The main aim of the article is to offer a systematic review of the literature on the relation between Intellectual Capital (IC) and Firm Performance (FP) through the statistical technique of meta-analysis (MA). MA synthesizes the quantitative results of different empirical studies on the relationship between explicative, independent variables (IC in our case) and dependent variables (FP) in a common metric called the effect size (Rosenthal, 1984, Hedges and Olkin, 1985, Hunter and Schmidt, 2004/1990). Meta-analysis is scarcely used in business sciences, even if applications in these research areas have increased in the last few years. The originality of the article consists in applying a relatively new technique for business sciences and particularly new for the IC literature. The research of a correlation between IC and FP has always been considered relevant for both a firm’s managers and Scholars (Ittner, 2008; Kamiyama et al., 2004), but has encountered many drawbacks, not least the difficulties in measuring IC. Researches on a correlation between IC and FP received a great boost after the publication of the VAICTM method by Ante Pulic, (1998, 2000, 2004) which allows the IC performance to be calculated by starting from accounting data. The article, despite the limits deriving from the effectiveness of the measures of the two variables and from a lack of reliability in the measurement of the studies included in the MA, tries to summarize, from a statistical point of view, for the first time in the IC literature, the researches existing on such a connection and, above all, to search for the variables responsible for the heterogeneity between studies (moderators). On the whole, there are two ways to conceptualize. MA: fixed-effects and random-effects MA (Hedges and Vevea, 1998; Hunter and Schmidt, 2004/1990). The fixed-effect methods assumes that all studies in an MA comes from a population with a fixed-average effect size, whilst random-effect methods assume that average effect size in the population vary from study to study. I chose to utilize a random-effects method, in particular the Hedges and colleagues’ method (Hedges and Olkin, 1985, Hedges and Vevea, 1998), since the data collected for the MA are real-world data, likely to have variable-population parameters and because the random-effects method allows inferences that generalize beyond the studies included in MA (Field, 2009). The moderator analysis has been carried out by using a multi-level regression model, due to the hierarchical nature of our data (multiple FP measures in the same study). With respect to the traditional meta-analytic techniques, multilevel modeling structure accounts for dependencies in the data whilst also allowing each study to contribute different effect sizes (Hox,2002; Van Den Noortgate and Onghena, 2003; Goldstein et al., 2000). Despite to its potential efficacy (Hofmann and Gavin, 1998), few published MA utilized this method, for a distinct lack of prescriptive instructions on how to conduct a MA using multilevel modeling (O’Mara et al., 2005). The main MA result is a positive true effect size of the correlation between IC and firm’s performance, whilst, from the moderator analysis, it emerges that the different economic context is the main responsible for the between-study variance, together with the industry and the FP measures.
The impact of Intellectual capital measurement on the financial markets: a meta-analysis approach
VELTRI, Stefania
2009-01-01
Abstract
The main aim of the article is to offer a systematic review of the literature on the relation between Intellectual Capital (IC) and Firm Performance (FP) through the statistical technique of meta-analysis (MA). MA synthesizes the quantitative results of different empirical studies on the relationship between explicative, independent variables (IC in our case) and dependent variables (FP) in a common metric called the effect size (Rosenthal, 1984, Hedges and Olkin, 1985, Hunter and Schmidt, 2004/1990). Meta-analysis is scarcely used in business sciences, even if applications in these research areas have increased in the last few years. The originality of the article consists in applying a relatively new technique for business sciences and particularly new for the IC literature. The research of a correlation between IC and FP has always been considered relevant for both a firm’s managers and Scholars (Ittner, 2008; Kamiyama et al., 2004), but has encountered many drawbacks, not least the difficulties in measuring IC. Researches on a correlation between IC and FP received a great boost after the publication of the VAICTM method by Ante Pulic, (1998, 2000, 2004) which allows the IC performance to be calculated by starting from accounting data. The article, despite the limits deriving from the effectiveness of the measures of the two variables and from a lack of reliability in the measurement of the studies included in the MA, tries to summarize, from a statistical point of view, for the first time in the IC literature, the researches existing on such a connection and, above all, to search for the variables responsible for the heterogeneity between studies (moderators). On the whole, there are two ways to conceptualize. MA: fixed-effects and random-effects MA (Hedges and Vevea, 1998; Hunter and Schmidt, 2004/1990). The fixed-effect methods assumes that all studies in an MA comes from a population with a fixed-average effect size, whilst random-effect methods assume that average effect size in the population vary from study to study. I chose to utilize a random-effects method, in particular the Hedges and colleagues’ method (Hedges and Olkin, 1985, Hedges and Vevea, 1998), since the data collected for the MA are real-world data, likely to have variable-population parameters and because the random-effects method allows inferences that generalize beyond the studies included in MA (Field, 2009). The moderator analysis has been carried out by using a multi-level regression model, due to the hierarchical nature of our data (multiple FP measures in the same study). With respect to the traditional meta-analytic techniques, multilevel modeling structure accounts for dependencies in the data whilst also allowing each study to contribute different effect sizes (Hox,2002; Van Den Noortgate and Onghena, 2003; Goldstein et al., 2000). Despite to its potential efficacy (Hofmann and Gavin, 1998), few published MA utilized this method, for a distinct lack of prescriptive instructions on how to conduct a MA using multilevel modeling (O’Mara et al., 2005). The main MA result is a positive true effect size of the correlation between IC and firm’s performance, whilst, from the moderator analysis, it emerges that the different economic context is the main responsible for the between-study variance, together with the industry and the FP measures.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
