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Competition and consumer pressure correlate with higher AI adoption and frugal innovation in Jordanian manufacturing, and firms with more mature AI use see stronger social impacts from frugal innovations.

The drivers of AI adoption maturity and the domino effects on frugal innovation adoption and social impact
Khaled Saleh Al-Omoush · December 17, 2025 · International Journal of Innovation Science
openalex correlational low evidence 7/10 relevance Summary only summary available; pdf_status=not_found DOI Source

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Using a cross-sectional survey of Jordanian manufacturers, the paper finds that market competition and consumer pressure are associated with higher AI adoption maturity and frugal innovation, and that greater AI maturity amplifies the social impact of frugal innovation.

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Purpose This paper aims to examine the role of market competition, artificial intelligence (AI) adoption maturity and consumer pressures significantly in frugal innovation in the manufacturing sector. It also explores the potential impact of market competition and consumer pressures on AI adoption maturity and the impact of these interactions on social impact. Design/methodology/approach The empirical data for this study were collected through a structured survey from 331 managers, heads of departments and AI specialists in the manufacturing sector of Jordan and analyzed using the Smart PLS program. Findings The results revealed that market competition and consumer pressures significantly impact AI adoption maturity and frugal innovation. The findings also confirmed that AI adoption maturity significantly improves frugal innovation efforts. Furthermore, it was found that AI adoption maturity significantly moderates the role of frugal innovation in creating social impact. Research limitations/implications This study is constrained by a sample of manufacturers in Jordan and one questionnaire that could limit generalization of results and predispose the study to bias. Furthermore, the quantitative cross-sectional type is not able to show the dynamics of the temporal nature or qualitative settings of the relationships between the variables. Therefore, this paper recommends further research, which is geographically and cross-sector extended, the utilization of longitudinal designs and the utilization of qualitative research to investigate causal associations and the processes of organization and culture in more depth. Practical implications The model can be applied in practice by businesses to understand how the adoption and maturity of AI can affect rational innovation and responsiveness to market and consumer pressures. It helps companies to develop strategies that could contribute to the increase of the positive social value of innovations and add competitiveness. The management can use this model to focus on the investments it has made in technology and expand the capacity needed to become a successful innovator. Originality/value The contribution of the present research lies in its focus on the institutional and social features of frugal innovation, which has a notably strong role to play to maximize it, but the maturity of AI plays a key role in this. This study can not only research the direct relationships between variables but also examine the relationship between the institutional forces, digital changes and innovation of firms. It provides practical suggestions on the necessity to invest in AI to become a sustainable, frugal innovator. Additionally, it is a good academic contribution because it integrates not only methodological novelty with AI but also social elements, in this way expanding the horizons of future studies in the area of AI adoption, frugal innovation and social impacts of innovation.

Summary

Main Finding

AI adoption maturity amplifies frugal innovation and its social impact. Market competition and consumer pressures both directly increase AI adoption maturity and stimulate frugal innovation; higher AI maturity both strengthens firms’ frugal-innovation outcomes and moderates (enhances) the ability of frugal innovation to generate positive social impact.

Key Points

  • Sample & context: survey of 331 managers, department heads, and AI specialists in Jordan’s manufacturing sector.
  • Direct effects:
    • Market competition → positively associated with AI adoption maturity.
    • Consumer pressures → positively associated with AI adoption maturity.
    • Market competition and consumer pressures → positively associated with frugal innovation.
    • AI adoption maturity → positively associated with frugal innovation.
  • Moderation effect: AI adoption maturity significantly moderates the relationship between frugal innovation and social impact (i.e., when AI maturity is higher, frugal innovation produces greater social impact).
  • Practical takeaway: Investing in AI capability and maturing AI adoption helps firms respond to market/consumer pressures and enhances frugal, socially valuable innovations.
  • Limitations: single-country (Jordan) manufacturing sample, single cross-sectional questionnaire (possible common-method bias), inability to capture temporal dynamics or in-depth causal processes.

Data & Methods

  • Data: structured survey of 331 manufacturing-sector respondents (managers, heads of departments, AI specialists) in Jordan.
  • Constructs (as reported): market competition, consumer pressures, AI adoption maturity, frugal innovation, social impact.
  • Analysis: Partial Least Squares Structural Equation Modeling (SmartPLS) to test direct effects and moderation (interaction) effects.
  • Study design: quantitative, cross-sectional. Authors note need for longitudinal and qualitative follow-ups to unpack causal mechanisms and organizational/cultural processes.

Implications for AI Economics

  • Complementarity and capability building: AI adoption maturity functions as a strategic capability that complements frugal-innovation processes, implying returns to investments in AI not only via productivity but also via improved socially beneficial innovation outcomes.
  • Demand- and competition-driven diffusion: Competitive markets and consumer pressures accelerate firms’ AI adoption; economic models of AI diffusion should incorporate demand-side and competitive incentives as key drivers.
  • Policy levers: Policymakers seeking socially beneficial, low-cost innovation (frugal innovation) can promote AI capability-building (training, subsidies, standards) to magnify social returns from firm-level innovation.
  • Research agenda for AI economics:
    • Extend evidence across countries and sectors to assess external validity and heterogeneous returns to AI maturity.
    • Use longitudinal and quasi-experimental designs to identify causal effects of AI maturity on innovation and social welfare.
    • Quantify welfare and distributional impacts: how AI-enabled frugal innovation affects consumer surplus, employment, and firm-level productivity in resource-constrained settings.
    • Explore mechanisms: complementarities between digital investments, organizational practices, and institutional forces that translate AI capability into socially valuable innovations.

Assessment

Paper Typecorrelational Evidence Strengthlow — Findings are based on self-reported, cross-sectional survey data and PLS-SEM associations, which are vulnerable to common-method bias, reverse causality and omitted variables; the single-country, single-sector sample and non-random sampling further limit causal claims and external validity. Methods Rigormedium — Sample size (n=331) is reasonable for SEM and PLS-SEM is an accepted approach for exploratory structural models and moderation tests, but rigor is reduced by cross-sectional design, likely non-random sampling, reliance on self-reported measures, and absence of stronger identification strategies (e.g., longitudinal data, exogenous shocks, or instruments). Sample331 respondents (managers, department heads and AI specialists) from firms in the manufacturing sector in Jordan; data collected via a structured, single cross-sectional questionnaire. Themesinnovation adoption org_design governance IdentificationCross-sectional survey of 331 managers/department heads/AI specialists in Jordanian manufacturing analyzed with PLS-SEM to estimate associations, direct effects and moderation; identification relies on model specification, measurement validity and control variables rather than exogenous variation or temporal ordering (no instrumental variables, experiments, or panel-based causal leverage). GeneralizabilitySingle-country (Jordan) context may not transfer to other institutional, regulatory or economic environments, Single-sector (manufacturing) limits applicability to services or other industries, Manager/AI-specialist self-reports may not reflect firm-level objective performance or social outcomes, Cross-sectional design prevents inference about temporal dynamics or causal direction, Potential non-random sampling limits representativeness across firm sizes and types, Measures of 'AI adoption maturity', 'frugal innovation' and 'social impact' likely subjective and context-specific

Claims (8)

ClaimDirectionOutcomeConfidence & EvidenceDetails
Market competition significantly impacts AI adoption maturity. Adoption Rate positive AI adoption maturity
Reading fidelity high
Study strength medium
n=331
0.3
Consumer pressures significantly impact AI adoption maturity. Adoption Rate positive AI adoption maturity
Reading fidelity high
Study strength medium
n=331
0.3
Market competition significantly impacts frugal innovation. Innovation Output positive frugal innovation
Reading fidelity high
Study strength medium
n=331
0.3
Consumer pressures significantly impact frugal innovation. Innovation Output positive frugal innovation
Reading fidelity high
Study strength medium
n=331
0.3
AI adoption maturity significantly improves frugal innovation efforts. Innovation Output positive frugal innovation
Reading fidelity high
Study strength medium
n=331
0.3
AI adoption maturity significantly moderates the role of frugal innovation in creating social impact. Consumer Welfare positive social impact (as influenced by frugal innovation)
Reading fidelity high
Study strength medium
n=331
0.3
The proposed model can be applied in practice by businesses to understand how AI adoption and maturity affect frugal innovation and responsiveness to market and consumer pressures, and to help increase the social value of innovations and competitiveness. Organizational Efficiency positive organizational responsiveness/competitiveness and social value of innovations
Reading fidelity medium
Study strength speculative
n=331
0.03
The study's contribution lies in focusing on the institutional and social features of frugal innovation and highlighting that AI maturity plays a key role in maximizing frugal innovation. Innovation Output positive conceptual advancement in understanding frugal innovation and AI maturity
Reading fidelity high
Study strength speculative
n=331
0.05

Notes