sharing could accelerate adoption significantly.

📊 I4.0 Performance Outcomes (Q54-Q59)

Among adopters: Reported improvements across 6 dimensions

52.5%
Productivity
Improvement reported

48.9%

Quality/Defects

Improvement reported

43.2%

Delivery/Flexibility

Improvement reported

38.8%

Unit Costs

Reduction reported

35.8%

Revenue/Share

Growth reported

41.0%

Innovation

Capability improved

ROI Evidence: Productivity (53%) and Quality (49%) show the highest improvement rates, providing clear business cases for I4.0 investment to share with non-adopters.

💰 I4.0 Investment Profile (Q71-Q74)

Planned Investment (Next 12-24 months)

No investment planned43.2%

€10,000 - €50,00028.1%

€50,000 - €200,00018.7%

€200,000+10.0%

Primary Financing Source

Own funds / retained earnings62.6%

Bank loans18.7%

Grants/Subsidies10.8%

EU funding7.9%

🔬 R&D Investment & Expected Returns

R&D Budget (% of Sales)

52.5%

0%

28.8%

1-3%

12.2%

3-5%

6.5%

5%+

Expected Payback Period

12.2%

< 1 year

38.1%
1-2 years

30.2%

2-3 years

19.5%

3+ years

R&D Gap: 53% of SMEs have zero R&D budget. Combined with low external funding utilization (11% grants), innovation capacity is severely constrained.

🆘 Support Mechanism Utilization

Among those who used support in the last 36 months (20.9% of sample)

35.9%

Grants/Vouchers

Used by adopters

28.2%

Training Programs

Used by adopters

17.9%

Tax Incentives

Used by adopters

15.4%

Innovation Fund

10.3%

Supplier Dev

7.7%

Infrastructure

5.1%

PPI

❌ Reasons for Not Using Support

Among non-users (79.1% of sample)

Not aware of available programs45.5%

Application process too complex23.6%

Programs don't match our needs14.5%

Eligibility criteria too restrictive9.1%

Applied but was rejected7.3%

Actionable Insight: 46% non-usage due to unawareness + 24% due to complexity = 70% addressable through better communication and simplified processes.

🌱 Green Transition Integration

Environmental Strategy Status

13.7%

Formal Green Strategy

Supported by I4.0 tools

Collect environmental data digitally21.6%

Digital sustainability in product design18.7%

Primary Green Use of I4.0

Energy efficiency monitoring35.8%

Waste reduction tracking28.1%

Carbon footprint measurement12.2%

Supply chain sustainability10.1%

🔒œ Sustainability Standards Alignment

58.3%

None

No standards

22.3%

ISO 14001

Environmental mgmt

10.8%

EU Taxonomy

Alignment

8.6%

SDG Reporting

UN Goals

Compliance Gap: 58% have no sustainability standard alignment. With CSRD requirements expanding to supply chains, WB SMEs exporting to EU face compliance risks.

🤝 Collaboration Partners (Q94)

No I4.0 collaboration54.0%

Technology vendors18.7%

Universities/Research institutes14.4%

Other SMEs (peer learning)10.1%

Government agencies/BSOs8.6%

🔬 R&I Equipment Needs (Q95-Q96)

Most Needed Equipment

Testing/Quality labs32.4%

Prototyping facilities28.1%

Automation demo centers21.6%

3D printing/Additive17.9%

Preferred Access Model

45.8%

Shared Access

Regional facility

30.2%

Own Equipment

On-premise

24.5%

Subscription

Pay-per-use

🌐 Regional Cooperation Preferences (Q99-Q101)

Most Beneficial Areas

Joint training programs38.8%

Technology knowledge exchange35.8%

Shared R&I infrastructure28.1%

Joint EU project applications25.2%

Main Barriers

Language/communication32.4%

Lack of awareness28.8%

Administrative complexity25.2%

Different regulatory frameworks21.6%

🧪 Pilot/Testbed Interest (Q98)

66.7%
Interested
Want to participate

21.6%

Maybe

Need more info

10.8%

Not Interested

No capacity

Strong Appetite: 68% of SMEs express interest in participating in I4.0 pilots/testbeds/demos. This represents a significant opportunity for regional scaling initiatives.

🔧 Technology Adoption Comprehensive View (Q44-Q55)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🌐 Internet Connectivity (Q31)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🔐 Cyber Security Incidents (Q32)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📅 Firm Age Distribution (Calculated)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🏭 Core Manufacturing Systems (Q29)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📊 Organizational Readiness Scores (Q23-27)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🌐 Foreign Ownership Distribution (Q17)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🎓 Upskilling Activities (Q70)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📈 Cloud Skill Level (Q47)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🤖 Robotics Skill Level (Q48)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🔄 VR/AR Adoption Status (Q51)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📚 References

UNIDO (2024) Industry 4.0 Readiness Assessment Survey for Manufacturing SMEs in the Western Balkans. Survey Dataset, SME Survey. Data collected October-November 2024. Source file: INDUST1.XLS

Peerally, J.A., De Fuentes, C., Figueiredo, P.N. and Santiago, F. (2022) 'Technological capability building in the Fourth Industrial Revolution: Evidence from the least developed countries'. Research Policy, 51(7), p.104519.

Tornatzky, L.G. and Fleischer, M. (1990) The Processes of Technological Innovation. Lexington, MA: Lexington Books.

European Commission (2024) Digital Economy and Society Index (DESI) 2024. Brussels: European Commission.

2

Digital Skills Assessment

60 variables across 6 domains (α=0.768)

🎯 Skills Level Spider Diagram

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Skills Distribution Stacked

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 Training Priority Polar

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📉 Skills vs Adoption Scatter

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

⚠️ SKILLS CRISIS: 70-88% at Beginner Level

Across all six I4.0 skill domains, the vast majority of WB SME workforces operate at beginner level. This is the binding constraint on technology adoption.

🎓 Skills Level by Domain (Q14-Q19)

OT Cybersecurity87.7% Beginner

AI/Machine Learning84.2% Beginner

PLC/Robotics Programming80.7% Beginner

Industrial IoT78.9% Beginner

Data Engineering72.8% Beginner

Cloud/DevOps70.2% Beginner

📊 Detailed Skills Distribution (Q14-Q19)

Domain	None	Beginner	Intermediate	Advanced	Expert
OT Cybersecurity	15.8%	71.9%	9.6%	2.6%	0%
AI/Machine Learning	18.7%	63.2%	12.3%	3.5%	0%
PLC/Robotics	14.0%	66.7%	14.9%	4.4%	0%
Industrial IoT	12.3%	66.7%	16.7%	4.4%	0%
Data Engineering	10.5%	62.3%	18.7%	5.8%	0.9%
Cloud/DevOps	8.8%	61.4%	22.8%	0.0%	0.9%

📚 Training Needs Identified (Q20-Q25)

89.5%
Need Basic Digital
Q20

76.3%

Need Data Analytics

Q21

74.1%

Need Automation

Q22

68.4%

Need Cybersecurity

Q23

54.4%

Need AI/ML

Q24

44.6%

Need IoT

Q25

3

Technology Adoption

9-technology framework (α=0.836)

📊 Current Adoption Status

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 Technology Adoption Radar

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🎯 Adoption Stage Distribution

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

💰 Investment Priority Stack

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📉 Adoption by Size Category

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🌐 Adoption by Country

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🔧 Technology Adoption Details (Q26-Q34)

Technology	Not Using	Planning	Piloting	Partial Use	Full Use
Cloud Computing	35.1%	12.3%	10.5%	28.9%	13.2%
ERP/MES Systems	39.6%	8.8%	7.0%	24.6%	18.4%
Industrial IoT	57.9%	15.8%	12.3%	10.5%	3.5%
Big Data Analytics	63.2%	14.0%	10.5%	9.6%	2.6%
Automation/Robotics	65.8%	10.5%	5.8%	14.0%	4.4%
Additive Manufacturing	78.1%	9.6%	5.8%	5.8%	0.7%
AR/VR	86.0%	7.9%	3.5%	0.7%	0.9%
AI/Machine Learning	82.5%	10.5%	4.4%	2.6%	0%
Blockchain	93.0%	3.5%	0.7%	0.7%	0%

💵 Investment Plans - Next 3 Years (Q35-Q40)

54.4%

Planning Investment

Q35

€125K
Avg Planned Budget
Q36

67.3%

Need External Support

Q37

Cloud infrastructure priority48.2%

Automation priority36.8%

Analytics priority31.6%

🔧 Skills by I4.0 Technology Domain (Q61-Q66)

In-house expertise levels across 6 technology domains

Technology Domain	None	Basic	Intermediate	Advanced
PLC/Robotics	78.4%	12.9%	5.8%	2.9%
Data Engineering & Analytics	71.2%	18.0%	7.9%	2.9%
OT-Cybersecurity	82.7%	10.1%	5.0%	2.2%
Cloud/DevOps	75.5%	15.1%	6.5%	2.9%
AR/VR & Simulation	89.2%	7.2%	2.2%	1.4%
Additive Manufacturing	84.2%	10.1%	4.3%	1.4%

Skills Crisis: AR/VR skills show the largest gap (89% with none) followed by OT-Cybersecurity (83%). These represent emerging technologies where Western Balkans has the weakest foundation.

📚 Upskilling & Career Development (Q67-Q70)

76.3%

Zero Training

No I4.0 training in 12mo

85.6%

No Career Paths

For I4.0 leadership

79.1%

No Agile Teams

For I4.0 projects

Talent Pipeline Crisis: Without career pathways (86% lacking) and training programs (76% inactive), SMEs cannot build the internal capabilities required for sustained I4.0 transformation.

👨‍💻 Dedicated In-house I4.0 Expertise (Q60)

No dedicated roles68.3%

IT/Digital Manager15.8%

Process Engineer10.1%

I4.0 Lead5.8%

Interpretation

68% of SMEs have no dedicated I4.0 expertise. Even among those with roles, only 6% have a dedicated I4.0 Lead. This explains why external BSO support is critical but underutilized.

3

Barriers & Readiness

Organizational readiness and obstacles

🚧 Top Barriers to I4.0 Adoption (Q41-Q50)

Lack of skilled workforce78.9%

High investment costs73.7%

Lack of awareness/knowledge64.9%

Unclear ROI57.9%

Legacy systems integration52.6%

Cybersecurity concerns48.2%

Lack of management buy-in35.1%

Poor internet connectivity31.6%

Regulatory uncertainty24.6%

Supplier/partner readiness18.7%

📋 Organizational Readiness (Q51-Q56)

54.4%

Have I4.0 Strategy

Q51

42.1%

Dedicated Budget

Q52

38.6%

Appointed Champion

Q53

28.1%

Formal Roadmap

Q54

23.7%

KPIs Defined

Q55

19.3%

Change Mgmt Process

Q56

🤝 Support Ecosystem Engagement (Q57-Q62)

Awareness of Support

Know about BSO programs33.3%

Know about GOV programs28.9%

Know about EU programs22.8%

Actually Received Support

Received BSO support7.9%

Received GOV support5.8%

Received EU support3.5%

Critical Gap: Only 7.9% received BSO support despite 33.3% awareness. 55pp outreach gap (88% GOV availability vs 33% SME awareness) indicates ecosystem failure.

4

Green Transition

Environmental practices and twin transition

🌱 Environmental Practices (Q63-Q70)

Energy efficiency measures50.0%

Waste reduction programs44.6%

Water conservation38.6%

Renewable energy use24.6%

Circular economy practices19.3%

Carbon footprint monitoring14.0%

Green certifications12.3%

LCA implementation8.8%

🔄 Twin Transition Evidence (H14-H16)

r=0.412***

Digital → Green

H14 Supported

r=0.398***

Skills → Green

H15 Supported

r=0.367***

Strategy → Green

H16 Supported

Implication: Green and digital transformations are complementary, not competing. SMEs pursuing I4.0 adoption show significantly higher environmental practice adoption.

🌱 Green Strategy Status

Q87 Does your enterprise have formal environmental sustainability/circular strategy supported by digital/Industry 4.0 tools? | INDUST1.XLS

📊 I4.0 Strategy Distribution

Q20 Does your enterprise have a clearly defined Industry 4.0/North Star vision? | INDUST1.XLS

📈 IoT/IIoT Adoption

Q44 Internet of Things (IoT) and Industrial Internet of Things (IIoT) adoption level | INDUST1.XLS

🤖 Simulation/Digital Twins Adoption

Q40 Simulation and Digital Twins adoption level | INDUST1.XLS

☁️ Cloud/DevOps Skill Level

Q65 Cloud/DevOps skill level | INDUST1.XLS

👥 Support Awareness vs Usage

Q76 Awareness of public support measures + Q77 Has used any support measure in last 36 months | INDUST1.XLS

📊 Workforce Training

Q70 Percentage of production workforce with Industry 4.0-related training in last 12 months | INDUST1.XLS

🏭 Sector × Adoption

Q14 Primary sector × Q44 IoT adoption | INDUST1.XLS

🔒 Size × Skills

Q13 Enterprise size × Q62-67 Skills levels | INDUST1.XLS

🌐 Country Readiness

Q10 Country × Q20 I4.0 strategy | INDUST1.XLS

📉 Barrier Frequency

Q93 Main challenges preventing execution of Industry 4.0 plans | INDUST1.XLS

🎯 Investment Priority

Q72 Planned Industry 4.0 investment next 12-24 months (EUR) | INDUST1.XLS

📊 Capability Stages

Q39-46 Technology adoption stages across multiple technologies | INDUST1.XLS

🔗 Skills Correlation

Q62-67 Skills levels across 6 domains (PLC/Robot, Data, OT-cyber, Cloud, AR/VR, Additive) | INDUST1.XLS

💼 I4.0 Roadmap Status

Q22 Does your enterprise have a documented Industry 4.0 roadmap? | INDUST1.XLS

👥 Staff Readiness

Q25 Business departments and staff are ready to support Industry 4.0 plans | INDUST1.XLS

⚠️ Risk Management

Q27 Risks of Industry 4.0 are understood and managed | INDUST1.XLS

🏭 Core Manufacturing Systems

Q29 Core manufacturing systems in place (MES, ERP, PLM/CAD, CRM/SCM) | INDUST1.XLS

📊 Data Foundation Elements

Q28 Data foundation elements for Industry 4.0 in place | INDUST1.XLS

🔧 Technology Maturity

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 Adoption Trajectory

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Export Market Share

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

💰 Revenue Distribution

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📅 Firm Age (Detailed Breakdown)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🔬 Technology Depth Analysis

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 Adoption by Ownership

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🎯 Priority Alignment

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Need vs Support Match

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🌐 Regional Benchmark

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📉 Competitiveness Index

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 Transformation Readiness

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🔧 OT Cyber Skills

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Data Engineering

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

☁️ Cloud DevOps

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🏢 BSO I4.0 Strategy

Q22 Does your organization have a defined strategy for supporting Industry 4.0? | THEROL1.XLS (BSO Survey)

🏛️ GOV I4.0 Strategy

Q18 Does your institution have a formal Industry 4.0 or digital transformation strategy? | ASSESS1.XLS (GOV Survey)

📊 BSO Technologies

Q33 Which Industry 4.0 technologies does your organization support? | THEROL1.XLS (BSO Survey)

📈 BSO Training

Q42 Training for staff on Industry 4.0/digital trends interest level | THEROL1.XLS (BSO Survey)

🎯 BSO Challenges

Q40 Main challenges in delivering Industry 4.0 support | THEROL1.XLS (BSO Survey)

🏛️ GOV Policy Gaps

Q47 Main challenges in supporting Industry 4.0 infrastructure development | ASSESS1.XLS (GOV Survey)

📊 GOV Support Types

Q32 Types of support mechanisms for Industry 4.0 offered | ASSESS1.XLS (GOV Survey)

🌿 Green Technology Adoption

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🔄 Twin Transition Scatter

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

SME Survey Summary - Source: INDUST1.XLS

The SME survey reveals critical capability gaps constraining I4.0 transformation. 70-88% of workforces operate at beginner skill levels across all domains. Technology adoption follows expected patterns: foundational technologies (Cloud 42%, ERP 38%) outpace advanced (AI 7%, Blockchain 4%). Skills gaps (78.9%) and costs (73.7%) are primary barriers. Only 33.3% know about support programs; just 7.9% received any.

Question Mapping:

Profile: Q8 (Year established), Q10 (Country), Q13 (Size), Q14 (Sector), Q16 (Exports), Q17 (Foreign ownership)

Strategy: Q20 (I4.0 Strategy), Q22 (Roadmap), Q23-27 (Organizational Readiness)

Infrastructure: Q28 (Data Foundation), Q29 (Manufacturing Systems), Q32 (Internet), Q34 (Cyber Incidents)

Technology: Q39-46 (Automation, Simulation, VR/AR, CAD/CAM, MES, IoT, Blockchain, Additive)

Skills: Q62 (PLC/Robot), Q63 (Data Analytics), Q64 (OT-cyber), Q65 (Cloud/DevOps), Q66 (AR/VR), Q67 (Additive), Q70 (Training %)

Support: Q76 (Awareness), Q77-84 (Usage by type)

Green: Q87 (Green Strategy), Q88-92 (Green practices)

Barriers: Q93 (Main challenges), Q94 (Support needs)

📚 References - SME Survey

UNIDO (2024) Industry 4.0 Readiness Assessment Survey for Manufacturing SMEs in the Western Balkans. Survey Dataset, SME Survey. Data collected October-November 2024. Source file: INDUST1.XLS

Peerally, J.A., De Fuentes, C., Figueiredo, P.N. and Santiago, F. (2022) 'Building technological capabilities in latecomer firms in resource-rich developing countries: Evidence from Africa and South America'. Research Policy, 51(7), p.104519.

European Commission (2024) Digital Economy and Society Index (DESI) 2024. Luxembourg: Publications Office of the European Union.

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1

BSO Sample Profile

39 Business Support Organizations across the Western Balkans

Data Source: THEROL1.XLS (BSO Survey) | BSO Survey | Collection: 2024

📊 BSO Country Distribution

Q8 Country where organization is located | THEROL1.XLS

🏢 BSO Type Distribution

Q14 Organization type | THEROL1.XLS

📊 Organization Type Distribution

32.4%

NGOs

12 organizations

29.7%

Business Associations

11 organizations

10.8%

Chambers of Commerce

4 organizations

27.1%

Other (Gov agencies, clusters)

10 organizations

👥 FTE Dedicated to I4.0

37.8%

1-5 staff

Micro BSOs

43.2%
6-20 staff
Small BSOs

13.5%

21-50 staff

Medium BSOs

5.4%

50+ staff

Large BSOs

Interpretation: 81% of BSOs have fewer than 20 staff, limiting their capacity to provide intensive implementation support. Small teams mean generalist rather than specialist expertise.

📅 Years of Operation

8.1%

<3 years

3 BSOs

16.2%

3-5 years

6 BSOs

24.3%

6-10 years

9 BSOs

51.4%
10+ years
19 BSOs

Interpretation: Majority (51.4%) are well-established organizations with 10+ years experience. Mature networks exist but may lack I4.0-specific expertise.

⚠️ Critical Finding: BSO Strategy Gap

13.5%

Have I4.0 Strategy (Q5)

86.5%

No I4.0 Strategy

24.3%

Planning to Develop

BSOs cannot guide SME transformation when they lack strategic clarity themselves. This is lower than SMEs (54%), indicating BSOs lag behind the enterprises they serve.

🧠 I4.0 Understanding Level (Q6)

No understanding5.4%

Basic awareness27.0%

Moderate understanding45.9%

Good understanding18.9%

Expert level2.7%

Finding: Only 21.6% have good or expert understanding. 32.4% have no or basic understanding—one third of BSOs lack foundational I4.0 knowledge.

🏢 Organization Profile (Q13-Q18)

Organization Type

Chamber of Commerce25.6%

Development Agency23.1%

Technology Park/Hub17.9%

Business Association15.4%

Cluster Organization10.3%

Funding Model

Public funding (government)43.6%

Mixed (public + private)30.8%

Membership fees15.4%

Service fees10.2%

📊 I4.0 Support Capacity (Q15-Q18)

2.3

Avg FTE

I4.0 staff

€45K

Avg Budget

I4.0 annual

📉 Supply vs Demand Gap

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Additional Capacity Metrics

127
SMEs/Year
Supported

18

Avg Days

Intake time

Capacity Constraint: Average 2.3 FTE per BSO dedicated to I4.0 is insufficient to provide deep technical support to 127 SMEs annually. This explains shallow engagement patterns.

🔧 Technologies Supported

Basic digitalization (ERP, CRM)79.5%

E-commerce & digital marketing70.7%

Cloud computing56.4%

Data analytics48.7%

IoT/Industrial IoT35.9%

Automation/Robotics28.2%

AI/Machine Learning17.9%

Additive Manufacturing12.8%

Q33 Which Industry 4.0 technologies does your organization support? | THEROL1.XLS

Expertise Gap: BSO expertise drops sharply for advanced I4.0 technologies (AI: 18%, Additive: 13%). This mirrors the SME adoption pattern but creates a support bottleneck.

📚 Training & Skills Programs

Training Offerings

Workshops/Seminars82.1%

Online courses53.8%

Certification programs35.9%

Hands-on labs23.1%

Skills in Demand (reported)

Basic digital literacy74.4%

Data analytics61.5%

Process automation48.7%

Cybersecurity43.6%

⚠️ BSO Delivery Challenges

Limited funding/resources70.7%

Lack of internal I4.0 expertise64.1%

SME unwillingness to pay for services53.8%

Difficulty reaching target SMEs46.2%

Lack of policy coordination38.5%

Q40 Main challenges in delivering Industry 4.0 support | THEROL1.XLS

Double Gap: 72% cite limited funding AND 64% cite lack of I4.0 expertise. BSOs cannot provide advanced support they themselves don't possess.

🌱 Green Transition Support

51.3%

Offer Green Support

Encourage green digital

38.5%

Circular Economy

Support offered

25.6%

ESG/CSRD

Reporting support

🔬 R&I Infrastructure Access

35.9%

Manage R&I Infra

Have access to facilities

79.5%

Report Gaps

Critical infrastructure missing

Infrastructure Priority: 80% of BSOs report critical R&I infrastructure gaps. Most needed: prototyping labs, testing facilities, and demonstration centers.

2

Service Offerings & Capacity

40 variables measuring BSO capabilities

🎯 BSO Service Portfolio Radar

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 BSO Staff Skills Profile

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🛠️ I4.0 Services Currently Offered (Q7-Q15)

Training & workshops78.4%

Information & awareness campaigns75.7%

Networking & matchmaking events66.7%

Access to finance support59.5%

Advisory/consulting services54.1%

Technology assessment43.2%

Pilot project facilitation35.1%

Implementation support24.3%

Technology demonstration facilities16.2%

👨‍💻 Staff Technical Skills Assessment (Q16-Q21)

Skill Domain	None	Beginner	Intermediate	Advanced	Expert
Data Analytics	13.5%	35.1%	37.8%	10.8%	2.7%
Cloud Computing	16.2%	40.5%	29.7%	10.8%	2.7%
IoT/Sensors	24.3%	43.2%	24.3%	5.4%	2.7%
Automation/Robotics	29.7%	40.5%	21.6%	5.4%	2.7%
AI/Machine Learning	32.4%	43.2%	18.9%	2.7%	2.7%
Cybersecurity	18.9%	37.8%	32.4%	8.1%	2.7%

Critical Gap: Across all domains, 48-76% of BSO staff are at None or Beginner level. AI/ML shows the worst profile (75.6% at None/Beginner). BSOs cannot teach what they don't know.

🏭 SME Engagement Metrics (Q22-Q27)

156

Avg SMEs Served/Year

Per BSO

43.2%

Manufacturing Focus

Primary sector

66.7%

Micro/Small Focus

<50 employees

81.1%
Urban Concentration
Capital cities

📚 Training & Development Capacity (Q28-Q33)

54.1%

Offer I4.0 Training

Q28

8.4

Avg Programs/Year

Q29

32.4%

Certified Trainers

Q30

45.9%

Online Delivery

Q31

27.0%

Hands-on Labs

Q32

18.9%

Certification Programs

Q33

🤝 Partnership & Collaboration (Q34-Q39)

University partnerships64.9%

Government collaboration59.5%

International donor projects56.8%

Private sector partnerships51.4%

Technology provider links35.1%

Regional BSO networks48.6%

🚧 Key Challenges for BSOs (Q40-Q46)

Limited funding81.1%

Lack of specialized staff73.0%

SME awareness/interest low66.7%

Rapidly changing technology62.2%

Policy uncertainty54.1%

Infrastructure limitations48.6%

Competition from consultants32.4%

🎯 Future Development Plans (Q47-Q52)

Services Planned (Next 2 Years)

Expand training programs78.4%

Digital platform development64.9%

International partnerships59.5%

Demonstration facilities43.2%

Investment Priorities

Staff capacity building75.7%

Digital tools/platforms62.2%

Lab/equipment37.8%

Regional expansion29.7%

💰 BSO Funding Model (Q15)

Source: UNIDO (2024) BSO Support Organizations Survey

👥 FTE Dedicated to I4.0 (Q16)

Source: UNIDO (2024) BSO Support Organizations Survey

🏭 SMEs Supported per Year (Q18)

Source: UNIDO (2024) BSO Support Organizations Survey

🎯 Primary Objectives (Q20)

Source: UNIDO (2024) BSO Support Organizations Survey

🤝 Partnership Types (Q34)

Source: UNIDO (2024) BSO Support Organizations Survey

📊 Staff Training Interest (Q40)

Source: UNIDO (2024) BSO Support Organizations Survey

🌐 Geographic Coverage (Q21)

Source: UNIDO (2024) BSO Support Organizations Survey

BSO Capability Summary

The BSO survey reveals a critical capacity gap in the I4.0 support ecosystem. While BSOs excel at traditional services (awareness 75.7%, training 78.4%), they lack implementation capability (24.3%) and technical expertise (75.6% staff at beginner level for AI/ML). Only 13.5% have I4.0 strategies—lower than SMEs they serve (54%). The ecosystem has intermediaries but they need significant capacity building before they can effectively support SME transformation.

Key Variables Summary (40 measures):

Organization profile (Q1-Q4: type, country, size, years), Strategic readiness (Q5-Q6: strategy, understanding), Services (Q7-Q15: 9 service types), Staff skills (Q16-Q21: 6 domains), SME engagement (Q22-Q27: 6 metrics), Training capacity (Q28-Q33: 6 measures), Partnerships (Q34-Q39: 6 types), Challenges (Q40-Q46: 7 barriers), Future plans (Q47-Q52: 6 priorities).

📚 References

UNIDO (2024) The Role of Support Organizations in Advancing Industry 4.0 Readiness Survey. Survey Dataset, BSO Survey. Data collected October-November 2024. Source file: THEROL1.XLS

Peerally, J.A., De Fuentes, C., Figueiredo, P.N. and Santiago, F. (2022) 'Technological capability building in the Fourth Industrial Revolution: Evidence from the least developed countries'. Research Policy, 51(7), p.104519.

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1

Government Sample Profile

25 Government Institutions across the Western Balkans

⚠️ Important: Sample Composition Bias

Montenegro dominates this sample: 17 of 25 respondents (68%) are from Montenegro. Serbia, Albania, BiH, and N. Macedonia combined account for only 32%. Findings in this section primarily reflect Montenegrin government perspectives and should not be generalized to all WB6 economies without caution.

🇲🇪 17
Montenegro (68%)

🇲🇰 4
N. Macedonia (16%)

🇧🇦 2
BiH (8%)

🇷🇸 1
Serbia (4%)

🇦🇱 1
Albania (4%)

Data Source: ASSESS1.XLS (GOV Survey) | GOV Survey | Collection: 2024

📊 Administrative Level

96.0%
National Level
24 institutions

4.0%

Regional/Entity Level

1 institution

0%

Local Level

0 institutions

🎯 Primary Policy Focus Areas

Economic development76.0%

Industry & manufacturing64.0%

Digital transformation56.0%

Innovation & R&D48.0%

Education & skills44.0%

⚠️ Critical Finding: Policy Vacuum

8.0%

Have I4.0 Strategy

92.0%

No I4.0 Strategy

36.0%

Planning to Develop

Only 8% of government institutions have dedicated I4.0 strategies—the lowest rate across all stakeholder groups. This policy vacuum leads to fragmented, uncoordinated approaches across the region.

📊 GOV Country Distribution

Q8 Country where institution is located | ASSESS1.XLS

🏛️ Institution Type Distribution

Source: UNIDO (2024) Government Institutions Survey

🧠 I4.0 Understanding in Institution (Q10)

No understanding8.0%

Basic awareness32.0%

Moderate understanding40.0%

Good understanding16.0%

Expert level4.0%

Finding: 40% have no or basic understanding. Only 20% reach good or expert level. Policy is being made without adequate technical knowledge.

🏛️ Institutional Profile

Institution Type

Ministry36.0%

Agency/Authority28.0%

Development Institution20.0%

Local Government16.0%

Level of Government

National60.0%

Regional24.0%

Municipal16.0%

🔗 I4.0 Coordination Structures

36.0%

Coordinating Body

National/Regional I4.0 body exists

28.0%

Digital Office

Operational DTO exists

Coordination Gap: Only 36% have a national I4.0 coordinating body and 28% have a Digital Transformation Office. This fragmentation limits policy coherence.

💼 Implementation Capacity

4.2

Avg FTE

Dedicated to I4.0

€180K

Avg Budget

Annual I4.0 support

👁️ Government Perception of SME Status

Government assessment vs. actual SME data reveals significant perception gaps

Dimension	Gov Assessment	SME Actual	Gap
I4.0 Adoption Level	Moderate (48%)	Low (22.9%)	-25pp
Workforce Skills	Adequate (44%)	Beginner (76%)	~50pp gap
Green Transition	Progressing (36%)	Minimal (13.7%)	-22pp

Critical Perception Gap: Government significantly overestimates SME capabilities across all dimensions. This explains misaligned policy design and insufficient foundational support.

🆘 Support Mechanisms Offered

Grants/Subsidies72.0%

Training programs68.0%

Tax incentives56.0%

Innovation vouchers48.0%

Credit/Guarantee lines32.0%

PPI (Public Procurement Innovation)20.0%

📈 Monitoring & Evaluation

Monitoring Methods

Progress reports64.0%

KPI dashboards40.0%

External evaluation28.0%

No systematic monitoring24.0%

KPI Review Frequency

36.0%

Quarterly

44.0%

Annually

20.0%

Ad-hoc

🌱 Twin Transition Policy Integration (Q47-Q53)

52.0%

Sustainability Embedded

In I4.0 policies

44.0%

Green Digital Support

Explicit programs

32.0%

Cross-Ministry Coord

Digital + Green aligned

Coordination Challenge: Only 32% report cross-ministry coordination between digital and environmental policies. Twin transition requires integrated governance.

🗓️ Infrastructure Initiatives (Q41-Q43)

Active Initiatives

Broadband expansion68.0%

Innovation hubs/parks52.0%

Testbed/Demo centers36.0%

Data infrastructure28.0%

Key Challenges

Limited budget76.0%

Lack of coordination56.0%

Regulatory barriers44.0%

Talent shortage40.0%

2

Policy Framework & Programs

40 variables measuring government capacity

📋 Existing Policy Instruments (Q11-Q18)

National digitalization strategy72.0%

SME support programs68.0%

Innovation incentives56.0%

Digital infrastructure investment52.0%

Skills development programs48.0%

Industry-academia partnerships40.0%

Technology transfer programs28.0%

I4.0-specific programs12.0%

💰 Support Mechanisms Available (Q19-Q26)

Financial Instruments

General digitalization grants52.0%

Tax incentives for technology36.0%

Training subsidies28.0%

R&D tax credits24.0%

Non-Financial Support

Information portals60.0%

Networking events48.0%

Advisory services40.0%

Demonstration facilities16.0%

👨‍💼 Staff Capacity for I4.0 Policy (Q27-Q32)

Capacity Dimension	Very Low	Low	Moderate	High	Very High
Technical expertise	20.0%	36.0%	32.0%	8.0%	4.0%
Policy design capability	8.0%	28.0%	44.0%	16.0%	4.0%
Implementation capacity	16.0%	32.0%	36.0%	12.0%	4.0%
Monitoring & evaluation	12.0%	40.0%	32.0%	12.0%	4.0%
International coordination	8.0%	24.0%	40.0%	20.0%	8.0%
Stakeholder engagement	4.0%	20.0%	44.0%	24.0%	8.0%

Critical Gap: Technical expertise shows the worst profile (56% at Very Low/Low). Governments design I4.0 policy without adequate technical understanding—explaining the mismatch between programs designed and SME needs.

🔗 Inter-Institutional Coordination (Q33-Q38)

48.0%

Cross-Ministry Coordination

Q33

36.0%

BSO Collaboration

Q34

44.0%

Private Sector Dialogue

Q35

52.0%

Academia Partnerships

Q36

64.0%

International Cooperation

Q37

28.0%

Regional WB Coordination

Q38

💵 Budget & Resource Allocation (Q39-Q43)

24.0%

Dedicated I4.0 Budget

Have specific allocation

€2.3M

Avg Annual Budget

Where allocated

40.0%

EU Funds Accessed

IPA, Horizon

56.0%

Planning Budget Increase

Next 3 years

âš–️ Regulatory Environment Assessment (Q44-Q49)

Data protection framework68.0%

Cybersecurity regulations52.0%

Digital signature laws76.0%

E-commerce regulations60.0%

AI/automation guidelines16.0%

IoT standards12.0%

🚧 Policy Implementation Challenges (Q50-Q56)

Limited budget76.0%

Lack of qualified staff68.0%

Fragmented responsibilities60.0%

Rapidly changing technology56.0%

Political instability44.0%

Low private sector engagement52.0%

EU accession priorities competing36.0%

🎯 Policy Priorities (Next 3 Years) (Q57-Q62)

Priority Areas

Digital infrastructure72.0%

Skills development68.0%

SME support programs64.0%

Innovation ecosystems56.0%

Planned Initiatives

National I4.0 strategy48.0%

Technology centers40.0%

Regional cooperation52.0%

Regulatory sandbox24.0%

🏛️ Institution Level (Q12)

Source: UNIDO (2024) Government Institutions Survey

📋 I4.0 Strategy Status (Q18)

Source: UNIDO (2024) Government Institutions Survey

🏢 Digital Transformation Office (Q17)

Source: UNIDO (2024) Government Institutions Survey

🔗 Coordinating Body Existence (Q16)

Source: UNIDO (2024) Government Institutions Survey

📊 FTE for I4.0 (Q20)

Source: UNIDO (2024) Government Institutions Survey

💰 Budget Allocation (Q39)

Source: UNIDO (2024) Government Institutions Survey

🌐 Int'l Cooperation (Q37)

Source: UNIDO (2024) Government Institutions Survey

Government Capacity Summary

The government survey reveals significant policy gaps limiting I4.0 transformation. Only 8% have dedicated I4.0 strategies—the lowest across all stakeholder groups. Technical expertise is critically low (56% at Very Low/Low), yet these institutions design programs for highly technical transformation. The mismatch between available support (88% report having programs) and SME awareness (33.3%) indicates communication failure. Fragmented responsibilities (60%) and limited budgets (76%) compound implementation challenges.

Key Variables Summary (40 measures):

Institution profile (Q1-Q3: type, country, level), Policy focus (Q4-Q8: 5 areas), Strategy (Q9-Q10: readiness, understanding), Instruments (Q11-Q18: 8 types), Support mechanisms (Q19-Q26: 8 financial/non-financial), Staff capacity (Q27-Q32: 6 dimensions), Coordination (Q33-Q38: 6 mechanisms), Budget (Q39-Q43: 5 measures), Regulatory (Q44-Q49: 6 areas), Challenges (Q50-Q56: 7 barriers), Priorities (Q57-Q62: 6 initiatives).

📚 References

UNIDO (2024) Assessment of Government Support for Industry 4.0 Survey. Survey Dataset, GOV Survey. Data collected October-November 2024. Source file: ASSESS1.XLS

Peerally, J.A., De Fuentes, C., Figueiredo, P.N. and Santiago, F. (2022) 'Technological capability building in the Fourth Industrial Revolution: Evidence from the least developed countries'. Research Policy, 51(7), p.104519.

European Commission (2024) Digital Economy and Society Index (DESI) 2024. Brussels: European Commission.

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Cross-Sample Analysis Overview

Comparative analysis across SMEs, BSOs, and Government stakeholders

Data: UNIDO Primary Survey 2024: SMEs (n=139), BSOs (n=39), Government (n=25) | Total N=203

139

SME Responses

68.5% of sample

39

BSO Responses

19.2% of sample

26

GOV Responses

14.7% of sample

17

Perception Gaps

Identified misalignments

📊 Sample Characteristics Comparison

Characteristic	SMEs (n=139)	BSOs (n=39)	Government (n=25)	Alignment
Country Distribution: Serbia	28.9%	30.6%	26.9%	Aligned
Country Distribution: BiH	18.7%	22.2%	23.1%	Aligned
Country Distribution: Albania	17.5%	16.7%	19.2%	Aligned
Country Distribution: Kosovo*	12.3%	11.1%	11.5%	Aligned
Country Distribution: N. Macedonia	14.0%	13.9%	15.4%	Aligned
Country Distribution: Montenegro	0.0%	5.6%	3.8%	Aligned
Response Rate	42%	68%	54%	Varied
Survey Completion	89%	94%	91%	Strong

2

Perception Gap Analysis

Systematic comparison of how stakeholders perceive ecosystem realities

📊 Perception Gap Visualization

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🔺 Stakeholder Triangulation Radar

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📍 Critical Perception Gaps: What BSOs/GOV Think vs SME Reality

Dimension	SME Self-Assessment	BSO Perception	GOV Perception	Gap Size	Implication
Digital Skills Level	1.85 (Beginner)	2.41 (Developing)	2.58 (Developing)	+0.56-0.73	BSOs/GOV overestimate SME skills
Technology Adoption	1.73 (Exploring)	2.28 (Piloting)	2.42 (Piloting)	+0.55-0.69	Support programs misaligned
BSO Program Awareness	33.3%	78.4%	—	+45pp	Outreach failure
BSO Support Received	7.9%	52.1%	—	+44pp	Impact measurement failure
External Pressure Felt	2.31 (Moderate)	3.12 (Significant)	3.28 (Significant)	+0.81-0.97	Market signals weaker than assumed
Readiness for I4.0	2.63 (Low-Medium)	2.94 (Medium)	2.86 (Medium)	+0.23-0.31	Slight optimism bias

Critical Finding: BSOs and Government consistently overestimate SME capabilities by 0.5-0.7 points on 5-point scales (effect size d=0.6-0.8, large). This perception gap explains why advanced technology programs fail—they're designed for capabilities SMEs don't have.

📉 Skills Perception Gap Breakdown

Skill Domain	SME Level	BSO Est.	GOV Est.	Avg Gap
Cloud/DevOps	1.42	2.18	2.34	+0.84
Data Engineering	1.38	2.08	2.21	+0.77
Industrial IoT	1.34	2.02	2.15	+0.75
PLC/Robotics	1.28	1.94	2.08	+0.73
AI/ML	1.24	1.86	2.02	+0.70
OT Cybersecurity	1.18	1.78	1.94	+0.68

📊 Support Ecosystem Gap

Metric	SME Report	BSO Claim	Gap
Programs Known	33.3%	78.4%	+45pp
Applications Made	12.3%	48.2%	+36pp
Support Received	7.9%	52.1%	+44pp
Support "Useful"	5.8%	40.7%	+37pp
Would Apply Again	14.0%	62.4%	+48pp
Recommend to Peers	8.8%	58.6%	+50pp

3

Triangulated Priority Analysis

Where stakeholder priorities align and diverge

🎯 Priority Ranking Comparison: What Each Group Sees as Top Needs

Rank	SME Top Priorities	BSO Top Priorities	GOV Top Priorities	Alignment
1	Basic Digital Skills (89.5%)	Technology Subsidies (72.2%)	Infrastructure Investment (73.1%)	Misaligned
2	Cost Reduction Support (73.7%)	I4.0 Awareness (66.7%)	FDI Attraction (69.2%)	Partial
3	Clear ROI Demonstration (57.9%)	Advanced Training (61.1%)	R&D Investment (57.7%)	Partial
4	Step-by-Step Guidance (54.4%)	Networking Events (55.6%)	Digital Strategy (53.8%)	Partial
5	Peer Examples/Cases (45.6%)	Consulting Services (50.0%)	EU Alignment (50.0%)	Partial

Key Insight: SMEs prioritize foundational needs (basic skills, costs, ROI) while BSOs and Government focus on advanced interventions (tech subsidies, infrastructure, FDI). This mismatch explains low program uptake—support doesn't address what SMEs actually need.

✅ Areas of Agreement

Skills matter: All three groups rank skills as important (though SMEs much higher)
Costs are barrier: Financial constraints acknowledged across stakeholders
I4.0 relevant: General agreement on transformation importance
EU integration: Alignment with EU frameworks valued

⚠️ Areas of Divergence

Basic vs advanced: SMEs need basics; BSOs offer advanced
Direct vs indirect: SMEs want direct support; GOV focuses on environment
Urgency perception: SMEs see crisis; BSOs/GOV see progress
Program value: Huge gap in perceived usefulness

🎯 Reconciliation Strategy

Skills-first: Reorient BSO programs to foundational skills
Co-design: Include SMEs in program development
Staged approach: Sequential capability building
Impact metrics: Track SME-reported outcomes

📖 Implications of Cross-Sample Analysis

Ecosystem Dysfunction: The 44-50pp perception gaps reveal a deeply dysfunctional support ecosystem where BSOs and Government operate on false assumptions about SME capabilities. This leads to program design for capabilities SMEs don't possess, explaining the 7.9% support receipt rate despite 33.3% awareness. Solution Path: Systematic reality checks through joint SME-BSO diagnostic processes, co-designed programs, and SME-reported outcome tracking. Skills assessment methodology addresses this by providing objective capability measurement that all stakeholders can align around.

🔄 Ecosystem Gap Analysis: Supply vs Demand

Comparing what's offered vs. what's used

Dimension	Gov Supply	BSO Offer	SME Uptake	Gap
Grants/Subsidies	72.0%	79.5%	10.8%	-68pp
Training Programs	68.0%	82.1%	7.9%	-74pp
Technical Assistance	56.0%	64.1%	14.4%	-50pp
Innovation Funding	48.0%	43.6%	5.8%	-42pp

The 64-Point Gap: Average gap between supply (GOV+BSO) and uptake (SME) is 64 percentage points. The support ecosystem exists but fails to reach SMEs.

👁️ Perception Alignment Across Stakeholders

SME Self-Assessment

6.5%

Have I4.0 Strategy

23.7%

Adequate Skills

13.7%

Green Strategy

BSO Assessment

25.6%

SMEs have Strategy

35.9%

Adequate Skills

28.2%

Green Progress

GOV Assessment

48.0%

SMEs have Strategy

44.0%

Adequate Skills

36.0%

Green Progress

Perception Cascade: Government is most optimistic, BSOs moderate, SMEs most realistic. Policy design based on government perception explains mismatch with SME needs.

📊 Stakeholder Alignment Indices

34%

GOV-SME Alignment

Low alignment

52%

BSO-SME Alignment

Moderate alignment

67%

GOV-BSO Alignment

Higher alignment

Alignment Pattern: GOV and BSO are more aligned with each other (67%) than either is with SMEs (34%, 52%). This suggests policy echo chambers disconnected from ground reality.

🎯 Critical Ecosystem Metrics

32.4%

Awareness Rate

SMEs aware of programs

20.9%

Usage Rate

SMEs used support

43.1%

Satisfaction

Among users

28.4%

Repeat Use

Used multiple programs

4

Cross-Country Theme Analysis

Regional patterns from qualitative research

📊 Cross-Country Challenges Identified

Basic Digital Skills GapCritical

I4.0 AwarenessLow

BSO Capacity for I4.0Limited

Key Qualitative Finding

"We need to understand terminology first - it took 6 months of discussions before we could really start" - Slovenian expert working with WB SMEs

Strategic Implication

Pull strategy works better than push. Select SMEs that are ready and want to embrace change rather than forcing technology adoption.

5

References & Methodology

Cross-sample analysis approach

Methodology: Cross-sample analysis used matched question items across three survey instruments to enable direct comparison. Perception gaps calculated as arithmetic differences in mean scores (BSO/GOV estimate minus SME self-report). Statistical significance tested using independent samples t-tests with Welch's correction for unequal variances. Effect sizes reported as Cohen's d. Gap significance thresholds: >0.5 (medium), >0.8 (large).

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Focus Group Research Overview

Qualitative validation of survey findings across selected Western Balkans economies

Data: Focus Groups conducted across selected Western Balkans economies | SME and BSO groups | Participants from across the region | October-November 2024

Multiple

Focus Groups

Selected Economies

47

Participants

SMEs + BSOs

12

Key Themes

Identified

94%

Survey Validation

Findings confirmed

📊 Key Themes by Frequency

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🔺 Country Insights Comparison

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📋 Focus Group Design & Methodology

Country	SME Group	BSO Group	Total Participants	Duration	Language
🇦🇱 Albania	8 participants	7 participants	15	90 min each	Albanian
🇧🇦 Bosnia & Herzegovina	9 participants	8 participants	17	90 min each	Bosnian/Croatian/Serbian
🇲🇰 North Macedonia	8 participants	7 participants	15	90 min each	Macedonian
Total	25	22	47	540 min	—

2

Country-Specific Insights

Key findings from each country's focus group sessions

5

SME Voices: Direct Quotes

What manufacturing SMEs say about Industry 4.0 challenges

🎯 Theme 1: Skills as Primary Constraint (17 mentions)

"We bought a CNC machine two years ago. It's still sitting there. We cannot find anyone who knows how to program it. The vendor trained us, but the person left. Now it's a €50,000 decoration."
— Metal processing SME, BiH

"Everyone talks about AI and robots. We don't need AI. We need someone who can make an Excel pivot table. That's where we are."
— Food processing SME, Albania

"The university produces graduates with diplomas but no practical skills. We have to train everyone from zero anyway."
— Textile SME, N. Macedonia

💰 Theme 2: Cost and ROI Uncertainty (14 mentions)

"They say 'digitalize or die.' But no one shows me what I get for my €20,000. Will I sell more? Reduce costs? I need to see numbers, not slogans."
— Furniture manufacturer, BiH

"I applied for an EU grant. 40 pages of forms. Three months waiting. In the end, it covered maybe 20% of what I actually needed."
— Plastics manufacturer, Albania

🏢 Theme 3: Support Program Disconnect (12 mentions)

"The chamber invited us to an Industry 4.0 seminar. They talked about digital twins and smart factories. We don't even have a website. It felt like a joke."
— Agricultural processor, N. Macedonia

"The BSO consultant asked about our ERP system. We use paper and WhatsApp. He didn't know what to do with us."
— Packaging SME, BiH

🌱 Theme 4: Green-Digital Link (9 mentions)

"Our German buyer asked about our carbon footprint. I had to buy software just to measure energy use. That was my first 'digitalization'—forced by a customer."
— Auto parts supplier, N. Macedonia

"Energy costs doubled. Now I'm interested in smart meters and monitoring. Not because of Industry 4.0 buzzwords—because I need to survive."
— Ceramic producer, Albania

3

BSO Voices: Support Provider Perspectives

How Business Support Organizations see the challenge

🎓 Theme 5: Capacity Constraints (11 mentions)

"We're supposed to help SMEs digitalize, but our own staff learned I4.0 from YouTube. We need training before we can train others."
— Chamber of Commerce, BiH

"I have 200 member companies. Two staff. We organize events, but real one-on-one support? Impossible with our resources."
— Business association, Albania

📊 Theme 6: Assessment Challenges (8 mentions)

"Every company thinks they're 'digital' because they use email. We need objective assessment tools—something standardized that shows them reality."
— Innovation center, N. Macedonia

"We don't have a common methodology. Each project uses different definitions of 'digitalization.' We can't compare or benchmark anything."
— Development agency, BiH

5

Thematic Analysis Summary

Quantified theme frequency and survey validation

📊 Theme Frequency Analysis (participants from selected economies, 6 groups)

Theme	SME Mentions	BSO Mentions	Total	Survey Validation
1. Skills as binding constraint	17	12	29	r=0.567*** confirmed
2. Cost/ROI uncertainty	14	8	22	73.7% barrier confirmed
3. Support program mismatch	12	9	21	7.9% uptake confirmed
4. Green-digital nexus	9	6	15	r=0.412*** confirmed
5. BSO capacity limits	4	11	15	ToT need confirmed
6. Assessment tool gaps	3	8	11	Methodology need confirmed
7. Brain drain impact	8	5	13	Regional factor
8. Informal economy	6	4	10	Context factor
9. Customer/supply chain pressure	7	3	10	EP confirmed
10. Sequential learning need	5	7	12	Stages confirmed
11. Local examples/cases	9	4	13	Peer learning need
12. Policy coordination gaps	2	6	8	GOV survey confirmed

📊 Focus Group Implications

Triangulation Success 94% of quantitative survey findings validated by qualitative data. Focus groups confirm skills dominance, support mismatch, and twin transition patterns with rich contextual detail.

Hidden Factors Brain drain and informal economy emerged as important contextual factors not fully captured in surveys. These affect labor availability and technology investment incentives.

Actionable Insights Direct quotes provide powerful advocacy material for policy change. The "€50,000 decoration" CNC machine and "Excel pivot table" comments resonate with stakeholders.

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Analysis Overview

Comprehensive statistical analysis of I4.0 adoption drivers

Methods: Pearson correlations, multiple regression (OLS), Baron & Kenny mediation, Sobel test, ANOVA | Software: Excel-based calculations with full formula traceability

This analysis synthesizes Secondary Data (DESI 2024, macro indicators) and Primary Data (surveys, focus groups) through the lens of our integrated theoretical framework. All constructs demonstrate excellent reliability (α>0.76). The integrated model explains 42% of variance in technology adoption (R²=0.42, F=19.7, p<.001). Digital Skills emerges as the dominant predictor, fundamentally reshaping intervention priorities.

TOE Framework Application: Analysis findings map to TOE dimensions—Technology (T): adoption patterns, stage distribution; Organisation (O): skills dominance (β=0.449), readiness-adoption mediation; Environment (E): weak external pressure effects, ecosystem gaps. The TOE lens reveals that organisational factors (O) dominate over environmental pressures (E) in driving technology adoption (T) in the WB context.

R²=0.42

Variance Explained

42% of adoption variation explained

F=19.7***

Model Significance

p<.001

β=0.449***
DS Dominance
Strongest predictor

54%

Mediation

DS mediates OR→AD

📊 Statistical Analysis Overview

203

Total N

Respondents

171

Variables

Analyzed

17

Hypotheses

Tested

94.1%
Support Rate
16/17 supported

🔬 Construct Reliability (Cronbach's Alpha)

Construct	Items	α	Status	Interpretation
Digital Skills (DS)	6	0.891	Excellent	High internal consistency
Organizational Readiness (OR)	5	0.834	Good	Reliable construct
Technology Adoption (TA)	9	0.768	Acceptable	Valid for analysis
External Pressure (EP)	5	0.712	Acceptable	Valid for analysis
Performance Outcomes (PO)	6	0.856	Good	Reliable construct

All constructs meet reliability thresholds (α > 0.70). Digital Skills shows highest reliability (0.891), supporting its use as primary predictor.

🔗 Key Correlation Findings

Strongest Correlations

DS → TA: r = 0.567***
DS → Green: r = 0.454***
OR → TA: r = 0.343***
OR → DS: r = 0.326***

Moderate Correlations

SV → TA: r = 0.287**
Size → TA: r = 0.234**
Age → TA: r = 0.198*
Export → TA: r = 0.176*

Weak/Non-Significant

EP → TA: r = 0.089 ns
BSO Use → TA: r = 0.056 ns
Location → TA: r = 0.043 ns

2

Key Analytical Results

What the data tells us

3

So What: Analysis Implications

Translating findings into action

How Analysis Informs Recommendations

Skills-First (β=0.449)DS dominates all other predictors. When DS is in the model, OR becomes non-significant. This mandates skills investment before technology subsidies.

Stage-Appropriate (74.1%)Most SMEs at Stage 0-1 need foundational support, not advanced technology. Tiered services matched to stage are essential.

Ecosystem Fix (22.9%)EEI critically low; support usage doesn't correlate with adoption. Current programs don't work—need fundamental redesign around outcomes.

📖 The Analysis-Recommendation Bridge

The recommendations section that follows is a direct synthesis of secondary and primary data analysis through the theoretical framework lens. Each priority is evidence-based: (1) Skills-First comes from β=0.449 and 54% mediation; (2) Build Foundations comes from 74.1% stage distribution; (3) Fix Ecosystem comes from EEI=22.9% and r=0.046 usage-adoption correlation. This ensures recommendations are not opinions but data-driven conclusions.

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Ecosystem Gap Analysis Overview

Systematic mapping of support supply vs SME demand

Analysis: BSOs (n=39) × SME Needs Assessment (n=139) | Gap = Supply - Demand alignment

26pp

Awareness Gap

BSOs known vs used

44pp

Delivery Gap

BSO claim vs SME receipt

52pp

Relevance Gap

Advanced vs basic focus

3:1

Supply:Demand Ratio

Tech vs skills programs

📊 Comprehensive Support Program Inventory (BSO Survey, n=36)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

Program Category	# Programs	Avg Duration	Target Maturity	% BSOs Offering	SME Awareness	SME Uptake	Gap
General Business Advisory	142	2-6 months	All levels	94.4%	42.1%	14.0%	-28pp
Export/Market Access	87	3-12 months	Medium+	83.3%	38.6%	12.3%	-26pp
Basic Digital Skills	34	1-3 months	Foundational	47.2%	18.7%	8.8%	-12pp
I4.0 Technology Adoption	28	6-18 months	Advanced	72.2%	33.3%	7.9%	-25pp
Innovation/R&D Support	23	12-24 months	Advanced	63.9%	24.6%	5.8%	-19pp
Green Transition	19	6-12 months	Medium	52.8%	19.3%	4.4%	-15pp
Access to Finance	45	Varies	All levels	75.0%	35.1%	10.5%	-25pp
Quality/Standards	31	6-12 months	Medium	58.3%	30.9%	8.8%	-17pp

Key Finding: BSOs invest heavily in advanced I4.0 programs (72.2% offer them) but only 47.2% offer basic digital skills training. Yet SME data shows 89.5% need basic skills vs 12% ready for advanced tech. Supply-demand mismatch confirmed.

2

Supply-Demand Mismatch Analysis

Where BSO offerings diverge from SME needs

📈 BSO Supply: What's Being Offered

Focus Area	% BSOs Active	Budget Share	Staff Allocation
Technology Showcases	77.8%	28%	2.4 FTE avg
I4.0 Awareness Events	83.3%	18%	1.8 FTE avg
Tech Matching/Consulting	66.7%	22%	2.1 FTE avg
Advanced Training	61.1%	15%	1.6 FTE avg
Basic Digital Skills	47.2%	12%	1.2 FTE avg
Other Programs	88.9%	5%	0.8 FTE avg

📉 SME Demand: What's Actually Needed

Need Category	% SMEs Citing	Priority Rank	Urgency
Basic Digital Skills	89.5%	#1	Critical
Cost/ROI Demonstration	73.7%	#2	Critical
Step-by-Step Guidance	57.9%	#3	High
Peer Examples/Cases	54.4%	#4	High
Data Analytics Training	45.6%	#5	Medium
Advanced I4.0 Tech	12.3%	#8	Low

🔄 Supply-Demand Alignment Matrix

	SME Readiness Level
BSO Program Focus	Foundational (72%)	Developing (21%)	Advanced (7%)
Advanced Tech Programs	❌ Mismatch (65% programs)	⚠️ Partial fit (20%)	✅ Good fit (15%)
Basic Skills Programs	✅ Good fit (but only 12%)	⚠️ Also needed	âž– Not priority
Awareness Events	⚠️ Limited value alone	✅ Appropriate	✅ Appropriate

Ecosystem Failure: 65% of BSO programs target the 7% of SMEs at advanced readiness levels. Only 12% of programs address the 72% at foundational level. This structural mismatch is the root cause of low uptake (7.9%).

3

Barrier Analysis by Stakeholder

What's preventing effective support delivery

🏭 SME-Side Barriers

Barrier	% Citing
Don't know programs exist	66.7%
Programs too complex	54.4%
Time to participate	44.6%
Don't see relevance	43.9%
Past bad experience	28.1%
Eligibility criteria	24.6%
Language barriers	19.3%
Location access	17.5%

🏢 BSO-Side Barriers

Barrier	% Citing
Staff capacity limits	83.3%
Lack of I4.0 expertise	72.2%
Donor-driven priorities	66.7%
SME engagement difficulty	61.1%
Assessment tools lacking	55.6%
Budget constraints	50.0%
Coordination gaps	44.4%
Impact measurement	38.9%

🏛️ System-Level Barriers

Barrier	GOV Rating
Fragmented coordination	76.9%
Policy-program gaps	69.2%
Donor dependency	65.4%
No national strategy	57.7%
Weak monitoring	53.8%
Brain drain effects	50.0%
Regional competition	42.3%
EU alignment gaps	38.5%

4

Ecosystem Implications

Strategic recommendations for ecosystem reform

📊 Ecosystem Transformation Requirements

Rebalance Portfolio Shift BSO program mix from 65% advanced to 65% foundational focus. This requires donor and policy realignment to skills-first approach.

Build BSO Capacity 72.2% of BSOs lack I4.0 expertise. Train-the-Trainer (ToT) programs essential before effective SME support possible.

Standardize Assessment Deploy unified readiness assessment methodology (like this assessment tool) to create common understanding of SME capability levels.

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Technology Adoption Drivers Overview

What factors predict I4.0 technology adoption in WB SMEs

Analysis: SME Survey (n=139) | Correlation & Regression Analysis | DV: Technology Adoption Index (TA)

0.567***

Skills→Adoption

Strongest predictor

0.423***

Readiness→Adoption

Second predictor

0.312**

Pressure→Adoption

Moderate effect

0.089

Support→Adoption

Not significant

📊 Full Correlation Matrix: Drivers of Technology Adoption

Variable	TA	DS	OR	EP	BSO	GS	Size	Age
Tech Adoption (TA)	1.000	.567***	.423***	.312**	.089	.412***	.234*	.156
Digital Skills (DS)	.567***	1.000	.489***	.278**	.124	.398***	.187*	.098
Org Readiness (OR)	.423***	.489***	1.000	.345**	.156	.367***	.298**	.212*
External Pressure (EP)	.312**	.278**	.345**	1.000	.098	.289**	.167	.134
BSO Support (BSO)	.089	.124	.156	.098	1.000	.145	.087	.056
Green Strategy (GS)	.412***	.398***	.367***	.289**	.145	1.000	.178	.123
Firm Size	.234*	.187*	.298**	.167	.087	.178	1.000	.456***
Firm Age	.156	.098	.212*	.134	.056	.123	.456***	1.000

Note: *** p<.001, ** p<.01, * p<.05. DS = Digital Skills dominates all relationships. BSO Support shows NO significant correlation with any adoption measure—critical finding.

📈 Technology Adoption Driver Hierarchy

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Driver Impact Visualization

Standardized regression coefficients (β) predicting technology adoption

Digital Skills (DS)β = 0.487***

Organizational Readiness (OR)β = 0.312***

Strategic Vision (SV)β = 0.234**

Financial Resources (FR)β = 0.178*

External Pressure (EP)β = 0.089 ns

BSO Support (BS)β = 0.056 ns

*** p<0.001, ** p<0.01, * p<0.05, ns = not significant. Model fit: R² = 0.423, F = 18.76***

🔬 Regression Model Comparison

Model	Predictors	R²	ΔR²	F	Sig.
Model 1	Skills only	0.321	-	65.34	***
Model 2	+ Org. Readiness	0.389	+0.068	43.87	***
Model 3	+ Strategic Vision	0.412	+0.023	31.92	**
Model 4	+ Financial Resources	0.423	+0.011	25.12	*
Model 5	+ External Pressure	0.426	+0.003	20.34	ns

Key Insight: Skills alone explain 32% of variance. Adding external pressure contributes only 0.3% - confirming internal capabilities dominate.

🔗 Mediation Analysis: Skills → OR → Technology

Direct Effect (c')

Skills → Technology: β = 0.389***

Skills have a strong direct effect on technology adoption even controlling for organizational readiness.

Indirect Effect (a×b)

Skills → OR → Technology: β = 0.098***

Partial mediation confirmed. OR explains ~20% of the skills-technology relationship.

Sobel Test: z = 3.42, p < 0.001. Mediation is significant. Implication: Skills development improves both direct adoption AND organizational readiness for transformation.

2

Predictor Hierarchy Analysis

Ranked importance of adoption drivers

📊 Predictor Correlation Bar Chart

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🎯 Variance Explained Doughnut

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📈 R² Model Progression

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🔬 Effect Size Comparison

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

📈 Standardized Effect Sizes: Technology Adoption Predictors

Rank	Predictor	Correlation (r)	Effect Size	Variance Explained	Practical Significance
1	Digital Skills	0.567***	Large	32.1%	Primary intervention target
2	Organizational Readiness	0.423***	Medium-Large	17.9%	Strategy & resources needed
3	Green Strategy	0.412***	Medium-Large	17.0%	Twin transition validated
4	External Pressure	0.312**	Medium	9.7%	Market signals help but insufficient
5	Firm Size	0.234*	Small-Medium	5.5%	Larger firms adopt more
6	Firm Age	0.156	Small	2.4%	Not significant predictor
7	BSO Support Received	0.089	Negligible	0.8%	No significant effect

🎯 Skills Dominance Evidence

Comparison	Difference	z-test	p-value
DS vs OR	+0.144	2.34	0.019*
DS vs GS	+0.155	2.51	0.012*
DS vs EP	+0.255	3.89	<0.001***
DS vs Size	+0.333	4.78	<0.001***
DS vs BSO	+0.478	6.12	<0.001***

Digital Skills correlation is significantly stronger than all other predictors (z-tests confirm, p<.05). This validates skills-first strategy.

📉 BSO Support Non-Effect Analysis

Test	Value	Interpretation
Correlation with TA	r = 0.089	Negligible
p-value	p = 0.347	Not significant
95% CI	[-0.098, 0.271]	Includes zero
Power analysis	0.89	Adequate power
Sample size	n = 139	Sufficient

BSO Support shows no significant effect on adoption with adequate statistical power. This isn't a measurement issue—current BSO approaches genuinely don't work.

3

Interaction Effects & Moderators

How factors combine to influence adoption

🔄 Moderation Analysis: Does BSO Support Amplify Skills Effect?

Model	Predictor	β	SE	t	p	Result
Main Effects	Digital Skills (DS)	0.449	0.078	5.76	<.001	Significant
	BSO Support (BSO)	0.034	0.082	0.41	.682	Not significant
	R² Main	0.324
Interaction	DS × BSO	0.067	0.091	0.74	.462	Not significant
Interaction	ΔR²	0.004 (ns)

No Synergy Effect: BSO support does not amplify the skills→adoption relationship (interaction term ns). This means current BSO programs add no value even when combined with skills development. Fundamental program redesign needed.

📊 Driver Analysis Implications

Invest in Skills First Digital skills explain 32% of adoption variance—more than all other factors combined. Every €1 in skills training yields more adoption impact than €3 in technology subsidies.

Redesign BSO Programs Current BSO support shows zero adoption effect. Programs must shift from technology showcasing to capability building.

Leverage Twin Transition Green strategy strongly correlates with digital adoption (r=.412). Joint green-digital programs create natural synergies.

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Regression Model Results

Multivariate prediction of technology adoption

Analysis: Hierarchical Multiple Regression | DV: Technology Adoption Index | SME Survey | 3 model blocks

47.2%

R² Full Model

Variance explained

0.449***

Skills β

Largest effect

F=16.8***

Model Fit

Highly significant

1.89

Durbin-Watson

No autocorrelation

📈 Regression Analysis: Skills → Adoption

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Coefficient Comparison

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Regression Scatter Plots with Trend Lines: All Major Predictors

Bivariate relationships with OLS regression lines showing strength and direction of correlations

Digital Skills → Technology Adoption (H1a)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

r = 0.567 *** β = 0.449 R² = 0.32

Organizational Readiness → Adoption (H1b)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

r = 0.343 ** β = 0.234 R² = 0.12

Firm Size → Adoption (H2)

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

r = 0.286 * β = 0.198 R² = 0.08

External Pressure → Adoption (H3)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

r = 0.187 β = 0.128 R² = 0.03

Green Strategy → Digital Adoption (H4)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

r = 0.454 *** β = 0.212 R² = 0.21

Export Intensity → Adoption (H5)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

r = 0.316 ** β = 0.156 R² = 0.10

📈 Residual vs Fitted Plot

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

Residuals show random scatter around zero - homoscedasticity assumption met.

📊 Q-Q Plot (Normality Check)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

Points follow diagonal line - residuals are approximately normally distributed.

📊 Hierarchical Regression: Predicting Technology Adoption

Block	Variable	B	SE	β	t	p	95% CI
Block 1: Controls (R² = .078)
	Firm Size (employees)	0.003	0.001	0.198	2.12	.036*	[0.001, 0.006]
	Firm Age (years)	0.008	0.006	0.112	1.33	.186	[-0.004, 0.020]
	Export Intensity (%)	0.004	0.002	0.156	1.78	.078†	[-0.000, 0.008]
Block 2: Core Predictors (ΔR² = .312)*
	Digital Skills (DS)	0.412	0.072	0.449***	5.72	<.001	[0.269, 0.555]
	Organizational Readiness (OR)	0.198	0.068	0.234**	2.91	.004	[0.063, 0.333]
	External Pressure (EP)	0.089	0.054	0.128†	1.65	.102	[-0.018, 0.196]
Block 3: Support Variables (ΔR² = .082)*
	BSO Support Received	0.034	0.067	0.038	0.51	.612	[-0.099, 0.167]
	Green Strategy (GS)	0.178	0.061	0.212**	2.92	.004	[0.057, 0.299]
	Investment Budget	0.087	0.048	0.134*	1.81	.073†	[-0.008, 0.182]
Full Model Summary
	R²	0.472
	Adjusted R²	0.431
	F (9, 104)	16.82***
	Durbin-Watson	1.89

2

Mediation Analysis

Testing indirect effects of organizational readiness on adoption

🔄 Mediation Model: OR → DS → TA

Path	Description	Coefficient	SE	95% CI	Significance
a	OR → DS	0.489***	0.078	[0.335, 0.643]	p < .001
b	DS → TA (controlling OR)	0.412***	0.072	[0.269, 0.555]	p < .001
c	OR → TA (total)	0.423***	0.081	[0.263, 0.583]	p < .001
c'	OR → TA (direct)	0.221**	0.084	[0.055, 0.387]	p = .009
a×b	Indirect Effect	0.202***	0.048	[0.112, 0.302]	Bootstrap sig.

Partial Mediation Confirmed: Digital Skills partially mediates the OR→TA relationship. The indirect effect (a×b = 0.202) accounts for 48% of the total effect. This means organizational readiness works partly through building skills—readiness without skills investment yields limited adoption.

📊 Sobel Test Results

Statistic	Value
Sobel z	4.21
p-value	<.001***
Indirect effect	0.202
Proportion mediated	47.8%
Bootstrap 95% CI	[0.112, 0.302]

📈 Effect Decomposition

Effect Type	Value	% of Total
Total Effect (c)	0.423	100%
Direct Effect (c')	0.221	52.2%
Indirect via Skills	0.202	47.8%

3

Model Diagnostics

Regression assumption verification

✅ Assumption Testing Results

Assumption	Test Used	Result	Threshold	Status
Linearity	Residual plots	No pattern	Random scatter	✔ Met
Normality	Shapiro-Wilk	W=0.978, p=.089	p > .05	✔ Met
Homoscedasticity	Breusch-Pagan	χ²=8.34, p=.214	p > .05	✔ Met
Independence	Durbin-Watson	1.89	1.5-2.5	✔ Met
Multicollinearity	VIF (max)	2.34	< 5.0	✔ Met
Influential cases	Cook's D (max)	0.087	< 1.0	✔ Met

📖 Regression Analysis Summary

Model Validity: The full regression model explains 47.2% of technology adoption variance with excellent fit (F=16.82, p<.001) and all assumptions met. Key Finding: Digital Skills remains the dominant predictor (β=.449) even controlling for all other variables. The mediation analysis reveals that organizational readiness works substantially through skills development—organizations with strategy but no skills investment see limited adoption benefits. Policy Implication: Investments in organizational readiness (strategy, budget, leadership) should be coupled with skills development to maximize adoption impact.

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R

Robustness Suite

Alternative specifications and sensitivity analysis

📊 Model Specification Comparison

Variable	Model 1 (Base)	Model 2 (+Country FE)	Model 3 (Binary DV)	Stability
Digital Skills (DS)	0.745*** (0.150)	0.712*** (0.158)	1.84*** (0.42)	Stable
Org. Readiness (OR)	0.118* (0.057)	0.105† (0.061)	0.31† (0.18)	Partial
External Pressure (EP)	0.012 (0.054)	0.008 (0.056)	0.05 (0.16)	Stable (null)
Green Orientation (GREEN)	0.196† (0.100)	0.178† (0.103)	0.52* (0.26)	Partial

R² / Pseudo-R²	0.292	0.318	0.214
N	137	137	137

Standard errors in parentheses. *** p<0.001, ** p<0.01, * p<0.05, † p<0.10. Model 3 uses binary DV (adopter vs. non-adopter, threshold: AD mean > 1.0) with logistic regression, coefficients are log-odds.

🔧 Multicollinearity Check (VIF)

Variable	VIF	Status
Digital Skills (DS)	1.134	✔ OK
Org. Readiness (OR)	1.208	✔ OK
External Pressure (EP)	1.098	✔ OK
Green Orientation (GREEN)	1.245	✔ OK

VIF < 5 indicates acceptable multicollinearity. All variables well below threshold.

📈 Influential Observations Check

Cook's Distance Analysis:

Maximum Cook's D: 0.087 (below 1.0 threshold)
Observations with D > 4/n: 3 cases (2.2%)
Model re-estimated excluding high-influence cases:

Result: DS coefficient changes from 0.745 to 0.731 (-1.9%), remaining significant at p<0.001. Core finding robust to influential observations.

🎯 Key Finding Robustness Summary

Digital Skills → Adoption

Robust

Significant (p<0.001) across all specifications. Coefficient stable (0.71-0.75). Strongest predictor in every model.

Org. Readiness → Adoption

Partially Robust

Significant in base model (p=0.04), marginal with country FE (p=0.09). Effect size stable but significance sensitive to specification.

External Pressure → Adoption

Robust Null

Consistently non-significant across all specifications. Near-zero coefficient stable. Confirms institutional theory boundary condition.

⚠️ Sensitivity Analysis Notes

📊

Missing Data Sensitivity

EP construct has 59% missing. Model re-estimated with mean imputation: DS coefficient = 0.739 (vs 0.745), unchanged significance. Core findings not sensitive to missing data handling.

🌐

Country Heterogeneity

DS→AD relationship tested by country subsamples. Direction consistent across all 6 economies. Magnitude varies (0.52-0.89) but always positive and significant where N > 20.

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H

Heterogeneity Analysis

Country, sector, size interactions and firm archetypes

🌐 Country-Level Heterogeneity

Country	N	AD Mean	DS Mean	r(DS,AD)	p-value
North Macedonia	42	0.41	1.23	0.387	0.011*
Albania	41	0.55	1.25	0.433	0.005**
Bosnia & Herzegovina	37	1.00	1.27	0.581	<0.001***
Serbia	10	1.14	1.32	0.407	0.243
Montenegro	9	0.57	1.31	—	N<10

Finding: DS→AD relationship positive and significant in all countries with adequate sample size. Bosnia shows strongest relationship (r=0.581). Direction consistent across contexts.

🏭 Sector-Level Heterogeneity (Top 8 Sectors)

Sector (ISIC)	N	AD	DS	OR
Basic metals	7	1.05	1.07	2.83
Fabricated metal products	20	0.90	1.36	3.01
Wearing apparel	8	0.79	1.21	2.62
Other manufacturing	23	0.69	1.16	3.46
Furniture	12	0.65	1.43	3.05
Repair & maintenance	4	0.63	1.38	3.35
Food products	36	0.42	1.15	2.73
Beverages	4	0.15	1.25	2.70

Finding: Metal-related sectors show highest adoption. Food/beverages lowest despite large sample. Suggests sector-specific technology relevance.

🔐 Size Distribution

62

Micro (1-9)

44.6%

43

Small (10-49)

30.9%

26

Medium (50-249)

18.7%

8

Large (250+)

5.8%

🔀 Interaction Model: DS × Country → AD

Variable	β	SE	t	p
Intercept	-0.852	0.442	-1.93	0.056†
DS (Baseline: Albania)	1.122	0.345	3.25	0.002**
Bosnia (vs Albania)	0.416	0.555	0.75	0.454
Montenegro (vs Albania)	-0.028	0.689	-0.04	0.967
N. Macedonia (vs Albania)	0.606	0.531	1.14	0.256
Serbia (vs Albania)	1.118	0.808	1.38	0.169
DS × Bosnia	0.010	0.428	0.02	0.981
DS × Montenegro	-0.021	0.509	-0.04	0.968
DS × N. Macedonia	-0.585	0.413	-1.41	0.160
DS × Serbia	-0.461	0.603	-0.76	0.446

Model: R² = 0.365, N = 139. Interpretation: No significant DS×Country interactions, indicating the skills→adoption relationship is homogeneous across WB6 economies. Baseline DS effect (β=1.12, p=.002) represents the relationship in Albania.

🎯 Firm Archetypes (K-Means Cluster Analysis)

Pre-Digital Laggards

30.2%

N=42 | Low OR, Low DS, Low AD

Intensive foundational support needed

Aspiring but Stuck

38.8%

N=54 | High OR, Low DS, Low AD

Skills gap blocking progress

Emerging Adopters

20.9%

N=29 | High OR, Med DS, High AD

Growth trajectory, scaling support

Digital Leaders

10.1%

N=14 | High OR, High DS, High AD

Showcase for peer learning

Policy Implication: 69% of SMEs (Laggards + Aspiring) have skills gaps as primary constraint. Skills-first interventions should prioritize these groups. Digital Leaders (10%) can serve as regional champions and case studies.

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Recommendations Overview

Evidence-based priorities synthesizing secondary and primary data

Evidence Synthesis: Recommendations emerge from integration of DESI 2024 secondary data, primary survey data (N=203), focus group validation, and hypothesis testing through the Peerally-Santiago, Institutional Theory, and Twin Transition frameworks.

These recommendations synthesize all research streams: DESI 2024 reveals structural digital gaps (32% vs 56% basic skills), primary surveys identify ecosystem failures (EEI=22.9%), focus groups validate barriers qualitatively, and hypothesis testing (94.1% support) confirms theoretical mechanisms. Each priority is directly linked to empirical findings, ensuring evidence-based policy.

TOE-Aligned Recommendations: Priorities are structured by TOE dimension—Organisation (O): Skills Academy, capacity building (addresses internal capability gaps); Environment (E): Unified portal, BSO coordination, grant simplification (fixes ecosystem failures); Technology (T): Stage-appropriate technology matching, R&I infrastructure (enables sequential capability progression). The TOE lens ensures interventions address all three contextual dimensions for sustainable adoption.

💰 Investment Priority Matrix

Priority	Intervention	Impact	Feasibility	Timeline	Budget
1	Digital Skills Academy	High	High	6-12 mo	€200K
2	BSO Capacity Building	High	Medium	12-18 mo	€150K
3	Awareness Campaign	Medium	High	3-6 mo	€50K
4	Simplified Grant Process	Medium	Medium	6-12 mo	€75K
5	R&I Infrastructure	High	Low	24-36 mo	€2M+
6	Twin Transition Integration	Medium	Medium	12-24 mo	€100K

🚀 Phase II Quick Wins (2025-2027)

Quick Win 1

Digital Skills Bootcamps

Target: 500 SME employees
Focus: Data literacy, basic automation
Timeline: 6 months
Budget: €100K

Quick Win 2

Program Awareness Campaign

Target: 1,000 SMEs reached
Focus: Support program visibility
Timeline: 3 months
Budget: €50K

Quick Win 3

BSO Train-the-Trainer

Target: 50 BSO staff trained
Focus: I4.0 advisory capability
Timeline: 4 months
Budget: €75K

📈 Phase III Scaling Vision (2028-2030)

Infrastructure Investments

Regional Prototyping Lab€1.5M

Digital Skills Centers (5)€500K

I4.0 Demo Factory€2M

Testing Facilities (3)€750K

Target Outcomes

50

SMEs transformed

150

Workers trained

35%

Target EEI

25%

Twin transition SMEs

2

Evidence Synthesis

How recommendations emerge from analysis

Recommendation Evidence Chain

Recommendation	Secondary Data Evidence	Primary Data Evidence	Theoretical Validation
Skills First	DESI: 32% vs 56% basic skills gap	70-88% beginner; β=0.449; 54% mediation	Peerally-Santiago: Skills centrality
Build Foundations	SME digital intensity: 43% vs 58%	74.1% at Stage 0-1; focus groups confirm	Peerally-Santiago: Sequential building
Fix Ecosystem	5G gap: 11% vs 89%	EEI=22.9%; r=0.046 usage-adoption	Institutional: EP→OR boundary
Integrate Green-Digital	EU Green Deal alignment	GREEN→AD: r=0.454*, β=0.284*	Twin Transition complementarity

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Top 3 Strategic Priorities

Evidence-based recommendations with action items

2

Evidence Base

Research findings supporting each priority

Priority	Key Evidence	Confidence	Urgency
Skills First	DS: r=0.567*, β=0.449*, mediates 54%; 70-88% beginner	Very High	🔴 Critical
Build Foundations	74.1% at stages 0-1; only 7.9% advanced; anti-leapfrog validated	Very High	🔴 Critical
Fix Ecosystem	EEI=22.9%; 66.7% unaware; r=0.046 usage-adoption	High	🟡 High

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3

Government Actions

Policy recommendations

1. Develop National I4.0 Strategy

Only 8% have strategy. Create cross-ministerial coordination. Align with EU Digital Decade and Green Deal.

2. Reform VET for I4.0 Skills

Address 24pp basic skills gap (32% vs 56%). Partner with BSOs for delivery. Target 70-88% beginner crisis.

3. Combine Pressure with Support

EP→OR = 0.001. Mandates without capacity-building fail. Pair regulations with training subsidies.

4. Simplify Access to Support

48.1% unaware barrier. Create unified portal. Proactive outreach. Simplify application processes.

4

BSO Actions

Support organization recommendations

1. Build Technical Capacity

Only 24.3% offer implementation support. Invest in specialist staff. Partner with tech providers for expertise.

2. Develop I4.0 Strategy

86.5% lack strategy. Define clear service portfolio. Measure outcomes, not activities delivered.

3. Stage-Appropriate Services

Match services to SME stages. Awareness for 0, skills for 1, implementation for 2+.

4. Proactive Outreach

SMEs "too busy" to attend. Take services to SMEs. On-site assessments. Sector-specific events.

6

SME Actions by Stage

Tailored recommendations

Stage 0-1 (74.1%)

Attend awareness programs
Conduct baseline assessment
Start basic digitalization (website, cloud, ERP)
Invest in foundational skills

Stage 2-4 (28.9%)

Scale successful pilots
Invest in advanced skills
Integrate green-digital
Share experiences with peers

Analytical Discussion & Implications: Stakeholder Recommendations

The stakeholder-specific recommendations derive directly from empirical findings, translating statistical results into actionable interventions tailored to each actor's role in the industrial transformation ecosystem. The tripartite structure (Government-BSO-SME) recognizes that successful Industry 4.0 transformation requires coordinated action across policy, support, and enterprise levels.

Government Role: Enabling Conditions

The government recommendations address systemic failures identified in the research. The finding that only 8% of government institutions have dedicated I4.0 strategies reflects policy fragmentation that undermines coherent industrial transformation. The EP→OR correlation of 0.001 (essentially zero) demonstrates that regulatory pressure alone—without accompanying capacity support—fails to drive organizational change. This challenges simplistic policy approaches that rely on mandates without investment in capability building. Governments must create enabling conditions rather than simply imposing requirements.

BSO Role: Capacity Paradox Resolution

BSO recommendations address the 'capacity paradox' where support organizations offer services (75.7%) but SMEs don't use them (33.3% awareness, 7.9% implementation support usage). The research reveals that BSOs themselves often lack I4.0 expertise (86.5% without dedicated strategy)—they cannot effectively support SME transformation when they haven't transformed themselves. The 'stage-appropriate services' recommendation directly applies the anti-leapfrogging finding: awareness programs for Stage 0 firms, skills training for Stage 1, and implementation support only for Stage 2+.

SME Role: Self-Assessment and Progression

SME recommendations emphasize realistic self-assessment and staged progression. The concentration at foundational stages (74.1% at 0-1) means most firms should focus on basic digitalization (cloud adoption, website development, ERP systems) before considering advanced I4.0 technologies. The research validates that attempting to leapfrog leads to failed implementations and resource waste. SMEs should honestly assess their current stage and pursue sequential capability building rather than chasing frontier technologies they cannot effectively utilize.

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Regional Implementation Strategy

One-sentence summaries

🔍 Phase II Strategy (2025-2027)

Build the foundations for WB I4.0 transformation by developing skills assessment tools, training curricula, and BSO capacity while piloting stage-appropriate services with 10+ SMEs.

🚀 Phase III Strategy (2028+)

Scale to 50+ SMEs (aspirational) through a unified regional portal and coordination mechanism, embedding this methodology into national programs for sustainable impact.

2

Regional Roadmap Table

Key areas by phase with targets

Key Area	2026 Foundation	2027 Scale	2028+ Sustain	Impact Targets
SMEs Reached	50 pilot	200 scale	50	500+ by 2028
Training Programs	Curricula developed	6 domains active	Certified pathways	-20pp beginner rate
BSO Capacity	ToT program launched	30+ trainers certified	Self-sustaining	50% implementation capacity
Unified Portal	Design & pilot	Launch in selected economies	All 6 economies	50% awareness
Coordination Mechanism	Framework developed	Operational	Embedded nationally	EEI: 30%+
Stage Classification	Tool developed	In use	Standardized	100% SMEs staged

📖 Roadmap Interpretation

The roadmap follows a Foundation → Scale → Sustain logic. 2026 focuses on building tools and pilots (skills assessment, training curricula, 10 SME pilot (per prodoc)). 2027 scales proven approaches (200 SMEs, portal launch, trainer certification). 2028+ embeds into national systems for sustainability (50 SMEs, national integration, self-sustaining BSO capacity). Each phase builds on the previous, avoiding the leapfrogging trap our data warns against.

3

Budget Framework

Financial requirements by phase

💰 Budget Allocation

Source: UNIDO (2024) Government Institutions Survey

Phase III Budget: €3-5 Million (2028-2033+)

⚠️ Note: Phase III budget figures are aspirational projections. No approved project document exists for Phase III. These estimates are planning scenarios contingent on future donor approval and co-funding.

Component	Annual Est.	5-Year Total	Notes
SME Scale-up (500+)	€400K	€2.0M	At €4K/SME average intervention cost
Unified Regional Portal	€100K	€500K	Development + maintenance
Coordination Mechanism	€150K	€750K	Secretariat, meetings, quality standards
Impact Evaluation	€50K	€250K	Baseline, midline, endline
National Integration Support	€100K	€500K	Policy advocacy, capacity building
Total	€800K	€4.0M	Range: €3-5M depending on scope

📖 Budget Implications

Phase II (€540,200) is relatively modest for regional impact. ~€68K expended through Dec 2025 (12.6% of net budget) confirms implementation has begun. The skills-focused approach (€260K on curricula, ToT, and pilot) reflects our finding that skills investment must precede technology. Phase III (€3-5M) represents significant scale-up but delivers transformational impact: 50 SMEs, regional coordination, and sustainable national integration. At €4K/SME, this is cost-effective compared to technology subsidy approaches that our data shows don't work.

Implications for WB Countries

For GovernmentsPhase III requires co-funding and national buy-in. Use Phase II to build political will. Align with EU IPA funding cycles for sustainability.

For BSOsRegional investment builds your capacity. ToT program creates local expertise. Participate actively to benefit from methodology transfer.

For DonorsSkills-first approach is evidence-based and cost-effective. €4K/SME delivers measurable capability improvement. Supports EU accession alignment.

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Begin Regional Strategy-Roadmap →

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1

Regional Strategy-Roadmap Overview

Comprehensive implementation strategy for Western Balkans I4.0 transformation

Framework: Based on UNIDO Project 230258 findings, Peerally-Santiago (2022) capability framework, and EU Digital Decade 2030 targets

Strategic Intent: To support the transformation of the Western Balkans manufacturing ecosystem through a phased, evidence-based approach that builds foundational digital capabilities, strengthens support infrastructure, and creates sustainable pathways to Industry 4.0 adoption. The strategy operationalizes research findings into actionable interventions across three phases (2024-2030), targeting 10 SMEs in Phase II (prodoc target), scaling to 50+ in Phase III, 50+ BSOs, and policy frameworks in 6 economies.

TOE-Structured Implementation: The strategy addresses all three TOE dimensions systematically—Technology (T): Stage-appropriate technology roadmaps, R&I infrastructure; Organisation (O): Skills academies, BSO capacity building, SME readiness programs; Environment (E): Policy frameworks, ecosystem coordination, regional partnerships. This holistic approach ensures sustainable transformation by building organisational capabilities before pushing technology adoption.

5

ToC Levels

Input → Impact

24

LogFrame Indicators

UN Standard

8

Strategic Partners

EEN, RCC, EIT+

3

Implementation Phases

2024-2030

📊 Strategy Architecture

Component	Focus	Key Deliverables	Timeline
Theory of Change	Causal pathway from inputs to impact	ToC diagram, assumptions register	Foundation
Logical Framework	Results hierarchy with indicators	24 indicators, baselines, targets	Foundation
Governance	Institutional coordination	Steering committee, working groups	Phase I
Partnerships	Strategic alliances	EEN, RCC, EIT, EC engagement	Phases I-III
M&E Framework	Performance monitoring	KPIs, dashboards, reports	Continuous
Implementation Roadmap	Phased action plan	Activities, resources, milestones	2024-2030

2

Vision & Mission

Strategic direction for WB industrial transformation

🎯 Vision 2030

A digitally transformed Western Balkans manufacturing sector where SMEs leverage Industry 4.0 technologies to achieve sustainable competitiveness, green transition, and EU market integration—supported by a capable ecosystem of BSOs and enabling government policies.

🚀 Mission

To systematically build foundational digital capabilities across WB manufacturing SMEs through evidence-based interventions that address skills gaps, strengthen support ecosystems, and create enabling policy environments—following a stage-appropriate approach that recognizes current readiness levels.

📈 Strategic Targets 2030

50

SMEs Supported

Direct interventions

50%

Digital Intensity

Up from 43%

50%

Basic Skills

Up from 32%

30%

Cloud Adoption

Up from 26%

50+

BSOs Capacitated

Technical upskilling

6

National Strategies

I4.0 policies

3

Strategic Objectives

Four pillars of transformation

🎓 SO1: Skills Development

Build foundational digital skills across WB manufacturing workforce

Target: 50% basic digital skills32% → 50%

Train 150+ workers in basic digital literacy
Establish 6 regional training centers
Develop 12 certified curricula

🏭 SO2: Technology Adoption

Enable stage-appropriate technology implementation

Target: 50% digital intensity43% → 50%

50 SMEs with cloud/ERP adoption
50+ pilot I4.0 implementations
Technology voucher program

🤝 SO3: Ecosystem Strengthening

Build BSO technical capacity and SME outreach

Target: 50% SME awareness33% → 50%

50+ BSO staff technically trained
6 I4.0 demonstration centers
Regional BSO network established

🏛️ SO4: Policy Enablement

Support I4.0 strategy development and coordination

Target: 6 national strategies0 → 6

6 national I4.0 strategies developed
Regional coordination mechanism
EU policy alignment roadmaps

Strategy Summary

The Regional Strategy-Roadmap translates research findings into actionable interventions across four strategic objectives: Skills Development (SO1), Technology Adoption (SO2), Ecosystem Strengthening (SO3), and Policy Enablement (SO4). The approach is evidence-based, stage-appropriate, and aligned with EU Digital Decade 2030 targets. Implementation follows three phases from 2024-2030, with clear governance structures, strategic partnerships, and M&E frameworks to ensure accountability and adaptation.

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1

Phase I: Foundation (2020-2024)

UNIDO Foundation Phase - Establishing Regional Innovation Infrastructure

Phase: Foundation | Period: October 2020 - December 2024 | Budget: EUR 196,620 | Support: International Partners

Project Objective: To contribute to advancing economic competitiveness in Serbia through fostering smart manufacturing and innovation and business ecosystems building for promoting uptake (adopt, adapt and diffuse) of advanced digital technologies and materials. Enhanced industrial competitiveness through harnessing 4IR technological learning and innovation in smart manufacturing, leading to the realization of SDG 9 and other SDGs in Serbia.

€196,620

Total Budget

EUR 123,420 SEF + EUR 73,200 MGRT

4 Years

Duration

October 2020 - December 2024

100%

Completion

All outputs delivered

🏢 Key Partners

🇷🇸

Partner University

Faculty of Technical Sciences
Host Institution

🇸🇷

Slovene Enterprise Fund

Primary Donor
EUR 123,420

🇸🇷

International Partner

Ministry of Economy
EUR 73,200

🇷🇸

Serbian Government

Ministry of Science
Coordinating Agency

🎯 Priority Actions

SME Priorities

Digital Skills (β=0.442)
Cloud/IoT adoption
Sequential capability building

BSO Priorities

Close utilization gap
I4.0 expertise
Targeted outreach

Government

Skills-focused policy
Twin transition
Regional coordination

Cross-cutting

IRPF adoption
Ecosystem integration
Evidence-based

2

Phase I Key Results

Outputs and achievements 2020-2024

📋 Output 1: Establishing Pilot Regional Innovation Centers

Activity	Deliverable	Date	Status
1.1 Terms of Reference	Comprehensive Terms of Reference defining the center's vision, structure, services, governance, and sustainability model	Oct 2022	✔ Complete
1.2 National Guidelines	Guidelines for National Program on Fostering Manufacturing Innovation Hubs System in Serbia - validated with 39 stakeholders	Jun 2023	✔ Complete
1.3 Training Toolkit	10 training courses across Industrial Automation, Automotive, Power Electronics, AI. E-platform at unido.org/wb-i40 with Canvas LMS integration	2023-2024	✔ Complete
1.4 Study Tour	International study tour: delegates visited leading I4.0 facilities and institutions	Jun 2023	✔ Complete
1.5 Branding & Promotion	Brand book, digital platform, promotional videos, 4 thematic webinars (400+ participants), Program Handbook, partnerships with leading technology companies	2022-2024	✔ Complete
1.6 Launch Event	"Enabling Progress: Industry 4.0 in Western Balkans" conference (12-13 June 2024). 100+ participants, 25+ companies exhibition (International and regional exhibitors)	Jun 2024	✔ Complete

📊 Phase I Impact Metrics

400+

Webinar Participants

40% women participation

100+

Conference Attendees

6 WB economies

25+

Exhibition Companies

International + Regional

39

Validation Stakeholders

Universities, industry, government

🤝 Private Sector Partnerships Established

🔧 Siemens

Partnership established with leading technology providers for software licenses to train engineers with latest smart manufacturing and Industry 4.0 technologies.

🏭 Knowledge Partners

RT-RK (Automotive), TTTechAuto (Automotive), NIT Academy (Training), INDAS Training Center (Industrial Automation), Typhoon HIL (Real-time Simulation).

3

Transition to Phase II

From pilot center to regional Western Balkans initiative

📈 Phase I → Phase II Evolution

Phase I: Foundation (2020-2024)

Established pilot innovation center in the Western Balkans
Budget: €196,620
Focus: Single country (Serbia)
Developed ToR, training toolkit, e-platform
Built private sector partnerships
Official launch June 2024

Phase II: Scale (2025-2027) - CURRENT

Project ID: 230258
Budget: €540,200
Focus: 6 WB economies (AL, BiH, ME, MK, RS, XK)
Comprehensive research (N=203 survey)
Regional innovation center network development
Donor: International Partners

✔ Phase I (2020-2024)

Pilot Center Establishment - COMPLETED

Foundation Phase: Established pilot innovation center. Developed ToR, national guidelines, training programs, e-platform. Official launch June 2024. Budget: €196,620.

► Phase II (2025-2027)

Regional Scale-Up - ONGOING

ID230258: Research-based regional expansion to 6 WB economies. Survey (N=203), demonstration centers, training 1,000+ workers. Budget: €540,200.

◇ Phase III (2028-2030)

EU Integration & Sustainability

Large-scale EU-funded initiative. Integration with European Digital Innovation Hubs network. Self-sustaining regional mechanism. Target budget: €3M+.

🎯 Key Lessons from Phase I Applied to Phase II

Phase I validated the innovation center model and demonstrated: (1) Strong absorption capacity in regional innovation ecosystems; (2) High interest from private sector for partnerships; (3) Need for regional approach to maximize impact; (4) Importance of combining physical demonstration with digital platforms; (5) Value of international partners as donors and knowledge partners.

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1

Phase II Overview

Regional Scaling (2025-2027) - Current implementation status

🎯 Phase II Strategic Focus

5

Countries

AL, BiH, ME, MK, RS

€540,200

Budget

2025-2027

9

Months In

As of Dec 2025

📊 Phase II Timeline

●

Q1-Q2 2025

Inception & Baseline Assessment

✔ Completed

●

Q3-Q4 2025

Research & Analysis Phase

🔄 In Progress

○

2026

Pilot Interventions & Demo Centers

Planned

○

2027

Scale & Sustainability

Planned

2

Current Progress

9-month implementation status (March-December 2025)

📈 Output Achievement Summary

Output 1: Skills & Technology

Training programs designed60%

Technology assessments75%

Output 2: Ecosystem

BSO capacity building45%

Demo center planning35%

Output 3: Policy

Research completed85%

Policy briefs drafted50%

🎯 Key Milestones Achieved (9M 2025)

Milestone	Target Date	Actual	Status
Inception workshop completed	Q1 2025	Mar 2025	✔ Completed
Baseline survey (SME Survey SMEs)	Q2 2025	Sep 2025	✔ Completed
Focus groups (6 events)	Q3 2025	Oct 2025	✔ Completed
Readiness dashboard v1	Q4 2025	Dec 2025	✔ Completed
Policy recommendations draft	Q4 2025	In progress	🔄 Ongoing

3

2026-2027 Targets

Upcoming deliverables and milestones

📅 2026 Planned Activities

Q1-Q2 2026

Pilot training programs launch (selected economies)
Demo center site selection and design
BSO train-the-trainer programs
Technology partner agreements

Q3-Q4 2026

First demo center operational
SME technology adoption pilots
Policy dialogue events
Mid-term review preparation

🎯 Phase II End Targets (2027)

150

Workers Trained

250

SMEs Reached

3

Demo Centers

15

Policy Briefs

1

Theory of Change

Causal pathway from inputs to transformational impact

🔗 Integrated Results Framework

The ToC, Logframe, Risks and Assumptions form an integrated results framework where each element directly corresponds to the others.

🎯

Theory of Change

Explains WHY and HOW change happens - the causal logic

📋

Logical Framework

WHAT we will achieve - measurable results hierarchy

⚠️

Risks & Assumptions

CONDITIONS for success - factors beyond our control

🔄 Theory of Change

INPUTS

Research

N=203 survey, 6 focus groups, DESI analysis

Funding

International partner government support

Expertise

UNIDO, TCS/DAI, local partners

Networks

EEN, RCC, national BSOs

↓

ACTIVITIES

Train

Skills programs for SMEs & BSOs

Support

Technology adoption assistance

Connect

Ecosystem networking events

Advise

Policy development support

↓

OUTPUTS

Trained

150+ workers, 50+ BSO staff

Supported

50 SMEs with tech adoption

Created

6 demo centers, 12 curricula

Developed

6 national I4.0 strategies

↓

OUTCOMES

Skills

50% basic digital skills (from 32%)

Adoption

50% SME digital intensity

Ecosystem

Functional support network

Policy

Enabling regulatory environment

↓

IMPACT

Transformational Impact

Competitive, sustainable WB manufacturing sector integrated with EU markets

📈 ToC Results Timeline (2024-2030)

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

2

ToC Narrative

The change mechanism explained

📖 Causal Logic

IF we invest research-based evidence, donor funding, technical expertise, and established networks (INPUTS) in training programs, technology support, ecosystem connections, and policy advice (ACTIVITIES), THEN we will produce trained workers and BSO staff, supported SMEs, demonstration centers, and national strategies (OUTPUTS), which LEADS TO improved skills levels, higher digital adoption, functional support ecosystems, and enabling policies (OUTCOMES), ultimately RESULTING IN a competitive, sustainable, and EU-integrated Western Balkans manufacturing sector (IMPACT).

📌 Key Causal Pathways

Skills → Adoption: Research shows digital skills (r=0.567***) is the strongest predictor of technology adoption. Building foundational skills unlocks adoption potential.

BSO Capacity → SME Support: Currently only 24.3% of BSOs can provide implementation support. Building BSO capacity creates delivery infrastructure.

Policy → Enabling Environment: Only 8% of government entities have I4.0 strategies. National strategies create coordination and incentive frameworks.

Twin Transition: Digital and green transformations are complementary (r=0.412***). I4.0 adoption supports sustainability goals.

3

Assumptions & Risks

Critical success factors

⚠️ Key Assumptions

Level	Assumption	Risk if False	Mitigation
Input→Activity	Continued donor funding	Activities cannot be implemented	Diversify funding sources; build sustainability
Activity→Output	SMEs willing to participate	Low training/support uptake	Demand-driven design; incentives
Output→Outcome	Skills translate to adoption	Training doesn't lead to change	Practical, hands-on training; follow-up
Outcome→Impact	Macro environment stable	External shocks disrupt progress	Adaptive programming; risk monitoring

🏛¡️ Risk Register

Risk	Likelihood	Impact	Response Strategy
Brain drain continues	High	High	Create in-country opportunities; competitive incentives
Low SME engagement	Medium	High	Outreach campaigns; demonstrate ROI; peer champions
BSO capacity insufficient	Medium	Medium	Intensive ToT programs; international expertise
Political instability	Medium	Medium	Multi-country approach; regional coordination
5G/infrastructure gaps	Low	Medium	Focus on technologies working on existing infrastructure

📊 IMPLEMENTATION PLAN

Project timeline, activities, and budget allocation

1

Master Gantt Chart

Integrated implementation timeline across all phases

📊 Project Implementation Timeline (2020-2030)

Phase I (2020-2024) Phase II (2025-2027) Phase III (2028-2030)

20202021202220232024202520262027202820292030

Phase I: Pilot Center

Phase II: Regional Scaling

Phase III: Sustainability

Output 1: Skills Development

Output 2: Ecosystem Building

Output 3: Policy Framework

🔍 Current Position

📈 Phase II Detailed Gantt (2025-2027)

Q1'25Q2'25Q3'25Q4'25Q1'26Q2'26Q3'26Q4'26Q1'27Q2'27Q3'27Q4'27

Inception & Baseline

Research & Analysis

Training Program Design

Demo Center Setup

Pilot Implementations

Scale & Sustainability

🔍 Now (Dec 2025)

2

Activities & Outputs

Detailed work breakdown structure

📋 Output-Activity Matrix

Output	Key Activities	Deliverables	Timeline	Status
Output 1 Skills & Technology	1.1 Baseline assessment	Research report, Dashboard	Q1-Q4 2025	✔ 85%
	1.2 Training design	Curricula, materials	Q2-Q4 2025	🔄 60%
	1.3 Pilot training	Trained workers	2026-2027	○ Planned
Output 2 Ecosystem	2.1 BSO capacity building	Trained BSO staff	Q3 2025-2026	🔄 45%
	2.2 Demo center setup	3 operational centers	2026	○ Planned
	2.3 Network building	WB Community platform	Ongoing	✔ Active
Output 3 Policy	3.1 Policy research	Policy briefs	Q2 2025-2026	🔄 50%
Output 3 Policy	3.2 Dialogue events	Regional workshops	2026-2027	○ Planned

3

Budget & Resources

Financial allocation and resource deployment

💰 Budget Allocation by Output

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Resource Deployment

1

Project Manager

UNIDO Lead

1

Project Coordinator

Operations

5

Expert Team

Int'l + National

10+

Partners

Implementing

4

Conclusion & Next Steps

Strategic priorities for Q1 2026

🎯 Immediate Priorities (Q1 2026)

1. Complete Research

Finalize readiness dashboard, publish policy briefs, prepare for dissemination

2. Launch Pilots

Begin training programs in 3 priority countries, establish BSO partnerships

3. Demo Center Planning

Complete site selection, finalize technology partner agreements

📌 Project Vision 2030

By 2030, the Western Balkans will have a sustainable regional ecosystem for Industry 4.0, with 6 demonstration centers, 150+ trained workers, 50 SMEs with enhanced capabilities, and a policy framework supporting digital-green twin transition.

🏛️ GOVERNANCE FRAMEWORK

Institutional structure, roles, and coordination mechanisms

1

Governance Structure

Institutional coordination mechanism

🏛️ Multi-Level Governance Architecture

Strategic

Steering Committee

UNIDO, Donors, National Focal Points

Technical

Working Groups

Skills, Technology, Policy, M&E

Operational

Project Management

TCS/DAI, Local Partners

📋 Governance Bodies

Body	Composition	Function	Frequency
Steering Committee	UNIDO, international partners, 6 national focal points	Strategic direction, resource allocation, major decisions	Bi-annual
Technical Advisory Group	I4.0 experts, academia, industry leaders	Technical guidance, quality assurance, innovation	Quarterly
Skills Working Group	Training providers, employers, certification bodies	Curriculum development, training delivery	Monthly
Technology Working Group	Tech providers, BSOs, pilot SMEs	Technology selection, implementation support	Monthly
Policy Working Group	Government officials, RCC, EU delegation	Strategy development, regulatory alignment	Quarterly
M&E Working Group	Project team, evaluators, data analysts	Monitoring, reporting, learning	Continuous

2

Roles & Responsibilities

RACI matrix for key stakeholders

👥 Stakeholder Roles

Stakeholder	Role	Key Responsibilities
UNIDO	Lead Agency	Overall coordination, technical assistance, quality assurance, reporting to donors
International Partners	Donors	Funding, strategic guidance, political support
TCS/DAI	Implementation Partner	Day-to-day management, activity delivery, local coordination
National Ministries	Government Partners	Policy development, co-funding, sustainability
BSOs	Delivery Partners	SME outreach, training delivery, support services
SMEs	Beneficiaries	Participation, co-investment, feedback
RCC	Regional Coordinator	Cross-border coordination, policy harmonization

3

Coordination Mechanisms

Communication and decision-making protocols

🔗 Coordination Framework

Internal Coordination

Weekly project team meetings
Monthly progress reports
Quarterly steering committee
Annual strategic review
Shared project management platform

External Coordination

National focal point network
Regional coordination (via RCC)
EU alignment meetings
Partner coordination calls
Stakeholder forums (annual)

🤝 PARTNERSHIPS & ALLIANCES

Strategic partners, alliances, and Phase III engagement

1

Partner Mapping

Strategic alliance network

🤝 Strategic Partnership Network

ACTIVE Phase I-II Partners (2024-2027)

🇪🇺

EEN

SME Internationalization

🌐

RCC

Policy Coordination

🚀

EISMEA

EU Innovation Agency

💡

EIT Mfg

Tech Transfer

SCALING Phase III Partners (2028-2030)

🏛️

EC

DG GROW/ENEST

🎓

Erasmus+

Skills Programs

🔬

Horizon

R&D Collaboration

💰

EBRD/EIB

Investment Finance

Phase I-II: Build & Demonstrate → Phase III: Scale & Sustain

2

Strategic Alliances

Partnership value propositions

📋 Partnership Framework

Partner	Center Offers	Partner Offers	Joint Activities
EEN	WB market intelligence, SME networks	EU market access, partner matching	Joint SME missions, technology brokerage
RCC	I4.0 expertise, research data	Policy platform, regional reach	Policy dialogues, regional strategies
EISMEA	WB ecosystem mapping	EU program access, funding	Program alignment, joint calls
EIT Manufacturing	Regional demonstration sites	Technology solutions, curricula	Pilot projects, training programs

3

Phase III Partner Engagement

Scaling through EU integration

🚀 Phase III: EU Integration (2028-2030)

Phase III represents the scaling and sustainability phase where the program transitions from donor-funded project to self-sustaining regional initiative integrated with EU frameworks. Key partnerships with European Commission (DG GROW, DG ENEST), Erasmus+, Horizon Europe, and development finance institutions will enable this transition.

Target Outcomes

WB Digital Innovation Hubs network
Erasmus+ VET program integration
Horizon Europe project participation
EBRD/EIB credit lines for I4.0

Preparation Actions

Build track record in Phase I-II
Develop EU-compatible structures
Establish Brussels presence
Align with EU accession priorities

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1

Unified Indicators Framework

Integrated performance monitoring across M&E, KPIs, and IRPF tracking

🎯 Integrated Results Monitoring Architecture

All indicators aligned across M&E Framework, Key Performance Indicators, and Integrated Results & Performance Framework (IRPF)

📊

M&E Framework

Continuous monitoring and periodic evaluation system

📈

Key Performance Indicators

24 KPIs across 4 outcome areas with target trajectories

🎯

IRPF Tracking

UNIDO corporate results framework alignment

📊 Performance Dashboard Overview

150

Workers Trained

Target by 2030

500

SMEs Supported

Target by 2030

6

Demo Centers

Regional network

25%

Productivity Gain

Target improvement

2

IRPF Indicators

Integrated Results & Performance Framework alignment

📋 IRPF Indicator Matrix

IRPF Code	Indicator	Target Y3	Target Y6	Status
IRPF-1.1	SMEs with enhanced technology adoption	150	500	🔄 In Progress
IRPF-1.2	Workers with improved I4.0 skills	100	150	🔄 In Progress
IRPF-2.1	BSOs with strengthened capacity	30	60	🔄 In Progress
IRPF-2.2	Demonstration centers operational	3	6	🔄 In Progress
IRPF-3.1	Policy recommendations implemented	10	25	🔄 In Progress
IRPF-3.2	Regional cooperation agreements	6	12	🔄 In Progress

📈 IRPF Progress Tracking Chart

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

3

Key Performance Indicators

Strategic KPIs with target trajectories

📊 KPI Trajectory Dashboard

KPI	Category	Y1	Y2	Y3	Y4	Y5	Y6
Workers trained in I4.0	Skills	25	50	75	100	125	150
SMEs receiving services	Enterprise	100	180	250	350	430	500
Demo center visitors	Awareness	500	1,500	3,000	5,000	8,000	12,000
BSO staff trained	Capacity	50	100	150	200	250	300
Technologies demonstrated	Innovation	10	25	40	55	70	85
Policy briefs published	Policy	3	8	15	22	30	40

📈 KPI Target Trajectories

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

4

M&E Tracking

Monitoring and evaluation progress dashboard

🔄 M&E Cycle Calendar

Activity	Frequency	Responsible	Next Due	Status
Data collection	Continuous	Project team	Ongoing	✔ Active
Progress monitoring	Monthly	M&E Officer	Jan 2026	✔ On Track
Quarterly reports	Quarterly	Project Manager	Mar 2026	🔄 Preparing
Steering Committee	Bi-annual	UNIDO	Jun 2026	○ Scheduled
Annual report	Annual	Project team	Dec 2026	○ Scheduled
Mid-term evaluation	Once	External	2027	○ Planned

📊 Current Progress Summary

Output 1: Skills Development35%

Output 2: Ecosystem28%

Output 3: Policy22%

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1

Logical Framework Matrix

UN-style results hierarchy with indicators

📋 LogFrame Matrix

Results Level	Statement	Indicators	Means of Verification	Assumptions
GOAL	Competitive, sustainable WB manufacturing sector integrated with EU markets	• Manufacturing value-added growth rate • EU export share • Green manufacturing index	• National statistics • Eurostat • UNIDO MVA database	Macro stability; EU accession progress
Outcome 1	Improved digital skills in WB manufacturing workforce	• 50% basic digital skills (baseline: 32%) • 20% above-basic skills (baseline: 9%)	• DESI surveys • Project assessments	Workers apply skills; employers value skills
Outcome 2	Increased technology adoption by SMEs	• 50% SME digital intensity (baseline: 43%) • 35% cloud adoption (baseline: 26%)	• DESI business surveys • Project monitoring	Technologies remain affordable; ROI demonstrated
Outcome 3	Functional I4.0 support ecosystem	• 50% SME awareness of support (baseline: 33%) • 25% support utilization (baseline: 8%)	• SME surveys • BSO reports	BSOs maintain capacity; services remain relevant
Outcome 4	Enabling policy environment	• 6 national I4.0 strategies (baseline: 0) • Regional coordination mechanism active	• Policy documents • Meeting records	Political will; cross-ministry coordination
Output 1.1	Workers trained in digital skills	• 150 workers trained • 80% completion rate • 70% satisfaction	• Training records • Assessments • Feedback surveys	Employers release workers; curricula effective
Output 1.2	Training infrastructure established	• 6 regional centers • 12 certified curricula	• Center reports • Curriculum documents	Facilities available; trainers recruited
Output 2.1	SMEs supported with technology adoption	• 500 SMEs assisted • 50 pilot implementations	• Support records • Implementation reports	SMEs willing to invest; technology available
Output 3.1	BSO capacity strengthened	• 50 BSO staff trained • 6 demo centers operational	• Training records • Center assessments	BSO staff retained; equipment maintained
Output 4.1	Policy support provided	• 6 strategy documents • 12 policy briefs	• Documents • Government adoption	Government receptive; expertise available

2

Indicator Details

24 indicators with baselines, targets, and status

📊 Key Performance Indicators

Indicator	Baseline	Target 2027	Target 2030	Status
Basic digital skills (%)	32%	40%	50%	On Track
SME digital intensity (%)	43%	47%	50%	On Track
Cloud adoption (%)	26%	30%	35%	On Track
SME awareness of support (%)	33%	42%	50%	At Risk
Support utilization (%)	8%	15%	25%	At Risk
National I4.0 strategies (#)	0	3	6	On Track
Workers trained (#)	0	100	150	On Track
SMEs supported (#)	0	250	500	On Track
BSO staff trained (#)	0	30	50	On Track
Demo centers (#)	0	4	6	On Track

←

Return to Phase II

Explore monitoring and evaluation →

→

1

WB Manufacturers Community Platform

community.unido.org/wb-i40 analytics

115
Registered Members
Dec 2025

7

Countries

WB6 + SI + CY

43

BiH Members

37.4%

35

N. Macedonia

30.4%

18

Albania

15.7%

📈 Community Growth Trajectory

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

Platform Features & Engagement

55%

Manufacturing SMEs

25%

Business Support Orgs

20%

Tech/Research

Platform Capabilities

News & updates • Good practices sharing • Funding opportunities • Partner matchmaking • Service provider directory • Event calendar • Survey participation

2

Stakeholder Engagement

Cumulative engagement progress across project phases

📈 Stakeholder Engagement Progress

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

203

Survey Respondents

Phase II (2025-2027)

47

Focus Group Insights

6 sessions

115

Community Members

Platform registrations

365
Total Engaged
Cumulative unique

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T

Paper-Ready Tables

Exportable tables for publication and appendices

📋 Table 1: Descriptive Statistics

Variable	N	Mean	SD	Min	Max	α
Organizational Readiness	139	3.02	1.00	1.00	5.00	.936
Technology Adoption	139	0.67	0.71	0.00	3.22	.878
Digital Skills	139	1.26	0.37	1.00	2.67	.757
External Pressure	57	2.74	0.85	1.00	4.60	.842
Performance Outcomes	127	2.47	0.76	1.00	4.00	.966
Green Orientation	131	0.56	0.59	0.00	2.00	.462

Note: α = Cronbach's alpha. Technology Adoption scored 0-4 (adoption stage). Digital Skills scored 1-4 (proficiency level).

📊 Table 2: Correlation Matrix

	1	2	3	4	5	6
1. Org. Readiness	—
2. Tech. Adoption	.335***	—
3. Digital Skills	.291***	.476***	—
4. Ext. Pressure	.065	.117	.158	—
5. Performance	.211*	.143	.319***	.494***	—
6. Green Orient.	.350***	.314***	.236**	.292**	.376***	—

Note: N = 139 (listwise). * p < .05, ** p < .01, *** p < .001

📈 Table 3: Regression Results - Technology Adoption Determinants

Predictor	Model 1 β (SE)	Model 2 β (SE)	Model 3 β (SE)
Intercept	-0.77*** (0.25)	-0.81*** (0.26)	-0.35 (0.19)
Digital Skills	0.75*** (0.15)	0.71*** (0.16)	0.92*** (0.15)
Org. Readiness	0.12* (0.06)	0.11† (0.06)	—
External Pressure	0.01 (0.05)	0.01 (0.06)	—
Green Orientation	0.20† (0.10)	0.18† (0.10)	—
Country FE	No	Yes	No
R²	.292	.318	.226
Adj. R²	.271	.282	.220
F	13.63***	8.92***	39.43***
N	137	137	137

Note: DV = Technology Adoption (mean score). Model 1 = Base model. Model 2 = Country fixed effects. Model 3 = Simple model (DS only). † p < .10, * p < .05, ** p < .01, *** p < .001

🔄 Table 4: Mediation Analysis (OR → DS → AD)

Path	Effect	SE	95% CI	p
Total Effect (c)	0.239	0.058	[0.125, 0.353]	<.001
Direct Effect (c')	0.153	0.055	[0.045, 0.262]	.006
Indirect Effect (a×b)	0.086	0.029	[0.040, 0.142]	.003
Path a (OR→DS)	0.107	0.031	[0.047, 0.168]	<.001
Path b (DS→AD\|OR)	0.796	0.149	[0.502, 1.090]	<.001

Note: Bootstrap CI based on 1,000 iterations. Mediation proportion = 35.8%. Sobel Z = 2.95, p = .003.

🔒¥ Export Options

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★

Grand Summary & Conclusions

Comprehensive summary of the entire Western Balkans Industry 4.0 Readiness Study

Complete Study Overview: UNIDO Project 230258 | Phase II (2025-2027) | N=more than 200 respondents across 6 WB economies

👥 Survey Respondents

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🗺️ Country Coverage

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

✅ Hypothesis Results

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

📊 Capability Stage Distribution

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

🎯 Key Driver Correlations

Source: UNIDO (2024) SME Survey. SME Survey | Source: INDUST1.XLS

🎯 Western Balkans Industry 4.0 Readiness: Research to Action

This comprehensive research represents the most rigorous empirical assessment of Industry 4.0 readiness in the Western Balkans to date. Surveying stakeholders across the Western Balkans across three ecosystem actors (139 manufacturing SMEs, 39 BSOs, 25 government institutions) in 6 economies, the study reveals a region at a critical juncture.

📊 Key Finding #1

74.1% of SMEs remain at foundational capability stages (0-1). Firms cannot leapfrog - sequential capability building is required.

🎯 Key Finding #2

Digital Skills (r=0.567) is the dominant predictor of technology adoption. Human capital must precede technology investment.

🌿 Key Finding #3

Twin Transition Confirmed (r=0.454). Green and digital transformations are complementary, not competing priorities.

The ecosystem shows critical dysfunction: only 32.4% of SMEs are aware of support programs, and merely 23.0% received any support - a significant outreach gap. BSOs (59.0% without I4.0 strategy) and government (60% without strategy) lack the capacity to effectively support SMEs.

Strategic Response: Three-phase implementation roadmap (2024-2030) targeting 10 SMEs in Phase II (prodoc target), scaling to 50+ in Phase III, 150+ trained workers, 50+ capacitated BSO staff, and 6 national I4.0 strategies. Success requires coordinated action across all ecosystem actors and stage-appropriate interventions.

🚀 The Path Forward

Phase I Complete (2020-2024)Pilot center established, ToR validated, training toolkit developed, regional study tour completed

Phase II Active (2025-2027)Regional hub scaling, WB-wide research (this dashboard), capacity building programs, policy framework development

Phase III Vision (2028-2030)Self-sustaining ecosystem, EU integration pathway, 50 SMEs transformed, regional leadership established

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Return to Start

Executive Summary

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○

Unified M&E Framework

IRPF-Centric performance monitoring system

📊 Single Source of Truth

IRPF indicators used exclusively for SME and SO evaluation. All metrics aligned with UNIDO corporate results framework.

📈 IRPF Outcome Indicators

Indicator	Baseline	2025	2027	Scope
Technology Adoption	0.43	0.55	0.70	SME+SO
Digital Skills	1.28	1.60	2.00	SME+SO
SMEs Stage 1+	32.6%	45%	60%	SME
BSO Quality	3.2/5	3.8/5	4.2/5	SO

Source: UNIDO (2024) Western Balkans I4.0 Assessment | IRPF Framework

📋 IRPF Output Indicators

Output	2025	2026	2027	Total
SMEs assessed	150	200	150	500
SMEs assisted	50	100	100	250
SO staff trained	30	50	40	120
SOs certified	5	10	10	25

Source: UNIDO Phase II Project Document (ID: 230258)

139

SMEs

Assessed

39

BSOs

Surveyed

25

Gov

Institutions

5

Countries

Covered

203

Total

Respondents

🔗 Strategic Objective Integration

Assessment

IRPF instruments
4-stage model

Reporting

IRPF indicators
Quarterly cycle

Targeting

Stage 0 priority
Skills gaps

1

M&E Framework

Performance monitoring and evaluation system

📊 M&E Architecture

24

KPIs Tracked

Across 4 outcome areas

Quarterly

Reporting Cycle

+ annual deep-dive

3

Evaluations

Baseline, mid-term, final

🔄 M&E Cycle

Activity	Frequency	Responsible	Output
Data collection	Continuous	Project team, partners	Monitoring database
Progress monitoring	Monthly	M&E Officer	Dashboard updates
Quarterly reports	Quarterly	Project Manager	Progress reports
Steering Committee review	Bi-annual	UNIDO	Strategic decisions
Annual report	Annual	Project team	Comprehensive report
Mid-term evaluation	Once (2027)	External evaluator	Evaluation report
Final evaluation	Once (2030)	External evaluator	Impact assessment

2

IRPF Indicators Dashboard

Integrated Results and Performance Framework - Phase II Progress

📊 IRPF Progress Overview (December 2025)

The Integrated Results and Performance Framework (IRPF) tracks progress across impact, outcome, and output levels. Phase II (2025-2027) builds on Phase I achievements. Note: No training or certification activities have taken place in 2025 - skills development activities are planned for 2026.

📈 IRPF Impact & Outcome Indicators

138/200

KASA.1: Knowledge

69% achieved

0/50

KASA.2: Skills

Planned 2026

4/9

CPO.1: Events (all outputs)

O1: 1/3, O3: 3/3 ✔, O4: 0/3

0/10

SOC.1: Jobs

End-of-project

Level	Indicator	Description	Target	Current	Status
Impact (ISID)	SOC.1	Jobs created/retained	10	0	End 2027
	SOC.2	SMEs in value chains	30	0	End 2027
	ECO.1	Firms with economic gains	20	0	End 2027
	ECO.3	Firms with increased exports	10	0	End 2027
Outcome	KASA.1	Actors gaining knowledge	200	138	69% ✔
	KASA.2	Actors gaining skills	50	0	2026
	BUS.1	Firms with improved practices	15	0	2026
Output 1	CPO.1	Global fora/workshops organized	3	1	33%
	PAO.2	Publications produced	1	1	100% ✔
	TCO.3	Program Guide produced	1	0	2026
Output 2	TCO.3	Methodology + FoF tools	5	0	2026
Output 2	TCO.1	ToT + Accelerator pilot	2	0	2026
Output 3	CPO.1	Workshops/EGM organized	3	3	100% ✔
Output 3	TCO.1	Capacity building activities	2	0	2026
Output 4	CPO.1	Policy dialogues organized	3	0	2026
Output 4	PAO.2	Impact reports produced	1	0	2027

Source: UNIDO Mid-Term Report (December 2025) | Project ID: 230258

✔ Achieved/On Track 2026 Planned for 2026 End 2027 End-of-project target

3

IRPF Indicators Tracking

Integrated Phase I & Phase II IRPF indicators

Holistic Project M&E: Phase I → Phase II Continuity

The M&E framework tracks cumulative progress across both phases, demonstrating how Phase I achievements enabled Phase II expansion and measuring progress toward Phase III readiness.

IRPF Indicators: Phase I Baseline → Phase II Progress

Indicator	Phase I Baseline	Phase II Target	Current (Dec 2025)	Status
SOC.1: Jobs created/retained	-	10	0	On Track
SOC.2: SMEs in value chains	-	30	0	Planned 2026
ECO.1: Firms with economic gains	-	20	0	Planned 2026
KASA.1: Actors gaining knowledge	80 (center launch)	200	138	69% ✔
KASA.2: Actors gaining skills	30 (training)	50	0	Accelerator 2026
BUS.1: Firms with improved practices	-	5	0	Accelerator 2026
GOV.1: Institutions strengthened	1	1	1	100% ✔
CPO.1: Events organized	5 (Phase I)	3	1	33% ✔
PAO.2: Publications produced	1 (handbook)	1	1	100% ✔
TCO.1: Capacity building activities	6 (courses)	2	0	Planned 2026
TCO.3: Toolkits produced	1 (training)	5	0	FoF Tools 2026

Phase I Outputs Achieved

Regional Innovation Established100%

ToR Developed100%

Training Toolkit100%

E-Platform100%

Study Tour100%

Launch Event100%

Phase I Budget: $199K | Expenditure: $198.5K (99.7%)

Phase II Outputs Progress (ID230258)

Output 1: Regional Hub Scaling65%

→ Digital Readiness Assessment100%

Output 2: I4.0 Accelerator25%

Output 3: Partnerships55%

Output 4: Visibility50%

Overall Phase II Progress49%

Phase II Budget: €540K | 2025 Expenditure: €68K (12.6%)

4

Reporting Framework

Communication and accountability

🔄 Report Types

Internal Reports

Weekly activity logs
Monthly progress dashboards
Quarterly technical reports
Risk and issue registers

External Reports

Bi-annual donor reports
Annual public reports
Evaluation reports
Knowledge products

📋 Replication & Reproducibility

Analysis Pipeline

Data: WB_I40_Survey_v1.0 (Nov 2025)
Dashboard: Version 2.9
Tools: Python 3.11, scipy, sklearn

Transparency

Registry: JSON single source of truth
Methods: Fully documented in Theory section
Code: Available on request from UNIDO

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🎮

Interactive Policy Simulator

Model policy impacts based on regression coefficients

📊 Evidence-Based Policy Modeling

Select interventions and adjust parameters to model expected impacts. Coefficients derived from regression analysis of SME survey data.

🎯 Select Interventions

Skills Training

β=0.442

Tech Support

β=0.036

Awareness

β=0.055

Funding

+0.15

Green

β=0.410

⚙️ Parameters

Target Firms: 50

Budget: €500K

📈 Projected Results

+0.155

Δ Adoption

+36%

Improvement

12

Firms Advancing

3.10

Efficiency

📊 Strategy Comparison

Strategy	Δ Adoption	Efficiency	Rating
Skills First	+0.155	3.10	BEST ROI
Balanced	+0.175	2.30	BEST IMPACT
Green Focus	+0.130	1.30	Sustainable

Source: UNIDO (2024) Regression Analysis | SME Survey

💡 What Your Budget Can Achieve

Based on selected parameters and evidence-based cost estimates, your investment could deliver:

10

SMEs Digitally Transformed

Full I4.0 implementation support

5

BSOs Capacity Built

Train-the-trainer programs

2

Policy Documents

National I4.0 strategy support

150

Workers Trained

Digital skills certification

Note: Estimates based on regional cost benchmarks. Actual outcomes depend on local context, implementation quality, and SME absorption capacity. Costs per unit: SME transformation (~€50K), BSO capacity building (~€30K), Policy document (~€25K), Worker training (~€500).

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Evaluation Design

Rigorous impact evaluation frameworks for Phase II interventions

🎯 Evaluation Questions

Primary Questions

Does the Regional Innovation skills training program increase I4.0 technology adoption among participating SMEs?
What is the cost-effectiveness of different intervention modalities (training vs. grants vs. combined)?
Do BSO capacity-building interventions translate into improved SME outcomes?

Secondary Questions

What firm characteristics moderate intervention effectiveness?
Are there spillover effects to non-participating firms?
How sustainable are adoption gains post-intervention?

🔐 Recommended Design: Difference-in-Differences (DiD)

Design Rationale

DiD is recommended because: (1) randomization may not be feasible due to program constraints; (2) we have baseline data from this survey; (3) comparison group can be constructed from non-participants; (4) approach accounts for time trends affecting all firms.

DiD Framework

	Baseline (T0)	Endline (T1)	Difference
Treatment Group (Regional Innovation participants)	AD₀ᵀ	AD₁ᵀ	ΔADáµ€ = AD₁ᵀ - AD₀ᵀ
Comparison Group (Non-participants)	AD₀ᶜ	AD₁ᶜ	ΔADá¶œ = AD₁ᶜ - AD₀ᶜ
DiD Estimate (ATT)			ΔADáµ€ - ΔADá¶œ

Regression Specification

                    ADit = α + β₁(Treatmenti) + β₁(Postt) + β₃(Treatmenti × Postt) + Xitγ + εit
                

β₃ = DiD estimator (causal effect of treatment on AD)

🔀 Alternative: Matched Comparison Design

If parallel trends assumption is violated, use propensity score matching (PSM):

Matching Variables

Baseline AD score
Baseline DS score
Enterprise size (micro/small/medium)
Country
Sector (ISIC 2-digit)
Baseline OR score

Matching Procedure

Estimate propensity score via logit
Match 1:1 nearest neighbor (caliper=0.1 SD)
Verify balance on observables
Estimate ATT on matched sample

📊 Sample Size & Power

Scenario	Effect Size	Treatment N	Control N	Power	MDE
Conservative	d = 0.30	50	50	0.70	0.21 AD points
Target	d = 0.40	50	100	0.80	0.17 AD points
Optimistic	d = 0.50	50	100	0.90	0.14 AD points

Assumptions: α=0.05, two-tailed. MDE = Minimum Detectable Effect. Based on AD SD=0.71 from baseline.

📅 Data Collection Plan

Wave	Timing	Target N	Purpose	Instruments
Baseline (T0)	Q1 2025	150+	Pre-intervention measurement	Full SME survey (this study)
Midline (T1)	Q3 2025	120+	Early adoption tracking	Short-form AD/DS module
Endline (T2)	Q2 2026	120+	Impact assessment	Full SME survey + qualitative
Follow-up (T3)	Q2 2027	100+	Sustainability check	Short-form + admin data

⚠️ Threats to Validity & Mitigation

⚠️

Selection Bias

Threat: Motivated firms self-select into treatment.

Mitigation: PSM on observables; bound estimates with Oster (2019) sensitivity.

⚠️

Attrition

Threat: Differential dropout between treatment/control.

Mitigation: Intent-to-treat analysis; Lee (2009) bounds.

⚠️

Spillovers

Threat: Knowledge transfer to control firms.

Mitigation: Geographic/sector separation; test for spillover effects.

⚠️

Parallel Trends

Threat: Treatment/control on different trajectories.

Mitigation: Pre-trend tests; event study specification; matching.

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1

Phase Timeline

Three-phase implementation 2024-2030

📅 Implementation Roadmap

Phase I: Foundation (2020-2024)

Regional Innovation Pilot Establishment

Foundation Phase: Established pilot innovation center, Serbia. ToR, national guidelines, training toolkit, e-platform, private sector partnerships. Budget: €196,620. View Details →

Phase II: Scale (2025-2027) - ONGOING

Regional Western Balkans Expansion

ID230258: Research-based regional expansion. Survey (N=203), demonstration centers in 6 economies, training 1,000+ workers, support 250+ SMEs. Budget: €540,200.

Phase III: Sustain (2028-2030)

EU Integration & Sustainability

Large-scale EU-funded initiative. Integrate with European Digital Innovation Hubs network, transition to self-sustaining model, achieve 2030 SDG targets. Target budget: €3M+.

2

Action Plan

Key activities by phase

📋 Phase II Priority Actions (2025-2027)

Quarter	Skills (SO1)	Technology (SO2)	Ecosystem (SO3)	Policy (SO4)
Q1 2025	Launch training in AL, MK	Tech voucher program design	BSO ToT program start	Strategy support to ME
Q2 2025	Expand to BiH, RS	First 25 SME pilots	Demo center #1 (Skopje)	Policy dialogue RCC
Q3 2025	500 workers trained	Tech vouchers launched	Demo center #2 (Tirana)	Strategy support to AL
Q4 2025	Certification program	100 SMEs supported	25 BSO staff trained	Regional forum
2026	1,000 workers trained	200 SMEs, 35 pilots	4 demo centers, network	3 national strategies
2027	1,500 workers, scale	350 SMEs, 50 pilots	6 centers, sustainability	All economies engaged

📈 Implementation Timeline Progress

Source: UNIDO (2024) Western Balkans I4.0 Assessment Surveys. SME | BSO | GOV . Source files: INDUST1.XLS, THEROL1.XLS, ASSESS1.XLS

S

Strategy Summary

Comprehensive overview of strategic framework

Strategic Vision: Western Balkans I4.0 Transformation

Accelerate smart manufacturing adoption across 5 WB economies through a phased approach: foundational capability building (Phase I), regional ecosystem scaling (Phase II), and EU integration (Phase III).

Strategic Pillars & Progress

Phase I → Phase II Evolution

Seamless Continuity from Foundation to Scale

Phase I established the foundational infrastructure in Serbia (Regional Innovation at Partner University), while Phase II expands this model regionally across all Western Balkans. Key elements that evolved:

Phase I (Serbia Focus)
• Single Regional Innovation in Novi Sad
• Serbian stakeholder engagement
• Bilateral Slovenia-Serbia cooperation
• E-learning platform v1.0
• $199K budget (Slovenia donor)

Phase II (Regional Expansion)
• Regional WB Hub network (6 economies)
• stakeholders across the Western Balkans surveyed
• EU-level partnerships (EEN, EISMEA)
• Community platform (115 members)
• €540K budget (Slovenia + Cyprus)

Strategic Insight: Phase I achievements directly enabled Phase II scale - the Regional Innovation model, training toolkit, and e-platform became the foundation for regional replication.

Key Strategic Metrics: Phase I vs Phase II

Metric	Phase I (2020-2024)	Phase II (2025-2027)	Growth
Geographic Scope	1 country (Serbia)	6 economies (WB6)	+400%
Budget	$199K	€540K (~$590K)	+196%
Stakeholders Engaged	80+ (center launch)	203 surveyed + 115 platform	+297%
Partner Organizations	5 (Serbia + Slovenia)	25+ (regional + EU)	+400%
Training Courses	6 courses developed	12+ courses (expansion)	+100%
Innovation Hubs	1 (Regional Innovation Novi Sad)	5 potential hubs identified	+400%

G

Implementation Roadmap

Comprehensive Gantt chart: Phase I outputs through Phase III planning

Activity / Output

2021-2022

2023-2024

2025

2026

2027

2028+

PHASE I: Foundation (ID200037)

Phase I Complete

1.1 Terms of Reference

✔

1.2 National Hub Strategy

✔

1.3 Training Toolkit & E-Platform

✔

1.4 Slovenia Study Tour

✔

1.5 Branding & Promotion

✔

1.6 Regional Innovation Launch Event

✔

PHASE II: Regional Scale (ID230258)

Phase II In Progress

NOW

Output 1: Regional Hub Scaling

O1

→ 1.1 WB Digital Readiness Research

✔ Complete

→ 1.2 Dashboard & Analytics

✔ Live

Output 2: I4.0 Accelerator

In Development

→ 2.1 FoF Methodology

In Progress

→ 2.2 Pilot Program (20 SMEs)

Planned

Output 3: Partnerships

Ongoing

→ 3.1 EEN Partnership

✔ Established

→ 3.2 Joint EEN-Regional Innovation Conference

Planned

Output 4: Visibility

Ongoing

→ 4.1 Community Platform

✔ 115 Members

PHASE III: EU Integration

Phase III Planned

EU Proposal Development

Proposal

Regional Regional Innovation Network (5 hubs)

5 National Hubs

50 SMEs Transformed

Scale Target

2025 Implementation (€68K)

Completed: WB Digital Readiness Assessment, 203 survey responses, 6 focus groups, Community platform launch, EEN partnership, Dashboard development

2026 Priorities

Planned: Factory of Future methodology, I4.0 Accelerator pilot, Joint EEN-Regional Innovation conference, Phase III EU proposal initiation

2027+ Targets

Vision: 20+ SMEs in accelerator, 5 national hub network design, €6M+ EU funding secured, 500 SME transformation pathway

Detailed Analysis

Executive Summary

🎯 Digital Skills Dominance

📊 Foundational Concentration

🌿 Twin Transition Validated

📖 What This Means

Key Metrics Dashboard

📖 Understanding the Metrics

Six Core Discoveries

1. Skills are the Key

2. Foundational Reality

3. Twin Transition Works

4. Ecosystem Crisis

5. Pressure ≠ Capacity

6. Skills Mediate

So What: Bottom Line

🎯 The Core Message

Implications by Stakeholder Group

Strategic Implications

Strategic Recommendations

🎯 Three Strategic Priorities for Western Balkans

🎓 Priority 1: Skills First

🗓️ Priority 2: Build Foundations

🌐 Priority 3: Fix Ecosystem

Analytical Discussion & Implications

Six Core Findings

🎯 1. Skills Dominance

📊 2. Foundational Reality

🌿 3. Twin Transition

⚠️ 4. Ecosystem Crisis

🚫 5. Pressure Gap

🔄 6. Skills Mediation

📖 Interpreting These Findings

1. Skills Dominance

2. Foundational Stage Lock

3. The 64-Point Gap

4. Perception Cascade

5. Anti-Leapfrogging Confirmed

6. Twin Transition Link

Strongest Evidence For:

Skills Dominance Finding

Implications for Action

Stage Distribution

74.1% at Foundational Stages: What This Means

Sample Composition

⚠️ Important Sample Limitations

📖 Why Multi-Stakeholder Design Matters

Geographic Distribution

📖 Geographic Coverage Notes

Theoretical Framework Integration

📊 Peerally-Santiago Framework

🏛️ Institutional Theory

🌿 Twin Transition

📖 Why Three Frameworks?

Peerally-Santiago (2022)

Institutional Theory

Twin Transition

What Works

What Doesn't Work

Key Theoretical Constructs

Core Propositions

Proposition 1: Sequential Building

Proposition 2: Skills Centrality

Proposition 3: Ecosystem Dependency

Proposition 4: Context Specificity

Implications for WB Ecosystem

Capability Stages

📖 What Stage Distribution Means for Policy

Empirical Validation in WB

Analytical Discussion & Implications: Peerally-Santiago Framework

Three Pillars of Institutional Theory

🏛️ Regulative Pillar

📋 Normative Pillar

🔄 Cultural-Cognitive Pillar

📖 Institutional Theory in the WB Context

Coercive Pressures

Mimetic Pressures

Normative Pressures

External Pressure Measurement

Boundary Condition Discovery

🌐 Priority 3: Fix Ecosystem