Understanding the Factors Driving Farmers to Adopt Peer-to-Peer Lending Sharing Economy

Peer-to-peer (P2P) lending is one of the forms of sharing economy offered by Fintech as an alternative solution to address the limited access of smallholder farmers to financing sources but its adoption is relatively low despite its numerous benefits. It is, however, important to understand the factors driving farmers to adopt this system. Therefore, this study was conducted to investigate the factors influencing the behavioral intention of farmers to adopt farming financing P2P lending using UTAUT2 and Theoretical Model of Participation variables as well as other variables in the form of values in the Sharing Economy. Data was collected from 371 farmers in West Java, Indonesia that has not adopted farming financing P2P lending and analyzed using Structural Equation Modelling (SEM). The results showed eight variables have significant correlation and they include effort expectancy, social influence, hedonic motivation, price value, habit, perceived risk, values, and trust through the application of perceived risk as an intervening variable. This means it is important for the P2P lending provider to educate the farmers on the easiness of using the platform, provide evidence for its legitimacy, and show that its security is guaranteed. It is also important to share the success story of the adopter farmers to encourage the non-adopters to embrace P2P lending.


INTRODUCTION
Sharing economy has been existing for approximately 10 years in Indonesia. It was initiated in the marketplace, introduced into the transportation sector with a ride-sharing application, and is currently being implemented in the agribusiness sector through the use of peer-to-peer (P2P) lending to finance farming activities. Agribusiness has been growing rapidly globally over the last decade but is still reported to be below other sectors (Goodman and Goodman, 2009;Mount, 2012;Si et al., 2015;Migliore et al., 2015;Forssell and Lankoski, 2015).
Several definitions and synonyms have been associated with the concept of sharing economy such as connected consumption (Schor, 2016), a collaborative economy (Botsman and Rogers, 2010), and an access-based economy (Belk, 2014). Benkler (2007) described the concept as a phenomenon of technology which enables connectivity through the mobile device while it was explained by Hamari et al. (2015) to consist of four elements which are social commerce, online collaboration, consumer ideology, and online sharing. Some activities observed to be in sharing economy include goods and services exchange, social connection as well as an increase in the utilization and sharing of productive assets (Codagnone and Martens, 2016). Moreover, Bardhi and Eckhardt (2012) showed that sharing of tangible and intangible assets through digital platform changes "assets ownership" to "the access to the assets" while collaborative consumption was described as the rapid explosion in terms of exchange, share, barter, sell, and rent which is enabled by the latest technology and P2P marketplace using method and scale previously considered impossible (Botsman and Rogers, 2010). These definitions are similar in their presentation of sharing economy or collaborative This Journal is licensed under a Creative Commons Attribution 4.0 International License consumption with continuous reference to online devices and the latest technology and this establishes a connection between collaborative consumption and digitalization (John, 2013).
Several benefits have been attributed to the use of collaborative consumption and these include the economic advantages such as cost efficiency and service quality as well as psychological benefits such as the sense of community belonging (Möhlmann, 2015). Hira and Reilly (2017) also showed its ability to reduce transactional cost by excluding the activities of middleman in the process of a transaction between producers and consumers and this, in addition to the affordability of mobile phones, increases the access to products and services and eliminate the needs for economies of scale for a marginal group without or with limited access to capital and infrastructure. According to Retamal and Dominish (2017), the main benefit of sharing economy in developing countries is the increment in consumption sustainability and support provided to entrepreneurship through its implementation and regulations. An academic study on industrial countries discovered that businesses sharing access to goods have the potentials to reduce the environmental impact of the production process due to the utilization of available goods thereby leading to a decrease in new productions.
The benefits of sharing economy in agribusiness have been established in some countries as observed in Farm Backup in Denmark and Machinery Link Sharing in America. These platforms offer P2P service through the lending of very useful farming machinery equipment to farmers thereby reducing their need to buy for themselves (Grigoras, 2017). Sharing economy has also been reported by Wineka (2019) to have assisted farmers through the provision of access to a wider market, fair price, fast payment, and easy way of selling farm produce. This means the implementation of the concept in the Indonesian agribusiness sector becomes is important considering the fact that the country is agricultural and a large part of its population work in the agricultural sector.
One of the forms of sharing economy observed to be growing rapidly in the agribusiness sector is the P2P lending farming funding. This service makes it easy for borrowers and lenders to connect via Internet through the use of either a mobile application or website and has also been reported to have provided new financial effectiveness for farmers (Bachmann et al., 2011). This concept is different from banking institutions lending due to its ability to assist farmers without the need for any collateral and the use of a profit-sharing scheme instead of interest which is considered favorable for the condition of smallholder farmers. P2P lending also provides an online marketing channel for farmers to sell their crops and this is not available in formal banking institutions. Some of the other benefits of this platform include the availability of field agents to assist farmers process funding proposals as well as professional agricultural advisors to advise on the best farming methods.
The acceptance rate of peer to peer lending is observed to be relatively low among farmers despite these numerous benefits. This was associated by Sembiring (2019) to the relatively minimal technology adoption rate in agribusiness by farmers and Boswell (2004) findings that technological development is not always well accepted by everyone. Meanwhile, information technology is reported to be an inseparable part of the sharing economy (Benkler, 2007;John, 2013;Codagnone and Martens, 2016). According to the Central Bureau of Statistics or BPS-Statistics Indonesia (2018), only 6.60% of households have accessed the internet to find new financial facilities. The limitation of farmers in using information and communication technology (ICT) is, therefore, related to the lack of ability, low awareness of its benefits, difficulty in usage, lack of technology infrastructure, inadequate training on the application, cost of technology, and availability of software (Taragola et al., 2005;Duerfeldt, 2014).
The low adoption rate of farming financing P2P lending among Indonesian farmers requires understanding the factors influencing the adoption of the platform. This study was, therefore, conducted to investigate the factors driving the farmers in Indonesia to adopt farming financing P2P lending.

Theoretical Model of Participation in the Sharing Economy
The participation model in the sharing economy was adopted from Social Exchange Theory (Homans, 1958;Thibaut and Kelley, 1959;Blau, 1964) and reported to have proposed trust and perceived usefulness to be contributing to the behavioral intention towards participating in the sharing economy. The model also proposes systemic support such as reputation, social presence, and benevolence as the factors used in building trust while the strongest factor influencing active participation has been reported by previous studies on online networks to be reputation (Wasko and Faraj, 2005;Slee, 2013). Moreover, the application of websites as the main factor in sharing economy leads to the development of social presence based on the online interaction among the users of the platform (Pavlou et al., 2007). Meanwhile, benevolence is defined as the confidence in the ability of the commercial sharing economy to increase consumers' wealth (Ba and Pavlou, 2002).
The participation model proposes trust to have the ability to reduce the perceived risk in the sharing economy (Lamberton and Rose, 2012) while the perceived risk is also used as the intermediary variable between trust and participation intention. Previous studies have reported the negative effects of perceived risk among consumers on their online shopping behavior (Hoffman et al., 1999;Salam et al., 2003, Park et al., 2004Almousa, 2011), and this further makes consumers stay away from online shopping. Pallab (1996) further showed worrisome on the part of consumers on internet security, especially as regards the use of credit cards, sharing of personal information, and ordering without seeing the real product. Moreover, Masoud (2013) also reported financial, product, delivery, and information security risks to have negative effects on online shopping behavior.
This model also shows the influence of unique variables of sharing economy such as social, economic, and epistemic benefits on perceived usefulness. The emergence of sharing economy has increased the need to understand consumer's participation intention. Kim et al. (2015) theoretically validated Social Exchange Theory's value in explaining the shift of consumer tendency from the traditional transaction, in which handing over the ownership of commercial goods, to the tendency to share. This theory also observes the factor of trust in sharing economy and explains the antecedent of behavioral intention in adopting sharing economy, which is a relative advantage.

P2P Lending
The concept of personal lending is not a new business model and has been defined as the conventional method of lending or borrowing money only without a mediator (Everett, 2010;Herrero-Lopez, 2009) but online P2P lending is tagged a new phenomenon due to the integration of the Internet in the transaction. Indonesian Financial Service Authority (OJK) regulation no. 77, year 2016 explained P2P lending as a financial service operation used in mediating the lender and borrower to have a lending agreement in Indonesian Rupiah directly via an electronic system using an internet network. The motive of this system is closely related to internet banking where transactions are mostly through the use of mobile applications and websites.
The analytical approach implemented in previous studies includes combining Technology Acceptance Model (TAM) and Theory of Planned Behavior (TPB) with the indicators being perceived security risk, social influence, trust, perceived financial cost, perceived ease of use, previous experience, perceived usefulness, and compatibility (Tan et al., 2017;Nisar and Prabhakar, 2017). The criteria used by consumers in selecting the preferred lending provider include interest rate, service fee, provider reputation, religion, facilities offered, security guarantee, compatibility, social influence, information availability, trustworthiness, and convenience (Ringim, 2014;Obeid and Kaabachi, 2016;Sayani and Miniaoui, 2013;Usman et al., 2017).

The Unified Theory of Acceptance and Use of Technology (UTAUT2)
Technology is considered a major part of P2P lending and this led to the adoption of The Unified Theory of Acceptance and Use of Technology (UTAUT) 2 variables to investigate the factors influencing farmers to adopt P2P lending in this research (Venkatesh et al., 2012). This model studies the acceptance and use of technology in the context of mobile application from consumer's point of view using hedonic motivation, price value, and habit as additional factors with direct or indirect impact on behavioral intention and use behavior. Moreover, price value is considered important due to the need for the consumers to bear the cost of purchasing the technology while voluntariness of use variable in prior theory, UTAUT (Venkatesh and Zhang, 2010), is removed and replaced with a new linkage between facilitating conditions and behavioral intention. UTAUT2 has a more in-depth explanatory ability in explaining behavioral intention and the use of technology more than UTAUT due to the fact that it does not only inherit the UTAUT structure but also adds new factors and relationships.

Schwartz Theory of Basic Values
Values are considered relevant to the study on farmers' behavior due to their use in identifying cultural groups, society, and individuals and also in tracing and explaining the underlying motivation of an attitude or behavior (Schwartz, 2012). The latest theory on values identifies ten kinds of motivation and these include stimulation, self-direction, universalism, benevolence, tradition, conformity, security, power, achievement, and hedonism (Schwartz, 2012).

The Research Model of Factors Influencing Behavioral Intention to Adopt P2P Lending
The literature review led to the development of the model for this research as presented in Figure 1 with ten variables

Research Hypotheses
The research hypotheses formulated include the following: H 1 : Performance expectancy has a positive effect on behavioral intention to adopt P2P lending H 2 : Effort expectancy has a positive effect on behavioral intention to adopt P2P lending H 3 : Facilitating condition has a positive effect on behavioral intention to adopt P2P lending H 4 : Social influence has a positive effect on behavioral intention to adopt P2P lending H 5 : Hedonic motivation has a positive effect on behavioral intention to adopt P2P lending H 6 : Price value has a positive effect on behavioral intention to adopt P2P lending H 7 : Habit has a positive effect on behavioral intention to adopt P2P lending H 8 : Trust has a positive effect on behavioral intention to adopt P2P lending H 9 : Trust has a negative effect on perceived risk H 10 : Perceived risk has a negative effect on behavioral intention to adopt P2P lending H 11 : Basic values have a positive effect on behavioral intention to adopt P2P lending.

METHODS
This research was conducted quantitatively using a confirmatory approach with 371 farmers in West Java, Indonesia as respondents with the samples used selected through purposive sampling method while questionnaire was applied as the survey instrument.
This research was conducted using dependent and independent variables with the dependent variable being behavioral intention while the independent variables are performance expectancy, effort expectancy, facilitating conditions, social influence, hedonic motivation, price value, habit, trust, values, and perceived risk adopted from UTAUT2 theory, Theoretical Model of Participation in The Sharing Economy, and Schwartz theory of basic values. The summary of variable operationalization is, therefore, presented in Table 1.
A SEM technique was used to test the relationship between research variables with the hypotheses tested using the farmers as the analysis unit. This technique was implemented due to its capability to investigate and test the hypothesis involving multiple regression analysis between a single dependent variable and a group of independent variables (Bentler and Chou, 1987).
According to Ghozali and Fuad (2008), SEM is an evolution from multiple linear regression developed from econometric principles combined with the regulation principles of psychology and sociology and has improved to be an integral part of academic managerial research.

Respondent Characteristics
The attributes of respondents presented in Table 2 shows majority represented by 91.47% are male while the average age with the most dominance is 40 years and above and this is in line with the agricultural survey by BPS-Statistics Indonesia (2018) which showed the majority of farmers' age in Indonesia are above 35 years old. Meanwhile, age has been reported to be one of the factors affecting the level of technology adoption (Martins et al., 2018). Moreover, male dominance is associated with the fact that farming is the main source of livelihood, and being a father makes an individual the decision-maker in the family.
The farmers were found to be mostly senior high school graduates as observed with 39.08% followed by elementary school graduates with 30.73%, junior high school graduates were 16.17% while only 12% were college graduates. Moreover, most of the respondents already own a smartphone  Uncertainty and worry due to the adoption of the P2P lending sharing economy Bhatnagar and Ghose (2002); Cases (2002); Forsythe and Shi (2003) Behavioral intention Tendency to adopt P2P lending Venkatesh et al. (2012); Davis (1989); Fishbein and Ajzen (1975) but some of them do not have as observed from the 24% recorded. This is also in line with the findings from BPS -Statistics Indonesia (2018) which showed only 13.4% of the total number of farmers in Indonesia use the internet and this means information technology adoption is low among farmers and this was found by Galloway and Mochrie (2005) due to the inappropriate supply and demand of technology infrastructure in suburban areas and low perception of the farmers towards the need for ICT. Table 3 shows borrowing experience is one of the considerations in P2P lending adoption. The farmers with experience in borrowing funds using offline conventional lending sources have a bigger tendency to adopt P2P lending, especially for children education followed by purchasing a vehicle and household equipment. The results also showed 59.75% borrow from a conventional bank and 32.08% from an informal financial entity such as moneylender, loan shark, and middleman which are usually patronized due to their much simpler requirements compared to formal financial institutions. Moreover, financial institutions, formal or informal, are highly needed by farmers for farming production and daily living cost before the crops are sold due to the current very limited access to the sources of capital or funding, particularly for smallholder farmers which make up the largest percentage of the society (Hermawan and Adrianyta, 2013;Mulyaqin et al., 2016;Nurmanaf, 2007). Middlemen play several roles such as the provision of capital as well as the formation of networks between different parties ranging from the farmers to traders and these have made the farmers to be highly dependent on them (Megasari, 2017).

Validity and Reliability Test
Reliability test was conducted to measure the consistency level of the research instrument which is considered reliable at composite reliability (CR) higher than 0.70. The results presented in Table 4 shows all the indicators are reliable.
The validity test was used to determine the validity of the questionnaire using a loading factor >0.50 and all the indicators were observed in Table 5 to be valid by having loading factor >0.50 and this means no variable was eliminated.

Hypotheses Verification Results
The factors affecting behavioral intention to adopt P2P lending were analyzed using farmers without prior experience in adopting  P2P lending and the results showed eight significant correlations as presented in Table 6.
Hypothesis 2 proposes that effort expectancy has a positive effect on behavioral intention to adopt P2P lending and a t-value of 2.28 indicated the existence of a significant correlation between the two variables, therefore, hypothesis 2 is accepted. This is in agreement with the findings of Tak and Panwar (2017) and Chen et al. (2017) and the reasonable explanation is due to the exposure of the respondents to the easiness of participating in P2P lending.
Hypothesis 4 proposes social influence has a positive effect on behavioral intention to adopt P2P lending and a significant correlation was found as indicated by the t-value of 2.21. The result is consistent with the findings of a previous study by Venkatesh et al. (2012) and this means farmers are interested in listening to the advice from reference groups such as farmers' community, family, or friends. Therefore, a community of farmers involving those that have adopted P2P lending needs to be built by the facilitators to allow the exchange of experience and knowledge and accelerate the adoption process.
A significant positive correlation was also discovered between hedonic motivation and behavioral intention with a t-value of 3.03 and this means hypothesis 5 is also accepted. The result is consistent with the findings of most preceding studies which showed hedonic motivation to have a positive effect on behavioral intention in adopting technology (Leong et al., 2017;Yapp et al., 2018;Martins et al., 2018;Lin et al., 2017;Tak and Panwar, 2017). The hedonic motivation was defined in this study based on farmers' perception towards the implementation and service of P2P lending to provide an exciting experience and easy accessibility. This means there is a need for the farmers to enjoy using the technology and create an exciting experience in order to drive the P2P lending adoption process.
Hypothesis 6 proposes price value has a positive effect on behavioral intention to adopt P2P lending and a significant correlation was discovered based on a t-value of 2.82, therefore, the hypothesis is accepted. The result aligns with the findings of a previous study by Tak and Panwar (2017) which showed a positive correlation between price value and behavioral intention to adopt online shop application technology. This means farmers are sensitive to the costs they have to spend and possible benefits from using any technology.
According to Tak and Panwar (2017), the use of online media is considered by several people as a habit and this submission was observed to be in line with hypothesis 7 test which showed a significant correlation between habit and behavioral intention to adopt P2P lending as indicated with a t-value of 2.16. This is consistent with the theory of UTAUT2 developed by Venkatesh et al. (2012) which shows the ability of the repeated use of a technology to form knowledge and eventually drive people to adopt such innovation. Therefore, the P2P lending provider has to educate and allow farmers to try the use of the platform regularly to make them familiar with the service.
The analysis also showed that trust does not show direct significant effect on behavioral intention to adopt P2P lending but has indirect influence through the use of perceived risk as a mediating variable, therefore, hypothesis 9 is also accepted with an absolute t-value score of 6.09. According to Kim et al. (2015), risk in the model refers to the subjective beliefs of users concerning the potential loss from a transaction. For example, an increase in consumers' trust in P2P lending reduces the perceived risk and this subsequently increases the propensity for adoption.
The risk in online platforms exists due to information asymmetry which produces uncertainty of identity and product and worrisome concerning the possibility of opportunistic behavior and these further reduce transactional intention (Pavlou and Gefen, 2004). This argument is consistent with the result of hypothesis 10 which   showed perceived risk to have a negative effect on behavioral intention to adopt P2P lending with an absolute t-value score of 2.13 and the most effecting indicator was discovered to be data security based on respondents' feeling that the data they submit to the platform might be stolen or misused.
The variables of values consist of indicators such as tradition including cultural values and religion, achievement, benevolence, and motivation. Hypothesis 11 test result showed values to have a significant positive correlation with behavioral intention to adopt P2P lending with a t-value of 4.69. This means farmers believe the use of P2P lending to finance their farming activities has the ability to make them successful by improving their personal and family's wealth and allowing them to try new methods of obtaining funds.

CONCLUSION
The factors influencing behavioral intention to adopt P2P lending sharing economy for farming funding were analyzed in this research and the result showed effort expectancy, social influence, hedonic motivation, price value, habit, perceived risk, basic values, and trust using perceived risk as the intermediary influence the behavioral intention to adopt P2P lending and these were found to be consistent with previous studies. Meanwhile, performance expectancy and facilitating conditions were found not to have any significant correlation with behavioral intention probably considering the respondents are farmers that have not adopted the system. It is, therefore, recommended that future research investigate the farmers that have adopted P2P lending, increase the number of respondents, widen the location area of research to allow better generalization.
The service provider of P2P lending needs to conduct an intensive offline approach and educate farmers through field agents considering their limitations in accessing the internet. The number of field agents also needs to be increased to reach more farmers in wider locations and, due to the fact that the data shows the usage of digital technology by farmers is relatively low, the government needs to provide infrastructure to allow farmers access ICT easily.