How Much Do Industry, Corporation, and Business Matter, Really? A Meta-Analysis
How Much Do Industry, Corporation, and Business Matter, Really? A Meta-Analysis
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DOI：
10.1287/stsc.2017.0029
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发表时间：
2017-05
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通讯作者：
Bart S. Vanneste
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作者：
Bart S. Vanneste
战略的学术领域试图解释企业绩效的差异。一个共识是，行业，公司和业务的影响一起占大多数性能差异，但有争议的每个因素解释多少。以前的研究使用了三种不同的效应量测量方法：平方和，方差或标准差。这些措施产生不同的结果，为一个给定的样本，这排除了直接比较。使用模拟分析，我表明，平方和措施是敏感的样本尺寸（例如，行业数量，每个行业的企业数量）。使用来自9项研究的25个样本（N = 212，112），我发现这种敏感性在实践中很强：仅知道样本的维度就足以很好地预测平方和测度。使用方差和标准差测量进行荟萃分析（16项研究的18个样本，N = 225，183）。在方差测度下，效应量为0.08。
The academic field of strategy seeks to explain differences in firm performance. A consensus exists that industry, corporate, and business effects together account for most performance differences, but there is debate over how much each factor explains. Previous studies have used three different effect size measures: sum of squares, variance, or standard deviation. These measures yield different results for a given sample, which precludes direct comparison. Using simulation analysis, I show that the sum-of-squares measure is sensitive to sample dimensions (e.g., the number of industries, the number of businesses per industry). Using 25 samples from nine studies (N = 212,112), I find that this sensitivity is strong in practice: knowing only the dimensions of a sample is sufficient to predict well the sum-of-squares measure. A meta-analysis is conducted using the variance and standard deviation measures instead (18 samples from 16 studies, N = 225,183). With the variance measure, the effect sizes are 0.08 f...