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Depth vs. Breadth: Network Strategy in Emerging Markets

Published online by Cambridge University Press:  20 January 2020

Shameen Prashantham*
Affiliation:
China Europe International Business School, China
Abby Jingzi Zhou
Affiliation:
University of Nottingham Ningbo, China
Charles Dhanaraj
Affiliation:
Temple University, USA
*
Corresponding author: Shameen Prashantham (sprashantham@ceibs.edu)
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Abstract

Using survey data from China and India, we explore the impact of network strategy of new ventures in emerging markets. We focus on two critical dimensions of network strategy, namely, broadening and deepening the network and two types of knowledge: market knowledge and technological knowledge. We find that proactive network deepening is associated with market knowledge and network broadening with technological knowledge. From a network perspective, our work highlights the counterintuitive outcomes of breadth versus depth orientation in network strategy, highlighting differences between advanced and emerging economies. We use a post-hoc multi-group analysis to show the differences even within the two emerging markets: India and China. The direct effect of partnering proactiveness on market knowledge in India is significantly higher than that in China but there is no significant difference as to the effect of technological knowledge. We use this exploratory study to highlight the opportunities for network and entrepreneurship scholars to study emerging markets and, in particular, undertake comparative studies between new ventures in China and India.

摘要

摘要

基于中国和印度的调查数据,我们探讨了新兴市场中新创企业网络战略的影响。我们重点研究了网络战略的两个关键维度:网络拓宽和网络深化,以及两类知识:市场知识和技术知识。我们发现,前瞻性的网络深化与市场知识相关,同时,积极的网络拓宽与技术知识相关。从网络的角度来看,我们的研究突出了网络战略中广度和深度目标与直觉相反的结果,并突出了发达经济体和新兴经济体之间的差异。我们通过后续的多群组分析来显示印度和中国两个新兴市场之间的差异。在合作主动性对市场知识的直接影响方面,印度明显高于中国,但其对技术知识的影响则没有显著差异。

Аннотация

АННОТАЦИЯ

На основании данных опроса из Китая и Индии, мы изучаем влияние сетевой стратегии, которую используют новые предприятия на развивающихся рынках. Мы рассматриваем два важных аспекта сетевой стратегии, а именно расширение и углубление сети, а также два типа знаний: знание рынка и технологическое знание. Мы обнаруживаем, что активное углубление сети связано со знанием рынка, а расширение сети с технологическими знаниями. С точки зрения сетевой теории, наша работа приходит к противоречивым выводам, которые связаны с аспектами широты и глубины в сетевой стратегии, подчеркивая различия между развитыми и развивающимися странами. Мы применяем ретроспективный анализ нескольких групп, чтобы показать различия даже на двух развивающихся рынках: в Индии и Китае. Прямое влияние партнерской активности на знание рынка в Индии значительно выше, чем в Китае, однако нет никаких существенных различий в отношении влияния технологических знаний. Мы проводим это предварительное исследование, чтобы показать возможности для изучения в сфере связей и предпринимательства на развивающихся рынках и, в особенности, для сравнительных исследований новых предприятий в Китае и Индии.

Resumen

RESUMEN

Usando datos de encuestas en China y en India, exploramos el impacto de la estrategia de redes en operaciones nuevas en mercados emergentes. Nos enfocamos en dos dimensiones críticas de la estrategia de redes, concretamente, ampliación y profundización de la red y en dos tipos de conocimiento: conocimiento del mercado y conocimiento tecnológico. Encontramos que una profundización proactiva de la red está asociada con un conocimiento del mercado, y una ampliación de la red con conocimiento tecnológico. Desde una perspectiva de redes, nuestro trabajo resalta los resultados contraintuitivos de la orientación de ampliar versus profundizar en la estrategia de redes, resaltando las diferencias entre economías avanzadas y emergentes. Usamos un análisis de grupos múltiples post hoc para mostrar las diferencias incluso entre dos mercados emergentes: India y China. El efecto directo de la proactividad de asociación en el conocimiento de mercado en India es más alto significativamente que en China, pero no hay una diferencia significativa en cuanto al conocimiento tecnológico. Usamos este estudio exploratorio para resaltar las oportunidades para estudiosos de las redes y el emprendimiento para estudiar mercados emergentes y, en particular que emprendan estudios comparados entre nuevas operaciones en China e India.

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Article
Copyright
Copyright © 2020 The International Association for Chinese Management Research

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INTRODUCTION

Knowledge is a strategic asset to new ventures in emerging economies. In a global economy, firms face a constant impetus to upgrade knowledge levels to keep up with competition. Often, faced with critical deficiencies in the institutional environments of emerging markets, new ventures resort to a network strategy, engaging in proactive behavior that deepens and/or broadens their relationships with other companies in order to generate valuable new knowledge (Batjargal et al., Reference Batjargal, Hitt, Tsui, Arregle, Webb and Miller2013; Li & Zhang, Reference Li and Zhang2007; Su, Xie, & Xang, Reference Su, Xie and Wang2015). While a network strategy is formidable in any context, it is especially vital for new ventures in emerging economies since these firms lack key resources and capabilities (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010; Zhou, Barnes, & Lu, Reference Zhou, Barnes and Lu2010). Absent a strong institutional environment, these firms seek to be ‘highly networked with external providers of requisite resources and information’ in order to upgrade their knowledge levels (Luo & Child, Reference Luo and Child2015: 398).

However, an important but as yet underexplored insight that emerges from Luo and Child (Reference Luo and Child2015) is that the nature of knowledge utilization – and hence how this is influenced by proactive network behaviors – may be different in emerging economies. Network behaviors emphasize agency, rather than network structures per se, and have particular relevance for new ventures that are unlikely to have extensive network structures in place, especially in emerging economies (Khanna, Reference Khanna2007). We start with the assumption that the role of network behaviors in fostering knowledge cannot be simply extrapolated from studies in advanced economies (e.g., Hansen, Reference Hansen1999; Phene, Fladmoe-Lindquist, & Marsh, Reference Phene, Fladmoe-Lindquist and Marsh2010).

Our goal here is to study the extent to which network strategy influences knowledge accumulation of new ventures in an emerging market context. Following network theorists, we focus on two distinct dimensions of network strategy – network broadening and network deepening – and study their role in fostering different knowledge types in emerging economy-based new ventures. Network broadening refers to the ‘the extent to which an entrepreneur reaches out to new people’ while network deepening describes ‘the extent to which an entrepreneur strengthens ties to existing personal network contacts’ (Vissa, Reference Vissa2012: 494). While new ventures make use of different types of knowledge to grow their business, we focus our attention on two critical knowledge types – technological knowledge and market knowledge – as the outcome variables of interest (Autio, George, & Alexy, Reference Autio, George and Alexy2011; Wiklund & Shepherd, Reference Wiklund and Shepherd2003; Zahra, Ireland, & Hitt, Reference Zahra, Ireland and Hitt2000). We recognize that in emerging market contexts, technological knowledge, as Luo and Child (Reference Luo and Child2015) suggest, may be predominantly know-what, but even then, is relatively scarce (Yip & McKern, Reference Yip and Mckern2016). And market knowledge is critical for ambitious new ventures that want to reach out to global markets for key components and technologies.

We ask: How does proactive networking strategy enable knowledge accumulation in new ventures from emerging markets? Our premise is that knowledge accumulation in an emerging economy context is qualitatively different than in developed economies owing to the nature of compositional approaches to technological know-what (Luo & Child, Reference Luo and Child2015) and institutional differences that affect the assimilation of market knowledge (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015). We draw on Hansen's (Reference Hansen1999) theorizing on search and transfer, framing the main challenge of accumulating technological knowledge as a search problem demanding network broadening and the challenge of market knowledge as a transfer problem demanding network deepening. Figure 1 provides an overview of our conceptual foundation. Our central argument is that for new ventures in an emerging economy, network broadening is primarily associated with technological knowledge levels, while network deepening is associated with market knowledge.

Figure 1. Transfer and search costs for new ventures in an emerging economy

Our empirical work draws on survey data from 187 information and communication technology (ICT) new ventures. Given our focus on emerging markets, we chose China and India – both with a vibrant entrepreneurial ecosystem. We focused on new ventures based in two locations, Bangalore in India and Zhongguancun in China. Notably, the former is referred to as the Silicon Valley of India (Lorenzen & Mudambi, Reference Lorenzen and Mudambi2013) and the latter, a district of Beijing, as the Silicon Valley of China (Khanna, Reference Khanna2007). Given the exploratory nature of our work and the small sample size, we used partial least squares, structural equation modelling (PLS – SEM) to analyze our model.

Emerging markets are not all alike (Kiss, Danis, & Cavusgil, Reference Kiss, Danis and Cavusgil2012). Our data gives us an opportunity to explore the differential effects of two large entrepreneurial nations. In a post-hoc analysis, we explore differences between the two countries in our sample, China and India. Khanna (Reference Khanna2007) examines how entrepreneurship is an important force in China and India, with similarities in both contexts, notably the challenge of dealing with immature institutions, but also points to important differences. Chatterjee and Sahasranamam (Reference Chatterjee and Sahasranamam2018) note that comparative studies between China and India present an untapped opportunity for researchers, and our post-hoc analysis provides a template to explore such questions further.

Our exploratory study indicates that proactive network deepening is associated with market knowledge and proactive network broadening with technological knowledge. Our post-hoc multi-group analysis shows a significant difference in the effect of partnering proactiveness on market knowledge between the Chinese and Indian subsamples (higher for India) but none for technological knowledge. We contribute by surfacing nuanced effects of proactive network deepening and broadening, and our findings are counterintuitive relative to previous work in advanced market settings (Hansen, Reference Hansen1999), thus echoing Xiao and Tsui's (Reference Xiao and Tsui2007) insight that theory developed in the West may not fully hold in emerging economies. Our work also expands upon Vissa's (Reference Vissa2012) study of network deepening and broadening, which was pioneering in unpacking network strategy but stopped short of considering their impact on firm outcomes. Furthermore, we make an agenda-setting contribution by highlighting differences between China and India; in so doing, we seek to stimulate larger-scale China-India comparisons in the future.

THEORETICAL BACKGROUND AND HYPOTHESES

New Ventures in Emerging Economies: Imperatives for Knowledge and Networks

New ventures in emerging economies face unique opportunities and challenges in relation to knowledge development since the entrepreneurial ecosystem is less mature (Li & Atuahene-Gima, Reference Li and Atuahene-Gima2001; Lu & Tao, Reference Lu and Tao2010; Peng, Lebedev, Vlas, & Wang, Reference Peng, Lebedev, Vlas, Wang and Shay2018). To understand how new ventures founded in emerging economies become more competitive by upgrading their stocks of knowledge, it is important to take into account the uniqueness of emerging economies. One approach that has highlighted this distinctiveness argues that emerging economy firms are adept at composition – that is, leveraging ordinary resources and integrating some level of product or service innovation with business model innovation at price-value ratios suitable to emerging economies (Luo & Child, Reference Luo and Child2015; Zhou, Li, Zhou, & Prashantham, Reference Zhou, Li, Zhou and Prashantham2019).

Luo and Child (Reference Luo and Child2015: 389) suggest that emerging economy firms tend to use a blend of imitation and innovation based on their ability ‘to synthesize and integrate disparate resources, including the open resources available to them’ while operating in an environment characterized by a ‘lack of advanced world-class and cutting-edge methods’ (380) and constraints ‘such as the lack of core technologies, low brand awareness and weak product differentiation’ (397). As a result, firms’ entrepreneurial behavior is crucial in coping with these institutional disadvantages (Bruton, Su, & Filatotchev, Reference Bruton, Su and Filatotchev2018). In emerging economies, these entrepreneurial attributes enable the effective exploitation of open resources (Luo & Child, Reference Luo and Child2015: 380).

Entrepreneurial behavior takes on added significance because even though the base level of knowledge is low, there is continuous pressure for firms to upgrade their knowledge base in a dynamic and competitive environment (Bruton et al., Reference Bruton, Su and Filatotchev2018; Luo & Bu, Reference Luo and Bu2018). While entrepreneurial behavior has multiple dimensions and manifestations, one that has been highlighted by scholars studying new ventures in emerging economies like China and India (Batjargal et al., Reference Batjargal, Hitt, Tsui, Arregle, Webb and Miller2013; Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010; Su, Xie, & Wang, Reference Su, Xie and Wang2015; Zhang & Li, 210; Zhou et al., Reference Zhou, Barnes and Lu2010) relates to entrepreneurially (in particular, proactively) building and leveraging network relationships. As Luo and Child (Reference Luo and Child2015) assert, entrepreneurs in emerging economies are potentially well networked with external sources of valuable resources and information, which is reflective of what they refer to as network competence. Leveraging networks is important as a means of building knowledge levels (Zhou et al., Reference Zhou, Barnes and Lu2010), yet little is known about the specifics of network competence, in particular regarding the differential effects of network actions (deepening vs. broadening, for instance) on different types of knowledge enhancement.

In sum, Luo and Child (Reference Luo and Child2015) as well as other work on new ventures in emerging economies (Batjargal et al., Reference Batjargal, Hitt, Tsui, Arregle, Webb and Miller2013; Bruton et al., Reference Bruton, Su and Filatotchev2018; Lu & Tao, Reference Lu and Tao2010; Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010; Su et al., Reference Su, Xie and Wang2015; Zhang & Li, Reference Zhang and Li2010; Zhou, Wu, & Luo, Reference Zhou, Wu and Luo2007) point to imperatives for emerging economy-based new ventures in the form of knowledge upgradation and knowledge-seeking networking. Gaining a deeper understanding of network agency in an emerging economy context is important because scholars have noted that ‘external knowledge search does not take place in a vacuum but is closely embedded in networks and ties of interpersonal relations…Resource and capability deficiencies, institutional voids, and structural obstacles… force managers to build ties’ (Liu, Chen, & Kittilaksanawong, Reference Liu, Chen and Kittilaksanawong2013).

Market and Technological Knowledge: Search and Transfer

In this study, we focus on two important types of knowledge: technological knowledge and market (i.e., internationalization) knowledge. Technological knowledge is specialized knowledge that forms the foundation for product and process development (Zahra et al., Reference Zahra, Ireland and Hitt2000). Market knowledge is knowledge that reflects a firm's resources and capabilities for international expansion (Johanson & Vahlne, Reference Johanson and Vahlne2009). Higher levels of technological knowledge provide the basis for a new venture's expansion (Autio, Sapienza, & Almeida, Reference Autio, Sapienza and Almeida2000) while higher levels of market knowledge give them the ability to execute a market expansion strategy (Johnson & Vahlne, 2009). These knowledge types are thus complementary and vital to the growth prospects of ambitious firms (Yli-Renko, Autio, & Tontti, Reference Yli-Renko, Autio and Tontti2002; Zahra et al., Reference Zahra, Ireland and Hitt2000).

We focus on these two types of knowledge because of their strong grounding in the entrepreneurship and international business (IB) literatures. While market knowledge builds downstream capabilities to sense and interpret customer needs, technological knowledge builds the capabilities to create the products or services to meet those needs (Fang, Wade, Delios, & Beamish, Reference Fang, Wade, Delios and Beamish2013). So, from the entrepreneurial context, these two variables ‘represent important knowledge-based resources applicable to a firm's ability to discover and exploit opportunities’ (Wiklund & Shepherd, Reference Wiklund and Shepherd2003: 1309). This is also consistent with the tradition in IB literature which has looked at two core variables, R&D and advertising intensity, as proxies for the two dimensions of firm specific advantage, in technological and market knowledge (Delios & Beamish, Reference Delios and Beamish2001; Rugman & Verbeke, Reference Rugman and Verbeke2001).

The relevance of both knowledge types can be seen in Luo and Child's (Reference Luo and Child2015) theorizing. While technological knowledge accumulation is typically portrayed in advanced economies as a process of absorption, Luo and Child (Reference Luo and Child2015) point out that, in emerging economies, the emphasis is on the composition of ‘suitable’ rather than ‘superior’ technologies. Unsurprisingly, new ventures in China and India have primarily focused on business model innovation rather than the technological breakthroughs and tend to operate on the basis of ‘suitable technology at lower cost by leveraging cheap R&D resources’ and ‘low-cost alternative technologies’ (397), not ‘superior resources per se’ (386). As such, it is more likely that these firms will engage in ‘adapting existing technologies and products rather than inventing entirely new ones’ (Luo & Child, Reference Luo and Child2015: 380). Thus, in the present context, we discuss know-what, which is easier to transfer than know-how. That said, finding technological knowledge still requires effort. In industries with standards-based technologies and interconnected interfirm networks such as the software industry[Footnote 1] ventures often seek to access, combine and build upon knowledge ‘components’ from global incumbents such as Microsoft or SAP, a process that is relatively nascent – and effortful – in emerging economies (Prashantham & Yip, Reference Prashantham and Yip2017).[Footnote 2]

In relation to market knowledge, we focus specifically on internationalization knowledge. One might ask why we focus on this construct given that emerging economies have large markets of their own that new ventures may focus on. The reasons are two-fold. First, as Luo and Child (Reference Luo and Child2015) have noted, smaller entrepreneurial firms in emerging economies, especially those located in reputed clusters, are able to source other forms of knowledge – notably technological knowledge – from ‘networks linked to domestic and foreign markets’ (393) and ‘the global open market for key components and technologies’ (397). This suggests that awareness of dealing with international markets is likely to be key to the overall knowledge upgradation process of new ventures in these settings. Second, in an emerging economy, entrepreneurial new ventures are known to explore international opportunities for accelerating their growth trajectories, and therefore seek to build ‘forward-looking knowledge about foreign markets from multiple sources of information’ (Zhou, Reference Zhou, Wu and Luo2007: 283).

In theorizing the effects of network behaviors on knowledge levels, we draw upon the distinction between search and transfer of knowledge (Hansen, Reference Hansen1999). Hansen's (Reference Hansen1999) seminal study focused on knowledge sharing for new product development within an organization. He made the distinction between the search for new knowledge and the transfer of this knowledge from one unit to another. Search becomes a necessary first step because relevant knowledge is often dispersed across units within an organization. Subsequently, the identified knowledge has to be transferred from the source unit. He showed that weak ties were conducive for search and strong ties for transfer, because more time is spent among the connected actors allowing the assimilation of the sourced knowledge. These insights hold relevance in an interorganizational context as well (Mariotti & Delbridge, Reference Mariotti and Delbridge2012).

In emerging markets like China and India, there are likely to be higher stocks of internationalization knowledge compared to technological knowledge that local ventures can tap into. It is notable that in emerging economies like China and India, international firms have been operating for a number of years since their respective liberalization policies began, i.e., for four decades in China and over 25 years in India. International firms that entered these markets have had to deploy and develop internationalization capabilities. Thus, local new ventures now have sources of gaining market knowledge, either through actual interaction or observation, that they could forge links with. At the same time, however, the local institutional contexts of emerging economy new ventures are likely to differ so much from those of their international firm ‘teachers’ that close interactions are likely required for learning to occur (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015).

The focus on upgradation to carry out innovation-related activities – by both foreign- and domestic-owned firms – is relatively recent in both China and India. Godinho and Ferreira (Reference Godinho and Ferreira2012) note the rise of IP patents in China and India but reckon these markets will catch up with advanced economies in a few decades. Thus, contemporary new ventures are operating with an improving but as yet immature technological environment. Of course, there have been many eye-catching business model innovations launched by startups in these economies that have gone on to become so-called unicorns; however, as previously noted, emerging economy firms appear to lag their advanced market peers in terms of core technology (Luo & Child, Reference Luo and Child2015). As such, technological knowledge is likely to be scarcer relative to internationalization knowledge for new ventures seeking to leverage their network relationships to augment their knowledge base.

Network Agency: Proactive Broadening and Deepening

External knowledge potentially accrues through networks that function as pipelines enabling information flows (Liu et al., Reference Liu, Chen and Kittilaksanawong2013). Building networks requires considerable effort. New ventures differ in their levels of network access partly due to differences in the extent of their partnering proactiveness, which is ‘the extent of efforts to identify potentially valuable partnering opportunities, and to initiate preemptive actions in response to identified opportunities’ (Sarkar, Echambadi, & Harrison, Reference Sarkar, Echambadi and Harrison2001: 702). A proactive stance towards partnering helps new ventures to expand their networks and leverage existing ties (Marion, Eddleston, Friar, & Deeds, Reference Marion, Eddleston, Friar and Deeds2015; Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010, Reference Prashantham and Dhanaraj2015).

Proactiveness implies an agency perspective of human action (Dhanaraj & Parkhe, Reference Dhanaraj and Parkhe2006; Emirbayer & Goodwin, Reference Emirbayer and Goodwin1994), in relation to network strategy. While network theory has historically posited a rich-get-richer account of network building whereby those endowed with ties are more likely to build new ones, scholars have recently challenged the implication that actors lacking ties are docile. For example, Ahuja, Soda, and Zaheer (Reference Ahuja, Soda and Zaheer2012: 435) note, ‘the potential role of conscious agency by network participants in creating network structures that benefit them…some deliberate network modifying actions by network actors in the present may have consequences for network structure later’. Their perspective resonates with that of others who have pointed out that entrepreneurs of new ventures may purposively manage impressions vis-à-vis potential partners (Zott & Huy, Reference Zott and Huy2007) or undertake catalyzing actions such as ‘dating’ multiple actors and gauging the sincerity of their interest in the focal actor (Hallen & Eisenhardt, Reference Hallen and Eisenhardt2012).

Firms that exhibit high levels of partnering proactiveness have higher-performing alliance portfolios (Sarkar et al., Reference Sarkar, Echambadi and Harrison2001) and generate more learning opportunities (Stam, Arzlanian, & Elfring, Reference Stam, Arzlanian and Elfring2014; Yli-Renko, Autio, & Sapienza, Reference Yli-Renko, Autio and Sapienza2001). Proactiveness can increase the diversity of ideas, information and advice flowing through networks (Gulati, Reference Gulati1999). Furthermore, a proactive disposition to partnering helps new ventures to extract value from their networks. When a venture adopts a proactive stance towards partnerships, its capacity to discern and adeptly channel attention to learning opportunities via existing relationships is enhanced since proactive behaviors are required to extract value from existing networks (Buckley & Prashantham, Reference Buckley and Prashantham2016). As Wang (Reference Wang2008: 640) notes, ‘proactive and extensive environmental scanning…serves as an impetus for information acquisition and dissemination, an important starting point for learning’. Echoing this viewpoint, Sarkar et al. (Reference Sarkar, Echambadi and Harrison2001: 703) observe, ‘Proactiveness may facilitate acquisition of specialized skills and knowledge’. In sum, partnering proactiveness can trigger the acquisition of external knowledge by expanding, diversifying, and extracting value from, networks (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010).

That said, proactiveness merely indicates that the action, which in this case relates to grasping partnering opportunities (Sarkar et al., Reference Sarkar, Echambadi and Harrison2001), is self-initiated. It does not, by definition, indicate the specific nature of the action. Here, a complementary advance over Sarkar et al. (Reference Sarkar, Echambadi and Harrison2001) is provided by Vissa (Reference Vissa2012), who distinguished between network broadening and deepening. Proactiveness can be manifested as network broadening or deepening strategies since the focal actor could scan for partnering opportunities among both existing and non-existing alters (Vissa, Reference Vissa2012). That is, new ventures could proactively look for a partner with certain complementary skills from among actors with which it has no links yet as well as from among its existing weak or latent ties (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015). Deeper engagement could then be forged if there is mutual interest in a partnership. Based on our agentic perspective, the network broadening-deepening distinction can be linked to Hansen's search-transfer notion.

Although prior work on knowledge search-transfer has tended to view networks, primarily in an intraorganizational context, as stocks (Hansen, Reference Hansen1999), for new ventures with emergent networks it seems more appropriate to focus on network behaviors (Vissa, Reference Vissa2012). Indeed, Hansen's follow-up work (Hansen, Mors, & Loval, Reference Hansen, Mors and Løvås2005) does allude to the processual aspect of knowledge search and transfer. They note that since it is not always clear during the search process who might be the relevant source of knowledge, the focal actor explores multiple weak ties within their wider set of network relationships as to their potential as knowledge sources. While Hansen et al. (Reference Hansen, Mors and Løvås2005) assume that the wider network is a given, for a new venture this is a process of network broadening. Subsequently, once useful knowledge sources have been identified, only these sources will be involved in the transfer process, not the wider network. The transfer process requires closer deep interactions between the source and destination of knowledge transfer. In the context of new ventures, this is a process of network deepening.

Thus, network broadening and network deepening are, respectively, behaviors that yield weak and strong ties (Sigfusson & Chetty, Reference Sigfusson and Chetty2013), and it can be inferred that these behaviors are especially important for knowledge search and transfer, respectively (Hansen, Reference Hansen1999). However, we know little about whether network broadening or deepening mediate the effects of partnering proactiveness in distinct ways, thereby leading to different outcomes in terms of new ventures’ level of technological knowledge and market knowledge. Our theorizing below considers the different levels of effort required in searching for and transferring technological knowledge and market knowledge in emerging markets. Note that there are qualitative differences in the level of the knowledge base and the institutional environment from the advanced economy setting used by Hansen and others. Tables 1 and 2 summarize key conceptual ideas and empirical studies, respectively, including effect sizes (where reported) for the latter.

Table 1. Summary of literature – Select theory-building papers

Table 2. Summary of Literature – Key Empirical Papers

Network Strategy and Knowledge Types

Our baseline expectation is that partnering proactiveness facilitates knowledge enhancement. In relation to technological knowledge, partnering proactiveness is likely to lead new ventures to engage in technology-related alliances and interactions that can foster innovative thinking and output (Yli-Renko et al., Reference Yli-Renko, Autio and Sapienza2001). This is important for new ventures since many focus on specific niches and thrive based on their specialist knowledge-base which needs to be continuously refined and refreshed (Zahra et al., Reference Zahra, Sapienza and Davidsson2006). Additionally, in terms of market knowledge, proactive ventures are more likely to gain exposure to actors both overseas and in their own home market possessing relevant routines and capabilities that lead to beneficial knowledge spillovers to the focal actor (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015). The general logic that partnering proactiveness facilitates the acquisition of external knowledge by expanding networks, diversifying them and extracting value from network ties can be expected to hold in the case of both technological knowledge and market knowledge (Yli-Renko et al., Reference Yli-Renko, Autio and Sapienza2001, Reference Yli-Renko, Autio and Tontti2002). In terms of formally laying out hypotheses, in the interest of highlighting the novel insights of the study, we focus on our mediating hypotheses, which we turn to next.

Network breadth and technological knowledge

The relative difficulty in accessing technological knowledge locally is well captured in a series of studies that insightfully highlight the value of returnees to China in creating technological knowledge spillovers (Filatotchev, Liu, Lu, & Wright, Reference Filatotchev, Liu, Lu and Wright2011; Liu, Lu, Filatotchev, Buck, & Wright, Reference Liu, Lu, Filatotchev, Buck and Wright2010; Liu, Wright, Filatotchev, Dai, & Lu, Reference Liu, Wright, Filatotchev, Dai and Lu2010). Also, in a context like India, returnees are known to facilitate knowledge spillovers (Lorenzen & Mudambi, Reference Lorenzen and Mudambi2013). What this shows is that local sources of technological knowledge are relatively scarce in emerging economies. Although the situation is changing (Yip & McKern, Reference Yip and Mckern2016), in relative terms, accessing technological knowledge is likely to require a wider search predicated on network broadening.

Searching for technological knowledge in emerging markets is not easy due to the institutional deficits prevalent in these settings that create ‘contexts of scarce resources and inferior technologies’ (Corredoira & McDermott, Reference Corredoira and Mcdermott2014: 699), thus constraining new venture innovation (Li & Zhang, Reference Li and Zhang2007). Searching for technological knowledge in emerging markets will typically involve screening a much larger selection of candidates than in advanced markets, as the hit rate of finding high-quality technological knowledge – often possessed by new partners like foreign firms[Footnote 3] – is lower in institutionally weaker settings. The apparent success of some technology-based firms stems more from business model and process innovations rather than cutting-edge scientific advances; that is, being technologically ‘good enough’ (Gadiesh, Leung, & Vestring, Reference Gadiesh, Leung and Vestring2007) is oftentimes sufficient for a so-called high-tech firm to be successful in emerging markets. Thus, identifying sources of valuable technological knowledge involves casting the net widely, and multiple actors will typically have to be engaged with before such sources can be identified (Prashantham & Birkinshaw, Reference Prashantham and Birkinshaw2008; Zhang & Li, Reference Zhang and Li2010), highlighting the importance of network broadening.

Once found, transferring technological knowledge in emerging markets is relatively easy. As stated earlier, this is because new ventures in emerging economies typically operate with knowledge components that they combine with extant ordinary resources in a process of composition rather than absorption (Luo & Child, Reference Luo and Child2015). Moreover, weaker institutions mean that knowledge leakages occur more readily. Even the existence of a patent does not effectively guard against technology being copied if there exists an ‘inadequate legal framework that defines and protects property rights’ (Li & Atuahene-Gima, Reference Li and Atuahene-Gima2001: 1125). The movement of people across firms might take place in emerging markets even if non-compete clauses attempt to restrict knowledge leakage, as people in these contexts are more ‘willing to make lateral moves’ (Ready, Hill, & Conger, Reference Ready, Hill and Conger2008). Therefore, network deepening is not that crucial to accessing technological knowledge.[Footnote 4]

Network depth and market knowledge

In the case of market knowledge, however, we expect the opposite situation. That is, searching for market knowledge is not difficult, but transferring it is. A source of market knowledge is foreign firms operating in emerging markets which are not difficult to identify. Other sources are readily identifiable domestic firms that have internationalized (especially into advanced markets) whose success cannot be easily feigned (Ramamurti, Reference Ramamurti2004). This applies not only to the celebrated success stories like China's Lenovo and India's Tata group but also relatively less well-known but nevertheless internationally successful firms that are accessible to local ventures (Elango & Pattnaik, Reference Elango and Pattnaik2007). Thus, casting the net widely is not an imperative, and so network broadening is not so crucial.

However, in direct contrast to technological knowledge, while finding market knowledge sources is not difficult, transferring it is more difficult. This difficulty largely stems from the tacit nature of this knowledge and the institutional distance between home and desirable host markets for emerging market firms (Zhou et al., Reference Zhou, Barnes and Lu2010), especially those for whom internationalization is a form of institutional escapism (Yamakawa, Deeds, & Peng, Reference Yamakawa, Peng and Deeds2008). For these firms, the locus of internationalization routines tends to be confined to distant foreign markets, with little overlap with their domestic ones. Moreover, the market knowledge built by these firms is likely highly idiosyncratic, resulting in greater causal ambiguity for external observers (Johanson & Vahlne, Reference Johanson and Vahlne2009). Hence market knowledge cannot be easily accessed through casual engagement at home; rather, close interactions and observation are typically required (Boso, Story, & Cadogan, Reference Boso, Story and Cadogan2013; Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015). This imperative is enhanced in Asian emerging economies where personal connections matter greatly for market learning.[Footnote 5] Therefore, network deepening seems especially important in market knowledge outcomes, since this provides the requisite access to understanding better other actors’ internationalization activities.

Thus, transferring internationalization knowledge into emerging economy ventures likely needs the formation of deep network ties with actors possessing such knowledge through their own internationalization activity (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015; Zhou et al., Reference Zhou, Wu and Luo2007). Here, the challenge is in understanding how to adapt to drastically different international business environments (Kiss et al., Reference Kiss, Danis and Cavusgil2012). Although there is opportunity to observe what others are doing, it is only through close interaction that the focal emerging economy new venture will be able to understand how to develop and execute an internationalization strategy (Tracey & Phillips, Reference Tracey and Phillips2011).

Synthesis

Extrapolating from Hansen's work (Hansen, Reference Hansen1999; Hansen et al., Reference Hansen, Mors and Løvås2005), a key premise of this study is that there are likely to be differential burdens associated with the search-transfer process vis-à-vis technological and market knowledge in prominent emerging markets such as China or India (Khavul, Pérez-Nordtvedt, & Wood, Reference Khavul, Pérez-Nordtvedt and Wood2010; Liu et al., Reference Liu, Chen and Kittilaksanawong2013). As depicted in Figure 1, technological knowledge is more difficult to search for because it is scarce, but easier to transfer because of the emphasis on composition-based on technological know-what (Luo & Child, Reference Luo and Child2015) and a weaker intellectual property regime (Li & Atuahene-Gima, Reference Li and Atuahene-Gima2001). In contrast, market knowledge is easier to search for because of the numerous international firms operating in these markets but harder to transfer because of the large gap between the firm's operational routines in the home market and what is required in distant international markets (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010).

Could network deepening enhance technological knowledge? Likewise, could network broadening help build market knowledge? This is certainly plausible, and one might expect this in an advanced economy context where deeper ties could be very important to transfer tacit technological knowhow while a broader set of ties could yield (international) market knowledge. However, we argue that these effects will be less dominant in emerging economies where the institutional context gives rise to differences in terms of the nature of technological knowledge (predominantly know-what rather than know-how) and market knowledge (more difficult to grasp because of inexperience with international markets).

Our arguments are the converse of the ones presented above: network deepening in itself may not suffice in tapping appropriate sources of technological knowledge since this requires a broader search in an emerging economy. Likewise, network broadening may not yield strong ties that facilitate market knowledge transfer given local inexperience and unfamiliarity with internationalization. Our logic is rooted in the existence of institutional differences that affect the relative burden of transfer and search of these knowledge ties (see Figure 1). The above arguments point to dominant roles for an aspect of network strategy (deepening or broadening) vis-à-vis knowledge type (market or technological), a situation that is likely different from advanced economies.

Synthesizing, we posit that for new ventures in emerging economies:

Hypothesis 1: Ceteris paribus, the effect of partnering proactiveness on technological knowledge will be mediated more by network broadening than by network deepening.

Hypothesis 2: Ceteris paribus, the effect of partnering proactiveness on market knowledge will be mediated more by network deepening than by network broadening.

METHODS

Data Collection

Our sample comprises information and communication technology (ICT) entrepreneurial ventures (Zahra et al., Reference Zahra, Ireland and Hitt2000). Given our interest in emerging markets, we focused on China and India. Both have been the setting for numerous previous studies on knowledge and networks (e.g., Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015; Zhou et al., Reference Zhou, Barnes and Lu2010). However, since both are vast economies, we deemed it prudent to narrow our focus to a single entrepreneurial ecosystem in each country. Specifically, survey data was collected from new ventures in the ICT industry in two emerging market locations: Bangalore, India and Zhongguancun, China. The former is referred to as the Silicon Valley of India (Lorenzen & Mudambi, Reference Lorenzen and Mudambi2013) and the latter, a district of Beijing, as the Silicon Valley of China (Khanna, Reference Khanna2007). By focusing on a high-reputation cluster in each country we felt we could reasonably expect that new ventures would have an interest in accumulating technological knowledge and market knowledge. Furthermore, this approach seemed more amenable to making meaningful comparisons between the two countries.

In Bangalore, a database of 530 firms was compiled from industry sources, notably the trade body for software services, Nasscom. In China, we obtained a list of 370 companies from the Administrative Committee of Zhongguancun Park. The data collection efforts yielded a sample of 216 ventures: 132 from India and 84 China. However, we discarded 15 questionnaires from the Indian subsample and 14 from the Chinese subsample owing to incomplete data. This left us with a total of 187 questionnaires, 117 from India and 70 from China. In the case of the latter, we worked with a slightly smaller database and the response rate was roughly similar (22% and 19%, respectively) in both markets.

Collecting survey data in emerging markets can be challenging, so we trained field agents in both markets to hand-collect the data. In order to ensure data quality, we trained the interviewers prior to the survey and kept regular and extensive contact with them throughout the data collection process. Survey progress, status reports, and data entry were checked on a weekly basis throughout the data collection process. After the survey was completed, we conducted further quality control by checking verifiable information from the questionnaires on the companies’ websites and conducting interviews with a few randomly selected firms. Thereafter, a second wave with a shorter version of the survey was conducted to obtain a different source of data on the dependent variables, which was used primarily as a validating mechanism to confirm the veracity of the data obtained in the first wave, as well as a robustness check for our analyses.

Variable Measures

Dependent variable measures

Key constructs were operationalized using measurement scale items taken from previous studies that are based on seven-point Likert-type scales ranging from 1= ‘strong disagree’ to 7= ‘strong agree’ (see Appendix I). Market knowledge was measured with four items using Likert-like scales (see Appendix I) from previous studies (Eriksson, Johanson, Majkgard, & Sharma, Reference Eriksson, Johanson, Majkgard, Sharma, Forsgren, Holm and Johanson1997) that capture the firm's understanding of how to undertake international business. Technological knowledge was similarly measured using four items (see Appendix) from previous studies (Autio et al., Reference Autio, Sapienza and Almeida2000).

Independent variable measures

Network deepening and network broadening vis-à-vis relationship-building with other companies were measured using seven and five items, respectively, taken from Vissa's (Reference Vissa2012) measures (see Appendix). Network proactiveness was measured with four items using Likert-like scales (see Appendix) from previous studies (Sarkar et al., Reference Sarkar, Echambadi and Harrison2001).[Footnote 6]

Control variable measures

Firm age and firm size were measured as the logarithm of the number of years since founding and the logarithm of the number of full-time employees, respectively (Stam et al., Reference Stam, Arzlanian and Elfring2014). We controlled for internationalization degree since market knowledge and technological knowledge can increase with internationalization (Zahra et al., Reference Zahra, Ireland and Hitt2000). The degree of internationalization was measured as the percentage of international revenues vis-a-vis total revenues. Based on the same reasoning, we controlled for overseas study experience (total of number of years studied abroad by the top management team), overseas work experience (total of number of years worked abroad by the top management team) and foreign work experience (total of number of years worked in foreign companies by the top management team). We also used a country dummy variable (China = 1, India = 0).

Data Analysis

We use multigroup structural equation modeling with PLS software to analyze our data. PLS works efficiently with complex models and small sample sizes (Fornell & Bookstein, Reference Fornell and Bookstein1982). In contrast to CB-SEM, which requires a minimum sample size of 200 (Afthanorhan, Reference Afthanorhan2013; Marsh, Hau, Balla, & Grayson, Reference Marsh, Hau, Balla and Grayson1998), PLS-SEM does not require a large sample size and can deal with complex models with many indicators and path relationships with relatively small samples (Hair, Ringle, & Sarstedt, Reference Hair, Ringle and Sarstedt2011). Most of the constructs in this study had multiple indicators, and an advantage of PLS is that it could weight indicator loadings on a construct in the context of the theoretical model rather than in isolation (Hair, Ringle, & Sarstedt, Reference Hair, Ringle and Sarstedt2013; Hulland, Reference Hulland1999).

More importantly, PLS does not face identification problems when analyzing reflective and formative measurement models simultaneously, and therefore it can be applied in a wide variety of research situations (Hair et al., Reference Hair, Ringle and Sarstedt2013). The multi-group analysis function in PLS also allowed us to compare our model across two groups of data (India vs. China) to see whether the path coefficients in our path model significantly differ between these two countries. Therefore, we decided to adopt PLS-SEM, and we conducted our data analysis following the guidelines suggested by Hair et al. (Reference Hair, Ringle and Sarstedt2013). The results of data analysis can be presented in two steps. First, in order to ensure the measurement validity and reliability of our theoretical framework, criteria on internal consistency, indicator reliability, convergent validity, and discriminant validity have been evaluated for our constructs.

To test for internal consistency, we checked to ensure that all the composite reliability values were higher than 0.7. We also tested indicator reliability. Most of our indicator loadings were higher than 0.6, and several of them were higher than 0.7. A small number of indicators with loadings lower than 0.5 were deleted to achieve an increase in both composite reliability and average variance extracted (AVE). In terms of convergent validity, two of the three AVE scores were above 0.49, and one was 0.48. All reflective constructs have a good discriminant validity since the indicators’ outer loadings on their own constructs were all higher than all their cross loadings with other constructs. The square root of the AVE of each construct was larger than its highest correlation with any other construct in the model, which established discriminant validity (Fornell & Larcker, Reference Fornell and Larcker1981).

We also evaluated the measurements of the formative constructs (network deepening and network broadening) by examining the collinearity issues and assessing the significance and relevance of the formative indicators. In terms of the significance and relevance of the formative indicators, we examined each indicator's outer weight (relative importance) and outer loadings (absolute importance) and used bootstrapping to assess their significance. Most of the indicators’ outer weights and outer loadings were significant. Second, to evaluate the structural model of our theoretical framework, we examined construct collinearity, the coefficient of determination (R2), the significance of path coefficients, and direct and mediation effects (Hair et al., Reference Hair, Ringle and Sarstedt2013). All of the R2 scores of variables in our model were above 0.25, and the R2 score for the final two dependent variables (market knowledge and technological knowledge) was 0.38 and 0.49, respectively. Following the suggestions of Lewin et al. (Reference Lewin, Chiu, Fey, Levine, McDermott, Murmann and Tsang2016), we also tested the model without the control variables. Without the control variables, the R2 score for the final two dependent variables (market knowledge and technological knowledge) was 0.36 and 0.47 respectively.

We also checked the goodness-of-fit (GoF) criterion in this study for diagnostic purposes (Tenenhaus, Vinzi, Chatelin, & Lauro, Reference Tenenhaus, Vinzi, Chatelin and Lauro2005). GoF (0 ≤ GoF ≤ 1) is defined as the geometric mean of the average communality and average R2 (for endogenous constructs). In line with the effect size for R2 (small: 0.02; medium: 0.13; large: 0.26) proposed by Cohen (Reference Cohen1988), Wetzels, Odekerken-Schröder, and Van Oppen (Reference Wetzels, Odekerken-Schröder and Van Oppen2009) suggest the following GoF criteria for small, medium, and large effect sizes of R2 by substituting the minimum average AVE of 0.5 and the effect sizes for R2 in the equation defining GoF (small = 0.1; medium = 0.25; large = 0.36). For the complete model in the study, we followed the guidelines of Wetzel et al. (Reference Wetzels, Odekerken-Schröder and Van Oppen2009) and obtained a GoF value of 0.42, which exceeds the cut-off value of 0.36 for large effect size of R2 and allows us to conclude that our model performs well compared to the baseline values defined above.

In addition, the tested model was expanded to examine construct collinearity, and the results were satisfactory (all of the VIF values were far below 5). This further suggests that multi-collinearity is not an issue (Hair et al., Reference Hair, Ringle and Sarstedt2013). Significance of path coefficients was calculated using a Bootstrapping Algorithm with 5000 subsamples for two-tailed test.

We adopted several approaches to avoid the problem of common method bias. First, in the questionnaire design, we mixed the order of the measurement of the predictor and criterion variables. During data collection, we conveyed to respondents that the purpose of this survey was only for academic research, and there were no ‘right’ or ‘wrong’ answers for the questions. These procedures could reduce our respondents’ apprehension over their responses and make them less likely to edit their choices to give what they perceive as the best answers (Podsakoff, MacKenzie, Lee, & Podsakoff, Reference Podsakoff, Mackenzie, Lee and Podsakoff2003). The respondents were also informed that their personal information and answers would be fully confidential and anonymous, therefore, they would be more aligned with the research goal.

Second, we followed Sarkar et al. (Reference Sarkar, Echambadi and Harrison2001) and conducted a Harman single-factor test (Podsakoff & Organ, Reference Podsakoff and Organ1986) by loading all the measures into an exploratory factor analysis with the assumption that the presence of common method variance (CMV) is indicated by the emergence of either a single factor or a general factor accounting for the majority of covariance among measures (Podsakoff et al., Reference Podsakoff, Mackenzie, Lee and Podsakoff2003). In our model, the variance of a single factor was 24%, therefore, the majority of the variance could not be explained by a single factor and we inferred this research does not have a problem of CMV.

Third, according to the suggestions of Podsakoff et al., (Reference Podsakoff, Mackenzie, Lee and Podsakoff2003) and the specific procedures in Liang, Saraf, Hu, and Xue (Reference Liang, Saraf, Hu and Xue2007), we included a common method factor, which included all the principal constructs’ indicators, in the model and calculated each indicator's variances substantively explained by the principal construct and by the method. The average variance explained by the substantive factor was 0.41, whereas the average variance of the common method factor was 0.01 and most of the method factor loadings were not significant. Based on the small magnitude and insignificance of method variance, we concluded common method bias is not a serious concern for this study.

Fourth, we followed the procedures and suggestions in Kock (Reference Kock2015) and tested all VIF scores of all variables via full collinearity tests, all of them are lower than 3.3. This result also indicated our model can be considered free of common method bias. Besides, to further address concerns about common method variance, we also re-ran the model using data collected from the second wave of the survey for the dependent variables and obtained consistent results. Although the second wave sample was small (96 Indian firms and 32 Chinese firms) and we did not use it in data analysis, the robustness checks provided us more confidence in the quality of our data and the veracity of the analyses.

RESULTS

The direct effects of partnering proactiveness on technological knowledge and market knowledge, respectively, were significant and positive (0.41, P value: 0.00; 0.24, P value: 0.03). We tested the mediating effects in H1 and H2. H1 was about the mediating role of network broadening in the relationship between partnering proactiveness and technological knowledge. The direct effect between partnering proactiveness and technological knowledge was significant. The relationships between partnering proactiveness and network broadening, and between network broadening and technological knowledge were also significant (0.51, P value: 0.00; 0.33, P value: 0.00, respectively). The variance accounted for (VAF) score for this mediation path was 0.29. In our data analysis, we also tested the relationship between network deepening and technological knowledge, but it was not significant (0.13, P value: 0.13). Therefore, we removed this relationship in testing our final model and have represented it as a dotted line (see Figure 2). Hence, the result indicates the relationship between partnering proactiveness and technological knowledge was partially mediated by network broadening rather than by network deepening; thus, H1 received support.

Notes: NB. Dotted lines represent non-significant relationships.

Figure 2. Model with results

H2 was about the mediating role of network deepening between partnering proactiveness and market knowledge. The direct effect between partnering proactiveness and market knowledge was significant. The relationships between partnering proactiveness and network deepening, and between network deepening and market knowledge were also significant (0.56, P value: 0.00; 0.35, P value: 0.00, respectively). The variance accounted for (VAF) score for this mediation path was 0.45. We also tested the relationship between network broadening and market knowledge, but it was not significant (0.17, P value: 0.14), which we have depicted as a dotted line in our final model (see Figure 2). Hence, the result indicates the relationship between partnering proactiveness and market knowledge was partially mediated by network deepening rather than by network broadening; thus, H2 was supported.

Post-Hoc Analysis: Exploring Contextual Differences

Having data from two important emerging economies provides us with an opportunity to respond, in an exploratory way, to Luo and Child's (Reference Luo and Child2015) call for ‘international comparative research’ (397), since there is ‘diversity and dynamism of different emerging economies’ (404). Indeed, a McKinsey (2018: 1) report observed that ‘the catchall term ‘emerging economies’ is misleading’. Given the paucity of cross-national comparisons of markets like China and India in prior research, it is challenging to put forward definitive hypotheses. One argument based on the idea that partnering proactiveness will have a greater payoff in resource-poorer contexts might suggest that Indian ventures would show a more significant relationship between partnering proactiveness and knowledge levels. This is because the scale and magnitude of state support is less than what the Chinese government provides (Khanna, Reference Khanna2007; Krishnan & Prashantham, Reference Krishnan and Prashantham2018; Ramamurti & Hillemann, Reference Ramamurti and Hillemann2018). Alternatively, a more munificent environment might provide a greater payoff for partnering proactiveness vis-à-vis technological knowledge since resources are more readily accessible locally.

Another line of argument might focus on the nature of the two markets’ dominant technological ecosystem-orchestrating actors. China has its own distinctive ecosystem players such as Baidu, Tencent, and Alibaba whereas in India it is typically multinational players such as Google, Facebook, and Microsoft that dominate. This might mean that new ventures in a China may not benefit as much from partnering proactiveness as the Indian firms, to gain (international) market knowledge.

With regard to the effect of partnering proactiveness on technological knowledge, although the path coefficients of Indian new ventures were higher (0.14) than the path coefficients of Chinese new ventures, the difference was not significant (P value: 0.36). By contrast, the effect of partnering proactiveness on market knowledge was significantly higher among Indian new ventures than Chinese new ventures (0.58, P value: 0.01). We speculate on the potential reasons for these differences in the Discussion section below. All of the foregoing results are shown in Figure 2, Table 3, and Table 4.

Table 3. Multi-group analysis results (India versus China)

Table 4. Latent variable correlation from PLS

DISCUSSION

Based on survey data from 187 technology-based new ventures in China and India, the present study examines the effects of network strategies on market knowledge and technological knowledge, highlighting differential effects of network broadening and deepening, and also differences between the Chinese and Indian subsamples. Below we discuss the study's contributions, limitations, research directions and practitioner implications.

Contributions

Our work makes two broad contributions to research on emerging markets. First, we contribute to research on new ventures in emerging markets, by providing a nuanced network strategy model that can be adapted to the institutional context in which it is based and the strategic intent a user wants to pursue. Counterintuitively, network broadening, which in advanced markets may lead to new market knowledge (Chetty & Pahlberg, Reference Chetty, Pahlberg, Fernhaber and Prashantham2015), is associated with technological knowledge. Similarly, network deepening is associated with market knowledge. Our insight lies in showing that in emerging markets’ institutional conditions (Hoskisson, Wright, Filatotchev, & Peng, Reference Hoskisson, Wright, Filatotchev and Peng2013; Kiss et al., Reference Kiss, Danis and Cavusgil2012) may alter the salience of network behavioral mechanisms for knowledge development outcomes of emerging economy new ventures.

By highlighting the differential outcomes of pursuing a network deepening versus network broadening strategy in emerging markets, our study suggests some nuanced differences from the typical observations in developed markets.[Footnote 7] This stems from differential burdens associated with searching for technological knowledge and transferring market knowledge (Dhanaraj, Lyles, Steensma, & Tihanyi, Reference Dhanaraj, Lyles, Steensma and Tihanyi2004). In addition to extending Vissa (Reference Vissa2012) who stopped short of highlighting the differential effects of network strategy, our work resonates with Luo and Child's (Reference Luo and Child2015: 404–405) composition-based view. They rightly argue that ‘management theories are a defining invention of Western scholarship… [But] these are no longer the preserve of the West…EEEs [emerging economy enterprises] have been deliberately pursuing their own distinctive paths, given the unique internal and external imperatives that they face’. Our work also highlights the distinctiveness of the knowledge enhancement challenges, and associated network strategy, of new ventures in emerging economies vis-a-vis those in advanced economies.

Second, we make an agenda-setting contribution by highlighting subtle differences in specific network context of emerging markets.[Footnote 8] There are likely to be differences in the extent to which partnering proactiveness impacts network outcomes: our study finds that this effect on market knowledge is significantly stronger for India than China. Thus, our work provides impetus for pursuing further comparative studies across China and India. While there are a multitude of relevant studies that focus either on India or on China, there are not enough studies that take a broader comparative look. As Khanna (Reference Khanna2007) points out, India and China provide two large natural experiments in economic evolution, and it may be worthwhile for more such studies to highlight nuanced applications in diverse contexts.

Why are there differences in the results between China and India, albeit only in relation to market knowledge? At this stage, our thoughts are speculative. As noted, partnering offsets disadvantages, and this effect becomes even more important in a less benign environment. While emerging economies on average have greater institutional weaknesses and voids compared to advanced economies (Batjargal et al., Reference Batjargal, Hitt, Tsui, Arregle, Webb and Miller2013), there are still differences in the level of institutional support among emerging markets. Although both the Chinese and Indian governments have made efforts to foster economic development, it would appear that China has the edge in terms of government-created advantages (Hoskisson et al., Reference Hoskisson, Wright, Filatotchev and Peng2013; Khanna, Reference Khanna2007; Ramamurti & Hillemann, Reference Ramamurti and Hillemann2018). Specifically, in relation to entrepreneurship, new ventures in China have far greater access to financial capital in the wake of policies such as ‘mass innovation, mass entrepreneurship’ (Lei, Reference Lei2017). When explicit government support is less forthcoming, we expect the partnering proactiveness of new ventures in that environment to ‘increase the potential pay-off for firms that can form links with partners that possess new and complementary competencies’ (Sarkar et al., Reference Sarkar, Echambadi and Harrison2001: 704).

Furthermore, the rapid growth of a distinct home-grown ecosystem dominated by giants such as Alibaba and Tencent – which can partly be attributed to government actions to promote local firms and restrict foreign ones (Yip & McKern, Reference Yip and Mckern2016) – gives Chinese startups alternative learning sources relative to their Indian counterparts who primarily coalesce around Western ecosystem leaders such as Google and Facebook. Additionally, the market structure in India may be driving more new ventures to seek foreign markets. Thus, our preliminary thinking is that the payoff for proactive behaviors is greater in an environment that is less benign compared to one that is more benign. That said, we also note that were no significant differences in relation to technological knowledge. Thus, part of the value of our study is in cautioning against overstating intercountry differences. Future research can fruitfully explore these ideas further.

Beyond emerging market research, a key implication of our work is that while undoubtedly network structural characteristics are important in explaining how organizational knowledge is developed, what new ventures are inclined to do vis-a-vis network actors – proactively pursuing partners and, more specifically, deepening extant ties or building new ones – matters greatly. The emphasis here shifts the focus from merely considering network structure to embracing network agency. In so doing our treatment of network behaviors incorporates more recent work done by Vissa (Reference Vissa2012) on the distinction between network broadening and deepening. The current research goes beyond his pioneering but limited focus on describing the two distinct processes rather than attempting to explore their differential effects, as we do in this study.

Limitations

The size of our sample in our study poses a significant limit on us. The novelty of the study lies in including survey data from both China and India. Comparative studies across these two markets are rare because of the complexity involved in gathering data but they can be valuable to gain a ‘more fine-grained understanding of the country context’ (Hoskisson et al., Reference Hoskisson, Wright, Filatotchev and Peng2013: 1295). Our study is a preliminary step in this direction. While we are conscious of the limitations of the small sample size, we submit that this study represents a useful exploratory effort that could further an agenda around getting a more nuanced understanding of emerging economy contexts. Given the constraint that our data imposes, we are not able to use more sophisticated models such as 2SLS to address the issues of endogeneity. We have, however, taken some steps to mitigate this issue. We controlled for CEO's overseas work experience, overseas work education, and work experience in foreign firms, and firm's age, size, and internationalization degree. Furthermore, the unobserved heterogeneity seems negligible considering the specific context of our data. We are dealing with new ventures that are typically at the lower end of possessing established networks (yet). The firms in our sample have an average age of 8 (years), with an SD of 2.5. Additionally, emerging economy clusters have less mature entrepreneurial ecosystems. Thus, the risk of endogeneity is even lower than if we had data from a sample of new ventures in an advanced economy. Also, given that we test in two different settings, the probability of endogeneity driving our results may be further reduced.[Footnote 9]

The boundary conditions of our work are akin to those of Luo and Child's (Reference Luo and Child2015) CBV: our results are more likely to hold at the business level for younger small firms in the early stages of organizational development. As more returnees, for instance, with higher knowledge bases get involved in such ventures, then their network behaviors may well reflect those we would expect in advanced economies.

Future Research

First, future research could examine larger samples from more settings. The relatively small sample in this study partly reflects our decision to focus on just one (important) cluster in each of the two countries. Future researchers should aim to collect bigger samples from multiple clusters in the region. Furthermore, as a cross-sectional study, the results may be inadequate in terms of confirming causality, i.e., ensuring against reverse causality.[Footnote 10]

Second, future research could also test the effects of network strategy on performance outcomes such as profitability or growth as our ultimate dependent variable. Our current model reflects the outcomes of particular interest to this study viz. technological knowledge and market knowledge and is thus consistent with our objectives. Moreover, to be candid, it is difficult to obtain reliable performance data from emerging market new ventures. The overwhelming majority of these firms are privately held. Thus, performance data cannot be obtained from secondary sources and the firms themselves tend to treat such information as highly confidential. We acknowledge that it would be of interest to include a further outcome such as performance in future research if such data can be obtained.

Third, future research could examine the impact of partner types, not merely network behaviors, on knowledge accumulation and performance. In dealing with network behaviors we make no reference to alters (i.e., partners). It would seem likely that which partners are proactively partnered with – for instance venture capital firms as distinct from distribution partners – will likely moderate the relationships in our model. Future research could usefully tease out the differential effects of different types of alters (partners) and would also do well to include samples from advanced markets and make comparisons so that we deepen our understanding of different network contexts.

Implications for Practitioners

This study yields valuable lessons for entrepreneurs in emerging markets. First, the study underlines the importance of partnering proactiveness not just for gaining new partners but also for facilitating knowledge outcomes. Put differently, not only is it important for new ventures to build and leverage networks actively they should also do so reflectively (that is, paying attention to learning outcomes in order to gain new knowledge). Skelta, an Indian firm described by Prashantham and Dhanaraj (Reference Prashantham and Dhanaraj2015), illustrates this well: the startup-built market and technological knowledge by learning from a network of partners that it built and progressively deepened the partnership with key actors, such as Microsoft.

Second, the study also provides nuanced understanding of how proactive behaviors can be manifested – deepening existing networks or broadening these by adding new ties – and that these manifestations yield differential information. This means that entrepreneurs should not only leverage networks actively, they should do so discerningly (that is, recognizing that different network actions likely produce different effects in terms of knowledge outcomes). Although no longer a startup, Xiaomi, a Chinese firm highlighted by Luo and Child (Reference Luo and Child2015), illustrates the benefits of nuanced partnering efforts by enhancing its technological diversity through broadening its partner base whereas deepening partnerships has helped it to grow its business in key markets like India.

Third, entrepreneurs should consider the peculiarities of their own institutional context. While emerging market ventures may have some broadly similar characteristics, such as institutional voids, the ‘devil is in the detail’: how these firms actually cultivate and leverage networks needs to match their own distinct institutional environment. While new ventures from any emerging market may benefit from partnering with foreign multinationals, for instance, to enhance their knowledge base (Prashantham & Birkinshaw, Reference Prashantham and Birkinshaw2008), the emphasis may be different in China versus India. Indian new ventures in the Microsoft Accelerator in Bangalore would do well to tap into global networks to internationalize whereas Chinese startups in the counterpart facility in Beijing may have rather different opportunities, such as aligning with the home market's government priorities (such as artificial intelligence) resulting in important associated opportunities (Prashantham & Yip, Reference Prashantham and Yip2017).

CONCLUSION

We started this study with the research question: How does proactive networking strategy enable knowledge accumulation in new ventures from emerging markets? Following Vissa (Reference Vissa2012), we framed network strategy as deepening versus broadening. Consistent with Wiklund and Shepherd (Reference Wiklund and Shepherd2003), we focused on two types of knowledge accumulation: technological and market. In essence, we have shown that the two types of network strategies differentially mediate the relationship between network proactiveness and knowledge accumulation type – network deepening in the case of market knowledge and broadening for technological knowledge.

Our study provides two main insights. First, network studies need to pay more attention to the context in which the networks are deployed. While the benefits of partnering proactiveness for new ventures may be universal, a more nuanced picture that takes into account the context of emerging markets will be more accurate to explain the impact of network strategy on knowledge accumulation. Second, even within emerging markets, there are subtle institutional differences that can shift the weight of these relationships. Our comparative study across China and India provides a possible way to generate a more holistic understanding of these dynamic contexts.

Since Coviello's (Reference Coviello2006) seminal work, our knowledge on the network dynamics of international new ventures has grown substantially. Our research over the past decade has focused on a better understanding of how such dynamics in the context of emerging markets (Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2010, Reference Prashantham and Dhanaraj2015; Prashantham & Yip, Reference Prashantham and Yip2017) play out in the growth of new ventures. While this current research advances this understanding further, it also signals the opportunity to advance our understanding of network theory leveraging the unique context of networks in emerging markets. Researchers should also keep in mind that there are substantive changes that are happening in the world of new ventures. New ventures are increasingly partnering with multinational enterprises, which enables them to grow faster both in size and market reach (Prashantham & Birkinshaw, Reference Prashantham and Birkinshaw2019; Prashantham & Dhanaraj, Reference Prashantham and Dhanaraj2015). Furthermore, digitalization is fundamentally reshaping the dynamics of networks and levelling the playing field in favor of new ventures in their quest for global domination (Banalieva & Dhanaraj, Reference Banalieva and Dhanaraj2019). Taken together, we see a new era dawning for advancing theoretical development and practical analysis – an era promising, yet daunting, for scholars of both networks and entrepreneurship across the globe.

APPENDIX I

Scale Items for Key Constructs

To what extent do you agree with the following statements?

(1 = strongly disagree; 7 = strongly agree)

Footnotes

Accepted by: Senior Editor Jiangyong Lu

We thank Senior Editor Jiangyong Lu and three anonymous reviewers for their constructive feedback and guidance. Earlier versions of this article were presented at the 2017 Academy of International Business conference in Dubai and the 2018 International Association of Chinese Management Research (IACMR) conference in Wuhan, where we received much appreciated feedback from Ron Burt. The first author acknowledges research support from CEIBS and NUBS China. We acknowledge Stella Yu for her research assistance at the early stages of the project and Li Meng as well as Rhea Li for support in the later stages.

[1] The software industry is the empirical focus of this study.

[2] We thank an anonymous reviewer for helping us clarify that given that technological knowledge in an emerging economy is, typically, not highly sophisticated, it is technological know-what, rather than know-how, that new ventures will seek to obtain through network behaviors in the first instance. Research suggests that technological knowhow may be obtained through other means, such as hiring knowledgeable individuals, such as technically qualified returnees (Filatotchev et al., Reference Filatotchev, Liu, Lu and Wright2011). Furthermore, while we are focusing on new ventures from high-reputation clusters within emerging economies, overall, the technological knowledge base even in these locations is significantly lower than in advanced economies (Lu & Tao, Reference Lu and Tao2010). Thus, ours is a nuanced depiction of the emerging economy context: technological know-what that contributes to innovation is relatively scarce but, when found, somewhat easier to digest than the more complex know-how associated with advanced economy settings.

[3] We thank an anonymous reviewer for highlighting this insight.

[4] Following this, we do not expect market deepening to mediate the relationship between partnering proactiveness and technological knowledge, and therefore, as will be seen, do not present any formal hypothesis about this relationship. Similarly, we do not envisage market broadening to be a mediator of the partnering proactiveness-market knowledge relationship and similarly do not state any hypothesis in this regard.

[5] We thank an anonymous reviewer for highlighting this insight.

[6] We used a set of six scale items that was refined, further to the Sarkar et al. (Reference Sarkar, Echambadi and Harrison2001) study. We found that two items did not load well and dropped these. We used the remaining four in our analyses; these are reported in the Appendix.

[7] We thank an anonymous reviewer for encouraging us to highlight these counterintuitive findings. As he or she points out, the seminal work of Xiao and Tsui (Reference Xiao and Tsui2007: 23) insightfully demonstrates the need for ‘a more contextualized view’ of theory developed in the West; thus structural holes, for instance, may not yield exactly the same benefits in a setting like China compared to the US. In a similar spirit, we highlight that differences such as an emphasis on know-what rather than know-how, and on composition rather than absorption, may lead to differential effects of network strategy on the accumulation of different types of knowledge in an emerging economy.

[8] This is especially valuable for the MOR readership in the context of the widened mission statement of the journal: ‘Management and Organization Review (MOR) aims to be the leading edge journal for advancing management and organization research with a contextual focus on China and all other transforming economies’.

[9] We thank the editor for suggesting this valid point.

[10] Our confidence in the directionality of the hypotheses stems from the empirical reality that in emerging markets, the initial stocks of technological knowledge and market knowledge in firms tends to be low. Thus, it seems more likely that network behaviors precede knowledge outcomes. Extensive field interviews accompanying this quantitative study (not reported in this study owing to paucity of space) yield consistent observations.

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Figure 0

Figure 1. Transfer and search costs for new ventures in an emerging economy

Figure 1

Table 1. Summary of literature – Select theory-building papers

Figure 2

Table 2. Summary of Literature – Key Empirical Papers

Figure 3

Figure 2. Model with results

Notes: NB. Dotted lines represent non-significant relationships.
Figure 4

Table 3. Multi-group analysis results (India versus China)

Figure 5

Table 4. Latent variable correlation from PLS