策略延迟下众筹项目的定价和激励决策

本文研究了投资者的策略延迟行为对众筹项目的定价以及激励决策的影响。策略延迟是指投资者在投资过程中自动按照估值在相应阶段投资,并且部分投资者会刻意延迟投资来搭便车的行为。本文采用了一个两阶段模型来刻画投资者的投资行为,并进一步对比序列到来模式（投资者没有策略延迟）与同时到来模式（投资者有策略延迟）中的贝叶斯均衡。研究结果表明,当发起人采用价格激励的方式提高成功率时,投资者的策略延迟行为可能会影响最优的激励策略。当存在固定目标约束时,如果投资者没有策略延迟行为,发起人应该激励前期投资者,通过弥补他们等待成本的方式提高成功率;反之如果投资者出现策略延迟行为,对项目估值较高的投资者会自动在前期投资并且愿意承受更高的价格,此时发起人应该反过来激励后期投资者。当不存在目标约束时,不论投资者是否有策略延迟行为,发起人都应该始终激励前期投资者以获得更高的成功率以及期望收益。     

奖励众筹融资绩效动态预测研究——来自“众筹网”数据的实证

奖励众筹融资绩效不仅受项目自身情况的影响，也与投资者行为有密切关系。本文研究了奖励众筹融资绩效与投资者投资和评论活动的动态变化关系，提出了投资曲线和评论曲线特征提取方法，并结合奖励众筹项目的投资特征、时滞评论特征以及项目自身特征，构建了奖励众筹项目融资绩效动态预测的函数主成分分析-广义回归神经网络(FPCA-GRNN)模型。基于“众筹网”交易数据进行了实证分析，结果表明FPCA-GRNN模型具有较好的融资绩效实时预测精度。     

基于复杂网络的电动汽车充电桩众筹市场信用风险建模与分析

因其经济优势及环境友好特性,电动汽车（EV）保有量持续攀升。而当前中国充电设施建设明显滞后于电动汽车的扩张速度,成为制约电动车发展潜力的主要瓶颈。由于充电设施具有高正外部性,同时投资回收期较长,难以吸引到传统融资,而基于互联网的众筹作为新型融资方式则受到了广泛关注。但由于缺乏强制性的信息披露制度及相关法律法规,众筹融资过程中的信息不对称问题相较传统融资方式更为严重,由此导致的高违约率也进一步削弱了投资者信心。本文利用复杂网络对电动汽车充电桩众筹融资市场进行建模,刻画了众筹投资者决策过程中的羊群效应,基于信息不对称使用多Agent模型对信用风险的产生、影响及针对性政策效果进行了模拟分析。结果表明,众筹投资者在决策过程中易受到社交网络中负面信息的影响,使得筹资人违约行为的不利影响进一步扩散。而严格的罚款机制可以在不影响融资市场均衡的情况下,使违约行为的恶性循环得到遏制。     

在线投资行为的本地偏好:基于在线融资市场的实证研究

传统的市场交易存在本地偏好，交易双方的地理位置趋同。然而，在线众筹平台打破了空间局限，地理位置的影响理应比传统市场小得多。为此提出三个问题：（1）众筹投资行为是否存在本地偏好？以及不同层次本地偏好有何差异？（2）本地偏好对投资行为和项目融资效果的影响；（3）对投资者的本地偏好进行机理分析和理论解释。以Kickstarter为研究对象，从国家级、区域级以及微观三个层次，分别构建了本地偏好模型以分析用户投资行为。研究表明，众筹项目的投资行为存在本地偏好，投资者偏好投资距离较近的项目，并且三个层次的本地偏好呈现不同模式。戏剧类项目的投资意愿受本地偏好的影响最强烈，其次是食品类，影响最弱的是游戏类。对于投融双方的距离来说，戏剧类项目的平均距离最小，为1474公里；而游戏类项目的平均距离最大，达到4624公里。众筹投资的本地偏好具有叠加效用，即融资者在异地进行融资的话，可以获得更高的融资成功率。最后，从信息传递理论、心理学、行为学等角度解释了本地偏好存在的原因以及影响。本文丰富了互联网金融研究以及用户行为模式研究，从投资行为角度为众筹项目研究和实践提供了新视角。     

奖励型众筹模式下考虑银行融资的企业最优运作策略研究

本文研究了企业在奖励型众筹模式下的最优运作策略,同时考虑了众筹融资不足时,企业继续向银行贷款的情况。通过分析众筹项目的运作机制,我们构建了企业在奖励型众筹模式下的决策模型,并研究得到了关于企业研发投入成本,产品生产量及众筹融资目标的最优策略及相关性质。最后,利用Kickstarter众筹平台上的实际数据进行数值实验,本文验证了相关的研究结论。研究表明:1)只有在众筹融资额达到一定阈值的前提下,企业才愿意投入研发成本,此时企业的研发投入成本及产品生产量都将随众筹投资者的需求增大;2)为保证投资者和银行对众筹项目具有投资意愿,同时也为确保众筹成功的项目具有盈利性,存在关于融资目标的可行域,使企业在启动众筹项目时需要将融资目标设置在该可行域内;3)企业通过众筹平台获得的期望融资额不会随融资目标增加,且企业的最优融资目标为融资目标在其可行域中的下界;4)此外,我们通过数值实验还发现最优众筹目标不会随企业的研发效率而增加。     

众筹平台上网络外部性的价值度量模型

在“互联网+”背景下,众筹模式逐渐崭露头角,成为电商营销的一种新形态.本文从众筹模式的特征分析出发,以其网络外部性特征和完全垄断的市场结构为切入点,建立众筹平台网络外部性的价值度量模型.该模型以考虑宏微观网络外部性的消费者效用函数和众筹发起人的收益函数为基础,在差异化定价策略、非差化定价策略和无网络外部性基础模型的三种情形下,应用两阶段博弈模型分别求解众筹发起人的最优价格向量、参与者的最优申购量和众筹发起人的最优收益水平,进而通过比较在不同策略下众筹发起人最优收益的差异得出众筹平台网络外部性的策略价值和内在价值.进一步,将模型的理论结果应用于推荐网络这一特殊的网络结构中,并设计出具有推荐功能的众筹平台.结果表明:本文所设计的具有推荐功能的众筹平台,可以显著提升众筹发起人的收益水平,对完善众筹平台的营销模式具有管理实践意义.     

现金流权不一致、利益冲突与控制权阶段转移

基于不完全合约理论,考虑风险投资者和创业者现金流权不一致所导致的双方利益冲突,在阶段投资框架下分析风险投资者在各个阶段的阶段决策,以及双方在阶段投资时刻对现金流权和控制权的再谈判,进而揭示控制权在不同阶段的转移过程及其影响因素.研究结果表明,阶段投资时刻项目的业绩较好,风险投资者将继续投资并向创业者释放控制权;进一步,项目发展前景的初始预期越好以及第2阶段不确定性越小,风险投资者在阶段投资时刻越倾向于继续投资及释放控制权.研究结论解释了Kaplan和Strmberg关于控制权动态转移与项目不确定性、业绩关系的实证结果.     

考虑支付痛苦钝化的在线产品预售定价策略

在线产品定价是平台经济的重要研究领域,两阶段混合预售是线上销售的一种主流模式。顾客在线上购买过程中,支付方式和预售模式对消费者行为和产品定价具有重要的影响。基于两阶段混合预售模式,本文考虑支付痛苦钝化效应对消费者购买心理的影响,建立相应的产品定价模型,得到了在线产品的最优预售价格、最优销量和最优利润,分析了支付痛苦钝化对消费者行为和在线产品最优定价的影响机制。理论和数值分析表明,两阶段混合预售策略更有利于卖方收益最大化;受支付痛苦钝化效应影响,折扣预售并非一定是产品最优选择策略,痛苦钝化因子是影响产品预售策略的重要因素。本文成果将进一步丰富平台经济下的顾客行为和产品定价研究,为平台企业和商家制定预售和产品定价策略提供决策依据。     

从众还是旁观?众筹市场中出资者行为的实证研究北大核心CSSCI

了解众筹市场中的出资者行为是提高众筹项目成功率的前提。潜在出资者面对已有筹资信息可能表现出两种截然不同的行为:羊群的从众行为和责任扩散的旁观行为。为了探讨众筹过程中已有筹资信息对潜在出资者决策的影响,本文基于羊群行为理论和责任扩散理论,以Demohour中393个众筹项目的面板数据为研究样本,分析了潜在出资者的行为模式。研究结果表明,潜在出资者行为会受到责任扩散效应影响,并且该效应会在已有经验型出资较多时或达到目标筹资额后得到强化,但面对目标筹资额相对较高的项目时,潜在出资者会因责任扩散效应弱化而提高出资额。同时,研究还发现捐赠类和回报类项目出资者均表现为责任扩散的旁观行为,但是其决策行为受到不同变量的调节效应。     

预售型众筹的性质分析及其法律风险

我国在互联网快速发展下,产生了一种新的融资方式——众筹。通过分析预售型众筹的性质可知其属于民事行为,应采取有效方法规避法律风险。回报不能使用服务或商品,要严格参照目前各网络平台的规定执行,使众筹无法形成犯罪,把法律风险降到最低。     

AON众筹模式下产品线设计与信息发布策略研究

针对众筹模式下如何激励消费者投资以实现众筹项目成功的问题，从信息发布和产品线设计的角度，通过两阶段博弈的理论模型，考虑了AON（all-or-nothing）模式下众筹方采用顺序发布和同时发布两种不同信息发布方式时，产品线的菜单价格和质量设计。研究表明：在顺序发布策略下产品线的质量差异化程度依赖于消费者异质性程度和高价值消费者的比例，和传统模式下产品质量相比，当消费者异质性很大同时高保留价格消费者的比例很高时，产品线质量差异和传统模式相同；当消费者异质性较小并且高保留价格消费者的比例较低时，产品线质量差异更小；当消费者异质性较小而高保留价格消费者比例较高时，产品线质量差异更大；而在同时发布策略下，产品线质量的差异化程度和传统模式相同，但价格差异与传统模式相比更大；相对于顺序发布策略而言，同时发布策略下由于极大程度的降低了消费者可能搭便车倾向，企业可以获取更多收益。这些结果将有助于采用AON众筹模式的企业在不同信息框架下做出最优的产品线质量设计和定价决策。     

Search for Yield

We present a model of the relationship between real interest rates, credit spreads, and the structure and risk of the banking system. Banks intermediate between entrepreneurs and investors, and can monitor entrepreneurs' projects. We characterize the equilibrium for a fixed aggregate supply of savings, showing that safer entrepreneurs will be funded by nonmonitoring banks and riskier entrepreneurs by monitoring banks. We show that an increase in savings reduces interest rates and spreads, and increases the relative size of the nonmonitoring banking system and the probability of failure of monitoring banks. We also show that the dynamic version of the model exhibits endogenous boom and bust cycles, and rationalizes the existence of countercyclical risk premia and the connection between low interest rates, tight credit spreads, and the buildup of risks during booms.     

Investigating investment decisions in equity crowdfunding: The role of projects' intellectual capital

The study aims to investigate the signalling role played by the intellectual capital of entrepreneurial projects towards crowdfunders. The study argues that information on; the intellectual capital of entrepreneurial projects posted on equity crowdfunding platforms can be perceived by external small investors or crowdfunders as signals of the venture's quality and can induce them to invest. By leveraging on the role of signalling theory in the mitigation of information asymmetries, the study argues that the intellectual capital of projects looking for equity crowdfunding is a powerful signal that positively affects the investment decisions of crowdfunders. Three components of entrepreneurial projects' intellectual capital – human, structural, and relational capital – are considered as factors affecting the success of equity crowdfunding campaigns, measured in terms of funding collected and the number of investors. The findings reveal that relational capital positively affects the investment decisions of equity crowdfunders and thus explains the success of equity crowdfunding campaigns. Factors related to human capital and structural capital have a positive impact on investment decisions, but only to a limited extent. This study provides valid implications for entrepreneurs, platform managers, and policy-makers/authorities.     

Identifying success factors for female entrepreneurs using the AMO framework: Empirical evidence from Japan

This study applies deep insights from the ability, motivation, and opportunity (AMO) framework as an overarching theoretical perspective to identify critical success factors for female entrepreneurs. In doing so, it investigates how entrepreneurial persistence (i.e., motivation or “M”), together with prior venture experience (i.e., ability or “A”) and competitive intensity (i.e., opportunity or “O”) influence the success of women's entrepreneurial activities. Using data from 308 Japanese female entrepreneurs, we show a positive association between entrepreneurial persistence and female entrepreneurs' venture growth, which becomes stronger when prior venture experience and competitive intensity are considered. Most importantly, the three-way interaction between these factors maximizes the business performance of female entrepreneurs. Thus, venture growth is the highest in the presence of high levels of entrepreneurial persistence, prior venture experience, and competitive intensity. Our findings suggest that policy makers should improve the competitiveness of women-led ventures and create supportive business environments for female entrepreneurs.     

The knowledge management of micro-firms in the crowd: key challenges for successful operations*

Abstract

Crowdfunding platforms are increasingly being used by small and micro-firms to fund the development and initial promotion of their new products. The transition from a crowdfunding campaign to the subsequent operational phase is critical to successfully fulfil the expectations of the backers who supported the micro-firm and expect to receive the new product. This paper analyzes the main challenges that must be faced by micro-firms during this transition, adopting the perspective of knowledge management theory. Based on an exploratory case study analysis, the paper highlights the particularities of these knowledge management challenges in terms of knowledge diversity, dependence and novelty. Asymmetry, dispersion, synchronization, a lack of a common understanding and backer lock-in are the main knowledge-based challenges for the operations of micro-firms that use crowdfunding. Various theoretical and practical implications of the findings are offered.     

社会资本与女性创业——基于GEM数据的跨国(地区)比较研究

从女性的社会地位和社会角色分工切入,探讨在不同经济发展水平和制度背景下社会资本对其创业选择的影响,并采用全球创业观察(global entrepreneurship monitor)和全球政府治理指标(worldwide governance index)两个大型国际数据库进行跨国(地区)比较研究,发现女性创业者社会资本与其创业选择呈倒U型关系,在一定阈值下社会资本能增加女性的创业选择,而超过该阈值社会资本的增加反而会降低女性的创业选择.同时,女性个人的人力资本和国家(地区)的经济发展水平会削弱社会资本的上述作用,制度环境则会增强社会资本的上述作用.这一研究揭示了社会地位、社会角色期望对女性参与创业活动的束缚,是将女性的社会角色纳入创业分析的一次有益探索.     

Impression management techniques in crowdfunding: An analysis of Kickstarter videos using artificial intelligence

Crowdfunding entrepreneurs increasingly use videos to present their venture and themselves to potential investors. To do this effectively, they consciously or unconsciously use several impression management techniques. Based on human and automated (AI-based analysis) of technology-related videos, we determine which impression management techniques help crowdfunding creators to attract a higher number of backers and meet their financial targets. Our results indicate that self-presentation and exemplification techniques are positively associated with crowdfunding success, while intimidation is negatively related to crowdfunding success. Our findings show that automated analysis of videos using advanced AI can replace human coding since measures based on human coding become insignificant when AI-based measures are included in the analysis. This illustrates the insights companies can create through the large scale automated analysis of video content using AI, including outside of a crowdfunding context.     

On flexible product-mix decision problems under randomness and fuzziness

This paper considers several models of product-mix decision problems and production planning problems under uncertain conditions, and shows that these are extensional and versatile models for resolving previous product-mix problems. These proposed models include randomness derived from statistical analysis based on historical data, ambiguity of decision maker's intuition and the quality of received information, and flexibility in accomplishing the original plan. Furthermore, given that the upper limit values of some constraints have flexibility, and given a decision maker's level of satisfaction, we propose a flexible product mix of problems using the theory of constraints (TOC), and develop an efficient solution method. We then provide a numerical example that compares our models with some previous basic models. Efficiency of flexibility is obtained when our proposed models are applied to several conditions, such as measurable changes from the expected value of future returns.     

Economic order quantity model with trade credit financing for non-decreasing demand

Researchers in the past have established their inventory lot-size models under trade credit financing by assuming that the demand rate is constant. However, from a product life cycle perspective, it is only in the maturity stage that demand is near constant. During the growth stage of a product life cycle (especially for high-tech products), the demand function increases with time. To obtain robust and generalized results, we extend the constant demand to a linear non-decreasing demand function of time. As a result, the fundamental theoretical results obtained here are suitable for both the growth and maturity stages of a product life cycle. In addition, we characterize the optimal solutions and obtain conclusions on important and relevant managerial phenomena. Lastly, we provide several numerical examples to illustrate the proposed model and its optimal solution.