Exploration versus exploitation the influence of network density on firm s strategic choice between two types of innovation

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Technology Analysis & Strategic Management

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Exploration versus exploitation: the influenceof network density on firm’s strategic choicebetween two types of innovation

Mingyu Tian, Yiwei Su & Zhong Yang

To cite this article: Mingyu Tian, Yiwei Su & Zhong Yang (2024) Exploration versus

exploitation: the influence of network density on firm’s strategic choice between two types of innovation, Technology Analysis & Strategic Management, 36:3, 605-618, DOI: 10.1080/09537325.2022.2046265

To link to this article: online: 01 Mar 2022.

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Exploration versus exploitation: the inﬂuence of network density

Mingyu Tian , Yiwei Su and Zhong Yang

School of Business, Nanjing University, Nanjing, People’s Republic of China

Extant research has shown equivocal evidences of brokerage on ﬁrm innovation. We try to unpack the mixed ﬁndings by studying how network density among aﬁrm’s alliance partners inﬂuences exploratory innovation. Based on the idea that it is optimum for aﬁrm to focus on one type of innovation when searching for resources from the alliance network, we posit that a low level, as well as a high level of network density, oﬀer a suitable environment for exploratory innovation, but a medium level of network density is more conductive to exploitative innovation. A longitudinal investigation of 277 biopharmaceuticalﬁrms indicates a U-shaped relationship between network density and exploratory innovation. This relationship is positively moderated by absorptive capacity. The study integrates related theories and provides a comprehensive framework of how the connect tightness among a ﬁrm’s partners impacts its exploration and exploitation behaviour.

Interorganizational relationship is one of the crucial factors toﬂourish business success, due to the access to knowledge and R&D capabilities from partners (Gulati, Nohria, and Zaheer2000). Under today’s increasingly competitive circumstances, it is far from suﬃcient for a ﬁrm to rely only on its internal resources to survive and gain a dominant position in the industry. As a consequence, ﬁrms in many industries are prone to form alliances to seek external resources for their R&D.

manage its alliances and take advantage of its position in the alliance network.

Prior research has explored various attributes of the alliance network on the focalﬁrm’s inno-vation performance. In addition to network ties (Liang and Liu2018; Kim, Narayanan, and Narasim-han2020; Yang et al.2021), network conﬁguration (Guan, Zhang, and Yan2015; Kumar and Zaheer 2019; Aggarwal2020) is investigated as well. There are two ideal types of network conﬁguration, namely, structural holes (Burt1992), and network closure (Coleman1990). A network full of structural

resources. As the opposite side of brokerage, closure typically leads to cooperation and trust. The advantages of brokerage and closure motivate scholars to explore which of them could enhance ﬁrm innovation. Some studies contend that broker ﬁrms produce greater innovation output than

as Ahuja (2000) and Fleming, Mingo, and Chen (2007) disagree and indicate a negative eﬀect of

CONTACTYiwei of Business, Nanjing University, Nanjing, 210093, People’s Republic ofChina

2024, VOL. 36, NO. 3, 605–618

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brokerage on innovation. Hence, we need to seek for a solution to disentangle the equivocal evi-dences of brokerage on innovation.

One possible solution for the perplexing evidences is to categorise innovation into exploratory innovation and exploitative innovation. Exploratory innovation refers to the utilisation of new knowl-edge departure from the existing research domain, and exploitative innovation mainly involves

diﬀerent attributes of exploratory innovation and exploitative innovation result in dissimilar knowl-edge search modes. Firms search for novel knowlknowl-edge to achieve exploratory innovation and seek incremental knowledge to exploitative innovation. Heterogeneous knowledge search modes lead to diﬀerent demands of external resources, and the impact of brokerage on exploratory innovation and exploitative innovation might be diﬀerent. Although existing research sheds light on this area, relevant conclusions are inconsistent. For example, Guan and Liu (2016) indicate that brokerage hinders exploitative innovation but favours exploratory innovation, however, Phelps (2010) points to the positive inﬂuence of closure on exploratory innovation.

The incoherent conclusions might be due to the fact that the range of relevant theories is limited. On the one hand, the positive impact of brokerage on innovation implies that closure, i.e. the low level of brokerage, is not preferred. On the other hand, closure accelerates innovation indicates that its opposite side, brokerage, is less beneﬁcial. These contradictory conclusions push us to integrate relevant theories and to examine what degree of brokerage is beneﬁcial for inno-vation. If we study this issue in-depth and understand the mechanism behind it, we may disen-tangle the puzzle.

In this study, we aim to extend the boundaries of relevant theories by exploring the impact of network density among aﬁrm’s alliance partners (network density for short) on exploratory inno-vation. The main purpose of this study is to integrate related theories and solve the inconsistent ﬁndings of the relationship between brokerage and ﬁrm innovation. We contend that, exploration

resources in the alliance network for exploratory innovation or exploitative innovation (Stettner

both nonredundant resources and social capital oriented from mutual trust help with novel ideas. In this sense, both a high level of network density and a low level of network density oﬀer a favour-able environment for searching novel ideas, thereby accelerating exploratory innovation. For the reason that a moderate level of network density may not promote exploratory innovation as a high level or a low level of network density does, and exploitative innovation is intrinsically preferred byﬁrms (Stuart and Podolny1996),ﬁrms are more willing to focus on exploitative innovation under a moderate level of network density. The foregoing implies a U-shaped eﬀect of network density on exploratory innovation.

network density and exploratory innovation. A focal ﬁrm’s absorptive capacity, i.e. the ability to

network. Thus, absorptive capacity intensiﬁes the U-shaped relationship. In summary, we explore the following questions: How doﬁrms adjust recourse allocation towards exploration or exploitation

under a certain level of brokerage? In this paper, we theorise and test the consequences of

In the empirical work reported here, we test these predictions on a panel of 277 leading

relationship between network density and exploratory innovation. Absorptive capacity positively moderates this relationship. The results suggest that when searching for resources from alliances, managers of aﬁrm should consider the connect tightness between its partners to ﬁnd out if they should extract resources for exploration or exploitation.

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2. Theory and hypothesis

exploitative innovation, we build on search and recombination theoretical base. From search and recombination perspective, organisations not only search for knowledge components, but are also dedicated to the way to combine them to achieve innovation (Arthur2007; Weitzman1998).

exploitative innovation, involving local search that builds on a ﬁrm’s existing knowledge base. The latter one is known as exploratory innovation, requiring a distant search of new knowledge

As a means of accessing external knowledge, the alliance network could be an eﬀective path of knowledge transfer and integration (Hamel1991). Through direct alliances with its partners, aﬁrm could be access to knowledge and skills of its partners, adding to existing knowledge and increasing the likelihood of innovation. In addition, the position that the focal ﬁrm occupies in the alliance network is considered to be crucial as well.

One stream of research on network conﬁguration stresses the importance of the brokerage pos-ition aﬁrm occupied (Burt1992; Kumar and Zaheer 2021; Kwon et al.2020). A triadic brokerage occurs when the focalﬁrm is connected with two alters that are not themselves connected. Accord-ing to Burt’s (1992) structural holes theory, a high level of brokerage provides the focalﬁrm with

research from diverse knowledge and skills, promoting successful recombination.

Another stream of research on network conﬁguration contends that network closure is beneﬁcial (Coleman1990; Phelps2010; Zhelyazkov2018). Triadic closure refers to the situation that two part-ners of the focalﬁrm are themselves connected. It allows the focal ﬁrm to learn about one of its part-ners from another, thereby reducing information asymmetries and increasing their trust (Gulati, Nohria, and Zaheer2000) towards repeated cooperation. Trust and reciprocity mitigate opportunism

resources in a social exchange relationship that are valuable and appropriable by partners. When partners of the focalﬁrm are densely connected, the corresponding social capital makes it easier

(Inkpen and Tsang2005). Therefore, aﬁrm could beneﬁt from densely connected partners as well.

2.1. The inﬂuence of network density on exploratory innovation

Before entering the inﬂuences of network density on two types of innovation, we ﬁrst clarify that exploratory innovation and exploitative innovation are exclusive when referring to the alliance network. There is a debate over whether exploratory innovation and exploitative innovation are exclusive or orthogonal. Although some research suggests that balancing exploration and exploita-tion is beneﬁcial for innovation (Cao, Gedajlovic, and Zhang 2009; Lucena 2016; March 1991), it

2011). Stettner and Lavie (2014) contend that balancing exploratory and exploitative within a

types of innovation, leading to knowledge misapplication of one type to another. Therefore,ﬁrms should focus on one type of innovation through the search mode of alliances. Under a certain struc-ture of the alliance network, aﬁrm should consider resource endowment to one type of innovation to achieve superior performance.

In addition, Gupta, Smith, and Shalley (2006) indicate that whenﬁrms need scarce resources for both types of innovation, exploration, and exploitation would be mutually exclusive. As March (1991) claims, although these two types of innovation are essential for organisations, they compete for

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scarce resources. Resources from the alliance network are considered to be scarce, since aﬁrm bears huge costs and risks to acquire external knowledge through the alliance network. More resources devoted to exploration imply fewer towards exploitation, and vice versa (Gupta, Smith, and Shalley2006). Thus, limited resources force aﬁrm to focus on one type of innovation when searching from the alliance network.

exploitative innovation of the focalﬁrm based on their exclusive relationship. Under a low level of network density, that is, a position with a high degree of brokerage, the focalﬁrm is easily accessed to diverse knowledge and information (Burt1992). Aﬁrm in the brokerage position is exposed to

ﬁrms (Kumar and Zaheer 2021; Kwon et al.2020). As a result, brokers may creatively synthesise diverse information and obtain a better vision of future innovation possibilities (Burt2005). Scholars contend that brokerageﬁrms acquire more information (Lee2007), get novel information earlier

Therefore, a low level of network density oﬀers a favourable environment for a ﬁrm to search for

exploratory innovation over exploitative innovation under such position. The foregoing analysis implies that the focalﬁrm should pursue exploratory innovation over exploitative innovation at a low level of network density.

At a high level of network density, that is, direct partners of the focalﬁrm are tightly connected to form a dense network, social capital composed of trust and reciprocity increases the willingness of partners to share knowledge (Coleman1990). Density allows the focalﬁrm to learn about a partner through a common third partner, thereby reducing information asymmetry and increasing their

more visible in the dense network, and opportunism behaviour could jeopardise its current alliances and reduce future alliance opportunities (Gulati1995). Tightly connectedness encourages reciprocity since recipients will repay the beneﬁts to avoid being recognised as opportunism (Coleman1988). We believe that aﬁrm could beneﬁt its exploratory innovation more from a high level of network density. As Phelps (2010) claims, a dense network leads to intense social interaction, joint problem solving, and experimentation, improving the ability of aﬁrm to absorb diverse partner knowledge from partners. Additionally, trust and reciprocity from network closure also increase joint

recombi-nation, thus promoting detection and transfer of diverse knowledge from partners (Dyer and Nobeoka2000). Furthermore, the rapidﬂow of information from tightly connected partners provides more opportunities to share and expand the understanding of technical issues and helps to establish a shared discourse mode (Powell and Smith-Doerr1994), allowing diverse partners to share with and learn from partners more eﬃciently (Kogut and Zander 1996). In summary, social capital from the dense network promotes knowledge transfer among partners, broadening the knowledge scope

about its interest of the incremental of existing knowledge and tries to extract from its partners, it might be viewed as opportunistic and thus may negatively inﬂuence its current and future alliances. Therefore,ﬁrms may focus more on exploratory innovation than exploitative innovation from a high level of network density.

At a moderate level of network density, that is, neither the ego network is full of structural

and information or trust and reciprocity decrease. Under this situation, a ﬁrm might not beneﬁt its exploratory innovation from not-so-dispersed knowledge, and a modest level of connection may not generate trust and reciprocity to maintain a strong relationship between partners. In addition, exploitative innovation is instinctively preferred byﬁrms, since it has a higher likelihood of success in areas of prior experience (Stuart and Podolny1996). Therefore, when the focalﬁrm

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does not have enough resources for exploratory innovation, it may allocate resources from the alliance network to exploitative innovation. Therefore, a moderate level of network density

eﬀorts to exploitative innovation.

In summary, we contend that both a high level and a low level of network density positively

Hypothesis (H1). Network density among aﬁrm’s alliance partners has a U-shaped relationship with the ﬁrm’ssubsequent exploratory innovation.

2.2. The moderating role of absorptive capacity

(2014) suggest, ‘Identifying and acquiring innovations from external sources is only half the battle’. Accordingly, to beneﬁt from the resources of the alliance network, a ﬁrm relies on its internal learning capacity to absorb and utilise external knowledge.

Absorptive capacity wasﬁrst deﬁned by Cohen and Levinthal (1990) and it is viewed as aﬁrm’s ability to acquire, assimilate, transform and exploit knowledge (Zahra and George2002). According to Zahra and George (2002), absorptive capacity contains aﬁrm’s ability to acquire and assimilate external knowledge and combine it with existing knowledge to further utilise it for new combi-nations. It is beneﬁcial to organisation learning and its R&D (Daghfous2004). Absorptive capacity

future information, thereby enhancing innovation (Schilling1998).

We argue that absorptive capacity intensiﬁes the U-shaped relationship between network density and exploratory innovation. Since aﬁrm could be access to external resources through the alliance network, its absorptive capacity helps with acquiring and assimilating knowledge from its partners more eﬃciently. It accelerates the procedure of knowledge acquiring and absorbing, thereby being ready for the recombination process. Thus, aﬁrm with suﬃcient absorptive capacity could beneﬁt more from partners for its innovation. In contrast, if aﬁrm lacks absorptive capacity, it would lead to a lower ability of theﬁrm to detect and absorb relative new knowledge from its partners, and it may hinder the eﬀect of external knowledge from alliances on exploratory innovation. In line with the above discussion, we hypothesise:

Hypothesis (H2). Absorptive capacity positively moderates the relationship between network density andexploratory innovation.

3. Methods

3.1. Data and sample

The research setting of this study was the global pharmaceutical industry (SICs through 2833–2836). We chose this industry mainly for two reasons. First, as a knowledge-intensive industry with knowl-edge complexity, locus of innovation is ubiquitous in this industry (Powell, Koput, and Smith-Doerr 1996). Second, ﬁrms in the pharmaceutical industry tend to patent their innovations (Paruchuri 2010), and it is convenient for us to measure innovation based on patent data.

We collected alliance data from the SDC Platinum database. Based on this database, we searched forﬁrms in the global pharmaceutical industry that announced their alliances from 1985 to 2014. There is scarce of alliances announced before 1985, so we began our search for alliances from

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type of alliances (Schilling and Phelps2007), and the scope of the true activity of an alliance is often greater than reported (Powell, Koput, and Smith-Doerr1996), we included all types of alliances in our study. Following the prior study, we deﬁne the network boundary as the following rules: each ﬁrm in the pharmaceutical industry must have a partner in the same industry (Østergaard, Timmermans,

Phelps2007). For the reason that alliance termination dates are rarely reported, we took a commonly usedﬁve-year rolling window approach to represent the duration of alliances (Guan and Liu2016). Therefore, we began in 1989, the last year of the duration of alliances that were announced in 1985. Then the alliance network was created to calculate network-based measures. Finally, our data con-tains 277ﬁrms and there are 845 observations of alliances.

Our patent data was collected from the frequently used Derwent Innovation Index database (DII). DII is one of the most comprehensive databases worldwide containing patent information of more than 100 countries and 40 patent oﬃces, including USPTO, JPO, EPO and SIPO, and so on. Our ﬁnal data contains an unbalanced panel of 277ﬁrms and 4132 ﬁrm-year observations, which is close to reality and is preferred for avoiding survivorship bias (Baum2006).

3.2. Measures

3.2.1. Dependent variable

each patent of the focalﬁrm in year t to exploratory innovation or exploitative innovation. Follow-ing GilsFollow-ing et al. (2008), the criterion is that, if any technological class of a given patent is not in the technological proﬁle of the focal ﬁrm in year t, it is considered as exploratory innovation. If all of the technological classes of a given patent are in the technological proﬁle of the focal ﬁrm, it is viewed as exploitative innovation. Here the technological proﬁle of the focal ﬁrm contains all technological classes of patents published within ﬁve years before year t. Existing research has

inventions (Ahuja2000). Following this criterion, we classiﬁed patents of the focal ﬁrm in year t into exploratory innovation category and exploitative innovation category. Then we applied a commonly used patent citation count measure for exploratory innovation and exploitative innovation.

Exploratory innovation. We measured the focalﬁrm’s exploratory innovation performance of year t as the forward citations of a granted exploratory patent p1 applied in year t as:

Network density. We used the ratio of the number of existing ties among alliance partners of the focal ﬁrm to the potential maximum number of ties among them in year t-1 to measure network density of the focalﬁrm (Phelps2010):

TNit−1 nit−1(nit−1− 1)/2

where TNit−1is the number of connections among alliance partners of the focalﬁrm i in year t-1. nit−1

is the number of alliance partners of the focalﬁrm, and nit−1(nit−1− 1)/2 is the potential maximum number of connections. It is valued at zero if there is no direct tie or only one direct tie of the focal ﬁrm.Figure 1presents examples of diﬀerent levels of network density.

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3.2.3. Moderator

Technological base (log). We used technological base to represent the absorptive capacity of the focal

Jaﬀe, and Trajtenberg2005) to calculate technological base of year t-1: t−1

(1− 15%)t−aPN

where PNiais the number of patents of the focalﬁrm i at yeara (a , t). Then following Funk (2014), we logged it, with 0 replaced by a small number of 0.0001 before the transformation.

3.2.4. Control variables

Direct ties. We controlled for direct ties of the focalﬁrm as the number of alliance partners of the focal ﬁrm in the network of year t-1, for the reason that the knowledge exchange through direct ties might be correlated with beneﬁts from brokerage position (Ahuja2000).

Indirect ties. To capture informal knowledge path through network (Ahuja2000), we measured indirect ties as the number of indirectly connected partners of the focalﬁrm in the network of year t-1.

Technological diversity. Based on the Blau Index of diversity, we controlled for technology diversity to account for the technological scope of the focalﬁrm (Kumar and Zaheer2019) as:

where j is the patent class and ppjrepresents the proportion of patents that is in class j. It is assigned to 0 if the focalﬁrm did not patent until year t-1. Higher Blau Index indicates a higher level of tech-nological heterogeneity (Kumar and Zaheer2019).

Alliance experience. We controlled for alliance experience as the number of alliances formed by the focal ﬁrm in the seven years before year t-1 to represent the collaborative capability of the focalﬁrm (Phelps2010).

Repeated ties. Prior collaboration between twoﬁrms could enhance their trust (Gulati1995). Fol-lowing Phelps (2010), we calculated repeat ties as the number of repeated alliances among its part-ners divided by the number of partpart-ners in year t-1.

Industry diﬀerence. We measured industry diﬀerence as the proportion of alliance partners that are not in the same four-digit SIC codes as the focalﬁrm at year t-1 to capture the eﬀect of market resource overlap (Kumar and Zaheer2019). High industry diﬀerence implies a low level of industry similarity.

International alliance. We controlled for heterogonous of alliance partners (Phelps2010) as the proportion of alliance partners that are not in the same country as the focalﬁrm in year t-1.

Figure 1.Diﬀerent levels of network density.

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Technological distance (cosine). To measure technological similarity between the focalﬁrm and its partners (Kumar and Zaheer2019), we constructed a vector I containing the cumulative distribution of patents of aﬁrm in diﬀerent patent classes until year t-1. Thus, each element of the K-dimensional vector measured the fraction of patents in class k, k = 1, 2,… , K. Then we calculated the cosine angular distance (Jaﬀe1986) between the focalﬁrm i and one of its partners j using:

cosineijt−1= Iit−1Ijt−1/ (Iit−1Iit−1)(Ijt−1Ijt−1)

The range of this measure is from 0 to 1, in which 1 represents complete similarity. Then we averaged the distance of the focalﬁrm and all alliance partners to get the aggregate measure.

Technological diversity (alliance partners). Similar to technological diversity of the focalﬁrm, we controlled for technological diversity of alliance partners in year t-1 as the average technological diversity of partners based on the Blau Index to capture the technological scope of partners of the focalﬁrm (Kumar and Zaheer2019).

Average network distance. We controlled for average network distance as the average distance between connected nodes in the network in year t-1 to capture its eﬀect on information diﬀusion and novel recombination (Schilling and Phelps2007).

control for the region of aﬁrm. ‘U.S.-Canada’ was coded 1 if a ﬁrm was in the U.S. or Canada and ‘Europe’ was coded 1 if a ﬁrm was in Europe. Asia was the omitted category.

3.3. Model estimation

hypothesis. This is because our dependent variable, Exploratory Innovation, is nonnegative and takes only integer values. Some values of our dependent variables are zero, therefore we cannot

our dependent variables results in E[ln (D + y|x)], and it cannot be transformed back to our interest E[y|x]. However, unconditional ﬁxed-eﬀects Poisson quasi-maximum likelihood estimator takes the form yit=aiex′itb+ 1it, and our interest could be captured as

E[yit|xit,ai]=aiex′itb= e(lnai+x′

itb), t= 1, 2, . . . , T, i = 1, 2, . . . , N,

here yit is the exploratory innovation ofﬁrm i in time t, xit includes independent variable, control variables, and time eﬀects,b is the coeﬃcient estimator, ai is aﬁrm-speciﬁc unobserved time-con-stant eﬀect, and 1it is the random error term.

4. Results

Table 1presents the descriptive statistics and correlations between variables. We checked for multi-collinearity through Variance Inﬂation Factor (VIF) and the highest VIF of the variables is 4.14, far below 10, indicating that there is no issue of multicollinearity.

Table 2presents the unconditionalﬁxed-eﬀects Poisson regression results. Model 1 is the baseline model only including control variables. Model 2 includes the independent variable, Network Density. The Moderating variable of Technological Base (Log) is added in Model 3. Model 4 is our full model with all variables and interactions included.

Hypothesis (H1) predicts that network density among the focalﬁrm’s alliance partners has a U-shaped eﬀect on exploratory innovation. The coeﬃcient of Network Density is negative and signiﬁ-cant (p, 0.01) and that of Network Density Squared is positive and signiﬁcant (p , 0.01) in Model 2, 3, and 4, supporting Hypothesis (H1). The focalﬁrm achieves better exploratory innovation under both the low level and the high level of network density, but the medium level of network density is not beneﬁcial for exploratory innovation. The results imply that the focal ﬁrm should

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focus on exploratory innovation under the low level as well as the high level of network density, while pursuing exploitative innovation under the medium level of network density.

Hypothesis (H2) proposes a positive moderating eﬀect of absorptive capacity on the relationship between Network Density and Exploratory Innovation. In line with Hypothesis (H2), the interaction between Network Density and Technological Base (Log) is positive (p, 0.01), and the interaction

4. Figure 2 illustrates this moderation eﬀect, showing that the U-shape is ﬂattened when the value of Technological Base (Log) is higher. These results provide supports for Hypothesis 2 (H2).

5. Discussion

This research is motivated by the seemingly perplexing theory and results regarding the inﬂuence of brokerage on innovation. Speciﬁcally, this study pays attention to the stream of research of alliance network and innovation, which neglects the beneﬁts of specialising resources from the alliance network on exploration versus exploitation and the correspondingly optimal choice between them at diﬀerent levels of brokerage.

Weﬁll in the gap by examining the inﬂuence of network density on exploratory innovation. The theoretical framework suggests that exploratory innovation and exploitative innovation are incom-patible when searching for resources from the alliance network and aﬁrm needs to specialise in one type of innovation. Since both spanning structural holes among direct partners and tightly con-nected partners are beneﬁcial for exploratory innovation, we posit that a low level, as well as a

Table 1.Descriptive statistics and correlations.

VariableMeanSDMin.Max.1234511.Technological Distance (cosine)0.200.340.001.000.090.170.050.480.6512.Technological Diversity (alliance11.Technological Distance (cosine)−0.000.060.370.310.48112.Technological Diversity (alliance

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