Determinants of the Utilization of Digital Technologies by Smallholder Farmers in Eastern Cape Province, South Africa

Fig. 1.

Fig. 2.

Factors affecting the adoption and utilization of digital technologies

Construct	Factors	Author(s)
Demographic	Age	(Shang et al., 2021) (Michels, von Hobe and Musshoff, 2020) (Khan et al., 2022) (Groher, Heitkämper and Umstätter, 2020) (Michels et al., 2020)
	Gender	(Drewry et al., 2019) (Groher, Heitkämper and Umstätter, 2020)
	Marital status	(Kaur, Walia and Singh, 2022) (Reisdorf, 2011) (Vimalkumar, Singh and Sharma, 2021)
	Educational levels (competences)	(Shang et al., 2021) (Drewry et al., 2019) (Carrer et al., 2022) (Khan et al., 2022) (Michels et al., 2020) (Giua, Materia and Camanzi, 2022a)
	Tenure	(Shang et al., 2021)
	Farming experience	(Shang et al., 2021) (Carrer et al., 2022)
Social	Social influence	(Giua, Materia and Camanzi, 2022a) (Sood, Bhardwaj and Sharma, 2022)
	Farm succession	(Shang et al., 2021)
Economic	Employment status	(Rodriguez Castelan et al., 2021)
	Income	(Shang et al., 2021) (Drewry et al., 2019)
	Source of income	(Shang et al., 2021)
Farm characteristics	Enterprise	(Shang et al., 2021)
	Farm size	(Drewry et al., 2019) (Carrer et al., 2022) (Khan et al., 2022) (Michels et al., 2020)
	Labour	(Shang et al., 2021)
Institutional	Extension	(Musyoki et al., 2022)
	Farmer groups	(Giua, Materia and Camanzi, 2022a)
	Distance to market	(Musyoki et al., 2022)
Technology characteristics	Technology attributes, performance expectation, complexity	(Giua, Materia and Camanzi, 2022a)
		(Shang et al., 2021)

Descriptive statistics

Question	Answer	%
1	2	3
Used any digital technologies	Yes	55.36
	No	44.64
Types digital technologies used	Digital sensors	1.55
	ICT (smartphones)	31.01
	Radio	27.91
	Smartphone and radio	39.53
Extent of digital technologies use	To some extent	47.29
	Large extent	22.48
	Very large extent	30.23
Duration of use (years)	1–4s	65.12
	5–10	29.46
	11–15	3.88
	Above 15 years	1.55
Are digital technologies beneficial	Yes	89.06
	No	10.94
How are digital technologies beneficial	Assists in accessing farming information	25.44
	Assist in seeking farming advices	20.18
	Improve communication between farmers and extension officers	16.67
	Assist in tracking market prices	4.39
	Assist farmers in looking for market to sell the produce	5.26
	Help to get information related to climate change /follow daily weather reports	26.32
	Communication between extension officers & farmers and also to look for market	1.75
Would you continue to use digital technologies	Yes	83.59
	No	16.41
Why would you continue to use digital technologies	Helps to track market and market prices	3.77
	Helps to learn about improved seeds and get educated about different cropping systems	30.19
	Helps to get climate change information	27.36
	Digital technologies improve farming skills and knowledge	33.02
	Promote better production and marketing	5.66
Extent of willingness to continue using digital technologies	Not at all	5.36
	To some extent	25.89
	Large extent	41.07
	To a very large extent	27.68
Why would you not continue to use digital technologies	Not beneficial to farmers' need	44.44
	Expensive	11.11
	Poor network coverage	5.56
	Expensive data and poor network coverage	33.33
	Expensive data bundles	5.56
Do you recommend digital technologies	yes	85.04
	No	14.96
Reason for recommending digital technologies	Digital technologies improve and make farming activities easy and interesting	17.39
	Digital technologies bridge the gap between extension officers & farmers and promote information dissemination	25.22
	Helps to access farm loans	5.22
	Provide farmers with knowledge and information about agriculture	43.48
	Not recommending it because it is expensive	6.96
	Improve farmers' marketing skills	1.74

Factors affecting the utilization of digital technologies by smallholder farmers in Port St Johns and Ingquza Hill Local Municipalities

Variable	β	Std. Error	Pvalue	Exp(B)
Gender	0.05	0.34	0.89	1.05
Age	−0.28	0.16	0.09	0.76
Marital status	0.37	0.20	0.06	1.45
Education	−0.91	0.25	0.00	0.40
Employment status	−0.11	0.15	0.44	0.89
Income source	−0.29	0.11	0.01	0.75
Monthly income	−0.28	0.25	0.25	0.75
Household size	−0.29	0.24	0.23	0.75
Farming activity	−0.10	0.17	0.54	0.90
Tenure	−0.49	0.69	0.48	0.61
Land size	1.14	0.30	0.00	3.13
Constant	2.41	1.02	0.02	11.19
Model summary
χ2	60.38		0.00
−2 Log Likelihood	259.94
Nagelkerke R2	0.31

Factors affecting the extent of digital technology utilization by smallholder farmers in Port St Johns and Ingquza Hill Local Municipalities

	Variable	β	Std. Error	Pvalue
1	2	3	4	5
Gender	Male	1.37	0.78	0.08
	Female*
Age	30–39	6.01	1.83	0.00
	40–49	4.98	1.85	0.01
	50–59	5.12	1.80	0.00
	60–69	6.81	1.69	0.00
	70 and above*
Marital status	Single	−2.64	1.52	0.08
	Married	−2.83	1.54	0.07
	Divorced	−24.33	0.00
	Widower/widow*
Education	No formal education	−9.03	2.48	0.00
	Primary education	−2.33	1.36	0.09
	Secondary education	−2.83	1.39	0.04
	Tertiary education*
Employment status	Unemployed	12.39	4.98	0.01
	Formal employed	11.04	5.38	0.04
	Self-employed	9.24	5.44	0.09
	Full-time farmer	12.16	5.23	0.02
	Part-time farmer	12.00	5.82	0.04
	Retiree*
Source of income	Social grants	−0.81	2.46	0.74
	Salary/wages	−2.35	3.56	0.51
	Agricultural activities	−2.05	2.89	0.48
	Remittances	−18.37	9203.61	1.00
	Social grant and Agricultural activities	−3.72	2.81	0.19
	Retirement pension funds*
	Social grant and remittances*
Income level	R500–R1000	−2.28	3.04	0.45
	R1001–R5000	−0.91	2.71	0.74
	R5001–10000	0.45	2.60	0.86
	More than R10000a
Household size	1–5 people	−20.59	1.23	0.00
	6–10 people	−19.35	1.18	0.00
	11–15 people	−14.35	0.00
	Above 15 people*
Farming enterprise	Crop production only	2.06	0.67	0.00
	Livestock production only	−0.08	1.31	0.95
	Mixed farming*
Land tenure	Communal land	−2.81	1.38	0.04
	Leased*
Land size (ha)	1–5	−2.96	2.02	0.14
	6–10	0.71	2.33	0.76
	11–20*
Model summary
	χ2	397.95		0.00
	–2 Log Likelihood	150.85
	Nagelkerke R2	0.68

Variables used in the logistic and ordered logistic models

Variable	Explanation	Measurement	Expected sign
Dependent
Y (logistic regression model)	Utilization of digital technologies	Binary: 0 – utilisation, 1 – otherwise
Y (ordered logistic regression model)	Extent of utilizing digital technologies	Ordered: 0 – to some extent, 1 – large extent, 2 – very large extent
Independent
GEN	Gender	Nominal: 0 – male, 1 – female	−
AGE	Age (years)	Ordinal: 0 – 30–39, 1 – 40–49, 2 – 50–59, 3 – 60–69, 4 – 70 and above	−
MARST	Marital status	Nominal: 0 – married, 1 – not married	−
EDU	Education level	Ordinal: 0 – none, 1 – primary, 2 – secondary, 3 – tertiary	+
EMPL	Employment status	Nominal: 0 – full-time farmer, 1 – part-time farmer	−
SOUINC	Source of income	Categorial: 0 – social grant, 1 – salary, 2 – agricultural activities, 4 – remittances	+/−
MI	Monthly income	Ordinal: 0 – less than R1000, 2 – R1001–R5000, 3 – R5001–R10000, 4 – more than R10000	+
HHS	Household size	Ordinal: 0 – 1–5, 1 – 6–10, 2 – 11–15, 3 – 15 and above	+/−
FEN	Farming enterprise	0 – crop production, 1 – livestock production, 2 – mixed farming	+/−
TEN	Tenure	Nominal: 0 – communal, 1 – leased	+
FZ	Farm size (ha)	Ordinal: 0 – 1–5, 1 – 6–10, 2 – 11–20	+