Mapping the Temporal Structure of Entrepreneurial Start-Up Activities

绘制创业活动的时间结构

• 批准号: 1561035
• 负责人: Mark Suchman
• 金额: $ 28.42万
• 依托单位: Brown University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-15 至 2021-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1561035&HistoricalAwards=false
• 关键词: Mapping; Temporal; Structure; Entrepreneurial; Start

Entrepreneurship is both an important source of employment and, especially within science and engineering, a powerful force for social and economic renewal. Despite this importance, however, entrepreneurship research has only recently expanded beyond studying precursor conditions (such as entrepreneurial personality traits and untapped market opportunities) to address the process of starting a business--developing a business model, incorporating, registering intellectual property, making first hires, and so on. The present project seeks to map the field of start-up activities and to identify the common pathways that nascent entrepreneurial ventures follow in transiting that field. The PI hypothesizes (a) that start-up trajectories cluster into distinct archetypes; (b) that the choice of archetype depends on identifiable attributes of the founding team, the new firm, and the social context; and (c) that different start-up sequences will yield different outcomes, depending on these team, firm, and context contingencies. If confirmed, these predictions would represent a significant advance over current "one size fits all" models of the start-up process. Such a shift would have a transformative impact on science, teaching, practice and policy in the field of entrepreneurship.This project advances the entrepreneurship literature's recent turn toward "process studies" that explore the contingent, unfolding nature of new firm emergence. Applying a set of under-utilized statistical techniques to data on the timing of 36 start-up activities recorded by the Panel Study of Entrepreneurial Dynamics (PSED I and II), the investigation seeks to identify temporal patterns in the early trajectories of nascent entrepreneurial ventures. First, the project will adapt multi-dimensional scaling (MDS) techniques from psychometry to map the set of start-up activities onto a multi-dimensional activity space, based on various measures of the activities' temporal similarity to one another. Second, the project will adapt sequence analysis (SA) techniques from genomics to cluster the observed start-up sequences into a limited number of archetypal trajectories through the activity space. Third, the project will assess the impact of exogenous conditions such as founder attributes, organization type, and environmental context in determining a new venture's particular trajectory. Fourth, the project will evaluate the interactive impact of these exogenous contingencies and archetypal trajectories in shaping the new venture's survival and performance outcomes. In furtherance of these analyses, the project will refine MDS and SA methodologies to better accommodate the unique features of social-scientific data such as the activity sequences in the PSED. The project will also supplement the existing PSED data sets with new measures of socio-political and economic conditions in respondents' environments. These new methods and variables will enhance the PSED's usefulness as a data infrastructure for exploring social vs. economic, local vs. global, and objective vs. subjective environmental influences on the start-up process, both for the current investigation and for other investigations in years to come.

创业精神既是就业的重要来源，也是社会和经济复兴的强大力量，特别是在科学和工程领域。 然而，尽管如此重要，创业研究直到最近才扩展到研究前兆条件之外（如企业家的个性特征和未开发的市场机会），以解决创业的过程-开发商业模式，合并，注册知识产权，第一次招聘，本项目力求绘制初创活动领域的地图，并查明新生的创业企业在过渡到该领域时所遵循的共同途径。 PI假设：（a）初创企业的发展轨迹会形成不同的原型;（B）原型的选择取决于创始团队、新公司和社会背景的可识别属性;（c）不同的初创企业序列会产生不同的结果，这取决于这些团队、公司和背景的偶然性。 如果得到证实，这些预测将是对目前“一刀切”的启动进程模式的重大进步。这种转变将对创业领域的科学、教学、实践和政策产生变革性的影响。本项目推动了创业文献最近转向“过程研究”，探讨新企业出现的偶然性和展开性。 应用一套未充分利用的统计技术的数据的创业动力学的小组研究（PSED I和II）记录的36个启动活动的时间，调查旨在确定时间模式的早期轨迹的新生创业企业。 首先，该项目将采用多维缩放（MDS）技术从心理测量映射到一个多维活动空间的启动活动的集合，基于各种措施的活动的时间相似性彼此。 其次，该项目将采用基因组学的序列分析（SA）技术，将观察到的启动序列聚类为有限数量的活动空间原型轨迹。 第三，该项目将评估外部条件的影响，如创始人属性，组织类型和环境背景，在确定一个新的企业的特定轨迹。 第四，该项目将评估这些外生意外事件和原型轨迹在塑造新企业的生存和绩效结果方面的互动影响。 为了促进这些分析，该项目将改进MDS和SA方法，以更好地适应社会科学数据的独特特征，例如PSED中的活动序列。 该项目还将用对答卷人环境中的社会政治和经济状况的新衡量标准来补充现有的PSED数据集。这些新的方法和变量将增强PSED作为数据基础设施的实用性，用于探索社会与经济，本地与全球，以及客观与主观环境对启动过程的影响，无论是对于当前的调查还是未来几年的其他调查。