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Digital motivation, knowledge, and skills: Pathways to adaptive millennial farmers Cover

Digital motivation, knowledge, and skills: Pathways to adaptive millennial farmers

Open Access
|Mar 2025

Figures & Tables

Figure 1

Hypothetical framework.
Hypothetical framework.

Results of the validity and reliability tests

Construct validityReliability
VariablesKaiser-Meyer-Olkin (KMO)Bartlett’s test of sphericity (sig.)Factor loadingCronbach’s α
Cut-off values [37,38,39]≥0.5≤0.5≥0.5≥0.6
Digital motivation0.6850.0010.599−0.9320.820
Digital knowledge0.8180.0010.790−0.9240.907
Digital skills0.6900.0010.554−0.9170.785
Online participation0.7710.0010.786−0.9530.848
Adaptive performance0.7400.0010.597−0.9890.934

List of survey items

VariablesIndicatorsSource
Digital motivation
MOV1Using digital ICT helps me more easily understand farming business[29], [30], [31]
MOV2I enjoy communicating using digital ICT
MOV3I use digital ICT because I believe it will increase efficiency in completing daily work
MOV4I am more effective in using digital ICT than other forms of communication
MOV5My digital ICT interactions are more productive than face-to-face interactions
MOV6I am highly motivated to use digital ICT to communicate with fellow farmers
MOV7I am highly motivated to use digital ICT to communicate with extension workers
Digital knowledge
KNW1I am very good at communicating through digital ICT[29], [30], [31]
KNW2I am never confused to say something through digital ICT
KNW3I understand very well how to communicate through digital ICT
KNW4I always know how to express myself through digital ICT
KNW5I know how to send messages through digital ICT
Digital skills
SKL1I can fix the root of the problem when the internet would not connect[29], [30], [31]
SKL2I can use various digital ICT programs/applications smoothly
SKL3I can use digital ICT to browse the latest and most up-to-date agricultural information
SKL4I can evaluate the credibility of agricultural information sources through digital ICT
SKL5I am proficient in using audio visuals to enhance the effectiveness of messages through digital ICT
Online participation
OLP1I often utilize digital ICTs to communicate with extension agents[32], [33]
OLP2I always utilize digital ICTs to communicate with other farmers
OLP3I often engage in discussions regarding agricultural business problem encountered through digital ICT
OLP4I often share weather and climate information through social media platforms
OLP5I share my farming technique through social media platforms
OLP6I often share agricultural technology information through social media platforms
OLP7I always disseminate market and price information through social media platforms
OLP8I share agricultural policy information through social media platforms
OLP9I often share risk management information through social media platforms
Adaptive performance
ADV1I sought assistance when encountering challenges in my farming business[34], [35]
ADV2I am open to criticism of my farming business
ADV3I endeavor to acquire knowledge from the feedback provided by various individuals regarding my farming business
ADV4I try to always update my farming knowledge
ADV5I try to always update my farming skills
ADV6I am able to cope well when difficult situations and setbacks occur in my farming business
ADV7I recover quickly, after difficult situations or downturns occur in the farming business
ADV8I am able to find creative solutions to new problems
ADV9I am able to cope well with uncertain and unpredictable situations in farming business
ADV10I easily adapt to changes in my farming business

Demographics of millennial farmers

DescriptorCategoryFrequencyPercentage (%)
GenderMale16648.12
Female17951.88
Age17–25 years old11633.62
26–35 years old17751.30
36–45 years old5215.07
EducationElementary school3610.43
Junior high school5114.78
Senior high school20258.55
University5616.23
Land ownership<5,000 m2 29986.67
5,000–10,000 m2 267.54
>10,000 m2 205.80
ICT ownershipSmartphone33897.97
Tab/tablet10.29
Laptop41.16
Computer/PC20.58
Internet access<1 h/day8624.93
1–4 h/day11433.04
>4 h/day14542.03

Summary mediation effect

Indirect effectCoeff.95% Conf. Int. t-valueSig.
Motivation → Knowledge → Skills0.401[0.247, 0.549]5.180Sig
Skills → Online participation → Adaptive0.158[0.112–0.211]6.255Sig

Convergent reliability and validity

Latent variableLoading factorCronbach’s α Composite reliabilityAVE
Cut-off value>0.7>0.7>0.7>0.5
Motivation0.721–0.7830.8230.8760.585
Knowledge0.717–0.8980.8990.9260.717
Skills0.739–0.8170.8410.8870.612
Online participation0.723–0.8440.9170.9300.624
Adaptive0.709–0.8470.9470.9540.676

Path coefficient and hypotheses testing

HypothesesPathPath coefficientSD t-valueHypotheses support
H1Motivation → Skills0.2830.0793.558Yes
H2Motivation → Knowledge0.7330.03719.648Yes
H3Knowledge → Skills0.5470.0965.712Yes
H4Skills → Adaptive0.3380.0665.129Yes
H5Skills → Online participation0.4280.0508.615Yes
H6Online participation → Adaptive0.3700.0487.796Yes

Measurement model-discriminant validity

Fornell−Larcker criterionHTMT ratio
ADVKNWMOVOLPSKLADVKNWMOVOLPSKL
ADV 0.822
KNW0.382 0.847 0.416
MOV0.4200.733 0.765 0.4740.852
OLP0.5150.4040.403 0.790 0.5130.4180.435
SKL0.4970.7540.6840.428 0.782 0.5500.8670.8140.457
Language: English
Submitted on: Dec 2, 2024
Accepted on: Feb 28, 2025
Published on: Mar 22, 2025
Published by: Sciendo
In partnership with: Paradigm Publishing Services

© 2025 Hari Otang Sasmita, Amiruddin Saleh, Wahyu Budi Priatna, Pudji Muljono, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 License.