H2a: External network broadening activities are positively related to the firm’s financial performance.

H1a: Stability of the STMT stability is positively related to network deepening activities.

H1b: Stability of the STMT stability is positively related to network broadening activities.

H2a: External network broadening activities are positively related to the firm’s financial performance.

H2b: External network deepening activities are positively related to the firm’s financial performance.

H3a: External network broadening activities positively mediate the relationship between STMT stability and the firm’s financial performance, such that stability increases future networking success in order to obtain financial benefits.

H3a: External network deepening activities s positively mediate the relationship between STMT stability and the firm’s financial performance, such that stability increases future networking success in order to obtain financial benefits.

STMT stability

External networking activities

Firm’s financial performance +

+ ?

THE MEDIATING EFFECT OF EXTERNAL NETWORKING ACTIVITIES BETWEEN STMT STABILITY AND FIRM

PERFORMANCE

PIETER VAN KONINGSVELD

S2739348

E-mail: p.r.van.koningsveld@student.rug.nl

Master thesis, MSc Human Resource Management

University of Groningen, Faculty of Economics and Business

Supervisor:

Y. Yuan

Second Assessor:

P. van der Meer

ABSTRACT

This paper examines the mediating effect of external networking activities on the relationship between STMT stability and the firm’s financial performance. Research on stability focusses primarily on internal consequences, and often overlooks the effects on external organizational activities. The distinction between network broadening and network deepening activities is taken into account. Quantitative, archival data is used. Contradicting with the existing literature, findings suggest that stability has a significant and negative effect on external network broadening and network deepening activities, and both external networking activities have an insignificant, negative effect on financial performance. For ROA, ROE, and ROI, both external networking activities appeared to positively mediate the relationship between STMT stability and financial performance. Theoretical and practical implications are discussed.

Keywords

External networking activities; Network broadening; Network deepening; STMT stability; Firm performance; Financial performance.

Word count: 8,585

INTRODUCTION

Our contemporary society is filled with frequent changes (Baker & Faulkner, 2017).

This also holds true for senior top management teams (STMT) and directorial boards of a firm (e.g., Halinen & Törnroos, 1998; Huggins, 2010), especially firms operating in the high- technology industry which is characterized by its changeability (Huggins, 2010), and competitiveness (Mu, Peng & Love, 2008). When a firm is performing poorly, and new opportunities present itself for the STMT members that are tempting enough, they might leave their current firm and join another (Crutchley, Garner & Marshall, 2002; Porter, Woo &

Campion, 2016). Likewise, when an STMT member performs poorly, he or she can quickly be replaced by the firm’s shareholders (Crutchley et al., 2002). Too much change, however, is detrimental for financial success (e.g. Henderson, Miller & Hambrick, 2006). There is not enough time to develop shared mental models between the STMT members (Heavey & Simsek, 2015), to become comfortable with another (Pfeffer, 1981; Harrison, Mohammed, McGrath, Florey & Van Der Stoep, 2003), or to fully comprehend the available organizational knowledge (Joshi, Pandey & Han, 2009). On the other hand, too much stability is also bad for financial performance. Groups become isolated from their external environment, neglecting outside information which will keep them up to date with industry trends or benchmarks (Mu et al., 2008). Firms should, therefore, carefully balance their STMT stability.

Existing literature about STMT stability has indicated several benefits. For example,

stability facilitates learning and coordination within the team (Savelsbergh, Poell & Beatrice,

2015). Higher team tenure also increases the creation of transactive memory, leading to an

overall increase in team performance (Moreland & Myaskovsky, 2000). Even though internal

consequences are important, most stability literature primarily focusses on this aspect, and often

overlooks the impact of external factors. It is essential to investigate this impact however as

STMT members have a great say in which information flows through the organization (Larcker

et al., 2013). They are in the highest positions in the firm’s hierarchy and therefore possess the power to control the strategic decisions and performance of the firm (Smith, Smith, Olian, Sims, O’Bannon & Scully, 1994). This information, that STMT members decide to distribute among the firm, is often obtained outside of the local firm via the act of networking. Networking offers new opportunities for the entire organization (Knoke, 1999; Muijs, West & Ainscow, 2010), increases financial performance (e.g., Gabbay & Leenders, 1999; Watson, 2007; Peng & Luo, 2010; Larcker, So & Wang, 2013), and is vital if the firm wishes to survive or grow (Watson, 2007; Vissa & Chacar, 2009). In short, this study hypothesizes and tests the effects of STMT stability on the firm’s financial performance via the mediator of external networking.

There are two forms of external networking activities, defined by Vissa (2012) as network-broadening activities and network-deepening activities. Network-broadening is used when actors actively seek out new contact to add to their existing network, while network- deepening is concerned with investing in existing relationships. However, a network often consists of a mix of both broad and deep contacts and is found to be a dynamic construct (Uzzi, 1996; Lazzarini & Zenger, 2007). Meaning that when a networker is investing time into broad contacts, less time is available to invest in deep contacts. Which strategy is best is what best suits the desired goals of the firm. Both forms of external networking are tested in this study in order to see its differences on financial performance.

Marrone (2010) has concluded in his literature review that external networking effects

on the performance of the team or firm are well researched. However, there is little to no

empirical evidence about the mediated use of external networking activities. It is important to

address this, in order to gain a complete understanding of the possible effect of networking. In

order to successfully test this, it is assumed that the networking behavior of STMT members is

agentic. Meaning that they can decide whether or not to take on certain connections as a result

of internal stability. In reality, taking on external contacts may also be receptive behavior where

the choice of the networker is little or not at all included. However, STMT stability will likely not influence the latter as stability often increases intrinsic motivation (e.g., Edmondson, 1999;

Putnam, 2000; Huckman, Staats & Upton, 2009). Therefore, only agentic networking behavior is examined.

THEORETICAL BACKGROUND AND HYPOTHESES

STMT Stability on Firm Financial Performance

In the literature, the firm is often represented by the behavior of a group of individuals.

The reason being that an individual’s ability to absorb all the available knowledge is limited;

for an individual to learn the unique knowledge possessed by other specialists is simply undoable (Grant, 1996). Therefore, individuals tend to stick together in order to supplement each other (Carley, 1991). This bundling of unique, specialized knowledge into a group that can carry out productive tasks repeatedly, is what happens in an organizational setting, and eventually creates value for the firm (Granovetter, 1992; Grant, 1996). However, for a group to stay productive, and keep creating value for the firm, it is important that a certain level of stability is achieved.

STMT stability can be referred to as the stability of all the directorial employees working

in a specific firm (Turrini, Cristofolo, Frosini & Nasi, 2009). It is the amount of change in the

membership of the team, only when no members leave or enter the STMT, it is considered

completely stable (Van Vugt, Jepson, Hart & De Cremer, 2004). STMTs on its own are by no

means a stable construct. Members might leave because they have found an opportunity

elsewhere (Crutchley et al., 2002; Porter et al., 2016), causing the exit of a member in one group

and the entrance of that member in another. On the other hand, a firm’s stakeholders can also

decide to involuntarily remove an STMT member (Crutchley et al., 2002). Juenke (2005)

conceptualized stability as the length of membership tenure in his study. Generally spoken when tenure increases, and the firm does not grow, this indicates that no members join or exit the STMT. When the firm does grow it is logical that new members are added in order to handle the new size of the firm, causing the STMT to be less stable.

A lot of research has been done on the effects of STMT stability on the firm’s financial performance, and many found that stable teams lead to superior performance. The reason behind this can be divided into two constructs: coordination and openness to relationships (Reagans, Argote & Brooks, 2005). With regard to the former, it is easier to develop shared mental models in groups that benefit from stable membership (Carley, 1991; Moreland, 1999), as people working together for a longer period of time create a shared knowledge (Wegner, 1987), and is found in literature to be beneficial for the firm’s financial performance (e.g., Heavey & Simsek, 2015). What is especially of interest to my study is the construct of openness to relationships. These can be separated into relationships within STMT and towards external actors. Locally, higher stability is found to lead to mutual trust between members (Coleman, 1988; Putnam, 2000), feelings of psychological safety (Edmondson, 1999), and team beliefs (Huckman, Staats & Upton, 2009). All leading to more openness to internal relationships, and thus increasing financial performance. Stable members are also found to be more willing to commit themselves to the group, resulting in a bigger motivation to put more effort into the group (Moreland & Levine, 1982; Nybakk & Jensen, 2012; Guchait, Paşamehmetoğlu &

Madera, 2016). However, the literature on local group stability in general is not unanimous

regarding its positive effects on firm performance. For example, Katz (1982) found that groups

that stick together ultimately become more isolated from external information, as this

information disrupts their comfortable way of working together. When a group neglects outside

information the accompanied benefits, such as learning and accessibility to new opportunities,

are lost (Hewstone, Rubin & Willis, 2002). Therefore, the relationship between STMT and a

firm’s financial performance is that of an inverted U-shape (Berman, Down & Hill, 2002;

Huckman, et al., 2009).

The stream of literature focused specifically on CEO and directorial stability found similar results on financial performance. First, Henderson, Miller & Hambrick (2006) found firm performance initially rises, but declines after a certain amount of CEO tenure. CEOs have the tendency to learn quickly. Once they gathered enough deeper knowledge of the firm and own the required skill repertoires, firm performance increases (Wu, Levitas & Priem, 2005;

Luo, Kanuri & Andrews, 2014). However, the longer a CEO stays at the firm the quality worsens. Indicating that if a STMT remains stable for too long, they start to poorly asses strategic risks and become blind to their environment (Levinthal and March, 1993; Simsek, 2007), leading to a decrease in firm performance. Again stating that the relationship between CEO tenure and the firm’s performance is likely to be that of an inverted U-shape (Henderson et al., 2006). Second, Huang & Hilary (2018) conducted the same research, but took the entire executive board as their subject group. The results were the same. They add that “for each additional year of tenure, the benefits of learning dominate for younger boards, whereas the costs of entrenchment dominate for older boards” (Huang & Hilary, 2018: 1289). Many other researchers join this point of view (e.g., Simsek, 2007; Lel & Miller, 2008; Luo, Kanuri &

Andrews, 2014; Setiawan, Kee Phua, Chee & Trinugroho, 2017).

In order to test the mediating effect of external networking between the firm’s local stability and its financial performance, each separate interactions between variables will be discussed in the following sections. Followed by the overall suggested effect of the mediating model.

STMT Stability on External Networking Activities

As said before, a large part of a STMT member’s job is to network externally (Larcker et al., 2013). But networking is a broad term that requires more clarification in order to comprehend it rightfully. Borgatti and Foster (2003: 922) define a network as “a set of actors connected by a set of ties”. These actors can be persons, teams or organizations. External networking activities are interactions with contacts, outside of the organization that the actor is part of, which can assist in achieving organizational goals (Ancona, 1990; Ancona & Caldwell, 1992).

Examples of actor’s interactions are information seeking, representing themselves towards outsiders, and task coordination between two actors (Ancona & Caldwell, 1992). Firms should engage in these networking activities if they wish to grow, develop, or gain a competitive advantage (Tang, Mu, & MacLachlan, 2008). It is the interaction with a firm’s external partners that gives the firm an edge over the competition through a better understanding of trends and by using industry-wide benchmarks (Mu, Peng & Love, 2008). They provide the networking firm with knowledge that otherwise wouldn’t have been available (Nahapiet & Ghoshal, 1998).

This newly accessible information helps firms to increase their innovation and creativity, and potentially performance, because existing knowledge is challenged, new possible solutions for existing problems are found, or new ideas are explored by bundling the knowledge of both firms (Mu et al., 2008).

Unfortunately, firms cannot simply keep adding endless numbers of new ties as it does not further increase the networking accompanied benefits (Ancona & Caldwell, 1992;

Carpenter & Westphal, 2001). The strategic context of networking is what matters. There are,

two broad constructs in which networking actions can be divided. Rugman and D’Cruz (2002)

made the distinction between these forms as openness to new network ties and strong network

ties. Later, Vissa (2012) refined these terms in his article to network-broadening – where an

actors seeks out new contacts to add to the current network – and network-deepening activities

– in which the actor manages and invests in existing relationships, respectively. As dynamic as

stability is, so too are networks and firms should carefully balance their investment into both types of relationships in order to achieve their desired organizational goals (Uzzi, 1996;

Lazzarini & Zenger, 2007).

According to the definitions of the two, it seems logical that network deepening is more suitable among stable STMTs. Network deepening is the strategy that requires the actor to engage with the external tie in a repeated manner, in order to establish mutual trust and social attachment (e.g., Ring & Van de Ven, 1994; Gulati, 1995). Frequent interactions between the networker and the external tie are necessary in order to create these social bonds (Nahapiet &

Ghosal, 1998; Johannisson, Ramirez-Pasillas & Karlsson, 2002). When a STMT is more stable it is more likely that the external tie will keep in contact with the same person from the STMT.

When the STMT experiences a lot of change, the existing contact from within the STMT might be replaced and the social bond will have to be rebuild again. This should mean that network broadening is more suitable for less stable STMTs, which is confirmed by the existing literature.

Having more change of membership matches better with having a wider range of external contacts, and opens up more opportunities to access a wider range of potentially useful knowledge and resources (Breschi & Malerba, 2001). However, the external environment also plays a large role on determining which strategy is best to use. For example, network broadening activities become more important when environmental uncertainty is high (Eisingerich, Bell & Tracey, 2010).

Additionally, it is also very challenging for, and demanding upon, the actors engaged in

both networking activities (Marrone, 2010). One of the reasons being that the actors constantly

have to balance their attention, not only between broadening and deepening ties, but also

between the ingroup and the outside relationships (Choi, 2002). This demands certain skills and

motivation from the networker (Marrone, 2010). When a group is stable they are familiar with

each other’s way of working (e.g., Harrison et al., 2003), and have more experience in their

jobs (Ancona & Caldwell, 1990). These factors lend external networkers a strong motivational driver for their external activities to become successful. Indicating that STMT stability leads to a positive influence on both networking forms. Joshi, Pandey & Han (2009) added that group members that are highly tenured possess unique organizational knowledge and therefore have gained the technical competence, further increasing the successful execution of both external networking activities.

So, based on the external characteristics of STMT stability, and what it provides their external ties, I assume it is more logical that network deepening activities are better suited for stable STMTs and network broadening activities for less stable STMTs. Based on the internal aspects of the stability, and what it provides the team itself, it seems that stability is better for both forms of networking activities. However, a moderate amount of stability is needed for both networking forms to be successful as too few stability will impede the required skills to in networking activities successfully (Joshi et al., 2009). On the other hand, when the STMT becomes too stable, it isolates itself from the outside world (Katz, 1982). Therefore, a moderate level of stability is good for both network broadening and network deepening activities.

Accordingly, the hypotheses state:

H1a: Stability of STMT stability is positively related to network broadening activities.

H1b: Stability of STMT stability is positively related to network deepening activities.

External Networking Activities on Firm Financial Performance

The existing literature provides many positive outcomes when firms are engaging in

external networking activities in general. For example, researchers found that senior top

management teams are important when it comes to securing resources obtained by the relationship with their external partners (Boyd, 1990; Hillman, Cannella, & Paetzold, 2000;

Pombo & Gutiérrez, 2011), by reducing environmental uncertainty (Pfeffer, 1972; Peng & Luo, 2000; Mu et al., 2008), by taking away the necessity to double-check (Fafchamps & Minten, 2002), and by increasing reliable information obtained (Greif, 1993). Fafchamps & Minten (2002) added that social capital is especially fruitful when a firm and her partner have established mutual trust. The firm can then place and take orders without barriers as it eases contractual obligations when unexpected dilemmas come to light (Bigsten, Collier, Dercon, Fafchamps, Gauthier, Gunning & Teal, 1998). All the benefits mentioned above enhance the efficiency and productivity of the firm by reducing the transaction costs with the partner and the search costs when looking for new technological information (Williamson, 1985;

Fafchamps & Minten, 2000). External networking activities are also found to increase knowledge sharing and innovation (Ancona & Caldwell, 1992; Tsai, 2001). Additionally, Muijs, West & Ainscow (2010) found that access to new information and unique knowledge provides firms with benefits such as organizational improvement, broadening opportunities and opening up possibilities for resource and knowledge sharing. Knowledge sharing and firm innovation, in turn, have a positive influence on financial performance (Calantone, Cavusgil &

Zhao, 2002; Wang & Wang, 2012), such as sales growth (Lee, Lee & Pennings, 2001), indicating that external networking activities in general have an indirect positive effect on the firm’s financial performance. Horton, Millo, & Serafeim (2012) concluded in their study that when directors engage in external networking activities, they increase the connectedness of their social network. This connectedness with other ties increases the firm’s current, overall performance and positively affects the firm’s future performance. Finally, Kandemir, Yaprak

& Cavusgil (2006) found that firms that are able to constructively use and develop their

networks will enjoy a competitive advantage over their counterparts not effectively using their

external partners. Constructively using a network includes “selecting the right partners, coordinating between, and learning from partners” (Ziggers & Henseler 2009: 799). These studies have all found networking consequences positively affecting firm performance or indirectly affecting a firm’s financial performance in a positive way.

Direct effects of external networking activities on the firm’s financial performance are less well researched. Nevertheless, it is important that the networking theory is to be expanded with the harder side of networking consequences (Powell, 1996). Since the article of Powell several authors have responded and researched possible direct financial effects. For example, Peng & Luo (2000) researched the effects of managerial ties using survey data from China.

They found that managerial ties were significantly and positively associated to market share

and return on assets (ROA). However, they also concluded that these positive effects were not

only due to managerial ties as, in order to obtain a good financial performance, the firm also

needs to invest in quality and advertising (Peng & Luo, 2000). Li, Zhang and Fung (2006) also

conducted their research on Chinese enterprises and found in their results that investments in

social capital significantly increased the firm’s profitability and sales, and thus increasing

overall financial performance. Likewise, Larcker et al., (2013) found that firms that have boards

actively networking externally experienced a significant increase in profitability, and enjoy

greater future growth in ROA. They also found that the firm’s realized margin exceeded the

expected returns. Lastly, Watson (2007) indicated that external networking is significantly and

positively correlated in an inverted U-shape to performance appraisals such as firm survival,

and to a lesser extent with firm growth. Networking is beneficial for the many reasons

mentioned above, however, it becomes detrimental when used excessively. In that case board

member’s attention becomes overloaded, which causes them to produce lower quality strategic

advice, and have lower monitoring capabilities in the focal firm (Fich & White, 2003; Fich &

Shivdasani, 2006). All in all, research examining the direct effect of networking on a firm’s financial performance concludes that the effect is positively and significantly related.

The different forms of networking provide the firm with different benefits. Network- broadening activities provide the company with more opportunities and more diverse resources (Renzulli & Aldrich, 2005; Vissa, 2012). While network-deepening activities are more likely to ease knowledge transfer between firms (Reagans & McEvily, 2003), and to get access to their ties’ private resources providing the focal firm with personalized opportunities (Uzzi, 1999). In reality there is no firm that uses only broadening or deepening activities. In order for broad ties to stay useful and provide the focal firm with new opportunities, the firm must invest in these ties. But, by doing so, the firm has less time available towards their existing deep ties (Lazzarini & Zenger, 2007). Environmental change can also induce churn in a firm’s current network. For example, it is possible that breaking the relationship with a long-term actor turns out to be the best option for the firm (Afuah, 2000). Thus, which strategy is better remains unclear and depends on many factors.

Based on the previously mentioned benefits of both networking types, such as access to new information and opportunities, reduction of environmental uncertainty, and an increase in innovation, on the overall performance of the firm, and the positive effects of networking on financial measurements, for example, profitability, sales, ROA, and growth, firms can be assumed to increase their financial performance when engaging in external networking activities due to the benefits obtained by social and network capital. This accounts for both the deepening and the broadening strategy, as both forms of capital can likely be achieved by both strategies. The hypotheses state:

H2a: External network broadening activities are

positively related to the firm’s financial performance.

H2b: External network deepening activities are positively related to the firm’s financial performance.

The Mediating Effect of External Networking Activities

It is likely that the relationship between STMT stability and the firm’s financial performance is mediated by external networking activities. Networking members of a firm are continuously in search for new external information (Carley, 1991; Mu et al., 2008), and new potential opportunities (Porter, Woo & Campion, 2016). It increases their knowledge, influence, and ultimately firm performance (Brass, Galaskiewicz, Greve & Tsai, 2004; Wong, 2004). In order to obtain this knowledge and access to new opportunities people look towards outside information. Information that the group already possesses is not new after all.

When STMT stability is high, members have generally spent more time in their network and have established mutual trust with their connections. It also means that these networkers have a better understanding of with whom they need to collaborate and whom they should avoid (Juenke, 2005). O’Toole and Meier (2003) have found that this has a strong positive effect on outcomes. Additionally, stability enables positive feelings for the actors engaging in network deepening and broadening activities which in turn predicts future networking success (Edmondson, 1999; Huckman et al., 2009). The rest of the team also benefits from successful network attempts. After all, reeling in valuable new knowledge from a recipient is beneficial for the firm and increases overall performance (e.g., Calantone, Cavusgil & Zhao, 2002; Wang

& Wang, 2012). When performance is better, employees are less likely to leave the current firm as they have become satisfied with the work that they have achieved (Saeed, Waseem, Sikander

& Rizwan, 2014). It also increases the commitment and motivation to contribute further to the

group (e.g., Nybakk & Jensen, 2012). When these positive feelings are increased, it again

becomes more likely to successfully reel in new network contacts. Thus, it seems that success leads to more success. This, however, neglects the influence of the firm’s environment.

It is up to the group what is done with the externally obtained information after that.

They can choose to accept the information and enjoy the benefits accompanied, or they could reject the outside information as it disrupts their comfortable way of working together (Katz, 1982). The latter is most likely when a group is excessively stable. Indicating that the effects of networking on performance hold true up to a certain amount of stability. Accordingly, I hypothesize:

H3a: External network broadening activities positively mediate the relationship between the stability of STMT stability and the firm’s financial performance, such that stability increases future networking success in order to obtain

financial benefits

H3b: External network deepening activities positively mediate the relationship between the stability of STMT stability and the firm’s financial performance, such that stability increases future networking success in order to obtain

financial benefits

METHODS

Sample and Data Collection

The hypotheses are tested with a set of 165 U.S. small capital firms operating in the high

technology industry. The high technology industry is marked by its high competitiveness (Mu

et al., 2008), high risks (George, Zahra & Wood, 2002), and changeability (Huggins, 2010). It

is harder for firms in this industry to obtain profitability and have to overcome great challenges

in order to survive (Oliver & Liebeskind, 1998). Fortunately, the associated risks can be

buffered by engaging in networking activities that can provide the firm with expertise and information otherwise not available (Mu et al., 2008), granting the networking firm a competitive advantage and ensuring survivability (Tang et al., 2008). Therefore, most of the firms operating in the high-technology industry will engage in external networking activities, making it a relevant industry to investigate.

The firm networking data that is used is archival data from BoardEx, and supplemented by financial data presented about these firms in Compustat. The networks investigated are that of senior management teams and other board member, as members of these team are representatives of the firm when engaging in networking activities. The networks of each member will be summed up to represent the firm’s network. Data stemmed from the period from 1990 to 2014 with every five years as a separate window. These time periods were chosen as from around 1990 most firms implemented their financial data into Compustat. Each time window is called a wave. So, for completeness, wave 1 consists of year 1990 – 1994, wave 2 includes 1995 – 1999, wave 3 includes 2000 – 2004, wave 4 includes 2004 – 2009, and wave 5 includes 2009 – 2014. Abbreviated W1 stands for wave 1. For financial data and control variables a single wave is used. In order to measure the change in stability or the churn in the network a slope is computed. This measured the difference between two waves, so for example for wave 2 to 3. How exactly all the measurements are computed is described below.

Measures

Independent variable – stability. STMT stability can be measured by among other things the desire of the members to stay within the group (e.g., Carley, 1991; Petersen, Dietz &

Frey, 2004), which is represented by the average tenure of its members. Shepard (1956) was

the first to investigate the effects of tenure, and made this measurable by taking the average

tenure of all the group members. Later studies, for example that of Turrini et al. (2009), also used the construct of group stability by using the measurement of mean group tenure. However, networking data provided via BoardEx had many missing cases for board member’s tenure. In order to bypass this absence, the slope of the number of external networkers for the next wave divided by the current wave is used. After all, Van Vugt et al. (2004) stated in their study that group stability is determined by the entrance and exit of its members. The closer this measure is to 1.00, the more stable the group is since the past wave.

Dependent variable – firm’s financial performance. Many different tools can be used to measure a firm’s financial performance. My study will consist of a combination of accounting- based measures of profitability and stock market-based measures (Gentry & Shen, 2010). The accounting measures used are return on assets (ROA), return on equity (ROE), sales growth (SG), and net income (NI), together with Tobin’s Q as a market-based measure. These indicators are commonly used among in the field of strategic performance and business financial literature (e.g., Morash, Dröge & Vickery, 1997; Kim, Kim & An, 2003; Gentry &

Shen, 2010; Pouraghajan, Emamgholipour, Lotfollahpour & Bagheri, 2012). These measures together are proven to predict profitable investment opportunities, managerial and business performance, and future performance of a firm. Measures such as ROA, ROE, SG and NI can be directly found from the data of Compustat. Tobin’s Q has to be calculated. The formula is stated below.

Tobin’s Q: 𝐴𝑇+(𝐶𝑆𝐻𝑂∗𝑃𝑅𝐶𝐶_𝐹) −𝐶𝐸𝑄

𝐴𝑇 .

Where AT is total assets, CSHO is common shares outstanding at year-end, PRCC_F is price

close at fiscal year-end, and CEQ is the book value of common equity. The data for these

constructs are directly available in the Compustat database.

Mediator – external networking activities. This study wants to measure the change between network deepening and broadening contacts in a firm’s network. When a firm decides to invest more into her deepening contacts, simultaneously reducing time invested in her broadening contact, what is the effect of this change on the financial performance? Therefore, the change of networking ties in a certain wave to another has been measured, which are called slopes.

External networking activities itself can be measured in many different ways. For example, the amount of time spent in a relationship (Granovetter, 1973), the frequency of contact (Lin, Dayton & Greenwald, 1978) or the closeness between contacts (Marsden &

Campbell, 1984). However, since this study uses two different constructs, a distinction had to be made. Network broadening activities are measured by the slope of the total number of recipients (frequency), while network deepening activities are measured by the slope of the total number of recipients (frequency) times the slope of the strength (number of connections) per recipients (on average). This way the distinction between having many ties in general, and the average strength of these ties can be measured. When a firm focusses more on adding new ties to the network the measure for network broadening will be higher. On the other hand, if a firm chooses to invest more time into its existing contacts, the strength of ties increases faster than the total amount of times leading to an increase in the deepening measure.

Control Variables

I controlled for several variables that were not directly of interest to this study, but may

be related to the dependent variable of firm’s financial performance which might give

alternative justification for the findings presented.

TMT size. As found by Dalton, Daily, Johnson & Ellstrand (1999), the size of the board is positively related to the financial performance of the firm. Data from Boardex is used in order to measure the TMT size of the different firms. In this data only the networking board members are taken into account. Meaning that a firm might have a larger board in reality, but non- networking members are not of interest to my study.

Firm size. The size of a firm is said to significantly, positively influence financial health (e.g., Abor, 2005; Papadogonas, 2007). Firm size was calculated by the common proxy of the logarithm of the total assets of the firm (Uyar, 2009). Total assets are directly derived from the data presented in Compustat.

Firm age. Research has indicated that firm age has an effect on financial performance.

Studies state that the effect can be a U-shape or non-linear, where performance begins when the newness fades but only up to a certain point (e.g., Coad, Segarra & Teruel, 2013; Akben- Selcuk, 2016; Coad, Holm, Krafft & Quatraro, 2018). Firm age was calculated for every wave by the amount of years that the firms has Compustat data, plus one year because it is very likely that the firms started their operations already before putting data into Compustat.

R&D intensity. R&D intensity is said to positively influence the firm’s financial performance. For example, firms that engage in R&D activities generate around 8% more profits and more sales than firms that neglect R&D investments (Rafiq, Salim & Smyth, 2015).

R&D intensity can be calculated by dividing research and development expenses (XRD) by total assets (AT), and is calculated with the data presented in Compustat.

Market value. Market value also influences the financial performance of the firm,

because companies with a high market value generally benefit from increased growth and a

better future performance (Selvam, Gayathri, Vasanth, Lingaraja & Marxiaoli, 2016). Market

value was controlled for with two different measures, directly via Compustat through

MKVALT and calculated separately by multiplying CSHO by PRCC_F. There was a small difference in market value in the earlier waves when computing the data, therefore, both are taken into consideration at first. In a later stadium will be defined which one to use.

RESULTS

Data Analysis

Table 1 indicates the total number of data available for all the firms for each available wave. The table shows that the data from wave 1 was incomplete as Compustat did not have the necessary data available. It is likely that most of the firms had the data available, but did not report it in Compustat until wave 2. Nevertheless, because a relatively large part of the data is unavailable, the first wave is not included in the analyses.

[INSERT TABLE 1 HERE]

The residual data still contained outliers. For every variable the data was computed visually in a histogram, outliers were then detected by filtering data that was 3.29*SD above the mean (Tabachnick & Fidell, 2013). The IV of stability follows a Poisson distribution.

In order to see whether the outliers would affect the results I ran simple mediation regressions

via PROCESS by Andrew F. Hayes in SPSS, one with and one without the outliers. With

outliers the total effect model with Tobin’s Q as DV, and network broadening as mediator gave

R

= .45, F(4,119) = 7.85, p < .001, while without gave R ² = .47, F(4,117) = 8.14, p < .001. The

results show nearly similar results and therefore, the stability outliers remained within the

dataset. For the DV, most of the outliers did not seem excessive outliers, mostly ranging only

<1 SD above the original measurement. The differences with and without were tested.

Removing Tobin’s Q outliers this way did not have a big influence the outcome as the new results, including stability outliers, gave R

= .47, F(4,117) = 8.22, p < .001. ROA gave with outliers R

= .21, F(4,114) = 1.31, p > .05, and without R

= .24, F(4,113) = 1.80, p > .05. ROI gave R

= .17, F(4,114) = .92, p > .05, and R

= .14, F(4,111) = .58, p > .05 respectively. ROE, however, gave with outliers an effect of R

= .29, F(5,110) = 2.04, p > .05 for the total model, and R

= .32, F(5,106) = 2.47, p < .05 without outliers. Thus, only for the DV variable ROE the model without outliers will be used. For the mediator of external networking activities there were some extreme outliers. For network broadening the formula M + (3.29*SD) = 4.50, which meant that two cases (with value 6.64 and 8.67) were excluded. For network deepening the formula gave 5.56, therefore two cases (with value 7.14 and 13.33) were removed.

In order to test the mediating effect of networking on the relationship between stability and the firm’s financial performance over a certain period of time, different sequences of waves for the variables were tested. The only sequence that was found significant was that of the IV wave 2 – 3, with a mediator from wave 4 – 5, and the DV of wave 5. This indicates that, without considering other factors, STMT stability needs around 10 years in order to benefit from the organizational expertise possessed by high tenured individuals to influence their external networking activities. A sequence with a five year window between stability and networking proved insignificant.

For stability, the slope from wave 2 – 3, there were N = 137 cases. However, there was

little overlap in the missing cases. For example, the cases that were empty in the stability slope

did give data for ROE, and the other way around. Additionally, there were also several double

cases of data included due to mergers or takeovers. For the companies that were taken over, this

resulted in no data for later waves, meaning that data for stability W2-3 was available, but the

outcome on, for example, ROE could not be tested. These cases were excluded in the results by

SPSS. Therefore, the amount of data used in this study is N = 119. Before heading into the mediation regressions, the correlation table was computed with the available data. Most of the data did not follow a normal distribution. Therefore, a Spearman correlation test was conducted.

The results are depicted in table 2.

[INSERT TABLE 2 HERE]

For the regression, every variable’s output, except the DV scores, were standardized in order to account for the different scales of measurement. It is preferred for the types of measurement used in this study to use standardized effect sizes over simple effects, because

“conceptually similar effects needed to be compared using different units of measurement”

(Baguley, 2009: 17). A 90% confidence interval is used due to the relatively small sample size (N = 119) compared to the overall population of U.S. firms operating in the high technology industry. This way it was easier to conclude that the effects are different from zero, enabling to interpret possible effects more clearly (Hair, Black, Babin & Anderson, 2009). However, the downside of this method is that it leaves more space to interpret results wrongly (Hair et al., 2009).

All of the control variables were tested individually on significance towards the

mediation effect. First of all, TMT tenure was dropped because of a very low amount of

available data (N = 55). From the other control variables, the analysis only resulted in significant

effects for several financial performance measures when using R&D intensity, market value,

and firm size for both network broadening and deepening activities. Not all the control variables

were significant on all the financial performance measures. The other control variables did not

show significant results. However, when combined many of the significant effects disappeared.

This is probably also due to the very small sample size, where the population is to a lesser extent represented by the sample size than would have been with a larger sample (Delice, 2010). Only TMT size was once significant for DV = Tobin’s Q with mediator of network broadening activities. However, since this only occurred once, the control variable is not taken into account for the rest of the study. Market value was computed in two different ways. Both control variables gave nearly the same outcomes and significance. For the sake of simplicity, only one is used in the following results.

Regression Analysis

Only the data from the significant sequence were included in the following tables.

Several mediation regressions were conducted using model four of Andrew F. Hayes’

PROCESS in SPSS. These outcome can be found for network broadening in table 3 and for network deepening in table 4.

[INSERT TABLE 3 HERE]

[INSERT TABLE 4 HERE]

The effect of stability on external networking activities. STMT stability positively

predicts external network broadening activities. The results differ slightly between the

dependent variables. NI (B = .26, p < .05), SG (B = .27, p < .001), ROA (B = .25, p < .01),

ROE (B = .26, p < .01), ROI (B = .25, p < .01), TQ (B = .25, p < .01). However, stability is an

inversed measurement. The closer the value of the stability measurement is to 1.00, the more

stability is present. This means that the found effects still account, but occur negatively rather

than positively. The same accounts for network deepening activities. Here the results are for every DV: NI (B = .26, p < .01), SG (B = .27, p < .001), ROA (B = .25, p < .01), ROE (B = .26, p < .01), ROI (B = .25, p < .01), TQ (B = .26, p < .01). Meaning that stability significantly, negatively predicts both external networking activities. Therefore, hypothesis 1a and 1b cannot be accepted.

The effect of external networking activities on financial performance. The results found are insignificant, nevertheless the effect of external network broadening activities negatively influences the firm’s financial performance for all the DV measures. NI (B = -10.41, p > .05), SG (B = -5.10, p > .05), ROA (B = -.01, p > .05), ROE (B = -.01, p > .05), ROI (B = -.01, p >

.05), TQ (B = -.05, p > .05). The entire model, however, is significant for NI (R

= .70, F(6,112)

= 17.69, p < .001), SG (R

= .47, F(6,112) = 5.41, p < .001), ROA (R

= .35, F(6,109) = 2.51, p < .05), ROE (R

= .34, F(6,105) = 2.31, p < .001), and TQ (R

= .50, F(6,112) = 6.08, p <

.001), but is not explained by the external network broadening activities. For external network deepening, all of the firm’s financial performance variables are also insignificantly, and negatively influenced. NI (B = -10.05, p > .05), SG (B = -4.13, p > .05), ROA (B = -.01, p >

.05), ROE (B = .00, p > .05), ROI (B = -.01, p > .05), TQ (B = -.07, p > .05). The entire model is significant for NI (R

= .70, F(6,112) = 17.61, p < .001), SG (R

= .47, F(6,112) = 5.39, p <

.001), ROA (R

= .35, F(6,109) = 2.49, p < .05), ROE (R

= .34, F(6,105) = 1.65, p < .001), and TQ (R

= .50, F(6,112) = 6.15, p < .001), but is not explained by external network deepening activities. Considering the negative effect and lack of significance, both hypothesis 2a and 2b cannot be accepted.

The mediating effect of external networking activities. The results for the mediating effect are mixed concerning the different financial performance variables. For network broadening activities NI (B = 6.88, p > 0.5), SG (B = 13.19, p > 0.5), and TQ (B = -.10, p >

0.5) turned out insignificant. ROA (B = -.02, p < 0.5), ROE (B = -.01, p < 0.5), and ROI (B = -

.02, p < 0.5) gave significant results. With the inversed measurement of stability in mind, the results are all relatively small, positive effects. Network deepening activities show similar results. NI (B = 6.88, p > 0.5), SG (B = 13.19, p > 0.5), and TQ (B = -.10, p > 0.5) were insignificant, while ROA (B = -.02, p < 0.5), ROE (B = -.01, p < 0.5), and ROI (B = -.02, p <

0.5) proved significant. Again the results are relatively small and positive, considering the inversed measurement. In conclusion, hypothesis 3a and 3b are both partly true. The relationship between STMT stability and the firm’s financial performance is mediated by external network broadening and deepening activities for three of the six financial variables.

These are ROA, ROE, and ROI. Therefore, hypothesis 3a and 3b can be accepted, as the mediation effect does increase financial performance.

DISCUSSION

The goal of my study was to examine the mediating effect of external network broadening and deepening activities between the relationship of STMT stability and the firm’s financial performance. Results show that there is a significant negative effect of STMT stability on both external network broadening activities and external network deepening activities. For the entire mediation model, both broadening as deepening activities were found to mediate the relationship between STMT stability and financial performance but only for ROA, ROE, and ROI. Next the theoretical and practical implications will be discussed.

Theoretical Implications

Existing literature has found a positive relationship between STMT stability and firm

performance (e.g., Henderson et al., 2006; Heavey & Simsek, 2015). However, the focus of

STMT stability is mainly on the internal effects of the firm causing the external implications

are often overlooked. This study contributes to the existing literature by examining the mediating effect of two different external network activities.

The negative effect of stability on external networking activities. This effect is contradicting with the existing literature. For example, Joshi et al. (2009) stated in their study that STMT stability leads to the possession of necessary skills and organizational knowledge in order to successfully network externally. One possible explanation might be that most of the research that includes STMT stability and external factors is exploratory, conducted with qualitative data (Marone, 2010), and this study uses quantitative data. This opens up space for a new kind of theory where STMT stability negatively influences external networking, both broadening and deepening. Since the effect was relatively large it interesting to deduct the underlying reasons. According to existing research, having a stable STMT increases familiarity with another and each other’s way of working, and external network broadening and deepening activities both bring in new information which disrupts the stability (Katz, 1982). Therefore, in order to keep their STMT stable, they engage in less external networking activities (Juenke, 2005).

The positive mediating effect of external networking activities. This finding, even though

relatively small, contributes to the expected effect of external network broadening and

deepening activities as mediator. This contributes to the existing literature by addressing the

importance of external factors for internal processes. However, the results only hold for ROA,

ROE and ROI. Inconsistence in the different performance measures may be caused by the small

sample size, and that it does not represent the population (Delice, 2010). Additionally, NI, SG,

and TQ are likely affected by other aspects that could not be controlled for, again, due to the

small sample size.

Practical Implications

First of all, noticeable is that when the results of table 3 and 4 are compared there is little difference between the outcomes of network broadening activities and network deepening activities. This finding is not unexpected. As stated before, networking is a balancing act as it is very dependent on the firm’s environment (Afuah, 2000). It is very likely that both options can provide the firm with financial performance when executed under the right conditions. The members of the firm’s STMT often engage in networking activities and are therefore expected to know what they are doing when they have gained some experience (Joshi et al., 2009). This can result in positive outcomes for both networking strategies. Networkers still have to carefully consider which contacts to maintain, as their attention and available time is limited (Lazzarini

& Zenger, 2007).

The negative effect of stability on external networking activities. The results gave no difference for network broadening and deepening, meaning that there is no specific level of stability required for one form of networking. If the firm wishes to engage in external network broadening and deepening activities it is advised that the STMT stability is kept on a moderate level, since Katz (1982) stated in his study that too much stability is detrimental for networking.

Too few stability is also bad, as it hinders the creation of transactive memory (Moreland &

Myaskovsky, 2000), and mutual trust (Putnam, 2000) among STMT members. Even though a quadratic regression in SPSS gave insignificant results between the relationship between STMT stability and networking, according to the literature it seems logical that it takes the form of an inverted U-shape. Firms and her STMT members should therefore carefully balance their amount of stability.

The positive mediating effect of external networking activities. It is important for firms

to keep a moderate amount of STMT stability in the firm, in order to keep them engaging in

external networking activities, both broadening and deepening if the desired outcome is to

improve their ROA, ROE or ROI. The results unfortunately gave no different results between broadening and deepening activities. Therefore, the advice is to keep mindful of the external environment in order to engage in the specific form of networking activities that best suit the organizational goals.

LIMITATIONS AND FUTURE RESEARCH

Like any other, there are some limitations to my research. First, my study ignores the effects of the external environment, or trends in the industry, on the decision of firms to engage in network broadening or network deepening activities. This effect is also driven by the other firms in the industry or sector. For example, when an industry-leader adopts more broad connections into her network, more opportunities become available to the other networking firms, leading towards a trend in network broadening activities (Kranton, 1996). Additionally, firms also have to handle according to the interest of among other things shareholders or investors. When these external agents interact, they can influence the external networking activities of firms in order to achieve the desired goals (DiMaggio & Power, 1983).

Another limitation is the measurement of stability. The provided data lacked sufficient information about STMT tenure. This lead to a different use of measurement for stability, namely the slope of the amount of networkers next wave divided by the amount of networkers in the current wave. One major pitfall of this measurement is that it does not solely capture stability. What happened in the dataset is that the firms that survived grew over the years.

Meaning that STMT and her networkers also grew, causing for very unstable teams. However,

this instability still significantly predicted external networking activities. The most obvious

reason for this is that an increase in STMT causes more external networking. Future research

should repeat the conducted regressions with STMT tenure as stability measure, or a different measure all together.

Lastly, the data sample used in this study is of a relatively small size considering the total population of high-technology firms operating in the U.S. Only 119 firms were used in the mediation regression. The small sample size limits the generalizability of the results (Delice, 2010), making the conducted regressions rather speculative. In order to raise generalizability and repeatability, future research replicate the study with a larger data set. The residual firms luckily offered all the desired data that was necessary. This provided me with the detailed information needed to test several quantitative measures for financial performance, stability and networking strategies. As mentioned before, the control variables also may have been insignificant because of the smaller sample size. Additionally, the gathered results are from U.S. firms in the high-technology industry only. It is possible that these findings cannot be generalized to other industries and/or cultures. Future research should further investigate whether the results differ in different industries or cultures, and whether the significance of certain measurements hold in a different industry or country.

All the limitations could be a possible cause of the negative results on hypothesis 1 and

2 which are contradicting the existing literature at hand. Future research should replicate this

study with the current limitations accounted for, in order to see whether the negative results

remain. Nevertheless, by examining the mediating effect of external networking activities

between STMT stability firm and the firm’s financial performance, this paper emphasizes that

more quantitative research on this topic is required. For hypothesis 3, it is fruitful to replicate

the study in the same setting with a larger sample in order to test whether the mediation remains

and whether the effect coefficient changes.

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