Three essays on likability factors, crowdfunding, and entrepreneurial performance

In this dissertation, I conduct three empirical studies exploring the relation between likability factors, crowdfunding characteristics and entrepreneurial performance. Together these studies integrate aspects of major entrepreneurial likability factors including liking of the entrepreneur (source attractiveness, credibility, personal traits) and liking of the message (verbal content and expression), and components of nonverbal and verbal cues. I apply computer-mediated communication (CMC) and persuasion theories, political and marketing literature to provide a more fine-grained understanding of likability on crowdfunding success. In the first essay, I study how the non-verbal cues of a crowdfunding video influence the crowdfunding success. By employing social presence theory, I argue, hypothesize and test that effective use of non-verbal cues in a pitch video increases funding success. In the second essay, I explore how verbal cues (readability and complexity) and non-verbal cues (smiling and professional attire) interact to influence crowdfunding outcome. Findings of this essay indicate that powerful persuasion results from both expression (verbal cues) and impression (non-verbal cues). The third essay examines the mediating effect of likability between nonverbal, verbal cues and crowdfunding success. According to the likability factors extracted from political and advertising campaign literature, I conclude five main dimensions of likability in crowdfunding context. The results show that message factors are more influential than source factors in affecting crowdfunding outcome. Findings of three essays show that entrepreneurs should be careful to deliver a message which is immediate, simple, informative, humorous, storytelling and less complimentary to their funders. The more their messages are liked, the more likely funders will back their projects, and then the more success their crowdfunding campaign will be

The Power of Words in Crowdfunding

In this chapter, the authors first provide an overview of the crowdfunding phenomenon. Through the literature review of crowdfunding success factors in the four models, the authors then summarize that the current entrepreneurial research focused on success factors has failed to sufficiently examine how the power of words would affect crowdfunding. Therefore, the authors propose that non-verbal and verbal cues are crucial to entrepreneurial financing success. Based on the insufficient research related with those cues, especially the non-verbal ones, the authors open an area of study on non-verbal and verbal cues in the entrepreneurial financing process by conducting and writing this chapter

How to Attract Low Prosocial Funders in Crowdfunding? Matching Among Funders, Project Descriptions, and Platform Types

The amount of crowdfunding research that investigates funding success factors has been increasing. The existing research shows inconsistent evidence regarding how a prosocial project description affects funding success and largely ignores the issue of alignment/misalignment among different factors in affecting funding success. We suggest that funders’ prosocial motivation can be an important factor for this inconsistent evidence. We integrate the elaboration likelihood model and language expectancy theory and demonstrate distinct decision-making patterns from high and low prosocial motivation funders. Through three experiments, we provide evidence for alignment/misalignment effects among funders’ prosocial motivation, prosocial project descriptions, and platform types (donation-based vs. reward-based). While there are no differences for participants with high prosocial motivation across conditions, we find that participants with low prosocial motivation are more willing to contribute to a project that has alignment between the different factors, namely to a project that has a high prosocial description on a donation-based platform, or to a project that has a low prosocial description on a reward-based platform. This research sheds light on the crowdfunding and prosocial motivation literature. This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in the journal Information & Management: https://doi.org/10.1016/j.im.2023.10384

How to attract low prosocial funders in crowdfunding? Matching among funders, project descriptions, and platform types

Highlights Crowdfunding research shows inconsistent evidence about the impact of prosocial project description on crowdfunding success. We integrate elaboration likelihood model and language expectancy theory and propose distinct decision-making patterns from high and low prosocial motivation funders. Our findings show low prosocial participants are more likely to contribute to a project that aligns platform types (donation-based vs. reward-based) and prosocial project descriptions (high vs. low). We did not find these alignment effects for high prosocial participants. Abstract The amount of crowdfunding research that investigates funding success factors has been increasing. The existing research shows inconsistent evidence regarding how a prosocial project description affects funding success and largely ignores the issue of alignment/misalignment among different factors in affecting funding success. We suggest that funders’ prosocial motivation can be an important factor for this inconsistent evidence. We integrate the elaboration likelihood model and language expectancy theory and demonstrate distinct decision-making patterns from high and low prosocial motivation funders. Through three experiments, we provide evidence for alignment/misalignment effects among funders’ prosocial motivation, prosocial project descriptions, and platform types (donation-based vs. reward-based). While there are no differences for participants with high prosocial motivation across conditions, we find that participants with low prosocial motivation are more willing to contribute to a project that has alignment between the different factors, namely to a project that has a high prosocial description on a donation-based platform, or to a project that has a low prosocial description on a reward-based platform. This research sheds light on the crowdfunding and prosocial motivation literature

Decoding hand movement velocity from electroencephalogram signals during a drawing task

<p>Abstract</p> <p>Background</p> <p>Decoding neural activities associated with limb movements is the key of motor prosthesis control. So far, most of these studies have been based on invasive approaches. Nevertheless, a few researchers have decoded kinematic parameters of single hand in non-invasive ways such as magnetoencephalogram (MEG) and electroencephalogram (EEG). Regarding these EEG studies, center-out reaching tasks have been employed. Yet whether hand velocity can be decoded using EEG recorded during a self-routed drawing task is unclear.</p> <p>Methods</p> <p>Here we collected whole-scalp EEG data of five subjects during a sequential 4-directional drawing task, and employed spatial filtering algorithms to extract the amplitude and power features of EEG in multiple frequency bands. From these features, we reconstructed hand movement velocity by Kalman filtering and a smoothing algorithm.</p> <p>Results</p> <p>The average Pearson correlation coefficients between the measured and the decoded velocities are 0.37 for the horizontal dimension and 0.24 for the vertical dimension. The channels on motor, posterior parietal and occipital areas are most involved for the decoding of hand velocity. By comparing the decoding performance of the features from different frequency bands, we found that not only slow potentials in 0.1-4 Hz band but also oscillatory rhythms in 24-28 Hz band may carry the information of hand velocity.</p> <p>Conclusions</p> <p>These results provide another support to neural control of motor prosthesis based on EEG signals and proper decoding methods.</p

A Semisupervised Support Vector Machines Algorithm for BCI Systems

As an emerging technology, brain-computer interfaces (BCIs) bring us new communication interfaces which translate brain activities into control signals for devices like computers, robots, and so forth. In this study, we propose a semisupervised support vector machine (SVM) algorithm for brain-computer interface (BCI) systems, aiming at reducing the time-consuming training process. In this algorithm, we apply a semisupervised SVM for translating the features extracted from the electrical recordings of brain into control signals. This SVM classifier is built from a small labeled data set and a large unlabeled data set. Meanwhile, to reduce the time for training semisupervised SVM, we propose a batch-mode incremental learning method, which can also be easily applied to the online BCI systems. Additionally, it is suggested in many studies that common spatial pattern (CSP) is very effective in discriminating two different brain states. However, CSP needs a sufficient labeled data set. In order to overcome the drawback of CSP, we suggest a two-stage feature extraction method for the semisupervised learning algorithm. We apply our algorithm to two BCI experimental data sets. The offline data analysis results demonstrate the effectiveness of our algorithm