Factors influencing the adoption of crowdfunding in Bangladesh: A study of start-up entrepreneurs

Abstract

Despite widespread adoption of crowdfunding for funding social donation projects, its adoption among start-up entrepreneurs is significantly low, in developing countries in particular. Research has been performed to investigate the crowdfunding adoption intention of start-up entrepreneurs in Bangladesh. This study aimed to identify the motivation behind the intentions of the entrepreneurs to adopt crowdfunding, using the Unified Theory of Acceptance and Use of Technology (UTAUT) model with extensions. Empirical data were collected from 317 respondents and analyzed using Partial Least Squares-based Structural Equation Modeling (PLS-SEM). The results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions and perceived trust mpact significantly ion the entrepreneurs’ behavioral intention to adopt crowdfunding. In contrast, trialability and perceived trust were not found to be significant determinants. However, trialability has a significant positive relation with use behavior or actual use, whereas no significant relationship has been identified between behavioral intention and use behavior.

Introduction

Start-ups are booming all over the world by providing innovative solutions and by introducing many new business models as solutions for social problems. Raising capital is one of the most challenging tasks for start-ups, especially for innovative start-ups (Berger and Black, 2011; Lee and Brown, 2017). Start-ups are not able to access funds from conventional sources like banks and stock markets due to lack of collateral assets and to not having previous credit history (Kim and Lee, 2014). In order to overcome the financing crisis of start-ups, recently some alternative sources have emerged and crowdfunding is one of them (Ahlers et al., 2015; Paschen, 2017). Crowdfunding is a well-suited source of start-up financing because it does not demand any collateral or previous credit records to assess the solvency of fund seekers. An innovative idea with an attractive business model is enough to draw the attention of investors and raise funds in crowdfunding platforms (Stemler, 2013). There are other benefits of using crowdfunding for start-up financing. First, it helps to reduce transaction costs and agency costs of fundraising, eliminating traditional expensive intermediaries. Secondly, it democratizes the fundraising process by getting funds from the crowd and start-ups can also enjoy suggestions and feedback from the crowd. Thirdly, the fundraising process is free of geographical constraints; entrepreneurs in any country can get capital from all over the world. Last, but not least, it provides a fast fundraising platform to raise targeted capital for innovative and appealing business ideas.

In studies of the World Bank (infoDev, 2013; Raymond, 2015), crowdfunding was found to be an emerging capital raising source in the developing countries. Bangladesh is a South Asian developing country with a growing economy, with 7.86 % GDP growth in 2017-2018 and USD 1,751 per capita income (Bangladesh Bureau of Statistics, 2018). A start-up supportive technological infrastructure is being ensured in Bangladesh. Approximately 96,176 of the people of Bangladesh (58 % of total population) are using the Internet (Bangladesh Telecommunications Regulatory Commissions, 2019). The government of Bangladesh is also taking many initiatives to foster start-ups under an iDEA (Innovation, Design & Entrepreneurship Academy) project, which is providing motivation, guidelines and completion-based small-scale funds for start-ups (Startup Bangladesh, 2018). But start-ups in Bangladesh are still facing fund raising problems (Reaz, 2015; Shams, 2017).

Adhikary and Kutsuna (2016) first identified and proposed crowdfunding for small businesses and start-ups in Bangladesh, evaluating all the available conventional funding options such as banks, stock markets and non-government organizations (NGOs) like Grameen Bank. Start-up entrepreneurs face difficulties in accessing funds from international crowdfunding platforms like Kickstarter, Indiegogo and GoFundMe, etc. because of restrictions on inbound-outbound remittance exchange and unavailability of credit transactions. Most of these platforms demand US residency. Oporajoy.org (Islam, 2018) and Projekt.co (Reaz, 2015) are the only two local crowdfunding platforms in Bangladesh. There are also social media-based platforms, specifically Crowdfunding Bangladesh, Crowdfunding Soft and Crowdfunding Association of Bangladesh (CAB), which provides fundraising options for fostering start-ups.

A study by Adhikary et al. (2018) pointed out that only 5% of 270 entrepreneurs have knowledge about crowdfunding, while 84% of them mentioned that they intend to use crowdfunding in future and 7% don’t want to use crowdfunding services for their business. There is no study to identify the factors influencing entrepreneurs to use crowdfunding. It is essential to identify and evaluate why crowdfunding is adopted and to better understand the key factors influencing start-up entrepreneurs’ acceptance and usage of crowdfunding in Bangladesh.

Research background

Crowdfunding is a shared financing concept which has emerged from the ideas of crowdsourcing and microfinance (Morduch, 1999). It is better suited for start-ups trying to convert new innovative ideas to feasible businesses and young businesses trying to grow and make a room in the competitive market (Stemler, 2013). The adoption of crowdfunding by start-ups is needed for their existence and growth in the challenging business market. The better acceptance of any new technology and innovation is influenced by many economic and psychological factors, which should be assessed to better understand the acceptance, nature and success of crowdfunding (Steinberg and DeMaria, 2012). Crowdfunding in developing countries is not well accepted as in the developed countries, which enjoy supportive tools and financial regulations as well as promotional campaigns for crowdfunding (Jegeleviciute and Valenciene, 2015; Turan, 2015). Nevertheless, crowdfunding has high potentiality in the developing nations (infoDev, 2013).

Crowdfunding is considered as a blessing in overcoming scarcity in the early funding of entrepreneurs (Hemer et al., 2011). Some empirical studies have been identified to address the motives and reasons for adopting crowdfunding by entrepreneurs. Conducting a semi-structured interview-based study, Gerber et al. (2012) identified financing, self-affirmation, networking, product awareness and success story replication as the five motivations for using crowdfunding. Entrepreneurs use crowdfunding to get the benefits of flexibility and speed in financing, testing products among consumers, avoid financial and legal obligations, enjoy a positive multiplier effect and ‘wisdom of the crowd’ for business tasks (Hemer et al., 2011; Hienerth and Riar, 2013; Macht and Weatherston, 2014). Fund raising, drawing public attention and receiving feedback on products or services are the main reasons for companies to adopt crowdfunding (Belleflamme et al., 2013). Gleasure (2015) identified risk related to the expected outcomes, or information disclosure and opportunity cost as the factors discouraging entrepreneurs from raising money from crowdfunding.

Although there are lots of studies in the field of crowdfunding, there are only a few in the context of Bangladesh. Adhikary and Kutsuna (2016) first identified crowdfunding as an alternative financing option for small businesses and start-ups in Bangladesh. In 2018, the readiness of Bangladesh for crowdfunding was assessed in terms of economic advances and citizens’ willingness (Adhikary et al., 2018). They found positive economic readiness for crowdfunding in Bangladesh. Hasan et al. (2017) made the first attempt to assess the behavioral and psychological readiness of participants, but considered limited constructs such as perceived expectations, technology awareness, perceived costs, social psychology, availability of resources, and access to other finance options.

Based on the above literature review, we can conclude that most of the crowdfunding research done has focused on backers or investors’ perspectives and no studies had been done on start-up entrepreneurs’ perspective. Most of the previous researches are conducted in the context of developed countries, for example, Australia (Ley and Weaven, 2011), China (Lee, 2016; Ya-Zheng Li, 2018), Korea (Kim and Jeon, 2017; Moon and Hwang, 2018), and Germany (Koch and Siering, 2015). It is important to extend the investigation to developing countries; theories and models developed in the developed nations’ context need to be re-examined in the environment of developing nations (Al-Somali et al., 2011; Hofstede, 1980). There is scarcity of crowdfunding research in developing countries and the pattern of crowdfunding adoption remains unexplored, especially in Bangladesh (Adhikary and Kutsuna, 2016; Hasan et al., 2017). In a World Bank report (infoDev, 2013), cultural and behavioral readiness to adopt crowdfunding should be assessed along with the readiness of technology, capital and regulations before implementing crowdfunding in developing countries. This study aims to fill this research gap. In order to identify the influencing factors for the adoption of crowdfunding by start-up entrepreneurs in Bangladesh and to contribute in technology acceptance research, this study is aimed to investigate the possible factors influencing crowdfunding adoption by start-up entrepreneurs in Bangladesh.

Research model and hypothesis development

Research model

The objective of this study is to examine the motivating factors for start-up entrepreneurs to accept crowdfunding in Bangladesh. The Unified Theory of Acceptance and Use of Technology (UTAUT) model was used for specific reasons. First, it was developed by combining eight previous and renowned technology acceptance models (Venkatesh et al., 2003). Secondly, it is proved to have high explanatory power (R ² ≥ 70%) compared to nearly 40% of other technology acceptance models (Al-Shafi and Weerakkody, 2009; Venkatesh, Morris et al., 2003). Finally, it is considered as the benchmark among other models for the technology acceptance literature for its realistic and complete explanation of user acceptance (Rosen, 2005). Like other technology acceptance models, the UTAUT model has been modified or adjusted in many earlier studies to improve the explanatory power of the model (Abbas et al., 2018; Aboelmaged and Gebba, 2013; Byun et al., 2018; Moon and Hwang, 2018). Therefore, the research model (Figure 1) is developed based on the UTAUT Model (Venkatesh et al., 2003) with the extension of three other factors.

Figure 1.

Research model and hypotheses.

One of the added factors is trialability of the Diffusion of Innovation Theory (Rogers, 2003) to examine the opportunities for start-up entrepreneurs to use crowdfunding platforms for fundraising in Bangladesh. Also, trust and risk factors were identified to analyze their influence on start-up entrepreneurs for crowdfunding acceptance decisions. In synopsis, the proposed research model claims that performance expectancy, effort expectancy, social influence, facilitating conditions, trialability and perceived trust positively affect the crowdfunding adoption intention of start-up entrepreneurs. On the other hand, perceived risk negatively affects start-up entrepreneurs’ adoption intention. As a final point, start-up entrepreneurs with the intention to adopt crowdfunding are likely to use crowdfunding for fund raising. It was hypothesized that trialability leads to use behavior or actual use of crowdfunding. Although behavioral intention is the mediator factor in this model, which is similar to the original UTAUT model, moderating factors like age, gender, experience of respondents and voluntariness of use, which are mentioned in original UTAUT model, are discarded in the proposed model for generalability and asymmetrical distribution like the studies of Tan (2013) and Van Schaik (2009).

Hypothesis development

Performance expectancy

Performance expectancy is represented as the first encouraging factor to influence start-up entrepreneurs to use crowdfunding platforms to raise funds for their start-ups. Performance expectancy is formally defined as “the degree to which an individual believes that using the system will help him or her attain gains in job performance” (Venkatesh et al., 2003, p. 447). Venkatesh et al. (2003) found performance expectancy to be the strongest influencing determinant to users’ intention to use any new system or technology. In this study, performance expectancy encompasses the issues of financial performance, quickness of fund raising and usefulness of crowdfunding. In case of crowdfunding platforms, perceived usefulness or benefit is found influential to user intention (Lacan and Desmet, 2017). From the crowdfunding investors or backers’ points of view, performance expectancy was found as a statistically significant motivator in some studies (Kim and Jeon, 2017; Lee et al., 2015; Li et al., 2018), whereas it was found not to be influential in some other studies (Moon and Hwang, 2018). The following hypothesis is proposed based on this discussion:

H1: Start-up entrepreneurs’ performance expectancy of crowdfunding is positively associated with their adoption intention of crowdfunding.

Effort expectancy

Effort expectancy refers to the degree of ease associated with the use of the system (Venkatesh et al., 2003, p. 450) and it indicates the perceived convenience of using a new information system or technology. Effort expectancy is found as a significant influencing factor of the behavioral intention of users to use information technology (Aggelidis and Chatzoglou, 2009). Effort expectancy is derived from the construct named ‘perceived ease of use’ by Davis (1989) and it was argued by Davis (1989) that use intention is significantly influenced by perceived ease of use through the mechanism of instrumentality and self-efficacy. Perceived less effort expectancy in handling information systems can improve the user performance. On the other hand, the more the systems are perceived easy by users, the greater users’ sense of self-efficacy can be expected (Bandura, 1982). Self-efficacy refers to users’ confidence in their ability to use an information system easily. Perceived easiness, clarity and understandability of instructions for use of crowdfunding are considered as effort expectancy in this study. In earlier crowdfunding acceptance studies, effort expectancy had a positive impact on users’ participation intention (Kim and Jeon, 2017; Moon and Hwang, 2018; Ya-Zheng et al., 2018). Based on this discussion, the researchers propose the following hypothesis for this study:

H2: Start-up entrepreneurs’ effort expectancy of crowdfunding is positively associated with their adoption intention.

Social influence

Social Influence refers to the degree to which users think that people important to them advocate the use or adoption of a new information system. The concept of social influence is similar to the concept of subjective norm, which means the extent of important persons’ influence on an individual’s behavior (Venkatesh et al., 2012). In this research, social influence represents the extent of encouragement and influence by entrepreneurs’ reference groups to use crowdfunding to raise funds for start-ups. There is evidence in previous studies that peer effects and social networks play a key role in influencing users to use new information systems at the early stage of adoption (Lu et al., 2005). Interpersonal networks, including acquaintances, friends and family, are important factors for successful usage of crowdfunding (Mollick, 2014). Project proponents such as close friends are considered as key positive factors for using crowdfunding and implementing a project successfully (Ordanini et al., 2011; Colombo et al., 2015). In terms of investors or backers of crowdfunding projects, the peer effect influences backers to invest in crowdfunding and control the investment behavior (Kim and Jeon, 2017; Park and Lee, 2016; Ya-Zheng et al., 2018). Thus, we propose the hypothesis below:

H3: Start-up entrepreneurs’ perceived social influence for crowdfunding is positively associated with their adoption intention.

Facilitating conditions

Facilitating conditions represent the degree to which users of information systems perceive that the organization and infrastructure are in place to facilitate the usage of that information system (Venkatesh et al., 2003). In this study, facilitating conditions are defined as the perceived technological and organizational availability of infrastructure to smoothly support the use of crowdfunding platforms. Although feedback channels and technical support of crowdfunding platforms foster the users’ intention, statistical presentation systems and inadequate information about fund raising activities are often considered as hindrances for crowdfunding initiatives (Kwon et al., 2014). Gerber et al. (2012) have shown that alternatives like social media and diverse information sourcing can foster the facilitating conditions of crowdfunding platforms to increase awareness of their use. Howeve, in some studies, facilitating conditions are not found to have significant influence on intention to participate in crowdfunding (e.g. Moon and Hwang, 2018). In consideration of this discussion, the researchers set the hypothesis below:

H4: Start-up entrepreneurs’ perceived facilitating conditions of crowdfunding are positively associated with their adoption intention.

Trialability

Trialability is defined as “the degree to which an innovation may be experimented with on a limited basis” (Rogers, 2003, p. 16). Crowdfunding is an innovation that helps solve constraints on finance or capital-based or donation-based projects. There is a faster rate of adoption of an innovation if there is more opportunity to try it first (Moore and Benbasat, 1991; Rogers, 2003). In this study, the opportunity to use and ease to become familiar with crowdfunding platforms are considered under trialability. In some earlier researches (Lee, 2004; Sahin and Thompson, 2006), trialability is an important motivating factor for adopting innovations or new information systems. Agarwal and Prasad (1997) found trialability as a significant determinant for actual use, whilst insignificant for behavioral intention to use new information systems. Hence, following hypotheses are proposed by this study:

H5: Start-up entrepreneurs’ perceived trialability of crowdfunding is positively associated with their adoption intention.

H6: Start-up entrepreneurs’ perceived trialability of crowdfunding is positively associated with their use behavior or actual use.

Perceived trust

Perceived trust represents the subjective belief of start-up entrepreneurs in the reliability, trustworthiness, integrity and technological appropriateness of crowdfunding platforms. In prior studies (Lee et al., 2015; Lee et al., 2016; Moon and Hwang, 2018; Ya-Zheng et al., 2018), trust, in terms of the stability and security of crowdfunding platforms, has been found to be positively influential in users’ participation in crowdfunding. Gerber and Hui (2013) found that trust level and transparency of crowdfunding platforms after posting the fundraising projects by entrepreneurs are the key factors behind the success of crowdfunding, and lack of trust is an obstacle to the use of crowdfunding. On web-based platforms, trust can be ensured by firewalls, by facilitating encrypted financial transactions, by ensuring seals for privacy and by using authentication mechanisms (Benassi, 1999; Bhimani, 1996). So, the following is hypothesized by this study based on prior research:

H7: Start-up entrepreneurs’ perceived trust in crowdfunding is positively associated with their adoption intention.

Perceived risk

Perceived risk refers to “the potential for loss in the pursuit of a desired outcome of using e-services” (Featherman and Pavlou, 2003, p. 454). Previous studies mentioned types of perceived risk such as privacy risk, performance risk, financial risk, time risk and psychological risk (Grewal et al., 1994; Mitchell, 1992). In this study, privacy risk, imitation risk of start-up ideas and monetary transaction information disclosure risk are considered as perceived risk. The outcomes of prior studies in term of the effect of perceived risk on users’ crowdfunding use intention are controversial. Some studies found that users, especially backers or investors, perceptions about risk have direct or indirect negative influence on crowdfunding participation intention (Lee, 2016; Moon and Hwang, 2018). On the other hand, Kim and Jeon (2017) found that perceived risk has no effect on crowdfunding users’ intention to participate, because as investors users need to invest a small amount. But the privacy risks and business idea imitation risks of start-up entrepreneurs may be negatively related with using crowdfunding platforms for fund raising. In this study, researchers thereby have framed the following hypothesis:

H8: Start-up entrepreneurs’ perceived risk of crowdfunding is negatively associated with their adoption intention.

Behavioral intention

Behavioral intention refers to the strength of users’ willingness to perform or not to perform a specific behavior (Ajzen, 2002). Behavioral intention has been found to have a positive relation to use behavior or actual use of information systems in previous researches (Ifinedo, 2012; Oliveira et al., 2014). In prior research, behavioral intention was found as a key mediator between predictor variables and the use of information systems. In this study, entrepreneurs’ plans and predictions about their use of crowdfunding in the near future is regarded as behavioral intention. Hence, the following hypothesis is developed:

H8: Start-up entrepreneurs’ behavioral intention to adopt crowdfunding is positively associated with their use behavior or actual use of crowdfunding.

Research methodology

Sampling and data collection

The target population of this study is the start-up entrepreneurs of Bangladesh. Non-probability purposive sampling technique was followed to include responses only from start-up entrepreneurs. The list of start-ups was carefully developed from different start-up support platforms like startupbangladesh.gov.bd and startupdhaka.org, etc. An online questionnaire was used to collect data. The link to that questionnaire was sent by email to the owners of start-ups together with a formal request to participate. The questionnaire was also sent to the members of different online social media platforms of start-up groups or incubation centers in Bangladesh. In order to increase the number of responses, reminder emails were sent after 3 weeks to the entrepreneurs who did not response within this period. Out of 334 filled received questionnaires, 317 usable responses were identified after missing data treatment and checking outliers. A recommended sample size of 300 is considered good for standard statistical analysis like structural equation modeling (Tabachnick and Fidell, 1996; Field, 2013). Based on previous literature and research examples, the sample of 317 usable responses in this study is considered adequate for structural equation modeling analysis.

Questionnaire development

A self-administered questionnaire survey was used to collect the responses. The questionnaire was structured and divided into two parts, A and B, after mentioning the research objectives, time needed to respond and ethical declaration by the researchers.

Part A focused on basic information about the start-ups and the demographic information of entrepreneurs. Part B contains questions related to different constructs in the proposed conceptual research model. All items of latent constructs were formulated based on previous literature and modified for use in the context of crowdfunding and start-ups (see Appendix A). A 5-point Likert scale was used to score measurement items coded as 1= strongly disagree, 2= disagree, 3= neutral, 4= agree and 5= strongly agree.

The developed questionnaire was tested by distributing among 20 volunteer respondents including academicians, start-up entrepreneurs and prospective entrepreneurs, for improving the initial questions and testing content clarification or understandability. After doing some minor modifications based on the feedback from the pilot study volunteers, the final version of questionnaire was distributed among the respondents.

Data screening

In order to prepare data for analysis, it is necessary to do data screening before performing data analysis (Pallant, 2005). The raw data was screened to find and avoid the problems of missing data, outliers and common method bias. All the dataset related problems were corrected before statistical analysis because data preparation leads to better prediction (Tabachnick and Fidell, 2007).

Data analysis

Partial Least Square (PLS) focused Structural Equation Modeling (SEM), a second generation multivariate data analysis method, was used in this study because SEM is a path modeling approach to test and validate a proposed research model and the hypothesized relationships among the constructs of that model (StatSoft, 2013). SEM is widely accepted for measuring the validity of theories with empirical data (Gotz et al., 2010) and is popular in social science and information systems research (Williams et al., 2015). SmartPLS 3.0, software for PLS-SEM, was used to analyze the quantitative data (Hair Jr et al., 2014). A two stage methodology (Anderson and Gerbing, 1988) was used to perform data analysis. In the first stage, content, convergent and discriminant validity was tested based on the measurement model, while in the second stage, the hypotheses and structural model was tested. The Statistical Package for the Social Science (SPSS) 25.0 was used for screening and preparation of data and cleaned data was imported to SmartPLS 3.0 for analysis.

Results

Demographic information

The demographic characteristics of respondents such as gender, age, education, profession and experience are shown in Table 1. 54.9% of the respondents are males compared to 45.1% are females. Only 4 (1.9%) respondents had age more than 36 years, which indicates the majority of young people’s presence in the survey. About 70% of respondents have completed graduate level education and about 22% had a masters’ degree. The responses are dominated by businessmen: 177 (55.8 %) of the respondents mentioned their professions as business, while 54 (17 %) were job holders. About half (159: 50.2%) of the entrepreneurs had 1 to 5 years previous business experience, while nearly 4% had more than 5 years business experience. A substantial number of respondents had business experience of less than 1 year (26.8%) and 61 (19.2%) had no business experience before their attempts at start-ups. A total of 281 (88.6%) survey participants had no crowdfunding experience, whereas 36 (11.4%) mentioned that they had used crowdfunding.

Table 1.

Demographic Information of Respondents.

Characteristics		Frequency	Percentage
Gender	Male	174	54.9%
Gender	Female	143	45.1%
Age	15 – 20 Years	37	11.7%
	21 – 25 Years	101	31.9%
	26 – 30 Years	153	48.3%
	31 – 35 Years	20	6.3%
	36 – 40 Years	4	1.9%
	More than 40 Years	0	0%
Educational Qualification	Secondary	0	0%
	Higher Secondary	25	7.9%
	Honors	223	70.3%
	Masters	69	21.8%
	Others	0	0%
Profession	Business	177	55.8%
	Job Holder	54	17%
	Student	78	24.6%
	Others	3	0.9%
Previous Business Experience	No Experience	61	19.2%
	Less than 1 Year	85	26.8%
	1-5 Years	159	50.2%
	6-10 Years	12	3.8%
	More than 10 Years	0	0%
Crowdfunding Use Experience	Yes	36	11.4%
Crowdfunding Use Experience	No	281	88.6%

Measurement model

Both reliability and validity of the constructs were tested to ensure the accuracy of the measurement items. Internal reliability, convergent validity and discriminant validity test criteria were used to assess the measurement model (Hair Jr et al., 2014) before testing the proposed hypotheses (Bagozzi et al., 1991).

Internal reliability

In this study, both Cronbach’s alpha and composite reliability were used to examine internal reliability (Fornell and Larcker, 1981). The values of Cronbach’s alpha and composite reliability should be greater than 0.70 to ensure satisfactory reliability (Hair and Tatham, 2006).

The calculated Cronbach’s alpha and composite reliability for assessing internal reliability is presented in Table 2. The values of Cronbach’s alpha range from 0.855 to 0.939 and the values of composite reliability range from 0.902 to 0.960, which are more than the recommended value of 0.7. Thus, it can be concluded that all the constructs have strong internal reliability.

Table 2.

Measurement Model.

Constructs	Items	Loadings	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)
Use Behavior (UB)	UB1	0.930	0.896	0.935	0.828
	UB2	0.906
	UB3	0.893
Behavioral Intention (BI)	BI1	0.938	0.923	0.951	0.867
	BI2	0.934
	BI3	0.921
Effort Expectancy (EE)	EE1	0.861	0.887	0.922	0.747
	EE2	0.865
	EE3	0.861
	EE4	0.870
Facilitating Conditions (FC)	FC1	0.881	0.893	0.926	0.757
	FC2	0.885
	FC3	0.861
	FC4	0.853
Performance Expectancy (PE)	PE1	0.870	0.855	0.902	0.697
	PE2	0.828
	PE3	0.820
	PE4	0.819
Perceived Risk (PR)	PR1	0.956	0.939	0.960	0.890
	PR2	0.935
	PR3	0.940
Perceived Trust (PT)	PT1	0.931	0.902	0.939	0.837
	PT2	0.920
	PT3	0.893
Social Influence (SI)	SI1	0.909	0.891	0.932	0.821
	SI2	0.900
	SI3	0.909
Trialability (TA)	T1	0.925	0.913	0.945	0.852
	T2	0.915
	T3	0.929

Convergent validity

Convergent validity refers to the degree to which an item of measurement has strong positive correlation with other items of the same construct (Hair Jr et al., 2014). Item loading and average variance extracted (AVE) are used to measure convergent validity (Fornell and Larcker, 1981). Fornell and Larcker (1981) suggested that the acceptable values of items loading and AVE should be at least 0.50 to ensure convergent validity of the construct. Table 2 shows that the values of item loading of all the constructs range from 0.819 to 0.956 and the values of AVE range from 0.697 to 0.890 which are more than the suggested thresholds. Therefore, the convergent validity condition in this study is satisfied.

Discriminant validity

Cross loading and the square root of average variance extracted (AVE) are the measurements of discriminant validity (Barclay et al., 1995). The value of the square root of AVE should be greater than its correlation among other constructs (Henseler et al., 2009). Heterotrait-monotrait (HTMT) ratio is an alternative method of checking discriminant validity and the acceptable ratios of all research constructs are less than 0.85 (Henseler et al., 2016).

The items loading in the cross loading matrix (Table 3) and the calculated values of square root of AVE of all constructs (Table 4) are more than their alternative corresponding correlations with other constructs. Heterotrait-monotrait (HTMT) ratios of all research constructs, presented in Table 5, are lower than the threshold of 0.85. Thus, there is adequate discriminant validity of data.

Table 3.

Matrix of Cross Loading.

	UB	BI	EE	FC	PE	PR	PT	SI	TA
UB1	0.930	0.009	0.046	0.050	-0.024	0.356	0.106	0.142	0.527
UB2	0.906	-0.002	0.053	0.037	0.013	0.327	0.076	0.108	0.481
UB3	0.893	-0.016	0.020	-0.008	0.013	0.348	0.044	0.069	0.473
BI1	0.002	0.938	0.661	0.662	0.392	0.123	0.642	0.722	-0.102
BI2	-0.014	0.934	0.638	0.644	0.395	0.130	0.617	0.715	-0.060
BI3	0.005	0.921	0.614	0.607	0.416	0.164	0.627	0.628	-0.046
EE1	0.027	0.584	0.861	0.468	0.428	0.154	0.445	0.418	-0.090
EE2	0.050	0.573	0.865	0.446	0.358	0.105	0.463	0.423	-0.054
EE3	-0.003	0.614	0.861	0.530	0.353	0.095	0.492	0.475	-0.048
EE4	0.079	0.599	0.870	0.546	0.393	0.155	0.458	0.490	-0.044
FC1	0.045	0.613	0.557	0.881	0.348	0.004	0.433	0.516	-0.032
FC2	-0.003	0.636	0.497	0.885	0.333	-0.002	0.459	0.523	-0.052
FC3	0.015	0.597	0.490	0.861	0.292	-0.027	0.422	0.461	-0.049
FC4	0.052	0.532	0.459	0.853	0.299	0.070	0.436	0.424	0.007
PE1	-0.019	0.417	0.390	0.367	0.870	0.015	0.293	0.281	-0.063
PE2	-0.040	0.330	0.358	0.334	0.828	0.088	0.248	0.248	-0.131
PE3	0.007	0.339	0.348	0.239	0.820	0.173	0.255	0.253	0.003
PE4	0.055	0.339	0.380	0.274	0.819	0.172	0.227	0.259	-0.030
PR1	0.378	0.152	0.132	0.006	0.126	0.956	0.184	0.202	0.367
PR2	0.351	0.147	0.163	0.015	0.119	0.935	0.181	0.213	0.337
PR3	0.336	0.115	0.116	0.009	0.115	0.940	0.182	0.168	0.340
PT1	0.089	0.639	0.487	0.471	0.276	0.155	0.931	0.490	0.044
PT2	0.088	0.627	0.512	0.468	0.298	0.214	0.920	0.481	0.032
PT3	0.052	0.585	0.477	0.439	0.271	0.162	0.893	0.435	0.034
SI1	0.042	0.672	0.479	0.507	0.328	0.178	0.476	0.909	-0.043
SI2	0.154	0.657	0.444	0.468	0.239	0.190	0.466	0.900	0.041
SI3	0.128	0.684	0.499	0.532	0.280	0.197	0.452	0.909	-0.015
TA1	0.488	-0.099	-0.082	-0.067	-0.073	0.318	0.019	-0.042	0.925
TA2	0.506	-0.024	-0.002	-0.001	-0.052	0.374	0.107	0.026	0.915
TA3	0.512	-0.084	-0.104	-0.038	-0.058	0.332	-0.012	-0.003	0.929

Table 4.

Correlation Matrix of Square Root of Average Variance Extracted (AVE).

Constructs	UB	BI	EE	FC	PE	PR	PT	SI	TA
UB	0.910
BI	-0.003	0.931
EE	0.044	0.686	0.864
FC	0.030	0.686	0.577	0.870
PE	0.000	0.430	0.443	0.366	0.835
PR	0.378	0.148	0.147	0.011	0.127	0.943
PT	0.084	0.675	0.538	0.503	0.308	0.193	0.915
SI	0.119	0.741	0.523	0.555	0.312	0.208	0.513	0.906
TA	0.544	-0.075	-0.068	-0.038	-0.066	0.370	0.041	-0.007	0.923

Table 5.

Correlation Matrix of Heterotrait-Monotrait (HTMT) Ratio.

Constructs	UB	BI	EE	FC	PE	PR	PT	SI
BI	0.014
EE	0.060	0.756
FC	0.045	0.751	0.645
PE	0.043	0.480	0.508	0.414
PR	0.410	0.158	0.159	0.037	0.149
PT	0.092	0.739	0.601	0.559	0.348	0.210
SI	0.132	0.615	0.587	0.619	0.356	0.225	0.572
TA	0.600	0.084	0.085	0.050	0.078	0.399	0.056	0.047

Structural model

The findings (Table 6) show that performance expectancy, effort expectancy, social influence, facilitating conditions and perceived trust were statistically significant in explaining the behavioral intention of start-up entrepreneurs to use crowdfunding. Trialability is found to affect use behavior of crowdfunding, whilst trialability and perceived risk had no statistically significant effect on behavioral intention to use crowdfunding to raise funds. Behavioral intention does not have a significant impact on actual use behavior of crowdfunding. Therefore, the proposed hypotheses, H1, H2, H3, H4, H6 and H7 were supported. On the other hand, H5, H8 and H9 were found to be not supported.

Table 6.

Structural Model.

Hypothesis	Path	Coefficient (β)	t-Statistics	p-Value	Comments
H1	PE→BI	0.150	3.256	0.001	Supported
H2	EE→BI	0.112	2.307	0.021	Supported
H3	SI→BI	0.365	8.905	0.000	Supported
H4	FC→BI	0.085	2.059	0.040	Supported
H5	TA→BI	-0.058	1.876	0.061	Not Supported
H6	TA→UB	0.547	12.107	0.000	Supported
H7	PT→BI	0.254	6.806	0.000	Supported
H8	PR→BI	0.004	0.134	0.894	Not Supported
H9	BI→UB	0.038	0.917	0.359	Not Supported

The structural model explains 75.9% of the variance in behavioral intention to use crowdfunding and 29.7% of the variance of actual use behavior of crowdfunding (Figure 2). Since the model accounted for more than 10% of variance of endogenous variables, it is a substantive and satisfactory structural model (Falk and Miller, 1992).

Figure 2.

Results of Hypothesis Testing.

Discussion

An extended UTAUT was applied in this study to determine the start-up entrepreneurs’ behavioral intention to adopt crowdfunding for financing in Bangladesh. Overall, the empirical findings of this research provided insight into the constructs such as performance expectancy, effort expectancy, social influence, facilitating conditions and perceived trust in influencing crowdfunding adoption. The majority of constructs and hypothesized relations are supported by empirical findings, which are consistent with the outcomes of previous studies using UTAUT in crowdfunding adoption.

The results indicate that performance expectancy is a significant positive determinant of intention to adopt crowdfunding (H1), supporting previous studies in crowdfunding adoption (Kim and Jeon, 2017; Lacan and Desmet, 2017). As business persons, it is logical for start-up entrepreneurs to express their intentions to use crowdfunding based on cost-benefit analysis. They found crowdfunding to be a useful financing platform to help them access capital quickly and increase their financial performance. Likewise, effort expectancy is identified as a significant contributor in explaining the start-up entrepreneurs’ intention to adopt crowdfunding (H2), which is consistent with many prior studies on users’ crowdfunding adoption (Kim and Jeon, 2017; Moon and Hwang, 2018). The entrepreneurs believe that they needed less effort to raise funds through crowdfunding because most of them have basic computer literacy and Internet use experience. Similarly, the third construct, social influence, is found to have the greatest impact on willingness to adopt crowdfunding (H3) compared to other exogenous variables. The adoption intentions of start-up entrepreneurs are heavily influenced by the opinions and recommendations, as well as the motivations of their reference groups or socially important people, which is also in line with prior studies (Colombo et al., 2015; Mollick, 2014; Ordanini et al., 2011). Facilitating conditions also have a positive effect on crowdfunding intention (H4). Perceived technical support, customer assistance and expertise of crowdfunding platforms motivate entrepreneurs to raise capital through this financing option. This also supports the suggestions for infrastructural development proposed by Kwon et al. (2014), to ensure crowdfunding success.

However, no significant relationship was observed between trialability and behavioral intention (H5). This reflects the lack of local crowdfunding platforms and restricted access to global ones. Consequently, entrepreneurs get little opportunity to experience crowdfunding. In contrast, trialability was found to be a very significant determinant of use behavior or actual use of crowdfunding (H6), which revealed that the crowdfunding experiment opportunity influences start-up entrepreneurs directly for using crowdfunding for fund raising without delay.

The relationship between perceived trust and crowdfunding intention was also investigated in this research (H7) and was found to be significant. The start-up entrepreneurs can rely on this type of online funding because of the trustworthiness, integrity, transparency and security of crowdfunding platforms, which confirms the trust ensuring recommendation for success in crowdfunding by Gerber and Hui (2013). On the other hand, another extended construct to the UTAUT model, perceived risk, is identified as a non-significant factor (H8), consistent with some prior studies in crowdfunding adoption (Kim and Jeon, 2017). The probable reason behind is that entrepreneurs are the receivers of funds from backers or investors in the crowdfunding platform and the information supposed to be provided in the crowdfunding platforms is less risk sensitive for entrepreneurs.

Finally, the association between behavioral intention and use behavior or actual use was examined (H9) and no relationship was identified, which conflicts with previous research in information system or technology acceptance studies (Ifinedo, 2012; Oliveira et al., 2014). This outcome indicates that a notable number of entrepreneurs have the intention to raise funds using crowdfunding, but cannot easily access crowdfunding platforms in or from Bangladesh. If more crowdfunding platforms are available in the future, this relation will be significant.

Implications

Theoretical Implications

This research presents empirical insights into the intention to adopt crowdfunding of start-ups in Bangladesh, which can be generalized for other South Asian developing countries with similar cultural and economic backgrounds. The research first validated the UTAUT framework in a study on start-up entrepreneurs. The UTAUT was extended with three additional constructs, namely, trialability, trust and risk, which is a significant contribution to the literature on crowdfunding and start-up research. The validity of the research model and the outcomes of hypothesis testing confirm that the start-up entrepreneurs’ crowdfunding intention can be predicted by using this model. Finally, the findings can lay the foundations for other researchers to investigate further to obtain more insights of crowdfunding adoption in developing countries.

Practical Implications

The empirical findings of this research have practical implications for crowdfunding platforms, policy makers and incubation centers and universities that are fostering start-ups. Crowdfunding platforms can use the findings in planning, designing and facilitating crowdfunding. In addition, start-up promoting organizations, such as incubation centers, universities and entrepreneur clubs, can design their training programs based on identified behavioral issues to increase the success of start-ups through crowdfunding. Start-up entrepreneurs in Bangladesh have been given the opportunity to view a macro level picture of their perceptions of crowdfunding. Also, considering the identified motivating and demotivating factors as a checklist, policy-making authorities can develop strategies and modify existing policies to foster start-ups as well as crowdfunding in Bangladesh.

Limitations and Future Directions

There are some clear limitations of this study. First, it is a cross-sectional study and cannot provide a comparative scenario of before and after crowdfunding adoption by start-up entrepreneurs. Secondly, the sampling method used in this study might not be representative of the entire population and this could cause sampling bias. Also, the online survey method could not capture the opinions and views of entrepreneurs who are not digitally accessible. Thirdly, the use of a mail based self-administered survey may only collect information from entrepreneurs who are computer literate. Besides, the participants were not provided with clarifications of each question. The participants might have been able to get clarification about any question, if data collection were done through interviews. Fourthly, only the hypothesized factors were tested to identify the motivating factors. This prevented the study from the possibility of enumerating all the underlying motivating factors.

Despite its limitations, this research provides a literature foundation and scope for start-up and crowdfunding researchers for future studies. A further study could be done using longitudinal data to get better insights of causal or over time relationships or changes among factors. The scope of research could be broadened in future by increasing the sample size and study area, specifically in the developing countries. The developed model can be applied for research focusing on other modes of crowdfunding, like donation crowdfunding rather than equity crowdfunding. Lastly, comparative studies with other developed and/or developing countries would be precious research contributions.

Conclusion

Although crowdfunding is a promising financing source for start-ups, its acceptance rate is not satisfactory in Bangladesh. In order to ensure positive social, economical and technological impacts on emerging innovative start-ups, the technical and non-technical challenges towards crowdfunding need to be identified and faced. The success of adoption and diffusion of crowdfunding depends on the engagement and acceptance of end users, both investors and entrepreneurs. This study has revealed the motivational perceptions of start-up entrepreneurs in the setting of a developing country. The extended UTAUT model was validated and confirmed for predicting start-up entrepreneurs’ crowdfunding adoption intention. The findings contribute to crowdfunding and start-up researches incorporating the behavioral intention aspects of start-up entrepreneurs. For practitioners, the identified factors can be helpful in designing, refining and implementing crowdfunding platforms or crowdfunding fostering programs. The government should work in collaboration with the private sector, including financial institutions, educational institutions and incubation centers, to ensure greater adoption of crowdfunding in Bangladesh.

Footnotes

ORCID iD

Mohammad Tariqul Islam

Appendix A.

Measurement Items of Questionnaire

Constructs	Items	Survey Questions	Items’ Sources
Performance Expectancy (PE)	PE1	Using crowdfunding is useful to raise capital for my start-up.	Venkatesh et al. (2003), Venkatesh et al. (2012)
	PE2	Using crowdfunding enables me to raise capital for my start-up more quickly.
	PE3	Using crowdfunding increase my financial performance and productivity.
	PE4	If I use crowdfunding, I will enhance the possibility of raising more funds for my start-up.
Effort Expectancy (EE)	EE1	The structure and user interface of crowdfunding platforms is clear and easy to understand.	Venkatesh et al. (2003), Ya-Zheng et al. (2018)
	EE2	Learning how to use crowdfunding platforms is easy for me.
	EE3	I find crowdfunding platforms easy to use for fund raising.
	EE4	It is easy for me to become skillful at using crowdfunding platforms.
Social Influence (SI)	SI1	People around me encourage me to raise funds from crowdfunding platform for my start-up.	Venkatesh et al. (2003), Moon and Hwang (2018)
	SI2	People who are important to me think that I should use crowdfunding for funds raising for my start-up.
	SI3	My friends are likely to follow if they encourage raising fund for their start-ups through crowdfunding platforms.
Facilitating Conditions (FC)	FC1	The crowdfunding platforms are capable to provide me sufficient technical support to solve any problems I encounter during raising funds for my start-up.	Venkatesh et al. (2003), Moon and Hwang (2018)
	FC2	The crowdfunding platforms have adequate transaction systems for fund raising for start-ups.
	FC3	Crowdfunding platforms have sufficient channels (chat and mail) to communicate with the appropriate technical support staffs.
	FC4	The crowdfunding platforms have enough experience and knowledge in managing and facilitating funds raising for start-ups.
Trialability (TA)	TA1	I have enough opportunity to try and use crowdfunding.	Moore and Benbasat (1991)
	TA2	I am able to experiment crowdfunding.
	TA3	I think I don’t have to expend much effort in trying to crowdfunding.
Perceived Trust (PT)	PT1	I can rely on crowdfunding platforms.	Kang et al. (2016), Ya-Zheng, et al. (2018)
	PT2	I think crowdfunding platform is trustworthy and have high integrity.
	PT3	I think my personal and start-up related information will not be exposed to unauthorized parties by crowdfunding platforms.
Perceived Risk (PR)	PR1	I think It would not be risky to use crowdfunding platform to raise funds.	Featherman and Pavlou (2003)
	PR2	I think crowdfunding platform would not hamper the financial and personal privacy.
	PR3	I think business model information exposure in crowdfunding platforms don’t have imitation risk.
Behavioral Intention (BI)	BI1	I have intention to raise capital for my start-up through crowdfunding platform in coming future.	Venkatesh et al. (2003), Ya-Zheng et al. (2018)
	BI2	I predict I would raise capital for my start-up through crowdfunding platform in near future.
	BI3	I have planned to use crowdfunding in near future.
Use Behavior (UB)	UB1	I frequently use crowdfunding platform to raise capital for my start-up.	Venkatesh et al. (2012)
	UB2	I frequently browse crowdfunding platforms to raise capital for my start-up.
	UB3	I frequently post on crowdfunding platforms to raise capital for start-up.

References

Abbas

Hassan

Asif

Ahmed

Hassan

Haider

(2018) Integration of TTF, UTAUT, and ITM for Mobile Banking Adoption. International Journal of Advanced Engineering, Management and Science (IJAEMS) 4(5): 375–379.

Aboelmaged

Gebba

(2013) Mobile banking adoption: an examination of technology acceptance model and theory of planned behavior. International Journal of Business Research and Development 2(1).

Adhikary

Kutsuna

Hoda

(2018) Crowdfunding Potential in Developing Countries—A Case of Bangladesh. In Adhikary

Kutsuna

Hoda

, Crowdfunding Lesson from Japan’s Approach. Singapore: SpringerBriefs in Economics. Springer, Singapore, pp. 77–94.

Adhikary

Kutsuna

(2016) Small Business Finance in Bangladesh: Can ‘Crowdfunding’ Be an Alternative? Review of Integrative Business and Economics Research 4(4): 1–21.

Agarwal

Prasad

(1997) The role of innovation characteristics, and perceived voluntariness in the acceptance of information technologies. Decision Sciences 28(3): 557–582.

Aggelidis

Chatzoglou

(2009) Using a modified technology acceptance model in hospitals. International journal of medical informatics 78(2): 115–126.

Ahlers

Cummings

Gunther

Schwelzer

(2015). Signalling in equity crowdfunding. Entrpreneurship Theory and Practice 39: 955–980.

Ajzen

(2002) Perceived behavioral control, self-efficacy, locus of control and the theory of planned behavior. Journal of Applied Social Psychology, 32: 665–683.

Aker

Kumar

Day

(2004) Pesquisa de marketing. Portugues: Atlas.

10.

AlAwadhi

Morris

(2008) The Use of the UTAUT Model in the Adoption of E-government Services in Kuwait. In: Hawaii International Conference on System Sciences, Proceedings of the 41st Annual, pp. 219–220. Hawaii: IEEE.

11.

Al-Shafi

Weerakkody

(2009) Understanding citizens’ behavioral intention in the adoption of e-goverrnment services in the state of Qatar. In: 17th European Conference on Information Systems (ECIS 2009). pp.1618–1629. Verona, Italy.

12.

Al-Somali

Gholami

Clegg

(2011) An investigation into the adoption of electronic commerce among Saudi Arabian SMEs. E-Commerce for Organizational Development and Competitive Advantage 9(2): 41–65.

13.

Anderson

Gerbing

(1988) Structural equation modeling in practice: a review and recommended two-step approach. Psychological Bulletin 103(3): 411–423.

14.

Babbie

(2004) Survey research. In: Babbie

, The practice of social research. Belmount, USA: Nelson Education, pp. 242–280.

15.

Bagozzi

Singh

(1991) On the use of structural equation models in experimental designs: Two extensions. International Journal of Research in Marketing 8(2): 125–140.

16.

Bandura

(1982) Self efficacy mechanism in human agency. American Psychologist 37(2): 122.

17.

Bangladesh Bureau of Statistics (2018) Gross Domestic Product (GDP) of Bangladesh. Available at: http://www.bbs.gov.bd/site/page/dc2bc6ce-7080-48b3-9a04-73cec782d0df/%E0%A6%9C%E0%A6%BF%E0%A6%A1%E0%A6%BF%E0%A6%AA%E0%A6%BF- (accessed 7 October 2018).

18.

Bangladesh Telecommunications Regulatory Commissions (2019) Internet subscribers Bangladesh, July 2019. Available at: http://www.btrc.gov.bd:http://www.btrc.gov.bd/content/internet-subscribers-bangladesh-july-2019 (accessed 10 August 2019).

19.

Barclay

Higgins

Thompson

(1995) The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology studies 2(2): 285–309.

20.

Belleflamme

Lambert

Schwienbacher

(2013) Individual crowdfunding practices. Journal of Venture Capital 15(4): 313–333.

21.

Benassi

(1999) TRUSTe: An online privacy seal program. Communication of the ACM 42(2): 56.

22.

Berger

Black

(2011) Bank size, lending technologies and small business finance. Journal of Banking and Finance 35(3): 724–735.

23.

Bhimani

(1996) Securing the commercial Internet. Communication of the ACM, 39(6): 29–35.

24.

Bryman

Cramer

(2005) Quantitative data analysis with SPSS 12 and 13: a guide for social scientists. Routledge: London.

25.

Byun

Chiu

Bae

(2018) Exploring the Adoption of Sports Brand Apps: An Application of the Modified Technology Acceptance Model. International Journal of Asian Business and Information Management (IJABIM) 9(1): 52–65.

26.

Chin

(1998) Commentary: Issues and opinion on structural equation modeling. MIS Quarterly Vol. 22

27.

Churchill

Iacobucci

(2002) Marketing research (8th ed.). Florida: Harcourt College Publishers.

28.

Colombo

Franzoni

Rossi-Lamastra

(2015) Internal social capital and the attraction of early contributions in crowdfunding. Entreprise Theory Practice (39): 75–100.

29.

Davis

(1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly 13(3): 319–340.

30.

Falk

Miller

(1992) A primer for soft modelling. Akron: University of Akron Press.

31.

Featherman

Pavlou

(2003) Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies 59(4): 451–474.

32.

Featherman

Pavlou

(2003) Predicting E-Services Adoption: A Perceived Risk Facets Perspective. International Journal of Human-Computer Studies 59(4): 451–474.

33.

Field

(2013) Discovering statistics using IBM SPSS statistics. London: SAGE.

34.

Fornell

Larcker

(1981) Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of marketing research 18(3): 382–388.

35.

Gefen

Straub

Boudreau

(2000) Structural equation modeling and regression:guidelines for research practice. Communications of Association for Information Systems 4(7): 1–77.

36.

Gerber

Hui

Kuo

(2012) Crowd-funding: Why people are motivated to post and fund projects on crowd funding platforms. In: the International Workshop on Design, Influence, and Social Technologies: Techniques, Impacts and Ethics 2:11. Northwestern University Evanston, IL.

37.

Gerber

Hui

(2013) Crowdfunding: Motivations and deterrents for participation. ACM Transactions on Computer-Human Interaction (20): 34.

38.

Gleasure

(2015) Resistance to crowdfunding among entrepreneurs: An impression management perspective. Journal of Strategic Information Systems 24(1): 219–233.

39.

We Are Social (2018) Global Digital Report. Available at: https://digitalreport.wearesocial.com/ (Accessed 7 October)

40.

Gotz

Liehr

Krafft

(2010) Evaluation of structural equation models using the partial least squares (PLS) approach. In: Handbook of Partial Least Squares. Berlin, Heidelberg: Springer, pp. 691–711.

41.

Grewal

Gotlieb

Marmorstein

(1994) The moderating effects of message framing and source credibility on the price-perceived risk relationship. Journal of Consumer Research 21: 145–153.

42.

Hair

Jr Hult

Ringle

Sarstedt

(2014) A primer on partial least squares structural equation modeling (PLS-SEM). California: SAGE.

43.

Hair

Tatham

(2006) Multivariate data analysis (Vol. 6th) . Upper Saddle River, NJ: Pearson Prentice Hall.

44.

Hair

Black

Babin

Anderson

Tatham

(2006) Multivariate Data Analysis (6th ed.). New Jersey: Pearson Prentice Hall, Pearson Education, Inc.

45.

Hair

Sarstedt

Ringle

Mena

(2012) An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the academy of marketing science 40(3): 414–433.

46.

Hasan

Ahmad

Rahman

Islam

(2017) Exploring the Prerequisites of Institutionalizing Crowdfunding Process in Bangladesh as an Alternative Financing Option for the Startups. Global Journal Of Management And Business Research 18(1): 1–16.

47.

Hemer

Schneider

Dornbusch

Frey

(2011) A snapshot on crowdfunding. Karlsruhe, Germany: Fraunhofer Institute for Systems and Innovation Research. Available at: http://www.legalefiscale.it/wp-content/uploads/2016/09/StudioCrowdfundig.pdf (accessed 28 August 2018)

48.

Henseler

Hubona

Ray

(2016) Using PLS path modeling in new technology research: Updated guidelines. Industrial Management and Data Systems 116(1): 2–20.

49.

Henseler

Ringle

Sinkovics

(2009) The use of partial least squares path modeling in international marketing. Advances in international marketing 20(1): 277–319.

50.

Hienerth

Riar

(2013) The wisdom of the crowd vs. expert evaluation: a conceptualization of evaluation validity. In: 35th DRUID celebration conference, Barcelona, Spain, pp. 17–19. Available at: http://druid8.sit.aau.dk/acc_papers/im4odl75ix421p8l11ry1qg6gko8.pdf

51.

Hofstede

(1980) Culture and organizations. International Studies of Management & Organization, 10(4): 15–41.

52.

Ifinedo

(2012) Technology acceptance by health profesionals in Canada: An analysis with a modified UTAUT model. In: Proceedings of the 45th Hawaii International Conference on System Sciences, Hawaii, USA, pp. 4–7.

53.

infoDev (2013) Crowdfunding’s Potential for the Developing World. Washington, DC: The World Bank, Finance and Private Sector Development.

54.

Islam

(2018) Oporajoy has introduced a crowdfunding platform. The Daily Asian Age, 18 July.

55.

Jegeleviciute

Valenciene

(2015) Comparative analysis of the ways crowdfunding is promoted. Procedia- Social and Behavioral Sciences 213: 268–274.

56.

Kang

Gao

Wang

Zheng

(2016) Understanding the determinants of funders’ investment intentions on crowdfunding platforms: A trust-based perspective. Industrial Management & Data Systems 116(8): 1800–1819.

57.

Kim

Jeon

(2017) Influencing Factors on the Acceptance for Crowd Funding—Focusing on Unified Theory of Acceptance and Use of Technoloyg. Journal of Korean Institute of Intellegence Systems (27): 150–156.

58.

Kim

Lee

(2014) Can Crowd Funding be the Economic Basis for the Independence of Small-Scale Culture & Arts?-A Study on Funding Experiences of Indie Musicians. Media Gend. Culture 29: 5–42.

59.

Klarner

Sarstedt

Hoeck

Ringle

(2013) Disentangling the effects of team competences, team adaptability, and client communication on the performance of management consulting teams. Long Range Planning 46(3): 258–286.

60.

Koch

Siering

(2015) Crowdfunding success factors: The characteristics of successfully funded projects on crowdfunding platforms. In: Proceedings of the 23rd European Conference on Information Systems (ECIS 2015), Muenster: SSRN, pp. 1–15.

61.

Kwon

Lee

(2014) Success and Failure Case Analysis of Crowdfunding: Focused on Community Art Project. Journal of Korea Contents Association 14: 125–136.

62.

Lacan

Desmet

(2017) Does the crowdfunding platform matter? Risks of negative attitudes in two-sided markets. Journal of Consumer Marketing 34(6): 472–479.

63.

Lee

Kim

Kwon

(2016) Promoting the Masses’ Investment through Crowdfunding Platform: Focusing on Lending based Crowdfunding Platform. Journal of Korea Contents Association 16: 644–660.

64.

Lee

Shin

(2015) Factor Analysis of the Motivation on Crowdfunding Participants: An Empirical Study of Funder Centered Reward-type Platform. The Journal of Society for e-Business Studies 20(1): 137–151.

65.

Lee

(2016) Effect of Perceived Risk, Perceived Benefit, and Korea Wave to Crowdfunding Participation in China. Journal of Korea Contents Association 16: 204–213.

66.

Lee

Brown

(2017) Innovation, SMEs and the liability of distance: The demand and supply of bank funding in peripheral UK regions. Journal of Economic Geography 17(1): 233–260.

67.

Lee

(2004) Nurses’ Adoption of Technology: Application of Rogers’ Innovation-Diffusion Model. Applied Nursing Research 17(4): 231–238.

68.

Ley

Weaven

(2011) Exploring Agency Dynamics Of Crowdfunding In Start-Up Capital Financing. Academy Of Entrepreneurship Journal, 85–110.

69.

Song

Yang

Zhou

(2018) Factors impacting donors’ intention to donate to charitable crowd-funding projects in China: A UTAUT-based model. Information Communication for Society 21: 404–415.

70.

Litwin

(1995) How to measure survey reliability and validity (Vol. 7). California: SAGE.

71.

Liu

(2005) Facilitating conditions, wireless trust and adoption intention. Journal of Computer Information Systems 46(1): 17–24.

72.

Macht

Weatherston

(2014) The benefits of online crowdfunding for fund seeking business ventures. Journal of Strategic Change 23(2): 1–14.

73.

Malhotra

(2008) Marketing research: An applied orientation (5th ed.). India: Pearson Education.

74.

Mitchell

(1992) Understanding consumers’ behavior: can perceived risk theory help? Management Decision 30(2): 26–31.

75.

Mollick

(2014) The dynamics of crowdfunding: An exploratory study. Journal of Business Venture 29: 1–16.

76.

Moon

Hwang

(2018) Crowdfunding as an Alternative Means for Funding Sustainable Appropriate Technology: Acceptance Determinants of Backers. Sustainability 10: 1–18.

77.

Moore

Benbasat

(1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Information systems research 2(3): 192–222.

78.

Moore

Benbasat

(1991) Development of an instrument to measure the perceptions of adopting an information technology innovation. Information System Research 12: 192–222.

79.

Morduch

(1999) The Microfinance Promise. Journal of Economic Literature 37(4): 1569–1614.

80.

Oliveira

Faria

Thomas

Popovic

(2014) Extending the understanding of mobile banking adopion: When UTAUT meets TTF and ITM. International Journal of Information Management 34(5): 689–703.

81.

Ordanini

Miceli

Pizzetti

Parasuraman

(2011) Crowd-funding: Transforming customers into investors through innovative service platforms. Journal of Service Management (22): 443–470.

82.

Orlikowski

Baroudi

(1991) Studying information technology in organizations: Research approaches and assumptions. Information systems research 2(1): 1–28.

83.

Pallant

(2005) SPSS Survival Guide. NSW: Allen & Unwin.

84.

Palvia

Salam

(2004) Research methodologies in MIS: An Update. Communications of the Association for Information Systems 14(1): 24.

85.

Park

Lee

(2016) An Exploratory Study on the Factors Affecting Crowdfunding: An Analysis on Online Donation. e-Business Studies (17): 55–69.

86.

Paschen

(2017) Choose wisely: Crowdfunding through the stages of the startup life cycle. Business Horizons 60(2): 179–188.

87.

Raymond

(2015) Crowdfunding in Emerging Markets: Lessons from East African Startups. Washington DC: World Bank Group.

88.

Reaz

(2015) Face time with Waiz Rahim, founder of the local crowd-funding platform Projekt.co. The Daily Star, 20 June. Available at: https://www.thedailystar.net/next-step/projektco-face-time-waiz-rahim-founder-the-local-crowd-funding-platform-72639

89.

Reaz

(2015) The startup culture. The Daily Star, 12 March. Available at: https://www.thedailystar.net/supplements/24th-anniversary-the-daily-star-part-3/the-startup-culture-71129.

90.

Rogers

(2003) Diffusion of Innovations (4th ed.). New York: Free Press.

91.

Rosen

(2005) The effect of personal innovativeness on technology acceptance and use. Oklahoma: Oklahoma Stae University.

92.

Sahin

Thompson

(2006) Using Rogers’ Theory to Interpret Instructional Computer Use by COE Faculty. Journal of Research on Technology in Education 39(1): 81–104.

93.

Santosa

Wei

Chan

(2005) User involvement and user satisfaction with information-seeking activity. European Journal of Information Systems 14(4): 361–370.

94.

Saunders

(2011) Research methods for business students (5th ed.). India: Pearson Education.

95.

Saunders

Lewis

Thornhill

(2003) Research methods for business students (3rd ed.). Harlow: Prentice Hall.

96.

Shams

(2017). Fundamentals of fundraising for startups: The Bangladesh perspective. The Independent, 14 July, Available at: http://www.theindependentbd.com/printversion/details/103838.

97.

Startup Bangladesh (2018) Available at: http://startupbangladesh.gov.bd:http://startupbangladesh.gov.bd/about/background/ (Accessed 7 October 2018).

98.

StartupExplore (2014) Understanding the differences in startup financing stages. Available at: https://startupxplore.com/en/blog/types-startup-investing/

99.

StatSoft

(2013) Electronic Statistics Textbook. Statsoft: Tulsa.

100.

Steinberg

DeMaria

(2012) The Crowdfunding Bible. Australia: Read Me.

101.

Stemler

(2013) The JOBS Act and crowdfunding: Harnessing the power and money of masses. Business Horizons 56(3): 271–275.

102.

Stemler

A. R.

(2013). The JOBS Act and Crowdfunding: Harnessing the power and money of the masses. Business Horizons, 56(3), 271–275.

103.

Tabachnick

Fidell

(1996) SPSS for Windows Workbook to Accompany Large Sample Examples of Using Multivariate Statistics. Harper Collins College Publishers.

104.

Tabachnick

Fidell

(2007) Profile analysis: the multivariate approach to repeated measures. In: Tabachnick

Fidell

, Using multivariate statistics (5th ed). Boston: Allyn & Bacon/Pearson Education, pp. 311–374.

105.

Tan

(2013) Applying the UTAUT to understand the factors affecting the use of English e-learning websites in Taiwan. Sage Open 3(4): 215.

106.

Turan

(2015) Financial Innovation Crowdfunding: Friend or Foe? Procedia - Social and Behavioral Sciences 195(1): 353 – 362.

107.

Van Schaik

(2009) Unified theory of acceptance and use for websites used by students in higher education. Journal of Educational Computing Research 40(2): 229–257.

108.

Venkatesh

Morris

Davis

(2003) User acceptance of information technology: Toward a unified view. MIS Quaterly 27: 425–478.

109.

Venkatesh

Thong

(2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quaterly 36: 57–178.

110.

Venkatesh

Thong

(2012) Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quaterly 36: 57–178.

111.

Williams

Rana

Dwivedi

(2015) The unified theory of acceptance and use of technology (UTAUT): a literature review. Journal of Enterprise Information Management 28(3): 443–488.

112.

Wright

(2005) Researching Internet-based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer-Mediated Communication 10(3).

113.

Ya-Zheng

Tong-Liang

Yi-Ran

Zheng

Rong-Ting

(2018) Factors impacting donors’ intention to donateo charitable crowd-funding projects in China: a UTAUT-based model. Information, Communication & Society 21(3): 404–415.