Motivational factors influencing the adoption of frugal innovation among agrifood SMEs in Surakarta, Indonesia

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Type of the article: Research Article

Abstract
Agrifood SMEs play a vital role in producing high-value-added processed products, enhancing the potential of local agricultural commodities, and supporting food availability and diversity. However, these SMEs face various challenges, including limited resources and capital, the need to adapt to rapid innovation and shifting consumer preferences, and volatile purchasing power amid economic fluctuations. This study aims to analyze the motivational factors influencing the adoption of frugal innovation among agrifood SMEs in Surakarta City, Indonesia. A quantitative method was employed, involving a sample of 150 agrifood SMEs in Surakarta. Data were collected through surveys and direct interviews between April and June 2025. The data were analyzed using structural equation modeling (SEM) with partial least squares (PLS) in SmartPLS 4. The majority of respondents’ businesses have been operating for less than 10 years and generate monthly revenue ranging from IDR 5,000,000 to IDR 40,000,000. The findings reveal that compatibility (ꞵ = 0.191, p < 0.05), low-cost manufacturing (ꞵ = 0.220, p < 0.05), digital platforms (ꞵ = 0.258, p < 0.05), sustainable leadership (ꞵ = 0.160, p < 0.05), technology turbulence (ꞵ = 0.188, p < 0.05), and external sources of knowledge (ꞵ = 0.172, p < 0.05) significantly encourage the adoption of frugal innovation. Moreover, external knowledge sources partially mediate the influence of sustainable leadership on the adoption of frugal innovation.

Acknowledgment
The authors are grateful to Universitas Sebelas Maret, Surakarta, Central Java, Indonesia, for funding this study with Contract No: 371/UN27.22/PT.01.03/2025.

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    • Figure 1. Conceptual model
    • Figure 2. Path model diagram
    • Table 1. Rule of thumb for outer model, structural model, and hypothesis testing
    • Table 2. Factor loading, AVE, Cronbach’s alpha, and composite reliability
    • Table 3. VIF and F-squared values
    • Table 4. R2 and Q2 values
    • Table 5. Bootstrapping
    • Table A1. Demographic characteristics
    • Table A2. Questionnaire
    • Table B1. Fornell–Larcker
    • Table B2. Heterotrait–monotrait ratio (HTMT)
    • Table B3. Cross-loading values
    • Conceptualization
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Data curation
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Formal Analysis
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Investigation
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Methodology
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Project administration
      Nuning Setyowati
    • Resources
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Software
      Nuning Setyowati
    • Supervision
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Validation
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Writing – original draft
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
    • Writing – review & editing
      Nuning Setyowati, Emi Widiyanti, Fanny Widadie
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Problems and Perspectives in Management