Managing the risks from high-tech Investments in India: differential strategies of foreign and domestic venture capital firms

FVCs have led this rally of growth of VC investments in India. In 2014, about 54% of the FDI received by India was in the form of VC or Private Equity. In fact, 80% of the VC funds invested in India are raised overseas (Ernst and Young, 2014). Several MNCs such as Intel, Qualcomm, SAP and Cisco have established corporate VC arms to leverage the technologies developed by the Indian start-ups (Planning Commission, 2012; VI, 2014). Other global technology giants such as Microsoft, Google and Amazon have set up their own business accelerators as well.

The growing dominance of FVCs in India must be understood in the context of the resource constraints encountered by their domestic counterparts. To start with, the investible funds of the DVCs are limited owing to the restrictions placed on the domestic pension funds, insurance companies and provident funds in making contributions towards VC fund corpuses (India, Planning Commission, 2012). On the contrary, several FVCs have raised large India-focused funds with the sole target of investing in India (VI, 2014). Additionally, DVCs have historically demonstrated limited opportunity recognition potential for evaluating deals in knowledge-based businesses resulting in several lost opportunities (Dossani and Kenney, 2002). The most prominent among these was the rejection of VC funding for Infosys Technologies- which later went on to list on NASDAQ and emerged among the top four software services firms from India. Even today, the portfolios of the DVCs are heavily skewed in favor of more conventional sectors (Venture Intelligence, 2014). On the contrary, the FVCs with their depth of investing experience in technology sectors are likely to provide not only finance but also other strategic inputs along with an exposure to international markets (Devigne et al., 2013). The provision of strategic business advice and exposure to international markets by FVCs has also well complemented pre-existing sources of institutional finance, especially in China and India, the major investment destinations of Western VCs in Asia (Devigne et al., 2013).

And yet, despite their growing influence, the FVCs intending to invest in India are likely to encounter remarkable challenges. To start with, India ranks 100 out of 158 on the Ease of Doing Business Index developed by the World Bank Group (2017) and 85 out of 175 on the Public Sector Corruption Index and recently has been regarded most corrupt among the South-Asian economies (Transparency International, 2014, 2017). Moreover, India is a country of multiple languages. Although English is widely spoken, language can be a deterrent if the investee venture is located in second-tier town. Further, India has just implemented a common Goods and Services Tax across the country and the institutional issues in implementing the same will take a while to resolve in the short-term (Forbes, 2017). Consequently, market fragmentation and cascading tax incidence is likely to persist over a longer time horizon which can be tedious for foreign businesses to navigate.

A recent study on VC investing showed that severe trust deficit exists with respect to prospective entrepreneurs (Panda and Dash, 2016), especially in early stage ventures. Given the fact that the legal system is yet to catch up with the rapidly evolving VC-entrepreneur ecosystem, it can be fairly difficult to enforce contracts. Moreover, Indian entrepreneurs still regard VC as largely a funding source and resist the overall involvement in other operational and strategic arenas of their ventures (Bain and Company, 2012). Furthermore, closing or winding up a business in India is a complex and time-consuming process (India, Planning Commission, 2012).

These risks are likely to be further enhanced if the domain of investment is high-tech focus. Although, the FVCs possess high opportunity recognition potential with respect to high-tech domains, they are likely to possess lower depth of local networks that guard them against information asymmetry risks (Wright et al., 2005). On the contrary although the DVCs are likely to possess better understanding of the socio-cultural-institutional context, traditionally high-tech investing has not been their forte. Naturally, each of these VCs firm categories is likely to use different strategies to negotiate the underlying risks.

According to RBV, firms comprise a historically determined collection of resources that are valuable, rare, imperfectly imitable, non-substitutable and tied semi-permanently to their management (Lockett and Thompson, 2001). The primary resources for a VC firm comprise its financial, human and social capital (Jääskeläinen, 2012). Adequacy and quality of these resources is essential in not only guaranteeing the VC firm access to superior quality deals but also in terms of monitoring and the value-add that the VCs can provide to their investee firms during the post-investment phases (Jääskeläinen, 2012). Based on the RBV, it may be inferred that both- the quantum and composition of resources are critical in managing risks emanating from information asymmetry.

However, the access to such resources involves incurring substantial transactions costs. As such, transactions costs are of three kinds - search and information costs, bargaining and decision costs and policing and enforcement costs (Williamson and Winter, 1993). In fact, all of these are relevant in the context of VC investing. Search and information costs are relevant during the deal selection phase and in the choice of appropriate syndication partners. Bargaining and decision costs become relevant during the process of contracting with the entrepreneurs and peer-VCs regarding the quantum of stake in the firm or exit related procedures. Policing and enforcement costs allude to the costs incurred for monitoring the investee firms to guard themselves against agency risks (Bruining et al., 2005). Typically, transactions costs of doing business are likely to be higher for FVCs as compared to their domestic counterparts given their limited understanding about the nature of the legal systems, local institutions and limited experience in the destination countries (Joshi, 2016, Gu et al., 2018).To sum up, any VC firm always tries to access resources such that it reduces the investment risks and at the same time, diminishes the transactions costs while doing so.

Since, they deal with overlapping phenomena, the two theoretical approaches of RBV and TCT are often considered mutually complementary (Williamson, 1999). Consequently, we use both these approaches to analyse how the VCs deal with information asymmetry risks from technology investing.

Technology firms encounter several risks in general such as - the risk of developing a new technological platform, commercializing a technology or developing internationalization strategies to enable them to compete in a global industry (Christensen, 2013). Moreover, young, high-tech companies face liabilities of newness and smallness driven by incomplete resource base, lack of organizational routines, networks, legitimacy in the marketplace and managerial expertise (Baum and Silverman, 2004).

Since, the technology start-ups are inevitably funded by VCs, the above-mentioned risks automatically spill-over to the investor VC firm. Among others, the risks arising from information asymmetry between VCs and entrepreneurs are particularly severe in the context of high-technology investments. (Dai et al., 2012). The foremost issue in case of technology investments is the intangibility of assets of the investee firms (which take the form of Intellectual Property). In an event of business failure, there is very little that the VCs can salvage (Gompers and Lerner, 2004). High involvement by VCs is hence required to guard against the underlying agency risks. Moreover, entrepreneurs seeking to use high innovation as an entry barrier for competitors require significant strategic inputs from outside advisors such as the VCs (Sapienza, 1992). The latter are known to perform such boundary-spanning functions quite well as they can bring in the best practices from their other successfully funded businesses to the current venture (Gomez –Mejia et al., 1990).

The presence of FVCs in the Asian markets in general, and India and China in particular have grown in leaps and bounds over the past one and a half decade. About 70% of the VC funding in the Asian markets originates from firms of foreign origin (Dai et al.; 2012). Given the geographic distance and the cultural differences between the countries of origin and the investment destinations; the magnitude of information asymmetry risks are bound to be particularly severe (Schertler and Tykvova, 2011). FVCs – by the virtue of their headquarters being located in another nation – are less positioned to process “soft” information about the opaque local firms and/or their local market conditions, completely by themselves. Although the FVCs have relatively rich experience in terms of exposure, international network and financial resources, they are often constrained by information friction while investing in geographies different from their countries of origin (Dai et al. 2012).

Studies pertaining to FDI in emerging economies lead further credence to the above fact. It has been found that firms in emerging economies are often controlled by founding families who make key strategic decisions, and rely heavily on network linkages (Filatotchev et al., 2007). While such businesses encounter significant corporate governance issues prompting the VCs to consciously stay away from the same; it must be emphasized that they also possess deep local networks that can be vital in reducing risks from information asymmetry (Joshi, 2016). Thus, the key question for FVCs would be – should they invest in such businesses or not!

Moreover, there exists a significant level of heterogeneity among emerging economies themselves and as such no two emerging economies are alike (Hosskisson et al., 2000). Comparative studies of China and India reveal that China and India are only ‘seemingly alike’ but differ distinctly in terms of their governments, institutional structures and innovation approaches (Huang and Khanna, 2003). Thus, the depth of investing experience in one emerging economy does not automatically spill-over to any other. Guillen (2000) argues that specialized skills possessed by incumbents make them adept in negotiating the governmental bottlenecks in the emerging economy, which is what the foreign MNCs often lack. In fact, all the above facts discussed in the FDI literature are directly relevant in the context of FVCs as well.

VCs are rarely generalists, rather they specialize in a niche domain – either by sector, industry, funding amount, geography or the funding-stage. Specialized domain knowledge by facilitating access to tacit knowledge and networks (Norton and Tenenbaum, 1993), may be considered a valuable resource in itself (De Clercq and Dimov, 2004). Economists since long have alluded to the advantages of ‘learning by doing’ and accordingly the learning curve benefits arising from the VC firm’s specialized activity have been documented as well (Sahlman, 1990). By aiding opportunity recognition and better monitoring, it guards against the opportunistic behaviour by entrepreneurs at both - deal funding and management stages; consequently, lowering the related transactions costs of technology investing for the concerned VC firm.

The DVCs in India are in general not as ‘specialized’ in terms of their respective investment domains (sector, stage, investment size, geography) as their western counterparts (Bain, 2014). The lack of market depth has often been advanced as an important reason for the same (Joshi, 2016). This often results in the classic situation of too much money chasing a few viable deals, thus inflating the deal valuation (VC Circle, 2014). Yet it needs to be mentioned that, these ‘generalist’ VCs share space with a small proportion of highly specialized VCs focussed on certain sectors or investment stages. Even when they exhibit sector-specialization, their respective arenas of sector-focus are likely to be distinctly different – with FVCs being more high-technology focused, while DVCs specializing in more conventional sectors (Joshi, 2016).

Based on the above, we put forth our first proposition:

The joint investment by two or more VCs in a deal is termed as syndication. While, several motives for syndication have been advanced, in general, it is well understood that syndication arises from the need for risk reduction via pooling of tangible and non-tangible resources, culminating in better selection and management of investments (Casamatta and Haritchabalet, 2007;) ultimately resulting in an enhanced venture valuation (Hellman and Puri, 2002).

At large, the core motivation for syndication is the need to leverage ‘external’ social capital (Sorenson and Stuart, 2008). Social capital refers to the relational and structural resources attained by leveraging the network of social relationships (Mosey and Wright, 2007). In the VC industry, where information on deals is rarely public, social capital in the form of inter-firm relationships (as represented by syndication) is likely to play a crucial role in granting access to better quality deals (Sorenson and Stuart, 2001).

Yet the process of syndication also implies significant transactions costs – particularly in terms of identifying a suitable partner, drawing up contracts with them and in especially in guarding one’s firm against the opportunistic behaviour of co-investors (Lerner, 1994).

In India, syndication has often been relied upon by VCs in diversifying into domains dissimilar from their erstwhile arenas of specialization (Joshi, 2016). Moreover, FVCs have invariably used it as a key strategy to get over their information asymmetries (Joshi and Pruthi, 2017) Also FVCs and DVCs are likely to exhibit diferent likelihoods of syndication, depending on the respective settings, experience and reputation (Wang, 2017) Of course, there are enough examples of opportunistic behaviour by the lead VCs as well. The classic case is that of Subhiksha, a retail brick and mortar grocery chain that involved significant corporate governance issues and relied extensively on window-dressing to cook up its books of account (Economic Times, 2007). Yet the first-stage VC sold it off to another prominent VC firm resulting in a lot of bad press for the VC industry. Thus, the advantages of resource-pooling need to be well matched with the transactions costs associated with such informational disadvantages. Based on the above, we advance our next proposition.

The role of social capital in accessing high-quality deals and accordingly impacting VC firm strategy is reasonably well understood (Sorenson and Stuart, 2001). The age of the VC firm and its investment experience in a certain geography, may be regarded as a proxy for its level of social capital (Huang et al., 2013).

Prior experience in the VC industry is likely to significantly enhance the contacts with the VC-entrepreneurial ecosystem thus augmenting the level of social capital and making the VCs adept in selecting and supporting their investees (Zarutskie, 2010). The skillsets arising from age and experience themselves constitute a niche resource, which is likely to lower the transactions costs of VC investing.

India, with its unflattering record on ‘ease of doing business’, the age and experience of the VCs could potentially play a vital role in acclimatizing the VCs with the business processes of obtaining permits and licenses, understanding tax breaks and other government procedures; through their established networks of lawyers, accountants, vendors and consultants (Bain and Company, 2014). Based on the above, we advance the next proposition.

Team size has been identified as an important determinant of the firm strategy and in general, larger teams imply more resources, greater range of analytical skills (Lockett and Wright, 2001) and superior spread of social capital. Therefore, bigger investment teams are likely to reduce the need for further complementary resources and consequently reduce the transactions costs as well. On the contrary, larger teams may be associated with greater conflicts and delays in decision-making, consequently enhancing the transactions costs (Amason and Sapienza, 1997).

The other important attribute pertaining to team composition relates to the prior experience and ethnicity of the team members. Specifically, erstwhile founding experience of VC professionals has been found to enrich their perceptions of the underlying risks, enhance their knowledge of market entry and augment their potential for identifying agency problems (Patzelt et al., 2009). Further, such experience is likely to enable them to better assist the venture during the post-investment period and better identify with the investee ventures and be supportive of them (Patzelt et al., 2009).

The other vital factor, particularly in context of FVCs pertains to the ethnicity of the team members. Evidence from China suggests that while investing in geographically distant destinations, FVCs consciously hire local professionals to manage their regional offices (Huang et al., 2013; Ahlmstrom et al., 2007). The resulting homophily sentiment is likely to facilitate the networking process. The impact of homophily resulting from similar experiences, organizational structures and goals has already been found to aid VC syndication (Sorenson and Stuart, 2008). Moreover, among the various known forms of homophily, the tendency to form ties based on ethnic/racial background has been acknowledged to be the strongest (Miller et al., 2001). Cultural distance has been found to raise barriers to information sharing, trust, and accordingly increasing the transaction costs (Li et al., 2014). Hiring professionals of the local ethnicity is one of the important ways of overcoming such cultural barriers by VCs.

In India, FVCs have always followed the strategy of hiring their investment teams from peer Indian VCs (Dossani and Kenney, 2002). In fact, in India, many a times the VC deals are cut in the most informal settings such pool-parlours or golf clubs (Joshi, 2016). Such informal interactions are better facilitated when the members belong to the same ethnicity (Joshi, 2016). At the same time, there exist other FVCs that operate completely from abroad and do not have even a single member of Indian ethnicity on their teams.

Based on the above we advance our next proposition:

Presence of a vibrant ecosystem in terms of the presence of incubators, accelerators, business angels and incubated companies (Joshi and Satyanarayana, 2014) is an important factor considered by the VCs in determining their geographical location. Co-location with other elements of the ecosystem possibly facilitates access to critical resources and also reduces the transactions costs of accessing the same.

The other vital element that possibly has foremost influence is the presence of ‘critical mass’ – i.e. pre- existence of a critical mass of relevant businesses and human capital. High quality human capital is the most vital resource for high-tech start-ups. Presence of a critical mass of high-technology businesses (including the pre-incubated companies and angel investments) implies that the requisite capital and skill sets are available in abundance. It is also well understood that localization of knowledge due to the presence of a critical mass results in significant positive externalities in the form of knowledge spillovers. These clusters allow employees of various start-ups to network with one another and make it easier for the firms to gain access to specialized suppliers, scientific knowledge, and technological expertise indigenous to the area (Ketels, 2003). Based on the above, we advance our next proposition.

In India, 80% of the VC firms and 90% of the start-ups are based out of just three clusters NCR, Mumbai and Bangalore.

Based on the above we advance our next proposition.

This study is based on secondary data obtained from VI (VI), a private database for VC and Private Equity deals in India. Additional information pertaining to team size and its composition was obtained from the respective VC firm websites. Additionally, we also conducted semi-structured interviews with the VC executives. These interviews that were conducted as a part of a larger project on VCs, examined in detail various attributes that are looked into by VCs at three stages of the VC lifecycle viz. deal selection, managing the investee firms and exits. Specifically, the methods used by the latter to assess and manage the information asymmetry risks were probed into in great detail.

The unit of analysis for the study is an individual VC firm. For this analysis, we selected only active VCs i.e. all those VCs that have funded at least 2 deals over the period of study viz. 2005–2013. The sample comprises of about 70 VCs (out of a total population around 300 VCs), spread across the major Indian cities – Bangalore, Mumbai, Chennai, National Capital Region (New Delhi, Gurgaon, NOIDA, Ghaziabad and Faridabad) and Hyderabad. 26 out of these are FVCs while 44 are DVCs. The 70 VCs in our sample contribute to more than 80% of the overall VC deals funded by VCs during the study period (VI, 2014). The FVCs belong to US, UK, Japan and Singapore.

Data were analysed in two steps. A preliminary data analysis was first conducted. This was followed by building the CART decision tree. The aim of this technique is to arrive at mutually exclusive segments of VCs based on their high-technology focus. The dependent variable was ‘high-tech focus’ while the independent variables were representative of the strategies pursued by them to address information risks. We identified separate segments for DVCs and FVCs, since the primary aim of this study was to distinguish between the strategies used by the two firm types. The analysis was performed using SPSS 21.0.0.0 software. For CART procedure, we report the Risk metrics, Gini coefficients, percent pairs correctly classified and the respective proportions of sample observations high-technology focused VCs in each segment.

CART is a term introduced by Leo Breiman to refer to Decision Tree algorithms that are for classification or regression predictive modelling problems (Loh, 2011). The representation for the CART model is a binary tree. A Decision Tree is based on a recursive partitioning approach; wherein each of the input nodes is split into two child nodes.

CART iteratively tries out each potential cut points, subdividing the data at each possible split and choosing as the best split i.e. the one that produces the most homogenous subgroups. Once the best split has been identified for every variable, the CART algorithm partitions the data using the best overall split among these best splits and assigns a predicted class to each subgroup by majority vote (i.e., a predicted class of 1 for a subgroup containing mostly 1 s). CART repeats this same process on each predictor in the model, identifying the best split by iteratively trying out all possible splits and settling on the split that produces the greatest reduction in impurity (or, equivalently, the most homogenous partitions). The selection of which input variable to use and the specific split or cut-point is chosen using a greedy algorithm to minimizes the Gini Index or Negative Cost Entropy. It is greedy because at each step of the tree building process, the best split is made at that particular step, rather than looking ahead and picking a split that will lead to a better tree in some future step.

CART proceeds recursively in this fashion until some stopping criterion is reached. Examples of stopping criteria include creating a prespecified number of nodes, or reaching a point at which no further reduction in node impurity is possible. If the algorithm is allowed to proceed indefinitely, the model will eventually find splits that are completely or nearly completely homogenous but that may have trivial sample sizes.

The most important property of the CART decision-tree approach is that it captures the non-linear relationships among the dependent and independent variables. In this aspect it scores above the linear regression models.

A comparison of important attributes for FVCs and DVCs has been presented in Table 1. In addition to their overall profile, we analyse their Investment Stage and Industry Focus, their Locational characteristics and the characteristics of their Investment Team. The analysis sample comprised 44 DVCs and 26 FVCs.

The average age of both DVCs and FVCs is almost the same at about 6 and 7 years respectively. Despite their years of operations being almost the same, the FVCs have invested in an average of about 35 deals per firm whereas DVCs have invested in just 20 deals. Possibly, the higher fund size of the FVCs enables them to support greater number of deals.

The average fund size of the DVCs is about $232 Mn whereas that for the FVCs is about $441 Mn. In general, the DVCs in India are highly financially constrained. This is because, the pension funds, insurance companies, provident funds, mutual funds or other entities with large pool of public reserves are still not permitted to contribute to the corpus of VC funds.

Securities and Exchange Board of India (SEBI) is the primary regulatory authority for investments in Indian stock markets. While it is mandatory for all DVCs with pooled funding sources to be registered with SEBI, the FVCs can legally by-pass SEBI and invest in India via the FDI route with automatic approval of the Reserve Bank of India (Rastogi, 2008). Due to various reasons, most FVCs investing in prefer the FDI route. Given the above policy, we find that about 50% of the DVCs are SEBI-registered as against 19% of FVCs. This provision makes the FVCs in India, fairly comparable to other forms of FDI.

About 45% of DVCs exhibit an early-stage focus in their investments as against that of 25% for FVCs. Moving on to industry focus, in general both FVCs and DVCs invest in about 6 sectors on an average. The value of the Herfindahl-Hirshmann index is also comparable for both.

Locational characteristics of the VCs are important as well. These are reflective of the prevalent eco-system therein. Among all cities, the start-up ecosystem in Bangalore is regarded to be the most vibrant (Bala Subrahmanya, 2017). Similarly, different Indian start-up hubs have firms focussed on different domains – Mumbai specializes in start-ups in the BFSI domain, Bangalore in the IT and ITeS domain, NCR in E-commerce domain, while Chennai in the cloud computing domain (Joshi, 2016). Mumbai emerges as the destination of choice for DVCs with 46% of them being located there whereas Bangalore appears to be so for FVCs with 48% of them being present there. Geographical proximity to their investee firms has been regarded as an important factor in VC investing. VCs tackle this in number of ways. One of the ways is establishing their offices at multiple locations. About 20% of the DVCs have established offices at multiple locations as against 15% for FVCs.

It is also important to look at the characteristics of the investment team itself as the same is critical to its ability to handle investment risks. The team size of both FVCs and DVCs is comparable at six members. As expected, DVCs also have the presence of greater proportion of members of Indian origin on their teams. The proportion of team members being of Indian origin is about 97% for DVCs as compared to 93% for FVCs. Regarding the other important attribute viz. erstwhile founding experience of the team members, we find that, about 27% of DVCs have erstwhile entrepreneurs as a part of their core investing teams as compared to that of 23% for FVCs.

The proportion of syndicated deals is slightly higher at 56% for FVCs as compared of that of 52% for the DVCs. This is distinctly different from what has been indicated in the relevant literature, which emphasizes on high syndication intensity of FVCs.

The process we follow for building and analysing the decision tree is as follows. We first build the decision tree using a set of variables that represent the strategies to address the information asymmetry risks from technology investing.

Since, the primary purpose of this study is to identify the differential risk management strategies relied upon by FVCs and DVCs, the first level of split in the decision tree has been forced on the variable that distinguishes FVCs from DVCs. We then identify the sub-segments within the FVCs and DVCs separately with the highest technology focus. We further profile each of these segments to identify the attributes that enable these VCs to tide over the risks from technology investing. The dependent variable in the CART decision tree was a binary one – whether the VC firm exhibits a High-tech focus or not. The independent variables are both binary and continuous/ordinal.

The decision tree presented in Fig. 1 has 5 levels in all. We specify the condition that each end-node should comprise at least 4 observations (VC firms) and last but one node should comprise at least 10. Usually it is the norm to have at least 1% of the records in the end node (Song and Ying, 2015). Since the sample size is 70, having 4 VCs in the end node seems to be the appropriate value.

To start with we find, that the segment of technology focused DVCs comprises VCs that have funded greater number of deals than the overall average. They have funded on an average of 23 deals so far, as compared to the sample average of just 18 deals. The social capital arising from greater investing experience is critical in handling information asymmetries.

The DVCs in this segment are also highly concentrated in the NCR region. In fact, for the entire sample put together, just 15% VCs are based out of NCR; however, in the high-technology focused DVC segment about 23% of the VCs are based out of NCR. Proximity to the central government in New Delhi, could be a conscious attempt to be close to corridors of power for the DVCs. In fact, the DVCs in this segment have extensively invested in start-ups in the arenas of financial inclusion and mobile/digital payments – both of which have been the top priorities of the Indian government since 2008 onwards. Thus, being closer to the government ecosystem, possibly makes them privy to upcoming governmental policy priorities, much before their competitors become aware of the same. This places them at an advantage of being pioneers in those domains.

The other strengths of the NCR start-up cluster are primarily in the domains of hardware and Business Process Outsourcing (BPOs). In fact, NCR is number one ITES-BPO destination in India (Khomiakova, 2007). Moreover, New Delhi being the capital city, the infrastructure and institutional strength of the city also favours both intra-cluster and extra cluster interactions for NCR based firms (Rao & Balasubrahmanya, 2017).; which could benefit the DVCs located there by means of reduction in their transactions costs.

Syndication seems to be a vital strategy relied on by them for reducing the informational risks from technology investing. Across all VCs in the sample, about 53% of the deals are syndicated; while for the segment under consideration, about 64% of the deals are syndicated. Thus, it is highly likely that the DVCs leverage the skills of their co-investors in managing the technology risks. Traditionally, DVCs in India have always invested in more conventional domains; however, syndicating with FVCs gives them the advantage of exploring other emerging domains (Joshi, 2016). Given the complementarity of their skillsets, syndication aids both – DVCs and FVCs. DVCs have access to networks and encounter lower cultural and institutional barriers while their foreign counterparts possess ‘domain’ knowledge (Devigne et al., 2013). Consequently, syndicating brings together best of both worlds.

The average fund-size of the DVCs in this segment is also significantly lower (at $63 Mn) as compared to the average for all DVCs in the sample ($ 210 Mn). Thus, higher syndication could also possibly arise from the need to leverage greater financial capital. However, in the Indian context it has been frequently mentioned that at earlier investment stages, especially of high-tech investments, it’s invariably the dearth of ‘opportunity recognition skills’ and not necessarily the capital that acts as a deterrent. Thus, having a prominent FVC firm with technology expertise make the first move is possibly an important signal for DVCs to follow (Joshi, 2016).

Another noteworthy characteristic of the DVCs in this segment is that 100% of them exhibit an early-stage focus in their investments as compared to the sample average of 37%. Moreover, about 46% of the VC investment teams have the presence of erstwhile technology founders on their investment teams as compared to the overall average of 26%. Thus, the VCs in this segment seem to be leveraging the opportunity recognition potential of the latter in identifying opportunities at earlier stages, that not only enable them to stay ahead of the competition but also gain a significant leverage over the magnitude of agency risks (Tufano, 1989). Moreover, since most of these tech entrepreneurs have established accomplished businesses in India itself, it gives them an extensive access to networks within the high-tech ecosystem thus reducing the magnitude of risks at the investment stage itself. In fact, the team members of the DVCs in this segment include who’s who of the India IT industry – one of VC executives has been a co-founder of Infosys Technologies - the top IT services firm in India. Another one has been the founder of a company called Snapfish, an online photo service with more than 100 Mn registered users. Another one has been the founder of Mumbai Angels, one of the most vibrant angel investor networks in India. Yet another one has been a part of the core team of UIDAI, a pioneering multi-million-dollar project sponsored by the Indian government, to develop a unique identification number for all Indians based on bio-metric information. A glimpse of such pioneering experiences of the VC executives in this tech-focused DVC segment is indicative of their depth of experience they bring in and how the same can be valuable to their funded deals.

The FVCs in this segment are distinctly younger in age and have funded a lower number of deals as compared to the overall average. They are about 2.5 years of age and have funded about just 4 deals per firm so far. Thus, they score extremely low on the magnitude of social capital they possess that emanates out of age and experience.

Interestingly they have much smaller teams with an average team size of just 3 members as compared to that 6 members on an average. Moreover, just 69% of the team members are of Indian origin. Ethnicity is an important parameter that enables VCs to conquer the asymmetric information and hence FVCs often hire members of Indian origin when they invest in India (Huang et al., 2013). Homophily sentiment enables them build local networks and interact in informal spaces thus minimizing the transactions costs of technology investing. However, the VCs in this segment are way behind in this aspect.

Quite interestingly, the VCs in this segment also exhibit the least amount of syndication, with just 33% of their deals being syndicated. There could be many possible reasons for the same: Social capital necessary for syndication is distinctly lower for the FVCs in this segment. The agency risks arising from opportunistic behaviour by other syndication partners are high – particularly if VCs are geographically distant from the investee ventures and other peer VCs. This is particularly true for the FVCs in this segment since, none of them have offices at multiple locations within India and in fact 50% do not even have a physical office in India. Accordingly, these FVCs are likely to encounter institutional barriers as also cultural barriers owing to low ethnicity composition (Li et al., 2014).

Given, the above, it becomes important to understand how these firms manage the information asymmetry risks of technology investing at all. This could be explained in the following manner: the FVCs from this segment exhibit a significantly higher early-stage focus with about 75% of their deals belonging to earlier investment stages as compared to the sample average of just 37%. Investing at earlier phases possibly enables them to keep a tab on opportunistic entrepreneurial behaviour (Gompers, 1995). It also empowers them to be in the driver’s seat impacting all the vital arenas of strategy. Furthermore, the FVCs invest in just 3 sectors (as compared to an average of 6 sectors) and exhibit a high value for the Herfindahl-Hirshmann index, which takes the value of 0.76 (as against the average of 0.52). Thus, domain specialization, both by stage and sector seems to an important strategy that enables them to minimize the risks of technology investing.

This is further facilitated by the fact that 100% of the FVCs belonging to this segment, hire erstwhile technology founders on their investing team. The VC executives in this segment are found to possess a depth of sector-specific experience. E.g. a FVC focused on Healthcare has two academicians from the field of medicine as a part of their core investing team. One of them has been a senior surgeon at a high-profile hospital in the United States while other has been the CEO of Siemens healthcare business. An investment executive with another VC has founded several successful start-ups in the Silicon Valley that have been acquired by prestigious technology firms such as Cisco. He has also been a General Partner with one of the respected VC firms based out of Menlo Park, California. Yet another VC firm has hired executives with a depth of experience in automobile and retail sectors. One of them is considered a pioneer in the Indian organized retail industry and is credited to have created world class retail innovations, product competencies and built one of India’s largest retail networks and distribution models.

Thus, it appears, that domain specialization coupled with deep sector experience of the investment team is the most core strategy that enables the FVCs belonging to this segment to effectively assess and overcome the information risks of technology investing. Consequently, the choice to hire erstwhile technology entrepreneurs or domain experts could possibly be a conscious one. In fact, most FVCs in this segment have been started by these erstwhile founders themselves.

The FVCs in this segment are the oldest among all sample VCs (7 years) and have historically funded the highest number of deals (31 deals per VC firm). Moreover, they exhibit a clear preference for non-early stage deals (growth-stages) – just 14% of the deals funded by them belong to earlier stages of investment. However, they do exhibit a strong sector focus. Although, they invest in 6 sectors on an average, the Herfindahl-Hirshmann index for the same is around 0.69, which is much higher than the sample average 0.51. Thus, it can be said that they counter the information risks of technology investing by avoiding early-stage deals in those domains. The sector focus facilitates the opportunity recognition process.

However, the vital parameter that stands out in the context of FVCs in this segment relates to their physical location in India. About 71% of the FVCs in this segment are based in Bangalore as against the sample average of 33%. Given the fact that Bangalore has the most vibrant entrepreneurial ecosystem for start-ups in India – with accelerators, incubators, educational institutions and human resources– the location becomes vital in terms of facilitating the access to resources and reducing the search and transactions costs in recognizing the same. Given that the VCs in this segment are growth stage focused, the presence of ‘critical mass’ in Bangalore becomes particularly important. In fact, the Bangalore IT cluster has been recognized as one of the largest and fastest growing software/knowledge clusters outside the USA. The city has been described as the Silicon Valley of India as well as the Outsourcing Capital of the World (Rao & Balasubrahmanya, 2017). Further, Bangalore is not only a hub for software-related industries but also houses several high-tech clusters (e.g. defense, aeronautics, and biotechnology) and is the scientific and engineering center of India in terms of research and training as well as manufacturing (Rao & Balasubrahmanya, 2017). The setting up of affiliates of MNCs and the location of affiliates of Bangalore based Indian MNCs elsewhere have led to a steady increase in both intra-cluster interactions within Bangalore and extra-cluster interactions between Bangalore and other clusters both in India and abroad.