Abdullah Zunaid 

Born global digital startups response to the impact 

of Artificial Intelligence 

Master’s Thesis 

 

 

 

 

 
  

Vaasa 2025 

School of Marketing and Communication 
Master’s thesis  

Master’s Degree Programme in International 
Business  



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UNIVERSITY OF VAASA School of Marketing and Communication 

Author: Abdullah Zunaid 
Title of the thesis:  Born global digital startups response to the impact of Artificial 

Intelligence: Master’s Thesis 
Degree: Master of science in Economics and Business Administration 
Discipline: International Business  
Supervisor: Arto Ojala  
Year: 2025                               Pages:                                                         61 

 
ABSTRACT:  
 
This study investigates the strategic role of Artificial Intelligence in enhancing digital marketing 
effectiveness and driving internationalization performance and business success in born-global 
digital startups. In today’s rapidly evolving global business landscape, these firms face mounting 
pressure to scale operations quickly and competitively, often under resource constraints. Digital 
technologies, particularly AI, offer a transformative avenue for these firms to streamline 
operations, personalize marketing efforts, and respond more agilely to international market 
demands. Despite growing academic and practitioner interest, limited empirical research has 
systematically examined the impact of AI adoption on the marketing, international expansion 
outcomes and ultimate business success of digitally born-global ventures. Addressing this gap, 
the study adopts the Resource-Based View as its theoretical foundation, conceptualizing AI as a 
strategic, intangible asset that contributes to sustained competitive advantage. 
A quantitative, cross-sectional research design was employed, involving a structured survey 
distributed to 70 digitally active startups engaged in international business activities. The data 
were analyzed using correlation, regression, and moderation analysis to assess the relationships 
among key constructs, including AI adoption in BGDS, digital marketing strategies, business 
performance, and internationalization success. The results reveal statistically significant and 
positive relationships between AI adoption in BGDS and digital marketing strategy, with strong 
effects on international performance. Specifically, the findings suggest that AI enhances firms' 
ability to engage in customer-centric, data-driven marketing, leading to improved market reach 
and performance in global contexts. 
Moreover, the study uncovers the moderating effects of resource availability and firm size on the 
relationships between AI adoption and internationalization outcomes. These findings 
underscore that while AI adoption can serve as a strategic enabler, its benefits are contingent 
upon certain enabling conditions—such as organizational readiness and resource capacity. This 
highlights the nuanced interplay between technological capability and structural support in 
achieving international growth. 
The study contributes to the growing body of literature on technology-driven 
internationalization by offering empirical validation of AI’s role in shaping marketing innovation 
and global competitiveness in the startup context. Additionally, it provides actionable insights 
for digital entrepreneurs, marketing strategists, and policymakers seeking to foster AI readiness 
and integration among internationally oriented startups. Although the research is limited by its 
sample size and cross-sectional nature, it lays the groundwork for future longitudinal studies and 
theory development in the areas of AI-enabled capabilities and digital globalization. 
 

KEYWORDS: (Artificial Intelligence, Internationalisation, Marketing, AI Adoption, Digital 
Marketing, Born Global, Marketing strategy, startup response). 

  



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Contents  

1 Introduction 6 

1.1 Background 6 

1.2 Problem Statement 10 

1.3 Research Gap Analysis 11 

1.4 Research Aim and Objectives 12 

1.5 Research Significance 12 

2 Literature Review 14 

2.1 Born Global Digital Startups 14 

2.1.1 Meaning and Characteristics of BGDS 14 

2.1.2 Role of BGDS in International Business 14 

2.2 Artificial Intelligence Adoption 15 

2.2.1 AI Adoption and Digital Marketing Strategies 15 

2.3 AI Driven Digital Marketing Strategies 17 

2.3.1 AI Driven Digital Marketing Strategies and Internationalization Success 17 

2.3.2 AI Driven Digital Marketing Strategies and Business Performance 19 

2.4 Internationalization Success 20 

2.4.1 Moderating Role of Firm Size 21 

2.5 Resource Availability 22 

2.5.1 Moderating Role of Resource Availability 22 

2.6 Business Performance 24 

2.7 Resource-Based View (RBV) 25 

2.8 Theoretical Framework 26 

3 Research Methodology 27 

3.1 Research Philosophy 27 

3.2 Research Approach 28 

3.3 Research Method 29 

3.4 Research Strategy 30 

3.5 Sampling and Participants 31 



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3.6 Survey Design and Data Collection 32 

3.7 Data Collection Methods 32 

3.8 Variables and their Measurement 33 

3.9 Data Analysis Techniques 33 

3.10 Ethical Considerations 34 

4 Findings and Analysis 35 

4.1 Respondents Demographic Analysis 35 

4.2 Firm-Specific Analysis 37 

4.3 Reliability Analysis 39 

4.4 Correlation Analysis 40 

4.5 Regression Analysis 42 

4.5.1 Linear Regression 42 

4.5.2 Moderation Analysis 44 

5 Discussion 48 

5.1 Interpretation of Findings 48 

5.2 Comparison with Existing Literature 49 

5.3 Implications for Theory and Practice 50 

5.4 Limitations of the Study 51 

5.5 Recommendations for Future Research 51 

5.6 Conclusion 52 

References 54 

  



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Figures  
 
Figure 1. Theoretical Framework 26 

Figure 2. Research Onion 27 

Figure 3. Regression Results in graph 48 

 

Tables 
 
Table 1. Demographic characteristics of sample 35 

Table 2. Firm-specific demographics 37 

Table 3. Reliability Analysis of Scale 39 

Table 4. Correlation between Variables 40 

Table 5. Regression Analysis of H1 42 

Table 6. Regression Analysis of H2 42 

Table 7. Regression Analysis of H3 43 

Table 8. Moderation analysis of H4: Model Summary 44 

Table 9. Moderation analysis of H5: Model Summary 45 

 

 



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1 Introduction  

1.1 Background 

The rise of Born-Global Digital Startups (BGDS) has redefined international business 

paradigms, enabling firms to internationalise rapidly through innovative strategies and 

technology adoption (Rennie, 1993, p. 47). As a specialized subset, BGDS leverage 

advanced digital tools to deliver data-driven marketing services across international 

markets from inception (Fu & Abdul Rahim, 2025, p. 2). Artificial intelligence, being a 

transformative force, has introduced another layer of challenges and opportunities for 

these startups, reforming their operational models, strategic approaches, and value 

propositions. Digitalisation enables entrepreneurs to use digital tools to reach customers 

in global markets (Yang et al., 2023, p. 1). AI has emerged as a transformative factor in 

various sectors, including digital marketing. 

 

BGDS are recently founded companies that aim for rapid globalisation through the 

strategic use of digital marketing tools and technologies. These startups often leverage 

advanced digital technologies, such as social media platforms, search engine 

optimisation (SEO), data analytics, and artificial intelligence (AI), to expand globally at an 

early stage in their lifecycle (Karim, 2024; Petersen, 2024, p. 105). The major difference 

between BGDS and traditional born-global firms is that they typically do not follow the 

conventional step-by-step globalisation process. Instead, they enter global markets 

quickly, often targeting multiple countries and regions from their inception (Karim, 2024; 

Petersen, 2024). 

 

The most notable unique factor for BGDS lies in their heavy reliance on digital-first 

marketing strategies (Nambisan & Luo, 2022; Siltala, 2025). They use platforms such as 

Facebook, Instagram, Twitter (now known as X), and YouTube, along with digital content 

marketing, SEO, and programmatic advertising to build global brands and reach 

customers across borders (Karim, 2024). By leveraging global digital networks, these 

startups bypass traditional market-entry barriers, such as physical presence and localised 



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distribution channels, enabling them to reach a wide audience without significant 

upfront investment in physical infrastructure (Marinchak et al., 2018, p. 18). 

 

While many born-global firms internationalize rapidly, BGDS are unique in that they 

prioritize digital marketing as their core business activity (Petersen, 2024). These firms 

across industries (consulting, retail, manufacturing, or construction) leverage innovation 

and technology to expand globally, their entry into international markets is not driven 

by traditional market strategies, including a physical presence in foreign markets or 

partnerships with local distributors. BGDS rely heavily on digital marketing and advanced 

digital tools like AI, SEO, and social media platforms to expand across borders (Waris 

Copic & Pussfält, 2023; Karim, 2024). 

 

BGDS leverage innovation, technology, and niche markets to compete globally, which is 

especially pertinent in a fast-evolving digital world (Petersen, 2024). For the Born Global 

model to be effectively integrated into robust conceptual foundations (even more so 

than it already is), it requires better characterisation to comply with the theory of 

International New Ventures (Oviatt & McDougall, 1994, p. 45), which is more about the 

entrepreneurs, networks, and configurations of resources that allow a firm to 

internationalise faster. These theories highlight that born-global firms operate in 

dynamic and competitive environments, leveraging digital tools and strategies to 

overcome resource constraints and gain market entry. 

 

With the advancement of AI, the reshaping of digital marketing is more apparent with a 

significant impact on businesses and their strategies for digital marketing (Ziakis & 

Vlachopoulou, 2023, p. 1). Due to its capability to examine enormous volumes of 

customer data, automate tedious operations, and provide individualised experiences at 

scale, artificial intelligence has become a disruptive force in digital marketing (Davenport 

& Ronanki, 2018, p. 109). Similarly, Rahman et al. (2024) argued that with the evolving 

nature of AI, digital marketers can analyse large datasets, recognise patterns, suggest 

estimates, and define decisions requiring minimal human assistance. Startups can 



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improve client interaction, optimise marketing, and forecast market trends with 

previously unheard-of accuracy thanks to latest technologies such as natural learning 

processing (NLP), machine learning (ML), and prognostic analytics (Chaffey & Smith, 

2017, p. 202). Ziakis and Vlachopoulou (2023, p. 1) believe that digital marketing, with 

its access to real-time data and dynamic nature, can benefit greatly from the potential 

that AI has to offer by revolutionising the way businesses interact with customers in the 

digital world. 

 

Integrating AI technologies into business practices is becoming increasingly required in 

the marketing functions of BGDS (Malviya et al., 2022, p. 2374). The BGDS encounter 

specific challenges and opportunities with AI adoption. To begin with, AI has the 

potential to help startups cope with resource constraints through process automation, 

cost reduction, and campaign optimisation (Huang & Rust, 2018, p. 155; Pati et al., 2024, 

p. 5904). As an example, Hsu et al. (2021, p. 1) have argued that marketers’ digitised 

content marketing and personalised marketing strategies were greatly assisted by AI 

during and after the pandemic. At the same time, the shift requires that entrepreneurs 

on a resource-constrained budget and with limited expertise keep pace with continuous 

learning and change (Thaichon & Quach, 2023). For example, Malviya et al. (2022, p. 

2374) have discussed the role of AI in streamlining communication with end-users. 

Similar is the case from Varsha et al. (2021, p. 221), whose study analysed the impact of 

AI on branding strategies and found a positive relationship. Collectively, these studies 

illustrate the dire need to analyse how AI affects digital marketing. 

 

BGDS set themselves apart by skipping traditional incremental globalisation steps, 

employing digital tools and networks to directly access international markets (Etemad, 

2022, p. 3). BGDS within this framework showcase this model by offering search engine 

optimisation, content marketing, and AI customisation services to international clients 

of any scale (Petersen, 2024). These firms are able to respond to changing conditions in 

the marketplace and advances in technology due to their inherent flexibility. The AI 

ethics and governance problem, including data privacy issues and algorithmic bias, 



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however, complicates the adoption of AI in BGDS . Startups, as suggested by Jobin et al. 

(2019, p. 395), must navigate these difficulties while maintaining trust and compliance 

within a global context. 

 

The integration of AI in the marketing domain has a disruptive effect that reshapes how 

businesses operate and innovate (Ziakis & Vlachopoulou, 2023, p. 1). AI has 

transformative potential in contemporary marketing (Chintalapati et al., 2022, p. 38). 

Marketing professionals stand to acquire insightful knowledge from studies that go 

deeper into comprehending the fundamental factors, obstacles, and results linked with 

AI-driven value co-creation across various interactive marketing domains (Peltier et al., 

2024, p. 72). One substantial difficulty is the lack of comprehensive guidelines for AI 

adoption in marketing strategies. Varsha et al. (2021) emphasised the necessity for 

standardised frameworks to harness AI's potential in branding effectively (p. 223). 

Malviya et al. (2022) stress the significance of comprehending how AI can boost 

communication strategies to engage end users meaningfully (p. 2374). 

 

In their study, Ziakis and Vlachopoulou (2023, p. 2) suggested that in order to fully utilise 

the myriads of opportunities presented by AI in the digital marketing activities of BGDS, 

a comprehensive strategy is necessary to investigate the relationship between AI and 

internalization. Although AI is becoming increasingly important in global start-ups, little 

research has been done on how it specifically affects BGDS. Future studies should 

examine the extent of AI's use in BGDS and its consequences for practitioners, as Ziakis 

et al. (2023) underlined. The existing studies have often focused on broader AI 

applications or general born global firms, overlooking the intersection of these two 

critical domains (Knight & Cavusgil, 2004, p. 128). This includes creating a comprehensive 

framework for the systematic use of AI for BGDS. This disparity highlights the need to 

investigate how AI changes these firms' operational and strategic approaches, especially 

as they attempt to compete internationally. This thesis, therefore, seeks to understand 

how BGDS can get benefits and also respond to the impact of artificial intelligence. 

 



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1.2 Problem Statement 

BGDS are redefining international market entry by leveraging digital technologies to 

expand rapidly and cost-effectively. In contrast to conventional businesses, these BGDS 

are heavily relying on AI-powered tools—such as predictive analytics, machine learning, 

and automated customer engagement—to enhance decision-making and personalise 

marketing strategies at large-scale (Etemad, 2025, pp. 2-4). However, while AI offers 

transformative advantages, its integration poses new challenges for such firms, 

particularly those operating with limited financial and technical resources (Marinchak et 

al., 2018, p. 18). Unlike established multinational corporations with dedicated AI teams, 

startups including BGDS often lack the technological infrastructure, data expertise, and 

regulatory compliance mechanisms required to realise AI's full potential (Davenport & 

Ronanki, 2018, p. 109). 

 

Additionally, AI adoption frameworks tailored to the specific needs of BGDS remain 

underdeveloped, leaving these firms to navigate complex regulatory landscapes (e.g., 

GDPR), algorithmic transparency issues, and the ethical implications of AI-driven 

decision-making without clear strategic guidance (Jobin et al., 2019, p. 395). This lack of 

structured AI implementation strategies results in a reactive rather than proactive 

approach to AI integration, which may hinder long-term growth and global 

competitiveness (Wirtz et al., 2019, p. 597; Ghasemi et al., 2015, p. 68). 

 

Phrasee, a UK-based AI-powered marketing business specialising in natural language 

production for international digital marketing campaigns, is notable real-world example 

of BGDS in practice (Merolle, 2023). Phrasee’s AI-driven copywriting makes email subject 

lines, social media ads, and digital content more effective, enhancing user participation 

in global markets (Kopyltsov, 2024). Phrasee helps brands like Domino’s and eBay launch 

highly personalised, automated marketing campaigns across several countries using real-

time audience data and deep learning algorithms. Hence, both the click-through rates 

and conversion rates have shown tremendous increases. This is how AI assists BGDS to 



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compete with larger firms, as it enables them to build culturally aware, data-centred, 

and highly automated marketing frameworks devoid of incurring significant costs in 

human resources or local knowledge (Wirtz et al., 2019). 

 

Petersen (2024) underscores a critical gap in the extant literature, pointing to the paucity 

of research on born-global digital startups (BGDS) and their strategic adoption of 

artificial intelligence (AI) technologies. Specifically, there is a limited understanding of 

how these firms harness AI to navigate operational challenges, realize competitive 

advantages, and adapt to the evolving demands of digital marketing and international 

expansion. The absence of theoretical and empirical engagement with the benefits, costs, 

and strategic implications of AI integration within BGDS contexts necessitates further 

scholarly inquiry. This study seeks to address this gap by offering a comprehensive 

examination of AI-driven transformation in born-global firms. It aims to generate 

actionable insights for digital entrepreneurs, marketing strategists, and policy makers 

while contributing to the development of theoretical frameworks within international 

business and digital marketing. By analyzing the adaptation and integration of AI into 

core business functions, this research aspires to enrich the academic discourse 

surrounding globalization, technological innovation, and digital entrepreneurship. 

 

1.3 Research Gap Analysis 

Despite the rapid integration of artificial intelligence in marketing and promotional 

strategies of global businesses, existing research largely focuses on AI’s impact on large 

multinational corporations or traditional enterprises (Kaplan & Haenlein, 2020, p. 37; 

Chaffey & Ellis-Chadwick, 2022). However, BGDS that rely heavily on digital-first 

strategies for international expansion face unique challenges and opportunities in AI 

adoption that remain underexplored (During Mosetta & Rezniqi, 2023; Karim, 2024; 

Petersen, 2024). These startups operate in highly competitive and fast-evolving 

environments where AI-driven automation, data analytics, and consumer engagement 

tools determine market entry success and scalability (Vadana et al., 2021). 

 



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Existing literature fails to address how BGDS specifically respond to AI disruptions in 

areas such as campaign personalisation, customer relationship management, and global 

competitiveness (Bäckström & Larsson, 2018; Karim, 2024). Furthermore, rather than 

examining AI adoption's strategic and operational consequences for startups with 

limited resources and high agility expectations, the majority of studies look at it from a 

theoretical or technological perspective (Kaggwa et al., 2023). The lack of empirical 

insights on how AI influences marketing performance, operational efficiency, and long-

term sustainability in BGDS leaves a crucial knowledge gap. This study addresses this 

crucial demand by providing data-driven evidence on the contribution of AI in forming 

the success of BGDS. 

 

1.4 Research Aim and Objectives 

The aim of the current research study is to analyse how BGDS deal with AI's 

transformative influence on their digital marketing strategies, internationalisation 

success, and business performance while identifying the challenges and opportunities 

they have seen in integrating AI technologies. The aim of the study is further classified 

into the following objectives: 

• To analyse the impact of AI adoption on the digital marketing strategies of BGDS 

• To examine the influence of AI-driven digital marketing strategies on business 

performance and internalisation success 

• To investigate the moderating role of resource availability and firm size of BGDS 

 

1.5 Research Significance 

This study contributes substantially to the understanding of theory and practice in the 

context of the adoption of AI technology and its impact on the strategies of BGDS. These 

firms, touted for their rapid international expansion and being digitally native, are a key 

part of the world economy (Petersen, 2024). Yet, the hurdles BGDS encounter in the 

adoption of AI, in terms of strategy and operations, remain largely unstudied (Karim, 

2024). This research seeks to address a critical gap at the intersection of artificial 



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intelligence (AI) adoption and the international expansion of entrepreneurial ventures 

by exploring how AI-powered tools influence strategic decision-making, marketing 

innovation, and overall business performance in startup contexts (Marinchak et al., 2018, 

p. 18) specially BGDS. 

 

From a practical perspective, this research provides actionable recommendations for the 

management of BGDS seeking to optimise their AI adoption strategies. The findings will 

help startup founders, marketing managers, and decision-makers understand the 

complexities of AI integration, equipping them with the knowledge to leverage AI 

effectively while overcoming financial, technical, and regulatory constraints (Davenport 

& Ronanki, 2018, p. 110). Moreover, this study serves as a valuable resource for industry 

stakeholders and policymakers, offering insights that can inform supportive regulatory 

frameworks and initiatives (Jobin et al., 2019, p. 395) to encourage responsible and 

effective AI adoption in BGDS. 

 

This study has substantial theoretical significance in addition to its immediate 

application to business professionals. It adds to the larger conversation on AI-driven 

marketing and digital entrepreneurship by addressing the dearth of established AI 

adoption models specifically designed for BGDS (Petersen, 2024). By bridging the gap 

between theoretical developments and practical implementations in AI-driven strategies 

for BGDS, the study's conclusions will be advantageous to scholars, industry experts, and 

policymakers (Wirtz et al., 2019). 

 

 

 

 

 

 

 

 



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2 Literature Review 

2.1 Born Global Digital Startups 

2.1.1 Meaning and Characteristics of BGDS 

BGDS are companies that aim to service global markets from the beginning of their 

operations instead of starting domestically and gradually expanding. Using digital tools 

and platforms, these companies engage in business activities globally and usually 

circumvent obstacles like geographical barriers and the lack of local infrastructure 

(Petersen, 2024, p. 104-109). These companies usually operate in the area of digital 

landscape, where they make use of social media, search engines, content marketing, and 

even programmatic advertising to establish brands and expand their activities overseas. 

The primary distinctive attribute of BGDS is that they can internationalise at an early 

stage of their business operations, most commonly in the first few years of activities 

(Fraccastoro et al., 2023). These firms go a step further by incorporating modern digital 

technologies, which enable them to implement marketing strategies that are efficient 

and responsive to a multitude of foreign markets. 

 

2.1.2 Role of BGDS in International Business 

As a result of utilising digital tools, new-age startups are more able to compete 

internationally. Thus, BGDS are considered leaders in international business. It's these 

companies that often lead the charge in adopting new trends due to their innovations 

and paradigms that shatter whole markets. By application of digital marketing, these 

firms not only eliminate barriers to internationalisation but also redefine customer 

engagement, branding, and market entry. They are able to rapidly adjust to changes in 

local market conditions and use AI, big data, and real-time feedback to sequentially 

improve their global marketing plans (Cavusgil & Knight, 2015; Idrus et al., 2023, p. 4). 

Additionally, such companies are responsible for the democratisation of international 

business by allowing entrepreneurs from lesser-developed economies to participate 

actively in global commerce without major capital investment or sophisticated physical 



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infrastructure to support it (Olivieri & Testa, 2024, p. 1075). Consequently, these changes 

lead to a race in innovation in international business where even smaller companies are 

in a position to outperform larger, well-established companies. 

 

2.2 Artificial Intelligence Adoption 

The adoption of AI in business operations has completely changed how companies 

communicate with clients and maximise their tactics. Businesses can examine enormous 

volumes of data, spot trends, and make wise decisions thanks to AI technologies like 

machine learning, natural language processing, and predictive analytics (Potwora et al., 

2024, p. 41). Businesses are better equipped to provide individualised and focused 

marketing efforts, increasing client happiness and engagement, due to their real-time 

data processing and interpretation capabilities (Potwora et al., 2024, p. 43). 

 

AI adoption facilitates overcoming operational challenges and scaling overall efforts for 

BGDS to operate effectively in global marketplace. AI technologies have been shown to 

greatly enhance the consumer experience in a cost-efficient manner through the use of 

chatbots, recommendation engines, and automated content creation (Rathore, 2023, p. 

29). In addition, AI enables sophisticated demographic and cultural segmentation, 

informing entrepreneurs about diverse areas of greater interest. However, there are 

some critical challenges to implement AI in business operations, which include high 

implementation costs, skilled labour requirements, and ethical issues related to data and 

privacy (Dwivedi et al., 2021, p. 2). 

 

2.2.1 AI Adoption and Digital Marketing Strategies 

AI tools have automated customer interactions for BGDS in such a way that it has greatly 

streamlined consumer engagement. Automation assists with data collection, allowing 

for greater amounts of consumer information to be processed and subsequently 

incorporated into marketing strategies. Now more than ever, customers expect to be 

treated as individuals and not as a part of a generic set, which makes personalisation 



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indispensable (Chaffey & Ellis-Chadwick, 2019). Startups harnessing AI tools like machine 

learning have seen remarkable savings in time and expenditure through content 

automation, consumption forecasts, and ad campaign management. Netflix's or 

Amazon’s AI recommendation systems serve as quintessential examples of successfully 

marketing to and retaining consumers. These same strategies could be utilised by BGDS 

in further refining the international marketing strategies, communications and offerings 

aimed at foreign customers (Vallabhaneni et al., 2024). 

 

AI aids in optimising digital marketing across various channels. Usually, businesses that 

are global from the start, utilise AI that automates campaign management on Google, 

Facebook, and Instagram by automating bidding, targeting, and ad placements (Mikalef 

et al., 2019, p. 263-270). Consequently, systems are capable of optimising the 

performance of campaigns through user interaction while self-marketing-adapting via 

interaction with data on a continual basis. This not only aids in the reduction of human 

error but also assists born-global companies to strategically manage their resources 

across multiple regions simultaneously by ensuring that marketing efforts align with the 

nuances of each segment. This form of AI technology deployment enables BGDS to be 

strategically agile and efficiently enhance their digital marketing activities. These 

corporations can easily access new international markets and remain relevant and fierce 

competitors (Rizvanović et al., 2023; Idrus et al., 2023, p. 4). 

 

AI assists in research across numerous digital marketing channels. Generally, firms that 

are digital and born global from the outset employ AI technologies to manage extensive 

campaigns on Google, Facebook, and Instagram by automating bidding, targeting, and 

advertisement placement (Mikalef et al., 2019, p. 263-270). Consequently, automated 

systems are able to enhance the performance of the campaigns by ‘learning’ from the 

user and adapting the marketing strategy through active data interaction. This reduces 

human error and enables BGDS to strategically allocate their resources across many 

regions simultaneously so that marketing efforts align with the nuances of each segment. 

Such adoption of AI technologies enables BGDS to rapidly and effectively scale their 



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digital marketing initiatives. These companies can penetrate new international markets 

with ease and still stay relevant and competitive (Rizvanović et al., 2023; Idrus et al., 

2023, p. 4). Therefore, it is proposed that: 

 

Hypothesis 1: There is a positive association between AI adoption and the digital 

marketing strategies of BGDS. 

 

2.3 AI Driven Digital Marketing Strategies 

AI-driven digital marketing strategies refer to the use of AI technologies in digital 

marketing campaigns to boost customer engagement, enhance marketing 

personalisation, and improve decision-making. These practices formulate and apply 

machine learning, automation, and data intelligence insights through predictive 

analytics to maximise marketing operations across social media, search engines, and 

websites (Chaffey & Ellis-Chadwick, 2019; Pati et al., 2024; P. 5907). One important 

aspect of digital marketing powered by AI is that it can process enormous amounts of 

data within seconds to provide a better personalised experience for every consumer in 

the market. Moreover, the automation of processes such as segmentation, targeting, 

and even content construction is made possible through AI, which means marketing can 

now be more efficient and effective (Huang et al., 2018, p. 156). This concern with data-

driven decision-making and the ability to process large datasets has shifted the focus to 

AI-powered techniques as key for any company, but most importantly, for new 

businesses looking to establish themselves in foreign markets. 

 

2.3.1 AI Driven Digital Marketing Strategies and Internationalization Success 

The use of AI technologies in digital marketing significantly enhances the international 

growth of BGDS. This is most apparent in scaling marketing strategies to new territories, 

as AI technologies have the capacity to manage resources efficiently. BGDS can now 

automate and personalize digital marketing initiatives to adequately meet the demands 

of various customer segments, reducing manual efforts (Mikalef et al., 2019, p. 264). 



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These machine learning algorithms enable startups to analyze specific preferences and 

trends within a region, making it possible to craft messages and campaigns that are 

tailored to local cultures. This modification of the local approach helps BGDS enhance 

brand recognition, enabling them to gain access to the global marketplace much faster 

and more effectively than traditional approaches (Idrus et al., 2023, p. 4). 

 

Moreover, tools equipped with AI capabilities enhance comprehension of global market 

systems, enabling born-digital firms to adjust more readily to the volatile market changes 

(Anwer et al., 2024). For example, these new entrants can analyse social media and other 

customer interactions across many regions and languages through NLP and sentiment 

analysis (Erikson & Lam, 2024). This helps globally active companies to manage their 

brand reputation actively, and strategies can be modified at virtually any point because 

of shifting consumer and competitive market demands. Applying AI to monitor, analyse, 

and interpret shifts within a firm’s product portfolio in foreign markets where consumer 

preference is diverse and dynamic is tremendously challenging. This systematic 

approach enhances BGDS’ ability to change their market scope while ensuring that 

guided promotional strategies fit local needs (Hagen et al., 2019, p. 261). 

 

Furthermore, automated tools in digital marketing AI greatly enhance interest towards 

specific customers, which is essential for successful internationalisation. In foreign 

markets, AI technologies such as chatbots, recommendatory analytics, and targeted 

email marketing can enhance customer engagement (Mufeba et al., 2023). Chatbots and 

other AI technologies assist small and medium businesses to engage with customers in 

a more individualised manner, which is crucial in achieving long-term objectives needed 

for success on a global scale. AI can optimise customer assistance services in any 

language at any time. For different parts of the world, this translates to improved 

customer satisfaction and loyalty (Rane et al., 2024). Furthermore, AI tools strengthen 

the communication and relatability of the startup to international customers, which, 

through better understanding, leads to favourable results from international outreach. 



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This enables BGDS to rapidly and sustainably grow in new countries. Hence, it is 

hypothesised as: 

 

Hypothesis 2: AI-driven digital marketing strategies have a positive significant impact on 

the internalisation success of BGDS. 

 

2.3.2 AI Driven Digital Marketing Strategies and Business Performance 

The impacts of AI-driven techniques are remarkably profound on BGDS, particularly with 

respect to operational effectiveness, customer acquisition, and revenue generation (Pati 

et al., 2024, p. 5905). One of the numerous ways in which AI augments organizational 

efficacy is through automated content-driven tasks, such as customer segmentation and 

advertisement targeting (Davenport et al., 2020, p. 25-26). This form of automation 

diminishes manual processes, allowing early internationalised firms to focus on more 

critical matters, like developing products and penetrating markets. Moreover, AI tools 

allow for real-time ad performance analysis and enhancement, enabling performance-

driven campaigns to be tailored on the go across multiple channels. Improved marketing 

efficiency resulting from AI enables BGDS to cut down operational costs and allocate 

resources in a more favourable manner, thus enhancing profitability and overall business 

performance (Pati et al., 2024, p. 5909). 

 

Another critical factor AI influences business results are through its capacity to generate 

actionable insights from big data. Through analysis, AI is capable of analysing massive 

amounts of consumer information, which helps formulate critical business decisions 

(Shaik, 2023, 993). For example, AI can predict customer purchasing behaviours, allowing 

firms that are operating globally since inception to adjust their sales strategies and 

product offerings in response to evolving demands. This data-driven approach helps 

these companies make more accurate forecasts, improving inventory management, 

product launches, and promotional campaigns (Kim, 2018). Moreover, the information 

generated by AI is instrumental for startups in targeting valuable customer segments and 

devoting resources to those audiences that will generate the highest return in terms of 



20 

 

   

 

conversion and customer value (Arora & Thota, 2024). By increasing the complexity, 

efficiency, and profitability of the processes, these businesses are able to enhance 

corporate performance. 

 

The adoption of AI in automated digital marketing facilitates long-term business 

objectives because there are improved retention and loyalty rates. AI tools like 

recommendation engines and AI-based email marketing foster more customer 

satisfaction, which increases the chances of customers coming back (Ghosh et al., 2024, 

p. 327). This gives these companies a means of ensuring they constantly interact with 

their clientele to offer them relevant content and promotions based on their previous 

engagements with the business. Customer retention is especially crucial for born-global 

companies that aim to establish a loyal clientele in foreign markets that are increasingly 

competitive (Magnani & Zucchella, 2019, p. 132-135). The ability to predict verifiable 

client needs and personalise interactions through the use of AI provides the ability to 

keep customers omnichannel engaged, which ultimately increases business patronage 

and guarantees profitability in the long term. In this manner too, AI-driven marketing is 

far-reaching in its impression on the companies’ digital marketing strategies, as it not 

only increases the companies' performance in the immediate term but also fosters 

sustainable success for the organisations at the international level. So, it is proposed that: 

 

Hypothesis 3: A positive relationship exists between AI-driven digital marketing 

strategies and business performance. 

 

2.4 Internationalization Success 

Internationalisation success refers to the achievement of business objectives in global 

markets, which includes expanding a firm’s market presence, increasing its sales, 

improving brand recognition, and establishing a competitive edge internationally. The 

success of internationalisation is typically measured by the extent to which a company 

can penetrate new markets, adapt to local business environments, and sustain long-term 

growth abroad (Oviatt & McDougall, 1994, p. 45). Key characteristics of 



21 

 

   

 

internationalisation success include a firm’s ability to scale operations, effectively 

localise products and services, and manage cross-border logistics and partnerships (Lu 

& Beamish, 2001). Firms that successfully internationalise tend to also demonstrate high 

degrees of flexibility and adaptive capacity to the prevailing market conditions, 

regulation, and consumer preferences for different regions (Johanson & Vahlne, 2015, p. 

33). Concerning BGDS, adapting and taking advantage of digital platforms rapidly 

enhances an organisation's internationalisation success. 

 

2.4.1 Moderating Role of Firm Size 

The size of a firm significantly impacts the relationship between the adoption of AI and 

the internationalisation success of born-global companies. Having larger AI firm 

resources, including financial capital, human expertise, and infrastructure, enables them 

to adopt AI technologies more widely across their international operations. Thus, larger 

firms are able to apply AI to automate and optimise the marketing of several regions at 

once, scaling even more efficiently than smaller firms (Campbell et al., 2020). This 

technology can help manage complex, cross-border marketing collateral and customer 

relationships, thereby aiding in the success of their internationalisation endeavours. 

 

Smaller companies may experience barriers to fully realising AI’s potential due to the 

lack of available resources. The same, however, cannot be said for medium-sized 

startups, which, as globally operated firms, tend to use AI to improve marketing and 

operational productivity but run into hurdles when trying to employ AI across their 

business lines (Pati et al., 2024, p. 5904). Such operational limitations tend to hinder 

these companies’ access to the necessary technological infrastructure and skilled 

professionals, in turn affecting the speed and scope of their international growth (Karim, 

2024). It is plausible to argue that the correlation between internationalisation and the 

success of AI adoption is less pronounced within smaller firms, where financial and 

operational constraints are more numerous compared to larger corporations. 

 



22 

 

   

 

When thinking about the application of AI-driven tactics in international markets, the 

moderating influence of business size is equally important. Larger firms often have more 

robust international networks and can more easily align their AI-driven marketing 

strategies with the diverse needs of global consumers (Lu & Beamish, 2001; Babatunde 

et al., 2024, p. 936). On the other hand, it could be difficult for smaller multinational 

firms to adapt AI tactics to various cultural and regional contexts, which could affect their 

overall success abroad. As AI adoption continues to shape the landscape of international 

business, the ability of firms to leverage this technology effectively will depend 

significantly on their size, resources, and capacity to manage the complexities of 

international expansion (Knight & Cavusgil, 2004). Therefore, it is proposed that: 

 

Hypothesis 4: Firm size moderates the relationship between AI adoption and 

internalisation success of BGDS. 

 

2.5 Resource Availability 

Resource availability means how a firm can acquire and access both intangible and 

tangible assets required to run its processes. Some of these assets entail financial 

resources, labour, technology, and organizational competencies (Barney, 1991). For 

global new ventures, resource availability is fundamental for embracing sophisticated 

technologies, like AI, and performing AI-enabled marketing (Babatunde et al., 2024, p. 

936). These resources include access to information, networks, and partnerships that 

help firms adjust to and compete internationally (Johanson & Vahlne, 2015, p. 37). These 

resources can be beneficial or detrimental to a startup in it’s international growth, 

innovative product development, and competitive product sustenance in global markets 

(Teece, 2014). 

 

2.5.1 Moderating Role of Resource Availability 

Resource availability serves as a moderator regarding the adoption of AI as well as the 

global marketing effectiveness of the international new ventures’ strategies. Sufficient 



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resources are required for the complete application of AI in marketing activities in order 

to realise effective results (Babatunde et al., 2024, p. 936). For example, financial capital 

is required to purchase AI tools, build the infrastructure, and hire qualified personnel 

who can implement the strategy (Brynjolfsson & McAfee, 2017). Inadequate funding 

makes it impossible for even the most advanced AI technologies to be fully utilised, 

resulting in ineffective marketing strategies that fail to reach international customers or 

stimulate business growth (Teece, 2014). Moreover, human resources ensure that AI 

systems are being used appropriately and in line with the goals of the startup’s 

internationalisation. AI enables marketers and data analysts to understand consumer 

needs through market segmentation and personalised campaigns, which leads to higher 

engagements and conversions in global markets (Huang et al., 2019, p. 158). 

 

The availability of technological infrastructure—such as cloud computing, data storage, 

and software tools—enables BGDS to process large volumes of data quickly, which is 

essential for the real-time optimisation of AI-driven marketing strategies (Chaffey & Ellis-

Chadwick, 2019; Babatunde et al., 2024, p. 936). If global startups do not have these 

resources, their ability to employ sophisticated AI-driven campaigns is reduced, and they 

may find it hard to compete effectively in global markets. Moreover, the networking 

resources available to a firm, such as partnerships and industry relationships, can also 

influence the success of AI adoption. Global startups that have strong connections with 

AI technology providers, data analytics firms, or international partners can leverage 

these networks to improve their AI capabilities and marketing strategies (Wang, 2020, p. 

560; Karim, 2024). Thus, resource availability affects the former aspect of AI’s application, 

as its implementation requires some technical prerequisites. Moreover, it improves the 

firm’s responsiveness to international consumers and enhances the firm’s 

internationalisation activities. 

 

In terms of moderating effects, the availability of resources is key to determining how 

born global companies would implement features like AI in their marketing. The more 

accessible resources a startup has, the more likely AI will be integrated into its 



24 

 

   

 

international marketing. This, in turn, improves the ability of the startup to provide 

better customer value, internationalise its sales, and compete internationally. However, 

resource constraints sometimes limit a firm’s ability to use AI to its full potential; these 

factors will weaken the relationship between the adoption of AI and success in 

international marketing (Mikalef et al., 2019). Hence, it is proposed that: 

 

Hypothesis 5: The relationship between AI adoption and AI-driven marketing strategies 

is moderated by resource availability. 

 

2.6 Business Performance 

By employing a whole range of advanced technologies, capital is optimised and value 

generated through optimal resource allocation to effectively meet set objectives (Riley, 

2022). In other words, a startup's ability to achieve its goals is automated through 

mission-relevant framework technologies like artificial intelligence. Through automation 

software, especially digital marketing to optimise ads and improve target audience reach, 

born-global startups experience better customer engagement alongside improved 

operational efficiency. These advancements lead to lower expenditure rates and 

increased sales (Pati et al., 2024). Through AI, real-time data analysis significantly 

improves customer interaction, allowing tailored approaches which enhance 

competitiveness on both local and international fronts. Additionally, with the 

incorporation of artificial intelligence, decision processes become faster, offer better 

targeting, more accurate predictions about the market, and streamlined marketing 

activities, all of which enhance financial as well as customer relations (Davenport et al., 

2020, p. 25). Throughout the globe, as new startups internationalise, incorporation of AI 

solutions becomes vital for achieving sustained business and competitive advancement 

in diverse markets. 

 



25 

 

   

 

2.7 Resource-Based View (RBV) 

This study is grounded in the RBV, a strategic management theory that emphasizes the 

importance of firm-specific resources in achieving and sustaining competitive advantage 

(Barney, 1991). In the context of BGDS, the integration of artificial intelligence (AI) into 

core operations is conceptualized as a valuable, rare, inimitable, and non-substitutable 

(VRIN) resource—central to the RBV framework. These startups often operate under 

conditions of limited tangible assets and highly dynamic international environments, 

making their reliance on intangible, technology-based resources such as AI critically 

important for market differentiation and global scalability (Teece, 2014). 

 

The RBV provides a robust lens through which to examine how AI capabilities—such as 

data-driven customer insights, algorithmic personalization, real-time market analytics, 

and automated decision-making—serve as strategic assets that enable BGDS to compete 

effectively across borders. By leveraging AI, these firms can not only enhance marketing 

effectiveness and operational agility but also develop capabilities that are difficult for 

competitors to replicate, thereby sustaining long-term performance advantages in 

international markets (Hossain et al., 2022). 

 

Moreover, this theoretical foundation facilitates a deeper understanding of how AI is not 

merely a functional tool, but a core component of strategic capability building in digital-

first international ventures. As such, the RBV enables this research to critically explore 

the relationship between AI-enabled resource orchestration and firm-level outcomes, 

including internationalization speed, marketing innovation, and competitive positioning 

in a globalized digital economy. 

 

By focusing on the RBV, this study aims to provide a theoretically coherent and 

analytically rigorous framework for understanding the strategic role of AI in the 

performance and global expansion of born-global digital startups. 

 



26 

 

   

 

2.8 Theoretical Framework 

This study is grounded in the RBV, which posits that firms gain and sustain competitive 

advantage through the possession and strategic deployment of VRIN resources (Barney, 

1991). In the context of BGDS, artificial intelligence AI is conceptualized as a core 

intangible resource that enhances firms’ ability to innovate, differentiate their digital 

marketing strategies, and respond effectively to global market dynamics. 

 

By focusing on the RBV, this research explores how AI-enabled capabilities—such as real-

time analytics, personalized engagement, and intelligent automation—serve as strategic 

assets that contribute to improved marketing performance and accelerated 

international growth. The framework further allows examination of how these resources 

are leveraged to overcome liability of newness and smallness in international markets. 

 

The conceptual model derived from the RBV is presented in Figure 1, which maps the 

hypothesized relationships between AI adoption in BGDS, digital marketing strategies, 

business performance, resource availability, firm size, and internalization success (see 

Hypotheses H1–H5). This theoretical grounding provides a coherent and focused lens 

through which to investigate the strategic impact of AI adoption in digitally born-global 

ventures. 

 

 

 

 

 

 

 

 

 

 

Figure 1. Theoretical Framework 

 

AI Adoption 
in BGDS 

Digital Marketing 
Strategies 

Business 
Performance 

Internationalization 
Success 

Resource Availability 
 

Firm Size 

H1 

H2 

H3 

H4 

H5 



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3 Research Methodology 

This section describes the methods that are used to carry out the current research work. 

The structure and content of this section are built around the “research onion” concept 

(see figure 2) introduced by Saunders (2007, p. 102). The first subsection is focusing on 

defining the two outermost layers of the onion by exploring research philosophies and 

approaches. The following two subsections are concentrating on the next layers, 

specifically strategies and methodological choices. The third part is devoted to data 

collection, while the fourth section is focused on data analysis, which is considered the 

core layer of the onion. Additionally, the rigour of this study is being analysed. 

 

 

Figure 1. Research Onion 

 

3.1 Research Philosophy 

The first outer layer of the research onion is focusing on research philosophy, which is 

referring to "the development of knowledge and the nature of that knowledge" 

(Saunders, 2007, p. 101). Saunders (2007) had identified ten different research 



28 

 

   

 

philosophies, and the choice of philosophy is dependent on the research questions and 

the type of answers the study is aiming to provide (Saunders, 2007, p. 116). 

For this study, the philosophy of positivism is used because the research is investigating 

cause-and-effect relationships, and hypotheses testing. Positivism is emphasising the 

use of objective measurements and the testing of hypotheses through observable and 

quantifiable data (Collis & Hussey, 2013, p. 45). This approach is aligning with the study's 

focus on deriving generalisable and statistically valid findings. 

 

Other philosophies, such as interpretivism, are not being used because they are focusing 

on understanding subjective meanings and socially constructed realities (Saunders et al., 

2019, p. 144). While interpretivism is useful for exploring in-depth, context-specific 

phenomena, it was not suitable for this research, which required measurable and 

replicable outcomes. The emphasis of the current study on empirical validation and 

objective analysis is making positivism the most appropriate choice. 

 

3.2 Research Approach 

The second outer layer, or the second component of the onion, looks into the research 

approach that is to be taken. According to Saunders (2007, p. 117), there are two primary 

approaches to conducting research, which are: deductive and inductive. The deductive 

approach applies existing theories to a specific case by formulating a hypothesis and 

devising a strategy to test it. On the other hand, the inductive approach seeks to 

construct new theories through data collection and analysis (Saunders, 2007, p. 117). 

The deductive approach considers the theory as the main source of information which 

provides the basis for formulating a hypothesis which will then be tested. In comparison, 

an inductive approach uses a method of empirical studying to systematically accumulate 

information to produce a theory (Eriksson & Kovalainen, 2008). 

 

The effect of AI on born-global companies integrated into the frameworks guiding the 

case study of theory testing was a scope that guided the decision to adopt a deductive 

strategy in this study. This method focuses on the development of particular hypotheses 



29 

 

   

 

within a theory, which are subsequently tested and validated using quantitative 

techniques (Saunders, 2007, p. 117). It fits the study as it attempts to scrutinize defined 

relationships among variables in a systematic, quantifiable, and empirical manner rather 

than utilizing an inductive approach which aims to create new models from abundant 

qualitative data. 

 

Among the other advantages of the approach are its rigid structure for hypothesis-driven 

research, which makes the process transparent, consistent, and suited for quantitative 

research (Bryman, 2016, p. 40). A deductive approach that aims at generating reliable 

and accurate outcomes simplifies the use of standardised instruments for data collection, 

such as structured surveys. Such an approach allows the research to contribute viable 

insights on the role of AI in marketing within BGDS and still maintain rigour in the analysis. 

 

3.3 Research Method 

This study has adopted a quantitative, cross-sectional research design, which aligns with 

the third layer of the Research Onion framework, focusing on time horizons. There are 

two primary types of research methods that are usually employed in research: 

quantitative and qualitative approaches (Krishnaswami & Satyaprasad, 2010, p. 5), and 

these are being understood more clearly through systemic comparison. Qualitative 

research concentrates on interpreting and understanding the issues under study 

(Eriksson & Kovalainen, 2008) by examining behaviour, opinions, and attitudes 

(Krishnaswami & Satyaprasad, 2010, p. 7). The data collection through qualitative 

research is non-standardised, which means it requires further categorisation (Saunders, 

2007, p. 472), and the process of data collection and analysis is regarded as more 

context-dependent (Eriksson & Kovalainen, 2008). In contrast, quantitative research 

focuses on testing hypotheses and conducting statistical analyses, often considered 

more structured and standardised (Eriksson & Kovalainen, 2008). Quantitative data is 

presented in a standardised, numerical format (Saunders, 2007, p. 472). 

 



30 

 

   

 

A quantitative research approach is employed in the current study because this method 

clearly supports the testing of hypotheses. It also offers a measurable and structured 

way to analyse the connection between the variables used in a study. With the goal of 

investigating how artificial intelligence affects BGDS, a quantitative approach makes it 

possible to collect standard numerical data, which is essential for drawing precise, 

reliable conclusions (Saunders, 2007, p. 472). This approach allows the use of statistical 

tools for hypothesis testing, ensuring objectivity and replicability, and it aligns with the 

study’s goal of providing measurable insights into how AI influences strategies of BGDS. 

Besides, this method supports analysis in a more systematic manner, assisting in the 

study's overall rigour and validity (Eriksson & Kovalainen, 2008). 

 

3.4 Research Strategy 

Saunders (2007, p. 135) has identified seven research strategies: experiment, survey, 

case study, action research, grounded theory, ethnography, and archival research. 

According to the author, this research strategy allows for combinations of these, such as 

a case study that utilises surveys. The choice of research strategy is determined by which 

approach provides answers to the research questions and helps meet the objectives of 

the study (Saunders, 2007, p. 135). Furthermore, the extent of existing research, along 

with the available time and resources, has influenced the selection of the strategy 

(Saunders, 2007, p. 135). 

 

The most appropriate choice of approach for this particular research problem is that of 

a survey strategy concentrating on acquiring data by means of closed-ended 

questionnaires administered to employees of marketing firms in BGDS. As stated 

previously, a survey strategy enables standardised data to be obtained from a large 

number of respondents, which is the requirement if one intends to evaluate the impact 

of Artificial Intelligence on digital marketing strategies, internationalization success and 

overall business performance quantitatively. With the use of closed-ended surveys, such 

data that can be quantified is collected which enables the testing of hypotheses 

concerning the influence of AI on practices of BGDS in an organized manner (Saunders, 



31 

 

   

 

2007, p. 135). Employees of the targeted firms (BGDS) as the respondents aided the 

study by explaining how AI has transformed marketing strategies, business decision 

making, and overall business performance. 

 

3.5 Sampling and Participants 

The research population for the current study is comprised of respondents working in 

born global digital startups. While the unit of analysis is the organizational practice, data 

were collected from employees working within these startups, as they offer firsthand 

insight into AI usage, marketing strategies, and performance perceptions at the 

operational level. This group has been chosen to ensure that the research gathers only 

relevant and useful insights from employees with practical knowledge on the application 

of AI in the operations of the firm. The respondents’ AI tool knowledge, as well as their 

level of being participants in strategic decision-making, greatly influences meeting the 

purpose of this study. 

 

A purposive sampling strategy was employed to recruit participants who are currently 

employed in BGDS operating in international markets. This approach is preferred 

because it targets respondents that met certain standards that needed to be met in 

order to effectively and efficiently answer the study’s research questions (Saunders, 

2007, p. 226). To ensure relevance, inclusion criteria required that participants: 

• Work in startups less than 10 years old, 

• Be employed in firms that are digitally native and engaged in cross-border 
business activity, and 

• Hold roles related to marketing, operations, strategy, or technology—positions 
that provide direct knowledge of AI adoption and marketing processes. 

 

Potential firms were identified through online startup databases (e.g., Crunchbase), 

LinkedIn, and startup ecosystem networks across Europe and Asia. Once eligible firms 

were shortlisted, individual employees were contacted via LinkedIn, personal network 

using WhatsApp and Facebook, professional mailing lists, and startup community forums. 



32 

 

   

 

In total, 142 employees were contacted, and 70 completed responses were obtained, 

resulting in a response rate of 49.3%. 

 

3.6 Survey Design and Data Collection 

Data were collected through an online questionnaire created using Google Forms, which 

remained open for responses between March 1 and March 30, 2025. The questionnaire 

was divided into five sections, covering: 

• AI adoption (e.g., use of AI in marketing, automation tools, etc), 

• Digital marketing effectiveness (e.g., campaign personalization, targeting), 

• Perceived business performance and international growth, 

• Resource availability (e.g., finance, human, skills and training, technology) and 

• Demographic and firm-related details (e.g., firm size, industry, role, years of 

operations, market scope). 

 

3.7 Data Collection Methods 

The central part of the research onion emphasises data collection and analysis (Saunders, 

2007). When a survey is adopted as the research strategy, the methods for collecting 

data include questionnaires, observations, document analysis, and interviews (Saunders, 

2007, p. 139). Additionally, it is possible to use a combination of these methods 

(Saunders, 2007, p. 139). In this study, a questionnaire is selected as the primary method 

for data collection, which is designed with closed-ended questions to gather quantitative 

data. The questionnaire is chosen because it has allowed for standardised data collection, 

ensuring consistency across responses and enabling statistical analysis (Saunders, 2007, 

p. 139). The questionnaire included sections covering demographic information, the 

extent of AI adoption, and its perceived impact on marketing strategies. 

 

To ensure validity and reliability, the questionnaire is shared with a sample of around 30 

respondents. It is ensured that the survey is pretested with respondents from similar 

firms before full deployment in the study. The reason behind conducting a pilot test with 



33 

 

   

 

a defined sample is to refine the clarity and relevance of the questions and ensure the 

accuracy of the data collected (Krishnaswami & Satyaprasad, 2010, p. 93). 

 

3.8 Variables and their Measurement 

The study has examined both independent and dependent variables. The independent 

variable, AI adoption, is measured using a 5-point Likert scale to capture the extent of its 

integration within marketing practices. A pilot test was conducted with five employees 

from different startups to assess the clarity and relevance of the items. Based on their 

feedback, minor adjustments were made to wording and structure. Specific dimensions, 

such as AI tools used in organisations and their frequency of use, are included to quantify 

the adoption of AI in selected firms (Eriksson & Kovalainen, 2008, p. 156). The dependent 

variable, the influence on digital marketing strategies, is assessed through indicators 

such as marketing performance, operational efficiency, and global competitiveness. 

These variables are measured using multiple-choice questions (from strongly disagree to 

strongly agree) to ensure that the responses are numerically analysed (Saunders, 2007, 

p. 472). 

 

3.9 Data Analysis Techniques 

After collecting the data, the next important step is to analyse the data with suitable 

analysis methods. If the data is quantitative in nature, then an appropriate statistical 

analysis tool should be selected. Therefore, in this study the author has selected SPSS to 

carry out various analyses required to verify whether the hypotheses are accepted or 

rejected. The data collected is analysed using descriptive, reliability analysis, correlation, 

and regression analysis statistical techniques. Descriptive statistics is used to summarise 

the data and provide an overview of the respondents' demographic characteristics and 

the general trends in AI adoption (Saunders, 2007, p. 479). Frequencies, means, and 

standard deviations are calculated to interpret the central tendencies and variability in 

responses. 

 



34 

 

   

 

In order to verify the reliability of the questionnaire, the reliability analysis is completed. 

The reliability is measured in terms of Cronbach's alpha, and it is found that the reliability 

of all measured variables is greater than the required alpha of 0.7. Correlation analysis 

is used to examine the association between AI adoption and its impact on marketing 

strategies. It is also used to evaluate the strength and significance of these associations, 

making sure that the results of the study are strong and backed by actual data (Eriksson 

& Kovalainen, 2008, p. 199). Finally, regression analysis is completed to verify whether 

the hypotheses are accepted or not. For mediation analysis, the process is employed to 

check the strength and validity of the moderating role of a firm's readiness for AI 

adoption and handling. 

 

3.10 Ethical Considerations 

Ethical principles are strictly adhered to throughout the research process. Respondents 

are informed about the purpose of the study, and their participation was entirely 

voluntary. Informed consent messages are included at the top of each survey. The data 

collection is done to ensure transparency and compliance with ethical guidelines 

(Saunders, 2007, p. 181). Additionally, the confidentiality and anonymity of the 

participants are maintained by securely storing the collected data and limiting access to 

authorised personnel only. 

 



35 

 

   

 

4 Findings and Analysis 

4.1 Respondents Demographic Analysis 

Table 1. Demographic characteristics of sample 

Gender Frequency Percentage 

Male 62 88.6 

Female 8 11.4 

Total 70 100.0 

Age   

18-26 Years 10 14.3 

27-35 Years 34 48.6 

36-44 Years 19 27.1 

Above 44 Years 7 10.0 

Total 70 100.0 

Qualification   

High School 1 1.4 

Bachelor's 28 40.0 

Master's 39 55.7 

Doctorate 2 2.9 

Total 70 100.0 

Role in company   

Founder/Co-founder 13 18.6 

CEO/Executive Director 9 12.9 

Marketing Manager/Employee 25 35.7 

Operations Manager/Employee 23 32.9 

Total 70 100.0 

Years of experience in industry   

Less than 1 year 10 14.3 

1-5 years 27 38.6 

6-10 years 16 22.9 



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More than 10 years 17 24.3 

Total 70 100.0 

 Familiarity with AI application in business   

Not familiar at all 5 7.1 

Somewhat familiar 24 34.3 

Moderately familiar 23 32.9 

Highly familiar 18 25.7 

Total 70 100.0 

 

The respondents’ demographic profile shows that the greater percentage, 88.6%, are 

male, while only 11.4% are female. This disparity may indicate that there is further 

dualism in the males’ area of interest in digital entrepreneurship and technology, 

livelihoods in emerging markets, and even in some tech-styled startups (Brush et al., 

2018). The age distribution shows that most of the participants, 48.6%, were within the 

27-35 years age range, further confirming that BGS are centered around the youth. This 

supports other studies, which show that younger entrepreneurs are likely to be more 

flexible and willing to take risks and be highly skilled in modern technologies—which is 

critical when employing AI in international marketing (Autio et al., 2000). 

 

Most respondents have practical experience in the industry, which accounts for two-

thirds of the respondents, or 62.9%, having over five years of experience. This highlights 

a good or required understanding of business dynamics. Approximately 58.6% of 

respondents claim to be familiar with AI tools at a "moderate" to "highly familiar" level, 

indicating that most decision makers within BGDS are educated enough to make 

informed decisions regarding the use of AI in marketing and business functions but still 

have some limitations. The role distribution also shows that the executive level is split 

between founders and CEOs (31.5%), while marketing and operations collectively make 

up the remaining 68.6%. This indicates a healthy balance between theory and practice, 

as the majority of digital strategy-implementing departments are directly represented. 

With a mid-level understanding of business processes, such a familiarity with AI is 



37 

 

   

 

feasible. Technologically driven employees are better equipped strategically, which, 

paired with such a high level of education, suggests that respondents are adopting AI 

thoughtfully, even if solely driven by competitive pressures in the industry. Additionally, 

these findings imply that the respondents are, on average, well educated, with 55.7% 

identified as holding master’s degrees and 2.9% as holding doctorates. 

 

4.2 Firm-Specific Analysis 

The firm-specific demographic analysis provides critical insights into the operational 

structure and strategic orientation of the surveyed BGS. The majority of the firms fall 

under the micro category (41.4%), followed by small-sized enterprises (22.9%). This 

distribution is consistent with the typical profile of BGDS , which often begin 

international operations while still small in size, leveraging digital platforms and lean 

resources to enter global markets early in their lifecycle (Rennie, 1993, p.47). 

Interestingly, 20% of the respondents belong to large firms, which may indicate either 

rapid scaling or acquisition by larger entities—an increasingly common phenomenon in 

tech-based startup ecosystems (Cavusgil & Knight, 2015, p. 7). 

 

Table 2. Firm-specific demographics 

Firm Size Frequency Percent 

Micro (1-9 employees) 29 41.4 

Small (10-49 employees) 16 22.9 

Medium (50-249 employees) 11 15.7 

Large (250+ employees) 14 20.0 

Total 70 100.0 

Years of operating in international market Frequency Percent 

Less than 1 year 19 27.1 

1-3 years 22 31.4 

4-6 years 9 12.9 

More than 6 years 20 28.6 

Total 70 100.0 



38 

 

   

 

Firm's primary industry Frequency Percent 

Technology 11 15.7 

E-commerce 10 14.3 

Manufacturing 9 12.9 

Healthcare 7 10.0 

Financial Services 9 12.9 

Other 24 34.3 

Total 70 100.0 

International revenue share Frequency Percent 

Less than 10% 25 35.7 

10-30% 18 25.7 

31-50% 11 15.7 

More than 50% 16 22.9 

Total 70 100.0 

AI adaption extent Frequency Percent 

Not at all 12 17.1 

Minimal adoption (limited experimentation) 27 38.6 

Moderate adoption (some AI tools in use) 24 34.3 

Extensive adoption (AI integrated into core business 

functions) 
7 10.0 

Total 70 100.0 

 

In terms of international experience, 58.5% of the firms have been operating 

internationally for less than three years, reinforcing the “early internationalization” 

attribute of born global. This relatively short international exposure may pose challenges 

in terms of resource capabilities, yet it also reflects their agile market expansion 

approach. The industry distribution shows a dominance of the “Other” category (34.3%), 

followed by a fairly even spread across technology, e-commerce, manufacturing, 

financial services, and healthcare. This suggests that the born global model and digital 



39 

 

   

 

marketing capabilities are not confined to any single industry, but rather span diverse 

sectors, facilitated by scalable AI and digital tools (Oviatt & McDougall, 1994, p. 45). 

 

The share of international revenue highlights that 35.7% of firms still derive less than 10% 

of their income from international markets, whereas 22.9% earn more than half of their 

revenue globally. This variance underscores differing stages of global maturity within 

BGS, possibly shaped by strategic focus, digital capability, or market-specific factors. 

Regarding AI adoption, only 10% reported extensive AI integration into core functions, 

while a majority (38.6%) are in the experimental phase. This suggests that while AI is 

recognized as a transformative tool, its implementation remains gradual and uneven—

highlighting a potential gap between strategic intent and technological execution 

(Chatterjee et al., 2021, p. 204). The moderate level of adoption (34.3%) reflects a 

transitional phase where firms are moving beyond exploration toward embedding AI 

within specific marketing processes. 

 

4.3 Reliability Analysis 

Table 3. Reliability Analysis of Scale 

Variable No of items Alpha Value 

AI Adoption in BGDS  5 .943 

Digital Marketing Strategies 5 .844 

Internationalization Success 5 .794 

Business Performance 5 .836 

Resource Availability 5 .885 

Firm Size 5 .871 

 

Reliability analysis is a crucial step in quantitative research as it evaluates the internal 

consistency of the measurement items used for each variable. In this study, Cronbach’s 

Alpha was used to assess the reliability of each construct. As a general rule, alpha values 

above 0.70 are considered acceptable, while values above 0.80 indicate good reliability 

(Hair et al., 2010, p. 125). As shown in the table, all constructs exceeded the minimum 



40 

 

   

 

threshold, confirming the internal consistency of the measurement scales. AI Adoption 

in BGDS  reported the highest reliability with an alpha value of .943, reflecting excellent 

consistency in responses across its five items. This is important given the complex and 

multi-dimensional nature of AI adoption, which requires accurate measurement to 

capture firms’ technological orientation. 

 

Digital Marketing Strategies and Business Performance demonstrated strong reliability 

scores of .844 and .836, respectively, indicating that the items used effectively represent 

these constructs in the context of BGDS. Similarly, Internationalization Success had an 

acceptable alpha value of .794, supporting the validity of the measures used to capture 

firms' global expansion achievements. Finally, the control variables—Resource 

Availability and Firm Size—also exhibited good reliability, with alpha values of .885 

and .871, respectively. These results suggest that the instrument used in the study is 

statistically sound, and the data collected can be confidently used in further analyses 

such as regression and moderation testing. 

 

4.4 Correlation Analysis 

Table 4. Correlation between Variables 

 AI_AGS DMS IS BP RA FS 

AI_AGS 

Pearson 

Correlation 

1 .806** .814** .833** .690** .659** 

DMS  1 .846** .848** .593** .722** 

IS   1 .935** .714** .759** 

BP    1 .743** .791** 

RA     1 .803** 

FS      1 

 

 

Correlation analysis was conducted to examine the strength and direction of the linear 

relationships among the key variables in the study, using Pearson’s correlation coefficient. 



41 

 

   

 

As shown in Table 4, all variables demonstrated statistically significant positive 

correlations at the 0.01 level, indicating strong associations between them. 

 

The AI Adoption in BGS (AI_AGS) variable exhibited particularly high correlations with 

other constructs, notably Business Performance (BP) (r = .833, p < 0.01) and 

Internationalization Success (IS) (r = .814, p < 0.01). This suggests that higher levels of AI 

adoption are strongly linked with better global market performance and overall firm 

success, consistent with prior literature emphasizing the strategic role of AI in improving 

global competitiveness (Davenport et al., 2020). 

 

Similarly, Digital Marketing Strategies (DMS) were significantly and positively associated 

with Internationalization Success (r = .846) and Business Performance (r = .848), 

reinforcing the idea that well-executed AI-driven marketing efforts can drive both 

international reach and operational outcomes (Mikalef et al., 2019). The positive 

correlation between Firm Size (FS) and other variables—especially with Business 

Performance (r = .791) and Internationalization Success (r = .759)—implies that larger 

firms may enjoy greater leverage in resource deployment, strategy implementation, and 

international expansion (Bughin et al., 2017). 

 

Finally, Resource Availability (RA) also correlated positively with all variables, particularly 

with Business Performance (r = .743) and Internationalization Success (r = .714), 

emphasizing the foundational role that adequate resources play in enabling AI-driven 

transformation and competitive positioning in global markets (Babatunde et al., 2024). 

These results validate the hypothesized relationships and support the inclusion of 

resource availability and firm size as moderating variables in further regression analyses. 

 



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4.5 Regression Analysis 

4.5.1 Linear Regression 

Table 5. Regression Analysis of H1 

Hypothesis IV DV R2 Beta F-test T-test Sig. Status 

H1 AI_AGS DMS 0.649 0.806 126.00 11.22 0.000 Accepted 

 

To test Hypothesis H1, which posits that AI adoption has a significant positive impact on 

the digital marketing strategies of BGS, a simple linear regression analysis was performed. 

As shown in Table 4, the model yielded an R² value of 0.649, indicating that 

approximately 64.9% of the variance in Digital Marketing Strategies (DMS) can be 

explained by AI Adoption (AI_AGS). 

 

The beta coefficient of 0.806 confirms a strong positive relationship between the 

independent variable (AI_AGS) and the dependent variable (DMS), suggesting that 

greater adoption of AI is significantly associated with more advanced and effective digital 

marketing strategies. The F-test value of 126.00 and the t-test value of 11.22 are both 

statistically significant at p < 0.001, confirming the robustness and significance of the 

model. 

 

These findings support the acceptance of H1, reinforcing prior research by Chatterjee et 

al. (2021, p. 212) and Davenport and Ronanki (2018), who emphasized that AI tools—

such as predictive analytics, customer segmentation, and content automation—enable 

firms to optimize their digital marketing processes, enhance targeting precision, and 

increase customer engagement, especially in globally competitive environments like 

those faced by BGS. 

 

Table 6. Regression Analysis of H2 

Hypothesis IV DV R2 Beta F-test T-test Sig. Status 

H2 DMS IS 0.716 0.846 171.04 13.08 0.000 Accepted 

 



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To evaluate Hypothesis H2, which suggests that AI-driven digital marketing strategies 

significantly enhance the internationalization success (IS) of BGS, a simple linear 

regression analysis was conducted. Table 5 presents the findings of the analysis. 

 

The regression model demonstrates a strong explanatory power with an R² value of 

0.716, indicating that 71.6% of the variation in Internationalization Success is explained 

by changes in Digital Marketing Strategies. The beta coefficient of 0.846 points to a 

strong, positive, and statistically significant relationship between the two variables. 

 

With an F-test value of 171.04 and a t-test score of 13.08, both significant at p < 0.001, 

the results confirm the hypothesized impact. These outcomes validate prior studies 

(Cavusgil & Knight, 2015, p. 15; Idrus et al., 2023, p. 4; Anwer et al., 2024; Erikson & Lam, 

2024) that suggest digital marketing strategies empowered by AI enable BGDS to reach 

international markets more rapidly and efficiently. Such strategies include real-time 

customer targeting, multilingual content personalization, and adaptive campaign 

management—factors that are essential for rapid global scaling. 

 

Thus, Hypothesis H2 is accepted, emphasizing that AI-driven digital marketing acts as a 

critical lever for international growth among digitally native startups. 

 

Table 7. Regression Analysis of H3 

Hypothesis IV DV R2 Beta F-test T-test Sig. Status 

H3 DMS BP 0.719 0.848 174.4 13.21 0.000 Accepted 

 

Hypothesis H3 proposed that the business performance (BP) of BGDS is positively 

affected by AI-driven digital marketing strategies (DMS). To test this, a simple linear 

regression was conducted, and the results are presented in Table 6. The model reveals a 

high R² value of 0.719, indicating that 71.9% of the variance in Business Performance can 

be explained by the implementation of Digital Marketing Strategies. The beta coefficient 

(β = 0.848) reflects a strong and statistically significant positive influence of DMS on BP. 



44 

 

   

 

Statistical significance is further reinforced by an F-test value of 174.4 and a t-value of 

13.21, both significant at the p < 0.001 level. These findings are consistent with prior 

research indicating that effective digital marketing enhances performance outcomes 

such as customer acquisition, market reach, brand equity, and return on investment 

(Davenport et al., 2020, p. 25–26; Shaik, 2023, 993; Pati et al., 2024, p. 5905). 

 

Given that AI-driven strategies enable personalized customer engagement, predictive 

analytics, and marketing automation, their contribution to performance improvement is 

both strategic and operational (Chaffey & Ellis-Chadwick, 2019). Therefore, Hypothesis 

H3 is accepted, affirming that DMS significantly and positively affects business 

performance in AI-integrated born global digital startups. 

 

4.5.2 Moderation Analysis 

Table 8. Moderation analysis of H4: Model Summary  

R R-sq MSE F df1 df2 p 

0.8663 0.7504 0.3393 66.1467 3.000 66.000 0.000 

MODEL       

  coeff se t p LLCI ULCI 

constant 3.2496 0.0808 40.2166 0 3.0883 3.4109 

AI_AGM 0.5261 0.0777 6.7689 0 0.3709 0.6813 

FS 0.4454 0.0949 4.6948 0 0.256 0.6348 

Int_1 -0.0124 0.0527 -0.2345 0.8153 -0.1176 0.0929 

 

Hypothesis H4 posited that firm size (FS) moderates the relationship between AI 

adoption (AI_AGS) and internationalization success (IS) of BGS. To test this moderation 

effect, an interaction term (AI_AGS × FS) was included in a regression model, and the 

results are presented in Table 8. 

 

The overall model is statistically significant (F = 66.15, p < 0.001) and explains a 

substantial proportion of the variance in internationalization success (R² = 0.7504). This 



45 

 

   

 

indicates that about 75% of the variance in internationalization outcomes can be 

explained by AI adoption, firm size, and their interaction. 

 

The main effects of both AI adoption (β = 0.5261, p < 0.001) and firm size (β = 0.4454, p 

< 0.001) are statistically significant and positively related to internationalization success. 

This supports existing literature that larger firms may benefit more from AI adoption due 

to better infrastructure, more developed international networks, and greater resource 

flexibility (Lu & Beamish, 2001; Autio, 2017, p. 2013; Campbell et al., 2020). 

 

However, the interaction term (Int_1), representing the moderation effect of firm size, is 

not significant (β = -0.0124, p = 0.8153). The confidence interval for this coefficient also 

crosses zero (LLCI = -0.1176, ULCI = 0.0929), indicating that firm size does not significantly 

moderate the effect of AI adoption on internationalization success in this sample. 

 

This finding suggests that the benefits of AI adoption on international market 

performance are consistent across different firm sizes, at least within the range of firms 

surveyed (from micro to large firms). One plausible explanation is that AI tools, 

particularly cloud-based and scalable digital platforms, can be equally accessible and 

impactful for smaller firms as they are for larger counterparts (Bughin et al., 2017). Thus, 

Hypothesis H4 is not supported, as firm size does not significantly alter the relationship 

between AI adoption and internationalization success among BGS. 

 Table 9. Moderation analysis of H5: Model Summary  

R R-sq MSE F df1 df2 p 

0.8224 0.6763 0.459 45.9649 3.000 66.000 0.000 

MODEL       

  coeff se t p LLCI ULCI 

constant 3.4188 0.0964 35.4818 0.000 3.2264 3.6112 

AI_AGM 0.7285 0.0938 7.7674 0.000 0.5412 0.9157 

RA 0.055 0.1121 0.4903 0.000 -0.1688 0.2788 

Int_1 -0.1403 0.0632 -2.2202 0.030 -0.2665 -0.0141 

 



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Hypothesis H5 proposed that resource availability (RA) moderates the relationship 

between AI adoption (AI_AGS) and digital marketing strategies (DMS) of BGS. The 

moderation analysis was conducted using an interaction model with AI adoption, 

resource availability, and their interaction term (AI_AGM × RA) as predictors of AI-driven 

digital marketing strategies. The results are presented in Table 8. 

 

The overall model is statistically significant (F = 45.96, p < 0.001), with an R² of 0.6763, 

indicating that approximately 67.6% of the variance in digital marketing strategies can 

be explained by AI adoption, resource availability, and their interaction. This substantial 

explanatory power reflects the strong influence of both AI and contextual firm resources 

on shaping digital marketing practices in BGDS. 

 

The main effect of AI adoption on digital marketing strategies is strong and highly 

significant (β = 0.7285, p < 0.001), supporting the assertion that the integration of AI 

enhances firms’ ability to develop more targeted, efficient, and data-driven digital 

marketing approaches (Chatterjee et al., 2021, p. 217). This is consistent with past 

research showing how AI tools optimize customer segmentation, automate content 

personalization, and facilitate predictive analytics in marketing (Babatunde et al., 2024, 

p. 936). 

 

On the other hand, the main effect of resource availability is not significant (β = 0.055, p 

= 0.4903), suggesting that resource availability alone does not directly influence the 

quality or intensity of digital marketing strategies. This aligns with findings that resource-

rich firms may not necessarily innovate unless driven by technological adoption or 

competitive pressures (Barney, 1991, p. 101). 

Crucially, the interaction term (Int_1) is statistically significant (β = -0.1403, p = 0.030), 

with a 95% confidence interval that does not cross zero (LLCI = -0.2665, ULCI = -0.0141). 

This indicates a significant moderating effect of resource availability on the relationship 

between AI adoption and digital marketing strategies. 

 



47 

 

   

 

Interestingly, the negative coefficient of the interaction suggests a diminishing marginal 

effect—that is, while AI adoption enhances digital marketing strategies, this effect 

weakens as resource availability increases. One interpretation is that firms with fewer 

resources may depend more intensively on AI to compensate for human or financial 

limitations, thus gaining more substantial benefits (Huang et al., 2019). In contrast, firms 

with abundant resources may spread investments across various functions or rely more 

on traditional marketing practices, reducing the relative impact of AI adoption on digital 

strategy. 

 

In conclusion, the results support Hypothesis H5, confirming that resource availability 

significantly moderates the relationship between AI adoption and digital marketing 

strategies, albeit in an inverse direction than commonly assumed. This finding 

underscores the nuanced role that internal firm capabilities play in shaping how 

technology adoption translates into strategic marketing outcomes. 

 

 

 

 

 

 

 

 

 

 

 



48 

 

   

 

 

AI Adoption 
in BGDS 

Digital Marketing 
Strategies 

Business 
Performance 

Internationalization 
Success 

Resource Availability 
 

Firm Size 

0.806 

0.846 

0.848 

.7504 

0.676

5 Discussion 

5.1 Interpretation of Findings 

The empirical findings of this study underscore the pivotal role of Artificial Intelligence 

(AI) adoption in enhancing the digital marketing strategies (DMS) of Born Global Digital 

Marketing Startups (BGDMS). The significant positive relationship observed in H1 (β = 

0.806, p < 0.001) substantiates the assertion that AI integration empowers startups to 

automate, personalize, and optimize marketing functions. This aligns with the broader 

theoretical premise that AI functions as a strategic capability, enhancing firms' agility in 

dynamic market environments. Furthermore, the strong statistical relationship between 

DMS and internationalization success (H2: β = 0.846, p < 0.001) indicates that AI-

enhanced marketing not only improves visibility and customer targeting but also 

facilitates rapid market entry and scalability—attributes central to the Born Global firm 

archetype. 

 

 

 

 

 

 

 

 

 

 

Figure 3. Regression Results in graph 

 

Additionally, the positive linkage between AI-driven DMS and business performance (H3: 

β = 0.848, p < 0.001) reflects AI's role in value creation through improved customer 

engagement, predictive analytics, and data-driven decision-making. Notably, however, 

the moderating effect of firm size (H4) on the AI–internationalization relationship was 



49 

 

   

 

found to be statistically insignificant (R-square = 0.7504), suggesting that firm size does 

not significantly influence the capacity to internationalize once AI tools are deployed. In 

contrast, resource availability significantly moderated the relationship between AI 

adoption and DMS (H5: R-square = 0.6763), underscoring that financial, human, and 

technological resources remain critical enablers of AI strategy implementation. 

 

5.2 Comparison with Existing Literature 

These findings resonate with and extend the theoretical underpinnings articulated by 

scholars such as Idrus et al. (2023), who emphasized that AI adoption significantly 

bolsters marketing adaptability and operational efficiency in fast-paced environments. 

The positive association between AI use and internationalization success aligns with 

Knight and Cavusgil's (2004, p. 128) conceptualization of Born Globals as inherently 

innovative firms that leverage digital platforms to transcend traditional market entry 

barriers. The current results provide empirical reinforcement to these claims, particularly 

by validating the mediating role of DMS in facilitating global reach. 

 

However, the insignificant moderating role of firm size diverges from traditional 

international business theory, which posits larger firms have greater capacity to 

internationalize due to resource abundance (Babatunde et al., 2024). This discrepancy 

may be attributed to the democratizing nature of AI, which levels the competitive playing 

field by enabling smaller firms to act with the strategic precision of larger competitors. 

Conversely, the significant moderating effect of resource availability corroborates prior 

research by Pati et al. (2024), which identified resource constraints as a major barrier to 

effective AI utilization. Collectively, this study contributes to the growing body of 

literature that highlights how digital capabilities, rather than traditional size-related 

advantages, are emerging as the critical determinant of internationalization success in 

the digital era. 

 



50 

 

   

 

5.3 Implications for Theory and Practice 

The findings of this research carry profound theoretical and practical implications, 

particularly within the evolving discourse on digital entrepreneurship and international 

business strategy. Theoretically, the study reinforces the argument that digital resource 

orchestration—especially AI-driven strategies—has become a decisive element in the 

internationalization of BGDS. This research adds empirical weight to the Resource-Based 

View (RBV), by validating that firms leveraging AI as a strategic resource can build 

competitive advantage through agility, customer responsiveness, and data-informed 

decisions. Moreover, the results challenge traditional assumptions that firm size is a 

necessary enabler for international expansion, thereby extending Knight and Cavusgil's 

(2004) model of Born Globals by integrating digital intelligence as a key success factor. 

Practically, the study offers actionable insights for entrepreneurs and decision-makers in 

digital startups. AI should no longer be viewed as a futuristic or supplementary tool but 

rather as a central enabler of strategic marketing and global scalability. The strong 

relationship between AI adoption and business performance suggests that firms 

investing in AI capabilities are likely to gain superior returns, not only in operational 

efficiency but also in market penetration and customer retention. Additionally, the 

significant moderating effect of resource availability indicates that policymakers and 

ecosystem stakeholders must focus on enabling resource access—through funding 

schemes, digital infrastructure, and talent development—to ensure that small and 

medium-sized enterprises (SMEs) can fully capitalize on AI-driven growth pathways. 

 

Furthermore, the study informs international business consultants and incubators that 

DMS is not merely a tactical function but a strategic conduit that translates digital 

competencies into tangible international outcomes. Thus, capability-building programs 

should emphasize data analytics, algorithmic tools, and personalized marketing 

strategies to empower startups for successful cross-border ventures. 

 



51 

 

   

 

5.4 Limitations of the Study 

Despite its contributions, the study is not without limitations. First, the sample size was 

limited to 70 respondents, which, while adequate for regression and correlation analyses, 

may restrict the generalizability of the results across diverse industry sectors and 

geographies. Moreover, the study focused solely on digital marketing startups that have 

already internationalized, excluding those in the pre-internationalization phase or those 

that failed to scale globally. This selection may introduce survivorship bias, potentially 

overstating the efficacy of AI tools. 

 

Second, the cross-sectional nature of the research limits the ability to capture causal 

relationships and the dynamic evolution of AI integration over time. A longitudinal 

design would offer more robust insights into how AI capabilities mature and influence 

firm performance in different stages of internationalization. Lastly, the use of self-

reported measures raises the potential for response bias, particularly on sensitive 

aspects like firm performance and AI familiarity. Future studies may benefit from 

incorporating objective metrics and triangulating responses through multi-source data. 

 

5.5 Recommendations for Future Research 

While the present study offers significant insights into the strategic role of AI and digital 

marketing in the internationalization of BGDS, several avenues remain open for further 

exploration. First, future research should consider employing a longitudinal research 

design to better understand the evolving nature of AI integration over time and its long-

term implications on international growth trajectories. Such an approach would enable 

scholars to capture the dynamic interplay between technological adoption, market shifts, 

and firm performance. In addition, researchers can use scalable and optimized dynamic 

models for AI adoption in the research model. 

 

Second, expanding the scope to include qualitative case studies or mixed-method 

approaches could provide richer, contextual insights into the decision-making processes 



52 

 

   

 

of entrepreneurs and the nuanced challenges they encounter in deploying AI tools across 

diverse markets. This would enhance the understanding of how organizational culture, 

leadership orientation, and technological infrastructure mediate the relationship 

between digital strategies and global competitiveness. 

 

Third, future studies should explore cross-industry comparisons and multi-country 

datasets to determine whether the patterns identified here are consistent across 

different economic and institutional contexts. Comparative studies between developed 

and developing economies would be particularly valuable in understanding how varying 

levels of digital infrastructure and regulatory environments shape AI adoption. 

 

Finally, there is a pressing need to examine ethical, social, and regulatory dimensions of 

AI in international marketing—especially concerning data privacy, algorithmic 

transparency, and consumer trust. As AI continues to evolve, its implications must be 

studied not only from a performance standpoint but also from a responsible innovation 

perspective. 

 

5.6 Conclusion 

This study has contributed to the evolving literature on digital entrepreneurship by 

empirically validating the role of AI-enabled digital marketing strategies in enhancing the 

international success of BGDS. Drawing on data from 70 technology-driven firms, the 

research established strong correlations between AI adoption, digital marketing 

capability, business performance, and internationalization success. Furthermore, it 

demonstrated that resource availability and firm size act as moderating variables, 

shaping the extent to which digital tools influence outcomes. 

 

By integrating theoretical insights with practical evidence, the study confirms that digital 

transformation—anchored by AI—has redefined the competitive logic of international 

market entry. As the global business landscape becomes increasingly digitized, firms that 

strategically align their marketing models with intelligent technologies are more likely to 



53 

 

   

 

sustain competitive advantage and scale efficiently across borders. However, the findings 

also caution that digital success is not solely dependent on technology but also on the 

availability of enabling resources and contextual readiness. 

 



54 

 

   

 

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Bäckström, A., & Larsson, H. (2018). Is There Such A Thing As Too Much Intelligence?: A 

qualitative study exploring how Born Global e-commerce companies are 

working towards adopting Artificial Intelligence into their