3.2.1. Unified Theory of Acceptance and Use of Technology Model
In this study, we employ the unified theory of acceptance and use of technology (UTAUT) model to analyze the factors that influence backers of crowdfunded appropriate technology projects. The UTAUT model is derived from the theory of reasoned action (TRA) based on social psychology. Fishbein and Ajzen [26
] suggest in TRA that attitudes toward behavior and subjective norms influence behavioral intention and often can lead to specific behaviors. TRA provides the rationale that certain behaviors, such as technology acceptance, can be predicted by understanding the factors that influence a user’s behavioral intentions. On the basis of TRA, Davis and Bagozzi [27
] suggest a technology acceptance model (TAM) which explains the reasons for adopting new technologies and information systems using two factors: perceived usefulness and perceived ease of use. However, the TAM model is limited in its application to specific details and it is difficult to analyze any correlation in an information technology context [28
]. To overcome these limitations, Venkatesh and Morris [29
] proposed the UTAUT model by integrating eight theories and models related to technology acceptance, such as TRA, TAM, the theory of planned behavior (TPB), and innovation diffusion theory (IDT). In the UTAUT model, individual behavior intention is influenced by several key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. It is also moderated by a user’s gender, age, experience, and voluntariness of use [29
]. The UTAUT model has been widely used in research on user acceptance of new information technology and new media. In this study, we analyze the factors affecting a backer’s intention to contribute to appropriate technology crowdfunding projects based on the UTAUT model.
As shown in Figure 1
, a crowdfunding project broadly comprises three components: a project proponent, a platform that provides information to backers and delivers the collected funds, and backers who make investment decisions related to the proposed project. Crowdfunded appropriate technology projects often have a different set of stakeholders than crowdfunded projects, particularly arts and cultural projects or those related to new technologies. Whereas backers in general crowdfunding projects might look for returns on their investments, backers of crowdfunded appropriate technology projects are rewarded vicariously through the benefits provided to a third party, usually in the form of the increased availability of an appropriate technology to the residents of developing countries. This structural difference implies that the backing for an appropriate technology project may be closer in nature to a donation than an investment. Thus, in applying the UTAUT model in this study, we reflect on the characteristics of the stakeholder structure particular to appropriate technology projects in defining the model’s variables.
3.2.2. Variable Definitions and Hypothesis Development
Performance expectancy (PE) refers to the extent of improvement in performance which one expects to observe as a result of the use of specific new information or technology [29
]. In this study, we define performance expectancy as the extent to which the challenges facing local communities are expected to be resolved through the crowdfunded appropriate technology. Earlier studies have stated that the expectations held by investors and backers regarding a project will influence their intention to participate in the project. A backer’s expectations of the satisfaction that will be derived as a result of the crowdfunded project has a significant effect on their intention to participate and may also directly lead to their continued participation [31
]. In addition to satisfaction, direct rewards have been found to directly influence participation intentions; concurrently, high performance expectancy has been found to influence participation intentions positively [32
]. In view of this discussion, we propose the following hypothesis for the purposes of this study:
Hypothesis 1 (H1).
Users’ performance expectancy has a positive effect on crowdfunding use intention.
Effort expectancy (EE) refers to the perceived extent of convenience presented by the use of some new information or technology [29
]. In this study, we define effort expectancy as the ease with which backers expect to be able to invest in appropriate technologies through crowdfunding. Earlier studies have stated that such ease of use has a positive effect on the participation intentions of backers [32
]. Thus, we propose the following hypothesis:
Hypothesis 2 (H2).
Users’ effort expectancy has a positive effect on crowdfunding use intention.
Facilitating conditions (FC) refer to the extent to which users perceive that the infrastructure and organization are sufficiently in place to facilitate the use of information systems [29
]. In this study, we define facilitating conditions as the perceived availability of organizational and technological infrastructure that supports the use of the crowdfunding platform, e.g., a customer center or payment systems. Earlier studies have stated that the official feedback channels of crowdfunding platforms contribute to facilitating user participation; however, inadequate information and statistical systems regarding the funds raised often presents obstacles to the activation of crowdfunding efforts [35
]. On this basis of this discussion, we propose the following hypothesis:
Hypothesis 3 (H3).
Users’ facilitating conditions have a positive effect on crowdfunding use intention.
Social influence (SI) refers to the extent to which the user believes that important people advocate the use of new information technology by individuals. This concept is similar to that of the subjective norm, which refers to the extent of influence that important persons have on an individual’s behavior [29
]. In this study, social influence is defined as the extent of influence exerted by a user’s surrounding reference group on the user’s decision to invest in crowdfunding and appropriate technology.
Previous studies have stated that social influences, such as social networks and peer effects, play an important role in influencing users. Interpersonal networks, which encompass friends and acquaintances, are a key factor for the success of crowdfunding [36
]. In particular, during the early stages of funding, the social capital of the project proponent, which includes close friends, is considered an important positive factor on the successful implementation of projects [15
]. In addition to the social capital available to the project proponent, a peer effect exists in which backers and their acquaintances mutually influence one another. The resulting social influence ultimately influences the investment behavior of crowdfunding users [32
]. In view of these considerations, we set the following hypothesis:
Hypothesis 4 (H4).
Users’ social influence has a positive effect on crowdfunding use intention.
In this study, we examine how the perceived risk (RI) and perceived trust (TR) associated with appropriate technology projects influence performance expectancy and user intention, in addition to the influence exerted by the key variables of the UTAUT model. Perceived risk refers to the investor’s perception of functional risks that might arise owing to insufficient information with respect to crowdfunded appropriate technology projects. Functional risk refers to the possibility of project failure owing to the inadequacy of the appropriate technology itself or inadequate capacity for project implementation. Perceived trust refers to the user’s subjective degree of belief in the expertise (knowledge and competency) and trustworthiness (public confidence and ethics) of a crowdfunding platform, and points to a platform’s capacity for monitoring and post-management.
Earlier studies on the effect of trust on crowdfunding users found that trust factors, such as the security and stability of a crowdfunding platform, had a similar significant effect on investment intention as well as a positive influence on participation intention as for other forms of e-commerce [33
]. Gerber and Hui [40
] identified a platform’s transparency and level of trust, established through post-management systems, as key factors behind crowdfunding success; additionally, they found that lack of trust represents an obstacle to user participation. In view of these points, we propose the following hypotheses:
Hypothesis 5 (H5).
Users’ perceived trust has a positive effect on the performance expectancy of crowdfunding.
Hypothesis 6 (H6).
Users’ perceived trust has a positive effect on crowdfunding use intention.
The findings of previous studies have varied regarding the effect of the perceived risk of appropriate technology projects on user behavior. While some studies have stated that perceived risk by investors/backers has a negative direct and/or indirect effect on participation intention [13
], others have countered that perceived risk has no effect on user intention because most crowdfunding users who make investments in small sums perceive a lesser degree of risk [32
]. To ascertain how perceived risk influences user intention, we set the following hypotheses in this study:
Hypothesis 7 (H7).
Users’ perceived risk has a negative effect on performance expectancy.
Hypothesis 8 (H8).
User’s perceived risk has a negative effect on use intention.
As described above, in this study we examine how the stakeholders of crowdfunded appropriate technology project—the project proponent and the platform—influence backers. In the case of the project proponent, we analyze the roles of performance expectancy and perceived risk. In the case of the platform, we analyze the roles of effort expectancy and facilitating conditions supporting the funding project, in addition to that of perceived trust. Furthermore, we examine the factors influencing individual investors/backers via social influence and the moderating variables. For this purpose, we consider moderating variables such as a user’s gender, age, and previous experience in backing other crowdfunded projects. A model constructed on the basis of the variables and hypotheses described earlier is expressed in Figure 2
. We employed this model to identify the factors influencing backers participating in appropriate technology projects via crowdfunding platforms, in addition to analyzing how these factors are interrelated.