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Frontiers in Human Dynamics ·

Volume 8 - 2026 | https://doi.org/10.3389/fhumd.2026.1861807

Institutional-based social network resources, social resource orchestration competence, and business growth of manufacturing small and medium enterprises: evidence from Kenya

  • Loise Wanjiru Kanotha Oburu1*corresponding author
  • Joseph Odhiambo Onyango1
  • Anne Ndirangu1
  1. Strathmore Business School, Strathmore University, Nairobi, Kenya
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Abstract

The sustainable growth of small and medium-sized enterprises (SMEs) in manufacturing in developing economies depends significantly on how firms harness institutional social networks as strategic resources. Despite growing scholarship on social capital and firm performance, the mechanisms by which institutionally based social network resources — comprising regulatory, normative, and cultural-cognitive networks — translate into business growth remain insufficiently theorised. Specifically, the moderating role of social resource orchestration competence (SROC) in converting institutional network resources into firm growth value has not been empirically examined among Kenyan manufacturing SMEs. A quantitative cross-sectional survey was conducted among 393 purposively selected respondents — comprising business owners, heads of operations, and heads of finance — drawn from Kenya Association of Manufacturers (KAM)-registered manufacturing SMEs across 14 sub-sectors in Nairobi County, Kenya. Structural Equation Modelling (SEM) was conducted in SmartPLS 4.0, preceded by Confirmatory Factor Analysis (CFA) to establish reliability and validity. Convergent and discriminant validity, as well as collinearity, were assessed using established thresholds before path analysis was conducted. Direct path analysis confirmed significant positive associations of regulatory (β = 0.332, p < 0.05), normative (β = 0.285, p < 0.05), and cultural-cognitive resource networks (β = 0.162, p < 0.05) with SME growth. When SROC was introduced as a moderator, it emerged as the dominant growth factor (β = 0.543, p < 0.05). SROC significantly amplified the association of regulatory resource networks with SME growth (β = 0.162, p < 0.05) and attenuated the contribution of cultural-cognitive networks (β = −0.429, p < 0.05), while having no significant moderating effect on normative resource networks. These findings advance Social Exchange Theory by offering preliminary evidence that the economic value of institutional network exchanges varies with the firm's SROC. The study introduces the Social Exchange Orchestration Grid (SEOG) — a novel theoretical framework that maps the differential economic value of institutional network archetypes against SROC — providing manufacturing SME managers with a strategic tool for prioritising institutional network investments. Implications for SME policy in sub-Saharan Africa are discussed, with an emphasis on building orchestration capabilities as a potentially more critical investment than network acquisition per se.

Keywords

Introduction

Institutional networks involve connections with business partners and government officials that can be harnessed to achieve business objectives (Ólafsdóttir et al., 2022). The concept of institutional networks covers two key domains: business relationships and interactions with managers from other firms — such as suppliers, buyers, competitors, and intermediaries (Karali et al., 2020) — and political ties with government officials, including the entrepreneur's social network of friends, business partners, and relatives who provide significant support across governmental, bureaucratic, and regulatory agencies (Kregar et al., 2019). An institutional-based social network is characterised by two or more firms engaging in ongoing exchange relationships without a formal institutional authority overseeing the exchange process (Berrone et al., 2020). Examples include interfirm social relations such as alliances, joint ventures, business groups, franchises, and research consortia. The broad characterisation of institutional networks encompasses government bodies, research institutions, business incubators, international development agencies, and non-governmental organisations (Alkahtani et al., 2020).

A strong institutional framework forms the basis under which social networks can be harnessed for business growth (Yan and Zhang, 2018). Such networks provide the framework for establishing connections with individuals directly or indirectly linked to the organisation (Möller et al., 2020). Institutional-based social networks are woven into their regulatory, normative, and cultural-cognitive elements that, when bundled together, offer a platform for resource allocation and distribution in a manner that brings stability to social life (Wang and Chung, 2020).

The business value of institutional-based network resources is, however, contingent on the orchestration competence of the firm — defined as the act of organising a company's resource portfolio, grouping those resources to create valuable capabilities, and utilising those capabilities to gain a competitive edge (Baquero, 2024). Resource orchestration outlines a crucial role for managers in ensuring resource innovation for improved organisational success, employing three techniques: structuring, bundling resources into capabilities, and leveraging (Andersén and Ljungkvist, 2021).

Extant literature links firm growth with the moderating effect of resource orchestration competence, since managers acquire capabilities to utilise available resources effectively (Schmelzle and Tate, 2022). The ability of the business to access and utilise network resources for competitive advantage has attracted considerable scholarly attention (Sidibe, 2020), as business growth is increasingly linked to resource endowment in social and institutional networks (Yin et al., 2022). However, the mechanism and extent to which SROC converts institutional-based social network resources into business growth value for SMEs has not been adequately explored.

Many manufacturing SMEs in Nairobi County face economic resource constraints, which impede their growth (Cyprien and Warue, 2019). These businesses often lack the resources needed for expansion and rely heavily on external actors or network relationships to bridge these gaps (Urbano et al., 2020). Institutional networks are crucial in supporting and fostering an environment conducive to business growth (Urbano et al., 2020). Yet, while networks are recognized as essential for facilitating business growth (Bong and Premaratne, 2019), research has often concentrated on social and business networks, leaving a gap in understanding the specific impact of institutional network resources on business growth.

Manufacturing SMEs are fundamental to a nation's economic and social fabric (Bong and Premaratne, 2019). Developing economies have placed a lot of emphasis on the performance and sustainability of manufacturing SMEs since they can produce and distribute products and services that are globally competitive. In Kenya, Nairobi City has the highest concentration of registered manufacturing SMEs in Kenya. Within the manufacturing environment, Nairobi has many professional bodies, government institutionals, and individuals that manufacturing SMEs can leverage for innovation, creativity, networking, collaborations, and partnerships. Further, through Vision 2030, the government is keen on the performance and growth of manufacturing SMEs to realize the much-anticipated industrial revolution by the national government, whose headquarters is in Nairobi. The aim of this study was therefore to examine how institutional-based social network resources impact business growth and whether the impact is moderated by the SROC of Kenyan manufacturing SMEs in Nairobi County.

Literature review

Many studies agree that social networks and capital are integral to resource orchestration, enabling SMEs to mobilise, access, and manage resources effectively. Studies emphasise the importance of social capital in orchestrating resources to drive venture performance, suggesting that strong social networks enhance the capacity of SMEs to manage their resources (Edafioghor et al., 2026; Jalali, 2025). The role of social intermediaries in orchestrating resources in Base of the Pyramid markets has also been highlighted, with Bals et al. (2023) supporting the idea that social networks facilitate social value creation and resource access. Building on this (Ólafsdóttir et al., 2022), underscores how the orchestration of human and social capital resources leads to improved firm performance.

Some studies, however, introduce important nuances that places more emphasis on social bricolage and networking strategies as alternative methods of orchestrating resources, indicating that resource orchestration can take diverse forms depending on SME context (Mensah et al., 2026). Similarly, (Kramer and Rodriguez, 2018) highlight that access to business networks mediates financial performance, suggesting resource orchestration operates differently across enterprise types (Teece, 2018) demonstrate that the intersection of digital innovation and social networks shapes orchestration capabilities, suggesting the integration of technology into social capital.

Systemaatic review of recent empirical literature highlight the important role of institutional-based social network resources with social resource orchestration as its mechanism for realizing business growth at both national and international marketplaces (Qi et al., 2026). However, extant scholarship (Juráček et al., 2025; Luge et al., 2026) underscore the unique resource constraint dynamics characterizing developing countries and how institutional resource networks differently interact with resource orchestrarion in shaping SME growth performance. Jalali (2025) for instance explored how institutional-based social network resources manifesting in government support influence SME growth performance. The results confirmed the salience of government support but highlighted that the nexus between government support and SME growth was tempered by the institional context. Within the institutional entrepreneurship literature, intermediary organisations such as innovation hubs have similarly been shown to help SMEs in African settings work around institutional voids by supplying capital access, infrastructure, and training (Züfle and Von Carlowitz, 2026). Relatedly, network capability research demonstrates that a firm's ability to develop and manage business network competence mediates the relationship between the institutional environment and SME performance, reinforcing the case for treating orchestration capability as central to the value SMEs extract from institutional resource networks (Torkkeli et al., 2019).

In Africa, the literature on institutional-based social network resources, social resource orchestration competence, and business growth of manufacturing SMEs is fragmented. A number of studies (Edafioghor et al., 2026; Mensah et al., 2026) have examined the connections firms forge with external actors in the broder community, thereby contributing to the body of knowledge on the economic value of harnessing both cultural cognitive and normative resource networks. The study by Mensah et al. (2026) in Ghana is particularly insightful as it reported that most SMEs studied engage in normative and cultural cognitive networks more than regulatory resource networks and derived business growth value through resource orchestration. However, the interaction between institutional-based social network resources, social resource orchestration competence, and business growth of manufacturing SMEs in this context was not demonstrated.

Knowledge gaps across the reviewed studies reveal a recurring trend of not fully exploring the relationship between social resource orchestration and SME growth. While (Mensah et al., 2026) found a positive correlation between social resources and resource orchestration value, the connection to actual business growth was not elaborated. Similar limitations were noted in (Kramer and Rodriguez, 2018; Teece, 2018), where the importance of social networks in SME performance was emphasised but the direct link to sustained growth was not addressed. Further, there is limited integration of SET, RBV, and Institutional Theory into a unified theoretical anchorage of the research findings. Together, the contextual, conceptual, and theoretical gaps provided the impetus for the study.

Institutional network resources

Institutional network resources involve connections with business partners and government officials that can be utilized for organizational objectives (Merton and Klein, 2021). It is the framework through which firm managers establish connections with individuals directly or indirectly linked to the organization (Barney and Hesterly, 2020). This definition of institutional networks covers two key areas: business relationships and interactions with managers from other firms, such as suppliers, buyers, competitors, and other intermediaries (Hafiz et al., 2021). The second domain comprises political ties and relationships with government officials and the entrepreneur's social network, which includes a content multiplicity of friends, business partners, and relatives who provide significant support for business growth at various levels of governmental, bureaucratic, and regulatory agencies (Scott and Thornton, 2021). In addition, institutional-based social networks provide opportunities for both firms to seek and bond social capital for information seeking (Jeanes and Sinclair, 2022). Based on the discussion and perspective of García et al. (2020), the study proposes three pillars of institutional-based social networks: regulatory, normative, and cultural-cognitive resources.

Regulatory resource networks

Regulatory resources include laws, regulations, rules, and government policies that encourage particular behaviors while restricting others in the business context (Adegbile et al., 2024). Regulatory resources provide the foundation for entrepreneurs to secure commercial support, fostering formal relationships with business networks, business associations, official and semi-official government agencies, and trade organizations dedicated to business support (Agyapong et al., 2018). Moreover, establishing peaceful and formal relationships between entrepreneurs and government officials, banks, or other administrative agencies creates a secure working environment and facilitates access to credit for business growth (Bals et al., 2023). Such relationships contribute to great economic transformation (Olafenwa, 2024). The social network linking entrepreneurs with regulatory agencies aids companies in acquiring essential resources, information, and knowledge for their operations. This network thus serves as a crucial buffer, helping businesses manage the high levels of uncertainty in the business environment (Ghalwash and Ismail, 2022).

Normative resource networks

A normative network resource involves various stakeholders, value frameworks, and business models to support system processes and internal controls to promote the firm's growth and sustainability (Hussain et al., 2023). It covers the vision and mission of the company, norms, policies, and procedures that aid in developing capabilities for firm survival. The normative dimension consists of social norms, values, beliefs, and assumptions about human nature and behaviour that individuals share and carry. Large companies invest substantially in exploring and explicating their values, visions, missions, and strategic goals to communicate and stabilize their identity. A normative network can also be defined as the association between organizations that can normatively monitor practices or institutions by sharing an understanding of norm compliance among various actors and forming a kind of solidarity through mutual monitoring and evaluation (Xie et al., 2024).

Cultural-cognitive networks

Cultural cognitive lens is used to expound on a network of resources that tends to build on the strong institutional culture (Samanta et al., 2021). That leverages the cognitive resources of the employees to enhance business growth. The aim is to explore and use the shared mental models that individuals use to see, decode, and understand to shape the behavior and thinking of managers and entrepreneurs for firm growth (Möller et al., 2020). The epistemological and ontological perspective provides that cultural cognitive resource is based on the entrepreneur's ability to draw from its network ties with religious and community leaders who shape the moral behaviour necessary for firm growth (Kregar et al., 2019).

Social resource orchestration

Resource orchestration competence is organizing a company's resource portfolio, grouping those resources to create valuable capabilities, and utilizing those capabilities to gain a competitive edge (Wang et al., 2020). It outlines a crucial role for managers in ensuring resource innovation for improved organizational success. Three techniques and related processes, structuring, bundling resources into capabilities, and leveraging, are used to achieve this (García et al., 2020). Thus, within every organization, the resource portfolio is structured through the following processes: the purchase or acquisition of resources, accumulation or development of resources, and divesting for more valuable ones.

Business growth

The concept of business growth is a subject of ongoing academic inquiry due to the inconclusive findings on its definition, conceptualization, and measurement (Li et al., 2020). Scholars argue that firm growth can be measured in terms of increased revenue (Chewaka and Zhang, 2021) and employment (Hossain et al., 2024) and is a key indicator for business growth and sustainability. Smith and Jones (2021) define business growth as the number of jobs created, economic efficiency, and poverty reduction. Growth is associated with increased products, expanding a firm's clientele and geographic reach, adding new networks and alliances, and acquiring new markets are all indicators of progress (Kim and Shin, 2022). Others include increased sales, profits, assets, the number of employees, and organization value (Kim and Shin, 2022). Growth is also associated with better organization practices and efficiency gains. Gilbert et al. as cited in Li et al. (2020) used the following scale to measure growth in their study: revenue/ sale, market share, and number of employees in an enterprise. These growth dimensions form the foundation under which economic growth and rapid industrialization are achieved.

Theoretical framework

The anchor theory for this study was Social Exchange Theory (SET), which conceptualises social exchange as a voluntary action based on anticipated benefits or returns (Rodriguez, 2022). Exchange relationships are founded not just on economic value but on social cost and benefits (Pidduck et al., 2024), and are typically personal, unofficial, and governed by norms of loyalty, trust, mutual commitment, and reciprocation (Krueger, 2020). In this study, SET is integrated with the Resource-Based View (RBV) and Institutional Theory to produce a unified theoretical lens for explaining the interaction between institutional networks, resource orchestration competence, and business growth.

As a complementary theory, the RBV explains how firms attain and sustain competitive advantage (Hussain et al., 2023). It posits that every organisation is a bundle of tangible and intangible resources and capabilities with an essential bearing on growth and performance. Firms that develop or acquire valuable strategic resources meeting the attributes of value, rarity, imitability, and sustainability add value to products, resulting in expanded markets and enhanced growth (Xie et al., 2024).

Institutional Theory was employed as a supplementary framework. Institutions are understood as frameworks encompassing formal and informal rules governing human interactions (Samanta et al., 2021). Institutional theory posits that organisations sustain their legitimacy by following the rules and norms established within their institutional environment (Rodriguez, 2022). It also highlights how regulatory, social, and cultural factors contribute to an organisation's survival and legitimacy beyond efficiency-driven behaviour (Pidduck et al., 2024). By complementing SET with RBV and Institutional Theory: RBV identifies what constitutes valuable institutional-based social resources; SET explains how the processes of exchange and relationship management operate; and Institutional Theory provides the contextual pressures that shape resource value.

Together, the three theories offer complementary perspectives that accounts for the various stages of business growth value creation from institutional resource networks. Institutional Theory explains why manufacturing SMEs form ties with regulatory, normative, and cultural-cognitive resource networks and how these network structures institutionally shape the availability and quality of network resources. Subsequently, RBV provides an explanation for the development of these network ties, and the capability through which value is extracted. Finally, the complementarity of SET to both institutional theory and RBV is found in its ability to explain relational mechanisms that facilitate the excxhanges necessary for firms to access and mobilise the institutiona resource networks. Integration of the three theories thus provide a more robust explanation of the link between institutional environment, social exchange processes, managerial capability, and manufacturing SME growth.

Hypotheses development

Four research hypotheses were tested:

  • H01 Regulatory resource networks do not significantly affect the business growth of Manufacturing Small and Medium Enterprises.
  • H02 Normative resource networks do not significantly affect the business growth of Manufacturing Small and Medium Enterprises.
  • H03 Cultural-cognitive networks have no significant effects on the business growth of manufacturing Small and Medium Enterprises.
  • H04 Social resource orchestration does not significantly moderate the relationship between Institutional network resources and business growth in Manufacturing Small and Medium Enterprises.

Materials and methods

A quantitative cross-sectional research design was employed to facilitate statistical analysis and quantification of the relationships between study variables. The research was conducted in Nairobi City County, which accounts for the largest concentration of manufacturing firms in Kenya. The target population comprised manufacturing SMEs registered by the Kenya Association of Manufacturers (KAM), spanning 14 sub-sectors: Agriculture, Food and Beverage; Leather Products and Footwear; Textile and Apparel; Pharmaceutical and Medical Equipment; Paper and Board; Timber, Wood and Furniture; Plastics and Rubber; Building, Mining and Construction; Motor Vehicles and Accessories; Metal and Allied; Chemical and Allied; Energy, Electrical and Electronics; and Service and Consultancy.

Data were collected from 393 purposively selected respondents comprising business owners, heads of operations, and heads of finance from KAM-registered manufacturing SMEs across the 14 sub-sectors. However, to minimize sampling bias, stratified sampling was first applied to ensure equitable representation of SMEs across all the 14 sub-sectors. Subsequently, the three executive categories were targeted because they are responsible for policy decisions regarding resources and operations.

A structured questionnaire was used to gather the data. Trained research assistants facilitated the distribution of questionnaires to participants. The instrument consisted of four sections. The first section addressed demographic information; the remaining sections utilised 5-point Likert-type scales asking respondents to rate their agreement with various statements regarding business growth, aspects of institutional network resources, and social orchestration competence, following established survey methodology (Clarke et al., 2021). For growth, scores ranged from 1 (lowest satisfaction) to 5 (highest satisfaction); for independent and moderating variables, scores ranged from 1 (strongly disagree) to 5 (strongly agree). The questionnaire was validated for consistency, accuracy, legibility, completeness, and response clarification. Although collecting data from the same respondents potentially exposed the study to the risk of common method variance, this risk was minimised by not disclosing the study's hypotheses to respondents and by psychologically separating the measurement of institutional-based social network resources, SROC, and business growth into separate sections. Further, the Variance Inflation Factor (VIF) was tested and found to be less than 3, which was within the required threshold. This ruled out any risk of common method variance.

Data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) in SmartPLS 4.0 software. Structural Equation Modelling (SEM) was employed to examine the relationships between institutional network resources, resource orchestration competence, and business growth, and to estimate path coefficients.

Results

A summary of the demogprahic profile of the respondents is presented in Table 1. The table shows that 58.5% of the respondents were male and 41.5% were female. Regarding education level, majority (52.9%) held a Bachelor's degree. A total of 77.4% of the respondents served as Administrators/Operations staff. About half (48.6%) of the respondents had at least 5 years of experience in manufacturing. the SMEs represented in the study operated in the following manufacturing subsectors: Energy, Electrical and Electronics (7.6%), Chemical and Allied (6.1%), Food and Beverage (17.8%), Plastics and Rubber (6.6%), Building, Mining and Construction (9.7%), Paper and Paper Board (3.3%), Timber, Wood and Furniture (6.6%), Automotive (4.6%), Metal and Allied (4.3%), Pharmaceutical and Medical Equipment (3.6%), Leather Products and Footwear (1.0%), Textile and Apparel (7.1%), Agriculture/Fresh Produce (5.3%), Service and Consultants (14.0%), and Other subsectors (2.3%).

Table 1. Demographic distribution of respondents.

Demographic variableResponse categoryFrequencyPercent
GenderMale23058.5%
Female16341.5%
Total393100.0%
Level of educationPhD degree3.8%
Master's degree317.9%
Bachelor's degree20852.9%
Diploma11830.0%
Certificate338.4%
Total393100.0%
Role/designationOwner41.0%
Owner- manager256.4%
Administrator/operations30477.4%
Accountant/head of finance6015.3%
Total393100.0%
Manufacturing business tenureLess than a year153.8%
1–5 years18747.6%
5–10 years7719.6%
10–15 years287.1%
Above 15 years8621.9%
Total393100.0%
Sub-sectorEnergy, electrical and electronics firms307.6%
Chemical and allied firms246.1%
Food and beverage firms7017.8%
Plastics and rubber266.6%
Building, mining and construction389.7%
Paper and paper board133.3%
Timber, wood and furniture266.6%
Automotive (motor vehicle assembly and accessories)184.6%
Metal and allied174.3%
Pharmaceutical and medical equipment143.6%
Leather products and footwear41.0%
Textile and apparel287.1%
Agriculture (fresh produce)215.3%
Service and consultants5514.0%
Other92.3%
Total393100.0%

The study utilised five reflective measurement models: Business Growth (B), Regulatory Resource Network (R), Normative Resource Network (N), Cultural Resource Network (C), and Social Resource Orchestration Competence (S). Confirmatory Factor Analysis (CFA) was conducted to assess validity, reliability, and collinearity prior to path analysis. Table 2 presents the effect sizes (f2) of the exogenous constructs on Business Growth.

Table 2. Effect size (f2) of exogenous constructs on business growth.

Predictorf-squareEffect size interpretation
Cultural resource network -> Business growth0.030Small
Normative resource network -> Business growth0.051Small
Regulatory resource network -> Business growth0.082Small

According to Cohen's guidelines (0.02 = small, 0.15 = medium, and 0.35 = large), all three resource network dimensions exhibited small effects on Business Growth. Regulatory Resource Network demonstrated the largest effect (f2 = 0.082), followed by Normative Resource Network (f2 = 0.051) and Cultural Resource Network (f2 = 0.030). Although these effects are small, they indicate that each construct contributes uniquely to explaining variations in Business Growth. Table 3 presents the global model fit indices for the structural model.

Table 3. Model Fit indices of the structural model.

Model fit indexSaturated modelRecommended thresholdInterpretation
SRMR0.063<0.08Good model fit
Chi-square (χ2)1,756.886Lower values indicate better fitaReported
NFI0.941≥0.90Good model fit

Chi-square is sensitive to sample size and model complexity and is therefore interpreted cautiously in PLS-SEM.

The Standardized Root Mean Square Residual (SRMR) was 0.063, which is below the recommended threshold of 0.08, indicating that the model demonstrates a good fit to the observed data. The Normed Fit Index (NFI) was 0.941, exceeding the recommended cutoff value of 0.90, further supporting satisfactory model fit. The chi-square statistic was 1,756.886 and is reported for completeness. However, because chi-square is highly sensitive to sample size and model complexity, it is not considered the primary criterion for model evaluation in PLS-SEM. Table 4 presents the predictive performance of the structural model for the endogenous construct, Business Growth.

Table 4. Predictive relevance of the structural model.

Endogenous constructQ2predictRMSEMAEInterpretation
Business growth0.4660.7330.628Strong predictive relevance

The Q2predict value of 0.466 is substantially greater than zero, indicating that the model possesses strong out-of-sample predictive relevance. The prediction errors, represented by the Root Mean Square Error (RMSE = 0.733) and Mean Absolute Error (MAE = 0.628), indicate acceptable predictive accuracy. Therefore, these findings suggest that the model has good predictive capability and is suitable for explaining variations in Business Growth.

Convergent reliability was established using Cronbach's alpha (α) and Composite Reliability (CR), both above 0.70. Convergent validity was confirmed when factor loadings exceeded 0.70 and the Average Variance Extracted (AVE) was greater than 0.50. Discriminant validity was assessed using the Fornell–Larcker Criterion (FLC) and the Heterotrait–Monotrait Ratio (HTMT), with criteria of AVE square roots exceeding inter-construct correlations and HTMT values below 0.90, respectively. Collinearity was evaluated via the Variance Inflation Factor (VIF), with acceptable thresholds < 10. During iterative CFA estimation, items with low loadings were removed, including two from the cultural resource network (C01, C04), one from the regulatory resource network (R01), one from the normative resource network (N05), and one from business growth (B02). The refined CFA was re-estimated, and the final measurement model, cross-loadings, and discriminant validity results are summarised in Tables 2–5.

Table 5. Results of the measurement model.

Constructs and itemsFactor loadingsCRαAVE
Business growth (B)
B010.7710.9200.8680.796
B030.926
B040.966
Regulatory resource network (R)
R020.7520.8540.8130.594
R030.781
R040.809
R050.740
Normative resource network (N)
N010.8470.9400.9150.797
N020.869
N030.927
N040.926
Cultural resource network (C)
C020.7610.9000.8520.752
C030.900
C050.932
Social resource orchestration competence (S)
S010.8860.9720.9680.795
S020.882
S030.881
S040.919
S050.913
S060.905
S070.904
S080.860
S090.875

CR, composite reliability; α, Cronbach's alpha; AVE, average variance extracted.

Having established the reliability and validity of the measurement model, the analysis proceeded to the structural model to assess the hypothesised relationships. Figure 1 presents the Direct Path Diagram Analysis.

Direct path analysis diagram. Path coefficients (*β*) with significance levels are shown for the direct effects of regulatory, normative, and cultural-cognitive resource networks on manufacturing SME business growth.
Figure 1. Direct path analysis diagram. Path coefficients (β) with significance levels are shown for the direct effects of regulatory, normative, and cultural-cognitive resource networks on manufacturing SME business growth.

Table 9 summarises the direct–path results shown in Figure 1. Regulatory (β = 0.332), normative (β = 0.285), and cultural–cognitive (β = 0.162) resource networks all had significant positive effects on business growth (p < 0.05), supporting H1, H2, and H3.

Table 6. Cross loadings of the measurement items.

ItemsBusiness growthRegulatory networkNormative networkCultural network
B010.7710.5410.4750.194
B030.9260.5180.5570.522
B040.9660.6500.6730.608
R020.2780.7520.5250.096
R030.3310.7810.5490.125
R040.3390.8090.5820.192
R050.7170.7400.6530.767
N010.4740.7100.8470.423
N020.6330.6280.8690.666
N030.5370.7370.9270.507
N040.6320.7250.9260.603
C020.2250.4160.5170.761
C030.3490.4120.5000.900
C050.6130.5380.6060.932

Diagonal elements (bold) represent the square root of AVE for each construct.

Table 7. Discriminant validity of measurement model.

Construct1234
1. Business growth0.892
2. Cultural resource network0.519 (0.500)0.867
3. Normative resource network0.646 (0.709)0.627 (0.686)0.893
4. Regulatory resource network0.641 (0.628)0.537 (0.438)0.781 (0.847)0.771

Diagonal elements (bold) represent the square root of AVE. HTMT values are in parentheses.

Table 9. Summary of hypothesis testing (direct paths).

HypothesisCoefficientt-stat.p-valueDecision
H1: Regulatory resource network → Business growth0.332* (0.066)5.0300.000Supported
H2: Normative resource network → Business growth0.285* (0.077)3.6970.000Supported
H3: Cultural resource network → Business growth0.162* (0.042)3.8800.000Supported

Standard errors in parentheses.

p < 0.05.

The moderation analysis was conducted in SmartPLS 4 using the Product Indicator approach, which is appropriate for estimating interactions between reflective latent constructs. Under this approach, the interaction construct was generated by multiplying the indicators of the independent variable with the corresponding indicators of the moderator to create product indicators. These product indicators were then used to estimate the latent interaction construct within the structural model, enabling the examination of whether the moderator significantly altered the relationship between the independent variable and Business Growth. The resulting moderation path diagram is presented in Figure 2. As shown in Table 6, each item loaded more strongly on its own construct than on any other, supporting item-level discriminant validity.

Moderation analysis path diagram. The diagram presents the moderated structural model, incorporating SROC as a moderator between institutional resource networks and business growth.
Figure 2. Moderation analysis path diagram. The diagram presents the moderated structural model, incorporating SROC as a moderator between institutional resource networks and business growth.

Table 10 presents the moderation results. In the moderation model, direct path analysis showed a significant positive effect of the regulatory resource network (β = 0.332, p < 0.05), whereas the effects of the normative resource network (β = 0.082, p > 0.05) and the cultural resource network (β = 0.067, p > 0.05) on business growth were not statistically significant. The moderation test revealed a strong positive main effect of SROC on business growth (β = 0.543, p < 0.05). SROC significantly enhanced the effect of regulatory resource networks (β = 0.162, p < 0.05) and reduced the effect of cultural-cognitive resource networks (β = −0.429, p < 0.05), but had no significant moderating effect on normative resource networks (β = 0.129, p > 0.05). Table 7 shows construct-level discriminant validity. All HTMT values were below 0.90 (highest 0.847), confirming that the constructs are distinct.

Table 10. Social resource orchestration competence (SROC) moderation results.

Hypothesis/PathCoefficientt-stat.p-valueDecision
H4a: SROC × Regulatory resource network → Business growth0.162* (0.056)2.8740.004Supported
H4b: SROC × Normative resource network → Business growth0.129 (0.073)1.7620.078Not significant
H4c: SROC × Cultural resource network → Business growth−0.429* (0.059)7.2650.000Supported
SROC → Business growth (main effect)0.543* (0.073)7.4730.000Significant
Regulatory resource network → Business growth0.258* (0.058)4.4630.000Significant
Normative resource network → Business growth0.082 (0.061)1.3380.181Not significant
Cultural resource network → Business growth0.067 (0.041)1.6360.102Not significant

Standard errors in parentheses. SROC, social resource orchestration competence.

p < 0.05.

To ensure the robustness of the moderation analysis, multicollinearity was assessed after introducing the interaction constructs by examining the inner variance inflation factor (VIF) values. The analysis showed that all VIF values were below the recommended threshold of 5, indicating the absence of problematic multicollinearity. These results confirm that the interaction term did not introduce excessive collinearity and that the estimated moderation effects are reliable and free from multicollinearity bias.

From Table 11 above, VIF values for the main effects ranged from 1.845 to 3.786, indicating low to moderate levels of collinearity. Cultural Resource Network recorded the lowest VIF (1.845), while Social Resource Orchestration Competence exhibited the highest VIF among the main predictor constructs (3.786).

Table 11. Inner VIFs for the structural model after incorporating moderation effects.

Structural RelationshipVIF
Cultural resource network -> Business growth1.845
Normative resource network -> Business growth3.660
Regulatory resource network -> Business growth3.208
Social resource orchestration competence -> Business growth3.786
Social resource orchestration competence × Cultural resource network -> Business growth3.000
Social resource orchestration competence × Normative resource network -> Business growth2.375
Social resource orchestration competence × Regulatory resource network -> Business growth3.036

The interaction terms generated using the Product Indicator approach also exhibited acceptable VIF values, ranging from 3.000 to 3.375. Specifically, the interaction between Social Resource Orchestration Competence and Cultural Resource Network had a VIF of 3.000, while the interaction terms involving Normative Resource Network and Regulatory Resource Network recorded VIF values of 3.375 and 3.036, respectively. These findings indicate that the inclusion of the interaction constructs did not introduce problematic multicollinearity into the structural model.

Overall, the VIF values were below the commonly accepted threshold of 5.0, indicating that multicollinearity was not a significant concern and that the estimated path coefficients and moderation effects were not adversely affected by collinearity among the predictor constructs.

As presented in Table 8, VIF values ranged from 1.099 to 7.389, all below 10, indicating no multicollinearity concerns.

Table 8. Variance inflation factor (VIF) results.

ItemVIF
B01 Sales turnover (Zane and DeCarolis, 2016)1.698
B03 Number of branches/outlets (Chambers and Munemo, 2017)6.129
B04 Market share (Wiklund et al., 2009)7.389
R02 We work towards ensuring good industrial relations (Odhong et al., 2014)3.991
R03 We are actively engaged in industry association events (Qiao et al., 2014)3.294
R04 We have established strong partnerships with local authorities (Li et al., 2022)3.781
R05 We enjoy government support for our business activities (Divanbeigi and Ramalho, 2015)1.099
N01 We have strong social ties with our host community (Chekh and Vinnyk, 2017)2.588
N02 We often get involved in the affairs of the community (Chekh and Vinnyk, 2017)2.366
N03 We intentionally participate in social networking events (Sheng et al., 2011)4.345
N04 Undertaking social responsibility activities is part of our business strategy (Songling et al., 2018)3.846
C02 We have established working relationships with local cultural institutions (Bartkus and Davis, 2010)2.041
C03 We undertake activities that preserve local norms and traditions (Skinner, 2018)3.011
C05 We do business in a way sensitive to the customs and norms of our host community (Randolph, 2016)2.080
S01 Our company can acquire more resources through our social network (Sun et al., 2022)4.409
S02 Our company has successfully accumulated resources from social connections (Nasution et al., 2022)4.440
S03 Our company has utilised social resources to divest our product portfolio (Kadam et al., 2019)3.800
S04 Our firm has worked to stabilise capabilities to enhance social resources (Kadam et al., 2019)5.386
S05 We have enriched our capabilities to add value to the social resource base (Escandon-Barbosa et al., 2022)5.102
S06 Our firm has pioneered new systems to improve our social resource endowment (Ramirrez-Urraya et al., 2024)4.613
S07 Our firm has integrated social networking capabilities for the business (Su and Linderman, 2016)4.654
S08 Our firm has provided mechanisms for deploying acquired social competencies (Ahuja and Chan, 2017)5.614
S09 Our firm has a mechanism for configuring social resources into a distinct capability (Berseck, 2018)6.048

Discussion

Direct path analysis revealed significant positive effects of regulatory (β = 0.332, p < 0.05) and normative (β = 0.285, p < 0.05) resource networks on SME business growth. These results align with the work of Ghalwash and Ismail (2022) and Hussain et al. (2023), whose studies focused on the role of social capital in resource mobilisation but placed greater emphasis on social bricolage and networking strategies as alternative approaches. Additional evidence showed a significant positive effect of cultural-cognitive resource networks on business growth (β = 0.162, p < 0.05). The moderation test showed a strong positive main effect of SROC (β = 0.543, p < 0.05), implying that business growth was more a function of SROC than of the institutional resources themselves — a finding that concurs with Samanta et al. (2021), who noted that entrepreneurs utilising institutional networks to obtain initial resources, favourable legal decisions, and political connections can enhance business performance.

Subsequent inferential results showed that SROC significantly enhanced the role of regulatory resource networks (β = 0.162, p < 0.05) and reduced the role of cultural-cognitive networks (β = −0.429, p < 0.05), but did not significantly moderate the role of normative resource networks in manufacturing SME growth. This finding suggests that the association between institutional resource networks and business growth was contingent on SROC, varying across institutional resource archetypes. This affirms Adegbile et al. (2024), who found a positive correlation between social resources and resource orchestration value, and aligns with studies emphasising the importance of social capital in orchestrating resources to drive venture performance (Edafioghor et al., 2026; Jalali, 2025). The reduced usefulness of cultural-cognitive networks under high SROC affirms the notion of managerial competence as a superordinate resource, in keeping with RBV. This means that manufacturing SMEs with high SROC may possess superior capabilities to sense, seize, integrate, and strategically prioritise and deploy high-value institutional resource networks, thereby reducing reliance on cultural-cognitive resource networks.

This study enriches Social Exchange Theory by empirically demonstrating how different types of institutional resource networks provide platforms for reciprocal exchanges that generate economic value through firm growth. The unique link between each institutional-based social resource network archetype and business growth shows that the economic value of each class of social exchange differs — both in its own right and at various levels of SROC. The present study reveals that regulatory resource networks make the greatest positive contribution to manufacturing SME growth, followed by normative networks, and lastly, cultural-cognitive networks. Furthermore, SROC enhances the benefits of regulatory resource networks but diminishes the relevance of cultural-cognitive networks.

By showing that SROC amplifies the utility of regulatory networks and tempers the relevance of cultural-cognitive networks, this study provides a case for extending SET by linking external network resources to internal capabilities, illustrating a dynamic interplay between institutional-based social resource endowment, orchestration capability, and business growth in manufacturing SMEs. This study therefore introduces the Social Exchange Orchestration Grid (SEOG) as a novel theoretical lens for making sense of how firms extract economic value from social exchanges within the context of institutional-based social network resources. A visual illustration is presented in Table 12.

Table 12. Social exchange orchestration grid (SEOG).

RangeLow orchestration competenceHigh orchestration competence
High economic valueQUADRANT ANormative resource networks(β = 0.285, p < 0.05; SROC: ns)QUADRANT BRegulatory resource networks(β = 0.332, p < 0.05; SROC amplifies: β = 0.162, p < 0.05)
Low economic valueQUADRANT CCultural-Cognitive Networks(β = 0.162, p < 0.05; SROC reduces: β = −0.429, p < 0.05)QUADRANT DSROC (independent effect)(main effect: β = 0.543, p < 0.05)
← Economic value of orchestration capability →

Empirical path coefficients from the moderation model are displayed within each quadrant. SROC, social resource orchestration competence; ns, non-significant.

The SEOG maps the economic value of social exchange against SME orchestration competence, operationalising how SMEs can convert social ties into quantifiable economic benefits. Normative resource networks (Quadrant A) indicate that the tangible value of social exchange is relatively high irrespective of SROC (β = 0.129, p > 0.05), suggesting firms can extract value from normative networks without high orchestration competencies. Regulatory resource networks (Quadrant B) are characterised by high SROC, which drives substantial increases in economic value (β = 0.162, p < 0.05), signalling the need to invest in social skills to unlock the full potential of regulatory network ties. Cultural-cognitive networks (Quadrant C) show limited economic value, as SROC significantly erodes their growth link (β = −0.429, p < 0.05). Quadrant D shows that economic value increases with high SROC itself (β = 0.543, p < 0.05), independent of social exchanges, emphasising that orchestration competence is the ultimate determinant of economic value from social exchanges.

The proposed SEOG is theoretically justified by the proposition that the utility of institutional-based social resource networks is subject to SROC. The grid extends SET theory from a relationship-focused explanation of SME growth to a capability-based account of economic value extraction from institutional-based resource networks by determining to what extent these networks yield growth. This is demonstrated by the intersection between the value inherent in institutional-based social resource networks and the SROC of each manufacturing SME. The SEOG thus suggests that each quadrant represents a unique configuration of conditions that cannot be replicated from one firm to another due to varying levels of SROC. That is, a firm either possesses high or low SROC, and thus its social exchanges either yield high or low business growth value. This classification deviates from existing resource orchestration frameworks, which focus on explaining how businesses structure, bundle, and leverage internal resources. Thus, the theoretical extension value of SEOG lies in integrating SET and institutional theory into a capability-based framework.

Conclusion

This study concludes that social resource orchestration competence is the main capability that powered the strength of institutional-based social network resources on SME growth. The main driver of manufacturing SME growth resided in the SMEs' ability to effectively manage, configure, and deploy organisational resources. Social resource structuring, bundling, and leveraging together shaped manufacturing SME growth trajectories far more than the institutional-based resource networks themselves.

Furthermore, SROC was not only associated with the business growth outcomes of the various institutional-based social resource networks but was also linked to how the different network archetypes translated into SME growth. SROC amplified the growth value of regulatory resource networks but tempered the business growth value of cultural-cognitive networks. SROC was not a necessary enhancement for normative resource networks, since its non-significant moderation suggests that shared norms retain their positive impact on manufacturing SME growth regardless of a firm's orchestration competence. The proposed SEOG provides a useful mental model that manufacturing SMEs with high SROC can use to prioritise investment in institutional-based resource networks promising the greatest return.

Limitations

Whereas the research has provided preliminary evidence of the value of institutional-based social network resources and social resource orchestration competence for the growth of manufacturing SMEs, the focus on KAM-registered SMEs introduces external validity constraints. Therefore, future research should examine these relationships longitudinally and in other counties in Kenya and across African manufacturing contexts to test the generalisability of the SEOG framework beyond Nairobi County.

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Footnotes

Edited by: Rohit Bhardwaj, Xavier Institute of Management and Entrepreneurship Bangalore, India

Reviewed by: Sugianto Sugianto, Politeknik Tonggak Equator, Indonesia

Saurabh Srivastava, Siddharth University Kapilvastu, India

Statements

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

The author(s) declared that financial support was not received for this work and/or its publication.

Data availability

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.

Ethics approval

The studies involving humans were approved by National Commission For Science, Technology & Innovation and Strathmore University Institutional Scientific and Ethical Review Committee. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

Author contributions

LW: Conceptualization, Data curation, Formal analysis, Methodology, Resources, Writing – original draft, Writing – review & editing. JO: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Validation, Visualization, Writing – review & editing. AN: Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Validation, Writing – review & editing.

Acknowledgements

The authors wish to thank the Kenya Association of Manufacturers (KAM) for facilitating access to the study population, and the research assistants who supported data collection. The authors also acknowledge the contribution of respondents across the 14 manufacturing sub-sectors who participated in the study.

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Citation

Oburu, L. W. K., et al. (2026). Institutional-based social network resources, social resource orchestration competence, and business growth of manufacturing small and medium enterprises: evidence from Kenya. Frontiers in Human Dynamics, Volume 8. https://doi.org/
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© 2026 Wanjiru Kanotha Oburu, Onyango and Ndirangu.

License: https://creativecommons.org/licenses/by/4.0/

*Correspondence: Loise Wanjiru Kanotha Oburu

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