Indian SMEs that recognize clear functional and efficiency benefits from AI report stronger innovation capabilities and durable competitive advantage; technical complexity nudges firms to build capabilities rather than blocking benefits, and regulation — if well-targeted — can amplify those gains.

Understanding AI-Driven Innovation and Sustainable Competitive Advantage in Indian SMEs

Aruna Polisetty, Sowmya G, Debarun Chakraborty, Nripendra P. Rana, Rishabh Rajan · August 11, 2026 · Journal of Computer Information Systems

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Aruna Polisetty provider ID
Sowmya G provider ID
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Among Indian SMEs, perceived functional benefits and efficiency gains from AI strongly predict perceived innovation capabilities and sustained competitive advantage, with innovation capabilities mediating these links, technical complexity operating indirectly via capability development, and regulatory support moderating outcomes while perceived costs are viewed as strategic investments.

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This study examines small and medium-sized enterprises (SMEs) operating in India, one of the world’s largest emerging economies, to understand how artificial intelligence (AI) value perceptions influence innovation capabilities and sustainable competitive advantage. Drawing on the Value Adoption Model (VAM) and Resource-Based View (RBV), the research investigates the role of functional benefits, efficiency gains, technical complexity, and perceived costs in shaping innovation outcomes. Using a mixed-method approach and PLS-SEM analysis on data from SME managers, the findings reveal that functional and efficiency benefits significantly enhance both perceived innovation capabilities (PIC) and sustained competitive advantage (SCA). While technical complexity contributes indirectly through capability development, perceived costs act as strategic investments rather than barriers. Innovation capabilities further mediate these relationships, reinforcing their importance as strategic assets. Regulatory support plays a nuanced moderating role, strengthening or constraining competitive outcomes depending on its intensity. The study contributes to information systems literature by integrating value perception and capability-based perspectives to explain AI-driven competitiveness in emerging economies, although the empirical evidence is derived exclusively from India.

Summary

Main Finding

Perceptions of AI value among Indian SMEs—especially functional benefits and efficiency gains—significantly strengthen perceived innovation capabilities (PIC) and sustained competitive advantage (SCA). Technical complexity influences competitiveness indirectly by driving capability development, while perceived costs are treated more as strategic investments than adoption barriers. Innovation capabilities mediate the effects of AI value perceptions on sustained advantage, and regulatory support moderates outcomes in a context-dependent way.

Key Points

Theoretical framing: integrates the Value Adoption Model (VAM) with the Resource-Based View (RBV) to link value perceptions to capability formation and competitive outcomes.
Positive drivers:
- Functional benefits (usefulness) → strong direct positive effects on PIC and SCA.
- Efficiency gains (productivity) → strong direct positive effects on PIC and SCA.
Complexity and cost:
- Technical complexity does not block outcomes directly but spurs capability development that then affects competitiveness (indirect effect).
- Perceived costs are interpreted by managers as strategic investments rather than straightforward deterrents to AI adoption.
Mediation and moderation:
- Innovation capabilities mediate the relationship between AI value perceptions and sustained competitive advantage.
- Regulatory support has a nuanced moderating role: appropriate support can strengthen competitive outcomes, while overly burdensome or weak regulation can constrain them.
Contribution: bridges information-systems value-perception approaches and capability-based views to explain AI-driven competitiveness in emerging-market SMEs.
Limitation: findings are based solely on data from India, limiting generalizability.

Data & Methods

Population: Small and medium-sized enterprises (SMEs) operating in India; respondents were SME managers.
Approach: Mixed-methods design (qualitative + quantitative components).
Quantitative analysis: Partial Least Squares Structural Equation Modeling (PLS-SEM) to test relationships among constructs (functional benefits, efficiency gains, technical complexity, perceived costs, perceived innovation capabilities, sustained competitive advantage) and moderation by regulatory support.
Empirical inference: Hypothesis testing focused on direct, indirect (mediation), and moderation effects; interpretation grounded in VAM and RBV.

Implications for AI Economics

For firm strategy and resource allocation:
- Treat AI-related expenditures as strategic investments that build firm-specific capabilities yielding durable returns, rather than as sunk adoption costs.
- Prioritize AI use-cases with clear functional and efficiency benefits to accelerate capability accumulation and competitive advantage.
- Invest in capability development (skills, processes, integration) to convert technical complexity into long-term value.
For policymakers and regulators:
- Calibrate regulatory support to enable capability building (e.g., standards, training subsidies, experimentation sandboxes) rather than imposing heavy compliance costs that could stifle SME competitiveness.
- Recognize that regulatory intensity matters: well-targeted, enabling regulation can accelerate productive AI diffusion in SMEs; overly strict or absent regulation can both have negative effects.
For economic modeling of AI diffusion:
- Incorporate heterogeneous firm perceptions and capability dynamics (not just adoption binary) into diffusion and productivity models for emerging economies.
- Model firms’ cost perceptions as investment decisions with option-value considerations and long-run capability returns, rather than as immediate adoption thresholds.
For generalizability and future research:
- Extend empirical testing beyond India to assess cross-country variation in how value perceptions, capability formation, and regulation interact.
- Quantify longer-term productivity and market-structure impacts of AI-driven capability accumulation among SMEs.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on cross-sectional, self-reported survey data and PLS-SEM associations; mediation/moderation are statistical but not causal without exogenous variation, temporal ordering, or robust controls for omitted variables and common-method bias. Methods Rigormedium — Appropriate latent-variable technique (PLS-SEM) and a mixed-methods design strengthen construct validity and theoretical integration, but risks include cross-sectional design, self-reported outcomes, potential common-method variance, unclear sampling representativeness, and lack of objective performance measures. SampleSME managers in India (small and medium-sized enterprises); mixed-methods design with qualitative component and a quantitative survey analyzed via PLS-SEM; exact sample size, sampling frame, sectoral breakdown, and sampling procedure not reported in supplied text. Themesinnovation adoption productivity org_design IdentificationCross-sectional survey of SME managers analyzed with PLS-SEM to estimate hypothesized direct, indirect (mediation) and interaction (moderation) paths; identification relies on theoretical priors (VAM and RBV) and statistical associations rather than exogenous variation, experiments, instruments, or longitudinal change. GeneralizabilitySingle-country (India) — cultural, institutional, and market factors may differ elsewhere, SMEs only — may not apply to large firms or multinationals, Manager-reported perceptions and self-assessed competitive advantage rather than objective performance metrics, Cross-sectional data — limited ability to infer temporal/causal dynamics, Unclear sampling frame/representativeness and potential sectoral heterogeneity

Claims (7)

Claim	Direction	Outcome	Confidence & Evidence	Details
Perceived functional benefits of AI have significant positive direct effects on SMEs' perceived innovation capabilities and sustained competitive advantage. Firm Productivity	positive	Perceived innovation capabilities and sustained competitive advantage	Reading fidelity high Study strength medium	not reported 0.3
Perceived AI efficiency gains have significant positive direct effects on SMEs' perceived innovation capabilities and sustained competitive advantage. Firm Productivity	positive	Perceived innovation capabilities and sustained competitive advantage	Reading fidelity high Study strength medium	not reported 0.3
Technical complexity does not directly block competitiveness; instead, it contributes indirectly by stimulating innovation capability development, which then affects sustained competitive advantage. Task Allocation	mixed	Innovation capability development and sustained competitive advantage	Reading fidelity high Study strength medium	not reported 0.3
Perceived AI-related costs are interpreted by SME managers as strategic investments rather than straightforward barriers to AI adoption. Adoption Rate	positive	Interpretation of AI costs and their role in adoption decisions	Reading fidelity high Study strength low	not reported 0.15
Innovation capabilities mediate the relationship between AI value perceptions and sustained competitive advantage among Indian SMEs. Firm Productivity	positive	Sustained competitive advantage through perceived innovation capabilities	Reading fidelity high Study strength medium	not reported 0.3
Regulatory support moderates the relationship between AI-related value perceptions, capability development, and competitive outcomes, with enabling support strengthening outcomes while overly burdensome or weak regulation can constrain them. Governance And Regulation	mixed	Competitive outcomes and capability development under different regulatory conditions	Reading fidelity high Study strength medium	not reported 0.3
The study's empirical findings are based solely on Indian SMEs, limiting their generalizability to other national contexts. Other	negative	External validity and cross-country generalizability	Reading fidelity high Study strength high	not reported 0.5