Entity-Relationship Mapping of 184 SME Internationalization Success Determinants for AI Feature Engineering: Integrating CSR, Deep Learning, and Stakeholder Insights

Abstract

Corporate Social Responsibility (CSR) is increasingly shaping the pathways of Small Medium-sized Enterprises (SMEs). This study presents an entity-relationship diagram (ERD) approach to 184 determinants of SME internationalization success, in order to provide structured inputs for Deep Learning (DL) Recommenders that can support CSR-aligned internationalization strategies. Employing Visual Paradigm 17.2 Professional software for modeling, the research synthesizes state-of-the-art findings on foreign market entry, and export performance, into ERDs. Then the market adoption drivers for such a DL tool are explored through semi-structured interviews with twelve stakeholders. The results reveal a propensity to adopt the DL recommender, with experts highlighting essential features for engagement, pricing, and implementation. The discussion contextualizes these findings, while the conclusion addresses gaps and future directions. The study’s focus in Portugal/Germany may limit worldwide extrapolation, yet it advances knowledge by consolidating success determinants, validating platform requirements, exposing gaps, and suggesting research in both CSR, AI and SME internationalization.

1. Introduction

The relationship between CSR and internationalization has become a core focus for scholars seeking to understand how firms can achieve legitimacy, stakeholder trust, and sustainable growth in global markets. For those studying the impact of CSR on SME internationalization, a key question is how SMEs can strategically leverage responsible practices to overcome the unique challenges they face when entering foreign markets [1,2,3]. Existing research demonstrates that well-integrated CSR initiatives can enhance firm performance, facilitate access to new markets, and strengthen stakeholder relationships [4,5]. Sectoral differences shape how CSR is deployed: consumer-facing firms often use CSR for differentiation, while industrial firms focus on operational and supply chain improvements. This sector-specific advantage emerges from heightened consumer sensitivity to ethical practices and the capacity for CSR activities to potentiate market offerings. Industrial sectors may achieve comparable strategic benefits through operational innovations that align sustainability goals–such as environmental protection, characterized by reducing waste and emissions and implementing advanced environmental management systems; social well-being, demonstrated by building organizational trust, social capital, and genuine CSR; and economic performance, achieved through fostering innovation, green finance, and sustained organizational outcomes–with production efficiency and supply chain optimization [6,7]. Scholars have also emphasized the importance of voluntary CSR adoption, cross-sector partnerships, and firm-specific capabilities in shaping these outcomes [8,9,10,11], while others have even alerted for the long-term risks/challenges of non-adoption, especially in the realm of artificial intelligence (AI) [12].

However, despite this growing interest in digital transformation, there is still limited understanding of how emerging technologies–particularly AI–can be harnessed to operationalize and scale CSR strategies in the context of SME internationalization. While AI has shown promise in optimizing supply chains, managing risk, and supporting sustainable business models [13,14,15,16,17,18,19], few studies have systematically examined how AI-enabled platforms can help SMEs identify and enter optimal foreign markets, especially while advancing CSR objectives. This gap is important (as per Barney definition) [20], as SMEs often encounter barriers to AI adoption–including limited expertise, high costs, and integration challenges [21,22]–and little is known about what features or business models would make AI-driven internationalization tools both effective and attractive to stakeholders [23].

To address this gap, this paper aims to summarize prior research efforts and probe what SME internationalization experts deem essential to a DL platform designed to assist SMEs in identifying optimal foreign markets. Building upon an already published comprehensive systematic literature review on internationalization success determinants and previous interviews with experts [24,25], this study updates the state-of-the-art and incorporates new insights from the same experts to provide a more nuanced understanding of AI’s potential in SME internationalization. Therefore, the research questions (RQ) guiding this study are: RQ1: How do CSR and AI influence the internationalization of SMEs? RQ2: How do experts across the internationalization spectrum perceive the potential of AI in identifying optimal foreign markets for SMEs? RQ3: Are stakeholders willing to pay for an AI-driven SME internationalization solution, and what key features must be present to invest?

With these questions in mind, the following research objectives were established: O1: Perform a Systematic Literature Review (SLR) between CSR and SME internationalization, and their intersection with AI. O2: Empirically validate the need for AI-assisted decision-making in SME internationalization. O3: Investigate the willingness of SME internationalization experts to pay for an AI-driven market selection tool and identify the key DL features required to drive its acquisition, based on insights from twelve expert interviews.

By developing an ERD that contains 184 SME internationalization determinants of success among its attributes and integrates long-established and state-of-the-art theoretical constructs with practical requirements drawn from highly experienced and market-leading SME international business stakeholders, this research offers a framework for future AI-driven tools in SME internationalization. The ERD serves as a backbone, translating advanced academic knowledge into a practical structure that may support and guide the next generation of AI solutions for SMEs entering global markets. The interviews provide evidence of the financial potential and hypothetical viability of such AI tools, highlighting the features practitioners deem necessary to invest in them. In line with established practices in innovation management research, this study investigates not only the propensity to buy, or in other words, the willingness to pay (WTP) for an AI-driven solution for SME internationalization, but also the specific features, functionalities, and outputs that potential users consider essential. The use of WTP to assess the perceived value of technological innovations and their features is well supported in the literature [26].

SMEs are the bedrock of the European economy, accounting for 99.8% of all EU enterprises and generating around 58% of the region’s GDP. According to the European Commission’s 2024/2025 SME Performance Review [27], there are 26.1 million SMEs in the EU, and their continued resilience is vital for economic stability and growth. While SMEs face persistent challenges–particularly in accessing finance and international markets–the adoption of digital and AI-driven solutions is increasingly recognized as a key enabler for overcoming these barriers and enhancing competitiveness. In this context, research that identifies the digital features and functionalities most valued by SMEs and their experts is crucial for informing policy and guiding effective digital transformation strategies across the EU.

To sum up, the novelty of this manuscript lies in developing a comprehensive set of interconnected ERDs consolidating 184 determinants of SME internationalization: 181 drawn from the established literature, 2 validated in prior empirical studies, and 1 (CSR) introduced here from the recent literature but not yet systematized as a determinant. Benchmarking established platforms such as Harvard’s Atlas of Economic Complexity (AEC) and MIT’s Observatory of Economic Complexity (OEC), which mainly incorporate about 30 core trade and economic complexity variables (such as HS codes, export and import values, country metadata and revealed comparative advantage), this schema greatly extends that scope by categorizing a richer set of determinants covering firm characteristics (age, knowledge, international experience), network dynamics (buyer and supplier entry, foreign partners, network capabilities), strategic factors (cultural distance, transaction costs, market orientation), performance outcomes (firm valuation, managerial behavior, export performance), among many others. Organized hierarchically, the framework addresses SME internationalization holistically, capturing its multidimensional complexity. Unlike prior efforts that list determinants, this ERD functions as a dynamic conceptual model that formalizes constructs and their interrelations into a computationally operable format. This innovation potentially enables development of a DL-based recommender system that leverages rigorous academic research to provide practical, data-driven market entry suggestions for SMEs. By bridging scholarly theory with AI and decision support tools, the framework transcends subjective heuristics in favor of an empirically grounded, transparent, and operational system. The inclusion of the CSR determinant reflects evolving priorities. Future research will focus on operationalizing these determinants computationally to enable rigorous empirical recommendations in AI-enabled global markets.

The article begins with this introduction, followed by a synthesis of relevant academic research to establish the theoretical framework. The method section then details the authors’ research approach, while the results section presents the findings. Afterwards, the discussion analyzes theoretical contributions, practical implications, research limitations, and further research potential. The article concludes with a summary of key insights and the impact of this study.

2. Literature Review

2.1. CSR and SME Internationalization

Strategic approaches to CSR, even in SMEs, as several researchers have claimed [1,2,3], demonstrate significant potential to enhance organizational performance across diverse industry contexts, when aligned with stakeholder expectations and integrated into core business operations.

Empirical evidence suggests that CSR initiatives yield stronger financial returns when embedded within corporate value chains, particularly in consumer-facing sectors where stakeholder engagement directly influences market positioning and brand equity [4,5]. This sector-specific advantage emerges from heightened consumer sensitivity to ethical practices and the capacity for CSR activities to potentiate market offerings. Industrial sectors may achieve comparable strategic benefits through operational innovations that align sustainability goals with production efficiency and supply chain optimization [6,7]. Thus, the strategic efficacy of CSR initiatives depends on multidimensional implementation frameworks that address business objectives and stakeholder expectations [8,9]. Therefore, organizations that adopt voluntary rather than compliance-driven approaches tend to realize greater performance benefits, as these strategies enable customization to unique competitive landscapes and operational contexts.

Also, cross-sector alliances further enhance strategic impact by facilitating knowledge transfer and innovation diffusion, particularly in addressing complex sustainability challenges that go beyond industry boundaries [10,11]. While macroeconomic conditions and regulatory environments influence CSR outcomes, firm-specific factors, including executive decision-making paradigms and organizational learning capabilities, often outweigh industry characteristics in determining implementation success [28,29].

Emerging research challenges conventional assumptions about industry determinism in CSR effectiveness, revealing nuanced relationships between sector characteristics and specific environmental, social and governance (ESG) dimensions. While consumer-facing industries may excel in community engagement metrics, industrial firms frequently demonstrate superior performance in environmental stewardship and workplace safety indicators [30]. This differentiation underscores the importance of strategic fit between CSR initiatives and core operational competencies, rather than the generic adoption of sectorial best practices.

Future research directions should explore dynamic capability development in CSR strategy formulation, particularly regarding the interplay between technological disruption and evolving stakeholder expectations in shaping sustainable competitive advantage.

While CSR is increasingly cited as a strategic determinant of SME international performance, existing studies rarely integrate it with emerging technological lenses such as AI, highlighting the need to explore CSR’s role in digitally enabled internationalization (RQ1).

2.2. AI in Marketing and Customer Relations

Previous studies have established the complexity of foreign market entry decisions for firms, especially SMEs. Causal and effectual approaches to market selection and entry can both lead to enhanced international performance through collaboration [31]. Heuristics play a significant role in decision-making under uncertainty for foreign market entry [32,33].

Non-traditional entry modes, such as virtual presence and managed ecosystems, are emerging because of digitalization [34]. An integrated approach to international market selection and entry mode selection is proposed for optimal strategy [35,36]. Platform-based companies must consider bilateral markets when developing entry strategies [37,38,39].

Digital artifacts and their characteristics can facilitate rapid internationalization for digital-based ventures [40]. Entry mode diversity can benefit advanced servitization providers [41]. Open innovation is suggested as an alternative foreign market entry strategy for high-tech SMEs [42].

In the broader context of SME internationalization, AI applications are being leveraged across various business functions. Drydakis [43] found that AI enables SMEs to target consumers online, forecast cash-flow, and facilitate HR activities, all of which are associated with reduced business risks, particularly during economic downturns.

This aligns with the broader trend of AI adoption in B2B sales processes, where international SMEs are implementing AI-enabled tasks for marketing, sales, and pricing strategies [43,44]. The long-term impact of these AI applications on SMEs’ international performance requires further investigation.

This suggests potential applications in enhancing customer relationships and decision-making processes for internationalizing SMEs, though the cross-cultural applicability of these findings warrants further examination.

Although AI is transforming SME marketing practices, current research does not fully clarify how these technologies are applied in foreign market identification, limiting their strategic potential in international expansion (RQ2).

2.3. AI in Partnership Management

Chatterjee et al. [45] discuss the integration of AI into partner relationship management (AI-PRM) to automate processes and improve operational performance, which is crucial for SMEs managing international partnerships. However, the specific challenges and success factors in implementing these AI-driven approaches in diverse international contexts remain to be fully explored.

Most studies exploring AI in partnership management focus on large firms, leaving a gap in understanding how internationalizing SMEs can leverage AI for cross-border alliances (RQ2).

2.4. Deep Learning for Market Analysis

In the context of SME internationalization, deep neural networks show remarkable capabilities in analyzing international market data, helping SMEs identify promising markets for entry. These sophisticated models can process complex variables, such as economic indicators, cultural factors, and competitive landscapes, to provide data-driven market recommendations [46].

By leveraging advanced pattern recognition techniques, DL algorithms enable more accurate prediction of potential risks in international markets, which is crucial for SMEs with limited resources to avoid costly missteps [47].

Advanced DL models excel at processing multi-modal data, including text, images, and user behavior, to create sophisticated customer profiles across different international markets. This capability allows SMEs to tailor their offerings more effectively and understand nuanced market dynamics [48].

DL techniques can enhance supply chain management for internationalizing SMEs by predicting demand, optimizing inventory, and identifying potential disruptions across global networks [49].

Despite progress in DL techniques, their relevance for SME-level international opportunity analysis remains under-examined in applied literature (RQ2).

2.5. Generative AI for Data Augmentation

Advancements in generative AI, a subset of DL, have shown promise for SMEs in international business contexts. These models can generate synthetic data to augment limited datasets, a common issue for SMEs entering new international markets.

Setting eyes on the horizon, future research should focus on integrating alternative perceptual systems into generative AI platforms to acquire competitive advantage [50], particularly in SME internationalization.

The potential use of generative AI in SME strategy is under-explored, particularly in terms of stakeholder perceptions, practical integration, and willingness to invest (RQ3).

2.6. AI Implementation Challenges in SMEs

Researchers emphasize the critical need for evaluating the impact and potential harm of these systems [51], as well as exploring their implications for academic research and knowledge evaluation [52]. The intersection of these challenges with the unique constraints faced by internationalizing SMEs presents a rich area for future research.

While the technical capabilities of DL offer significant potential for enhancing SME internationalization processes, it is crucial to consider the broader implications of these technologies. SMEs adopting AI-driven solutions must navigate ethical challenges related to data privacy and algorithmic bias, particularly when operating across different regulatory environments [53,54].

Wei and Pardo [55] highlight how SMEs are leveraging AI platforms to integrate AI technologies across multiple layers, adopting various roles. This multi-layered approach is relevant for SMEs seeking to optimize their international operations.

Despite growing interest in the application of AI to SME internationalization, the literature lacks an integrated framework that systematically identifies, organizes, and prioritizes the full range of variables relevant to this process. Furthermore, there is a notable absence of empirical studies that connect expert-driven feature prioritization with economic valuation (willingness to pay) and translate these insights into actionable AI system design. This study addresses these gaps by synthesizing 184 variables into an ERD and empirically linking feature preferences to market value, thereby informing the development of AI tools tailored to SME internationalization.

While AI adoption offers significant opportunities for SMEs, it also introduces distinct ethical risks that are amplified by SMEs’ resource constraints. Many SMEs lack the financial capacity and specialized IT staff required to implement comprehensive data governance and ethical oversight frameworks [56]. The absence of dedicated roles such as Data Protection Officers (DPOs) increases vulnerability to data privacy breaches, algorithmic bias, and non-compliance with evolving regulatory requirements. Additionally, limited access to expertise can hinder the identification and mitigation of unintended ethical consequences, such as discriminatory outcomes or lack of transparency in automated decision-making. Addressing these risks requires not only investment in staff training and ethical awareness, but also the development of accessible guidelines and collaborative networks that support responsible AI adoption in the SME sector.

There is currently limited empirical insight into the practical, financial, and ethical barriers facing SMEs who might adopt AI tools for internationalization, leaving unanswered questions around willingness to pay and perceived value (RQ3).

Across these six thematic areas, the literature suggests that CSR and AI are increasingly relevant to SME internationalization, yet are rarely considered in combination. While some conceptual patterns are emerging, important questions remain around how these themes intersect in strategy and practice—especially from the perspective of SME decision-makers. These observations set the stage for the empirical investigation that follows, which focuses on AI-related priorities, perceived value, and adoption constraints as reported by domain experts. Broader gaps—particularly concerning CSR’s integration—are revisited in the discussion

3. Method

A mixed-methods sequential exploratory design was chosen to address the research objectives. This approach is effective for complex, under-explored topics where initial qualitative insights can inform subsequent quantitative analysis and system design [57]. The research proceeded in three stages: (1) a systematic literature review (SLR) to identify and structure relevant variables, (2) expert interviews to validate and prioritize features as well as assess willingness to pay, and (3) integration of findings into the development of an AI-based decision support framework for SME internationalization.

This design is congruent with the study’s aims, as it allows thematic gaps identified in the literature (linked to RQ1–RQ3) to inform qualitative inquiry with practitioners and a scholar, which grounds the development of applied decision-support tools for practice and academia. The priority of the qualitative phase reflects the exploratory nature of the research, which also seeks to identify variables and relationships not yet codified in previous frameworks.

Following two previous SLRs, the authors conducted an updated review to identify any newly reported determinants of SME internationalization success, with particular attention to the integration of CSR factors, as surfaced in the recent literature. Semi-structured interviews were also held with domain experts to explore their specific requirements for an AI platform designed to support foreign market selection. Therefore, this mixed methods study employs a sequential exploratory design [57], integrating the latest literature findings with expert insights to inform the development of a DL recommender system.

These two previous systematic literature reviews served as foundational resources for defining the scope, search terms, and inclusion criteria of the current SLR. No primary data or results were reused; instead, these prior studies informed the conceptual framework and methodological rigor applied in this research.

3.1. Designing the SLR

A SLR was executed in accordance with the PRISMA 2020 statement [58], ensuring transparent and comprehensive reporting of the review’s rationale, methodology, and findings. As illustrated in Figure 1, this process synthesized the principal determinants shaping SME internationalization and engagement with CSR. The resulting insights inform the specification of relevant inputs for the development of AI applications in this field.

The literature search was conducted in the Scopus database (last accessed in April 2025), across all fields, using the keyword terms “SME Internationalization” and “SME Internationalisation” to capture both American and British English spellings, and limited to publications between 2020 and 2025 to focus on recent literature. The initial search string was “(ALL(‘SME Internationalization’) OR ALL(‘SME internationalisation’)) AND PUBYEAR>2019 AND PUBYEAR<2026,” yielding 2693 records. The results were refined by document type to include only articles, reviews, books, or book chapters (represented by the search string (LIMIT-TO/DOCTYPE, ‘ar’ OR ‘re’ OR ‘bk’ OR ‘ch’)), excluding 109 records and leaving 2584 for screening. Next, the phrase “corporate social responsibility” was applied using the “Search within results” function. This broader approach produced a large volume of irrelevant articles, generating noise unrelated to SME internationalization. After interrater discussion, adoption of the more restrictive TITLE(“corporate social responsibility”) filter was agreed by majority, as it ensured that only studies explicitly addressing CSR within SME internationalization were retained. This refinement removed 2569 records and left 15 for further quality screening. These remaining records underwent additional quality screening: four studies were excluded because they were published in journals not ranked in Q1 according to the Scimago Journal Rank, and two were excluded due to lack of citations. While articles in press were also considered as a possible exclusion criterion, no such cases were identified. The final included set therefore comprised nine Q1 studies.

In determining the most suitable bibliographic source, leading academic repositories including Google Scholar, Scopus, Web of Science, IEEE Xplore, and ScienceDirect were reviewed. Google Scholar provides the largest citation volume, but nearly half of its references (48–65%) are non-journal sources, which do not align with the scientific rigor required for this study. In addition, it omits a significant share of Scopus-indexed journal articles. IEEE Xplore and ScienceDirect, though valuable for technical disciplines such as AI, offer limited coverage relevant to SME internationalization. Scopus was therefore selected as the primary database due to its stronger representation in Business and Economics (35% vs. 28% in WoS) [59].

As Scopus does not enable direct filtering of articles by journal quartile, journals of retrieved articles were cross-referenced with the SCImago Journal Rank (SJR) database, which classifies journals into quartiles based on citation impact and prestige within their subject areas. Q1 journals represent the top 25% in each field and are widely acknowledged for publishing high-impact, rigorously peer-reviewed research. Prioritizing articles from these outlets ensured that the review included the most influential and methodologically robust studies, thereby enhancing the validity and credibility of the findings.

3.2. Semi-Structured Interviews

Although some of the foundational methods and procedural frameworks for semi-structured interviewing originate in fields such as health sciences and education, they have since been broadly adopted in management, entrepreneurship, and innovation research for their reliability and adaptability to real-world practice. The interview design in this study draws on these established protocols to ensure thematic depth, conceptual transparency, and alignment with the exploratory goals of the research.

Semi-structured interviews represent a critical qualitative research method that balances structured inquiry with conversational flexibility. The development of an effective interview guide follows systematic frameworks, such as the five-phase model proposed by Kallio et al. [60], which includes assessing prerequisites, reviewing prior knowledge, formulating questions, pilot testing, and refining the guide.

Researchers are encouraged to employ innovative techniques, such as the critical incident method or visual elicitation tools, to deepen participant engagement and enrich data quality [61].

Ethical considerations remain paramount, particularly in participant selection and interaction. DeJonckheere and Vaughn [62] emphasize the necessity of informed consent, confidentiality protocols, and strategies to mitigate power imbalances during recruitment and data collection. They also indicate that building trust and rapport with participants is foundational to eliciting meaningful responses.

Weiss [63] and Adams [64] highlight the importance of creating a supportive environment through clear communication, active listening, and adaptive questioning that balances a structured agenda with opportunities for spontaneous dialog and interviewee cooperation.

Sampling strategies must align with research objectives, prioritizing purposive selection to ensure participants possess relevant experiences or perspectives [65]. While face-to-face interactions may enhance nonverbal communication and rapport, telephone or video interviews can achieve comparable depth, albeit with distinct interaction dynamics [66].

Qualitative methodologies often complement quantitative approaches, particularly in addressing multifaceted challenges in health sciences [67]. Participatory approaches to guide development, such as involving community stakeholders or interdisciplinary teams, can enhance cultural sensitivity and methodological rigor in healthcare studies [68].

Fieldwork demands flexibility and reflexivity, as researchers must navigate logistical challenges, unexpected participant responses, and evolving contexts while maintaining methodological consistency [69,70]. By integrating ethical rigor, systematic guide development, and adaptive execution, semi-structured interviews can yield rich, nuanced insights while upholding academic integrity.

3.3. Qualitative Data Collection and Analysis

3.3.1. Sampling Strategy

The study also employed purposive sampling initially, followed by theoretical sampling [71], resulting in 12 participants (E1–E12) representing diverse stakeholders in SME internationalization. The sample was purposefully drawn to include individuals actively engaged in internationalization processes. Eleven experts were based in Portugal, while one, located in Germany, specializes in providing legal advisory services to Portuguese companies aiming to enter or expand within the German market. Selection criteria prioritized individuals holding senior leadership positions with direct involvement in strategic decision-making related to foreign trade, ensuring informed and authoritative perspectives on SME internationalization.

Thus, the participants’ pool encompassed a range of experts in the field, including:

• SME executives (E1, E2, E3) from various sectors, chosen to represent distinct stages and approaches to internationalization;
• Industry association leaders (E4, E5) selected for their broad perspective on sector-wide internationalization trends;
• Specialized service providers (E6, E7) included for their expertise in consultancy and legal aspects of cross-border operations;
• Government agency representatives (E8, E9) chosen for their role in policy implementation, and years of helping SMEs to grow and internationalize;
• A venture capitalist (E10) selected to provide insights on financial aspects and service-sector internationalization;
• An executive from a large exporter (E11) included to contrast SME experiences with those of more established international players; and
• An academic expert (E12) chosen to bridge theory and practice in SME internationalization research.

This carefully curated sample aimed to provide a comprehensive, multi-faceted examination of SME internationalization processes, challenges, and success factors, aligning with current methodological best practices in international business research.

3.3.2. Data Collection

Between September 2021 and May 2023, semi-structured interviews, lasting approximately 30 min each, were conducted with twelve decision-makers from sectors related to SME internationalization. The interview protocol, comprising fifteen open-ended questions, was developed based on literature review findings and validated by three independent experts in international business.

3.3.3. Data Analysis

Qualitative data analysis utilized webQDA 4.0 software, following the thematic analysis approach outlined by Braun and Clarke [72]. Two researchers independently coded the entire dataset, achieving a Cohen’s kappa intercoder agreement of 0.85 [73] considered excellent by McHugh [74]. Double-coding was mostly convergent, with approximately 20% involving discrepancies. These disagreements were resolved through discussion, with final coding decisions reached by consensus or, in cases of continued divergence, by majority rule.

3.3.4. Ethical Considerations

Approval from the University of Aveiro was secured, and research followed ethical principles regarding human subjects, with recorded informed consent from all twelve participants, though one requested not to be quoted. Thus, for coherency, the authors decided to anonymize all data. Confidentiality was safeguarded with aliases. Participation was voluntary and guaranteed through the right to decline any questions at any time. No personal data was disclosed, nor sensitive professional information was discussed.

3.3.5. ERD Integration

The entity-relationship diagram (ERD) was integrated into this research as a robust tool for visualizing and structuring complex, multivariate information. ERDs have been widely used in decision analysis and knowledge management to clarify relationships among variables and support the design of data-driven decision support systems. In the context of SME internationalization, ERD enables the systematic mapping of critical factors and their interdependencies, facilitating both theoretical synthesis and practical application in AI system development [75,76,77].

4. Findings

Table 1. SLR Results and Quality assessment.

SLR Results
Author	Title				Publication	Y	P	I	V	C
Ivanova-Gongne et al. [78]	Cultural sensemaking of corporate social responsibility: A dyadic view of Russian–Finnish business relationships				Ind. Mark. Manag.	2022	153– 164		101	33
Oduro et al. [1]	Corporate social responsibility (CSR) in SMEs: what we know, what we don’t know, and what we should know				J. Small Bus. En- trep.	2024	207– 238	2	36	30
Nyuur et al. [79]	Corporate social responsibility and employee attitudes: The moderating role of employee age				Bus. Ethics Envi- ron. Responsib.	2022	100– 117	1	31	26
Siddiqui et al. [80]	The role of corporate governance and reputation in the disclosure of corp. soc. resp. and firm performance				Heliyon	2023		5	9	21
Torkkeli & Durst [81]	Corporate Social Responsibility of SMEs: Learning Orientation and Performance Outcomes				Sustainability	2022		11	14	11
Bastian & Poussing [82]	Analyzing the employee/employer relationships in the c.s.r. context: An empirical investigation of SMEs				Corp. Soc. Respon- sib. Environ. Manag.	2023	2011–2020	4	30	5
Pillai et al. [83]	Unlocking corporate social responsibility in smaller firms: Compliance, conviction, burden, or opportunity?				Thunderbird Int. Bus. Rev.	2022	627– 646	6	64	5
Myyryläinen & Torkkeli [84]	C. S. R. in Social SMEs: Discourses of Prosocial Behavior in Individual, Organizational, and Societal Levels				Sustainability	2022		11	14	3
Le et al. [85]	The roles of c.s.r., int. entrep. orientation, dynamic and tech. capabilities in the performance of int. new ventures				Int. Entrep. Manag. J.	2024	3403–3438	4	20	1
Quality Assement
Author	Study design	Sample	Context	Relevance	Sel. Bias Risk	Info. Bias Risk			Quality
[78]	Qualitative (interviews)	5 dyads (SMEs)	Finland, Russia	Cultural CSR	High (pilot dyad reliance)	Moderate (manual coding)			High
[1]	Theoretical (review)	166 articles	Global	CSR in SME performance	Moderate (database limits)	Moderate (abstracts reliance)			High
[79]	Quantitative (survey)	322 employees	Ghana	CSR and employees	Moderate (single country)	Moderate (self-report)			Moderate
[80]	Quantitative (longitudinal)	833 firms (3588 obs.)	31 countries	CSR governance	Moderate (listed firms only)	Low-Moderate (robust methods)			High
[81]	Quantitative (survey)	148 SMEs	Finland	CSR learning orientation	Moderate-High (14% response rate)	Moderate (self-report)			High
[82]	Quantitative (survey)	755 SMEs	Luxembourg	CSR and employees	Moderate (dated data)	Moderate (self-report)			High
[83]	Qualitative (interviews)	31 SMEs	Singapore	CSR and stakeholders	High (self-selection)	Moderate (social desirability)			High
[84]	Quantitative (case study)	11 SMEs (3 Countries)	Finland, Estonia, Latvia	CSR in Social SMEs	High (small-sample)	Moderate (interpretative coding)			High
[85]	Quantitative (survey)	468 INVs	Vietnam	Entrepreneurs and CSR	Low (random sampling)	Low (validated instruments)			High

Authors’ own elaboration via SCOPUS, last accessed on 21 April 2025 (T—Type; Y—Year; P—Pages; I—Issue; V—Volume; C—Citations).

reveals the top cited SCOPUS authors on the topic of CSR and SME internationalization, according to this study criteria (Q1 ordered by citations, 2020–2025) and a brief quality assessment of the nine final studies.

4.1. Entity-Relationship Diagram (ERD)

Figure 2, Figure 3 and Figure 4 represent the modeling of the 184 variables in an ERD [86,87], via Visual Paradigm 17.2 Professional software [88,89].

Entities were created and nested according to the Determinants hierarchy and its inherent categories, such as Determinants > Determinants of Internationalization > Antecedents of Internationalization, and given a numbered title to facilitate the data specification.

The root Primary Key (PK) is dossibID. The others were inherited from each new entity to create a foreign primary key. Composite PKs are composed of three levels: Class, Entity, and Category. When necessary, composite PKs were created and linked to ensure strict legacy normalization principles are applied, to facilitate the compatibility with legacy systems and older sources of open data.

4.2. ERD Data Specification

ERD Data specification for variables used in this study, grouped by category of determinants, is shown in Appendix A,

Table A3. Represents the ERD Data specification for variables used in this study, grouped by category of determinants, as referred in Section 4.2. Each sub-table presents variables with consistent headers for clarity.

A3. Determinants of SME Internationalization Success
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
A3.1. Determinants of Internationalization
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
diID	integer(10)	√	χ	χ	Determinants of Internationalization ID
A3.1.1. Antecedents of Internationalization
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
diID	integer(10)	√	χ	χ	Determinants of Internationalization ID
aiID	integer(10)	√	χ	χ	Antecedents of Internationalization ID
Age, knowledge, imitable	varchar(255)	χ	√	χ	Autio et al. (2000) as cited in (*)
Buyer & supplier entry, size	varchar(255)	χ	√	χ	Martin et al. (1998) as cited in (*)
Industry factors, networks	varchar(255)	χ	√	χ	Sarkar, Cavusgil, & Aulakh (1999) as cited in (*)
Information internationalization	varchar(255)	χ	√	χ	Liesch & Knight (1999) as cited in (*)
Int. experience, foreign partners, speed	varchar(255)	χ	√	χ	Reuber & Fisch (1997) as cited in (*)
Internationalization R&D intensity, sales	varchar(255)	χ	√	χ	Fiegenbaum et al. (1997) as cited in (*)
Knowledge of International Business (IB)	varchar(255)	χ	√	χ	Eriksson et al. (1997) as cited in (*)
Privatization, network capabilities	varchar(255)	χ	√	χ	Doh (2000) as cited in (*)
Product diversification and performance	varchar(255)	χ	√	χ	Delios & Beamish (1999) as cited in (*)
Top management factors	varchar(255)	χ	√	χ	Tihanvi et al. (2000) as cited in (*)
Top management foreign experience	varchar(255)	χ	√	χ	Sambharva (1996) as cited in (*)
A3.1.2. Description and Measurement
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
diID	integer(10)	√	χ	χ	Determinants of Internationalization ID
damID	integer(10)	√	χ	χ	Description and Measurement ID
Comment on measurement	varchar(255)	χ	√	χ	Ramaswamy et al. (1996) as cited in (*)
Measurement of globalization	varchar(255)	χ	√	χ	Makhiia et al. (1997) as cited in (*)
Process of Internationalization	varchar(255)	χ	√	χ	Hendry (1996) as cited in (*)
Reply on measurement	varchar(255)	χ	√	χ	Sullivan (1996) as cited in (*)
A3.1.3. Consequences of Internationalization
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
diID	integer(10)	√	χ	χ	Determinants of Internationalization ID
ciID	integer(10)	√	χ	χ	Consequences of Internationalization ID
Advice network density	varchar(255)	χ	√	χ	Athanassiou & Nigh (1999) as cited in (*)
CEO international experience	varchar(255)	χ	√	χ	Daily, Certo, & Dalton (2000) as cited in (*)
CEO pay. Management team factors	varchar(255)	χ	√	χ	Sanders & Carpenter (1998) as cited in (*)
Curvilinear performance effect	varchar(255)	χ	√	χ	Gomes & Ramaswamy (1999) as cited in (*)
Entrepreneurial firms	varchar(255)	χ	√	χ	McDougall & Oviatt (2000) as cited in (*)
Firm valuation, investment & incentives	varchar(255)	χ	√	χ	Mishra & Gobeli (1998) as cited in (*)
Management team behavior & knowledge	varchar(255)	χ	√	χ	Athanassiou & Nigh (2000) as cited in (*)
MNE risk & leverage, market conditions	varchar(255)	χ	√	χ	Kwok & Reeb (2000) as cited in (*)
Performance & technological learning	varchar(255)	χ	√	χ	Zahra, Ireland, & Hitt (2000) as cited in (*)
Perf., cult. distance & experience moderators	varchar(255)	χ	√	χ	Luo (1999) as cited in (*)
Performance, cultural diversity	varchar(255)	χ	√	χ	Palich & Gomez-Meija (1999) as cited in (*)
Performance, diversification, time	varchar(255)	χ	√	χ	Geringer, Tallman, & Olsen (2000) as cited in (*)
Performance, diversification moderator	varchar(255)	χ	√	χ	Hitt, Hoskisson, & Kim (1997) as cited in (*)
Performance, expansion decisions	varchar(255)	χ	√	χ	Syam (2000) as cited in (*)
Performance, product diversity moderator	varchar(255)	χ	√	χ	Tallman & Li (1996) as cited in (*)
Performance, psychic distance moderator	varchar(255)	χ	√	χ	O’Grady & Lane (1996) as cited in (*)
Performance, timing moderator	varchar(255)	χ	√	χ	Luo (1998) as cited in (*)
Performance. timing of withdrawal	varchar(255)	χ	√	χ	Meznar, Nigh, & Kwok (1998) as cited in (*)
Performance cultural diversity	varchar(255)	χ	√	χ	Gomez-Meiia & Palich (1997) as cited in (*)
A3.2. Determinants of Entry Mode Decisions
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
doemdID	integer(10)	√	χ	χ	Determinants of Entry Mode Decisions ID
A3.2.1. Predictors of Entry Mode Choice
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
doemdID	integer(10)	√	χ	χ	Determinants of Entry Mode Decisions ID
poemcID	integer(10)	√	χ	χ	Predictors of Entry Mode Choice ID
Cooperative arrangements, risk sharing	varchar(255)	χ	√	χ	Pan & Tse (1999) as cited in (*)
Corporate visual identity	varchar(255)	χ	√	χ	Melewar & Saunders (1999) as cited in (*)
Costs, cultural factors, market structure	varchar(255)	χ	√	χ	Buckley & Casson (1998) as cited in (*)
Cultural distance, licensor competition	varchar(255)	χ	√	χ	Arora & Fosfuri (2000) as cited in (*)
Digestibility, information asymmetry	varchar(255)	χ	√	χ	Hennart & Reddy (2000) as cited in (*)
Experience with entry mode	varchar(255)	χ	√	χ	Padmanabhan & Cho (1999) as cited in (*)
Firm structure, strategy, & country factors	varchar(255)	χ	√	χ	Contractor & Kundu (1998) as cited in (*)
Home, host industry, & operation factors	varchar(255)	χ	√	χ	Tse, Pan, & Au (1997) as cited in (*)
Host, home, & ind. factors, mode hierarchy	varchar(255)	χ	√	χ	Pan & Ise (2000) as cited in (*)
Industry, partner experience	varchar(255)	χ	√	χ	Swan & Ettlie (1997) as cited in (*)
Internal institutional pressures	varchar(255)	χ	√	χ	Davis, Desai, & Francis (2000) as cited in (*)
Organizational capabilities, transaction costs	varchar(255)	χ	√	χ	Madhok (1997) as cited in (*)
Sequential pattern of entry	varchar(255)	χ	√	χ	Penner-Hahn (1998) as cited in (*)
Target digestibility. industry growth	varchar(255)	χ	√	χ	Hennart & Reddy (1997) as cited in (*)
Tech, characteristics of industry sectors	varchar(255)	χ	√	χ	Hagedoorn & Naruja (1996) as cited in (*)
Transaction costs, national culture	varchar(255)	χ	√	χ	Makino & Neupert (2000) as cited in (*)
Transaction costs, strategic options	varchar(255)	χ	√	χ	Chi & McGuire (1996) as cited in (*)
A3.2.2. Predictors of Equity Ownership Levels
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
doemdID	integer(10)	√	χ	χ	Determinants of Entry Mode Decisions ID
poeolID	integer(10)	√	χ	χ	Predictors of Equity Ownership Levels ID
Cultural distance & characteristics	varchar(255)	χ	√	χ	Hennart & Larimo (1998) as cited in (*)
Experience & institutional factors	varchar(255)	χ	√	χ	Delios & Beamish (1999) as cited in (*)
Firm specific advantages	varchar(255)	χ	√	χ	Erramilli, Agarwal, & Kim (1997) as cited in (*)
Home national culture & economic factors	varchar(255)	χ	√	χ	Erramilli (1996) as cited in (*)
Institutional, cultural, & TC factors	varchar(255)	χ	√	χ	Brouthers & Brouthers (2000) as cited in (*)
Ownership, location, & int. factors	varchar(255)	χ	√	χ	Pan (1996) as cited in (*)
Private & public expropriation hazards	varchar(255)	χ	√	χ	Delios & Henisz (2000) as cited in (*)
Product & multinational diversity	varchar(255)	χ	√	χ	Barkema & Vernneulen (1998) as cited in (*)
A3.2.3. Consequences of Entry Mode
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
doemdID	integer(10)	√	χ	χ	Determinants of Entry Mode Decisions ID
cemID	integer(10)	√	χ	χ	Consequences of Entry Mode ID
Longevity, cultural distance	varchar(255)	χ	√	χ	Barkena & Pennings (1996) as cited in (*)
Performance, firm capabilities & mode fit	varchar(255)	χ	√	χ	Anand & Delios (1997) as cited in (*)
Performance, ILO & mode fit	varchar(255)	χ	√	χ	Brouthers et al. (1999) as cited in (*)
Performance, strategy & mode fit	varchar(255)	χ	√	χ	Busiia, O’Neill & Zeithami (1997) as cited in (*)
Responses environment change	varchar(255)	χ	√	χ	Bogner, Thomas & McGee (1996) as cited in (*)
A3.3. Determinants of International Exchange
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
ieID	integer(10)	√	χ	χ	Determinants of International Exchange ID
A3.3.1. Determinants of Exporting
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
ieID	integer(10)	√	χ	χ	Determinants of International Exchange ID
deID	integer(10)	√	χ	χ	Determinants of Exporting ID
Existing interpersonal links	varchar(255)	χ	√	χ	Ellis (2000) as cited in (*)
Home/host location factors, ownership adv.	varchar(255)	χ	√	χ	Campa & Guillen (1999) as cited in (*)
Market orientation	varchar(255)	χ	√	χ	Cadogan et al. (1999) as cited in (*)
Market size, income, size, imports	varchar(255)	χ	√	χ	Andersson & Fredriksson (1196) as cited in (*)
Review of process & determinants	varchar(255)	χ	√	χ	Leonidou & Katsikeas (1996) as cited in (*)
A3.3.2. Exchange Overviews
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
ieID	integer(10)	√	χ	χ	Determinants of International Exchange ID
eoID	integer(10)	√	χ	χ	Exchange Overviews ID
Effect of lagging adj., social networks	varchar(255)	χ	√	χ	Rangan (2000) as cited in (*)
Integrating importing/exporting decisions	varchar(255)	χ	√	χ	Liang & Parkhe (1997) as cited in (*)
Types of exchanges and their enforcement	varchar(255)	χ	√	χ	Choi, Lee & Kim (1999) as cited in (*)
A3.3.3. Export Intermediaries
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
ieID	integer(10)	√	χ	χ	Determinants of International Exchange ID
eiID	integer(10)	√	χ	χ	Export Intermediaries ID
Performance, market distance, products	varchar(255)	χ	√	χ	Peng, Hill & Wang (2000) as cited in (*)
Service offer., size, role, suppliers, products	varchar(255)	χ	√	χ	Balabanis (2000) as cited in (*)
Use, products. market distance/familiarity	varchar(255)	χ	√	χ	Peng & Ilinitch (1998) as cited in (*)
A3.3.4. Consequences of Exporting
Determinant	Data Type	PK	N	U	Description
dossibID	integer(10)	√	χ	√	Determinants of SME International. Success ID
ieID	integer(10)	√	χ	χ	Determinants of International Exchange ID
coeID	integer(10)	√	χ	χ	Consequences of Exporting ID
Export & econ. perf., gray market activity	varchar(255)	χ	√	χ	Myers (1199) as cited in (*)
Export performance, strategic fit	varchar(255)	χ	√	χ	Aulakh. Kotabe, & Teegen (2000) as cited in (*)
Export ratio, firm perf., market share, size	varchar(255)	χ	√	χ	Ito (2000) as cited in (*)
Export ratio, keiretsu membership	varchar(255)	χ	√	χ	Hundley & Jacobson (1998) as cited in (*)

Authors’ own elaboration—adapted from Werner [90]. (PK—Primary Key; N—Nullable; U—Unique). (*) [24] Note: The format “Author (Year) as cited in ()” (e.g., “Balabanis (2000) as cited in ()”) means that the original source of the construct—including author, paper, publisher, and year—is fully detailed in the prior article (). To verify any construct’s original source, please consult the reference list of (), which serves as the comprehensive source table for the constructs presented here.”.

. Each sub-table presents variables with consistent headers for clarity. A total of 184 determinants of success were defined and mapped as attributes across multiple entities in an ERD. These 184 attributes serve as SME internationalization variables and future features for any subsequent AI embedding and analysis. The ERD model provides a theoretical foundation by mapping the complex relationships among internationalization variables identified in the literature. This structure guided the design of expert interviews, ensuring that practical insights could be systematically integrated with the theoretical framework. The subsequent analysis bridges theory and practice by examining how experts prioritize features and assign economic value, leading to actionable recommendations for AI system development. To further illustrate these findings, a summary money map (Figure 5—a combination of a heat map and a tree map, depicting these responses across all twelve interviewees) presents the distribution of “propensity to buy” across user categories, supporting the development of a preliminary typology of potential adopters.

4.3. Insights from the Semi-Stuctured Interviews

Table 2. Propensity to buy the DL platform (authors’ own elaboration via webQDA 4.0 software).

Int.	Org.	Sector	Type of Company	Gender	Function	Propensity to Buy (p)
E12	C7	Education	University	F	Scholar	p = 0.05 × S (€)
E1	C1	Automation	SME	M	CEO	p = 0.01 × (1 + 0.10) × S (€)
E5	C6	Chambers of Commerce	Chamber	M	Director	€1000/month
E4	C3	Automotive	Association	M	Secretary-general	€10/month **
E8	C10	Government	State Agency	M	Director	€100 ≤ pmonth ≤ €150
E11	C8	Wines	SME	M	Director	€2000 ≤ p ≤ €2500
E2	C1	Automation	SME	F	Board Member	€1000
E10	C4	Venture Capital	Business Angel	M	Partner	p = {0,a,10a,100a} (€) **
E7	C11	Lawyers	SME	F	Lawyer	€0
E9	C5	Government	State Agency	M	Director	€0 ***
E6	C9	Consulting	SME	F	Owner	n.a. (€)
E3	C2	Toys	SME	M	CEO	n.a. (€)

** The interviewee said the solution will not work. *** The interviewee said the platform has market value and would sell. a = unknown amount; S = total sales in that market; n.a. = not answered.

describes the interviewees, their organization, their sector, type of firm, gender and job function and each answer regarding the propensity to buy.

E1 reported that would pay 1% for each 10% of increased sales on the market suggested by the platform (represented in Table 2 by p = 0.01 × (1 + 0.10) × S). E2 alerted that it would be ready to acquire the solution for €1000. E3 did not answer. E4 clearly stated that such a solution would not work in the automotive industry, since it is so closed on itself, especially in the Tier 1 niche of suppliers, and compared the platform to an advanced “yellow pages”. When requested to give the first amount that came to mind, the value was €10 a month. E5 said that building such a platform is not enough, since many have tried in the past. It is also critical to feed and update it and would pay €1000 a month for the solution. E6 did not answer. E7 also stated that it could not value such a solution and would not acquire it. E8 stated that the number of contacts on the platform will be crucial, and for a credible solution could pay between €100 to €150 a month. E9 clarified that the state agency would not pay for such a solution, since it is already developing one similar, but that the platform has market value and would sell. E10 stated that the solution technically will not work, since it requires resources that would make it unprofitable. In the case such a solution is implemented, would not be interested in paying only to know the market, but would pay tenfold the amount if it supplied the right contact and other ten times if the solution could somehow schedule a meeting with that person (represented in Table 2 by p = {0, a, 10a, 100a}). E11 would pay for such a solution, between €2000 to €2500 punctually, which is what they are used to paying for traditional market research. E12 sees such platforms as crucial and would pay 5% of the volume expected for each market suggestion (represented in Table 2 by p = 0.05 × S).

Results reveal that most interviewees expressed willingness to pay for such a solution. Seven responded affirmatively, three declined, and two did not answer. Among those willing to pay, three indicated a monthly payment, two a percentage of sales, and two a one-time fee, as just shown in Table 2 and Figure 5.

Figure 6 was obtained via webQDA 4.0 and samples a coded interview exchange with one of the interviewees, in this case E1, while discussing the features needed for the expert to buy such a DL solution.

The interviews with exporting firms, business associations, and internationalization decision makers revealed a multifaceted and highly pragmatic perspective on market entry, the barriers encountered, and the conditions under which interviewee would be willing to adopt and finance a DL-driven recommender system for international market selection. Three main themes emerged: (i) willingness to pay, (ii) barriers to internationalization, and (iii) functional prerequisites for such recommender systems.

(i) Willingness to Pay—Contrary to the current assumption that digital tools for market intelligence need to be free to ensure adoption, a respondent explicitly rejected this notion, stating “if it’s free, people don’t value it”. Instead, experts articulated significant WTP, provided that the system generates actionable outputs. Some assessed the investment through absolute figures, with estimations oscillating between “10,000 EUR per year, 20,000 EUR, I don’t know” to “It should be around 2000 to 2500 EUR, at the very least”. Others tied WTP explicitly to expected revenues: “It is always a percentage of what we expect in sales volume for that market. And it is usually around 5%”. Smaller firms pointed to more conservative ranges, such as “I don’t know, 1000 EUR annually” or “I could subscribe to something monthly for 100 or 150 EUR.”

Interestingly, one participant followed a WTP exponential logic depending on the level of actionable intelligence: “I would even pay more if it scheduled a meeting, much more, it’s exponential… I would pay you 10 euros for you to tell me which country it is. It is 10 times more useful for you to tell me the entry point and 10 times more useful for you to schedule me a meeting.” This evidence suggests that highly credible players in SME internationalization attribute radically higher value not to general country recommendations but to the delivery of qualified, personalized contacts and facilitated interactions.

(ii) Barriers to Internationalization—At the same time, interviewees consistently stressed the structural and operational barriers that limit the effectiveness of current internationalization efforts and platforms. One recurring theme was the cultural factor, formulated as: “The cultural factor cannot be minimized”. They also underscored the importance of hiring local staff to manage expansion attempts: in the German market, for example, a director with prior ties to major firms and distribution chains was deemed critical. Another barrier was product-market fit. Differences in consumer expectations and standards, ranging from sizing in the textile industry to compulsory certifications, often prevented success. As noted by one executive: “A major mistake is starting marketing promotion without knowing if the product is ready for that market… without certification, any investment is wasted.” Processual complexity also emerged, particularly among firms transitioning from B2C to B2B operations. One case exemplified this clearly: “150,000 EUR in exports and we had management control costs of 1 million.” Other respondents highlighted the difficulty of long sales cycles: “For Latin America, we have to court or nurture the potential client for 6 years and only after 6 years do results come.” Equally important were the perceived weaknesses in existing digital matching platforms. Participants criticized their lack of reliability, outdated data, and insufficient interactivity: “We need to ensure the application is continuously fed with updates, always permanent and dynamic.” Credibility, freshness, and maintenance of information emerged as non-negotiable conditions for adoption. Finally, an interviewee also underscored the structural disadvantages of isolation, since collective strategies offer efficiency and legitimacy (“A company alone is always weaker than a group of companies”), and complained against a certain operational individualism, thought to characterize a big part of the Portuguese business environment, that still resists coordinated public–private frameworks, not allowing critical mass to facilitate governmental international lobbying and bridge-building.

(iii) Functional requirements of a DL foreign market recommender system—from these barriers emerged a clear set of functional prerequisites for any AI- or DL-based recommender. First, experts demand precision in identifying partners and distribution networks. Screening should involve filters to exclude competitors’ existing distributors and to map channel presence: “Partner screening—for me that would be the most important thing… I need to understand who my competitors are, how they are distributed in the market, which channels they use, what channels the market has, and what is the weight of each channel.” Second, the platform should go beyond descriptive outputs and deliver actionable intelligence. Mere assessments such as “Germany is indicated for export” were dismissed as inadequate. Instead, interviewees expected concrete leads and meetings: “It would be an added value: direct contacts with companies… going to talk with purchasing managers”. Third, industry-specificity was viewed as indispensable, and sometimes even insurmountable to AI tools: “the specificity of this industry is such that it is not possible”. Through this lens, one-size-fits-all solutions appear destined to underperform if they fail to accommodate adaptation to these needs. Fourth, the tool must integrate strategic and operational layers. Desired data included not only financial profiles and consumption trends but also political, economic, social, and technological (PEST) conditions: “PEST analysis is fundamental for us to make a decision.” Similarly, information about the state of the labor market and human resource availability was singled out: “The state of the art of each market, according to our business area… how the labor market works and what type of human resources there are”. Fifth, firms required solutions that were usable, intuitive, and accessible: “no programming required, and usable by an administrator, a salesperson, or even an accountant.”, in other words, user-friendly. While desktop was viewed as the prime working environment for data-heavy tasks, mobile accessibility (even in lightweight versions) was praised for its flexibility. Integration of communication channels was also necessary, given that “WhatsApp is crazy popular for my markets. It has become the communication tool. It’s now almost on par with email.”

Most striking insights: exponential payment model, long-term ROI risks, self-inflicted market entry complexity and interviewees’ frustration with existing tools, that lack updated, credible data, making them seem regarded as nearly useless, and the willingness to pay for a trustworthy AI recommendation, able to capture budgets of 2 k–10 k EUR per market entry project.

Synthesis: the empirical evidence illustrates that firms are not unwilling to pay for advanced foreign market entry recommender tools, but strongly condition their WTP on trustworthiness, contextualization, and actionability. Consistently, the highest valuation is attributed to systems that reduce uncertainty at the partner level—transforming high-level market recommendations into verified contacts and facilitated meetings. Barriers were also revealed to be multidimensional, spanning culture, regulatory requirements, product adaptation, cost structures, and institutional support gaps. For recommender systems to demonstrate utility and legitimacy, they must converge these domains—integrating macro-level variables with micro-level partner identification—while ensuring a continuous process of updating and validation.

Integration of Findings with Research Questions

RQ1: The literature clearly positions CSR as a key determinant of SME internationalization, most notably for its ability to enhance reputational capital and stakeholder legitimacy in foreign markets. Evidence also suggests that AI, particularly when applied to market analysis and risk assessment, can improve decision quality in ways that complement CSR. However, the effectiveness of both CSR and AI depends on sector-specific and contextual factors. Their impact is not uniform, and firms must tailor their strategies to local conditions.

RQ2: Expert interviews confirm the practical value of AI-driven tools for market selection, especially when these tools are reliable, data-driven, and adapted to sector needs. Most interviewees reported that they would adopt such solutions if they demonstrably improved market entry outcomes, such as increased sales or higher quality commercial contacts. At the same time, experts in specialized sectors expressed reservation, citing concerns about data sufficiency, sectoral fit, and the risk of over-promising technological capabilities. This direct feedback underscores that AI’s real-world value in internationalization is contingent on both technical sophistication and contextual relevance.

RQ3: Most interviewees expressed a willingness to pay for AI-based internationalization platforms, with preferences varying according to payment model and the demonstrable impact on business outcomes. Willingness to pay is closely linked to the platform’s ability to deliver measurable benefits, such as sales growth, access to qualified leads, or traceable results. Experts also emphasize the importance of sector-specific customization, flexible integration, and actionable insights, with a clear preference for tools that can be tailored to their needs and constraints.

Synthesis: These findings collectively show that CSR and AI both matter for SME internationalization, but their real-world adoption and effectiveness require attention to context, sectoral fit, and proven value. CSR’s importance remains well supported by the literature, while practitioners consistently value AI-driven tools if they are transparent, reliable, and tailored. The demand for such platforms is real, provided they can demonstrate real impact and adapt to the diverse needs of SMEs across sectors.

CSR data can be operationalized for AI/ML analysis of SME internationalization through a relational structure in which primary keys uniquely identify firms, foreign keys link firm characteristics to CSR performance and market environments, and composite keys such as (Firm_ID, Market_ID, Year) capture observations across contexts and time. A streamlined data dictionary would include firm and market identifiers, temporal indicators, standardized CSR dimensions (environmental, social, and governance scores), financial attributes like revenue, sector classifications, an AI readiness index, and a market risk index. These inputs provide the foundation for an analytical pipeline. Data ingestion aligns firm- and market-level attributes, followed by cleaning and transformation to standardize CSR measures, resolve missing values, and harmonize sector codes. Feature engineering then derives sector-adjusted risk scores, AI readiness composites, and trend indicators capturing CSR improvement over time. Modeling approaches may involve recommender systems for identifying promising markets or supervised predictors such as gradient boosting and neural networks to estimate entry success. Model performance can be evaluated through lead quality precision, F1-score, and ROI uplift indicators. To ensure interpretability, explanation methods such as SHAP values and partial dependence plots clarify the relative weight of CSR and contextual features, while error analysis safeguards against biases related to sectoral differences, overfitting, or the uneven quality of CSR reporting. For example, a European textile SME assessing expansion into Vietnam and Malaysia could leverage the pipeline to benchmark its governance and social scores against regional standards; the models may indicate Vietnam as a higher-return opportunity, but explanation layers would highlight governance as the decisive driver, while error analysis would detect any skew arising from overemphasis on environmental indicators. This demonstrates how systematically structured CSR data, integrated with AI/ML tools, can enhance SME internationalization strategies, while also exposing the risks of biased or incomplete CSR measures.

By closely mapping empirical results to each research question, this study advances the discourse on the digital and responsible transformation of SME internationalization strategies, laying a credible foundation for the subsequent development and empirical testing of AI-driven solutions in this domain.

5. Discussion

5.1. Theoretical Contribution

With the empirical integration of our findings and research questions now clearly articulated, several key theoretical and practical implications emerge. These broader lessons—for how CSR, AI, and expert perspectives together shape SME internationalization—invite closer debate about the conditions under which digital and responsible transformation can succeed, as well as the real-world barriers and trade-offs that may constrain their impact.

Interviews and data analysis, from Portugal and Germany, reveal evidence of a positive propensity in the market for DL recommenders that assist internationalizing SMEs in identifying their optimal foreign markets. This, together with the authors’ own knowledge and their prior literature reviews about internationalization, convinced the authors they could contribute to science and society with an innovative ERD model, inspired in both the state-of-the-art of academia, but also the direct feedback of practitioners in SME Internationalization.

This study makes a distinctive contribution to the literature on firm internationalization by systematically operationalizing both scholarly and practitioner knowledge into a comprehensive set of software-ready variables. While platforms such as MIT’s Observatory of Economic Complexity (OEC) and Harvard’s Atlas of Economic Complexity have pioneered data-driven approaches to international market analysis, these typically focus on a limited set of variables, often between 6 and 30, and are primarily grounded in either scholarly research or practitioner needs, but rarely both.

In contrast, this study draws on a much broader and more integrative foundation. By directly engaging domain experts in SME internationalization, the research identifies the features and information practitioners would require in a practical decision-support platform. These practitioner insights are then systematically merged with an extensive review of scholarly variables, resulting in a robust ERD that encompasses 184 variables. This ERD has the potential to serve as the Electronic Control Unit (ECU) or core logic for future AI-driven platforms, providing a detailed and actionable architecture that goes well beyond the scope of existing tools.

Rather than simply cataloging variables or demonstrating a proof of concept, the study bridges the theory-practice gap by translating both academic and market-driven insights into a unified, software-compatible structure. This approach enables future AI platforms to deliver individualized, evidence-based foreign market recommendations to firm owners, thereby facilitating the practical application of internationalization theory.

While acknowledging the valuable contributions of prior platforms, this study distinguishes itself through its unprecedented breadth, its explicit integration of both scholarly and practitioner perspectives, and its focus on systematizing these insights for direct implementation in AI-based decision-support systems. As such, it advances the field by providing a replicable framework that scholars can use to further operationalize theory and practitioners can leverage for real-world strategic decision-making.

Traditionally, DL models are developed using large, often unstructured datasets, with minimal explicit integration of domain theory or expert knowledge. By systematically translating state-of-the-art theoretical constructs and expert stakeholder insights into a structured ERD, this study ensures that the variables and relationships embedded in any AI model are grounded in both academic rigor and real-world relevance. This bridges the gap between abstract theory and practical, data-driven AI applications–an integration rarely seen in the SME internationalization literature.

Feature engineering is also a critical, yet often ad hoc, aspect of DL model development. The present method uses the ERD as a formal, transparent blueprint for feature selection and data architecture. This not only increases model interpretability and replicability but also ensures that the resulting AI tool is aligned with the most relevant and validated determinants of SME internationalization success.

It is noteworthy to add that DL models are frequently criticized for their “black box” nature. By anchoring the model’s features in an ERD derived from both theory and expert practice, researchers and practitioners can enhance the explainability of the AI’s recommendations. Last but not least, stakeholders can also trace predictions back to well-defined variables, increasing trust and adoption potential among SMEs and policymakers.

To the authors knowledge, this is the first study to apply an ERD-based variable framework as the backbone for a generative AI tool in the context of SME internationalization. While ERDs are common in database design, their use as a methodological bridge between qualitative theory and quantitative AI modeling in this field seems unprecedented.

Therefore, this ERD provides a reusable and extensible structure for future AI applications, enabling other researchers and practitioners to build upon a validated, theory-driven feature set. This accelerates the development of more robust, transparent, and effective AI tools for SMEs.

Figure 7 aligns qualitative findings with the data architecture.

5.2. Managerial and Policy Recommendations

The implications of this research extend beyond academic discourse, offering significant insights for both managerial strategy and policy formulation in the realm of SME internationalization. The findings suggest a paradigm shift in how SMEs approach their international market entry strategies. Rather than persisting with traditional, often ineffective methods such as repeated attendance at international trade shows, which frequently yield a multitude of business cards, yet a scarcity of tangible business outcomes, SMEs can now focus their resources more efficiently.

Several policy recommendations emerge for CSR in SMEs. Public authorities should implement “soft policies” to raise awareness among SMEs through trainings, workshops and conferences, and facilitate CSR adoption as a way to improve social dialog and increase business performance [1].

Policymakers should create platforms to allow SMEs to share and match their CSR interests with societal needs, reducing search efforts and helping managers allocate resources efficiently, and policies should be designed to support SMEs in integrating CSR into their core business strategy and operations, since there is a need for cohesive government strategies to level the playing field for SMEs to contribute effectively to economic and social contexts [83].

Finally, government authorities should recognize that SMEs in different sectors may require tailored approaches, as firms in highly regulated industries tend to focus on compliance-based CSR while those in less regulated sectors may be more proactive.

Figure 8 was conceptualized via CmapTools software, version 6.04, in order to synthesize and make more prominent the recommendations here presented, so any policymaker or SME owner, could easily implement them.

5.3. Gaps

• CSR-Employee Engagement: Systematic analysis of how CSR initiative characteristics (e.g., scope, authenticity) influence employee engagement is lacking, particularly regarding individual CSR dimensions’ differential impacts and age’s moderating role in CSR-job attitude relationships [79].
• CSR Disclosure-Performance Linkages: Inconsistencies persist in linking CSR disclosure to organizational performance, with limited empirical support for mediating/moderating variables and underdeveloped causal pathways [80].
• SME CSR Communication: SMEs in emerging markets lack strategic CSR communication frameworks tailored to resource constraints [78].
• Learning Orientation and CSR Innovation: The role of SME learning orientation (e.g., knowledge absorption) in catalyzing CSR innovation (e.g., ethical supply chains) to drive internationalization and performance remains under-explored [81].
• CSR in Social Enterprises: Ambiguities persist in CSR’s role within social enterprises, including fragmented micro-foundational drivers (e.g., founder motivations) and disconnects between sociopsychological processes (e.g., trust-building) and macro-level policy impacts [84].
• SME-Specific CSR Dynamics: Systematic reviews struggle to account for SME sectoral variations (e.g., manufacturing vs. agriculture) in CSR drivers, reporting, and performance, alongside inadequate cross-cultural validation and overreliance on qualitative metrics [1].
• Employee–Employer CSR Interactions: Employee–employer bidirectional CSR interactions are underexplored, with sparse SME-specific studies on resource-driven engagement barriers and overuse of binary CSR measures [82].
• Stakeholder Prioritization in SMEs: Gaps exist in stakeholder salience hierarchies (e.g., communities vs. suppliers) and methodological biases (e.g., self-selection) in SME CSR decision-making [83].

Cross-Cutting Issues: Longitudinal data scarcity, sector-specific investigations (e.g., family firms), and culturally sensitive collaboration models requiring validation.

5.4. Limitations and Future Research

Despite the sampling design and the authors’ best effort, this study, due to its limited geolocation (2 countries in the same Continent) and sample size, may suffer from interpretation bias and limited generalizability. Nevertheless, the sample is intentionally diverse, including micro, small, and medium-sized enterprises, as well as state agencies dedicated to SME support. The inclusion of these agencies, despite their size, is justified by their exclusive focus on facilitating SME internationalization. While most participants are based in Portugal and one in Germany, the selection of experts was purposive, targeting stakeholders directly involved in internationalization processes, rather than a random sample of SMEs.

It is important to note that the core set of variables operationalized in this study is derived from the established international literature on SME internationalization. As such, these variables reflect a body of knowledge that has been validated across multiple countries and contexts, supporting the broader applicability and relevance of the resulting framework. The expert insights collected serve primarily to enhance the practical relevance and implementation of these variables; therefore, any limitations regarding generalizability pertain mainly to the contextual nuances of expert opinion, rather than to the foundational variable set itself.

Moreover, while cultural and contextual differences may influence specific internationalization pathways, the fundamental principles of successful SME exporting–such as market demand, resource allocation, and strategic fit–tend to be consistent across geographies. The authors acknowledge that further research could extend the geographic and sectoral scope of expert input. However, it is anticipated that the core findings and the universal business logic underpinning successful internationalization would remain robust, as success in international markets is governed by widely recognized principles rather than local idiosyncrasies.

Accordingly, the framework developed in this study is positioned as globally relevant, grounded in long-established principles of business performance, and informed by both scholarly literature and targeted practitioner insight. Future research may further validate and refine this approach across additional contexts, but the foundational logic is expected to hold true internationally. This study also provides valuable insights into CSR and AI-driven SME internationalization, but it is important to acknowledge its limitations. The sample of the interviews, while diverse in expertise, was composed of European participants. This geographical concentration may limit the extrapolation of findings to a global context, despite efforts to include a broader international sample, since resources and time constraints prevented the inclusion of additional foreign perspectives.

Future research should address this limitation by replicating the study with a more geographically diverse sample, ideally encompassing global participants. This expanded scope would provide a more comprehensive understanding of the cross-cultural demand and challenges for AI-driven internationalization solutions.

Upcoming research is also needed to enhance the assessment of CEO integrity, further explore its moderating effects, extend the analysis to SMEs to determine potential differences in outcomes, and examine additional corporate governance characteristics such as family ownership and executive compensation [80]. Subsequent studies are encouraged to examine additional mediators or moderators, such as marital status, and replicate the study across multiple countries to account for regional variations in CSR and employee outcomes [79]. Scholars could also address self-selection and social desirability biases to obtain more comprehensive data, while also exploring CSR practices among SMEs not currently engaged in such initiatives to yield valuable new insights [83].

Academia may benefit from collecting longitudinal data to explore the causal effects between CSR adoption and employee–employer relationships, while using more nuanced measures of CSR beyond a binary variable to capture the complexity of these practices [82]. Forthcoming studies can also use longitudinal and comparative methods to investigate how CSR, learning orientation, and market orientation interact in diverse SMEs, including family firms and start-ups, across various countries and emerging markets, with qualitative approaches enriching our understanding of entrepreneurial learning and sustainable development [81].

Further investigation should compare social entrepreneurial elements across contexts (starting, managing, growing enterprises); explore prosocial behaviors in institutional settings as social entrepreneurship; understand motivational and impact aspects of prosocial behaviors; study the interplay between micro-social and macro-level processes related to social mission and CSR; investigate growth challenges and internationalization of social SMEs; conduct cross-country and cross-cultural comparisons; and examine individual, company-level, and societal processes alongside sociopsychological and sociocultural contexts of prosocial motivation [84].

To advance understanding, future work may examine digitalization’s effects on SME internationalization, diverse inter-firm relationships, and the interplay between CSR practices, institutional contexts, and international partnerships–particularly among resource-constrained and emerging market firms [78].

There is also scope for new studies to further examine SMEs’ CSR disclosures, reporting practices, and their association with sustainability reporting quality and CSR performance-considering both financial and non-financial dimensions–by investigating how CSR is integrated into supply chain activities, management control systems, global production networks, and green/ethical practices, adopting mixed methodologies, replicating findings across quantitative and qualitative studies, empirically testing existing models and frameworks, developing more theoretically grounded studies that move beyond performance-based theories toward cognitive and emotion-based perspectives, and expanding the geographical focus to include emerging markets such as Africa and South America [1].

6. Conclusions

This study confirms that CSR is a recognized determinant of SME internationalization in the literature, while expert interviews reveal that generative AI-driven market selection tools are valued in practice and attract a measurable willingness to pay. The adoption and impact of these two factors are shaped by contextual variables such as sectoral fit and data quality. These findings, while grounded in distinct sources of evidence, suggest that both CSR and AI are increasingly relevant for SME internationalization.

Recent research on CSR in SMEs highlights its growing importance and impact. Studies show that CSR practices positively influence employee engagement and satisfaction [79], and contribute to firm performance and reputation [80]. CSR in SMEs is influenced by cultural backgrounds and contextual environments [78], with learning orientation playing a crucial role in CSR development [81]. SMEs engage in CSR through prosocial behavior at individual, organizational, and societal levels [84]. While some SMEs practice CSR because of compliance, others do so out of conviction, depending on their sector’s normativity [83]. The field of CSR in SMEs is booming, with research predominantly focused on firm performance and European contexts [1]. Employee–employer relationships in the CSR context are also gaining attention [82].

This article advances knowledge by updating the feedback of key players in the global market but also by clarifying that SME owners and other internationalization executives would welcome an AI solution that could facilitate the complex decision that is to choose the optimal foreign market, for any product or service.

The authors have also been able to review the influence of CSR in SME internationalization and to validate the need for a DL solution, from SME owners, who are experts in the field. Testimonies describe such necessity and even the requisites such a platform should have. It could also determine the value experts would be willing to pay for such a solution (minimum €10 and maximum 1% from the sales on the identified optimal market), thus the research questions RQ1, RQ2 and RQ3, and their O1, O2 and O3 objectives were achieved.

As stated, the study reveals that there is a propensity for acquisition of a DL solution that could predict the optimal foreign market, with potential profits going from ten euros/month to one percent of the new country’s sales.

The SLR and interviews also allowed the authors to uncover one more variable, or determinant of success, for such a platform, that adds to the already pinpointed “Foreign Champion” and “Small Print” of legal contracts [25] which is CSR.

The authors close by proposing that future investigations could focus on expanding the interviews, namely by evaluating the results of more countries and to validate the correlation between variables and country specific idiosyncrasies and conceptualize an intelligent decision support system, a DL foreign market recommender based on generative AI.