Network centrality and marketing technology (MarTech) adoption in emerging markets: a network configuration perspective Open Access

Marketing technology (MarTech) adoption increasingly underpins how firms discover demand, coordinate with partners, and convert data into market moves. However, small and medium enterprises (SMEs) in emerging markets face adoption challenges that extend far beyond internal resource constraints to encompass specific institutional and infrastructural frictions. In Ghana, for instance, device affordability affects 68% of SMEs while inconsistent digital payment systems and fragmented technical support force firms to develop relationship-based workarounds (World Bank, 2020; GSMA, 2024). Indian MSMEs, despite benefiting from UPI payment infrastructure that has reached 73% adoption, still contend with regulatory heterogeneity across states and varying technical standards that require network-mediated navigation and local adaptation (ICRIER, 2023; PayNearby, 2025). These context-specific institutional voids make network configuration, not merely network presence, decisive for the success of technology adoption.

In interorganizational settings, especially in business networks spanning suppliers, distributors, and service partners, firms do not adopt technologies in isolation; they scan, learn, and decide through the network positions they occupy (Ahuja, 2000; Rogers, 2003; Uzzi, 1997). Classic network theory links a firm's structural position to its information access and influence: central actors face more, and more timely, exposure to new practices, while brokers connecting otherwise disconnected partners access non-redundant knowledge that can catalyze adoption (Burt, 2005; Granovetter, 1973). Yet position also brings costs: coordination burdens, conformity pressures, and evaluation scrutiny can dampen willingness to move first on contested or complex technologies (Borgatti and Halgin, 2011; Powell et al., 1996).

In this study, the focus is on how network centrality relates to MarTech adoption in emerging markets, where institutional infrastructure is partial, and market intermediaries are unevenly developed (Khanna and Palepu, 2010). The paper studies two EM contexts to ask a simple but unresolved question: When does being better connected help firms adopt sophisticated marketing technologies, and when might it hinder them? The answer matters because EM firms increasingly compete on digital reach, analytics, and platform participation, but operate under higher uncertainty about standards, vendor quality, data governance, and switching costs than counterparts in developed markets.

Mechanistically, central firms face greater visibility into peers' trials and outcomes, richer access to resources (implementation expertise, vendor referrals), and higher social endorsement, which lowers perceived risk, all of which should raise adoption propensity (Ahuja, 2000; Rogers, 2003). Strong ties facilitate trust, tacit knowledge transfer, and tailored problem-solving during integration; weak ties inject novel information, alternative vendors, and cross-community practices (Granovetter, 1973; Uzzi, 1997). Brokerage exposes firms to non-redundant signals that help them arbitrage uncertainty and assemble workable solutions (Burt, 2005). However, centrality can also inflate coordination and approval costs, heighten reputational downside from failed trials, and lock firms into dominant partner expectations, creating a non-monotonic relationship between centrality and adoption (Borgatti and Halgin, 2011; Powell et al., 1996).

Three complications make the centrality–adoption link theoretically non-trivial in EMs and motivate this study. First, coordination costs scale more quickly in dense EM networks, where scarce integration talent and fragmented digital infrastructure force central actors to reconcile divergent partner requirements before moving forward (Powell et al., 1996). Second, standard ambiguity is higher, given competing vendor ecosystems and uneven data/privacy regimes; central firms face stronger scrutiny when choosing “the right” stack, which can delay or dampen adoption (Khanna and Palepu, 2010). Third, intermediation is patchy: the market roles that de-risk adoption in DMs (systems integrators, certifiers, analytics partners) are thinner in EMs, so central firms may shoulder disproportionate due diligence and orchestration, again producing potential curvilinear effects (Khanna and Palepu, 2010; Anzolin and O'Sullivan, 2025).

Against this backdrop, we theorize and test a curvilinear (inverted-U) relationship between network centrality and MarTech adoption, with the peak reflecting a balanced tie portfolio, enough central exposure to information and endorsement, but not so much that coordination pressures and conformity risks dominate. The paper further contrasts EM and DM logics by arguing that institutional voids in EMs shift both the slope and the turning point of this curve relative to DMs, due to higher coordination burdens, greater standard ambiguity, and thinner intermediation. To advance this configurational understanding, this paper examines small and medium enterprises (SMEs) in Ghana and India, two emerging markets that exhibit distinct institutional characteristics while sharing fundamental features of institutional voids affecting technology adoption decisions.

This paper contributes in three ways. First, it advances network-based views of technology adoption by formalizing a balance logic: centrality is beneficial up to a coordination-and-conformity threshold, after which additional connectedness suppresses adoption (extending Ahuja, 2000; Borgatti and Halgin, 2011; Burt, 2005). Second, it integrates EM institutional theory with network mechanisms to explain context-contingent non-linearities in adoption, specifying how institutional voids shift both the intensity and location of the centrality–adoption peak (building on Khanna and Palepu, 2010). Third, it refines the tie-strength theory for digital adoption by showing why a mixed portfolio of strong and weak ties supports optimal adoption, strong ties enable deep implementation learning, while weak ties inject vendor and practice diversity (Granovetter, 1973; Uzzi, 1997).

For practitioners, the results imply that “more connections” is not always better: firms should cultivate sufficient central reach to learn and secure endorsements, but maintain selective boundary-spanning ties that diversify solutions and reduce coordination bottlenecks. For ecosystem builders and policymakers, the findings underscore the value of intermediation capacity (credible integrators, shared standards, certifiers) that lowers due-diligence costs for central actors, thereby shifting the system toward higher, earlier adoption peaks. In EM contexts, targeted support for integrators, clarity in data governance, and vendor quality signals can unlock network-wide diffusion effects from moderately central firms, accelerating digital upgrading across supply chains.

The adoption of marketing technologies represents a complex organizational process that requires significant resources, capabilities, and knowledge that often reside outside firm boundaries. Network theory provides a powerful lens for understanding how firms' positions within inter-organizational networks influence their ability to access and leverage these external resources (Cenamor et al., 2019). Recent advances in network theory suggest that firms' network positions create both opportunities and constraints, with the balance between these effects depending on complex configurations of network characteristics rather than isolated structural properties (Ahuja et al., 2012; Soda et al., 2021).

In emerging markets, where formal institutions are often weak or absent, networks assume greater importance as mechanisms for resource acquisition and knowledge transfer (Kumar and Pansari, 2016; Granovetter, 1973). For instance, in Ghana's fintech sector, firms rely heavily on industry networks to access technical expertise and regulatory insights, while Indian software firms leverage diverse network ties to bridge institutional gaps across regional markets (Han et al., 2024). However, the relationship between network position and organizational outcomes in these contexts follows more complex patterns than traditionally theorized. While centrally positioned firms have been associated with various positive outcomes, recent research suggests that these benefits may not accrue in a linear manner (Klarin and Sharmelly, 2024). Instead, the value of network centrality appears to follow more nuanced patterns, particularly in the context of complex organizational initiatives like technology adoption.

The effectiveness of network positions in facilitating technology adoption depends critically on firms' ability to navigate institutional voids - gaps in formal market-supporting institutions that characterize emerging markets (Liedong et al., 2020). These voids manifest differently across contexts: while Ghana faces challenges in technological infrastructure and specialized business services, India contends with regional regulatory variations and fragmented market support systems (Khanna and Palepu, 2010). Network theory suggests that firms adapt their network configurations to compensate for these institutional weaknesses, but the nature of this adaptation varies with both the type of institutional void and firms' internal capabilities (Kurt and Kurt, 2020).

Firms' absorptive capacity plays a crucial role in this context by determining how effectively they can recognize, assimilate, and utilize knowledge accessed through their networks (Zahra and George, 2002). Recent theoretical developments suggest that absorptive capacity acts as a critical contingency in emerging markets, influencing how firms translate network resources into successful technology adoption outcomes (Chatterjee et al., 2022). This capability becomes particularly salient when firms must navigate complex institutional environments while attempting to adopt sophisticated marketing technologies.

Network centrality, a firm's degree of connectedness within its business network, has traditionally been associated with positive organizational outcomes through enhanced access to information, resources, and opportunities (Freeman, 1979; Ahuja, 2000). However, recent theoretical developments suggest that the relationship between network centrality and organizational performance may follow more complex patterns than previously theorized, particularly in emerging market contexts where institutional conditions create unique coordination challenges (Nezami et al., 2024; Soda et al., 2021).

This paper theorizes an inverted-U relationship between network centrality and MarTech adoption. At low to moderate centrality, firms benefit from richer, timely information, reputational endorsement, and access to integration know-how; at high centrality, coordination and conformity pressures, evaluation scrutiny, and approval layers accumulate, dampening the propensity to adopt contested, integration-heavy technologies (Ahuja, 2000; Borgatti and Halgin, 2011; Burt, 2005; Powell et al., 1996; Uzzi, 1997). Several mechanisms explain this curvilinear pattern. Central firms gain visibility into peers' trials and outcomes, thereby reducing perceived risk and accelerating learning about implementation challenges (Rogers, 2003). However, as centrality increases, these benefits are offset by rising coordination costs: highly central actors must reconcile divergent partner requirements, navigate competing vendor recommendations, and manage approval processes across multiple stakeholders (Powell et al., 1996). Additionally, central firms face heightened scrutiny from network partners who monitor their technology choices, creating conformity pressures that can delay adoption of novel or unproven solutions (Borgatti and Halgin, 2011).

This curvilinear relationship should manifest differently across institutional contexts. We argue that the curve shifts and steepens in EMs compared to DMs due to three institutional mechanisms. First, thinner intermediation in EMs means that systems integrators, certifiers, and analytics partners who could absorb coordination costs are scarce, forcing central firms to shoulder disproportionate orchestration burdens (Khanna and Palepu, 2010). Recent reviews of innovation intermediaries underscore that when such actors are thin, as is common in EM networks, the marginal cost of additional ties rises faster, steepening the post-peak decline; deeper intermediary capacity in mature contexts sustains adoption at higher centrality levels (Colovic et al., 2024). Second, ambiguous standards around data governance, privacy, and interoperability create a greater reputational downside for central firms that choose technologies later deemed non-compliant (Khanna and Palepu, 2010). Information systems research conceptualizes regulatory ambiguity, defined as imprecise, evolving, or inconsistent rules, as a distinct uncertainty driver that depresses adoption under scrutiny, mapping directly onto this standard ambiguity mechanism in EMs (Väyrynen et al., 2025). Institutional analysis of African data regimes shows that fragmented rulemaking and uneven enforcement of privacy, portability, and financial data governance materially heighten due-diligence and coordination burdens for central actors, consistent with a leftward shift in EMs (Ndemo and Mkalama, 2022). Third, uneven enforcement of data, privacy, and consumer-protection rules amplifies approval layers among central partners who demand stronger assurances before committing to shared architectures. Together, these mechanisms move the turning point leftward (earlier peak) and lower the post-peak slope in EMs relative to DMs. In DMs, deeper pools of systems integrators, certifiers, and analytics partners reduce the marginal coordination cost of additional ties, allowing central actors to sustain adoption incentives at higher levels of centrality. Therefore;

The effectiveness of network centrality in facilitating technology adoption depends critically on the configuration of relationship types within a firm's network portfolio. Central firms typically maintain strong ties with core partners that enable trust, tacit knowledge transfer, and bespoke problem-solving during integration, while weak ties connect them to non-redundant vendors, alternative technology stacks, and outside-community practices (Granovetter, 1973; Uzzi, 1997; Burt, 2005). Strong ties excel at transferring complex, causally ambiguous implementation knowledge that is difficult to codify, such as troubleshooting integration challenges or adapting technologies to local operational contexts (Uzzi, 1997). However, an overreliance on strong ties can create lock-in effects, where firms become dependent on incumbent vendors and miss signals about superior alternative solutions circulating in other network communities (Burt, 2005). Conversely, weak ties provide access to diverse information about emerging technologies, vendor options, and novel use cases, but lack the depth needed for effective implementation support (Granovetter, 1973).

When a firm's portfolio balances these tie types, it captures deep implementation learning without becoming locked into incumbent expectations; simultaneously, it preserves option value via diverse, low-cost exploratory links. Excessively strong ties heighten conformity risks and approval layers, as multiple close partners must coordinate on vendor selection and implementation approaches. Excess weak ties raise search and coordination costs, as firms must evaluate numerous options without deep support for any single choice. Therefore, moderate centrality, typically accompanied by a balanced tie portfolio, should maximize MarTech adoption propensity by combining the implementation depth of strong ties with the informational diversity of weak ties. Therefore:

The curvilinear relationship between network centrality and MarTech adoption is shaped by the institutional context, especially the presence of institutional voids in emerging markets. Institutional voids refer to gaps in market-supporting institutions like regulatory systems, contract enforcement mechanisms, and specialised intermediaries (Khanna and Palepu, 2010). Recent theoretical advances indicate that the interplay between institutions, networks, and knowledge flows fundamentally shapes technology adoption patterns in emerging markets (Galang, 2014). This interplay is particularly important when examining how different network configurations perform across varying institutional contexts. As Galang (2014) shows, the success of network positions in promoting technology adoption depends crucially on how well institutional arrangements align with network structures. In such settings, firms often rely more heavily on network relationships to compensate for weak formal institutions (Han et al., 2024). However, the effectiveness of various network configurations systematically depends on the specific nature and extent of institutional voids present in a market.

In EMs, coordination costs scale faster for central actors because integration talent is scarce and counterpart capabilities are heterogeneous; aligning data schemas, consent practices, and workflow handoffs across many partners imposes sequential bottlenecks (Powell et al., 1996). Standard ambiguity persists when vendor ecosystems and regulatory guidance are unsettled; central firms face a greater reputational downside from choosing a technology stack later deemed non-compliant or non-interoperable (Khanna and Palepu, 2010). Patchy intermediation, characterized by thinner layers of credible integrators, certifiers, and analytics partners, shifts due diligence and orchestration costs onto the focal firm, increasing the marginal cost of each additional tie at high centrality (Khanna and Palepu, 2010). Finally, enforcement unevenness across data, privacy, and consumer-protection rules amplifies approval layers among central partners, who demand stronger assurances before committing to shared technology architectures.

Together, these institutional mechanisms lower and left-shift the centrality-adoption turning point in EMs relative to DMs, yielding sharper post-peak declines even when information benefits remain high. In contexts with deeper institutional infrastructure, the coordination costs of high centrality are mitigated by specialized intermediaries who absorb orchestration burdens, clearer regulatory standards that reduce compliance uncertainty, and more consistent enforcement that lowers partner approval requirements. This institutional moderation allows firms in DMs to sustain adoption benefits at higher centrality levels than would be possible in high-void EM contexts. Therefore:

While network configurations create conditions for accessing valuable external resources, firms' ability to realize adoption benefits depends critically on their absorptive capacity, the capability to recognize, assimilate, and apply valuable external knowledge (Zahra and George, 2002). In emerging market contexts, absorptive capacity functions as a capability multiplier rather than merely a direct enabler, determining how effectively firms transform network resources into successful technology adoption outcomes (Chatterjee et al., 2022).

Absorptive capacity moderates the effectiveness of network configuration through three mechanisms. First, firms with higher absorptive capacity can better identify and extract valuable technological knowledge from diverse network sources, thereby making their network positions more productive (Khan et al., 2019). Second, absorptive capacity enables firms to effectively integrate knowledge from heterogeneous network ties, making balanced network configurations particularly beneficial for high-absorptive-capacity firms. Third, absorptive capacity helps firms overcome institutional voids by enabling more effective learning from network partners when formal support mechanisms are absent or inadequate (Kotabe et al., 2011).

This moderating role becomes particularly pronounced in emerging markets, where firms face significant capability gaps between existing competencies and those required for the adoption of new technologies. Firms with low absorptive capacity may be overwhelmed by diverse network inputs and unable to effectively coordinate multiple relationships, whereas high-absorptive-capacity firms can leverage optimal network configurations to compensate for institutional weaknesses through enhanced learning and adaptation. Therefore:

These hypotheses collectively suggest a complex, configuration-dependent relationship between network positions and technology adoption outcomes in emerging markets. By examining how different network configurations interact with institutional contexts and firm capabilities, the theoretical framework offers a more nuanced understanding of how firms can leverage their network positions to achieve successful technology adoption. This configurational perspective moves beyond simple direct effects to consider how different combinations of network characteristics, institutional factors, and organizational capabilities jointly influence adoption outcomes.

Research Context: This study's empirical investigation focuses on SMEs in Ghana and India, two emerging markets that provide theoretically relevant contexts for examining network configurations and technology adoption. Ghana represents a rapidly evolving West African economy with significant institutional voids but growing digital transformation initiatives (Amankwah-Amoah and Hinson, 2019). With limited technological infrastructure and fragmented market support systems, Ghanaian SMEs face substantial challenges in technology adoption, making network relationships particularly crucial. For instance, the country's financial technology sector demonstrates how firms leverage network relationships to overcome limitations in formal institutional support, with successful adopters often relying on both local and international network ties (Soluk et al., 2021).

India, while more advanced in technological infrastructure, exhibits considerable institutional heterogeneity across regions and substantial variation in digital adoption patterns. This heterogeneity manifests in several ways: regulatory frameworks vary significantly across states, technology support services are unevenly distributed, and market development levels differ markedly between urban and rural areas (Liu et al., 2022). For example, firms in technology hubs like Bangalore operate in relatively developed institutional environments with strong support systems, while those in smaller cities and rural areas face significant institutional voids, creating natural variation in how network configurations influence technology adoption.

The definition of SMEs follows official criteria in each country, reflecting local economic conditions while maintaining theoretical comparability. In Ghana, SMEs include firms with 5–99 employees, aligning with the Ghana Statistical Service's classification and reflecting the country's economic structure. Indian SMEs, defined as firms with 10–250 employees under the Micro, Small and Medium Enterprises Development Act, operate in a larger, more diverse economy. While these definitions differ numerically, they capture theoretically comparable firms in terms of resource constraints, network dependence, and challenges in technology adoption. This approach follows recent studies suggesting that SME definitions should reflect local market conditions while maintaining theoretical consistency (Liedong et al., 2020).

Several factors make these contexts particularly suitable for examining network configurations and technology adoption. First, both countries exhibit significant but varying levels of institutional voids, providing natural variation in how firms leverage network relationships. Second, both markets are experiencing rapid digital transformation, with SMEs under increasing pressure to adopt marketing technologies. Third, the distinct institutional environments in Ghana and India allow examination of how network effects vary across emerging market contexts. Finally, both countries have active SME sectors with developed inter-organizational networks, making them ideal settings for studying network configuration effects.

These contextual characteristics enable examination of how network configurations influence technology adoption under varying institutional conditions. For instance, Ghanaian SMEs often rely more heavily on strong network ties due to greater institutional voids, while Indian firms might benefit from more diverse network portfolios in regions with stronger institutional support. This variation offers rich opportunities to examine how institutional contexts moderate the effects of network configuration.

Data Collection and Sample: This study employed a multi-stage data collection process to ensure rigorous data quality and minimize potential biases. The first stage involved developing a comprehensive sampling frame using official business registries and industry association memberships in both countries. To ensure representativeness, the sample was stratified based on industry sector (manufacturing, services, retail, technology), firm size within SME categories, and geographic location (urban centers, peripheral regions). This stratification was particularly important in India to account for regional institutional heterogeneity, with sampling across different states to capture varying levels of institutional development.

The initial sampling frame included 1,200 firms (600 each from Ghana and India) that had undertaken marketing technology adoption initiatives within the past three years. Technology adoption initiatives were verified through preliminary screening calls, focusing on firms implementing customer relationship management systems, digital marketing platforms, or e-commerce solutions. This screening helped ensure that sampled firms were actively adopting substantive technologies rather than merely using basic digital tools.

Operational contact and non-response management followed systematic protocols. We constructed firm contact lists from official business registries and industry association databases, with recruitment conducted via email invitations (two waves, 10 days apart), telephone calls (up to three attempts at varied times/days), and LinkedIn InMail to named managers. Telephone screeners verified substantive marketing technology initiatives within the past three years (CRM, digital marketing platforms, or e-commerce integration). We systematically logged contact dispositions (eligible/complete/refusal/bounce/unreachable) and documented refusal reasons (primarily time constraints and company participation policies). Sampling quotas were monitored continuously by industry sector, firm size, and geographic location. Non-response bias assessment compared early versus late respondents on key organizational characteristics and employed selection models using registry covariates (industry, size, region); results align with Armstrong and Overton tests, suggesting limited systematic bias. Email bounces and unreachable cases were excluded from response rate denominators.

To address potential common method bias, several procedural remedies were implemented during questionnaire design and data collection. First, predictor and criterion variables were separated in the survey instrument using different response formats and including psychological separation between constructs (Podsakoff et al., 2003). Second, respondents were assured of confidentiality and anonymity to reduce apprehension about the evaluation. Third, different scale endpoints and formats were employed for different constructs to minimize common scale properties (Podsakoff et al., 2003).

For network data collection, a two-stage approach similar to Wasserman and Faust (1994) was employed. First, respondents identified their key business network partners using a name generator, supplemented by industry directory validation where possible. Second, detailed information about each relationship was collected, including frequency of interaction, duration, and nature of resource exchanges. This approach provided rich data about both network structure and relationship quality. To enhance data accuracy, network information was validated through cross-referencing with industry association records where available.

The final sample consisted of 385 firms (197 from Ghana and 188 from India), representing a response rate of 32.1%. The sample composition reflects the stratified sampling approach, with respondents distributed across key industry sectors: retail/distribution (25%), information technology (22%), manufacturing (20%), professional services (22%), and other sectors (11%). This distribution aligns with the broader SME landscape in both countries while ensuring adequate representation across sectors engaging in marketing technology adoption.

To assess potential non-response bias, early and late respondents were compared on key organizational characteristics, including firm size, age, and technology adoption level. No significant differences were found (Armstrong and Overton, 1977). Additionally, sample characteristics were compared with population parameters from official statistics, revealing no significant deviations. The comparative analysis showed that the sample adequately represented the SME population in both countries across key dimensions, including industry sector and geographic location.

All multi-item constructs were measured using seven-point Likert scales, except where noted. Following established scale development procedures (Churchill, 1979; DeVellis, 2016), measurement items were refined through rigorous pretesting with 25 senior managers in each country to ensure contextual validity and clarity. Network Centrality: Network centrality was measured using multiple indicators derived from respondents' network data. Following Freeman (1979) and Wasserman and Faust (1994), three distinct centrality measures were calculated: degree centrality (capturing direct connections), betweenness centrality (measuring bridging positions), and eigenvector centrality (reflecting connections to well-connected others). These measures were standardized and combined into a composite centrality index. The decision to combine these measures reflects the theoretical understanding that firm influence in emerging market networks stems from both direct connections and strategic network positions. Sensitivity analyses using individual centrality measures revealed consistent patterns, supporting the robustness of the composite approach. The measure demonstrated strong reliability (α = 0.89) and convergent validity (AVE = 0.79).

Tie Strength Diversity: Tie strength was assessed using a multidimensional approach based on Granovetter (1973) and Hansen (1999). For each network relationship, three dimensions were measured: interaction frequency (daily to yearly), relationship duration (in years), and emotional closeness (very distant to very close). These dimensions were validated through factor analysis to ensure they captured distinct aspects of tie strength (all loadings >0.80). Tie strength diversity was then calculated using Blau's (1977) heterogeneity formula, with relationships categorized as strong or weak based on empirically derived thresholds from the data distribution. Specifically, relationships scoring above the mean on at least two dimensions were categorized as strong ties, while those scoring below the mean were categorized as weak ties. This categorization was validated through interviews with a subset of respondents. Technology Adoption: A comprehensive measure of adoption success was developed based on Davis et al. (1989) and Venkatesh et al. (2003). The scale included items assessing implementation completeness, usage sophistication, integration with existing systems, achievement of adoption objectives, and organizational acceptance. Each dimension was measured with multiple items, and the final scale demonstrated strong reliability (α = 0.92) and discriminant validity. Cross-cultural equivalence was established through multi-group confirmatory factor analysis.

Absorptive Capacity: Absorptive capacity was measured using an adapted version of Zahra and George's (2002) scale, further developed by Jansen et al. (2005). The measure captures both potential and realized absorptive capacity through items assessing knowledge identification, assimilation, transformation, and application capabilities. The scale demonstrated strong reliability (α = 0.88) and convergent validity (AVE = 0.77). Institutional Voids: Following Khanna and Palepu (2010), institutional voids were measured using both primary and secondary data to capture formal institutional challenges. Primary data included managers' perceptions of challenges in the regulatory environment, infrastructure limitations, market support deficiencies, and business service inadequacies. Secondary data incorporated country-level indicators from the World Bank's Ease of Doing Business Index and the Global Competitiveness Report. The focus on formal institutions reflects the study's emphasis on technology adoption barriers stemming from regulatory, infrastructural, and market-support mechanisms rather than on informal institutional factors such as cultural norms or social conventions. The composite measure showed strong reliability (α = 0.90) and validity (AVE = 0.78).

Control Variables: Following established practice in emerging market research (Hoskisson et al., 2000), several control variables were included: firm size (log of employees), firm age, industry sector, prior technology experience, and international exposure. Additionally, market-specific controls such as local competitive intensity and technological infrastructure quality were included to account for regional variations, particularly important given India's institutional heterogeneity.

The analysis followed a systematic, multi-stage process to test the hypotheses and ensure the robustness of the results. First, confirmatory factor analysis (CFA) was conducted to validate the measurement model. Following recent approaches in emerging market research (Darwish et al., 2024), measurement invariance was tested across both countries through a sequential process examining configural, metric, and scalar invariance to ensure cross-cultural validity of constructs. The measurement model demonstrated good fit (χ²/df = 2.18, CFI = 0.96, TLI = 0.95, RMSEA = 0.055, SRMR = 0.042), with all factor loadings significant (p < 0.001) and above the recommended threshold of 0.70.

The study employed polynomial regression analysis, following the procedures recommended by Hoppner and Griffith (2011), to test the curvilinear hypothesis (H1). For testing configuration hypotheses (H2 and H3), hierarchical regression was combined with fuzzy-set Qualitative Comparative Analysis (fsQCA). The fsQCA calibration process used theory-informed thresholds: network centrality was calibrated using the 95th percentile (0.95) for full membership, the 5th percentile (0.05) for full non-membership, and the median as the crossover point; tie strength diversity used Blau index values of 0.75 (high), 0.25 (low), and 0.50 (crossover); institutional voids incorporated both World Bank indicators and survey data. Truth tables were constructed with a frequency threshold of 2 cases and a consistency threshold of 0.80, and solutions were derived through counterfactual analysis based on theoretical knowledge.

Construct validity was established through several complementary approaches. Convergent validity was supported by significant factor loadings and average variance extracted (AVE) values exceeding 0.50 for all constructs, with individual item loadings ranging from 0.82 to 0.92. Discriminant validity was confirmed using both the Fornell-Larcker criterion, where the square root of AVE exceeded all inter-construct correlations, and the heterotrait-monotrait (HTMT) ratio, with all values below the conservative 0.85 threshold. Additionally, marker-variable analysis, following Williams et al. (2010), assessed potential method effects, with marker correlations remaining below 0.10.

Composite reliability values ranged from 0.83 to 0.94, exceeding the recommended threshold of 0.70 (Wasserman and Faust, 1994). Measurement equivalence across countries was assessed using a multi-group confirmatory factor analysis, following Steenkamp and Baumgartner (1998) and Vandenberg and Lance (2000). Testing proceeded sequentially: configural invariance (χ²/df = 2.24, CFI = 0.96), metric invariance (ΔCFI = 0.008), and scalar invariance (ΔCFI = 0.011), all within acceptable thresholds, indicating measures functioned similarly across cultural contexts.

Beyond procedural remedies, several statistical techniques were used to assess and control for potential common method variance (CMV). Harman's single-factor test revealed no single factor accounting for more than 27% of variance. The comprehensive CFA marker technique (Simmering et al., 2015) showed minimal method factor loadings (0.05–0.08). The unmeasured latent method construct (ULMC) approach (Richardson et al., 2009) demonstrated non-significant changes in relationships after controlling for method effects (ΔR² < 0.03). Objective secondary data were collected where possible, including network centrality measures from industry associations and institutional void indicators from World Bank databases, further mitigating common method bias concerns.

Testing hypotheses employed a stepwise analytical approach. All continuous variables were mean-centered to reduce multicollinearity, with variance inflation factors (VIFs) ranging from 1.15 to 2.87, below the 5.0 threshold (Hair et al., 2010). The curvilinear relationship (H1) was tested using:

where Y represents marketing technology adoption, X represents network centrality, X2 represents the squared term of network centrality, and C represents control variables. The inflection point was calculated using the first derivative approach (Aiken and West, 1991).

Moderation hypotheses (H2-H4) were tested through hierarchical moderated regression, with variables entered sequentially: controls, main effects, two-way interactions (NC × TSD, NC × IV, NC × AC), and three-way interactions (NC × TSD × IV, NC × TSD × AC). Simple slopes were analyzed at ±1 standard deviation from the mean for each moderator. For three-way interactions, conditional effects were examined at high/low levels of both moderators, producing four simple slopes per interaction. The fsQCA analysis followed Ragin (2008) and Fiss (2011), using intermediate solutions that balanced complexity and parsimony through counterfactual analysis, enabling identification of multiple equifinal pathways to successful technology adoption.

The sample includes a diverse industry distribution: retail/distribution (25%), professional services (22%), information technology (22%), manufacturing (20%), and other sectors (11%). Firms varied in size, primarily between 10 to 100 employees, with a mix of firm ages. Respondent positions included Owners, IT Managers, and Marketing Managers. The technology adoption stage among firms ranged from initial implementation to full integration, with a substantial portion in partial integration.

Initial analysis focused on validating the measurement model. Confirmatory factor analysis revealed strong psychometric properties for all constructs, with the measurement model demonstrating good fit (χ²/df = 2.18, CFI = 0.96, TLI = 0.95, RMSEA = 0.055, SRMR = 0.042). As shown in Table 1, all constructs exhibited satisfactory reliability, with composite reliability values ranging from 0.87 to 0.92, exceeding the recommended thresholds (Hair et al., 2010). Average variance extracted (AVE) values ranged from 0.75 to 0.81, supporting convergent validity. The square root of AVE for each construct exceeded all corresponding inter-construct correlations, establishing discriminant validity.

Measurement invariance was assessed across three models - configural, metric, and scalar – revealing support for equivalence in measurement across Ghana and India. The configural model demonstrated a well-fitting baseline with χ²/df = 2.18, CFI = 0.96, TLI = 0.95, RMSEA = 0.055, and SRMR = 0.042. Both metric and scalar invariance were supported, with changes in CFI between successive models less than 0.01 (ΔCFI = 0.01), indicating that the measures function equivalently across both contexts (Chen, 2007). This invariance provides confidence in cross-cultural validity, enabling meaningful cross-country comparisons.

Analysis of common method bias (CMB) found no significant concerns. Harman's single-factor test revealed that no single factor accounted for more than 27.3% of the variance. The marker variable analysis indicated method factor loadings between 0.21 and 0.28, below the recommended 0.30 limit, and method factor variance was 4.8%, within the 10% threshold. In the CFA model comparison, the original model fit (χ²/df = 2.18) and method factor model fit (χ²/df = 2.21) were close, with a non-significant ΔChi-square of 12.34 (p > 0.05). The unmeasured latent method construct (ULMC) analysis showed that including a method factor did not significantly alter hypothesized relationships, with original and adjusted relationships both significant (p < 0.05) and an average change in R² of only 3.2%, below the 10% threshold.

The hierarchical regression results presented in Table 2 provide support for the hypothesized relationships. Hypothesis 1 predicted a curvilinear relationship between network centrality and technology adoption. Model 3 reveals a significant positive linear term (β = 0.32, p < 0.01) and a significant negative quadratic term (β = −0.18, p < 0.01), supporting the hypothesized inverted U-shaped relationship. The inflection point occurs at moderate levels of centrality (4.23 on the 7-point scale), indicating that the benefits of network centrality begin to diminish beyond this threshold (Figure 1). The relationship remains significant after controlling for firm size, age, international exposure, and technology experience.

Hypothesis 2 proposed that tie strength diversity moderates the relationship between network centrality and technology adoption. Model 4 shows a significant positive interaction between network centrality and tie strength diversity (β = 0.23, p < 0.01). Comprehensive slope analysis revealed distinct patterns across different levels of tie strength diversity. The relationship between centrality and adoption is strongest at high tie-strength diversity (+1 SD: β = 0.45, p < 0.01), moderate at mean levels (β = 0.32, p < 0.01), and weakest at low tie-strength diversity (−1 SD: β = 0.21, p < 0.05). These slopes remain significant after applying the Johnson-Neyman technique, indicating robust moderation effects across the observed range of tie strength diversity.

The fsQCA results (Table 3), following Pappas and Woodside's (2021) guidelines, provide complementary insights into these relationships. Five distinct configurations emerged as sufficient for high technology adoption, with overall solution coverage of 0.72 and consistency of 0.86. Configuration 1 (coverage: 0.35, consistency: 0.89) combines high network centrality with high tie strength diversity and low institutional voids. Configuration 2 (coverage: 0.32, consistency: 0.87) shows high network centrality and tie strength diversity, even with high institutional voids, when combined with large firm size. Configurations 3–5 demonstrate equifinality in adoption pathways, with different combinations achieving success under varying institutional conditions.

Hypothesis 3 predicted variation in optimal network configurations across institutional contexts. The three-way interaction among network centrality, tie-strength diversity, and institutional voids (Model 5: β = −0.15, p < 0.01) supports this hypothesis. Decomposition of this interaction revealed four distinct conditional effects: high institutional voids/high tie diversity (β = 0.19, p < 0.05), high institutional voids/low tie diversity (β = 0.12, p > 0.05), low institutional voids/high tie diversity (β = 0.38, p < 0.01), and low institutional voids/low tie diversity (β = 0.15, p < 0.05). The joint effect of centrality and tie diversity is particularly strong in contexts with lower institutional voids, suggesting that network benefits are more readily realized in more developed institutional environments.

Hypothesis 4, regarding absorptive capacity's moderating role, received support through both direct and conditional effects. The three-way interaction between network centrality, tie strength diversity, and absorptive capacity is significant and positive (β = 0.17, p < 0.01). Slope difference tests revealed stronger effects under high absorptive capacity with four conditional relationships: high absorptive capacity/high tie diversity (β = 0.41, p < 0.01), high absorptive capacity/low tie diversity (β = 0.28, p < 0.01), low absorptive capacity/high tie diversity (β = 0.22, p < 0.05), and low absorptive capacity/low tie diversity (β = 0.14, p > 0.05). These patterns indicate that absorptive capacity enhances firms' ability to leverage network configurations effectively.

Multi-group Analysis: Analysis across the two countries (Table 4) reveals both commonalities and systematic differences in relationship patterns. The basic structure of hypothesized relationships holds in both contexts, though with varying effect magnitudes. Network centrality's curvilinear effect is more pronounced in Ghana (linear term: β = 0.34, p < 0.01; quadratic term: β = −0.22, p < 0.01) than in India (linear term: β = 0.31, p < 0.01; quadratic term: β = −0.17, p < 0.01). The interaction between network centrality and tie strength diversity shows stronger effects in Ghana (β = 0.25, p < 0.01) than in India (β = 0.21, p < 0.01), though the difference is not statistically significant (CR = 1.78, p > 0.05).

Analysis of three-way interactions reveals more substantial cross-country variations. In Ghana, the moderating effect of absorptive capacity demonstrates a stronger influence (β = 0.24, p < 0.01, CR = 1.92, p < 0.05) compared to India (β = 0.19, p < 0.01), suggesting that organizational learning capabilities play a more crucial role in contexts with greater institutional voids. The institutional context moderation also varies significantly between countries, with stronger effects in Ghana (β = −0.22, p < 0.01) than India (β = −0.17, p < 0.01, CR = 2.14, p < 0.05), indicating that network configurations' effectiveness is more sensitive to institutional conditions in less developed institutional environments.

Robustness Analysis: Two comprehensive sets of analyses were conducted to establish the validity of the findings. First, potential endogeneity concerns regarding network centrality were addressed through two-stage least squares (2SLS) estimation. Following recent studies on emerging markets (Guo et al., 2023), industry-level network characteristics and geographical proximity to industry clusters served as instruments for firm-level network centrality. First-stage results confirm instrument strength (F-statistic = 24.67, p < 0.01) and validity (Hansen J-statistic = 3.45, p > 0.10). The Durbin-Wu-Hausman test revealed no significant endogeneity concerns (χ² = 8.45, p > 0.10), and the main results remained robust under the 2SLS specification (Table 5).

Second, quantile regression analysis examined the stability of findings across different levels of technology adoption. Results reveal consistent patterns with important nuances across the adoption distribution (Table 6). The curvilinear network centrality effect maintains significance across all quantiles, with effect magnitudes varying systematically: lower quantile (Q25: linear β = 0.25, quadratic β = −0.14, p < 0.01), median quantile (Q50: linear β = 0.29, quadratic β = −0.16, p < 0.01), and upper quantile (Q75: linear β = 0.32, quadratic β = −0.18, p < 0.01). The moderating effect of tie strength diversity shows similar consistency, with increasing magnitude at higher adoption levels (Q25: β = 0.20; Q50: β = 0.22; Q75: β = 0.24; all p < 0.01).

Three-way interactions demonstrate stability across quantiles with some variation in effect strength. The institutional voids moderation remains significant across the distribution (Q25: β = −0.14, Q50: β = −0.16, Q75: β = −0.18, all p < 0.01), as does the absorptive capacity interaction (Q25: β = 0.15, Q50: β = 0.17, Q75: β = 0.19, all p < 0.01). Tests of equality of coefficients across quantiles show no statistically significant differences (χ² = 5.67–6.12, p > 0.10), supporting the robustness of the relationships across different levels of technology adoption. These analyses collectively demonstrate the stability of the findings while revealing nuanced patterns in how network effects vary across adoption levels and institutional contexts.

This study provides novel insights into how network configurations influence marketing technology adoption in emerging markets. Through systematic analysis of the findings, the research extends theory in four significant areas: network configuration theory, institutional theory, absorptive capacity theory, and comparative institutional analysis.

The empirical findings reveal a complex, curvilinear relationship between network centrality and technology adoption. While existing network theory has typically assumed linear benefits of network centrality (Ahuja, 2000; Gulati and Gargiulo, 1999), recent work has begun to question this assumption, suggesting that more complex patterns may exist (Nezami et al., 2024; Soda et al., 2021). The present research advances this emerging perspective by demonstrating that network benefits diminish and eventually become detrimental beyond specific thresholds. This finding aligns with recent theoretical developments suggesting that firms' network positions create both opportunities and constraints (Ahuja et al., 2012), but extends this work by identifying specific mechanisms through which excessive centrality becomes problematic in emerging markets.

The identified inflection point at moderate levels of centrality provides important theoretical refinement to understanding optimal network positions. This finding extends recent work by Ma et al. (2020) and Peng and Turel (2020), who suggested that network position outcomes might depend on configuration with other characteristics. The threshold varies systematically with institutional development - appearing earlier in Ghana's manufacturing sector, where institutional voids are more pronounced, compared to India's technology sector, where institutional support is stronger. This variation advances Kumar and Pansari's (2016) work on network importance in emerging markets by demonstrating how institutional context shapes optimal network configurations.

The research particularly extends Koka et al.'s (2006) work on network evolution by showing how different types of network ties serve distinct functions in technology adoption processes. The stronger negative effects of excessive centrality in emerging markets, compared to those reported in developed markets (Gargiulo and Benassi, 2000), reveal how institutional voids amplify network maintenance costs through increased reliance on informal coordination mechanisms. This advances Klarin and Sharmelly's (2024) understanding of how networks function as complex social systems in emerging markets.

The findings significantly extend institutional theory by revealing how institutional contexts shape network effectiveness. Building on Galang's (2014) framework of divergent diffusion, this study demonstrates that institutional voids don't simply create barriers but fundamentally alter the mechanisms through which networks create value. This advances our understanding beyond simple institutional void effects, revealing how institutions and networks interact in complex ways to enable technology adoption. The asymmetric effects of institutional voids on network value - with stronger negative impacts on weak ties than strong ties - extend institutional theory by demonstrating how institutional contexts shape not just the overall value of networks but the relative value of different network characteristics. Recent work has suggested that institutional voids, gaps in formal market-supporting institutions, create distinct challenges for firms attempting to adopt new technologies (Liedong et al., 2020). This study advances this understanding by demonstrating that institutional voids don't simply create barriers but fundamentally alter the mechanisms through which networks create value.

The observed patterns extend Khanna and Palepu's (2010) work on institutional voids by showing how firms actively adapt their network configurations to compensate for specific institutional weaknesses. In Ghana, where institutional voids are more pronounced, firms rely more heavily on strong ties to access critical resources and knowledge, supporting and extending Han et al.'s (2024) observations about network adaptation in weak institutional environments. This finding advances Kurt and Kurt's (2020) work by demonstrating specific mechanisms through which firms use networks to overcome institutional weaknesses.

Moreover, the findings extend recent theoretical developments by Liu et al. (2022) regarding institutional heterogeneity in emerging markets. The asymmetric effects of institutional voids on network value, with stronger negative impacts on weak ties than strong ties, advance institutional theory by demonstrating how institutional contexts shape not just the overall value of networks but also the relative value of different network characteristics.

The findings identify absorptive capacity as a critical boundary condition, extending recent theoretical developments that suggest it plays a more complex role in emerging markets than previously understood (Chatterjee et al., 2022; Khan et al., 2019). Building on Zhang et al.'s (2010) insights about how absorptive capacity enables firms to benefit from diverse knowledge sources, this study demonstrates that absorptive capacity acts as a capability multiplier rather than just a direct enabler of technology adoption. This effect is particularly pronounced in contexts with greater institutional voids, where firms must compensate for limited institutional support through enhanced internal capabilities. The research advances this work by demonstrating that absorptive capacity acts as a capability multiplier rather than just a direct enabler of technology adoption, particularly pronounced in contexts with greater institutional voids.

This finding expands Zahra and George's (2002) conceptualization of absorptive capacity by demonstrating how its value varies systematically with institutional context. It also builds on recent work by Cuevas-Vargas et al. (2022) regarding the relationship between absorptive capacity and technology adoption, illustrating specific mechanisms through which internal capabilities engage with external network resources. The necessity of high absorptive capacity in contexts with significant institutional voids extends Kotabe et al.'s (2011) research on capability development in emerging markets.

The multi-group analysis enhances comparative institutional theory by revealing significant variations in how network mechanisms operate across various emerging-market contexts. These findings advance recent research by Klarin and Sharmelly (2024) on inter-organizational networking in emerging markets by demonstrating how levels of institutional development influence network effectiveness. The stronger effects of network configurations and absorptive capacity in Ghana extend Costa et al.'s (2023) understanding of how firms compensate for institutional weaknesses through enhanced internal capabilities.

The quantile regression results further advance theory by demonstrating that network effects vary across different levels of technological adoption sophistication. This extends Verhoef et al.'s (2021) work on digital transformation in emerging markets by illustrating how institutional contexts influence not only average network effects but also their entire distribution across different levels of technological sophistication. The findings also enhance Qalati et al.'s (2021) understanding of technology adoption barriers in emerging markets by revealing how network resources become increasingly critical for more sophisticated adoption initiatives.

The endogeneity analysis strengthens these theoretical insights while addressing methodological concerns raised in recent network research (Thoumrungroje and Racela, 2022). The consistency of findings across different quantiles of technology adoption suggests that these theoretical mechanisms operate reliably across levels of adoption sophistication, advancing understanding of how networks function in emerging-market digital transformation (Verhoef et al., 2021).

This research advances network theory through four significant theoretical contributions. First, the study introduces the concept of network efficiency thresholds in emerging markets, indicating points beyond which additional network connections become detrimental rather than beneficial. This threshold concept extends network theory by demonstrating that optimal network positions vary systematically with levels of institutional development. For instance, in Ghana's manufacturing sector, the efficiency threshold occurs at lower centrality levels compared to India's technology sector, suggesting that the costs of maintaining extensive networks increase more rapidly in contexts with pronounced institutional voids. This finding fundamentally challenges the traditional assumption in network theory that more connections invariably lead to better outcomes (Ahuja, 2000; Gulati and Gargiulo, 1999).

Secondly, the identification of multiple successful network configurations advances configuration theory by demonstrating equifinality in network strategies across emerging markets. While prior research has often sought universal “best practices” for network development (Klarin and Sharmelly, 2024), this study reveals that firms can achieve successful technology adoption through various configurational pathways. However, these pathways are not equally viable across all contexts. In environments characterised by significant institutional voids, configurations emphasizing strong ties and high absorptive capacity become more prevalent, while contexts with stronger institutions support more diverse network portfolios. This finding extends institutional theory by illustrating how institutional contexts shape the viability of different network strategies.

Third, the study advances our understanding of how firms adapt their networks to institutional voids through specific compensatory mechanisms. When formal institutions are weak, firms develop deeper relationships with key network partners who serve multiple functions. Technology providers become both implementation partners and informal consultants, while industry peers serve as both knowledge sources and validation references. This multi-functional approach to relationship development extends current theory on network adaptation in emerging markets (Kurt and Kurt, 2020) by revealing specific mechanisms through which firms leverage network relationships to overcome institutional weaknesses.

Fourth, the research extends absorptive capacity theory by demonstrating its role as a capability multiplier in network resource utilization. Previous studies have generally treated absorptive capacity as a direct enabler of technology adoption (Zahra and George, 2002). This study demonstrates that its value systematically varies with both network configurations and institutional contexts. High absorptive capacity becomes particularly crucial in environments characterised by significant institutional voids, where firms must compensate for limited institutional support through enhanced internal capabilities.

Our findings indicate that connection breadth alone is not a sufficient condition for digital upgrading. Firms that occupy moderate positions in their business networks, and that sustain mixed portfolios of strong and weak ties, are positioned to absorb vendor and practice diversity while coordinating deeply enough to make complex technologies work. For decision-makers in Ghana and India, this suggests that adoption planning should attend to the composition of relational portfolios as much as their size. Strong ties enable the troubleshooting and tacit knowledge exchange required for successful integration, while weak ties prevent single-vendor lock-in and widen access to alternative analytics stacks and data partners. The implication is not to maximize connections indiscriminately, but rather to cultivate selective boundary-spanning ties that diversify solution options while maintaining core partnerships for implementation depth.

Ecosystem stewards and policymakers can support these dynamics by enhancing the credibility and reach of intermediation functions, including systems integration services, certification bodies, and shared data standards. Such intermediaries reduce the due-diligence burdens that disproportionately accrue to highly central firms, effectively shifting the adoption peak rightward and upward. Where regulatory guidance on privacy and data portability becomes clearer, the reputational penalty from early adoption diminishes, allowing central actors to maintain adoption propensity at higher centrality levels. Country-specific evidence illustrates these mechanisms: digital business and data privacy law in Ghana shows a moving regulatory frontier that firms must interpret in practice, reinforcing the argument that institutional clarity shifts the adoption peak rightward (ICLG, 2025). Survey evidence from African SMEs similarly highlights persistent governance and support gaps beyond finance that complicate technology uptake, consistent with our coordination and intermediation mechanisms (Quaye et al., 2024).

In practical terms, the most reliable diffusion pathways in EM settings originate from firms that are central enough to influence network norms but not so central that coordination and conformity pressures overwhelm the benefits of experimentation. For technology vendors and platform providers, this implies that targeting moderately central firms as early adopters may be more effective than focusing exclusively on the most connected actors. These moderately central firms can serve as credible reference points for adoption without triggering the coordination gridlock that highly central firms face. For policymakers seeking to accelerate digital transformation, investments in intermediation capacity (training integrators, establishing certification programs, clarifying data governance rules) offer high leverage by reducing the coordination costs that depress adoption among well-connected firms.

While the study offers important insights into network configurations and technology adoption in emerging markets, several limitations suggest promising avenues for future research. Firstly, the cross-sectional research design, while appropriate for examining network configurations, cannot fully capture the dynamic evolution of network relationships during technology adoption processes. Future research could employ longitudinal designs to examine how network configurations evolve over time and how firms actively reshape their networks as they progress through different stages of technology adoption. Such research might reveal temporal patterns in network optimization strategies and provide insights into the co-evolution of network structures and technology adoption capabilities. Secondly, although the two-country comparison provides valuable insights into institutional effects, future research could explore a broader range of emerging market contexts to develop a more nuanced theoretical understanding of how institutional configurations influence network effectiveness. Particularly valuable would be studies examining markets at different stages of institutional development, which could help identify critical institutional thresholds that trigger shifts in optimal network configurations. Such research might also explore how different dimensions of institutional voids (e.g. regulatory, normative, cognitive) affect network strategy effectiveness in varying ways.

Third, the focus on SMEs, although theoretically justified, limits our understanding of how firm size and resource endowments impact network configuration effects. Future studies could examine how these relationships manifest in larger organizations or compare network effects across different organizational size categories. Additionally, researchers might explore how subsidiary networks of multinational enterprises interact with local SME networks in emerging markets, potentially revealing complex patterns of network influence across organizational boundaries. Fourth, while the study addresses marketing technology adoption in broad terms, future research could examine how network effects vary across various types of technologies or digital transformation initiatives. For instance, studies might examine whether certain network configurations prove more effective for customer-facing technologies compared to back-office systems, or for incremental versus radical technological innovations. Such research could aid in developing more nuanced theories of network influence in technology adoption. Fifth, while the measurement of absorptive capacity is comprehensive, it primarily focuses on general learning capabilities. Future research could explore more technology-specific dimensions of absorptive capacity and how these interact with network configurations. Researchers might also examine how firms develop specialized absorptive capacities for different types of marketing technologies and how these capabilities affect network resource utilization.

The digitalization of emerging markets represents one of the most significant transformational forces in contemporary business, fundamentally reshaping how firms compete and create value. This study advances the understanding of this phenomenon by illuminating the complex interplay between network configurations, institutional contexts, and organizational capabilities in enabling successful marketing technology adoption. Through rigorous empirical analysis across two distinct emerging market contexts, the research demonstrates how different combinations of network characteristics create unique pathways to adoption success, while also revealing important boundary conditions and contingencies that shape these relationships.

Beyond its immediate findings, this research points to the need for more sophisticated theoretical frameworks capable of capturing the configurational nature of organizational success in emerging markets. The identification of network efficiency thresholds, which vary systematically with levels of institutional development, challenges traditional assumptions about network benefits and suggests a more nuanced approach to network strategy. Likewise, the discovery of multiple effective network configurations, each tailored to different institutional contexts, signifies that future theoretical development must transcend simple linear models in favor of more complex, context-sensitive approaches.

The establishment of absorptive capacity as a capability multiplier, rather than merely a direct enabler of technology adoption, offers fresh insights into how firms can effectively leverage their network resources. This finding is particularly relevant in emerging market contexts, where firms often need to offset institutional weaknesses by developing stronger internal capabilities. The systematic variation in the effectiveness of network configurations across institutional settings further implies that theories of technology adoption in emerging markets must consider both organizational capabilities and levels of institutional development.

Looking forward, several promising research directions emerge from this study. Future research could examine how network efficiency thresholds evolve as markets develop institutionally, potentially revealing patterns of network strategy adaptation over time. Additionally, investigating how different types of institutional voids affect optimal network configurations could provide more nuanced guidance for firms operating across diverse emerging market contexts. The role of absorptive capacity in enabling effective network resource utilization also merits further investigation, particularly in understanding how firms can develop this crucial capability in resource-constrained environments.

As emerging markets continue their digital transformation, understanding how to achieve and maintain optimal network configurations is increasingly crucial. The findings suggest that successful technology adoption necessitates a sophisticated balancing act, optimizing network positions, aligning relationship portfolios with institutional contexts, and developing suitable organizational capabilities. This balanced approach, customized for specific institutional environments, will become ever more critical as digital technologies continually reshape competitive dynamics in emerging markets.

Ultimately, this research contributes to both scholarly understanding and management practice by revealing how firms can successfully navigate technology adoption through strategic network configuration. As digital transformation continues to accelerate across emerging markets, these insights will become increasingly valuable for firms seeking to enhance their competitive positions through effective technology adoption strategies.

During the preparation of this paper, the author used Claude, an AI assistant created by Anthropic, in order to assist with language refinement, grammar checking, and clarity improvements in the manuscript. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.