NETWORKING STRATEGIES AND FIRM PERFORMANCE: THE MODERATING ROLE OF FIRM LIFE CYCLE

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NETWORKING STRATEGIES AND FIRM PERFORMANCE: THE

MODERATING ROLE OF FIRM LIFE CYCLE

Master thesis, Msc Human Resource Management University of Groningen, Faculty of Economics and Business

June 28, 2020 KARLIE ZONDAG Student number: S2966646 Nettelbosje 2 9747AE Groningen tel.: +31(0)50-3634624 e-mail: k.y.zondag@student.rug.nl Supervisor/field of study Y. Yuan Co-assessed by P. van der Meer

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ABSTRACT

Networking activities are important for firm performance as they allow access to resources to achieve competitive advantages. Networking involves a dynamic aspect, arising the importance of researching network dynamics. This quantitative study uses linear regression methods to explore the relationship between networking strategies on firm performance and the extent to which firm’s life cycle stages influence this relationship. Findings show that networking strategies have a negative relationship with firm performance, however firm life cycle stage mitigates this negative effect. Firms benefit differently from networking for certain stages of the life cycle and managers should allocate networking resources appropriately. Future research should investigate non-relationships between networking and firm performance. Taking into account that excessive networking behaviour might be counterproductive and networking efforts should be aimed towards the ‘right’ ties because some might be more important than others.

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Defined as ‘the process of sharing contacts and obtaining resources’ (Sawyerr, Mcgee, & Peterson, 2003), networking ties between organisations are everywhere. In 2004 alone, networking activities such as meetings, conventions as well as the exhibition industry have generated in total $122 billion in total direct spending, which ranks spending in this category to contribute as 29th

largest to gross national product (Ingram & Morris, 2007).

Networking activities are important because they allow for access to resources held by others that firms alone are not able to develop or acquire (Hite and Hesterly, 2001; Larson, 1992; Powell, Koput, & Smith-Doerr, 1996). They provide firms of valuable resources, competencies, information and status (Davis & Eisenhardt, 2011) to achieve competitive advantages over others (Ostgaard & Birley, 1996). External networking activities that a firm engages in are found to have a critical influence on firm performance (Hallen & Eisenhardt, 2012; Kalm, 2012; Sawyerr & Mcgee, 2003; Watson, 2007).

Earlier literature focuses mainly on the static perspectives of network ties on performance (Borgatti & Foster, 2003; Ostgaard & Birley, 1996; Watson, 2007), using concepts as diversity (Lungeanu & Contractor, 2014; Martinez & Aldrich, 2011) and tie strength (Gulati & Zaheer, 2000). Yet networking is of a dynamic nature, which has been largely overlooked (Baum & Rowley, 2008; Burt, 2000). Networks and networking are essentially different concepts, (Shaw, 1997) because networks are about interconnections between ties (Neergaard, 2005) at a given point in time and networking is associated with behaviour over time (Burt, 2000). Strategies that firms use to form ties (Hallen & Eisenhardt, 2012) and manage existing ties over time (Vissa, 2012) are underdeveloped, emerging the importance of exploring the link between dynamic networking strategies and firm performance.

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reconnecting with past partners, current partner’s ties (Vissa, 2012) and new ties (Ahuja, 2000) or end relationships with existing ones (Chan & Makino, 2007; Reuer & Zollo, 2005). These networking strategies have been identified and categorized into network-broadening actions, a networking strategy that concerns increasing the range of new network contacts and networking-deepening actions, a networking strategy that pertains to managing existing contacts (Vissa, 2012). Broadening and strengthening their network allows firms to obtain resources, to upgrade its capabilities in all aspects (Baoshan, G., Hisrich, R. D., Dong, 2009).

Each networking strategy is expected influence firms differently. To illustrate, firms growing the range of collaborative efforts (broadening strategy) results in getting placed into more information-rich positions, leading to information advantages (Powell, Koput, & Smith-Doerr, 1996) and expansion of the number of ties to access finance (Shane & Stuart, 2000). Broadening the network with more distinct ties, helps to broaden knowledge and skills (Hite & Hesterly, 2001). On the other hand, deepening strategy, increasing cohesiveness of the network and building stronger relationships with existing network partners leads to solidarity, commitment advantages and higher trust over time (Martinez & Aldrich, 2011). Therefore, both networking strategies are expected to positively influence firm performance.

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than in other stages. As firms grow and mature, firms face increased resource acquisition challenges such as uncertainty, access and availability (Hite & Hesterly, 2001). They need to constantly adapt to the environment in order to survive (Tian, Han & Zhang, 2015). Firms require investments and extensive market information to reinvest or innovate (Frielinghaus, Mostert & Firer, 2005). Broadening strategy, adding ties increases innovative capabilities and performance (Baum et al., 2000; Watson, 2007. At the decline of a business, the main goal is to decide on withdrawal or recovery (Dickinson, 2011) and extra funding is crucial. Firms often need to renegotiate debt, or look for recapitalization (Frielinghaus et al., 2005), hence network broadening is positively related to funding chances (Steier & Greenwood, 2000).

The network literature has drastically expanded over the last years (Borgatti & Foster, 2003). Despite increasing consensus that networking is important, specific effects of networking on organisational performance remain unclear (Ahuja, 2000), especially from a dynamic perspective. Scholars do not agree on the type of social structures (Ahuja, 2000) or networking strategy that firms profit most from at different stages of a firm’s life cycle (Hite & Hesterly, 2001). A longitudinal approach allows for a better understanding of whether and when networking influence performance (Hoang & Antoncic, 2003). The lack of research of this topic is mainly because networking literature is relatively young and had not established legitimacy until recently (Borgatti & Foster, 2003). Network outcomes, when combined with theoretical insights from findings on network dynamics can be far richer and more nuanced theoretically (Neergaard, 2005).

The aim of the research is to further investigate a firm’s networking strategies and its influence on firm performance and how these strategies have an impact at different stages of the firm’s life. To date, there is little empirical evidence of the association between these concepts. Giving rise to the following research question:

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THEORETICAL FRAMEWORK AND HYPOTHESES

Networking strategies

Network ties that firms interact with determine the nature and scope of resources that is available through networking (Zhao & Aram, 1995). Therefore, it is important to understand how these relationships are built through looking at the networking strategies firms use to establish and maintain their networks. Networking strategies, (Vissa, 2012) or negotiating strategies (Hallen & Eisenhardt, 2012) explain the variation in networking actions. A networking strategy is used as a dynamic mechanism to shape selection of new ties and manage existing exchange partners, for which, two concepts are identified; network-broadening and network-deepening strategy (Vissa, 2012).

Network broadening Strategy. Network broadening activities are concerned with the number of people the actor reaches out to (Vissa, 2012). Throughout this study, network broadening activities refer to networking efforts by firms that aim towards creating a broader network over time.

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activities in turn lead to more new economic exchanges as firms become less dependent on others (Vissa, 2012). Given this, firms have a broader network base, and increased chances to reach the right network ties that can provide firms with the needed resources at a given point in time.

Network deepening Strategy. Network deepening activities explain the extent to which existing ties are strengthened and managed (Vissa, 2012). Throughout this study, network deepening actions pertain to efforts made to create a stronger relationship with the current network partners. It reflects the activities that firms engage in order to keep and manage existing ties (Vissa, 2012).

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resources from going lost as there is a natural tendency for relationships to diminish over time (Burt, 2000). Network deepening actions can further be measured through time-based interaction pacing, increasing the duration of the relationship, and relational embedding, including social aspects to relationships with current network partners (Vissa, 2012).

Networking Strategies and Firm Performance

Network Broadening and Firm Performance. The relationship between network broadening activities and its advantages have received previous attention throughout the literature. As firms increase the range of the network relationships to draw from, they allow themselves greater access to instrumental resources in comparison to drawing contacts from limited groups (Zhao & Aram, 1995). Broadening your network over time leads to wider-ranging information sources, more alternative points of view (Martinez & Aldrich, 2011), increasing knowledge from potential markets, innovations, business locations, sources of capital, investment opportunities (Burt, 2005; Davidsson & Honig, 2003; Granovetter, 1973). Next to that, broadening the firm’s network, is positively related to funding chances of firms (Steier & Greenwood, 2000) and better firm performance (Florin, Lubatkin & Schulze., 2003). Adding new ties continues to move firms in more central network positions and helps companies in later network formation as well as performance (Ozcan & Eisenhardt, 2009; Powell, Koput, & Smith-Doerr, 1996).

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claim, Omze, Ruer & Gulati (2013) find that if a firm is perceived to have too many and unnecessary ties to venture capital firms, the relationship with subsequent alliance formation was found to be negative. This can be harmful for both missing out on potential investors, but also on other important potential network ties, therefore putting firm performance at risk. However, this evidence is only circumstantial and therefore expecting network broadening to have a positive relationship with firm performance.

H1: Network broadening has a positive influence on firm performance

Network Deepening and Firm Performance. The relationship between network deepening activities and firm performance has not been researched before. Prior research focus on tie strength (Granovetter, 1973) and network intensity (Watson, 2007) from a static point of view. Strengthening relationships with network ties can provide benefits such as stronger advantages (Larson, 1992), high-quality information sharing (Rowley et al., 2000) and solidarity and commitment (Martinez & Aldrich, 2011). The stronger the relationship with network ties, the more trustworthy they become, therefore encouraging the resource exchange amongst each other, decreasing the likelihood of performing unethical behaviour (Baoshan, Hisrich & Dong, 2009), and reducing opportunistic behaviour (Ahuja, 2000). More trust leads to a deeper relationship with network ties (Hite, 2003) and eventually positively influences firm performance. By engaging in network deepening activities over time, actions by firms and network become predictable as well as mutually acceptable (Powell, 1990). Expectations reduce the level of transaction costs that come from monitoring and renegotiating (Jones et al., 1997), and as a result, firm performance will increase.

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profitable relationships with other viable partners (Gulati, Nohria & Zaheer., 2000). In the alliance portfolio context, strengthening relationships with only a few ties may negatively influence the firm performance due to a loss of flexibility and not responding to market trends appropriately (Capaldo, 2007). However, this loss of flexibility does not necessarily have to be true for all firms, therefore expecting a positive relationship between network deepening and firm performance.

H2: Network deepening has a positive influence on firm performance

Firm life cycle stage

Networking strategies are costly as they require resources (Ingram & Morris, 2007). Choosing to pursue a network broadening strategy means that firms are not engaging in network deepening. Different strategic needs over the firm’s lifetime, might influence the extent to which firms benefit from networking to access ties and draw out capabilities and resources (Borgatti & Foster, 2003; Podolny, 1994; Ozcan & Eisenhardt, 2009), consequently, the need for vast or stronger interfirm relationships changes over time. The life cycle approach is introduced as a way to explain when firms benefit most from networking and alliances (Hwang & Park, 2007).

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The introduction stage refers to when the firm is legally founded (Gartner, Bird & Starr, 1992; Gartner and Bush, 1999), and the main strategic goal is organisational survival (Hite & Hesterly, 2001). This stage is characterized with high degrees of uncertainty (Gartner et al., 1992). Capital is needed for R&D investments to launch products or services and raising capital is a challenge (Neergaard, 2005) and thus firms are more dependent on external networks for resources, capabilities and investment (Jarillo, 1989). Network ties weaken the notion of liabilities of newness and smallness during new venture creation (Hite & Hesterly, 2001; Larson, 1992; Zhang & White, 2016). Establishing trust from network ties to increase the willingness of sharing these needed resources is important (Vissa, 2012). Network deepening strategy, increasing frequency of contact, leads to emotional commitment advantages, resource sharing and higher trust (Martinez & Aldrich, 2011).

Because firms move from one stage to the other, extra resources are needed to support business growth (Hwang & Park, 2007). This growth stage is characterized by increasing competition (Yoo, Lee & Park), rapid sales growth, distinctive competences and product diversification (Miller & Friesen, 1984). The strategic goal is to grow beyond survival (Churchill and Lewis, 1983). A broader scope of resources than in the introduction stage is needed because of resource acquisition challenges such as uncertainty, access and availability (Hite & Hesterly, 2001). Network broadening strategy is therefore found to be relevant during growth (Steier & Greenwood, 2000; Vissa, 2012; Watson, 2007). On the contrary, Baoshan, Hisrich & Dong (2009) argue that building stronger network ties, thus network deepening is actually crucial for the growth of the firm.

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broadening is crucial in the later stages of a venture’s life cycle (Martinez & Aldrich, 2011). Next to that, increasing the number of different capital networks positively relates to higher chances of acquiring funds at later stages (Steier and Greenwood, 2000).

The shake-out stage is characterized by decreasing growth and profitability, intense competition and cost reduction (Dietrich & Kraft, 2012). The strategic goal for firms in shake-out is to adapt and reinvest in facilities, markets technologies to prevent going out of business and return to growth or maturity (Tian, Han & Zhang, 2015). External networks can provide such new ideas and advice (Birley, 1987). Networking behaviour to acquire those resources and knowledge (Macpherson, Jones & Zhang, 2004) leads to better adaptation to a complex environment, boosts innovative output (Ahuja, 2000; Powell et al., 1996) and stimulates co-creation (Miles & Snow; Powell et al, 1996). Broadening by adding more exchange partners increases innovative performance to outperform competition (Baum et al., 2000; Watson, 2007.

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H3: Network broadening activities have a more positive influence on firm performance in the growth, maturity, shakeout and decline stage than in the introduction stage

H4: Network deepening activities have a more positive influence on firm performance in the introduction stage than in growth, maturity, shakeout and decline

Conceptual model

Arguing that that firm life cycle stage moderates the relationship between networking strategies and firm performance, expecting network broadening activities to have a more positive effect on firm performance in the growth stage, maturity stage, shake-out stage and decline stage. On the other hand, network deepening actions are expected to have a more positive effect on firm performance in the introduction stage. These expectations are conceptualized below:

Figure 1. Conceptual model

METHODS

Data and Sample

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acquired from annual data provided by CompuSTAT. Data is collected on a firm-level and captures 15 years of data separated in time periods of 5 years, referred to as waves, from 1995 to 20101_.

Networking activity is an ongoing process and product of two waves. The independent variable (IV) is measured as the change between wave 2 (1995-1999) and wave 3 (2000-2004). The moderator of life cycle stage and control variables are also from wave 3. The dependent variable of firm performance was sourced from wave 4 (2005-2010).

Measures

Networking activities. There are no distinct operationalizations for measuring networking strategies as deepening and broadening (Vissa, 2012). The dynamics between wave 2 and 3 are used to capture both strategies as follows. The network broadening strategy is measured as the firm’s expansion of connected firms in two ways: (1) the growth of new contacts is captured by the slope of the total number of ties; (2) the growth of new distinct contacts as captured by the slope of the total number of distinct recipients. The network deepening strategy is measured as the increase in strength with connections in two ways: (1) the increase of connection frequency, (i.e. how many directors are connected) with connected firms, operationalized as the slope of the sum of recipients x tie strength (number of ties per recipient); and (2) the increase of duration with connections (i.e. how long the connection with the connected firm lasts), operationalized by the slope of sum of recipients x duration (length of ties per recipient).

Firm performance. Firm performance is a multidimensional construct, consisting of different elements (Richard, Devinney & Johnson, 2009). Firm performance is measured at wave 4 using the most common indicators from strategy and accounting literature (Gentry & Shen, 2010). The following indicators are used: (1) Net Income data is taken directly from CompuSTAT

1_{The original dataset consisted of 5 waves between 1990 and 2014. Wave 1 is dropped due to lack of data and}

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database. (2) Sales growth is computed using the variable SALE = (Sales/Turnover). Growth is captured by calculating the slope of the average sales growth from one wave to another. (3) Return

on Assets is calculated using the formula ROA = (NI/AT). Where NI refers to Net income and AT

to total assets. (4) Return on Equity is computed using the formula ROE = (NI/CHSO x PRCC_F).

CSHO stands for Common Shares outstanding, and PRCC_F is company price close at fiscal year

end. (5) Return on Investment illustrates how well companies are able to transform investments into a profitable outcome relative to the cost of investment (Zamfir & Manea, & Ionescu, 2016). The formula used for ROI = NI/ICAPT. Where ICAPT refers to total invested capital. (6) Tobin’s

Q. A crucial accounting profitability measurement (Aliabadi, Dorestani & Balsara, 2013) which

stands for the ratio firm’s market value and replacement costs of capital stock as Q (Brainard & Tobin, 1968). The database does not contain readily information about Tobin Q and is therefore computed using the formula: Tobin’s Q = [AT + CSHO x PRCC_F)-CEQ/AT] as provided by CompuSTAT. CEQ refers to book value of common equity.

Firm Life Cycle Stage. Firm life cycle stage is used to understand the phases firms go through during their life. It is important to take into account that firm life cycle stages are not necessarily sequential and can be cyclical in nature (Dickinson, 2011). Next to that, a firm can enter the decline stage regardless of firm age (Jaafar & Halim, 2016) and can be preceded by any of the previous stages (Dickinson, 2011).

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firm’s assigned a 1 are classified in the introduction phase and firms assigned a 5 in the decline stage.

Control variables. Several controls at wave 3 are included to see if they potentially relate to networking and firm performance. In particular those that might influence firm performance. The following variables are accounted for: (1) firm size is measured using the logarithm of total assets according to Uyar (2009). (2) firm age is computed using the first year that financial data is found in CompuSTAT + 1 year. (3) R&D intensity is used to control the relationship as there are contradicting findings to the relationship between R&D and financial performance (Yoo, Lee & Park, 2019) and measured by the formula RD = XRD/AT. Where XRD refers to research and development expenses. (4) Market value is expected to influence future performance due to its correlation with historical profitability, firm growth levels and future performance (Selvam, Gayathri, Vasanth, Lingaraja & Maxiaoli, 2016). Using the MKVALT variable as directly provided by CompuSTAT database and is expressed in millions of US dollars. (5) TMT size is controlled for, because in times of complex decision making and non-routine environments, such as the high-tech industry, larger teams are more likely to yield higher performance (Hoffman et al., 1997). TMT size is retrieved from BoardEx. (6) Performance indicator wave 3. We control for the influence of the dependent variable wave 3 on wave 4 to address endogeneity issues.

RESULTS

Data analysis

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entire U.S. firms operating in the technology industry, a 90% confidence interval level is used to be able to interpret results (Hair, Black, Babin & Anderson, 2009). However, limiting the ability to generalize the research outcomes (Delice, 2010). The final sample consists of 180 cases. On average, these firms grew their number of ties by 1.56 and their distinct contacts by 1.60. Next to that, connection frequency increases on average by 2.22 years and the duration of the relationship increases by 1.62 years.

Correlations. The complete correlations table can be found in Table B1.

Moderation. The moderator Life Cycle Stage is a multiple categorical variable. Table C1 presents descriptive statistics of the firm performance indicators at different values of the moderator.

Regression analysis

Models are tested including all control variables. Due to the small sample size of this study, control variables that are found to be non-significant are removed to increase the parsimoniousness of the model. Moderation analysis yields N=133 valid cases. A detailed overview of the results is attached in Tables D1 to D4.

The main effect of network broadening on firm performance. (1) Network broadening: growth of new contacts. Table D1 illustrates that for performance indicators Net Income, Sales growth and Tobin’s Q, we find no support for this relationship. However, growth of new contacts, has a significant negative relationship with ROA (b=-0.02, p< 0.05), ROE (b=0.02, p<0.1), and

ROI (b=-0.05, p<0.05). (2) Network broadening: growth of number of distinct recipients. As

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The main effect of network deepening on firm performance. (1) Network deepening: increasing connection frequency. Table D3, shows that for performance Net Income, Sales growth and Tobin’s Q, we find no support for the relationship between the increase of connection frequency and these indicators. The increase of connection frequency has a significant negative relationship on ROA (b=-0.02, p<0.01), ROE (b=-0.04, p<0.00) and ROI (b=-0.05, p<0.02). (2) Network deepening: increase duration of relationship. Table D4, shows that for performance Net Income, Sales growth, ROE and Tobin’s Q, we find no support for the relationship between the increase of duration of the relationship and these indicators. The increase of the duration of the relationship has a significant negative relationship with ROA (b=-0.02, p<0.1) and (b=-0.03,

p<0.10).

H2 is not supported as we find no significant positive relationship between network deepening strategy on any of the performance indicators. Yet a negative relationship was found between networking deepening strategy and several corporate performance measures.

The moderating effect of life cycle stage on network broadening and firm performance. We find life cycle moderates the relationship between network broadening and firm performance for some of the indicators but not the others. For Net income, Sales growth, and Tobin’s Q no significant interaction was found. A detailed overview of results can be found in Table D1 and D2. Figures E1 to E3 illustrate the significant interaction effect between network broadening and life cycle stages for performance indicators ROA, ROE and ROI.

(1) Network broadening: growth of new contacts. There is a significant interaction effect between networking broadening; growth of new contacts and life cycle stages; growth (b=0.14,

p<0.00), maturity (b=0.12, p<0.00), shakeout (b=0.12, p<0.00) and decline (b=0.12, p<0.00) for

ROA when compared to network broadening in the introduction stage. There is also a significant interaction effect between network broadening; adding new contacts and life cycle stages growth

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introduction stage. Next to that, we find a significant interaction effect between network broadening and life cycle stages; growth (b=0.17, p<0.00), maturity (b=0.14, p<0.00), shakeout

(b=0.15, p<0.05) and decline (b=0.14, p<0.05) for ROI compared to networking at the

introduction stage. We see that life cycle stage mitigates the negative relationship between the growth of new contacts and performance at some life cycle stages. Network broadening, the growth of new contacts has a steep negative slope at the introduction stage for ROA, ROI and ROI (see Figure E1 till E3). The negative effect decreases from negative to non-negative as firms move from introduction to growth. However, the negative effect increases again from growth to shake down and decline.

(2) Network broadening: increase of number of distinct recipients. We find no significant interaction effect between increase of the nr of distinct recipients and life cycle stages for performance indicators Net Income, Sales growth, ROA, ROE, ROI and Tobin’s Q. Even though the overall models for NI (R2_{=0.48, F(11,118)=9.81, p<0.00), ROA (R}2_{=0.16, F(11, 120)=2.02,}

p<0.05) and Tobin’s Q (R2_{=0.55, F(12, 117)=12.09, p<0.00) are significant, they do not explain}

the interaction through life cycle stages.

H3 is supported to the extent that we find a general pattern for the relationship between network broadening and firm performance from negative to less negative for the later stages compared to introduction. Therefore, an overall positive moderation effect of life cycle stage on the relationship between networking broadening and firm performance is supported. This effect is indeed more positive for the later stages of the firm’s life cycle compared to the introduction, however detailed deviations per stage are present.

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E7 illustrate the significant interaction effect between network deepening and life cycle stage for performance indicators ROA, ROE and ROI.

(1) Network deepening: increasing connection frequency. We find a significant interaction effect between networking and life cycle stages; growth (b=0.14, p<0.00), maturity (b=0.12,

p<0.00), shakeout (b=0.12, p<0.00) and decline (b=0.12, p<0.00) for ROA when compared to

increasing connection frequency in the introduction stage. There is also a significant interaction effect between networking and life cycle stages; growth (b=0.11, p<0.05) and maturity (b=0.11,

p<0.05) for ROE when compared to networking in the introduction stage. Next to that, we find a

significant interaction effect between increasing connection frequency and life cycle stages; growth (b=0.17, p<0.00), maturity (b=0.14, p<0.00), shakeout (b=0.15, p<0.05) and decline

(b=0.14, p<0.05) for ROI compared to networking at the introduction stage. When looking at the

increase of connection frequency, network deepening is most negative for firm performance in the introduction stage. However, this negative effect decreases up until the maturity stage and turns non-negative at the shake-out stage. When the firm reaches the decline stage, the negative effect is more powerful again.

(2) Increasing duration of the relationship. We find a significant interaction effect between increasing the duration of the relationship and life cycle stages; growth (b=0.08, p<0.05), maturity

(b=0.08, p<0.05), shakeout (b=0.07, p<0.01) and decline (b=0.09, p<0.10) for ROA when

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H4 is not supported as we find that network deepening is most detrimental at the introduction stage compared to later stages. Contrastingly, an overall positive moderation is present for the relationship between deepening and firm performance for the later stages of the firm’s life cycle compared to the introduction.

DISCUSSION AND CONCLUSION

Existing literature lacks an elaboration on which network strategy is most beneficial at which stage of the firm life cycle. This study contributes to the network literature by addressing the dynamic nature of network ties at different stages of a firm’s life cycle. Life cycle stages are of theoretical relevance and can be very useful for practical purposes.

Theoretical contributions

The goal of this research is to find out what the effect of networking is on firm performance, and how networking might impact firms differently at certain stages of the firm’s life cycle. Literature on static views of networks have found a positive relationship between different types of networks on different aspects of firm performance (Baum et al., 2000; Watson, 2007). This study contributes by addressing the dynamic features of networking and the implications it has on several performance indicators. Next to that, the research investigates in which stage, firms might benefit most from certain networking strategies.

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activities and firm performance appears to be negative. A possible justification for the negative relationship between networking and firm performance is the amount of networking behaviour. This study concerns higher levels of a certain networking behaviour that have a positive implication on firm performance at different stages of the firm’s life cycle. It does not account for the proportion of networking where the influence might turn negative. An example, there might be limited benefits when increasing the number of ties over a certain point (Hallen & Eisenhardt, 2012; Wassmer, 2010). Findings with regards to survival and growth propose there is an optimum level of resources that should be allocated to networking, and more than 3 times a year or more than 6 networks is considered counterproductive (Watson, 2007).

Furthermore, firms might overestimate the value of the network portfolio as a whole due to increasing redundancy across ties (Vassolo, Anand & Folta, 2004). Besides, they often engage with multiple network partners at the same, which leads to problems managing the entire portfolio of ties (Gulati, 1998; Hoffmann, 2005; Lavie & Miller, 2008), and decreasing the effectiveness of networking (Martinez & Aldrich, 2011).

Besides, network broadening and network deepening strategies are taken separately in this research, but they can take place simultaneously (Vissa, 2012). Networking strategies combined is not captured throughout this study, but the combined effect on performance might be larger than the sum of its parts. Together, it is possible that these strategies do have a positive relationship on firm performance.

Practical implications

Our findings offer practical implications for increasing organisational performance through providing a better understanding of the effect that networking strategies have at different stages of the firm’s life cycle.

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Firms in the introduction stage should focus their efforts on survival (Hite & Hesterly, 2001), addressing high degrees of uncertainty (Gartner et al., 1992) and funding (Neergaard, 2005) and adding new contacts does not seem helpful to achieve such results. Therefore, managers are advised to be careful when they allocate their resources to network broadening in the introduction stage, as this is harmful for firm performance. However, for the growth stage, results are related to Vissa (2012)’s expectations. The negative relationship between network broadening and performance turns non-negative. Which means that firms benefit more from network broadening at the growth stage. This decreasing negative effect is strongest for ROI and can be explained as investing activities might be more related to networking. Investment projects, requiring more knowledge, capital, know-how and a more extensive amount of broader network of ties are needed to achieve positive return on investments (Lungeanu & Contractor, 2014) than for instance assets or common shares. The negative relationship increases again from maturity to shakedown. Which illustrates that network broadening: adding new contacts has a stronger negative influence on firm performance in the shakeout stage. In this stage, the main goal for firms is to adapt to changes in a complex environment focussing on cost reductions (Dietrich & Kraft, 2012) or reinvestments (Tian, Han & Zhang, 2015). Therefore, it is important that resources are allocated to the most crucial places and adding new contacts do not yield desired results and can even distract from ‘revival’.

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might need to be more responsive. Even though creating stronger relationships with network ties lead to commitment advantages and higher trust (Martinez & Aldrich, 2011). Managers should take into consideration that increasing connection frequency with ties does not necessarily lead to having a stronger relationship and better performance. As firms grow and mature, increasing connection frequency has a more positive influence on firm performance, because they have already established viable network partners. These findings align more with Baoshan et al., (2009). For the shake-out stage, findings are different. Contrastingly, shakeout stage decreases the negative effect of increasing connection frequency on performance, while on the other hand, the negative effect of increasing the duration with relationships is found to increase again at the shakeout stage. Therefore network deepening has two sides, which should be taken into consideration when allocating resources for networking.

In the decline stage, both increasing connection frequency and increasing duration seem to have the least negative influence on firm performance. In line with findings about network frequency (Watson, 2006), firms might benefit from network deepening activities. This can be explained by the fact that stagnating or declining businesses (Yoo et al., 2019) have high need for external funding. Building stronger relationships with connections for advice and funding, can indeed provide firms with valuable support (Smith & Lohrke, 2008) to keep the business alive.

Limitations and recommendations for future research

Where previous studies neglected the dynamic nature of networking, this study gives an insight into the influence of a firm’s networking behaviour at different stages in the firm life cycle on firm performance. Nevertheless, there are several aspects to be taken into account.

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effective in for each life cycle stage, but those efforts are directed at the ‘wrong’ ties, the influence on firm performance might be different. A firm’s behaviour towards managing the network of relationships can have a negative side if for instance, deepening activities are focused towards unproductive network partners, which might impede firms from having relationships with other viable partners (Gulati et al., 2000).

Therefore, it matters with which type of network members the firm interacts that determine the nature and scope of resources that is available through networking (Zhao & Aram, 1995). Besides, the extent to which a particular tie is able to provide necessary resources as expertise, financing and legitimacy should therefore be taken into account as not all ties are an equal in doing so (Hite & Hesterly, 2001). And even if the right ties have been identified, the ability or willingness of those partners to share their knowledge and capabilities is questionable (Ganchev, Curado & Kassler, 2014). Future research directed at the nature of these ties, for instance with whom the connection is made and the quality of the connection can possibly provide valuable additions to the relationship between networking and firm performance.

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Performance measurements. Furthermore, this study measures financial performance only, which might be insufficient to determine overall firm performance (Kulmala & Lonnqvist, 2006). Including non-financial measures can provide better understanding of networking strategies on business success, given networking is considered to be a social interaction rather than a transactional one. Examples of non-financial that link to the high-tech industry are; organisational supply chain quality, customer value, job satisfaction, perceived product or service quality and customer satisfaction (Ittner, Larcker & Rajan, 1998). In this area of research, overall business success including non-financial measures can be interesting to combine with financial ratios ROA, ROE and ROI, rather than net income or sales growth.

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APPENDIX A: LIFE CYCLE CLASSIFICATION

TABLE A1

Cash flow patterns and classification of life cycle stages

Introduction Growth Mature Shake-out Decline

Cash flow operating activities - + + +/- -

Cash flow from investing activities - - - +/- +

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APPENDIX B: CORRELATIONS TABLE

TABLE B1

Descriptive Statistics and Correlations

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APPENDIX C: DESCRIPTIVES MODERATOR

TABLE C1

Performance indicators at different values of the moderator

Net Income Sales growth Return on _Assets Return on _Equity _InvestmentReturn on Tobin's Q

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APPENDIX D: REGRESSION RESULTS TABLE D1

Network broadening: growth of new contacts

Net income Sales growth ROA ROE ROI Tobin's Q

B SE P 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI Main effect - Independent variable (IV): growth of

new contacts

Intercept 39.46 5.29 0.00 [30.70, 48.22] 17.46 11.65 0.14 [-1.83, 36.75] 0.03 0.11 0.03 [0.01, 0.04] -0.01 0.01 0.68 [-0.03, 0.02] 0.03* 0.02 0.09 [0,00, 0.07] 2.05*** 0.70 0.00 [1.93, 2.16] Control: TMT size -0.03** 0.01 0.02 [-0.05, -0.01]

Control: Firm size -0.62*** 0.10 0.00 [-0.79, -0.46]

Control: R&D intensity 0.23*** 0.08 0.00 [-0.10, 0.36]

Control: Market value 103.25*** 10.17 0.00 [86.41, 120.09] 0.54*** 0.15 0.00 [0.288, 0.79]

IV: Growth of new contacts

0.77 5.13 0.88 [-7.73, 9.28] 4.75 11.60 0.68 [-14.44, 23.95] -0.02** 0.11 0.04 [-0.04, -0.01] -0.023* 0.01 0.08 [-0.04, 0.00] -0.04** 0.02 0.05 [-0.07, -0.01] -0.07 0.07 0.28 [-0.19, 0.04] R2 = 0.44, F(2, 132)=51.81, p =0.00 R2 = 0.00, F(1,145)=0.17, p =0.68 R2_{= 0.03, F(1, 145)=4.45, p = 0.04} _R2_{= 0.06, F(1,141)=4.29, p = 0.02} _R2_{= 0.03, F(1,145)=4.02, p = 0.05} _R2_{= 0.44, F(3,127)=24.43, p = 0.00}

Moderation IV: growth of new contacts

Intercept -44.33 36.32 0.22 [-39.55, 37.96] 5.33 42.52 0.90 [-63.15, 75.81] -0.06 0.02 0.00 [-0.09, -0.02] -0.10*** 0.03 0.00 [-0.19, -0.03] -0.08*** 0.03 0.01 [-0.13, 0.03] 1.25 0.26 0.00 [0.82, 1.68] Control: corresponding

control variable W3

-0.04 0.04 0.37 [-0.11, 0.03] -0.46** 0.22 0.04 [-0.83, -0.09] -0.02 0.04 0.64 [-0.08, 0.04] 0.00 0.00 0.52 [-0.00, 0.00] -0.04* 0.02 0.08 [-0.06, 0.03] 0.34 0.05 0.00 [0.24, 0.44] Control: Firm size -0.02** 0.01 0.01 [-0.03, -0.01] -0.05*** 0.02 0.00 [-0.07, -0.03] -0.52 0.09 0.00 [-1.68, -0,36]

Control: Market value 0.05** 0.02 0.02 [0.01, 0.08] 0.33 0.015 0.03 [0.09, 0.57]

Life cycle stage

Growth 28.47 25.82 0.27 [-14.33, 71.28] 40.20 46.94 0.39 [-37.59,117.99] -0.09*** 0.02 0.00 [0.05, 0.13] 0.10*** 0.04 0.01 [0.04, 0.16] 0.12*** 0.03 0.00 [0.07, 0.18] -0.19 0.02 0.42 [-0.57, 0.19] Maturity 42.02 26.13 0.11 [-1.30, 85.33] 6.52 47.47 0.89 [-72.15, 85.20] 0.13*** 0.02 0.00 [0.09, 0.16] 0.13*** 0.04 0.00 [0.06, 0.19] 0.19*** 0.03 0.00 [0.13, 0.24] 0.25 0.23 0.28 [-0.14, 0.64] Shake out 21.23 37.20 0.57 [-40.42, 82.89] -37.40 69.41 0.59 [-153.05, 77.05] 0.09*** 0.03 0.01 [0.04, 0.14] 0.07 0.06 0.25 [-0.03, 0.16] 0.13** 0.04 0.01 [0.04, 0.21] 0.20 0.34 0.55 [-0.36, 0.77] Decline 7.44 52.89 0.89 [-80.22, 95.10] 1.49 95.75 0.99 [157.22, 160.19] 0.10** 0.05 0.03 [0.03, 0.17] 0.12 0.08 0.12 [0.00, 0.24] 0.13* 0.07 0.06 [0.02, 0.24] 0.21 0.45 0.65 -0.54, 0.96] Interaction effect IV*Growth 53.66 37.67 0.16 [-8.78, 116.09] 16.02 63.86 0.80 [-89.83, 121.87] 0.14*** 0.03 0.00 [0.09, 0.19] 0.11** 0.05 0.03 [0.03, 0.19] 0.17*** 0.04 0.00 [0.09, 0.24] 0.37 0.30 0.23 [-0.13, 0.88] IV*Maturity 46.84 37.88 0.22 [-15.95, 109.62] -11.69 64.56 0.86 [-118.60, 95.41] 0.12*** 0.03 0.00 [0.08, 0.17] 0.11** 0.05 0.04 [0.03, 0.19] 0.14*** 0.04 0.00 [0.07, 0.21] 0.18 0.31 0.56 [-0.33, 0.69] IV*Shake out 45.17 43.79 0.30 [-27.41, 117.75] -69.18 75.21 0.36 [-193.84, 55.47] 0.12*** 0.03 0.00 [0.07, 0.18] 0.06 0.06 0.33 [-0.04, 0.16] 0.15** 0.05 0.01 [0.06, 0.24] 0.40 0.36 0.27 [-0.20, 0.38] IV*Decline 44.91 52.56 0.39 [-42.20, 132.03] -15.99 92.13 0.86 [-168.70, 136.72] 0.12*** 0.04 0.00 [0.05, 0.19] 0.10 0.07 0.15 [-0.02, 0.22] 0.14** 0.06 0.04 [0.03, 0.24] -0.34 0.44 0.43 [-1.06, 0.38] IV: growth of new

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TABLE D2

Network broadening: increasing nr of distinct recipients

B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI

Main effect (IV): growth of nr of distinct recipients

[-0.03, 0.02]

Intercept 39.46 5.29 0.00 [30.70, 48.23] 17.44 11.66 0.14 [-1.87, 36.74] 0.03 0.01 0.03 [0.01, 0.05] -0.01 0.01 0.70 0.03 0.02 0.09 [0.00, 0.07] 2.05*** 0.07 0.00 [1.94, 2.17]

Control: TMT size -0.03** 0.01 0.02 [-0.05, -0.01]

Control: Firm size -0.62*** 0.10 0.00 [-0.78, -0.46]

Control: R&D intensity 0.23*** 0.08 0.00 [0.11, 0.36] Control: Market value 103.30*** 10.19 0.00 [86.42, 120.17] 0.52*** 0.16 0.00 [0.26, 0.78] IV: growth of nr of distinct recipients -0.24 5.08 0.96 [-8.66, 8.19] 0.65 11.55 0.96 [-18,46, 19.77] -0.02 0.01 0.18 [-0.03, 0.00] -0.01 0.01 0.33 [-0.03, 0.09] -0.03 0.02 0.17 [-0.06, 0.01] -0.08 0.07 0.25 [-0.19, 0.03] R2_{= 0.44, F(2, 132) =51.80, p =0.00} _R2_{= 0.00, F(1, 145)=0.00, p = 0.96} _R2_{= 0.01, F(1, 145)=1.84, p = 0.18} _R2_{= 0.04, F(1,141)=3.23, p = 0.04} _{R2 = 0.01, F(1,145) =1.94, p = 0.17} _R2_{= 0.44, F(3, 127) = 24,49, p = 0.00} Moderation (IV): increasing nr of distinct recipients Intercept 18.68 18.49 0.31 [-11.96, 49.33] -2.86 67.14 0.97 [-70.79, 74.86] -0.04 0.02 0.10 [-0.08, 0.00] -0.06* 0.04 0.09 [-0.12, -0.00] -0.06* 0.03 0.09 [-0.11, -0.00] 1.26*** 0.26 0.00 [0.83, 1.70] Control: corresponding control variable W3 -0.07** 0.03 0.03 [-0.12, -0.02] -0.47** 0.22 0.03 [-0.84, -0.11] -0.05 0.04 0.19 [-0.12, 0.01] 0.00 0.00 0.52 [-0.00, 0.00] -0.04* 0.02 0.06 [-0.08, -0.01] 0.34*** 0.06 0.00 [0.24, 0.45]

Control: Firm size -0.02** 0.01 0.02 [-0.03, 0.00] -0.04*** 0.01 0.00 [-0.76, -0.01] -0.52*** 0.10 0.00 [-0.68, -0.35] Control: Market

value

104.62*** 10.54 0.00 [87.15, 122.09] 0.05** 0.02 0.03 [0.01, 0.08] 0.32** 0.15 0.04 [0.07, 0.56]

Life cycle stage

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TABLE D3

Network deepening: increasing connection frequency

B SE P 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI

Main effect (IV): increase connection frequency

Intercept 39.25*** 5.28 0.00 [30.50, 48.00] 17.37 11.63 0.14 [-1.89, 36.63] 0.03** 0.01 0.02 [0.01, 0.04] -0.01 0.01 0.61 [-0.03, 0.02] 0.03* 0.02 0.09 [0.00, 0.07] 2.05*** 0.07 0.00 [1.93, 2.16]

Control: TMT size -0.03** 0.01 0.02 [-0.05, 0.01]

Control: Firm size -0.64*** 0.10 0.00 [0.80,

-0.48]

Control: R&D intensity 0.23*** 0.08 0.00 [0.10, 0.36]

Control: Market value 104.91*** 10.29 0.00 [87.86, 121.97] 0.557*** 0.16 0.00 [0.30, 0.82] IV: increase connection

frequency -4.81 5.65 0.40 [-14.17, 4.56] -9.45 11.50 0.41 [-28.49, 9.58] 0.04*** 0.01 0.00 [0.05, -0.02] -0.04*** 0.01 0.00 [0.06, -0.02] -0.05** 0.02 0.02 [0.08, -0.02] -0.05 0.07 0.55 [-0.17, 0.08] R2_{= 0.44, F(2, 132)=52.44), p = 0.00} R2_{= 0.01, F(1, 145)=0.68, p = 0.41} _R2_{= 0.07, F(1, 145) =10.42, p = 0.00} _R2_{= 0.11, F(1, 141)=8.27, p = 0.00} _R2_{= 0.04, F(1, 145)=6.01, p = 0.02} _R2_{=0.43, F(4, 127)=24.07, p = 0.00}

Moderation (IV): increase connection frequency Intercept 26.94 16.65 0.11 [-0.66, 54.54] 1.73 37.88 0.96 [-61.06, 64.53] -0.02 0.02 0.38 [-.05, 0.02] -0.06* 0.03 0.06 [0.11, -0.01] -0.02 0.03 0.41 [-0.07, 0.02] 1.33 0.25 0.00 [0.91, 1.74] Control: corresponding control variable W3 -0.07** 0.03 0.02 [-0.12, -0.02] -0.39 0.22 0.08 [-0.76, -0.03] -0.06 0.04 0.12 [-0.12, 0.00] 0.00 0.00 0.58 [-0.00, 0.00] -0.04** 0.02 0.04 [0.08, -0.01] 0.35*** 0.06 0.00 [0.25, 0.45]

Control: Firm size -0.02** 0.01 0 .01 [0.03, -0.01]

-0.05*** 0.01 0.00 [0.07, -0.03]

-0.49*** 0.09 0.00 [0.65, -0.34] Control: Market value 105.68*** 10.86 0.00 [87.67, 123.68 -0.05** 0.02 0.02 [0.13, 0.08] 0.32** 0.15 0.04 [0.07, 0.57]

Life cycle stage

Growth 9.98 15.31 0.95 [-21.03, 41.00] 38.03 43.01 0.38 [-33.26, 109.31] 0.04** 0.02 0.05 [0.01, 0.08] 0.06* 0.04 0.09 [0.00, 0.12] 0.06* 0.03 0.05 [0.01, 0.11] -0.32 0.21 0.14 [-0.67, 0.03] Maturity 18.56 18.82 0.33 [-12.64, 49.76] 9.04 43.30 0.83 [-62.72, 80.81] 0.08** 0.02 0.02 [0.05, 0.12] 0.09** 0.04 0.01 [0.03, 0.15] 0.13*** 0.03 0.00 [0.07, 0.18] 0.12 0.21 0.57 [-0.24, 0.49] Shake out 16.30 29.17 0.58 [-32.07, 64.66] -37.68 72.41 0.60 [-157.70, 82.32] 0.05 0.03 0.15 [-0.01, 0.10] 0.03 0.06 0.58 [-0.06, 0.12] 0.08 0.05 0.13 [-0.01, 0.16] 0.11 0.34 0.74 [-0.45, 0.68] Decline 0.17 71.34 0.99 [-118.11, 118.44] -11.62 173.59 0.94 [-299.33, 275.09] 0.01 0.08 0.88 [-0.12, 0.15] 0.07 0.14 0.63 [-0.16, 0.30] 0.01 0.12 0.95 [-0.19, 0.21] -1,28 0.82 0.12 [-2.63, 0.08] Interaction effect IV*Growth -6.86 18.34 0.71 [37.27, 23.55] -14.68 44.48 0.74 [-88.40, 59.04] 0.0441** 0.02 0.03 [0.01, 0.08] 0.00 0.04 0.91 [-0.05, 0.06] 0.06* 0.03 0.08 [0.00, 0.11] 0.02 0.21 0.93 [-0.33, 0.37] IV*Maturity -5.70 17.89 0.75 [-35.35, 23.95] -8.13 42.84 0.85 [-79.13, 62.88] 0.06*** 0.02 0.00 [[0.03, 0.09] 0.01 0.03 0.74 [-0.05, 0.07] 0.07** 0.03 0.02 [0.02, 0.12] 0.07 0.20 0.72 [-0.27, 0.41] IV*Shake out 0.78 32.81 0.98 [-53.52, 55.18] -8.18 74.48 0.91 [-131.62, 115.25] 0.07** 0.03 0.03 [0.02, 0.13] 0.07 0.06 0.30 [-0.04, 0.17] 0.12** 0.06 0.03 [0.03, 0.22] 0.15 0.38 0.69 [4.07, -0.25] IV*Decline -3.02 100.54 0.98 [-169.71, 163.67] -25.55 244.96 0.92 [-431.56, 380.46] 0.02 0.11 0.89 [-0.17, 0.20] -0.00 0.19 0.98 [-0.33, 0.32] -0.03 0.17 0.88 [-0.31, 0.26] -2.16* 1.15 0.06 [4.07, -0.25] IV: increase connection

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TABLE D4

Network deepening: increasing duration of relationship

B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI B SE p 90% CI

Main effect (IV): increase duration of relationship

Intercept 39.49*** 5.29 0.00 [30.72, 48.25] 17.44 11.66 0.14 [-1.86, 36.75] 0.03** 0.01 0.03 [0.01, 0.05] -0.01 0.01 0.71 [-0.03, 0.02] 0.03* 0.02 0.08 [0.00, 0.07] 2.05*** 0.07 0.00 [1.94, 2.17] Control: TMT size -0.02* 0.01 0.09 [-0.04, 0.00] -0.03** 0.01 0.02 [-0.05, -0.01]

Control: Firm size -0.63*** 0.10 0.00 [0.79,

-0.46]

Control: R&D intensity 0.23*** 0.08 0.00 [0.10, 0.36]

Control: Market value 103.28*** 10.15 0.00 [86.46, 120,10] 0.52*** 0.16 0.00 [0.27, 0.78] IV: increase duration of

relationship

-1.09 5.23 0.84 [-9.75, 7.57] -0.17 11.93 0.99 [-19.91, 19.58] -0.02* 0.01 0.08 [-0.04, 0.00] -0.02 0.01 0.13 [-0.04, 0.00] -0.03* 0.02 0.09 [-0.07, 0.00] -0.08 0.07 0.26 [-0.19, 0.04]

R2_{=0.44, F(2, 132)=51.83=0.00} _R2_{= 0.00, F(1, 145)=0.00 =0.99} _R2_{=0.04, F(1, 142) = 3.14, p=0.05} _R2_{=0.05, F(1, 141)=3.91, p = 0.02} _R2_{= 0.02, F(1, 145)=2.98, p = 0.09} _R2_{= 0.44, F(4, 127)=24.47, p = 0.00}

Moderation (IV): increase duration of relationship Intercept 19.41 17.88 0.28 [-10.23, 49.05] 3.44 42.46 0.94 [-66.93, 73.81] -0.04** 0.02 0.05 [0.08, -0.01] -0.07** 0.03 0.04 [-0.13, 0.01] -0.06** 0.03 0.05 [-0.12, -0.01] 1.25*** 0.26 0.00 [0.83, 1.68] Control: corresponding control variable W3 -0.06** 0.03 0.04 [-0.11, -0.01] -0.48** 0.22 0.03 [-0.85, -0.11] -0.04 0.04 0.29 [-0.11, 0.02] 0.00 0.00 0.52 [-0.00, 0.00] -0.04* 0.02 0.05 [0.07, -0.00] 0.34*** 0.06 0.00 [0.24, 0.44]

Control: Firm size -0.02** 0.01 0.02 [0.03, -0.00]

-0.05*** 0.01 0.00 [0.07, -0.03]

-0.52*** 0.10 0.00 [0.68, -0.36] Control: Market value 109.96*** 10.45 0.00 [86.65, 121.28] 0.05** 0.02 0.03 [0.01, 0.08] 0.32** 0.15 0.03 [0.08, 0.57]

Life cycle stage

Growth 18.47 19.67 0.35 [-14.113, 51.08] 40.96 46.83 0.38 [-36.66, 118, 58] 0.07*** 0.02 0.00 [0.03, 0.11] 0.08** 0.03 0.05 [0.01, 0.14] 0.11*** 0.03 0.00 [0.05, 0.16] -0.20 0.23 0.39 [-0.58, 0.18] Maturity 25.63 19.89 0.20 [-7.35, 58.61] 8.61 47.83 0.39 [-69.88, 87.10] 0.11*** 0.02 0.00 [0.07, 0.15] 0.10** 0.04 0.01 [0.04, 0.16] 0.17*** 0.04 0.00 [011, 0.22] -0.23 0.23 0.33 [-0.16, 0.62] Shake out 24.17 29.32 0.41 [-24.44, 72.78] -33.43 69.66 0.86 [-148.89, 82.03] 0.07** 0.03 0.03 [0.02, 0.13] 0.04 0.05 0.47 [-0.05, 0.13] 0.11** 0.05 0.04 [0.02, 0.19] 0.19 0.34 0.58 [-0.38, 0.76] Decline 7.01 41.58 0.87 [-61.94, 75.95] 7.09 100.61 0.63 [-159.66, 173.85] 0.07** 0.05 0.03 [0.02, 0.13] 0.09 0.008 0.24 [-0.04, 0.23] 0.11 0.07 0.13 [-0.01, 0.23] 0.44 0.48 0.37 [-0.36, 1.23] Interaction effect IV*Growth 31.12 30.73 0.31 [-19.83, 82.07] 13.67 71.02 0.85 [-104.04, 131.39] 0.08** 0.03 0.02 [0.02, 0.14] 0.02 0.05 0.72 [-0.07, 0.12] 0.12** 0.05 0.03 [0.03, 0.20] 0.30 0.35 0.39 [-0.28, 0.89] IV*Maturity 33.50 30.59 0.28 [17.22, 84.21] -6.52 70.65 0.93 [-123.78, 110.74] 0.08** 0.03 0.02 [0.02, 0.14] 0.04 0.05 0.54 [-0.06, 0.13] 0.11* 0.05 0.03 [0.03, 0.20] 0.29 0.35 0.41 [-0.29, 0.86] IV*Shake out 23.59 35.48 0.50 [-33.59, 80.76] -65.21 80.55 0.42 [-198.72, 68.30] 0.07* 0.04 0.06 [0.01, 0.14] -0.03 0.06 0.66 [-0.14, 0.08] 0.11* 0.06 0.06 [0.01, 0.21] 0.45 0.40 0.27 [-0.22, 1.11] IV*Decline 36.06 40.91 0.38 [-31.76, 103.88] -9.97 97.93 0.92 [-170.78, 68.30] 0.09* 0.05 0.07 [0.01, 0.16] 0.03 0.08 0.73 [-0.10, 0.16] 0.11 0.07 0.11 [-0.00, 0.23] -0.27 0.47 0.57 [-1.05, 0.51] IV: increase duration of

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APPENDIX E: INTERACTION EFFECTS

Figure E1: Network broadening: Growth of new contacts and ROA

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Figure E3: Network broadening: Growth of new contacts and ROI

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Figure E5: Network deepening: increasing connection frequency and ROI

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NETWORKING STRATEGIES AND FIRM PERFORMANCE: THE MODERATING ROLE OF FIRM LIFE CYCLE

NETWORKING STRATEGIES AND FIRM PERFORMANCE: THE

MODERATING ROLE OF FIRM LIFE CYCLE

Table of contents