ESPACIO EN DONDE SE VA A CONSTRUIR

Enabling the investigation of moderator variables is amongst the salient advantages of meta-analyses, as moderators assist in the delineation of boundary conditions of a theory (Aguinis et al., 2011; Viswesvaran & Ones, 1995). Alternative methods are used to detect the presence of moderator variables and the two commonly employed methods are the Schmidt and Hunter‘s 75 percent rule and the Hedges and Olkin procedures. In the Schmidt and Hunter method, a 75 percent threshold is used for examining the variance in correlations, for detecting the presence of moderators (Hunter & Schmidt, 1990). Although in frequent use and driven by strong rationales, the applicability of this method in fields other than psychometrics, for which it was originally formulated, has been called into question (Borenstein et al., 2009). Therefore, this method was not adopted in the current study.

In the Hedges and Olkin method, the

χ

2 (Chi-square) test of homogeneity is

employed to evaluate if the variance of effect sizes can be solely attributed or

not to sampling error (Borenstein et al., 2009). If the

χ

2 value is statistically

significant, examination of possible moderators becomes necessary (Kirca et

al., 2005). The

χ

2 test is frequently used in meta-analyses and was employed

in the current study owing to its statistical rigour and scholarly acceptance outside the field of psychometrics. Considering the categorical nature of potential moderators in the study, subgroup analyses were used to ascertain the moderation effects on the PIC–firm performance relationship. The variables were coded only when unambiguous information was reported in studies, in order to avoid confounding moderation results. Studies not

reporting explicit information on moderators were coded ‗NA‘ (Not Available),

in the corresponding column of the coding sheet.

Borenstein et al. (2009) recommend that at least 10 studies must be used for the analysis of each purported moderator variable, and the current study

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meets this heuristic. Measures enabling the coding of moderator variables and evaluation of their effects were adopted on the basis of: the convention followed in meta-analyses; the scope of the current study and, the operationalisation of PIC as a dynamic capability (e.g., see Grinstein, 2008a; 2008b; Kirca et al., 2011; Rosenbusch et al., 2011; Rubera & Kirca, 2012). The coding of moderators is now outlined.

4.4.4.1. Industry type

The type of industry was divided into two broad categories (i.e., manufacturing and service) to enable the testing of any moderation effect via subgroup analysis. Information on industry type was extracted from studies to group effect sizes by industry type. Grouping was simply based on whether a study

investigated a sample of manufacturing or service firms and the two groups

were subsequently subjected to analysis to determine if statistically significant differences existed between them. Manufacturing and service firm samples

were coded as ―manufac‖ and ―service‖ respectively. Such coding for

categorical variables is prescribed by meta-analysts (e.g., Aguinis & Pierce, 1998). Thirty one studies provided information on industry type (i.e., manufacturing or services), thereby enabling a sub-group analysis.

4.4.4.2. Firm size

Based on contentions concerning the potential impact of firm size on the relationship of interest (presented in Chapters-2 and 3), effect sizes were grouped in accordance with SMEs and large firms to examine differences between the two groups. To differentiate SMEs from large corporations, different benchmarks have been used in the literature, and there is no universally applied rule of thumb for such categorisation (Rosenbusch et al., 2011). As the largest number of studies incorporated in the present research originated from the US (i.e., 20 studies out of 57), the SME definition applied in this study corresponded with the US threshold for SME classification. The US threshold stipulates 500 full-time employees as the demarcation between SMEs and large firms. This dichotomy is also consistent with the threshold level used in many primary and meta-analytic studies (e.g., Acs & Audretsch,

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1987; Rosenbusch et al., 2011). Consequently, the correlations from studies investigating firms with less than or equal to 500 employees were grouped in the SME cluster. A total of 27 studies supplied data on the size (SMEs or large firms) of the sampled firms.

4.4.4.3. Technological turbulence

The metrics used for dichotomising technological turbulence as high or low in this study were: the country-level R&D expenditure as the percentage of Gross Domestic Product (GDP), the number of researchers in R&D per million people, and the number of patent applications by local residents per million people.

These measures are reflective of the characteristics of technological turbulence (as outlined in Chapters-2 and 3), and identical or similar measures are frequently adopted in meta-analyses and primary studies (e.g., Grinstein, 2008b; Nelson, 1993; Song et al., 2005; Thornhill, 2006; Wilhelm et al., 2015). The use of R&D expenditure as a percentage of GDP for operationalising technological turbulence at country-level (in this study) was predicated on, and analogous to, the use of R&D intensity as a measure of industry- and firm-level R&D efforts (e.g., see Neely & Hii, 1998; Scherer, 1965; Thornhill, 2006). The number of researchers in R&D per million people is an established measure for calibrating knowledge assets/intellectual capital (see DeCarolis & Deeds, 1999; Thornhill, 2006). The number of patent applications by local residents per million people served as a measure of technological outputs, the key contributor to turbulence. The three measures calibrated resource commitments towards, and also outcomes relating to, technological advances. Hence, it is argued that the measures collectively constituted a valid and comprehensive metric for assessing technological turbulence prevailing in a specific country, and for evaluating its moderation effects. Other meta-analyses have examined the moderation effects of technological turbulence with similar measures (e.g., see Grinstein, 2008b). In addition to the appropriateness of these measures for operationalising technological turbulence, they also overlap with the frequently used measures

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for market dynamism. Through the inclusion of R&D expenditure as a percentage of GDP in the metric, technological turbulence was operationalised in a manner similar to the firm/industry-level operationalisation

of market dynamism via R&D intensity (e.g., see Audretsch & Acs, 1991;

Thornhill, 2006; Zhang et al., 2013). Furthermore, employing the number of researchers in R&D per million people corresponds to the measure of knowledge assets/skilled human capital, as sometimes used to calibrate market dynamism (e.g., Thornhill, 2006). Thus, due to the significant overlap between the measures of technological turbulence and market dynamism, the suitability of employing technological turbulence as a proxy for market dynamism in the current study is further supported.

The data for coding technological turbulence as high or low was obtained from the online Science and Technology Databases of The World Bank. These databases provided information on several country-specific indicators of science and technological activities such as high technology exports, scientific and technical publications, and R&D expenditure (as a percentage of GDP), amongst others (see http://data.worldbank.org/topic/science-and-technology, for details). The composite country scores (obtained by combining country- specific scores for each of the three measures) were used to determine the level of technological turbulence prevailing in a market. In total, 45 studies were coded for technological turbulence to enable a sub-group analysis. The moderator was coded as high or low, depending upon whether the composite score of a country fell above or below the median score, respectively.

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