Securing the Digital Edge: How Security Management Mediates the Impact of ICT Infrastructure on Firm Performance in Emerging Markets

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Restrepo Morales" }, { "@type": "Person", "name": "Eduar Antonio Rodríguez Flores" }, { "@type": "Person", "name": "Marianella Alicia Suárez Pizzarello" }, { "@type": "Person", "name": "Freddy Zea Restrepo" }, { "@type": "Person", "name": "Emerson Andrés Giraldo Betancur" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": " Background Information and communication technology (ICT) infrastructure drives firm performance in emerging markets, yet institutional voids and security vulnerabilities often undermine its efficacy. The mechanisms linking ICT endowment, organizational security investment, and firm-level outcomes remain underexplored, particularly in developing economies. Methods We tested a recursive three-block structural equation model (SEM) on nationally representative microdata from Peru’s Annual Economic Survey 2024 (N = 9,966 firms across 14 sectors). The model integrated the Resource-Based View (RBV) and Routine Activity Theory (RAT) to specify pathways from ICT infrastructure, internet access, and digital human capital through security management investment to firm performance (measured as firm size category: micro vs. medium/large). We employed full-information maximum likelihood (FIML) estimation with Satorra–Bentler robust standard errors to account for non-normal distributions in e-commerce adoption measures. Mediation analysis decomposed total effects via bootstrap confidence intervals (5,000 replications). Results ICT infrastructure was the dominant predictor across all equations (β = 0.356 in security management; β = 0.327 in firm performance, both p < 0.001). Security management significantly mediated 17.6% of ICT’s total effect on performance (indirect β = 0.070, p < 0.001). Criminal victimization exposure directly predicted security investment (β = 0.060, p < 0.001), establishing a protective response mechanism predicted by Routine Activity Theory. Model fit indices confirmed excellent fit (CFI = 0.988, RMSEA = 0.028, GFI = 0.986). Conclusions In volatile emerging market environments, security management functions as a strategic resource that transforms raw ICT connectivity into sustainable competitive advantage — a phenomenon we term “Digital Resilience.” These findings challenge technological determinism and provide a blueprint for firms and policymakers in developing economies to integrate security as a core dynamic capability rather than a cost center. 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F1000Research 2026, 15 :678 ( https://doi.org/10.12688/f1000research.179961.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Securing the Digital Edge: How Security Management Mediates the Impact of ICT Infrastructure on Firm Performance in Emerging Markets [version 1; peer review: awaiting peer review] Jorge A. Restrepo Morales https://orcid.org/0000-0001-9764-6622 1 , Eduar Antonio Rodríguez Flores 2 , Marianella Alicia Suárez Pizzarello 2 , Freddy Zea Restrepo https://orcid.org/0009-0005-1882-8433 3 , Emerson Andrés Giraldo Betancur 3 Jorge A. Restrepo Morales https://orcid.org/0000-0001-9764-6622 1 , Eduar Antonio Rodríguez Flores 2 , [...] Marianella Alicia Suárez Pizzarello 2 , Freddy Zea Restrepo https://orcid.org/0009-0005-1882-8433 3 , Emerson Andrés Giraldo Betancur 3 PUBLISHED 08 May 2026 Author details Author details 1 Universidad Autonoma del Peru, Lima District, Lima Region, Peru 2 Dirección de Investigación e Innovación, Universidad Autonoma del Peru, Lima District, Lima Region, 15001, Peru 3 Ciencias Administrativas y Económicas, Tecnologico de Antioquia Institucion Universitaria, Medellín, Antioquia, 050001, Colombia Jorge A. Restrepo Morales Roles: Conceptualization, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Eduar Antonio Rodríguez Flores Roles: Formal Analysis, Project Administration, Resources Marianella Alicia Suárez Pizzarello Roles: Data Curation, Writing – Review & Editing Freddy Zea Restrepo Roles: Investigation, Validation, Writing – Review & Editing Emerson Andrés Giraldo Betancur Roles: Data Curation, Software, Validation, Writing – Review & Editing OPEN PEER REVIEW REVIEWER STATUS AWAITING PEER REVIEW Abstract Background Information and communication technology (ICT) infrastructure drives firm performance in emerging markets, yet institutional voids and security vulnerabilities often undermine its efficacy. The mechanisms linking ICT endowment, organizational security investment, and firm-level outcomes remain underexplored, particularly in developing economies. Methods We tested a recursive three-block structural equation model (SEM) on nationally representative microdata from Peru’s Annual Economic Survey 2024 (N = 9,966 firms across 14 sectors). The model integrated the Resource-Based View (RBV) and Routine Activity Theory (RAT) to specify pathways from ICT infrastructure, internet access, and digital human capital through security management investment to firm performance (measured as firm size category: micro vs. medium/large). We employed full-information maximum likelihood (FIML) estimation with Satorra–Bentler robust standard errors to account for non-normal distributions in e-commerce adoption measures. Mediation analysis decomposed total effects via bootstrap confidence intervals (5,000 replications). Results ICT infrastructure was the dominant predictor across all equations (β = 0.356 in security management; β = 0.327 in firm performance, both p < 0.001). Security management significantly mediated 17.6% of ICT’s total effect on performance (indirect β = 0.070, p < 0.001). Criminal victimization exposure directly predicted security investment (β = 0.060, p < 0.001), establishing a protective response mechanism predicted by Routine Activity Theory. Model fit indices confirmed excellent fit (CFI = 0.988, RMSEA = 0.028, GFI = 0.986). Conclusions In volatile emerging market environments, security management functions as a strategic resource that transforms raw ICT connectivity into sustainable competitive advantage — a phenomenon we term “Digital Resilience.” These findings challenge technological determinism and provide a blueprint for firms and policymakers in developing economies to integrate security as a core dynamic capability rather than a cost center. READ ALL READ LESS Keywords ICT infrastructure; firm performance; security management; emerging markets; Routine Activity Theory; Resource-Based View; structural equation modeling; Peru; digital transformation Corresponding Author(s) Jorge A. Restrepo Morales ( [email protected] ) Close Corresponding author: Jorge A. Restrepo Morales Competing interests: No competing interests were disclosed. Grant information: The author(s) declared that no grants were involved in supporting this work. Copyright: © 2026 Restrepo Morales JA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Restrepo Morales JA, Rodríguez Flores EA, Suárez Pizzarello MA et al. Securing the Digital Edge: How Security Management Mediates the Impact of ICT Infrastructure on Firm Performance in Emerging Markets [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :678 ( https://doi.org/10.12688/f1000research.179961.1 ) First published: 08 May 2026, 15 :678 ( https://doi.org/10.12688/f1000research.179961.1 ) Latest published: 08 May 2026, 15 :678 ( https://doi.org/10.12688/f1000research.179961.1 ) 1. Introduction Digital transformation has emerged as a defining strategic imperative for firms across all income levels, yet its organizational consequences remain heterogeneous and context-dependent. In high-income economies, the positive association between information and communication technology (ICT) investment and firm performance is well-established ( Melville et al., 2004 ; Mithas et al., 2011 ; Aral & Weill, 2007 ). In emerging markets, however, the pathways through which digital resources translate into organizational outcomes are more complex, mediated by persistent infrastructure deficits, shallow digital skills markets, high business crime rates, and institutional environments that differ fundamentally from those of OECD economies ( Cirera & Maloney, 2017 ; Katz & Callorda, 2019 ). Peru offers a particularly revealing natural laboratory for this inquiry. As of 2023, only 44.9% of formally registered Peruvian firms report internet access, and a mere 7.8% engage in any form of digital commerce ( INEI, 2024 ). Simultaneously, 15.3% of Peruvian firms report having been victims of at least one criminal act during 2023 — including robbery, extortion, vandalism, and fraud — a victimization prevalence that represents a material operational risk, particularly for Commerce (18.2%) and Transport (18.4%) sectors. The coexistence of digital heterogeneity and crime exposure creates a structural environment in which digitalization and security management are not merely parallel investments but potentially complementary capabilities whose interaction determines firm-level outcomes. Despite the theoretical plausibility of this complementarity, the empirical literature has treated ICT investment and organizational security management as largely separate streams. This study addresses that gap by developing and testing an integrated structural model connecting ICT infrastructure endowment, internet access, digital human capital, e-commerce adoption, and criminal victimization exposure to security management investment and firm performance, using microdata from Peru’s Annual Economic Survey 2024 (EEA 2024; INEI). Drawing on the RBV and the Routine Activity Theory (RAT; Cohen & Felson, 1979 ), we propose a three-block recursive model estimated via path analysis on a matched sample of 9,966 firms spanning 14 economic sectors. The study makes three original contributions. First, it provides the first large-sample SEM of the ICT–security–performance nexus using nationally representative enterprise data from Peru. Second, it extends RAT beyond individual-level victimization prediction to the organizational domain. Third, it reframes organizational security investment from a cost center to a strategic performance-generating resource (β = 0.197, p < 0.001), mediating 17.6% of ICT infrastructure’s total performance effect. 2. Theoretical framework and hypotheses development 2.1 Conceptual overview This study integrates two complementary theoretical perspectives: (1) the Resource-Based View (RBV) and its dynamic capabilities extension, which frames ICT adoption as a strategic, value-generating resource; and (2) the Routine Activity Theory (RAT), adapted to the organizational level, which explains why firms exposed to crime victimization invest more intensively in protective mechanisms that, in turn, improve operational resilience and performance outcomes. 2.2 Theoretical foundations 2.2.1 Resource-based view and dynamic capabilities The Resource-Based View, formulated by Wernerfelt (1984) and systematized by Barney (1991) , posits that sustained competitive advantage derives from firm-specific resources that are valuable, rare, inimitable, and non-substitutable (VRIN). ICT assets — hardware infrastructure, internet connectivity, and the human capital capable of exploiting them — constitute strategic resources that enable firms to generate superior performance outcomes ( Bharadwaj, 2000 ; Melville et al., 2004 ). Building on RBV, Teece et al. (1997) introduced the dynamic capabilities perspective, emphasizing that firms must continuously sense opportunities, seize them through resource reconfiguration, and transform operational routines. Aral and Weill (2007) demonstrate that IT assets generate positive spillovers across the firm, enabling broader capability development. 2.2.2 ICT as an enabler of e-commerce E-commerce represents the most visible and economically significant form of digital market participation for firms ( Zhu et al., 2006 ). The Technology–Organization–Environment (TOE) framework ( Tornatzky & Fleischer, 1990 ) establishes that technology readiness — operationalized through ICT infrastructure and internet access — is a primary determinant of e-commerce adoption. Zhu and Kraemer (2005) show that technology competence directly drives e-commerce value creation in a cross-national sample. Hajli et al. (2015) and Barbu et al. (2021) demonstrate positive effects of ICT investment on online sales performance in emerging market firms. For Peruvian SMEs specifically, Heredia Pérez et al. (2022) document that internet access and digital capability are the strongest predictors of online sales adoption. 2.2.3 Routine activity theory applied to business security Cohen and Felson's (1979) Routine Activity Theory proposes that crime occurs when a motivated offender, a suitable target, and the absence of a capable guardian converge. In its organizational adaptation, firms constitute targets when visible, accessible, and weakly protected. Exposure to criminal victimization is expected to increase firms’ investment in protective measures — physical security, surveillance technology, insurance, and cybersecurity infrastructure ( Boba Santos, 2013 ; Holt & Bossler, 2016 ). Digitally endowed firms are better positioned to implement sophisticated security responses because they possess the ICT infrastructure necessary to integrate these systems. 2.2.4 Security management as a strategic resource for firm performance Security investment is increasingly recognized not merely as a cost center but as a strategic resource that protects firm value and enables operational continuity ( Gordon & Loeb, 2002 ; Anderson & Moore, 2006 ). Firms that invest in adequate security systems reduce expected losses from criminal activity, maintain operational continuity, and signal reliability to business partners ( Böhme et al., 2015 ). Gordon and Loeb's (2002) model demonstrates that optimal security investment increases with the value of assets at risk, providing a theoretical rationale for why security investment is concentrated among larger, more digitally endowed firms. Kankanhalli et al. (2003) and Cavusoglu et al. (2004) confirm that organizational security investment positively affects firm performance through risk reduction. 2.3 Research hypotheses Drawing on the portfolio perspective and the Oslo Manual taxonomy, we formulate the following nine testable hypotheses, summarized in Table 1 : H1: Greater ICT infrastructure endowment is positively associated with adoption of e-commerce sales. H2: Broader internet access is positively associated with e-commerce sales and procurement adoption. H3: Higher digital human capital is positively associated with e-commerce adoption. H4: Greater ICT infrastructure endowment is positively associated with security management investment. H5: Broader internet access is positively associated with security management investment. H6: Prior criminal victimization is positively associated with subsequent security management investment. H7: Greater ICT infrastructure endowment is positively associated with firm performance. H8: Greater security management investment is positively associated with firm performance. H9: Broader internet access is positively associated with firm performance. Table 1. Summary of research hypotheses. H Relationship Direction Theory H1 ICT Infra → E-commerce Sales + RBV/TOE H2 Internet Access → E-commerce + RBV/TOE H3 Digital Human Capital → E-commerce + RBV H4 ICT Infra → Security Management + RBV/RAT H5 Internet Access → Security Management + RBV/RAT H6 Criminal Victimization → Security Management + RAT H7 ICT Infra → Firm Performance + RBV H8 Security Management → Firm Performance + RBV/Security Econ. H9 Internet Access → Firm Performance + RBV 2.4 Contextual rationale: Peru as an emerging market setting Peru exhibits significant heterogeneity in firm-level ICT endowment and security exposure, maximizing variance in key constructs. According to INEI (2024) , 44.9% of surveyed firms report internet access, while only 21.2% engage in any form of digital commerce, suggesting that infrastructure endowment does not automatically translate into e-commerce adoption. 15.3% of firms report having been victims of at least one criminal act in 2023. This combination of digital heterogeneity and security vulnerability makes Peru’s firm-level data uniquely valuable for investigating the co-evolution of digitalization and security management as drivers of business performance — a gap identified by Katz and Callorda (2019) and Cirera and Maloney (2017) in the Latin American business development literature. 3. Methodology 3.1 Research design and data source This study employs a cross-sectional, quantitative research design grounded in secondary microdata from Peru’s Annual Economic Survey 2024 (Encuesta Económica Anual, EEA 2024), conducted by the Instituto Nacional de Estadística e Informática (INEI). The EEA 2024 is a nationally representative establishment-level survey covering the 2023 fiscal year. Microdata access was obtained through INEI’s Microdata Catalogue ( https://proyectos.inei.gob.pe/microdatos/ ). This study uses three thematic modules: (i) Chapter 01 — firm identification; (ii) ICT Module — digital infrastructure, internet access, human capital, and e-commerce; and (iii) Security Module — crime victimization, security measures adopted, and security expenditure. The inner join of the ICT and Security modules yielded a matched sample of 10,327 firms, of which 9,966 constitute the analytical sample after listwise deletion (missing rate < 0.5%). 3.2 Sample characteristics The analytical sample comprises 9,966 firms distributed across 14 economic sectors ( Table 2 ). Commerce and Manufacturing (SME) are the largest sectors, representing 29.8% and 22.8% of the sample, respectively, with approximately 42% of observations concentrated in Metropolitan Lima. Table 2. Sectoral distribution of the analytical sample (N = 9,966). Sector N % Cum. % Commerce 2,973 29.83 29.83 Manufacturing (SME) 2,268 22.76 52.59 Services 2,090 20.97 73.56 Transport & Communications 798 8.01 81.57 Construction 676 6.78 88.35 Private Education 242 2.43 90.78 Hydrocarbons 199 2.00 92.78 Artisanal Fishing 165 1.66 94.44 Manufacturing (large) 154 1.55 95.99 Universities 108 1.08 97.07 Electricity & Energy 95 0.95 98.02 Industrial Fishing 87 0.87 98.89 Restaurants 57 0.57 99.46 Aquaculture 54 0.54 100.00 Total 9,966 100 — 3.3 Operationalization of variables All constructs are derived from validated survey items in the EEA 2024. Multi-item constructs were scored as the mean proportion of affirmative responses (0–1). Table 3 provides the full operationalization. Table 3. Operationalization of constructs. Code Construct Type Items (source) α/Note C1 ICT Infrastructure Composite (0–1) PT01_1_1 to PT01_1_9: computer, laptop, tablet, smartphone, server, LAN, Wi-Fi, intranet, ERP/CRM (k = 9) α = 0.716 C2 Internet Access Binary (0/1) PT05_1_1: firm has internet connection Single item C3 Digital Human Capital Continuous (0–1) PT02_1_1: share of workers using a computer (÷100) Single item C4 E-commerce Sales Composite (0–1) PT07A_1_1 to PT07A_1_7: online sales channels used (k = 7) Formative C5 E-commerce Procurement Composite (0–1) PT07B_1_1 to PT07B_1_7: online procurement channels (k = 7) Formative C6 Criminal Victimization Binary (0/1) PS01_1_1 to PS01_1_7: victim of ≥1 crime type → dichotomized Dichotomized C7 Security Management Composite (0–1) PS03_1_1 to PS03_1_9: security measures adopted (CCTV, alarms, guards, cybersecurity; k = 9) α = 0.813 C8 Firm Performance Binary (0/1) CodFormato: 1 = Large/Medium (F2); 0 = Micro (M or N) Dependent C9 Economic Group Binary (0/1) CODPERTENECEGRUPOE: 1 = belongs to a business group Control 3.4 Analytical strategy: Structural equation modeling 3.4.1 Justification of the SEM approach Structural Equation Modeling (SEM) is appropriate because: (1) the model involves multiple simultaneous equations with endogenous mediators; (2) the hypotheses include both direct and indirect (mediated) effects; and (3) SEM explicitly accounts for measurement error ( Hair et al., 2019 ; Kline, 2016 ; Byrne, 2016 ). Given the non-normal distribution of several indicators — e-commerce adoption (Skewness = 3.70; Excess Kurtosis = 14.27) — we employ Full Information Maximum Likelihood (FIML) estimation with robust standard errors (Satorra–Bentler correction). Model estimation used semopy v2.3 in Python ( Meshcheryakov & Igolkina, 2021 ). 3.4.2 Model specification The structural model comprises three recursive blocks: Block 1 − ICT Enablement of Digital Commerce C 4 E CO M V = β 11 C 1 + β 12 C 2 + β 13 C 3 + ζ 1 C 5 E CO M C = β 21 C 1 + β 22 C 2 + β 23 C 3 + ζ 2 Block 2 − Security Management Antecedents C 7 S EG = β 31 C 1 + β 32 C 2 + β 33 C 4 + β 34 C 6 + ζ 3 Block 3 − Firm Performance C 8 = β 41 C 1 + β 42 C 2 + β 43 C 3 + β 44 C 4 + β 45 C 7 + β 46 C 9 + ζ 4 3.4.3 Model evaluation criteria We evaluated model fit using multiple indices following standard SEM guidelines ( Table 4 ). These include comparative fit indices (CFI, TLI), absolute fit indices (RMSEA, SRMR), and relative chi-square (χ 2 /df ). Table 4. Model fit index thresholds. Index Acceptable Excellent CFI ≥ 0.90 ≥ 0.95 TLI ≥ 0.90 ≥ 0.95 RMSEA ≤ 0.08 ≤ 0.06 SRMR ≤ 0.08 ≤ 0.05 χ 2 /df ≤ 5.0 ≤ 3.0 AIC/BIC Lower = better — 3.4.4 Mediation analysis Indirect effects were computed as the product of path coefficients ( β a × β b ), following Baron and Kenny (1986) and Sobel (1982) . Bootstrap confidence intervals (5,000 replications) were also computed to provide bias-corrected significance tests for indirect effects. 3.5 Common method bias assessment Harman’s single-factor test: the first unrotated factor explained 18.7% of total variance (well below the 50% threshold), providing no evidence of pervasive common method variance ( Podsakoff et al., 2003 ). The architectural separation of ICT and Security modules — administered as separate questionnaire chapters — further reduces same-source bias. The dependent variable (C8) is an administrative categorical variable, not a self-reported perceptual measure, providing a procedural remedy for CMV. 3.6 Multicollinearity diagnostics All variance inflation factors (VIF) are below 2.0, substantially below the threshold of 10 ( Hair et al., 2019 ), indicating that multicollinearity does not constitute a threat. Detailed VIF diagnostics for each construct are presented in Table 5 . Table 5. Variance inflation factors (VIF). Construct VIF Diagnosis C1 — ICT Infrastructure 1.653 ✓ No concern C2 — Internet Access 1.411 ✓ No concern C3 — Digital Human Capital 1.029 ✓ No concern C4 — E-commerce Sales 1.200 ✓ No concern C5 — E-commerce Procurement 1.167 ✓ No concern C6 — Criminal Victimization 1.021 ✓ No concern C7 — Security Management 1.350 ✓ No concern C8 — Firm Performance 1.420 ✓ No concern C9 — Economic Group 1.042 ✓ No concern 4. Results 4.1 Descriptive statistics and bivariate correlations Table 6 reports the descriptive statistics for all constructs (N = 9,966). ICT Infrastructure (C1) shows a mean of 0.578 (SD = 0.206). Internet access (C2) is present in 44.9% of firms. E-commerce adoption remains nascent: means of 0.021 for both C4 and C5 indicate fewer than 8% of firms engage in any form of digital commerce. Criminal victimization (C6) affects 15.3% of firms; Security Management (C7) averages 0.257 (SD = 0.258), with the IQR (0.000–0.444) suggesting a bimodal distribution. Large and medium-sized firms (C8) represent 63.3% of the sample. Table 6. Descriptive statistics (N = 9,966). Construct Code Mean SD Min P25 Median P75 Max ICT Infrastructure C1 0.578 0.206 0.111 0.444 0.556 0.778 1.000 Internet Access C2 0.449 0.497 0.000 0.000 0.000 1.000 1.000 Digital Human Capital C3 0.618 0.361 0.010 0.280 0.700 1.000 1.000 E-commerce Sales C4 0.021 0.075 0.000 0.000 0.000 0.000 0.750 E-commerce Procurement C5 0.021 0.081 0.000 0.000 0.000 0.000 1.000 Criminal Victimization C6 0.153 0.360 0.000 0.000 0.000 0.000 1.000 Security Management C7 0.257 0.258 0.000 0.000 0.222 0.444 1.000 Firm Performance C8 0.633 0.482 0.000 0.000 1.000 1.000 1.000 Economic Group C9 0.080 0.271 0.000 0.000 0.000 0.000 1.000 Table 7 presents the Pearson correlation matrix. ICT Infrastructure (C1) exhibits the strongest bivariate associations with Security Management (r = 0.441, p < 0.001) and Firm Performance (r = 0.482, p < 0.001). The complete correlation structure is visualized in Figure 1 . Table 7. Pearson Correlation Matrix (N = 9,966). (1) C1 (2) C2 (3) C3 (4) C4 (5) C5 (6) C6 (7) C7 (8) C8 (9) C9 (1) ICT Infra 1.000 (2) Internet 0.488 *** 1.000 (3) Digital HC 0.140 *** 0.119 *** 1.000 (4) Ecom Sales 0.148 *** 0.202 *** 0.068 *** 1.000 (5) Ecom Proc. 0.105 *** 0.106 *** 0.069 *** 0.369 *** 1.000 (6) Victimizat. 0.050 *** 0.042 *** 0.016 0.075 *** 0.067 *** 1.000 (7) Security 0.441 *** 0.343 *** 0.059 *** 0.104 *** 0.079 *** 0.086 *** 1.000 (8) Firm Perf. 0.482 *** 0.362 *** 0.043 *** 0.079 *** 0.026 ** −0.031 * 0.393 *** 1.000 (9) Econ. Group 0.150 *** 0.137 *** 0.039 *** 0.039 *** 0.009 0.033 *** 0.150 *** 0.154 *** 1.000 *** p < 0.001; ** p < 0.01; * p < 0.05. Lower triangular matrix. Figure 1. Pearson correlation matrix for all nine constructs (N = 9,966). *** p < 0.001, ** p < 0.01, * p < 0.05. Source: EEA 2024, INEI. 4.2 Structural model results Table 8 presents the standardized path coefficients, standard errors, t-statistics, and p-values for all structural paths. Table 8. Structural path coefficients (Standardized). Block/H Path β SE t p Sig. R 2 Block 1: ICT → E-commerce H1 C1 ICT → C4 E-com. Sales 0.060 0.011 5.333 <0.001 *** H2 C2 Internet → C4 E-com. Sales 0.168 0.011 14.970 <0.001 *** 0.046 H3 C3 Digital HC → C4 E-com. Sales 0.040 0.010 4.013 <0.001 *** H1 C1 ICT → C5 E-com. Procurement 0.064 0.011 5.597 <0.001 *** H2 C2 Internet → C5 E-com. Procurement 0.068 0.011 5.994 <0.001 *** 0.018 H3 C3 Digital HC → C5 E-com. Procurement 0.052 0.010 5.197 <0.001 *** Block 2: ICT + Victimization → Security H4 C1 ICT → C7 Security Mgmt. 0.356 0.010 35.101 <0.001 *** H5 C2 Internet → C7 Security Mgmt. 0.164 0.010 15.989 <0.001 *** 0.220 — C4 E-com. → C7 Security 0.014 0.009 1.543 0.123 n.s. H6 C6 Victimization → C7 Security Mgmt. 0.060 0.009 6.748 <0.001 *** Block 3: Digitalization + Security → Performance H7 C1 ICT → C8 Firm Performance 0.327 0.010 31.659 <0.001 *** H9 C2 Internet → C8 Firm Performance 0.134 0.010 13.526 <0.001 *** — C3 Digital HC → C8 Firm Performance −0.032 0.009 −3.754 <0.001 *** 0.290 — C4 E-com. → C8 Firm Performance −0.017 0.009 −1.948 0.051 n.s. H8 C7 Security → C8 Firm Performance 0.197 0.010 20.560 <0.001 *** — C9 Economic Group → C8 Firm Performance 0.059 0.009 6.849 <0.001 *** 4.3 Model fit To assess the adequacy of the structural model, we evaluated fit indices following standard SEM guidelines ( Table 9 ). Model fit indices confirm an excellent fit between the theoretical model and the empirical data. CFI (0.988) and TLI (0.976) both exceed the 0.95 threshold. RMSEA (0.028) is well below 0.06. These fit statistics, along with the full path diagram ( Figure 2 ), demonstrate that the theoretical model adequately represents empirical relationships. The χ 2 /df ratio above 3.0 is expected with N > 9,000 given the χ 2 statistic’s sensitivity to large samples ( Kline, 2016 ). Table 9. Model fit indices. Index Value Reference Threshold Diagnosis CFI 0.988 ≥ 0.95 ✓ Excellent TLI 0.976 ≥ 0.90 ✓ Excellent RMSEA 0.028 ≤ 0.06 ✓ Excellent SRMR 0.041 ≤ 0.08 ✓ Excellent GFI 0.986 ≥ 0.95 ✓ Excellent χ 2 /df 4.12 ≤ 5.0 ✓ Acceptable AIC 84,231 Lower = better — BIC 84,419 Lower = better — Figure 2. Full path diagram of the three-block recursive SEM model. CFI = 0.988, TLI = 0.976, RMSEA = 0.028, GFI = 0.986. Solid lines = significant paths (p < 0.001). Source: EEA 2024, INEI. Source: semopy v2.3 estimation on EEA 2024 microdata. 4.4 Hypothesis testing All nine hypotheses were supported ( Table 10 ). ICT Infrastructure emerged as the dominant predictor across all equations, with the largest direct effect on Firm Performance (β = 0.327, p < 0.001). Security Management mediated significant indirect effects from both ICT Infrastructure and Internet Access. Table 10. Summary of hypothesis testing. H Relationship β (std.) Result H1 ICT Infrastructure → E-commerce Sales 0.060 *** Supported H2 Internet Access → E-commerce Sales 0.168 *** Supported H3 Digital Human Capital → E-commerce Sales 0.040 *** Supported H4 ICT Infrastructure → Security Management 0.356 *** Supported (strong) H5 Internet Access → Security Management 0.164 *** Supported H6 Criminal Victimization → Security Management 0.060 *** Supported H7 ICT Infrastructure → Firm Performance 0.327 *** Supported (strong) H8 Security Management → Firm Performance 0.197 *** Supported (strong) H9 Internet Access → Firm Performance 0.134 *** Supported *** p < 0.001. All nine hypotheses supported. 4.5 Effect sizes and explanatory power Cohen’s f 2 values confirm the economic significance of the structural relationships. Block 2 (Security Management): R 2 = 0.220, f 2 = 0.282 — medium-to-large effect. Block 3 (Firm Performance): R 2 = 0.290, f 2 = 0.408 — large effect. Block 1 (E-commerce Sales: R 2 = 0.046, f 2 = 0.048; Procurement: R 2 = 0.018, f 2 = 0.018) — small effects, consistent with Peru’s early-stage adoption environment. ICT Infrastructure produces the largest standardized coefficient in both the Security Management equation (β = 0.356) and the Firm Performance equation (β = 0.327). 4.6 Mediation analysis: Indirect effects Table 11 presents the decomposition of total effects into direct and indirect components. Table 11. Direct, indirect, and total effects on firm performance (C8). Predictor Direct Effect (β) Indirect via C7 (β) Total Effect (β) C1 ICT Infrastructure 0.327 *** 0.356 × 0.197 = 0.070 *** 0.397 *** C2 Internet Access 0.134 *** 0.164 × 0.197 = 0.032 *** 0.166 *** C6 Criminal Victimization — 0.060 × 0.197 = 0.012 *** 0.012 *** *** p < 0.001. Indirect path from C1 (β = 0.070) = 17.6% of total effect (β = 0.397). Criminal victimization operates exclusively through the security management channel. 4.7 Sector-level analysis Table 12 presents construct means by economic sector. Universities (ICT Infra = 0.738; Internet = 0.926; Security = 0.422) and Electricity & Energy (ICT Infra = 0.689; Security = 0.359) exhibit the highest levels of digitalization and security investment. Transport & Communications (Victimization = 0.184) and Commerce (Victimization = 0.182) report the highest victimization rates — positions these sectors as prime contexts for the security-performance mediation effect. Sector-level heterogeneity is illustrated in Figure 3 . Table 12. Construct means by economic sector. Sector N ICT Infra Internet E-com Sales Security Firm Size Victimization Universities 108 0.738 0.926 0.074 0.422 0.991 0.222 Restaurants 57 0.727 0.667 0.000 0.366 1.000 0.193 Electricity & Energy 95 0.689 0.568 0.000 0.359 1.000 0.168 Hydrocarbons 199 0.674 0.704 0.013 0.321 0.573 0.101 Manufacturing (large) 154 0.651 0.675 0.093 0.282 0.455 0.117 Industrial Fishing 87 0.645 0.851 0.078 0.208 0.529 0.126 Transport 798 0.625 0.555 0.021 0.300 0.709 0.184 Services 2,090 0.586 0.500 0.018 0.269 0.583 0.157 Private Education 242 0.576 0.500 0.025 0.272 0.665 0.161 Manufacturing (SME) 2,268 0.565 0.458 0.015 0.233 0.536 0.110 Construction 676 0.554 0.395 0.003 0.219 0.657 0.154 Commerce 2,973 0.552 0.333 0.028 0.247 0.696 0.182 Artisanal Fishing 165 0.605 0.291 0.005 0.284 0.739 0.048 Aquaculture 54 0.484 0.222 0.005 0.214 0.389 0.111 Figure 3. Construct means by economic sector (N = 9,966). Sectors sorted by ICT infrastructure mean. Source: EEA 2024, INEI. 4.8 Unexpected finding: Negative effect of digital human capital on performance Digital Human Capital (C3) exhibits a small but statistically significant negative direct coefficient on Firm Performance (β = −0.032, p < 0.001). This likely reflects a compositional artifact: micro-enterprises frequently report high PC-utilization rates because their small, homogeneous workforces are predominantly engaged in computer-based tasks, whereas large and medium enterprises have more heterogeneous workforces with substantial portions in non-digital roles (logistics, production, field operations), yielding comparatively lower average PC utilization ratios. The indirect effect of C3 through e-commerce is positive (β = +0.040 × 0.197 = +0.008), partially offsetting the negative direct path. 5. Discussion 5.1 Overview and theoretical integration: The digital resilience framework This study provides robust evidence for a three-block theoretical architecture where ICT infrastructure, security management, and firm performance co-evolve (R 2 = 0.220 for Security; R 2 = 0.290 for Performance). The theoretical novelty lies in the strategic synthesis of the Resource-Based View and Routine Activity Theory within the digital ecosystem of an emerging economy. By integrating these perspectives, we propose that in volatile environments like Peru’s, firm performance is not merely a product of technological endowment, but a result of “Digital Resilience” — a dynamic capability where security acts as a strategic mediator that ensures the continuity and integrity of digital rent-seeking activities. 5.2 Implications for emerging markets: The resilience gap Our findings challenge the technological determinism prevalent in studies from developed economies. In Peru, where institutional frameworks for cybersecurity and physical protection are still maturing, the firm becomes its own primary “guardian.” The significant mediating role of Security Management suggests that in environments characterized by institutional voids, security is a survival-critical capability. Unlike firms in Global North contexts that rely on robust external legal protections, Peruvian firms must internalize security as a core dynamic capability to prevent “value leakage” from their ICT investments. This “Digital Resilience” becomes a source of competitive advantage that is harder to imitate than mere hardware acquisition. 5.3 Block analysis and boundary conditions ICT Infrastructure is the dominant predictor across all equations (β = 0.356 in Security; β = 0.327 in Performance). E-commerce adoption faces a structural ceiling in Peru due to the low penetration of 7.8%. The non-significant path from E-commerce to Security (β = 0.014, p = 0.123) suggests a “security paradox”: firms are adopting digital channels faster than they are implementing the protective measures required to sustain them — a common vulnerability in rapidly digitizing emerging markets. 6. Conclusion This study investigated how ICT resources and security management interact to determine performance in 9,966 Peruvian firms (EEA 2024). ICT infrastructure is the keystone resource, acting as a platform that cascades its effects across security adoption and organizational scale. 6.1 Theoretical contributions This research extends RAT to the organizational level, demonstrating that the “capable guardian” mechanism is mediated by technological endowment. It also contributes to the RBV by empirically validating security management as a strategic resource — accounting for 17.6% of the total effect of ICT on performance. This reframes security from a “cost center” to a “value protector” in management literature. 6.2 Managerial implications • Security as a Profit Center: Managers must shift their perception of security from an operational expense to a strategic capability. Security management is the “translator” that allows ICT investments to yield actual profitability by ensuring business continuity. • Holistic Digital Human Capital: Connectivity alone is insufficient. Successful mediation depends on integrating human capital capable of managing risks. Firms should prioritize “digital hygiene” training to complement technical infrastructure. 6.3 Policy implications • Beyond Connectivity: Public policies in developing nations often focus solely on access. “Unprotected access” is inefficient. Governments should incentivize cybersecurity certification frameworks (e.g., ISO 27001) for SMEs to secure the national digital economy. • Targeted Infrastructure: Sectors like Commerce and Construction lag in connectivity (below 40%). Broadband expansion should be prioritized over general equipment subsidies to trigger the performance cascades identified in this model. 6.4 Limitations and future research While the sample size provides exceptional statistical power, the cross-sectional nature of EEA 2024 limits causal claims. Future research should exploit the longitudinal structure of the survey (2001–2024) to estimate dynamic panel models with lagged predictors. Furthermore, future studies should incorporate objective cybersecurity log data to triangulate self-reported security measures and extend the analysis to other Latin American EEA-equivalent surveys (DANE Colombia, INEGI Mexico) to assess regional generalizability. Data availability The analytical dataset underlying this study is publicly available in Zenodo: Dataset DOI: https://doi.org/10.5281/zenodo.19430763 Citation: Restrepo Morales, et. al., (2024) https://doi.org/10.5281/zenodo.19430763 License: CC-BY 4.0 . Contents: The dataset comprises 9,966 observations across 34 variables spanning nine constructs: ICT Infrastructure (C1), Internet Access (C2), Digital Human Capital (C3), E-commerce Sales (C4), E-commerce Procurement (C5), Criminal Victimization (C6), Security Management (C7), Firm Performance (C8), and Economic Group Membership (C9). Complete codebook and SEM estimation code (Python/semopy) are included in the repository. Access: Openly accessible, no login required. Data Source: Instituto Nacional de Estadística e Informática (INEI), Peru’s Annual Economic Survey (Encuesta Económica Anual) 2024. References Anderson R, Moore T: The economics of information security. Science. 2006; 314 (5799): 610–613. 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PubMed Abstract | Publisher Full Text Restrepo Morales JA, Rodríguez Flores EA, Suárez Pizzarello MA, et al. : Peru Annual Economic Survey 2024 (EEA 2024) - ICT and Security Modules. [Dataset]. Zenodo. 2024. Publisher Full Text Sobel ME: Asymptotic confidence intervals for indirect effects in structural equation models. Sociological Methodology. 1982; 13 : 290–312. Publisher Full Text Teece DJ, Pisano G, Shuen A: Dynamic capabilities and strategic management. Strategic Management Journal. 1997; 18 (7): 509–533. Publisher Full Text Tornatzky LG, Fleischer M: The processes of technological innovation. Lexington Books; 1990. Wernerfelt B: A resource-based view of the firm. Strategic Management Journal. 1984; 5 (2): 171–180. Publisher Full Text Zhu K, Kraemer KL: Post-adoption variations in usage and value of e-business by organizations: Cross-country evidence from the retail industry. Information Systems Research. 2005; 16 (1): 61–84. Publisher Full Text Zhu K, Kraemer KL, Xu S: The process of innovation assimilation by firms in different countries: A technology diffusion perspective on e-business. Management Science. 2006; 52 (10): 1557–1576. Publisher Full Text Comments on this article Comments (0) Version 1 VERSION 1 PUBLISHED 08 May 2026 ADD YOUR COMMENT Comment Author details Author details 1 Universidad Autonoma del Peru, Lima District, Lima Region, Peru 2 Dirección de Investigación e Innovación, Universidad Autonoma del Peru, Lima District, Lima Region, 15001, Peru 3 Ciencias Administrativas y Económicas, Tecnologico de Antioquia Institucion Universitaria, Medellín, Antioquia, 050001, Colombia Jorge A. Restrepo Morales Roles: Conceptualization, Methodology, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Eduar Antonio Rodríguez Flores Roles: Formal Analysis, Project Administration, Resources Marianella Alicia Suárez Pizzarello Roles: Data Curation, Writing – Review & Editing Freddy Zea Restrepo Roles: Investigation, Validation, Writing – Review & Editing Emerson Andrés Giraldo Betancur Roles: Data Curation, Software, Validation, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information The author(s) declared that no grants were involved in supporting this work. Article Versions (1) version 1 Published: 08 May 2026, 15:678 https://doi.org/10.12688/f1000research.179961.1 Copyright © 2026 Restrepo Morales JA et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Restrepo Morales JA, Rodríguez Flores EA, Suárez Pizzarello MA et al. Securing the Digital Edge: How Security Management Mediates the Impact of ICT Infrastructure on Firm Performance in Emerging Markets [version 1; peer review: awaiting peer review] . F1000Research 2026, 15 :678 ( https://doi.org/10.12688/f1000research.179961.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. 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