The Relationship Between Intellectual Capital and Competitive Advantage: A Meta-Analysis Study

Yasin Kiliçli
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Abstract

Purpose: The aim of this study is to examine the results of previous studies examining the relationship between intellectual capital (IC) and competitive advantage (CA) with a meta-analytic approach. Design/Methodology: Studies examining the relationship between IC and CA were reached by scanning a total of 14 national and international online academic databases. The Pearson correlation (r) coefficient was taken as a criterion in studies examining the relationship between IC and CA. Analyzes were performed using CMA software. A total of 15,625 samples from 71 studies were included in the meta-analysis process. In this study, the random effect model was used when interpreting the mean effect size. Findings: As a result of the study, it was understood that the calculated average effect size was 0.490 and this value corresponded to a high effect. This suggests that a higher IC is associated with a higher CA. This result supports common hypotheses and salient findings in the literature. Limitations: The meta-analysis applied in this study has some limitations due to its inherent shortcomings. Originality/Value: As a result of a comprehensive literature review, no studies examining the relationship between IC and CA with a meta-analytic approach were found. This study, which deals with the relationship between IC and CA with a meta-analytic approach for the first time, will provide a broader perspective on the literature in this field by calculating the average effect value between the mentioned variables over a large sample of 15.625.
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智力资本与竞争优势关系的元分析研究
目的:本研究的目的是用元分析的方法检验以往关于智力资本(IC)与竞争优势(CA)关系的研究结果。设计/方法:通过扫描14个国家和国际在线学术数据库,研究了IC和CA之间的关系。在检验IC和CA之间关系的研究中,采用Pearson相关系数(r)作为标准。使用CMA软件进行分析。来自71项研究的15,625个样本被纳入meta分析过程。在本研究中,在解释平均效应大小时采用随机效应模型。研究结果:研究结果表明,计算的平均效应值为0.490,该值对应于高效应。这表明较高的IC与较高的CA相关。这一结果支持了文献中的常见假设和重要发现。局限性:本研究采用的meta分析由于其固有的不足,存在一定的局限性。原创性/价值:通过全面的文献回顾,没有发现用元分析方法研究IC和CA之间关系的研究。本研究首次采用元分析的方法来研究IC与CA之间的关系,通过在15.625个大样本中计算上述变量之间的平均效应值,将为该领域的文献提供更广阔的视角。
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