This conceptual paper focuses on how equity crowdfunding investors navigate uncertainty in their decision-making. We demonstrate shortcomings of prior research focusing on the attributes that are assessed in micro-investment decisionmaking without considering the heuristic processes by which these attributes are appraised. To overcome these shortcomings, we propose the development of a comprehensive model of micro-investment decision-making, the first of its kind to our knowledge.
Equity crowdfunding is an online mechanism for attracting
financial contributions from large numbers of individual
investors through public offerings of unlisted shares (Ahlers
et al.
To investigate how micro-investors navigate uncertainty in their decision-making, we review the emerging equity crowdfunding literature. Due to limited research on equity crowdfunding, we furthermore turn to the entrepreneurial finance literature for theory on investment decision-making, and to the decision-making literature for insights into decision-making under uncertainty.
To the best of our knowledge, no prior study has systematically analyzed crowdfunding within the theoretical framework of decision-making under uncertainty. We consequently make two contributions to the crowdfunding literature: 1) we provide the to date most comprehensive overview and classification of the constituent elements of microCopyright © 2018 for this paper by its authors. Copying permitted for private and academic purposes. investment decision-making, and 2) based on this overview and classification, we develop the first comprehensive model of how investors make decisions under uncertainty (both to be included in the full paper).
Crowdfunding is a subset of crowdsourcing defined as the
act of outsourcing a task to an undefined network of people
in the form of an open call that is broadcast online
In recent years, crowdfunding has become an increasingly viable alternative to conventional sources of early-stage capital. The most recent global crowdfunding industry report estimated crowdfunding volume in 2015 at $34.4 billion globally, up from $16.2 billion in 2014 and $6.1 billion in 2013 (Massolution 2015). The industry showed continued growth in 2016 to a market volume of $35.2 billion in the Americas alone with more than 218,000 businesses across the Americas raising funds from online alternative finance channels in 2016 (Ziegler et al. 2017).
In addition to its significant practical applications,
crowdfunding is an emerging research area, which has gained
momentum with an increasing number of publications over the
past decade. This literature is mostly empirical, and
overwhelmingly focused on identifying determinants of funding
success to further our understanding of the factors that
support successful funding outcomes
The current study seeks to remedy these shortcomings by developing the first comprehensive model of micro-investment decision-making under uncertainty. Based on our review of the crowdfunding literature, we argue that as the quality of entrepreneurial ventures is unobservable under uncertainty, micro-investors must base their investment decisions on observable quality signals assumed to co-vary with the underlying, but unobservable quality of investment opportunities (Ahlers et al. 2015; Agrawal, Catalini, and Goldfarb 2014; Belleflamme and Lambert 2014; Burtch 2013; Mollick 2014).
Consequently, investment decisions rely on a range of quality signals, which can be observed, and which are therefore the success factors identified in the literature. However, while previous studies have provided important groundwork on factors influencing the performance of crowdfunding projects, they have stopped short of conceptualizing these in terms of the decision-making process to which they contribute (Kang et al. 2016). To the best of our knowledge, our study is therefore the first to analyze how quality signals trigger a kind of cognitive shortcut in the decision-making process by substituting for the underlying, but unobservable quality of the investment opportunity, and thereby reducing uncertainty for potential investors.
As outlined in the previous section, quality signals are typically conceptualized as funding success factors in the literature. In their review of the crowdfunding literature, Belleflamme and Lambert argue that “contributors respond to quality signals” in equity crowdfunding, and conclude that equity crowdfunding is most successful when entrepreneurs reduce uncertainty for potential investors by signaling quality (p. 4). This line of argument is based partly on the study by Ahlers and his co-authors (2015), who demonstrate empirically that quality signals may "strongly impact the probability of funding success", and consequently classify quality signals as “determinants of funding success” (p. 955 and Figure 1).Yet, they stop short of analyzing the role that quality signals play in investment decision-making, and ultimately conclude that "further analysis would be needed to understand ... individual investors' decision-making processes" (ibid., p. 975).
Finally, in his highly cited paper on the dynamics of crowdfunding, Mollick (2014) finds that potential investors respond to quality signals in all forms of crowdfunding, before coming to the conclusion that “projects that signal a higher quality level are more likely to be funded” (p. 2). However, as signaling is less well defined in crowdfunding than in “traditional new venture settings”, Mollick recommends further research into "the decision-making process in crowdfunding to gain insight into the ... signaling process." (ibid., p. 14).
Thus, these studies all focus on quality signals as determinants of funding success, while acknowledging that little is known about the significant role that quality signals play in investment decision-making. Furthermore, the list of quality signals discovered in the literature is arguably so long and categories so unstable as to provide confusing and often contradictory evidence concerning their role in the decision-making process. Consequently, our key contribution in the following is to demonstrate how micro-investors use heuristics to appraise quality signals, and make decisions under uncertainty. We thereby account not only for the factors that inform the decision process, but also for the process by which these factors are appraised.
According to the heuristics and biases program, heuristics
can be defined as a cognitive process in which a highly
accessible attribute is substituted for a less accessible attribute
of a judgment object to reduce the complexity of a particular
judgement
In equity crowdfunding, all three conditions are typically satisfied as information asymmetry and uncertainty make investment target attributes inaccessible, forcing micro-investors to rely instead on highly accessible quality signals. Consequently, the various quality signals discovered in the research can be characterized as heuristic attributes substituting for the underlying, but unobservable target attributes under information asymmetry and uncertainty.
As micro-investors cannot possibly take all possible quality signals into consideration, they furthermore use specific decision-making heuristics to ignore some of the information, and come to a decision (see e.g. Gigerenzer 2008, Table 2 for an overview of 10 different heuristics). Decision-making heuristics are strategies of bounded rationality that ignore information to make more accurate judgments than strategies that use more information and computation, for instance under uncertainty (Gigerenzer and Gaissmaier 2011). These heuristics determine where to search for cues (search rules), when to stop searching without computing an optimal stopping point (stopping rules), and how to make a decision after search is stopped (decision rules) (Gigerenzer and Gaissmaier 2011; Goldstein and Gigerenzer 2002).
In the crowdfunding setting, quality signals serve as cues,
whereas the relevance of specific cues and their cue values
depends on investor decision criteria and their choice of
decision-making heuristics. Several heuristics would appear
be relevant in the crowdfunding context, including social
heuristics such as imitate-the-majority, which investors
arguably use when basing their investment decisions on the
quality signal provided by capital accumulated in the course
of a crowdfunding campaign
Ultimately, heuristics thus enable micro-investors to base their decisions on quality signals substituting for unobservable attributes of potential investment targets, and to prevent paralysis by analysis given the huge number of investment opportunities and even greater number of quality signals associated with these opportunities. Without heuristics, microinvestors would struggle to process a potentially overwhelming amount of information, while simultaneously being overwhelmed by information asymmetry and uncertainty.
While discovering a wide range of signals that inform the investment decision, crowdfunding research has largely neglected the process whereby investors make decisions. Consequently, this line of research does not observe how quality signals substitute investment target attributes in the decision-making process, or how this heuristic process of attribute substitution leads to intuitive, gut feel decisions.
As the quality of early-stage entrepreneurial ventures is
difficult to gauge due to information asymmetry and
uncertainty, micro-investors must base their decisions on a range
of quality signals that substitute for the underlying, but
unobservable quality of the investment opportunity. As such,
these quality signals are heuristic attributes substituting for
target attributes that are unobservable due to uncertainty,
information asymmetry and the unknowable quality of
earlystage entrepreneurial ventures (Ahlers et al.
We therefore posit that it would not be possible for microinvestors to make decisions under conditions of uncertainty if they did not use heuristics, as they cannot possibly employ a fully compensatory decision-making model to balance out large numbers of attributes under these conditions. Instead, they use non-compensatory decision-making heuristics to reduce the number of investment opportunities, and identify their investment targets by focusing on quality signals relevant to their decision criteria, which in turn are derived from their objectives and motivations. As such, the focus on heuristic attribution amounts to a first step towards modeling observable phenomena and their relationships. In our forthcoming full paper, we will therefore present the first comprehensive model of micro-investment decision-making, which will include not only quality signals and heuristics, but also investor decision criteria, motivations and objectives.
The implications are manifold as failing to fully understand the process of decision-making at the core of crowdfunding, and focusing on quality signals, as crowdfunding success factors will likely cause all kinds of problems for researchers and practitioners within the field. Unless the role of quality signals as heuristic attributes is understood, using these quality signals to ensure crowdfunding success is bound to be hit and miss. Not only is the list of quality signals discovered in the literature long, but also the list is continuously growing, as there is no natural limit to how quality may be signaled to reduce uncertainty for potential investors. Quality signals should therefore be studied in their own right as low-level constructs, but more important is the function of these constructs in the decision-making process.
Both from a theoretical and practical perspective, we need to turn to the process of decision-making rather than the laundry lists of quality signals produced in the literature to understand how quality signals may trigger an investment decision by matching the decision criteria that micro-investors derive from their objectives and motivations. Ultimately, this will deepen our understanding of how microinvestors make decisions, shedding important light on how they use heuristics to guide their decision-making under uncertainty, when all they have to go by are a range of ambiguous quality signals thought to co-vary with the underlying, but unobservable quality of potential investment targets.
Finally, this approach offers rich opportunities for further research. The model proposed in this paper is empirically grounded to the degree that it is based on previous empirical studies, but the model itself has yet to be empirically tested in a set-up where the decision-making process is the focus of the research, and where the different factors are therefore not reduced to determinants of crowdfunding success as the lowest common denominator of crowdfunding research.