Understanding factors affecting non-participants’ interest in community-supported agriculture

doi:10.1016/j.regsus.2024.100160

Abstract

Abstract:

Community-supported agriculture (CSA) has emerged as a viable solution for addressing the agricultural challenges faced by countries like Indonesia. This study uses the well-established unified theory of acceptance and use of technology (UTAUT2) model to examine the interest in CSA of potential customers in Indonesia. A standardized questionnaire was distributed to 1200 respondents, and the data were analyzed using structural equation model-partial least square (SEM-PLS) in SmartPLS 4.0 software. The results capture potential CSA consumer interest and will help to improve CSA development strategies in Indonesia. The model explains 44.4% of customers’ intentions, and identifies performance expectancy as the decisive factor in customers’ willingness to participate in CSA. Performance expectancy (0.292), hedonic motivation (0.262), social influence (0.259), and facilitating conditions (0.086) positively influence customers’ interest in participating in a CSA program. The adoption of CSA programs by both farmers and customers could be increased by implementing regulations that provide tax incentives and subsidies, offering training on sustainable farming practices, facilitating the establishment of distribution channels, and establishing guidelines for fair price and quality standards. This study shows the high potential for the implementation of CSA in Indonesia. It could also be used as a foundation for the development of new policies regarding sustainable agriculture markets in Indonesia.

Key words: Community-supported agriculture (CSA), Sustainable agriculture, Behavioral intention, Performance expectancy, Unified theory of acceptance and use of technology model, Indonesia

Maula Fadhilata RAHMATIKA, Agus SUMAN, Wildan SYAFITRI, Sri MULJANINGSIH. Understanding factors affecting non-participants’ interest in community-supported agriculture[J]. Regional Sustainability, 2024, 5(3): 100160.

Figures/Tables 8

Table 1

Description of variables used in the well-established unified theory of acceptance and use of technology (UTAUT2) model."

Variable	Definition	Reference
Behavioral intention (BI)	BI refers to an individual’s willingness to perform a specific action. In the context of technology adoption, it represents the level of desire to use a particular technology or innovation. This variable serves as a strong predictor of whether an individual will actually engage in the desired behavior.	Davis (1989); Venkatesh et al. (2003, 2012, 2016)
Effort expectancy (EE)	EE refers to the perceived ease of use of a technology or innovation. This variable reflects how much effort someone believes is required to use the technology effectively. Higher EE indicates that the technology is perceived as easier to use, which can positively impact adoption and continued use, as individuals are more likely to participate if they find the technology easy to use.	Davis et al. (1992); Venkatesh et al. (2003)
Facilitating conditions (FC)	FC refers to the resources and support that help individuals use technology or innovations, including infrastructure, technical support, and other necessary resources. The higher the FC, the better the support and resources available.	Venkatesh et al. (2003, 2012, 2016)
Habit (H)	H refers to the repeated and automatic use of technology. The higher the H, the more ingrained and automatic the usage.	Venkatesh et al. (2012, 2016)
Hedonic motivation (HM)	HM refers to the enjoyment people get from using technology or innovations. This variable reflects how much satisfaction the technology provides in terms of pleasure and enjoyment. A higher level of HM means greater enjoyment from using the technology.	Venkatesh et al. (2012, 2016)
Performance expectancy (PE)	PE refers to the expectation that a technology or innovation will enhance task performance or achieve desired outcomes. This variable reflects how useful a technology is perceived to be, with higher PE indicating greater perceived benefits.	Venkatesh et al. (2003, 2012, 2016)
Price value (PV)	PV refers to how much individuals believe a technology or innovation is worth, based on the benefits they receive and the price they pay. A higher PV means people think the benefits are worth the cost.	Venkatesh et al. (2012, 2016)
Social influence (SI)	SI refers to the degree to which an individual believes other people, such as friends, family, or society more generally, expect them to use a technology or innovation.	Venkatesh et al. (2003); Chen et al. (2019)

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Variable	Description	Frequency	Percentage (%)
Gender	Male	363	30.25
Gender	Female	837	69.75
Age (years old)	20-30	1147	95.58
	31-40	41	3.42
	41-50	12	1.00
Education	Middle school	5	0.42
	High school	398	33.17
	Undergraduate	763	63.58
	Postgraduate	34	2.83
Income (USD/month)	<73.6	831	69.25
	73.6-368.0	286	23.83
	>368.0	83	6.92
Place of origin	Urban areas	966	80.50
Place of origin	Rural areas	234	19.50

Latent variable	Indicator	Outer loading	Latent variable	Indicator	Outer loading
BI	BI4	0.874	HM	HM2	0.815
	BI5	0.907		HM3	0.840
	BI6	0.886		HM4	0.766
EE	EE1	0.765		HM5	0.811
	EE2	0.743	PE	PE1	0.779
	EE3	0.740		PE2	0.821
	EE4	0.746		PE3	0.757
	EE5	0.780		PE4	0.787
FC	FC1	0.745		PE5	0.805
	FC2	0.798		PE6	0.816
	FC3	0.799	PV	PV1	0.900
	FC4	0.777		PV2	0.873
H	H1	0.865		PV3	0.895
	H2	0.857		PV4	0.816
	H3	0.871	SI	SI1	0.916
	H4	0.862		SI2	0.912
HM	HM1	0.804		SI3	0.898

Construct	Cronbach’s alpha	Composite reliability	AVE	Construct	Cronbach’s alpha	Composite reliability	AVE
BI	0.868	0.919	0.791	HM	0.866	0.903	0.652
EE	0.813	0.869	0.570	PE	0.883	0.911	0.631
FC	0.785	0.861	0.608	PV	0.894	0.927	0.760
H	0.886	0.921	0.746	SI	0.895	0.934	0.826

	BI	EE	FC	H	HM	PE	PV	SI
BI	0.889
EE	0.457	0.755
FC	0.496	0.550	0.780
H	0.430	0.590	0.592	0.864
HM	0.563	0.584	0.659	0.677	0.807
PE	0.547	0.571	0.585	0.579	0.649	0.794
PV	0.419	0.509	0.658	0.672	0.679	0.613	0.872
SI	0.456	0.458	0.406	0.278	0.367	0.275	0.250	0.909

	BI	EE	FC	H	HM	PE	PV
EE	0.538
FC	0.599	0.681
H	0.490	0.683	0.710
HM	0.648	0.681	0.801	0.773
PE	0.623	0.662	0.700	0.654	0.739
PV	0.474	0.585	0.785	0.756	0.772	0.689
SI	0.515	0.545	0.482	0.310	0.415	0.305	0.277