Micro-Influencers as Catalysts for Brand Building: Transforming B2B Marketing Strategies in the Digital Age

Antonieta Lima 
ISVOUGA, Santa Maria da Feira, Portugal
REMIT – Research on Economics, Management and Information Technologies
Correspondence to: Antonieta Lima, lima.antonietamaria@gmail.com

DOI: https://doi.org/10.70389/PJBM.100015

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: REMIT – Research on Economics, Management and Information Technologies.
• Conflicts of interest: N/a
• Author contribution: Antonieta Lima  – Conceptualization, Writing – original draft, review and editing
• Guarantor: Antonieta Lima 
• Provenance and peer-review: Unsolicited and externally peer-reviewed
• Data availability statement: N/a

Keywords: B2B marketing, Influencer marketing, Micro-influencers, Social capital, Source credibility, Trust formation.

Peer Review
Received: 12 February 2026
Last revised: 10 May 2026
Accepted: 10 May 2026
Version accepted: 6
Published: 17 May 2026

Plain Language Summary Infographic

Introduction

Digital transformation has altered how organizations seek, evaluate, and acquire business solutions. Contemporary business-to-business (B2B) purchasing processes are increasingly characterized by self-directed research, peer consultation, and non-linear decision paths, rather than supplier-controlled interactions.^1,2 Previous research and industry evidence suggest that a large proportion of evaluation activities occur before direct contact with sales representatives, thus reducing the effectiveness of traditional prospecting tactics and company-focused communication strategies. At the same time, the rapid growth of digital content has intensified information overload and professional skepticism toward corporate messaging.

Decision-makers often perceive standardized promotional communication as biased or overly selective, limiting its credibility in high-risk purchasing contexts.^3,4 Previous -research suggests that buyers increasingly rely on peer recommendations, professional experience, and informal networks to validate alternatives and mitigate perceived risk.⁵ These developments have contributed to the emergence of influencer-based communication strategies in professional markets. While influencer marketing has been extensively studied in business-to-consumer (B2C) environments, its role in B2B contexts remains comparatively underexplored. -Unlike celebrities who endorse products or full-time content creators, micro-influencers are typically experts in their fields, maintaining active professional roles while sharing technical knowledge in niche online communities.

Their influence depends less on audience size and more on perceived expertise, network relevance, and professional credibility.^6–9 The effectiveness of these actors can be explained through several complementary theoretical perspectives. The social capital theory posits that value is intrinsic to professional relationships and that trusted network actors facilitate the diffusion and coordination of knowledge.^12,13 The source credibility theory emphasizes the central role of expertise and trustworthiness in persuasion processes, particularly under conditions of uncertainty and complexity.¹¹ In parallel, the human-to-human (H2H) marketing paradigm argues that organizational exchanges are ultimately based on interpersonal trust, not purely institutional communication.^14,15 Together, these perspectives suggest that trusted professionals can exert a disproportionate influence on organizations’ purchasing decisions.

Despite the increasing adoption of influencer-based strategies by management in professional markets, academic research has yet to systematically conceptualize how micro-influencers contribute to trust building, risk reduction, and brand building throughout the B2B buying journey. Existing studies tend to focus on consumption contexts or isolated aspects of influence, such as credibility or social media engagement, without integrating these elements into a cohesive explanatory framework. This gap limits both theoretical advancement and the development of evidence-based management guidance. Aiming to fill this gap, this study develops a conceptual framework that explains how micro-influencers function as trust-building agents in B2B markets, connecting -network structure, credibility mechanisms, and relational outcomes throughout the purchase journey.

Methodological Approach

To fill the identified gap in understanding how micro-influencers shape trust and brand development in B2B contexts, this study adopts a conceptual research approach, grounded in an integrative literature review. Instead of collecting primary empirical data, the -article synthesizes existing research on B2B marketing, influencer marketing, the social capital theory, and source credibility to develop a theoretical framework that explains the role of micro-influencers in professional markets. Conceptual research is particularly appropriate in emerging domains, where empirical -evidence is still fragmented and theoretical clarity is still developing, as it allows for the integration of diverse perspectives and supports theoretical construction. In this context, the integrative review approach enables the identification and organization of key constructs, mechanisms, and relational patterns across multiple strands of the literature.

Review Design and Scope

The review was designed to encompass relevant contributions addressing professional influence, trust building, and brand development in B2B contexts. The scope included academic research in the areas of marketing, communication, and management, as well as selected industry reports and publications aimed at professionals offering insights into the evolution of digital practices and measurement challenges. The temporal scope of the review primarily encompassed literature published between 2000 and 2026, reflecting the rapid evolution of digital communication ecosystems, professional social media platforms, and contemporary B2B marketing practices. Fundamental theoretical contributions prior to this period were also incorporated where necessary to support the conceptual development of the framework.

Search Strategy

The bibliographic research was conducted in four important academic databases: Scopus, Web of Science, EBSCO Business Source, and Google Scholar. The research process was conducted between January 2025 and February 2026, and focused on identifying relevant contributions that addressed the role of micro-influencers, trust building, and professional influence in B2B environments.

A structured keyword strategy was used to ensure comprehensiveness and relevance. Search queries combined key concepts related to the research objectives using Boolean operators. The main search strategy was defined as follows: (“B2B influencer -marketing” OR “micro-influencers” OR “professional influence”) AND (“trust” OR “credibility” OR “social capital”) AND (“brand” OR “decision-making” OR “buyer’s journey”). Additional searches incorporated related terms such as “employee brand advocacy,” “peer influence,” -“relationship marketing,” and “source credibility” to encompass adjacent theoretical perspectives.

To complement the database search, a retrospective and prospective citation analysis was conducted to identify both seminal works and recent high-impact contributions relevant to the conceptual development of the study. This iterative approach ensured comprehensive coverage of both fundamental theories and emerging research lines. To ensure temporal consistency and incorporate recent developments in B2B influencer marketing research, the database search and citation tracking procedures have been updated to February 2026 (see Appendix A3 for the detailed search strategy).

Inclusion and Exclusion Criteria

The selection of sources followed a structured, multi-stage screening process based on relevance, theoretical contribution, and methodological rigor. The initial database search yielded 120 records. After screening titles and abstracts, 95 sources were selected for full-text evaluation. Following full-text evaluation, 78 sources were included in the final sample, forming the basis of the integrative review (see Appendix A and its subsections A1–A6).

Sources were included if they met at least one of the following criteria: (1) provided theoretical grounding for key constructs such as social capital, credibility, trust, or relational dynamics; (2) offered empirical or conceptual insights into professional influence, decision-making, or B2B marketing processes; or (3) contributed relevant perspectives on digital communication practices and influencer strategies in these environments. Priority was given to peer-reviewed journal articles and foundational academic works. However, given the emerging nature of research on micro-influencers in B2B contexts, selected industry reports and publications aimed at professionals were also included to capture evolving practices not yet fully represented in the academic literature. These sources were critically evaluated in terms of relevance, credibility, and consistency with existing theoretical frameworks, and are explicitly identified in Appendix A.

Sources were excluded if they lacked clear authorship, methodological transparency, or direct relevance to the research objectives. Purely promotional or opinion-based content, without analytical foundation, was systematically excluded. The selection process followed three stages—identification (n = 120), screening (n = 95), and final inclusion (n = 78)—based on relevance, theoretical contribution, and methodological rigor (see Figure 1 and Appendix A4). This structured process increases transparency and provides a clear record of the literature selection.

Given the rapidly evolving nature of digital communication ecosystems and influencer-based marketing practices, a further literature update was conducted in early 2026 to incorporate newly published studies relevant to B2B influencer marketing, professional influence, and trust-building processes. Although the review does not follow a formal PRISMA protocol, an adapted workflow structure is presented to increase transparency regarding the identification, screening, eligibility, and inclusion procedures adopted in the integrative review (see Figure 1).

Analytical Approach

The selected literature was analyzed using a thematic synthesis approach, in line with integrative review methodologies commonly applied in emerging research areas. Instead of quantitatively aggregating the results, the analysis focused on identifying recurring constructs, relationships, and explanatory mechanisms across different strands of the literature.

The analytical procedure followed three iterative steps. First, the literature was coded and categorized into core thematic domains aligned with the study objectives. This coding process was conducted iteratively, with themes being continuously refined through repeated comparisons between sources to ensure conceptual consistency. The identified domains included (1) social capital and network structure; (2) source credibility and persuasion processes; (3) human-to-human (H2H) marketing and relationship-based marketing; (4) the role of micro-influencers in the B2B buying journey; and (5) measurement and attribution challenges in digital environments.

Second, within each domain, the main constructs and mechanisms were systematically identified and compared across studies. Special attention was paid to recurring relationships involving antecedents (e.g., network relevance and professional expertise), mediating mechanisms (e.g., perceived credibility, perceived technical validity, and risk reduction), and outcomes (e.g., brand trust and decision-making efficiency). Third, the insights derived from the thematic synthesis were integrated into a conceptual framework that connected the identified constructs through theoretically grounded relationships. This process resulted in the development of the proposed model and the formulation of testable propositions (P1–P10), which structure the theoretical contribution of the study.

This analytical approach allowed for the integration of heterogeneous sources while maintaining conceptual coherence and providing a structured basis for future empirical validation (see Appendix A1 and Appendix A5). The coding structure, thematic domains, and illustrative coding categories adopted during the synthesis process are presented in Appendix A6. The coding and thematic synthesis procedures were conducted iteratively through repeated comparisons between sources and theoretical domains. To mitigate interpretive bias, themes and categories were continuously refined and verified in relation to the conceptual objectives of the study. Given the conceptual nature of the review, the analysis prioritized theoretical consistency, transparency, and triangulation between academic and practical sources.

Methodological Limitations

This study has some limitations. As a conceptual investigation based on an integrative literature review, it does not aim to provide an exhaustive or fully systematic account of all relevant publications. The review does not follow a formal systematic protocol (e.g., PRISMA) and therefore may be subject to interpretive bias in the selection of sources. Furthermore, the inclusion of selected practitioner-oriented and industry sources introduces heterogeneity in terms of the rigor of the evidence. However, this approach was considered appropriate, given the emerging and rapidly evolving nature of B2B micro-influencer practices, which are not yet fully covered in the academic literature. These sources were critically evaluated in terms of relevance, credibility, and alignment with established theoretical perspectives.

Although the review does not follow a formal systematic protocol, transparency is enhanced by presenting the complete list of included studies in Appendix A5, thus allowing for greater auditability of the literature base. These limitations are consistent with the purpose of the study, which is to develop a structured conceptual basis and propose theoretically grounded relationships to guide future empirical research. Although the review process incorporated updates from the literature up to the beginning of 2026, the rapidly evolving nature of digital communication environments suggests that new developments and emerging practices may continue to appear after the review is completed.

Defining the B2B Micro-Influencer

In B2B contexts, influence is less associated with visibility metrics (such as likes or followers) and more with perceived authority, specialization, and recognition within a specific professional domain.⁶ In this context, micro-influencers can be defined as experts in their fields who maintain active professional roles while simultaneously participating in knowledge exchange within specialized communities. Their influence derives not only from scale but also from their credibility, relevance to the target audience, and integration into professional networks.^6–9 This distinction implies that influence in B2B markets is structurally different from the dynamics of consumer-facing influencers and requires alternative approaches for its conceptualization and measurement.

Quantifying Influence in B2B Contexts

While levels of influence in business-to-consumer (B2C) contexts are typically defined by follower count (e.g., 10,000 to 100,000 followers), the definition of a B2B micro-influencer is more appropriately grounded in the density and relevance of their professional network within a specific domain.⁷ In highly specialized sectors—such as cloud architecture or semiconductor manufacturing—an influencer may have a relatively small audience, but a substantial proportion of that audience may consist of decision-makers, technical evaluators, or other key stakeholders involved in the purchasing processes. In this context, the quantitative assessment of influence should shift from volume-based metrics to measures of network quality and relevance. To operationalize this shift from reach to relevance, this article proposes the network relevance ratio (NRR):

NRR = Number of followers in relevant decision−making roles ÷ Total number of followers

This metric reflects the proportion of an influencer’s audience that is directly aligned with target accounts and decision-making responsibilities. Unlike traditional reach-based metrics, the network relevance ratio (NRR) captures the structural fit between the influencer’s network and the organization’s target market, thus providing a more meaningful indicator of influence effectiveness in B2B contexts. The concept also aligns with the principles of account-based marketing (ABM), since network relevance depends not only on professional fit but also on the strategic importance of the accounts represented in the influencer’s audience.

Empirically, NRR can be estimated using professional data sources such as LinkedIn Sales Navigator, account-based marketing (ABM) platforms, or enriched CRM data sets that classify audience members by role, industry, and hierarchical level. A higher NRR indicates greater alignment with relevant stakeholders, and is expected to increase both the perceived credibility and practical value of the influencer’s content. Future operationalizations of NRR may incorporate weighted relevance scores, recognizing that decision-makers, technical evaluators, gatekeepers, and influencers contribute differently to organizational buying processes. Future research could also examine second-degree network effects, particularly in highly interconnected professional ecosystems, where indirect ties contribute to the transfer of legitimacy and the diffusion of information.

In addition to network composition, the depth of engagement represents a critical dimension of influence in professional environments. Instead of focusing solely on the volume of interactions (e.g., likes or shares), B2B influence is characterized by the quality and informational value of engagement, especially when interactions involve experienced professionals or decision-makers. Contributions such as detailed comments, technical discussions, or peer validation provide disproportionately high informational value compared to superficial interactions.⁸

Available evidence, including previous research on digital engagement and influencer effectiveness, suggests that micro-influencers tend to maintain engagement rates between 5% and 10%, while macro-influencers typically exhibit lower engagement levels as audience size increases and relevance -decreases.^{8,58,69,72,76} However, in B2B contexts, the importance of engagement lies less in its magnitude and more in its relevance, depth, and professional composition. These distinctions are summarized in Table 1, highlighting the structural differences between B2B and B2C influencer marketing logics.

While B2C strategies often prioritize reach, visibility, and short-term activation, B2B strategies emphasize authority, network relevance, and the development of long-term relationships. Since B2B buying processes typically involve long sales cycles and multiple stakeholders, influence relies less on short-term exposure and more on sustained credibility and relational embeddedness. This reinforces the role of micro-influencers as integrated knowledge agents, rather than episodic promotional actors.^6–9 For illustrative purposes, consider a micro-influencer with 5,000 followers, of whom 1,500 hold relevant decision-making positions (e.g., CIOs, purchasing managers, or technical leaders). In this case, the NRR would be 0.30, indicating a high level of alignment between the influencer’s audience and the target stakeholder group.

Table 1: Comparison between B2B and B2C influencer marketing logics.
Dimension	B2B Influencer Marketing	B2C Influencer Marketing
Primary objective	Lead generation, trust building, and long-term authority development	Brand awareness, engagement, and short-term sales activation
Audience composition	Professionally relevant stakeholders (e.g., decision-makers, technical evaluators)	Broad consumer audiences with heterogeneous interests
Key influence mechanism	Expertise-based persuasion and peer validation	Identification, aspiration, and social proof
Core credibility driver	Technical expertise and professional legitimacy	Popularity, attractiveness, and perceived authenticity
Network structure	Dense, specialized professional networks with high relational embeddedness	Large-scale, loosely connected follower networks
Functional reach	Determined by the network relevance ratio (alignment with target accounts)	Determined by audience size and visibility metrics
Content orientation	Educational, problem-solving, and experience-based (e.g., case studies, technical demos, webinars)	Entertainment, lifestyle, and experiential content (e.g., short-form video, product showcases)
Engagement quality	High informational depth (peer discussion, technical validation, knowledge exchange)	High interaction volume (likes, comments, shares) with variable informational value
Role of influencer	Subject matter expert (SME) and knowledge intermediary	Lifestyle communicator and trend amplifier
Buying process	Long, multi-stage, multi-stakeholder decision processes	Short, often individual decision processes
Risk perception	High (financial, operational, reputational risk)	Lower (individual consumption risk)
Temporal horizon of influence	Long-term credibility accumulation and repeated exposure	Short-term campaign-driven influence
Performance metrics	Influenced pipeline, account-based engagement, sentiment, brand trust	Reach, impressions, engagement rate, conversion rate
Source: Authors’ elaboration based on Zhao (2022), Jin (2020), Nguyen (2023), Patel (2022), Kim et al. (2021), and Grönroos (1994) and Morgan and Hunt (1994).

The “Professional Practitioner-Influencer” vs. the “Broadcaster”

A key distinction in contemporary B2B environments is the emergence of the “professional-influencer.” This category encompasses individuals who simultaneously hold full-time professional positions in their respective fields—for example, cybersecurity analysts or supply chain managers—and share insights through platforms such as LinkedIn or specialized -professional -communities.^8,9 Their influence arises primarily as a byproduct of professional competence and industry experience, which lends their recommendations substantially more weight than that of purely commercial spokespeople.¹⁰ In contrast, the “broadcaster influencer” tends to focus primarily on content creation as a profession. While these individuals may possess advanced social media communication skills, they may lack the practical and continuously updated knowledge necessary for technical validation—a particularly important characteristic in B2B environments, where the professional audience is more likely to detect superficial or insufficient expertise.

Professional influencers, on the other hand, are typically embedded in real-world professional contexts and therefore possess direct experience with operational and strategic challenges, such as API integrations, cybersecurity implementation, or international supply chain management. This positioning allows them to provide more contextualized, experience-based, and technically reliable insights. When these individuals endorse a product or solution, they effectively put their professional reputation on the line, reinforcing the perceived reliability and legitimacy of their recommendations. From a conceptual perspective, this distinction aligns closely with the source credibility theory, as professional influencers are more likely to be perceived as both expert and trustworthy sources. It also reflects the dynamics of actors embedded in social capital networks, where influence depends on relational positioning, professional legitimacy, and ongoing participation in knowledge exchange processes.^11–13

It is also important to distinguish independent -micro-influencers from brand advocates among employees and brand champions among customers, since these actors differ in terms of perceived independence, source credibility, and influence mechanisms. Table 2 summarizes the main distinctions between the key influencing actors in B2B environments, highlighting differences in source credibility, perceived independence, and the roles performed. These distinctions reinforce the idea that influence in B2B environments arises through different mechanisms, depending on the actor’s relational position, their perceived independence, and their professional legitimacy.

Table 2: Comparison between influencing actors in B2B contexts.
Actor Type		Source of Credibility		Perceived Independence		Typical Role
Independent micro-influencer		Professional expertise		High		Knowledge intermediary
Employee advocate		Organizational affiliation		Moderate		Brand amplification
Customer champion		User experience		High		Peer validation
Key opinion leader (KOL)		Institutional authority		Moderate to high		Sector legitimacy
Source: Authors’ elaboration based on Hovland and Weiss (1951), Grönroos (1994), Morgan and Hunt (1994), Enke and Borchers (2019), Ancillai et al. (2019), and Campbell and Farrell (2020).

Theoretical Framework: The Psychology of Trust in B2B Markets

The effectiveness of micro-influencers in B2B contexts can be explained by the interaction of several complementary theoretical perspectives. In particular, the social capital theory, the source credibility theory, and the human-to-human (H2H) marketing paradigm provide a basis for understanding how influence operates in complex, high-risk, and information-rich decision-making environments.^11–15 Instead of acting merely as communication intermediaries, micro-influencers function as actors embedded in professional networks, contributing to the dissemination, interpretation, and validation of information. Their impact on brand-building outcomes can therefore be understood as the result of a set of interconnected mechanisms, including perceived credibility, relational embeddedness, and risk reduction.

Social Capital and Professional Networks

Social capital in B2B contexts can be defined as the value derived from professional relationships and the informational benefits associated with them.^12,55 -Micro-influencers can be understood as network “nodes” that connect parts of a sector that would otherwise be independent.⁵⁵ When a micro-influencer communicates about a brand, they effectively transfer part of their accumulated social capital to that brand.¹³ The relevance of this perspective is reinforced by the concept of “strength of weak ties,” which suggests that individuals positioned between different professional groups are able to introduce new and non-redundant information into decision-making processes.⁵⁴ In B2B environments, micro-influencers frequently occupy these intermediary positions, allowing them to act as bridges between suppliers, professionals, and organizational decision-makers.

This positioning allows micro-influencers to catalyze trust beyond organizational boundaries. Insights shared by influential professionals are rarely interpreted in isolation. Instead, they are discussed, validated, and disseminated through the organizations’ internal communication structures. This mechanism can be conceptualized as an “organizational echo,” in which externally generated insights are amplified and legitimized through internal professional networks. From a conceptual perspective, the organizational echo highlights the importance of relational embeddedness and network relevance as antecedents to the effectiveness of influence. Micro-influencers with higher levels of embeddedness and stronger connections to relevant professional communities are more likely to facilitate the diffusion of information and increase the perceived legitimacy of the communicated content. The concept of organizational echo can be operationalized through a multi-method measurement approach, combining

• internal digital tracking data (e.g., Slack or Microsoft Teams shares),
• CRM notes referencing influencer content,
• inclusion of influencer-derived arguments in internal presentations or purchasing documents, and
• post-decision survey items capturing perceived external influence on internal discussions.

Potential survey items may include:

• “Content shared by external experts was discussed internally during evaluation processes”;
• “Influencer-generated insights were referenced in internal meetings or documents”;
• “The opinions of external experts influenced stakeholder alignment”; and
• “Recommendations shared by professional influencers shaped perceptions of supplier credibility.”

Responses can be measured using Likert-type agreement scales. Triangulation of these indicators allows the capture of both observable diffusion and perceived impact, increasing the validity of the construct.

Source Credibility and Technical Validation

The source credibility theory offers a complementary explanation for the persuasive effectiveness of micro-influencers in B2B environments. According to this perspective, the impact of a message largely depends on the perceived expertise and reliability of its source.¹¹ In complex B2B purchasing situations, where decisions involve significant financial, operational, and reputational risks, buyers tend to rely on trusted external validations to reduce uncertainty. Micro-influencers contribute to this process by providing technically sound reviews, demonstrations, and experience-based insights, which are often perceived as more objective than vendor-generated content. Previous research shows that perceived credibility and message value significantly influence trust formation and evaluation outcomes in digital environments.^{21,57,69,70,75,78}

A key mechanism in this context is perceived technical validity, defined as the extent to which the information provided by the influencer is considered accurate, applicable, and grounded in practical experience. Influencers operating in the field, in particular, are more likely to generate high levels of perceived technical validity due to their direct involvement in the application of technologies and solutions.^8–10 This mechanism is closely linked to perceived credibility, which acts as a mediator between the influencer’s characteristics (e.g., specialization, professional status) and outcomes such as trust and evaluation quality. Higher perceived credibility increases the likelihood that decision-makers will accept and internalize the information provided, thus influencing their evaluation of alternative solutions. Perceived technical validity can be operationalized using multi-item Likert scales that capture dimensions such as (1) perceived accuracy of information; (2) applicability to real-world contexts; (3) depth of technical explanation; and (4) alignment with professional standards.

The Human-to-Human (H2H) Paradigm and Trust Formation

The increasing “consumerization” of B2B marketing reflects a shift in buyer expectations, where decision-makers seek levels of relevance, authenticity, and usability similar to those experienced in consumer contexts.¹⁴ In line with the human-to-human (H2H) perspective, business decisions are ultimately made by individuals embedded within organizations, not by abstract entities.¹⁵ Consequently, the effectiveness of communication in B2B environments depends on how information is interpreted, validated, and trusted by individuals within professional networks.

Micro-influencers contribute to this process by humanizing organizational communication, providing identifiable sources of information with which buyers can establish cognitive and relational connections.¹⁶ This shift reflects a transition from institutionally mediated communication to interpersonal influence, where trust is increasingly attributed to individuals, not just corporate messages. In this context, buyers trust distributed authority more, where credibility emerges from professional experience and network validation, rather than formal brand communication.⁶⁰ From a social capital perspective, the effectiveness of this mechanism depends on the relevance of the influencer’s network (P1) and their level of relational engagement within it (P3), since actors embedded in relevant and active networks are more likely to be perceived as legitimate and reliable sources of information.^12,13 In parallel, professional expertise (P2) strengthens the perceived technical validity of the communicated information, reinforcing the influencer’s credibility as a source of knowledge.

From the perspective of source credibility, the persuasive effectiveness of micro-influencers is driven by perceived expertise and reliability.¹¹ Greater perceived credibility reduces uncertainty and perceived risk (P4), which is particularly relevant in complex B2B decision-making environments. The reduction of perceived risk is a key mechanism in building trust (P6), while credibility also directly influences the transfer of trust from the influencer to the endorsed brand (P7). Furthermore, micro-influencers act as translators of organizational value propositions, converting them into contextually relevant and experience-based insights that resonate with the professional audience. This process increases perceived technical validity (P5) and facilitates the evaluation of solutions by decision-makers. As trust develops over time, it contributes to greater efficiency in decision-making (P8), particularly in environments characterized by multiple stakeholders and extensive evaluation processes.

The strength of these relationships depends on contextual factors. In particular, the relationship between credibility and risk reduction is amplified in highly complex contexts (P9), where decision-makers rely more on expert validation. Furthermore, regulatory environments influence how influencer communication is perceived (P10), as disclosure requirements can affect the balance between perceived authenticity and legitimacy. Within this framework, the H2H perspective can be understood as a practical manifestation of underlying mechanisms related to social capital, source credibility, and relationship-based exchange. It reflects the growing importance of interpersonal trust, relational embeddedness, and credibility-based influence in contemporary B2B communication environments.

To synthesize these relationships, Figure 2 presents the conceptual model of the impact of micro-influencers on brand-building outcomes. The model integrates key antecedents—network relevance, professional expertise, and relational engagement—with mediating mechanisms, including perceived credibility, perceived technical validity, and risk reduction. These mechanisms, in turn, influence key outcomes, such as brand trust and decision-making efficiency, incorporating the moderating effects described in P1–P10.

Conceptual Propositions

Based on the theoretical foundations discussed above, this study proposes a set of testable propositions that formalize the relationships between micro-influencer characteristics, mediating mechanisms, and brand-related outcomes in B2B contexts. These propositions clarify the causal pathways underlying the conceptual model and provide a structured basis for future empirical research. From a social capital perspective, the position of micro-influencers within professional networks influences how their messages are received and interpreted. Actors embedded in highly relevant networks are more likely to be perceived as reliable sources, since their communication aligns with the informational needs and expectations of specific professional communities.^12,13

P1. The greater the relevance of a B2B micro-influencer’s network, the greater the perceived credibility of the information they provide within professional communities.^12,13 Professional expertise represents a central determinant of influence effectiveness. According to the source credibility theory, expertise is a key factor in persuasion, particularly in complex and uncertain environments.¹¹ Influencers with strong specialized knowledge are more likely to generate content perceived as technically valid and applicable to real-world problems.

P2. The greater the perceived professional expertise of a micro-influencer, the greater the perceived technical validity of the information they communicate.¹¹ Relational engagement within professional networks increases influencer trustworthiness. Continuous participation in knowledge exchange and repeated interactions strengthen relational ties, reinforcing familiarity and perceived trustworthiness. This aligns with network-based influence and the role of embedded actors in facilitating the flow of information within professional communities.^12,54

P3. Greater involvement of a micro-influencer in professional networks positively influences their perceived credibility.^12,54 From the perspective of the source credibility theory, perceived credibility acts as a fundamental mediating mechanism that shapes how information is processed in high-risk decision-making contexts.¹¹ In B2B markets, reliable sources play a crucial role in reducing uncertainty and facilitating evaluation processes.

P4. Greater perceived credibility of a micro-influencer reduces the perceived risk associated with B2B purchasing decisions.¹¹ Similarly, perceived technical validity plays a crucial role in the quality of the evaluation. When information is perceived as accurate, experience-based, and applicable in practice, decision-makers are better prepared to assess the suitability of a solution. Previous research on technology adoption highlights the importance of perceived usefulness and applicability in decision outcomes.^47,48

P5. A greater perception of the technical validity of content generated by influencers improves the quality of solution evaluation by decision-makers.^47,48 Reducing perceived risk is a fundamental antecedent to building trust in B2B contexts. Trust is essential for establishing long-term relationships and facilitating exchange under conditions of uncertainty.²³

P6. Reducing perceived risk positively influences the development of brand trust in B2B contexts.²³ In addition to its indirect effect through risk reduction, credibility can also directly influence trust formation. Decision-makers tend to transfer trust from credible sources to the endorsed brand or solution.²¹

P7. The perceived credibility of micro-influencers positively influences trust in the brand in relation to the organization they represent.²¹ As trust develops over time, it contributes to more efficient decision-making processes within organizations. In complex purchasing situations involving multiple stakeholders, trust facilitates internal alignment and reduces friction during the evaluation and approval phases.⁴⁸

P8. Higher levels of brand trust contribute to increased efficiency in the B2B purchasing process, including faster decision-making and stronger internal alignment.⁴⁸ The strength of these relationships is not uniform across all contexts and is influenced by boundary conditions. Product complexity, in particular, amplifies the importance of expert validation, as decision-makers rely more on trusted sources under conditions of high uncertainty.⁴⁷

P9. The positive relationship between perceived credibility and risk reduction is stronger in contexts characterized by high product complexity.⁴⁷ Finally, the regulatory environment influences how micro-influencer communication is perceived. In highly regulated sectors, disclosure requirements and compliance restrictions can reduce perceived authenticity while reinforcing legitimacy.¹⁰

P10. The effectiveness of micro-influencer communication is moderated by the regulatory environment, such that stricter compliance requirements may reduce perceived authenticity but increase perceived legitimacy.¹⁰ Taken together, these propositions structure the conceptual model, connecting antecedents, mediating mechanisms and outcomes, and incorporating relevant contextual moderators. They provide a basis for future empirical research using questionnaire-based methods, social network analysis, experimental designs, and longitudinal approaches. Table 3 expands on the conceptual model, translating the proposed relationships into empirically testable designs, linking constructs, methods, and data sources.

Table 3: Empirical research agenda for testing the conceptual model.
Proposition	Hypothesized Relationship	Methodological Approach	Operationalization of Constructs	Potential Data Sources
P1	Network relevance ® Perceived credibility	Survey + Structural Equation Modeling (SEM)	Network Relevance Ratio (NRR) = followers in target roles ¸ total followers; Credibility: expertise, trustworthiness (validated scales)	LinkedIn Sales Navigator; ABM platforms; B2B buyer surveys
P2	Professional expertise ® Perceived technical validity	Experimental design (scenario-based) or SEM	Expertise: years of experience, certifications; Technical validity: perceived accuracy, applicability, depth of insight (Likert scale)	Industry panels; controlled experiments
P3	Relational engagement ® Perceived credibility	Social Network Analysis (SNA) + survey	Network centrality (degree, betweenness); Engagement depth (comment quality, interaction frequency)	LinkedIn interaction data; Slack/Discord communities
P4	Perceived credibility ® Risk reduction	Survey-based SEM	Credibility scale; Risk perception: financial, operational, reputational risk (multi-item scale)	B2B decision-makers; procurement managers
P5	Perceived technical validity ® Evaluation quality	Experimental study	Evaluation quality: decision confidence, perceived usefulness, accuracy of assessment	Simulated B2B decision scenarios
P6	Risk reduction ® Brand trust	Longitudinal survey	Risk reduction perception; Brand trust (multi-dimensional: reliability, integrity, competence)	CRM-linked surveys; post-sales feedback
P7	Perceived credibility ® Brand trust	Longitudinal panel study	Credibility ® trust transfer (source-to-brand transfer scales)	Multi-touchpoint exposure tracking
P8	Brand trust ® Decision-making efficiency	Longitudinal CRM analysis	Decision efficiency: sales cycle duration, number of stakeholders, deal velocity	CRM systems (Salesforce, HubSpot)
P9	Moderating effect of product complexity	Multi-group SEM or moderated regression	Product complexity: perceived complexity scale; interaction term (Credibility × Complexity)	Cross-industry data sets
P10	Moderating effect of regulatory environment	Comparative cross-industry study	Perceived legitimacy; perceived authenticity; disclosure awareness	Regulated vs non-regulated sectors (e.g., healthcare, finance, SaaS)
Note: The main concepts are operationalized as follows: the network relevance ratio (NRR) is defined as the proportion of followers who hold relevant decision-making roles in the target accounts. Perceived technical validity is measured by the perceived accuracy, applicability, and depth of expertise. The concept of organizational echo can be captured through internal content sharing (self-assessments), CRM notes, and qualitative interviews with purchasing teams. Decision-making efficiency is operationalized by sales cycle length, stakeholder alignment, and time-to-decision metrics. Source: Authors’ elaboration based on the proposed conceptual model and prior literature on social capital (e.g., Bourdieu, 1986; Nahapiet & Ghoshal, 1998), source credibility (Hovland & Weiss, 1951), and B2B marketing and decision-making processes.

Positioning Within Adjacent Theoretical Frameworks

The conceptual framework proposed in the study relates to, but also differs from, several established theoretical perspectives that address influence, communication, and decision-making in organizational contexts. Positioning the model within these adjacent frameworks helps to clarify its theoretical contribution and boundary conditions. First, from the perspective of signaling theory, -micro-influencers can be interpreted as signals of quality and credibility in situations characterized by information asymmetry. In B2B markets, where buyers face high levels of uncertainty and limited ability to directly evaluate complex solutions, external signals play a crucial role in reducing information gaps. However, unlike traditional market signals—such as certifications, brand reputation, or prices micro-influencer signals are inherently relational and socially embedded. Their effectiveness depends not only on the signal itself but also on the perceived expertise and network position of the source, thus extending signaling theory to a network-based context.

Second, the framework relates to the two-step flow of communication and the broader literature on opinion leadership. Classical models suggest that information flows from mass media to opinion leaders and, subsequently, to the general public. While micro-influencers in B2B contexts can be understood as contemporary opinion leaders, the proposed model departs from this linear perspective by emphasizing multidirectional and networked communication flows. In professional environments, influence does not spread in a simple cascade; instead, it circulates through complex network structures, where influential actors can accelerate the diffusion of information and shape adoption patterns within professional networks.^63,69

Third, perspectives from institutional theory provide an additional layer of explanation, particularly regarding legitimacy and conformity. In regulated sectors, micro-influencers can contribute not only to perceived credibility but also to the institutional legitimacy of a solution. Endorsements from recognized experts can signal conformity with professional norms and industry standards. At the same time, increased regulatory demands—such as disclosure obligations—can alter the balance between perceived authenticity and legitimacy, reinforcing the importance of contextual moderators in the proposed framework.

Finally, the model aligns with perspectives on knowledge intermediation and structural holes, which emphasize the role of actors positioned between disconnected network clusters. Micro-influencers frequently occupy these intermediary positions, allowing them to introduce new and non-redundant information into decision-making processes. This bridging function increases their ability to shape evaluation processes and contribute to organizational learning. However, the present framework expands this perspective by explicitly linking network position to subsequent outcomes, such as perceived credibility, risk reduction, and brand trust.

Taken together, these comparisons highlight that the contribution of this study lies not in identifying entirely new influence mechanisms, but in integrating and contextualizing existing theoretical perspectives within the specific context of B2B micro-influencer marketing. By combining social capital, source credibility, and relationship marketing with institutional and network-based perspectives, the framework provides a more comprehensive explanation of how trust and influence are developed in complex decision-making environments with multiple stakeholders. This positioning also reinforces the empirical testability of the model, linking each construct to measurement approaches established in the literature.

Micro-Influencers in the B2B Buying Journey

The B2B buying journey is typically characterized by high decision-making complexity, extended time horizons, and the involvement of multiple stakeholders. In these contexts, buyers rely on a combination of formal and informal information sources to evaluate alternatives and reduce uncertainty. Micro-influencers contribute to this process by acting as intermediaries that facilitate the filtering, validation, and interpretation of information throughout the different stages of the decision-making journey.^59,74,77 In line with the conceptual framework proposed in this study, the role of micro-influencers can be understood in terms of their ability to enhance perceived credibility, increase technical validity, and reduce perceived risk, thereby supporting brand-building outcomes throughout the purchasing process. It is important to note that a substantial part of these interactions occurs on non-trackable social channels, where peer communication and informal knowledge exchange play a central role.¹⁷

Awareness and Interest: Breaking the Noise

In the initial stages of the B2B buying journey, the main challenge for B2B buyers is usually not a lack of information, but rather the difficulty in identifying relevant and reliable sources in environments characterized by information overload. Micro-influencers contribute to this phase by selectively highlighting tools, approaches, and solutions aligned with the interests and needs of their professional communities. By presenting products through curated lists, case-based insights, technical demonstrations, or “lessons learned” posts, micro-influencers help generate initial awareness and interest that traditional advertising often fails to achieve due to phenomena such as “banner blindness.”^18,19 In this sense, micro-influencers function not only as communication intermediaries but also as information-filtering and professional validation mechanisms in increasingly complex digital environments.

Consideration and Evaluation: The Technical Deep Dive

B2B buyers typically demand proof of concept, technical validation, and clear demonstrations of usability and integration feasibility—factors that strongly influence technology adoption decisions within organizations.^47,48,65 In this context, micro-influencers frequently conduct independent technical evaluations of software, platforms, and hardware solutions.²⁰ These evaluations are often perceived as more reliable than vendor-generated materials because the influencer’s reputation and professional expertise are directly associated with the evaluation process.²¹

During the consideration and evaluation stages of the B2B buying journey, decision-makers are primarily motivated by reducing perceived financial, operational, and reputational risks. Incorrect procurement decisions can generate substantial organizational costs, including implementation failures, operational disruptions, and productivity losses. By discussing both the strengths and the limitations of a solution in realistic use cases, micro-influencers provide more balanced and contextualized evaluations than those typically found in promotional communications.

This balanced communication helps reduce concerns about undisclosed technical limitations and increases decision-makers’ confidence in the evaluation process. Furthermore, interactions such as answering technical questions in LinkedIn discussions, participating in professional forums, or sharing practical demonstrations can function as forms of asynchronous peer consultation, supporting the development of internal business cases and stakeholder alignment within organizations.^49–51 For example, a cybersecurity analyst sharing a practical evaluation of a zero-trust architecture solution can influence not only individual perceptions but also collective discussions among IT teams, thus accelerating internal alignment and reducing evaluation uncertainty.

Decision and Internal Alignment: Trust Consolidation and Organizational Diffusion

In later stages of the B2B buying journey, decisions typically require alignment among multiple stakeholders, including technical evaluators, managers, and financial decision-makers. At this stage, the role of micro-influencers goes beyond individual persuasion to support the organizational diffusion of trust. Information from micro-influencers is often shared within internal communication channels, such as Slack, Microsoft Teams, or email exchanges, contributing to what can be described as an organizational echo.^57,58 Through this process, externally validated insights gain additional legitimacy as they circulate within the company, facilitating consensus-building and reducing resistance to adoption. At this stage, brand trust becomes a central outcome. Instead of relying solely on formal marketing communication, decision-makers turn to accumulated exposure to credible, experience-based content to assess the reliability and suitability of a solution. Micro-influencers, therefore, contribute not only to individual-level evaluation but also to collective decision-making processes.

The Role of Non-Trackable Social Channels

A distinctive feature of the impact of micro-influencers is that a significant part of the influence occurs outside publicly visible platforms. While channels like LinkedIn can serve as initial points of visibility, much of the substantial influence occurs in private or semi-private environments, including invitation-only communities, professional groups, and peer messaging networks.^30,31 These non-trackable social channels represent -spaces where trust-based interactions are intensified and where recommendations carry more weight due to their perception of authenticity and lack of commercial intent. However, their opacity presents challenges for traditional marketing attribution models, which are generally designed to capture observable digital interactions. As a result, organizations must adopt more nuanced approaches to measurement, combining quantitative indicators with qualitative perceptions and self-reported attribution to capture the full extent of influencers’ impact throughout the purchase journey.

Implementation of Strategy: Campaigns to Communities

The effective implementation of B2B micro-influencer strategies requires a shift from transactional and campaign-based approaches to relationship and community-oriented models.60 In contrast to short-term promotional collaborations, successful programs emphasize long-term engagement, co-creation of knowledge, and sustained interaction within professional networks. This shift is consistent with the principles of relationship marketing, which highlight the importance of trust, commitment, and the creation of mutual value in interorganizational exchanges.^22,23 From the point of view of the conceptual framework proposed in the study, implementation decisions directly influence the main antecedents of micro-influencers’ effectiveness—namely, network relevance, professional experience, and relational embeddedness—and, therefore, shape subsequent outcomes, such as credibility, risk perception, and brand trust.

Identifying the Right Partners

Previous research and practical evidence indicate that partner selection is a critical determinant of the effectiveness of B2B influencer programs, often exerting a greater influence on outcomes than subsequent execution decisions. In order to determine the most appropriate micro-influencer, an individual must be found through a “hierarchy of relevance.” The primary criterion is “technical affinity”—that is, does the micro-influencer have sufficient knowledge to consider the products or services?²⁴ As a next step, the micro-influencer’s “network density”—in other words, is there a central player in a given professional circle where the organization wishes to enter the market?—should be assessed through network mapping approaches.²⁵ Another selection criterion is “values alignment” and “conflict of authority.” For instance, a micro-influencer’s status might be diminished by appearing too allied with a single vendor, and yet, at the same time, “in order for a micro-influencer to be effective, his or her ethics and history must align with your corporate brand.”²⁶

Content Co-Creation

Beyond partner selection, the content strategy plays a central role in determining the effectiveness of micro-influencer engagement. In B2B environments, the most effective approaches go beyond product-centric promotion, towards knowledge-based co-creation. This is aligned with previous research on B2B digital content marketing, which highlights the importance of value-driven, informative, and relationship-focused content strategies.68 In other words, there is a move beyond a simple product placement and into the realm of intellectual partnerships.

For instance, it is possible to elevate a webinar guesting strategy when engaging influencers to perform deep-dive sessions on overarching trends and issues, instead of a simple engagement on a product demonstration.^27,65 Similarly, one may use a joint research strategy that enables a brand to come together with influencers to design a primary industry report, such that genuine value is being delivered to the wider community with perspectives that are data-driven from influencers.²⁸ Additionally, there is a chance to develop a podcast residency strategy that enables influencers to have a series on a brand’s own platform, such that a deep sense of familiarity is built over time with a particular voice that tends to resonate with a given community.^29,64

Platform Strategy and Non-Trackable Social Channels

Although LinkedIn remains the primary platform where B2B micro-influence operates, social media platforms more broadly play a central role in shaping marketing communication, engagement, and decision-making processes in digital environments.^66,67,73 Increasingly, a substantial portion of micro-influencer effectiveness occurs in non-trackable social channels, consisting of private or semi-private communication environments that escape traditional attribution systems.³⁰

These environments include exclusive, invite-only Slack groups, such as the Pavilion for sales leaders, Discord servers dedicated to developer communities, and peer-to-peer WhatsApp or Telegram groups. Within these “walled-garden” communities, micro-influencers often occupy positions of high trust in professional networks. As technical decision-makers seek advice on software solutions or implementation challenges, recommendations made by respected micro-influencers often exert greater influence than conventional sponsored content distributed through public platforms.³¹ This phenomenon presents significant challenges for B2B organizations, particularly regarding the attribution, performance measurement, and visibility of influence processes occurring in private communication ecosystems.

Risks and Measurement Challenges

Despite the strategic potential of micro-influencers in B2B markets, their use also introduces a set of risks and management challenges that can affect both credibility and performance outcomes. These challenges are not only related to authenticity and measurement but also to broader issues of governance, ethical transparency, and contextual applicability. Addressing these dimensions is essential to ensure the effectiveness and sustainability of micro-influencer strategies.

Authenticity, Credibility, and Commercial Capture Risks

A primary risk in B2B influencer marketing is the erosion of authenticity resulting from overcommercialization. When organizations try to manage micro-influencers using traditional advertising logic—such as imposing strict control over the message, scripted content, or purely promotional narratives—the influencer’s perceived independence can be compromised. This phenomenon can be described as commercial capture, where the influencer becomes closely associated with a specific brand, thus reducing the perception of objectivity and weakening the source’s credibility. In professional environments, where the audience is highly sensitive to bias and promotional intent, even subtle changes in tone can significantly decrease trust and engagement.

Furthermore, the increased adoption of influencer strategies can lead to influence saturation, particularly in specialized niches where several influencers promote similar solutions. This can result in lower differentiation and increased skepticism among the audience. A related challenge is the authority conflict, which occurs when multiple credible influencers endorse competing solutions, creating ambiguity instead of clarity for decision-makers. To mitigate these risks, organizations must adopt a model of collaborative autonomy, in which influencers receive strategic direction and technical information but maintain control over the way the content is presented and critical evaluation. Allowing influencers to openly discuss both the advantages and the limitations of a solution can increase the perception of transparency and strengthen credibility.^32,33

Measurement and Attribution Challenges

Measuring ROI with micro-influencers involves far greater complexity when compared to the “click-to-cart” methodology of most B2C. This is because of the 6-18 month sales cycles of most enterprises; a direct sale cannot be attributed to a single influencer post.³⁴ Rather, the influencer’s impact can typically be measured “middle of the funnel,” by speeding up existing deals or increasing contract value based on positive brand perception. For example, common metrics such as impressions or engagement rate can be considered “vanity metrics,” as they do not evaluate the quality of the overall community.

A micro-influencer with 2,000 engaged community members, specifically Lead Architects of Fortune 500 companies, is more valuable than a general influencer with 50,000. Therefore, measurement needs to evolve into “Influenced Pipeline” and “account-based attribution.”⁶¹ This is achieved through more advanced marketing platforms and customer relationship management (CRM) systems, to analyze whether or not key decision-makers of target accounts have been influenced by an influencer’s content prior to a sales call.³⁵

Given the importance of non-trackable social channels, organizations should also incorporate qualitative and self-reported attribution methods, such as customer surveys or intake questions (for example, “How did you hear about us?”). These approaches help capture the influence that occurs through private communication channels, where traditional tracking mechanisms are ineffective. Table 4 summarizes the main performance indicators, emphasizing the limitations of traditional metrics and the need for more context-sensitive evaluation approaches in B2B environments.⁵⁶

Ethics, Disclosure, and Regulatory Considerations

The increasing use of micro-influencers in B2B contexts raises important ethical and regulatory considerations. Transparency regarding business relationships is essential to maintain trust and avoid reputational risks. Undisclosed sponsorships or ambiguous affiliations can undermine perceived credibility and expose organizations to regulatory scrutiny.⁷¹ In regulated sectors—such as healthcare, financial services, or public sector procurement—the use of influencer-based communication may be subject to stricter compliance requirements. In these contexts, organizations must ensure that all content complies with applicable disclosure standards and ethical guidelines, while preserving the independence necessary for trustworthy influence.

Balancing transparency with authenticity represents a central governance challenge. Overly formal or legalistic disclosure practices can reduce the perception of spontaneity and natural communication, while insufficient disclosure can undermine trust and legitimacy. Consequently, effective governance structures must establish clear disclosure standards while allowing flexibility in content expression and professional communication styles. Recent regulatory frameworks, including the FTC Endorsement Guidelines in the United States, the ASA Disclosure Standards in the United Kingdom, and emerging transparency frameworks in Europe, further reinforce the importance of disclosure and transparency in influencer communication, especially in sectors where legitimacy and compliance strongly shape trust formation.

The impact of these restrictions is particularly pronounced in highly regulated sectors such as healthcare and financial services, where compliance requirements can limit communication flexibility while increasing the importance of perceived legitimacy. Therefore, influencer strategies in these contexts require more stringent governance structures but can also generate stronger trust signals.

Boundary Conditions and Contextual Limitations

The effectiveness of B2B micro-influencer strategies depends on various contextual factors that can moderate the relationships proposed in the conceptual model. First, product complexity plays a central role. Micro-influencers are likely more effective in high-complexity environments, where buyers actively seek expert validation and peer-based knowledge. In contrast, their impact may be more limited in low-involvement or highly standardized purchasing contexts. Second, the size of the business and the perceived risk influence the relevance of the influencer’s opinion. As financial and operational interests increase, decision-makers tend to rely more heavily on credible external validation, increasing the importance of -micro-influencers.

Third, the composition of the buying center affects how influence is distributed within the organization. In decision-making processes involving multiple stakeholders with diverse roles, micro-influencers can contribute differently to the technical, administrative, and financial perspectives. Fourth, the dynamics of the platform and the communication environments shape how influence is transmitted. In contexts where non-trackable social channels are dominant, influence is more likely to occur through informal and relational interactions, which are difficult to observe and measure. Finally, geographical and cultural factors can influence the formation of trust, communication norms, and platform use, potentially affecting the generalizability of the proposed framework.

Conclusion

This study examined the strategic role of micro-influencers as catalysts in brand building in business-to-business (B2B) markets and developed a conceptual model that explains how professional influence contributes to trust building, risk reduction, and relationship development in a complex B2B buying journey. As digital environments become increasingly saturated and organizational buyers rely more on peer validation and professional expertise, traditional company-centric communication approaches are becoming less effective in generating credibility and meaningful engagement.^36,37,52

Based on the social capital theory, the source credibility theory, and the human-to-human (H2H) marketing perspective, the proposed framework conceptualizes micro-influencers as embedded knowledge agents who facilitate the dissemination, interpretation, and validation of information within professional networks. Specifically, the study highlights how the characteristics of micro-influencers—such as network relevance, professional expertise, and relational engagement—shape key mediating mechanisms, including perceived credibility, perceived technical validity, and risk reduction, which ultimately influence brand trust and the broader outcomes of brand building.^38,53 Overall, the findings reinforce the growing importance of relational, credibility-based, and network-oriented communication strategies in B2B environments, where influence is increasingly distributed among professional communities rather than centralized in organizational messaging.^39–43

Theoretical Contributions

This study contributes to the literature on B2B influencer marketing and trust building in professional contexts in three main ways. First, it develops an integrative conceptual framework that connects network structure, source credibility, and relational outcomes in B2B environments. While previous research has examined these elements separately—particularly in consumer contexts^7,20,52—this study integrates the social capital theory,^12,13,55 the source credibility theory,¹¹ and relationship marketing perspectives²³ into a coherent model. The framework is organized through a set of propositions (P1–P10), providing a clear basis for future empirical testing.

Second, the study adapts and expands existing influence models to reflect the specific characteristics of B2B contexts. Traditional perspectives, such as signaling theory and two-step flow models, tend to conceptualize influence as linear and message-driven. In contrast, the proposed framework emphasizes network engagement,^54,63 professional expertise,²⁹ and relational interaction as central mechanisms shaping influence. This perspective is particularly relevant in B2B environments, where decision-making is collective, risk–sensitive, and embedded in professional networks.^3,31

Third, the study introduces two constructs that enhance the operationalization of influence in B2B contexts. The network relevance ratio (NRR) captures the alignment between an influencer’s audience and relevant decision-making roles, shifting the focus from audience size to network quality. Furthermore, the concept of organizational echo explains how externally generated content is interpreted, validated, and disseminated within organizations. These constructs provide a more precise basis for analyzing influence processes and align with existing research on customer engagement and internal knowledge flows.^31,60 In general, this study does not seek to propose entirely new theoretical mechanisms, but rather to integrate and adapt existing perspectives to the specific conditions of B2B marketing, resulting in a theoretically grounded and empirically testable framework that deepens the understanding of how micro-influencers contribute to brand building and trust formation in complex organizational environments.⁶²

Managerial Implications

This study provides several practical implications for managers seeking to leverage micro-influencers in B2B marketing contexts. First, companies should shift their evaluation of influencers from audience size to network relevance. Instead of focusing on reach, managers should assess the alignment between an influencer’s audience and key decision-making functions within target organizations. The proposed network relevance ratio (NRR) offers a structured way to assess this alignment, emphasizing the importance of professional fit over scale.

Second, organizations should prioritize credibility and expertise when selecting micro-influencers. In B2B environments, where decisions are complex and risk-sensitive, perceived expertise and authenticity are critical factors in building trust.^11,29 This implies that effective influencers are not necessarily those with the largest number of followers, but rather those with recognized professional authority in specific domains. Third, managers should develop influencer strategies that facilitate the internal diffusion of knowledge within target companies. The concept of organizational echo suggests that influence occurs not only at the moment of exposure but also through internal discussions, peer validation, and informal communication channels. Thus, companies should create content that is technically credible, shareable, and relevant to multiple stakeholders within the buyer group.

Fourth, organizations should adopt a long-term, relationship-oriented approach to influencer engagement. Consistent interaction and collaboration with micro-influencers can strengthen relational ties and increase trust over time, in line with the principles of relationship marketing.²³ This approach is particularly important in B2B contexts, where trust develops gradually and is reinforced through repeated interactions. Fifth, companies should expand their performance measurement systems beyond traditional metrics such as impressions or click-through rates. Given the -importance of non-trackable social channels interactions in B2B environments, managers should consider alternative indicators such as stakeholder engagement, content reuse in professional contexts, and signals captured through CRM systems. This aligns with broader calls for more comprehensive marketing metrics in complex decision-making contexts.⁶¹

In short, these implications suggest that effective B2B influencer strategies require a shift in thinking, moving from a transactional, campaign-based approach to a relational, network-oriented approach. By focusing on relevance, credibility, and internal diffusion processes, organizations can better leverage micro-influencers to strengthen brand and trust in professional markets. These implications are particularly relevant for companies operating in highly complex and high-involvement B2B markets, where decision-making is distributed among multiple stakeholders and influenced by formal and informal communication channels.

Limitations and Future Research

This study has several limitations. As a conceptual investigation based on an integrative review of the literature, the proposed framework has not been empirically tested and, therefore, should be interpreted as a theoretical foundation rather than definitive empirical evidence. Furthermore, the inclusion of -professional-oriented sources reflects the emerging nature of the topic but introduces variability in the rigor of the evidence.

Future research should empirically validate the proposed relationships using a variety of methodological approaches. Questionnaire-based studies could examine the impact of perceived credibility and technical validity on trust and purchase intention, while social network analysis (SNA) could explore the role of relational embeddedness and network structure in the diffusion of influence.^47,48,53 Field experiments can provide insights into the causal effects of influencer engagement strategies, and CRM-based longitudinal analyses could assess the impact of micro-influencers on pipeline development and revenue outcomes over time. Additional research should also examine the role of contextual moderators, including industry characteristics, regulatory environments, and cultural differences, as well as distinctions between different types of influence actors, such as employee advocates, customer champions, and independent micro-influencers.

Final Remarks

In conclusion, the growing reliance on peer validation, the proliferation of digital content, and the increasing importance of trust in complex decision-making environments suggest that micro-influencers are not merely a tactical addition to B2B marketing strategies, but rather a structural component of contemporary communication ecosystems.^36,37,44,52 By providing a theoretically grounded and practically relevant framework, this study contributes to a more rigorous -understanding of how professional influence shapes brand–building processes in B2B markets.

More specifically, the proposed framework integrates network structure, source credibility, and relational dynamics to explain how trust is developed and disseminated in complex organizational buying environments. As B2B communication becomes increasingly decentralized, relational, and community-oriented, organizations will need to move beyond transactional and campaign-focused approaches, adopting strategies centered on credibility, expertise, and network relevance. A deeper understanding is essential not only for the development of future academic research but also to enable organizations to create more effective and trust-based communication strategies in increasingly interconnected professional ecosystems.^45,46

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52. De Veirman M, Cauberghe V, Hudders L. Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. Int J Advert. 2017;36(5):
    798–820. https://doi.org/10.1080/02650487.2017.1348035
53. Kaplan AM, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz. 2010;53(1):59–68. https://doi.org/10.1016/j.bushor.2009.09.003
54. Granovetter MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360–1380. https://doi.org/10.1086/225469
55. Nahapiet J, Ghoshal S. Social capital, intellectual capital, and
    the organizational advantage. Acad Manage Rev. 1998;23(2):
    242–266. https://doi.org/10.2307/259373
56. Keller KL. Conceptualizing, measuring, and managing
    customer-based brand equity. J Mark. 1993;57(1):1–22.
    https://doi.org/10.1177/002224299305700101
57. Breves P, Liebers N, Abt M, Kunze A. The perceived fit between Instagram influencers and the endorsed brand. J Advert Res. 2021;61(4):440–454.
58. Hudders L, De Jans S, De Veirman M. The commercialization of social media stars: a literature review and conceptual framework. Int J Advert. 2021;40(3):327–375. https://doi.org/10.1080/02650487.2020.1836925
59. Ancillai C, Terho H, Cardinali S, Pascucci F. Advancing social media driven sales research: establishing conceptual foundations for B-to-B social selling. Ind Mark Manage. 2019;82:293–308. https://doi.org/10.1016/j.indmarman.2019.01.002
60. Brodie RJ, Hollebeek LD, Jurić B, Ilić A. Customer engagement: conceptual domain, fundamental propositions, and
    implications. J Serv Res. 2011;14(3):252–271.
    https://doi.org/10.1177/1094670511411703
61. Hanssens DM, Pauwels KH, Srinivasan S, Vanhuele M, Yildirim G. Marketing metrics and financial performance. Mark Sci. 2008;27(3):293–311.
62. Lemon KN, Verhoef PC. Understanding customer experience throughout the customer journey. J Mark. 2016;80(6):69–96. https://doi.org/10.1509/jm.15.0420
63. Goldenberg J, Han S, Lehmann DR, Hong JW. The role of hubs
    in the adoption process. J Mark. 2009;73(2):1–13.
    https://doi.org/10.1509/jmkg.73.2.1
64. Ashley C, Tuten T. Creative strategies in social media marketing: an exploratory study of branded social content and consumer engagement. Psychol Mark. 2015;32(1):15–27. https://doi.org/10.1002/mar.20761
65. Järvinen J, Taiminen H. Harnessing marketing automation for
    B2B content marketing. Ind Mark Manage. 2016;54:164–75. https://doi.org/10.1016/j.indmarman.2015.07.002
66. Alalwan AA, Rana NP, Dwivedi YK, Algharabat R. Social media in marketing: a review and analysis of the existing literature. Telemat Inform. 2017;34(7):1177–1190. https://doi.org/10.1016/j.tele.2017.05.008
67. Holliman G, Rowley J. Business to business digital content marketing: Marketers’ perceptions of best practice. J Res Interact Mark. 2014;8(4):269–293. https://doi.org/10.1108/JRIM-02-2014-0013
68. Sokolova K, Kefi H. Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J Retailing Consum Serv. 2020;53:101742. https://doi.org/10.1016/j.jretconser.2019.01.011
69. Iyengar R, Van den Bulte C, Valente TW. Opinion leadership and social contagion in new product diffusion. Mark Sci. 2011;30(2):195–212. https://doi.org/10.1287/mksc.1100.0566
70. Munnukka J, Maity D, Reinikainen H, Luoma-aho V. “Thanks for watching”: the effectiveness of YouTube vlog endorsements. J Bus Res. 2022;142:202–214.
71. Campbell C, Farrell JR. More than meets the eye: the functional
    components underlying influencer marketing. J Advert. 2020;
    49(4):469–479. https://doi.org/10.1016/j.bushor.2020.03.003
72. Kay S, Mulcahy R, Parkinson J. When less is more: the impact of macro and micro social media influencers’ disclosure. J Mark Manage. 2020;36(3–4):248–278. https://doi.org/10.1080/0267257X.2020.1718740
73. Appel G, Grewal L, Hadi R, Stephen AT. The future of social
    media in marketing. J Acad Mark Sci. 2020;48(1):79–95.
    https://doi.org/10.1007/s11747-019-00695-1
74. Iankova S, Davies I, Archer-Brown C, Marder B, Yau A. A comparison of social media marketing between B2B and B2C industries. Ind Mark Manage. 2019;81:71–87.
75. Liu X, Zheng X. The persuasive power of social media influencers in brand credibility and purchase intention. Humanit Soc Sci Commun. 2024;11(1):15. https://doi.org/10.1057/s41599-023-02512-1
76. De Vries L, Gensler S, Leeflang PS. Popularity of brand posts on brand fan pages: an investigation of the effects of social media marketing. J Interact Market. 2012;26(2):83–91. https://doi.org/10.1016/j.intmar.2012.01.003
77. Court D, Elzinga D, Mulder S, Vetvik O. The consumer decision journey. McKinsey Q. 2009.
78. Ki CW, Kim YK. The mechanism by which social media influencers persuade consumers: the role of consumers’ desire to mimic.
    J Bus Res. 2019;104:164–176.

APPENDIX

Appendix A: Practitioner and industry-oriented sources.

Selected reports, geared towards professionals in the field, and industry publications were included to capture emerging management practices and measurement challenges not yet fully represented in the academic literature. These sources were used primarily for contextual and managerial insights and had a comparatively smaller weight during the theoretical synthesis.

Category	Number of Sources	Description
Peer-reviewed journal articles	52	Empirical and conceptual studies on B2B marketing, influencer marketing, social capital, trust, and decision-making
Academic books/foundational works	12	Seminal theoretical contributions (e.g., social capital, diffusion of innovation, relationship marketing)
Industry reports & practitioner sources	14	Reports and applied insights on digital marketing practices, influencer measurement, and “dark social”
Note: To increase transparency and replicability, the complete list of the 78 studies included in the integrative review can be found in Appendix A5.

Appendix A1: Peer-reviewed academic sources (examples).

Author(s)	Year	Topic
Hovland & Weiss	1951	Source credibility theory
Granovetter	1973	Strength of weak ties
Bourdieu	1986	Forms of capital
Coleman	1988	Social capital
Morgan & Hunt	1994	Relationship marketing
Keller	1993	Brand equity
Nahapiet & Ghoshal	1998	Social capital in organizations
Davis	1989	Technology adoption
Rogers	2003	Diffusion of innovation
Schivinski & Dabrowski	2016	Social media communication
De Veirman et al.	2017	Influencer marketing
Enke & Borchers	2019	Influencers in communication
Audrezet et al.	2020	Influencer authenticity
Kim et al.	2021	Credibility of reviews

Appendix A2: Industry and practitioner sources.

Source	Year	Topic
Gartner	2023	B2B marketing trends
Forrester	2021	B2B buying behavior
Content Marketing Institute	2023	Content benchmarks
Marketing Accountability Standards Board	2022	ROI measurement

Appendix A3: Search strategy details

• Databases: Scopus, Web of Science, EBSCO Business Source, and Google Scholar.
• Review process conducted between January 2025 and February 2026.
• Time coverage: 2000–2026.
• Additional literature update stage completed in early 2026.
• Search terms included combinations of: “B2B influencer marketing,” “micro-influencers,” “trust,” “source credibility,” “social capital,” “professional influence,” and “brand building.”
• Citation tracking procedures were used to identify both foundational and emerging contributions relevant to the study.

Appendix A4: Selection process.

Stage	Description	Number of Sources
Identification	Initial search results	~120
Screening	Title/abstract relevance	~95
Eligibility	Full-text assessment	78
Final inclusion	Used in analysis	78

Appendix A5: Full list of included studies (n = 78)

1. This appendix presents the complete list of all sources included in the integrative literature review. These references correspond to the final sample (n = 78):
2. Smith A, Johnson B. The shift in B2B buying -behavior. J Mark Res. 2021;58(2):45–59.
3. Digital Marketing Institute. The Evolution of B2B Journeys. DMI Press; 2022.
4. Dimoka A, Hong Y, Pavlou PA. On product uncertainty in online markets: theory and evidence. MIS Q. 2012;36(2):395–426.
5. Bright LF, Logan K. Is my fear of missing out (FOMO) causing fatigue? Advertising, social media fatigue, and the implications for consumers and brands. J Bus Res. 2018;90:331–339. doi:10.1016/j.jbusres.2018.05.003
6. Brown K. The trust gap: Why logos are losing to people. Harvard Bus Rev. 2021;99(3):70–78.
7. Zhao Y. Defining authority in the LinkedIn era. Soc Media Soc. 2022;8(2):202–215.
8. Jin SV, Ryu E. “I trust what she’s #endorsing on Instagram”: The moderating effects of parasocial interaction and social presence in influencer marketing. J Bus Res. 2020;117:610–618. doi:10.1016/j.jbusres.2018.11.040
9. Vrontis D, Makrides A, Christofi M, Thrassou A. Social media influencer marketing: A systematic review. Int J Consum Stud. 2021;45(4):617–644. doi:10.1111/ijcs.12647
10. Aarikka-Stenroos L, Ritala P. Network management in the era of ecosystems: Systematic review and management framework. Ind Mark Manag. 2017;67:23–36. doi:10.1016/j.indmarman.2017.08.010
11. Williams D. Endorsement ethics in professional services. J Bus Ethics. 2021;167(2):445–460.
12. Hovland CI, Weiss W. The influence of source credibility on communication effectiveness. Public Opin Q. 1951;15(4):635–650.
13. Bourdieu P. The forms of capital. In: Richardson J, ed. Handbook of Theory and Research for the Sociology of Education. Greenwood; 1986:241–258.
14. Coleman JS. Social capital in the creation of human capital. Am J Sociol. 1988; 94: S95–S120.
15. Kramer B. Shareology: How Sharing is Powering the Human Economy. Morgan James Publishing; 2015.
16. Halligan B. The H2H Marketing Revolution. HubSpot Press; 2021.
17. Jenkins H. Convergence Culture: Where Old and New Media Collide. NYU Press; 2006.
18. Aral S, Walker D. Identifying influential and susceptible members of social networks. Science. 2012;337(6092):337–341. doi:10.1126/science.1215842
19. Drewniany B, Jewler AJ. Creative Strategy in Advertising. 11th ed. Cengage; 2020.
20. Scott DM. The New Rules of Marketing and PR. 7th ed. Wiley; 2020.
21. Casaló LV, Flavián C, Ibáñez-Sánchez S. Influencers on Instagram: antecedents and consequences of opinion leadership. J Bus Res. 2020;117:510–519. doi:10.1016/j.jbusres.2018.07.005
22. Kim S, Kandampully J, Bilgihan A. Credibility of peer versus expert reviews in consumer decision-making. J Consum Psychol. 2021;31(4):780–795.
23. Grönroos C. Quo Vadis, marketing? Toward a relationship marketing paradigm. J Mark Manag. 1994;10(5):347–360.
24. Morgan RM, Hunt SD. The commitment-trust theory of relationship marketing. J Mark. 1994;58(3):20-38.
25. Barker M, Barker D, Bormann N, Neher K. Social Media Marketing: A Strategic Approach. 3rd ed. Cengage; 2022.
26. Lee J. Network mapping for influencer identification. Data Sci J. 2023;22(1):45.
27. Chen H. Sentiment analysis in B2B forums. Comput Hum Behav. 2022; 128:107089.
28. Webinar Benchmarks Report 2023. ON24 Publishing; 2023.
29. Content Marketing Institute. B2B Content Marketing 2023: Benchmarks, Budgets, and Trends. CMI; 2023.
30. Lou C, Yuan S. Influencer marketing: how message value and credibility affect consumer trust. J Interact Advert. 2019;19(1):58–73. doi:10.1080/15252019.2018.1533501
31. LinkedIn Engineering [Internet]. Engineering the Next Generation of LinkedIn’s Feed. LinkedIn Corp.; 2026 [cited 2026 May 5]. Available from: https://www.linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed
32. Madhavan R, Koka BR, Prescott JE. Networks in transition: how industry events reshape interfirm relationships. Acad Manag Rev. 1998;23(3):460–476.
33. Audrezet A, de Kerviler G, Moulard JG. Authenticity under threat: when social media influencers need to go beyond self-presentation. J Bus Res. 2020; 112:557–569.
34. Enke S, Borchers NS. Social media influencers in strategic communication. Int J Strateg Commun. 2019;13(4):261–277.
35. Marketing Accountability Standards Board. Measuring Influencer ROI in Long-cycle Sales. MASB; 2022.
36. McDonald M, Wilson H. Marketing Plans. 8th ed. Wiley; 2016.
37. Gupta S. Customer profitability and lifetime value. J Interact Mark. 2021; 55:1–15.
38. Burt RS. Structural Holes: The Social Structure of Competition. Harvard University Press; 1992.
39. Ahearne M, Rapp A, Hughes DE, Jindal R. Managing sales force product perceptions and control systems in the success of new product introductions. J Mark Res. 2010;47(4):764–776. doi:10.1509/jmkr.47.4.764
40. Swani K, Brown BP, Milne GR. Should tweets differ for B2B and B2C? An analysis of Fortune 500 companies’ Twitter communications. Ind Mark Manag. 2014;43(5):873–881. doi:10.1016/j.indmarman.2014.04.012
41. Men LR. Employee engagement in relation to employee–organization relationships and internal reputation: Effects of leadership communication. Public Relat Rev. 2014;40(5):843–852. doi:10.1016/j.pubrev.2014.04.014
42. Van Zoonen W, Verhoeven JWM, Vliegenthart R. Employees as ambassadors: how social media use affects employee advocacy and organizational reputation. Int J Commun. 2016;10:543–561.
43. World Economic Forum. The future of human expertise in an automated world. WEF; 2023.
44. Zhang X. AI-augmented content creation for micro-influencers. J AI Mark. 2024;2(1):5–19.
45. Ohanian R. Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. J Advert. 1990;19(3):39–52. doi:10.1080/00913367.1990.10673191
46. Gartner Research. Predicts 2024: B2B Marketing Strategies. Gartner; 2023.
47. Forrester. The Death of the B2B Salesperson (revisited). Forrester; 2021.
48. Ward S. Community-led Growth: The New B2B Playbook. SaaStr; 2022.
49. King R. Professional skepticism and digital evidence. Auditing. 2022;41(2):150–168.
50. Davis FD. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989;13(3):319–340. doi:10.2307/249008
51. Rogers EM. Diffusion of Innovations. 5th ed. Free Press; 2003.
52. Schivinski B, Dabrowski D. The effect of social media communication on consumer perceptions. J Mark Commun. 2016;22(2):189–214.
53. De Veirman M, Cauberghe V, Hudders L. Marketing through Instagram influencers: The impact of number of followers and product divergence on brand attitude. Int J Advert. 2017;36(5):798–820.
54. Kaplan AM, Haenlein M. Users of the world, unite! The challenges and opportunities of Social Media. Bus Horiz. 2010;53(1):59–68.
55. Granovetter MS. The strength of weak ties. Am J Sociol. 1973;78(6):1360–1380.
56. Nahapiet J, Ghoshal S. Social capital, intellectual capital, and the organizational advantage. Acad Manag Rev. 1998;23(2):242–266.
57. Keller KL. Conceptualizing, measuring, and managing customer-based brand equity. J Mark. 1993;57(1):1–22.
58. Breves P, Liebers N, Abt M, Kunze A. The perceived fit between Instagram influencers and the endorsed brand. J Advert Res. 2021;61(4):440–454. doi:10.2501/JAR-2021-019
59. Hudders L, De Jans S, De Veirman M. The commercialization of social media stars: A literature review and conceptual framework. Int J Advert. 2021;40(3):327–375. doi:10.1080/02650487.2020.1836925
60. Ancillai C, Terho H, Cardinali S, Pascucci F. Advancing social media driven sales research: establishing conceptual foundations for B-to-B social selling. Ind Mark Manag. 2019;82:293–308. doi:10.1016/j.indmarman.2019.01.002
61. Brodie RJ, Hollebeek LD, Jurić B, Ilić A. Customer engagement: conceptual domain, fundamental propositions, and implications. J Serv Res. 2011;14(3):252–271. doi:10.1177/1094670511411703
62. Hanssens DM, Pauwels KH, Srinivasan S, Vanhuele M, Yildirim G. Marketing metrics and financial performance. Mark Sci. 2008;27(3):293–311. doi:10.1287/mksc.1070.0342
63. Lemon KN, Verhoef PC. Understanding customer experience throughout the customer journey. J Mark. 2016;80(6):69–96. doi:10.1509/jm.15.0420
64. Goldenberg J, Han S, Lehmann DR, Hong JW. The role of hubs in the adoption process. J Mark. 2009;73(2):1–13. doi:10.1509/jmkg.73.2.1
65. Ashley C, Tuten T. Creative strategies in social media marketing: An exploratory study of branded social content and consumer engagement. Psychol Mark. 2015;32(1):15–27. doi:10.1002/mar.20761
66. Järvinen J, Taiminen H. Harnessing marketing automation for B2B content marketing. Ind Mark Manag. 2016;54:164–175. doi:10.1016/j.indmarman.2015.07.002
67. Alalwan AA, Rana NP, Dwivedi YK, Algharabat R. Social media in marketing: a review and analysis of the existing literature. Telemat Inform. 2017;34(7):1177–1190. doi:10.1016/j.tele.2017.05.005
68. Holliman G, Rowley J. Business to business digital content marketing: Marketers’ perceptions of best practice. J Res Interact Mark. 2014;8(4):269–293. doi:10.1108/JRIM-02-2014-0013
69. Sokolova K, Kefi H. Instagram and YouTube bloggers promote it, why should I buy? How credibility and parasocial interaction influence purchase intentions. J Retail Consum Serv. 2020;53:101742. doi:10.1016/j.jretconser.2019.01.011
70. Iyengar R, Van den Bulte C, Valente TW. Opinion leadership and social contagion in new product diffusion. Mark Sci. 2011;30(2):195–212. doi:10.1287/mksc.1100.0566
71. Munnukka J, Maity D, Reinikainen H, Luoma-aho V. “Thanks for watching”: the effectiveness of YouTube vlog endorsements. J Bus Res. 2022;142:202–214. doi:10.1016/j.jbusres.2021.12.031
72. Campbell C, Farrell JR. More than meets the eye: the functional components underlying influencer marketing. J Advert. 2020;49(4):469–479. doi:10.1080/00913367.2020.1784765
73. Kay S, Mulcahy R, Parkinson J. When less is more: the impact of macro and micro social media influencers’ disclosure. J Mark Manag. 2020;36(3–4):248–278. doi:10.1080/0267257X.2020.1718740
74. Appel G, Grewal L, Hadi R, Stephen AT. The future of social media in marketing. J Acad Mark Sci. 2020;48:79–95. doi:10.1007/s11747-019-00695-1
75. Iankova S, Davies I, Archer-Brown C, Marder B, Yau A. A comparison of social media marketing between B2B and B2C industries. Ind Mark Manag. 2019;81:71–87. doi:10.1016/j.indmarman.2018.07.013
76. Liu X, Zheng X. The persuasive power of social media influencers in brand credibility and purchase intention. Humanit Soc Sci Commun. 2024;11:15. doi:10.1057/s41599-023-02512-1
77. De Vries L, Gensler S, Leeflang PSH. Popularity of brand posts on brand fan pages: An investigation of the effects of social media marketing. J Interact Mark. 2012;26(2):83–91. doi:10.1016/j.intmar.2012.01.003
78. Court D, Elzinga D, Mulder S, Vetvik O. The consumer decision journey. McKinsey Q. 2009.
79. Ki CW, Kim YK. The mechanism by which social media influencers persuade consumers: The role of consumers’ desire to mimic. J Bus Res. 2019;104:164–176. doi:10.1016/j.jbusres.2019.07.015
80. Appendix A6: Coding schema and thematic categorization
81. This appendix presents the coding structure adopted during the thematic synthesis process. The coding procedure aimed to identify recurring constructs, relational mechanisms, and theoretical patterns in the literature on B2B influencer marketing, trust building, social capital, and professional digital communication.
82. The coding process was conducted iteratively through repeated comparisons between sources and thematic domains. Themes and categories were continuously refined to ensure conceptual consistency and alignment with the study’s objectives.

Cite this article as:
Lima A. Micro-Influencers as Catalysts for Brand Building: Transforming B2B Marketing Strategies in the Digital Age. Premier Journal of Business and Management 2026;8:100015

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