AI Innovation
Ongoing academic research

AI adoption and entrepreneurial innovation in Romanian agri-food SMEs

How far has artificial intelligence reached Romanian farms and food plants, what speeds it up and what holds it back? A study built on real data from entrepreneurs — you can contribute in about 12 minutes, anonymously.

Take the questionnaire About the research
Agriculture for Life, Life for Agriculture (16th edition) — USAMV București · 3–5 June 2027
—validated responses
7thematic sections
≈ 12′completion time
Anonymousno mandatory contact data
Context

Why AI matters for the agri-food sector

The Romanian agri-food sector consists largely of small and medium-sized enterprises with limited resources, strong seasonality and uneven access to digital infrastructure. At the same time, AI tools — from generative models to sensors, satellite imagery and forecasting — are becoming accessible even to small firms.

Eurostat statistics on the use of AI by enterprises show a marked gap between member states, and Romania is among those with low usage. Detailed evidence on agri-food SMEs is still missing: who adopts, why, and with what results for innovation. This study aims to fill that gap.

Research question

“Which technological, organisational and environmental factors explain AI adoption in Romanian agri-food SMEs, and how is it linked to entrepreneurial innovation?”

Paper prepared for the international conference Agriculture for Life, Life for Agriculture (16th edition) — AI for Agriculture and Food Systems: from Data to Decisions, from Innovation to Impact.

Scope of the study

From field to fork: six families of activities

We include SMEs from the whole agri-food chain to compare how AI adoption differs between production, processing and services.

Crop farming

Cereals, industrial crops, field vegetables — the base of the agri-food chain.

Livestock

Livestock and mixed farms, from smallholdings to commercial holdings.

Horticulture and viticulture

Orchards, greenhouse vegetables, vineyards and wine — high value-added sectors.

Dairy and meat

Small and medium processors: traceability, quality and food safety.

Bakery and beverages

Bakeries, mills, beverage producers — digitalisation and production planning.

Inputs, machinery, services

Agricultural trade and services, agritourism and short supply chains.

Conceptual framework

The study model

We combine the Technology–Organisation–Environment (TOE) framework with diffusion of innovations and entrepreneurial orientation to explain AI adoption and its effects on innovation.

TechnologyUsefulness · Ease · CompatibilityH1 OrganisationLeadership · Skills · Resources/dataH2 EnvironmentPressure · Support · InfrastructureH3 · Barriers H4 (−) AI adoptionAIX index 0–100 H5 Innovation and performanceProduct · Process · MarketingOrganisation · SustainabilityRelative performance Entrepreneurial orientationInnovativeness · Proactiveness · Risk H6 moderation Controls: firm size, sector, export orientation.
TechnologyUsefulness · Ease · Compatibility · H1
OrganisationLeadership · Skills · Resources/data · H2
EnvironmentPressure · Support · Infrastructure · H3 · Barriers H4 (−)
↓
AI adoptionAIX index 0–100
↓ H5
Innovation and performanceProduct · Process · Marketing · Organisation · Sustainability
Entrepreneurial orientationmoderates the adoption–innovation link · H6
Hypotheses

What we test

H1

Technological factors (usefulness, ease of use, compatibility) are positively associated with AI adoption.

H2

Organisational factors (management support, skills, resources and data) are positively associated with AI adoption.

H3

Environmental pressure and support are positively associated with AI adoption.

H4

Perceived barriers are negatively associated with AI adoption.

H5

AI adoption is positively associated with innovation intensity (product, process, marketing, organisational, sustainability).

H5b

AI adoption is positively associated with perceived relative performance.

H6

Entrepreneurial orientation strengthens the relationship between AI adoption and innovation (moderation).

Methodology

How the study is conducted

  1. Design

    Cross-sectional quantitative study based on a structured online questionnaire (1–5 Likert scales, single/multiple choice and 3 optional open questions).

  2. Population and sample

    SMEs (as defined by Recommendation 2003/361/EC) in agriculture, food processing and related services in Romania. Convenience sampling with distribution through professional networks (snowball); preliminary target: 150 valid responses.

  3. Instrument

    Constructs are adapted from the literature (TOE, TAM/UTAUT, entrepreneurial orientation, Oslo Manual) and will be checked for reliability (Cronbach’s alpha) and piloted.

  4. Analysis

    Descriptive statistics, Spearman correlations, Kruskal–Wallis group tests, multiple regression and moderation analysis for H6. Data are analysed in aggregate.

The instrument at a glance

A. The firm and the respondent B. Artificial intelligence in your firm C. Factors shaping the decision D. Obstacles to AI adoption E. Entrepreneurial culture of the firm F. Innovation and outcomes G. Closing

Questions adapt automatically: if the firm does not use AI, the usage questions are skipped. At the end every participant receives a personal profile compared with the sample average.

01

Anonymous

We do not ask for the company name, tax ID or personal data. The IP address is not stored in clear, only as an irreversible fingerprint used to detect duplicate submissions.

02

Voluntary

You may stop at any time, return through a resume link, or delete your own answers at the end of the questionnaire. Data are used for research only.

03

Secure (GDPR)

Encrypted connection, database on a dedicated EU server, encrypted backups. The optional e-mail is stored encrypted and separately from the answers.

Full privacy notice

Research team

Joint research: entrepreneurial practice and agri-food expertise

The paper and the practical study are carried out jointly by two PhD engineers with complementary experience in training, entrepreneurship and the agri-food field.

Dr. ing. Panait Mihai Vlad

Dr. ing. Panait Mihai Vlad

Doctor in professional training · Trainer · Certified coach · Entrepreneur

University teaching staff, Artifex University of Bucharest — Faculty of Management-Marketing

University teaching staff and trainer with hands-on experience in business development, adult professional training and working with entrepreneurs.

EntrepreneurshipProfessional trainingLeadershipDigital marketing

panaitmihaivlad.ro

Centrul de Formare Profesională Eurodeal

Administrator of the centre — an authorised adult vocational-training provider, active since 2002. In the AI area: the online course “Artificial Intelligence – Digital Marketing from A to AI” (live sessions, 2 weeks).

About EurodealThe artificial intelligence course
Dr. ing. Ștefan Petrică

Dr. ing. Ștefan Petrică

University lecturer · PhD in agronomy · Trainer

USAMV Bucharest — Faculty of Management and Rural Development

University teaching staff since 2008, currently university lecturer, specialised in the agri-food and public-catering fields: culinary technology, agritourism, quality and innovation management in the agri-food sector. Economic engineer in Public Catering and Agritourism, PhD in agronomy (USAMV Bucharest, 2011), trainer with a pedagogical module and certified quality auditor.

Agri-foodCulinary technologyQuality and innovation managementAgritourism
How to cite
Panait Mihai, V., & Petrică, Ș. (2026). AI Adoption and Entrepreneurial Innovation in Romanian Agri-Food SMEs (online questionnaire, data collection in progress). https://panaitmihaivlad.ro/ai-innovation
FAQ

Frequently asked questions

Who can take part?

Owners, managers or decision-makers of Romanian agri-food SMEs (farming, processing, agricultural trade/services, agritourism). You do not need to use AI — answers from firms that do not use it are equally valuable.

How long does it take and can I continue later?

About 12 minutes. Progress is saved after each section and you receive a resume link on screen.

What do I get in return?

A personal AI-adoption profile compared with the sample average (once enough responses exist) and, optionally, a summary of the study results.

How are the data used?

Only in aggregate, in the scientific paper and research materials. Individual answers are never published and data are never sold or passed on.

Help build an overview of AI in the Romanian agri-food sector

12 minutes, anonymous, with results useful for the whole sector.

Start the questionnaire
Selected references
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