Evidence map›Paper›PMID 39866395›Full record

ArticleHeliyon2025

Unearthing the determinants of digital innovation adoption in the agricultural sector: The role of food security awareness and agricultural experience.

David Aboagye-Darko, Peter Mkhize

Abstract read
In one paragraph

Article in Heliyon, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

David Aboagye-DarkoDepartment of Information Technology Studies, University of Professional Studies, Accra, Ghana.
Peter MkhizeDepartment of Information Systems, College of Science, Engineering, and Technology, University of South Africa, Johannesburg, South Africa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Extant research has widely acknowledged the role of digital innovation as a facilitator of digital transformation, presenting solutions for various challenges in various industries. However, prior research demonstrates inadequate discussions on the determinants of digital innovation adoption for digital transformation in developing countries, particularly in the agricultural sector. To address this gap, this study investigates the effect of food security awareness, innovation characteristics, and the moderating role of agricultural experience on behavioral intention to adopt digital innovation in the agricultural sector. A dyadic model based on diffusion innovation theory and the technology acceptance model is proposed to investigate the phenomenon. This study employed a cross-sectional quantitative approach to investigate the phenomenon based on survey data collected from 207 study participants in Ghana's agricultural sector and the partial least square structural equation modeling technique. The study's findings revealed that personal innovativeness significantly affects food security awareness (β = 0.574; p < 0.000), relative advantage (β = 0.699; p < 0.000), compatibility (β = 0.687; p < 0.000), and complexity (β = 0.312; p < 0.000). In addition, food security awareness (β = 0.336; p < 0.000), compatibility (β = 0.257; p < 0.000), and agricultural experience (β = 0.238; p < 0.003) significantly affect behavioral intention to adopt digital innovation. Furthermore, the study revealed that agricultural experience (β = -0.145; p < 0.036) moderates the relationship between compatibility and behavioral intention. Together, these variables explain 78.9 % of the variance in behavioral intention to adopt digital innovation in the agricultural sector in Ghana. The study contributes to the literature on digital innovation adoption in the agricultural sector in developing countries and proffers actionable insights for practitioners.

Indexed as

Agricultural experienceAgricultural sectorDigital innovationFood security awarenessGhana

Identifiers

PMID39866395
PMCPMC11760315

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.