Skip to main content
NCBI home page
Elsevier - PMC COVID-19 Collection logoLink to Elsevier - PMC COVID-19 Collection
. 2022 Nov 7;155:113433. doi: 10.1016/j.jbusres.2022.113433

Investors’ confidence in the crowdlending platform and the impact of Covid-19

José María Ferrer a, Klaus Ulrich b, Cristina Blanco-González-Tejero c,⁎, Enrique Caño-Marín c
PMCID: PMC9637522  PMID: 36373084

Abstract

Crowdlending platforms have gained importance in recent years due to digitalization. The Covid-19 pandemic has severely impacted the entire economy, including repercussions on digital transactions. Investors considering transactions on these platforms require confidence in both the platform and the project in order to make the right decision. For this reason, this study analyzes the links between the parties involved in the process. A survey was sent to 135 investors on the Colectual platform in January 2022, and the research method adopted is Fuzzy Set Qualitative Comparative Analysis (fsQCA). The study examines the connections between the platform, the investors, and the project developers, to assess the influence exerted by the platform on the confidence of the parties seeking and providing investment. The main result is that Covid-19 has been a key factor in the confidence of investors in the platform, and it has had a significant influence on the investments made.

Keywords: Crowdlending, Crowdfunding, Fintech, Finance, Covid-19

1. Introduction

The pandemic has led to changes in consumer trends. Digitization has allowed the economy to continue, and already-digitized activities continued to operate despite being directly impacted by Covid 19. Digital capabilities have a positive impact on firm performance (Hereida et al., 2022). Financial transactions have also been affected, but, broadly speaking, this sector has played a strategic role in mitigating the crisis (Acharya & Steffen, 2020).

Innovation contributes to sustainable competitive business advantage (Ortigueira-Sánchez et al., 2022). Hence, continuous and open innovation has influenced and changed the focus of business strategies (Chesbrough, 2003). In this sense, the innovative roles of technologies and digital transformation are relevant to the crisis caused by the pandemic. In recent years, physical and digital systems have been increasingly integrated, achieving a total convergence in many of the services offered, meaning that many commercial interactions have moved online.

In the financial sector, crowdfunding (Belleflamme et al., 2013) and microfinance (Khavul, 2010) have become more important sources of financing because, in crises, bank lending and venture capital investment are reduced (Piva & Rossi-Lamastra, 2018). In addition, crowdfunding has been considered by Dijkstraet al., (2022) as a market driver that allows organizations to attract early adopters.

With crowdfunding, an entrepreneur can acquire external funding from a crowd (Belleflamme et al., 2014), which makes it easier to manage and implement their projects. At the same time, the relationship between technology and financing has led to the development of ‘FinTech’, a portmanteau of “finance” and “technology” which refers to businesses that use technology to enhance or automate financial services and processes (Zalan & Toufaily, 2017). Consequently, this topic has been considered as a technology-driven financial innovation (Ye et al., 2022).

Fintech is currently of great strategic importance for financial services (Puschmann, 2017). Some Fintech companies offer customer value in alternative ways, differing in their business models, value propositions, and operating mechanisms (Giaretta & Chesini, 2021). Some offer alternative financial services (Zalan & Toufaily, 2017). Within the Fintech sector, we wish to highlight services such as crowdfunding, peer-to-peer lending, venture capital, private equity, or other forms of financing that include technology (Arner et al., 2015, Zalan and Toufaily, 2017). In this sense, technology have changed the way in which business finance is provided (Vismara, 2016). Covid-19 has positively affected Fintech, particularly crowdfunding, as indicated by Wahjono et al. (2021), so it is now important to analyze users’ perceptions of this type of platform. Since authors such as Al-Omoush (2021) have considered that due to large pandemics, due to the data available on social media have generated contradictions, lack of trust and changes in the perceived value of crowdsourcing. To this end, this study uses primary data generated via a survey of 135 investors on the Colectual platform and tested through Fuzzy Set Qualitative Comparative Analysis (fsQCA).

Crowdfunding, as an innovative financing method (Ribeiro-Navarrete et al., 2021c), has received much attention in recent years, which has boosted and expanded the general literature in this field (Ribeiro-Navarrete et al., 2021b; Grundy & Ohmer, 2016). Furthermore, authors such as Cumming et al., (2021) compared the differential impact of Covid-19 between different alternative sources of capital available to entrepreneurs such as bank consumer loans, P2P loans, and ECF and highlighted the relationship between them. Hence, this topic has sparked research interest in terms of exploring how financial products and services are produced, delivered, and consumed (Allen et al., 2021). However, many factors can impact every investment decision (Ribeiro-Navarrete et al., 2021a; Mollick, 2014), so this study examines Covid-19 and capital markets from a new perspective. Therefore, we contribute to the existing literature with new knowledge that helps to answer the implications of Covid-19 on the global economy, since, as authors such as Nozawa & Qiu, (2021) state, it has not yet been fully revealed. The research focuses on the links between the parties involved in the financing process through this type of platform. The key elements of analysis are the platform, the investor and the founders of the projects seeking funding. Therefore, this research work provides an analysis of the link between the investor's profile, the value of the information present in each project and the positioning of the platform. These three aspects are addressed due to the fact that the investor profile is no longer an isolated element in this type of financing, and digital elements such as the type of platform and the value of the project described in it, are elements that become relevant due to the amount of irrelevant information available on the internet. In this way, these factors are involved in crowdfunding operations, and the consideration of these elements by entrepreneurs and government policies is essential for the correct functioning and positioning of this type of financing model. In this way, it avoids unethical practices. Furthermore, it highlights the key concepts on which investors must focus their attention in order to be able to evaluate the signals and confidence of the platform after the Covid-19 situation.

This research is developed as follows. It begins with a literature review of the financing models linked to technology and their impact on SMEs. It focuses on the motivations that have driven investors to carry out transactions through crowdlending platforms during the Covid-19 situation. This is followed by the data and methodology section in which the research design is outlined. Then the results are presented and discussed, followed by future lines of research and the limitations of the study.

1.1. Theoretical background

Financing is one of the most problematic considerations when an initiative is being launched. Companies need finance to develop their entrepreneurship, their capacity for innovation and the adoption and transfer of new technology. Thus, entrepreneurship, innovation, and technology adoption and transfer should be considered to be highly dependent on business financing (Audretsch et al., 2016). Moreover, the adoption of new algorithms and big data approaches in this funding model has helped to overcome the limitations of traditional funding models (Allen et al., 2021).

Traditional financing processes are often arduous and time-consuming, posing a range of impediments for entrepreneurs in the market. The existing constraints on entrepreneurs have led some to opt for microfinance processes (Kimmit & Dimov, 2021). Profound social and organizational changes in the economy have severely affected all financing processes. Traditional financing methods have lost ground to the new financial models such as crowdfunding, which has involved developing and implementing new technologies. Similarly, the standardization of banking products has also diminished banks’ ability to differentiate appropriately between different funding applications (Lundahl et al., 2009).

Crowdfunding, which is a crowdsourcing method of financing companies, represents a rapidly growing online ecosystem (Frydrych et al., 2014). Authors such as Mačiulienė & Skaržauskienė (2021) have described crowdfunding as a civic technology since the platforms through which it operates offer solutions to community needs. New funding models require platforms that connect entrepreneurs with funders through the internet (Vrontis et al., 2020). Crowdfunding investors are interested both in providing support for new project ideas and in continuing support after the end of a campaign. These platforms can also be helpful throughout the development and implementation of the project. The management of the platform is complicated because it needs to consider the different parties involved, such as creators, investors, and platforms (Leone et al., 2018). The user’s experience is critical in their decision to use one platform or another.

Crowdfunding platforms vary, mainly according to the purpose of the projects they intend to support. Under the most recognized classification, they can be donation-based, reward-based, loan-based, or equity-based (Parhankangas et al., 2019). In donation-based platforms, success depends on engaging backers based on the emotional value of their projects (Rhue et al., 2018) and on the use of social networks for rapid dissemination (Korolov et al., 2016). Their usual focus is altruistic projects such as communities, musicians, filmmakers, and artists (Ribeiro-Navarrete et al., 2021a). This model makes it possible to raise funds in response to financial difficulties and medical and natural disasters (Saleh et al., 2020). It has therefore been one of the most widely used models during the pandemic. The most common arrangement is reward-based funding: attracting investors seeking rewards (Belleflamme et al., 2014, Mollick, 2014). Equity-based crowdfunding is the most novel model since it allows entrepreneurs to advertize the sale of their own shares, usually in the start-up, to internet users (Agrawal et al., 2014, Ahlers et al., 2015). Crowdfunding can also be solicited through loans: this is called crowdlending, and it allows investors to receive back the principal loaned plus interest (Lin & Viswanathan, 2016).

1.2. Investor profile

The motivations of investors in crowdlending are usually either financial or social (Rey-Martí et al., 2016). Following the growing success of crowdfunding platforms, many authors have studied the motivations of these users (Calic & Mosakowski, 2016; Ribeiro-Navarrete et al., 2021a; Short & Anglin, 2019).

Scholars such as Lee and Suh (2022) have argued that the economic performance of all firms, and especially of crowdfunded projects is influenced by customer and shareholder loyalty and their ability to generate long-term shareholder value and sustained financial wealth. Many variables have been linked to the success of crowdfunding (Kuppuswamy & Bayus, 2017), one of the most important of which is dynamic changes in uncertain markets. The transactions developed by this type of platform have been impacted by education and by the professional experience or gender of the person seeking funds (Barbi and Mattioli, 2019, Ahlers et al., 2015). Experience enables them to act differently based on prior learning (Greenberg & Gerber, 2014). Ultimately, crowdfunding performance influences professional funding (Roma et al., 2017) and consumer perceptions of the product (Wehnert et al., 2019).

Investors in crowdlending platforms may or may not be professionals. Professionals invest in crowdfunding to obtain monetary returns (Goethner et al., 2021). Consequently, larger amounts of money can be invested in this type of platform. Professional investors create a cascade effect on the other users willing to invest. Thus, individuals with less accurate information tend to follow the lead of individuals with relevant empirical information (Zhang & Liu 2012). Therefore, these ‘lead investors’ can become opinion leaders who can influence the dissemination of information and attitudes, according to Watts and Dodds (2007).

Barbi and Mattioli (2019) highlight the role of human capital as a hallmark of quality of and entrepreneurial initiative in crowdfunding, and (Beckman et al. (2007) argue that it is another critical factor in investment decisions. From this perspective, the individual plays a determining role, so the training and experience of the founder and their team (Kaplan & Strömberg, 2004) will influence the final decision. In this pandemic-affected funding environment, the availability of capital from accredited investors through crowdfunding platforms is a significant opportunity for companies. This study, therefore, proposes to analyze the investor profiles, considered through gender, age, education, and the percentage of income invested, on the assumption that they will be significant elements in the investments placed in this type of platform.

Proposition 1

Accredited investors have developed greater confidence towards the crowdlending platform following the impact of the pandemic.

Proposition 2

Users who invest a significant part of their wealth in the crowdlending platform now have greater confidence.

1.3. The value of project information

Financial transactions are based on trust (Guiso et al., 2008), so this variable is relevant in crowdfunding models, especially during the pandemic. Crowdlending platforms become central to the building of trust (Moysidou & Hausberg, 2020). Not all platforms have the same characteristics, nor do they provide the same benefit. Hence, it is necessary to consider the differences between platforms on the basis of their perceived usefulness (Venkatesh et al., 2003). The platform needs an endorsement—publicized so that it is known by both lenders and borrowers—that allows it to be seen as secure (Rogers, 2003). The feeling of trust, norms, and expectations, should be considered as influential variables in the funding process (Madrazo-Lemarroy et al., 2019). The development and success of this financing model depend on lenders continuing to lend money to borrowers (Yum et al., 2012). Therefore, the project information provided by the platform must be analyzed if the usual risks in the lender-borrower relationship are to be avoided.

Due to the digitalization of transactions, both lenders and borrowers need to be persuaded that they can trust the other ‘side’ before undertaking transactions. Thus, it becomes relevant to highlight studies that link social capital with other variables such as collective efficacy, social trust, and reciprocity (Harpham et al., 2002). Therefore, reliable sources of information are the basis for acquiring credible knowledge and building social trust in times of crisis (Zhong et al., 2021). Bi et al. (2017) have highlighted that the success of investments usually depends on the availability of existing information from these projects. Several studies show how important it is to potential transactions that information is communicated to investors (Cappa et al., 2021). Therefore, information communication is a key element of crowdfunding projects’ success (Kraus et al., 2016). Accordingly, the crucial factors are the quality of the project analysis Mollick (2013), its description (Gafni et al., 2019), and the creator’s record of obtaining backing for other projects (Koch & Siering, 2015).

Society interacts at an ever-increasing pace in the digital environment, with the user being the generator of content on digital platforms and social networks. Moreover, the volume of shared information is increasing—especially during the pandemic—and platforms have also increased their digital activities. Faced with this increased volume of information, the platform needs to generate a link with its users, which also helps it differentiate itself. Engagement has been defined as the commitment between customer and seller that makes for a lasting exchange (Sashi, 2012). There will be greater engagement when better signaling between the parties is facilitated by communication. Thus, companies will have to find ways to clearly signal their value to small investors to attract their attention and get them to invest in them (Ahlers et al., 2015). Crowdfunding platforms play a crucial role because they enable relationships (Belleflamme et al., 2014). Scholars such as Block et al. (2018) have noted the number of comments, the length and speed of responses, and the language used in the success of campaigns. Crowdlending platforms function as intermediaries and generate value between the parties (Sriram et al., 2015). The motivation and bonding between campaign contributors and sponsors on platforms can be important factors in generating support (Wang et al., 2018). Thus, a range of elements influences the engagement between the parties in transactions through crowdlending platforms.

Authors such as Thies et al. (2014) have considered the influence of social media activity. Xu et al. (2014) argued that updating content in this type of media has become critical. Therefore, the social media activity is related to key business development outcomes, and the social media presence of crowdfunding projects is a key component of their overall campaign strategy (Wolfe et al., 2021). However, this apparent advantage may turn out not to be so. As indicated by Saura et al. (2021), users should be aware of the security issues posed by the use or misuse of digital devices, so the increase in technological capacity in the market might well increase vulnerabilities (Almeida et al., 2020).

The projects available on the platforms during Covid have changed significantly, so the information related to them has been updated and sent to investors through various means of communication. Consequently, the relevance of the information shared by the crowdfunding platforms on funding decisions, and the trust between the parties influenced by Covid-19, should be analyzed.

Proposition 3

Users who support the decisions made by the platform to try to mitigate the negative effects of the pandemic on the loan portfolio have greater confidence in the platform.

Proposition 4

Investors who have positively valued the platform’s communication of extraordinary activity and information during the pandemic have confidence in the platform.

1.4. Platform positioning vis-à-vis the investor

Given the current uncertainty, confidence is necessary to invest and for the crowdfunding platform to gain relevance. The perceived risk of transaction complexity can prevent many transactions from being completed, but trust is an enabling mechanism (McKnight et al., 1998). Wahjono et al. (2021) note that Covid-19 has increased the use of Fintech. However, the perceived risk in crowdfunding platforms is mainly due to the loss of contact and lack of information between the parties. (Zheng et al. (2016) therefore suggest that, as trust is fundamental for the success of this type of investment, platforms should manage this variable more carefully in order to increase fundraising significantly. There is an unfavorable risk distribution for the funder (Hommerová, 2020) since the entrepreneur has all the project information. Therefore, the platform must pay attention to the users and offers them adequate information, thus enhancing the impression that the investor has of the service provided by the platform.

With regard to information, signage strongly influences the frequency of use of the crowdlending platform. One of the variables used to analyze the behavior of investors is the frequency of their visits to the platforms and the numbers of people participating in crowdfunding activities (Venkatesh et al., 2012, Block et al., 2018). Accordingly, the influence of information asymmetries and signaling on decisions to use crowdfunding has been considered in a range of studies, including Ribeiro-Navarrete et al., (2021a); Burtch et al., 2013, Courtney et al., 2017.

Social capital and the contributions of others play an important role in crowdfunders’ decision-making because they reduce uncertainties and perceived information asymmetries (Herzenstein et al., 2011). The transfer of trust from the lending platform to the lending project and the creator reflects the effectiveness of trust-building measures established by the platform (Moysidou & Hausberg, 2020). Accordingly, positive trust factors seem to lead to success in loan-based crowdfunding, with the crowdfunding platform as a whole being the most important driver.

Proposition 5

Investors who use the platform more frequently have a better image of the platform after the impact of Covid-19 on their operations.

Proposition 6

Longer-standing investors on the platform have developed a more positive view of the platform in the pandemic situation.

2. Methodology

A Qualitative Comparative Analysis (QCA) was developed in order to study the complex relationship between the conditions that the literature suggests may affect after-pandemic confidence in the crowdlending platform under investigation. For this purpose, a survey was sent to 135 investors on the Colectual platform. This methodology has been used by authors such as De Crescenzo et al. (2021) and Ribeiro-Navarrete et al., (2021a) to analyze the combination of causal conditions that produce the result, making it highly relevant in this research since it allows the analysis of propositions through configurations. Likewise, we continue in the research line of Martinez et al. (2021), who worked in the same market and the same methodology in the area of crowdlending investor motivations.

QCA considers the relationship between conditions and the studied outcome and reveals the different equifinal configurations that lead to an increase in confidence in the platform after the covid-19 pandemic. QCA considers the relationship between variables and avoids the limitations of alternative symmetric quantitative methods (Woodside, 2013, Zhang et al., 2017).

We selected the following factors as conditions that may cause increased investor confidence: the customer being a professional investor, the period the user was active in the platform, how frequently the investor browed the web, the percentage of their wealth they invest in Colectual, how much the investor agreed with the extraordinary measures adopted by the company to reduce the adverse effects of the pandemic on the loan portfolio and investors’ perceived satisfaction with the corporate communication of the platform see Table 1. .

Table 1.

Variables.

CONDITIONS & OUTCOME DEFINITION
ACR CONDITION Professional investor
ACT CONDITION Active period in the platform
FRQ CONDITION Web browsing frequency
INV CONDITION % of wealth invested in the platform
MEAS CONDITION Degree of agreement with the extraordinary measures adopted by the platform
COM CONDITION Perception of the communication of the platform during the pandemic
CONF OUTCOME Investors confidence in the platform after the pandemic
Open in a new tab

The non-dichotomous conditions were calibrated in a 1–5 Likert scale and reconfigured into a 0–1 range Table 2. .

Table 2.

Calibration.

ACT FRQ INV MEAS COM CONF
Full membership (90th percentile) 4 4 5 4 4 4
Crossover point (median) 3 2 2 3 3 3
Full non-membership (10th percentile) 2 1 1 2 2 2
Open in a new tab

Causal condition ACR was not calibrated as it is a dichotomous condition whose value is 1 if the investor is professional and 0 if they are not.

The non-dichotomous causal conditions were set as follows: full-membership at percentile 0.9, the maximal ambiguity point, also known as crossover, at 0.5, and full non-membership at 0.9, following Ragin (2007). For ACT, MEAS, and COM, full-membership was set at 4, crossover point at 3, and full non-membership at 2. For causal condition INV, the corresponding calibration points were at values 5, 2, and 1. For condition FRQ, the settings were at values 4, 2, and 1. Outcome CONF the settings were: full membership point 4, crossover point 3, and full non-membership point 2.

3. Results

3.1. Analysis of necessary conditions

Necessary conditions are those that must be present to make an outcome happen. In Qualitative Comparative Analysis, conditions are considered necessary when they reach a consistency level of at least 0.9; that is, a high level of consistency means that a condition is empirically important (Dusa, 2019, Greckhamer et al., 2018).

As Table 3 shows, the degree of investor agreement with the platform’s measures to reduce the impact of Covid 19 in the loan portfolio is a necessary condition for increased confidence in the company after the pandemic.

Table 3.

Analysis of necessary conditions.

PRESENCE OF THE OUTCOME
 ABSENCE OF THE OUTCOME
Consistency Coverage Consistency Coverage
ACR 0.184307 0.522400 0.186155 0.477600
∼ACR 0.815693 0.525454 0.813844 0.474546
ACT 0.879904 0.531045 0.927502 0.506686
∼ACT 0.182614 0.735645 0.141565 0.516202
FRQ 0.790291 0.606848 0.748362 0.520156
∼FRQ 0.375106 0.622191 0.434362 0.652153
INV 0.314000 0.795211 0.261771 0.600072
∼INV 0.842083 0.557559 0.910664 0.545787
MEAS 0.968671 0.738620 0.780792 0.538900
∼MEAS 0.395287 0.665795 0.621296 0.947231
COM 0.861417 0.766064 0.645306 0.519453
∼COM 0.459639 0.588756 0.709385 0.822487
Open in a new tab

3.2. Analysis of sufficient conditions

Sufficient conditions are complex approaches that indicate the presence of a recipe of antecedent conditions, i.e., multiple paths leading to the same result (Woodside, 2016). Configurational multiplicity presents different configurations of factors (Park et al., 2020). The elaborated sufficiency analysis determined the various configurations of conditions that lead to the presence or absence of the outcome. To conduct the analysis, we focused on two main indicators, consistency and raw coverage. Consistency expresses the degree to which a condition is a subset of the result. Raw coverage shows the proportion of cases explained by a configuration (Skarmeas et al., 2014). Valid solutions should register consistency levels of at least 0.8 and raw coverage above 0.25 (Eng & Woodside, 2012).

Following Fiss (2011), solutions are illustrated here by their “core” structures Table 4. . Black circles indicate the presence of the condition, white circles indicate the absence of the condition, large circles indicate a core condition (i.e., the condition appears in both the parsimonious solution and the intermediate solution), and small circles indicate that the condition only appears in the intermediate solution. Blank spaces indicate that the condition is irrelevant.

Table 4.

Analysis of sufficient conditions.

PRESENCE OF CONFIDENCE INCREASEMENT AFTER THE PANDEMIC ABSENCE OF CONFIDENCE INCREASEMENT AFTER THE PANDEMIC
1 2 3 4 5 6 7
ACR ○ ○
ACT graphic file with name fx1_lrg.gif ● ● ● ● ●
FRQ ● ● ○ ● ●
INV ○ ○ graphic file with name fx1_lrg.gif graphic file with name fx2_lrg.gif graphic file with name fx2_lrg.gif graphic file with name fx2_lrg.gif
MEAS ● ● graphic file with name fx1_lrg.gif graphic file with name fx2_lrg.gif graphic file with name fx2_lrg.gif ●
COM graphic file with name fx1_lrg.gif ● graphic file with name fx2_lrg.gif graphic file with name fx2_lrg.gif ○ graphic file with name fx2_lrg.gif
Raw coverage 0.652 0.573 0.273 0.497 0.285 0.323 0.384
Unique coverage 0.170 0.091 0.104 0.056 0.029 0.027 0.054
Consistency 0.832 0.841 0.879 0.859 0.977 0.908 0.917
Solution coverage 0.847 0.621
Consistency 0.834 0.863
Open in a new tab

4. Discussion

4.1. Presence of the outcome

Configuration 1, which explains the 65.2 % of the cases in which investors had increased confidence in the platform after the pandemic crisis, suggests that the conditions for that result to occur are as follows: customers are usually long-term users of the platform, they have a positive perception of the communication of the measures adopted, they support these decisions, and they do not invest a large part of their wealth in the platform. This configuration registers a consistency level of 0.832.

Configuration 2 shows that customers who increased their confidence in the platform after the impact of the pandemic in their investments and frequently browse the platform, support the Covid measures adopted by the platform, believe that the company communicated its decisions well, and do not invest high amounts of their wealth.

Configuration 3 registers the highest consistency score. It indicates that the crowdlending customers who had a high level of confidence invested a significant proportion of their wealth in the platform, agreed with the platform’s Covid measures, have usually been active users from long ago, and often browse in the platform. This solution has a raw coverage of 0.273.

4.2. Absence of the outcome

Configuration 5, which has the highest consistency score (0.977), suggests that investors who do not invest large amounts of their wealth in the crowdlending platform do not agree at all with the adopted measures to reduce the negative consequences of the pandemic in their investments, disapprove of how those actions were communicated, do not usually browse the website, are not active for long periods and have not increased their confidence levels in the company after the pandemic. Configuration 5 registers a raw coverage level of 0.285.

Configuration 4, which explains 49.7 % of the cases, indicates that customers who invest relatively little in the platform, have neutral perceptions of the platform’s communication ability on its Covid 19 measures, who are not professional investors and have not been active for a long time ago in the marketplace, do not typically increase their confidence in the platform after the pandemic. This configuration has a consistency level of 0.859.

Configuration 7 shows that investors with low capital invested in the platform and a negative perception of the communication of the platform about their Covid 19 measures, who have not been active for a long time in the marketplace, and who usually browse it and support the measures adopted by the platform against the pandemic have not increased their confidence levels in the platform after the Covid crisis.

5. Conclusions

The Covid-19 pandemic has significantly impacted many parts of the economy. The pandemic situation has influenced the behaviour of the investor, the entrepreneur and the way both deal with the financing process. Traditional financing was already being replaced gradually by Fintech due to digitalization and the support of particular technological tools in financial services. However, the pandemic has necessitated changes and adjustments to financing platforms to improve the process between investors and projects. This study has analyzed the characteristics of the investors in the Colectual platform. It has considered the accredited professional investor, the investors’ periods of activity and frequency of use of the platform, the percentage of their wealth invested in the transactions, the degree of trust provided by active communication, the extraordinary measures adopted during the Covid-19, and the confidence and engagement generated by it. In this way, the research gap is addressed through the FsQCA analysis.

Covid-19 has had a significant influence on the investments made, and it is a determining factor in the confidence placed by investors in the platform after the pandemic. In this way, trust becomes a key element to be borne in mind by entrepreneurs, so that a project that generates greater trust will be more likely to be invested in. The trust placed in the platform by investors who have a continuous relationship with it, understand the communication made by the platform, and invest moderate proportions of their assets, encourages moderate and long-term investments in Colectual. Thus, it has become clear that the signaling developed by the platform (Burtch et al., 2013) is key to investor confidence. It will therefore be crucial to consider the practical implications of this research since they can lead to improved relationships between the parties. The more information we have about the investor profile, the easier it will be to direct communications to that target audience, thus generating greater confidence in the service provided and allowing a real link between the parties, increasing engagement. Therefore, the positioning of the platform vis-à-vis the investor, the investor profile and the value of the project information are key elements for the progress of this financing model.

5.1. Theoretical and practical implications

The results obtained enable a series of theoretical and practical implications to emerge. In this way, government policies should consider the trust that certain platforms offer to potential investors, and the veracity of the projects provided by entrepreneurs. To this end, new policies or strategies can be designed to verify the information.

5.2. Limitations and future research lines

This study is not without limitations. Firstly, the Covid-19 pandemic has significantly influenced personal and professional decisions and therefore affected the level of market activity, so further research should be conducted after the end of the crisis in order to compare the present results with new findings. Secondly, the research looked specifically at the investors of the Colectual platform, so a study on other platforms would allow comparisons between platforms, giving us a broader perspective of the influence and value represented by the variables analyzed. Thirdly, there are other variables that may influence investor confidence, such as family tradition, financial backing or education. Finally, the data for the analysis is limited to a sample of 135 investors. However, by using FsQCA, the analysis becomes particularly relevant, as Mas-Verdú et al. (2015) proposes that this methodology allows to give robustness to the results despite having limited samples.

Therefore, this research provides an important basis for future research on investors’ perceptions of, and engagement with, crowdfunding platforms. Furthermore, given the results obtained and the improvements they could imply, further rigorous and solid conceptual and empirical research will be needed to improve and develop existing theories on the basis of the new results. In addition, a larger sample, and/or a sample segmented by territories and/or other demographic characteristics, will allow for comparison of results and provide new scientific insights. Last, further cross-cultural studies could be developed to focus on investor confidence in digital finances with the aim of increasing creditworthiness and confidence in these platforms.

CRediT authorship contribution statement

José María Ferrer: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing - original draft; Writing - review & editing. Klaus Ulrich: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing - original draft; Writing - review & editing. Cristina Blanco-González-Tejero: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing - original draft; Writing - review & editing. Enrique Caño-Marín: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Resources; Software; Validation; Visualization; Writing - original draft; Writing - review & editing.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Biographies

José María Ferrer General Manager at Colectual and Economist with more than 18 years of experience in Business Development in the Consulting, Private Healthcare and Renewable Energy sectors. Correspondence to: Valencia University, Valencia, Spain, e-mail: jmferrer@colectual.com ORCID https://orcid.org/0000-0002-4279-9338

Klaus Ulrich Professor and researcher at ESIC Business & Marketing School. His lines of research focus on entrepreneurship, blockchain and finance. Correspondence to: ESIC Business & Marketing School, Valencia, Spain, ORCID https://orcid.org/0000-0003-1003-0847

Cristina Blanco González-Tejero Her main lines of research are the development of Soft Skills, agile methodologies and the impact in entrepreneurial and economic activity. Correspondence to: School of Economics, Business and Tourism, University of Alcalá, Madrid, Spain

Enrique Caño His main lines of research are: Social network analysis (SNA), Artificial Intelligence (AI), machine learning and automation applied. He holds a BSc in Industrial Engineering and a MSc in Robotics and Automation. Correspondence to: University of Alcalá, Madrid, Spain, e-mail: enrique.cano@outlook.com ORCID https://orcid.org/0000-0002-7948-1657

References

  1. Acharya V.V., Steffen S. The risk of being a fallen angel and the corporate dash for cash in the midst of COVID. The Review of Corporate Finance Studies. 2020;9(3):430–471. doi: 10.1093/rcfs/cfaa013. [DOI] [Google Scholar]
  2. Agrawal A., Catalini C., Goldfarb A. Some simple economics of crowdfunding. Innovation Policy and the Economy. 2014;14(1):63–97. doi: 10.1086/674021. [DOI] [Google Scholar]
  3. Ahlers G.K., Cumming D., Günther C., Schweizer D. Signaling in equity crowdfunding. Entrepreneurship Theory and Practice. 2015;39(4):955–980. doi: 10.1111/etap.12157. [DOI] [Google Scholar]
  4. Allen F., Gu X., Jagtiani J. A survey of fintech research and policy discussion. Review of Corporate Finance. 2021;1:259–339. [Google Scholar]
  5. Almeida F., Santos J.D., Monteiro J.A. The challenges and opportunities in the digitalization of companies in a post-COVID-19 World. IEEE Engineering Management Review. 2020;48(3):97–103. [Google Scholar]
  6. Al-Omoush K.S., Zardini A., Al-Qirem R.M., Ribeiro-Navarrete S. Big crisis data, contradictions and perceived value of social media crowdsourcing in pandemics. Economic Research-Ekonomska Istraživanja. 2021;34(1):450–468. doi: 10.1080/1331677X.2020.1867604. [DOI] [Google Scholar]
  7. Arner D.W., Barberis J., Buckley R.P. The evolution of Fintech: A new post-crisis paradigm. Georgetown Journal of International Law. 2015;47:1271. [Google Scholar]
  8. Audretsch D.B., Lehmann E.E., Paleari S., Vismara S. Entrepreneurial finance and technology transfer. The Journal of Technology Transfer. 2016;41(1):1–9. doi: 10.1007/s10961-014-9381-8. [DOI] [Google Scholar]
  9. Barbi M., Mattioli S. Human capital, investor trust, and equity crowdfunding. Research in International Business and Finance. 2019;49:1–12. doi: 10.1016/j.ribaf.2019.02.005. [DOI] [Google Scholar]
  10. Beckman C.M., Burton M.D., O’Reilly C. Early teams: The impact of team demography on VC financing and going public. Journal of Business Venturing. 2007;22(2):147–173. doi: 10.1016/j.jbusvent.2006.02.001. [DOI] [Google Scholar]
  11. Belleflamme P., Lambert T., Schwienbacher A. Individual crowdfunding practices. Venture Capital. 2013;15(4):313–333. doi: 10.1080/13691066.2013.785151. [DOI] [Google Scholar]
  12. Belleflamme P., Lambert T., Schwienbacher A. Crowdfunding: Tapping the right crowd. Journal of business venturing. 2014;29(5):585–609. doi: 10.1016/j.jbusvent.2013.07.003. [DOI] [Google Scholar]
  13. Bi S., Liu Z., Usman K. The influence of online information on investing decisions of reward-based crowdfunding. Journal of Business Research. 2017;71:10–18. doi: 10.1016/j.jbusres.2016.10.001. [DOI] [Google Scholar]
  14. Block J., Hornuf L., Moritz A. Which updates during an equity crowdfunding campaign increase crowd participation? Small Business Economics. 2018;50(1):3–27. doi: 10.1007/s11187-017-9876-4. [DOI] [Google Scholar]
  15. Burtch G., Ghose A., Wattal S. An empirical examination of the antecedents and consequences of contribution patterns in crowdfunded markets. Information Systems Research. 2013;24(3):499–519. doi: 10.1287/isre.1120.0468. [DOI] [Google Scholar]
  16. Calic G., Mosakowski E. Kicking off social entrepreneurship: How a sustainability orientation influences crowdfunding success. Journal of Management Studies. 2016;53(5):738–767. doi: 10.1111/joms.12201. [DOI] [Google Scholar]
  17. Cappa F., Pinelli M., Maiolini R., Leone M.I. “Pledge” me your ears! The role of narratives and narrator experience in explaining crowdfunding success. Small Business Economics. 2021;57(2):953–973. doi: 10.1007/s11187-020-00334-y. [DOI] [Google Scholar]
  18. Chesbrough H.W. Harvard Business Press; 2003. Open innovation: The new imperative for creating and profiting from technology. [Google Scholar]
  19. Courtney C., Dutta S., Li Y. Resolving information asymmetry: Signaling, endorsement, and crowdfunding success. Entrepreneurship Theory and Practice. 2017;41(2):265–290. doi: 10.1111/etap.12267. [DOI] [Google Scholar]
  20. Cumming D.J., Martinez-Salgueiro A., Reardon R.S., Sewaid A. COVID-19 bust, policy response, and rebound: Equity crowdfunding and P2P versus banks. The Journal of Technology Transfer. 2021:1–22. doi: 10.1007/s10961-021-09899-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. De Crescenzo V., Monfort A., Felício J.A., Ribeiro-Navarrete S. Communication and the role of third-party endorsement in social crowdfunding. The Service Industries Journal. 2021;1–28 doi: 10.1080/02642069.2021.1963437. [DOI] [Google Scholar]
  22. Dijkstra H., van Beukering P., Brouwer R. Marine plastic entrepreneurship; Exploring drivers, barriers and value creation in the blue economy. Sustainable Technology and Entrepreneurship. 2022;100018 [Google Scholar]
  23. Dusa A. Springer; 2019. QCA with R. [Google Scholar]
  24. Eng S., Woodside A.G. Configural analysis of the drinking man: Fuzzy-set qualitative comparative analyses. Addict. Behav. 2012;37(4):541–545. doi: 10.1016/j.addbeh.2011.11.034. [DOI] [PubMed] [Google Scholar]
  25. Frydrych D., Bock A.J., Kinder T., Koeck B. Exploring entrepreneurial legitimacy in reward-based crowdfunding. Venture capital. 2014;16(3):247–269. doi: 10.1080/13691066.2014.916512. [DOI] [Google Scholar]
  26. Gafni H., Marom D., Sade O. Are the life and death of an early-stage venture indeed in the power of the tongue? Lessons from online crowdfunding pitches. Strategic Entrepreneurship Journal. 2019;13(1):3–23. doi: 10.1002/sej.1293. [DOI] [Google Scholar]
  27. Giaretta E., Chesini G. The determinants of debt financing: The case of fintech start-ups. Journal of Innovation & Knowledge. 2021;6(4):268–279. doi: 10.1016/j.jik.2021.10.001. [DOI] [Google Scholar]
  28. Goethner M., Hornuf L., Regner T. Protecting investors in equity crowdfunding: An empirical analysis of the small investor protection act. Technological Forecasting and Social Change. 2021;162 doi: 10.1016/j.techfore.2020.120352. [DOI] [Google Scholar]
  29. Greckhamer T., Furnari S., Fiss P.C., Aguilera R.V. Studying configurations with qualitative comparative analysis: Best practices in strategy and organization research. Strategic Organization. 2018;16(4) [Google Scholar]
  30. Greenberg, M. D., & Gerber, E. M. (2014, April). Learning to fail: experiencing public failure online through crowdfunding. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 581-590). Doi: 10.1145/2556288.2557110.
  31. Grundy D., Ohmer C. German crowd-investing platforms: Literature review and survey. Cogent Business & Management. 2016;3(1):1138849. doi: 10.1080/23311975.2016.1138849. [DOI] [Google Scholar]
  32. Guiso L., Sapienza P., Zingales L. Trusting the stock market. the. Journal of Finance. 2008;63(6):2557–2600. doi: 10.1111/j.1540-6261.2008.01408.x. [DOI] [Google Scholar]
  33. Harpham T., Grant E., Thomas E. Measuring social capital within health surveys: Key issues. Health Policy and Planning. 2002;17(1):106–111. doi: 10.1093/heapol/17.1.106. [DOI] [PubMed] [Google Scholar]
  34. Hereida J., Castillo-Vergara M., Geldes C., Gamarra F.M.C., Flores A., Heredia W. How do digital capabilities affect firm performance? The mediating role of technological capabilities in the “new normal”. Journal of Innovation & Knowledge. 2022;7(2) doi: 10.1016/j.jik.2022.100171. [DOI] [Google Scholar]
  35. Herzenstein M., Dholakia U.M., Andrews R.L. Strategic herding behavior in peer-to-peer loan auctions. Journal of Interactive Marketing. 2011;25(1):27–36. doi: 10.1016/j.intmar.2010.07.001. [DOI] [Google Scholar]
  36. Hommerová D. Financing Nonprofit Organizations. Routledge; 2020. Crowdfunding as a new model of nonprofit funding; pp. 173–187. [Google Scholar]
  37. Kaplan S.N., Strömberg P.E. Characteristics, contracts, and actions: Evidence from venture capitalist analyses. The Journal of Finance. 2004;59(5):2177–2210. doi: 10.1111/j.1540-6261.2004.00696.x. [DOI] [Google Scholar]
  38. Khavul S. Microfinance: Creating opportunities for the poor? Academy of Management Perspectives. 2010;24(3):58–72. doi: 10.5465/amp.24.3.58. [DOI] [Google Scholar]
  39. Kimmitt J., Dimov D. The recursive interplay of capabilities and constraints amongst microfinance entrepreneurs. International Journal of Entrepreneurial Behavior & Research. 2021 doi: 10.1108/IJEBR-10-2018-0642. [DOI] [Google Scholar]
  40. Koch, J. A., & Siering, M. (2015). Crowdfunding success factors: The characteristics of successfully funded projects on crowdfunding platforms.
  41. Korolov R., Peabody J., Lavoie A., Das S., Magdon-Ismail M., Wallace W. Predicting charitable donations using social media. Social Network Analysis and Mining. 2016;6(1):1–10. doi: 10.1007/s13278-016-0341-1. [DOI] [Google Scholar]
  42. Kraus S., Richter C., Brem A., Cheng C.F., Chang M.L. Strategies for reward-based crowdfunding campaigns. Journal of Innovation & Knowledge. 2016;1(1):13–23. doi: 10.1016/j.jik.2016.01.010. [DOI] [Google Scholar]
  43. Kuppuswamy V., Bayus B.L. Does my contribution to your crowdfunding project matter? Journal of Business Venturing. 2017;32(1):72–89. doi: 10.1016/j.jbusvent.2016.10.004. [DOI] [Google Scholar]
  44. Lee M.T., Suh I. Understanding the effects of Environment, Social, and Governance conduct on financial performance: Arguments for a process and integrated modelling approach. Sustainable Technology and Entrepreneurship. 2022;1(1) doi: 10.1016/j.stae.2022.100004. [DOI] [Google Scholar]
  45. Leone D., Schiavone F., Dezi L. Post-campaign operational problems hindering promised rewards in crowdfunding projects. Journal of Innovation Economics Management. 2018;2:173–194. [Google Scholar]
  46. Lin M., Viswanathan S. Home bias in online investments: An empirical study of an online crowdfunding market. Management Science. 2016;62(5):1393–1414. doi: 10.1287/mnsc.2015.2206. [DOI] [Google Scholar]
  47. Lundahl N., Vegholm F., Silver L. Technical and functional determinants of customer satisfaction in the bank-SME relationship. Managing Service Quality: An. International Journal. 2009 [Google Scholar]
  48. Mačiulienė M., Skaržauskienė A. Building the capacities of civic tech communities through digital data analytics. Journal of Innovation & Knowledge. 2020;5(4):244–250. doi: 10.1016/j.jik.2019.11.005. [DOI] [Google Scholar]
  49. Madrazo-Lemarroy P., Barajas-Portas K., Tovar M.E.L. Analyzing campaign’s outcome in reward-based crowdfunding: Social capital as a determinant factor. Internet Research. 2019 doi: 10.1108/INTR-03-2018-0115. [DOI] [Google Scholar]
  50. Mas-Verdú F., Ribeiro-Soriano D., Roig-Tierno N. Firm survival: The role of incubators and business characteristics. Journal of Business Research. 2015;68(4):793–796. doi: 10.1016/j.jbusres.2014.11.030. [DOI] [Google Scholar]
  51. McKnight D.H., Cummings L.L., Chervany N.L. Initial trust formation in new organizational relationships. Academy of Management Review. 1998;23(3):473–490. doi: 10.5465/amr.1998.926622. [DOI] [Google Scholar]
  52. Mollick E. The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing. 2014;29(1):1–16. doi: 10.1016/j.jbusvent.2013.06.005. [DOI] [Google Scholar]
  53. Moysidou K., Hausberg J.P. In crowdfunding we trust: A trust-building model in lending crowdfunding. Journal of Small Business Management. 2020;58(3):511–543. doi: 10.1080/00472778.2019.1661682. [DOI] [Google Scholar]
  54. Nozawa Y., Qiu Y. Corporate bond market reactions to quantitative easing during the COVID-19 pandemic. Journal of Banking & Finance. 2021;106153 doi: 10.1016/j.jbankfin.2021.106153. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Ortigueira-Sánchez L.C., Welsh D.H., Stein W.C. Innovation drivers for export performance. Sustainable Technology and Entrepreneurship. 2022;1(2) [Google Scholar]
  56. Parhankangas A., Mason C., Landström H. Edward Elgar Publishing; In Handbook of Research on Crowdfunding: 2019. Crowdfunding: An introduction. [Google Scholar]
  57. Park Y., Fiss P.C., El Sawy O.A. Theorizing the Multiplicity of Digital Phenomena: The Ecology of Configurations, Causal Recipes and Guidelines for Applying QCA. MIS Quarterly. 2020;44(4):1493–1520. [Google Scholar]
  58. Piva E., Rossi-Lamastra C. Human capital signals and entrepreneurs’ success in equity crowdfunding. Small Business Economics. 2018;51(3):667–686. doi: 10.1007/s11187-017-9950-y. [DOI] [Google Scholar]
  59. Puschmann T. Fintech. Business & Information. Systems Engineering. 2017;59(1):69–76. doi: 10.1007/s12599-017-0464-6. [DOI] [Google Scholar]
  60. Ragin, C. C. (2007). Fuzzy sets: calibration versus measurement. Methodology volume of Oxford handbooks of political science.
  61. Rey-Martí A., Ribeiro-Soriano D., Sánchez-García J.L. Giving back to society: Job creation through social entrepreneurship. Journal of Business Research. 2016;69(6):2067–2072. doi: 10.1016/j.jbusres.2015.12.010. [DOI] [Google Scholar]
  62. Rhue, L., & Robert, L. P. (2018, April). Emotional delivery in pro-social crowdfunding success. In Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-6). Doi: 10.1145/3170427.3188534.
  63. Ribeiro-Navarrete S., Palacios-Marqués D., Lassala C., Ulrich K. Key factors of information management for crowdfunding investor satisfaction. International Journal of Information Management. 2021;59 doi: 10.1016/j.ijinfomgt.2021.102354. [DOI] [Google Scholar]
  64. Ribeiro-Navarrete S., Palacios-Marqués D., Martín J.M.M., Martínez J.M.G. A synthetic indicator of market leaders in the crowdlending sector. International Journal of Entrepreneurial Behavior & Research. 2021 doi: 10.1108/IJEBR-05-2021-0348. [DOI] [Google Scholar]
  65. Ribeiro-Navarrete S., Piñeiro-Chousa J., López-Cabarcos M., Palacios-Marqués D. Crowdlending: Mapping the core literature and research frontiers. Review of Managerial Science. 2021;1–31 doi: 10.1007/s11846-021-00491-8. [DOI] [Google Scholar]
  66. Rogers E.M. 5th ed. Free Press; New York: 2003. Diffusion of innovations. [Google Scholar]
  67. Roma P., Petruzzelli A.M., Perrone G. From the crowd to the market: The role of reward-based crowdfunding performance in attracting professional investors. Research Policy. 2017;46(9):1606–1628. doi: 10.1016/j.respol.2017.07.012. [DOI] [Google Scholar]
  68. Saleh S.N., Ajufo E., Lehmann C.U., Medford R.J. A Comparison of Online Medical Crowdfunding in Canada, the UK, and the US. JAMA Network Open. 2020;3(10):e2021684–e. doi: 10.1001/jamanetworkopen.2020.21684. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Sashi C.M. Customer engagement, buyer-seller relationships, and social media. Management Decision. 2012 doi: 10.1108/00251741211203551. [DOI] [Google Scholar]
  70. Saura J.R., Palacios-Marqués D., Ribeiro-Soriano D. Using data mining techniques to explore security issues in smart living environments in Twitter. Computer Communications. 2021;179:285–295. [Google Scholar]
  71. Short J.C., Anglin A.H. Is leadership language ‘rewarded’ in crowdfunding? Replicating social entrepreneurship research in a rewards-based context. Journal of Business Venturing Insights. 2019;11:e00121. [Google Scholar]
  72. Sriram S., Manchanda P., Bravo M.E., Chu J., Ma L., Song M., et al. Platforms: A multiplicity of research opportunities. Marketing Letters. 2015;26(2):141–152. doi: 10.1007/s11002-014-9314-1. [DOI] [Google Scholar]
  73. Thies, Ferdinand, Michael Wessel, and Alexander Benlian. “Understanding the dynamic interplay of social buzz and contribution behavior within and between online platforms–evidence from crowdfunding.” (2014).
  74. Venkatesh V., Thong J.Y., Xu X. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly. 2012;157–178 [Google Scholar]
  75. Venkatesh V., et al. User acceptance of information technology: Toward a unified view. MIS Quarterly. 2003:425–478. doi: 10.2307/30036540. [DOI] [Google Scholar]
  76. Vismara S. Equity retention and social network theory in equity crowdfunding. Small Business Economics. 2016;46(4):579–590. doi: 10.1007/s11187-016-9710-4. [DOI] [Google Scholar]
  77. Vrontis D., Christofi M., Battisti E., Graziano E.A. Intellectual capital, knowledge sharing and equity crowdfunding. Journal of Intellectual Capital. 2020 doi: 10.1108/JIC-11-2019-0258. [DOI] [Google Scholar]
  78. Wahjono S.I., Fam S.F., Pakkanna M., Rasulong I., Marina A. Promoting Creators Intentions: Measurement of Crowdfunding Performance. International Journal of Business and Society. 2021;22(3):1084–1101. doi: 10.33736/ijbs.4285.2021. [DOI] [Google Scholar]
  79. Wang N., Li Q., Liang H., Ye T., Ge S. Understanding the importance of interaction between creators and backers in crowdfunding success. Electronic Commerce Research and Applications. 2018;27:106–117. doi: 10.1016/j.elerap.2017.12.004. [DOI] [Google Scholar]
  80. Watts D.J., Dodds P.S. Influentials, networks, and public opinion formation. Journal of Consumer Research. 2007;34(4):441–458. doi: 10.1086/518527. [DOI] [Google Scholar]
  81. Wehnert P., Baccarella C.V., Beckmann M. In crowdfunding we trust? Investigating crowdfunding success as a signal for enhancing trust in sustainable product features. Technological Forecasting and Social Change. 2019;141:128–137. doi: 10.1016/j.techfore.2018.06.036. [DOI] [Google Scholar]
  82. Wolfe M.T., Patel P.C., Manikas A.S. Shock and awe: Loudness and unpredictability in Twitter messages and crowdfunding campaign success. Journal of Innovation & Knowledge. 2021;6(4):246–256. doi: 10.1016/j.jik.2021.06.002. [DOI] [Google Scholar]
  83. Woodside A.G. Moving Beyond Multiple Regression Analysis to Algorithms: Calling for Adoption of a Paradigm Shift from Symmetric to Asymmetric Thinking in Data Analysis and Crafting Theory. Journal of Business Research. 2013;66(4):463–472. [Google Scholar]
  84. Woodside A.G. The good practices manifesto: Overcoming bad practices pervasive in current research in business. Journal of Business Research. 2016;69:365–381. [Google Scholar]
  85. Xu, Anbang, et al. “Show me the money! An analysis of project updates during crowdfunding campaigns.” Proceedings of the SIGCHI conference on human factors in computing systems. 2014. Doi: 10.1145/2556288.2557045.
  86. Ye Y., Chen S., Li C. Financial technology as a driver of poverty alleviation in China: Evidence from an innovative regression approach. Journal of Innovation & Knowledge. 2022;7(1) doi: 10.1016/j.jik.2022.100164. [DOI] [Google Scholar]
  87. Yum H., Lee B., Chae M. From the wisdom of crowds to my own judgment in microfinance through online peer-to-peer lending platforms. Electronic Commerce Research and Applications. 2012;11(5):469–483. doi: 10.1016/j.elerap.2012.05.003. [DOI] [Google Scholar]
  88. Zalan T., Toufaily E. The promise of Fintech in emerging markets: Not as disruptive. Contemporary Economics. 2017;11(4):415–431. [Google Scholar]
  89. Zhang J., Liu P. Rational herding in microloan markets. Management Science. 2012;58(5):892–912. doi: 10.1287/mnsc.1110.1459. [DOI] [Google Scholar]
  90. Zhang W., Yan X., Chen Y. Configurational Path to Financing Performance of Crowdfunding Projects Using Fuzzy Set Qualitative Comparative Analysis. Inzinerine Ekonomika-Engineering Economics. 2017;28(1):25–34. [Google Scholar]
  91. Zheng H., Hung J.L., Qi Z., Xu B. The role of trust management in reward-based crowdfunding. Online Information Review. 2016 doi: 10.1108/OIR-04-2015-0099. [DOI] [Google Scholar]
  92. Zhong Y., Liu W., Lee T.Y., Zhao H., Ji J. Risk perception, knowledge, information sources and emotional states among COVID-19 patients in Wuhan, China. Nursing Outlook. 2021;69(1):13–21. doi: 10.1016/j.outlook.2020.08.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Journal of Business Research are provided here courtesy of Elsevier

RESOURCES