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Modeling Levels and Time in Entrepreneurship Research: An Illustration With Growth Strategies and Post-IPO Performance

机译:创业研究中的建模水平和时间:以增长策略和首次公开募股后的业绩为例

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摘要

New ventures lack resources, are buffeted by environmental factors, and often experience rapid growth and organizational transformations that can have profound effects on performance and survival. This indicates that factors at multiple levels and across time affect new venture outcomes. Research examining these outcomes often address relationships that cross levels or time, but rarely both. Because scholars potentially can make rich theoretical contributions by simultaneously investigating temporal relationships that cross levels, the authors illustrate multiyear, multilevel model building with random coefficient modeling (RCM) using language that is accessible to entrepreneurship scholars. Specifically, they model the effects of strategic growth actions on new venture performance using a longitudinal data set of young, IPO-stage firms. Their illustration demonstrates the statistical advantages of modeling levels and time simultaneously and offers a roadmap for entrepreneur-ship scholars interested in examining these effects, including a step-by-step guide with SAS code for working with these data. They also describe some specific research questions to help advance theory development using RCM.
机译:新企业缺乏资源,受环境因素的影响,经常经历快速增长和组织变革,这些变革可能对绩效和生存产生深远影响。这表明跨多个级别且跨时间的因素会影响新的风险投资结果。研究这些结果的研究通常涉及跨层次或跨时间的关系,但很少涉及两者。由于学者们可以通过同时调查跨层次的时间关系来潜在地做出丰富的理论贡献,因此作者使用企业家精神学者可以访问的语言,说明了使用随机系数建模(RCM)的多年,多层模型构建。具体而言,他们使用年轻的,处于IPO阶段的公司的纵向数据集来模拟战略增长行动对新企业绩效的影响。他们的插图展示了同时建模水平和时间的统计优势,并为有兴趣研究这些影响的企业家学者提供了路线图,其中包括使用SAS代码的逐步指南以处理这些数据。他们还描述了一些具体的研究问题,以帮助推动使用RCM进行理论开发。

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