Entrepreneurial networking: a blessing or a curse? Differential effects for low, medium and high performing franchisees

Many studies have found that networking improves entrepreneurial performance by providing entrepreneurs with access to a variety of important resources (e.g. Aldrich and Zimmer 1986; Hoang and Antoncic 2003; Slotte-Kock and Coviello 2009). However, other studies have pointed at the downsides of entrepreneurial networking—such as opportunity costs and governance problems—that may negatively affect firm performance (e.g. Watson 2007; Jack 2010; Rauch et al. 2016). Overall, there is still little consensus on how and under what conditions entrepreneurial networking affects firm performance (Stam et al. 2014). This paper contributes to this discussion by arguing and empirically demonstrating that networking can have differential effects for different types of entrepreneurs; more specifically, we find that for some entrepreneurs networking is a blessing, whereas for others it can be a curse.

Recent literature reviews and meta-analyses point at several critical issues in the networking-performance literature that may have caused the debate on the networking-performance link. First, the literature has suffered from conceptual vagueness regarding the types of resources shared, the types of networks in which they are shared and the types of entrepreneurial performance under study (Hoang and Antoncic 2003; Jack 2010). Second, researchers have largely focused on the effect of isolated network characteristics, such as an entrepreneur’s network position or relationship quality, on entrepreneurial outcomes. This has resulted in a lack of consistent theoretical frameworks that take into account multiple facets of entrepreneurial networking in explaining performance (Jack 2010; Rauch et al. 2016). Third, only relatively few studies have taken into account contingencies, such as firm or industry characteristics, that might condition the effects of networking on entrepreneurial performance (Stam et al. 2014; Rauch et al. 2016).

Related to the abovementioned critical issues, our study contributes to the literature on the entrepreneurial networking-performance link in several ways. First, we decrease conceptual vagueness and provide more detailed knowledge of networking effects by focusing on franchise systems as a specific type of entrepreneurial context in which franchisees share a specific type of resource (i.e. ideas and knowledge on local marketing) with their fellow franchisees within the same franchise system (i.e. peers), resulting in a specific type of entrepreneurial performance (i.e. franchisee unit sales). Besides the fact that franchise systems are an important entrepreneurial context, such systems are very suitable for studying entrepreneurial phenomena since franchise systems provide a ‘natural laboratory’ where several conditions are standardized across franchisee-entrepreneurs (Szulanski and Jensen 2008).

Our second contribution is that we develop and test a multi-faceted framework of entrepreneurs’ network characteristics and their effects on performance. We build on recent studies (Batjargal 2003; Stam et al. 2014) to distinguish three facets of an entrepreneur’s network: structural, resource and relational characteristics. In recent years, several studies on the networking-performance link in different organizational contexts have argued for combining structural network characteristics, such as an actor’s network position, with other types of network variables, such as relational quality (Moran 2005), conduct (Afuah 2013) or partner attributes (Rodan and Galunic 2004). However, so far, these studies have not provided a consistent framework capturing the most relevant facets at once. Our study provides such a framework.

Our final contribution to the networking literature is that our study is among the few to include a contingency that may influence the effects of networking on entrepreneurial outcomes (following Stam et al. 2014; Rauch et al. 2016). Contingencies can be present at different levels, such as the individual entrepreneur (e.g. Ritter and Gemünden 2003), the firm (Stam et al. 2014), the type of industry (Rauch et al. 2016) or the economy (Stam et al. 2014). Some general networking studies have looked at actors’ network utilization by studying the skills and abilities that influence how actors are able to utilize the resources acquired via their networks (e.g. Tsai 2001; Ritter and Gemünden 2003; Baker et al. 2016). However, entrepreneurship studies have rarely accounted for the heterogeneity among entrepreneurs in terms of how they utilize the resources deriving from their entrepreneurial networks (see Hayter (2015) for a brief discussion and Arenius and De Clercq (2005) and Seo et al. (2014) for exceptions). Our study contributes by including the entrepreneur’s firm performance as an important contingency variable (cf. Seo et al. 2014). We propose that the level of performance reached by an entrepreneur affects the benefits the entrepreneur is able to draw from his networking activity.

We focus on an important and unique type of entrepreneurial context, namely business format franchise systems. As such, we also contribute to a considerable stream of literature aimed at describing and explaining franchising as an entrepreneurial phenomenon (Combs et al. 2011; Ketchen et al. 2011). Franchise systems are important since in many countries, they account for a major share of business; for example, they account for about 40, 52 and 32% of retailing sales in respectively the USA, Australia and Germany (Dant et al. 2011). Franchise systems are unique because franchisees are semi-autonomous entrepreneurs who operate their businesses in a specific geographical location under a standardized business format with a uniform strategic positioning towards customers (Kaufmann and Eroglu 1998). Franchisees are part of a franchise system with the franchisor as the umbrella organization and with fellow franchisees (i.e. peers) and possibly franchisor-owned units operating under the same business format in different locations (Gassenheimer et al. 1996).

Our study addresses an important knowledge gap in the franchising literature by studying how franchisees acquire their local knowledge and how this affects their unit performance. Knowledge is crucial in business format franchise systems (e.g. Darr et al. 1995; Gorovaia and Windsperger 2013), and many researchers have used knowledge and learning perspectives regarding franchising (e.g. Szulanski and Jensen 2008; Winter et al. 2012). These studies have focused on various research questions, such as what factors affect a franchisor’s knowledge transfer mechanisms (Gorovaia and Windsperger 2013), whether systems or units within systems benefit more from the standardized knowledge of their franchisors or from the franchisees’ local knowledge (e.g. Kalnins and Mayer 2004; Jensen and Szulanski 2007) or how specific types of units influence organizational learning and hence system performance (e.g. Darr et al. 1995; Sorenson and Sørensen 2001). These studies typically assume that franchisees have both the room and the inclination to use their local knowledge to adapt the franchisor’s business format to their own circumstances to improve their unit performance (Kaufmann and Eroglu 1998; Sorenson and Sørensen 2001). To attain such an optimal local fit, franchisees will try to increase the quantity and quality of their local knowledge. Since the franchisor has knowledge on the system level and, consequently, less knowledge on the franchisee’s local level, an important question is how franchisees use their personal contact networks to acquire their local knowledge and how this affects their unit performance. To our knowledge, this question has not yet been addressed in the franchising literature. Since franchisees have a huge impact on their franchise system’s success (Michael and Combs 2008), the lack of understanding on franchisee local knowledge acquisition and its impact on unit performance forms an important research gap, which we narrow by means of our study.

In studying the performance effects of franchisee networking activities, we aim to avoid the aforementioned three critical issues in the networking-performance literature. First, we prevent conceptual vagueness by focusing specifically on franchisees’ acquisition of local marketing knowledge from their franchisee peers and the impact on franchisees’ unit sales performance. Franchising literature so far has paid very little attention to knowledge sharing among franchisee peers. Only the studies of Darr et al. (1995), Darr and Kurtzberg (2000) and Turner and Pennington (2015) have explicitly focused on knowledge transfer among franchisees within franchise systems. Darr et al. (1995) found that operational knowledge is mostly shared between units owned by the same franchisee and less likely to be shared among units owned by different franchisees, whereas Darr and Kurtzberg (2000) found that strategic similarity of franchisees facilitates knowledge transfer among them. More recently, Turner and Pennington (2015) studied antecedents of franchisees’ inclination to share knowledge. However, the effects of knowledge sharing on franchisee unit performance have remained unclear. Second, we avoid a focus on some isolated network characteristics by developing and testing a multi-faceted theoretical framework distinguishing a network’s structural, resource and relational characteristics (Batjargal 2003; Stam et al. 2014). Finally, we take into account an important contingency variable by proposing that different franchisee types (i.e. low performers, medium and high performers) experience differential effects from their peer networking activities. This approach also fits with a more general tendency in the franchising literature of including the role of idiosyncratic franchisee characteristics in explaining unit-level outcomes (e.g. Kidwell et al. 2007; Cochet et al. 2008).

Franchisors and franchisees have very different types of knowledge (Kalnins and Mayer 2004; Szulanski and Jensen 2006). The franchisor mainly has generic knowledge at the system level: ideally, the franchisor knows which attributes of the business format are replicable and worth replicating, how these attributes are created and in which types of environments they are worth replicating (Winter and Szulanski 2001). Franchisors typically codify their knowledge and distribute standardized routines in the form of a defined business format to their franchisees (Szulanski and Jensen 2008; Winter et al. 2012; Gorovaia and Windsperger 2013). As a result, the knowledge provided by the franchisor will not be perfect for any one location, but it should be generic enough to be valuable to franchisees at all locations. However, each franchisee needs knowledge about the local environment of its unit (e.g. customer preferences, competition, labour market developments) and the management of its unit (e.g. local HRM policies, local marketing activities) to run the local unit effectively. We define this type of knowledge as franchisee local knowledge. A franchisee’s local knowledge is idiosyncratic, and it is mostly non-codified as opposed to codified (Kalnins and Mayer 2004; Knott 2003). Consequently, the franchisee’s local knowledge is generally not possessed by the franchisor, and franchisees need other sources (their peers in this study) for acquiring their local knowledge.

‘Franchisee local knowledge’ is still a general classification, and different types of local knowledge can be relevant in a franchise setting. We focus on a franchisee’s local marketing knowledge: knowledge about the local market needs, the competitive situation and the marketing instruments that can be used locally next to the marketing instruments as imposed by the franchisor. Such marketing instruments typically include the products and services offered, the unit interior and exterior, promotion activities and price levels. A franchise system’s centralization level (cf. Windsperger 2004; Mumdžiev and Windsperger 2011) determines how much freedom franchisees have in adopting local marketing instruments, for example offering new products/services or adopting their own promotion activities. It is not uncommon for franchisors to allow their franchisees to develop local tailor-made marketing activities, although usually these are developed in addition to the system-wide marketing activities executed by the franchisor (Windsperger 2004). In sum, our study focuses on the characteristics of franchisees’ peer networks for acquiring local marketing knowledge, which we link to an objective marketing performance measure, namely unit sales levels.

In recent years, several studies (e.g. Tsai 2001; Hansen 2002; Baker et al. 2016) have combined a networking perspective with knowledge and learning perspectives to explain a range of outcomes of networking behaviour. The main idea behind such studies is that actors’ learning from network partners requires access to valuable knowledge via those partners and, subsequently, actors need to be able to utilize this knowledge.

Having access to valuable knowledge relates to three different facets of an entrepreneur’s network (Batjargal 2003, Stam et al. 2014): structural characteristics (‘where you reach’), resource characteristics (‘whom you reach’) and relational characteristics (‘how you reach’). First, the structural characteristics refer to the structure of an actor’s overall network of relations and include for example the entrepreneur’s network size or network position (Tsai 2001; Batjargal 2003; Reinholt et al. 2011). We focus on the entrepreneur’s network position as this oft-studied type of characteristic captures the informational (dis)advantages that result from an entrepreneur’s position in the knowledge sharing network. The more strategic the position of an actor within a network, the more (and the more timely) access the actor has to the knowledge and expertise of others in the network (Reinholt et al. 2011; Stam et al. 2014; Hayter 2015). This type of variable is important because it affects the quantity of knowledge to which an actor has easy access to.

Second, the network’s resource characteristics refer to the resources that an actor has access to through the entrepreneur’s network partners. These partner resources are important because they determine the potential value that an actor can derive from its partners (Adler and Kwon 2002; Zaheer and Bell 2005). Examples of such variables reflecting the value of network partners are ‘resource richness’ (Batjargal 2003), partners’ skills, qualities or know-how (Kwon and Adler 2014) or partners’ innovative capabilities (Zaheer and Bell 2005). In this study, we use the term partner quality to indicate the level of relevant marketing-related skills, qualities and know-how of the network partners which provides an actor with access to a high quality of knowledge.

The third and final group of networking variables are the relational characteristics, reflecting the nature of interactions between an actor and its network partners. These include, for example, the duration of relationships (Kim and Aldrich 2005) or the geographic distance (Kolympiris and Kalaitzandonakes 2013). Prior research has pointed at tie strength as an important relational network characteristic affecting the ease of knowledge transfer between partners (e.g. Uzzi 1997; Van Wijk et al. 2008). Strong ties consist of frequent interaction with peers nearby, while weak ties point at limited access to peers located far away. We use this concept of tie strength to capture the ease of knowledge transfer between an actor and its network partners.

The above three groups of network characteristics impact the quantity, the quality and the ease of transfer of knowledge that an actor has access to via its network partners. However, the access an actor has to valuable knowledge is only one side of the coin; the extent to which knowledge access translates into firm performance also depends on how well actors can actually utilize this knowledge. We refer to this as an actor’s ‘network utilization’ (Baker et al. 2016). Recent work by Stam et al. (2014), Hayter (2015) and Rauch et al. (2016) provides examples of studies demonstrating that characteristics and capacities of actors affect if and to what extent they can benefit from the network resources they have access to. The meta-analysis of Rauch et al. (2016), however, clearly demonstrates that the majority of network studies still does not include such contingencies. Building on learning and knowledge perspectives, an actor’s network utilization is contingent upon two characteristics. The first one is the actor’s ability to recognize, assimilate and apply new knowledge. We refer to this as absorptive capacity (following many authors, such as Cohen and Levinthal 1990, Tsai 2001). The second one is the extent to which the acquired knowledge is useful given the entrepreneur’s idiosyncratic circumstances (Seo et al. 2014; Baker et al. 2016). The literature points at two important aspects of such knowledge usefulness. First, new knowledge is particularly useful for an actor if it stems from a partner that has a high partner quality but that is also more knowledgeable than the actor itself (cf. Lane and Lubatkin 1998; Monteiro et al. 2008). This relative position of the actor vis-à-vis peers determines the potential added value to be gained from peers. High quality actors often have less to learn, particularly from lower quality partners with a small knowledge base. Second, the usefulness of knowledge for a specific actor depends on whether the knowledge offered fits with the actor’s knowledge needs, which in turn depends on a firm’s characteristics such as a firm’s innovation orientation (Xu 2015) or firm performance (Seo et al. 2014). For example, Xu (2015) demonstrates that firms that aim for radical innovation need access to broad rather than deep knowledge, and Seo et al. (2014) find that low performing firms benefit more from access to basic knowledge on primary business functions rather than knowledge on secondary business functions, whereas for high performers it is the other way around.

Translating the above arguments to our theoretical framework for a franchise context, we posit that a franchisee’s structural (i.e. network position), resource (i.e. partner quality) and relational (i.e. tie strength) network characteristics affect the quantity and quality of the knowledge to which the franchisee has access and the ease of knowledge transfer. Moreover, our framework points at network utilization as an important contingency variable influencing the actual utilization of the accessible knowledge by the franchisee. Following Seo et al. (2014), we posit that attained franchisee unit performance is an adequate indicator of such network utilization for three reasons. First, linked to the absorptive capacity argument, high-performance franchisees are likely to have a higher absorptive capacity than low-performance franchisees; high performers already have the experience and knowledge needed to identify and successfully implement new knowledge in their units. Second, linked to the relative position argument, we propose that high-performance franchisees may benefit differently from their peers compared to low performing franchisees since the relative knowledge position of high performers is strong. Finally, related to the knowledge needs argument, we posit that low performing franchisees require basic knowledge on how to attain higher sales levels, whereas the high performing franchisees already understand their business and need broader and more complex knowledge to take their business to the next level (cf. Seo et al. 2014; Xu 2015).

Figure 1 depicts an overview of the theoretical framework. In Section 2.3, we present our study’s hypotheses.

Table 1 summarizes the measurement properties of the variables in this study.

In our analyses, we combine traditional ordinary least squares (OLS) with Quantile Regression (QR) and compare the results for both analyses. This also enables us to assess the value of applying each method, as is often done in other QR studies (e.g. Goedhuys and Sleuwaegen 2010; Ramdani and Van Witteloostuijn 2010). We provide a brief primer to QR in the Appendix.

Traditional regression methods, such as OLS or logistic regression, are focused on the mean: they summarize the relationship between an outcome and a set of explanatory variables by describing the mean outcome for each fixed value of the explanatory variables—OLS therefore is also known as conditional mean modelling. A drawback of such a model is that it does not describe non-central locations, such as the effect that the explanatory variables may have specifically on observations in the lower tail or upper tail of the distribution. Koenker and Bassett (1978) introduced a natural extension of the linear regression model called quantile regression, which models conditional quantiles as functions of a set of explanatory variables. Whereas the traditional linear regression model only specifies the change in the conditional mean of the dependent variable associated with a change in the explanatory variables, the QR model specifies the changes in the conditional quantile. The researcher can choose which quantiles (or ‘percentiles’) are of relevance to the research at hand—when the .5 quantile is chosen the model is better known as median regression.

Since our hypotheses make a distinction between three groups of franchisees (those in the lower tail of the performance distribution, those in the middle and those in the upper tail), we focus on three percentiles: .25 (representing the effect of the explanatory variables on a franchisee that only does better than 25% of the franchisees in the sample), .50 (representing the effect on a franchisee with median performance) and .75 (representing the effect on a franchisee that achieves a performance level that is better than that of 75% of the sample). In this way, we estimate regression coefficients that pertain to franchisees at each of these performance levels and compare coefficients and statistical significance between them. QR has several statistical advantages over regular regression: it is robust to outliers as well as to distributional assumptions. Whereas OLS often relies on larger sample sizes in order to remedy potentially violated distributional assumptions, QR does not require larger sample sizes for this purpose. QR can deal naturally with highly skewed data such as income or sales, without the need to transform such data into mode well-behaved data shapes (Koenker 2005; Hao and Naiman 2007).

The key descriptives and statistics for all variables appear in Tables 2 and 3. Table 3 shows that performance correlates positively with occupying a central position in the peer network, with networking with peers with high sales quality and with having more experience as a franchisee in the franchise system. There is a moderate positive significant correlation between the average sales quality of a franchisee’s peers and the peers’ average operational quality. This makes sense, because higher sales are likely to contribute positively to operational results. Unexperienced franchisees also put in more work floor hours. Separate analyses (not shown) indicate that there are no multicollinearity issues in any of our analyses.

We present the results of the OLS and QR models in Table 4. QR estimates the regression coefficients of the explanatory variables for franchisees at particular quantiles in the overall performance distribution: .25 (i.e. low performers), .50 (i.e. medium performers) and .75 (i.e. high performers). In the distribution of sales in our sample, the 25th quantile equals a total sales volume (over the 43 weeks of measurement) of about €159K, the median sales level is about €228K and the high performing franchisees at the .75th quantile of the distribution have a sales level of about €321K. In OLS analyses, it is common to transform sales volume before running the analysis (usually, by taking the logarithm), but this is unnecessary in QR analyses (see the Appendix).

Since we hypothesize that networking characteristics may have differential effects on a franchisee depending on the position of the franchisee in the performance distribution, we expect differences in parameter estimates and statistical significance between firms along the three considered quantiles. In Table 4, we present both the results of the OLS regression and the three quantile regressions. In the OLS model, we leave the sales variable untransformed. This maximizes comparability between OLS and the QR results; besides this, the Shapiro-Wilk statistic of sales is not significant (p < .72), which suggests that sales is quite normally distributed.

The type of hypothesis that follows from our quantile-based arguments (i.e. the ‘b’-versions of the hypotheses) consists of several components; in a strict sense, the hypothesis would be rejected as soon as only one of the components does not hold. As a result, it is reasonable and more informative to discuss the extent to which each hypothesis is supported, rather than only drawing conclusions about the consolidated hypothesis per se. Besides a discussion of the empirical findings based on Table 4, we summarize the empirical findings graphically in Fig. 2, allowing for the visual comparison of the various effects.

Finally, to assess the robustness of the empirical findings to alternative model specifications, we ran several alternative QR models: such as a model without peer sales quality, a model without peer operational quality, a model without both peer sales quality and peer operational quality and a model without peer distance (while always maintaining the controls). Either of these models yielded essentially the same results as those presented in Table 4. This suggests that the results can be considered robust.

H1a hypothesized that a franchisee’s betweenness centrality in the peer network is positively associated with unit performance, and H1b proposed that the strength of this association is stronger for high performers than for medium and low performers. Supporting H1a, the OLS regression indeed shows a positive and statistically significant association: the more a franchisee is located on shortest paths between its peers, the higher its unit performance. Consistent with H1b, we find that the positive effect of centrality is stronger for high performers (a positive, statistically significant coefficient) than for low performers. In fact, the coefficient for low performers is not statistically significant, which indicates that low performers do not benefit from having a central position, whereas medium and high performers do. Because the difference between high and medium performers is not statistically significant (F = 1.13, p > .1), the hypothesis is largely supported.

These findings are in concert with the absorptive capacity argument (Tsai 2001; Van Wijk et al. 2008; Monteiro et al. 2008); as a result of their high absorptive capacity, high performers may benefit the most from having access to knowledge. The finding that low performers do not benefit at all also fits with this argument: their absorptive capacity is simply too low to recognize and be able to internalize the value of available knowledge in their peer network.

A franchisee’s partner quality refers to peer sales quality (H2a and b) and peer operational quality (H3a and b). We expected peer sales quality to be positively associated with unit performance (H2a) and that this positive association is stronger for low performers than for medium and high performers (H2b). The OLS model does not uncover a statistically significant effect, so H2a is not supported. However, the QR results do show differential effects of peer sales quality on unit performance. Since an OLS model generates a single coefficient for the (conditionally) ‘average population’, it misses the differentiating effect of peer sales quality on unit performance. Consistent with H2b, we find that low performers benefit from a high peer sales quality (with p < .088, which is reasonable given the modest sample size), whereas the high and medium performers do not. In fact, high performers even suffer from a (small) negative association between partner sales quality and unit performance. Although the difference in coefficients is small, the difference is statistically significant (F = 7.13, p < .01). Medium performers benefit nor suffer from having high or low average performing contacts. Apart from the coefficient for the high performers turning slightly negative, H2b is essentially supported. An explanation for this negative association can be found in the marginal benefits for the high performers relative to the costs (Watson 2007). The high performers in our data set already have a high level of performance and will thus learn less from other high sales performers, whereas they still suffer from the costs of networking. For the high performers, the costs of networking thus outweigh the benefits.

Regarding peer operational quality, we expected a positive association with unit performance (H3a) and that this positive association is stronger for high performers than for medium and for low performers (H3b). The OLS model again does not uncover an effect (thus rejecting H3a), whereas the QR model shows that effects actually differ along performance levels. Consistent with H3b, the QR results show that high performers benefit positively and significantly from peer operational quality, whereas medium and low performers do not. The effect for low performers even turns negative (albeit only significant at p < .09). This difference between the coefficients is statistically significant (F = 8.29, p < .01). Again, medium performers do not appear to benefit (or suffer) from having contacts with high financial performance. H3b is essentially supported; as expected, the high performers indeed benefit the most from the high operational quality partners; however, the slight negative association for the low performers was unexpected. An explanation for this latter result may be that the low performers suffer the costs from networking (Watson 2007), but on top of that, the knowledge from their network partners is too complex for them and does not fit their specific knowledge needs. Given these low performers’ low absorptive capacity, this combination of costly and complex knowledge is likely to cause problems.

Hypotheses 4 and 5 relate to a franchisee’s tie strength, consisting of peer distance and peer communication frequency. Regarding peer distance, we expected a negative association between peer distance and unit performance (H4a) with a stronger negative association for the low performers than for the medium and high performers (H4b). Both the OLS and QR models show no significant effects; hence, H4a and H4b are not supported. Even though several networking studies have argued for and/or found partner distance as an important network characteristic affecting performance (e.g. Cramton 2001; Kolympiris and Kalaitzandonakes 2013; Dolfsma and Van der Eijk 2015), our study does not find statistically significant results. An explanation may be the relative homogeneity of units within franchise systems compared to other types of networks of entrepreneurs. Since franchisees all operate under the same business format in relatively similar local circumstances (especially in our study as explained in Section 3.1.1), the problems with knowledge transfer and interpretation as suggested by previous studies may be smaller. Moreover, developments in ICT in general (e.g. email and internet) and in franchise systems (e.g. benchmarking systems and intranet) may have facilitated knowledge transfer and the interpretation of knowledge from different local units (Brooks 2012).

Regarding peer communication frequency, we hypothesized that communication frequency is negatively associated with unit performance (H5a) and that this negative association is stronger for high performers than for medium and low performers (H5b). The OLS model does not uncover a significant effect (rejecting H5a), whereas the QR models show that the negative effect of interaction frequency is stronger for high and medium performers (negative, statistically significant coefficients) than for low performers (seen from the statistically insignificant coefficient). For medium and high performers, having increased frequency of promotion-related contact with peers is negatively associated with their performance. The difference in effect between medium and high performers is not statistically significant (F = 1.27, p > .1), so they both appear to suffer negative consequences of similar strength. An explanation is that for medium and high performers the marginal benefits of having frequent contacts are outweighed by the costs of networking because the frequent contacts are likely to produce redundant knowledge (cf. Watson 2007; Stam et al. 2014). Hence, H5b is largely supported.