Experts’ Mindsets on Generative AI in Business-to-Business Professional Service Exports: A Q Methodology

Abstract

The internationalization of business-to-business (B2B) professional services is being reshaped by generative artificial intelligence (GenAI). Despite its potential to enhance productivity and reduce export uncertainty, existing research has focused on B2C contexts, leaving a gap in understanding how B2B experts perceive and exploit this technology. This research, using a Q methodology, seeks to explore the discursive framework of experts’ mindsets regarding exploitation of GenAI to develop B2B professional services exports. Using 32 experts from five countries (Iran, United States, United Kingdom, Germany, and India), four mindsets were identified: (1) Human–GenAI Synergy, (2) Export Innovation Catalyst, (3) Facilitator of Managerial Mindset, and (4) Moral Hazard Paradox. By conceptualizing mindsets as intangible resources within the Resource-Based View (RBV) and interpreting their role through the Uppsala model, this study makes three contributions: enriching theory through discourse-based analysis of expert mindsets, extending Q methodology to B2B export research, and providing practical insights for human–GenAI collaboration, export innovation, and ethical governance. The findings indicate that successful GenAI-enabled export development depends not only on technological capabilities but also on how experts interpret, adopt, and utilize the technology. The results highlight the need to balance innovation with ethical risks to achieve export growth.

1. Introduction

In today’s digital age, the emergence of transformative technologies such as Generative Artificial Intelligence (GenAI) has reshaped social dynamics and business practices worldwide [1,2,3]. As a subset of AI, GenAI can generate new content, analytics, and solutions using advanced algorithms, thereby increasing productivity and streamlining many organizational processes [4]. Unlike many traditional applications of AI, GenAI can provide human-like reasoning to solve complex problems and business [5]. Capabilities such as demand forecasting, marketing content generation, multilingual and cross-cultural translation for business negotiations, risk assessment, and reduction in language barriers make this technology a potential tool for reducing uncertainty in international interactions and facilitating export activities [6]. For this reason, GenAI has made a significant leap in marketing activities and is increasingly being considered as a source of strategic value creation in organizations [7].

However, integrating this technology into organizational activities is not without challenges. The use of GenAI can raise new operational, ethical, and regulatory issues [8] and even lead to paradoxes in service delivery; for example, services can be simultaneously perceived as personalized but intrusive, cheaper but at a higher price, or higher quality but with less human empathy [3]. Such tensions have been addressed in the customer service arena, but their implications for B2B professional services are not yet fully clear.

This ambiguity is particularly important in B2B professional services firms, whose value creation depends on specialized knowledge, professional judgment, and close client interactions. Although many studies have emphasized the potential benefits of GenAI for improving organizational productivity, innovation, and performance, empirical evidence suggests that managers and professionals are not in agreement about the actual role of this technology in professional activities. For example, the results of a large-scale study among legal, finance, tax, and accounting professionals showed that despite the significant benefits of specialized GenAI tools, more than 60% of professional services employees do not use these tools, and concerns such as data confidentiality, reliability of outputs, training requirements, and professional implications of using GenAI continue to hinder its widespread adoption [9].

Similarly, recent research suggests that GenAI adoption in B2B companies is not simply a function of the technology’s capabilities, but is also influenced by managers’ different perceptions and interpretations of its opportunities and risks [10]. Even more recent studies argue that the competitive performance resulting from GenAI depends more on how organizational capabilities, governance mechanisms, and ways of exploiting the technology are aligned than on its mere adoption [11]. On the other hand, there is evidence that successful use of AI requires fundamental changes in the mindset of managers and professionals regarding the role of technology in value creation and innovation [12]. As a result, while organizations are under increasing pressure to adopt GenAI, there is still no clear consensus on exactly what opportunities the technology presents, what risks it poses, and how it should be applied in professional and international activities. This ambiguity is even more important in the context of professional services exports, as decisions about entering foreign markets, engaging with international clients, and managing long-term B2B relationships rely on professional judgment and subjective interpretations of experts, in addition to technological capabilities.

At the same time, a significant portion of the existing research on GenAI has focused mainly on applications of this technology in the business-to-consumer (B2C) domain [13,14,15,16,17,18]. Although attention to AI applications in B2B markets has increased in recent years, the literature still has limited ability to explain the complex interactions among emerging technologies, environmental conditions, and organizations’ strategic decision-making [19,20].

More importantly, most existing studies have focused on variables such as technology acceptance, intention to use, technological capabilities, or performance outcomes [10,11]. The fundamental distinction of “mindset” from traditional technology acceptance approaches is that acceptance models such as TAM and UTAUT consider users as rational decision-makers evaluating usefulness and performance [21], while “mindset” is a deeper cognitive-interpretive construct that shapes how users “make sense” of technology and how they transform contradictory experiences into learning opportunities [21,22]. Furthermore, recent research has shown that managers have different belief configurations and that no single belief uniformly explains engagement with AI [23]; these findings underscore the need to move away from linear approaches to technology adoption towards an understanding of “configural mindsets.” However, there is limited understanding of how professionals interpret GenAI and the mental models that emerge when they encounter this technology.

This distinction is important because in B2B professional services, export and internationalization decisions are not made solely on technical capabilities but are also influenced by the perceptions, judgments, and interpretive frameworks of the professionals who design, deliver, and manage these services. As a result, the existing literature focuses mainly on the factors facilitating GenAI adoption, but less on how professionals make sense of and interpret the opportunities, risks, and consequences of this technology. Since professionals may have different, even conflicting, perceptions of the role of GenAI in the development of service exports, approaches based on technology acceptance alone cannot reveal the mental structures and competing discourses that shape these interpretations. These limitations are even more pronounced in international trade in services, as service firms face increasing pressure to expand their operations into global markets [24]. Service exports have become a key component of the global economy. They are projected to grow significantly by 2024 [25], a trend reinforced by the increasing entry of service firms into foreign markets to attract new clients [26]. This development reflects the growing importance of professional services in the global economy, especially in sectors such as information technology and consulting that are heavily dependent on digital technologies [27,28,29,30].

The main gap in this research is therefore not simply a lack of knowledge about GenAI adoption, but rather a lack of systematic understanding of the distinct and sometimes conflicting mindsets that B2B professional services professionals employ in interpreting the opportunities, capabilities, risks, and implications of GenAI for the development of service exports. While the existing literature explains which factors facilitate or constrain GenAI adoption, it remains unclear how practitioners interpret this technology in the context of B2B professional services exports and what distinct mental frameworks have emerged in this context.

To explain this issue, the present study draws on three theoretical lenses. First, based on resource-based theory, this study considers experts’ mindsets as intangible resources that can create sustainable competitive advantages for companies by exploiting artificial intelligence [31]. The focus of this framework is not only on the functional capabilities of generative AI, but also on how it is interpreted and applied by experts. Second, in this framework, the interactions between generative AI and export performance are modeled on the Uppsala model [32]. Export development is affected by the uncertainty of foreign markets. In this regard, the Uppsala model explains how GenAI can reduce uncertainty and facilitate gradual entry into foreign markets.

However, the application and exploitation of these capabilities, and their conversion into desirable export performance, depend on experts’ mindsets. Third, the theory of critical discourse analysis [33] provides a linguistic analysis of B2B exports. Since experts’ mindsets are expressed through narratives, arguments, and interpretations of the opportunities and challenges of generative AI, discourse, as a dialectical tool [34], provides a suitable platform for identifying and explaining the semantic structures underlying these subjectivities.

Finally, resource-based theory explains how experts’ mindsets can be considered a strategic resource; the Uppsala model explains the role of these mindsets in reducing uncertainty and forming gradual commitment; and critical discourse analysis theory provides a platform for representing these mindsets. By combining these three theories, the research’s theoretical framework was developed. In this regard, the main research question is as follows: What is the discursive framework of the expert mindset in the development of B2B professional services exports based on GenAI, and by what mechanisms is it formed?

By exploring the experts’ mindsets in the exploitation of generative AI, this study makes three key theoretical contributions to international management and the industrial marketing literature. First, unlike previous studies that focus on the practical applications of generative AI in regional trade [35], customer service with paradoxes [3], marketing [5], or overall adoption by B2B managers [10] and green innovation [1], this study uses Q methodology to identify experts’ mindsets as a central construct in the exploitation of generative AI for the development of B2B professional services exports.

This approach fills a gap in the literature, in which subjective perceptions, such as customer service paradoxes, have often been investigated using purely qualitative methods [3]. This research deepens the existing literature using the Q-method to uncover hidden patterns. It provides a framework for the discursive analysis of subjective perceptions, paving the way for practical applications, such as human–GenAI hybrid models for export negotiations or hybrid training for companies.

Second, this study conceptualizes mindsets as key drivers of the strategic exploitation of generative AI in B2B professional services exports. This conceptualization is not only discursive but also provides a context for prioritizing virtual networking in export innovation. The framework extends the literature from general applications of AI platforms in digital service delivery [36] to specific mindsets for B2B exports, where mindsets transform generative AI capabilities into competitive advantage through service innovation, even under environmental dynamics [19]. Unlike previous studies that focus on operational innovations, this framework highlights the role of mindsets as a bridge between operational and exploitative innovations for export development.

Third, from a methodological perspective, this study extends the application of Q methodology as a tool to explore experts’ mindsets regarding the exploitation of generative AI in developing B2B professional services exports, a focus less common in recent studies of B2B exports. This contribution pushes the literature towards mixed methods and provides a model for global generalizability. Finally, this study addresses these gaps by providing a discursive framework for experts’ mindsets and offering insights for policymakers and managers.

2. Background

2.1. Generative AI and Its Transformative Potential in B2B Professional Services Exports

In the era of Industry 4.0, various types of transformative technologies have emerged, including big data analytics [37], blockchain [38], and artificial intelligence [39]. Among these technologies, artificial intelligence has evolved significantly over the years, with a notable advance being the development of GenAI [40]. GenAI, which represents a deep extension of machine learning [13,41,42], is capable of learning from large amounts of data to transform inputs such as written instructions into a variety of outputs, including video, text, images, audio, and code [42,43]. For example, GenAI models such as Midjourney can transform text inputs into images [42].

GenAI has applications in various sectors, including education [44], marketing [7], hospitality [45], and banking [46]. However, it is an emerging topic in business research and B2B marketing. As it is at the beginning of a broader transformation and digitalization process, there is still no comprehensive view of how GenAI is applied to marketing practices in the literature [5].

Studies show that deploying generative AI in companies can offer unprecedented opportunities for the international trade industry through service diversification, personalized recommendations, automated customer service, intelligent marketing, and data analytics [6,47]. These generative AI functions can help overcome language barriers in B2B professional services exports. However, the path to fully exploiting generative AI in trade is fraught with challenges, especially in countries such as those in Africa, due to poor infrastructure [35].

Previous studies have tended to focus on the customer-centric perspective of generative AI, leaving researchers in the dark about the drivers, dynamics, and outcomes of managerial adoption of generative AI, particularly in B2B companies [10]. On the other hand, the frameworks presented in previous studies cannot fully capture the broad and transformative impact of generative AI on B2B service innovation [19].

For example, ref. [19] showed that generative AI capabilities lead to competitive advantage through service concept, service process, and customer experience innovation as key mediators. However, unexpected findings show that environmental dynamics weaken the mediating effects of service concept innovation and customer experience innovation. A review of the literature shows that recent studies have focused more on the practical and innovative role of generative AI. However, it remains unclear how experts interpret the opportunities, capabilities, and challenges of this technology in the context of B2B professional services exports. Therefore, by filling these gaps, the present study seeks to explore the discursive frameworks of experts’ mindsets in exploiting generative AI to develop B2B professional services exports and, focusing on discursive frameworks, conceptualizes mindsets as key facilitators.

2.2. Internationalization of B2B Professional Services

B2B professional services, characterized by their complexity [48], knowledge intensity, and the importance of specialized skills and knowledge [49], are inherently performed across national borders and in critical contact with foreign cultures [50]. This sector is one of the fastest-growing in the global economy [28], but it faces numerous challenges in internationalization, including regulatory and socio-cultural barriers [51,52]. On the other hand, research on B2B service topics, such as service delivery (e.g., [53,54]), service innovation [55,56,57], and sales–service interaction (e.g., [58]) is steadily increasing [59]. However, it has not received sufficient attention in international trade research for several reasons, including limited data availability, and it remains relatively scattered, with insufficient focus on productive AI aspects [28,30,60]. This fragmentation is particularly pronounced in the integration of transformative technologies, such as GenAI, to reduce cultural complexity in B2B exports. This gap highlights how GenAI can be used to overcome barriers (such as cultural translation and risk assessment), which the present research addresses by focusing on discursive frameworks of subjectivities as key facilitators [61].

Classic studies on the internationalization of services show that, within the framework of the Uppsala model [32], the firm’s formal and informal relationship networks [62], customer credibility and trust [63], and adaptation to local conditions and cost management [64] are key factors in this area. Ref. [49] emphasize high customization and innovation, while [65] explore cultural uncertainties. These studies show that success in international markets, especially in the services sector, depends on managing uncertainties and adapting to foreign markets.

Furthermore, from a resource-based perspective [31], these uncertainties, based on [66] cultural dimensions framework, can affect the adoption of digital technologies in export markets. However, a review of empirical findings suggests gaps in this theoretical convergence. For example, studies by [67] show that firms overcome constraints by leveraging innovation, relying on government relations, and networking; however, ref. [61] challenge this optimism by citing evidence of negative growth in the scope of internationalization of small, as opposed to large, B2B service firms.

Ref. [68] emphasize that B2B service companies, especially those with a global presence, should dynamically use a combination of physical and virtual networking, challenge the traditional resource-based approach to ignoring digital capabilities. Among them, the use of digital platforms [69] and the assessment of organizational readiness to use these platforms [70] are emphasized. However, although [70] describe how exporters use platforms to build lasting relationships with foreign business customers, it must be acknowledged that each platform has its own mechanisms and characteristics that influence the emergence of customer relationships. Therefore, simply having access to new technologies is not enough for export success, and how these technologies are interpreted and understood, taking into account different contextual and cultural conditions, is important. From a resource-based perspective, these mindsets can be considered intangible resources within the organization, serving as key facilitators of organizational readiness to exploit digital technologies such as generative AI in B2B exports.

While Q-methods allow for the systematic identification and classification of these shared mental patterns among experts, discourse theory [33] serves as an interpretive lens for understanding the resulting mental perspectives. Therefore, critical discourse analysis and Q-methods are complementary approaches, with the former providing a theoretical basis for understanding discourses and the latter providing the empirical tools needed to explore and compare them.

3. Research Methodology

3.1. Research Design

The present study employs the Q methodology to investigate the discursive framework of experts’ mindsets in leveraging GenAI to enhance B2B professional services exports. The Q methodology, developed by [71], is a hybrid approach that allows for a systematic and in-depth examination of respondents’ mindsets and opinions through a combination of qualitative and quantitative analyses [72]. Combining qualitative (e.g., generating discourse expressions from interviews and literature) and quantitative (e.g., rating and factor analysis) components transforms individual perspectives into meaningful clusters (factors) [35,73]. Hence, it is suitable for exploring the discursive frameworks of mindsets in complex contexts such as B2B service exports. The choice of the Q methodology is justified by gaps in the literature, such as the limited focus on managerial mindsets in GenAI in the B2B context [10]. Where purely qualitative methods have high analytical depth but limited generalizability to quantitative methods [74], and purely quantitative methods also ignore hidden mindsets. By integrating these elements, the Q methodology creates a bridge that allows the discovery of mental patterns in the exploitation of GenAI. The research process is designed according to the standard procedures outlined by [75] It includes four main stages: (1) development of discourse space, (2) data collection, (3) data analysis, and (4) interpretation of factors.

3.2. Participants

To ensure diversity of perspectives and comprehensiveness of results, 32 participants were purposively selected. In Q studies, participants act as key variables. Therefore, a small sample size (ranging from 8 to 40 people) is sufficient to achieve robust results [75]. The inclusion criteria for the study were: 1. At least 5 years of experience in B2B professional services export. 2. Direct experience in developing or implementing digital technologies with a focus on industrial diversity (e.g., IT, consulting, finance, etc.). 3. Awareness or experience using productive artificial intelligence (e.g., data analysis, demand forecasting, or international negotiations). Participants were identified and invited through professional networks, including LinkedIn (to reach B2B managers) and Upwork and Toptal (to reach experts in service export).

To enhance participants’ credibility, their expertise was verified through professional profile screening prior to recruitment. Public LinkedIn profiles, organizational roles, years of professional experience, participation in international B2B projects, and evidence of GenAI-related professional activities were reviewed. For participants recruited through Upwork and Toptal, professional ratings, project portfolios, and client histories were reviewed. Only those who met both the eligibility criteria and evidence-based verification requirements were invited to participate.

To maximize the inclusiveness of the discursive space and benefit from cultural diversity, participants were selected from five geographical regions: Iran, India, the United States, the United Kingdom, and Germany. Iran (as a symbol of an economy with international sanctions and developing digital infrastructure), the United States and the United Kingdom (as representatives of free and developed markets with a lead in GenAI technologies), Germany (as a symbol of strict European regulations such as GDPR), and India (as a representative of an emerging economy with an advanced IT industry). Although the inclusion of other regions such as Southeast Asia, Africa, and Latin America would have enriched the discourse, Q methodology prioritizes the identification of diverse mindsets rather than geographic representativeness.

Nevertheless, the findings of this study provide an “analytical framework” that can serve as a basis for future comparative research in the missing regions [76]. This geographical diversity was not guided solely by [66] cultural dimensions; rather, it was designed to enable the emergence of a broader range of discourses (including optimistic, pessimistic, conservative, and innovative perspectives). According to the Q-method approach, this diversity is not intended to be statistically generalizable to society, but rather to mean analytical generalizability and to ensure the coverage of diverse mental models in the discursive space [75]. The demographic characteristics of the participants are summarized in

Table 1. Demographic Characteristics of Interview Participants (n = 32).

Gender	Male n = 16 (%50)			Female n = 16 (%50)
Education	Master’s n = 19 (%59)			PhD n = 13 (%41)
Age	35–44 n = 13 (%41)	45–55 n = 13 (%41)		55 and up n = 6 (%18)
Career History	5–10 n = 10 (%31)	10–15 n = 13 (%40)		15 and up n = 9 (%29)
Job Field	Export Consultant n = 6 (%19)	AI/GenAI Specialist n = 10 (%31)		B2B Sales Manager B2B n = 10 (%31)	B2B Freelancer n = 6 (%19)
Industry	IT n = 13 (%40)	Professional Consulting n = 10 (%30)		Financial n = 6 (%20)	Other n = 3 (%10)
Country	Iran n = 12 (%37.5)	USA n = 6 (%18.75)	England n = 6 (%18.75)	India n = 4 (%12.5)	Germany n = 4 (%12.5)

.

3.3. Methodology

The present study follows the four-step process of the Q methodology:

Stage 1 involves developing a discourse space from which the final set of statements (the Q set) is selected. The discourse for the present study was extracted from two sources: 1. A comprehensive review of the theoretical literature, 2. Semi-structured interviews with experts. The process of developing the discourse space began with the extraction of 76 initial phrases from the literature review and semi-structured interviews (n = 32); theoretical saturation was determined based on the recurrence of themes after 32 interviews. All interviews with individuals were conducted via video call. The average interview time was 55 min. After the interviews were transcribed, thematic analysis [77] was used to code and extract the initial Q phrase. Two independent researchers screened the initial 76 phrases, and duplicate and irrelevant phrases were removed (reducing to 51 phrases). Then, 5 expert professors of international management (none of whom participated in the interviews) rated the phrases on a 5-point Likert scale based on three main criteria: clarity, conceptual distinction (no overlap with other phrases), and discursive relevance (role in representing GenAI opportunities/challenges). Phrases with an average score of less than 3.5 or a low inter-user agreement coefficient (less than 0.7) were removed, resulting in a final 39 phrases.

The next steps included (2) data collection (Q-sorting), in which 20 individuals were purposively selected from the first 32 experts for the sorting stage to sort these expressions in an unstructured manner. In this method, individuals act as variables, and the small size of the P-set is sufficient to extract meaningful factors. This reduction resulted from two factors: four participants dropped out due to time constraints; the remaining eight individuals from the initial set overlapped with each other in terms of country, industry, seniority level, and history of interaction with GenAI after comparing demographic and professional profiles, and the presence of both in the P-set created redundancy without increasing discourse diversity. The final selection decision was based on a diversity matrix in which each position in the country × industry × seniority-level combination had at most one representative, and gender, education, and occupation diversity were also maintained as much as possible. The demographic characteristics of these 20 individuals are listed in

Table 2. Demographic Characteristics of Q-Sort Participants (n = 20).

Gender	Male n = 12 (%60)			Female n = 8 (%40)
Education	Master’s n = 10 (%50)			PhD n = 10 (%50)
Age	35–44 n = 8 (%40)	45–55 n = 9 (%45)		55 and up n = 3 (%15)
Career History	5–10 n = 5 (%25)	10–15 n = 9 (%45)		15 and up n = 6 (%30)
Job Field	Export Consultant n = 5 (%25)	AI/GenAI Specialist n = 5 (%25)		B2B Sales Manager B2B n = 6 (%30)	B2B Freelancer n = 4 (%20)
Industry	IT n = 7 (%35)	Professional Consulting n = 6 (%30)		Financial n = 4 (%20)	Other n = 3 (%15)
Country	Iran n = 5 (%25)	USA n = 4 (%20)	England n = 4 (%20)	India n = 4 (%20)	Germany n = 3 (%15)

.

(3) Data analysis: The data obtained from the sorting of participants were entered into the SPSS (SPSS version 25, IBM Corp., Armonk, NY, USA) software to identify, with the help of Q-factor analysis, the discourse framework and mental patterns related to the development of B2B professional services exports based on GenAI.

(4) Factor interpretation: The extracted factors were interpreted and named based on factor arrays and standard scores of each phrase.

To ensure methodological rigor and robustness, this study followed four complementary procedures within the Q methodology framework [73,75].

First, content validity was ensured by systematically collecting discourse space from two independent sources, including a literature review and 32 semi-structured interviews. Next, the initial 76 statements were assessed by five experts in the field of international business and management based on criteria of discursive relevance, clarity, and conceptual distinction, and the Q set was finally reduced to 39 statements. This process ensured that the final set covered the diverse range of existing perspectives on the role of GenAI in the development of B2B professional services exports [75].

Second, Q-sort reliability was assessed through a test–retest method. Six participants (30% of the sample) re-ran the sorting process after a two-week interval. The Pearson correlation coefficients between the two steps ranged from 0.951 to 0.971 (

Table 3. Test–Retest reliability test results.

Participant (P)	First and Second Time Correlation (Pearson r)	Significance Level (p-Value)
P4	0.957	0.01>
P6	0.971	0.01>
P10	0.951	0.01>
P11	0.966	0.01>
P17	0.969	0.01>
P20	0.963	0.01>

), indicating a very high stability of the sorting over time [78].

Third, interpretive validity and factor differentiation were assessed by examining the rotated factor structure. The results showed that all participants had a significant factor loading higher than 0.413 at the 99% confidence level, and each factor contained at least five significant factor loadings. Also, the dominant factor loadings on each factor ranged from 0.735 to 0.900, indicating intra-factor consistency and adequate differentiation among the extracted factors. It should be noted that none of the participants had cross-loading on two factors, and each participant had a significant load on only one factor, which strengthens the differentiation and independence of the extracted factors [75].

Fourth, factor retention was performed based on the integration of three complementary criteria: Kaiser criterion (eigenvalue greater than 1), percentage of cumulative variance explained, and theoretical interpretability of the factors. As can be seen in

Table 4. Q-phrases.

Number	Q-Phrase
1	GenAI facilitates cultural translation in B2B professional services export negotiations.
2	GenAI democratizes companies’ access to global B2B professional services markets.
3	GenAI combines demand forecasting with sentiment analysis in B2B professional services exports.
4	GenAI accelerates B2B professional services innovation with scenario simulation.
5	GenAI generates ethically compliant personalized B2B professional services export marketing content.
6	GenAI innovates B2B professional services export partner interactions.
7	GenAI enhances virtual networking in B2B professional services exports.
8	GenAI increases B2B professional services export productivity and revenue streams.
9	GenAI predicts geopolitical risk in B2B professional services export supply chains.
10	GenAI threatens trust and long-term relationships in B2B professional services exports with automation without human intervention.
11	GenAI enhances contractual risk assessment in B2B exports.
12	GenAI creates regulatory challenges in B2B professional services exports.
13	GenAI violates privacy in B2B professional services export negotiations.
14	GenAI reduces human empathy in B2B export services.
15	GenAI exacerbates the negative effects of market changes on B2B professional services exports.
16	GenAI creates illusion and low accuracy in B2B professional services export contracts.
17	GenAI increases upfront costs in B2B professional services exports.
18	GenAI exacerbates cultural biases in B2B professional services export translations.
19	GenAI deepens the digital divide in B2B professional services exports.
20	GenAI threatens job inequality in B2B professional services exports.
21	GenAI integrates a hybrid human-AI model into B2B professional services export negotiations.
22	GenAI highlights markets ready for innovation in B2B professional services exports.
23	B2B executives’ mindset towards GenAI increases trust in professional services exports.
24	Ethical leadership facilitates the adoption of GenAI in B2B professional services exports.
25	GenAI drives adaptive marketing in B2B professional services export internationalization.
26	GenAI favors automated customer service in B2B professional services digital exports.
27	GenAI facilitates hybrid learning for companies to adopt in B2B professional services exports.
28	Management mindsets in B2B professional services exports limit GenAI adoption.
29	GenAI enhances the resilience of B2B professional services exports through technological innovation.
30	GenAI defines platform features for B2B digital service delivery.
31	GenAI increases the focus on technology in B2B professional services export strategies.
32	GenAI slows adoption in emerging B2B markets with infrastructure barriers.
33	GenAI slows adoption in B2B professional services due to strategic misalignment.
34	GenAI complicates ethical issues in B2B professional services.
35	GenAI complicates ethical regulations in B2B professional services exports.
36	GenAI challenges data quality in B2B professional services exports.
37	GenAI has limited impact in B2B service exports due to startup challenges.
38	GenAI drives personalization in ethical B2B services export marketing.
39	GenAI enhances predictive insights for B2B professional services export sales pipeline.

, the four extracted factors had eigenvalues of 7.248, 3.463, 2.734, and 1.972, explaining a total of 77.089 percent of the total variance. In addition, examination of the Scree Plot showed a clear break after the fourth factor. Since extracting more factors would have reduced theoretical coherence and made interpretation difficult, a four-factor solution was chosen as the most appropriate final structure [73,75].

4. Research Findings

In the Q-card method, the cards can be distributed on the spectrum in two ways: optional (free) or mandatory. We used the optional method because, in an optional distribution, participants have greater freedom in their sorting [79]. In this case, the participant can place 39 Q-phrase cards in a given spectrum (ranging from 1 to 9) at the desired number of degrees along it.

4.1. Q-Set Formation and Sorting Steps

The researchers identified Q-phrases after conducting semi-structured interviews and reviewing library studies; experts subsequently approved these phrases. The set of Q-phrases obtained is shown in Table 4.

4.2. Q Factor Analysis

A correlation matrix, a standard method in factor analysis, was used to identify the discursive framework of B2B professional services export development based on GenAI. The factors were rotated using the Varimax method, which is a type of orthogonal rotation. The numbers extracted from Q factor analysis are shown in

Table 5. Eigenvalues and explained variance of the retained factors.

Component	Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	7.248	36.242	36.242	4.067	20.335	20.335
2	3.463	17.317	53.559	3.913	19.566	39.901
3	2.734	13.672	67.231	3.816	19.081	58.982
4	1.972	9.858	77.089	3.621	18.107	77.089

.

In addition to the criterion of an eigenvalue greater than one, the explained variance diagram and the conceptual interpretability of the factors were also examined. The results showed that extracting more than four factors reduced theoretical coherence and made the factors difficult to interpret. In contrast, the four-factor solution, in addition to covering 77.089 percent of the total variance, exhibited a clear conceptual distinction among the extracted concepts. According to the results in Table 5, four factors were retained based on three criteria: (1) an eigenvalue greater than 1 according to the Kaiser criterion, (2) the percentage of cumulative explained variance, and (3) the theoretical interpretability and conceptual coherence of the extracted factors. As shown in the table, the eigenvalues of the first to fourth factors were 7.248, 3.463, 2.734, and 1.972, respectively, explaining 77.089 percent of the variance.

Since these four factors have good conceptual coherence and cover a significant share of the variance, the four-factor solution was chosen as the final structure of the research. The first mental model accounts for 36.242%, the second mental model accounts for 17.317%, the third mental model accounts for 13.672%, and the fourth mental model accounts for 9.858% of the total variance. Table 5 shows the rotated factor matrix. According to this matrix, the participants who fall into each of these four mental models are identified.

As shown in

Table 6. Rotated matrix of components.

Participant	Component
	1	2	3	4
P5	0.892	0.061	0.136	0.059
P2	0.878	0.137	0.155	0.136
P18	0.847	0.078	0.287	0.157
P20	0.799	−0.031	0.259	0.103
P15	0.735	−0.052	0.212	0.051
P8	−0.071	0.880	0.203	0.170
P4	0.201	0.877	−0.029	0.015
P19	0.243	0.863	0.152	0.086
P7	−0.081	0.823	0.058	0.205
P13	−0.063	0.818	0.152	0.002
P16	0.165	−0.033	0.900	0.141
P9	0.156	0.138	0.838	0.192
P1	0.178	0.125	0.834	0.054
P17	0.324	0.176	0.784	0.114
P3	0.397	0.223	0.775	0.077
P10	−0.058	0.165	0.085	0.883
P6	0.233	0.068	0.158	0.837
P12	0.145	0.055	0.075	0.833
P11	−0.014	−0.045	0.150	0.827
P14	0.258	0.305	0.053	0.739

, all participants with factor loadings are greater than $\frac{2.58}{\sqrt{39}}=0.413$ can be considered significant with 99% confidence [79]. Therefore, participants 5, 2, 18, 20, and 15 jointly constitute the first factor; participants 8, 4, 19, 7, and 13 jointly constitute the second factor; participants 16, 9, 1, 17, and 3 jointly constitute the third factor; and participants 10, 6, 12, 11, and 14 jointly constitute the fourth factor. Varimax rotation results showed that each factor has a distinct set of participants who share similar patterns in terms of mindsets toward utilizing GenAI in B2B professional services export development.

4.3. Identifying Mental Patterns

By calculating the score arrays for the three groups (mental patterns) identified, and by sorting the factor arrays within each factor (mental group), the statements that were most agreed upon or disagreed with in each mental group were identified. The results of the analysis are shown in

Table 7. The most important statements of agreement and disagreement of the first mentality.

Most Important Agreement Statements
Ranking	Items	Score
39	GenAI integrates a hybrid human-AI model into B2B professional services export negotiations.	2.104
38	B2B executives’ mindset towards GenAI increases trust in professional services exports.	2.054
37	GenAI facilitates hybrid learning for companies to adopt in B2B professional services exports.	2.053
36	GenAI favors automated customer service in B2B professional services digital exports.	2.011
35	GenAI drives adaptive marketing in B2B professional services export internationalization.	1.978
The Most Important Opposition Statements
Ranking	Items	Score
1	GenAI predicts geopolitical risk in B2B professional services export supply chains.	−2.200
2	GenAI increases upfront costs in B2B professional services exports.	−2.451

,

Table 8. The most important statements of agreement and disagreement of the second mentality.

Most Important Agreement Statements
Ranking	Items	Score
39	GenAI enhances virtual networking in B2B professional services exports.	2.191
38	GenAI combines demand forecasting with sentiment analysis in B2B professional services exports.	2.172
37	GenAI generates ethically compliant personalized B2B professional services export marketing content.	2.157
36	GenAI increases B2B professional services export productivity and revenue streams.	2.028
35	GenAI accelerates B2B professional services innovation with scenario simulation.	1.894
The Most Important Opposition Statements
Ranking	Items	Score
1	GenAI exacerbates cultural biases in B2B professional services export translations.	−2.106
2	GenAI threatens job inequality in B2B professional services exports.	−2.014

,

Table 9. The most important statements of agreement and disagreement of the third mentality.

Most Important Agreement Statements
Ranking	Items	Score
39	GenAI enhances the resilience of B2B professional services exports through technological innovation.	2.119
38	Management mindsets in B2B professional services exports limit GenAI adoption.	2.092
37	Ethical leadership facilitates the adoption of GenAI in B2B professional services exports.	2.071
36	GenAI highlights markets ready for innovation in B2B professional services exports.	1.968
35	GenAI increases the focus on technology in B2B professional services export strategies.	1.867
The Most Important Opposition Statements
Ranking	Items	Score
1	GenAI complicates ethical issues in B2B professional services.	−2.108
2	GenAI defines platform features for B2B digital service delivery.	−2.063

and

Table 10. The most important statements of agreement and disagreement with the fourth mentality.

Most Important Agreement Statements
Ranking	Items	Score
39	GenAI exacerbates the negative effects of market changes on B2B professional services exports.	2.105
38	GenAI creates regulatory challenges in B2B professional services exports.	2.088
37	GenAI reduces human empathy in B2B export services.	2.028
36	GenAI threatens trust and long-term relationships in B2B professional services exports with automation without human intervention.	2.025
35	GenAI violates privacy in B2B professional services export negotiations.	1.992
The Most Important Opposition Statements
Ranking	Items	Score
1	GenAI threatens job inequality in B2B professional services exports.	−2.315
2	GenAI slows adoption in B2B professional services due to strategic misalignment.	−2.142

. To interpret the factors, the statements with the highest and lowest scores on each factor were considered Distinguishing Statements because they played the greatest role in distinguishing each mindset from others.

In this mindset, as shown in Table 7, the phrases “GenAI integrates a hybrid human–AI model into B2B professional services export negotiations.”, “B2B executives’ mindset towards GenAI increases trust in professional services exports.”, “GenAI facilitates hybrid learning for companies to adopt in B2B professional services exports.”, “GenAI favors automated customer service in B2B professional services digital exports,” and “GenAI drives adaptive marketing in B2B professional services export internationalization” have higher scores. The phrases “GenAI predicts geopolitical risk in B2B professional services export supply chains” and “GenAI increases upfront costs in B2B professional services exports” have lower scores. Therefore, considering the phrases that have achieved the highest scores, the first perspective was named the GenAI-Human Synergy Mindset.

In this mindset, as shown in Table 8, the phrases “GenAI enhances virtual networking in B2B professional services exports.”, “GenAI combines demand forecasting with sentiment analysis in B2B professional services exports”. “GenAI generates ethically compliant personalized B2B professional services export marketing content.”, “GenAI increases B2B professional services export productivity and revenue streams,” and “GenAI accelerates B2B professional services innovation with scenario simulation” have higher scores. The phrases “GenAI exacerbates cultural biases in B2B professional services export translations” and “GenAI threatens job inequality in B2B professional services exports” have lower scores. Therefore, considering the phrases that have achieved the highest scores, the first perspective was named the Export Innovation Catalyst Mindset.

In this mindset, as shown in Table 9, the phrases “GenAI enhances the resilience of B2B professional services exports through technological innovation.”, “Management mindsets in B2B professional services exports limit GenAI adoption.”, “Ethical leadership facilitates the adoption of GenAI in B2B professional services exports.”, “GenAI highlights markets ready for innovation in B2B professional services exports,” and “GenAI increases the focus on technology in B2B professional services export strategies” have higher scores. The phrases “GenAI complicates ethical issues in B2B professional services” and “GenAI defines platform features for B2B digital service delivery” have lower scores. Therefore, considering the phrases that have achieved the highest scores, the first perspective was named the Facilitator of Managerial Mindset.

In this mindset, Table 10, the phrase “GenAI exacerbates the negative effects of market changes on B2B professional services exports.”, “GenAI creates regulatory challenges in B2B professional services exports.”, “GenAI reduces human empathy in B2B export services.”, “GenAI threatens trust and long-term relationships in B2B professional services exports with automation without human intervention,” and “GenAI violates privacy in B2B professional services export negotiations,” have higher scores. The phrases “GenAI threatens job inequality in B2B professional services exports” and “GenAI slows adoption in B2B professional services due to strategic misalignment” have lower scores. Therefore, considering the phrases that have achieved the highest scores, the first perspective was named the Moral Hazard Paradox Mindset.

5. Discussion

5.1. Theoretical Implications

The landscape of internationalization in B2B professional services is changing due to the emergence of GenAI. Organizational readiness to adopt and leverage GenAI is crucial for achieving a global competitive advantage This research employs the Q methodology to explore the discursive frameworks underlying experts’ mindsets for leveraging Generative AI in the development of B2B professional services exports. By identifying four key discursive frameworks: (1) Human–GenAI synergy, (2) Export innovation catalyst, (3) Facilitator of managerial mindset, and (4) Moral hazard paradox, it shows that these patterns not only depict dynamic narratives of mindsets, but also transform the technical capabilities of GenAI into practical tools for transforming export challenges into competitive opportunities in global markets. These findings, directly aligned with the main research objective—to explore the discursive frameworks of experts’ mindsets—enrich the literature on international management and industrial marketing, but in contrast to previous studies that focus on the adoption intention of GenAI (e.g., [10]), highlight the role of the discourse framework of mindsets as a moderator between technology and export performance and fill the gaps in examining the subtle interactions between technology, managerial mindsets, and B2B export dynamics.

From the perspective of critical discourse analysis theory [33], the four identified experts’ mindsets can be considered as four different discursive frameworks through which experts make sense of the role of generative AI in the development of B2B professional services exports. These mentalities do not simply express the experts’ agreement or disagreement with the role of generative AI in the development of B2B professional services exports; rather, each shows four distinct interpretive logics of its role, interpreting it from different angles.

While the Human–GenAI synergy Mindset, emphasizing the complementary relationship between humans and generative AI capabilities, focuses on the positive aspects of this relationship in export development, the Moral hazard paradox Mindset highlights the darker aspects of AI, such as the decline of human empathy and ethical challenges. While these two mindsets indicate the opportunities and risks of generative AI, the Export Innovation Catalyst Mindset, which emphasizes the functional implications of generative AI, considers it a driver of transformation in service exports. In contrast, the Facilitator of managerial mindset shows that the realization of these benefits does not result solely from its technical capabilities but also depends on how managers interpret and represent them. As long as such a mindset is not formed, generative AI capabilities alone cannot lead to sustainable competitive advantage or increased export performance.

Also, from the perspective of the Uppsala model [32], these mindsets affect the perception of uncertainty in foreign markets, experiential learning, the formation of trust, and the gradual development of international relationships, and determine the extent to which firms exploit generative AI capabilities to internationalize their services. Finally, based on the resource-based theory [31], these mindsets can be considered as intangible knowledge-based resources that guide the exploitation of generative AI capabilities and, if they are difficult to imitate by competitors, can lead to a sustainable competitive advantage in the development of B2B professional services exports.

The first mindset, which accounted for the largest share of variance at 36.24%, was named the GenAI-Human Synergy. This perspective represents GenAI not as a competitor to human labor, but as a complementary partner in export processes. In this mindset, experts believe that GenAI integrates hybrid human-AI models into B2B professional services export negotiations. This integration leverages B2B managers’ mindset to enhance trust, utilizes hybrid training to facilitate technology adoption within companies, and employs automated customer service and adaptive marketing to increase scalability. In contrast to the limited focus of previous studies on B2B managers’ intention to adopt GenAI [10], this perspective highlights the role of mindset as a strategic bridge between technology and human relationships. It extends the literature from customer service paradoxes [3] to B2B export contexts. From this perspective, the findings of the present study, while confirming the importance of complementarity between humans and AI in value creation, challenge the implicit assumption in the literature that the benefits of GenAI are mainly due to its technical capabilities, and show that the way this technology is interpreted and integrated into human interactions is also part of its source of value creation. However, the opposition to the prediction of geopolitical risk and increased initial costs points to environmental adjustments that [19] have emphasized, showing that global dynamics can negatively affect service innovation; therefore, this mental model suggests that synergy should be complemented with environmental adaptive strategies to ensure long-term sustainability.

The second mindset, accounting for 17.31% of the variance, was named the Export Innovation Catalyst, which views GenAI as a driver of practical transformations in B2B professional services exports. In this framework, experts focus on enhancing virtual networking, forecasting demand through sentiment analysis, and generating personalized content based on ethical considerations, thereby increasing productivity and revenue streams and accelerating service innovation through scenario simulation. This pattern is consistent with the findings of [6], who see GenAI as a tool for diversifying and customizing services in international trade, and move it beyond general applications of GenAI to the specific context of B2B professional services exports. The findings of the present study expand on this literature and move the literature from general applications of generative AI in international trade to the specific context of B2B professional services exports. However, downplaying threats such as the intensification of cultural biases in translations and the deepening of the digital divide reveals an over-optimism that challenges some assumptions in the literature on infrastructural constraints and the digital divide [35] and may lead to practical failures in emerging markets with uneven infrastructure; hence, this model could be enriched by incorporating mechanisms for assessing cultural bias to make innovation more realistic.

The third mindset, Facilitator of Managerial with a variance of 13.67%, emphasizes the role of GenAI in enhancing export resilience through technological innovation. While identifying managers’ viewpoints as limiting the adoption of GenAI and introducing ethical leadership as a facilitator of its adoption. In this view, GenAI not only highlights markets ready for innovation but also increases the focus on it in export strategies, serving as a smart market identifier. Experts’ opposition to ethical complexities and defining platform features demonstrates a realistic perspective that sees challenges as manageable. The findings of this model extend the existing literature because, unlike many studies that attribute the success of generative AI implementation primarily to technological features or organizational resources, it suggests that managers’ mindsets can act as a cognitive prerequisite for converting technological capabilities into export performance. It also highlights gaps, such as the neglect of cultural diversity in ethical leadership, which could be addressed by culturally adaptive leadership models (e.g., [66]). This finding is consistent with the study by [8], which acknowledged that integrating generative AI poses ethical and regulatory challenges. Therefore, it is suggested that this framework be expanded to include comparative studies across geographies to increase its global generalizability.

Finally, the fourth mindset, the Moral Hazard Paradox, with a variance contribution of 9.85%, highlights the dark side of GenAI, including the aggravation of adverse market effects, regulatory challenges, reduced human empathy, threats to trust through automation without human intervention, and privacy violations. This view is consistent with [3], who emphasize the reduction in empathy in GenAI services and direct the literature from a focus solely on opportunities to a balance between risk and reward. However, the experts’ opposition to job threats and strategic inconsistency reveals a paradox that downplays risks, while [80] reports indicate widespread concerns among managers. This finding, while confirming concerns raised about reduced human empathy and ethical challenges [3,8], extends the existing literature by transferring these risks from the customer service domain to the B2B professional services export context, where trust, long-term relationships, and professional credibility are particularly important.

Institutional and cultural contexts influence these four mindsets. For example, the moral hazard paradox mindset is likely to be more pronounced in European markets (with strict regulations (GDPR). In contrast, the export innovation catalyst mindset appears stronger in the US market (with a market-oriented approach). In contrast, in Asian countries such as Iran and India, infrastructure challenges and higher power distance [66] may place greater emphasis on the Facilitator of managerial mindset, and the need for ethical leadership. These differences suggest that mindsets are not only individual but also highly contextual.

Overall, this discursive framework transforms the Q methodology from a tool for discovering mindsets to an analytical framework (

Table 11. Discourse framework.

Mental Model	Variance (%)	Mentality Discourse	Practical Example
Human–GenAI Synergy	36.24	Strategic Bridge Between Technology and Human Relations	Hybrid Negotiation Models
Export Innovation Catalyst	17.32	Driver of Practical GenAI Transformation	Demand Forecasting with Sentiment Analysis
Facilitator of Managerial Mindset	13.67	Adjusting Cognitive Limitations with Leadership	Resilience through Technological Innovation
The Moral Hazard Paradox	9.86	Balancing Risk/Opportunity on the Dark Side	Managing Privacy Violations

) for international management. By filling gaps in the literature, addressing the lack of examination of subtle interactions between mindsets and technology, and focusing on B2B, this research achieves its goal of providing policymakers and managers with practical insights, including hybrid models for export negotiations.

5.2. Practical Implications

The findings of this research, drawing on the discursive frameworks of the expert mindset, provide a practical framework for B2B professional services managers to use GenAI as a catalyst for export development. These frameworks can help managers better understand the opportunities and challenges associated with employing GenAI in B2B professional services export development and adjust their decisions accordingly.

Within the GenAI-Human Synergy framework, which assigns the greatest weight to the most significant variance, the framework emphasizes the integration of technology with human relationships. Accordingly, managers are advised to use hybrid solutions to overcome the challenges of B2B professional services export negotiations. While generative AI tools can provide initial proposals, the human negotiator focuses on the finer points of relationships and trust—critical elements for long-term B2B contracts that can become mechanical and ineffective without human intervention. Therefore, rather than completely replacing human negotiators, B2B managers are advised to design structures in which GenAI outputs are considered as initial drafts rather than final answers, with the human team responsible for emotional and contextual validation. However, the opposition of this mindset to ineffective forecasting of geopolitical risks and upfront costs leads managers to prioritize hybrid training programs. Based on this mindset, export consultants should focus on designing hybrid human-AI processes to deliver advisory services, so that AI-produced analyses are complemented by human judgment and knowledge of foreign markets.

The Export Innovation Catalyst encourages managers to leverage GenAI to personalize adaptive marketing and forecast demand. Accordingly, this perspective suggests that experts’ mindset GenAI as a potential enabler of export performance through enhanced demand forecasting, adaptive marketing, and service innovation. Although experts associated with this mindset reject the view that GenAI exacerbates cultural biases, managers are nevertheless advised to remain vigilant for possible cultural biases in GenAI outputs. Therefore, regularly reviewing GenAI outputs for potential cultural biases—especially when serving international and emerging markets—can help prevent the deepening of the digital divide and ensure inclusive innovation. Based on this mindset, digital services companies have an opportunity to develop GenAI-based solutions for international marketing personalization, export market analytics, and foreign customer relationship management.

The Facilitator of Managerial Mindset emphasizes the role of managerial cognition and ethical leadership in shaping GenAI adoption. Based on this mindset, managers can pave the way for more effective adoption of GenAI in export activities by strengthening AI literacy, developing digital leadership skills, and creating a supportive environment for responsible use of technology. Accordingly, managers are suggested to facilitate the adoption of new products and services by focusing on technologies in export strategies. Managers can strengthen client trust by communicating transparently about the role of GenAI in service delivery and by establishing clear ethical guidelines for its use. This perspective highlights the importance of ethical leadership and managerial openness toward technology as key enablers of GenAI adoption. It also suggests to executives at B2B professional services firms that investing in improving AI literacy, developing digital leadership skills, and fostering a positive attitude toward technology can increase the effectiveness of GenAI adoption and use in export processes.

Ultimately, Moral Hazard Paradox, by highlighting the dark side of GenAI, guides managers toward a balance between automation and human intervention. This is particularly important in B2B sales, which rely on personal relationships and mutual trust, and in export contracts, which are often multi-year. The decline in human empathy and the threat to long-term relationships with unsupervised automation force managers to retain ultimate human control, At the same time, managers should ensure that automation initiatives remain aligned with organizational objectives while preserving customer trust, human oversight, and relationship quality. Collectively, these frameworks guide managers toward a mix of policies that minimize moral hazard and sustain the development of B2B service exports. Consistent with the Moral Hazard Paradox mindset, policymakers may need to pay more attention to issues such as data privacy, accountability, and human oversight in the applications of GenAI in export activities.

6. Conclusions

Finally, by delving deeply into the discursive frameworks of the expert mindset, this research demonstrates that the exploitation of GenAI in B2B professional digital services export development is used as a complement to, rather than a replacement for, human experts. Therefore, market expertise and customer relations remain critical skill sets that AI cannot replicate. However, the identification of four key mindset patterns—human–GenAI synergy as a bridge between technology and relationships, the export innovation catalyst as a driver of practical transformation, Facilitator of Managerial Mindset as a moderator of cognitive constraints, and the moral hazard paradox as a reminder of the dark sides of innovation—opens new horizons in the literature of digitalization of services, international management, and industrial marketing. These models, beyond mere description, serve as an analytical lens that reconstructs the dynamics of B2B professional services exports from the perspective of experts’ minds, in which generative AI technology shapes new patterns of interaction between human and machine capabilities.

Contrary to a significant body of literature that directly links technology adoption to export performance, this study found that the value creation of generative AI in B2B professional services export development does not depend solely on the technical capabilities of the technology, but also on how managers and experts interpret, make sense of, and apply it. Findings suggest that even when faced with a single technology, different mental frameworks can lead to radically different perceptions of opportunities, risks, and export outcomes. In this light, the present study challenges the implicit assumption in the body of literature that export success is a direct consequence of technology adoption. It suggests that managerial mindsets play an important role in transforming generative AI capabilities into export-competitive advantage.

Drawing on the Q method, this study not only fills gaps in examining the subtle interactions between managerial perceptions and the practical applications of GenAI but also provides a conceptual framework that helps policymakers and managers seize opportunities for the internationalization of professional services while accounting for cultural and ethical complexities. Although these findings emphasize that GenAI is transforming B2B professional services, its full potential in this sector has not yet been realized, and sustainable success lies not in unquestioningly embracing the technology, but in aligning it with managers’ mindsets and understanding its limitations; mindsets that can transform challenges such as cultural biases or geopolitical risks into competitive strengths. Thus, this research not only adds to the theoretical richness of the literature but also builds a practical bridge between academic research and strategic decision-making, emphasizing the need for hybrid approaches—the thoughtful integration of generative AI tools with human capabilities—to enhance the effectiveness of export strategies in B2B professional services.

Although the Q methodology is accurate in uncovering mental models, its reliance on a purposive sample of 32 experts—mostly from specific geographical regions such as the US, Iran, the UK, Germany, India—may limit the generalizability of the findings to more culturally diverse contexts, such as African or Latin American markets, where the infrastructural and regulatory dynamics of GenAI are different [35]. Furthermore, the exploratory nature of this approach, which focuses on ranking mental perceptions, may overlook quantitative aspects such as measuring the direct economic effects of mental models on export performance. Consequently, the findings serve as a basis for predictive modeling rather than definitive conclusions. Another challenge is the rapid dynamism of GenAI technology. The study’s references span the year 2025. However, future developments, such as more advanced multimodal models (e.g., GPT-4 or equivalent), could transform mental models, rendering this research a snapshot of a changing landscape. From a methodological perspective, integrating semi-structured interviews with thematic analysis ensured qualitative depth; however, the lack of digital tools to collect larger datasets (e.g., large-scale online surveys) may reduce the diversity of perspectives, especially among less digitized companies.

7. Limitations and Directions for Future Research

Although the Q methodology does not seek statistical generalizability, the study’s reliance on a purposive sample of experts from five countries (Iran, the United States, the United Kingdom, Germany, and India) may limit the transferability of the findings to other institutional contexts. In particular, the perspectives of experts from Southeast Asia, Africa, and Latin America are not represented in this study. These regions have different regulatory environments, digital infrastructures, and AI adoption patterns, which could lead to different interpretations of the role of GenAI in B2B service exports. On the other hand, due to the non-random nature of purposive sampling, there is a possibility of bias in sample selection, and the experts’ views may be influenced by their professional experiences and level of exposure to GenAI technologies, which can affect how phrases are ranked. Furthermore, it should be emphasized that the geographical diversity of the sample in this study served the purpose of discursive comprehensiveness, not statistical generalization. The four identified mental patterns do not claim to represent the distribution of these mentalities among all global professionals; rather, they indicate that such patterns exist and are identifiable in the discursive space of this field.

The exploratory nature of the Q-method and its focus on ranking subjective perceptions do not allow for direct testing of the causal effects of GenAI-based decision-making. For example, it cannot be said with certainty that the GenAI-based “human synergy” mindset actually leads to increased trust in export negotiations or improved export performance. Therefore, future research is recommended to use experimental designs and triangulation with digital behavioral data.

Furthermore, the results strongly depend on the quality and balance of the selected statements, and understanding these factors requires the researcher’s interpretation, which can introduce bias. The generalizability of this method is of the “analytic generalization” type rather than statistical generalization, which is less common and more “qualitative”. Therefore, the findings of this study cannot be generalized quantitatively to the entire population. Another challenge is the rapid evolution of generative AI technology; the references in this study cover published sources up to 2025, but future developments, such as more advanced multimodal models (e.g., GPT-4 or its equivalents), could transform mental models. To examine these dynamics, longitudinal studies and continuous or repeated measurements to survey the attitudes of specific individuals over long periods are suggested.

However, these limitations are not weaknesses but rather an invitation to future research that can broaden horizons through mixed approaches. For example, longitudinal studies with quantitative methods such as structural equation modeling (SEM) can test the identified mental models against performance variables such as export growth rate or profit margin. Also, future research can develop more quantitative measures based on the four expert mindsets, examine their validity in larger samples, and derive maturity models for exploiting generative AI in the context of B2B exports. Comparative studies between developed and developing countries could also shed more light on the role of cultural, institutional, and regulatory differences (such as differences between European, Asian, and American markets) in shaping these mindsets.

From a practical perspective, future research could translate mindsets into concrete management practices, hybrid human–GenAI decision-making, and practical recommendations for different levels of digital maturity of internationalization. From a critical perspective, future research could focus on feminist or postcolonial aspects, examining how mindsets in developing countries view generative AI not as a catalyst for innovation but as a tool for reproducing global inequalities; this perspective, which is lacking in current findings, could be enriched by analyzing anti-Western discourses in African or Asian interviews. Also, empirical tests in simulated environments, such as using virtual reality for B2B negotiations, could challenge the moral hazard paradox and show whether hybrid models truly preserve human empathy or reduce it to a digital illusion.