Micro-level: Microfoundational Assumptions and Their Impact on GVC
Microfoundations refer to generic human behavioral conditions that impact firm-level (and, in the case of GVCs, network-level) outcomes (Kano & Verbeke, 2019). Scholars have argued that individual-level characteristics, such as bounded rationality, bounded reliability, cognitive biases, and entrepreneurial orientation, impact GVC governance (Denicolai, Strange, & Zucchella, 2015; Kano, 2018; Levy, 1995; Verbeke & Kano, 2016), in terms of how transactions are organized and orchestrated. Therefore, systematic attention to microfoundations is necessary in order to meaningfully advance the GVC research agenda. However, few empirical studies directly observe or measure individual-level variables. Further, while certain behavioral assumptions are frequently implied – e.g., the nature of individual-level knowledge and capabilities is inherent in the idea of learning and upgrading; the need for knowledge sharing across units implies bounded rationality of individual actors and associated information asymmetries; the notions of power balance and the need for intellectual property (IP) protection assume a certain level of bounded reliability of actors involved – these assumptions are, for the most part, neither articulated explicitly nor examined empirically.
Only seven studies in our sample directly address the impact of microfoundations (either stated or implied) on GVC geographic configurations, knowledge acquisition and dissemination within the GVC network, and efficient functioning and orchestration of the network. In an early qualitative study of supply chain management, Akkermans, Bogerd and Vos (1999) discuss how bounded rationality, as expressed in supply chain partners’ diverging beliefs and goals, contributes to functional silos and erects barriers to effective value chain management. Lipparini, Lorenzoni and Ferriani (2014) argue that GVC networks that benefit the most from knowledge transfer among partners are those where partners share common identity and language. These features serve as safeguards against the potential threat of opportunism and allow participating firms to learn from partners with reduced risk of proprietary knowledge spillover outside of the immediate network. Eriksson, Nummella and Saarenketo (2014) suggest that individual-level cognitive and managerial capabilities of lead firm managers, such as cultural awareness, entrepreneurial orientation, global mindset, interface competences and analytical capabilities, constitute a critical building block for firm-level ability to successfully orchestrate cross-border transactions in a GVC. Seppälä, Kenney and Ali-Yrkkö (2018) focus on boundedly rational accounting decisions in lead MNEs, and argue that lead firms’ accounting systems may misrepresent where the most value is created in a GVC. This mismatch implies that GVC activities to which value is allocated may be selected somewhat arbitrarily, and this further impacts location decisions. Kano (2018) argues that bounded rationality and reliability of decision-makers in participating firms impact the efficiency of the GVC; as such, the role of lead firm managers is to control bounded rationality and reliability through a mix of relational mechanisms, so as to improve the likelihood that the GVC will be sustainable over time. Treiblmaier (2018) theoretically predicts structural and managerial changes introduced into GVCs by blockchain technologies, by analyzing four behavioral assumptions of major economic theories: bounded rationality, opportunism, goal conflict, and trust. Finally, Sinkovics, Choksy, Sinkovics and Mudambi (2019: 151) explore the relationship between three variables – information complexity, information codifiability, and supplier capabilities – and knowledge connectivity in a GVC, and conclude that individual characteristics of lead firm managers – specifically, their risk perceptions and associated “comfort zones” – moderate this relationship.
GVC Level: Components of GVC Governance
The term “governance” refers to the organizational framework within which economic exchange takes place, including the processes associated with the exchange (Zaheer & Venkatraman, 1995). In the context of a GVC, governance includes the overarching principles, structures and decision making processes that guide the “checks and balances” in network functioning, so as to make sure that the interests of the entire network (and broader societal/environmental interests where relevant) are served above and beyond localized interests of participating firms and individual decision-makers within these firms. These principles, structures and processes encompass considerations related to boundaries of the network and its geographic make-up, control and orchestration mechanisms for economic activities performed within the GVC, value distribution, relationship management, and direction of knowledge flows. Outcomes of successful governance include meeting of individual participants’ performance goals, as well as, ultimately, long-term sustainability of the GVC as a whole.
Here, a distinction can be made between structural and strategic governance of the GVC, as shown in Figure 1. The former refers to the actual structure governing economic activities, e.g., make versus buy decisions, organizational structure of the network (number of players, power balance, boundaries, etc.), geographic and functional allocation of activities, level of centralization of decision-making, and so on. In contrast, strategic governance is concerned with dynamics of actors’ behavior in respect to strategic decision making (Schmidt & Brauer, 2006; Zaheer & Venkatraman, 1995). In the context of GVCs, strategic governance is about orchestrating the usage of resources, through codified and uncodified routines and managerial practices, to ensure smooth functioning of the entire network (Kano, 2018). Our review identified six broad, interrelated conceptual dimensions (Figure 1) that constitute critical elements of structural and strategic governance of a GVC. These dimensions, as well as outcomes of governance practices, are discussed below.
Control
Control decisions establish the governance structure of the GVC, that is, whether each value chain activity should be internalized, outsourced, or controlled through hybrid forms such as joint ventures (JVs) (Buckley et al., 2019). It has been argued that in a GVC, control of critical knowledge and intangible assets (e.g., brand names and technological platforms) takes precedence over ownership of physical assets (Buckley, 2011, 2014; Mudambi, 2008), and ownership advantages can be exploited without internalizing operations (Strange & Newton, 2006). This core premise underlying the GVC is supported in Hillemann and Gestrin’s (2016) analysis of OECD data on foreign direct investment (FDI) and cross-border mergers and acquisitions (M&As), which shows that cross-border financial flows related to intangible assets continue to increase relative to those related to tangible assets. An analysis of about 25,000 Italian firms also suggests that control of GVC activities, as compared to ownership, yields benefits in terms of greater propensity toward innovation, increased productivity, and faster sales growth (Brancati, Brancati, & Maresca, 2017). The preference for control without ownership is enabled by increasing digital connectivity, which allows lead firms to influence various units in the GVC without directly managing them (Foster, Graham, Mann, Waema, & Friederici, 2018).
To some extent, control decisions are impacted by host countries’ regulatory environments, particularly when national political institutions create pressure for local content on MNEs that are trying to gain access to large downstream markets in emerging economies (Lund-Thomsen & Coe, 2015; Morris & Staritz, 2014; Sturgeon, Van Biesebroeck, & Gereffi, 2008). This is the case with “obligated embeddedness” (Liu & Dicken, 2006: 1238) of automotive MNEs in China, where the government’s industrial policy dictates that inward FDI should take a JV form. Further, control decisions are linked to sectoral and functional factors – for example, lead MNEs operating in high- and medium-technology sectors and/or locating knowledge-intensive functions (e.g., innovation) in host markets are more likely to pursue ownership in jurisdictions that offer weaker IP protection (Ascani, Crescenzi, & Iammarino, 2016). Ownership allows the MNE to have better control over the creation, transfer and leakage of propriety knowledge, and is thus a pre-emptive measure for knowledge protection.
However, considerable heterogeneity in control decisions exists among lead firms operating in the same geographic regions and industry sectors, which suggests that firm-level strategic considerations, and not only macro-level forces, are powerful drivers of control patterns in GVCs (Dallas, 2015; Sako & Zylberberg, 2019). These considerations include lead firms’ levels of specialization, the nature of their relationships with partners, the need for flexibility versus stability in offshore operations, and the value of the operations to the lead firm (Amendolagine, Presbitero, Rabellotti, & Sanfilippo, 2019; Dallas, 2015; Kleibert, 2016). Control decisions can be also driven by the level of local adaptation required, whereby the lead MNE may need to source external expertise in order to perform the desired degree of customization. Here, a carefully designed mix of internalized and externalized, yet managerially or technologically linked, activities is argued to allow the lead firm to achieve the ultimate balance between integration and responsiveness (Buckley, 2014).
Location
Location decisions determine the most advantageous geographical configuration of the GVC, namely, where activities should be located, and how they should be distributed in order to maximize the value created in and captured through the GVC. Location decisions encompass such considerations as the regional effect (Rugman & Verbeke, 2004), the nature of industrial clusters (Turkina & Van Assche, 2018), and the links between GVCs and local clusters. Location decisions are tightly intertwined with control decisions discussed earlier. For example, FDI (as opposed to market contracting) enables the MNE to construct a regional, or even global, network under its control to supply wide-ranging, differentiated and low cost products in a flexible manner. Chen’s (2003) study of electronics firms in Taiwan indicates that FDI often starts at a location close to the home base, where resources from domestic networks can be drawn, and subsequently moves on to more distant locations, after the lead firm has developed a regional sub-network to support its further expansion.
Location considerations are linked to macro-level characteristics of host and home countries, including level of economic development and corresponding factors such as cost of labor, technological environment, and institutional quality. Among these factors, favorable business regulations, IP protection, and significant education spending typically attract technologically and functionally sophisticated activities (Amendolagine et al., 2019; Ascani et al., 2016; Pipkin & Fuentes, 2017). Control of the GVC resides in the hands of technology and/or market leaders, which are typically (although not always) located in developed economies and extract value from their GVCs through global orchestration capabilities (Buckley & Tian, 2017). Countries with more advanced production technologies are naturally engaged more in the upstream segments of the GVC, and become key suppliers to other countries in the region, thus supporting regional integration of production (Amendolagine et al., 2019; Suder, Liesch, Inomata, Mihailova, & Meng, 2015).
Most empirical studies address location of production activities, whereby labor cost emerges as one of the core determinants for GVCs led by both advanced economy MNEs (AMNEs) and emerging economy MNEs (EMNEs). For example, Asian tier 1 suppliers to MNEs and OEMs become GVC lead firms in their own right by shifting production to lower cost locations in the region (Azmeh & Nadvi, 2014; Chen, Wei, Hu, & Muralidharan, 2016). Yet efficiency-seeking offshoring may create strategic issues, particularly when inefficient local institutions fail to prevent unwanted knowledge dissipation. Issues can also emerge on the demand side due to sustainability and ethical breaches in large MNEs’ value chains, as evidenced in multiple, recent instances of public backlash in response to poor working conditions in manufacturing factories in South and Southeast Asia (Malesky & Mosley, 2018). Funk et al.’s (2010) survey of US consumers suggests that developed economy consumers’ willingness to purchase is negatively affected by partial production shifts to animosity-invoking countries (countries with poor human rights records/with poor diplomatic relationships with the home country). As the wave of consumer movement spreads to less developed countries, it is in the best interest of the lead firm to evaluate carefully the undesirable attributes of a potential host country when making FDI decisions (Amendolagine et al., 2019; Morris & Staritz, 2014).
Desire to access large and fast-growing consumer markets drives production activities close to end markets, for example, when host country governments in emerging markets pressure MNEs for local operations (Sturgeon et al., 2008). Co-location of manufacturing and sales also allows lead firms to be more responsive to customer demands, and to off-set the costs of globally dispersed activities by reducing investment in transportation and logistics (Lampel & Giachetti, 2013).
Strategic asset seeking by lead firms and suppliers explains much of the geographic configuration of GVCs, whereby MNEs locate value chain activities in globally specialized units to exploit international division of labor (Asmussen, Pedersen, & Petersen, 2007). This is particularly pronounced in knowledge-intensive industries, where lead firms often locate operations in innovation hubs and global cities (Taylor, Derudder, Faulconbridge, Hoyler, & Ni, 2014). In their analysis of clusters in the aerospace, biopharma, and ICT industries, Turkina and Van Assche (2018) demonstrate that innovation in knowledge-intensive clusters benefits from horizontal connection to global hotspots, as opposed to labor-intensive clusters where innovation gains from vertical GVC connections.
While much has been written about fine-slicing and fragmentation of value chain activities in a GVC (Buckley, 2009a, b), few empirical studies measure the costs and benefits of geographic diversification of operations within the same part of the value chain. Lampel and Giachetti (2013) address a relationship between international diversification of manufacturing and financial performance in the context of the global automotive industry, and find an inverted U-shaped relationship, whereby advantages of diversified manufacturing (i.e., greater flexibility and access to internationally dispersed strategic resources) are eventually off-set by increased organizational complexity and managerial inefficiencies. Further, location decisions are tied to firms’ strategic priorities beyond cost reduction – for example, increased needs for customer responsiveness and/or enhanced quality control. Focus on such priorities may prompt backshoring initiatives (Ancarani, Di Mauro, & Mascali, 2019). Yet, geographic diversification may serve strategic purposes such as IP protection. Gooris and Peeters’ (2016) survey of offshore service production units demonstrates that lead firms may opt to fragment their global business processes across multiple service production units, rather than co-locating processes, with the explicit purpose of reducing the hazard of knowledge misappropriation.
Finally, technological advances continue to shape geographic make-up of GVCs (MacCarthy, Blome, Olhager, Srai, & Zhao, 2016). Few studies in our sample measure the impact of digital technologies on location choice, but several studies address current and potential influences of technology indirectly and/or conceptually. Ancarani et al. (2019) suggest that adoption of labor-saving technologies leads to backshoring in instances when lead firms compete on quality, rather than on cost. While digital connectivity enables exploiting complementarities between geographically dispersed processes (Gooris & Peeters, 2016), it may limit participation by suppliers located in technologically underdeveloped regions (Foster et al., 2018). Further, the latest technology, such as 3D printing, is likely to impact GVCs of relevant industries by making them shorter, more dispersed, more local, and closer to end users (Laplume et al., 2016; Rehnberg & Ponte, 2018).
Network structure
Network structure refers to the structural make-up of a GVC and has been well theorized in some of the most cited GVC conceptual frameworks (e.g., Coe & Yeung, 2015; Gereffi, 2018; Gereffi et al., 2005; Henderson et al., 2002). While a GVC can typically be conceptualized as an asymmetrical or high centrality network with a lead firm at its centre (Kano, 2018), these networks can also be heterogeneous in terms of such characteristics as depth, density, openness, and the presence of structural holes (Capaldo, 2007; Rowley, 1997). These characteristics affect power relations in the GVC, the level of control afforded to the lead firm, and innovation and business performance. Not surprisingly, a large number of empirical studies in our review address various dimensions of the nature and/or role of network structures in GVC governance and performance outcomes.
The network structure in a typical GVC can be dyadic or multi-actor in nature, and can affect knowledge flows (Lipparini et al., 2014), new venture formation (Carnovale & Yeniyurt, 2014), and operational performance (Golini, Deflorin, & Scherrer, 2016). A firm with high centrality (i.e., most links in a network) has greater power over other firms in a dyadic or multi-actor network, whereby control can be exerted by the lead firm beyond its legal boundaries over independent – but captive – suppliers (Yamin, 2011). In supply chain management, Carnovale and Yeniyurt’s (2014) study of automotive OEMs and automotive parts suppliers shows that manufacturing JV formation between lead firms and potential partners can be enhanced by higher network centrality of either the lead firm or the potential JV partner. This network centrality is seen as a proxy for greater legitimacy and credibility within the network. However, the study found mixed outcomes in relation to network density. High network density is not necessarily favorable to new JV formation due to “lock-in” effects through structural homophily. This network structure in turn limits access of lead firms to a diverse set of potential partners and hinders learning and innovation. Similarly, the studies of manufacturing plants in various countries by Golini et al. (2016) and Golini and Gualandris (2018) demonstrate that a higher level of external supply chain integration (e.g., through GVC activities) can improve the operational performance of and the adoption of sustainable production by manufacturing MNEs due to information sharing, learning, and innovation through supply chain partners.
The density of network structure in GVCs, however, may change over time in relation to the emergence of new technologies and platforms, some of which may favor greater density in localized networks. In their perspective article on 3D printing and GVCs, Laplume et al. (2016) question if technological advancements can influence the relative density of globally dispersed and localized production networks. As more local firms can participate in the production of high-value components through 3D printing, their need for technological acquisition and/or specialized components through MNE lead firms in GVCs may be reduced, leading to what Rehnberg and Ponte (2018) call “unbundling” and “rebundling” of GVC activities towards regionalized or even localized GVCs. In this scenario for decentralized GVC network structure, local producers can engage in more transactions with each other, and thus localized production networks may get denser over time.
In addition to centrality and density, network structures in GVCs can also be distinguished by linkage heterogeneity – the mix of horizontal linkages (between firms with similar value chain specialization) and vertical MNE-supplier linkages (with different value chain specialization). This structural mix has significant influence on the innovation performance of firms in different industries (Amendolagine et al., 2019; Brancati et al., 2017). Drawing on a social network approach, Turkina and Van Assche’s (2018) study of industrial clusters shows that network structures underpinned by dense horizontal linkages among local firms tend to enhance innovation performance in knowledge-intensive industries, whereas strong vertical linkages between local firms and MNEs can promote innovation in labor-intensive clusters. The former network structure tends to promote innovation through intra-task knowledge capability development among horizontally linked firms. As to the latter case of local suppliers in labor-intensive industries, inter-task capability development can be better served through vertical and international linkages with global lead firms.
Finally, power relations among GVC actors play out very differently in different network structures (Dallas, Ponte, & Sturgeon, 2019; Grabs & Ponte, 2019). In one of the earliest studies of industrial upgrading through GVC participation, Humphrey and Schmitz (2002) observed that network structures characterized by quasi-hierarchical power relations in favor of one party – often global lead firms or global buyers – were generally not conducive to the upgrading of local firms. Sturgeon et al. (2008) followed up with this line of research by examining major American and Japanese automotive lead firms and over 150 suppliers in North America. They found that upgrading of local suppliers was more likely if the GVC network structure moved towards a relational form of power dynamics. Such a relational form of network structure tends to favor inter-firm cooperation and credible commitment (e.g., IKEA and its suppliers in Ivarsson & Alvstam, 2011 and tuna canning firms in Havice & Campling, 2017). Similarly, Khan, Lew and Sinkovics’s (2015) study of the Pakistani automotive industry shows that local firms are more likely to acquire technological know-how and develop new capabilities by participating in geographically dispersed rather than locally oriented networks. Through international JVs (IJVs) with global lead firms, these local firms can access different knowledge base and know-how in those international networks.
As noted earlier, network structures are embedded in different national and institutional contexts. Pipkin & Fuentes (2017) find that domestic institutional environment, such as state policies and support from business associations, is more significant than lead firms’ influence in shaping network dynamics in developing countries. Horner and Murphy’s (2018) study of manufacturing firms in India’s pharmaceutical industry shows that network structures characterized by firms from similar national contexts (e.g., the Global South) can be more open and cooperative in relation to production and quality standards, market access, and innovation. This greater openness in South–South GVCs entails different business practices toward their end markets due to lower entry barriers, lower margins, and higher volumes. The opportunities for learning in these GVCs are also different from those tightly controlled and coordinated by lead firms from the Global North. Another study of chocolate GVCs in Indonesia by Neilson, Pritchard, Fold and Dwiartama (2018) also points to the importance of contextual heterogeneity in shaping the influence of different network structures on lead firm behavior and relationships with suppliers and distributors. Drawing upon Yeung and Coe’s (2015) GPN 2.0 theory, Neilson et al. (2018) argue that network structures differ significantly between branded chocolate manufacturing and cocoa farming/processing in agrofood manufacturing. Owing to domestic industrial policy and international business lobbying, the role of national context is much more pronounced in the network structure of cocoa farming/processing that favors inter-firm partnership and cooperative learning.
Learning
Conceptual studies have identified knowledge diffusion and transfer as an important aspect of network governance (Ernst & Kim, 2002; Inkpen & Tsang, 2005). Empirical studies take note of this topic and examine various dimensions of learning in a GVC. Most of such studies in our sample focus on interfirm learning in the context of capability development, technological catch-up and upgrading by peripheral GVC actors – that is, emerging economy suppliers’ progression from OEM to original design manufacturing (ODM) and to own brand manufacturing (OBM). As touched upon in the previous section, macro-level conditions such as market forces and state policies, rather than lead firm initiatives, are argued to be the main force in spurring supplier upgrading (Pipkin & Fuentes, 2017). Upgrading initiatives can produce a wide range of results, from incremental to significant leaps in market position (Pipkin & Fuentes, 2017), depending on a number of factors. Eng and Spickett-Jones (2009) argue that upgrading hinges on suppliers’ ability to simultaneously develop three sets of marketing capabilities: product development, marketing communication, and channel management. Wang, Wei, Liu, Wang and Lin’s (2014) study of manufacturing firms in China indicates that the presence of MNEs alone does not guarantee knowledge spillovers, and may in fact have a negative impact on indigenous firms’ domestic performance due to increased competition. Hatani (2009) describes barriers to learning by emerging market GVC suppliers. Her study of autoparts suppliers in China suggests that excessive inward FDI limits interactions between lead firms and local suppliers and thus creates structural obstacles to technology spillovers to lower GVC tiers. Also researching the autoparts industry (but in Argentina rather than China), McDermott and Corredoira (2010) suggest that supplier upgrading is facilitated by regular, disciplined discussions with the lead firm about product and process improvement; in this context, a limited amount of direct social ties to international assemblers appears to be the most beneficial.
In a follow-up study, Corredoira and McDermott (2014) find that lead firms alone do not help process upgrading, but add value particularly when emerging market suppliers’ ties to MNEs are augmented with multiple, strong ties to non-market institutions (e.g., universities and business associations), which act as knowledge-bridgers and help suppliers tap into knowledge embedded in the home country. These types of ties are particularly useful for accessing knowledge for the development of exploitative innovation, while exploratory innovation is best achieved through participation in trade fairs and collaboration with international (rather than domestic) institutions, according to the study of Pakistani motorcycle part suppliers by Khan, Rao-Nicholson and Tarba (2018). Similarly, Jean’s (2014) study of new technology ventures in China indicates that firms that participate in trade shows and have strong quality control practices are more likely to develop requisite knowledge to pursue upgrading, while firms engaging in Internet-based business-to-business transactions are less likely to upgrade. Based on their studies of the garment and toy industries, Azmeh and Nadvi (2014) as well as Chen et al. (2016) describe alternative paths to upgrading: some OEMs invest in R&D to enter the ODM business, or invest in marketing and branding and move toward the downstream end of the value chain to become OBMs. Others achieve competitive gains by shifting production to different locations and learning how to effectively coordinate multiple production locations (see also detailed case studies of ODMs from Taiwan and Singapore and OBMs from South Korea in Yeung, 2016). Buckley (2009b) suggests that both options – incremental upgrading within the established GVC and developing a new GVC under local control – are difficult in that they require mobilization of entrepreneurial abilities and development of sophisticated managerial skills. Successful upgrading hinges not only on suppliers’ acquisition of knowledge, but also on their ability to absorb it and transform it into innovation, which ultimately improves suppliers’ position in GVCs (Khan et al., 2019).
Specific knowledge acquisition strategies required for upgrading vary depending on the nature of home institutions and labor markets (Barrientos, Knorringa, Evers, Visser, & Opondo, 2016; Pipkin & Fuentes, 2017; Werner, 2012). Weak home institutions hinder the transformation of knowledge into actual innovative products and processes (Jean, 2014). This explains why catch-up and upgrading by GVC suppliers often mirrors the evolution of home institutions (Kumaraswamy, Mudambi, Saranga, & Tripathy, 2012): as institutions evolve toward liberalization, upgrading strategies change from upgrading technical competencies through licensing and collaborations, to upgrading internal R&D and developing strong relationships with lead firms. The weakness of local institutions can be overcome by gaining knowledge through participation in international networks and collaboration with global suppliers (Khan et al., 2018).
The nature of relationship among parties in GVCs matters for technological knowledge transfer, as network ties are channels through which knowledge flows. Khan et al.’s (2015) above-mentioned study indicates that IJVs represent a governance vehicle that facilitates the creation of social capital between focal MNEs and automotive parts suppliers located in emerging economies, and thus facilitate development and acquisition of complex technological knowledge by local firms.
Learning and knowledge accumulation and diffusion in the lead firm, as well as lead-firm initiated network-wide learning, garnered significantly less scholarly attention, with one notable exception. Through analyzing Italian motorcycle industry projects carried out via dyads of buyers and suppliers, Lipparini et al. (2014) develop a framework that addresses multi-directional, multilevel and multiphase knowledge flows in a GVC, and describe practices implemented by lead firms to successfully cultivate creation, transfer and recombination of specialized knowledge to facilitate network-wide learning. In such a dynamic and somewhat open context of knowledge sharing, the threat of opportunism is likely to be outweighed by the advantages of learning from other network members.
There appears to be consensus in the literature that strong linkages within the GVC – frequently referred to as embeddedness of actors in the network (Henderson et al., 2002) – are conducive to transferring various types of knowledge, including production processes, sourcing practices, technological knowledge, and innovation capabilities (Golini et al., 2016; Golini & Gualandris, 2018; Ivarsson & Alvstam, 2011). Such linkages are the most effective when purposefully facilitated by strong lead firms. Lead firms can impel capability upgrading on peripheral units by leveraging their central positions and complementary assets, as indicated by the acquisition of UK-based Dynex by China’s Times Electric (He, Khan, & Shenkar, 2018). Ivarsson and Alvstam’s (2011) case study of IKEA and its suppliers in China and Southeast Asia similarly shows that lead firms can contribute to peripheral units’ upgrading by fostering close, long-term interactions, and by offering technological support. Conversely, weak strategic coupling between lead firms and peripheral units hurts knowledge transfer and capability development (Yeung, 2016). For example, Pavlínek’s (2018) study of automotive firms in Slovakia suggests that weak and dependent supplier linkages between MNEs and domestic firms undermine the potential for technology and knowledge transfer from the former to the domestic economy.
Lead firms are often motivated to drive their suppliers’ capability upgrading, because they themselves benefit from suppliers’ enhanced capabilities through improved sourcing efficiency, higher-quality inputs, and more generally valuable knowledge diffusion throughout the GVC. In the next section, we discuss how characteristics of the lead firm impact its position and role in the GVC.
Impact of lead firm
Extant conceptual research has acknowledged that smooth and efficient functioning of the GVC is contingent on the lead firm’s ability to establish, coordinate and lead the network (Kano, 2018; Yamin, 2011; Yeung, 2016; Yeung & Coe, 2015). Buckley (2009a) argues that the role of headquarters is more important in a GVC than in a conventional hierarchical MNE, because leading a GVC demands specific management capabilities such as the ability to fine-slice the value chain, control information, and coordinate strategies of external organizations. Yet few studies directly investigate the specific impact of lead firm characteristics on the boundaries, configurations and performance of the GVC. The studies that do use lead firm features as independent variables focus on such aspects of the lead firm as size (small versus large), industry sector (and associated sector-specific value chain strategies), location (headquarters location in a particular region/in emerging versus developed markets, and proximity to clusters), and technological leadership.
Lead firm size appears to be seen as a proxy for power and influence in a network. Eriksson et al. (2014), in a case study of a Finnish high-tech SME at the centre of a globally dispersed value chain, argue that SMEs face additional liabilities of smallness and newness when managing a GVC, and suggest that in order to manage successfully a GVC over the long term, the SME must develop three distinct yet related sets of dynamic capabilities: cognitive, managerial, and organizational. Dallas (2015) takes a finer-grained view of firm size as a determinant of GVC management strategy. While his analysis of transactional data of Chinese electronics/light industry firms uses size as a control, rather than independent, variable, he concludes that ways in which GVCs are organized vary not simply by lead firm size and productivity, but also by other heterogeneous firm level features, such as distinct governance channels available to lead firms. Dallas (2015) thus cautions GVC researchers not to make assumptions about the distinctiveness of large lead firms as a group, and to focus on other potential sources of heterogeneity, which can be linked to sector-specific features as well as firm-level strategies.
One of such sources of heterogeneity appears to be the level of economic development of home country, dichotomized in some GVC papers as emerging versus advanced. Two studies explore differences in GVCs led by EMNEs versus AMNEs. He et al. (2018), based on a case analysis of China’s Times Electric-led GVC, argue that power relationships in the GVC seem to be more balanced when EMNEs, rather than AMNEs, are in lead positions. Buckley and Tian (2017) compare internationalization patterns of top non-financial EMNEs and AMNEs, and find that AMNEs are more likely to achieve profitability through global GVC orchestration, while EMNEs’ ability to develop orchestration know-how is restricted by home institutions. Therefore, EMNEs are more likely to extract monopoly-based rents from internationalization, but to remain constrained to the periphery position in GVCs.
It follows, then, that control of the GVC is likely to remain in the hands of technology leaders (Buckley & Tian, 2017). Jacobides and Tae (2015) describe such technology leaders as “kingpins,” operationalized as firms with superior market capitalization and comparatively high R&D investment. In their study of firms active in various segments in the US computer industry, the authors show that “kingpins” impact value distribution and migration through the value chain. Technological and R&D capabilities, however, need to be accompanied by global orchestration know-how in order for lead firms to achieve profitability from fragmented, globally dispersed operations (Buckley & Tian, 2017). We address GVC orchestration in the next section.
GVC orchestration
Orchestration refers to decisions and actions by lead firm managers – a managerial toolkit – aimed at connecting, coordinating, leading, and serving GVC partners, and ultimately shaping the network’s strategy (Rugman & D’Cruz, 1997). Orchestration encompasses such elements as, inter alia, formal and informal components of each relationship within the network, the entrepreneurial element of resource bundling, interest alignment among parties achieved through strategic leadership by the lead firm, knowledge management4, and value distribution.
Formal orchestration tools – that is, codified rules, specific contractual choices to manage partner relationships, and price-like incentives and penalties – are typically easier to observe and operationalize than informal tools such as social mechanisms deployed by lead firms to govern relationships. Yet, only a few studies in our sample investigate contractual choices in a GVC. Lojacono, Misani and Tallman (2017) examine nuances of cooperative governance in the dispersed value chain of the home appliances industry, and find that more complex transactions requiring greater coordination are more likely to be governed through equity participation. Specifically, non-equity contracts are more efficient for coordinating offshore production, while equity JVs are preferable for managing local strategic relationships, such as production alliances whose primary objective is to serve local markets. Chiarvesio and Di Maria (2009) explore differences in GVC orchestration between lead firms located within industrial districts versus those located outside. Their quantitative study of Italian firms active in the country’s four dominant industries – furniture, engineering, fashion, and food – shows that there are subtle differences in ways district and non-district lead firms manage their GVCs to achieve optimal efficiency: while lead firms located within industrial districts rely more on local systems through subcontracting networks, non-district firms invest in national level subcontracting. Here, local subcontracting networks allow lead firms to exploit flexibility, and national subcontracting facilitates greater efficiency and acquisition of value-added competences through the GVC. Of note, these differences decrease as firm size increases. Finally, Enderwick (2018) conceptually studies responsibility boundaries in a GVC, and argues that the full extent of lead firm responsibility for actions of indirect GVC participants depends on whether indirect partners’ contracts are exclusive or non-exclusive.
Entrepreneurial guidance by the lead firm is an important component of GVC orchestration (Buckley, 2009a), as it serves to redirect GVC resources and tasks toward creating innovation. While most research in our sample implicitly assumes the lead firm’s entrepreneurial role in generating value, two empirical studies take a close look at the process of entrepreneurial resource recombination in a GVC, initiated by the lead firm. In a multiple case study of engineering firms, Zhang and Gregory (2011) identify mechanisms of value creation in global engineering networks: efficiency, innovation, and flexibility. The efficacy of these mechanisms depends on which part of the engineering value chain is the core focus of the operations: product development/production, design/idea generation, or service/support. Ivarsson and Alvstam (2011) discuss how IKEA manages resources to generate greater value and stimulate innovation capabilities in its supply chain. Their case study reveals that IKEA provides access to inputs through global sourcing, shares business intelligence, implements management systems and business policies across the network, and fosters informal R&D collaborations with suppliers.
Relational governance, as perhaps the most important of the five types of GVC governance in Gereffi et al.’s (2005) typology, emerged as a key tool for network orchestration. There appears to be a broad consensus in our sample that cultivating informal relationships, as a means of network orchestration, has a potential to facilitate knowledge transfer, secure commitments, enhance innovation, respond to legislation, and improve overall GVC efficiency. In fact, Brancati et al. (2017) show, based on a survey of about 25,000 Italian firms, that GVCs comprised of firms with strong relationships and active decisional roles in the value chain have a 4-6% higher probability of engaging in innovation and R&D, and display greater productivity and sales growth. Benstead, Hendry and Stevenson (2018) argue that relational capital facilitates successful horizontal collaboration among GVC members, which allows participating firms to respond more effectively to modern slavery legislation in the textiles and fashion industry, and consequently improve reputation and performance. In a case study of major American and Japanese automotive lead firms and their suppliers, Sturgeon et al. (2008) find that relational governance is necessitated by rising product complexity, low process codifiability and a paucity of industry-level standards. These relational links explain continued dominance of regional structures in the industry.
Studies have described specific relational strategies deployed by lead firms. These include promoting regular communication between suppliers and buyers (McDermott & Corredoira, 2010), adapting communication strategies to cultural contexts where GVC partners are embedded (Griffith & Myers, 2005), involving multiple actors in establishing functioning principles for the GVC, facilitating shared identity and common language (Lipparini et al., 2014), extending the network to include non-market institutions (Corredoira & McDermott, 2014; Kano, 2018; Pipkin & Fuentes, 2017), investing into image building (Horner & Murphy, 2018), and establishing a long-term horizon for inter-unit relationships to facilitate repeated interactions (Ivarsson & Alvstam, 2011).
Finally, extant research identifies GVC value distribution as the responsibility of the orchestrating firm. The lead firm must ensure that partners receive an equitable share of value created in the GVC, as a function of their respective contributions to the network (Dhanaraj & Parkhe, 2006). In most studies in our sample, a power view of the GVC is assumed, whereby value distribution is seen to be a result of the power struggle between the lead firm and the periphery. Typically, lead firms – particularly those that possess valuable technological knowledge and/or intangibles such as brand names and patents – are argued to capture the lion share of the value (Jacobides & Tae, 2015), while most peripheral players appear in a subordinate position and under high cost pressures (Taplin, Winterton, & Winterton, 2003), and must deploy strategies to counter the power of the lead firm (Grabs & Ponte, 2019; Havice & Campling, 2017; Pipkin & Fuentes, 2017), including attempts to move up the value chain, as discussed above. This power imbalance appears to be more pronounced in GVCs led by AMNEs than those led by EMNEs, because lead EMNEs are likely to build their GVCs with a knowledge-seeking objective, by enlisting AMNEs that possess desired knowledge (He et al., 2018).
Some conceptual studies in our sample approach the issue of value distribution as a deliberate orchestration tool on behalf of the lead firm. Kano (2018) argues that equitable value distribution improves reliability of partners and enhances sustainability of the GVC over time. Of note, equitable value distribution undermines potential efficiency gains achieved through externalization of activities; however, as argued by Yamin (2011), such sacrifice in terms of loss of efficiency may be necessary in order to ensure legitimacy and survival of the network.
Governance and performance outcomes
A significant proportion of papers in our sample is concerned with developing typologies, mapping linkages in GVCs, analyzing configurations, and investigating processes, without an explicit focus on performance. Studies that addresses performance per se conceptualize and measure performance outcomes in a variety of ways, depending on research questions and units of analysis. Most studies focusing on GVC suppliers are concerned with upgrading as a performance goal, as evidenced by suppliers’ development of technological and/or branding capabilities, or by their ability to reconfigure activities so as to become lead firms in their own right (e.g., Azmeh & Nadvi, 2014; Buckley 2009b; Chen et al., 2016).
Studies focusing on lead firms are more likely to use financial performance measures as indicators of GVC success: for example, value capture as measured by comparative market capitalizations of various industrial sectors (Jacobides & Tae, 2015), sales and profit growth (Griffith & Myers, 2005), and return on assets (Buckley & Tian, 2017; Lampel & Giachetti, 2013). Other conceptualizations of lead firm performance include, inter alia, its ability to exercise control over independent partners and coordinate division of labor (Casson, 2013; Strange & Newton, 2006), ability to minimize the total sum of transaction costs (Buckley, 2009a), capability development (Eriksson et al., 2014), and corporate social responsibility (CSR) performance (Enderwick, 2018).
Studies concerned with performance of the GVC network as a whole naturally explore more complex aspects of performance, such as flexibility/dynamism of the production process, access to a wide range of resources, operational efficiency, cohesiveness/connectivity, innovation/ability to transform ideas into commercial products, and sustainability of the GVC over time (Akkermans et al., 1999; Buckley, 2011; Chen, 2003; Colotla, Shi, & Gregory, 2003; Kano, 2018; Karlsson, 2003; Sinkovics et al., 2019; Yamin, 2011; Zhang & Gregory, 2011). Notably, studies in the social sciences group may focus on development and sustainability outcomes of GVC governance, such as industrial/economic development and positive institutional change (e.g., Coe et al., 2004; Fuller & Phelps, 2018; Henderson et al., 2002; Lund-Thomsen & Coe, 2015; Pavlínek, 2018; Yeung, 2016). Due to its complexity and multifariousness, GVC-level performance is difficult to operationalize quantitatively, and is mostly addressed in qualitative and conceptual studies in our sample.
Macro-level: Interaction of Home and Host Environment Characteristics and GVC Governance
GVC organization is contingent on a number of location characteristics, including levels of economic development (Mudambi, 2007), IP and FDI protection regimes (Johns & Wellhausen, 2016), trade and tariff regimes (Curran, Nadvi, & Campling, 2019; Kim, Milner, Bernauer, Osgood, Spilker, & Tingley, 2019), regulatory environments and government policy interventions, labor costs, level of technological sophistication, and societal norms (Dunning, 1988). The role of the state, in particular, can significantly shape the organization and evolution of GVCs over time (Alford & Phillips, 2018; Coe & Yeung, 2019; Smith, 2015; Yeung, 2016). Macro-level impacts on GVC governance have been discussed in the preceding sections, but we summarize the key themes and findings below.
Institutional factors, such as trade regulations and the strength of local institutions, are major determinants of GVC governance attributes, including geographic and structural configuration, operating mode choices, power balance, and possibility of upgrading by peripheral players. Host country institutions can both attract investment by lead firms through policies encouraging local content and promoting local supplier linkages (Amendolagine et al., 2019; Dawley, MacKinnon, & Pollock, 2019; Liu & Dicken, 2006; Sturgeon et al., 2008; Yeung, 2016), and deter such investment due to insufficient IP protection and underdeveloped legal systems (Gooris & Peeters, 2016). However, the impact of host country institutional environment on GVCs is heterogeneous: while it is tempting to assume that lead firms are attracted by favorable local business regulations and strong institutions, this impact in fact varies across GVCs, depending on specific functions/activities being offshored, internationalization motives, and lead firm-level strategies and capabilities (Ascani et al., 2016; Morris & Staritz, 2014).
One conclusion that can be drawn from our review is that institutions greatly impact GVCs’ abilities to engage in, and profit from, innovation. Inadequate local institutions prevent domestic firms from transforming R&D into innovative products and services (Buckley & Tian, 2017; Jean, 2014), and thus effectively hinder supplier catch-up and upgrading. This likely explains why most GVCs are controlled by MNEs that stem from developed institutional environments and, consequently, display technological leadership. Peripheral players in GVCs can respond to this challenge by entering into international collaborations, engaging with international institutions, and more broadly becoming embedded in international networks that off-set the weakness of local institutions (Khan et al., 2015, 2018; Pipkin & Fuentes, 2017). This is a crucial dimension of strategic coupling in GPN 2.0 theory (Coe & Yeung, 2015; Yeung, 2009, 2016). It is important to note that the impact of institutions is dynamic. As trade, liberalization and economic development in emerging markets progress, so do suppliers’ strategies. Internal R&D becomes a dominant strategy for upgrading (Kumaraswamy et al., 2012), and suppliers with more advanced technologies become core players in their regional networks (Suder et al., 2015).
Economic factors, such as labor cost and supply, markets and competition (MacCarthy et al., 2016), impact GVC configurations and, more recently, determine further production shifts in GVCs, whereby tier 1 GVC suppliers begin disintegrating their own value chains, in search of both greater efficiency (as a response to rising labor costs) and better production capabilities (Azmeh & Nadvi, 2014; Suder et al., 2015). In the terminology of GPN 2.0 (Coe & Yeung, 2015), this simultaneous attainment of both cost efficiency and production capabilities is translated into lower cost-capability ratios in favor of strategic partners and suppliers of global lead firms. This strategy is an alternative to functional upgrading discussed above (Chen et al., 2016; Humphrey & Schmitz, 2002; Sako & Zylberberg, 2019), and represents a different type of upgrading, where major suppliers become MNEs in their own right, e.g., leading ODMs such as Quanta and Wistron and contract manufacturers such as Foxconn, Flex, and Venture from East Asian economies (Yeung, 2016).
The impact of macro-level cultural characteristics is considered in a smaller subset of studies, and mainly in relation to the lead firm’s strategic governance routines. Griffith and Myers (2005) suggest that host country cultural expectations impact GVC performance by affecting the lead firm’s ability to effectively deploy relational strategies across the network. They argue that cultural adaptation of relational governance results in improved performance. Sturgeon et al. (2008) discuss the impact of home country cultural characteristics on American and Japanese lead firms’ abilities to successfully engage in relational governance. Only one study (Funk et al., 2010) analyzes the broader impact of home country consumers’ cultural characteristics on GVC profitability, using Schwartz’s (2006) theory of values.
It is acknowledged that technology is one of the major macro-level factors impacting a GVC over its lifecycle (MacCarthy et al., 2016). In the prior section, we have discussed ways in which advanced technologies impact structural and strategic governance decisions in a GVC, mostly in the context of facilitating connectivity and determining innovation and power loci in the network. Some studies in our sample investigate a direct impact of the latest, advanced technologies on GVC configurations. Laplume et al. (2016) analyze potential impact of 3D printing technologies on GVC structure and geographic reach. Treiblmaier (2018) discusses potential implications of blockchain technology for various aspect of GVC management, including boundaries, structures and relationships.
GVCs are not only impacted by, but also influence the macro-environment; specifically, sustainability impacts of GVCs and associated policy implications have to date invited much scholarly and practitioner dialogue (Coe & Yeung, 2015; Gereffi, 2018). This interest is to some extent reflected in our sample, yet few studies explicitly address ways in which GVCs affect social, economic and environmental conditions in host countries. For example, labor standards have become one critical frontier of GVC organization (Hastings, 2019; Malesky & Mosley, 2018). Lund-Thomsen and Coe (2015) studied Nike’s main football supplier factory in Pakistan, and investigated whether CSR initiatives by the lead firm can facilitate or constrain labor agency in GVCs. Their results indicate that lead firms are limited in their ability to shape local labor agency, as it is impacted by wider economic forces, relationships with local and national actors, and local regulatory frameworks; these factors can place clear limits on lead firms’ efforts to facilitate responsible forms of GVC. Barrientos et al. (2016) address the impact of diffusion by global and regional supermarkets in “global South” – South Africa, Kenya, and Uganda – and find that entry by large global retailers provides new opportunities for strategic diversification to the most skilled local horticultural producers and workers. This facilitates economic and social upgrading; yet, persisting economic downgrading pressures mean that many less skilled suppliers are excluded from both global and regional value chains. Kleibert (2016) explores local impacts of the Philippinean offshore service offices’ participation in GVCs, and finds that the majority of these offshore offices are characterized by foreign ownership and a high degree of dependency. However, participation in the GVC increases the number and quality of jobs in the region, and creates new opportunities in the labor force – particularly for young college graduates, who suffer from a high level of unemployment in the region. Finally, in a longitudinal study of the international canned tuna industry, Havice and Campling (2017: 309) argue that value chain governance and environmental governance are “mutually constituted”: lead firm power dynamic is inextricable from the environmental conditions of production, and interfirm strategies work not only with, but also through, environmental governance.