Data, sample size, and common method bias

A first limitation is related to the sample that was used to investigate the mediating role of mass customization capability on the relation between strategic flexibility and operational efficiency. The data was obtained from a survey conducted by a team of researcher from the University of San Diego in the context of a larger research project on organizational capabilities in dynamic market. However, the data used does not pro- vide any information about the level of environmental dynamics faced by the companies included in the survey. Nor do I have any information about whether a selection mech- anism was applied when selecting the companies for the survey. Although I obtained data about the industry affiliation of the companies in the sample, which would at least allow for approximate controlling for industry-specific dynamics, I was not able to in- tegrate this variable, as the sample size is too small. An integration of these variables would dramatically reduce the explanatory power of the empirical analysis. I could only assume that the industries represented in the sample are characterized by high levels of dynamics; however, there is no indication for this. Therefore, the findings are limited and should be interpreted with caution.

However, there is another and more general concern regarding this study that re- lates to the sample size and chosen analytical methodology, which is discussed in the

following. It is frequently argued that partial least squares (PLS) approaches are suit- able for the analyse of even the smallest sample sizes (Chin et al., 2003; Peng & Lai, 2012). The ten-times (sometimes even five-times) rule is an often cited guideline which suggests that the minimum sample size needs to be at least ten times (five times) as large as the number of path relations directed to the most complex construct in a given model (Hair et al., 2012). I followed this suggestion when analyzing the mediating role of mass customization capability on the relation between strategic flexibility and opera- tional efficiency. However, this ten-times rule is not without criticism. The results of a Monte Carlo simulation study conducted by Goodhue et al. (2006) suggest that using bootstrapping in order to obtain p-values in PLS is not superior to other methods, as it also lacks the adequate statistical power to detect small or even medium-sized effects in small sample sizes. According to these results, the ten-times rule is no longer viable. Overall, the contradictory guidance provided in the literature suggests that results ob- tained from PLS estimates using small samples should be considered with caution. In light of this, I attempted to get larger sample sizes in the other studies of this thesis in order to avoid the difficulties discussed above.

In the following, I focus in detail on limitations that can be associated with the kind of data used in this thesis. In this thesis. All (papers I and II) or most of (paper III) the data used for analyses in this thesis are cross-sectional and based on perceptions of single informants per company (self-reported data). Although this is consistent with common practice in research (Huang et al., 2010; Lisboa et al., 2011), as it is frequently difficult to acquire objective data and collect company-specific data over time, these data provide only a snapshot of a company’s status quo, thereby limiting the ability to understand causality between the factors. Moreover, another potential downside of using only self- reported data, such as in research paper I, is that common method variance can bias the results of the empirical analyses. Common method bias is defined as the variance induced by the applied methodological measurement approach and survey procedure rather than by the objective variance of constructs. It may occur when the values of both the independent and the dependent variable are obtained from the same survey respondent, as in the case of my first research paper3. Moreover, common method variance can result from the item characteristics as well as the context in which an item is placed (Podsakoff et al., 2003). Different studies reveal that common method bias

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represents a widespread problem. For example, a meta-study by Cote and Buckley (1987) finds that approximately one quarter of the variance in research measures that has been investigated is caused by measurement error such as common method bias. The size and direction of the effect caused by method variance can differ, and thus it can cause Type I as well as Type II errors (Podsakoff et al., 2003). Although the strength of a present effect of common method variance varies between studies, on average their effect is significant (Podsakoff et al., 2003). Therefore, it is broadly accepted in the scientific literature that researchers need to test whether common method variance is an issue in survey-based empirical studies. Different methods are provided in the literature that allow testing for common method variance. The single-factor test suggested by Harman (cf. Harman, 1967; Podsakoff & Organ, 1986) is one of the most frequently used test procedures (e.g. Atuahene-Gima et al., 2005; Zhang et al., 2009; Liu et al., 2012) and has the underlying hypothesis that common method variance is an issue in a study if most of its variables load on a single factor when conducting an exploratory factor analysis. Therefore, I followed common practice by using this test procedure in research paper I.

However, the single-factor test is not without criticism (Podsakoff et al., 2003): (1) It does not provide statistical evidence of the absence of common method bias, (2) it remains unclear how much of the variance between the items needs to be accounted for the major factor, and (3) depending on the number of items in a study, the num- ber of extracted factors likely increases as well, thereby reducing the validity of the test. Podsakoff et al. (2003) therefore believe that the single-factor test is not a valid eva- luation instrument for detecting common method variance, although it is still frequently applied, and that it only represents a test procedure that indicates whether the common method variance might be an issue. In order to obtain more valid assessments with regard to the effect of common method variance, more advanced methods are suggested, for example, partial correlation techniques (e.g. Lindell & Whitney, 2001).

According to the arguments presented above, it can be concluded that the results obtained for the chosen test procedure in the first research paper should only be regarded as a first indication that common method variance is likely not a problem, rather than as a sufficient criterion to rule out the presence of such a bias in my study. It would be beneficial for future research to rely on objective data or longitudinal data in order to address potential shortcomings associated with the use of self-reported data. Using

longitudinal data allows detecting and controlling for time-induced changes in the value of the dependent variable (Hsiao, 2003). Additionally, as the sample is larger, the use of longitudinal data reduces the threat of collinearity among the independent variables (Hsiao, 2003). Thus, I also suggest replicating at least the first studies of this thesis using longitudinal data to gain a more valid understanding of the relationships in an empirical model.

To prevent common method bias and to increase the validity of the model, I used subjective and objective data to measure company success in the third research paper. However, as the financial data were extracted from the last available annual report of a company, within the sample ranging from 2011-13, there is a time offset between the survey data and the objective financial data, which limits the explanatory power of the results. Although it seems likely that the conditions of the surveyed companies will not have changed essentially across the relatively short time offset, there is no guarantee for this. I suggest repeating the analysis as soon as all financial data of the companies for 2013 are available.