Crowd-based Platforms

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    Vulnerable Populations in Prosocial Crowdfunding: Does the Framing Matter for Female and Rural Entrepreneurs?
    ( 2021-01-05) Figueroa-Armijos, Maria ; Berns, John P.
    Prosocial crowdfunding was originally conceived as a financial mechanism to assist vulnerable unbanked populations, typically excluded from formal financial markets. It subsequently grew into a billion-dollar scheme in a multi-billion-dollar crowdfunding industry. However, recent evidence claims prosocial crowdfunding may be shifting away from its goal to support the poor and underserved. Drawing on a composite social responsibility and framing theory framework, we examine the role that vulnerability plays in successfully raising funds in a prosocial crowdfunding context. We conduct multilevel logistic regressions on a sample of microloans allocated to 105,727 ventures in 64 countries. Our results indicate that applying for funds through a field partner which caters to vulnerable populations may in fact have a negative effect on the entrepreneur’s request to be fully funded. Notwithstanding, framing the entrepreneur as being female or rural as key characteristics of individual vulnerability increases the project’s likelihood to be fully funded.
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    Understanding the “Holiday Effect” in Online Restaurant Ratings
    ( 2021-01-05) Deng, Lingfei ; Xu, Dapeng ; Ye, Qiang
    Plenty of studies have demonstrated the holiday effect in human decision-makings. However, extant research fails to explore whether and how a holiday effect exists in online word-of-mouth generation. This work utilizes online restaurant reviews obtained from the most popular review platform in China to investigate this question with multiple empirical tests. The results suggest that diners are more likely to give a lower online rating on holidays, and this relationship is driven by a combination of restaurants’ specific reasons and diners’ specific factors. Specifically, the level of crowdedness and the quality of the restaurant can partly explain this relationship. Moreover, reviewers are found to be driven by cognitive mental processes instead of being carried away by emotions when they post online ratings on holidays. However, those who need to work overtime during holidays are found to be driven by bad mood when they post online ratings.
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    Initial Coin Offering (ICO): a systematic review of the literature
    ( 2021-01-05) Moxoto, Ana Claudia De ; Melo, Paulo ; Soukiazes, Elias
    Initial ICO coin offerings have emerged as a new business financing mechanism. ICOs have raised more than $ 31.14 billion by 2019, sparking interest in finance studies. Despite ongoing scientific research on the topic, academic knowledge remains limited and fragmented. This study aims to conduct a systematic review of the literature with 30 contributions from journals published until January 2020. Based on an in-depth analysis of the publications identified, we describe the landscape of the field of ICOs focusing on two aspects. First, we conducted an analysis of the empirical articles that addressed the success determinants of ICOs. Second, we categorize relevant contributions in five different perspectives: human and social capital, technological characteristics, governance and legal aspects and financial details of the campaign. Thematic analysis was carried out to address dominant themes and subthemes in each perspective.
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    I’m Not a Chatbot: An Empirical Investigation of Humanized Profiles of Social Media Customer Service Representatives
    ( 2021-01-05) Cheng, Huai-Tzu ; Pan, Yang
    While artificial intelligence is robotizing customer service at an unprecedented pace, there is great concern that robotized customer service could undermine customer satisfaction. This study searches for a solution that humanizes customer service to address this concern. Aiming to increase humanization, U.S. telecom giant T-Mobile recently added personal identities to its customer service representatives’ profiles on Twitter. Here, we examine the effect of humanized profiles on customers’ expressions of emotion or complaints via public tweets. The study provides novel insight explaining why customers are more likely to express positive emotions and fewer complaints if they are interacting with customer service representatives with a humanized profile on a social media platform. Interestingly, this effect is stronger among female users. We also discuss the implications for research and practice.
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    Graph Neural Network for Customer Engagement Prediction on Social Media Platforms
    ( 2021-01-05) Ma, Tengteng ; Hu, Yuheng ; Lu, Yingda ; Bhattacharyya, Siddhartha
    Social media platforms such as Twitter and Facebook play a pivotal role in companies’ strategy of engaging customers. How to target potential customers on social media effectively and efficiently is an important yet unsolved question. Predicting customer engagement on social media platforms is facing several challenges that cannot be solved by traditional methods. In this work, we design a framework that leverages individual behavior on Facebook together with network contextual information to predict customer engagement (like/comment/share) of a brand’s posts. We first build a meta-path based Heterogeneous Information Network (HIN) to exploit large-scale content consumption information. We then design a Graph Neural Network (GNN) model combined with attention mechanism to learn structural feature representations of users to make the customer-brand engagement prediction. The proposed model is examined using a large-scale Facebook dataset and the result shows significant performance improvement compared with state-of-the-art baselines. Besides, the effectiveness of attention mechanism reveals the potential interpretability of the proposed model for the prediction results.